diff --git a/.gitignore b/.gitignore index 3624d12269612..debad77ec2ad3 100644 --- a/.gitignore +++ b/.gitignore @@ -66,6 +66,7 @@ scalastyle-output.xml R-unit-tests.log R/unit-tests.out python/lib/pyspark.zip +lint-r-report.log # For Hive metastore_db/ diff --git a/.rat-excludes b/.rat-excludes index c0f81b57fe09d..c24667c18dbda 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -28,6 +28,7 @@ spark-env.sh spark-env.cmd spark-env.sh.template log4j-defaults.properties +log4j-defaults-repl.properties bootstrap-tooltip.js jquery-1.11.1.min.js d3.min.js @@ -80,5 +81,9 @@ local-1425081759269/* local-1426533911241/* local-1426633911242/* local-1430917381534/* +local-1430917381535_1 +local-1430917381535_2 DESCRIPTION NAMESPACE +test_support/* +.lintr diff --git a/LICENSE b/LICENSE index b2001f029a4f0..42010d9f5f0e6 100644 --- a/LICENSE +++ b/LICENSE @@ -836,6 +836,68 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +======================================================================== +For vis.js (core/src/main/resources/org/apache/spark/ui/static/vis.min.js): +======================================================================== +Copyright (C) 2010-2015 Almende B.V. + +Vis.js is dual licensed under both + + * The Apache 2.0 License + http://www.apache.org/licenses/LICENSE-2.0 + +and + + * The MIT License + http://opensource.org/licenses/MIT + +Vis.js may be distributed under either license. + +======================================================================== +For dagre-d3 (core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js): +======================================================================== +Copyright (c) 2013 Chris Pettitt + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. + +======================================================================== +For graphlib-dot (core/src/main/resources/org/apache/spark/ui/static/graphlib-dot.min.js): +======================================================================== +Copyright (c) 2012-2013 Chris Pettitt + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. ======================================================================== BSD-style licenses @@ -845,7 +907,7 @@ The following components are provided under a BSD-style license. See project lin (BSD 3 Clause) core (com.github.fommil.netlib:core:1.1.2 - https://github.com/fommil/netlib-java/core) (BSD 3 Clause) JPMML-Model (org.jpmml:pmml-model:1.1.15 - https://github.com/jpmml/jpmml-model) - (BSD 3-clause style license) jblas (org.jblas:jblas:1.2.3 - http://jblas.org/) + (BSD 3-clause style license) jblas (org.jblas:jblas:1.2.4 - http://jblas.org/) (BSD License) AntLR Parser Generator (antlr:antlr:2.7.7 - http://www.antlr.org/) (BSD License) Javolution (javolution:javolution:5.5.1 - http://javolution.org) (BSD licence) ANTLR ST4 4.0.4 (org.antlr:ST4:4.0.4 - http://www.stringtemplate.org) @@ -888,3 +950,4 @@ The following components are provided under the MIT License. See project link fo (MIT License) scopt (com.github.scopt:scopt_2.10:3.2.0 - https://github.com/scopt/scopt) (The MIT License) Mockito (org.mockito:mockito-all:1.8.5 - http://www.mockito.org) (MIT License) jquery (https://jquery.org/license/) + (MIT License) AnchorJS (https://github.com/bryanbraun/anchorjs) diff --git a/R/README.md b/R/README.md index a6970e39b55f3..d7d65b4f0eca5 100644 --- a/R/README.md +++ b/R/README.md @@ -52,7 +52,7 @@ The SparkR documentation (Rd files and HTML files) are not a part of the source SparkR comes with several sample programs in the `examples/src/main/r` directory. To run one of them, use `./bin/sparkR `. For example: - ./bin/sparkR examples/src/main/r/pi.R local[2] + ./bin/sparkR examples/src/main/r/dataframe.R You can also run the unit-tests for SparkR by running (you need to install the [testthat](http://cran.r-project.org/web/packages/testthat/index.html) package first): @@ -63,5 +63,5 @@ You can also run the unit-tests for SparkR by running (you need to install the [ The `./bin/spark-submit` and `./bin/sparkR` can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run ``` export YARN_CONF_DIR=/etc/hadoop/conf -./bin/spark-submit --master yarn examples/src/main/r/pi.R 4 +./bin/spark-submit --master yarn examples/src/main/r/dataframe.R ``` diff --git a/R/create-docs.sh b/R/create-docs.sh index 4194172a2e115..6a4687b06ecb9 100755 --- a/R/create-docs.sh +++ b/R/create-docs.sh @@ -23,14 +23,14 @@ # After running this script the html docs can be found in # $SPARK_HOME/R/pkg/html +set -o pipefail +set -e + # Figure out where the script is export FWDIR="$(cd "`dirname "$0"`"; pwd)" pushd $FWDIR -# Generate Rd file -Rscript -e 'library(devtools); devtools::document(pkg="./pkg", roclets=c("rd"))' - -# Install the package +# Install the package (this will also generate the Rd files) ./install-dev.sh # Now create HTML files diff --git a/R/install-dev.sh b/R/install-dev.sh index 55ed6f4be1a4a..1edd551f8d243 100755 --- a/R/install-dev.sh +++ b/R/install-dev.sh @@ -26,11 +26,20 @@ # NOTE(shivaram): Right now we use $SPARK_HOME/R/lib to be the installation directory # to load the SparkR package on the worker nodes. +set -o pipefail +set -e FWDIR="$(cd `dirname $0`; pwd)" LIB_DIR="$FWDIR/lib" mkdir -p $LIB_DIR -# Install R +pushd $FWDIR + +# Generate Rd files if devtools is installed +Rscript -e ' if("devtools" %in% rownames(installed.packages())) { library(devtools); devtools::document(pkg="./pkg", roclets=c("rd")) }' + +# Install SparkR to $LIB_DIR R CMD INSTALL --library=$LIB_DIR $FWDIR/pkg/ + +popd diff --git a/R/log4j.properties b/R/log4j.properties index 701adb2a3da1d..cce8d9152d32d 100644 --- a/R/log4j.properties +++ b/R/log4j.properties @@ -19,7 +19,7 @@ log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=true -log4j.appender.file.file=R-unit-tests.log +log4j.appender.file.file=R/target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n diff --git a/R/pkg/.lintr b/R/pkg/.lintr new file mode 100644 index 0000000000000..b10ebd35c4ca7 --- /dev/null +++ b/R/pkg/.lintr @@ -0,0 +1,2 @@ +linters: with_defaults(line_length_linter(100), camel_case_linter = NULL) +exclusions: list("inst/profile/general.R" = 1, "inst/profile/shell.R") diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE index 7611f479a628b..7f857222452d4 100644 --- a/R/pkg/NAMESPACE +++ b/R/pkg/NAMESPACE @@ -1,25 +1,37 @@ # Imports from base R importFrom(methods, setGeneric, setMethod, setOldClass) -useDynLib(SparkR, stringHashCode) + +# Disable native libraries till we figure out how to package it +# See SPARKR-7839 +#useDynLib(SparkR, stringHashCode) # S3 methods exported export("sparkR.init") export("sparkR.stop") export("print.jobj") +# Job group lifecycle management methods +export("setJobGroup", + "clearJobGroup", + "cancelJobGroup") + exportClasses("DataFrame") -exportMethods("cache", +exportMethods("arrange", + "cache", "collect", "columns", "count", "describe", "distinct", + "dropna", "dtypes", "except", "explain", + "fillna", "filter", "first", + "group_by", "groupBy", "head", "insertInto", @@ -28,12 +40,15 @@ exportMethods("cache", "join", "limit", "orderBy", + "mutate", "names", "persist", "printSchema", "registerTempTable", + "rename", "repartition", - "sampleDF", + "sample", + "sample_frac", "saveAsParquetFile", "saveAsTable", "saveDF", @@ -42,42 +57,68 @@ exportMethods("cache", "selectExpr", "show", "showDF", - "sortDF", + "summarize", "take", "unionAll", "unpersist", "where", "withColumn", - "withColumnRenamed") + "withColumnRenamed", + "write.df") exportClasses("Column") exportMethods("abs", + "acos", "alias", "approxCountDistinct", "asc", + "asin", + "atan", + "atan2", "avg", "cast", + "cbrt", + "ceiling", "contains", + "cos", + "cosh", "countDistinct", "desc", "endsWith", + "exp", + "expm1", + "floor", "getField", "getItem", + "hypot", "isNotNull", "isNull", "last", "like", + "log", + "log10", + "log1p", "lower", "max", "mean", "min", + "n", + "n_distinct", + "rint", "rlike", + "sign", + "sin", + "sinh", "sqrt", "startsWith", "substr", "sum", "sumDistinct", + "tan", + "tanh", + "toDegrees", + "toRadians", "upper") exportClasses("GroupedData") @@ -94,6 +135,7 @@ export("cacheTable", "jsonFile", "loadDF", "parquetFile", + "read.df", "sql", "table", "tableNames", diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index 354642e7bc307..0af5cb8881e35 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -65,9 +65,9 @@ dataFrame <- function(sdf, isCached = FALSE) { #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' printSchema(df) #'} setMethod("printSchema", @@ -88,9 +88,9 @@ setMethod("printSchema", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' dfSchema <- schema(df) #'} setMethod("schema", @@ -110,9 +110,9 @@ setMethod("schema", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' explain(df, TRUE) #'} setMethod("explain", @@ -139,9 +139,9 @@ setMethod("explain", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' isLocal(df) #'} setMethod("isLocal", @@ -150,7 +150,7 @@ setMethod("isLocal", callJMethod(x@sdf, "isLocal") }) -#' ShowDF +#' showDF #' #' Print the first numRows rows of a DataFrame #' @@ -162,15 +162,16 @@ setMethod("isLocal", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' showDF(df) #'} setMethod("showDF", signature(x = "DataFrame"), function(x, numRows = 20) { - callJMethod(x@sdf, "showString", numToInt(numRows)) + s <- callJMethod(x@sdf, "showString", numToInt(numRows)) + cat(s) }) #' show @@ -184,10 +185,10 @@ setMethod("showDF", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) -#' show(df) +#' df <- jsonFile(sqlContext, path) +#' df #'} setMethod("show", "DataFrame", function(object) { @@ -209,9 +210,9 @@ setMethod("show", "DataFrame", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' dtypes(df) #'} setMethod("dtypes", @@ -233,9 +234,9 @@ setMethod("dtypes", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' columns(df) #'} setMethod("columns", @@ -266,11 +267,11 @@ setMethod("names", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' registerTempTable(df, "json_df") -#' new_df <- sql(sqlCtx, "SELECT * FROM json_df") +#' new_df <- sql(sqlContext, "SELECT * FROM json_df") #'} setMethod("registerTempTable", signature(x = "DataFrame", tableName = "character"), @@ -292,9 +293,9 @@ setMethod("registerTempTable", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df <- loadDF(sqlCtx, path, "parquet") -#' df2 <- loadDF(sqlCtx, path2, "parquet") +#' sqlContext <- sparkRSQL.init(sc) +#' df <- read.df(sqlContext, path, "parquet") +#' df2 <- read.df(sqlContext, path2, "parquet") #' registerTempTable(df, "table1") #' insertInto(df2, "table1", overwrite = TRUE) #'} @@ -315,9 +316,9 @@ setMethod("insertInto", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' cache(df) #'} setMethod("cache", @@ -340,9 +341,9 @@ setMethod("cache", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' persist(df, "MEMORY_AND_DISK") #'} setMethod("persist", @@ -365,9 +366,9 @@ setMethod("persist", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' persist(df, "MEMORY_AND_DISK") #' unpersist(df) #'} @@ -390,9 +391,9 @@ setMethod("unpersist", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' newDF <- repartition(df, 2L) #'} setMethod("repartition", @@ -414,9 +415,9 @@ setMethod("repartition", # @examples #\dontrun{ # sc <- sparkR.init() -# sqlCtx <- sparkRSQL.init(sc) +# sqlContext <- sparkRSQL.init(sc) # path <- "path/to/file.json" -# df <- jsonFile(sqlCtx, path) +# df <- jsonFile(sqlContext, path) # newRDD <- toJSON(df) #} setMethod("toJSON", @@ -439,9 +440,9 @@ setMethod("toJSON", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' saveAsParquetFile(df, "/tmp/sparkr-tmp/") #'} setMethod("saveAsParquetFile", @@ -460,9 +461,9 @@ setMethod("saveAsParquetFile", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' distinctDF <- distinct(df) #'} setMethod("distinct", @@ -472,25 +473,26 @@ setMethod("distinct", dataFrame(sdf) }) -#' SampleDF +#' Sample #' #' Return a sampled subset of this DataFrame using a random seed. #' #' @param x A SparkSQL DataFrame #' @param withReplacement Sampling with replacement or not #' @param fraction The (rough) sample target fraction -#' @rdname sampleDF +#' @rdname sample +#' @aliases sample_frac #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) -#' collect(sampleDF(df, FALSE, 0.5)) -#' collect(sampleDF(df, TRUE, 0.5)) +#' df <- jsonFile(sqlContext, path) +#' collect(sample(df, FALSE, 0.5)) +#' collect(sample(df, TRUE, 0.5)) #'} -setMethod("sampleDF", +setMethod("sample", # TODO : Figure out how to send integer as java.lang.Long to JVM so # we can send seed as an argument through callJMethod signature(x = "DataFrame", withReplacement = "logical", @@ -501,6 +503,15 @@ setMethod("sampleDF", dataFrame(sdf) }) +#' @rdname sample +#' @aliases sample +setMethod("sample_frac", + signature(x = "DataFrame", withReplacement = "logical", + fraction = "numeric"), + function(x, withReplacement, fraction) { + sample(x, withReplacement, fraction) + }) + #' Count #' #' Returns the number of rows in a DataFrame @@ -512,9 +523,9 @@ setMethod("sampleDF", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' count(df) #' } setMethod("count", @@ -534,9 +545,9 @@ setMethod("count", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' collected <- collect(df) #' firstName <- collected[[1]]$name #' } @@ -569,9 +580,9 @@ setMethod("collect", #' @examples #' \dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' limitedDF <- limit(df, 10) #' } setMethod("limit", @@ -588,9 +599,9 @@ setMethod("limit", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' take(df, 2) #' } setMethod("take", @@ -615,9 +626,9 @@ setMethod("take", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' head(df) #' } setMethod("head", @@ -636,9 +647,9 @@ setMethod("head", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' first(df) #' } setMethod("first", @@ -658,9 +669,9 @@ setMethod("first", # @examples #\dontrun{ # sc <- sparkR.init() -# sqlCtx <- sparkRSQL.init(sc) +# sqlContext <- sparkRSQL.init(sc) # path <- "path/to/file.json" -# df <- jsonFile(sqlCtx, path) +# df <- jsonFile(sqlContext, path) # rdd <- toRDD(df) # } setMethod("toRDD", @@ -682,7 +693,8 @@ setMethod("toRDD", #' @param x a DataFrame #' @return a GroupedData #' @seealso GroupedData -#' @rdname DataFrame +#' @aliases group_by +#' @rdname groupBy #' @export #' @examples #' \dontrun{ @@ -705,12 +717,21 @@ setMethod("groupBy", groupedData(sgd) }) -#' Agg +#' @rdname groupBy +#' @aliases group_by +setMethod("group_by", + signature(x = "DataFrame"), + function(x, ...) { + groupBy(x, ...) + }) + +#' Summarize data across columns #' #' Compute aggregates by specifying a list of columns #' #' @param x a DataFrame #' @rdname DataFrame +#' @aliases summarize #' @export setMethod("agg", signature(x = "DataFrame"), @@ -718,6 +739,14 @@ setMethod("agg", agg(groupBy(x), ...) }) +#' @rdname DataFrame +#' @aliases agg +setMethod("summarize", + signature(x = "DataFrame"), + function(x, ...) { + agg(x, ...) + }) + ############################## RDD Map Functions ################################## # All of the following functions mirror the existing RDD map functions, # @@ -886,7 +915,7 @@ setMethod("select", signature(x = "DataFrame", col = "list"), function(x, col) { cols <- lapply(col, function(c) { - if (class(c)== "Column") { + if (class(c) == "Column") { c@jc } else { col(c)@jc @@ -909,9 +938,9 @@ setMethod("select", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' selectExpr(df, "col1", "(col2 * 5) as newCol") #' } setMethod("selectExpr", @@ -935,9 +964,9 @@ setMethod("selectExpr", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' newDF <- withColumn(df, "newCol", df$col1 * 5) #' } setMethod("withColumn", @@ -946,6 +975,42 @@ setMethod("withColumn", select(x, x$"*", alias(col, colName)) }) +#' Mutate +#' +#' Return a new DataFrame with the specified columns added. +#' +#' @param x A DataFrame +#' @param col a named argument of the form name = col +#' @return A new DataFrame with the new columns added. +#' @rdname withColumn +#' @aliases withColumn +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlContext <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlContext, path) +#' newDF <- mutate(df, newCol = df$col1 * 5, newCol2 = df$col1 * 2) +#' names(newDF) # Will contain newCol, newCol2 +#' } +setMethod("mutate", + signature(x = "DataFrame"), + function(x, ...) { + cols <- list(...) + stopifnot(length(cols) > 0) + stopifnot(class(cols[[1]]) == "Column") + ns <- names(cols) + if (!is.null(ns)) { + for (n in ns) { + if (n != "") { + cols[[n]] <- alias(cols[[n]], n) + } + } + } + do.call(select, c(x, x$"*", cols)) + }) + #' WithColumnRenamed #' #' Rename an existing column in a DataFrame. @@ -959,9 +1024,9 @@ setMethod("withColumn", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' newDF <- withColumnRenamed(df, "col1", "newCol1") #' } setMethod("withColumnRenamed", @@ -977,9 +1042,47 @@ setMethod("withColumnRenamed", select(x, cols) }) +#' Rename +#' +#' Rename an existing column in a DataFrame. +#' +#' @param x A DataFrame +#' @param newCol A named pair of the form new_column_name = existing_column +#' @return A DataFrame with the column name changed. +#' @rdname withColumnRenamed +#' @aliases withColumnRenamed +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlContext <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlContext, path) +#' newDF <- rename(df, col1 = df$newCol1) +#' } +setMethod("rename", + signature(x = "DataFrame"), + function(x, ...) { + renameCols <- list(...) + stopifnot(length(renameCols) > 0) + stopifnot(class(renameCols[[1]]) == "Column") + newNames <- names(renameCols) + oldNames <- lapply(renameCols, function(col) { + callJMethod(col@jc, "toString") + }) + cols <- lapply(columns(x), function(c) { + if (c %in% oldNames) { + alias(col(c), newNames[[match(c, oldNames)]]) + } else { + col(c) + } + }) + select(x, cols) + }) + setClassUnion("characterOrColumn", c("character", "Column")) -#' SortDF +#' Arrange #' #' Sort a DataFrame by the specified column(s). #' @@ -987,19 +1090,19 @@ setClassUnion("characterOrColumn", c("character", "Column")) #' @param col Either a Column object or character vector indicating the field to sort on #' @param ... Additional sorting fields #' @return A DataFrame where all elements are sorted. -#' @rdname sortDF +#' @rdname arrange #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) -#' sortDF(df, df$col1) -#' sortDF(df, "col1") -#' sortDF(df, asc(df$col1), desc(abs(df$col2))) +#' df <- jsonFile(sqlContext, path) +#' arrange(df, df$col1) +#' arrange(df, "col1") +#' arrange(df, asc(df$col1), desc(abs(df$col2))) #' } -setMethod("sortDF", +setMethod("arrange", signature(x = "DataFrame", col = "characterOrColumn"), function(x, col, ...) { if (class(col) == "character") { @@ -1013,12 +1116,12 @@ setMethod("sortDF", dataFrame(sdf) }) -#' @rdname sortDF +#' @rdname arrange #' @aliases orderBy,DataFrame,function-method setMethod("orderBy", signature(x = "DataFrame", col = "characterOrColumn"), function(x, col) { - sortDF(x, col) + arrange(x, col) }) #' Filter @@ -1026,7 +1129,7 @@ setMethod("orderBy", #' Filter the rows of a DataFrame according to a given condition. #' #' @param x A DataFrame to be sorted. -#' @param condition The condition to sort on. This may either be a Column expression +#' @param condition The condition to filter on. This may either be a Column expression #' or a string containing a SQL statement #' @return A DataFrame containing only the rows that meet the condition. #' @rdname filter @@ -1034,9 +1137,9 @@ setMethod("orderBy", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' filter(df, "col1 > 0") #' filter(df, df$col2 != "abcdefg") #' } @@ -1074,9 +1177,9 @@ setMethod("where", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df1 <- jsonFile(sqlCtx, path) -#' df2 <- jsonFile(sqlCtx, path2) +#' sqlContext <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlContext, path) +#' df2 <- jsonFile(sqlContext, path2) #' join(df1, df2) # Performs a Cartesian #' join(df1, df2, df1$col1 == df2$col2) # Performs an inner join based on expression #' join(df1, df2, df1$col1 == df2$col2, "right_outer") @@ -1106,6 +1209,7 @@ setMethod("join", #' #' Return a new DataFrame containing the union of rows in this DataFrame #' and another DataFrame. This is equivalent to `UNION ALL` in SQL. +#' Note that this does not remove duplicate rows across the two DataFrames. #' #' @param x A Spark DataFrame #' @param y A Spark DataFrame @@ -1115,9 +1219,9 @@ setMethod("join", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df1 <- jsonFile(sqlCtx, path) -#' df2 <- jsonFile(sqlCtx, path2) +#' sqlContext <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlContext, path) +#' df2 <- jsonFile(sqlContext, path2) #' unioned <- unionAll(df, df2) #' } setMethod("unionAll", @@ -1140,9 +1244,9 @@ setMethod("unionAll", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df1 <- jsonFile(sqlCtx, path) -#' df2 <- jsonFile(sqlCtx, path2) +#' sqlContext <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlContext, path) +#' df2 <- jsonFile(sqlContext, path2) #' intersectDF <- intersect(df, df2) #' } setMethod("intersect", @@ -1165,9 +1269,9 @@ setMethod("intersect", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df1 <- jsonFile(sqlCtx, path) -#' df2 <- jsonFile(sqlCtx, path2) +#' sqlContext <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlContext, path) +#' df2 <- jsonFile(sqlContext, path2) #' exceptDF <- except(df, df2) #' } #' @rdname except @@ -1199,23 +1303,22 @@ setMethod("except", #' @param source A name for external data source #' @param mode One of 'append', 'overwrite', 'error', 'ignore' #' -#' @rdname saveAsTable +#' @rdname write.df #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) -#' saveAsTable(df, "myfile") +#' df <- jsonFile(sqlContext, path) +#' write.df(df, "myfile", "parquet", "overwrite") #' } -setMethod("saveDF", - signature(df = "DataFrame", path = 'character', source = 'character', - mode = 'character'), - function(df, path = NULL, source = NULL, mode = "append", ...){ +setMethod("write.df", + signature(df = "DataFrame", path = 'character'), + function(df, path, source = NULL, mode = "append", ...){ if (is.null(source)) { - sqlCtx <- get(".sparkRSQLsc", envir = .sparkREnv) - source <- callJMethod(sqlCtx, "getConf", "spark.sql.sources.default", + sqlContext <- get(".sparkRSQLsc", envir = .sparkREnv) + source <- callJMethod(sqlContext, "getConf", "spark.sql.sources.default", "org.apache.spark.sql.parquet") } allModes <- c("append", "overwrite", "error", "ignore") @@ -1230,6 +1333,14 @@ setMethod("saveDF", callJMethod(df@sdf, "save", source, jmode, options) }) +#' @rdname write.df +#' @aliases saveDF +#' @export +setMethod("saveDF", + signature(df = "DataFrame", path = 'character'), + function(df, path, source = NULL, mode = "append", ...){ + write.df(df, path, source, mode, ...) + }) #' saveAsTable #' @@ -1258,9 +1369,9 @@ setMethod("saveDF", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' saveAsTable(df, "myfile") #' } setMethod("saveAsTable", @@ -1268,8 +1379,8 @@ setMethod("saveAsTable", mode = 'character'), function(df, tableName, source = NULL, mode="append", ...){ if (is.null(source)) { - sqlCtx <- get(".sparkRSQLsc", envir = .sparkREnv) - source <- callJMethod(sqlCtx, "getConf", "spark.sql.sources.default", + sqlContext <- get(".sparkRSQLsc", envir = .sparkREnv) + source <- callJMethod(sqlContext, "getConf", "spark.sql.sources.default", "org.apache.spark.sql.parquet") } allModes <- c("append", "overwrite", "error", "ignore") @@ -1295,9 +1406,9 @@ setMethod("saveAsTable", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' describe(df) #' describe(df, "col1") #' describe(df, "col1", "col2") @@ -1318,3 +1429,128 @@ setMethod("describe", sdf <- callJMethod(x@sdf, "describe", listToSeq(colList)) dataFrame(sdf) }) + +#' dropna +#' +#' Returns a new DataFrame omitting rows with null values. +#' +#' @param x A SparkSQL DataFrame. +#' @param how "any" or "all". +#' if "any", drop a row if it contains any nulls. +#' if "all", drop a row only if all its values are null. +#' if minNonNulls is specified, how is ignored. +#' @param minNonNulls If specified, drop rows that have less than +#' minNonNulls non-null values. +#' This overwrites the how parameter. +#' @param cols Optional list of column names to consider. +#' @return A DataFrame +#' +#' @rdname nafunctions +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' dropna(df) +#' } +setMethod("dropna", + signature(x = "DataFrame"), + function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) { + how <- match.arg(how) + if (is.null(cols)) { + cols <- columns(x) + } + if (is.null(minNonNulls)) { + minNonNulls <- if (how == "any") { length(cols) } else { 1 } + } + + naFunctions <- callJMethod(x@sdf, "na") + sdf <- callJMethod(naFunctions, "drop", + as.integer(minNonNulls), listToSeq(as.list(cols))) + dataFrame(sdf) + }) + +#' @aliases dropna +#' @export +setMethod("na.omit", + signature(x = "DataFrame"), + function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) { + dropna(x, how, minNonNulls, cols) + }) + +#' fillna +#' +#' Replace null values. +#' +#' @param x A SparkSQL DataFrame. +#' @param value Value to replace null values with. +#' Should be an integer, numeric, character or named list. +#' If the value is a named list, then cols is ignored and +#' value must be a mapping from column name (character) to +#' replacement value. The replacement value must be an +#' integer, numeric or character. +#' @param cols optional list of column names to consider. +#' Columns specified in cols that do not have matching data +#' type are ignored. For example, if value is a character, and +#' subset contains a non-character column, then the non-character +#' column is simply ignored. +#' @return A DataFrame +#' +#' @rdname nafunctions +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' fillna(df, 1) +#' fillna(df, list("age" = 20, "name" = "unknown")) +#' } +setMethod("fillna", + signature(x = "DataFrame"), + function(x, value, cols = NULL) { + if (!(class(value) %in% c("integer", "numeric", "character", "list"))) { + stop("value should be an integer, numeric, charactor or named list.") + } + + if (class(value) == "list") { + # Check column names in the named list + colNames <- names(value) + if (length(colNames) == 0 || !all(colNames != "")) { + stop("value should be an a named list with each name being a column name.") + } + + # Convert to the named list to an environment to be passed to JVM + valueMap <- new.env() + for (col in colNames) { + # Check each item in the named list is of valid type + v <- value[[col]] + if (!(class(v) %in% c("integer", "numeric", "character"))) { + stop("Each item in value should be an integer, numeric or charactor.") + } + valueMap[[col]] <- v + } + + # When value is a named list, caller is expected not to pass in cols + if (!is.null(cols)) { + warning("When value is a named list, cols is ignored!") + cols <- NULL + } + + value <- valueMap + } else if (is.integer(value)) { + # Cast an integer to a numeric + value <- as.numeric(value) + } + + naFunctions <- callJMethod(x@sdf, "na") + sdf <- if (length(cols) == 0) { + callJMethod(naFunctions, "fill", value) + } else { + callJMethod(naFunctions, "fill", value, listToSeq(as.list(cols))) + } + dataFrame(sdf) + }) diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index 73999a6737032..0513299515644 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -67,8 +67,8 @@ setMethod("initialize", "RDD", function(.Object, jrdd, serializedMode, }) setMethod("show", "RDD", - function(.Object) { - cat(paste(callJMethod(.Object@jrdd, "toString"), "\n", sep="")) + function(object) { + cat(paste(callJMethod(getJRDD(object), "toString"), "\n", sep="")) }) setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) { @@ -239,7 +239,7 @@ setMethod("cache", # @aliases persist,RDD-method setMethod("persist", signature(x = "RDD", newLevel = "character"), - function(x, newLevel) { + function(x, newLevel = "MEMORY_ONLY") { callJMethod(getJRDD(x), "persist", getStorageLevel(newLevel)) x@env$isCached <- TRUE x @@ -927,7 +927,7 @@ setMethod("takeSample", signature(x = "RDD", withReplacement = "logical", MAXINT))))) # TODO(zongheng): investigate if this call is an in-place shuffle? - sample(samples)[1:total] + base::sample(samples)[1:total] }) # Creates tuples of the elements in this RDD by applying a function. @@ -996,7 +996,7 @@ setMethod("coalesce", if (shuffle || numPartitions > SparkR:::numPartitions(x)) { func <- function(partIndex, part) { set.seed(partIndex) # partIndex as seed - start <- as.integer(sample(numPartitions, 1) - 1) + start <- as.integer(base::sample(numPartitions, 1) - 1) lapply(seq_along(part), function(i) { pos <- (start + i) %% numPartitions diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R index cae06e6af2bff..22a4b5bf86ebd 100644 --- a/R/pkg/R/SQLContext.R +++ b/R/pkg/R/SQLContext.R @@ -69,7 +69,7 @@ infer_type <- function(x) { #' #' Converts an RDD to a DataFrame by infer the types. #' -#' @param sqlCtx A SQLContext +#' @param sqlContext A SQLContext #' @param data An RDD or list or data.frame #' @param schema a list of column names or named list (StructType), optional #' @return an DataFrame @@ -77,13 +77,13 @@ infer_type <- function(x) { #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x))) -#' df <- createDataFrame(sqlCtx, rdd) +#' df <- createDataFrame(sqlContext, rdd) #' } # TODO(davies): support sampling and infer type from NA -createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { +createDataFrame <- function(sqlContext, data, schema = NULL, samplingRatio = 1.0) { if (is.data.frame(data)) { # get the names of columns, they will be put into RDD schema <- names(data) @@ -102,7 +102,7 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { }) } if (is.list(data)) { - sc <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "getJavaSparkContext", sqlCtx) + sc <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "getJavaSparkContext", sqlContext) rdd <- parallelize(sc, data) } else if (inherits(data, "RDD")) { rdd <- data @@ -146,7 +146,7 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { jrdd <- getJRDD(lapply(rdd, function(x) x), "row") srdd <- callJMethod(jrdd, "rdd") sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "createDF", - srdd, schema$jobj, sqlCtx) + srdd, schema$jobj, sqlContext) dataFrame(sdf) } @@ -161,7 +161,7 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { # @examples #\dontrun{ # sc <- sparkR.init() -# sqlCtx <- sparkRSQL.init(sc) +# sqlContext <- sparkRSQL.init(sc) # rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x))) # df <- toDF(rdd) # } @@ -170,14 +170,14 @@ setGeneric("toDF", function(x, ...) { standardGeneric("toDF") }) setMethod("toDF", signature(x = "RDD"), function(x, ...) { - sqlCtx <- if (exists(".sparkRHivesc", envir = .sparkREnv)) { + sqlContext <- if (exists(".sparkRHivesc", envir = .sparkREnv)) { get(".sparkRHivesc", envir = .sparkREnv) } else if (exists(".sparkRSQLsc", envir = .sparkREnv)) { get(".sparkRSQLsc", envir = .sparkREnv) } else { stop("no SQL context available") } - createDataFrame(sqlCtx, x, ...) + createDataFrame(sqlContext, x, ...) }) #' Create a DataFrame from a JSON file. @@ -185,24 +185,24 @@ setMethod("toDF", signature(x = "RDD"), #' Loads a JSON file (one object per line), returning the result as a DataFrame #' It goes through the entire dataset once to determine the schema. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param path Path of file to read. A vector of multiple paths is allowed. #' @return DataFrame #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' } -jsonFile <- function(sqlCtx, path) { +jsonFile <- function(sqlContext, path) { # Allow the user to have a more flexible definiton of the text file path path <- normalizePath(path) # Convert a string vector of paths to a string containing comma separated paths path <- paste(path, collapse = ",") - sdf <- callJMethod(sqlCtx, "jsonFile", path) + sdf <- callJMethod(sqlContext, "jsonFile", path) dataFrame(sdf) } @@ -211,7 +211,7 @@ jsonFile <- function(sqlCtx, path) { # # Loads an RDD storing one JSON object per string as a DataFrame. # -# @param sqlCtx SQLContext to use +# @param sqlContext SQLContext to use # @param rdd An RDD of JSON string # @param schema A StructType object to use as schema # @param samplingRatio The ratio of simpling used to infer the schema @@ -220,16 +220,16 @@ jsonFile <- function(sqlCtx, path) { # @examples #\dontrun{ # sc <- sparkR.init() -# sqlCtx <- sparkRSQL.init(sc) +# sqlContext <- sparkRSQL.init(sc) # rdd <- texFile(sc, "path/to/json") -# df <- jsonRDD(sqlCtx, rdd) +# df <- jsonRDD(sqlContext, rdd) # } # TODO: support schema -jsonRDD <- function(sqlCtx, rdd, schema = NULL, samplingRatio = 1.0) { +jsonRDD <- function(sqlContext, rdd, schema = NULL, samplingRatio = 1.0) { rdd <- serializeToString(rdd) if (is.null(schema)) { - sdf <- callJMethod(sqlCtx, "jsonRDD", callJMethod(getJRDD(rdd), "rdd"), samplingRatio) + sdf <- callJMethod(sqlContext, "jsonRDD", callJMethod(getJRDD(rdd), "rdd"), samplingRatio) dataFrame(sdf) } else { stop("not implemented") @@ -241,64 +241,63 @@ jsonRDD <- function(sqlCtx, rdd, schema = NULL, samplingRatio = 1.0) { #' #' Loads a Parquet file, returning the result as a DataFrame. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param ... Path(s) of parquet file(s) to read. #' @return DataFrame #' @export # TODO: Implement saveasParquetFile and write examples for both -parquetFile <- function(sqlCtx, ...) { +parquetFile <- function(sqlContext, ...) { # Allow the user to have a more flexible definiton of the text file path paths <- lapply(list(...), normalizePath) - sdf <- callJMethod(sqlCtx, "parquetFile", paths) + sdf <- callJMethod(sqlContext, "parquetFile", paths) dataFrame(sdf) } #' SQL Query -#' +#' #' Executes a SQL query using Spark, returning the result as a DataFrame. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param sqlQuery A character vector containing the SQL query #' @return DataFrame #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' registerTempTable(df, "table") -#' new_df <- sql(sqlCtx, "SELECT * FROM table") +#' new_df <- sql(sqlContext, "SELECT * FROM table") #' } -sql <- function(sqlCtx, sqlQuery) { - sdf <- callJMethod(sqlCtx, "sql", sqlQuery) - dataFrame(sdf) +sql <- function(sqlContext, sqlQuery) { + sdf <- callJMethod(sqlContext, "sql", sqlQuery) + dataFrame(sdf) } - #' Create a DataFrame from a SparkSQL Table #' #' Returns the specified Table as a DataFrame. The Table must have already been registered #' in the SQLContext. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param tableName The SparkSQL Table to convert to a DataFrame. #' @return DataFrame #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' registerTempTable(df, "table") -#' new_df <- table(sqlCtx, "table") +#' new_df <- table(sqlContext, "table") #' } -table <- function(sqlCtx, tableName) { - sdf <- callJMethod(sqlCtx, "table", tableName) +table <- function(sqlContext, tableName) { + sdf <- callJMethod(sqlContext, "table", tableName) dataFrame(sdf) } @@ -307,22 +306,22 @@ table <- function(sqlCtx, tableName) { #' #' Returns a DataFrame containing names of tables in the given database. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param databaseName name of the database #' @return a DataFrame #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' tables(sqlCtx, "hive") +#' sqlContext <- sparkRSQL.init(sc) +#' tables(sqlContext, "hive") #' } -tables <- function(sqlCtx, databaseName = NULL) { +tables <- function(sqlContext, databaseName = NULL) { jdf <- if (is.null(databaseName)) { - callJMethod(sqlCtx, "tables") + callJMethod(sqlContext, "tables") } else { - callJMethod(sqlCtx, "tables", databaseName) + callJMethod(sqlContext, "tables", databaseName) } dataFrame(jdf) } @@ -332,22 +331,22 @@ tables <- function(sqlCtx, databaseName = NULL) { #' #' Returns the names of tables in the given database as an array. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param databaseName name of the database #' @return a list of table names #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' tableNames(sqlCtx, "hive") +#' sqlContext <- sparkRSQL.init(sc) +#' tableNames(sqlContext, "hive") #' } -tableNames <- function(sqlCtx, databaseName = NULL) { +tableNames <- function(sqlContext, databaseName = NULL) { if (is.null(databaseName)) { - callJMethod(sqlCtx, "tableNames") + callJMethod(sqlContext, "tableNames") } else { - callJMethod(sqlCtx, "tableNames", databaseName) + callJMethod(sqlContext, "tableNames", databaseName) } } @@ -356,58 +355,58 @@ tableNames <- function(sqlCtx, databaseName = NULL) { #' #' Caches the specified table in-memory. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param tableName The name of the table being cached #' @return DataFrame #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' registerTempTable(df, "table") -#' cacheTable(sqlCtx, "table") +#' cacheTable(sqlContext, "table") #' } -cacheTable <- function(sqlCtx, tableName) { - callJMethod(sqlCtx, "cacheTable", tableName) +cacheTable <- function(sqlContext, tableName) { + callJMethod(sqlContext, "cacheTable", tableName) } #' Uncache Table #' #' Removes the specified table from the in-memory cache. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param tableName The name of the table being uncached #' @return DataFrame #' @export #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #' path <- "path/to/file.json" -#' df <- jsonFile(sqlCtx, path) +#' df <- jsonFile(sqlContext, path) #' registerTempTable(df, "table") -#' uncacheTable(sqlCtx, "table") +#' uncacheTable(sqlContext, "table") #' } -uncacheTable <- function(sqlCtx, tableName) { - callJMethod(sqlCtx, "uncacheTable", tableName) +uncacheTable <- function(sqlContext, tableName) { + callJMethod(sqlContext, "uncacheTable", tableName) } #' Clear Cache #' #' Removes all cached tables from the in-memory cache. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @examples #' \dontrun{ -#' clearCache(sqlCtx) +#' clearCache(sqlContext) #' } -clearCache <- function(sqlCtx) { - callJMethod(sqlCtx, "clearCache") +clearCache <- function(sqlContext) { + callJMethod(sqlContext, "clearCache") } #' Drop Temporary Table @@ -415,22 +414,22 @@ clearCache <- function(sqlCtx) { #' Drops the temporary table with the given table name in the catalog. #' If the table has been cached/persisted before, it's also unpersisted. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param tableName The name of the SparkSQL table to be dropped. #' @examples #' \dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df <- loadDF(sqlCtx, path, "parquet") +#' sqlContext <- sparkRSQL.init(sc) +#' df <- read.df(sqlContext, path, "parquet") #' registerTempTable(df, "table") -#' dropTempTable(sqlCtx, "table") +#' dropTempTable(sqlContext, "table") #' } -dropTempTable <- function(sqlCtx, tableName) { +dropTempTable <- function(sqlContext, tableName) { if (class(tableName) != "character") { stop("tableName must be a string.") } - callJMethod(sqlCtx, "dropTempTable", tableName) + callJMethod(sqlContext, "dropTempTable", tableName) } #' Load an DataFrame @@ -441,7 +440,7 @@ dropTempTable <- function(sqlCtx, tableName) { #' If `source` is not specified, the default data source configured by #' "spark.sql.sources.default" will be used. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param path The path of files to load #' @param source the name of external data source #' @return DataFrame @@ -449,19 +448,37 @@ dropTempTable <- function(sqlCtx, tableName) { #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df <- load(sqlCtx, "path/to/file.json", source = "json") +#' sqlContext <- sparkRSQL.init(sc) +#' df <- read.df(sqlContext, "path/to/file.json", source = "json") #' } -loadDF <- function(sqlCtx, path = NULL, source = NULL, ...) { +read.df <- function(sqlContext, path = NULL, source = NULL, schema = NULL, ...) { options <- varargsToEnv(...) if (!is.null(path)) { options[['path']] <- path } - sdf <- callJMethod(sqlCtx, "load", source, options) + if (is.null(source)) { + sqlContext <- get(".sparkRSQLsc", envir = .sparkREnv) + source <- callJMethod(sqlContext, "getConf", "spark.sql.sources.default", + "org.apache.spark.sql.parquet") + } + if (!is.null(schema)) { + stopifnot(class(schema) == "structType") + sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "loadDF", sqlContext, source, + schema$jobj, options) + } else { + sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "loadDF", sqlContext, source, options) + } dataFrame(sdf) } +#' @aliases loadDF +#' @export + +loadDF <- function(sqlContext, path = NULL, source = NULL, schema = NULL, ...) { + read.df(sqlContext, path, source, schema, ...) +} + #' Create an external table #' #' Creates an external table based on the dataset in a data source, @@ -471,7 +488,7 @@ loadDF <- function(sqlCtx, path = NULL, source = NULL, ...) { #' If `source` is not specified, the default data source configured by #' "spark.sql.sources.default" will be used. #' -#' @param sqlCtx SQLContext to use +#' @param sqlContext SQLContext to use #' @param tableName A name of the table #' @param path The path of files to load #' @param source the name of external data source @@ -480,15 +497,15 @@ loadDF <- function(sqlCtx, path = NULL, source = NULL, ...) { #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) -#' df <- sparkRSQL.createExternalTable(sqlCtx, "myjson", path="path/to/json", source="json") +#' sqlContext <- sparkRSQL.init(sc) +#' df <- sparkRSQL.createExternalTable(sqlContext, "myjson", path="path/to/json", source="json") #' } -createExternalTable <- function(sqlCtx, tableName, path = NULL, source = NULL, ...) { +createExternalTable <- function(sqlContext, tableName, path = NULL, source = NULL, ...) { options <- varargsToEnv(...) if (!is.null(path)) { options[['path']] <- path } - sdf <- callJMethod(sqlCtx, "createExternalTable", tableName, source, options) + sdf <- callJMethod(sqlContext, "createExternalTable", tableName, source, options) dataFrame(sdf) } diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R index 95fb9ff0887b6..80e92d3105a36 100644 --- a/R/pkg/R/column.R +++ b/R/pkg/R/column.R @@ -55,12 +55,17 @@ operators <- list( "+" = "plus", "-" = "minus", "*" = "multiply", "/" = "divide", "%%" = "mod", "==" = "equalTo", ">" = "gt", "<" = "lt", "!=" = "notEqual", "<=" = "leq", ">=" = "geq", # we can not override `&&` and `||`, so use `&` and `|` instead - "&" = "and", "|" = "or" #, "!" = "unary_$bang" + "&" = "and", "|" = "or", #, "!" = "unary_$bang" + "^" = "pow" ) column_functions1 <- c("asc", "desc", "isNull", "isNotNull") column_functions2 <- c("like", "rlike", "startsWith", "endsWith", "getField", "getItem", "contains") functions <- c("min", "max", "sum", "avg", "mean", "count", "abs", "sqrt", - "first", "last", "lower", "upper", "sumDistinct") + "first", "last", "lower", "upper", "sumDistinct", + "acos", "asin", "atan", "cbrt", "ceiling", "cos", "cosh", "exp", + "expm1", "floor", "log", "log10", "log1p", "rint", "sign", + "sin", "sinh", "tan", "tanh", "toDegrees", "toRadians") +binary_mathfunctions<- c("atan2", "hypot") createOperator <- function(op) { setMethod(op, @@ -76,7 +81,11 @@ createOperator <- function(op) { if (class(e2) == "Column") { e2 <- e2@jc } - callJMethod(e1@jc, operators[[op]], e2) + if (op == "^") { + jc <- callJStatic("org.apache.spark.sql.functions", operators[[op]], e1@jc, e2) + } else { + callJMethod(e1@jc, operators[[op]], e2) + } } column(jc) }) @@ -106,11 +115,29 @@ createStaticFunction <- function(name) { setMethod(name, signature(x = "Column"), function(x) { + if (name == "ceiling") { + name <- "ceil" + } + if (name == "sign") { + name <- "signum" + } jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc) column(jc) }) } +createBinaryMathfunctions <- function(name) { + setMethod(name, + signature(y = "Column"), + function(y, x) { + if (class(x) == "Column") { + x <- x@jc + } + jc <- callJStatic("org.apache.spark.sql.functions", name, y@jc, x) + column(jc) + }) +} + createMethods <- function() { for (op in names(operators)) { createOperator(op) @@ -124,6 +151,9 @@ createMethods <- function() { for (x in functions) { createStaticFunction(x) } + for (name in binary_mathfunctions) { + createBinaryMathfunctions(name) + } } createMethods() @@ -131,6 +161,8 @@ createMethods() #' alias #' #' Set a new name for a column + +#' @rdname column setMethod("alias", signature(object = "Column"), function(object, data) { @@ -141,8 +173,12 @@ setMethod("alias", } }) +#' substr +#' #' An expression that returns a substring. #' +#' @rdname column +#' #' @param start starting position #' @param stop ending position setMethod("substr", signature(x = "Column"), @@ -152,6 +188,9 @@ setMethod("substr", signature(x = "Column"), }) #' Casts the column to a different data type. +#' +#' @rdname column +#' #' @examples #' \dontrun{ #' cast(df$age, "string") @@ -173,8 +212,8 @@ setMethod("cast", #' Approx Count Distinct #' -#' Returns the approximate number of distinct items in a group. -#' +#' @rdname column +#' @return the approximate number of distinct items in a group. setMethod("approxCountDistinct", signature(x = "Column"), function(x, rsd = 0.95) { @@ -184,8 +223,8 @@ setMethod("approxCountDistinct", #' Count Distinct #' -#' returns the number of distinct items in a group. -#' +#' @rdname column +#' @return the number of distinct items in a group. setMethod("countDistinct", signature(x = "Column"), function(x, ...) { @@ -197,3 +236,18 @@ setMethod("countDistinct", column(jc) }) +#' @rdname column +#' @aliases countDistinct +setMethod("n_distinct", + signature(x = "Column"), + function(x, ...) { + countDistinct(x, ...) + }) + +#' @rdname column +#' @aliases count +setMethod("n", + signature(x = "Column"), + function(x) { + count(x) + }) diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R index 380e8ebe8c8f4..12e09176c9f92 100644 --- a/R/pkg/R/generics.R +++ b/R/pkg/R/generics.R @@ -380,6 +380,14 @@ setGeneric("value", function(bcast) { standardGeneric("value") }) #################### DataFrame Methods ######################## +#' @rdname agg +#' @export +setGeneric("agg", function (x, ...) { standardGeneric("agg") }) + +#' @rdname arrange +#' @export +setGeneric("arrange", function(x, col, ...) { standardGeneric("arrange") }) + #' @rdname schema #' @export setGeneric("columns", function(x) {standardGeneric("columns") }) @@ -388,6 +396,20 @@ setGeneric("columns", function(x) {standardGeneric("columns") }) #' @export setGeneric("describe", function(x, col, ...) { standardGeneric("describe") }) +#' @rdname nafunctions +#' @export +setGeneric("dropna", + function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) { + standardGeneric("dropna") + }) + +#' @rdname nafunctions +#' @export +setGeneric("na.omit", + function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) { + standardGeneric("na.omit") + }) + #' @rdname schema #' @export setGeneric("dtypes", function(x) { standardGeneric("dtypes") }) @@ -400,10 +422,18 @@ setGeneric("explain", function(x, ...) { standardGeneric("explain") }) #' @export setGeneric("except", function(x, y) { standardGeneric("except") }) +#' @rdname nafunctions +#' @export +setGeneric("fillna", function(x, value, cols = NULL) { standardGeneric("fillna") }) + #' @rdname filter #' @export setGeneric("filter", function(x, condition) { standardGeneric("filter") }) +#' @rdname groupBy +#' @export +setGeneric("group_by", function(x, ...) { standardGeneric("group_by") }) + #' @rdname DataFrame #' @export setGeneric("groupBy", function(x, ...) { standardGeneric("groupBy") }) @@ -424,7 +454,11 @@ setGeneric("isLocal", function(x) { standardGeneric("isLocal") }) #' @export setGeneric("limit", function(x, num) {standardGeneric("limit") }) -#' @rdname sortDF +#' @rdname withColumn +#' @export +setGeneric("mutate", function(x, ...) {standardGeneric("mutate") }) + +#' @rdname arrange #' @export setGeneric("orderBy", function(x, col) { standardGeneric("orderBy") }) @@ -432,16 +466,27 @@ setGeneric("orderBy", function(x, col) { standardGeneric("orderBy") }) #' @export setGeneric("printSchema", function(x) { standardGeneric("printSchema") }) +#' @rdname withColumnRenamed +#' @export +setGeneric("rename", function(x, ...) { standardGeneric("rename") }) + #' @rdname registerTempTable #' @export setGeneric("registerTempTable", function(x, tableName) { standardGeneric("registerTempTable") }) -#' @rdname sampleDF +#' @rdname sample +#' @export +setGeneric("sample", + function(x, withReplacement, fraction, seed) { + standardGeneric("sample") + }) + +#' @rdname sample #' @export -setGeneric("sampleDF", +setGeneric("sample_frac", function(x, withReplacement, fraction, seed) { - standardGeneric("sampleDF") - }) + standardGeneric("sample_frac") + }) #' @rdname saveAsParquetFile #' @export @@ -453,9 +498,13 @@ setGeneric("saveAsTable", function(df, tableName, source, mode, ...) { standardGeneric("saveAsTable") }) -#' @rdname saveAsTable +#' @rdname write.df +#' @export +setGeneric("write.df", function(df, path, ...) { standardGeneric("write.df") }) + +#' @rdname write.df #' @export -setGeneric("saveDF", function(df, path, source, mode, ...) { standardGeneric("saveDF") }) +setGeneric("saveDF", function(df, path, ...) { standardGeneric("saveDF") }) #' @rdname schema #' @export @@ -473,9 +522,9 @@ setGeneric("selectExpr", function(x, expr, ...) { standardGeneric("selectExpr") #' @export setGeneric("showDF", function(x,...) { standardGeneric("showDF") }) -#' @rdname sortDF +#' @rdname agg #' @export -setGeneric("sortDF", function(x, col, ...) { standardGeneric("sortDF") }) +setGeneric("summarize", function(x,...) { standardGeneric("summarize") }) # @rdname tojson # @export @@ -521,6 +570,10 @@ setGeneric("avg", function(x, ...) { standardGeneric("avg") }) #' @export setGeneric("cast", function(x, dataType) { standardGeneric("cast") }) +#' @rdname column +#' @export +setGeneric("cbrt", function(x) { standardGeneric("cbrt") }) + #' @rdname column #' @export setGeneric("contains", function(x, ...) { standardGeneric("contains") }) @@ -544,6 +597,10 @@ setGeneric("getField", function(x, ...) { standardGeneric("getField") }) #' @export setGeneric("getItem", function(x, ...) { standardGeneric("getItem") }) +#' @rdname column +#' @export +setGeneric("hypot", function(y, x) { standardGeneric("hypot") }) + #' @rdname column #' @export setGeneric("isNull", function(x) { standardGeneric("isNull") }) @@ -564,6 +621,18 @@ setGeneric("like", function(x, ...) { standardGeneric("like") }) #' @export setGeneric("lower", function(x) { standardGeneric("lower") }) +#' @rdname column +#' @export +setGeneric("n", function(x) { standardGeneric("n") }) + +#' @rdname column +#' @export +setGeneric("n_distinct", function(x, ...) { standardGeneric("n_distinct") }) + +#' @rdname column +#' @export +setGeneric("rint", function(x, ...) { standardGeneric("rint") }) + #' @rdname column #' @export setGeneric("rlike", function(x, ...) { standardGeneric("rlike") }) @@ -576,6 +645,14 @@ setGeneric("startsWith", function(x, ...) { standardGeneric("startsWith") }) #' @export setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") }) +#' @rdname column +#' @export +setGeneric("toDegrees", function(x) { standardGeneric("toDegrees") }) + +#' @rdname column +#' @export +setGeneric("toRadians", function(x) { standardGeneric("toRadians") }) + #' @rdname column #' @export setGeneric("upper", function(x) { standardGeneric("upper") }) diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R index 02237b3672d6b..b758481997574 100644 --- a/R/pkg/R/group.R +++ b/R/pkg/R/group.R @@ -56,6 +56,7 @@ setMethod("show", "GroupedData", #' #' @param x a GroupedData #' @return a DataFrame +#' @rdname agg #' @export #' @examples #' \dontrun{ @@ -83,8 +84,6 @@ setMethod("count", #' df2 <- agg(df, age = "sum") # new column name will be created as 'SUM(age#0)' #' df2 <- agg(df, ageSum = sum(df$age)) # Creates a new column named ageSum #' } -setGeneric("agg", function (x, ...) { standardGeneric("agg") }) - setMethod("agg", signature(x = "GroupedData"), function(x, ...) { @@ -103,15 +102,20 @@ setMethod("agg", } } jcols <- lapply(cols, function(c) { c@jc }) - # the GroupedData.agg(col, cols*) API does not contain grouping Column - sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "aggWithGrouping", - x@sgd, listToSeq(jcols)) + sdf <- callJMethod(x@sgd, "agg", jcols[[1]], listToSeq(jcols[-1])) } else { stop("agg can only support Column or character") } dataFrame(sdf) }) +#' @rdname agg +#' @aliases agg +setMethod("summarize", + signature(x = "GroupedData"), + function(x, ...) { + agg(x, ...) + }) # sum/mean/avg/min/max methods <- c("sum", "mean", "avg", "min", "max") diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R index 7694652856da5..1e24286dbcae2 100644 --- a/R/pkg/R/pairRDD.R +++ b/R/pkg/R/pairRDD.R @@ -329,7 +329,7 @@ setMethod("reduceByKey", convertEnvsToList(keys, vals) } locallyReduced <- lapplyPartition(x, reduceVals) - shuffled <- partitionBy(locallyReduced, numPartitions) + shuffled <- partitionBy(locallyReduced, numToInt(numPartitions)) lapplyPartition(shuffled, reduceVals) }) @@ -436,7 +436,7 @@ setMethod("combineByKey", convertEnvsToList(keys, combiners) } locallyCombined <- lapplyPartition(x, combineLocally) - shuffled <- partitionBy(locallyCombined, numPartitions) + shuffled <- partitionBy(locallyCombined, numToInt(numPartitions)) mergeAfterShuffle <- function(part) { combiners <- new.env() keys <- new.env() diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R index c53d0a961016f..3169d7968f8fe 100644 --- a/R/pkg/R/serialize.R +++ b/R/pkg/R/serialize.R @@ -37,6 +37,14 @@ writeObject <- function(con, object, writeType = TRUE) { # passing in vectors as arrays and instead require arrays to be passed # as lists. type <- class(object)[[1]] # class of POSIXlt is c("POSIXlt", "POSIXt") + # Checking types is needed here, since ‘is.na’ only handles atomic vectors, + # lists and pairlists + if (type %in% c("integer", "character", "logical", "double", "numeric")) { + if (is.na(object)) { + object <- NULL + type <- "NULL" + } + } if (writeType) { writeType(con, type) } @@ -160,6 +168,14 @@ writeList <- function(con, arr) { } } +# Used to pass arrays where the elements can be of different types +writeGenericList <- function(con, list) { + writeInt(con, length(list)) + for (elem in list) { + writeObject(con, elem) + } +} + # Used to pass in hash maps required on Java side. writeEnv <- function(con, env) { len <- length(env) @@ -168,7 +184,7 @@ writeEnv <- function(con, env) { if (len > 0) { writeList(con, as.list(ls(env))) vals <- lapply(ls(env), function(x) { env[[x]] }) - writeList(con, as.list(vals)) + writeGenericList(con, as.list(vals)) } } diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R index bc82df01f0fff..2efd4f0742e77 100644 --- a/R/pkg/R/sparkR.R +++ b/R/pkg/R/sparkR.R @@ -222,19 +222,26 @@ sparkR.init <- function( #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRSQL.init(sc) +#' sqlContext <- sparkRSQL.init(sc) #'} -sparkRSQL.init <- function(jsc) { +sparkRSQL.init <- function(jsc = NULL) { if (exists(".sparkRSQLsc", envir = .sparkREnv)) { return(get(".sparkRSQLsc", envir = .sparkREnv)) } - sqlCtx <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", - "createSQLContext", - jsc) - assign(".sparkRSQLsc", sqlCtx, envir = .sparkREnv) - sqlCtx + # If jsc is NULL, create a Spark Context + sc <- if (is.null(jsc)) { + sparkR.init() + } else { + jsc + } + + sqlContext <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", + "createSQLContext", + sc) + assign(".sparkRSQLsc", sqlContext, envir = .sparkREnv) + sqlContext } #' Initialize a new HiveContext. @@ -246,15 +253,22 @@ sparkRSQL.init <- function(jsc) { #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' sqlCtx <- sparkRHive.init(sc) +#' sqlContext <- sparkRHive.init(sc) #'} -sparkRHive.init <- function(jsc) { +sparkRHive.init <- function(jsc = NULL) { if (exists(".sparkRHivesc", envir = .sparkREnv)) { return(get(".sparkRHivesc", envir = .sparkREnv)) } - ssc <- callJMethod(jsc, "sc") + # If jsc is NULL, create a Spark Context + sc <- if (is.null(jsc)) { + sparkR.init() + } else { + jsc + } + + ssc <- callJMethod(sc, "sc") hiveCtx <- tryCatch({ newJObject("org.apache.spark.sql.hive.HiveContext", ssc) }, error = function(err) { @@ -264,3 +278,47 @@ sparkRHive.init <- function(jsc) { assign(".sparkRHivesc", hiveCtx, envir = .sparkREnv) hiveCtx } + +#' Assigns a group ID to all the jobs started by this thread until the group ID is set to a +#' different value or cleared. +#' +#' @param sc existing spark context +#' @param groupid the ID to be assigned to job groups +#' @param description description for the the job group ID +#' @param interruptOnCancel flag to indicate if the job is interrupted on job cancellation +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' setJobGroup(sc, "myJobGroup", "My job group description", TRUE) +#'} + +setJobGroup <- function(sc, groupId, description, interruptOnCancel) { + callJMethod(sc, "setJobGroup", groupId, description, interruptOnCancel) +} + +#' Clear current job group ID and its description +#' +#' @param sc existing spark context +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' clearJobGroup(sc) +#'} + +clearJobGroup <- function(sc) { + callJMethod(sc, "clearJobGroup") +} + +#' Cancel active jobs for the specified group +#' +#' @param sc existing spark context +#' @param groupId the ID of job group to be cancelled +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' cancelJobGroup(sc, "myJobGroup") +#'} + +cancelJobGroup <- function(sc, groupId) { + callJMethod(sc, "cancelJobGroup", groupId) +} diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R index 0e7b7bd5a5b34..69b2700191c9a 100644 --- a/R/pkg/R/utils.R +++ b/R/pkg/R/utils.R @@ -122,13 +122,49 @@ hashCode <- function(key) { intBits <- packBits(rawToBits(rawVec), "integer") as.integer(bitwXor(intBits[2], intBits[1])) } else if (class(key) == "character") { - .Call("stringHashCode", key) + # TODO: SPARK-7839 means we might not have the native library available + if (is.loaded("stringHashCode")) { + .Call("stringHashCode", key) + } else { + n <- nchar(key) + if (n == 0) { + 0L + } else { + asciiVals <- sapply(charToRaw(key), function(x) { strtoi(x, 16L) }) + hashC <- 0 + for (k in 1:length(asciiVals)) { + hashC <- mult31AndAdd(hashC, asciiVals[k]) + } + as.integer(hashC) + } + } } else { warning(paste("Could not hash object, returning 0", sep = "")) as.integer(0) } } +# Helper function used to wrap a 'numeric' value to integer bounds. +# Useful for implementing C-like integer arithmetic +wrapInt <- function(value) { + if (value > .Machine$integer.max) { + value <- value - 2 * .Machine$integer.max - 2 + } else if (value < -1 * .Machine$integer.max) { + value <- 2 * .Machine$integer.max + value + 2 + } + value +} + +# Multiply `val` by 31 and add `addVal` to the result. Ensures that +# integer-overflows are handled at every step. +mult31AndAdd <- function(val, addVal) { + vec <- c(bitwShiftL(val, c(4,3,2,1,0)), addVal) + Reduce(function(a, b) { + wrapInt(as.numeric(a) + as.numeric(b)) + }, + vec) +} + # Create a new RDD with serializedMode == "byte". # Return itself if already in "byte" format. serializeToBytes <- function(rdd) { diff --git a/R/pkg/inst/profile/shell.R b/R/pkg/inst/profile/shell.R index 33478d9e29995..773b6ecf582d9 100644 --- a/R/pkg/inst/profile/shell.R +++ b/R/pkg/inst/profile/shell.R @@ -24,10 +24,10 @@ old <- getOption("defaultPackages") options(defaultPackages = c(old, "SparkR")) - sc <- SparkR::sparkR.init(Sys.getenv("MASTER", unset = "")) + sc <- SparkR::sparkR.init() assign("sc", sc, envir=.GlobalEnv) - sqlCtx <- SparkR::sparkRSQL.init(sc) - assign("sqlCtx", sqlCtx, envir=.GlobalEnv) + sqlContext <- SparkR::sparkRSQL.init(sc) + assign("sqlContext", sqlContext, envir=.GlobalEnv) cat("\n Welcome to SparkR!") - cat("\n Spark context is available as sc, SQL context is available as sqlCtx\n") + cat("\n Spark context is available as sc, SQL context is available as sqlContext\n") } diff --git a/R/pkg/inst/tests/test_context.R b/R/pkg/inst/tests/test_context.R index e4aab37436a74..513bbc8e62059 100644 --- a/R/pkg/inst/tests/test_context.R +++ b/R/pkg/inst/tests/test_context.R @@ -48,3 +48,10 @@ test_that("rdd GC across sparkR.stop", { count(rdd3) count(rdd4) }) + +test_that("job group functions can be called", { + sc <- sparkR.init() + setJobGroup(sc, "groupId", "job description", TRUE) + cancelJobGroup(sc, "groupId") + clearJobGroup(sc) +}) diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R index 7a42e289fcd9e..8946348ef801c 100644 --- a/R/pkg/inst/tests/test_sparkSQL.R +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -23,7 +23,7 @@ context("SparkSQL functions") sc <- sparkR.init() -sqlCtx <- sparkRSQL.init(sc) +sqlContext <- sparkRSQL.init(sc) mockLines <- c("{\"name\":\"Michael\"}", "{\"name\":\"Andy\", \"age\":30}", @@ -32,6 +32,15 @@ jsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp") parquetPath <- tempfile(pattern="sparkr-test", fileext=".parquet") writeLines(mockLines, jsonPath) +# For test nafunctions, like dropna(), fillna(),... +mockLinesNa <- c("{\"name\":\"Bob\",\"age\":16,\"height\":176.5}", + "{\"name\":\"Alice\",\"age\":null,\"height\":164.3}", + "{\"name\":\"David\",\"age\":60,\"height\":null}", + "{\"name\":\"Amy\",\"age\":null,\"height\":null}", + "{\"name\":null,\"age\":null,\"height\":null}") +jsonPathNa <- tempfile(pattern="sparkr-test", fileext=".tmp") +writeLines(mockLinesNa, jsonPathNa) + test_that("infer types", { expect_equal(infer_type(1L), "integer") expect_equal(infer_type(1.0), "double") @@ -67,31 +76,68 @@ test_that("structType and structField", { test_that("create DataFrame from RDD", { rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) - df <- createDataFrame(sqlCtx, rdd, list("a", "b")) + df <- createDataFrame(sqlContext, rdd, list("a", "b")) expect_true(inherits(df, "DataFrame")) expect_true(count(df) == 10) expect_equal(columns(df), c("a", "b")) expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) - df <- createDataFrame(sqlCtx, rdd) + df <- createDataFrame(sqlContext, rdd) expect_true(inherits(df, "DataFrame")) expect_equal(columns(df), c("_1", "_2")) schema <- structType(structField(x = "a", type = "integer", nullable = TRUE), structField(x = "b", type = "string", nullable = TRUE)) - df <- createDataFrame(sqlCtx, rdd, schema) + df <- createDataFrame(sqlContext, rdd, schema) expect_true(inherits(df, "DataFrame")) expect_equal(columns(df), c("a", "b")) expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) }) - df <- createDataFrame(sqlCtx, rdd) + df <- createDataFrame(sqlContext, rdd) expect_true(inherits(df, "DataFrame")) expect_true(count(df) == 10) expect_equal(columns(df), c("a", "b")) expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) }) +test_that("convert NAs to null type in DataFrames", { + rdd <- parallelize(sc, list(list(1L, 2L), list(NA, 4L))) + df <- createDataFrame(sqlContext, rdd, list("a", "b")) + expect_true(is.na(collect(df)[2, "a"])) + expect_equal(collect(df)[2, "b"], 4L) + + l <- data.frame(x = 1L, y = c(1L, NA_integer_, 3L)) + df <- createDataFrame(sqlContext, l) + expect_equal(collect(df)[2, "x"], 1L) + expect_true(is.na(collect(df)[2, "y"])) + + rdd <- parallelize(sc, list(list(1, 2), list(NA, 4))) + df <- createDataFrame(sqlContext, rdd, list("a", "b")) + expect_true(is.na(collect(df)[2, "a"])) + expect_equal(collect(df)[2, "b"], 4) + + l <- data.frame(x = 1, y = c(1, NA_real_, 3)) + df <- createDataFrame(sqlContext, l) + expect_equal(collect(df)[2, "x"], 1) + expect_true(is.na(collect(df)[2, "y"])) + + l <- list("a", "b", NA, "d") + df <- createDataFrame(sqlContext, l) + expect_true(is.na(collect(df)[3, "_1"])) + expect_equal(collect(df)[4, "_1"], "d") + + l <- list("a", "b", NA_character_, "d") + df <- createDataFrame(sqlContext, l) + expect_true(is.na(collect(df)[3, "_1"])) + expect_equal(collect(df)[4, "_1"], "d") + + l <- list(TRUE, FALSE, NA, TRUE) + df <- createDataFrame(sqlContext, l) + expect_true(is.na(collect(df)[3, "_1"])) + expect_equal(collect(df)[4, "_1"], TRUE) +}) + test_that("toDF", { rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) df <- toDF(rdd, list("a", "b")) @@ -121,17 +167,17 @@ test_that("toDF", { test_that("create DataFrame from list or data.frame", { l <- list(list(1, 2), list(3, 4)) - df <- createDataFrame(sqlCtx, l, c("a", "b")) + df <- createDataFrame(sqlContext, l, c("a", "b")) expect_equal(columns(df), c("a", "b")) l <- list(list(a=1, b=2), list(a=3, b=4)) - df <- createDataFrame(sqlCtx, l) + df <- createDataFrame(sqlContext, l) expect_equal(columns(df), c("a", "b")) a <- 1:3 b <- c("a", "b", "c") ldf <- data.frame(a, b) - df <- createDataFrame(sqlCtx, ldf) + df <- createDataFrame(sqlContext, ldf) expect_equal(columns(df), c("a", "b")) expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) expect_equal(count(df), 3) @@ -142,7 +188,7 @@ test_that("create DataFrame from list or data.frame", { test_that("create DataFrame with different data types", { l <- list(a = 1L, b = 2, c = TRUE, d = "ss", e = as.Date("2012-12-13"), f = as.POSIXct("2015-03-15 12:13:14.056")) - df <- createDataFrame(sqlCtx, list(l)) + df <- createDataFrame(sqlContext, list(l)) expect_equal(dtypes(df), list(c("a", "int"), c("b", "double"), c("c", "boolean"), c("d", "string"), c("e", "date"), c("f", "timestamp"))) expect_equal(count(df), 1) @@ -154,7 +200,7 @@ test_that("create DataFrame with different data types", { # e <- new.env() # assign("n", 3L, envir = e) # l <- list(1:10, list("a", "b"), e, list(a="aa", b=3L)) -# df <- createDataFrame(sqlCtx, list(l), c("a", "b", "c", "d")) +# df <- createDataFrame(sqlContext, list(l), c("a", "b", "c", "d")) # expect_equal(dtypes(df), list(c("a", "array"), c("b", "array"), # c("c", "map"), c("d", "struct"))) # expect_equal(count(df), 1) @@ -163,7 +209,7 @@ test_that("create DataFrame with different data types", { #}) test_that("jsonFile() on a local file returns a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) expect_true(inherits(df, "DataFrame")) expect_true(count(df) == 3) }) @@ -171,88 +217,88 @@ test_that("jsonFile() on a local file returns a DataFrame", { test_that("jsonRDD() on a RDD with json string", { rdd <- parallelize(sc, mockLines) expect_true(count(rdd) == 3) - df <- jsonRDD(sqlCtx, rdd) + df <- jsonRDD(sqlContext, rdd) expect_true(inherits(df, "DataFrame")) expect_true(count(df) == 3) rdd2 <- flatMap(rdd, function(x) c(x, x)) - df <- jsonRDD(sqlCtx, rdd2) + df <- jsonRDD(sqlContext, rdd2) expect_true(inherits(df, "DataFrame")) expect_true(count(df) == 6) }) test_that("test cache, uncache and clearCache", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) registerTempTable(df, "table1") - cacheTable(sqlCtx, "table1") - uncacheTable(sqlCtx, "table1") - clearCache(sqlCtx) - dropTempTable(sqlCtx, "table1") + cacheTable(sqlContext, "table1") + uncacheTable(sqlContext, "table1") + clearCache(sqlContext) + dropTempTable(sqlContext, "table1") }) test_that("test tableNames and tables", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) registerTempTable(df, "table1") - expect_true(length(tableNames(sqlCtx)) == 1) - df <- tables(sqlCtx) + expect_true(length(tableNames(sqlContext)) == 1) + df <- tables(sqlContext) expect_true(count(df) == 1) - dropTempTable(sqlCtx, "table1") + dropTempTable(sqlContext, "table1") }) test_that("registerTempTable() results in a queryable table and sql() results in a new DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) registerTempTable(df, "table1") - newdf <- sql(sqlCtx, "SELECT * FROM table1 where name = 'Michael'") + newdf <- sql(sqlContext, "SELECT * FROM table1 where name = 'Michael'") expect_true(inherits(newdf, "DataFrame")) expect_true(count(newdf) == 1) - dropTempTable(sqlCtx, "table1") + dropTempTable(sqlContext, "table1") }) test_that("insertInto() on a registered table", { - df <- loadDF(sqlCtx, jsonPath, "json") - saveDF(df, parquetPath, "parquet", "overwrite") - dfParquet <- loadDF(sqlCtx, parquetPath, "parquet") + df <- read.df(sqlContext, jsonPath, "json") + write.df(df, parquetPath, "parquet", "overwrite") + dfParquet <- read.df(sqlContext, parquetPath, "parquet") lines <- c("{\"name\":\"Bob\", \"age\":24}", "{\"name\":\"James\", \"age\":35}") jsonPath2 <- tempfile(pattern="jsonPath2", fileext=".tmp") parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet") writeLines(lines, jsonPath2) - df2 <- loadDF(sqlCtx, jsonPath2, "json") - saveDF(df2, parquetPath2, "parquet", "overwrite") - dfParquet2 <- loadDF(sqlCtx, parquetPath2, "parquet") + df2 <- read.df(sqlContext, jsonPath2, "json") + write.df(df2, parquetPath2, "parquet", "overwrite") + dfParquet2 <- read.df(sqlContext, parquetPath2, "parquet") registerTempTable(dfParquet, "table1") insertInto(dfParquet2, "table1") - expect_true(count(sql(sqlCtx, "select * from table1")) == 5) - expect_true(first(sql(sqlCtx, "select * from table1 order by age"))$name == "Michael") - dropTempTable(sqlCtx, "table1") + expect_true(count(sql(sqlContext, "select * from table1")) == 5) + expect_true(first(sql(sqlContext, "select * from table1 order by age"))$name == "Michael") + dropTempTable(sqlContext, "table1") registerTempTable(dfParquet, "table1") insertInto(dfParquet2, "table1", overwrite = TRUE) - expect_true(count(sql(sqlCtx, "select * from table1")) == 2) - expect_true(first(sql(sqlCtx, "select * from table1 order by age"))$name == "Bob") - dropTempTable(sqlCtx, "table1") + expect_true(count(sql(sqlContext, "select * from table1")) == 2) + expect_true(first(sql(sqlContext, "select * from table1 order by age"))$name == "Bob") + dropTempTable(sqlContext, "table1") }) test_that("table() returns a new DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) registerTempTable(df, "table1") - tabledf <- table(sqlCtx, "table1") + tabledf <- table(sqlContext, "table1") expect_true(inherits(tabledf, "DataFrame")) expect_true(count(tabledf) == 3) - dropTempTable(sqlCtx, "table1") + dropTempTable(sqlContext, "table1") }) test_that("toRDD() returns an RRDD", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) testRDD <- toRDD(df) expect_true(inherits(testRDD, "RDD")) expect_true(count(testRDD) == 3) }) test_that("union on two RDDs created from DataFrames returns an RRDD", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) RDD1 <- toRDD(df) RDD2 <- toRDD(df) unioned <- unionRDD(RDD1, RDD2) @@ -274,7 +320,7 @@ test_that("union on mixed serialization types correctly returns a byte RRDD", { writeLines(textLines, textPath) textRDD <- textFile(sc, textPath) - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) dfRDD <- toRDD(df) unionByte <- unionRDD(rdd, dfRDD) @@ -292,7 +338,7 @@ test_that("union on mixed serialization types correctly returns a byte RRDD", { test_that("objectFile() works with row serialization", { objectPath <- tempfile(pattern="spark-test", fileext=".tmp") - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) dfRDD <- toRDD(df) saveAsObjectFile(coalesce(dfRDD, 1L), objectPath) objectIn <- objectFile(sc, objectPath) @@ -303,7 +349,7 @@ test_that("objectFile() works with row serialization", { }) test_that("lapply() on a DataFrame returns an RDD with the correct columns", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) testRDD <- lapply(df, function(row) { row$newCol <- row$age + 5 row @@ -315,7 +361,7 @@ test_that("lapply() on a DataFrame returns an RDD with the correct columns", { }) test_that("collect() returns a data.frame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) rdf <- collect(df) expect_true(is.data.frame(rdf)) expect_true(names(rdf)[1] == "age") @@ -324,20 +370,20 @@ test_that("collect() returns a data.frame", { }) test_that("limit() returns DataFrame with the correct number of rows", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) dfLimited <- limit(df, 2) expect_true(inherits(dfLimited, "DataFrame")) expect_true(count(dfLimited) == 2) }) test_that("collect() and take() on a DataFrame return the same number of rows and columns", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) expect_true(nrow(collect(df)) == nrow(take(df, 10))) expect_true(ncol(collect(df)) == ncol(take(df, 10))) }) test_that("multiple pipeline transformations starting with a DataFrame result in an RDD with the correct values", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) first <- lapply(df, function(row) { row$age <- row$age + 5 row @@ -354,7 +400,7 @@ test_that("multiple pipeline transformations starting with a DataFrame result in }) test_that("cache(), persist(), and unpersist() on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) expect_false(df@env$isCached) cache(df) expect_true(df@env$isCached) @@ -373,7 +419,7 @@ test_that("cache(), persist(), and unpersist() on a DataFrame", { }) test_that("schema(), dtypes(), columns(), names() return the correct values/format", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) testSchema <- schema(df) expect_true(length(testSchema$fields()) == 2) expect_true(testSchema$fields()[[1]]$dataType.toString() == "LongType") @@ -394,7 +440,7 @@ test_that("schema(), dtypes(), columns(), names() return the correct values/form }) test_that("head() and first() return the correct data", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) testHead <- head(df) expect_true(nrow(testHead) == 3) expect_true(ncol(testHead) == 2) @@ -415,23 +461,27 @@ test_that("distinct() on DataFrames", { jsonPathWithDup <- tempfile(pattern="sparkr-test", fileext=".tmp") writeLines(lines, jsonPathWithDup) - df <- jsonFile(sqlCtx, jsonPathWithDup) + df <- jsonFile(sqlContext, jsonPathWithDup) uniques <- distinct(df) expect_true(inherits(uniques, "DataFrame")) expect_true(count(uniques) == 3) }) -test_that("sampleDF on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) - sampled <- sampleDF(df, FALSE, 1.0) +test_that("sample on a DataFrame", { + df <- jsonFile(sqlContext, jsonPath) + sampled <- sample(df, FALSE, 1.0) expect_equal(nrow(collect(sampled)), count(df)) expect_true(inherits(sampled, "DataFrame")) - sampled2 <- sampleDF(df, FALSE, 0.1) + sampled2 <- sample(df, FALSE, 0.1) expect_true(count(sampled2) < 3) + + # Also test sample_frac + sampled3 <- sample_frac(df, FALSE, 0.1) + expect_true(count(sampled3) < 3) }) test_that("select operators", { - df <- select(jsonFile(sqlCtx, jsonPath), "name", "age") + df <- select(jsonFile(sqlContext, jsonPath), "name", "age") expect_true(inherits(df$name, "Column")) expect_true(inherits(df[[2]], "Column")) expect_true(inherits(df[["age"]], "Column")) @@ -457,7 +507,7 @@ test_that("select operators", { }) test_that("select with column", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) df1 <- select(df, "name") expect_true(columns(df1) == c("name")) expect_true(count(df1) == 3) @@ -468,7 +518,7 @@ test_that("select with column", { }) test_that("selectExpr() on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) selected <- selectExpr(df, "age * 2") expect_true(names(selected) == "(age * 2)") expect_equal(collect(selected), collect(select(df, df$age * 2L))) @@ -479,7 +529,7 @@ test_that("selectExpr() on a DataFrame", { }) test_that("column calculation", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) d <- collect(select(df, alias(df$age + 1, "age2"))) expect_true(names(d) == c("age2")) df2 <- select(df, lower(df$name), abs(df$age)) @@ -487,16 +537,29 @@ test_that("column calculation", { expect_true(count(df2) == 3) }) -test_that("load() from json file", { - df <- loadDF(sqlCtx, jsonPath, "json") +test_that("read.df() from json file", { + df <- read.df(sqlContext, jsonPath, "json") expect_true(inherits(df, "DataFrame")) expect_true(count(df) == 3) + + # Check if we can apply a user defined schema + schema <- structType(structField("name", type = "string"), + structField("age", type = "double")) + + df1 <- read.df(sqlContext, jsonPath, "json", schema) + expect_true(inherits(df1, "DataFrame")) + expect_equal(dtypes(df1), list(c("name", "string"), c("age", "double"))) + + # Run the same with loadDF + df2 <- loadDF(sqlContext, jsonPath, "json", schema) + expect_true(inherits(df2, "DataFrame")) + expect_equal(dtypes(df2), list(c("name", "string"), c("age", "double"))) }) -test_that("save() as parquet file", { - df <- loadDF(sqlCtx, jsonPath, "json") - saveDF(df, parquetPath, "parquet", mode="overwrite") - df2 <- loadDF(sqlCtx, parquetPath, "parquet") +test_that("write.df() as parquet file", { + df <- read.df(sqlContext, jsonPath, "json") + write.df(df, parquetPath, "parquet", mode="overwrite") + df2 <- read.df(sqlContext, parquetPath, "parquet") expect_true(inherits(df2, "DataFrame")) expect_true(count(df2) == 3) }) @@ -526,6 +589,7 @@ test_that("column operators", { c2 <- (- c + 1 - 2) * 3 / 4.0 c3 <- (c + c2 - c2) * c2 %% c2 c4 <- (c > c2) & (c2 <= c3) | (c == c2) & (c2 != c3) + c5 <- c2 ^ c3 ^ c4 }) test_that("column functions", { @@ -533,10 +597,34 @@ test_that("column functions", { c2 <- min(c) + max(c) + sum(c) + avg(c) + count(c) + abs(c) + sqrt(c) c3 <- lower(c) + upper(c) + first(c) + last(c) c4 <- approxCountDistinct(c) + countDistinct(c) + cast(c, "string") + c5 <- n(c) + n_distinct(c) + c5 <- acos(c) + asin(c) + atan(c) + cbrt(c) + c6 <- ceiling(c) + cos(c) + cosh(c) + exp(c) + expm1(c) + c7 <- floor(c) + log(c) + log10(c) + log1p(c) + rint(c) + c8 <- sign(c) + sin(c) + sinh(c) + tan(c) + tanh(c) + c9 <- toDegrees(c) + toRadians(c) +}) + +test_that("column binary mathfunctions", { + lines <- c("{\"a\":1, \"b\":5}", + "{\"a\":2, \"b\":6}", + "{\"a\":3, \"b\":7}", + "{\"a\":4, \"b\":8}") + jsonPathWithDup <- tempfile(pattern="sparkr-test", fileext=".tmp") + writeLines(lines, jsonPathWithDup) + df <- jsonFile(sqlContext, jsonPathWithDup) + expect_equal(collect(select(df, atan2(df$a, df$b)))[1, "ATAN2(a, b)"], atan2(1, 5)) + expect_equal(collect(select(df, atan2(df$a, df$b)))[2, "ATAN2(a, b)"], atan2(2, 6)) + expect_equal(collect(select(df, atan2(df$a, df$b)))[3, "ATAN2(a, b)"], atan2(3, 7)) + expect_equal(collect(select(df, atan2(df$a, df$b)))[4, "ATAN2(a, b)"], atan2(4, 8)) + expect_equal(collect(select(df, hypot(df$a, df$b)))[1, "HYPOT(a, b)"], sqrt(1^2 + 5^2)) + expect_equal(collect(select(df, hypot(df$a, df$b)))[2, "HYPOT(a, b)"], sqrt(2^2 + 6^2)) + expect_equal(collect(select(df, hypot(df$a, df$b)))[3, "HYPOT(a, b)"], sqrt(3^2 + 7^2)) + expect_equal(collect(select(df, hypot(df$a, df$b)))[4, "HYPOT(a, b)"], sqrt(4^2 + 8^2)) }) test_that("string operators", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) expect_equal(count(where(df, like(df$name, "A%"))), 1) expect_equal(count(where(df, startsWith(df$name, "A"))), 1) expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi") @@ -544,7 +632,7 @@ test_that("string operators", { }) test_that("group by", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) df1 <- agg(df, name = "max", age = "sum") expect_true(1 == count(df1)) df1 <- agg(df, age2 = max(df$age)) @@ -557,6 +645,13 @@ test_that("group by", { expect_true(inherits(df2, "DataFrame")) expect_true(3 == count(df2)) + # Also test group_by, summarize, mean + gd1 <- group_by(df, "name") + expect_true(inherits(gd1, "GroupedData")) + df_summarized <- summarize(gd, mean_age = mean(df$age)) + expect_true(inherits(df_summarized, "DataFrame")) + expect_true(3 == count(df_summarized)) + df3 <- agg(gd, age = "sum") expect_true(inherits(df3, "DataFrame")) expect_true(3 == count(df3)) @@ -573,12 +668,12 @@ test_that("group by", { expect_true(3 == count(max(gd, "age"))) }) -test_that("sortDF() and orderBy() on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) - sorted <- sortDF(df, df$age) +test_that("arrange() and orderBy() on a DataFrame", { + df <- jsonFile(sqlContext, jsonPath) + sorted <- arrange(df, df$age) expect_true(collect(sorted)[1,2] == "Michael") - sorted2 <- sortDF(df, "name") + sorted2 <- arrange(df, "name") expect_true(collect(sorted2)[2,"age"] == 19) sorted3 <- orderBy(df, asc(df$age)) @@ -591,7 +686,7 @@ test_that("sortDF() and orderBy() on a DataFrame", { }) test_that("filter() on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) filtered <- filter(df, "age > 20") expect_true(count(filtered) == 1) expect_true(collect(filtered)$name == "Andy") @@ -601,7 +696,7 @@ test_that("filter() on a DataFrame", { }) test_that("join() on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) mockLines2 <- c("{\"name\":\"Michael\", \"test\": \"yes\"}", "{\"name\":\"Andy\", \"test\": \"no\"}", @@ -609,7 +704,7 @@ test_that("join() on a DataFrame", { "{\"name\":\"Bob\", \"test\": \"yes\"}") jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") writeLines(mockLines2, jsonPath2) - df2 <- jsonFile(sqlCtx, jsonPath2) + df2 <- jsonFile(sqlContext, jsonPath2) joined <- join(df, df2) expect_equal(names(joined), c("age", "name", "name", "test")) @@ -632,7 +727,7 @@ test_that("join() on a DataFrame", { }) test_that("toJSON() returns an RDD of the correct values", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) testRDD <- toJSON(df) expect_true(inherits(testRDD, "RDD")) expect_true(SparkR:::getSerializedMode(testRDD) == "string") @@ -640,43 +735,44 @@ test_that("toJSON() returns an RDD of the correct values", { }) test_that("showDF()", { - df <- jsonFile(sqlCtx, jsonPath) - expect_output(showDF(df), "+----+-------+\n| age| name|\n+----+-------+\n|null|Michael|\n| 30| Andy|\n| 19| Justin|\n+----+-------+\n") + df <- jsonFile(sqlContext, jsonPath) + s <- capture.output(showDF(df)) + expect_output(s , "+----+-------+\n| age| name|\n+----+-------+\n|null|Michael|\n| 30| Andy|\n| 19| Justin|\n+----+-------+\n") }) test_that("isLocal()", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) expect_false(isLocal(df)) }) test_that("unionAll(), except(), and intersect() on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) lines <- c("{\"name\":\"Bob\", \"age\":24}", "{\"name\":\"Andy\", \"age\":30}", "{\"name\":\"James\", \"age\":35}") jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") writeLines(lines, jsonPath2) - df2 <- loadDF(sqlCtx, jsonPath2, "json") + df2 <- read.df(sqlContext, jsonPath2, "json") - unioned <- sortDF(unionAll(df, df2), df$age) + unioned <- arrange(unionAll(df, df2), df$age) expect_true(inherits(unioned, "DataFrame")) expect_true(count(unioned) == 6) expect_true(first(unioned)$name == "Michael") - excepted <- sortDF(except(df, df2), desc(df$age)) + excepted <- arrange(except(df, df2), desc(df$age)) expect_true(inherits(unioned, "DataFrame")) expect_true(count(excepted) == 2) expect_true(first(excepted)$name == "Justin") - intersected <- sortDF(intersect(df, df2), df$age) + intersected <- arrange(intersect(df, df2), df$age) expect_true(inherits(unioned, "DataFrame")) expect_true(count(intersected) == 1) expect_true(first(intersected)$name == "Andy") }) test_that("withColumn() and withColumnRenamed()", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) newDF <- withColumn(df, "newAge", df$age + 2) expect_true(length(columns(newDF)) == 3) expect_true(columns(newDF)[3] == "newAge") @@ -687,34 +783,146 @@ test_that("withColumn() and withColumnRenamed()", { expect_true(columns(newDF2)[1] == "newerAge") }) -test_that("saveDF() on DataFrame and works with parquetFile", { - df <- jsonFile(sqlCtx, jsonPath) - saveDF(df, parquetPath, "parquet", mode="overwrite") - parquetDF <- parquetFile(sqlCtx, parquetPath) +test_that("mutate() and rename()", { + df <- jsonFile(sqlContext, jsonPath) + newDF <- mutate(df, newAge = df$age + 2) + expect_true(length(columns(newDF)) == 3) + expect_true(columns(newDF)[3] == "newAge") + expect_true(first(filter(newDF, df$name != "Michael"))$newAge == 32) + + newDF2 <- rename(df, newerAge = df$age) + expect_true(length(columns(newDF2)) == 2) + expect_true(columns(newDF2)[1] == "newerAge") +}) + +test_that("write.df() on DataFrame and works with parquetFile", { + df <- jsonFile(sqlContext, jsonPath) + write.df(df, parquetPath, "parquet", mode="overwrite") + parquetDF <- parquetFile(sqlContext, parquetPath) expect_true(inherits(parquetDF, "DataFrame")) expect_equal(count(df), count(parquetDF)) }) test_that("parquetFile works with multiple input paths", { - df <- jsonFile(sqlCtx, jsonPath) - saveDF(df, parquetPath, "parquet", mode="overwrite") + df <- jsonFile(sqlContext, jsonPath) + write.df(df, parquetPath, "parquet", mode="overwrite") parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet") - saveDF(df, parquetPath2, "parquet", mode="overwrite") - parquetDF <- parquetFile(sqlCtx, parquetPath, parquetPath2) + write.df(df, parquetPath2, "parquet", mode="overwrite") + parquetDF <- parquetFile(sqlContext, parquetPath, parquetPath2) expect_true(inherits(parquetDF, "DataFrame")) expect_true(count(parquetDF) == count(df)*2) }) test_that("describe() on a DataFrame", { - df <- jsonFile(sqlCtx, jsonPath) + df <- jsonFile(sqlContext, jsonPath) stats <- describe(df, "age") - expect_true(collect(stats)[1, "summary"] == "count") - expect_true(collect(stats)[2, "age"] == 24.5) - expect_true(collect(stats)[3, "age"] == 5.5) + expect_equal(collect(stats)[1, "summary"], "count") + expect_equal(collect(stats)[2, "age"], "24.5") + expect_equal(collect(stats)[3, "age"], "5.5") stats <- describe(df) - expect_true(collect(stats)[4, "name"] == "Andy") - expect_true(collect(stats)[5, "age"] == 30.0) + expect_equal(collect(stats)[4, "name"], "Andy") + expect_equal(collect(stats)[5, "age"], "30") +}) + +test_that("dropna() on a DataFrame", { + df <- jsonFile(sqlContext, jsonPathNa) + rows <- collect(df) + + # drop with columns + + expected <- rows[!is.na(rows$name),] + actual <- collect(dropna(df, cols = "name")) + expect_true(identical(expected, actual)) + + expected <- rows[!is.na(rows$age),] + actual <- collect(dropna(df, cols = "age")) + row.names(expected) <- row.names(actual) + # identical on two dataframes does not work here. Don't know why. + # use identical on all columns as a workaround. + expect_true(identical(expected$age, actual$age)) + expect_true(identical(expected$height, actual$height)) + expect_true(identical(expected$name, actual$name)) + + expected <- rows[!is.na(rows$age) & !is.na(rows$height),] + actual <- collect(dropna(df, cols = c("age", "height"))) + expect_true(identical(expected, actual)) + + expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),] + actual <- collect(dropna(df)) + expect_true(identical(expected, actual)) + + # drop with how + + expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),] + actual <- collect(dropna(df)) + expect_true(identical(expected, actual)) + + expected <- rows[!is.na(rows$age) | !is.na(rows$height) | !is.na(rows$name),] + actual <- collect(dropna(df, "all")) + expect_true(identical(expected, actual)) + + expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),] + actual <- collect(dropna(df, "any")) + expect_true(identical(expected, actual)) + + expected <- rows[!is.na(rows$age) & !is.na(rows$height),] + actual <- collect(dropna(df, "any", cols = c("age", "height"))) + expect_true(identical(expected, actual)) + + expected <- rows[!is.na(rows$age) | !is.na(rows$height),] + actual <- collect(dropna(df, "all", cols = c("age", "height"))) + expect_true(identical(expected, actual)) + + # drop with threshold + + expected <- rows[as.integer(!is.na(rows$age)) + as.integer(!is.na(rows$height)) >= 2,] + actual <- collect(dropna(df, minNonNulls = 2, cols = c("age", "height"))) + expect_true(identical(expected, actual)) + + expected <- rows[as.integer(!is.na(rows$age)) + + as.integer(!is.na(rows$height)) + + as.integer(!is.na(rows$name)) >= 3,] + actual <- collect(dropna(df, minNonNulls = 3, cols = c("name", "age", "height"))) + expect_true(identical(expected, actual)) +}) + +test_that("fillna() on a DataFrame", { + df <- jsonFile(sqlContext, jsonPathNa) + rows <- collect(df) + + # fill with value + + expected <- rows + expected$age[is.na(expected$age)] <- 50 + expected$height[is.na(expected$height)] <- 50.6 + actual <- collect(fillna(df, 50.6)) + expect_true(identical(expected, actual)) + + expected <- rows + expected$name[is.na(expected$name)] <- "unknown" + actual <- collect(fillna(df, "unknown")) + expect_true(identical(expected, actual)) + + expected <- rows + expected$age[is.na(expected$age)] <- 50 + actual <- collect(fillna(df, 50.6, "age")) + expect_true(identical(expected, actual)) + + expected <- rows + expected$name[is.na(expected$name)] <- "unknown" + actual <- collect(fillna(df, "unknown", c("age", "name"))) + expect_true(identical(expected, actual)) + + # fill with named list + + expected <- rows + expected$age[is.na(expected$age)] <- 50 + expected$height[is.na(expected$height)] <- 50.6 + expected$name[is.na(expected$name)] <- "unknown" + actual <- collect(fillna(df, list("age" = 50, "height" = 50.6, "name" = "unknown"))) + expect_true(identical(expected, actual)) }) unlink(parquetPath) unlink(jsonPath) +unlink(jsonPathNa) diff --git a/R/pkg/src/Makefile b/R/pkg/src-native/Makefile similarity index 100% rename from R/pkg/src/Makefile rename to R/pkg/src-native/Makefile diff --git a/R/pkg/src/Makefile.win b/R/pkg/src-native/Makefile.win similarity index 100% rename from R/pkg/src/Makefile.win rename to R/pkg/src-native/Makefile.win diff --git a/R/pkg/src/string_hash_code.c b/R/pkg/src-native/string_hash_code.c similarity index 100% rename from R/pkg/src/string_hash_code.c rename to R/pkg/src-native/string_hash_code.c diff --git a/README.md b/README.md index c3afc4db9c63c..380422ca00dbe 100644 --- a/README.md +++ b/README.md @@ -3,8 +3,8 @@ Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a -rich set of higher-level tools including Spark SQL for SQL and structured -data processing, MLlib for machine learning, GraphX for graph processing, +rich set of higher-level tools including Spark SQL for SQL and DataFrames, +MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing. @@ -22,7 +22,7 @@ This README file only contains basic setup instructions. Spark is built using [Apache Maven](http://maven.apache.org/). To build Spark and its example programs, run: - mvn -DskipTests clean package + build/mvn -DskipTests clean package (You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at @@ -43,7 +43,7 @@ Try the following command, which should return 1000: Alternatively, if you prefer Python, you can use the Python shell: ./bin/pyspark - + And run the following command, which should also return 1000: >>> sc.parallelize(range(1000)).count() @@ -58,9 +58,9 @@ To run one of them, use `./bin/run-example [params]`. For example: will run the Pi example locally. You can set the MASTER environment variable when running examples to submit -examples to a cluster. This can be a mesos:// or spark:// URL, -"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run -locally with one thread, or "local[N]" to run locally with N threads. You +examples to a cluster. This can be a mesos:// or spark:// URL, +"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run +locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the `examples` package. For instance: @@ -75,8 +75,8 @@ can be run using: ./dev/run-tests -Please see the guidance on how to -[run all automated tests](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting). +Please see the guidance on how to +[run tests for a module, or individual tests](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools). ## A Note About Hadoop Versions diff --git a/assembly/pom.xml b/assembly/pom.xml index 626c8577e31fe..e9c6d26ccddc7 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml diff --git a/bagel/pom.xml b/bagel/pom.xml index 1f3dec91314f2..ed5c37e595a96 100644 --- a/bagel/pom.xml +++ b/bagel/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -40,6 +40,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.scalacheck scalacheck_${scala.binary.version} diff --git a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala index ccb262a4ee02a..fb10d734ac74b 100644 --- a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala +++ b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.bagel -import org.scalatest.{BeforeAndAfter, FunSuite, Assertions} +import org.scalatest.{BeforeAndAfter, Assertions} import org.scalatest.concurrent.Timeouts import org.scalatest.time.SpanSugar._ @@ -27,7 +27,7 @@ import org.apache.spark.storage.StorageLevel class TestVertex(val active: Boolean, val age: Int) extends Vertex with Serializable class TestMessage(val targetId: String) extends Message[String] with Serializable -class BagelSuite extends FunSuite with Assertions with BeforeAndAfter with Timeouts { +class BagelSuite extends SparkFunSuite with Assertions with BeforeAndAfter with Timeouts { var sc: SparkContext = _ diff --git a/bin/pyspark b/bin/pyspark index 8acad6113797d..f9dbddfa53560 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -17,24 +17,10 @@ # limitations under the License. # -# Figure out where Spark is installed export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" source "$SPARK_HOME"/bin/load-spark-env.sh - -function usage() { - if [ -n "$1" ]; then - echo $1 - fi - echo "Usage: ./bin/pyspark [options]" 1>&2 - "$SPARK_HOME"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 - exit $2 -} -export -f usage - -if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then - usage -fi +export _SPARK_CMD_USAGE="Usage: ./bin/pyspark [options]" # In Spark <= 1.1, setting IPYTHON=1 would cause the driver to be launched using the `ipython` # executable, while the worker would still be launched using PYSPARK_PYTHON. @@ -90,11 +76,7 @@ if [[ -n "$SPARK_TESTING" ]]; then unset YARN_CONF_DIR unset HADOOP_CONF_DIR export PYTHONHASHSEED=0 - if [[ -n "$PYSPARK_DOC_TEST" ]]; then - exec "$PYSPARK_DRIVER_PYTHON" -m doctest $1 - else - exec "$PYSPARK_DRIVER_PYTHON" $1 - fi + exec "$PYSPARK_DRIVER_PYTHON" -m $1 exit fi diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd index 09b4149c2a439..45e9e3def5121 100644 --- a/bin/pyspark2.cmd +++ b/bin/pyspark2.cmd @@ -21,6 +21,7 @@ rem Figure out where the Spark framework is installed set SPARK_HOME=%~dp0.. call %SPARK_HOME%\bin\load-spark-env.cmd +set _SPARK_CMD_USAGE=Usage: bin\pyspark.cmd [options] rem Figure out which Python to use. if "x%PYSPARK_DRIVER_PYTHON%"=="x" ( diff --git a/bin/spark-class b/bin/spark-class index c49d97ce5cf25..2b59e5df5736f 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -16,18 +16,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # -set -e # Figure out where Spark is installed export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" . "$SPARK_HOME"/bin/load-spark-env.sh -if [ -z "$1" ]; then - echo "Usage: spark-class []" 1>&2 - exit 1 -fi - # Find the java binary if [ -n "${JAVA_HOME}" ]; then RUNNER="${JAVA_HOME}/bin/java" @@ -64,24 +58,6 @@ fi SPARK_ASSEMBLY_JAR="${ASSEMBLY_DIR}/${ASSEMBLY_JARS}" -# Verify that versions of java used to build the jars and run Spark are compatible -if [ -n "$JAVA_HOME" ]; then - JAR_CMD="$JAVA_HOME/bin/jar" -else - JAR_CMD="jar" -fi - -if [ $(command -v "$JAR_CMD") ] ; then - jar_error_check=$("$JAR_CMD" -tf "$SPARK_ASSEMBLY_JAR" nonexistent/class/path 2>&1) - if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then - echo "Loading Spark jar with '$JAR_CMD' failed. " 1>&2 - echo "This is likely because Spark was compiled with Java 7 and run " 1>&2 - echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark " 1>&2 - echo "or build Spark with Java 6." 1>&2 - exit 1 - fi -fi - LAUNCH_CLASSPATH="$SPARK_ASSEMBLY_JAR" # Add the launcher build dir to the classpath if requested. @@ -98,9 +74,4 @@ CMD=() while IFS= read -d '' -r ARG; do CMD+=("$ARG") done < <("$RUNNER" -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@") - -if [ "${CMD[0]}" = "usage" ]; then - "${CMD[@]}" -else - exec "${CMD[@]}" -fi +exec "${CMD[@]}" diff --git a/bin/spark-shell b/bin/spark-shell index b3761b5e1375b..a6dc863d83fc6 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -29,20 +29,7 @@ esac set -o posix export FWDIR="$(cd "`dirname "$0"`"/..; pwd)" - -usage() { - if [ -n "$1" ]; then - echo "$1" - fi - echo "Usage: ./bin/spark-shell [options]" - "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 - exit "$2" -} -export -f usage - -if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then - usage "" 0 -fi +export _SPARK_CMD_USAGE="Usage: ./bin/spark-shell [options]" # SPARK-4161: scala does not assume use of the java classpath, # so we need to add the "-Dscala.usejavacp=true" flag manually. We diff --git a/bin/spark-shell2.cmd b/bin/spark-shell2.cmd index 00fd30fa38d36..251309d67f860 100644 --- a/bin/spark-shell2.cmd +++ b/bin/spark-shell2.cmd @@ -18,12 +18,7 @@ rem limitations under the License. rem set SPARK_HOME=%~dp0.. - -echo "%*" | findstr " \<--help\> \<-h\>" >nul -if %ERRORLEVEL% equ 0 ( - call :usage - exit /b 0 -) +set _SPARK_CMD_USAGE=Usage: .\bin\spark-shell.cmd [options] rem SPARK-4161: scala does not assume use of the java classpath, rem so we need to add the "-Dscala.usejavacp=true" flag manually. We @@ -37,16 +32,4 @@ if "x%SPARK_SUBMIT_OPTS%"=="x" ( set SPARK_SUBMIT_OPTS="%SPARK_SUBMIT_OPTS% -Dscala.usejavacp=true" :run_shell -call %SPARK_HOME%\bin\spark-submit2.cmd --class org.apache.spark.repl.Main %* -set SPARK_ERROR_LEVEL=%ERRORLEVEL% -if not "x%SPARK_LAUNCHER_USAGE_ERROR%"=="x" ( - call :usage - exit /b 1 -) -exit /b %SPARK_ERROR_LEVEL% - -:usage -echo %SPARK_LAUNCHER_USAGE_ERROR% -echo "Usage: .\bin\spark-shell.cmd [options]" >&2 -call %SPARK_HOME%\bin\spark-submit2.cmd --help 2>&1 | findstr /V "Usage" 1>&2 -goto :eof +%SPARK_HOME%\bin\spark-submit2.cmd --class org.apache.spark.repl.Main %* diff --git a/bin/spark-sql b/bin/spark-sql index ca1729f4cfcb4..4ea7bc6e39c07 100755 --- a/bin/spark-sql +++ b/bin/spark-sql @@ -17,41 +17,6 @@ # limitations under the License. # -# -# Shell script for starting the Spark SQL CLI - -# Enter posix mode for bash -set -o posix - -# NOTE: This exact class name is matched downstream by SparkSubmit. -# Any changes need to be reflected there. -export CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver" - -# Figure out where Spark is installed export FWDIR="$(cd "`dirname "$0"`"/..; pwd)" - -function usage { - if [ -n "$1" ]; then - echo "$1" - fi - echo "Usage: ./bin/spark-sql [options] [cli option]" - pattern="usage" - pattern+="\|Spark assembly has been built with Hive" - pattern+="\|NOTE: SPARK_PREPEND_CLASSES is set" - pattern+="\|Spark Command: " - pattern+="\|--help" - pattern+="\|=======" - - "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 - echo - echo "CLI options:" - "$FWDIR"/bin/spark-class "$CLASS" --help 2>&1 | grep -v "$pattern" 1>&2 - exit "$2" -} -export -f usage - -if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then - usage "" 0 -fi - -exec "$FWDIR"/bin/spark-submit --class "$CLASS" "$@" +export _SPARK_CMD_USAGE="Usage: ./bin/spark-sql [options] [cli option]" +exec "$FWDIR"/bin/spark-submit --class org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver "$@" diff --git a/bin/spark-submit b/bin/spark-submit index 0e0afe71a0f05..255378b0f077c 100755 --- a/bin/spark-submit +++ b/bin/spark-submit @@ -22,16 +22,4 @@ SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" # disable randomized hash for string in Python 3.3+ export PYTHONHASHSEED=0 -# Only define a usage function if an upstream script hasn't done so. -if ! type -t usage >/dev/null 2>&1; then - usage() { - if [ -n "$1" ]; then - echo "$1" - fi - "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit --help - exit "$2" - } - export -f usage -fi - exec "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@" diff --git a/bin/spark-submit2.cmd b/bin/spark-submit2.cmd index d3fc4a5cc3f6e..651376e526928 100644 --- a/bin/spark-submit2.cmd +++ b/bin/spark-submit2.cmd @@ -24,15 +24,4 @@ rem disable randomized hash for string in Python 3.3+ set PYTHONHASHSEED=0 set CLASS=org.apache.spark.deploy.SparkSubmit -call %~dp0spark-class2.cmd %CLASS% %* -set SPARK_ERROR_LEVEL=%ERRORLEVEL% -if not "x%SPARK_LAUNCHER_USAGE_ERROR%"=="x" ( - call :usage - exit /b 1 -) -exit /b %SPARK_ERROR_LEVEL% - -:usage -echo %SPARK_LAUNCHER_USAGE_ERROR% -call %SPARK_HOME%\bin\spark-class2.cmd %CLASS% --help -goto :eof +%~dp0spark-class2.cmd %CLASS% %* diff --git a/bin/sparkR b/bin/sparkR index 8c918e2b09aef..464c29f369424 100755 --- a/bin/sparkR +++ b/bin/sparkR @@ -17,23 +17,7 @@ # limitations under the License. # -# Figure out where Spark is installed export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" - source "$SPARK_HOME"/bin/load-spark-env.sh - -function usage() { - if [ -n "$1" ]; then - echo $1 - fi - echo "Usage: ./bin/sparkR [options]" 1>&2 - "$SPARK_HOME"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 - exit $2 -} -export -f usage - -if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then - usage -fi - +export _SPARK_CMD_USAGE="Usage: ./bin/sparkR [options]" exec "$SPARK_HOME"/bin/spark-submit sparkr-shell-main "$@" diff --git a/build/mvn b/build/mvn index 3561110a4c019..e8364181e8230 100755 --- a/build/mvn +++ b/build/mvn @@ -69,11 +69,14 @@ install_app() { # Install maven under the build/ folder install_mvn() { + local MVN_VERSION="3.3.3" + install_app \ - "http://archive.apache.org/dist/maven/maven-3/3.2.5/binaries" \ - "apache-maven-3.2.5-bin.tar.gz" \ - "apache-maven-3.2.5/bin/mvn" - MVN_BIN="${_DIR}/apache-maven-3.2.5/bin/mvn" + "http://archive.apache.org/dist/maven/maven-3/${MVN_VERSION}/binaries" \ + "apache-maven-${MVN_VERSION}-bin.tar.gz" \ + "apache-maven-${MVN_VERSION}/bin/mvn" + + MVN_BIN="${_DIR}/apache-maven-${MVN_VERSION}/bin/mvn" } # Install zinc under the build/ folder @@ -105,28 +108,16 @@ install_scala() { SCALA_LIBRARY="$(cd "$(dirname ${scala_bin})/../lib" && pwd)/scala-library.jar" } -# Determines if a given application is already installed. If not, will attempt -# to install -## Arg1 - application name -## Arg2 - Alternate path to local install under build/ dir -check_and_install_app() { - # create the local environment variable in uppercase - local app_bin="`echo $1 | awk '{print toupper(\$0)}'`_BIN" - # some black magic to set the generated app variable (i.e. MVN_BIN) into the - # environment - eval "${app_bin}=`which $1 2>/dev/null`" - - if [ -z "`which $1 2>/dev/null`" ]; then - install_$1 - fi -} - # Setup healthy defaults for the Zinc port if none were provided from # the environment ZINC_PORT=${ZINC_PORT:-"3030"} -# Check and install all applications necessary to build Spark -check_and_install_app "mvn" +# Install Maven if necessary +MVN_BIN="$(command -v mvn)" + +if [ ! "$MVN_BIN" ]; then + install_mvn +fi # Install the proper version of Scala and Zinc for the build install_zinc diff --git a/conf/metrics.properties.template b/conf/metrics.properties.template index 2e0cb5db170ac..7f17bc7eea4f5 100644 --- a/conf/metrics.properties.template +++ b/conf/metrics.properties.template @@ -4,7 +4,7 @@ # divided into instances which correspond to internal components. # Each instance can be configured to report its metrics to one or more sinks. # Accepted values for [instance] are "master", "worker", "executor", "driver", -# and "applications". A wild card "*" can be used as an instance name, in +# and "applications". A wildcard "*" can be used as an instance name, in # which case all instances will inherit the supplied property. # # Within an instance, a "source" specifies a particular set of grouped metrics. @@ -32,7 +32,7 @@ # name (see examples below). # 2. Some sinks involve a polling period. The minimum allowed polling period # is 1 second. -# 3. Wild card properties can be overridden by more specific properties. +# 3. Wildcard properties can be overridden by more specific properties. # For example, master.sink.console.period takes precedence over # *.sink.console.period. # 4. A metrics specific configuration @@ -47,6 +47,13 @@ # instance master and applications. MetricsServlet may not be configured by self. # +## List of available common sources and their properties. + +# org.apache.spark.metrics.source.JvmSource +# Note: Currently, JvmSource is the only available common source +# to add additionaly to an instance, to enable this, +# set the "class" option to its fully qulified class name (see examples below) + ## List of available sinks and their properties. # org.apache.spark.metrics.sink.ConsoleSink @@ -126,9 +133,9 @@ #*.sink.slf4j.class=org.apache.spark.metrics.sink.Slf4jSink # Polling period for Slf4JSink -#*.sink.sl4j.period=1 +#*.sink.slf4j.period=1 -#*.sink.sl4j.unit=minutes +#*.sink.slf4j.unit=minutes # Enable jvm source for instance master, worker, driver and executor diff --git a/core/pom.xml b/core/pom.xml index fc42f48973fe9..40a64beccdc24 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -338,6 +338,12 @@ org.seleniumhq.selenium selenium-java + + + com.google.guava + guava + + test @@ -361,15 +367,31 @@ junit test + + org.hamcrest + hamcrest-core + test + + + org.hamcrest + hamcrest-library + test + com.novocode junit-interface test - org.spark-project + net.razorvine pyrolite 4.4 + + + net.razorvine + serpent + + net.sf.py4j @@ -381,35 +403,6 @@ target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes - - - org.apache.maven.plugins - maven-antrun-plugin - - - generate-resources - - run - - - - - - - - - - - maven-clean-plugin - - - - ${basedir}/../python/build - - - true - - org.apache.maven.plugins maven-dependency-plugin @@ -438,24 +431,6 @@ - - - - src/main/resources - - - ../python - - pyspark/*.py - - - - ../python/build - - py4j/*.py - - - diff --git a/core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java new file mode 100644 index 0000000000000..d3d6280284beb --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java @@ -0,0 +1,184 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.sort; + +import java.io.File; +import java.io.FileInputStream; +import java.io.FileOutputStream; +import java.io.IOException; + +import scala.Product2; +import scala.Tuple2; +import scala.collection.Iterator; + +import com.google.common.io.Closeables; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import org.apache.spark.Partitioner; +import org.apache.spark.SparkConf; +import org.apache.spark.TaskContext; +import org.apache.spark.executor.ShuffleWriteMetrics; +import org.apache.spark.serializer.Serializer; +import org.apache.spark.serializer.SerializerInstance; +import org.apache.spark.storage.*; +import org.apache.spark.util.Utils; + +/** + * This class implements sort-based shuffle's hash-style shuffle fallback path. This write path + * writes incoming records to separate files, one file per reduce partition, then concatenates these + * per-partition files to form a single output file, regions of which are served to reducers. + * Records are not buffered in memory. This is essentially identical to + * {@link org.apache.spark.shuffle.hash.HashShuffleWriter}, except that it writes output in a format + * that can be served / consumed via {@link org.apache.spark.shuffle.IndexShuffleBlockResolver}. + *

+ * This write path is inefficient for shuffles with large numbers of reduce partitions because it + * simultaneously opens separate serializers and file streams for all partitions. As a result, + * {@link SortShuffleManager} only selects this write path when + *

    + *
  • no Ordering is specified,
    • + *
  • no Aggregator is specific, and
    • + *
  • the number of partitions is less than + * spark.shuffle.sort.bypassMergeThreshold.
    • + *
+ * + * This code used to be part of {@link org.apache.spark.util.collection.ExternalSorter} but was + * refactored into its own class in order to reduce code complexity; see SPARK-7855 for details. + *

+ * There have been proposals to completely remove this code path; see SPARK-6026 for details. + */ +final class BypassMergeSortShuffleWriter implements SortShuffleFileWriter { + + private final Logger logger = LoggerFactory.getLogger(BypassMergeSortShuffleWriter.class); + + private final int fileBufferSize; + private final boolean transferToEnabled; + private final int numPartitions; + private final BlockManager blockManager; + private final Partitioner partitioner; + private final ShuffleWriteMetrics writeMetrics; + private final Serializer serializer; + + /** Array of file writers, one for each partition */ + private BlockObjectWriter[] partitionWriters; + + public BypassMergeSortShuffleWriter( + SparkConf conf, + BlockManager blockManager, + Partitioner partitioner, + ShuffleWriteMetrics writeMetrics, + Serializer serializer) { + // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided + this.fileBufferSize = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024; + this.transferToEnabled = conf.getBoolean("spark.file.transferTo", true); + this.numPartitions = partitioner.numPartitions(); + this.blockManager = blockManager; + this.partitioner = partitioner; + this.writeMetrics = writeMetrics; + this.serializer = serializer; + } + + @Override + public void insertAll(Iterator> records) throws IOException { + assert (partitionWriters == null); + if (!records.hasNext()) { + return; + } + final SerializerInstance serInstance = serializer.newInstance(); + final long openStartTime = System.nanoTime(); + partitionWriters = new BlockObjectWriter[numPartitions]; + for (int i = 0; i < numPartitions; i++) { + final Tuple2 tempShuffleBlockIdPlusFile = + blockManager.diskBlockManager().createTempShuffleBlock(); + final File file = tempShuffleBlockIdPlusFile._2(); + final BlockId blockId = tempShuffleBlockIdPlusFile._1(); + partitionWriters[i] = + blockManager.getDiskWriter(blockId, file, serInstance, fileBufferSize, writeMetrics).open(); + } + // Creating the file to write to and creating a disk writer both involve interacting with + // the disk, and can take a long time in aggregate when we open many files, so should be + // included in the shuffle write time. + writeMetrics.incShuffleWriteTime(System.nanoTime() - openStartTime); + + while (records.hasNext()) { + final Product2 record = records.next(); + final K key = record._1(); + partitionWriters[partitioner.getPartition(key)].write(key, record._2()); + } + + for (BlockObjectWriter writer : partitionWriters) { + writer.commitAndClose(); + } + } + + @Override + public long[] writePartitionedFile( + BlockId blockId, + TaskContext context, + File outputFile) throws IOException { + // Track location of the partition starts in the output file + final long[] lengths = new long[numPartitions]; + if (partitionWriters == null) { + // We were passed an empty iterator + return lengths; + } + + final FileOutputStream out = new FileOutputStream(outputFile, true); + final long writeStartTime = System.nanoTime(); + boolean threwException = true; + try { + for (int i = 0; i < numPartitions; i++) { + final FileInputStream in = new FileInputStream(partitionWriters[i].fileSegment().file()); + boolean copyThrewException = true; + try { + lengths[i] = Utils.copyStream(in, out, false, transferToEnabled); + copyThrewException = false; + } finally { + Closeables.close(in, copyThrewException); + } + if (!blockManager.diskBlockManager().getFile(partitionWriters[i].blockId()).delete()) { + logger.error("Unable to delete file for partition {}", i); + } + } + threwException = false; + } finally { + Closeables.close(out, threwException); + writeMetrics.incShuffleWriteTime(System.nanoTime() - writeStartTime); + } + partitionWriters = null; + return lengths; + } + + @Override + public void stop() throws IOException { + if (partitionWriters != null) { + try { + final DiskBlockManager diskBlockManager = blockManager.diskBlockManager(); + for (BlockObjectWriter writer : partitionWriters) { + // This method explicitly does _not_ throw exceptions: + writer.revertPartialWritesAndClose(); + if (!diskBlockManager.getFile(writer.blockId()).delete()) { + logger.error("Error while deleting file for block {}", writer.blockId()); + } + } + } finally { + partitionWriters = null; + } + } + } +} diff --git a/core/src/main/java/org/apache/spark/shuffle/sort/SortShuffleFileWriter.java b/core/src/main/java/org/apache/spark/shuffle/sort/SortShuffleFileWriter.java new file mode 100644 index 0000000000000..656ea0401a144 --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/sort/SortShuffleFileWriter.java @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.sort; + +import java.io.File; +import java.io.IOException; + +import scala.Product2; +import scala.collection.Iterator; + +import org.apache.spark.annotation.Private; +import org.apache.spark.TaskContext; +import org.apache.spark.storage.BlockId; + +/** + * Interface for objects that {@link SortShuffleWriter} uses to write its output files. + */ +@Private +public interface SortShuffleFileWriter { + + void insertAll(Iterator> records) throws IOException; + + /** + * Write all the data added into this shuffle sorter into a file in the disk store. This is + * called by the SortShuffleWriter and can go through an efficient path of just concatenating + * binary files if we decided to avoid merge-sorting. + * + * @param blockId block ID to write to. The index file will be blockId.name + ".index". + * @param context a TaskContext for a running Spark task, for us to update shuffle metrics. + * @return array of lengths, in bytes, of each partition of the file (used by map output tracker) + */ + long[] writePartitionedFile( + BlockId blockId, + TaskContext context, + File outputFile) throws IOException; + + void stop() throws IOException; +} diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/DummySerializerInstance.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/DummySerializerInstance.java new file mode 100644 index 0000000000000..3f746b886bc9b --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/DummySerializerInstance.java @@ -0,0 +1,93 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.unsafe; + +import java.io.IOException; +import java.io.InputStream; +import java.io.OutputStream; +import java.nio.ByteBuffer; + +import scala.reflect.ClassTag; + +import org.apache.spark.serializer.DeserializationStream; +import org.apache.spark.serializer.SerializationStream; +import org.apache.spark.serializer.SerializerInstance; +import org.apache.spark.unsafe.PlatformDependent; + +/** + * Unfortunately, we need a serializer instance in order to construct a DiskBlockObjectWriter. + * Our shuffle write path doesn't actually use this serializer (since we end up calling the + * `write() OutputStream methods), but DiskBlockObjectWriter still calls some methods on it. To work + * around this, we pass a dummy no-op serializer. + */ +final class DummySerializerInstance extends SerializerInstance { + + public static final DummySerializerInstance INSTANCE = new DummySerializerInstance(); + + private DummySerializerInstance() { } + + @Override + public SerializationStream serializeStream(final OutputStream s) { + return new SerializationStream() { + @Override + public void flush() { + // Need to implement this because DiskObjectWriter uses it to flush the compression stream + try { + s.flush(); + } catch (IOException e) { + PlatformDependent.throwException(e); + } + } + + @Override + public SerializationStream writeObject(T t, ClassTag ev1) { + throw new UnsupportedOperationException(); + } + + @Override + public void close() { + // Need to implement this because DiskObjectWriter uses it to close the compression stream + try { + s.close(); + } catch (IOException e) { + PlatformDependent.throwException(e); + } + } + }; + } + + @Override + public ByteBuffer serialize(T t, ClassTag ev1) { + throw new UnsupportedOperationException(); + } + + @Override + public DeserializationStream deserializeStream(InputStream s) { + throw new UnsupportedOperationException(); + } + + @Override + public T deserialize(ByteBuffer bytes, ClassLoader loader, ClassTag ev1) { + throw new UnsupportedOperationException(); + } + + @Override + public T deserialize(ByteBuffer bytes, ClassTag ev1) { + throw new UnsupportedOperationException(); + } +} diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/PackedRecordPointer.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/PackedRecordPointer.java new file mode 100644 index 0000000000000..4ee6a82c0423e --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/PackedRecordPointer.java @@ -0,0 +1,92 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.unsafe; + +/** + * Wrapper around an 8-byte word that holds a 24-bit partition number and 40-bit record pointer. + *

+ * Within the long, the data is laid out as follows: + * 

+ *   [24 bit partition number][13 bit memory page number][27 bit offset in page]
+ *
+ * This implies that the maximum addressable page size is 2^27 bits = 128 megabytes, assuming that + * our offsets in pages are not 8-byte-word-aligned. Since we have 2^13 pages (based off the + * 13-bit page numbers assigned by {@link org.apache.spark.unsafe.memory.TaskMemoryManager}), this + * implies that we can address 2^13 * 128 megabytes = 1 terabyte of RAM per task. + *

+ * Assuming word-alignment would allow for a 1 gigabyte maximum page size, but we leave this + * optimization to future work as it will require more careful design to ensure that addresses are + * properly aligned (e.g. by padding records). + */ +final class PackedRecordPointer { + + static final int MAXIMUM_PAGE_SIZE_BYTES = 1 << 27; // 128 megabytes + + /** + * The maximum partition identifier that can be encoded. Note that partition ids start from 0. + */ + static final int MAXIMUM_PARTITION_ID = (1 << 24) - 1; // 16777215 + + /** Bit mask for the lower 40 bits of a long. */ + private static final long MASK_LONG_LOWER_40_BITS = (1L << 40) - 1; + + /** Bit mask for the upper 24 bits of a long */ + private static final long MASK_LONG_UPPER_24_BITS = ~MASK_LONG_LOWER_40_BITS; + + /** Bit mask for the lower 27 bits of a long. */ + private static final long MASK_LONG_LOWER_27_BITS = (1L << 27) - 1; + + /** Bit mask for the lower 51 bits of a long. */ + private static final long MASK_LONG_LOWER_51_BITS = (1L << 51) - 1; + + /** Bit mask for the upper 13 bits of a long */ + private static final long MASK_LONG_UPPER_13_BITS = ~MASK_LONG_LOWER_51_BITS; + + /** + * Pack a record address and partition id into a single word. + * + * @param recordPointer a record pointer encoded by TaskMemoryManager. + * @param partitionId a shuffle partition id (maximum value of 2^24). + * @return a packed pointer that can be decoded using the {@link PackedRecordPointer} class. + */ + public static long packPointer(long recordPointer, int partitionId) { + assert (partitionId <= MAXIMUM_PARTITION_ID); + // Note that without word alignment we can address 2^27 bytes = 128 megabytes per page. + // Also note that this relies on some internals of how TaskMemoryManager encodes its addresses. + final long pageNumber = (recordPointer & MASK_LONG_UPPER_13_BITS) >>> 24; + final long compressedAddress = pageNumber | (recordPointer & MASK_LONG_LOWER_27_BITS); + return (((long) partitionId) << 40) | compressedAddress; + } + + private long packedRecordPointer; + + public void set(long packedRecordPointer) { + this.packedRecordPointer = packedRecordPointer; + } + + public int getPartitionId() { + return (int) ((packedRecordPointer & MASK_LONG_UPPER_24_BITS) >>> 40); + } + + public long getRecordPointer() { + final long pageNumber = (packedRecordPointer << 24) & MASK_LONG_UPPER_13_BITS; + final long offsetInPage = packedRecordPointer & MASK_LONG_LOWER_27_BITS; + return pageNumber | offsetInPage; + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala b/core/src/main/java/org/apache/spark/shuffle/unsafe/SpillInfo.java similarity index 51% rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala rename to core/src/main/java/org/apache/spark/shuffle/unsafe/SpillInfo.java index a4a3a66b8b229..7bac0dc0bbeb6 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/SpillInfo.java @@ -15,22 +15,23 @@ * limitations under the License. */ -package org.apache.spark.sql.catalyst.optimizer +package org.apache.spark.shuffle.unsafe; -import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical._ +import java.io.File; + +import org.apache.spark.storage.TempShuffleBlockId; /** - * Overrides our expression evaluation tests and reruns them after optimization has occured. This - * is to ensure that constant folding and other optimizations do not break anything. + * Metadata for a block of data written by {@link UnsafeShuffleExternalSorter}. */ -class ExpressionOptimizationSuite extends ExpressionEvaluationSuite { - override def checkEvaluation( - expression: Expression, - expected: Any, - inputRow: Row = EmptyRow): Unit = { - val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation) - val optimizedPlan = DefaultOptimizer.execute(plan) - super.checkEvaluation(optimizedPlan.expressions.head, expected, inputRow) +final class SpillInfo { + final long[] partitionLengths; + final File file; + final TempShuffleBlockId blockId; + + public SpillInfo(int numPartitions, File file, TempShuffleBlockId blockId) { + this.partitionLengths = new long[numPartitions]; + this.file = file; + this.blockId = blockId; } } diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java new file mode 100644 index 0000000000000..9e9ed94b7890c --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java @@ -0,0 +1,422 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.unsafe; + +import java.io.File; +import java.io.IOException; +import java.util.LinkedList; + +import scala.Tuple2; + +import com.google.common.annotations.VisibleForTesting; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import org.apache.spark.SparkConf; +import org.apache.spark.TaskContext; +import org.apache.spark.executor.ShuffleWriteMetrics; +import org.apache.spark.serializer.SerializerInstance; +import org.apache.spark.shuffle.ShuffleMemoryManager; +import org.apache.spark.storage.*; +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.memory.MemoryBlock; +import org.apache.spark.unsafe.memory.TaskMemoryManager; +import org.apache.spark.util.Utils; + +/** + * An external sorter that is specialized for sort-based shuffle. + *

+ * Incoming records are appended to data pages. When all records have been inserted (or when the + * current thread's shuffle memory limit is reached), the in-memory records are sorted according to + * their partition ids (using a {@link UnsafeShuffleInMemorySorter}). The sorted records are then + * written to a single output file (or multiple files, if we've spilled). The format of the output + * files is the same as the format of the final output file written by + * {@link org.apache.spark.shuffle.sort.SortShuffleWriter}: each output partition's records are + * written as a single serialized, compressed stream that can be read with a new decompression and + * deserialization stream. + *

+ * Unlike {@link org.apache.spark.util.collection.ExternalSorter}, this sorter does not merge its + * spill files. Instead, this merging is performed in {@link UnsafeShuffleWriter}, which uses a + * specialized merge procedure that avoids extra serialization/deserialization. + */ +final class UnsafeShuffleExternalSorter { + + private final Logger logger = LoggerFactory.getLogger(UnsafeShuffleExternalSorter.class); + + private static final int PAGE_SIZE = PackedRecordPointer.MAXIMUM_PAGE_SIZE_BYTES; + @VisibleForTesting + static final int DISK_WRITE_BUFFER_SIZE = 1024 * 1024; + @VisibleForTesting + static final int MAX_RECORD_SIZE = PAGE_SIZE - 4; + + private final int initialSize; + private final int numPartitions; + private final TaskMemoryManager memoryManager; + private final ShuffleMemoryManager shuffleMemoryManager; + private final BlockManager blockManager; + private final TaskContext taskContext; + private final ShuffleWriteMetrics writeMetrics; + + /** The buffer size to use when writing spills using DiskBlockObjectWriter */ + private final int fileBufferSizeBytes; + + /** + * Memory pages that hold the records being sorted. The pages in this list are freed when + * spilling, although in principle we could recycle these pages across spills (on the other hand, + * this might not be necessary if we maintained a pool of re-usable pages in the TaskMemoryManager + * itself). + */ + private final LinkedList allocatedPages = new LinkedList(); + + private final LinkedList spills = new LinkedList(); + + // These variables are reset after spilling: + private UnsafeShuffleInMemorySorter sorter; + private MemoryBlock currentPage = null; + private long currentPagePosition = -1; + private long freeSpaceInCurrentPage = 0; + + public UnsafeShuffleExternalSorter( + TaskMemoryManager memoryManager, + ShuffleMemoryManager shuffleMemoryManager, + BlockManager blockManager, + TaskContext taskContext, + int initialSize, + int numPartitions, + SparkConf conf, + ShuffleWriteMetrics writeMetrics) throws IOException { + this.memoryManager = memoryManager; + this.shuffleMemoryManager = shuffleMemoryManager; + this.blockManager = blockManager; + this.taskContext = taskContext; + this.initialSize = initialSize; + this.numPartitions = numPartitions; + // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided + this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024; + + this.writeMetrics = writeMetrics; + initializeForWriting(); + } + + /** + * Allocates new sort data structures. Called when creating the sorter and after each spill. + */ + private void initializeForWriting() throws IOException { + // TODO: move this sizing calculation logic into a static method of sorter: + final long memoryRequested = initialSize * 8L; + final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryRequested); + if (memoryAcquired != memoryRequested) { + shuffleMemoryManager.release(memoryAcquired); + throw new IOException("Could not acquire " + memoryRequested + " bytes of memory"); + } + + this.sorter = new UnsafeShuffleInMemorySorter(initialSize); + } + + /** + * Sorts the in-memory records and writes the sorted records to an on-disk file. + * This method does not free the sort data structures. + * + * @param isLastFile if true, this indicates that we're writing the final output file and that the + * bytes written should be counted towards shuffle spill metrics rather than + * shuffle write metrics. + */ + private void writeSortedFile(boolean isLastFile) throws IOException { + + final ShuffleWriteMetrics writeMetricsToUse; + + if (isLastFile) { + // We're writing the final non-spill file, so we _do_ want to count this as shuffle bytes. + writeMetricsToUse = writeMetrics; + } else { + // We're spilling, so bytes written should be counted towards spill rather than write. + // Create a dummy WriteMetrics object to absorb these metrics, since we don't want to count + // them towards shuffle bytes written. + writeMetricsToUse = new ShuffleWriteMetrics(); + } + + // This call performs the actual sort. + final UnsafeShuffleInMemorySorter.UnsafeShuffleSorterIterator sortedRecords = + sorter.getSortedIterator(); + + // Currently, we need to open a new DiskBlockObjectWriter for each partition; we can avoid this + // after SPARK-5581 is fixed. + BlockObjectWriter writer; + + // Small writes to DiskBlockObjectWriter will be fairly inefficient. Since there doesn't seem to + // be an API to directly transfer bytes from managed memory to the disk writer, we buffer + // data through a byte array. This array does not need to be large enough to hold a single + // record; + final byte[] writeBuffer = new byte[DISK_WRITE_BUFFER_SIZE]; + + // Because this output will be read during shuffle, its compression codec must be controlled by + // spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use + // createTempShuffleBlock here; see SPARK-3426 for more details. + final Tuple2 spilledFileInfo = + blockManager.diskBlockManager().createTempShuffleBlock(); + final File file = spilledFileInfo._2(); + final TempShuffleBlockId blockId = spilledFileInfo._1(); + final SpillInfo spillInfo = new SpillInfo(numPartitions, file, blockId); + + // Unfortunately, we need a serializer instance in order to construct a DiskBlockObjectWriter. + // Our write path doesn't actually use this serializer (since we end up calling the `write()` + // OutputStream methods), but DiskBlockObjectWriter still calls some methods on it. To work + // around this, we pass a dummy no-op serializer. + final SerializerInstance ser = DummySerializerInstance.INSTANCE; + + writer = blockManager.getDiskWriter(blockId, file, ser, fileBufferSizeBytes, writeMetricsToUse); + + int currentPartition = -1; + while (sortedRecords.hasNext()) { + sortedRecords.loadNext(); + final int partition = sortedRecords.packedRecordPointer.getPartitionId(); + assert (partition >= currentPartition); + if (partition != currentPartition) { + // Switch to the new partition + if (currentPartition != -1) { + writer.commitAndClose(); + spillInfo.partitionLengths[currentPartition] = writer.fileSegment().length(); + } + currentPartition = partition; + writer = + blockManager.getDiskWriter(blockId, file, ser, fileBufferSizeBytes, writeMetricsToUse); + } + + final long recordPointer = sortedRecords.packedRecordPointer.getRecordPointer(); + final Object recordPage = memoryManager.getPage(recordPointer); + final long recordOffsetInPage = memoryManager.getOffsetInPage(recordPointer); + int dataRemaining = PlatformDependent.UNSAFE.getInt(recordPage, recordOffsetInPage); + long recordReadPosition = recordOffsetInPage + 4; // skip over record length + while (dataRemaining > 0) { + final int toTransfer = Math.min(DISK_WRITE_BUFFER_SIZE, dataRemaining); + PlatformDependent.copyMemory( + recordPage, + recordReadPosition, + writeBuffer, + PlatformDependent.BYTE_ARRAY_OFFSET, + toTransfer); + writer.write(writeBuffer, 0, toTransfer); + recordReadPosition += toTransfer; + dataRemaining -= toTransfer; + } + writer.recordWritten(); + } + + if (writer != null) { + writer.commitAndClose(); + // If `writeSortedFile()` was called from `closeAndGetSpills()` and no records were inserted, + // then the file might be empty. Note that it might be better to avoid calling + // writeSortedFile() in that case. + if (currentPartition != -1) { + spillInfo.partitionLengths[currentPartition] = writer.fileSegment().length(); + spills.add(spillInfo); + } + } + + if (!isLastFile) { // i.e. this is a spill file + // The current semantics of `shuffleRecordsWritten` seem to be that it's updated when records + // are written to disk, not when they enter the shuffle sorting code. DiskBlockObjectWriter + // relies on its `recordWritten()` method being called in order to trigger periodic updates to + // `shuffleBytesWritten`. If we were to remove the `recordWritten()` call and increment that + // counter at a higher-level, then the in-progress metrics for records written and bytes + // written would get out of sync. + // + // When writing the last file, we pass `writeMetrics` directly to the DiskBlockObjectWriter; + // in all other cases, we pass in a dummy write metrics to capture metrics, then copy those + // metrics to the true write metrics here. The reason for performing this copying is so that + // we can avoid reporting spilled bytes as shuffle write bytes. + // + // Note that we intentionally ignore the value of `writeMetricsToUse.shuffleWriteTime()`. + // Consistent with ExternalSorter, we do not count this IO towards shuffle write time. + // This means that this IO time is not accounted for anywhere; SPARK-3577 will fix this. + writeMetrics.incShuffleRecordsWritten(writeMetricsToUse.shuffleRecordsWritten()); + taskContext.taskMetrics().incDiskBytesSpilled(writeMetricsToUse.shuffleBytesWritten()); + } + } + + /** + * Sort and spill the current records in response to memory pressure. + */ + @VisibleForTesting + void spill() throws IOException { + logger.info("Thread {} spilling sort data of {} to disk ({} {} so far)", + Thread.currentThread().getId(), + Utils.bytesToString(getMemoryUsage()), + spills.size(), + spills.size() > 1 ? " times" : " time"); + + writeSortedFile(false); + final long sorterMemoryUsage = sorter.getMemoryUsage(); + sorter = null; + shuffleMemoryManager.release(sorterMemoryUsage); + final long spillSize = freeMemory(); + taskContext.taskMetrics().incMemoryBytesSpilled(spillSize); + + initializeForWriting(); + } + + private long getMemoryUsage() { + return sorter.getMemoryUsage() + (allocatedPages.size() * (long) PAGE_SIZE); + } + + private long freeMemory() { + long memoryFreed = 0; + for (MemoryBlock block : allocatedPages) { + memoryManager.freePage(block); + shuffleMemoryManager.release(block.size()); + memoryFreed += block.size(); + } + allocatedPages.clear(); + currentPage = null; + currentPagePosition = -1; + freeSpaceInCurrentPage = 0; + return memoryFreed; + } + + /** + * Force all memory and spill files to be deleted; called by shuffle error-handling code. + */ + public void cleanupAfterError() { + freeMemory(); + for (SpillInfo spill : spills) { + if (spill.file.exists() && !spill.file.delete()) { + logger.error("Unable to delete spill file {}", spill.file.getPath()); + } + } + if (sorter != null) { + shuffleMemoryManager.release(sorter.getMemoryUsage()); + sorter = null; + } + } + + /** + * Checks whether there is enough space to insert a new record into the sorter. + * + * @param requiredSpace the required space in the data page, in bytes, including space for storing + * the record size. + + * @return true if the record can be inserted without requiring more allocations, false otherwise. + */ + private boolean haveSpaceForRecord(int requiredSpace) { + assert (requiredSpace > 0); + return (sorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage)); + } + + /** + * Allocates more memory in order to insert an additional record. This will request additional + * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be + * obtained. + * + * @param requiredSpace the required space in the data page, in bytes, including space for storing + * the record size. + */ + private void allocateSpaceForRecord(int requiredSpace) throws IOException { + if (!sorter.hasSpaceForAnotherRecord()) { + logger.debug("Attempting to expand sort pointer array"); + final long oldPointerArrayMemoryUsage = sorter.getMemoryUsage(); + final long memoryToGrowPointerArray = oldPointerArrayMemoryUsage * 2; + final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryToGrowPointerArray); + if (memoryAcquired < memoryToGrowPointerArray) { + shuffleMemoryManager.release(memoryAcquired); + spill(); + } else { + sorter.expandPointerArray(); + shuffleMemoryManager.release(oldPointerArrayMemoryUsage); + } + } + if (requiredSpace > freeSpaceInCurrentPage) { + logger.trace("Required space {} is less than free space in current page ({})", requiredSpace, + freeSpaceInCurrentPage); + // TODO: we should track metrics on the amount of space wasted when we roll over to a new page + // without using the free space at the end of the current page. We should also do this for + // BytesToBytesMap. + if (requiredSpace > PAGE_SIZE) { + throw new IOException("Required space " + requiredSpace + " is greater than page size (" + + PAGE_SIZE + ")"); + } else { + final long memoryAcquired = shuffleMemoryManager.tryToAcquire(PAGE_SIZE); + if (memoryAcquired < PAGE_SIZE) { + shuffleMemoryManager.release(memoryAcquired); + spill(); + final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(PAGE_SIZE); + if (memoryAcquiredAfterSpilling != PAGE_SIZE) { + shuffleMemoryManager.release(memoryAcquiredAfterSpilling); + throw new IOException("Unable to acquire " + PAGE_SIZE + " bytes of memory"); + } + } + currentPage = memoryManager.allocatePage(PAGE_SIZE); + currentPagePosition = currentPage.getBaseOffset(); + freeSpaceInCurrentPage = PAGE_SIZE; + allocatedPages.add(currentPage); + } + } + } + + /** + * Write a record to the shuffle sorter. + */ + public void insertRecord( + Object recordBaseObject, + long recordBaseOffset, + int lengthInBytes, + int partitionId) throws IOException { + // Need 4 bytes to store the record length. + final int totalSpaceRequired = lengthInBytes + 4; + if (!haveSpaceForRecord(totalSpaceRequired)) { + allocateSpaceForRecord(totalSpaceRequired); + } + + final long recordAddress = + memoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition); + final Object dataPageBaseObject = currentPage.getBaseObject(); + PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, lengthInBytes); + currentPagePosition += 4; + freeSpaceInCurrentPage -= 4; + PlatformDependent.copyMemory( + recordBaseObject, + recordBaseOffset, + dataPageBaseObject, + currentPagePosition, + lengthInBytes); + currentPagePosition += lengthInBytes; + freeSpaceInCurrentPage -= lengthInBytes; + sorter.insertRecord(recordAddress, partitionId); + } + + /** + * Close the sorter, causing any buffered data to be sorted and written out to disk. + * + * @return metadata for the spill files written by this sorter. If no records were ever inserted + * into this sorter, then this will return an empty array. + * @throws IOException + */ + public SpillInfo[] closeAndGetSpills() throws IOException { + try { + if (sorter != null) { + // Do not count the final file towards the spill count. + writeSortedFile(true); + freeMemory(); + } + return spills.toArray(new SpillInfo[spills.size()]); + } catch (IOException e) { + cleanupAfterError(); + throw e; + } + } + +} diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorter.java new file mode 100644 index 0000000000000..5bab501da9364 --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorter.java @@ -0,0 +1,124 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.unsafe; + +import java.util.Comparator; + +import org.apache.spark.util.collection.Sorter; + +final class UnsafeShuffleInMemorySorter { + + private final Sorter sorter; + private static final class SortComparator implements Comparator { + @Override + public int compare(PackedRecordPointer left, PackedRecordPointer right) { + return left.getPartitionId() - right.getPartitionId(); + } + } + private static final SortComparator SORT_COMPARATOR = new SortComparator(); + + /** + * An array of record pointers and partition ids that have been encoded by + * {@link PackedRecordPointer}. The sort operates on this array instead of directly manipulating + * records. + */ + private long[] pointerArray; + + /** + * The position in the pointer array where new records can be inserted. + */ + private int pointerArrayInsertPosition = 0; + + public UnsafeShuffleInMemorySorter(int initialSize) { + assert (initialSize > 0); + this.pointerArray = new long[initialSize]; + this.sorter = new Sorter(UnsafeShuffleSortDataFormat.INSTANCE); + } + + public void expandPointerArray() { + final long[] oldArray = pointerArray; + // Guard against overflow: + final int newLength = oldArray.length * 2 > 0 ? (oldArray.length * 2) : Integer.MAX_VALUE; + pointerArray = new long[newLength]; + System.arraycopy(oldArray, 0, pointerArray, 0, oldArray.length); + } + + public boolean hasSpaceForAnotherRecord() { + return pointerArrayInsertPosition + 1 < pointerArray.length; + } + + public long getMemoryUsage() { + return pointerArray.length * 8L; + } + + /** + * Inserts a record to be sorted. + * + * @param recordPointer a pointer to the record, encoded by the task memory manager. Due to + * certain pointer compression techniques used by the sorter, the sort can + * only operate on pointers that point to locations in the first + * {@link PackedRecordPointer#MAXIMUM_PAGE_SIZE_BYTES} bytes of a data page. + * @param partitionId the partition id, which must be less than or equal to + * {@link PackedRecordPointer#MAXIMUM_PARTITION_ID}. + */ + public void insertRecord(long recordPointer, int partitionId) { + if (!hasSpaceForAnotherRecord()) { + if (pointerArray.length == Integer.MAX_VALUE) { + throw new IllegalStateException("Sort pointer array has reached maximum size"); + } else { + expandPointerArray(); + } + } + pointerArray[pointerArrayInsertPosition] = + PackedRecordPointer.packPointer(recordPointer, partitionId); + pointerArrayInsertPosition++; + } + + /** + * An iterator-like class that's used instead of Java's Iterator in order to facilitate inlining. + */ + public static final class UnsafeShuffleSorterIterator { + + private final long[] pointerArray; + private final int numRecords; + final PackedRecordPointer packedRecordPointer = new PackedRecordPointer(); + private int position = 0; + + public UnsafeShuffleSorterIterator(int numRecords, long[] pointerArray) { + this.numRecords = numRecords; + this.pointerArray = pointerArray; + } + + public boolean hasNext() { + return position < numRecords; + } + + public void loadNext() { + packedRecordPointer.set(pointerArray[position]); + position++; + } + } + + /** + * Return an iterator over record pointers in sorted order. + */ + public UnsafeShuffleSorterIterator getSortedIterator() { + sorter.sort(pointerArray, 0, pointerArrayInsertPosition, SORT_COMPARATOR); + return new UnsafeShuffleSorterIterator(pointerArrayInsertPosition, pointerArray); + } +} diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleSortDataFormat.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleSortDataFormat.java new file mode 100644 index 0000000000000..a66d74ee44782 --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleSortDataFormat.java @@ -0,0 +1,67 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.unsafe; + +import org.apache.spark.util.collection.SortDataFormat; + +final class UnsafeShuffleSortDataFormat extends SortDataFormat { + + public static final UnsafeShuffleSortDataFormat INSTANCE = new UnsafeShuffleSortDataFormat(); + + private UnsafeShuffleSortDataFormat() { } + + @Override + public PackedRecordPointer getKey(long[] data, int pos) { + // Since we re-use keys, this method shouldn't be called. + throw new UnsupportedOperationException(); + } + + @Override + public PackedRecordPointer newKey() { + return new PackedRecordPointer(); + } + + @Override + public PackedRecordPointer getKey(long[] data, int pos, PackedRecordPointer reuse) { + reuse.set(data[pos]); + return reuse; + } + + @Override + public void swap(long[] data, int pos0, int pos1) { + final long temp = data[pos0]; + data[pos0] = data[pos1]; + data[pos1] = temp; + } + + @Override + public void copyElement(long[] src, int srcPos, long[] dst, int dstPos) { + dst[dstPos] = src[srcPos]; + } + + @Override + public void copyRange(long[] src, int srcPos, long[] dst, int dstPos, int length) { + System.arraycopy(src, srcPos, dst, dstPos, length); + } + + @Override + public long[] allocate(int length) { + return new long[length]; + } + +} diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java new file mode 100644 index 0000000000000..ad7eb04afcd8c --- /dev/null +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java @@ -0,0 +1,438 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.unsafe; + +import java.io.*; +import java.nio.channels.FileChannel; +import java.util.Iterator; +import javax.annotation.Nullable; + +import scala.Option; +import scala.Product2; +import scala.collection.JavaConversions; +import scala.reflect.ClassTag; +import scala.reflect.ClassTag$; + +import com.google.common.annotations.VisibleForTesting; +import com.google.common.io.ByteStreams; +import com.google.common.io.Closeables; +import com.google.common.io.Files; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import org.apache.spark.*; +import org.apache.spark.annotation.Private; +import org.apache.spark.io.CompressionCodec; +import org.apache.spark.io.CompressionCodec$; +import org.apache.spark.io.LZFCompressionCodec; +import org.apache.spark.executor.ShuffleWriteMetrics; +import org.apache.spark.network.util.LimitedInputStream; +import org.apache.spark.scheduler.MapStatus; +import org.apache.spark.scheduler.MapStatus$; +import org.apache.spark.serializer.SerializationStream; +import org.apache.spark.serializer.Serializer; +import org.apache.spark.serializer.SerializerInstance; +import org.apache.spark.shuffle.IndexShuffleBlockResolver; +import org.apache.spark.shuffle.ShuffleMemoryManager; +import org.apache.spark.shuffle.ShuffleWriter; +import org.apache.spark.storage.BlockManager; +import org.apache.spark.storage.TimeTrackingOutputStream; +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.memory.TaskMemoryManager; + +@Private +public class UnsafeShuffleWriter extends ShuffleWriter { + + private final Logger logger = LoggerFactory.getLogger(UnsafeShuffleWriter.class); + + private static final ClassTag OBJECT_CLASS_TAG = ClassTag$.MODULE$.Object(); + + @VisibleForTesting + static final int INITIAL_SORT_BUFFER_SIZE = 4096; + + private final BlockManager blockManager; + private final IndexShuffleBlockResolver shuffleBlockResolver; + private final TaskMemoryManager memoryManager; + private final ShuffleMemoryManager shuffleMemoryManager; + private final SerializerInstance serializer; + private final Partitioner partitioner; + private final ShuffleWriteMetrics writeMetrics; + private final int shuffleId; + private final int mapId; + private final TaskContext taskContext; + private final SparkConf sparkConf; + private final boolean transferToEnabled; + + private MapStatus mapStatus = null; + private UnsafeShuffleExternalSorter sorter = null; + + /** Subclass of ByteArrayOutputStream that exposes `buf` directly. */ + private static final class MyByteArrayOutputStream extends ByteArrayOutputStream { + public MyByteArrayOutputStream(int size) { super(size); } + public byte[] getBuf() { return buf; } + } + + private MyByteArrayOutputStream serBuffer; + private SerializationStream serOutputStream; + + /** + * Are we in the process of stopping? Because map tasks can call stop() with success = true + * and then call stop() with success = false if they get an exception, we want to make sure + * we don't try deleting files, etc twice. + */ + private boolean stopping = false; + + public UnsafeShuffleWriter( + BlockManager blockManager, + IndexShuffleBlockResolver shuffleBlockResolver, + TaskMemoryManager memoryManager, + ShuffleMemoryManager shuffleMemoryManager, + UnsafeShuffleHandle handle, + int mapId, + TaskContext taskContext, + SparkConf sparkConf) throws IOException { + final int numPartitions = handle.dependency().partitioner().numPartitions(); + if (numPartitions > UnsafeShuffleManager.MAX_SHUFFLE_OUTPUT_PARTITIONS()) { + throw new IllegalArgumentException( + "UnsafeShuffleWriter can only be used for shuffles with at most " + + UnsafeShuffleManager.MAX_SHUFFLE_OUTPUT_PARTITIONS() + " reduce partitions"); + } + this.blockManager = blockManager; + this.shuffleBlockResolver = shuffleBlockResolver; + this.memoryManager = memoryManager; + this.shuffleMemoryManager = shuffleMemoryManager; + this.mapId = mapId; + final ShuffleDependency dep = handle.dependency(); + this.shuffleId = dep.shuffleId(); + this.serializer = Serializer.getSerializer(dep.serializer()).newInstance(); + this.partitioner = dep.partitioner(); + this.writeMetrics = new ShuffleWriteMetrics(); + taskContext.taskMetrics().shuffleWriteMetrics_$eq(Option.apply(writeMetrics)); + this.taskContext = taskContext; + this.sparkConf = sparkConf; + this.transferToEnabled = sparkConf.getBoolean("spark.file.transferTo", true); + open(); + } + + /** + * This convenience method should only be called in test code. + */ + @VisibleForTesting + public void write(Iterator> records) throws IOException { + write(JavaConversions.asScalaIterator(records)); + } + + @Override + public void write(scala.collection.Iterator> records) throws IOException { + boolean success = false; + try { + while (records.hasNext()) { + insertRecordIntoSorter(records.next()); + } + closeAndWriteOutput(); + success = true; + } finally { + if (!success) { + sorter.cleanupAfterError(); + } + } + } + + private void open() throws IOException { + assert (sorter == null); + sorter = new UnsafeShuffleExternalSorter( + memoryManager, + shuffleMemoryManager, + blockManager, + taskContext, + INITIAL_SORT_BUFFER_SIZE, + partitioner.numPartitions(), + sparkConf, + writeMetrics); + serBuffer = new MyByteArrayOutputStream(1024 * 1024); + serOutputStream = serializer.serializeStream(serBuffer); + } + + @VisibleForTesting + void closeAndWriteOutput() throws IOException { + serBuffer = null; + serOutputStream = null; + final SpillInfo[] spills = sorter.closeAndGetSpills(); + sorter = null; + final long[] partitionLengths; + try { + partitionLengths = mergeSpills(spills); + } finally { + for (SpillInfo spill : spills) { + if (spill.file.exists() && ! spill.file.delete()) { + logger.error("Error while deleting spill file {}", spill.file.getPath()); + } + } + } + shuffleBlockResolver.writeIndexFile(shuffleId, mapId, partitionLengths); + mapStatus = MapStatus$.MODULE$.apply(blockManager.shuffleServerId(), partitionLengths); + } + + @VisibleForTesting + void insertRecordIntoSorter(Product2 record) throws IOException { + final K key = record._1(); + final int partitionId = partitioner.getPartition(key); + serBuffer.reset(); + serOutputStream.writeKey(key, OBJECT_CLASS_TAG); + serOutputStream.writeValue(record._2(), OBJECT_CLASS_TAG); + serOutputStream.flush(); + + final int serializedRecordSize = serBuffer.size(); + assert (serializedRecordSize > 0); + + sorter.insertRecord( + serBuffer.getBuf(), PlatformDependent.BYTE_ARRAY_OFFSET, serializedRecordSize, partitionId); + } + + @VisibleForTesting + void forceSorterToSpill() throws IOException { + assert (sorter != null); + sorter.spill(); + } + + /** + * Merge zero or more spill files together, choosing the fastest merging strategy based on the + * number of spills and the IO compression codec. + * + * @return the partition lengths in the merged file. + */ + private long[] mergeSpills(SpillInfo[] spills) throws IOException { + final File outputFile = shuffleBlockResolver.getDataFile(shuffleId, mapId); + final boolean compressionEnabled = sparkConf.getBoolean("spark.shuffle.compress", true); + final CompressionCodec compressionCodec = CompressionCodec$.MODULE$.createCodec(sparkConf); + final boolean fastMergeEnabled = + sparkConf.getBoolean("spark.shuffle.unsafe.fastMergeEnabled", true); + final boolean fastMergeIsSupported = + !compressionEnabled || compressionCodec instanceof LZFCompressionCodec; + try { + if (spills.length == 0) { + new FileOutputStream(outputFile).close(); // Create an empty file + return new long[partitioner.numPartitions()]; + } else if (spills.length == 1) { + // Here, we don't need to perform any metrics updates because the bytes written to this + // output file would have already been counted as shuffle bytes written. + Files.move(spills[0].file, outputFile); + return spills[0].partitionLengths; + } else { + final long[] partitionLengths; + // There are multiple spills to merge, so none of these spill files' lengths were counted + // towards our shuffle write count or shuffle write time. If we use the slow merge path, + // then the final output file's size won't necessarily be equal to the sum of the spill + // files' sizes. To guard against this case, we look at the output file's actual size when + // computing shuffle bytes written. + // + // We allow the individual merge methods to report their own IO times since different merge + // strategies use different IO techniques. We count IO during merge towards the shuffle + // shuffle write time, which appears to be consistent with the "not bypassing merge-sort" + // branch in ExternalSorter. + if (fastMergeEnabled && fastMergeIsSupported) { + // Compression is disabled or we are using an IO compression codec that supports + // decompression of concatenated compressed streams, so we can perform a fast spill merge + // that doesn't need to interpret the spilled bytes. + if (transferToEnabled) { + logger.debug("Using transferTo-based fast merge"); + partitionLengths = mergeSpillsWithTransferTo(spills, outputFile); + } else { + logger.debug("Using fileStream-based fast merge"); + partitionLengths = mergeSpillsWithFileStream(spills, outputFile, null); + } + } else { + logger.debug("Using slow merge"); + partitionLengths = mergeSpillsWithFileStream(spills, outputFile, compressionCodec); + } + // When closing an UnsafeShuffleExternalSorter that has already spilled once but also has + // in-memory records, we write out the in-memory records to a file but do not count that + // final write as bytes spilled (instead, it's accounted as shuffle write). The merge needs + // to be counted as shuffle write, but this will lead to double-counting of the final + // SpillInfo's bytes. + writeMetrics.decShuffleBytesWritten(spills[spills.length - 1].file.length()); + writeMetrics.incShuffleBytesWritten(outputFile.length()); + return partitionLengths; + } + } catch (IOException e) { + if (outputFile.exists() && !outputFile.delete()) { + logger.error("Unable to delete output file {}", outputFile.getPath()); + } + throw e; + } + } + + /** + * Merges spill files using Java FileStreams. This code path is slower than the NIO-based merge, + * {@link UnsafeShuffleWriter#mergeSpillsWithTransferTo(SpillInfo[], File)}, so it's only used in + * cases where the IO compression codec does not support concatenation of compressed data, or in + * cases where users have explicitly disabled use of {@code transferTo} in order to work around + * kernel bugs. + * + * @param spills the spills to merge. + * @param outputFile the file to write the merged data to. + * @param compressionCodec the IO compression codec, or null if shuffle compression is disabled. + * @return the partition lengths in the merged file. + */ + private long[] mergeSpillsWithFileStream( + SpillInfo[] spills, + File outputFile, + @Nullable CompressionCodec compressionCodec) throws IOException { + assert (spills.length >= 2); + final int numPartitions = partitioner.numPartitions(); + final long[] partitionLengths = new long[numPartitions]; + final InputStream[] spillInputStreams = new FileInputStream[spills.length]; + OutputStream mergedFileOutputStream = null; + + boolean threwException = true; + try { + for (int i = 0; i < spills.length; i++) { + spillInputStreams[i] = new FileInputStream(spills[i].file); + } + for (int partition = 0; partition < numPartitions; partition++) { + final long initialFileLength = outputFile.length(); + mergedFileOutputStream = + new TimeTrackingOutputStream(writeMetrics, new FileOutputStream(outputFile, true)); + if (compressionCodec != null) { + mergedFileOutputStream = compressionCodec.compressedOutputStream(mergedFileOutputStream); + } + + for (int i = 0; i < spills.length; i++) { + final long partitionLengthInSpill = spills[i].partitionLengths[partition]; + if (partitionLengthInSpill > 0) { + InputStream partitionInputStream = + new LimitedInputStream(spillInputStreams[i], partitionLengthInSpill); + if (compressionCodec != null) { + partitionInputStream = compressionCodec.compressedInputStream(partitionInputStream); + } + ByteStreams.copy(partitionInputStream, mergedFileOutputStream); + } + } + mergedFileOutputStream.flush(); + mergedFileOutputStream.close(); + partitionLengths[partition] = (outputFile.length() - initialFileLength); + } + threwException = false; + } finally { + // To avoid masking exceptions that caused us to prematurely enter the finally block, only + // throw exceptions during cleanup if threwException == false. + for (InputStream stream : spillInputStreams) { + Closeables.close(stream, threwException); + } + Closeables.close(mergedFileOutputStream, threwException); + } + return partitionLengths; + } + + /** + * Merges spill files by using NIO's transferTo to concatenate spill partitions' bytes. + * This is only safe when the IO compression codec and serializer support concatenation of + * serialized streams. + * + * @return the partition lengths in the merged file. + */ + private long[] mergeSpillsWithTransferTo(SpillInfo[] spills, File outputFile) throws IOException { + assert (spills.length >= 2); + final int numPartitions = partitioner.numPartitions(); + final long[] partitionLengths = new long[numPartitions]; + final FileChannel[] spillInputChannels = new FileChannel[spills.length]; + final long[] spillInputChannelPositions = new long[spills.length]; + FileChannel mergedFileOutputChannel = null; + + boolean threwException = true; + try { + for (int i = 0; i < spills.length; i++) { + spillInputChannels[i] = new FileInputStream(spills[i].file).getChannel(); + } + // This file needs to opened in append mode in order to work around a Linux kernel bug that + // affects transferTo; see SPARK-3948 for more details. + mergedFileOutputChannel = new FileOutputStream(outputFile, true).getChannel(); + + long bytesWrittenToMergedFile = 0; + for (int partition = 0; partition < numPartitions; partition++) { + for (int i = 0; i < spills.length; i++) { + final long partitionLengthInSpill = spills[i].partitionLengths[partition]; + long bytesToTransfer = partitionLengthInSpill; + final FileChannel spillInputChannel = spillInputChannels[i]; + final long writeStartTime = System.nanoTime(); + while (bytesToTransfer > 0) { + final long actualBytesTransferred = spillInputChannel.transferTo( + spillInputChannelPositions[i], + bytesToTransfer, + mergedFileOutputChannel); + spillInputChannelPositions[i] += actualBytesTransferred; + bytesToTransfer -= actualBytesTransferred; + } + writeMetrics.incShuffleWriteTime(System.nanoTime() - writeStartTime); + bytesWrittenToMergedFile += partitionLengthInSpill; + partitionLengths[partition] += partitionLengthInSpill; + } + } + // Check the position after transferTo loop to see if it is in the right position and raise an + // exception if it is incorrect. The position will not be increased to the expected length + // after calling transferTo in kernel version 2.6.32. This issue is described at + // https://bugs.openjdk.java.net/browse/JDK-7052359 and SPARK-3948. + if (mergedFileOutputChannel.position() != bytesWrittenToMergedFile) { + throw new IOException( + "Current position " + mergedFileOutputChannel.position() + " does not equal expected " + + "position " + bytesWrittenToMergedFile + " after transferTo. Please check your kernel" + + " version to see if it is 2.6.32, as there is a kernel bug which will lead to " + + "unexpected behavior when using transferTo. You can set spark.file.transferTo=false " + + "to disable this NIO feature." + ); + } + threwException = false; + } finally { + // To avoid masking exceptions that caused us to prematurely enter the finally block, only + // throw exceptions during cleanup if threwException == false. + for (int i = 0; i < spills.length; i++) { + assert(spillInputChannelPositions[i] == spills[i].file.length()); + Closeables.close(spillInputChannels[i], threwException); + } + Closeables.close(mergedFileOutputChannel, threwException); + } + return partitionLengths; + } + + @Override + public Option stop(boolean success) { + try { + if (stopping) { + return Option.apply(null); + } else { + stopping = true; + if (success) { + if (mapStatus == null) { + throw new IllegalStateException("Cannot call stop(true) without having called write()"); + } + return Option.apply(mapStatus); + } else { + // The map task failed, so delete our output data. + shuffleBlockResolver.removeDataByMap(shuffleId, mapId); + return Option.apply(null); + } + } + } finally { + if (sorter != null) { + // If sorter is non-null, then this implies that we called stop() in response to an error, + // so we need to clean up memory and spill files created by the sorter + sorter.cleanupAfterError(); + } + } + } +} diff --git a/core/src/main/java/org/apache/spark/storage/TimeTrackingOutputStream.java b/core/src/main/java/org/apache/spark/storage/TimeTrackingOutputStream.java new file mode 100644 index 0000000000000..dc2aa30466cc6 --- /dev/null +++ b/core/src/main/java/org/apache/spark/storage/TimeTrackingOutputStream.java @@ -0,0 +1,75 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage; + +import java.io.IOException; +import java.io.OutputStream; + +import org.apache.spark.annotation.Private; +import org.apache.spark.executor.ShuffleWriteMetrics; + +/** + * Intercepts write calls and tracks total time spent writing in order to update shuffle write + * metrics. Not thread safe. + */ +@Private +public final class TimeTrackingOutputStream extends OutputStream { + + private final ShuffleWriteMetrics writeMetrics; + private final OutputStream outputStream; + + public TimeTrackingOutputStream(ShuffleWriteMetrics writeMetrics, OutputStream outputStream) { + this.writeMetrics = writeMetrics; + this.outputStream = outputStream; + } + + @Override + public void write(int b) throws IOException { + final long startTime = System.nanoTime(); + outputStream.write(b); + writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime); + } + + @Override + public void write(byte[] b) throws IOException { + final long startTime = System.nanoTime(); + outputStream.write(b); + writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime); + } + + @Override + public void write(byte[] b, int off, int len) throws IOException { + final long startTime = System.nanoTime(); + outputStream.write(b, off, len); + writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime); + } + + @Override + public void flush() throws IOException { + final long startTime = System.nanoTime(); + outputStream.flush(); + writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime); + } + + @Override + public void close() throws IOException { + final long startTime = System.nanoTime(); + outputStream.close(); + writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime); + } +} diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults-repl.properties b/core/src/main/resources/org/apache/spark/log4j-defaults-repl.properties new file mode 100644 index 0000000000000..b146f8a784127 --- /dev/null +++ b/core/src/main/resources/org/apache/spark/log4j-defaults-repl.properties @@ -0,0 +1,12 @@ +# Set everything to be logged to the console +log4j.rootCategory=WARN, console +log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.target=System.err +log4j.appender.console.layout=org.apache.log4j.PatternLayout +log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + +# Settings to quiet third party logs that are too verbose +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR +log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO +log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO diff --git a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js index 013db8df9b363..0b450dc76bc38 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js +++ b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js @@ -50,4 +50,9 @@ $(function() { $("span.additional-metric-title").click(function() { $(this).parent().find('input[type="checkbox"]').trigger('click'); }); + + // Trigger a double click on the span to show full job description. + $(".description-input").dblclick(function() { + $(this).removeClass("description-input").addClass("description-input-full"); + }); }); diff --git a/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js b/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js index 6beb7db855ca0..2d9262b972a59 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js +++ b/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js @@ -1,4 +1,4 @@ -!function(e){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=e();else if("function"==typeof define&&define.amd)define([],e);else{var f;"undefined"!=typeof window?f=window:"undefined"!=typeof global?f=global:"undefined"!=typeof self&&(f=self),f.dagreD3=e()}}(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o0}},{}],14:[function(require,module,exports){module.exports=intersectNode;function intersectNode(node,point){return node.intersect(point)}},{}],15:[function(require,module,exports){var intersectLine=require("./intersect-line");module.exports=intersectPolygon;function intersectPolygon(node,polyPoints,point){var x1=node.x;var y1=node.y;var intersections=[];var minX=Number.POSITIVE_INFINITY,minY=Number.POSITIVE_INFINITY;polyPoints.forEach(function(entry){minX=Math.min(minX,entry.x);minY=Math.min(minY,entry.y)});var left=x1-node.width/2-minX;var top=y1-node.height/2-minY;for(var i=0;i1){intersections.sort(function(p,q){var pdx=p.x-point.x,pdy=p.y-point.y,distp=Math.sqrt(pdx*pdx+pdy*pdy),qdx=q.x-point.x,qdy=q.y-point.y,distq=Math.sqrt(qdx*qdx+qdy*qdy);return distpMath.abs(dx)*h){if(dy<0){h=-h}sx=dy===0?0:h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=dx===0?0:w*dy/dx}return{x:x+sx,y:y+sy}}},{}],17:[function(require,module,exports){var util=require("../util");module.exports=addHtmlLabel;function addHtmlLabel(root,node){var fo=root.append("foreignObject").attr("width","100000");var div=fo.append("xhtml:div");var label=node.label;switch(typeof label){case"function":div.insert(label);break;case"object":div.insert(function(){return label});break;default:div.html(label)}util.applyStyle(div,node.labelStyle);div.style("display","inline-block");div.style("white-space","nowrap");var w,h;div.each(function(){w=this.clientWidth;h=this.clientHeight});fo.attr("width",w).attr("height",h);return fo}},{"../util":25}],18:[function(require,module,exports){var addTextLabel=require("./add-text-label"),addHtmlLabel=require("./add-html-label");module.exports=addLabel;function addLabel(root,node){var label=node.label;var labelSvg=root.append("g");if(typeof label!=="string"||node.labelType==="html"){addHtmlLabel(labelSvg,node)}else{addTextLabel(labelSvg,node)}var labelBBox=labelSvg.node().getBBox();labelSvg.attr("transform","translate("+-labelBBox.width/2+","+-labelBBox.height/2+")");return labelSvg}},{"./add-html-label":17,"./add-text-label":19}],19:[function(require,module,exports){var util=require("../util");module.exports=addTextLabel;function addTextLabel(root,node){var domNode=root.append("text");var lines=processEscapeSequences(node.label).split("\n");for(var i=0;i0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,entry,true));break}}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.each(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors){results.push({v:edge.v,w:edge.w})}uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.each(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.each(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.each(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});var zeroIdx=maxIn+1;_.each(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out){buckets[0].enqueue(entry)}else if(!entry["in"]){buckets[buckets.length-1].enqueue(entry)}else{buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}}},{"./data/list":31,"./graphlib":33,"./lodash":36}],35:[function(require,module,exports){"use strict";var _=require("./lodash"),acyclic=require("./acyclic"),normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time(" buildLayoutGraph",function(){return buildLayoutGraph(g)});time(" runLayout",function(){runLayout(layoutGraph,time)});time(" updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time(" makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time(" removeSelfEdges",function(){removeSelfEdges(g)});time(" acyclic",function(){acyclic.run(g)});time(" nestingGraph.run",function(){nestingGraph.run(g)});time(" rank",function(){rank(util.asNonCompoundGraph(g))});time(" injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time(" removeEmptyRanks",function(){removeEmptyRanks(g)});time(" nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time(" normalizeRanks",function(){normalizeRanks(g)});time(" assignRankMinMax",function(){assignRankMinMax(g)});time(" removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time(" normalize.run",function(){normalize.run(g)});time(" parentDummyChains",function(){parentDummyChains(g)});time(" addBorderSegments",function(){addBorderSegments(g)});time(" order",function(){order(g)});time(" insertSelfEdges",function(){insertSelfEdges(g)});time(" adjustCoordinateSystem",function(){coordinateSystem.adjust(g)});time(" position",function(){position(g)});time(" positionSelfEdges",function(){positionSelfEdges(g)});time(" removeBorderNodes",function(){removeBorderNodes(g)});time(" normalize.undo",function(){normalize.undo(g)});time(" fixupEdgeLabelCoords",function(){fixupEdgeLabelCoords(g)});time(" undoCoordinateSystem",function(){coordinateSystem.undo(g)});time(" translateGraph",function(){translateGraph(g)});time(" assignNodeIntersects",function(){assignNodeIntersects(g)});time(" reversePoints",function(){reversePointsForReversedEdges(g)});time(" acyclic.undo",function(){acyclic.undo(g)})}function updateInputGraph(inputGraph,layoutGraph){_.each(inputGraph.nodes(),function(v){var inputLabel=inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.each(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs))); +module.exports={graphlib:require("./lib/graphlib"),dagre:require("./lib/dagre"),intersect:require("./lib/intersect"),render:require("./lib/render"),util:require("./lib/util"),version:require("./lib/version")}},{"./lib/dagre":8,"./lib/graphlib":9,"./lib/intersect":10,"./lib/render":23,"./lib/util":25,"./lib/version":26}],2:[function(require,module,exports){var util=require("./util");module.exports={"default":normal,normal:normal,vee:vee,undirected:undirected};function normal(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function vee(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 L 4 5 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function undirected(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 5 L 10 5").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}},{"./util":25}],3:[function(require,module,exports){var _=require("./lodash"),addLabel=require("./label/add-label"),util=require("./util");module.exports=createClusters;function createClusters(selection,g){var clusters=g.nodes().filter(function(v){return util.isSubgraph(g,v)}),svgClusters=selection.selectAll("g.cluster").data(clusters,function(v){return v});var makeClusterIdentifier=function(v){return"cluster_"+v.replace(/^cluster/,"")};svgClusters.enter().append("g").attr("class",makeClusterIdentifier).attr("name",function(v){return g.node(v).label}).classed("cluster",true).style("opacity",0).append("rect");var sortedClusters=util.orderByRank(g,svgClusters.data());for(var i=0;i0}},{}],14:[function(require,module,exports){module.exports=intersectNode;function intersectNode(node,point){return node.intersect(point)}},{}],15:[function(require,module,exports){var intersectLine=require("./intersect-line");module.exports=intersectPolygon;function intersectPolygon(node,polyPoints,point){var x1=node.x;var y1=node.y;var intersections=[];var minX=Number.POSITIVE_INFINITY,minY=Number.POSITIVE_INFINITY;polyPoints.forEach(function(entry){minX=Math.min(minX,entry.x);minY=Math.min(minY,entry.y)});var left=x1-node.width/2-minX;var top=y1-node.height/2-minY;for(var i=0;i1){intersections.sort(function(p,q){var pdx=p.x-point.x,pdy=p.y-point.y,distp=Math.sqrt(pdx*pdx+pdy*pdy),qdx=q.x-point.x,qdy=q.y-point.y,distq=Math.sqrt(qdx*qdx+qdy*qdy);return distpMath.abs(dx)*h){if(dy<0){h=-h}sx=dy===0?0:h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=dx===0?0:w*dy/dx}return{x:x+sx,y:y+sy}}},{}],17:[function(require,module,exports){var util=require("../util");module.exports=addHtmlLabel;function addHtmlLabel(root,node){var fo=root.append("foreignObject").attr("width","100000");var div=fo.append("xhtml:div");var label=node.label;switch(typeof label){case"function":div.insert(label);break;case"object":div.insert(function(){return label});break;default:div.html(label)}util.applyStyle(div,node.labelStyle);div.style("display","inline-block");div.style("white-space","nowrap");var w,h;div.each(function(){w=this.clientWidth;h=this.clientHeight});fo.attr("width",w).attr("height",h);return fo}},{"../util":25}],18:[function(require,module,exports){var addTextLabel=require("./add-text-label"),addHtmlLabel=require("./add-html-label");module.exports=addLabel;function addLabel(root,node){var label=node.label;var labelSvg=root.append("g");if(typeof label!=="string"||node.labelType==="html"){addHtmlLabel(labelSvg,node)}else{addTextLabel(labelSvg,node)}var labelBBox=labelSvg.node().getBBox();labelSvg.attr("transform","translate("+-labelBBox.width/2+","+-labelBBox.height/2+")");return labelSvg}},{"./add-html-label":17,"./add-text-label":19}],19:[function(require,module,exports){var util=require("../util");module.exports=addTextLabel;function addTextLabel(root,node){var domNode=root.append("text");var lines=processEscapeSequences(node.label).split("\n");for(var i=0;imaxPadding){maxPadding=child.paddingTop}}return maxPadding}function getRank(g,v){var maxRank=0;var children=g.children(v);for(var i=0;imaxRank){maxRank=thisRank}}return maxRank}function orderByRank(g,nodes){return nodes.sort(function(x,y){return getRank(g,x)-getRank(g,y)})}function edgeToId(e){return escapeId(e.v)+":"+escapeId(e.w)+":"+escapeId(e.name)}var ID_DELIM=/:/g;function escapeId(str){return str?String(str).replace(ID_DELIM,"\\:"):""}function applyStyle(dom,styleFn){if(styleFn){dom.attr("style",styleFn)}}function applyClass(dom,classFn,otherClasses){if(classFn){dom.attr("class",classFn).attr("class",otherClasses+" "+dom.attr("class"))}}function applyTransition(selection,g){var graph=g.graph();if(_.isPlainObject(graph)){var transition=graph.transition;if(_.isFunction(transition)){return transition(selection)}}return selection}},{"./lodash":20}],26:[function(require,module,exports){module.exports="0.4.4-pre"},{}],27:[function(require,module,exports){module.exports={graphlib:require("./lib/graphlib"),layout:require("./lib/layout"),debug:require("./lib/debug"),util:{time:require("./lib/util").time,notime:require("./lib/util").notime},version:require("./lib/version")}},{"./lib/debug":32,"./lib/graphlib":33,"./lib/layout":35,"./lib/util":55,"./lib/version":56}],28:[function(require,module,exports){"use strict";var _=require("./lodash"),greedyFAS=require("./greedy-fas");module.exports={run:run,undo:undo};function run(g){var fas=g.graph().acyclicer==="greedy"?greedyFAS(g,weightFn(g)):dfsFAS(g);_.each(fas,function(e){var label=g.edge(e);g.removeEdge(e);label.forwardName=e.name;label.reversed=true;g.setEdge(e.w,e.v,label,_.uniqueId("rev"))});function weightFn(g){return function(e){return g.edge(e).weight}}}function dfsFAS(g){var fas=[],stack={},visited={};function dfs(v){if(_.has(visited,v)){return}visited[v]=true;stack[v]=true;_.each(g.outEdges(v),function(e){if(_.has(stack,e.w)){fas.push(e)}else{dfs(e.w)}});delete stack[v]}_.each(g.nodes(),dfs);return fas}function undo(g){_.each(g.edges(),function(e){var label=g.edge(e);if(label.reversed){g.removeEdge(e);var forwardName=label.forwardName;delete label.reversed;delete label.forwardName;g.setEdge(e.w,e.v,label,forwardName)}})}},{"./greedy-fas":34,"./lodash":36}],29:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports=addBorderSegments;function addBorderSegments(g){function dfs(v){var children=g.children(v),node=g.node(v);if(children.length){_.each(children,dfs)}if(_.has(node,"minRank")){node.borderLeft=[];node.borderRight=[];for(var rank=node.minRank,maxRank=node.maxRank+1;rank0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,entry,true));break}}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.each(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors){results.push({v:edge.v,w:edge.w})}uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.each(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.each(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.each(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});var zeroIdx=maxIn+1;_.each(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out){buckets[0].enqueue(entry)}else if(!entry["in"]){buckets[buckets.length-1].enqueue(entry)}else{buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}}},{"./data/list":31,"./graphlib":33,"./lodash":36}],35:[function(require,module,exports){"use strict";var _=require("./lodash"),acyclic=require("./acyclic"),normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time(" buildLayoutGraph",function(){return buildLayoutGraph(g)});time(" runLayout",function(){runLayout(layoutGraph,time)});time(" updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time(" makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time(" removeSelfEdges",function(){removeSelfEdges(g)});time(" acyclic",function(){acyclic.run(g)});time(" nestingGraph.run",function(){nestingGraph.run(g)});time(" rank",function(){rank(util.asNonCompoundGraph(g))});time(" injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time(" removeEmptyRanks",function(){removeEmptyRanks(g)});time(" nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time(" normalizeRanks",function(){normalizeRanks(g)});time(" assignRankMinMax",function(){assignRankMinMax(g)});time(" removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time(" normalize.run",function(){normalize.run(g)});time(" parentDummyChains",function(){ +parentDummyChains(g)});time(" addBorderSegments",function(){addBorderSegments(g)});time(" order",function(){order(g)});time(" insertSelfEdges",function(){insertSelfEdges(g)});time(" adjustCoordinateSystem",function(){coordinateSystem.adjust(g)});time(" position",function(){position(g)});time(" positionSelfEdges",function(){positionSelfEdges(g)});time(" removeBorderNodes",function(){removeBorderNodes(g)});time(" normalize.undo",function(){normalize.undo(g)});time(" fixupEdgeLabelCoords",function(){fixupEdgeLabelCoords(g)});time(" undoCoordinateSystem",function(){coordinateSystem.undo(g)});time(" translateGraph",function(){translateGraph(g)});time(" assignNodeIntersects",function(){assignNodeIntersects(g)});time(" reversePoints",function(){reversePointsForReversedEdges(g)});time(" acyclic.undo",function(){acyclic.undo(g)})}function updateInputGraph(inputGraph,layoutGraph){_.each(inputGraph.nodes(),function(v){var inputLabel=inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.each(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));_.each(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.each(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph();graph.ranksep/=2;_.each(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c"){if(graph.rankdir==="TB"||graph.rankdir==="BT"){edge.width+=edge.labeloffset}else{edge.height+=edge.labeloffset}}})}function injectEdgeLabelProxies(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.each(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.each(g.nodes(),function(v){getExtremes(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){getExtremes(edge)}});minX-=marginX;minY-=marginY;_.each(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x")){edge.x-=minX}if(_.has(edge,"y")){edge.y-=minY}});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.each(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r"){edge.width-=edge.labeloffset}switch(edge.labelpos){case"l":edge.x-=edge.width/2+edge.labeloffset;break;case"r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.reversed){edge.points.reverse()}})}function removeBorderNodes(g){_.each(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.each(g.nodes(),function(v){if(g.node(v).dummy==="border"){g.removeNode(v)}})}function removeSelfEdges(g){_.each(g.edges(),function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges){node.selfEdges=[]}node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.each(layers,function(layer){var orderShift=0;_.each(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.each(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.each(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":28,"./add-border-segments":29,"./coordinate-system":30,"./graphlib":33,"./lodash":36,"./nesting-graph":37,"./normalize":38,"./order":43,"./parent-dummy-chains":48,"./position":50,"./rank":52,"./util":55}],36:[function(require,module,exports){module.exports=require(20)},{"/Users/andrew/Documents/dev/dagre-d3/lib/lodash.js":20,lodash:77}],37:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root"),depths=treeDepths(g),height=_.max(depths)-1,nodeSep=2*height+1;g.graph().nestingRoot=root;_.each(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.each(g.children(),function(child){dfs(g,root,nodeSep,weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root){g.setEdge(root,v,{weight:0,minlen:nodeSep})}return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.each(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v)){g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}}function treeDepths(g){var depths={};function dfs(v,depth){var children=g.children(v);if(children&&children.length){_.each(children,function(child){dfs(child,depth+1)})}depths[v]=depth}_.each(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge){g.removeEdge(e)}})}},{"./lodash":36,"./util":55}],38:[function(require,module,exports){"use strict";var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.each(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenterentryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRanklow||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.each(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.each(g.children(),dfs);return result}},{"./lodash":36}],49:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.each(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.each(layer.slice(scanPos,i+1),function(scanNode){_.each(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPosnextNorthBorder)){addConflict(conflicts,u,v)}})}})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.each(south,function(v,southLookahead){if(g.node(v).dummy==="border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy){return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}}function addConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV){conflicts[v]=conflictsV={}}conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.each(layering,function(layer){_.each(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.each(layering,function(layer){var prevIdx=-1;_.each(layer,function(v){var ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdx0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenterentryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRanklow||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.each(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.each(g.children(),dfs);return result}},{"./lodash":36}],49:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.each(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.each(layer.slice(scanPos,i+1),function(scanNode){_.each(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPosnextNorthBorder)){addConflict(conflicts,u,v)}})}})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.each(south,function(v,southLookahead){if(g.node(v).dummy==="border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy){return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}}function addConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV){conflicts[v]=conflictsV={}}conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.each(layering,function(layer){_.each(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.each(layering,function(layer){var prevIdx=-1;_.each(layer,function(v){var ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdxwLabel.lim){tailLabel=wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.min(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);_.each(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":33,"../lodash":36,"../util":55,"./feasible-tree":51,"./util":54}],54:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v)){return label.rank}visited[v]=true;var rank=_.min(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY){rank=0}return label.rank=rank}_.each(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":36}],55:[function(require,module,exports){"use strict";var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v; +if(!g.hasEdge(v,w)){v=edge.w;w=edge.v}var vLabel=t.node(v),wLabel=t.node(w),tailLabel=vLabel,flip=false;if(vLabel.lim>wLabel.lim){tailLabel=wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.min(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);_.each(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":33,"../lodash":36,"../util":55,"./feasible-tree":51,"./util":54}],54:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v)){return label.rank}visited[v]=true;var rank=_.min(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY){rank=0}return label.rank=rank}_.each(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":36}],55:[function(require,module,exports){"use strict";var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v;do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":75,"./dijkstra":61}],61:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":71,"../lodash":75}],62:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"../lodash":75,"./tarjan":69}],63:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":71,"../graph":72,"../lodash":75}],69:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack){entry.lowlink=Math.min(entry.lowlink,visited[w].index)}});if(entry.lowlink===entry.index){var cmpt=[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v)){dfs(v)}});return results}},{"../lodash":75}],70:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node)){throw new CycleException}if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount()){throw new CycleException}return results}function CycleException(){}},{"../lodash":75}],71:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined){return this._arr[index].priority}};PriorityQueue.prototype.min=function(){if(this.size()===0){throw new Error("Queue underflow")}return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority){throw new Error("New priority is greater than current priority. "+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l>1;if(arr[parent].priority1){this.setNode(v,value)}else{this.setNode(v)}},this);return this};Graph.prototype.setNode=function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1){this._nodes[v]=value}return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete this._children[this._parent[v]][v]};Graph.prototype.parent=function(v){if(this._isCompound){var parent=this._parent[v];if(parent!==GRAPH_NODE){return parent}}};Graph.prototype.children=function(v){if(_.isUndefined(v)){v=GRAPH_NODE}if(this._isCompound){var children=this._children[v];if(children){return _.keys(children)}}else if(v===GRAPH_NODE){return this.nodes()}else if(this.hasNode(v)){return[]}};Graph.prototype.predecessors=function(v){var predsV=this._preds[v];if(predsV){return _.keys(predsV)}};Graph.prototype.successors=function(v){var sucsV=this._sucs[v];if(sucsV){return _.keys(sucsV)}};Graph.prototype.neighbors=function(v){var preds=this.predecessors(v);if(preds){return _.union(preds,this.successors(v))}};Graph.prototype.setDefaultEdgeLabel=function(newDefault){if(!_.isFunction(newDefault)){newDefault=_.constant(newDefault)}this._defaultEdgeLabelFn=newDefault;return this};Graph.prototype.edgeCount=function(){return this._edgeCount};Graph.prototype.edges=function(){return _.values(this._edgeObjs)};Graph.prototype.setPath=function(vs,value){var self=this,args=arguments;_.reduce(vs,function(v,w){if(args.length>1){self.setEdge(v,w,value)}else{self.setEdge(v,w)}return w});return this};Graph.prototype.setEdge=function(){var v,w,name,value,valueSpecified=false;if(_.isPlainObject(arguments[0])){v=arguments[0].v;w=arguments[0].w;name=arguments[0].name;if(arguments.length===2){value=arguments[1];valueSpecified=true}}else{v=arguments[0];w=arguments[1];name=arguments[3];if(arguments.length>2){value=arguments[2];valueSpecified=true}}v=""+v;w=""+w;if(!_.isUndefined(name)){name=""+name}var e=edgeArgsToId(this._isDirected,v,w,name);if(_.has(this._edgeLabels,e)){if(valueSpecified){this._edgeLabels[e]=value}return this}if(!_.isUndefined(name)&&!this._isMultigraph){throw new Error("Cannot set a named edge when isMultigraph = false")}this.setNode(v);this.setNode(w);this._edgeLabels[e]=valueSpecified?value:this._defaultEdgeLabelFn(v,w,name);var edgeObj=edgeArgsToObj(this._isDirected,v,w,name);v=edgeObj.v;w=edgeObj.w;Object.freeze(edgeObj);this._edgeObjs[e]=edgeObj;incrementOrInitEntry(this._preds[w],v);incrementOrInitEntry(this._sucs[v],w);this._in[w][e]=edgeObj;this._out[v][e]=edgeObj;this._edgeCount++;return this};Graph.prototype.edge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return this._edgeLabels[e]};Graph.prototype.hasEdge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return _.has(this._edgeLabels,e)};Graph.prototype.removeEdge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name),edge=this._edgeObjs[e];if(edge){v=edge.v;w=edge.w;delete this._edgeLabels[e];delete this._edgeObjs[e];decrementOrRemoveEntry(this._preds[w],v);decrementOrRemoveEntry(this._sucs[v],w);delete this._in[w][e];delete this._out[v][e];this._edgeCount--}return this};Graph.prototype.inEdges=function(v,u){var inV=this._in[v];if(inV){var edges=_.values(inV);if(!u){return edges}return _.filter(edges,function(edge){return edge.v===u})}};Graph.prototype.outEdges=function(v,w){var outV=this._out[v];if(outV){var edges=_.values(outV);if(!w){return edges}return _.filter(edges,function(edge){return edge.w===w})}};Graph.prototype.nodeEdges=function(v,w){var inEdges=this.inEdges(v,w);if(inEdges){return inEdges.concat(this.outEdges(v,w))}};function incrementOrInitEntry(map,k){if(_.has(map,k)){map[k]++}else{map[k]=1}}function decrementOrRemoveEntry(map,k){if(!--map[k]){delete map[k]}}function edgeArgsToId(isDirected,v,w,name){if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}return v+EDGE_KEY_DELIM+w+EDGE_KEY_DELIM+(_.isUndefined(name)?DEFAULT_EDGE_NAME:name)}function edgeArgsToObj(isDirected,v,w,name){if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}var edgeObj={v:v,w:w};if(name){edgeObj.name=name}return edgeObj}function edgeObjToId(isDirected,edgeObj){return edgeArgsToId(isDirected,edgeObj.v,edgeObj.w,edgeObj.name)}},{"./lodash":75}],73:[function(require,module,exports){module.exports={Graph:require("./graph"),version:require("./version")}},{"./graph":72,"./version":76}],74:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graph");module.exports={write:write,read:read};function write(g){var json={options:{directed:g.isDirected(),multigraph:g.isMultigraph(),compound:g.isCompound()},nodes:writeNodes(g),edges:writeEdges(g)};if(!_.isUndefined(g.graph())){json.value=_.clone(g.graph())}return json}function writeNodes(g){return _.map(g.nodes(),function(v){var nodeValue=g.node(v),parent=g.parent(v),node={v:v};if(!_.isUndefined(nodeValue)){node.value=nodeValue}if(!_.isUndefined(parent)){node.parent=parent}return node})}function writeEdges(g){return _.map(g.edges(),function(e){var edgeValue=g.edge(e),edge={v:e.v,w:e.w};if(!_.isUndefined(e.name)){edge.name=e.name}if(!_.isUndefined(edgeValue)){edge.value=edgeValue}return edge})}function read(json){var g=new Graph(json.options).setGraph(json.value);_.each(json.nodes,function(entry){g.setNode(entry.v,entry.value);if(entry.parent){g.setParent(entry.v,entry.parent)}});_.each(json.edges,function(entry){g.setEdge({v:entry.v,w:entry.w,name:entry.name},entry.value)});return g}},{"./graph":72,"./lodash":75}],75:[function(require,module,exports){module.exports=require(20)},{"/Users/andrew/Documents/dev/dagre-d3/lib/lodash.js":20,lodash:77}],76:[function(require,module,exports){module.exports="1.0.1"},{}],77:[function(require,module,exports){(function(global){(function(){var undefined;var arrayPool=[],objectPool=[];var idCounter=0;var keyPrefix=+new Date+"";var largeArraySize=75;var maxPoolSize=40;var whitespace=" \f \ufeff"+"\n\r\u2028\u2029"+" ᠎             　";var reEmptyStringLeading=/\b__p \+= '';/g,reEmptyStringMiddle=/\b(__p \+=) '' \+/g,reEmptyStringTrailing=/(__e\(.*?\)|\b__t\)) \+\n'';/g;var reEsTemplate=/\$\{([^\\}]*(?:\\.[^\\}]*)*)\}/g;var reFlags=/\w*$/;var reFuncName=/^\s*function[ \n\r\t]+\w/;var reInterpolate=/<%=([\s\S]+?)%>/g;var reLeadingSpacesAndZeros=RegExp("^["+whitespace+"]*0+(?=.$)");var reNoMatch=/($^)/;var reThis=/\bthis\b/;var reUnescapedString=/['\n\r\t\u2028\u2029\\]/g;var contextProps=["Array","Boolean","Date","Function","Math","Number","Object","RegExp","String","_","attachEvent","clearTimeout","isFinite","isNaN","parseInt","setTimeout"];var templateCounter=0;var argsClass="[object Arguments]",arrayClass="[object Array]",boolClass="[object Boolean]",dateClass="[object Date]",funcClass="[object Function]",numberClass="[object Number]",objectClass="[object Object]",regexpClass="[object RegExp]",stringClass="[object String]";var cloneableClasses={};cloneableClasses[funcClass]=false;cloneableClasses[argsClass]=cloneableClasses[arrayClass]=cloneableClasses[boolClass]=cloneableClasses[dateClass]=cloneableClasses[numberClass]=cloneableClasses[objectClass]=cloneableClasses[regexpClass]=cloneableClasses[stringClass]=true;var debounceOptions={leading:false,maxWait:0,trailing:false};var descriptor={configurable:false,enumerable:false,value:null,writable:false};var objectTypes={"boolean":false,"function":true,object:true,number:false,string:false,undefined:false};var stringEscapes={"\\":"\\","'":"'","\n":"n","\r":"r"," ":"t","\u2028":"u2028","\u2029":"u2029"};var root=objectTypes[typeof window]&&window||this;var freeExports=objectTypes[typeof exports]&&exports&&!exports.nodeType&&exports;var freeModule=objectTypes[typeof module]&&module&&!module.nodeType&&module;var moduleExports=freeModule&&freeModule.exports===freeExports&&freeExports;var freeGlobal=objectTypes[typeof global]&&global;if(freeGlobal&&(freeGlobal.global===freeGlobal||freeGlobal.window===freeGlobal)){root=freeGlobal}function baseIndexOf(array,value,fromIndex){var index=(fromIndex||0)-1,length=array?array.length:0;while(++index-1?0:-1:cache?0:-1}function cachePush(value){var cache=this.cache,type=typeof value;if(type=="boolean"||value==null){cache[value]=true}else{if(type!="number"&&type!="string"){type="object"}var key=type=="number"?value:keyPrefix+value,typeCache=cache[type]||(cache[type]={});if(type=="object"){(typeCache[key]||(typeCache[key]=[])).push(value)}else{typeCache[key]=true}}}function charAtCallback(value){return value.charCodeAt(0)}function compareAscending(a,b){var ac=a.criteria,bc=b.criteria,index=-1,length=ac.length;while(++indexother||typeof value=="undefined"){return 1}if(value/g,evaluate:/<%([\s\S]+?)%>/g,interpolate:reInterpolate,variable:"",imports:{_:lodash}};function baseBind(bindData){var func=bindData[0],partialArgs=bindData[2],thisArg=bindData[4];function bound(){if(partialArgs){var args=slice(partialArgs);push.apply(args,arguments)}if(this instanceof bound){var thisBinding=baseCreate(func.prototype),result=func.apply(thisBinding,args||arguments);return isObject(result)?result:thisBinding}return func.apply(thisArg,args||arguments)}setBindData(bound,bindData);return bound}function baseClone(value,isDeep,callback,stackA,stackB){if(callback){var result=callback(value);if(typeof result!="undefined"){return result}}var isObj=isObject(value);if(isObj){var className=toString.call(value);if(!cloneableClasses[className]){return value}var ctor=ctorByClass[className];switch(className){case boolClass:case dateClass:return new ctor(+value);case numberClass:case stringClass:return new ctor(value);case regexpClass:result=ctor(value.source,reFlags.exec(value));result.lastIndex=value.lastIndex;return result}}else{return value}var isArr=isArray(value);if(isDeep){var initedStack=!stackA;stackA||(stackA=getArray());stackB||(stackB=getArray());var length=stackA.length;while(length--){if(stackA[length]==value){return stackB[length]}}result=isArr?ctor(value.length):{}}else{result=isArr?slice(value):assign({},value)}if(isArr){if(hasOwnProperty.call(value,"index")){result.index=value.index}if(hasOwnProperty.call(value,"input")){result.input=value.input}}if(!isDeep){return result}stackA.push(value);stackB.push(result);(isArr?forEach:forOwn)(value,function(objValue,key){result[key]=baseClone(objValue,isDeep,callback,stackA,stackB)});if(initedStack){releaseArray(stackA);releaseArray(stackB)}return result}function baseCreate(prototype,properties){return isObject(prototype)?nativeCreate(prototype):{}; -do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":75,"./dijkstra":61}],61:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":71,"../lodash":75}],62:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"../lodash":75,"./tarjan":69}],63:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":71,"../graph":72,"../lodash":75}],69:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack){entry.lowlink=Math.min(entry.lowlink,visited[w].index)}});if(entry.lowlink===entry.index){var cmpt=[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v)){dfs(v)}});return results}},{"../lodash":75}],70:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node)){throw new CycleException}if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount()){throw new CycleException}return results}function CycleException(){}},{"../lodash":75}],71:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined){return this._arr[index].priority}};PriorityQueue.prototype.min=function(){if(this.size()===0){throw new Error("Queue underflow")}return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority){throw new Error("New priority is greater than current priority. "+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l>1;if(arr[parent].priority1){this.setNode(v,value)}else{this.setNode(v)}},this);return this};Graph.prototype.setNode=function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1){this._nodes[v]=value}return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete this._children[this._parent[v]][v]};Graph.prototype.parent=function(v){if(this._isCompound){var parent=this._parent[v];if(parent!==GRAPH_NODE){return parent}}};Graph.prototype.children=function(v){if(_.isUndefined(v)){v=GRAPH_NODE}if(this._isCompound){var children=this._children[v];if(children){return _.keys(children)}}else if(v===GRAPH_NODE){return this.nodes()}else if(this.hasNode(v)){return[]}};Graph.prototype.predecessors=function(v){var predsV=this._preds[v];if(predsV){return _.keys(predsV)}};Graph.prototype.successors=function(v){var sucsV=this._sucs[v];if(sucsV){return _.keys(sucsV)}};Graph.prototype.neighbors=function(v){var preds=this.predecessors(v);if(preds){return _.union(preds,this.successors(v))}};Graph.prototype.setDefaultEdgeLabel=function(newDefault){if(!_.isFunction(newDefault)){newDefault=_.constant(newDefault)}this._defaultEdgeLabelFn=newDefault;return this};Graph.prototype.edgeCount=function(){return this._edgeCount};Graph.prototype.edges=function(){return _.values(this._edgeObjs)};Graph.prototype.setPath=function(vs,value){var self=this,args=arguments;_.reduce(vs,function(v,w){if(args.length>1){self.setEdge(v,w,value)}else{self.setEdge(v,w)}return w});return this};Graph.prototype.setEdge=function(){var v,w,name,value,valueSpecified=false;if(_.isPlainObject(arguments[0])){v=arguments[0].v;w=arguments[0].w;name=arguments[0].name;if(arguments.length===2){value=arguments[1];valueSpecified=true}}else{v=arguments[0];w=arguments[1];name=arguments[3];if(arguments.length>2){value=arguments[2];valueSpecified=true}}v=""+v;w=""+w;if(!_.isUndefined(name)){name=""+name}var e=edgeArgsToId(this._isDirected,v,w,name);if(_.has(this._edgeLabels,e)){if(valueSpecified){this._edgeLabels[e]=value}return this}if(!_.isUndefined(name)&&!this._isMultigraph){throw new Error("Cannot set a named edge when isMultigraph = false")}this.setNode(v);this.setNode(w);this._edgeLabels[e]=valueSpecified?value:this._defaultEdgeLabelFn(v,w,name);var 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' 5' : ' ▾'; this.appendChild(sortrevind); return; } @@ -113,7 +113,7 @@ sorttable = { this.removeChild(document.getElementById('sorttable_sortrevind')); sortfwdind = document.createElement('span'); sortfwdind.id = "sorttable_sortfwdind"; - sortfwdind.innerHTML = stIsIE ? ' 6' : ' ▾'; + sortfwdind.innerHTML = stIsIE ? ' 6' : ' ▴'; this.appendChild(sortfwdind); return; } @@ -134,7 +134,7 @@ sorttable = { this.className += ' sorttable_sorted'; sortfwdind = document.createElement('span'); sortfwdind.id = "sorttable_sortfwdind"; - sortfwdind.innerHTML = stIsIE ? ' 6' : ' ▾'; + sortfwdind.innerHTML = stIsIE ? ' 6' : ' ▴'; this.appendChild(sortfwdind); // build an array to sort. This is a Schwartzian transform thing, diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css index 18c72694f3e2d..3b4ae2ed354b8 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css +++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css @@ -15,33 +15,26 @@ * limitations under the License. */ -#dag-viz-graph svg path { - stroke: #444; - stroke-width: 1.5px; +#dag-viz-graph a, #dag-viz-graph a:hover { + text-decoration: none; } -#dag-viz-graph svg g.cluster rect { - stroke-width: 1px; +#dag-viz-graph .label { + font-weight: normal; + text-shadow: none; } -#dag-viz-graph svg g.node circle { - fill: #444; +#dag-viz-graph svg path { + stroke: #444; + stroke-width: 1.5px; } -#dag-viz-graph svg g.node rect { - fill: #C3EBFF; - stroke: #3EC0FF; +#dag-viz-graph svg g.cluster rect { stroke-width: 1px; } -#dag-viz-graph svg g.node.cached circle { - fill: #444; -} - -#dag-viz-graph svg g.node.cached rect { - fill: #B3F5C5; - stroke: #56F578; - stroke-width: 1px; +#dag-viz-graph div#empty-dag-viz-message { + margin: 15px; } /* Job page specific styles */ @@ -57,12 +50,23 @@ stroke-width: 1px; } -#dag-viz-graph svg.job g.cluster[id*="stage"] rect { +#dag-viz-graph svg.job g.cluster.skipped rect { + fill: #D6D6D6; + stroke: #B7B7B7; + stroke-width: 1px; +} + +#dag-viz-graph svg.job g.cluster.stage rect { fill: #FFFFFF; stroke: #FF99AC; stroke-width: 1px; } +#dag-viz-graph svg.job g.cluster.stage.skipped rect { + stroke: #ADADAD; + stroke-width: 1px; +} + #dag-viz-graph svg.job g#cross-stage-edges path { fill: none; } @@ -71,6 +75,20 @@ fill: #333; } +#dag-viz-graph svg.job g.cluster.skipped text { + fill: #666; +} + +#dag-viz-graph svg.job g.node circle { + fill: #444; +} + +#dag-viz-graph svg.job g.node.cached circle { + fill: #A3F545; + stroke: #52C366; + stroke-width: 2px; +} + /* Stage page specific styles */ #dag-viz-graph svg.stage g.cluster rect { @@ -79,7 +97,7 @@ stroke-width: 1px; } -#dag-viz-graph svg.stage g.cluster[id*="stage"] rect { +#dag-viz-graph svg.stage g.cluster.stage rect { fill: #FFFFFF; stroke: #FFA6B6; stroke-width: 1px; @@ -93,11 +111,14 @@ fill: #333; } -#dag-viz-graph a, #dag-viz-graph a:hover { - text-decoration: none; +#dag-viz-graph svg.stage g.node rect { + fill: #C3EBFF; + stroke: #3EC0FF; + stroke-width: 1px; } -#dag-viz-graph .label { - font-weight: normal; - text-shadow: none; +#dag-viz-graph svg.stage g.node.cached rect { + fill: #B3F5C5; + stroke: #52C366; + stroke-width: 2px; } diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js index 764dd2cfcd76f..9fa53baaf4212 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js +++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js @@ -57,9 +57,7 @@ var VizConstants = { stageSep: 40, graphPrefix: "graph_", nodePrefix: "node_", - stagePrefix: "stage_", - clusterPrefix: "cluster_", - stageClusterPrefix: "cluster_stage_" + clusterPrefix: "cluster_" }; var JobPageVizConstants = { @@ -86,7 +84,7 @@ function toggleDagViz(forJob) { $(arrowSelector).toggleClass('arrow-open'); var shouldShow = $(arrowSelector).hasClass("arrow-open"); if (shouldShow) { - var shouldRender = graphContainer().select("svg").empty(); + var shouldRender = graphContainer().select("*").empty(); if (shouldRender) { renderDagViz(forJob); } @@ -108,7 +106,7 @@ function toggleDagViz(forJob) { * Output DOM hierarchy: * div#dag-viz-graph > * svg > - * g#cluster_stage_[stageId] + * g.cluster_stage_[stageId] * * Note that the input metadata is populated by o.a.s.ui.UIUtils.showDagViz. * Any changes in the input format here must be reflected there. @@ -117,17 +115,23 @@ function renderDagViz(forJob) { // If there is not a dot file to render, fail fast and report error var jobOrStage = forJob ? "job" : "stage"; - if (metadataContainer().empty()) { - graphContainer() - .append("div") - .text("No visualization information available for this " + jobOrStage); + if (metadataContainer().empty() || + metadataContainer().selectAll("div").empty()) { + var message = + "No visualization information available for this " + jobOrStage + "!
" + + "If this is an old " + jobOrStage + ", its visualization metadata may have been " + + "cleaned up over time.
You may consider increasing the value of "; + if (forJob) { + message += "spark.ui.retainedJobs and spark.ui.retainedStages."; + } else { + message += "spark.ui.retainedStages"; + } + graphContainer().append("div").attr("id", "empty-dag-viz-message").html(message); return; } // Render - var svg = graphContainer() - .append("svg") - .attr("class", jobOrStage); + var svg = graphContainer().append("svg").attr("class", jobOrStage); if (forJob) { renderDagVizForJob(svg); } else { @@ -136,22 +140,21 @@ function renderDagViz(forJob) { // Find cached RDDs and mark them as such metadataContainer().selectAll(".cached-rdd").each(function(v) { - var nodeId = VizConstants.nodePrefix + d3.select(this).text(); - svg.selectAll("#" + nodeId).classed("cached", true); + var rddId = d3.select(this).text().trim(); + var nodeId = VizConstants.nodePrefix + rddId; + svg.selectAll("g." + nodeId).classed("cached", true); }); - // More post-processing - drawClusterLabels(svg, forJob); resizeSvg(svg); } /* Render the RDD DAG visualization on the stage page. */ function renderDagVizForStage(svgContainer) { var metadata = metadataContainer().select(".stage-metadata"); - var dot = metadata.select(".dot-file").text(); + var dot = metadata.select(".dot-file").text().trim(); var containerId = VizConstants.graphPrefix + metadata.attr("stage-id"); var container = svgContainer.append("g").attr("id", containerId); - renderDot(dot, container, StagePageVizConstants.rankSep); + renderDot(dot, container, false); // Round corners on rectangles svgContainer @@ -179,29 +182,35 @@ function renderDagVizForJob(svgContainer) { var dot = metadata.select(".dot-file").text(); var stageId = metadata.attr("stage-id"); var containerId = VizConstants.graphPrefix + stageId; - // Link each graph to the corresponding stage page (TODO: handle stage attempts) - var stageLink = "/stages/stage/?id=" + - stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0&expandDagViz=true"; - var container = svgContainer - .append("a") - .attr("xlink:href", stageLink) - .append("g") - .attr("id", containerId); + var isSkipped = metadata.attr("skipped") == "true"; + var container; + if (isSkipped) { + container = svgContainer + .append("g") + .attr("id", containerId) + .attr("skipped", "true"); + } else { + // Link each graph to the corresponding stage page (TODO: handle stage attempts) + // Use the link from the stage table so it also works for the history server + var attemptId = 0 + var stageLink = d3.select("#stage-" + stageId + "-" + attemptId) + .select("a.name-link") + .attr("href") + "&expandDagViz=true"; + container = svgContainer + .append("a") + .attr("xlink:href", stageLink) + .append("g") + .attr("id", containerId); + } // Now we need to shift the container for this stage so it doesn't overlap with // existing ones, taking into account the position and width of the last stage's // container. We do not need to do this for the first stage of this job. if (i > 0) { - var existingStages = svgContainer - .selectAll("g.cluster") - .filter("[id*=\"" + VizConstants.stageClusterPrefix + "\"]"); + var existingStages = svgContainer.selectAll("g.cluster.stage") if (!existingStages.empty()) { var lastStage = d3.select(existingStages[0].pop()); - var lastStageId = lastStage.attr("id"); - var lastStageWidth = toFloat(svgContainer - .select("#" + lastStageId) - .select("rect") - .attr("width")); + var lastStageWidth = toFloat(lastStage.select("rect").attr("width")); var lastStagePosition = getAbsolutePosition(lastStage); var offset = lastStagePosition.x + lastStageWidth + VizConstants.stageSep; container.attr("transform", "translate(" + offset + ", 0)"); @@ -209,7 +218,13 @@ function renderDagVizForJob(svgContainer) { } // Actually render the stage - renderDot(dot, container, JobPageVizConstants.rankSep); + renderDot(dot, container, true); + + // Mark elements as skipped if appropriate. Unfortunately we need to mark all + // elements instead of the parent container because of CSS override rules. + if (isSkipped) { + container.selectAll("g").classed("skipped", true); + } // Round corners on rectangles container @@ -221,7 +236,7 @@ function renderDagVizForJob(svgContainer) { // them separately later. Note that we cannot draw them now because we need to // put these edges in a separate container that is on top of all stage graphs. metadata.selectAll(".incoming-edge").each(function(v) { - var edge = d3.select(this).text().split(","); // e.g. 3,4 => [3, 4] + var edge = d3.select(this).text().trim().split(","); // e.g. 3,4 => [3, 4] crossStageEdges.push(edge); }); }); @@ -231,15 +246,18 @@ function renderDagVizForJob(svgContainer) { } /* Render the dot file as an SVG in the given container. */ -function renderDot(dot, container, rankSep) { +function renderDot(dot, container, forJob) { var escaped_dot = dot .replace(/</g, "<") .replace(/>/g, ">") .replace(/"/g, "\""); var g = graphlibDot.read(escaped_dot); - g.graph().rankSep = rankSep; var renderer = new dagreD3.render(); + preprocessGraphLayout(g, forJob); renderer(container, g); + + // Find the stage cluster and mark it for styling and post-processing + container.selectAll("g.cluster[name*=\"Stage\"]").classed("stage", true); } /* -------------------- * @@ -251,50 +269,38 @@ function graphContainer() { return d3.select("#dag-viz-graph"); } function metadataContainer() { return d3.select("#dag-viz-metadata"); } /* - * Helper function to create draw a label for each cluster. - * - * We need to do this manually because dagre-d3 does not support labeling clusters. - * In general, the clustering support for dagre-d3 is quite limited at this point. + * Helper function to pre-process the graph layout. + * This step is necessary for certain styles that affect the positioning + * and sizes of graph elements, e.g. padding, font style, shape. */ -function drawClusterLabels(svgContainer, forJob) { - var clusterLabelSize, stageClusterLabelSize; +function preprocessGraphLayout(g, forJob) { + var nodes = g.nodes(); + for (var i = 0; i < nodes.length; i++) { + var isCluster = g.children(nodes[i]).length > 0; + if (!isCluster) { + var node = g.node(nodes[i]); + if (forJob) { + // Do not display RDD name on job page + node.shape = "circle"; + node.labelStyle = "font-size: 0px"; + } else { + node.labelStyle = "font-size: 12px"; + } + node.padding = "5"; + } + } + // Curve the edges + var edges = g.edges(); + for (var j = 0; j < edges.length; j++) { + var edge = g.edge(edges[j]); + edge.lineInterpolate = "basis"; + } + // Adjust vertical separation between nodes if (forJob) { - clusterLabelSize = JobPageVizConstants.clusterLabelSize; - stageClusterLabelSize = JobPageVizConstants.stageClusterLabelSize; + g.graph().rankSep = JobPageVizConstants.rankSep; } else { - clusterLabelSize = StagePageVizConstants.clusterLabelSize; - stageClusterLabelSize = StagePageVizConstants.stageClusterLabelSize; + g.graph().rankSep = StagePageVizConstants.rankSep; } - svgContainer.selectAll("g.cluster").each(function() { - var cluster = d3.select(this); - var isStage = cluster.attr("id").indexOf(VizConstants.stageClusterPrefix) > -1; - var labelSize = isStage ? stageClusterLabelSize : clusterLabelSize; - drawClusterLabel(cluster, labelSize); - }); -} - -/* - * Helper function to draw a label for the given cluster element based on its name. - * - * In the process, we need to expand the bounding box to make room for the label. - * We need to do this because dagre-d3 did not take this into account when it first - * rendered the bounding boxes. Note that this means we need to adjust the view box - * of the SVG afterwards since we shifted a few boxes around. - */ -function drawClusterLabel(d3cluster, fontSize) { - var cluster = d3cluster; - var rect = d3cluster.select("rect"); - rect.attr("y", toFloat(rect.attr("y")) - fontSize); - rect.attr("height", toFloat(rect.attr("height")) + fontSize); - var labelX = toFloat(rect.attr("x")) + toFloat(rect.attr("width")) - fontSize / 2; - var labelY = toFloat(rect.attr("y")) + fontSize * 1.5; - var labelText = cluster.attr("name").replace(VizConstants.clusterPrefix, ""); - cluster.append("text") - .attr("x", labelX) - .attr("y", labelY) - .attr("text-anchor", "end") - .style("font-size", fontSize + "px") - .text(labelText); } /* @@ -386,14 +392,14 @@ function getAbsolutePosition(d3selection) { function connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer) { var fromNodeId = VizConstants.nodePrefix + fromRDDId; var toNodeId = VizConstants.nodePrefix + toRDDId; - var fromPos = getAbsolutePosition(svgContainer.select("#" + fromNodeId)); - var toPos = getAbsolutePosition(svgContainer.select("#" + toNodeId)); + var fromPos = getAbsolutePosition(svgContainer.select("g." + fromNodeId)); + var toPos = getAbsolutePosition(svgContainer.select("g." + toNodeId)); // On the job page, RDDs are rendered as dots (circles). When rendering the path, // we need to account for the radii of these circles. Otherwise the arrow heads // will bleed into the circle itself. var delta = toFloat(svgContainer - .select("g.node#" + toNodeId) + .select("g.node." + toNodeId) .select("circle") .attr("r")); if (fromPos.x < toPos.x) { @@ -444,11 +450,36 @@ function addTooltipsForRDDs(svgContainer) { if (tooltipText) { node.select("circle") .attr("data-toggle", "tooltip") - .attr("data-placement", "right") - .attr("title", tooltipText) + .attr("data-placement", "bottom") + .attr("title", tooltipText); } + // Link tooltips for all nodes that belong to the same RDD + node.on("mouseenter", function() { triggerTooltipForRDD(node, true); }); + node.on("mouseleave", function() { triggerTooltipForRDD(node, false); }); }); - $("[data-toggle=tooltip]").tooltip({container: "body"}); + + $("[data-toggle=tooltip]") + .filter("g.node circle") + .tooltip({ container: "body", trigger: "manual" }); +} + +/* + * (Job page only) Helper function to show or hide tooltips for all nodes + * in the graph that refer to the same RDD the specified node represents. + */ +function triggerTooltipForRDD(d3node, show) { + var classes = d3node.node().classList; + for (var i = 0; i < classes.length; i++) { + var clazz = classes[i]; + var isRDDClass = clazz.indexOf(VizConstants.nodePrefix) == 0; + if (isRDDClass) { + graphContainer().selectAll("g." + clazz).each(function() { + var circle = d3.select(this).select("circle").node(); + var showOrHide = show ? "show" : "hide"; + $(circle).tooltip(showOrHide); + }); + } + } } /* Helper function to convert attributes to numeric values. */ diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css index d40de704229c3..0f400461c5293 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css +++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css @@ -19,10 +19,70 @@ div#application-timeline, div#job-timeline { margin-bottom: 30px; } -#application-timeline div.legend-area { +#application-timeline div.legend-area, +#job-timeline div.legend-area { margin-top: 5px; } +#task-assignment-timeline div.legend-area { + width: 574px; +} + +#task-assignment-timeline .legend-area > svg { + width: 100%; + height: 55px; +} + +#task-assignment-timeline div.item.range { + padding: 0px; + height: 26px; + border-width: 0; +} + +.task-assignment-timeline-content { + width: 100%; +} + +.task-assignment-timeline-duration-bar { + width: 100%; + height: 26px; +} + +rect.scheduler-delay-proportion { + fill: #80B1D3; + stroke: #6B94B0; +} + +rect.deserialization-time-proportion { + fill: #FB8072; + stroke: #D26B5F; +} + +rect.shuffle-read-time-proportion { + fill: #FDB462; + stroke: #D39651; +} + +rect.executor-runtime-proportion { + fill: #B3DE69; + stroke: #95B957; +} + +rect.shuffle-write-time-proportion { + fill: #FFED6F; + stroke: #D5C65C; +} + +rect.serialization-time-proportion { + fill: #BC80BD; + stroke: #9D6B9E; +} + +rect.getting-result-time-proportion { + fill: #8DD3C7; + stroke: #75B0A6; +} + .vis.timeline { line-height: 14px; } @@ -177,6 +237,10 @@ tr.corresponding-item-hover > td, tr.corresponding-item-hover > th { display: none; } +#task-assignment-timeline.collapsed { + display: none; +} + .control-panel { margin-bottom: 5px; } @@ -185,7 +249,8 @@ tr.corresponding-item-hover > td, tr.corresponding-item-hover > th { margin: 0; } -span.expand-application-timeline, span.expand-job-timeline { +span.expand-application-timeline, span.expand-job-timeline, +span.expand-task-assignment-timeline { cursor: pointer; } diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js index 48fbb33b1155b..ca74ef9d7e94e 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js +++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js @@ -39,23 +39,24 @@ function drawApplicationTimeline(groupArray, eventObjArray, startTime) { function setupJobEventAction() { $(".item.range.job.application-timeline-object").each(function() { - var getJobId = function(baseElem) { + var getSelectorForJobEntry = function(baseElem) { var jobIdText = $($(baseElem).find(".application-timeline-content")[0]).text(); var jobId = jobIdText.match("\\(Job (\\d+)\\)")[1]; - return jobId; + return "#job-" + jobId; }; $(this).click(function() { - window.location.href = "job/?id=" + getJobId(this); + var jobPagePath = $(getSelectorForJobEntry(this)).find("a.name-link").attr("href") + window.location.href = jobPagePath }); $(this).hover( function() { - $("#job-" + getJobId(this)).addClass("corresponding-item-hover"); + $(getSelectorForJobEntry(this)).addClass("corresponding-item-hover"); $($(this).find("div.application-timeline-content")[0]).tooltip("show"); }, function() { - $("#job-" + getJobId(this)).removeClass("corresponding-item-hover"); + $(getSelectorForJobEntry(this)).removeClass("corresponding-item-hover"); $($(this).find("div.application-timeline-content")[0]).tooltip("hide"); } ); @@ -97,32 +98,24 @@ function drawJobTimeline(groupArray, eventObjArray, startTime) { function setupStageEventAction() { $(".item.range.stage.job-timeline-object").each(function() { - var getStageIdAndAttempt = function(baseElem) { + var getSelectorForStageEntry = function(baseElem) { var stageIdText = $($(baseElem).find(".job-timeline-content")[0]).text(); var stageIdAndAttempt = stageIdText.match("\\(Stage (\\d+\\.\\d+)\\)")[1].split("."); - return stageIdAndAttempt; + return "#stage-" + stageIdAndAttempt[0] + "-" + stageIdAndAttempt[1]; }; $(this).click(function() { - var idAndAttempt = getStageIdAndAttempt(this); - var id = idAndAttempt[0]; - var attempt = idAndAttempt[1]; - window.location.href = "../../stages/stage/?id=" + id + "&attempt=" + attempt; + var stagePagePath = $(getSelectorForStageEntry(this)).find("a.name-link").attr("href") + window.location.href = stagePagePath }); $(this).hover( function() { - var idAndAttempt = getStageIdAndAttempt(this); - var id = idAndAttempt[0]; - var attempt = idAndAttempt[1]; - $("#stage-" + id + "-" + attempt).addClass("corresponding-item-hover"); + $(getSelectorForStageEntry(this)).addClass("corresponding-item-hover"); $($(this).find("div.job-timeline-content")[0]).tooltip("show"); }, function() { - var idAndAttempt = getStageIdAndAttempt(this); - var id = idAndAttempt[0]; - var attempt = idAndAttempt[1]; - $("#stage-" + id + "-" + attempt).removeClass("corresponding-item-hover"); + $(getSelectorForStageEntry(this)).removeClass("corresponding-item-hover"); $($(this).find("div.job-timeline-content")[0]).tooltip("hide"); } ); @@ -140,6 +133,57 @@ function drawJobTimeline(groupArray, eventObjArray, startTime) { }); } +function drawTaskAssignmentTimeline(groupArray, eventObjArray, minLaunchTime, maxFinishTime) { + var groups = new vis.DataSet(groupArray); + var items = new vis.DataSet(eventObjArray); + var container = $("#task-assignment-timeline")[0] + var options = { + groupOrder: function(a, b) { + return a.value - b.value + }, + editable: false, + align: 'left', + selectable: false, + showCurrentTime: false, + min: minLaunchTime, + max: maxFinishTime, + zoomable: false + }; + + var taskTimeline = new vis.Timeline(container) + taskTimeline.setOptions(options); + taskTimeline.setGroups(groups); + taskTimeline.setItems(items); + + // If a user zooms while a tooltip is displayed, the user may zoom such that the cursor is no + // longer over the task that the tooltip corresponds to. So, when a user zooms, we should hide + // any currently displayed tooltips. + var currentDisplayedTooltip = null; + $("#task-assignment-timeline").on({ + "mouseenter": function() { + currentDisplayedTooltip = this; + }, + "mouseleave": function() { + currentDisplayedTooltip = null; + } + }, ".task-assignment-timeline-content"); + taskTimeline.on("rangechange", function(prop) { + if (currentDisplayedTooltip !== null) { + $(currentDisplayedTooltip).tooltip("hide"); + } + }); + + setupZoomable("#task-assignment-timeline-zoom-lock", taskTimeline); + + $("span.expand-task-assignment-timeline").click(function() { + $("#task-assignment-timeline").toggleClass("collapsed"); + + // Switch the class of the arrow from open to closed. + $(this).find(".expand-task-assignment-timeline-arrow").toggleClass("arrow-open"); + $(this).find(".expand-task-assignment-timeline-arrow").toggleClass("arrow-closed"); + }); +} + function setupExecutorEventAction() { $(".item.box.executor").each(function () { $(this).hover( @@ -154,7 +198,7 @@ function setupExecutorEventAction() { } function setupZoomable(id, timeline) { - $(id + '>input[type="checkbox"]').click(function() { + $(id + ' > input[type="checkbox"]').click(function() { if (this.checked) { timeline.setOptions({zoomable: true}); } else { @@ -162,7 +206,7 @@ function setupZoomable(id, timeline) { } }); - $(id + ">span").click(function() { + $(id + " > span").click(function() { $(this).parent().find('input:checkbox').trigger('click'); }); } diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css index e7c1d475d4e52..b1cef47042247 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/webui.css +++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css @@ -135,6 +135,14 @@ pre { display: block; } +.description-input-full { + overflow: hidden; + text-overflow: ellipsis; + width: 100%; + white-space: normal; + display: block; +} + .stacktrace-details { max-height: 300px; overflow-y: auto; diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala index 330df1d59a9b1..5a8d17bd99933 100644 --- a/core/src/main/scala/org/apache/spark/Accumulators.scala +++ b/core/src/main/scala/org/apache/spark/Accumulators.scala @@ -228,7 +228,7 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa * @tparam T result type */ class Accumulator[T](@transient initialValue: T, param: AccumulatorParam[T], name: Option[String]) - extends Accumulable[T,T](initialValue, param, name) { + extends Accumulable[T, T](initialValue, param, name) { def this(initialValue: T, param: AccumulatorParam[T]) = this(initialValue, param, None) } diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala index af9765d313e9e..ceeb58075d345 100644 --- a/core/src/main/scala/org/apache/spark/Aggregator.scala +++ b/core/src/main/scala/org/apache/spark/Aggregator.scala @@ -34,8 +34,8 @@ case class Aggregator[K, V, C] ( mergeValue: (C, V) => C, mergeCombiners: (C, C) => C) { - // When spilling is enabled sorting will happen externally, but not necessarily with an - // ExternalSorter. + // When spilling is enabled sorting will happen externally, but not necessarily with an + // ExternalSorter. private val isSpillEnabled = SparkEnv.get.conf.getBoolean("spark.shuffle.spill", true) @deprecated("use combineValuesByKey with TaskContext argument", "0.9.0") @@ -45,7 +45,7 @@ case class Aggregator[K, V, C] ( def combineValuesByKey(iter: Iterator[_ <: Product2[K, V]], context: TaskContext): Iterator[(K, C)] = { if (!isSpillEnabled) { - val combiners = new AppendOnlyMap[K,C] + val combiners = new AppendOnlyMap[K, C] var kv: Product2[K, V] = null val update = (hadValue: Boolean, oldValue: C) => { if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2) @@ -76,7 +76,7 @@ case class Aggregator[K, V, C] ( : Iterator[(K, C)] = { if (!isSpillEnabled) { - val combiners = new AppendOnlyMap[K,C] + val combiners = new AppendOnlyMap[K, C] var kc: Product2[K, C] = null val update = (hadValue: Boolean, oldValue: C) => { if (hadValue) mergeCombiners(oldValue, kc._2) else kc._2 diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index 66bda68088502..49329423dca76 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -91,7 +91,7 @@ private[spark] class ExecutorAllocationManager( // How long there must be backlogged tasks for before an addition is triggered (seconds) private val schedulerBacklogTimeoutS = conf.getTimeAsSeconds( - "spark.dynamicAllocation.schedulerBacklogTimeout", "5s") + "spark.dynamicAllocation.schedulerBacklogTimeout", "1s") // Same as above, but used only after `schedulerBacklogTimeoutS` is exceeded private val sustainedSchedulerBacklogTimeoutS = conf.getTimeAsSeconds( @@ -99,7 +99,10 @@ private[spark] class ExecutorAllocationManager( // How long an executor must be idle for before it is removed (seconds) private val executorIdleTimeoutS = conf.getTimeAsSeconds( - "spark.dynamicAllocation.executorIdleTimeout", "600s") + "spark.dynamicAllocation.executorIdleTimeout", "60s") + + private val cachedExecutorIdleTimeoutS = conf.getTimeAsSeconds( + "spark.dynamicAllocation.cachedExecutorIdleTimeout", s"${2 * executorIdleTimeoutS}s") // During testing, the methods to actually kill and add executors are mocked out private val testing = conf.getBoolean("spark.dynamicAllocation.testing", false) @@ -150,6 +153,13 @@ private[spark] class ExecutorAllocationManager( // Metric source for ExecutorAllocationManager to expose internal status to MetricsSystem. val executorAllocationManagerSource = new ExecutorAllocationManagerSource + // Whether we are still waiting for the initial set of executors to be allocated. + // While this is true, we will not cancel outstanding executor requests. This is + // set to false when: + // (1) a stage is submitted, or + // (2) an executor idle timeout has elapsed. + @volatile private var initializing: Boolean = true + /** * Verify that the settings specified through the config are valid. * If not, throw an appropriate exception. @@ -240,6 +250,7 @@ private[spark] class ExecutorAllocationManager( removeTimes.retain { case (executorId, expireTime) => val expired = now >= expireTime if (expired) { + initializing = false removeExecutor(executorId) } !expired @@ -261,13 +272,23 @@ private[spark] class ExecutorAllocationManager( private def updateAndSyncNumExecutorsTarget(now: Long): Int = synchronized { val maxNeeded = maxNumExecutorsNeeded - if (maxNeeded < numExecutorsTarget) { + if (initializing) { + // Do not change our target while we are still initializing, + // Otherwise the first job may have to ramp up unnecessarily + 0 + } else if (maxNeeded < numExecutorsTarget) { // The target number exceeds the number we actually need, so stop adding new // executors and inform the cluster manager to cancel the extra pending requests val oldNumExecutorsTarget = numExecutorsTarget numExecutorsTarget = math.max(maxNeeded, minNumExecutors) - client.requestTotalExecutors(numExecutorsTarget) numExecutorsToAdd = 1 + + // If the new target has not changed, avoid sending a message to the cluster manager + if (numExecutorsTarget < oldNumExecutorsTarget) { + client.requestTotalExecutors(numExecutorsTarget) + logDebug(s"Lowering target number of executors to $numExecutorsTarget (previously " + + s"$oldNumExecutorsTarget) because not all requested executors are actually needed") + } numExecutorsTarget - oldNumExecutorsTarget } else if (addTime != NOT_SET && now >= addTime) { val delta = addExecutors(maxNeeded) @@ -292,9 +313,8 @@ private[spark] class ExecutorAllocationManager( private def addExecutors(maxNumExecutorsNeeded: Int): Int = { // Do not request more executors if it would put our target over the upper bound if (numExecutorsTarget >= maxNumExecutors) { - val numExecutorsPending = numExecutorsTarget - executorIds.size - logDebug(s"Not adding executors because there are already ${executorIds.size} registered " + - s"and ${numExecutorsPending} pending executor(s) (limit $maxNumExecutors)") + logDebug(s"Not adding executors because our current target total " + + s"is already $numExecutorsTarget (limit $maxNumExecutors)") numExecutorsToAdd = 1 return 0 } @@ -310,10 +330,19 @@ private[spark] class ExecutorAllocationManager( // Ensure that our target fits within configured bounds: numExecutorsTarget = math.max(math.min(numExecutorsTarget, maxNumExecutors), minNumExecutors) + val delta = numExecutorsTarget - oldNumExecutorsTarget + + // If our target has not changed, do not send a message + // to the cluster manager and reset our exponential growth + if (delta == 0) { + numExecutorsToAdd = 1 + return 0 + } + val addRequestAcknowledged = testing || client.requestTotalExecutors(numExecutorsTarget) if (addRequestAcknowledged) { - val delta = numExecutorsTarget - oldNumExecutorsTarget - logInfo(s"Requesting $delta new executor(s) because tasks are backlogged" + + val executorsString = "executor" + { if (delta > 1) "s" else "" } + logInfo(s"Requesting $delta new $executorsString because tasks are backlogged" + s" (new desired total will be $numExecutorsTarget)") numExecutorsToAdd = if (delta == numExecutorsToAdd) { numExecutorsToAdd * 2 @@ -420,7 +449,7 @@ private[spark] class ExecutorAllocationManager( * This resets all variables used for adding executors. */ private def onSchedulerQueueEmpty(): Unit = synchronized { - logDebug(s"Clearing timer to add executors because there are no more pending tasks") + logDebug("Clearing timer to add executors because there are no more pending tasks") addTime = NOT_SET numExecutorsToAdd = 1 } @@ -433,9 +462,23 @@ private[spark] class ExecutorAllocationManager( private def onExecutorIdle(executorId: String): Unit = synchronized { if (executorIds.contains(executorId)) { if (!removeTimes.contains(executorId) && !executorsPendingToRemove.contains(executorId)) { + // Note that it is not necessary to query the executors since all the cached + // blocks we are concerned with are reported to the driver. Note that this + // does not include broadcast blocks. + val hasCachedBlocks = SparkEnv.get.blockManager.master.hasCachedBlocks(executorId) + val now = clock.getTimeMillis() + val timeout = { + if (hasCachedBlocks) { + // Use a different timeout if the executor has cached blocks. + now + cachedExecutorIdleTimeoutS * 1000 + } else { + now + executorIdleTimeoutS * 1000 + } + } + val realTimeout = if (timeout <= 0) Long.MaxValue else timeout // overflow + removeTimes(executorId) = realTimeout logDebug(s"Starting idle timer for $executorId because there are no more tasks " + - s"scheduled to run on the executor (to expire in $executorIdleTimeoutS seconds)") - removeTimes(executorId) = clock.getTimeMillis + executorIdleTimeoutS * 1000 + s"scheduled to run on the executor (to expire in ${(realTimeout - now)/1000} seconds)") } } else { logWarning(s"Attempted to mark unknown executor $executorId idle") @@ -467,6 +510,7 @@ private[spark] class ExecutorAllocationManager( private var numRunningTasks: Int = _ override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = { + initializing = false val stageId = stageSubmitted.stageInfo.stageId val numTasks = stageSubmitted.stageInfo.numTasks allocationManager.synchronized { diff --git a/core/src/main/scala/org/apache/spark/FutureAction.scala b/core/src/main/scala/org/apache/spark/FutureAction.scala index 91f9ef8ce7185..48792a958130c 100644 --- a/core/src/main/scala/org/apache/spark/FutureAction.scala +++ b/core/src/main/scala/org/apache/spark/FutureAction.scala @@ -150,7 +150,7 @@ class SimpleFutureAction[T] private[spark](jobWaiter: JobWaiter[_], resultFunc: } override def isCompleted: Boolean = jobWaiter.jobFinished - + override def isCancelled: Boolean = _cancelled override def value: Option[Try[T]] = { diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index f2b024ff6cb67..6909015ff66e6 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -29,7 +29,7 @@ import org.apache.spark.util.{ThreadUtils, Utils} /** * A heartbeat from executors to the driver. This is a shared message used by several internal - * components to convey liveness or execution information for in-progress tasks. It will also + * components to convey liveness or execution information for in-progress tasks. It will also * expire the hosts that have not heartbeated for more than spark.network.timeout. */ private[spark] case class Heartbeat( @@ -43,8 +43,8 @@ private[spark] case class Heartbeat( */ private[spark] case object TaskSchedulerIsSet -private[spark] case object ExpireDeadHosts - +private[spark] case object ExpireDeadHosts + private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean) /** @@ -62,18 +62,18 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) // "spark.network.timeout" uses "seconds", while `spark.storage.blockManagerSlaveTimeoutMs` uses // "milliseconds" - private val slaveTimeoutMs = + private val slaveTimeoutMs = sc.conf.getTimeAsMs("spark.storage.blockManagerSlaveTimeoutMs", "120s") - private val executorTimeoutMs = + private val executorTimeoutMs = sc.conf.getTimeAsSeconds("spark.network.timeout", s"${slaveTimeoutMs}ms") * 1000 - + // "spark.network.timeoutInterval" uses "seconds", while // "spark.storage.blockManagerTimeoutIntervalMs" uses "milliseconds" - private val timeoutIntervalMs = + private val timeoutIntervalMs = sc.conf.getTimeAsMs("spark.storage.blockManagerTimeoutIntervalMs", "60s") - private val checkTimeoutIntervalMs = + private val checkTimeoutIntervalMs = sc.conf.getTimeAsSeconds("spark.network.timeoutInterval", s"${timeoutIntervalMs}ms") * 1000 - + private var timeoutCheckingTask: ScheduledFuture[_] = null // "eventLoopThread" is used to run some pretty fast actions. The actions running in it should not @@ -140,7 +140,7 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) } } } - + override def onStop(): Unit = { if (timeoutCheckingTask != null) { timeoutCheckingTask.cancel(true) diff --git a/core/src/main/scala/org/apache/spark/HttpFileServer.scala b/core/src/main/scala/org/apache/spark/HttpFileServer.scala index 7e706bcc42f04..7cf7bc0dc6810 100644 --- a/core/src/main/scala/org/apache/spark/HttpFileServer.scala +++ b/core/src/main/scala/org/apache/spark/HttpFileServer.scala @@ -50,8 +50,8 @@ private[spark] class HttpFileServer( def stop() { httpServer.stop() - - // If we only stop sc, but the driver process still run as a services then we need to delete + + // If we only stop sc, but the driver process still run as a services then we need to delete // the tmp dir, if not, it will create too many tmp dirs try { Utils.deleteRecursively(baseDir) diff --git a/core/src/main/scala/org/apache/spark/Logging.scala b/core/src/main/scala/org/apache/spark/Logging.scala index 419d093d55643..7fcb7830e7b0b 100644 --- a/core/src/main/scala/org/apache/spark/Logging.scala +++ b/core/src/main/scala/org/apache/spark/Logging.scala @@ -121,13 +121,25 @@ trait Logging { if (usingLog4j12) { val log4j12Initialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements if (!log4j12Initialized) { - val defaultLogProps = "org/apache/spark/log4j-defaults.properties" - Option(Utils.getSparkClassLoader.getResource(defaultLogProps)) match { - case Some(url) => - PropertyConfigurator.configure(url) - System.err.println(s"Using Spark's default log4j profile: $defaultLogProps") - case None => - System.err.println(s"Spark was unable to load $defaultLogProps") + if (Utils.isInInterpreter) { + val replDefaultLogProps = "org/apache/spark/log4j-defaults-repl.properties" + Option(Utils.getSparkClassLoader.getResource(replDefaultLogProps)) match { + case Some(url) => + PropertyConfigurator.configure(url) + System.err.println(s"Using Spark's repl log4j profile: $replDefaultLogProps") + System.err.println("To adjust logging level use sc.setLogLevel(\"INFO\")") + case None => + System.err.println(s"Spark was unable to load $replDefaultLogProps") + } + } else { + val defaultLogProps = "org/apache/spark/log4j-defaults.properties" + Option(Utils.getSparkClassLoader.getResource(defaultLogProps)) match { + case Some(url) => + PropertyConfigurator.configure(url) + System.err.println(s"Using Spark's default log4j profile: $defaultLogProps") + case None => + System.err.println(s"Spark was unable to load $defaultLogProps") + } } } } diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index 018422827e1c8..862ffe868f58f 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -21,7 +21,7 @@ import java.io._ import java.util.concurrent.ConcurrentHashMap import java.util.zip.{GZIPInputStream, GZIPOutputStream} -import scala.collection.mutable.{HashSet, Map} +import scala.collection.mutable.{HashMap, HashSet, Map} import scala.collection.JavaConversions._ import scala.reflect.ClassTag @@ -284,6 +284,53 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf) cachedSerializedStatuses.contains(shuffleId) || mapStatuses.contains(shuffleId) } + /** + * Return a list of locations that each have fraction of map output greater than the specified + * threshold. + * + * @param shuffleId id of the shuffle + * @param reducerId id of the reduce task + * @param numReducers total number of reducers in the shuffle + * @param fractionThreshold fraction of total map output size that a location must have + * for it to be considered large. + * + * This method is not thread-safe. + */ + def getLocationsWithLargestOutputs( + shuffleId: Int, + reducerId: Int, + numReducers: Int, + fractionThreshold: Double) + : Option[Array[BlockManagerId]] = { + + if (mapStatuses.contains(shuffleId)) { + val statuses = mapStatuses(shuffleId) + if (statuses.nonEmpty) { + // HashMap to add up sizes of all blocks at the same location + val locs = new HashMap[BlockManagerId, Long] + var totalOutputSize = 0L + var mapIdx = 0 + while (mapIdx < statuses.length) { + val status = statuses(mapIdx) + val blockSize = status.getSizeForBlock(reducerId) + if (blockSize > 0) { + locs(status.location) = locs.getOrElse(status.location, 0L) + blockSize + totalOutputSize += blockSize + } + mapIdx = mapIdx + 1 + } + val topLocs = locs.filter { case (loc, size) => + size.toDouble / totalOutputSize >= fractionThreshold + } + // Return if we have any locations which satisfy the required threshold + if (topLocs.nonEmpty) { + return Some(topLocs.map(_._1).toArray) + } + } + } + None + } + def incrementEpoch() { epochLock.synchronized { epoch += 1 diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index b8d244408bc5b..82889bcd30988 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -103,7 +103,7 @@ class HashPartitioner(partitions: Int) extends Partitioner { */ class RangePartitioner[K : Ordering : ClassTag, V]( @transient partitions: Int, - @transient rdd: RDD[_ <: Product2[K,V]], + @transient rdd: RDD[_ <: Product2[K, V]], private var ascending: Boolean = true) extends Partitioner { @@ -185,7 +185,7 @@ class RangePartitioner[K : Ordering : ClassTag, V]( } override def equals(other: Any): Boolean = other match { - case r: RangePartitioner[_,_] => + case r: RangePartitioner[_, _] => r.rangeBounds.sameElements(rangeBounds) && r.ascending == ascending case _ => false @@ -249,7 +249,7 @@ private[spark] object RangePartitioner { * @param sampleSizePerPartition max sample size per partition * @return (total number of items, an array of (partitionId, number of items, sample)) */ - def sketch[K:ClassTag]( + def sketch[K : ClassTag]( rdd: RDD[K], sampleSizePerPartition: Int): (Long, Array[(Int, Int, Array[K])]) = { val shift = rdd.id @@ -272,7 +272,7 @@ private[spark] object RangePartitioner { * @param partitions number of partitions * @return selected bounds */ - def determineBounds[K:Ordering:ClassTag]( + def determineBounds[K : Ordering : ClassTag]( candidates: ArrayBuffer[(K, Float)], partitions: Int): Array[K] = { val ordering = implicitly[Ordering[K]] diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala index 8aed1e20e0686..673ef49e7c1c5 100644 --- a/core/src/main/scala/org/apache/spark/SecurityManager.scala +++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala @@ -192,7 +192,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf) // key used to store the spark secret in the Hadoop UGI private val sparkSecretLookupKey = "sparkCookie" - private val authOn = sparkConf.getBoolean("spark.authenticate", false) + private val authOn = sparkConf.getBoolean(SecurityManager.SPARK_AUTH_CONF, false) // keep spark.ui.acls.enable for backwards compatibility with 1.0 private var aclsOn = sparkConf.getBoolean("spark.acls.enable", sparkConf.getBoolean("spark.ui.acls.enable", false)) @@ -365,10 +365,12 @@ private[spark] class SecurityManager(sparkConf: SparkConf) cookie } else { // user must have set spark.authenticate.secret config - sparkConf.getOption("spark.authenticate.secret") match { + // For Master/Worker, auth secret is in conf; for Executors, it is in env variable + sys.env.get(SecurityManager.ENV_AUTH_SECRET) + .orElse(sparkConf.getOption(SecurityManager.SPARK_AUTH_SECRET_CONF)) match { case Some(value) => value case None => throw new Exception("Error: a secret key must be specified via the " + - "spark.authenticate.secret config") + SecurityManager.SPARK_AUTH_SECRET_CONF + " config") } } sCookie @@ -449,3 +451,12 @@ private[spark] class SecurityManager(sparkConf: SparkConf) override def getSaslUser(appId: String): String = getSaslUser() override def getSecretKey(appId: String): String = getSecretKey() } + +private[spark] object SecurityManager { + + val SPARK_AUTH_CONF: String = "spark.authenticate" + val SPARK_AUTH_SECRET_CONF: String = "spark.authenticate.secret" + // This is used to set auth secret to an executor's env variable. It should have the same + // value as SPARK_AUTH_SECERET_CONF set in SparkConf + val ENV_AUTH_SECRET = "_SPARK_AUTH_SECRET" +} diff --git a/core/src/main/scala/org/apache/spark/SerializableWritable.scala b/core/src/main/scala/org/apache/spark/SerializableWritable.scala index cb2cae185256a..beb2e27254725 100644 --- a/core/src/main/scala/org/apache/spark/SerializableWritable.scala +++ b/core/src/main/scala/org/apache/spark/SerializableWritable.scala @@ -41,7 +41,7 @@ class SerializableWritable[T <: Writable](@transient var t: T) extends Serializa private def readObject(in: ObjectInputStream): Unit = Utils.tryOrIOException { in.defaultReadObject() val ow = new ObjectWritable() - ow.setConf(new Configuration()) + ow.setConf(new Configuration(false)) ow.readFields(in) t = ow.get().asInstanceOf[T] } diff --git a/core/src/main/scala/org/apache/spark/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/SizeEstimator.scala deleted file mode 100644 index 54fc3a856adfa..0000000000000 --- a/core/src/main/scala/org/apache/spark/SizeEstimator.scala +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark - -import org.apache.spark.annotation.DeveloperApi - -/** - * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in - * memory-aware caches. - * - * Based on the following JavaWorld article: - * http://www.javaworld.com/javaworld/javaqa/2003-12/02-qa-1226-sizeof.html - */ -@DeveloperApi -object SizeEstimator { - /** - * :: DeveloperApi :: - * Estimate the number of bytes that the given object takes up on the JVM heap. The estimate - * includes space taken up by objects referenced by the given object, their references, and so on - * and so forth. - * - * This is useful for determining the amount of heap space a broadcast variable will occupy on - * each executor or the amount of space each object will take when caching objects in - * deserialized form. This is not the same as the serialized size of the object, which will - * typically be much smaller. - */ - @DeveloperApi - def estimate(obj: AnyRef): Long = org.apache.spark.util.SizeEstimator.estimate(obj) -} diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index a8fc90ad2050e..6cf36fbbd6254 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -227,7 +227,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { def getSizeAsBytes(key: String, defaultValue: String): Long = { Utils.byteStringAsBytes(get(key, defaultValue)) } - + /** * Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set. If no * suffix is provided then Kibibytes are assumed. @@ -244,7 +244,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { def getSizeAsKb(key: String, defaultValue: String): Long = { Utils.byteStringAsKb(get(key, defaultValue)) } - + /** * Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set. If no * suffix is provided then Mebibytes are assumed. @@ -261,7 +261,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { def getSizeAsMb(key: String, defaultValue: String): Long = { Utils.byteStringAsMb(get(key, defaultValue)) } - + /** * Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set. If no * suffix is provided then Gibibytes are assumed. @@ -278,7 +278,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { def getSizeAsGb(key: String, defaultValue: String): Long = { Utils.byteStringAsGb(get(key, defaultValue)) } - + /** Get a parameter as an Option */ def getOption(key: String): Option[String] = { Option(settings.get(key)).orElse(getDeprecatedConfig(key, this)) @@ -480,8 +480,8 @@ private[spark] object SparkConf extends Logging { "spark.kryoserializer.buffer.mb was previously specified as '0.064'. Fractional values " + "are no longer accepted. To specify the equivalent now, one may use '64k'.") ) - - Map(configs.map { cfg => (cfg.key -> cfg) }:_*) + + Map(configs.map { cfg => (cfg.key -> cfg) } : _*) } /** @@ -508,8 +508,8 @@ private[spark] object SparkConf extends Logging { "spark.reducer.maxSizeInFlight" -> Seq( AlternateConfig("spark.reducer.maxMbInFlight", "1.4")), "spark.kryoserializer.buffer" -> - Seq(AlternateConfig("spark.kryoserializer.buffer.mb", "1.4", - translation = s => s"${s.toDouble * 1000}k")), + Seq(AlternateConfig("spark.kryoserializer.buffer.mb", "1.4", + translation = s => s"${(s.toDouble * 1000).toInt}k")), "spark.kryoserializer.buffer.max" -> Seq( AlternateConfig("spark.kryoserializer.buffer.max.mb", "1.4")), "spark.shuffle.file.buffer" -> Seq( @@ -557,7 +557,7 @@ private[spark] object SparkConf extends Logging { def isExecutorStartupConf(name: String): Boolean = { isAkkaConf(name) || name.startsWith("spark.akka") || - name.startsWith("spark.auth") || + (name.startsWith("spark.auth") && name != SecurityManager.SPARK_AUTH_SECRET_CONF) || name.startsWith("spark.ssl") || isSparkPortConf(name) } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index b59f562d05ead..141276ac901fb 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -371,6 +371,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli throw new SparkException("An application name must be set in your configuration") } + // System property spark.yarn.app.id must be set if user code ran by AM on a YARN cluster + // yarn-standalone is deprecated, but still supported + if ((master == "yarn-cluster" || master == "yarn-standalone") && + !_conf.contains("spark.yarn.app.id")) { + throw new SparkException("Detected yarn-cluster mode, but isn't running on a cluster. " + + "Deployment to YARN is not supported directly by SparkContext. Please use spark-submit.") + } + if (_conf.getBoolean("spark.logConf", false)) { logInfo("Spark configuration:\n" + _conf.toDebugString) } @@ -381,7 +389,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER) - _jars =_conf.getOption("spark.jars").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten + _jars = _conf.getOption("spark.jars").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten _files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.size != 0)) .toSeq.flatten @@ -430,7 +438,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _ui = if (conf.getBoolean("spark.ui.enabled", true)) { Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener, - _env.securityManager,appName, startTime = startTime)) + _env.securityManager, appName, startTime = startTime)) } else { // For tests, do not enable the UI None @@ -670,7 +678,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * Note: Return statements are NOT allowed in the given body. */ - private def withScope[U](body: => U): U = RDDOperationScope.withScope[U](this)(body) + private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](this)(body) // Methods for creating RDDs @@ -689,6 +697,78 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]()) } + /** + * Creates a new RDD[Long] containing elements from `start` to `end`(exclusive), increased by + * `step` every element. + * + * @note if we need to cache this RDD, we should make sure each partition does not exceed limit. + * + * @param start the start value. + * @param end the end value. + * @param step the incremental step + * @param numSlices the partition number of the new RDD. + * @return + */ + def range( + start: Long, + end: Long, + step: Long = 1, + numSlices: Int = defaultParallelism): RDD[Long] = withScope { + assertNotStopped() + // when step is 0, range will run infinitely + require(step != 0, "step cannot be 0") + val numElements: BigInt = { + val safeStart = BigInt(start) + val safeEnd = BigInt(end) + if ((safeEnd - safeStart) % step == 0 || safeEnd > safeStart ^ step > 0) { + (safeEnd - safeStart) / step + } else { + // the remainder has the same sign with range, could add 1 more + (safeEnd - safeStart) / step + 1 + } + } + parallelize(0 until numSlices, numSlices).mapPartitionsWithIndex((i, _) => { + val partitionStart = (i * numElements) / numSlices * step + start + val partitionEnd = (((i + 1) * numElements) / numSlices) * step + start + def getSafeMargin(bi: BigInt): Long = + if (bi.isValidLong) { + bi.toLong + } else if (bi > 0) { + Long.MaxValue + } else { + Long.MinValue + } + val safePartitionStart = getSafeMargin(partitionStart) + val safePartitionEnd = getSafeMargin(partitionEnd) + + new Iterator[Long] { + private[this] var number: Long = safePartitionStart + private[this] var overflow: Boolean = false + + override def hasNext = + if (!overflow) { + if (step > 0) { + number < safePartitionEnd + } else { + number > safePartitionEnd + } + } else false + + override def next() = { + val ret = number + number += step + if (number < ret ^ step < 0) { + // we have Long.MaxValue + Long.MaxValue < Long.MaxValue + // and Long.MinValue + Long.MinValue > Long.MinValue, so iff the step causes a step + // back, we are pretty sure that we have an overflow. + overflow = true + } + ret + } + } + }) + } + /** Distribute a local Scala collection to form an RDD. * * This method is identical to `parallelize`. @@ -837,7 +917,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli classOf[FixedLengthBinaryInputFormat], classOf[LongWritable], classOf[BytesWritable], - conf=conf) + conf = conf) val data = br.map { case (k, v) => val bytes = v.getBytes assert(bytes.length == recordLength, "Byte array does not have correct length") @@ -894,7 +974,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope { assertNotStopped() // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it. - val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration)) + val confBroadcast = broadcast(new SerializableConfiguration(hadoopConfiguration)) val setInputPathsFunc = (jobConf: JobConf) => FileInputFormat.setInputPaths(jobConf, path) new HadoopRDD( this, @@ -1079,8 +1159,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]): RDD[(K, V)] = { withScope { assertNotStopped() - val kc = kcf() - val vc = vcf() + val kc = clean(kcf)() + val vc = clean(vcf)() val format = classOf[SequenceFileInputFormat[Writable, Writable]] val writables = hadoopFile(path, format, kc.writableClass(km).asInstanceOf[Class[Writable]], @@ -1187,7 +1267,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable: ClassTag, T] (initialValue: R): Accumulable[R, T] = { - val param = new GrowableAccumulableParam[R,T] + val param = new GrowableAccumulableParam[R, T] val acc = new Accumulable(initialValue, param) cleaner.foreach(_.registerAccumulatorForCleanup(acc)) acc @@ -1236,7 +1316,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli val uri = new URI(path) val schemeCorrectedPath = uri.getScheme match { case null | "local" => new File(path).getCanonicalFile.toURI.toString - case _ => path + case _ => path } val hadoopPath = new Path(schemeCorrectedPath) @@ -1804,7 +1884,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * @param f the closure to clean * @param checkSerializable whether or not to immediately check f for serializability - * @throws SparkException if checkSerializable is set but f is not + * @throws SparkException if checkSerializable is set but f is not * serializable */ private[spark] def clean[F <: AnyRef](f: F, checkSerializable: Boolean = true): F = { @@ -1911,7 +1991,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // Note: this code assumes that the task scheduler has been initialized and has contacted // the cluster manager to get an application ID (in case the cluster manager provides one). listenerBus.post(SparkListenerApplicationStart(appName, Some(applicationId), - startTime, sparkUser, applicationAttemptId)) + startTime, sparkUser, applicationAttemptId, schedulerBackend.getDriverLogUrls)) } /** Post the application end event */ diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 0c4d28f786edd..b0665570e2681 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -20,6 +20,8 @@ package org.apache.spark import java.io.File import java.net.Socket +import akka.actor.ActorSystem + import scala.collection.JavaConversions._ import scala.collection.mutable import scala.util.Properties @@ -75,7 +77,8 @@ class SparkEnv ( val conf: SparkConf) extends Logging { // TODO Remove actorSystem - val actorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem + @deprecated("Actor system is no longer supported as of 1.4") + val actorSystem: ActorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem private[spark] var isStopped = false private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]() @@ -298,7 +301,7 @@ object SparkEnv extends Logging { } } - val mapOutputTracker = if (isDriver) { + val mapOutputTracker = if (isDriver) { new MapOutputTrackerMaster(conf) } else { new MapOutputTrackerWorker(conf) @@ -313,7 +316,8 @@ object SparkEnv extends Logging { // Let the user specify short names for shuffle managers val shortShuffleMgrNames = Map( "hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager", - "sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager") + "sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager", + "tungsten-sort" -> "org.apache.spark.shuffle.unsafe.UnsafeShuffleManager") val shuffleMgrName = conf.get("spark.shuffle.manager", "sort") val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName) val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass) @@ -347,7 +351,7 @@ object SparkEnv extends Logging { val fileServerPort = conf.getInt("spark.fileserver.port", 0) val server = new HttpFileServer(conf, securityManager, fileServerPort) server.initialize() - conf.set("spark.fileserver.uri", server.serverUri) + conf.set("spark.fileserver.uri", server.serverUri) server } else { null @@ -378,7 +382,7 @@ object SparkEnv extends Logging { } val outputCommitCoordinator = mockOutputCommitCoordinator.getOrElse { - new OutputCommitCoordinator(conf) + new OutputCommitCoordinator(conf, isDriver) } val outputCommitCoordinatorRef = registerOrLookupEndpoint("OutputCommitCoordinator", new OutputCommitCoordinatorEndpoint(rpcEnv, outputCommitCoordinator)) diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala index 2ec42d3aea169..f5dd36cbcfe6d 100644 --- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala +++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala @@ -28,6 +28,7 @@ import org.apache.hadoop.fs.Path import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.rdd.HadoopRDD +import org.apache.spark.util.SerializableJobConf /** * Internal helper class that saves an RDD using a Hadoop OutputFormat. @@ -42,7 +43,7 @@ class SparkHadoopWriter(@transient jobConf: JobConf) with Serializable { private val now = new Date() - private val conf = new SerializableWritable(jobConf) + private val conf = new SerializableJobConf(jobConf) private var jobID = 0 private var splitID = 0 @@ -50,8 +51,8 @@ class SparkHadoopWriter(@transient jobConf: JobConf) private var jID: SerializableWritable[JobID] = null private var taID: SerializableWritable[TaskAttemptID] = null - @transient private var writer: RecordWriter[AnyRef,AnyRef] = null - @transient private var format: OutputFormat[AnyRef,AnyRef] = null + @transient private var writer: RecordWriter[AnyRef, AnyRef] = null + @transient private var format: OutputFormat[AnyRef, AnyRef] = null @transient private var committer: OutputCommitter = null @transient private var jobContext: JobContext = null @transient private var taskContext: TaskAttemptContext = null @@ -114,10 +115,10 @@ class SparkHadoopWriter(@transient jobConf: JobConf) // ********* Private Functions ********* - private def getOutputFormat(): OutputFormat[AnyRef,AnyRef] = { + private def getOutputFormat(): OutputFormat[AnyRef, AnyRef] = { if (format == null) { format = conf.value.getOutputFormat() - .asInstanceOf[OutputFormat[AnyRef,AnyRef]] + .asInstanceOf[OutputFormat[AnyRef, AnyRef]] } format } @@ -138,7 +139,7 @@ class SparkHadoopWriter(@transient jobConf: JobConf) private def getTaskContext(): TaskAttemptContext = { if (taskContext == null) { - taskContext = newTaskAttemptContext(conf.value, taID.value) + taskContext = newTaskAttemptContext(conf.value, taID.value) } taskContext } diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala index 398ca41e16151..a1ebbecf93b7b 100644 --- a/core/src/main/scala/org/apache/spark/TestUtils.scala +++ b/core/src/main/scala/org/apache/spark/TestUtils.scala @@ -51,7 +51,7 @@ private[spark] object TestUtils { classpathUrls: Seq[URL] = Seq()): URL = { val tempDir = Utils.createTempDir() val files1 = for (name <- classNames) yield { - createCompiledClass(name, tempDir, toStringValue, classpathUrls = classpathUrls) + createCompiledClass(name, tempDir, toStringValue, classpathUrls = classpathUrls) } val files2 = for ((childName, baseName) <- classNamesWithBase) yield { createCompiledClass(childName, tempDir, toStringValue, baseName, classpathUrls) @@ -105,23 +105,18 @@ private[spark] object TestUtils { URI.create(s"string:///${name.replace(".", "/")}${SOURCE.extension}") } - private class JavaSourceFromString(val name: String, val code: String) + private[spark] class JavaSourceFromString(val name: String, val code: String) extends SimpleJavaFileObject(createURI(name), SOURCE) { override def getCharContent(ignoreEncodingErrors: Boolean): String = code } - /** Creates a compiled class with the given name. Class file will be placed in destDir. */ + /** Creates a compiled class with the source file. Class file will be placed in destDir. */ def createCompiledClass( className: String, destDir: File, - toStringValue: String = "", - baseClass: String = null, - classpathUrls: Seq[URL] = Seq()): File = { + sourceFile: JavaSourceFromString, + classpathUrls: Seq[URL]): File = { val compiler = ToolProvider.getSystemJavaCompiler - val extendsText = Option(baseClass).map { c => s" extends ${c}" }.getOrElse("") - val sourceFile = new JavaSourceFromString(className, - "public class " + className + extendsText + " implements java.io.Serializable {" + - " @Override public String toString() { return \"" + toStringValue + "\"; }}") // Calling this outputs a class file in pwd. It's easier to just rename the file than // build a custom FileManager that controls the output location. @@ -144,4 +139,18 @@ private[spark] object TestUtils { assert(out.exists(), "Destination file not moved: " + out.getAbsolutePath()) out } + + /** Creates a compiled class with the given name. Class file will be placed in destDir. */ + def createCompiledClass( + className: String, + destDir: File, + toStringValue: String = "", + baseClass: String = null, + classpathUrls: Seq[URL] = Seq()): File = { + val extendsText = Option(baseClass).map { c => s" extends ${c}" }.getOrElse("") + val sourceFile = new JavaSourceFromString(className, + "public class " + className + extendsText + " implements java.io.Serializable {" + + " @Override public String toString() { return \"" + toStringValue + "\"; }}") + createCompiledClass(className, destDir, sourceFile, classpathUrls) + } } diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala index 61af867b11b9c..a650df605b92e 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala @@ -137,7 +137,7 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) */ def sample(withReplacement: Boolean, fraction: JDouble): JavaDoubleRDD = sample(withReplacement, fraction, Utils.random.nextLong) - + /** * Return a sampled subset of this RDD. */ diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala index db4e996feb31c..ed312770ee131 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala @@ -101,7 +101,7 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) /** * Return a sampled subset of this RDD. - * + * * @param withReplacement can elements be sampled multiple times (replaced when sampled out) * @param fraction expected size of the sample as a fraction of this RDD's size * without replacement: probability that each element is chosen; fraction must be [0, 1] @@ -109,10 +109,10 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) */ def sample(withReplacement: Boolean, fraction: Double): JavaRDD[T] = sample(withReplacement, fraction, Utils.random.nextLong) - + /** * Return a sampled subset of this RDD. - * + * * @param withReplacement can elements be sampled multiple times (replaced when sampled out) * @param fraction expected size of the sample as a fraction of this RDD's size * without replacement: probability that each element is chosen; fraction must be [0, 1] diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index 8bf0627fc420d..c95615a5a9307 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -60,10 +60,13 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { @deprecated("Use partitions() instead.", "1.1.0") def splits: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq) - + /** Set of partitions in this RDD. */ def partitions: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq) + /** The partitioner of this RDD. */ + def partitioner: Optional[Partitioner] = JavaUtils.optionToOptional(rdd.partitioner) + /** The [[org.apache.spark.SparkContext]] that this RDD was created on. */ def context: SparkContext = rdd.context @@ -96,7 +99,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def mapPartitionsWithIndex[R]( f: JFunction2[jl.Integer, java.util.Iterator[T], java.util.Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R] = - new JavaRDD(rdd.mapPartitionsWithIndex(((a,b) => f(a,asJavaIterator(b))), + new JavaRDD(rdd.mapPartitionsWithIndex(((a, b) => f(a, asJavaIterator(b))), preservesPartitioning)(fakeClassTag))(fakeClassTag) /** @@ -386,9 +389,16 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { /** * Aggregate the elements of each partition, and then the results for all the partitions, using a - * given associative function and a neutral "zero value". The function op(t1, t2) is allowed to - * modify t1 and return it as its result value to avoid object allocation; however, it should not - * modify t2. + * given associative and commutative function and a neutral "zero value". The function + * op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object + * allocation; however, it should not modify t2. + * + * This behaves somewhat differently from fold operations implemented for non-distributed + * collections in functional languages like Scala. This fold operation may be applied to + * partitions individually, and then fold those results into the final result, rather than + * apply the fold to each element sequentially in some defined ordering. For functions + * that are not commutative, the result may differ from that of a fold applied to a + * non-distributed collection. */ def fold(zeroValue: T)(f: JFunction2[T, T, T]): T = rdd.fold(zeroValue)(f) @@ -485,9 +495,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { new java.util.ArrayList(arr) } - def takeSample(withReplacement: Boolean, num: Int): JList[T] = + def takeSample(withReplacement: Boolean, num: Int): JList[T] = takeSample(withReplacement, num, Utils.random.nextLong) - + def takeSample(withReplacement: Boolean, num: Int, seed: Long): JList[T] = { import scala.collection.JavaConversions._ val arr: java.util.Collection[T] = rdd.takeSample(withReplacement, num, seed).toSeq diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala index c9181a29d4756..b959b683d1674 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala @@ -19,8 +19,8 @@ package org.apache.spark.api.python import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.RDD -import org.apache.spark.util.Utils -import org.apache.spark.{Logging, SerializableWritable, SparkException} +import org.apache.spark.util.{SerializableConfiguration, Utils} +import org.apache.spark.{Logging, SparkException} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.io._ import scala.util.{Failure, Success, Try} @@ -61,7 +61,7 @@ private[python] object Converter extends Logging { * Other objects are passed through without conversion. */ private[python] class WritableToJavaConverter( - conf: Broadcast[SerializableWritable[Configuration]]) extends Converter[Any, Any] { + conf: Broadcast[SerializableConfiguration]) extends Converter[Any, Any] { /** * Converts a [[org.apache.hadoop.io.Writable]] to the underlying primitive, String or diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index 7409dc2d866f6..dc9f62f39e6d5 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -36,7 +36,7 @@ import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} import org.apache.spark.broadcast.Broadcast import org.apache.spark.input.PortableDataStream import org.apache.spark.rdd.RDD -import org.apache.spark.util.Utils +import org.apache.spark.util.{SerializableConfiguration, Utils} import scala.util.control.NonFatal @@ -47,6 +47,7 @@ private[spark] class PythonRDD( pythonIncludes: JList[String], preservePartitoning: Boolean, pythonExec: String, + pythonVer: String, broadcastVars: JList[Broadcast[PythonBroadcast]], accumulator: Accumulator[JList[Array[Byte]]]) extends RDD[Array[Byte]](parent) { @@ -210,6 +211,8 @@ private[spark] class PythonRDD( val dataOut = new DataOutputStream(stream) // Partition index dataOut.writeInt(split.index) + // Python version of driver + PythonRDD.writeUTF(pythonVer, dataOut) // sparkFilesDir PythonRDD.writeUTF(SparkFiles.getRootDirectory, dataOut) // Python includes (*.zip and *.egg files) @@ -442,7 +445,7 @@ private[spark] object PythonRDD extends Logging { val kc = Utils.classForName(keyClass).asInstanceOf[Class[K]] val vc = Utils.classForName(valueClass).asInstanceOf[Class[V]] val rdd = sc.sc.sequenceFile[K, V](path, kc, vc, minSplits) - val confBroadcasted = sc.sc.broadcast(new SerializableWritable(sc.hadoopConfiguration())) + val confBroadcasted = sc.sc.broadcast(new SerializableConfiguration(sc.hadoopConfiguration())) val converted = convertRDD(rdd, keyConverterClass, valueConverterClass, new WritableToJavaConverter(confBroadcasted)) JavaRDD.fromRDD(SerDeUtil.pairRDDToPython(converted, batchSize)) @@ -468,7 +471,7 @@ private[spark] object PythonRDD extends Logging { val rdd = newAPIHadoopRDDFromClassNames[K, V, F](sc, Some(path), inputFormatClass, keyClass, valueClass, mergedConf) - val confBroadcasted = sc.sc.broadcast(new SerializableWritable(mergedConf)) + val confBroadcasted = sc.sc.broadcast(new SerializableConfiguration(mergedConf)) val converted = convertRDD(rdd, keyConverterClass, valueConverterClass, new WritableToJavaConverter(confBroadcasted)) JavaRDD.fromRDD(SerDeUtil.pairRDDToPython(converted, batchSize)) @@ -494,7 +497,7 @@ private[spark] object PythonRDD extends Logging { val rdd = newAPIHadoopRDDFromClassNames[K, V, F](sc, None, inputFormatClass, keyClass, valueClass, conf) - val confBroadcasted = sc.sc.broadcast(new SerializableWritable(conf)) + val confBroadcasted = sc.sc.broadcast(new SerializableConfiguration(conf)) val converted = convertRDD(rdd, keyConverterClass, valueConverterClass, new WritableToJavaConverter(confBroadcasted)) JavaRDD.fromRDD(SerDeUtil.pairRDDToPython(converted, batchSize)) @@ -537,7 +540,7 @@ private[spark] object PythonRDD extends Logging { val rdd = hadoopRDDFromClassNames[K, V, F](sc, Some(path), inputFormatClass, keyClass, valueClass, mergedConf) - val confBroadcasted = sc.sc.broadcast(new SerializableWritable(mergedConf)) + val confBroadcasted = sc.sc.broadcast(new SerializableConfiguration(mergedConf)) val converted = convertRDD(rdd, keyConverterClass, valueConverterClass, new WritableToJavaConverter(confBroadcasted)) JavaRDD.fromRDD(SerDeUtil.pairRDDToPython(converted, batchSize)) @@ -563,7 +566,7 @@ private[spark] object PythonRDD extends Logging { val rdd = hadoopRDDFromClassNames[K, V, F](sc, None, inputFormatClass, keyClass, valueClass, conf) - val confBroadcasted = sc.sc.broadcast(new SerializableWritable(conf)) + val confBroadcasted = sc.sc.broadcast(new SerializableConfiguration(conf)) val converted = convertRDD(rdd, keyConverterClass, valueConverterClass, new WritableToJavaConverter(confBroadcasted)) JavaRDD.fromRDD(SerDeUtil.pairRDDToPython(converted, batchSize)) @@ -720,7 +723,7 @@ private[spark] object PythonRDD extends Logging { val converted = convertRDD(rdd, keyConverterClass, valueConverterClass, new JavaToWritableConverter) val fc = Utils.classForName(outputFormatClass).asInstanceOf[Class[F]] - converted.saveAsHadoopFile(path, kc, vc, fc, new JobConf(mergedConf), codec=codec) + converted.saveAsHadoopFile(path, kc, vc, fc, new JobConf(mergedConf), codec = codec) } /** @@ -794,10 +797,10 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort: val bufferSize = SparkEnv.get.conf.getInt("spark.buffer.size", 65536) - /** + /** * We try to reuse a single Socket to transfer accumulator updates, as they are all added * by the DAGScheduler's single-threaded actor anyway. - */ + */ @transient var socket: Socket = _ def openSocket(): Socket = synchronized { @@ -840,6 +843,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort: * An Wrapper for Python Broadcast, which is written into disk by Python. It also will * write the data into disk after deserialization, then Python can read it from disks. */ +// scalastyle:off no.finalize private[spark] class PythonBroadcast(@transient var path: String) extends Serializable { /** @@ -881,3 +885,4 @@ private[spark] class PythonBroadcast(@transient var path: String) extends Serial } } } +// scalastyle:on no.finalize diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala index acbaba6791850..90dacaeb93429 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala @@ -31,7 +31,7 @@ private[spark] object PythonUtils { def sparkPythonPath: String = { val pythonPath = new ArrayBuffer[String] for (sparkHome <- sys.env.get("SPARK_HOME")) { - pythonPath += Seq(sparkHome, "python").mkString(File.separator) + pythonPath += Seq(sparkHome, "python", "lib", "pyspark.zip").mkString(File.separator) pythonPath += Seq(sparkHome, "python", "lib", "py4j-0.8.2.1-src.zip").mkString(File.separator) } pythonPath ++= SparkContext.jarOfObject(this) @@ -50,7 +50,21 @@ private[spark] object PythonUtils { /** * Convert list of T into seq of T (for calling API with varargs) */ - def toSeq[T](cols: JList[T]): Seq[T] = { - cols.toList.toSeq + def toSeq[T](vs: JList[T]): Seq[T] = { + vs.toList.toSeq + } + + /** + * Convert list of T into array of T (for calling API with array) + */ + def toArray[T](vs: JList[T]): Array[T] = { + vs.toArray().asInstanceOf[Array[T]] + } + + /** + * Convert java map of K, V into Map of K, V (for calling API with varargs) + */ + def toScalaMap[K, V](jm: java.util.Map[K, V]): Map[K, V] = { + jm.toMap } } diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala index 3a2c94bd9d875..1a5f2bca26c2b 100644 --- a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala +++ b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala @@ -18,7 +18,7 @@ package org.apache.spark.api.r import java.io.{DataOutputStream, File, FileOutputStream, IOException} -import java.net.{InetSocketAddress, ServerSocket} +import java.net.{InetAddress, InetSocketAddress, ServerSocket} import java.util.concurrent.TimeUnit import io.netty.bootstrap.ServerBootstrap @@ -29,7 +29,7 @@ import io.netty.channel.socket.nio.NioServerSocketChannel import io.netty.handler.codec.LengthFieldBasedFrameDecoder import io.netty.handler.codec.bytes.{ByteArrayDecoder, ByteArrayEncoder} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkConf} /** * Netty-based backend server that is used to communicate between R and Java. @@ -41,14 +41,15 @@ private[spark] class RBackend { private[this] var bossGroup: EventLoopGroup = null def init(): Int = { - bossGroup = new NioEventLoopGroup(2) + val conf = new SparkConf() + bossGroup = new NioEventLoopGroup(conf.getInt("spark.r.numRBackendThreads", 2)) val workerGroup = bossGroup val handler = new RBackendHandler(this) - + bootstrap = new ServerBootstrap() .group(bossGroup, workerGroup) .channel(classOf[NioServerSocketChannel]) - + bootstrap.childHandler(new ChannelInitializer[SocketChannel]() { def initChannel(ch: SocketChannel): Unit = { ch.pipeline() @@ -65,7 +66,7 @@ private[spark] class RBackend { } }) - channelFuture = bootstrap.bind(new InetSocketAddress(0)) + channelFuture = bootstrap.bind(new InetSocketAddress("localhost", 0)) channelFuture.syncUninterruptibly() channelFuture.channel().localAddress().asInstanceOf[InetSocketAddress].getPort() } @@ -101,7 +102,7 @@ private[spark] object RBackend extends Logging { try { // bind to random port val boundPort = sparkRBackend.init() - val serverSocket = new ServerSocket(0, 1) + val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost")) val listenPort = serverSocket.getLocalPort() // tell the R process via temporary file diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala index 0075d963711f1..2e86984c66b3a 100644 --- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala +++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala @@ -77,7 +77,7 @@ private[r] class RBackendHandler(server: RBackend) val reply = bos.toByteArray ctx.write(reply) } - + override def channelReadComplete(ctx: ChannelHandlerContext): Unit = { ctx.flush() } @@ -124,7 +124,7 @@ private[r] class RBackendHandler(server: RBackend) } throw new Exception(s"No matched method found for $cls.$methodName") } - val ret = methods.head.invoke(obj, args:_*) + val ret = methods.head.invoke(obj, args : _*) // Write status bit writeInt(dos, 0) @@ -135,7 +135,7 @@ private[r] class RBackendHandler(server: RBackend) matchMethod(numArgs, args, x.getParameterTypes) }.head - val obj = ctor.newInstance(args:_*) + val obj = ctor.newInstance(args : _*) writeInt(dos, 0) writeObject(dos, obj.asInstanceOf[AnyRef]) diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala index 6fea5e1144f2f..4dfa7325934ff 100644 --- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala @@ -18,7 +18,7 @@ package org.apache.spark.api.r import java.io._ -import java.net.ServerSocket +import java.net.{InetAddress, ServerSocket} import java.util.{Map => JMap} import scala.collection.JavaConversions._ @@ -55,7 +55,7 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag]( val parentIterator = firstParent[T].iterator(partition, context) // we expect two connections - val serverSocket = new ServerSocket(0, 2) + val serverSocket = new ServerSocket(0, 2, InetAddress.getByName("localhost")) val listenPort = serverSocket.getLocalPort() // The stdout/stderr is shared by multiple tasks, because we use one daemon @@ -309,7 +309,7 @@ private class StringRRDD[T: ClassTag]( } private object SpecialLengths { - val TIMING_DATA = -1 + val TIMING_DATA = -1 } private[r] class BufferedStreamThread( @@ -355,7 +355,6 @@ private[r] object RRDD { val sparkConf = new SparkConf().setAppName(appName) .setSparkHome(sparkHome) - .setJars(jars) // Override `master` if we have a user-specified value if (master != "") { @@ -373,7 +372,11 @@ private[r] object RRDD { sparkConf.setExecutorEnv(name.asInstanceOf[String], value.asInstanceOf[String]) } - new JavaSparkContext(sparkConf) + val jsc = new JavaSparkContext(sparkConf) + jars.foreach { jar => + jsc.addJar(jar) + } + jsc } /** @@ -414,7 +417,7 @@ private[r] object RRDD { synchronized { if (daemonChannel == null) { // we expect one connections - val serverSocket = new ServerSocket(0, 1) + val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost")) val daemonPort = serverSocket.getLocalPort errThread = createRProcess(rLibDir, daemonPort, "daemon.R") // the socket used to send out the input of task diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala index 371dfe454d1a2..56adc857d4ce0 100644 --- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala +++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala @@ -18,7 +18,7 @@ package org.apache.spark.api.r import java.io.{DataInputStream, DataOutputStream} -import java.sql.{Date, Time} +import java.sql.{Timestamp, Date, Time} import scala.collection.JavaConversions._ @@ -107,9 +107,12 @@ private[spark] object SerDe { Date.valueOf(readString(in)) } - def readTime(in: DataInputStream): Time = { - val t = in.readDouble() - new Time((t * 1000L).toLong) + def readTime(in: DataInputStream): Timestamp = { + val seconds = in.readDouble() + val sec = Math.floor(seconds).toLong + val t = new Timestamp(sec * 1000L) + t.setNanos(((seconds - sec) * 1e9).toInt) + t } def readBytesArr(in: DataInputStream): Array[Array[Byte]] = { @@ -157,9 +160,11 @@ private[spark] object SerDe { val keysLen = readInt(in) val keys = (0 until keysLen).map(_ => readTypedObject(in, keysType)) - val valuesType = readObjectType(in) val valuesLen = readInt(in) - val values = (0 until valuesLen).map(_ => readTypedObject(in, valuesType)) + val values = (0 until valuesLen).map(_ => { + val valueType = readObjectType(in) + readTypedObject(in, valueType) + }) mapAsJavaMap(keys.zip(values).toMap) } else { new java.util.HashMap[Object, Object]() @@ -225,6 +230,9 @@ private[spark] object SerDe { case "java.sql.Time" => writeType(dos, "time") writeTime(dos, value.asInstanceOf[Time]) + case "java.sql.Timestamp" => + writeType(dos, "time") + writeTime(dos, value.asInstanceOf[Timestamp]) case "[B" => writeType(dos, "raw") writeBytes(dos, value.asInstanceOf[Array[Byte]]) @@ -287,6 +295,9 @@ private[spark] object SerDe { out.writeDouble(value.getTime.toDouble / 1000.0) } + def writeTime(out: DataOutputStream, value: Timestamp): Unit = { + out.writeDouble((value.getTime / 1000).toDouble + value.getNanos.toDouble / 1e9) + } // NOTE: Only works for ASCII right now def writeString(out: DataOutputStream, value: String): Unit = { diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index 4457c75e8b0fc..b69af639f7862 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -125,7 +125,7 @@ private[broadcast] object HttpBroadcast extends Logging { securityManager = securityMgr if (isDriver) { createServer(conf) - conf.set("spark.httpBroadcast.uri", serverUri) + conf.set("spark.httpBroadcast.uri", serverUri) } serverUri = conf.get("spark.httpBroadcast.uri") cleaner = new MetadataCleaner(MetadataCleanerType.HTTP_BROADCAST, cleanup, conf) @@ -187,7 +187,7 @@ private[broadcast] object HttpBroadcast extends Logging { } private def read[T: ClassTag](id: Long): T = { - logDebug("broadcast read server: " + serverUri + " id: broadcast-" + id) + logDebug("broadcast read server: " + serverUri + " id: broadcast-" + id) val url = serverUri + "/" + BroadcastBlockId(id).name var uc: URLConnection = null diff --git a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala index c048b78910f38..b4edb6109e839 100644 --- a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala +++ b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala @@ -65,7 +65,7 @@ private object FaultToleranceTest extends App with Logging { private val workers = ListBuffer[TestWorkerInfo]() private var sc: SparkContext = _ - private val zk = SparkCuratorUtil.newClient(conf) + private val zk = SparkCuratorUtil.newClient(conf) private var numPassed = 0 private var numFailed = 0 diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala index f0e77c2ba982b..0550f00a172ab 100644 --- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala @@ -43,15 +43,21 @@ class LocalSparkCluster( private val localHostname = Utils.localHostName() private val masterActorSystems = ArrayBuffer[ActorSystem]() private val workerActorSystems = ArrayBuffer[ActorSystem]() + // exposed for testing + var masterWebUIPort = -1 def start(): Array[String] = { logInfo("Starting a local Spark cluster with " + numWorkers + " workers.") // Disable REST server on Master in this mode unless otherwise specified - val _conf = conf.clone().setIfMissing("spark.master.rest.enabled", "false") + val _conf = conf.clone() + .setIfMissing("spark.master.rest.enabled", "false") + .set("spark.shuffle.service.enabled", "false") /* Start the Master */ - val (masterSystem, masterPort, _, _) = Master.startSystemAndActor(localHostname, 0, 0, _conf) + val (masterSystem, masterPort, webUiPort, _) = + Master.startSystemAndActor(localHostname, 0, 0, _conf) + masterWebUIPort = webUiPort masterActorSystems += masterSystem val masterUrl = "spark://" + Utils.localHostNameForURI() + ":" + masterPort val masters = Array(masterUrl) diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala index 53e18c4bcec23..c2ed43a5397d6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala @@ -18,9 +18,11 @@ package org.apache.spark.deploy import java.net.URI +import java.io.File import scala.collection.mutable.ArrayBuffer import scala.collection.JavaConversions._ +import scala.util.Try import org.apache.spark.api.python.PythonUtils import org.apache.spark.util.{RedirectThread, Utils} @@ -81,16 +83,13 @@ object PythonRunner { throw new IllegalArgumentException("Launching Python applications through " + s"spark-submit is currently only supported for local files: $path") } - val windows = Utils.isWindows || testWindows - var formattedPath = if (windows) Utils.formatWindowsPath(path) else path - - // Strip the URI scheme from the path - formattedPath = - new URI(formattedPath).getScheme match { - case null => formattedPath - case Utils.windowsDrive(d) if windows => formattedPath - case _ => new URI(formattedPath).getPath - } + // get path when scheme is file. + val uri = Try(new URI(path)).getOrElse(new File(path).toURI) + var formattedPath = uri.getScheme match { + case null => path + case "file" | "local" => uri.getPath + case _ => null + } // Guard against malformed paths potentially throwing NPE if (formattedPath == null) { @@ -99,7 +98,9 @@ object PythonRunner { // In Windows, the drive should not be prefixed with "/" // For instance, python does not understand "/C:/path/to/sheep.py" - formattedPath = if (windows) formattedPath.stripPrefix("/") else formattedPath + if (Utils.isWindows && formattedPath.matches("/[a-zA-Z]:/.*")) { + formattedPath = formattedPath.stripPrefix("/") + } formattedPath } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index b563034457a91..7fa75ac8c2b54 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -22,22 +22,22 @@ import java.lang.reflect.Method import java.security.PrivilegedExceptionAction import java.util.{Arrays, Comparator} +import scala.collection.JavaConversions._ +import scala.concurrent.duration._ +import scala.language.postfixOps + import com.google.common.primitives.Longs import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter} import org.apache.hadoop.fs.FileSystem.Statistics +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter} import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.JobContext import org.apache.hadoop.security.{Credentials, UserGroupInformation} -import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils - -import scala.collection.JavaConversions._ -import scala.concurrent.duration._ -import scala.language.postfixOps +import org.apache.spark.{Logging, SparkConf, SparkException} /** * :: DeveloperApi :: @@ -199,13 +199,43 @@ class SparkHadoopUtil extends Logging { * that file. */ def listLeafStatuses(fs: FileSystem, basePath: Path): Seq[FileStatus] = { - def recurse(path: Path): Array[FileStatus] = { - val (directories, leaves) = fs.listStatus(path).partition(_.isDir) - leaves ++ directories.flatMap(f => listLeafStatuses(fs, f.getPath)) + listLeafStatuses(fs, fs.getFileStatus(basePath)) + } + + /** + * Get [[FileStatus]] objects for all leaf children (files) under the given base path. If the + * given path points to a file, return a single-element collection containing [[FileStatus]] of + * that file. + */ + def listLeafStatuses(fs: FileSystem, baseStatus: FileStatus): Seq[FileStatus] = { + def recurse(status: FileStatus): Seq[FileStatus] = { + val (directories, leaves) = fs.listStatus(status.getPath).partition(_.isDir) + leaves ++ directories.flatMap(f => listLeafStatuses(fs, f)) } - val baseStatus = fs.getFileStatus(basePath) - if (baseStatus.isDir) recurse(basePath) else Array(baseStatus) + if (baseStatus.isDir) recurse(baseStatus) else Seq(baseStatus) + } + + def listLeafDirStatuses(fs: FileSystem, basePath: Path): Seq[FileStatus] = { + listLeafDirStatuses(fs, fs.getFileStatus(basePath)) + } + + def listLeafDirStatuses(fs: FileSystem, baseStatus: FileStatus): Seq[FileStatus] = { + def recurse(status: FileStatus): Seq[FileStatus] = { + val (directories, files) = fs.listStatus(status.getPath).partition(_.isDir) + val leaves = if (directories.isEmpty) Seq(status) else Seq.empty[FileStatus] + leaves ++ directories.flatMap(dir => listLeafDirStatuses(fs, dir)) + } + + assert(baseStatus.isDir) + recurse(baseStatus) + } + + def globPath(pattern: Path): Seq[Path] = { + val fs = pattern.getFileSystem(conf) + Option(fs.globStatus(pattern)).map { statuses => + statuses.map(_.getPath.makeQualified(fs.getUri, fs.getWorkingDirectory)).toSeq + }.getOrElse(Seq.empty[Path]) } /** diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 329fa06ba8ba5..abf222757a95b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -35,7 +35,8 @@ import org.apache.ivy.core.resolve.ResolveOptions import org.apache.ivy.core.retrieve.RetrieveOptions import org.apache.ivy.core.settings.IvySettings import org.apache.ivy.plugins.matcher.GlobPatternMatcher -import org.apache.ivy.plugins.resolver.{ChainResolver, IBiblioResolver} +import org.apache.ivy.plugins.repository.file.FileRepository +import org.apache.ivy.plugins.resolver.{FileSystemResolver, ChainResolver, IBiblioResolver} import org.apache.spark.SPARK_VERSION import org.apache.spark.deploy.rest._ import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils} @@ -82,13 +83,13 @@ object SparkSubmit { private val CLASS_NOT_FOUND_EXIT_STATUS = 101 // Exposed for testing - private[spark] var exitFn: () => Unit = () => System.exit(1) + private[spark] var exitFn: Int => Unit = (exitCode: Int) => System.exit(exitCode) private[spark] var printStream: PrintStream = System.err private[spark] def printWarning(str: String): Unit = printStream.println("Warning: " + str) private[spark] def printErrorAndExit(str: String): Unit = { printStream.println("Error: " + str) printStream.println("Run with --help for usage help or --verbose for debug output") - exitFn() + exitFn(1) } private[spark] def printVersionAndExit(): Unit = { printStream.println("""Welcome to @@ -99,7 +100,7 @@ object SparkSubmit { /_/ """.format(SPARK_VERSION)) printStream.println("Type --help for more information.") - exitFn() + exitFn(0) } def main(args: Array[String]): Unit = { @@ -160,7 +161,7 @@ object SparkSubmit { // detect exceptions with empty stack traces here, and treat them differently. if (e.getStackTrace().length == 0) { printStream.println(s"ERROR: ${e.getClass().getName()}: ${e.getMessage()}") - exitFn() + exitFn(1) } else { throw e } @@ -324,55 +325,20 @@ object SparkSubmit { // Usage: PythonAppRunner
[app arguments] args.mainClass = "org.apache.spark.deploy.PythonRunner" args.childArgs = ArrayBuffer(args.primaryResource, args.pyFiles) ++ args.childArgs - args.files = mergeFileLists(args.files, args.primaryResource) + if (clusterManager != YARN) { + // The YARN backend distributes the primary file differently, so don't merge it. + args.files = mergeFileLists(args.files, args.primaryResource) + } + } + if (clusterManager != YARN) { + // The YARN backend handles python files differently, so don't merge the lists. + args.files = mergeFileLists(args.files, args.pyFiles) } - args.files = mergeFileLists(args.files, args.pyFiles) if (args.pyFiles != null) { sysProps("spark.submit.pyFiles") = args.pyFiles } } - // In yarn mode for a python app, add pyspark archives to files - // that can be distributed with the job - if (args.isPython && clusterManager == YARN) { - var pyArchives: String = null - val pyArchivesEnvOpt = sys.env.get("PYSPARK_ARCHIVES_PATH") - if (pyArchivesEnvOpt.isDefined) { - pyArchives = pyArchivesEnvOpt.get - } else { - if (!sys.env.contains("SPARK_HOME")) { - printErrorAndExit("SPARK_HOME does not exist for python application in yarn mode.") - } - val pythonPath = new ArrayBuffer[String] - for (sparkHome <- sys.env.get("SPARK_HOME")) { - val pyLibPath = Seq(sparkHome, "python", "lib").mkString(File.separator) - val pyArchivesFile = new File(pyLibPath, "pyspark.zip") - if (!pyArchivesFile.exists()) { - printErrorAndExit("pyspark.zip does not exist for python application in yarn mode.") - } - val py4jFile = new File(pyLibPath, "py4j-0.8.2.1-src.zip") - if (!py4jFile.exists()) { - printErrorAndExit("py4j-0.8.2.1-src.zip does not exist for python application " + - "in yarn mode.") - } - pythonPath += pyArchivesFile.getAbsolutePath() - pythonPath += py4jFile.getAbsolutePath() - } - pyArchives = pythonPath.mkString(",") - } - - pyArchives = pyArchives.split(",").map { localPath=> - val localURI = Utils.resolveURI(localPath) - if (localURI.getScheme != "local") { - args.files = mergeFileLists(args.files, localURI.toString) - new Path(localPath).getName - } else { - localURI.getPath - } - }.mkString(File.pathSeparator) - sysProps("spark.submit.pyArchives") = pyArchives - } - // If we're running a R app, set the main class to our specific R runner if (args.isR && deployMode == CLIENT) { if (args.primaryResource == SPARKR_SHELL) { @@ -386,19 +352,10 @@ object SparkSubmit { } } - if (isYarnCluster) { - // In yarn-cluster mode for a python app, add primary resource and pyFiles to files - // that can be distributed with the job - if (args.isPython) { - args.files = mergeFileLists(args.files, args.primaryResource) - args.files = mergeFileLists(args.files, args.pyFiles) - } - + if (isYarnCluster && args.isR) { // In yarn-cluster mode for a R app, add primary resource to files // that can be distributed with the job - if (args.isR) { - args.files = mergeFileLists(args.files, args.primaryResource) - } + args.files = mergeFileLists(args.files, args.primaryResource) } // Special flag to avoid deprecation warnings at the client @@ -425,9 +382,10 @@ object SparkSubmit { // Yarn client only OptionAssigner(args.queue, YARN, CLIENT, sysProp = "spark.yarn.queue"), OptionAssigner(args.numExecutors, YARN, CLIENT, sysProp = "spark.executor.instances"), - OptionAssigner(args.executorCores, YARN, CLIENT, sysProp = "spark.executor.cores"), OptionAssigner(args.files, YARN, CLIENT, sysProp = "spark.yarn.dist.files"), OptionAssigner(args.archives, YARN, CLIENT, sysProp = "spark.yarn.dist.archives"), + OptionAssigner(args.principal, YARN, CLIENT, sysProp = "spark.yarn.principal"), + OptionAssigner(args.keytab, YARN, CLIENT, sysProp = "spark.yarn.keytab"), // Yarn cluster only OptionAssigner(args.name, YARN, CLUSTER, clOption = "--name"), @@ -440,13 +398,11 @@ object SparkSubmit { OptionAssigner(args.files, YARN, CLUSTER, clOption = "--files"), OptionAssigner(args.archives, YARN, CLUSTER, clOption = "--archives"), OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"), - - // Yarn client or cluster - OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, clOption = "--principal"), - OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, clOption = "--keytab"), + OptionAssigner(args.principal, YARN, CLUSTER, clOption = "--principal"), + OptionAssigner(args.keytab, YARN, CLUSTER, clOption = "--keytab"), // Other options - OptionAssigner(args.executorCores, STANDALONE, ALL_DEPLOY_MODES, + OptionAssigner(args.executorCores, STANDALONE | YARN, ALL_DEPLOY_MODES, sysProp = "spark.executor.cores"), OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, ALL_DEPLOY_MODES, sysProp = "spark.executor.memory"), @@ -516,17 +472,18 @@ object SparkSubmit { } } + // Let YARN know it's a pyspark app, so it distributes needed libraries. + if (clusterManager == YARN && args.isPython) { + sysProps.put("spark.yarn.isPython", "true") + } + // In yarn-cluster mode, use yarn.Client as a wrapper around the user class if (isYarnCluster) { childMainClass = "org.apache.spark.deploy.yarn.Client" if (args.isPython) { - val mainPyFile = new Path(args.primaryResource).getName - childArgs += ("--primary-py-file", mainPyFile) + childArgs += ("--primary-py-file", args.primaryResource) if (args.pyFiles != null) { - // These files will be distributed to each machine's working directory, so strip the - // path prefix - val pyFilesNames = args.pyFiles.split(",").map(p => (new Path(p)).getName).mkString(",") - childArgs += ("--py-files", pyFilesNames) + childArgs += ("--py-files", args.pyFiles) } childArgs += ("--class", "org.apache.spark.deploy.PythonRunner") } else if (args.isR) { @@ -700,7 +657,7 @@ object SparkSubmit { /** * Return whether the given main class represents a sql shell. */ - private def isSqlShell(mainClass: String): Boolean = { + private[deploy] def isSqlShell(mainClass: String): Boolean = { mainClass == "org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver" } @@ -753,7 +710,9 @@ private[spark] object SparkSubmitUtils { * @param artifactId the artifactId of the coordinate * @param version the version of the coordinate */ - private[deploy] case class MavenCoordinate(groupId: String, artifactId: String, version: String) + private[deploy] case class MavenCoordinate(groupId: String, artifactId: String, version: String) { + override def toString: String = s"$groupId:$artifactId:$version" + } /** * Extracts maven coordinates from a comma-delimited string. Coordinates should be provided @@ -776,6 +735,16 @@ private[spark] object SparkSubmitUtils { } } + /** Path of the local Maven cache. */ + private[spark] def m2Path: File = { + if (Utils.isTesting) { + // test builds delete the maven cache, and this can cause flakiness + new File("dummy", ".m2" + File.separator + "repository") + } else { + new File(System.getProperty("user.home"), ".m2" + File.separator + "repository") + } + } + /** * Extracts maven coordinates from a comma-delimited string * @param remoteRepos Comma-delimited string of remote repositories @@ -789,18 +758,18 @@ private[spark] object SparkSubmitUtils { val localM2 = new IBiblioResolver localM2.setM2compatible(true) - val m2Path = ".m2" + File.separator + "repository" + File.separator - localM2.setRoot(new File(System.getProperty("user.home"), m2Path).toURI.toString) + localM2.setRoot(m2Path.toURI.toString) localM2.setUsepoms(true) localM2.setName("local-m2-cache") cr.add(localM2) - val localIvy = new IBiblioResolver - localIvy.setRoot(new File(ivySettings.getDefaultIvyUserDir, - "local" + File.separator).toURI.toString) + val localIvy = new FileSystemResolver + val localIvyRoot = new File(ivySettings.getDefaultIvyUserDir, "local") + localIvy.setLocal(true) + localIvy.setRepository(new FileRepository(localIvyRoot)) val ivyPattern = Seq("[organisation]", "[module]", "[revision]", "[type]s", "[artifact](-[classifier]).[ext]").mkString(File.separator) - localIvy.setPattern(ivyPattern) + localIvy.addIvyPattern(localIvyRoot.getAbsolutePath + File.separator + ivyPattern) localIvy.setName("local-ivy-cache") cr.add(localIvy) @@ -864,18 +833,14 @@ private[spark] object SparkSubmitUtils { md.addDependency(dd) } } - + /** Add exclusion rules for dependencies already included in the spark-assembly */ def addExclusionRules( ivySettings: IvySettings, ivyConfName: String, md: DefaultModuleDescriptor): Unit = { // Add scala exclusion rule - val scalaArtifacts = new ArtifactId(new ModuleId("*", "scala-library"), "*", "*", "*") - val scalaDependencyExcludeRule = - new DefaultExcludeRule(scalaArtifacts, ivySettings.getMatcher("glob"), null) - scalaDependencyExcludeRule.addConfiguration(ivyConfName) - md.addExcludeRule(scalaDependencyExcludeRule) + md.addExcludeRule(createExclusion("*:scala-library:*", ivySettings, ivyConfName)) // We need to specify each component explicitly, otherwise we miss spark-streaming-kafka and // other spark-streaming utility components. Underscore is there to differentiate between @@ -884,13 +849,8 @@ private[spark] object SparkSubmitUtils { "sql_", "streaming_", "yarn_", "network-common_", "network-shuffle_", "network-yarn_") components.foreach { comp => - val sparkArtifacts = - new ArtifactId(new ModuleId("org.apache.spark", s"spark-$comp*"), "*", "*", "*") - val sparkDependencyExcludeRule = - new DefaultExcludeRule(sparkArtifacts, ivySettings.getMatcher("glob"), null) - sparkDependencyExcludeRule.addConfiguration(ivyConfName) - - md.addExcludeRule(sparkDependencyExcludeRule) + md.addExcludeRule(createExclusion(s"org.apache.spark:spark-$comp*:*", ivySettings, + ivyConfName)) } } @@ -903,6 +863,7 @@ private[spark] object SparkSubmitUtils { * @param coordinates Comma-delimited string of maven coordinates * @param remoteRepos Comma-delimited string of remote repositories other than maven central * @param ivyPath The path to the local ivy repository + * @param exclusions Exclusions to apply when resolving transitive dependencies * @return The comma-delimited path to the jars of the given maven artifacts including their * transitive dependencies */ @@ -910,76 +871,96 @@ private[spark] object SparkSubmitUtils { coordinates: String, remoteRepos: Option[String], ivyPath: Option[String], + exclusions: Seq[String] = Nil, isTest: Boolean = false): String = { if (coordinates == null || coordinates.trim.isEmpty) { "" } else { val sysOut = System.out - // To prevent ivy from logging to system out - System.setOut(printStream) - val artifacts = extractMavenCoordinates(coordinates) - // Default configuration name for ivy - val ivyConfName = "default" - // set ivy settings for location of cache - val ivySettings: IvySettings = new IvySettings - // Directories for caching downloads through ivy and storing the jars when maven coordinates - // are supplied to spark-submit - val alternateIvyCache = ivyPath.getOrElse("") - val packagesDirectory: File = - if (alternateIvyCache.trim.isEmpty) { - new File(ivySettings.getDefaultIvyUserDir, "jars") + try { + // To prevent ivy from logging to system out + System.setOut(printStream) + val artifacts = extractMavenCoordinates(coordinates) + // Default configuration name for ivy + val ivyConfName = "default" + // set ivy settings for location of cache + val ivySettings: IvySettings = new IvySettings + // Directories for caching downloads through ivy and storing the jars when maven coordinates + // are supplied to spark-submit + val alternateIvyCache = ivyPath.getOrElse("") + val packagesDirectory: File = + if (alternateIvyCache.trim.isEmpty) { + new File(ivySettings.getDefaultIvyUserDir, "jars") + } else { + ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache)) + ivySettings.setDefaultCache(new File(alternateIvyCache, "cache")) + new File(alternateIvyCache, "jars") + } + printStream.println( + s"Ivy Default Cache set to: ${ivySettings.getDefaultCache.getAbsolutePath}") + printStream.println(s"The jars for the packages stored in: $packagesDirectory") + // create a pattern matcher + ivySettings.addMatcher(new GlobPatternMatcher) + // create the dependency resolvers + val repoResolver = createRepoResolvers(remoteRepos, ivySettings) + ivySettings.addResolver(repoResolver) + ivySettings.setDefaultResolver(repoResolver.getName) + + val ivy = Ivy.newInstance(ivySettings) + // Set resolve options to download transitive dependencies as well + val resolveOptions = new ResolveOptions + resolveOptions.setTransitive(true) + val retrieveOptions = new RetrieveOptions + // Turn downloading and logging off for testing + if (isTest) { + resolveOptions.setDownload(false) + resolveOptions.setLog(LogOptions.LOG_QUIET) + retrieveOptions.setLog(LogOptions.LOG_QUIET) } else { - ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache)) - ivySettings.setDefaultCache(new File(alternateIvyCache, "cache")) - new File(alternateIvyCache, "jars") + resolveOptions.setDownload(true) } - printStream.println( - s"Ivy Default Cache set to: ${ivySettings.getDefaultCache.getAbsolutePath}") - printStream.println(s"The jars for the packages stored in: $packagesDirectory") - // create a pattern matcher - ivySettings.addMatcher(new GlobPatternMatcher) - // create the dependency resolvers - val repoResolver = createRepoResolvers(remoteRepos, ivySettings) - ivySettings.addResolver(repoResolver) - ivySettings.setDefaultResolver(repoResolver.getName) - - val ivy = Ivy.newInstance(ivySettings) - // Set resolve options to download transitive dependencies as well - val resolveOptions = new ResolveOptions - resolveOptions.setTransitive(true) - val retrieveOptions = new RetrieveOptions - // Turn downloading and logging off for testing - if (isTest) { - resolveOptions.setDownload(false) - resolveOptions.setLog(LogOptions.LOG_QUIET) - retrieveOptions.setLog(LogOptions.LOG_QUIET) - } else { - resolveOptions.setDownload(true) - } - // A Module descriptor must be specified. Entries are dummy strings - val md = getModuleDescriptor - md.setDefaultConf(ivyConfName) + // A Module descriptor must be specified. Entries are dummy strings + val md = getModuleDescriptor + md.setDefaultConf(ivyConfName) + + // Add exclusion rules for Spark and Scala Library + addExclusionRules(ivySettings, ivyConfName, md) + // add all supplied maven artifacts as dependencies + addDependenciesToIvy(md, artifacts, ivyConfName) - // Add exclusion rules for Spark and Scala Library - addExclusionRules(ivySettings, ivyConfName, md) - // add all supplied maven artifacts as dependencies - addDependenciesToIvy(md, artifacts, ivyConfName) + exclusions.foreach { e => + md.addExcludeRule(createExclusion(e + ":*", ivySettings, ivyConfName)) + } - // resolve dependencies - val rr: ResolveReport = ivy.resolve(md, resolveOptions) - if (rr.hasError) { - throw new RuntimeException(rr.getAllProblemMessages.toString) + // resolve dependencies + val rr: ResolveReport = ivy.resolve(md, resolveOptions) + if (rr.hasError) { + throw new RuntimeException(rr.getAllProblemMessages.toString) + } + // retrieve all resolved dependencies + ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, + packagesDirectory.getAbsolutePath + File.separator + + "[organization]_[artifact]-[revision].[ext]", + retrieveOptions.setConfs(Array(ivyConfName))) + resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) + } finally { + System.setOut(sysOut) } - // retrieve all resolved dependencies - ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, - packagesDirectory.getAbsolutePath + File.separator + - "[organization]_[artifact]-[revision].[ext]", - retrieveOptions.setConfs(Array(ivyConfName))) - System.setOut(sysOut) - resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) } } + + private def createExclusion( + coords: String, + ivySettings: IvySettings, + ivyConfName: String): ExcludeRule = { + val c = extractMavenCoordinates(coords)(0) + val id = new ArtifactId(new ModuleId(c.groupId, c.artifactId), "*", "*", "*") + val rule = new DefaultExcludeRule(id, ivySettings.getMatcher("glob"), null) + rule.addConfiguration(ivyConfName) + rule + } + } /** diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index c0e4c771908b3..b7429a901e162 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -17,12 +17,15 @@ package org.apache.spark.deploy +import java.io.{ByteArrayOutputStream, PrintStream} +import java.lang.reflect.InvocationTargetException import java.net.URI import java.util.{List => JList} import java.util.jar.JarFile import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap} +import scala.io.Source import org.apache.spark.deploy.SparkSubmitAction._ import org.apache.spark.launcher.SparkSubmitArgumentsParser @@ -169,6 +172,8 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S deployMode = Option(deployMode).orElse(env.get("DEPLOY_MODE")).orNull numExecutors = Option(numExecutors) .getOrElse(sparkProperties.get("spark.executor.instances").orNull) + keytab = Option(keytab).orElse(sparkProperties.get("spark.yarn.keytab")).orNull + principal = Option(principal).orElse(sparkProperties.get("spark.yarn.principal")).orNull // Try to set main class from JAR if no --class argument is given if (mainClass == null && !isPython && !isR && primaryResource != null) { @@ -410,6 +415,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S case VERSION => SparkSubmit.printVersionAndExit() + case USAGE_ERROR => + printUsageAndExit(1) + case _ => throw new IllegalArgumentException(s"Unexpected argument '$opt'.") } @@ -447,11 +455,14 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S if (unknownParam != null) { outStream.println("Unknown/unsupported param " + unknownParam) } - outStream.println( + val command = sys.env.get("_SPARK_CMD_USAGE").getOrElse( """Usage: spark-submit [options] [app arguments] |Usage: spark-submit --kill [submission ID] --master [spark://...] - |Usage: spark-submit --status [submission ID] --master [spark://...] - | + |Usage: spark-submit --status [submission ID] --master [spark://...]""".stripMargin) + outStream.println(command) + + outStream.println( + """ |Options: | --master MASTER_URL spark://host:port, mesos://host:port, yarn, or local. | --deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or @@ -523,6 +534,65 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S | delegation tokens periodically. """.stripMargin ) - SparkSubmit.exitFn() + + if (SparkSubmit.isSqlShell(mainClass)) { + outStream.println("CLI options:") + outStream.println(getSqlShellOptions()) + } + + SparkSubmit.exitFn(exitCode) } + + /** + * Run the Spark SQL CLI main class with the "--help" option and catch its output. Then filter + * the results to remove unwanted lines. + * + * Since the CLI will call `System.exit()`, we install a security manager to prevent that call + * from working, and restore the original one afterwards. + */ + private def getSqlShellOptions(): String = { + val currentOut = System.out + val currentErr = System.err + val currentSm = System.getSecurityManager() + try { + val out = new ByteArrayOutputStream() + val stream = new PrintStream(out) + System.setOut(stream) + System.setErr(stream) + + val sm = new SecurityManager() { + override def checkExit(status: Int): Unit = { + throw new SecurityException() + } + + override def checkPermission(perm: java.security.Permission): Unit = {} + } + System.setSecurityManager(sm) + + try { + Class.forName(mainClass).getMethod("main", classOf[Array[String]]) + .invoke(null, Array(HELP)) + } catch { + case e: InvocationTargetException => + // Ignore SecurityException, since we throw it above. + if (!e.getCause().isInstanceOf[SecurityException]) { + throw e + } + } + + stream.flush() + + // Get the output and discard any unnecessary lines from it. + Source.fromString(new String(out.toByteArray())).getLines + .filter { line => + !line.startsWith("log4j") && !line.startsWith("usage") + } + .mkString("\n") + } finally { + System.setSecurityManager(currentSm) + System.setOut(currentOut) + System.setErr(currentErr) + } + } + } diff --git a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala index 298a8201960d1..5f5e0fe1c34d7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala @@ -17,6 +17,9 @@ package org.apache.spark.deploy.history +import java.util.zip.ZipOutputStream + +import org.apache.spark.SparkException import org.apache.spark.ui.SparkUI private[spark] case class ApplicationAttemptInfo( @@ -62,4 +65,12 @@ private[history] abstract class ApplicationHistoryProvider { */ def getConfig(): Map[String, String] = Map() + /** + * Writes out the event logs to the output stream provided. The logs will be compressed into a + * single zip file and written out. + * @throws SparkException if the logs for the app id cannot be found. + */ + @throws(classOf[SparkException]) + def writeEventLogs(appId: String, attemptId: Option[String], zipStream: ZipOutputStream): Unit + } diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 45c2be34c8680..db383b9823d3c 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -17,16 +17,18 @@ package org.apache.spark.deploy.history -import java.io.{BufferedInputStream, FileNotFoundException, IOException, InputStream} +import java.io.{BufferedInputStream, FileNotFoundException, InputStream, IOException, OutputStream} import java.util.concurrent.{ExecutorService, Executors, TimeUnit} +import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.mutable +import com.google.common.io.ByteStreams import com.google.common.util.concurrent.{MoreExecutors, ThreadFactoryBuilder} -import org.apache.hadoop.fs.{FileStatus, Path} +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} import org.apache.hadoop.fs.permission.AccessControlException -import org.apache.spark.{Logging, SecurityManager, SparkConf} +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.io.CompressionCodec import org.apache.spark.scheduler._ @@ -59,7 +61,8 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) .map { d => Utils.resolveURI(d).toString } .getOrElse(DEFAULT_LOG_DIR) - private val fs = Utils.getHadoopFileSystem(logDir, SparkHadoopUtil.get.newConfiguration(conf)) + private val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) + private val fs = Utils.getHadoopFileSystem(logDir, hadoopConf) // Used by check event thread and clean log thread. // Scheduled thread pool size must be one, otherwise it will have concurrent issues about fs @@ -157,7 +160,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) replayBus.addListener(appListener) val appInfo = replay(fs.getFileStatus(new Path(logDir, attempt.logPath)), replayBus) - ui.setAppName(s"${appInfo.name} ($appId)") + appInfo.foreach { app => ui.setAppName(s"${app.name} ($appId)") } val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false) ui.getSecurityManager.setAcls(uiAclsEnabled) @@ -219,6 +222,58 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) } } + override def writeEventLogs( + appId: String, + attemptId: Option[String], + zipStream: ZipOutputStream): Unit = { + + /** + * This method compresses the files passed in, and writes the compressed data out into the + * [[OutputStream]] passed in. Each file is written as a new [[ZipEntry]] with its name being + * the name of the file being compressed. + */ + def zipFileToStream(file: Path, entryName: String, outputStream: ZipOutputStream): Unit = { + val fs = FileSystem.get(hadoopConf) + val inputStream = fs.open(file, 1 * 1024 * 1024) // 1MB Buffer + try { + outputStream.putNextEntry(new ZipEntry(entryName)) + ByteStreams.copy(inputStream, outputStream) + outputStream.closeEntry() + } finally { + inputStream.close() + } + } + + applications.get(appId) match { + case Some(appInfo) => + try { + // If no attempt is specified, or there is no attemptId for attempts, return all attempts + appInfo.attempts.filter { attempt => + attempt.attemptId.isEmpty || attemptId.isEmpty || attempt.attemptId.get == attemptId.get + }.foreach { attempt => + val logPath = new Path(logDir, attempt.logPath) + // If this is a legacy directory, then add the directory to the zipStream and add + // each file to that directory. + if (isLegacyLogDirectory(fs.getFileStatus(logPath))) { + val files = fs.listStatus(logPath) + zipStream.putNextEntry(new ZipEntry(attempt.logPath + "/")) + zipStream.closeEntry() + files.foreach { file => + val path = file.getPath + zipFileToStream(path, attempt.logPath + Path.SEPARATOR + path.getName, zipStream) + } + } else { + zipFileToStream(new Path(logDir, attempt.logPath), attempt.logPath, zipStream) + } + } + } finally { + zipStream.close() + } + case None => throw new SparkException(s"Logs for $appId not found.") + } + } + + /** * Replay the log files in the list and merge the list of old applications with new ones */ @@ -227,8 +282,12 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) val newAttempts = logs.flatMap { fileStatus => try { val res = replay(fileStatus, bus) - logInfo(s"Application log ${res.logPath} loaded successfully.") - Some(res) + res match { + case Some(r) => logDebug(s"Application log ${r.logPath} loaded successfully.") + case None => logWarning(s"Failed to load application log ${fileStatus.getPath}. " + + "The application may have not started.") + } + res } catch { case e: Exception => logError( @@ -374,9 +433,11 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) /** * Replays the events in the specified log file and returns information about the associated - * application. + * application. Return `None` if the application ID cannot be located. */ - private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationAttemptInfo = { + private def replay( + eventLog: FileStatus, + bus: ReplayListenerBus): Option[FsApplicationAttemptInfo] = { val logPath = eventLog.getPath() logInfo(s"Replaying log path: $logPath") val logInput = @@ -390,16 +451,18 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) val appCompleted = isApplicationCompleted(eventLog) bus.addListener(appListener) bus.replay(logInput, logPath.toString, !appCompleted) - new FsApplicationAttemptInfo( - logPath.getName(), - appListener.appName.getOrElse(NOT_STARTED), - appListener.appId.getOrElse(logPath.getName()), - appListener.appAttemptId, - appListener.startTime.getOrElse(-1L), - appListener.endTime.getOrElse(-1L), - getModificationTime(eventLog).get, - appListener.sparkUser.getOrElse(NOT_STARTED), - appCompleted) + appListener.appId.map { appId => + new FsApplicationAttemptInfo( + logPath.getName(), + appListener.appName.getOrElse(NOT_STARTED), + appId, + appListener.appAttemptId, + appListener.startTime.getOrElse(-1L), + appListener.endTime.getOrElse(-1L), + getModificationTime(eventLog).get, + appListener.sparkUser.getOrElse(NOT_STARTED), + appCompleted) + } } finally { logInput.close() } diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala index 517cbe5176241..10638afb74900 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala @@ -18,6 +18,7 @@ package org.apache.spark.deploy.history import java.util.NoSuchElementException +import java.util.zip.ZipOutputStream import javax.servlet.http.{HttpServlet, HttpServletRequest, HttpServletResponse} import com.google.common.cache._ @@ -25,7 +26,8 @@ import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder} import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.deploy.SparkHadoopUtil -import org.apache.spark.status.api.v1.{ApplicationInfo, ApplicationsListResource, JsonRootResource, UIRoot} +import org.apache.spark.status.api.v1.{ApiRootResource, ApplicationInfo, ApplicationsListResource, + UIRoot} import org.apache.spark.ui.{SparkUI, UIUtils, WebUI} import org.apache.spark.ui.JettyUtils._ import org.apache.spark.util.{SignalLogger, Utils} @@ -125,7 +127,7 @@ class HistoryServer( def initialize() { attachPage(new HistoryPage(this)) - attachHandler(JsonRootResource.getJsonServlet(this)) + attachHandler(ApiRootResource.getServletHandler(this)) attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static")) @@ -172,6 +174,13 @@ class HistoryServer( getApplicationList().iterator.map(ApplicationsListResource.appHistoryInfoToPublicAppInfo) } + override def writeEventLogs( + appId: String, + attemptId: Option[String], + zipStream: ZipOutputStream): Unit = { + provider.writeEventLogs(appId, attemptId, zipStream) + } + /** * Returns the provider configuration to show in the listing page. * diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala index a2a97a7877ce7..4692d22651c93 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala @@ -23,7 +23,7 @@ import org.apache.spark.util.Utils /** * Command-line parser for the master. */ -private[history] class HistoryServerArguments(conf: SparkConf, args: Array[String]) +private[history] class HistoryServerArguments(conf: SparkConf, args: Array[String]) extends Logging { private var propertiesFile: String = null diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala index 9c3f79f1244b7..66a9ff38678c6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala @@ -30,6 +30,11 @@ private[spark] class MasterSource(val master: Master) extends Source { override def getValue: Int = master.workers.size }) + // Gauge for alive worker numbers in cluster + metricRegistry.register(MetricRegistry.name("aliveWorkers"), new Gauge[Int]{ + override def getValue: Int = master.workers.filter(_.state == WorkerState.ALIVE).size + }) + // Gauge for application numbers in cluster metricRegistry.register(MetricRegistry.name("apps"), new Gauge[Int] { override def getValue: Int = master.apps.size diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala index 80db6d474b5c1..328d95a7a0c68 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala @@ -32,7 +32,7 @@ import org.apache.spark.deploy.SparkCuratorUtil private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serialization: Serialization) extends PersistenceEngine with Logging { - + private val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/master_status" private val zk: CuratorFramework = SparkCuratorUtil.newClient(conf) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala index 756927682cd24..6a7c74020bace 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala @@ -75,6 +75,7 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { val workerHeaders = Seq("Worker Id", "Address", "State", "Cores", "Memory") val workers = state.workers.sortBy(_.id) + val aliveWorkers = state.workers.filter(_.state == WorkerState.ALIVE) val workerTable = UIUtils.listingTable(workerHeaders, workerRow, workers) val appHeaders = Seq("Application ID", "Name", "Cores", "Memory per Node", "Submitted Time", @@ -108,12 +109,12 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { }.getOrElse { Seq.empty } } -
  • Workers: {state.workers.size}
    • -
  • Cores: {state.workers.map(_.cores).sum} Total, - {state.workers.map(_.coresUsed).sum} Used
    • -
  • Memory: - {Utils.megabytesToString(state.workers.map(_.memory).sum)} Total, - {Utils.megabytesToString(state.workers.map(_.memoryUsed).sum)} Used
    • +
  • Alive Workers: {aliveWorkers.size}
    • +
  • Cores in use: {aliveWorkers.map(_.cores).sum} Total, + {aliveWorkers.map(_.coresUsed).sum} Used
    • +
  • Memory in use: + {Utils.megabytesToString(aliveWorkers.map(_.memory).sum)} Total, + {Utils.megabytesToString(aliveWorkers.map(_.memoryUsed).sum)} Used
  • Applications: {state.activeApps.size} Running, {state.completedApps.size} Completed
    • diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index eb26e9f99c70b..2111a8581f2e4 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -19,7 +19,8 @@ package org.apache.spark.deploy.master.ui import org.apache.spark.Logging import org.apache.spark.deploy.master.Master -import org.apache.spark.status.api.v1.{ApplicationsListResource, ApplicationInfo, JsonRootResource, UIRoot} +import org.apache.spark.status.api.v1.{ApiRootResource, ApplicationsListResource, ApplicationInfo, + UIRoot} import org.apache.spark.ui.{SparkUI, WebUI} import org.apache.spark.ui.JettyUtils._ import org.apache.spark.util.RpcUtils @@ -47,7 +48,7 @@ class MasterWebUI(val master: Master, requestedPort: Int) attachPage(new HistoryNotFoundPage(this)) attachPage(masterPage) attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static")) - attachHandler(JsonRootResource.getJsonServlet(this)) + attachHandler(ApiRootResource.getServletHandler(this)) attachHandler(createRedirectHandler( "/app/kill", "/", masterPage.handleAppKillRequest, httpMethods = Set("POST"))) attachHandler(createRedirectHandler( diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala index be8560d10fc62..e8ef60bd5428a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala @@ -68,7 +68,7 @@ private[ui] class DriverPage(parent: MesosClusterUI) extends WebUIPage("driver") retryHeaders, retryRow, Iterable.apply(driverState.description.retryState)) val content =

    Driver state information for driver id {driverId}

    - Back to Drivers + Back to Drivers

    Driver state: {driverState.state}

    diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala index 6078f50518ba4..1fe956320a1b8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala @@ -57,7 +57,11 @@ private[spark] class RestSubmissionClient(master: String) extends Logging { private val supportedMasterPrefixes = Seq("spark://", "mesos://") - private val masters: Array[String] = Utils.parseStandaloneMasterUrls(master) + private val masters: Array[String] = if (master.startsWith("spark://")) { + Utils.parseStandaloneMasterUrls(master) + } else { + Array(master) + } // Set of masters that lost contact with us, used to keep track of // whether there are masters still alive for us to communicate with diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala index 0a1d60f58bc58..45a3f43045437 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala @@ -24,6 +24,7 @@ import scala.collection.JavaConversions._ import scala.collection.Map import org.apache.spark.Logging +import org.apache.spark.SecurityManager import org.apache.spark.deploy.Command import org.apache.spark.launcher.WorkerCommandBuilder import org.apache.spark.util.Utils @@ -40,12 +41,14 @@ object CommandUtils extends Logging { */ def buildProcessBuilder( command: Command, + securityMgr: SecurityManager, memory: Int, sparkHome: String, substituteArguments: String => String, classPaths: Seq[String] = Seq[String](), env: Map[String, String] = sys.env): ProcessBuilder = { - val localCommand = buildLocalCommand(command, substituteArguments, classPaths, env) + val localCommand = buildLocalCommand( + command, securityMgr, substituteArguments, classPaths, env) val commandSeq = buildCommandSeq(localCommand, memory, sparkHome) val builder = new ProcessBuilder(commandSeq: _*) val environment = builder.environment() @@ -69,6 +72,7 @@ object CommandUtils extends Logging { */ private def buildLocalCommand( command: Command, + securityMgr: SecurityManager, substituteArguments: String => String, classPath: Seq[String] = Seq[String](), env: Map[String, String]): Command = { @@ -76,20 +80,26 @@ object CommandUtils extends Logging { val libraryPathEntries = command.libraryPathEntries val cmdLibraryPath = command.environment.get(libraryPathName) - val newEnvironment = if (libraryPathEntries.nonEmpty && libraryPathName.nonEmpty) { + var newEnvironment = if (libraryPathEntries.nonEmpty && libraryPathName.nonEmpty) { val libraryPaths = libraryPathEntries ++ cmdLibraryPath ++ env.get(libraryPathName) command.environment + ((libraryPathName, libraryPaths.mkString(File.pathSeparator))) } else { command.environment } + // set auth secret to env variable if needed + if (securityMgr.isAuthenticationEnabled) { + newEnvironment += (SecurityManager.ENV_AUTH_SECRET -> securityMgr.getSecretKey) + } + Command( command.mainClass, command.arguments.map(substituteArguments), newEnvironment, command.classPathEntries ++ classPath, Seq[String](), // library path already captured in environment variable - command.javaOpts) + // filter out auth secret from java options + command.javaOpts.filterNot(_.startsWith("-D" + SecurityManager.SPARK_AUTH_SECRET_CONF))) } /** Spawn a thread that will redirect a given stream to a file */ diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala index ef7a703bffe67..1386055eb8c48 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala @@ -85,8 +85,8 @@ private[deploy] class DriverRunner( } // TODO: If we add ability to submit multiple jars they should also be added here - val builder = CommandUtils.buildProcessBuilder(driverDesc.command, driverDesc.mem, - sparkHome.getAbsolutePath, substituteVariables) + val builder = CommandUtils.buildProcessBuilder(driverDesc.command, securityManager, + driverDesc.mem, sparkHome.getAbsolutePath, substituteVariables) launchDriver(builder, driverDir, driverDesc.supervise) } catch { diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala index 7aa85b732fc87..fff17e1095042 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala @@ -25,7 +25,7 @@ import akka.actor.ActorRef import com.google.common.base.Charsets.UTF_8 import com.google.common.io.Files -import org.apache.spark.{SparkConf, Logging} +import org.apache.spark.{SecurityManager, SparkConf, Logging} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged import org.apache.spark.util.Utils @@ -125,8 +125,8 @@ private[deploy] class ExecutorRunner( private def fetchAndRunExecutor() { try { // Launch the process - val builder = CommandUtils.buildProcessBuilder(appDesc.command, memory, - sparkHome.getAbsolutePath, substituteVariables) + val builder = CommandUtils.buildProcessBuilder(appDesc.command, new SecurityManager(conf), + memory, sparkHome.getAbsolutePath, substituteVariables) val command = builder.command() logInfo("Launch command: " + command.mkString("\"", "\" \"", "\"")) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index 8f3cc54051048..ebc6cd76c6afd 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -324,9 +324,6 @@ private[worker] class Worker( map(e => new ExecutorDescription(e.appId, e.execId, e.cores, e.state)) sender ! WorkerSchedulerStateResponse(workerId, execs.toList, drivers.keys.toSeq) - case Heartbeat => - logInfo(s"Received heartbeat from driver ${sender.path}") - case RegisterWorkerFailed(message) => if (!registered) { logError("Worker registration failed: " + message) @@ -557,7 +554,7 @@ private[deploy] object Worker extends Logging { conf = conf, securityManager = securityMgr) val masterAkkaUrls = masterUrls.map(Master.toAkkaUrl(_, AkkaUtils.protocol(actorSystem))) actorSystem.actorOf(Props(classOf[Worker], host, boundPort, webUiPort, cores, memory, - masterAkkaUrls, systemName, actorName, workDir, conf, securityMgr), name = actorName) + masterAkkaUrls, systemName, actorName, workDir, conf, securityMgr), name = actorName) (actorSystem, boundPort) } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala index 88170d4df3053..5a1d06eb87db9 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala @@ -17,6 +17,8 @@ package org.apache.spark.deploy.worker.ui +import java.io.File +import java.net.URI import javax.servlet.http.HttpServletRequest import scala.xml.Node @@ -29,6 +31,7 @@ import org.apache.spark.util.logging.RollingFileAppender private[ui] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") with Logging { private val worker = parent.worker private val workDir = parent.workDir + private val supportedLogTypes = Set("stderr", "stdout") def renderLog(request: HttpServletRequest): String = { val defaultBytes = 100 * 1024 @@ -129,6 +132,18 @@ private[ui] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") with offsetOption: Option[Long], byteLength: Int ): (String, Long, Long, Long) = { + + if (!supportedLogTypes.contains(logType)) { + return ("Error: Log type must be one of " + supportedLogTypes.mkString(", "), 0, 0, 0) + } + + // Verify that the normalized path of the log directory is in the working directory + val normalizedUri = new URI(logDirectory).normalize() + val normalizedLogDir = new File(normalizedUri.getPath) + if (!Utils.isInDirectory(workDir, normalizedLogDir)) { + return ("Error: invalid log directory " + logDirectory, 0, 0, 0) + } + try { val files = RollingFileAppender.getSortedRolledOverFiles(logDirectory, logType) logDebug(s"Sorted log files of type $logType in $logDirectory:\n${files.mkString("\n")}") @@ -144,7 +159,7 @@ private[ui] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") with offset } } - val endIndex = math.min(startIndex + totalLength, totalLength) + val endIndex = math.min(startIndex + byteLength, totalLength) logDebug(s"Getting log from $startIndex to $endIndex") val logText = Utils.offsetBytes(files, startIndex, endIndex) logDebug(s"Got log of length ${logText.length} bytes") diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index ed159dec4f998..f3a26f54a81fb 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -33,7 +33,7 @@ import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.TaskDescription import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.serializer.SerializerInstance -import org.apache.spark.util.{SignalLogger, Utils} +import org.apache.spark.util.{ThreadUtils, SignalLogger, Utils} private[spark] class CoarseGrainedExecutorBackend( override val rpcEnv: RpcEnv, @@ -55,18 +55,19 @@ private[spark] class CoarseGrainedExecutorBackend( private[this] val ser: SerializerInstance = env.closureSerializer.newInstance() override def onStart() { - import scala.concurrent.ExecutionContext.Implicits.global logInfo("Connecting to driver: " + driverUrl) rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref => + // This is a very fast action so we can use "ThreadUtils.sameThread" driver = Some(ref) ref.ask[RegisteredExecutor.type]( RegisterExecutor(executorId, self, hostPort, cores, extractLogUrls)) - } onComplete { + }(ThreadUtils.sameThread).onComplete { + // This is a very fast action so we can use "ThreadUtils.sameThread" case Success(msg) => Utils.tryLogNonFatalError { Option(self).foreach(_.send(msg)) // msg must be RegisteredExecutor } case Failure(e) => logError(s"Cannot register with driver: $driverUrl", e) - } + }(ThreadUtils.sameThread) } def extractLogUrls: Map[String, String] = { diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala index 06152f16ae618..a3b4561b07e7f 100644 --- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala +++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala @@ -43,22 +43,22 @@ class TaskMetrics extends Serializable { private var _hostname: String = _ def hostname: String = _hostname private[spark] def setHostname(value: String) = _hostname = value - + /** * Time taken on the executor to deserialize this task */ private var _executorDeserializeTime: Long = _ def executorDeserializeTime: Long = _executorDeserializeTime private[spark] def setExecutorDeserializeTime(value: Long) = _executorDeserializeTime = value - - + + /** * Time the executor spends actually running the task (including fetching shuffle data) */ private var _executorRunTime: Long = _ def executorRunTime: Long = _executorRunTime private[spark] def setExecutorRunTime(value: Long) = _executorRunTime = value - + /** * The number of bytes this task transmitted back to the driver as the TaskResult */ @@ -94,8 +94,8 @@ class TaskMetrics extends Serializable { */ private var _diskBytesSpilled: Long = _ def diskBytesSpilled: Long = _diskBytesSpilled - def incDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled += value - def decDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled -= value + private[spark] def incDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled += value + private[spark] def decDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled -= value /** * If this task reads from a HadoopRDD or from persisted data, metrics on how much data was read @@ -261,7 +261,7 @@ case class InputMetrics(readMethod: DataReadMethod.Value) { */ private var _recordsRead: Long = _ def recordsRead: Long = _recordsRead - def incRecordsRead(records: Long): Unit = _recordsRead += records + def incRecordsRead(records: Long): Unit = _recordsRead += records /** * Invoke the bytesReadCallback and mutate bytesRead. @@ -315,7 +315,7 @@ class ShuffleReadMetrics extends Serializable { def remoteBlocksFetched: Int = _remoteBlocksFetched private[spark] def incRemoteBlocksFetched(value: Int) = _remoteBlocksFetched += value private[spark] def decRemoteBlocksFetched(value: Int) = _remoteBlocksFetched -= value - + /** * Number of local blocks fetched in this shuffle by this task */ @@ -333,7 +333,7 @@ class ShuffleReadMetrics extends Serializable { def fetchWaitTime: Long = _fetchWaitTime private[spark] def incFetchWaitTime(value: Long) = _fetchWaitTime += value private[spark] def decFetchWaitTime(value: Long) = _fetchWaitTime -= value - + /** * Total number of remote bytes read from the shuffle by this task */ @@ -381,7 +381,7 @@ class ShuffleWriteMetrics extends Serializable { def shuffleBytesWritten: Long = _shuffleBytesWritten private[spark] def incShuffleBytesWritten(value: Long) = _shuffleBytesWritten += value private[spark] def decShuffleBytesWritten(value: Long) = _shuffleBytesWritten -= value - + /** * Time the task spent blocking on writes to disk or buffer cache, in nanoseconds */ @@ -389,7 +389,7 @@ class ShuffleWriteMetrics extends Serializable { def shuffleWriteTime: Long = _shuffleWriteTime private[spark] def incShuffleWriteTime(value: Long) = _shuffleWriteTime += value private[spark] def decShuffleWriteTime(value: Long) = _shuffleWriteTime -= value - + /** * Total number of records written to the shuffle by this task */ diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala index 0756cdb2ed8e6..0d8ac1f80a9f4 100644 --- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala +++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala @@ -17,7 +17,7 @@ package org.apache.spark.io -import java.io.{InputStream, OutputStream} +import java.io.{IOException, InputStream, OutputStream} import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} import net.jpountz.lz4.{LZ4BlockInputStream, LZ4BlockOutputStream} @@ -154,8 +154,53 @@ class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { val blockSize = conf.getSizeAsBytes("spark.io.compression.snappy.blockSize", "32k").toInt - new SnappyOutputStream(s, blockSize) + new SnappyOutputStreamWrapper(new SnappyOutputStream(s, blockSize)) } override def compressedInputStream(s: InputStream): InputStream = new SnappyInputStream(s) } + +/** + * Wrapper over [[SnappyOutputStream]] which guards against write-after-close and double-close + * issues. See SPARK-7660 for more details. This wrapping can be removed if we upgrade to a version + * of snappy-java that contains the fix for https://github.com/xerial/snappy-java/issues/107. + */ +private final class SnappyOutputStreamWrapper(os: SnappyOutputStream) extends OutputStream { + + private[this] var closed: Boolean = false + + override def write(b: Int): Unit = { + if (closed) { + throw new IOException("Stream is closed") + } + os.write(b) + } + + override def write(b: Array[Byte]): Unit = { + if (closed) { + throw new IOException("Stream is closed") + } + os.write(b) + } + + override def write(b: Array[Byte], off: Int, len: Int): Unit = { + if (closed) { + throw new IOException("Stream is closed") + } + os.write(b, off, len) + } + + override def flush(): Unit = { + if (closed) { + throw new IOException("Stream is closed") + } + os.flush() + } + + override def close(): Unit = { + if (!closed) { + closed = true + os.close() + } + } +} diff --git a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala index cfd20392d12f1..390d148bc97f9 100644 --- a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala +++ b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala @@ -60,7 +60,7 @@ trait SparkHadoopMapReduceUtil { val taskTypeClass = Class.forName("org.apache.hadoop.mapreduce.TaskType") .asInstanceOf[Class[Enum[_]]] val taskType = taskTypeClass.getMethod("valueOf", classOf[String]).invoke( - taskTypeClass, if(isMap) "MAP" else "REDUCE") + taskTypeClass, if (isMap) "MAP" else "REDUCE") val ctor = klass.getDeclaredConstructor(classOf[String], classOf[Int], taskTypeClass, classOf[Int], classOf[Int]) ctor.newInstance(jtIdentifier, new JInteger(jobId), taskType, new JInteger(taskId), diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala index 8edf493780687..d7495551ad233 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala @@ -23,10 +23,10 @@ import java.util.Properties import scala.collection.mutable import scala.util.matching.Regex -import org.apache.spark.Logging import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SparkConf} -private[spark] class MetricsConfig(val configFile: Option[String]) extends Logging { +private[spark] class MetricsConfig(conf: SparkConf) extends Logging { private val DEFAULT_PREFIX = "*" private val INSTANCE_REGEX = "^(\\*|[a-zA-Z]+)\\.(.+)".r @@ -46,23 +46,14 @@ private[spark] class MetricsConfig(val configFile: Option[String]) extends Loggi // Add default properties in case there's no properties file setDefaultProperties(properties) - // If spark.metrics.conf is not set, try to get file in class path - val isOpt: Option[InputStream] = configFile.map(new FileInputStream(_)).orElse { - try { - Option(Utils.getSparkClassLoader.getResourceAsStream(DEFAULT_METRICS_CONF_FILENAME)) - } catch { - case e: Exception => - logError("Error loading default configuration file", e) - None - } - } + loadPropertiesFromFile(conf.getOption("spark.metrics.conf")) - isOpt.foreach { is => - try { - properties.load(is) - } finally { - is.close() - } + // Also look for the properties in provided Spark configuration + val prefix = "spark.metrics.conf." + conf.getAll.foreach { + case (k, v) if k.startsWith(prefix) => + properties.setProperty(k.substring(prefix.length()), v) + case _ => } propertyCategories = subProperties(properties, INSTANCE_REGEX) @@ -97,5 +88,31 @@ private[spark] class MetricsConfig(val configFile: Option[String]) extends Loggi case None => propertyCategories.getOrElse(DEFAULT_PREFIX, new Properties) } } -} + /** + * Loads configuration from a config file. If no config file is provided, try to get file + * in class path. + */ + private[this] def loadPropertiesFromFile(path: Option[String]): Unit = { + var is: InputStream = null + try { + is = path match { + case Some(f) => new FileInputStream(f) + case None => Utils.getSparkClassLoader.getResourceAsStream(DEFAULT_METRICS_CONF_FILENAME) + } + + if (is != null) { + properties.load(is) + } + } catch { + case e: Exception => + val file = path.getOrElse(DEFAULT_METRICS_CONF_FILENAME) + logError(s"Error loading configuration file $file", e) + } finally { + if (is != null) { + is.close() + } + } + } + +} diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala index 9150ad35712a1..ed5131c79fdc5 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala @@ -70,8 +70,7 @@ private[spark] class MetricsSystem private ( securityMgr: SecurityManager) extends Logging { - private[this] val confFile = conf.get("spark.metrics.conf", null) - private[this] val metricsConfig = new MetricsConfig(Option(confFile)) + private[this] val metricsConfig = new MetricsConfig(conf) private val sinks = new mutable.ArrayBuffer[Sink] private val sources = new mutable.ArrayBuffer[Source] diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/Slf4jSink.scala b/core/src/main/scala/org/apache/spark/metrics/sink/Slf4jSink.scala index e8b3074e8f1a6..11dfcfe2f04e1 100644 --- a/core/src/main/scala/org/apache/spark/metrics/sink/Slf4jSink.scala +++ b/core/src/main/scala/org/apache/spark/metrics/sink/Slf4jSink.scala @@ -26,9 +26,9 @@ import org.apache.spark.SecurityManager import org.apache.spark.metrics.MetricsSystem private[spark] class Slf4jSink( - val property: Properties, + val property: Properties, val registry: MetricRegistry, - securityMgr: SecurityManager) + securityMgr: SecurityManager) extends Sink { val SLF4J_DEFAULT_PERIOD = 10 val SLF4J_DEFAULT_UNIT = "SECONDS" diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/package.scala b/core/src/main/scala/org/apache/spark/metrics/sink/package.scala index 90e3aa70b99ef..670e683663324 100644 --- a/core/src/main/scala/org/apache/spark/metrics/sink/package.scala +++ b/core/src/main/scala/org/apache/spark/metrics/sink/package.scala @@ -20,4 +20,4 @@ package org.apache.spark.metrics /** * Sinks used in Spark's metrics system. */ -package object sink +package object sink diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala index 6181c0ee9fa2b..d650d5fe73087 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala @@ -59,12 +59,22 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage } transportContext = new TransportContext(transportConf, rpcHandler) clientFactory = transportContext.createClientFactory(clientBootstrap.toList) - server = transportContext.createServer(conf.getInt("spark.blockManager.port", 0), - serverBootstrap.toList) + server = createServer(serverBootstrap.toList) appId = conf.getAppId logInfo("Server created on " + server.getPort) } + /** Creates and binds the TransportServer, possibly trying multiple ports. */ + private def createServer(bootstraps: List[TransportServerBootstrap]): TransportServer = { + def startService(port: Int): (TransportServer, Int) = { + val server = transportContext.createServer(port, bootstraps) + (server, server.getPort) + } + + val portToTry = conf.getInt("spark.blockManager.port", 0) + Utils.startServiceOnPort(portToTry, startService, conf, getClass.getName)._1 + } + override def fetchBlocks( host: String, port: Int, diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala index b573f1a8a5fcb..67a376102994c 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala @@ -110,7 +110,7 @@ private[nio] class BlockMessage() { def getType: Int = typ def getId: BlockId = id def getData: ByteBuffer = data - def getLevel: StorageLevel = level + def getLevel: StorageLevel = level def toBufferMessage: BufferMessage = { val buffers = new ArrayBuffer[ByteBuffer]() @@ -155,7 +155,7 @@ private[nio] class BlockMessage() { override def toString: String = { "BlockMessage [type = " + typ + ", id = " + id + ", level = " + level + - ", data = " + (if (data != null) data.remaining.toString else "null") + "]" + ", data = " + (if (data != null) data.remaining.toString else "null") + "]" } } diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala index 1ba25aa74aa02..7d0806f0c2580 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala @@ -114,8 +114,8 @@ private[nio] object BlockMessageArray { val blockMessages = (0 until 10).map { i => if (i % 2 == 0) { - val buffer = ByteBuffer.allocate(100) - buffer.clear + val buffer = ByteBuffer.allocate(100) + buffer.clear() BlockMessage.fromPutBlock(PutBlock(TestBlockId(i.toString), buffer, StorageLevel.MEMORY_ONLY_SER)) } else { diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala index 6b898bd4bfc1b..1499da07bb83b 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala @@ -326,15 +326,14 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, // MUST be called within the selector loop def connect() { - try{ + try { channel.register(selector, SelectionKey.OP_CONNECT) channel.connect(address) logInfo("Initiating connection to [" + address + "]") } catch { - case e: Exception => { + case e: Exception => logError("Error connecting to " + address, e) callOnExceptionCallbacks(e) - } } } diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index 497871ed6d5e5..c0bca2c4bc994 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -635,12 +635,11 @@ private[nio] class ConnectionManager( val message = securityMsgResp.toBufferMessage if (message == null) throw new IOException("Error creating security message") sendSecurityMessage(waitingConn.getRemoteConnectionManagerId(), message) - } catch { - case e: Exception => { + } catch { + case e: Exception => logError("Error handling sasl client authentication", e) waitingConn.close() throw new IOException("Error evaluating sasl response: ", e) - } } } } diff --git a/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala index 747a2088a7258..232c552f9865d 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala @@ -75,7 +75,7 @@ private[nio] class SecurityMessage extends Logging { for (i <- 1 to idLength) { idBuilder += buffer.getChar() } - connectionId = idBuilder.toString() + connectionId = idBuilder.toString() val tokenLength = buffer.getInt() token = new Array[Byte](tokenLength) diff --git a/core/src/main/scala/org/apache/spark/package.scala b/core/src/main/scala/org/apache/spark/package.scala index 2ab41ba488ff6..8ae76c5f72f2e 100644 --- a/core/src/main/scala/org/apache/spark/package.scala +++ b/core/src/main/scala/org/apache/spark/package.scala @@ -43,5 +43,5 @@ package org.apache package object spark { // For package docs only - val SPARK_VERSION = "1.4.0-SNAPSHOT" + val SPARK_VERSION = "1.5.0-SNAPSHOT" } diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala index 3ef3cc219dec6..91b07ce3af1b6 100644 --- a/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala +++ b/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala @@ -32,12 +32,12 @@ import org.apache.spark.util.collection.OpenHashMap * An ApproximateEvaluator for counts by key. Returns a map of key to confidence interval. */ private[spark] class GroupedCountEvaluator[T : ClassTag](totalOutputs: Int, confidence: Double) - extends ApproximateEvaluator[OpenHashMap[T,Long], Map[T, BoundedDouble]] { + extends ApproximateEvaluator[OpenHashMap[T, Long], Map[T, BoundedDouble]] { var outputsMerged = 0 - var sums = new OpenHashMap[T,Long]() // Sum of counts for each key + var sums = new OpenHashMap[T, Long]() // Sum of counts for each key - override def merge(outputId: Int, taskResult: OpenHashMap[T,Long]) { + override def merge(outputId: Int, taskResult: OpenHashMap[T, Long]) { outputsMerged += 1 taskResult.foreach { case (key, value) => sums.changeValue(key, value, _ + value) diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala index ec185340c3a2d..ca1eb1f4e4a9a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -19,8 +19,10 @@ package org.apache.spark.rdd import java.util.concurrent.atomic.AtomicLong +import org.apache.spark.util.ThreadUtils + import scala.collection.mutable.ArrayBuffer -import scala.concurrent.ExecutionContext.Implicits.global +import scala.concurrent.ExecutionContext import scala.reflect.ClassTag import org.apache.spark.{ComplexFutureAction, FutureAction, Logging} @@ -66,6 +68,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi val f = new ComplexFutureAction[Seq[T]] f.run { + // This is a blocking action so we should use "AsyncRDDActions.futureExecutionContext" which + // is a cached thread pool. val results = new ArrayBuffer[T](num) val totalParts = self.partitions.length var partsScanned = 0 @@ -81,9 +85,9 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi numPartsToTry = partsScanned * 4 } else { // the left side of max is >=1 whenever partsScanned >= 2 - numPartsToTry = Math.max(1, + numPartsToTry = Math.max(1, (1.5 * num * partsScanned / results.size).toInt - partsScanned) - numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) } } @@ -101,7 +105,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi partsScanned += numPartsToTry } results.toSeq - } + }(AsyncRDDActions.futureExecutionContext) f } @@ -123,3 +127,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi (index, data) => Unit, Unit) } } + +private object AsyncRDDActions { + val futureExecutionContext = ExecutionContext.fromExecutorService( + ThreadUtils.newDaemonCachedThreadPool("AsyncRDDActions-future", 128)) +} diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala index 0d130dd4c7a60..33e6998b2cb10 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -21,13 +21,12 @@ import java.io.IOException import scala.reflect.ClassTag -import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.spark._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.SparkHadoopUtil -import org.apache.spark.util.Utils +import org.apache.spark.util.{SerializableConfiguration, Utils} private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {} @@ -38,7 +37,7 @@ private[spark] class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String) extends RDD[T](sc, Nil) { - val broadcastedConf = sc.broadcast(new SerializableWritable(sc.hadoopConfiguration)) + val broadcastedConf = sc.broadcast(new SerializableConfiguration(sc.hadoopConfiguration)) @transient val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration) @@ -49,7 +48,7 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String) if (fs.exists(cpath)) { val dirContents = fs.listStatus(cpath).map(_.getPath) val partitionFiles = dirContents.filter(_.getName.startsWith("part-")).map(_.toString).sorted - val numPart = partitionFiles.length + val numPart = partitionFiles.length if (numPart > 0 && (! partitionFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) || ! partitionFiles(numPart-1).endsWith(CheckpointRDD.splitIdToFile(numPart-1)))) { throw new SparkException("Invalid checkpoint directory: " + checkpointPath) @@ -87,7 +86,7 @@ private[spark] object CheckpointRDD extends Logging { def writeToFile[T: ClassTag]( path: String, - broadcastedConf: Broadcast[SerializableWritable[Configuration]], + broadcastedConf: Broadcast[SerializableConfiguration], blockSize: Int = -1 )(ctx: TaskContext, iterator: Iterator[T]) { val env = SparkEnv.get @@ -135,7 +134,7 @@ private[spark] object CheckpointRDD extends Logging { def readFromFile[T]( path: Path, - broadcastedConf: Broadcast[SerializableWritable[Configuration]], + broadcastedConf: Broadcast[SerializableConfiguration], context: TaskContext ): Iterator[T] = { val env = SparkEnv.get @@ -164,7 +163,7 @@ private[spark] object CheckpointRDD extends Logging { val path = new Path(hdfsPath, "temp") val conf = SparkHadoopUtil.get.newConfiguration(new SparkConf()) val fs = path.getFileSystem(conf) - val broadcastedConf = sc.broadcast(new SerializableWritable(conf)) + val broadcastedConf = sc.broadcast(new SerializableConfiguration(conf)) sc.runJob(rdd, CheckpointRDD.writeToFile[Int](path.toString, broadcastedConf, 1024) _) val cpRDD = new CheckpointRDD[Int](sc, path.toString) assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same") diff --git a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala index 0c1b02c07d09f..663eebb8e4191 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala @@ -310,11 +310,11 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: def throwBalls() { if (noLocality) { // no preferredLocations in parent RDD, no randomization needed if (maxPartitions > groupArr.size) { // just return prev.partitions - for ((p,i) <- prev.partitions.zipWithIndex) { + for ((p, i) <- prev.partitions.zipWithIndex) { groupArr(i).arr += p } } else { // no locality available, then simply split partitions based on positions in array - for(i <- 0 until maxPartitions) { + for (i <- 0 until maxPartitions) { val rangeStart = ((i.toLong * prev.partitions.length) / maxPartitions).toInt val rangeEnd = (((i.toLong + 1) * prev.partitions.length) / maxPartitions).toInt (rangeStart until rangeEnd).foreach{ j => groupArr(i).arr += prev.partitions(j) } diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala index 2cefe63d44b20..bee59a437f120 100644 --- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala @@ -44,7 +44,7 @@ import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.executor.DataReadMethod import org.apache.spark.rdd.HadoopRDD.HadoopMapPartitionsWithSplitRDD -import org.apache.spark.util.{NextIterator, Utils} +import org.apache.spark.util.{SerializableConfiguration, NextIterator, Utils} import org.apache.spark.scheduler.{HostTaskLocation, HDFSCacheTaskLocation} import org.apache.spark.storage.StorageLevel @@ -100,7 +100,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp @DeveloperApi class HadoopRDD[K, V]( @transient sc: SparkContext, - broadcastedConf: Broadcast[SerializableWritable[Configuration]], + broadcastedConf: Broadcast[SerializableConfiguration], initLocalJobConfFuncOpt: Option[JobConf => Unit], inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], @@ -121,8 +121,8 @@ class HadoopRDD[K, V]( minPartitions: Int) = { this( sc, - sc.broadcast(new SerializableWritable(conf)) - .asInstanceOf[Broadcast[SerializableWritable[Configuration]]], + sc.broadcast(new SerializableConfiguration(conf)) + .asInstanceOf[Broadcast[SerializableConfiguration]], None /* initLocalJobConfFuncOpt */, inputFormatClass, keyClass, diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala index 2ab967f4bb313..f827270ee6a44 100644 --- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala @@ -33,7 +33,7 @@ import org.apache.spark._ import org.apache.spark.executor.DataReadMethod import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD -import org.apache.spark.util.Utils +import org.apache.spark.util.{SerializableConfiguration, Utils} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.storage.StorageLevel @@ -74,7 +74,7 @@ class NewHadoopRDD[K, V]( with Logging { // A Hadoop Configuration can be about 10 KB, which is pretty big, so broadcast it - private val confBroadcast = sc.broadcast(new SerializableWritable(conf)) + private val confBroadcast = sc.broadcast(new SerializableConfiguration(conf)) // private val serializableConf = new SerializableWritable(conf) private val jobTrackerId: String = { @@ -196,7 +196,7 @@ class NewHadoopRDD[K, V]( override def getPreferredLocations(hsplit: Partition): Seq[String] = { val split = hsplit.asInstanceOf[NewHadoopPartition].serializableHadoopSplit.value val locs = HadoopRDD.SPLIT_INFO_REFLECTIONS match { - case Some(c) => + case Some(c) => try { val infos = c.newGetLocationInfo.invoke(split).asInstanceOf[Array[AnyRef]] Some(HadoopRDD.convertSplitLocationInfo(infos)) diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index a6d5d2c94e17f..91a6a2d039852 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -44,7 +44,7 @@ import org.apache.spark.executor.{DataWriteMethod, OutputMetrics} import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.partial.{BoundedDouble, PartialResult} import org.apache.spark.serializer.Serializer -import org.apache.spark.util.Utils +import org.apache.spark.util.{SerializableConfiguration, Utils} import org.apache.spark.util.collection.CompactBuffer import org.apache.spark.util.random.StratifiedSamplingUtils @@ -296,6 +296,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * before sending results to a reducer, similarly to a "combiner" in MapReduce. */ def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = self.withScope { + val cleanedF = self.sparkContext.clean(func) if (keyClass.isArray) { throw new SparkException("reduceByKeyLocally() does not support array keys") @@ -305,7 +306,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val map = new JHashMap[K, V] iter.foreach { pair => val old = map.get(pair._1) - map.put(pair._1, if (old == null) pair._2 else func(old, pair._2)) + map.put(pair._1, if (old == null) pair._2 else cleanedF(old, pair._2)) } Iterator(map) } : Iterator[JHashMap[K, V]] @@ -313,7 +314,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val mergeMaps = (m1: JHashMap[K, V], m2: JHashMap[K, V]) => { m2.foreach { pair => val old = m1.get(pair._1) - m1.put(pair._1, if (old == null) pair._2 else func(old, pair._2)) + m1.put(pair._1, if (old == null) pair._2 else cleanedF(old, pair._2)) } m1 } : JHashMap[K, V] @@ -327,7 +328,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) reduceByKeyLocally(func) } - /** + /** * Count the number of elements for each key, collecting the results to a local Map. * * Note that this method should only be used if the resulting map is expected to be small, as @@ -466,7 +467,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2 val bufs = combineByKey[CompactBuffer[V]]( - createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine=false) + createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false) bufs.asInstanceOf[RDD[(K, Iterable[V])]] } @@ -1001,7 +1002,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val formatter = new SimpleDateFormat("yyyyMMddHHmm") val jobtrackerID = formatter.format(new Date()) val stageId = self.id - val wrappedConf = new SerializableWritable(job.getConfiguration) + val wrappedConf = new SerializableConfiguration(job.getConfiguration) val outfmt = job.getOutputFormatClass val jobFormat = outfmt.newInstance @@ -1010,7 +1011,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) jobFormat.checkOutputSpecs(job) } - val writeShard = (context: TaskContext, iter: Iterator[(K,V)]) => { + val writeShard = (context: TaskContext, iter: Iterator[(K, V)]) => { val config = wrappedConf.value /* "reduce task" */ val attemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = false, context.partitionId, @@ -1026,7 +1027,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val (outputMetrics, bytesWrittenCallback) = initHadoopOutputMetrics(context) - val writer = format.getRecordWriter(hadoopContext).asInstanceOf[NewRecordWriter[K,V]] + val writer = format.getRecordWriter(hadoopContext).asInstanceOf[NewRecordWriter[K, V]] require(writer != null, "Unable to obtain RecordWriter") var recordsWritten = 0L Utils.tryWithSafeFinally { @@ -1064,7 +1065,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf - val wrappedConf = new SerializableWritable(hadoopConf) + val wrappedConf = new SerializableConfiguration(hadoopConf) val outputFormatInstance = hadoopConf.getOutputFormat val keyClass = hadoopConf.getOutputKeyClass val valueClass = hadoopConf.getOutputValueClass diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala index 7598ff617b399..9e3880714a79f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala @@ -86,7 +86,7 @@ class PartitionerAwareUnionRDD[T: ClassTag]( } val location = if (locations.isEmpty) { None - } else { + } else { // Find the location that maximum number of parent partitions prefer Some(locations.groupBy(x => x).maxBy(_._2.length)._1) } diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index b3b60578c92e8..10610f4b6f1ff 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -434,11 +434,11 @@ abstract class RDD[T: ClassTag]( * @return A random sub-sample of the RDD without replacement. */ private[spark] def randomSampleWithRange(lb: Double, ub: Double, seed: Long): RDD[T] = { - this.mapPartitionsWithIndex { case (index, partition) => + this.mapPartitionsWithIndex( { (index, partition) => val sampler = new BernoulliCellSampler[T](lb, ub) sampler.setSeed(seed + index) sampler.sample(partition) - } + }, preservesPartitioning = true) } /** @@ -454,7 +454,7 @@ abstract class RDD[T: ClassTag]( withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] = { - val numStDev = 10.0 + val numStDev = 10.0 if (num < 0) { throw new IllegalArgumentException("Negative number of elements requested") @@ -717,7 +717,8 @@ abstract class RDD[T: ClassTag]( def mapPartitionsWithContext[U: ClassTag]( f: (TaskContext, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = withScope { - val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(context, iter) + val cleanF = sc.clean(f) + val func = (context: TaskContext, index: Int, iter: Iterator[T]) => cleanF(context, iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -741,9 +742,11 @@ abstract class RDD[T: ClassTag]( def mapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) (f: (T, A) => U): RDD[U] = withScope { + val cleanF = sc.clean(f) + val cleanA = sc.clean(constructA) mapPartitionsWithIndex((index, iter) => { - val a = constructA(index) - iter.map(t => f(t, a)) + val a = cleanA(index) + iter.map(t => cleanF(t, a)) }, preservesPartitioning) } @@ -756,9 +759,11 @@ abstract class RDD[T: ClassTag]( def flatMapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) (f: (T, A) => Seq[U]): RDD[U] = withScope { + val cleanF = sc.clean(f) + val cleanA = sc.clean(constructA) mapPartitionsWithIndex((index, iter) => { - val a = constructA(index) - iter.flatMap(t => f(t, a)) + val a = cleanA(index) + iter.flatMap(t => cleanF(t, a)) }, preservesPartitioning) } @@ -769,9 +774,11 @@ abstract class RDD[T: ClassTag]( */ @deprecated("use mapPartitionsWithIndex and foreach", "1.0.0") def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit): Unit = withScope { + val cleanF = sc.clean(f) + val cleanA = sc.clean(constructA) mapPartitionsWithIndex { (index, iter) => - val a = constructA(index) - iter.map(t => {f(t, a); t}) + val a = cleanA(index) + iter.map(t => {cleanF(t, a); t}) } } @@ -782,9 +789,11 @@ abstract class RDD[T: ClassTag]( */ @deprecated("use mapPartitionsWithIndex and filter", "1.0.0") def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = withScope { + val cleanP = sc.clean(p) + val cleanA = sc.clean(constructA) mapPartitionsWithIndex((index, iter) => { - val a = constructA(index) - iter.filter(t => p(t, a)) + val a = cleanA(index) + iter.filter(t => cleanP(t, a)) }, preservesPartitioning = true) } @@ -901,7 +910,8 @@ abstract class RDD[T: ClassTag]( * Return an RDD that contains all matching values by applying `f`. */ def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = withScope { - filter(f.isDefinedAt).map(f) + val cleanF = sc.clean(f) + filter(cleanF.isDefinedAt).map(cleanF) } /** @@ -1005,9 +1015,16 @@ abstract class RDD[T: ClassTag]( /** * Aggregate the elements of each partition, and then the results for all the partitions, using a - * given associative function and a neutral "zero value". The function op(t1, t2) is allowed to - * modify t1 and return it as its result value to avoid object allocation; however, it should not - * modify t2. + * given associative and commutative function and a neutral "zero value". The function + * op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object + * allocation; however, it should not modify t2. + * + * This behaves somewhat differently from fold operations implemented for non-distributed + * collections in functional languages like Scala. This fold operation may be applied to + * partitions individually, and then fold those results into the final result, rather than + * apply the fold to each element sequentially in some defined ordering. For functions + * that are not commutative, the result may differ from that of a fold applied to a + * non-distributed collection. */ def fold(zeroValue: T)(op: (T, T) => T): T = withScope { // Clone the zero value since we will also be serializing it as part of tasks @@ -1121,8 +1138,8 @@ abstract class RDD[T: ClassTag]( if (elementClassTag.runtimeClass.isArray) { throw new SparkException("countByValueApprox() does not support arrays") } - val countPartition: (TaskContext, Iterator[T]) => OpenHashMap[T,Long] = { (ctx, iter) => - val map = new OpenHashMap[T,Long] + val countPartition: (TaskContext, Iterator[T]) => OpenHashMap[T, Long] = { (ctx, iter) => + val map = new OpenHashMap[T, Long] iter.foreach { t => map.changeValue(t, 1L, _ + 1L) } @@ -1151,8 +1168,8 @@ abstract class RDD[T: ClassTag]( */ @Experimental def countApproxDistinct(p: Int, sp: Int): Long = withScope { - require(p >= 4, s"p ($p) must be at least 4") - require(sp <= 32, s"sp ($sp) cannot be greater than 32") + require(p >= 4, s"p ($p) must be >= 4") + require(sp <= 32, s"sp ($sp) must be <= 32") require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)") val zeroCounter = new HyperLogLogPlus(p, sp) aggregate(zeroCounter)( @@ -1177,8 +1194,9 @@ abstract class RDD[T: ClassTag]( * It must be greater than 0.000017. */ def countApproxDistinct(relativeSD: Double = 0.05): Long = withScope { + require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017") val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt - countApproxDistinct(p, 0) + countApproxDistinct(if (p < 4) 4 else p, 0) } /** @@ -1512,13 +1530,15 @@ abstract class RDD[T: ClassTag]( * has completed (therefore the RDD has been materialized and potentially stored in memory). * doCheckpoint() is called recursively on the parent RDDs. */ - private[spark] def doCheckpoint() { - if (!doCheckpointCalled) { - doCheckpointCalled = true - if (checkpointData.isDefined) { - checkpointData.get.doCheckpoint() - } else { - dependencies.foreach(_.rdd.doCheckpoint()) + private[spark] def doCheckpoint(): Unit = { + RDDOperationScope.withScope(sc, "checkpoint", allowNesting = false, ignoreParent = true) { + if (!doCheckpointCalled) { + doCheckpointCalled = true + if (checkpointData.isDefined) { + checkpointData.get.doCheckpoint() + } else { + dependencies.foreach(_.rdd.doCheckpoint()) + } } } } @@ -1565,15 +1585,15 @@ abstract class RDD[T: ClassTag]( case 0 => Seq.empty case 1 => val d = rdd.dependencies.head - debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_,_,_]], true) + debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_, _, _]], true) case _ => val frontDeps = rdd.dependencies.take(len - 1) val frontDepStrings = frontDeps.flatMap( - d => debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_,_,_]])) + d => debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_, _, _]])) val lastDep = rdd.dependencies.last val lastDepStrings = - debugString(lastDep.rdd, prefix, lastDep.isInstanceOf[ShuffleDependency[_,_,_]], true) + debugString(lastDep.rdd, prefix, lastDep.isInstanceOf[ShuffleDependency[_, _, _]], true) (frontDepStrings ++ lastDepStrings) } diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala index 1722c27e55003..acbd31aacdf59 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala @@ -23,6 +23,7 @@ import org.apache.hadoop.fs.Path import org.apache.spark._ import org.apache.spark.scheduler.{ResultTask, ShuffleMapTask} +import org.apache.spark.util.SerializableConfiguration /** * Enumeration to manage state transitions of an RDD through checkpointing @@ -91,7 +92,7 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) // Save to file, and reload it as an RDD val broadcastedConf = rdd.context.broadcast( - new SerializableWritable(rdd.context.hadoopConfiguration)) + new SerializableConfiguration(rdd.context.hadoopConfiguration)) val newRDD = new CheckpointRDD[T](rdd.context, path.toString) if (rdd.conf.getBoolean("spark.cleaner.referenceTracking.cleanCheckpoints", false)) { rdd.context.cleaner.foreach { cleaner => diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala index 9440d456edf15..44667281c1063 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala @@ -24,7 +24,7 @@ import com.fasterxml.jackson.annotation.JsonInclude.Include import com.fasterxml.jackson.databind.ObjectMapper import com.fasterxml.jackson.module.scala.DefaultScalaModule -import org.apache.spark.SparkContext +import org.apache.spark.{Logging, SparkContext} /** * A general, named code block representing an operation that instantiates RDDs. @@ -43,9 +43,8 @@ import org.apache.spark.SparkContext @JsonPropertyOrder(Array("id", "name", "parent")) private[spark] class RDDOperationScope( val name: String, - val parent: Option[RDDOperationScope] = None) { - - val id: Int = RDDOperationScope.nextScopeId() + val parent: Option[RDDOperationScope] = None, + val id: String = RDDOperationScope.nextScopeId().toString) { def toJson: String = { RDDOperationScope.jsonMapper.writeValueAsString(this) @@ -75,7 +74,7 @@ private[spark] class RDDOperationScope( * A collection of utility methods to construct a hierarchical representation of RDD scopes. * An RDD scope tracks the series of operations that created a given RDD. */ -private[spark] object RDDOperationScope { +private[spark] object RDDOperationScope extends Logging { private val jsonMapper = new ObjectMapper().registerModule(DefaultScalaModule) private val scopeCounter = new AtomicInteger(0) @@ -88,30 +87,45 @@ private[spark] object RDDOperationScope { /** * Execute the given body such that all RDDs created in this body will have the same scope. - * The name of the scope will be the name of the method that immediately encloses this one. + * The name of the scope will be the first method name in the stack trace that is not the + * same as this method's. * * Note: Return statements are NOT allowed in body. */ private[spark] def withScope[T]( sc: SparkContext, allowNesting: Boolean = false)(body: => T): T = { - val callerMethodName = Thread.currentThread.getStackTrace()(3).getMethodName - withScope[T](sc, callerMethodName, allowNesting)(body) + val ourMethodName = "withScope" + val callerMethodName = Thread.currentThread.getStackTrace() + .dropWhile(_.getMethodName != ourMethodName) + .find(_.getMethodName != ourMethodName) + .map(_.getMethodName) + .getOrElse { + // Log a warning just in case, but this should almost certainly never happen + logWarning("No valid method name for this RDD operation scope!") + "N/A" + } + withScope[T](sc, callerMethodName, allowNesting, ignoreParent = false)(body) } /** * Execute the given body such that all RDDs created in this body will have the same scope. * - * If nesting is allowed, this concatenates the previous scope with the new one in a way that - * signifies the hierarchy. Otherwise, if nesting is not allowed, then any children calls to - * this method executed in the body will have no effect. + * If nesting is allowed, any subsequent calls to this method in the given body will instantiate + * child scopes that are nested within our scope. Otherwise, these calls will take no effect. + * + * Additionally, the caller of this method may optionally ignore the configurations and scopes + * set by the higher level caller. In this case, this method will ignore the parent caller's + * intention to disallow nesting, and the new scope instantiated will not have a parent. This + * is useful for scoping physical operations in Spark SQL, for instance. * * Note: Return statements are NOT allowed in body. */ private[spark] def withScope[T]( sc: SparkContext, name: String, - allowNesting: Boolean)(body: => T): T = { + allowNesting: Boolean, + ignoreParent: Boolean)(body: => T): T = { // Save the old scope to restore it later val scopeKey = SparkContext.RDD_SCOPE_KEY val noOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY @@ -119,8 +133,11 @@ private[spark] object RDDOperationScope { val oldScope = Option(oldScopeJson).map(RDDOperationScope.fromJson) val oldNoOverride = sc.getLocalProperty(noOverrideKey) try { - // Set the scope only if the higher level caller allows us to do so - if (sc.getLocalProperty(noOverrideKey) == null) { + if (ignoreParent) { + // Ignore all parent settings and scopes and start afresh with our own root scope + sc.setLocalProperty(scopeKey, new RDDOperationScope(name).toJson) + } else if (sc.getLocalProperty(noOverrideKey) == null) { + // Otherwise, set the scope only if the higher level caller allows us to do so sc.setLocalProperty(scopeKey, new RDDOperationScope(name, oldScope).toJson) } // Optionally disallow the child body to override our scope diff --git a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala index 3dfcf67f0eb66..4b5f15dd06b85 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala @@ -104,13 +104,13 @@ class SequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable : ClassTag if (!convertKey && !convertValue) { self.saveAsHadoopFile(path, keyWritableClass, valueWritableClass, format, jobConf, codec) } else if (!convertKey && convertValue) { - self.map(x => (x._1,anyToWritable(x._2))).saveAsHadoopFile( + self.map(x => (x._1, anyToWritable(x._2))).saveAsHadoopFile( path, keyWritableClass, valueWritableClass, format, jobConf, codec) } else if (convertKey && !convertValue) { - self.map(x => (anyToWritable(x._1),x._2)).saveAsHadoopFile( + self.map(x => (anyToWritable(x._1), x._2)).saveAsHadoopFile( path, keyWritableClass, valueWritableClass, format, jobConf, codec) } else if (convertKey && convertValue) { - self.map(x => (anyToWritable(x._1),anyToWritable(x._2))).saveAsHadoopFile( + self.map(x => (anyToWritable(x._1), anyToWritable(x._2))).saveAsHadoopFile( path, keyWritableClass, valueWritableClass, format, jobConf, codec) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala index 633aeba3bbae6..f7cb1791d4ac6 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala @@ -125,7 +125,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( integrate(0, t => getSeq(t._1) += t._2) // the second dep is rdd2; remove all of its keys integrate(1, t => map.remove(t._1)) - map.iterator.map { t => t._2.iterator.map { (t._1, _) } }.flatten + map.iterator.map { t => t._2.iterator.map { (t._1, _) } }.flatten } override def clearDependencies() { diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala index a96b6c3d23454..81f40ad33aa5d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala @@ -123,7 +123,7 @@ private[spark] class ZippedPartitionsRDD3 } private[spark] class ZippedPartitionsRDD4 - [A: ClassTag, B: ClassTag, C: ClassTag, D:ClassTag, V: ClassTag]( + [A: ClassTag, B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag]( sc: SparkContext, var f: (Iterator[A], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V], var rdd1: RDD[A], diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala index ba0d468f111ef..0161962cde073 100644 --- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala @@ -29,9 +29,11 @@ import akka.actor.{ActorSystem, ExtendedActorSystem, Actor, ActorRef, Props, Add import akka.event.Logging.Error import akka.pattern.{ask => akkaAsk} import akka.remote.{AssociationEvent, AssociatedEvent, DisassociatedEvent, AssociationErrorEvent} +import com.google.common.util.concurrent.MoreExecutors + import org.apache.spark.{SparkException, Logging, SparkConf} import org.apache.spark.rpc._ -import org.apache.spark.util.{ActorLogReceive, AkkaUtils} +import org.apache.spark.util.{ActorLogReceive, AkkaUtils, ThreadUtils} /** * A RpcEnv implementation based on Akka. @@ -294,8 +296,8 @@ private[akka] class AkkaRpcEndpointRef( } override def ask[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = { - import scala.concurrent.ExecutionContext.Implicits.global actorRef.ask(AkkaMessage(message, true))(timeout).flatMap { + // The function will run in the calling thread, so it should be short and never block. case msg @ AkkaMessage(message, reply) => if (reply) { logError(s"Receive $msg but the sender cannot reply") @@ -305,7 +307,7 @@ private[akka] class AkkaRpcEndpointRef( } case AkkaFailure(e) => Future.failed(e) - }.mapTo[T] + }(ThreadUtils.sameThread).mapTo[T] } override def toString: String = s"${getClass.getSimpleName}($actorRef)" diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 5d812918a13d1..aea6674ed20be 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -137,6 +137,22 @@ class DAGScheduler( private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this) taskScheduler.setDAGScheduler(this) + // Flag to control if reduce tasks are assigned preferred locations + private val shuffleLocalityEnabled = + sc.getConf.getBoolean("spark.shuffle.reduceLocality.enabled", true) + // Number of map, reduce tasks above which we do not assign preferred locations + // based on map output sizes. We limit the size of jobs for which assign preferred locations + // as computing the top locations by size becomes expensive. + private[this] val SHUFFLE_PREF_MAP_THRESHOLD = 1000 + // NOTE: This should be less than 2000 as we use HighlyCompressedMapStatus beyond that + private[this] val SHUFFLE_PREF_REDUCE_THRESHOLD = 1000 + + // Fraction of total map output that must be at a location for it to considered as a preferred + // location for a reduce task. + // Making this larger will focus on fewer locations where most data can be read locally, but + // may lead to more delay in scheduling if those locations are busy. + private[scheduler] val REDUCER_PREF_LOCS_FRACTION = 0.2 + // Called by TaskScheduler to report task's starting. def taskStarted(task: Task[_], taskInfo: TaskInfo) { eventProcessLoop.post(BeginEvent(task, taskInfo)) @@ -193,9 +209,15 @@ class DAGScheduler( def getCacheLocs(rdd: RDD[_]): Seq[Seq[TaskLocation]] = cacheLocs.synchronized { // Note: this doesn't use `getOrElse()` because this method is called O(num tasks) times if (!cacheLocs.contains(rdd.id)) { - val blockIds = rdd.partitions.indices.map(index => RDDBlockId(rdd.id, index)).toArray[BlockId] - val locs: Seq[Seq[TaskLocation]] = blockManagerMaster.getLocations(blockIds).map { bms => - bms.map(bm => TaskLocation(bm.host, bm.executorId)) + // Note: if the storage level is NONE, we don't need to get locations from block manager. + val locs: Seq[Seq[TaskLocation]] = if (rdd.getStorageLevel == StorageLevel.NONE) { + Seq.fill(rdd.partitions.size)(Nil) + } else { + val blockIds = + rdd.partitions.indices.map(index => RDDBlockId(rdd.id, index)).toArray[BlockId] + blockManagerMaster.getLocations(blockIds).map { bms => + bms.map(bm => TaskLocation(bm.host, bm.executorId)) + } } cacheLocs(rdd.id) = locs } @@ -208,19 +230,17 @@ class DAGScheduler( /** * Get or create a shuffle map stage for the given shuffle dependency's map side. - * The jobId value passed in will be used if the stage doesn't already exist with - * a lower jobId (jobId always increases across jobs.) */ private def getShuffleMapStage( shuffleDep: ShuffleDependency[_, _, _], - jobId: Int): ShuffleMapStage = { + firstJobId: Int): ShuffleMapStage = { shuffleToMapStage.get(shuffleDep.shuffleId) match { case Some(stage) => stage case None => // We are going to register ancestor shuffle dependencies - registerShuffleDependencies(shuffleDep, jobId) + registerShuffleDependencies(shuffleDep, firstJobId) // Then register current shuffleDep - val stage = newOrUsedShuffleStage(shuffleDep, jobId) + val stage = newOrUsedShuffleStage(shuffleDep, firstJobId) shuffleToMapStage(shuffleDep.shuffleId) = stage stage @@ -230,15 +250,15 @@ class DAGScheduler( /** * Helper function to eliminate some code re-use when creating new stages. */ - private def getParentStagesAndId(rdd: RDD[_], jobId: Int): (List[Stage], Int) = { - val parentStages = getParentStages(rdd, jobId) + private def getParentStagesAndId(rdd: RDD[_], firstJobId: Int): (List[Stage], Int) = { + val parentStages = getParentStages(rdd, firstJobId) val id = nextStageId.getAndIncrement() (parentStages, id) } /** * Create a ShuffleMapStage as part of the (re)-creation of a shuffle map stage in - * newOrUsedShuffleStage. The stage will be associated with the provided jobId. + * newOrUsedShuffleStage. The stage will be associated with the provided firstJobId. * Production of shuffle map stages should always use newOrUsedShuffleStage, not * newShuffleMapStage directly. */ @@ -246,21 +266,19 @@ class DAGScheduler( rdd: RDD[_], numTasks: Int, shuffleDep: ShuffleDependency[_, _, _], - jobId: Int, + firstJobId: Int, callSite: CallSite): ShuffleMapStage = { - val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId) + val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, firstJobId) val stage: ShuffleMapStage = new ShuffleMapStage(id, rdd, numTasks, parentStages, - jobId, callSite, shuffleDep) + firstJobId, callSite, shuffleDep) stageIdToStage(id) = stage - updateJobIdStageIdMaps(jobId, stage) + updateJobIdStageIdMaps(firstJobId, stage) stage } /** - * Create a ResultStage -- either directly for use as a result stage, or as part of the - * (re)-creation of a shuffle map stage in newOrUsedShuffleStage. The stage will be associated - * with the provided jobId. + * Create a ResultStage associated with the provided jobId. */ private def newResultStage( rdd: RDD[_], @@ -277,16 +295,16 @@ class DAGScheduler( /** * Create a shuffle map Stage for the given RDD. The stage will also be associated with the - * provided jobId. If a stage for the shuffleId existed previously so that the shuffleId is + * provided firstJobId. If a stage for the shuffleId existed previously so that the shuffleId is * present in the MapOutputTracker, then the number and location of available outputs are * recovered from the MapOutputTracker */ private def newOrUsedShuffleStage( shuffleDep: ShuffleDependency[_, _, _], - jobId: Int): ShuffleMapStage = { + firstJobId: Int): ShuffleMapStage = { val rdd = shuffleDep.rdd val numTasks = rdd.partitions.size - val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, jobId, rdd.creationSite) + val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, firstJobId, rdd.creationSite) if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) { val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId) val locs = MapOutputTracker.deserializeMapStatuses(serLocs) @@ -304,10 +322,10 @@ class DAGScheduler( } /** - * Get or create the list of parent stages for a given RDD. The stages will be assigned the - * provided jobId if they haven't already been created with a lower jobId. + * Get or create the list of parent stages for a given RDD. The new Stages will be created with + * the provided firstJobId. */ - private def getParentStages(rdd: RDD[_], jobId: Int): List[Stage] = { + private def getParentStages(rdd: RDD[_], firstJobId: Int): List[Stage] = { val parents = new HashSet[Stage] val visited = new HashSet[RDD[_]] // We are manually maintaining a stack here to prevent StackOverflowError @@ -321,7 +339,7 @@ class DAGScheduler( for (dep <- r.dependencies) { dep match { case shufDep: ShuffleDependency[_, _, _] => - parents += getShuffleMapStage(shufDep, jobId) + parents += getShuffleMapStage(shufDep, firstJobId) case _ => waitingForVisit.push(dep.rdd) } @@ -336,11 +354,11 @@ class DAGScheduler( } /** Find ancestor missing shuffle dependencies and register into shuffleToMapStage */ - private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], jobId: Int) { + private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], firstJobId: Int) { val parentsWithNoMapStage = getAncestorShuffleDependencies(shuffleDep.rdd) while (parentsWithNoMapStage.nonEmpty) { val currentShufDep = parentsWithNoMapStage.pop() - val stage = newOrUsedShuffleStage(currentShufDep, jobId) + val stage = newOrUsedShuffleStage(currentShufDep, firstJobId) shuffleToMapStage(currentShufDep.shuffleId) = stage } } @@ -386,11 +404,12 @@ class DAGScheduler( def visit(rdd: RDD[_]) { if (!visited(rdd)) { visited += rdd - if (getCacheLocs(rdd).contains(Nil)) { + val rddHasUncachedPartitions = getCacheLocs(rdd).contains(Nil) + if (rddHasUncachedPartitions) { for (dep <- rdd.dependencies) { dep match { case shufDep: ShuffleDependency[_, _, _] => - val mapStage = getShuffleMapStage(shufDep, stage.jobId) + val mapStage = getShuffleMapStage(shufDep, stage.firstJobId) if (!mapStage.isAvailable) { missing += mapStage } @@ -577,7 +596,7 @@ class DAGScheduler( private[scheduler] def doCancelAllJobs() { // Cancel all running jobs. - runningStages.map(_.jobId).foreach(handleJobCancellation(_, + runningStages.map(_.firstJobId).foreach(handleJobCancellation(_, reason = "as part of cancellation of all jobs")) activeJobs.clear() // These should already be empty by this point, jobIdToActiveJob.clear() // but just in case we lost track of some jobs... @@ -603,7 +622,7 @@ class DAGScheduler( clearCacheLocs() val failedStagesCopy = failedStages.toArray failedStages.clear() - for (stage <- failedStagesCopy.sortBy(_.jobId)) { + for (stage <- failedStagesCopy.sortBy(_.firstJobId)) { submitStage(stage) } } @@ -623,7 +642,7 @@ class DAGScheduler( logTrace("failed: " + failedStages) val waitingStagesCopy = waitingStages.toArray waitingStages.clear() - for (stage <- waitingStagesCopy.sortBy(_.jobId)) { + for (stage <- waitingStagesCopy.sortBy(_.firstJobId)) { submitStage(stage) } } @@ -843,7 +862,7 @@ class DAGScheduler( } } - val properties = jobIdToActiveJob.get(stage.jobId).map(_.properties).orNull + val properties = jobIdToActiveJob.get(stage.firstJobId).map(_.properties).orNull runningStages += stage // SparkListenerStageSubmitted should be posted before testing whether tasks are @@ -909,7 +928,7 @@ class DAGScheduler( stage.pendingTasks ++= tasks logDebug("New pending tasks: " + stage.pendingTasks) taskScheduler.submitTasks( - new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties)) + new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.firstJobId, properties)) stage.latestInfo.submissionTime = Some(clock.getTimeMillis()) } else { // Because we posted SparkListenerStageSubmitted earlier, we should mark @@ -1323,7 +1342,7 @@ class DAGScheduler( for (dep <- rdd.dependencies) { dep match { case shufDep: ShuffleDependency[_, _, _] => - val mapStage = getShuffleMapStage(shufDep, stage.jobId) + val mapStage = getShuffleMapStage(shufDep, stage.firstJobId) if (!mapStage.isAvailable) { waitingForVisit.push(mapStage.rdd) } // Otherwise there's no need to follow the dependency back @@ -1364,10 +1383,10 @@ class DAGScheduler( private def getPreferredLocsInternal( rdd: RDD[_], partition: Int, - visited: HashSet[(RDD[_],Int)]): Seq[TaskLocation] = { + visited: HashSet[(RDD[_], Int)]): Seq[TaskLocation] = { // If the partition has already been visited, no need to re-visit. // This avoids exponential path exploration. SPARK-695 - if (!visited.add((rdd,partition))) { + if (!visited.add((rdd, partition))) { // Nil has already been returned for previously visited partitions. return Nil } @@ -1381,17 +1400,32 @@ class DAGScheduler( if (rddPrefs.nonEmpty) { return rddPrefs.map(TaskLocation(_)) } - // If the RDD has narrow dependencies, pick the first partition of the first narrow dep - // that has any placement preferences. Ideally we would choose based on transfer sizes, - // but this will do for now. + rdd.dependencies.foreach { case n: NarrowDependency[_] => + // If the RDD has narrow dependencies, pick the first partition of the first narrow dep + // that has any placement preferences. Ideally we would choose based on transfer sizes, + // but this will do for now. for (inPart <- n.getParents(partition)) { val locs = getPreferredLocsInternal(n.rdd, inPart, visited) if (locs != Nil) { return locs } } + case s: ShuffleDependency[_, _, _] => + // For shuffle dependencies, pick locations which have at least REDUCER_PREF_LOCS_FRACTION + // of data as preferred locations + if (shuffleLocalityEnabled && + rdd.partitions.size < SHUFFLE_PREF_REDUCE_THRESHOLD && + s.rdd.partitions.size < SHUFFLE_PREF_MAP_THRESHOLD) { + // Get the preferred map output locations for this reducer + val topLocsForReducer = mapOutputTracker.getLocationsWithLargestOutputs(s.shuffleId, + partition, rdd.partitions.size, REDUCER_PREF_LOCS_FRACTION) + if (topLocsForReducer.nonEmpty) { + return topLocsForReducer.get.map(loc => TaskLocation(loc.host, loc.executorId)) + } + } + case _ => } Nil diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala index 12668b6c0988e..02c67073af6a0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala @@ -17,9 +17,8 @@ package org.apache.spark.scheduler -import com.codahale.metrics.{Gauge,MetricRegistry} +import com.codahale.metrics.{Gauge, MetricRegistry} -import org.apache.spark.SparkContext import org.apache.spark.metrics.source.Source private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler) diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index 0b1d47cff3746..8321037cdc026 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -38,7 +38,7 @@ private case class AskPermissionToCommitOutput(stage: Int, task: Long, taskAttem * This class was introduced in SPARK-4879; see that JIRA issue (and the associated pull requests) * for an extensive design discussion. */ -private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { +private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean) extends Logging { // Initialized by SparkEnv var coordinatorRef: Option[RpcEndpointRef] = None @@ -129,9 +129,11 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { } def stop(): Unit = synchronized { - coordinatorRef.foreach(_ send StopCoordinator) - coordinatorRef = None - authorizedCommittersByStage.clear() + if (isDriver) { + coordinatorRef.foreach(_ send StopCoordinator) + coordinatorRef = None + authorizedCommittersByStage.clear() + } } // Marked private[scheduler] instead of private so this can be mocked in tests diff --git a/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala index 86f357abb8723..c6d957b65f3fb 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala @@ -41,7 +41,7 @@ private[spark] class ReplayListenerBus extends SparkListenerBus with Logging { * * @param logData Stream containing event log data. * @param sourceName Filename (or other source identifier) from whence @logData is being read - * @param maybeTruncated Indicate whether log file might be truncated (some abnormal situations + * @param maybeTruncated Indicate whether log file might be truncated (some abnormal situations * encountered, log file might not finished writing) or not */ def replay( @@ -62,7 +62,7 @@ private[spark] class ReplayListenerBus extends SparkListenerBus with Logging { if (!maybeTruncated || lines.hasNext) { throw jpe } else { - logWarning(s"Got JsonParseException from log file $sourceName" + + logWarning(s"Got JsonParseException from log file $sourceName" + s" at line $lineNumber, the file might not have finished writing cleanly.") } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala index c0f3d5a13d623..bf81b9aca4810 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala @@ -28,9 +28,9 @@ private[spark] class ResultStage( rdd: RDD[_], numTasks: Int, parents: List[Stage], - jobId: Int, + firstJobId: Int, callSite: CallSite) - extends Stage(id, rdd, numTasks, parents, jobId, callSite) { + extends Stage(id, rdd, numTasks, parents, firstJobId, callSite) { // The active job for this result stage. Will be empty if the job has already finished // (e.g., because the job was cancelled). diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala index 646820520ea1b..8801a761afae3 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala @@ -49,4 +49,11 @@ private[spark] trait SchedulerBackend { */ def applicationAttemptId(): Option[String] = None + /** + * Get the URLs for the driver logs. These URLs are used to display the links in the UI + * Executors tab for the driver. + * @return Map containing the log names and their respective URLs + */ + def getDriverLogUrls: Option[Map[String, String]] = None + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulingAlgorithm.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulingAlgorithm.scala index 5e62c8468f007..864941d468af9 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulingAlgorithm.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulingAlgorithm.scala @@ -56,7 +56,7 @@ private[spark] class FairSchedulingAlgorithm extends SchedulingAlgorithm { val minShareRatio2 = runningTasks2.toDouble / math.max(minShare2, 1.0).toDouble val taskToWeightRatio1 = runningTasks1.toDouble / s1.weight.toDouble val taskToWeightRatio2 = runningTasks2.toDouble / s2.weight.toDouble - var compare:Int = 0 + var compare: Int = 0 if (s1Needy && !s2Needy) { return true diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala index d02210743484c..66c75f325fcde 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala @@ -30,10 +30,10 @@ private[spark] class ShuffleMapStage( rdd: RDD[_], numTasks: Int, parents: List[Stage], - jobId: Int, + firstJobId: Int, callSite: CallSite, val shuffleDep: ShuffleDependency[_, _, _]) - extends Stage(id, rdd, numTasks, parents, jobId, callSite) { + extends Stage(id, rdd, numTasks, parents, firstJobId, callSite) { override def toString: String = "ShuffleMapStage " + id diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala index 169d4fd3a94f0..9620915f495ab 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala @@ -110,8 +110,13 @@ case class SparkListenerExecutorMetricsUpdate( extends SparkListenerEvent @DeveloperApi -case class SparkListenerApplicationStart(appName: String, appId: Option[String], - time: Long, sparkUser: String, appAttemptId: Option[String]) extends SparkListenerEvent +case class SparkListenerApplicationStart( + appName: String, + appId: Option[String], + time: Long, + sparkUser: String, + appAttemptId: Option[String], + driverLogs: Option[Map[String, String]] = None) extends SparkListenerEvent @DeveloperApi case class SparkListenerApplicationEnd(time: Long) extends SparkListenerEvent @@ -265,7 +270,7 @@ class StatsReportListener extends SparkListener with Logging { private[spark] object StatsReportListener extends Logging { // For profiling, the extremes are more interesting - val percentiles = Array[Int](0,5,10,25,50,75,90,95,100) + val percentiles = Array[Int](0, 5, 10, 25, 50, 75, 90, 95, 100) val probabilities = percentiles.map(_ / 100.0) val percentilesHeader = "\t" + percentiles.mkString("%\t") + "%" @@ -299,7 +304,7 @@ private[spark] object StatsReportListener extends Logging { dOpt.foreach { d => showDistribution(heading, d, formatNumber)} } - def showDistribution(heading: String, dOpt: Option[Distribution], format:String) { + def showDistribution(heading: String, dOpt: Option[Distribution], format: String) { def f(d: Double): String = format.format(d) showDistribution(heading, dOpt, f _) } @@ -313,7 +318,7 @@ private[spark] object StatsReportListener extends Logging { } def showBytesDistribution( - heading:String, + heading: String, getMetric: (TaskInfo, TaskMetrics) => Option[Long], taskInfoMetrics: Seq[(TaskInfo, TaskMetrics)]) { showBytesDistribution(heading, extractLongDistribution(taskInfoMetrics, getMetric)) diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala index 5d0ddb8377c33..c59d6e4f5bc04 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala @@ -34,7 +34,7 @@ import org.apache.spark.util.CallSite * initiated a job (e.g. count(), save(), etc). For shuffle map stages, we also track the nodes * that each output partition is on. * - * Each Stage also has a jobId, identifying the job that first submitted the stage. When FIFO + * Each Stage also has a firstJobId, identifying the job that first submitted the stage. When FIFO * scheduling is used, this allows Stages from earlier jobs to be computed first or recovered * faster on failure. * @@ -51,7 +51,7 @@ private[spark] abstract class Stage( val rdd: RDD[_], val numTasks: Int, val parents: List[Stage], - val jobId: Int, + val firstJobId: Int, val callSite: CallSite) extends Logging { diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index 586d1e06204c1..15101c64f0503 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -125,7 +125,7 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex if (interruptThread && taskThread != null) { taskThread.interrupt() } - } + } } /** diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala index 1f114a0207f7b..8b2a742b96988 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala @@ -40,6 +40,9 @@ class DirectTaskResult[T](var valueBytes: ByteBuffer, var accumUpdates: Map[Long var metrics: TaskMetrics) extends TaskResult[T] with Externalizable { + private var valueObjectDeserialized = false + private var valueObject: T = _ + def this() = this(null.asInstanceOf[ByteBuffer], null, null) override def writeExternal(out: ObjectOutput): Unit = Utils.tryOrIOException { @@ -72,10 +75,26 @@ class DirectTaskResult[T](var valueBytes: ByteBuffer, var accumUpdates: Map[Long } } metrics = in.readObject().asInstanceOf[TaskMetrics] + valueObjectDeserialized = false } + /** + * When `value()` is called at the first time, it needs to deserialize `valueObject` from + * `valueBytes`. It may cost dozens of seconds for a large instance. So when calling `value` at + * the first time, the caller should avoid to block other threads. + * + * After the first time, `value()` is trivial and just returns the deserialized `valueObject`. + */ def value(): T = { - val resultSer = SparkEnv.get.serializer.newInstance() - resultSer.deserialize(valueBytes) + if (valueObjectDeserialized) { + valueObject + } else { + // This should not run when holding a lock because it may cost dozens of seconds for a large + // value. + val resultSer = SparkEnv.get.serializer.newInstance() + valueObject = resultSer.deserialize(valueBytes) + valueObjectDeserialized = true + valueObject + } } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala index 391827c1d2156..46a6f6537e2ee 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -54,6 +54,10 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul if (!taskSetManager.canFetchMoreResults(serializedData.limit())) { return } + // deserialize "value" without holding any lock so that it won't block other threads. + // We should call it here, so that when it's called again in + // "TaskSetManager.handleSuccessfulTask", it does not need to deserialize the value. + directResult.value() (directResult, serializedData.limit()) case IndirectTaskResult(blockId, size) => if (!taskSetManager.canFetchMoreResults(size)) { diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index b4b8a630694bb..ed3dde0fc3055 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -19,9 +19,9 @@ package org.apache.spark.scheduler import java.nio.ByteBuffer import java.util.{TimerTask, Timer} +import java.util.concurrent.TimeUnit import java.util.concurrent.atomic.AtomicLong -import scala.concurrent.duration._ import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap import scala.collection.mutable.HashSet @@ -32,7 +32,7 @@ import org.apache.spark._ import org.apache.spark.TaskState.TaskState import org.apache.spark.scheduler.SchedulingMode.SchedulingMode import org.apache.spark.scheduler.TaskLocality.TaskLocality -import org.apache.spark.util.Utils +import org.apache.spark.util.{ThreadUtils, Utils} import org.apache.spark.executor.TaskMetrics import org.apache.spark.storage.BlockManagerId @@ -64,6 +64,9 @@ private[spark] class TaskSchedulerImpl( // How often to check for speculative tasks val SPECULATION_INTERVAL_MS = conf.getTimeAsMs("spark.speculation.interval", "100ms") + private val speculationScheduler = + ThreadUtils.newDaemonSingleThreadScheduledExecutor("task-scheduler-speculation") + // Threshold above which we warn user initial TaskSet may be starved val STARVATION_TIMEOUT_MS = conf.getTimeAsMs("spark.starvation.timeout", "15s") @@ -142,10 +145,11 @@ private[spark] class TaskSchedulerImpl( if (!isLocal && conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") - sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL_MS milliseconds, - SPECULATION_INTERVAL_MS milliseconds) { - Utils.tryOrStopSparkContext(sc) { checkSpeculatableTasks() } - }(sc.env.actorSystem.dispatcher) + speculationScheduler.scheduleAtFixedRate(new Runnable { + override def run(): Unit = Utils.tryOrStopSparkContext(sc) { + checkSpeculatableTasks() + } + }, SPECULATION_INTERVAL_MS, SPECULATION_INTERVAL_MS, TimeUnit.MILLISECONDS) } } @@ -412,6 +416,7 @@ private[spark] class TaskSchedulerImpl( } override def stop() { + speculationScheduler.shutdown() if (backend != null) { backend.stop() } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index 7dc325283d961..82455b0426a5d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -46,7 +46,7 @@ import org.apache.spark.util.{Clock, SystemClock, Utils} * * @param sched the TaskSchedulerImpl associated with the TaskSetManager * @param taskSet the TaskSet to manage scheduling for - * @param maxTaskFailures if any particular task fails more than this number of times, the entire + * @param maxTaskFailures if any particular task fails this number of times, the entire * task set will be aborted */ private[spark] class TaskSetManager( @@ -620,6 +620,12 @@ private[spark] class TaskSetManager( val index = info.index info.markSuccessful() removeRunningTask(tid) + // This method is called by "TaskSchedulerImpl.handleSuccessfulTask" which holds the + // "TaskSchedulerImpl" lock until exiting. To avoid the SPARK-7655 issue, we should not + // "deserialize" the value when holding a lock to avoid blocking other threads. So we call + // "result.value()" in "TaskResultGetter.enqueueSuccessfulTask" before reaching here. + // Note: "result.value()" only deserializes the value when it's called at the first time, so + // here "result.value()" just returns the value and won't block other threads. sched.dagScheduler.taskEnded( tasks(index), Success, result.value(), result.accumUpdates, info, result.metrics) if (!successful(index)) { @@ -775,10 +781,10 @@ private[spark] class TaskSetManager( // that it's okay if we add a task to the same queue twice (if it had multiple preferred // locations), because dequeueTaskFromList will skip already-running tasks. for (index <- getPendingTasksForExecutor(execId)) { - addPendingTask(index, readding=true) + addPendingTask(index, readding = true) } for (index <- getPendingTasksForHost(host)) { - addPendingTask(index, readding=true) + addPendingTask(index, readding = true) } // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage, @@ -855,9 +861,9 @@ private[spark] class TaskSetManager( case TaskLocality.RACK_LOCAL => "spark.locality.wait.rack" case _ => null } - + if (localityWaitKey != null) { - conf.getTimeAsMs(localityWaitKey, defaultWait) + conf.getTimeAsMs(localityWaitKey, defaultWait) } else { 0L } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala index 70364cea62a80..4be1eda2e9291 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala @@ -75,7 +75,8 @@ private[spark] object CoarseGrainedClusterMessages { case class SetupDriver(driver: RpcEndpointRef) extends CoarseGrainedClusterMessage // Exchanged between the driver and the AM in Yarn client mode - case class AddWebUIFilter(filterName:String, filterParams: Map[String, String], proxyBase: String) + case class AddWebUIFilter( + filterName: String, filterParams: Map[String, String], proxyBase: String) extends CoarseGrainedClusterMessage // Messages exchanged between the driver and the cluster manager for executor allocation diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index f107148f3b8c6..7c7f70d8a193b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -69,6 +69,11 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) extends ThreadSafeRpcEndpoint with Logging { + // If this DriverEndpoint is changed to support multiple threads, + // then this may need to be changed so that we don't share the serializer + // instance across threads + private val ser = SparkEnv.get.closureSerializer.newInstance() + override protected def log = CoarseGrainedSchedulerBackend.this.log private val addressToExecutorId = new HashMap[RpcAddress, String] @@ -79,7 +84,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp override def onStart() { // Periodically revive offers to allow delay scheduling to work val reviveIntervalMs = conf.getTimeAsMs("spark.scheduler.revive.interval", "1s") - + reviveThread.scheduleAtFixedRate(new Runnable { override def run(): Unit = Utils.tryLogNonFatalError { Option(self).foreach(_.send(ReviveOffers)) @@ -98,7 +103,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp case None => // Ignoring the update since we don't know about the executor. logWarning(s"Ignored task status update ($taskId state $state) " + - "from unknown executor $sender with ID $executorId") + s"from unknown executor with ID $executorId") } } @@ -163,7 +168,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp } // Make fake resource offers on all executors - def makeOffers() { + private def makeOffers() { launchTasks(scheduler.resourceOffers(executorDataMap.map { case (id, executorData) => new WorkerOffer(id, executorData.executorHost, executorData.freeCores) }.toSeq)) @@ -175,16 +180,15 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp } // Make fake resource offers on just one executor - def makeOffers(executorId: String) { + private def makeOffers(executorId: String) { val executorData = executorDataMap(executorId) launchTasks(scheduler.resourceOffers( Seq(new WorkerOffer(executorId, executorData.executorHost, executorData.freeCores)))) } // Launch tasks returned by a set of resource offers - def launchTasks(tasks: Seq[Seq[TaskDescription]]) { + private def launchTasks(tasks: Seq[Seq[TaskDescription]]) { for (task <- tasks.flatten) { - val ser = SparkEnv.get.closureSerializer.newInstance() val serializedTask = ser.serialize(task) if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) { val taskSetId = scheduler.taskIdToTaskSetId(task.taskId) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index 2a3a5d925d06f..190ff61d689d1 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -149,7 +149,7 @@ private[spark] abstract class YarnSchedulerBackend( } } - override def onStop(): Unit ={ + override def onStop(): Unit = { askAmThreadPool.shutdownNow() } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index dc59545b43314..6b8edca5aa485 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -25,9 +25,10 @@ import scala.collection.mutable.{HashMap, HashSet} import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _} import org.apache.mesos.{Scheduler => MScheduler, _} +import org.apache.spark.rpc.RpcAddress import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.Utils import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState} /** @@ -51,7 +52,7 @@ private[spark] class CoarseMesosSchedulerBackend( val MAX_SLAVE_FAILURES = 2 // Blacklist a slave after this many failures // Maximum number of cores to acquire (TODO: we'll need more flexible controls here) - val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt + val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt // Cores we have acquired with each Mesos task ID val coresByTaskId = new HashMap[Int, Int] @@ -115,11 +116,9 @@ private[spark] class CoarseMesosSchedulerBackend( } val command = CommandInfo.newBuilder() .setEnvironment(environment) - val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(sc.env.actorSystem), + val driverUrl = sc.env.rpcEnv.uriOf( SparkEnv.driverActorSystemName, - conf.get("spark.driver.host"), - conf.get("spark.driver.port"), + RpcAddress(conf.get("spark.driver.host"), conf.get("spark.driver.port").toInt), CoarseGrainedSchedulerBackend.ENDPOINT_NAME) val uri = conf.getOption("spark.executor.uri") diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index db0a080b3b0c0..49de85ef48ada 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -146,7 +146,7 @@ private[spark] class MesosSchedulerBackend( private def createExecArg(): Array[Byte] = { if (execArgs == null) { val props = new HashMap[String, String] - for ((key,value) <- sc.conf.getAll) { + for ((key, value) <- sc.conf.getAll) { props(key) = value } // Serialize the map as an array of (String, String) pairs diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala index 928c5cfed417a..e79c543a9de27 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala @@ -37,14 +37,14 @@ private[mesos] object MesosSchedulerBackendUtil extends Logging { .newBuilder() .setMode(Volume.Mode.RW) spec match { - case Array(container_path) => + case Array(container_path) => Some(vol.setContainerPath(container_path)) case Array(container_path, "rw") => Some(vol.setContainerPath(container_path)) case Array(container_path, "ro") => Some(vol.setContainerPath(container_path) .setMode(Volume.Mode.RO)) - case Array(host_path, container_path) => + case Array(host_path, container_path) => Some(vol.setContainerPath(container_path) .setHostPath(host_path)) case Array(host_path, container_path, "rw") => @@ -108,7 +108,7 @@ private[mesos] object MesosSchedulerBackendUtil extends Logging { image: String, volumes: Option[List[Volume]] = None, network: Option[ContainerInfo.DockerInfo.Network] = None, - portmaps: Option[List[ContainerInfo.DockerInfo.PortMapping]] = None):Unit = { + portmaps: Option[List[ContainerInfo.DockerInfo.PortMapping]] = None): Unit = { val docker = ContainerInfo.DockerInfo.newBuilder().setImage(image) diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index e64d06c4d3cfc..3078a1b10be8b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -18,14 +18,12 @@ package org.apache.spark.scheduler.local import java.nio.ByteBuffer -import java.util.concurrent.TimeUnit import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv, TaskState} import org.apache.spark.TaskState.TaskState import org.apache.spark.executor.{Executor, ExecutorBackend} -import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcCallContext, RpcEndpointRef, RpcEnv} +import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint} import org.apache.spark.scheduler.{SchedulerBackend, TaskSchedulerImpl, WorkerOffer} -import org.apache.spark.util.{ThreadUtils, Utils} private case class ReviveOffers() @@ -47,9 +45,6 @@ private[spark] class LocalEndpoint( private val totalCores: Int) extends ThreadSafeRpcEndpoint with Logging { - private val reviveThread = - ThreadUtils.newDaemonSingleThreadScheduledExecutor("local-revive-thread") - private var freeCores = totalCores private val localExecutorId = SparkContext.DRIVER_IDENTIFIER @@ -79,27 +74,13 @@ private[spark] class LocalEndpoint( context.reply(true) } - def reviveOffers() { val offers = Seq(new WorkerOffer(localExecutorId, localExecutorHostname, freeCores)) - val tasks = scheduler.resourceOffers(offers).flatten - for (task <- tasks) { + for (task <- scheduler.resourceOffers(offers).flatten) { freeCores -= scheduler.CPUS_PER_TASK executor.launchTask(executorBackend, taskId = task.taskId, attemptNumber = task.attemptNumber, task.name, task.serializedTask) } - if (tasks.isEmpty && scheduler.activeTaskSets.nonEmpty) { - // Try to reviveOffer after 1 second, because scheduler may wait for locality timeout - reviveThread.schedule(new Runnable { - override def run(): Unit = Utils.tryLogNonFatalError { - Option(self).foreach(_.send(ReviveOffers)) - } - }, 1000, TimeUnit.MILLISECONDS) - } - } - - override def onStop(): Unit = { - reviveThread.shutdownNow() } } diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala index dfbde7c8a1b0d..698d1384d580d 100644 --- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala @@ -121,6 +121,8 @@ class JavaSerializer(conf: SparkConf) extends Serializer with Externalizable { private var counterReset = conf.getInt("spark.serializer.objectStreamReset", 100) private var extraDebugInfo = conf.getBoolean("spark.serializer.extraDebugInfo", true) + protected def this() = this(new SparkConf()) // For deserialization only + override def newInstance(): SerializerInstance = { val classLoader = defaultClassLoader.getOrElse(Thread.currentThread.getContextClassLoader) new JavaSerializerInstance(counterReset, extraDebugInfo, classLoader) diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index 64ba27f34d2f1..cd8a82347a1e9 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -17,8 +17,9 @@ package org.apache.spark.serializer -import java.io.{EOFException, InputStream, OutputStream} +import java.io.{EOFException, IOException, InputStream, OutputStream} import java.nio.ByteBuffer +import javax.annotation.Nullable import scala.reflect.ClassTag @@ -51,7 +52,7 @@ class KryoSerializer(conf: SparkConf) with Serializable { private val bufferSizeKb = conf.getSizeAsKb("spark.kryoserializer.buffer", "64k") - + if (bufferSizeKb >= ByteUnit.GiB.toKiB(2)) { throw new IllegalArgumentException("spark.kryoserializer.buffer must be less than " + s"2048 mb, got: + ${ByteUnit.KiB.toMiB(bufferSizeKb)} mb.") @@ -136,21 +137,45 @@ class KryoSerializer(conf: SparkConf) } private[spark] -class KryoSerializationStream(kryo: Kryo, outStream: OutputStream) extends SerializationStream { - val output = new KryoOutput(outStream) +class KryoSerializationStream( + serInstance: KryoSerializerInstance, + outStream: OutputStream) extends SerializationStream { + + private[this] var output: KryoOutput = new KryoOutput(outStream) + private[this] var kryo: Kryo = serInstance.borrowKryo() override def writeObject[T: ClassTag](t: T): SerializationStream = { kryo.writeClassAndObject(output, t) this } - override def flush() { output.flush() } - override def close() { output.close() } + override def flush() { + if (output == null) { + throw new IOException("Stream is closed") + } + output.flush() + } + + override def close() { + if (output != null) { + try { + output.close() + } finally { + serInstance.releaseKryo(kryo) + kryo = null + output = null + } + } + } } private[spark] -class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends DeserializationStream { - private val input = new KryoInput(inStream) +class KryoDeserializationStream( + serInstance: KryoSerializerInstance, + inStream: InputStream) extends DeserializationStream { + + private[this] var input: KryoInput = new KryoInput(inStream) + private[this] var kryo: Kryo = serInstance.borrowKryo() override def readObject[T: ClassTag](): T = { try { @@ -163,50 +188,105 @@ class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends Deser } override def close() { - // Kryo's Input automatically closes the input stream it is using. - input.close() + if (input != null) { + try { + // Kryo's Input automatically closes the input stream it is using. + input.close() + } finally { + serInstance.releaseKryo(kryo) + kryo = null + input = null + } + } } } private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends SerializerInstance { - private val kryo = ks.newKryo() - // Make these lazy vals to avoid creating a buffer unless we use them + /** + * A re-used [[Kryo]] instance. Methods will borrow this instance by calling `borrowKryo()`, do + * their work, then release the instance by calling `releaseKryo()`. Logically, this is a caching + * pool of size one. SerializerInstances are not thread-safe, hence accesses to this field are + * not synchronized. + */ + @Nullable private[this] var cachedKryo: Kryo = borrowKryo() + + /** + * Borrows a [[Kryo]] instance. If possible, this tries to re-use a cached Kryo instance; + * otherwise, it allocates a new instance. + */ + private[serializer] def borrowKryo(): Kryo = { + if (cachedKryo != null) { + val kryo = cachedKryo + // As a defensive measure, call reset() to clear any Kryo state that might have been modified + // by the last operation to borrow this instance (see SPARK-7766 for discussion of this issue) + kryo.reset() + cachedKryo = null + kryo + } else { + ks.newKryo() + } + } + + /** + * Release a borrowed [[Kryo]] instance. If this serializer instance already has a cached Kryo + * instance, then the given Kryo instance is discarded; otherwise, the Kryo is stored for later + * re-use. + */ + private[serializer] def releaseKryo(kryo: Kryo): Unit = { + if (cachedKryo == null) { + cachedKryo = kryo + } + } + + // Make these lazy vals to avoid creating a buffer unless we use them. private lazy val output = ks.newKryoOutput() private lazy val input = new KryoInput() override def serialize[T: ClassTag](t: T): ByteBuffer = { output.clear() + val kryo = borrowKryo() try { kryo.writeClassAndObject(output, t) } catch { case e: KryoException if e.getMessage.startsWith("Buffer overflow") => throw new SparkException(s"Kryo serialization failed: ${e.getMessage}. To avoid this, " + "increase spark.kryoserializer.buffer.max value.") + } finally { + releaseKryo(kryo) } ByteBuffer.wrap(output.toBytes) } override def deserialize[T: ClassTag](bytes: ByteBuffer): T = { - input.setBuffer(bytes.array) - kryo.readClassAndObject(input).asInstanceOf[T] + val kryo = borrowKryo() + try { + input.setBuffer(bytes.array) + kryo.readClassAndObject(input).asInstanceOf[T] + } finally { + releaseKryo(kryo) + } } override def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T = { + val kryo = borrowKryo() val oldClassLoader = kryo.getClassLoader - kryo.setClassLoader(loader) - input.setBuffer(bytes.array) - val obj = kryo.readClassAndObject(input).asInstanceOf[T] - kryo.setClassLoader(oldClassLoader) - obj + try { + kryo.setClassLoader(loader) + input.setBuffer(bytes.array) + kryo.readClassAndObject(input).asInstanceOf[T] + } finally { + kryo.setClassLoader(oldClassLoader) + releaseKryo(kryo) + } } override def serializeStream(s: OutputStream): SerializationStream = { - new KryoSerializationStream(kryo, s) + new KryoSerializationStream(this, s) } override def deserializeStream(s: InputStream): DeserializationStream = { - new KryoDeserializationStream(kryo, s) + new KryoDeserializationStream(this, s) } /** @@ -216,7 +296,12 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ def getAutoReset(): Boolean = { val field = classOf[Kryo].getDeclaredField("autoReset") field.setAccessible(true) - field.get(kryo).asInstanceOf[Boolean] + val kryo = borrowKryo() + try { + field.get(kryo).asInstanceOf[Boolean] + } finally { + releaseKryo(kryo) + } } } diff --git a/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala b/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala index 5abfa467c0ec8..cc2f0506817d3 100644 --- a/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala +++ b/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala @@ -17,7 +17,7 @@ package org.apache.spark.serializer -import java.io.{NotSerializableException, ObjectOutput, ObjectStreamClass, ObjectStreamField} +import java.io._ import java.lang.reflect.{Field, Method} import java.security.AccessController @@ -27,7 +27,7 @@ import scala.util.control.NonFatal import org.apache.spark.Logging -private[serializer] object SerializationDebugger extends Logging { +private[spark] object SerializationDebugger extends Logging { /** * Improve the given NotSerializableException with the serialization path leading from the given @@ -62,7 +62,7 @@ private[serializer] object SerializationDebugger extends Logging { * * It does not yet handle writeObject override, but that shouldn't be too hard to do either. */ - def find(obj: Any): List[String] = { + private[serializer] def find(obj: Any): List[String] = { new SerializationDebugger().visit(obj, List.empty) } @@ -125,6 +125,12 @@ private[serializer] object SerializationDebugger extends Logging { return List.empty } + /** + * Visit an externalizable object. + * Since writeExternal() can choose to add arbitrary objects at the time of serialization, + * the only way to capture all the objects it will serialize is by using a + * dummy ObjectOutput that collects all the relevant objects for further testing. + */ private def visitExternalizable(o: java.io.Externalizable, stack: List[String]): List[String] = { val fieldList = new ListObjectOutput @@ -145,17 +151,50 @@ private[serializer] object SerializationDebugger extends Logging { // An object contains multiple slots in serialization. // Get the slots and visit fields in all of them. val (finalObj, desc) = findObjectAndDescriptor(o) + + // If the object has been replaced using writeReplace(), + // then call visit() on it again to test its type again. + if (!finalObj.eq(o)) { + return visit(finalObj, s"writeReplace data (class: ${finalObj.getClass.getName})" :: stack) + } + + // Every class is associated with one or more "slots", each slot refers to the parent + // classes of this class. These slots are used by the ObjectOutputStream + // serialization code to recursively serialize the fields of an object and + // its parent classes. For example, if there are the following classes. + // + // class ParentClass(parentField: Int) + // class ChildClass(childField: Int) extends ParentClass(1) + // + // Then serializing the an object Obj of type ChildClass requires first serializing the fields + // of ParentClass (that is, parentField), and then serializing the fields of ChildClass + // (that is, childField). Correspondingly, there will be two slots related to this object: + // + // 1. ParentClass slot, which will be used to serialize parentField of Obj + // 2. ChildClass slot, which will be used to serialize childField fields of Obj + // + // The following code uses the description of each slot to find the fields in the + // corresponding object to visit. + // val slotDescs = desc.getSlotDescs var i = 0 while (i < slotDescs.length) { val slotDesc = slotDescs(i) if (slotDesc.hasWriteObjectMethod) { - // TODO: Handle classes that specify writeObject method. + // If the class type corresponding to current slot has writeObject() defined, + // then its not obvious which fields of the class will be serialized as the writeObject() + // can choose arbitrary fields for serialization. This case is handled separately. + val elem = s"writeObject data (class: ${slotDesc.getName})" + val childStack = visitSerializableWithWriteObjectMethod(finalObj, elem :: stack) + if (childStack.nonEmpty) { + return childStack + } } else { + // Visit all the fields objects of the class corresponding to the current slot. val fields: Array[ObjectStreamField] = slotDesc.getFields val objFieldValues: Array[Object] = new Array[Object](slotDesc.getNumObjFields) val numPrims = fields.length - objFieldValues.length - desc.getObjFieldValues(finalObj, objFieldValues) + slotDesc.getObjFieldValues(finalObj, objFieldValues) var j = 0 while (j < objFieldValues.length) { @@ -169,18 +208,54 @@ private[serializer] object SerializationDebugger extends Logging { } j += 1 } - } i += 1 } return List.empty } + + /** + * Visit a serializable object which has the writeObject() defined. + * Since writeObject() can choose to add arbitrary objects at the time of serialization, + * the only way to capture all the objects it will serialize is by using a + * dummy ObjectOutputStream that collects all the relevant fields for further testing. + * This is similar to how externalizable objects are visited. + */ + private def visitSerializableWithWriteObjectMethod( + o: Object, stack: List[String]): List[String] = { + val innerObjectsCatcher = new ListObjectOutputStream + var notSerializableFound = false + try { + innerObjectsCatcher.writeObject(o) + } catch { + case io: IOException => + notSerializableFound = true + } + + // If something was not serializable, then visit the captured objects. + // Otherwise, all the captured objects are safely serializable, so no need to visit them. + // As an optimization, just added them to the visited list. + if (notSerializableFound) { + val innerObjects = innerObjectsCatcher.outputArray + var k = 0 + while (k < innerObjects.length) { + val childStack = visit(innerObjects(k), stack) + if (childStack.nonEmpty) { + return childStack + } + k += 1 + } + } else { + visited ++= innerObjectsCatcher.outputArray + } + return List.empty + } } /** * Find the object to serialize and the associated [[ObjectStreamClass]]. This method handles * writeReplace in Serializable. It starts with the object itself, and keeps calling the - * writeReplace method until there is no more + * writeReplace method until there is no more. */ @tailrec private def findObjectAndDescriptor(o: Object): (Object, ObjectStreamClass) = { @@ -220,6 +295,31 @@ private[serializer] object SerializationDebugger extends Logging { override def writeByte(i: Int): Unit = {} } + /** An output stream that emulates /dev/null */ + private class NullOutputStream extends OutputStream { + override def write(b: Int) { } + } + + /** + * A dummy [[ObjectOutputStream]] that saves the list of objects written to it and returns + * them through `outputArray`. This works by using the [[ObjectOutputStream]]'s `replaceObject()` + * method which gets called on every object, only if replacing is enabled. So this subclass + * of [[ObjectOutputStream]] enabled replacing, and uses replaceObject to get the objects that + * are being serializabled. The serialized bytes are ignored by sending them to a + * [[NullOutputStream]], which acts like a /dev/null. + */ + private class ListObjectOutputStream extends ObjectOutputStream(new NullOutputStream) { + private val output = new mutable.ArrayBuffer[Any] + this.enableReplaceObject(true) + + def outputArray: Array[Any] = output.toArray + + override def replaceObject(obj: Object): Object = { + output += obj + obj + } + } + /** An implicit class that allows us to call private methods of ObjectStreamClass. */ implicit class ObjectStreamClassMethods(val desc: ObjectStreamClass) extends AnyVal { def getSlotDescs: Array[ObjectStreamClass] = { diff --git a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala index 6078c9d433ebf..bd2704dc81871 100644 --- a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala @@ -19,6 +19,7 @@ package org.apache.spark.serializer import java.io._ import java.nio.ByteBuffer +import javax.annotation.concurrent.NotThreadSafe import scala.reflect.ClassTag @@ -114,8 +115,12 @@ object Serializer { /** * :: DeveloperApi :: * An instance of a serializer, for use by one thread at a time. + * + * It is legal to create multiple serialization / deserialization streams from the same + * SerializerInstance as long as those streams are all used within the same thread. */ @DeveloperApi +@NotThreadSafe abstract class SerializerInstance { def serialize[T: ClassTag](t: T): ByteBuffer @@ -177,6 +182,7 @@ abstract class DeserializationStream { } catch { case eof: EOFException => finished = true + null } } diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala index 6ad427bcac7f9..6c3b3080d2605 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala @@ -76,7 +76,7 @@ private[spark] class FileShuffleBlockResolver(conf: SparkConf) private val consolidateShuffleFiles = conf.getBoolean("spark.shuffle.consolidateFiles", false) - // Use getSizeAsKb (not bytes) to maintain backwards compatibility of on units are provided + // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided private val bufferSize = conf.getSizeAsKb("spark.shuffle.file.buffer", "32k").toInt * 1024 /** diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala index f6e6fe5defe09..4cc4ef5f1886e 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala @@ -17,14 +17,17 @@ package org.apache.spark.shuffle +import java.io.IOException + import org.apache.spark.scheduler.MapStatus /** * Obtained inside a map task to write out records to the shuffle system. */ -private[spark] trait ShuffleWriter[K, V] { +private[spark] abstract class ShuffleWriter[K, V] { /** Write a sequence of records to this task's output */ - def write(records: Iterator[_ <: Product2[K, V]]): Unit + @throws[IOException] + def write(records: Iterator[Product2[K, V]]): Unit /** Close this writer, passing along whether the map completed */ def stop(success: Boolean): Option[MapStatus] diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala index 80374adc44296..597d46a3d2223 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala @@ -80,7 +80,7 @@ private[hash] object BlockStoreShuffleFetcher extends Logging { blocksByAddress, serializer, // Note: we use getSizeAsMb when no suffix is provided for backwards compatibility - SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024) + SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024) val itr = blockFetcherItr.flatMap(unpackBlock) val completionIter = CompletionIterator[T, Iterator[T]](itr, { diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala index 897f0a5dc5bcc..eb87cee15903c 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala @@ -49,7 +49,7 @@ private[spark] class HashShuffleWriter[K, V]( writeMetrics) /** Write a bunch of records to this task's output */ - override def write(records: Iterator[_ <: Product2[K, V]]): Unit = { + override def write(records: Iterator[Product2[K, V]]): Unit = { val iter = if (dep.aggregator.isDefined) { if (dep.mapSideCombine) { dep.aggregator.get.combineValuesByKey(records, context) diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala index 15842941daaab..d7fab351ca3b8 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala @@ -72,7 +72,7 @@ private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager true } - override def shuffleBlockResolver: IndexShuffleBlockResolver = { + override val shuffleBlockResolver: IndexShuffleBlockResolver = { indexShuffleBlockResolver } diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala index add2656294ca2..5865e7640c1cf 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala @@ -17,9 +17,10 @@ package org.apache.spark.shuffle.sort -import org.apache.spark.{MapOutputTracker, SparkEnv, Logging, TaskContext} +import org.apache.spark._ import org.apache.spark.executor.ShuffleWriteMetrics import org.apache.spark.scheduler.MapStatus +import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.{IndexShuffleBlockResolver, ShuffleWriter, BaseShuffleHandle} import org.apache.spark.storage.ShuffleBlockId import org.apache.spark.util.collection.ExternalSorter @@ -35,7 +36,7 @@ private[spark] class SortShuffleWriter[K, V, C]( private val blockManager = SparkEnv.get.blockManager - private var sorter: ExternalSorter[K, V, _] = null + private var sorter: SortShuffleFileWriter[K, V] = null // Are we in the process of stopping? Because map tasks can call stop() with success = true // and then call stop() with success = false if they get an exception, we want to make sure @@ -48,19 +49,28 @@ private[spark] class SortShuffleWriter[K, V, C]( context.taskMetrics.shuffleWriteMetrics = Some(writeMetrics) /** Write a bunch of records to this task's output */ - override def write(records: Iterator[_ <: Product2[K, V]]): Unit = { - if (dep.mapSideCombine) { + override def write(records: Iterator[Product2[K, V]]): Unit = { + sorter = if (dep.mapSideCombine) { require(dep.aggregator.isDefined, "Map-side combine without Aggregator specified!") - sorter = new ExternalSorter[K, V, C]( + new ExternalSorter[K, V, C]( dep.aggregator, Some(dep.partitioner), dep.keyOrdering, dep.serializer) - sorter.insertAll(records) + } else if (SortShuffleWriter.shouldBypassMergeSort( + SparkEnv.get.conf, dep.partitioner.numPartitions, aggregator = None, keyOrdering = None)) { + // If there are fewer than spark.shuffle.sort.bypassMergeThreshold partitions and we don't + // need local aggregation and sorting, write numPartitions files directly and just concatenate + // them at the end. This avoids doing serialization and deserialization twice to merge + // together the spilled files, which would happen with the normal code path. The downside is + // having multiple files open at a time and thus more memory allocated to buffers. + new BypassMergeSortShuffleWriter[K, V](SparkEnv.get.conf, blockManager, dep.partitioner, + writeMetrics, Serializer.getSerializer(dep.serializer)) } else { // In this case we pass neither an aggregator nor an ordering to the sorter, because we don't // care whether the keys get sorted in each partition; that will be done on the reduce side // if the operation being run is sortByKey. - sorter = new ExternalSorter[K, V, V](None, Some(dep.partitioner), None, dep.serializer) - sorter.insertAll(records) + new ExternalSorter[K, V, V]( + aggregator = None, Some(dep.partitioner), ordering = None, dep.serializer) } + sorter.insertAll(records) // Don't bother including the time to open the merged output file in the shuffle write time, // because it just opens a single file, so is typically too fast to measure accurately @@ -100,3 +110,13 @@ private[spark] class SortShuffleWriter[K, V, C]( } } +private[spark] object SortShuffleWriter { + def shouldBypassMergeSort( + conf: SparkConf, + numPartitions: Int, + aggregator: Option[Aggregator[_, _, _]], + keyOrdering: Option[Ordering[_]]): Boolean = { + val bypassMergeThreshold: Int = conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) + numPartitions <= bypassMergeThreshold && aggregator.isEmpty && keyOrdering.isEmpty + } +} diff --git a/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala new file mode 100644 index 0000000000000..df7bbd64247dd --- /dev/null +++ b/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala @@ -0,0 +1,202 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.shuffle.unsafe + +import java.util.Collections +import java.util.concurrent.ConcurrentHashMap + +import org.apache.spark._ +import org.apache.spark.serializer.Serializer +import org.apache.spark.shuffle._ +import org.apache.spark.shuffle.sort.SortShuffleManager + +/** + * Subclass of [[BaseShuffleHandle]], used to identify when we've chosen to use the new shuffle. + */ +private[spark] class UnsafeShuffleHandle[K, V]( + shuffleId: Int, + numMaps: Int, + dependency: ShuffleDependency[K, V, V]) + extends BaseShuffleHandle(shuffleId, numMaps, dependency) { +} + +private[spark] object UnsafeShuffleManager extends Logging { + + /** + * The maximum number of shuffle output partitions that UnsafeShuffleManager supports. + */ + val MAX_SHUFFLE_OUTPUT_PARTITIONS = PackedRecordPointer.MAXIMUM_PARTITION_ID + 1 + + /** + * Helper method for determining whether a shuffle should use the optimized unsafe shuffle + * path or whether it should fall back to the original sort-based shuffle. + */ + def canUseUnsafeShuffle[K, V, C](dependency: ShuffleDependency[K, V, C]): Boolean = { + val shufId = dependency.shuffleId + val serializer = Serializer.getSerializer(dependency.serializer) + if (!serializer.supportsRelocationOfSerializedObjects) { + log.debug(s"Can't use UnsafeShuffle for shuffle $shufId because the serializer, " + + s"${serializer.getClass.getName}, does not support object relocation") + false + } else if (dependency.aggregator.isDefined) { + log.debug(s"Can't use UnsafeShuffle for shuffle $shufId because an aggregator is defined") + false + } else if (dependency.partitioner.numPartitions > MAX_SHUFFLE_OUTPUT_PARTITIONS) { + log.debug(s"Can't use UnsafeShuffle for shuffle $shufId because it has more than " + + s"$MAX_SHUFFLE_OUTPUT_PARTITIONS partitions") + false + } else { + log.debug(s"Can use UnsafeShuffle for shuffle $shufId") + true + } + } +} + +/** + * A shuffle implementation that uses directly-managed memory to implement several performance + * optimizations for certain types of shuffles. In cases where the new performance optimizations + * cannot be applied, this shuffle manager delegates to [[SortShuffleManager]] to handle those + * shuffles. + * + * UnsafeShuffleManager's optimizations will apply when _all_ of the following conditions hold: + * + * - The shuffle dependency specifies no aggregation or output ordering. + * - The shuffle serializer supports relocation of serialized values (this is currently supported + * by KryoSerializer and Spark SQL's custom serializers). + * - The shuffle produces fewer than 16777216 output partitions. + * - No individual record is larger than 128 MB when serialized. + * + * In addition, extra spill-merging optimizations are automatically applied when the shuffle + * compression codec supports concatenation of serialized streams. This is currently supported by + * Spark's LZF serializer. + * + * At a high-level, UnsafeShuffleManager's design is similar to Spark's existing SortShuffleManager. + * In sort-based shuffle, incoming records are sorted according to their target partition ids, then + * written to a single map output file. Reducers fetch contiguous regions of this file in order to + * read their portion of the map output. In cases where the map output data is too large to fit in + * memory, sorted subsets of the output can are spilled to disk and those on-disk files are merged + * to produce the final output file. + * + * UnsafeShuffleManager optimizes this process in several ways: + * + * - Its sort operates on serialized binary data rather than Java objects, which reduces memory + * consumption and GC overheads. This optimization requires the record serializer to have certain + * properties to allow serialized records to be re-ordered without requiring deserialization. + * See SPARK-4550, where this optimization was first proposed and implemented, for more details. + * + * - It uses a specialized cache-efficient sorter ([[UnsafeShuffleExternalSorter]]) that sorts + * arrays of compressed record pointers and partition ids. By using only 8 bytes of space per + * record in the sorting array, this fits more of the array into cache. + * + * - The spill merging procedure operates on blocks of serialized records that belong to the same + * partition and does not need to deserialize records during the merge. + * + * - When the spill compression codec supports concatenation of compressed data, the spill merge + * simply concatenates the serialized and compressed spill partitions to produce the final output + * partition. This allows efficient data copying methods, like NIO's `transferTo`, to be used + * and avoids the need to allocate decompression or copying buffers during the merge. + * + * For more details on UnsafeShuffleManager's design, see SPARK-7081. + */ +private[spark] class UnsafeShuffleManager(conf: SparkConf) extends ShuffleManager with Logging { + + if (!conf.getBoolean("spark.shuffle.spill", true)) { + logWarning( + "spark.shuffle.spill was set to false, but this is ignored by the tungsten-sort shuffle " + + "manager; its optimized shuffles will continue to spill to disk when necessary.") + } + + private[this] val sortShuffleManager: SortShuffleManager = new SortShuffleManager(conf) + private[this] val shufflesThatFellBackToSortShuffle = + Collections.newSetFromMap(new ConcurrentHashMap[Int, java.lang.Boolean]()) + private[this] val numMapsForShufflesThatUsedNewPath = new ConcurrentHashMap[Int, Int]() + + /** + * Register a shuffle with the manager and obtain a handle for it to pass to tasks. + */ + override def registerShuffle[K, V, C]( + shuffleId: Int, + numMaps: Int, + dependency: ShuffleDependency[K, V, C]): ShuffleHandle = { + if (UnsafeShuffleManager.canUseUnsafeShuffle(dependency)) { + new UnsafeShuffleHandle[K, V]( + shuffleId, numMaps, dependency.asInstanceOf[ShuffleDependency[K, V, V]]) + } else { + new BaseShuffleHandle(shuffleId, numMaps, dependency) + } + } + + /** + * Get a reader for a range of reduce partitions (startPartition to endPartition-1, inclusive). + * Called on executors by reduce tasks. + */ + override def getReader[K, C]( + handle: ShuffleHandle, + startPartition: Int, + endPartition: Int, + context: TaskContext): ShuffleReader[K, C] = { + sortShuffleManager.getReader(handle, startPartition, endPartition, context) + } + + /** Get a writer for a given partition. Called on executors by map tasks. */ + override def getWriter[K, V]( + handle: ShuffleHandle, + mapId: Int, + context: TaskContext): ShuffleWriter[K, V] = { + handle match { + case unsafeShuffleHandle: UnsafeShuffleHandle[K, V] => + numMapsForShufflesThatUsedNewPath.putIfAbsent(handle.shuffleId, unsafeShuffleHandle.numMaps) + val env = SparkEnv.get + new UnsafeShuffleWriter( + env.blockManager, + shuffleBlockResolver.asInstanceOf[IndexShuffleBlockResolver], + context.taskMemoryManager(), + env.shuffleMemoryManager, + unsafeShuffleHandle, + mapId, + context, + env.conf) + case other => + shufflesThatFellBackToSortShuffle.add(handle.shuffleId) + sortShuffleManager.getWriter(handle, mapId, context) + } + } + + /** Remove a shuffle's metadata from the ShuffleManager. */ + override def unregisterShuffle(shuffleId: Int): Boolean = { + if (shufflesThatFellBackToSortShuffle.remove(shuffleId)) { + sortShuffleManager.unregisterShuffle(shuffleId) + } else { + Option(numMapsForShufflesThatUsedNewPath.remove(shuffleId)).foreach { numMaps => + (0 until numMaps).foreach { mapId => + shuffleBlockResolver.removeDataByMap(shuffleId, mapId) + } + } + true + } + } + + override val shuffleBlockResolver: IndexShuffleBlockResolver = { + sortShuffleManager.shuffleBlockResolver + } + + /** Shut down this ShuffleManager. */ + override def stop(): Unit = { + sortShuffleManager.stop() + } +} diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala index 50608588f09ae..390c136df79b3 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala @@ -169,7 +169,7 @@ private[v1] object AllStagesResource { val outputMetrics: Option[OutputMetricDistributions] = new MetricHelper[InternalOutputMetrics, OutputMetricDistributions](rawMetrics, quantiles) { - def getSubmetrics(raw:InternalTaskMetrics): Option[InternalOutputMetrics] = { + def getSubmetrics(raw: InternalTaskMetrics): Option[InternalOutputMetrics] = { raw.outputMetrics } def build: OutputMetricDistributions = new OutputMetricDistributions( @@ -284,7 +284,7 @@ private[v1] object AllStagesResource { * the options (returning None if the metrics are all empty), and extract the quantiles for each * metric. After creating an instance, call metricOption to get the result type. */ -private[v1] abstract class MetricHelper[I,O]( +private[v1] abstract class MetricHelper[I, O]( rawMetrics: Seq[InternalTaskMetrics], quantiles: Array[Double]) { diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala similarity index 86% rename from core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala rename to core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala index c3ec45f54681b..50b6ba67e9931 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala @@ -16,6 +16,7 @@ */ package org.apache.spark.status.api.v1 +import java.util.zip.ZipOutputStream import javax.servlet.ServletContext import javax.ws.rs._ import javax.ws.rs.core.{Context, Response} @@ -39,7 +40,7 @@ import org.apache.spark.ui.SparkUI * HistoryServerSuite. */ @Path("/v1") -private[v1] class JsonRootResource extends UIRootFromServletContext { +private[v1] class ApiRootResource extends UIRootFromServletContext { @Path("applications") def getApplicationList(): ApplicationListResource = { @@ -101,7 +102,7 @@ private[v1] class JsonRootResource extends UIRootFromServletContext { @Path("applications/{appId}/stages") - def getStages(@PathParam("appId") appId: String): AllStagesResource= { + def getStages(@PathParam("appId") appId: String): AllStagesResource = { uiRoot.withSparkUI(appId, None) { ui => new AllStagesResource(ui) } @@ -110,14 +111,14 @@ private[v1] class JsonRootResource extends UIRootFromServletContext { @Path("applications/{appId}/{attemptId}/stages") def getStages( @PathParam("appId") appId: String, - @PathParam("attemptId") attemptId: String): AllStagesResource= { + @PathParam("attemptId") attemptId: String): AllStagesResource = { uiRoot.withSparkUI(appId, Some(attemptId)) { ui => new AllStagesResource(ui) } } @Path("applications/{appId}/stages/{stageId: \\d+}") - def getStage(@PathParam("appId") appId: String): OneStageResource= { + def getStage(@PathParam("appId") appId: String): OneStageResource = { uiRoot.withSparkUI(appId, None) { ui => new OneStageResource(ui) } @@ -164,14 +165,26 @@ private[v1] class JsonRootResource extends UIRootFromServletContext { } } + @Path("applications/{appId}/logs") + def getEventLogs( + @PathParam("appId") appId: String): EventLogDownloadResource = { + new EventLogDownloadResource(uiRoot, appId, None) + } + + @Path("applications/{appId}/{attemptId}/logs") + def getEventLogs( + @PathParam("appId") appId: String, + @PathParam("attemptId") attemptId: String): EventLogDownloadResource = { + new EventLogDownloadResource(uiRoot, appId, Some(attemptId)) + } } -private[spark] object JsonRootResource { +private[spark] object ApiRootResource { - def getJsonServlet(uiRoot: UIRoot): ServletContextHandler = { + def getServletHandler(uiRoot: UIRoot): ServletContextHandler = { val jerseyContext = new ServletContextHandler(ServletContextHandler.NO_SESSIONS) - jerseyContext.setContextPath("/json") - val holder:ServletHolder = new ServletHolder(classOf[ServletContainer]) + jerseyContext.setContextPath("/api") + val holder: ServletHolder = new ServletHolder(classOf[ServletContainer]) holder.setInitParameter("com.sun.jersey.config.property.resourceConfigClass", "com.sun.jersey.api.core.PackagesResourceConfig") holder.setInitParameter("com.sun.jersey.config.property.packages", @@ -193,6 +206,17 @@ private[spark] trait UIRoot { def getSparkUI(appKey: String): Option[SparkUI] def getApplicationInfoList: Iterator[ApplicationInfo] + /** + * Write the event logs for the given app to the [[ZipOutputStream]] instance. If attemptId is + * [[None]], event logs for all attempts of this application will be written out. + */ + def writeEventLogs(appId: String, attemptId: Option[String], zipStream: ZipOutputStream): Unit = { + Response.serverError() + .entity("Event logs are only available through the history server.") + .status(Response.Status.SERVICE_UNAVAILABLE) + .build() + } + /** * Get the spark UI with the given appID, and apply a function * to it. If there is no such app, throw an appropriate exception diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/EventLogDownloadResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/EventLogDownloadResource.scala new file mode 100644 index 0000000000000..22e21f0c62a29 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/status/api/v1/EventLogDownloadResource.scala @@ -0,0 +1,70 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.status.api.v1 + +import java.io.OutputStream +import java.util.zip.ZipOutputStream +import javax.ws.rs.{GET, Produces} +import javax.ws.rs.core.{MediaType, Response, StreamingOutput} + +import scala.util.control.NonFatal + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.deploy.SparkHadoopUtil + +@Produces(Array(MediaType.APPLICATION_OCTET_STREAM)) +private[v1] class EventLogDownloadResource( + val uIRoot: UIRoot, + val appId: String, + val attemptId: Option[String]) extends Logging { + val conf = SparkHadoopUtil.get.newConfiguration(new SparkConf) + + @GET + def getEventLogs(): Response = { + try { + val fileName = { + attemptId match { + case Some(id) => s"eventLogs-$appId-$id.zip" + case None => s"eventLogs-$appId.zip" + } + } + + val stream = new StreamingOutput { + override def write(output: OutputStream): Unit = { + val zipStream = new ZipOutputStream(output) + try { + uIRoot.writeEventLogs(appId, attemptId, zipStream) + } finally { + zipStream.close() + } + + } + } + + Response.ok(stream) + .header("Content-Disposition", s"attachment; filename=$fileName") + .header("Content-Type", MediaType.APPLICATION_OCTET_STREAM) + .build() + } catch { + case NonFatal(e) => + Response.serverError() + .entity(s"Event logs are not available for app: $appId.") + .status(Response.Status.SERVICE_UNAVAILABLE) + .build() + } + } +} diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala index 07b224fac4786..dfdc09c6caf3b 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala @@ -25,7 +25,7 @@ import org.apache.spark.ui.SparkUI private[v1] class OneRDDResource(ui: SparkUI) { @GET - def rddData(@PathParam("rddId") rddId: Int): RDDStorageInfo = { + def rddData(@PathParam("rddId") rddId: Int): RDDStorageInfo = { AllRDDResource.getRDDStorageInfo(rddId, ui.storageListener, true).getOrElse( throw new NotFoundException(s"no rdd found w/ id $rddId") ) diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala index fd24aea63a8a1..f9812f06cf527 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala @@ -83,7 +83,7 @@ private[v1] class OneStageResource(ui: SparkUI) { withStageAttempt(stageId, stageAttemptId) { stage => val tasks = stage.ui.taskData.values.map{AllStagesResource.convertTaskData}.toIndexedSeq .sorted(OneStageResource.ordering(sortBy)) - tasks.slice(offset, offset + length) + tasks.slice(offset, offset + length) } } diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala b/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala index cee29786c3019..0c71cd2382225 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala @@ -16,40 +16,33 @@ */ package org.apache.spark.status.api.v1 -import java.text.SimpleDateFormat +import java.text.{ParseException, SimpleDateFormat} import java.util.TimeZone import javax.ws.rs.WebApplicationException import javax.ws.rs.core.Response import javax.ws.rs.core.Response.Status -import scala.util.Try - private[v1] class SimpleDateParam(val originalValue: String) { - val timestamp: Long = { - SimpleDateParam.formats.collectFirst { - case fmt if Try(fmt.parse(originalValue)).isSuccess => - fmt.parse(originalValue).getTime() - }.getOrElse( - throw new WebApplicationException( - Response - .status(Status.BAD_REQUEST) - .entity("Couldn't parse date: " + originalValue) - .build() - ) - ) - } -} -private[v1] object SimpleDateParam { - - val formats: Seq[SimpleDateFormat] = { - - val gmtDay = new SimpleDateFormat("yyyy-MM-dd") - gmtDay.setTimeZone(TimeZone.getTimeZone("GMT")) - - Seq( - new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSz"), - gmtDay - ) + val timestamp: Long = { + val format = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSz") + try { + format.parse(originalValue).getTime() + } catch { + case _: ParseException => + val gmtDay = new SimpleDateFormat("yyyy-MM-dd") + gmtDay.setTimeZone(TimeZone.getTimeZone("GMT")) + try { + gmtDay.parse(originalValue).getTime() + } catch { + case _: ParseException => + throw new WebApplicationException( + Response + .status(Status.BAD_REQUEST) + .entity("Couldn't parse date: " + originalValue) + .build() + ) + } + } } } diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala index ef3c8570d8186..2bec64f2ef02b 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala @@ -134,7 +134,7 @@ class StageData private[spark]( val accumulatorUpdates: Seq[AccumulableInfo], val tasks: Option[Map[Long, TaskData]], - val executorSummary:Option[Map[String,ExecutorStageSummary]]) + val executorSummary: Option[Map[String, ExecutorStageSummary]]) class TaskData private[spark]( val taskId: Long, diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index cc794e5c90ffa..1beafa1771448 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -17,12 +17,11 @@ package org.apache.spark.storage -import java.io.{BufferedOutputStream, ByteArrayOutputStream, File, InputStream, OutputStream} +import java.io._ import java.nio.{ByteBuffer, MappedByteBuffer} import scala.collection.mutable.{ArrayBuffer, HashMap} -import scala.concurrent.{Await, Future} -import scala.concurrent.ExecutionContext.Implicits.global +import scala.concurrent.{ExecutionContext, Await, Future} import scala.concurrent.duration._ import scala.util.Random @@ -77,12 +76,17 @@ private[spark] class BlockManager( private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo] + private val futureExecutionContext = ExecutionContext.fromExecutorService( + ThreadUtils.newDaemonCachedThreadPool("block-manager-future", 128)) + // Actual storage of where blocks are kept private var externalBlockStoreInitialized = false private[spark] val memoryStore = new MemoryStore(this, maxMemory) private[spark] val diskStore = new DiskStore(this, diskBlockManager) - private[spark] lazy val externalBlockStore: ExternalBlockStore = + private[spark] lazy val externalBlockStore: ExternalBlockStore = { + externalBlockStoreInitialized = true new ExternalBlockStore(this, executorId) + } private[spark] val externalShuffleServiceEnabled = conf.getBoolean("spark.shuffle.service.enabled", false) @@ -266,11 +270,13 @@ private[spark] class BlockManager( asyncReregisterLock.synchronized { if (asyncReregisterTask == null) { asyncReregisterTask = Future[Unit] { + // This is a blocking action and should run in futureExecutionContext which is a cached + // thread pool reregister() asyncReregisterLock.synchronized { asyncReregisterTask = null } - } + }(futureExecutionContext) } } } @@ -485,16 +491,17 @@ private[spark] class BlockManager( if (level.useOffHeap) { logDebug(s"Getting block $blockId from ExternalBlockStore") if (externalBlockStore.contains(blockId)) { - externalBlockStore.getBytes(blockId) match { - case Some(bytes) => - if (!asBlockResult) { - return Some(bytes) - } else { - return Some(new BlockResult( - dataDeserialize(blockId, bytes), DataReadMethod.Memory, info.size)) - } + val result = if (asBlockResult) { + externalBlockStore.getValues(blockId) + .map(new BlockResult(_, DataReadMethod.Memory, info.size)) + } else { + externalBlockStore.getBytes(blockId) + } + result match { + case Some(values) => + return result case None => - logDebug(s"Block $blockId not found in externalBlockStore") + logDebug(s"Block $blockId not found in ExternalBlockStore") } } } @@ -744,7 +751,11 @@ private[spark] class BlockManager( case b: ByteBufferValues if putLevel.replication > 1 => // Duplicate doesn't copy the bytes, but just creates a wrapper val bufferView = b.buffer.duplicate() - Future { replicate(blockId, bufferView, putLevel) } + Future { + // This is a blocking action and should run in futureExecutionContext which is a cached + // thread pool + replicate(blockId, bufferView, putLevel) + }(futureExecutionContext) case _ => null } @@ -1198,8 +1209,19 @@ private[spark] class BlockManager( bytes: ByteBuffer, serializer: Serializer = defaultSerializer): Iterator[Any] = { bytes.rewind() - val stream = wrapForCompression(blockId, new ByteBufferInputStream(bytes, true)) - serializer.newInstance().deserializeStream(stream).asIterator + dataDeserializeStream(blockId, new ByteBufferInputStream(bytes, true), serializer) + } + + /** + * Deserializes a InputStream into an iterator of values and disposes of it when the end of + * the iterator is reached. + */ + def dataDeserializeStream( + blockId: BlockId, + inputStream: InputStream, + serializer: Serializer = defaultSerializer): Iterator[Any] = { + val stream = new BufferedInputStream(inputStream) + serializer.newInstance().deserializeStream(wrapForCompression(blockId, stream)).asIterator } def stop(): Unit = { @@ -1218,6 +1240,7 @@ private[spark] class BlockManager( } metadataCleaner.cancel() broadcastCleaner.cancel() + futureExecutionContext.shutdownNow() logInfo("BlockManager stopped") } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index a85e1c7632973..7cdae22b0e253 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -17,13 +17,14 @@ package org.apache.spark.storage +import scala.collection.Iterable +import scala.collection.generic.CanBuildFrom import scala.concurrent.{Await, Future} -import scala.concurrent.ExecutionContext.Implicits.global import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.RpcUtils +import org.apache.spark.util.{ThreadUtils, RpcUtils} private[spark] class BlockManagerMaster( @@ -102,8 +103,8 @@ class BlockManagerMaster( val future = driverEndpoint.askWithRetry[Future[Seq[Int]]](RemoveRdd(rddId)) future.onFailure { case e: Exception => - logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}") - } + logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}", e) + }(ThreadUtils.sameThread) if (blocking) { Await.result(future, timeout) } @@ -114,8 +115,8 @@ class BlockManagerMaster( val future = driverEndpoint.askWithRetry[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) future.onFailure { case e: Exception => - logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}") - } + logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}", e) + }(ThreadUtils.sameThread) if (blocking) { Await.result(future, timeout) } @@ -128,8 +129,8 @@ class BlockManagerMaster( future.onFailure { case e: Exception => logWarning(s"Failed to remove broadcast $broadcastId" + - s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}") - } + s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}", e) + }(ThreadUtils.sameThread) if (blocking) { Await.result(future, timeout) } @@ -169,11 +170,17 @@ class BlockManagerMaster( val response = driverEndpoint. askWithRetry[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) val (blockManagerIds, futures) = response.unzip - val result = Await.result(Future.sequence(futures), timeout) - if (result == null) { + implicit val sameThread = ThreadUtils.sameThread + val cbf = + implicitly[ + CanBuildFrom[Iterable[Future[Option[BlockStatus]]], + Option[BlockStatus], + Iterable[Option[BlockStatus]]]] + val blockStatus = Await.result( + Future.sequence[Option[BlockStatus], Iterable](futures)(cbf, ThreadUtils.sameThread), timeout) + if (blockStatus == null) { throw new SparkException("BlockManager returned null for BlockStatus query: " + blockId) } - val blockStatus = result.asInstanceOf[Iterable[Option[BlockStatus]]] blockManagerIds.zip(blockStatus).flatMap { case (blockManagerId, status) => status.map { s => (blockManagerId, s) } }.toMap @@ -195,6 +202,14 @@ class BlockManagerMaster( Await.result(future, timeout) } + /** + * Find out if the executor has cached blocks. This method does not consider broadcast blocks, + * since they are not reported the master. + */ + def hasCachedBlocks(executorId: String): Boolean = { + driverEndpoint.askWithRetry[Boolean](HasCachedBlocks(executorId)) + } + /** Stop the driver endpoint, called only on the Spark driver node */ def stop() { if (driverEndpoint != null && isDriver) { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 3afb4c3c02e2d..68ed9096731c5 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -19,6 +19,7 @@ package org.apache.spark.storage import java.util.{HashMap => JHashMap} +import scala.collection.immutable.HashSet import scala.collection.mutable import scala.collection.JavaConversions._ import scala.concurrent.{ExecutionContext, Future} @@ -112,6 +113,17 @@ class BlockManagerMasterEndpoint( case BlockManagerHeartbeat(blockManagerId) => context.reply(heartbeatReceived(blockManagerId)) + case HasCachedBlocks(executorId) => + blockManagerIdByExecutor.get(executorId) match { + case Some(bm) => + if (blockManagerInfo.contains(bm)) { + val bmInfo = blockManagerInfo(bm) + context.reply(bmInfo.cachedBlocks.nonEmpty) + } else { + context.reply(false) + } + case None => context.reply(false) + } } private def removeRdd(rddId: Int): Future[Seq[Int]] = { @@ -292,16 +304,16 @@ class BlockManagerMasterEndpoint( blockManagerIdByExecutor.get(id.executorId) match { case Some(oldId) => // A block manager of the same executor already exists, so remove it (assumed dead) - logError("Got two different block manager registrations on same executor - " + logError("Got two different block manager registrations on same executor - " + s" will replace old one $oldId with new one $id") - removeExecutor(id.executorId) + removeExecutor(id.executorId) case None => } logInfo("Registering block manager %s with %s RAM, %s".format( id.hostPort, Utils.bytesToString(maxMemSize), id)) - + blockManagerIdByExecutor(id.executorId) = id - + blockManagerInfo(id) = new BlockManagerInfo( id, System.currentTimeMillis(), maxMemSize, slaveEndpoint) } @@ -418,6 +430,9 @@ private[spark] class BlockManagerInfo( // Mapping from block id to its status. private val _blocks = new JHashMap[BlockId, BlockStatus] + // Cached blocks held by this BlockManager. This does not include broadcast blocks. + private val _cachedBlocks = new mutable.HashSet[BlockId] + def getStatus(blockId: BlockId): Option[BlockStatus] = Option(_blocks.get(blockId)) def updateLastSeenMs() { @@ -451,27 +466,35 @@ private[spark] class BlockManagerInfo( * and the diskSize here indicates the data size in or dropped to disk. * They can be both larger than 0, when a block is dropped from memory to disk. * Therefore, a safe way to set BlockStatus is to set its info in accurate modes. */ + var blockStatus: BlockStatus = null if (storageLevel.useMemory) { - _blocks.put(blockId, BlockStatus(storageLevel, memSize, 0, 0)) + blockStatus = BlockStatus(storageLevel, memSize, 0, 0) + _blocks.put(blockId, blockStatus) _remainingMem -= memSize logInfo("Added %s in memory on %s (size: %s, free: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(memSize), Utils.bytesToString(_remainingMem))) } if (storageLevel.useDisk) { - _blocks.put(blockId, BlockStatus(storageLevel, 0, diskSize, 0)) + blockStatus = BlockStatus(storageLevel, 0, diskSize, 0) + _blocks.put(blockId, blockStatus) logInfo("Added %s on disk on %s (size: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(diskSize))) } if (storageLevel.useOffHeap) { - _blocks.put(blockId, BlockStatus(storageLevel, 0, 0, externalBlockStoreSize)) + blockStatus = BlockStatus(storageLevel, 0, 0, externalBlockStoreSize) + _blocks.put(blockId, blockStatus) logInfo("Added %s on ExternalBlockStore on %s (size: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(externalBlockStoreSize))) } + if (!blockId.isBroadcast && blockStatus.isCached) { + _cachedBlocks += blockId + } } else if (_blocks.containsKey(blockId)) { // If isValid is not true, drop the block. val blockStatus: BlockStatus = _blocks.get(blockId) _blocks.remove(blockId) + _cachedBlocks -= blockId if (blockStatus.storageLevel.useMemory) { logInfo("Removed %s on %s in memory (size: %s, free: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(blockStatus.memSize), @@ -494,6 +517,7 @@ private[spark] class BlockManagerInfo( _remainingMem += _blocks.get(blockId).memSize _blocks.remove(blockId) } + _cachedBlocks -= blockId } def remainingMem: Long = _remainingMem @@ -502,6 +526,9 @@ private[spark] class BlockManagerInfo( def blocks: JHashMap[BlockId, BlockStatus] = _blocks + // This does not include broadcast blocks. + def cachedBlocks: collection.Set[BlockId] = _cachedBlocks + override def toString: String = "BlockManagerInfo " + timeMs + " " + _remainingMem def clear() { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala index 1683576067fe8..376e9eb48843d 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -42,7 +42,6 @@ private[spark] object BlockManagerMessages { case class RemoveBroadcast(broadcastId: Long, removeFromDriver: Boolean = true) extends ToBlockManagerSlave - ////////////////////////////////////////////////////////////////////////////////// // Messages from slaves to the master. ////////////////////////////////////////////////////////////////////////////////// @@ -108,4 +107,6 @@ private[spark] object BlockManagerMessages { extends ToBlockManagerMaster case class BlockManagerHeartbeat(blockManagerId: BlockManagerId) extends ToBlockManagerMaster + + case class HasCachedBlocks(executorId: String) extends ToBlockManagerMaster } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala index 543df4e1350dd..7478ab0fc2f7a 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala @@ -40,7 +40,7 @@ class BlockManagerSlaveEndpoint( private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool) // Operations that involve removing blocks may be slow and should be done asynchronously - override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case RemoveBlock(blockId) => doAsync[Boolean]("removing block " + blockId, context) { blockManager.removeBlock(blockId) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala index 8569c6f3cbbc3..c5ba9af3e2658 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala @@ -17,9 +17,8 @@ package org.apache.spark.storage -import com.codahale.metrics.{Gauge,MetricRegistry} +import com.codahale.metrics.{Gauge, MetricRegistry} -import org.apache.spark.SparkContext import org.apache.spark.metrics.source.Source private[spark] class BlockManagerSource(val blockManager: BlockManager) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index 8bc4e205bc3c6..7eeabd1e0489c 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -86,16 +86,6 @@ private[spark] class DiskBlockObjectWriter( extends BlockObjectWriter(blockId) with Logging { - /** Intercepts write calls and tracks total time spent writing. Not thread safe. */ - private class TimeTrackingOutputStream(out: OutputStream) extends OutputStream { - override def write(i: Int): Unit = callWithTiming(out.write(i)) - override def write(b: Array[Byte]): Unit = callWithTiming(out.write(b)) - override def write(b: Array[Byte], off: Int, len: Int): Unit = { - callWithTiming(out.write(b, off, len)) - } - override def close(): Unit = out.close() - override def flush(): Unit = out.flush() - } /** The file channel, used for repositioning / truncating the file. */ private var channel: FileChannel = null @@ -105,6 +95,7 @@ private[spark] class DiskBlockObjectWriter( private var objOut: SerializationStream = null private var initialized = false private var hasBeenClosed = false + private var commitAndCloseHasBeenCalled = false /** * Cursors used to represent positions in the file. @@ -136,7 +127,7 @@ private[spark] class DiskBlockObjectWriter( throw new IllegalStateException("Writer already closed. Cannot be reopened.") } fos = new FileOutputStream(file, true) - ts = new TimeTrackingOutputStream(fos) + ts = new TimeTrackingOutputStream(writeMetrics, fos) channel = fos.getChannel() bs = compressStream(new BufferedOutputStream(ts, bufferSize)) objOut = serializerInstance.serializeStream(bs) @@ -150,9 +141,9 @@ private[spark] class DiskBlockObjectWriter( if (syncWrites) { // Force outstanding writes to disk and track how long it takes objOut.flush() - callWithTiming { - fos.getFD.sync() - } + val start = System.nanoTime() + fos.getFD.sync() + writeMetrics.incShuffleWriteTime(System.nanoTime() - start) } } { objOut.close() @@ -177,20 +168,22 @@ private[spark] class DiskBlockObjectWriter( objOut.flush() bs.flush() close() + finalPosition = file.length() + // In certain compression codecs, more bytes are written after close() is called + writeMetrics.incShuffleBytesWritten(finalPosition - reportedPosition) + } else { + finalPosition = file.length() } - finalPosition = file.length() - // In certain compression codecs, more bytes are written after close() is called - writeMetrics.incShuffleBytesWritten(finalPosition - reportedPosition) + commitAndCloseHasBeenCalled = true } // Discard current writes. We do this by flushing the outstanding writes and then // truncating the file to its initial position. override def revertPartialWritesAndClose() { try { - writeMetrics.decShuffleBytesWritten(reportedPosition - initialPosition) - writeMetrics.decShuffleRecordsWritten(numRecordsWritten) - if (initialized) { + writeMetrics.decShuffleBytesWritten(reportedPosition - initialPosition) + writeMetrics.decShuffleRecordsWritten(numRecordsWritten) objOut.flush() bs.flush() close() @@ -238,6 +231,10 @@ private[spark] class DiskBlockObjectWriter( } override def fileSegment(): FileSegment = { + if (!commitAndCloseHasBeenCalled) { + throw new IllegalStateException( + "fileSegment() is only valid after commitAndClose() has been called") + } new FileSegment(file, initialPosition, finalPosition - initialPosition) } @@ -251,12 +248,6 @@ private[spark] class DiskBlockObjectWriter( reportedPosition = pos } - private def callWithTiming(f: => Unit) = { - val start = System.nanoTime() - f - writeMetrics.incShuffleWriteTime(System.nanoTime() - start) - } - // For testing private[spark] override def flush() { objOut.flush() diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index 2a4447705fa65..91ef86389a0c3 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -139,8 +139,8 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon } private def addShutdownHook(): AnyRef = { - Utils.addShutdownHook { () => - logDebug("Shutdown hook called") + Utils.addShutdownHook(Utils.TEMP_DIR_SHUTDOWN_PRIORITY + 1) { () => + logInfo("Shutdown hook called") DiskBlockManager.this.doStop() } } @@ -151,7 +151,7 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon try { Utils.removeShutdownHook(shutdownHook) } catch { - case e: Exception => + case e: Exception => logError(s"Exception while removing shutdown hook.", e) } doStop() diff --git a/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala index 8964762df6af3..f39325a12d244 100644 --- a/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala @@ -32,6 +32,8 @@ import java.nio.ByteBuffer */ private[spark] abstract class ExternalBlockManager { + protected var blockManager: BlockManager = _ + override def toString: String = {"External Block Store"} /** @@ -41,7 +43,9 @@ private[spark] abstract class ExternalBlockManager { * * @throws java.io.IOException if there is any file system failure during the initialization. */ - def init(blockManager: BlockManager, executorId: String): Unit + def init(blockManager: BlockManager, executorId: String): Unit = { + this.blockManager = blockManager + } /** * Drop the block from underlying external block store, if it exists.. @@ -73,6 +77,11 @@ private[spark] abstract class ExternalBlockManager { */ def putBytes(blockId: BlockId, bytes: ByteBuffer): Unit + def putValues(blockId: BlockId, values: Iterator[_]): Unit = { + val bytes = blockManager.dataSerialize(blockId, values) + putBytes(blockId, bytes) + } + /** * Retrieve the block bytes. * @return Some(ByteBuffer) if the block bytes is successfully retrieved @@ -82,6 +91,17 @@ private[spark] abstract class ExternalBlockManager { */ def getBytes(blockId: BlockId): Option[ByteBuffer] + /** + * Retrieve the block data. + * @return Some(Iterator[Any]) if the block data is successfully retrieved + * None if the block does not exist in the external block store. + * + * @throws java.io.IOException if there is any file system failure in getting the block. + */ + def getValues(blockId: BlockId): Option[Iterator[_]] = { + getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer)) + } + /** * Get the size of the block saved in the underlying external block store, * which is saved before by putBytes. diff --git a/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala index 0bf770306ae9b..291394ed34816 100644 --- a/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala @@ -18,9 +18,11 @@ package org.apache.spark.storage import java.nio.ByteBuffer + +import scala.util.control.NonFatal + import org.apache.spark.Logging import org.apache.spark.util.Utils -import scala.util.control.NonFatal /** @@ -40,7 +42,7 @@ private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: externalBlockManager.map(_.getSize(blockId)).getOrElse(0) } catch { case NonFatal(t) => - logError(s"error in getSize from $blockId", t) + logError(s"Error in getSize($blockId)", t) 0L } } @@ -54,7 +56,7 @@ private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: values: Array[Any], level: StorageLevel, returnValues: Boolean): PutResult = { - putIterator(blockId, values.toIterator, level, returnValues) + putIntoExternalBlockStore(blockId, values.toIterator, returnValues) } override def putIterator( @@ -62,42 +64,70 @@ private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: values: Iterator[Any], level: StorageLevel, returnValues: Boolean): PutResult = { - logDebug(s"Attempting to write values for block $blockId") - val bytes = blockManager.dataSerialize(blockId, values) - putIntoExternalBlockStore(blockId, bytes, returnValues) + putIntoExternalBlockStore(blockId, values, returnValues) } private def putIntoExternalBlockStore( blockId: BlockId, - bytes: ByteBuffer, + values: Iterator[_], returnValues: Boolean): PutResult = { - // So that we do not modify the input offsets ! - // duplicate does not copy buffer, so inexpensive - val byteBuffer = bytes.duplicate() - byteBuffer.rewind() - logDebug(s"Attempting to put block $blockId into ExtBlk store") + logTrace(s"Attempting to put block $blockId into ExternalBlockStore") // we should never hit here if externalBlockManager is None. Handle it anyway for safety. try { val startTime = System.currentTimeMillis if (externalBlockManager.isDefined) { - externalBlockManager.get.putBytes(blockId, bytes) + externalBlockManager.get.putValues(blockId, values) + val size = getSize(blockId) + val data = if (returnValues) { + Left(getValues(blockId).get) + } else { + null + } val finishTime = System.currentTimeMillis logDebug("Block %s stored as %s file in ExternalBlockStore in %d ms".format( - blockId, Utils.bytesToString(byteBuffer.limit), finishTime - startTime)) + blockId, Utils.bytesToString(size), finishTime - startTime)) + PutResult(size, data) + } else { + logError(s"Error in putValues($blockId): no ExternalBlockManager has been configured") + PutResult(-1, null, Seq((blockId, BlockStatus.empty))) + } + } catch { + case NonFatal(t) => + logError(s"Error in putValues($blockId)", t) + PutResult(-1, null, Seq((blockId, BlockStatus.empty))) + } + } - if (returnValues) { - PutResult(bytes.limit(), Right(bytes.duplicate())) + private def putIntoExternalBlockStore( + blockId: BlockId, + bytes: ByteBuffer, + returnValues: Boolean): PutResult = { + logTrace(s"Attempting to put block $blockId into ExternalBlockStore") + // we should never hit here if externalBlockManager is None. Handle it anyway for safety. + try { + val startTime = System.currentTimeMillis + if (externalBlockManager.isDefined) { + val byteBuffer = bytes.duplicate() + byteBuffer.rewind() + externalBlockManager.get.putBytes(blockId, byteBuffer) + val size = bytes.limit() + val data = if (returnValues) { + Right(bytes) } else { - PutResult(bytes.limit(), null) + null } + val finishTime = System.currentTimeMillis + logDebug("Block %s stored as %s file in ExternalBlockStore in %d ms".format( + blockId, Utils.bytesToString(size), finishTime - startTime)) + PutResult(size, data) } else { - logError(s"error in putBytes $blockId") - PutResult(bytes.limit(), null, Seq((blockId, BlockStatus.empty))) + logError(s"Error in putBytes($blockId): no ExternalBlockManager has been configured") + PutResult(-1, null, Seq((blockId, BlockStatus.empty))) } } catch { case NonFatal(t) => - logError(s"error in putBytes $blockId", t) - PutResult(bytes.limit(), null, Seq((blockId, BlockStatus.empty))) + logError(s"Error in putBytes($blockId)", t) + PutResult(-1, null, Seq((blockId, BlockStatus.empty))) } } @@ -107,13 +137,19 @@ private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: externalBlockManager.map(_.removeBlock(blockId)).getOrElse(true) } catch { case NonFatal(t) => - logError(s"error in removing $blockId", t) + logError(s"Error in removeBlock($blockId)", t) true } } override def getValues(blockId: BlockId): Option[Iterator[Any]] = { - getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer)) + try { + externalBlockManager.flatMap(_.getValues(blockId)) + } catch { + case NonFatal(t) => + logError(s"Error in getValues($blockId)", t) + None + } } override def getBytes(blockId: BlockId): Option[ByteBuffer] = { @@ -121,7 +157,7 @@ private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: externalBlockManager.flatMap(_.getBytes(blockId)) } catch { case NonFatal(t) => - logError(s"error in getBytes from $blockId", t) + logError(s"Error in getBytes($blockId)", t) None } } @@ -130,13 +166,13 @@ private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: try { val ret = externalBlockManager.map(_.blockExists(blockId)).getOrElse(false) if (!ret) { - logInfo(s"remove block $blockId") + logInfo(s"Remove block $blockId") blockManager.removeBlock(blockId, true) } ret } catch { case NonFatal(t) => - logError(s"error in getBytes from $blockId", t) + logError(s"Error in getBytes($blockId)", t) false } } diff --git a/core/src/main/scala/org/apache/spark/storage/FileSegment.scala b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala index 95e2d688d9b17..021a9facfb0b2 100644 --- a/core/src/main/scala/org/apache/spark/storage/FileSegment.scala +++ b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala @@ -24,6 +24,8 @@ import java.io.File * based off an offset and a length. */ private[spark] class FileSegment(val file: File, val offset: Long, val length: Long) { + require(offset >= 0, s"File segment offset cannot be negative (got $offset)") + require(length >= 0, s"File segment length cannot be negative (got $length)") override def toString: String = { "(name=%s, offset=%d, length=%d)".format(file.getName, offset, length) } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index bdc6276e41915..b53c86e89a273 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -22,7 +22,10 @@ import java.nio.ByteBuffer import java.text.SimpleDateFormat import java.util.{Date, Random} +import scala.util.control.NonFatal + import com.google.common.io.ByteStreams + import tachyon.client.{ReadType, WriteType, TachyonFS, TachyonFile} import tachyon.TachyonURI @@ -38,7 +41,6 @@ import org.apache.spark.util.Utils */ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Logging { - var blockManager: BlockManager =_ var rootDirs: String = _ var master: String = _ var client: tachyon.client.TachyonFS = _ @@ -52,7 +54,7 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log override def init(blockManager: BlockManager, executorId: String): Unit = { - this.blockManager = blockManager + super.init(blockManager, executorId) val storeDir = blockManager.conf.get(ExternalBlockStore.BASE_DIR, "/tmp_spark_tachyon") val appFolderName = blockManager.conf.get(ExternalBlockStore.FOLD_NAME) @@ -95,8 +97,29 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log override def putBytes(blockId: BlockId, bytes: ByteBuffer): Unit = { val file = getFile(blockId) val os = file.getOutStream(WriteType.TRY_CACHE) - os.write(bytes.array()) - os.close() + try { + os.write(bytes.array()) + } catch { + case NonFatal(e) => + logWarning(s"Failed to put bytes of block $blockId into Tachyon", e) + os.cancel() + } finally { + os.close() + } + } + + override def putValues(blockId: BlockId, values: Iterator[_]): Unit = { + val file = getFile(blockId) + val os = file.getOutStream(WriteType.TRY_CACHE) + try { + blockManager.dataSerializeStream(blockId, os, values) + } catch { + case NonFatal(e) => + logWarning(s"Failed to put values of block $blockId into Tachyon", e) + os.cancel() + } finally { + os.close() + } } override def getBytes(blockId: BlockId): Option[ByteBuffer] = { @@ -105,21 +128,31 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log return None } val is = file.getInStream(ReadType.CACHE) - assert (is != null) try { val size = file.length val bs = new Array[Byte](size.asInstanceOf[Int]) ByteStreams.readFully(is, bs) Some(ByteBuffer.wrap(bs)) } catch { - case ioe: IOException => - logWarning(s"Failed to fetch the block $blockId from Tachyon", ioe) + case NonFatal(e) => + logWarning(s"Failed to get bytes of block $blockId from Tachyon", e) None } finally { is.close() } } + override def getValues(blockId: BlockId): Option[Iterator[_]] = { + val file = getFile(blockId) + if (file == null || file.getLocationHosts().size() == 0) { + return None + } + val is = file.getInStream(ReadType.CACHE) + Option(is).map { is => + blockManager.dataDeserializeStream(blockId, is) + } + } + override def getSize(blockId: BlockId): Long = { getFile(blockId.name).length } @@ -184,7 +217,7 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log tachyonDir = client.getFile(path) } } catch { - case e: Exception => + case NonFatal(e) => logWarning("Attempt " + tries + " to create tachyon dir " + tachyonDir + " failed", e) } } @@ -206,7 +239,7 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log Utils.deleteRecursively(tachyonDir, client) } } catch { - case e: Exception => + case NonFatal(e) => logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) } } diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala index dfd6fdb5e9993..06e616220c706 100644 --- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala @@ -78,6 +78,9 @@ private[spark] object JettyUtils extends Logging { } catch { case e: IllegalArgumentException => response.sendError(HttpServletResponse.SC_BAD_REQUEST, e.getMessage) + case e: Exception => + logWarning(s"GET ${request.getRequestURI} failed: $e", e) + throw e } } // SPARK-5983 ensure TRACE is not supported @@ -217,6 +220,9 @@ private[spark] object JettyUtils extends Logging { val pool = new QueuedThreadPool pool.setDaemon(true) server.setThreadPool(pool) + val errorHandler = new ErrorHandler() + errorHandler.setShowStacks(true) + server.addBean(errorHandler) server.setHandler(collection) try { server.start() diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index bfe4a180e8a6f..3788916cf39bb 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -19,7 +19,8 @@ package org.apache.spark.ui import java.util.Date -import org.apache.spark.status.api.v1.{ApplicationAttemptInfo, ApplicationInfo, JsonRootResource, UIRoot} +import org.apache.spark.status.api.v1.{ApiRootResource, ApplicationAttemptInfo, ApplicationInfo, + UIRoot} import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext} import org.apache.spark.scheduler._ import org.apache.spark.storage.StorageStatusListener @@ -64,7 +65,7 @@ private[spark] class SparkUI private ( attachTab(new ExecutorsTab(this)) attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static")) attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath)) - attachHandler(JsonRootResource.getJsonServlet(this)) + attachHandler(ApiRootResource.getServletHandler(this)) // This should be POST only, but, the YARN AM proxy won't proxy POSTs attachHandler(createRedirectHandler( "/stages/stage/kill", "/stages", stagesTab.handleKillRequest, @@ -136,7 +137,7 @@ private[spark] object SparkUI { jobProgressListener: JobProgressListener, securityManager: SecurityManager, appName: String, - startTime: Long): SparkUI = { + startTime: Long): SparkUI = { create(Some(sc), conf, listenerBus, securityManager, appName, jobProgressListener = Some(jobProgressListener), startTime = startTime) } diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala index 6a0f5c5d16daa..7898039519201 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -194,11 +194,7 @@ private[spark] object UIUtils extends Logging { {tab.name} } - val helpButton: Seq[Node] = helpText.map { helpText => - - (?) - - }.getOrElse(Seq.empty) + val helpButton: Seq[Node] = helpText.map(tooltip(_, "bottom")).getOrElse(Seq.empty) @@ -313,7 +309,7 @@ private[spark] object UIUtils extends Logging { started: Int, completed: Int, failed: Int, - skipped:Int, + skipped: Int, total: Int): Seq[Node] = { val completeWidth = "width: %s%%".format((completed.toDouble/total)*100) val startWidth = "width: %s%%".format((started.toDouble/total)*100) @@ -356,15 +352,17 @@ private[spark] object UIUtils extends Logging {
    -
    + @@ -237,11 +249,11 @@ public class GaussianMixtureExample { GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd()); // Save and load GaussianMixtureModel - gmm.save(sc, "myGMMModel") - GaussianMixtureModel sameModel = GaussianMixtureModel.load(sc, "myGMMModel") + gmm.save(sc.sc(), "myGMMModel"); + GaussianMixtureModel sameModel = GaussianMixtureModel.load(sc.sc(), "myGMMModel"); // Output the parameters of the mixture model for(int j=0; j println(s"${a.id} -> ${a.cluster}") } + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = PowerIterationClusteringModel.load(sc, "myModelPath") {% endhighlight %} A full example that produces the experiment described in the PIC paper can be found under @@ -360,6 +376,10 @@ PowerIterationClusteringModel model = pic.run(similarities); for (PowerIterationClustering.Assignment a: model.assignments().toJavaRDD().collect()) { System.out.println(a.id() + " -> " + a.cluster()); } + +// Save and load model +model.save(sc.sc(), "myModelPath"); +PowerIterationClusteringModel sameModel = PowerIterationClusteringModel.load(sc.sc(), "myModelPath"); {% endhighlight %}
    @@ -377,11 +397,11 @@ LDA can be thought of as a clustering algorithm as follows: on a statistical model of how text documents are generated. LDA takes in a collection of documents as vectors of word counts. -It learns clustering using [expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) -on the likelihood function. After fitting on the documents, LDA provides: +It supports different inference algorithms via `setOptimizer` function. EMLDAOptimizer learns clustering using [expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) +on the likelihood function and yields comprehensive results, while OnlineLDAOptimizer uses iterative mini-batch sampling for [online variational inference](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf) and is generally memory friendly. After fitting on the documents, LDA provides: * Topics: Inferred topics, each of which is a probability distribution over terms (words). -* Topic distributions for documents: For each document in the training set, LDA gives a probability distribution over topics. +* Topic distributions for documents: For each document in the training set, LDA gives a probability distribution over topics. (EM only) LDA takes the following parameters: @@ -572,15 +592,55 @@ model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print() ssc.start() ssc.awaitTermination() +{% endhighlight %} +
    + +
    +First we import the neccessary classes. + +{% highlight python %} +from pyspark.mllib.linalg import Vectors +from pyspark.mllib.regression import LabeledPoint +from pyspark.mllib.clustering import StreamingKMeans +{% endhighlight %} + +Then we make an input stream of vectors for training, as well as a stream of labeled data +points for testing. We assume a StreamingContext `ssc` has been created, see +[Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for more info. + +{% highlight python %} +def parse(lp): + label = float(lp[lp.find('(') + 1: lp.find(',')]) + vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(',')) + return LabeledPoint(label, vec) + +trainingData = ssc.textFileStream("/training/data/dir").map(Vectors.parse) +testData = ssc.textFileStream("/testing/data/dir").map(parse) {% endhighlight %} +We create a model with random clusters and specify the number of clusters to find + +{% highlight python %} +model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(3, 1.0, 0) +{% endhighlight %} + +Now register the streams for training and testing and start the job, printing +the predicted cluster assignments on new data points as they arrive. + +{% highlight python %} +model.trainOn(trainingData) +print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features)))) + +ssc.start() +ssc.awaitTermination() +{% endhighlight %} +
    + +
    + As you add new text files with data the cluster centers will update. Each training point should be formatted as `[x1, x2, x3]`, and each test data point should be formatted as `(y, [x1, x2, x3])`, where `y` is some useful label or identifier (e.g. a true category assignment). Anytime a text file is placed in `/training/data/dir` the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions. With new data, the cluster centers will change! - - - - diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index 7b397e30b2d90..dfdf6216b270c 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -107,7 +107,8 @@ other signals), you can use the `trainImplicit` method to get better results. {% highlight scala %} val alpha = 0.01 -val model = ALS.trainImplicit(ratings, rank, numIterations, alpha) +val lambda = 0.01 +val model = ALS.trainImplicit(ratings, rank, numIterations, lambda, alpha) {% endhighlight %} diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md index 4f2a2f71048f7..d824dab1d7f7b 100644 --- a/docs/mllib-data-types.md +++ b/docs/mllib-data-types.md @@ -31,7 +31,7 @@ The base class of local vectors is implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and [`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend using the factory methods implemented in -[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors. +[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) to create local vectors. {% highlight scala %} import org.apache.spark.mllib.linalg.{Vector, Vectors} @@ -57,7 +57,7 @@ The base class of local vectors is implementations: [`DenseVector`](api/java/org/apache/spark/mllib/linalg/DenseVector.html) and [`SparseVector`](api/java/org/apache/spark/mllib/linalg/SparseVector.html). We recommend using the factory methods implemented in -[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vector.html) to create local vectors. +[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vectors.html) to create local vectors. {% highlight java %} import org.apache.spark.mllib.linalg.Vector; @@ -84,7 +84,7 @@ and the following as sparse vectors: with a single column We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented -in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) to create sparse vectors. +in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors) to create sparse vectors. {% highlight python %} import numpy as np @@ -241,7 +241,7 @@ The base class of local matrices is [`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix). We recommend using the factory methods implemented -in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local +in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$) to create local matrices. {% highlight scala %} @@ -296,70 +296,6 @@ backed by an RDD of its entries. The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. In general the use of non-deterministic RDDs can lead to errors. -### BlockMatrix - -A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock`s, where a `MatrixBlock` is -a tuple of `((Int, Int), Matrix)`, where the `(Int, Int)` is the index of the block, and `Matrix` is -the sub-matrix at the given index with size `rowsPerBlock` x `colsPerBlock`. -`BlockMatrix` supports methods such as `add` and `multiply` with another `BlockMatrix`. -`BlockMatrix` also has a helper function `validate` which can be used to check whether the -`BlockMatrix` is set up properly. - -
    -
    - -A [`BlockMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.BlockMatrix) can be -most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. -`toBlockMatrix` creates blocks of size 1024 x 1024 by default. -Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. - -{% highlight scala %} -import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry} - -val entries: RDD[MatrixEntry] = ... // an RDD of (i, j, v) matrix entries -// Create a CoordinateMatrix from an RDD[MatrixEntry]. -val coordMat: CoordinateMatrix = new CoordinateMatrix(entries) -// Transform the CoordinateMatrix to a BlockMatrix -val matA: BlockMatrix = coordMat.toBlockMatrix().cache() - -// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. -// Nothing happens if it is valid. -matA.validate() - -// Calculate A^T A. -val ata = matA.transpose.multiply(matA) -{% endhighlight %} -
    - -
    - -A [`BlockMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/BlockMatrix.html) can be -most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. -`toBlockMatrix` creates blocks of size 1024 x 1024 by default. -Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. - -{% highlight java %} -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.linalg.distributed.BlockMatrix; -import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; -import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; - -JavaRDD entries = ... // a JavaRDD of (i, j, v) Matrix Entries -// Create a CoordinateMatrix from a JavaRDD. -CoordinateMatrix coordMat = new CoordinateMatrix(entries.rdd()); -// Transform the CoordinateMatrix to a BlockMatrix -BlockMatrix matA = coordMat.toBlockMatrix().cache(); - -// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. -// Nothing happens if it is valid. -matA.validate(); - -// Calculate A^T A. -BlockMatrix ata = matA.transpose().multiply(matA); -{% endhighlight %} -
    -
    - ### RowMatrix A `RowMatrix` is a row-oriented distributed matrix without meaningful row indices, backed by an RDD @@ -530,3 +466,67 @@ IndexedRowMatrix indexedRowMatrix = mat.toIndexedRowMatrix(); {% endhighlight %} + +### BlockMatrix + +A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock`s, where a `MatrixBlock` is +a tuple of `((Int, Int), Matrix)`, where the `(Int, Int)` is the index of the block, and `Matrix` is +the sub-matrix at the given index with size `rowsPerBlock` x `colsPerBlock`. +`BlockMatrix` supports methods such as `add` and `multiply` with another `BlockMatrix`. +`BlockMatrix` also has a helper function `validate` which can be used to check whether the +`BlockMatrix` is set up properly. + +
    +
    + +A [`BlockMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.BlockMatrix) can be +most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. +`toBlockMatrix` creates blocks of size 1024 x 1024 by default. +Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. + +{% highlight scala %} +import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry} + +val entries: RDD[MatrixEntry] = ... // an RDD of (i, j, v) matrix entries +// Create a CoordinateMatrix from an RDD[MatrixEntry]. +val coordMat: CoordinateMatrix = new CoordinateMatrix(entries) +// Transform the CoordinateMatrix to a BlockMatrix +val matA: BlockMatrix = coordMat.toBlockMatrix().cache() + +// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. +// Nothing happens if it is valid. +matA.validate() + +// Calculate A^T A. +val ata = matA.transpose.multiply(matA) +{% endhighlight %} +
    + +
    + +A [`BlockMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/BlockMatrix.html) can be +most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. +`toBlockMatrix` creates blocks of size 1024 x 1024 by default. +Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. + +{% highlight java %} +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.mllib.linalg.distributed.BlockMatrix; +import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; +import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; + +JavaRDD entries = ... // a JavaRDD of (i, j, v) Matrix Entries +// Create a CoordinateMatrix from a JavaRDD. +CoordinateMatrix coordMat = new CoordinateMatrix(entries.rdd()); +// Transform the CoordinateMatrix to a BlockMatrix +BlockMatrix matA = coordMat.toBlockMatrix().cache(); + +// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. +// Nothing happens if it is valid. +matA.validate(); + +// Calculate A^T A. +BlockMatrix ata = matA.transpose().multiply(matA); +{% endhighlight %} +
    +
    diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md index 870fed6cc5024..05f51168d837c 100644 --- a/docs/mllib-dimensionality-reduction.md +++ b/docs/mllib-dimensionality-reduction.md @@ -137,7 +137,7 @@ statistical method to find a rotation such that the first coordinate has the lar possible, and each succeeding coordinate in turn has the largest variance possible. The columns of the rotation matrix are called principal components. PCA is used widely in dimensionality reduction. -MLlib supports PCA for tall-and-skinny matrices stored in row-oriented format. +MLlib supports PCA for tall-and-skinny matrices stored in row-oriented format and any Vectors.
    @@ -157,6 +157,23 @@ val pc: Matrix = mat.computePrincipalComponents(10) // Principal components are // Project the rows to the linear space spanned by the top 10 principal components. val projected: RowMatrix = mat.multiply(pc) {% endhighlight %} + +The following code demonstrates how to compute principal components on source vectors +and use them to project the vectors into a low-dimensional space while keeping associated labels: + +{% highlight scala %} +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.feature.PCA + +val data: RDD[LabeledPoint] = ... + +// Compute the top 10 principal components. +val pca = new PCA(10).fit(data.map(_.features)) + +// Project vectors to the linear space spanned by the top 10 principal components, keeping the label +val projected = data.map(p => p.copy(features = pca.transform(p.features))) +{% endhighlight %} +
    diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index 03fedd01016b9..83e937635a55b 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -188,7 +188,7 @@ Here we assume the extracted file is `text8` and in same directory as you run th import org.apache.spark._ import org.apache.spark.rdd._ import org.apache.spark.SparkContext._ -import org.apache.spark.mllib.feature.Word2Vec +import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel} val input = sc.textFile("text8").map(line => line.split(" ").toSeq) @@ -201,6 +201,10 @@ val synonyms = model.findSynonyms("china", 40) for((synonym, cosineSimilarity) <- synonyms) { println(s"$synonym $cosineSimilarity") } + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = Word2VecModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -410,6 +414,7 @@ import org.apache.spark.SparkContext._ import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.feature.ChiSqSelector // Load some data in libsvm format val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") @@ -505,8 +510,7 @@ v_N ### Example -This example below demonstrates how to load a simple vectors file, extract a set of vectors, then transform those vectors using a transforming vector value. - +This example below demonstrates how to transform vectors using a transforming vector value.
    @@ -515,19 +519,124 @@ import org.apache.spark.SparkContext._ import org.apache.spark.mllib.feature.ElementwiseProduct import org.apache.spark.mllib.linalg.Vectors -// Load and parse the data: -val data = sc.textFile("data/mllib/kmeans_data.txt") -val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble))) +// Create some vector data; also works for sparse vectors +val data = sc.parallelize(Array(Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0))) val transformingVector = Vectors.dense(0.0, 1.0, 2.0) val transformer = new ElementwiseProduct(transformingVector) // Batch transform and per-row transform give the same results: -val transformedData = transformer.transform(parsedData) -val transformedData2 = parsedData.map(x => transformer.transform(x)) +val transformedData = transformer.transform(data) +val transformedData2 = data.map(x => transformer.transform(x)) {% endhighlight %}
    + +
    +{% highlight java %} +import java.util.Arrays; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.feature.ElementwiseProduct; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; + +// Create some vector data; also works for sparse vectors +JavaRDD data = sc.parallelize(Arrays.asList( + Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0))); +Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0); +ElementwiseProduct transformer = new ElementwiseProduct(transformingVector); + +// Batch transform and per-row transform give the same results: +JavaRDD transformedData = transformer.transform(data); +JavaRDD transformedData2 = data.map( + new Function() { + @Override + public Vector call(Vector v) { + return transformer.transform(v); + } + } +); + +{% endhighlight %}
    +
    +{% highlight python %} +from pyspark import SparkContext +from pyspark.mllib.linalg import Vectors +from pyspark.mllib.feature import ElementwiseProduct +# Load and parse the data +sc = SparkContext() +data = sc.textFile("data/mllib/kmeans_data.txt") +parsedData = data.map(lambda x: [float(t) for t in x.split(" ")]) + +# Create weight vector. +transformingVector = Vectors.dense([0.0, 1.0, 2.0]) +transformer = ElementwiseProduct(transformingVector) + +# Batch transform +transformedData = transformer.transform(parsedData) +# Single-row transform +transformedData2 = transformer.transform(parsedData.first()) + +{% endhighlight %} +
    +
    + + +## PCA + +A feature transformer that projects vectors to a low-dimensional space using PCA. +Details you can read at [dimensionality reduction](mllib-dimensionality-reduction.html). + +### Example + +The following code demonstrates how to compute principal components on a `Vector` +and use them to project the vectors into a low-dimensional space while keeping associated labels +for calculation a [Linear Regression]((mllib-linear-methods.html)) + +
    +
    +{% highlight scala %} +import org.apache.spark.mllib.regression.LinearRegressionWithSGD +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.feature.PCA + +val data = sc.textFile("data/mllib/ridge-data/lpsa.data").map { line => + val parts = line.split(',') + LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble))) +}.cache() + +val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) +val training = splits(0).cache() +val test = splits(1) + +val pca = new PCA(training.first().features.size/2).fit(data.map(_.features)) +val training_pca = training.map(p => p.copy(features = pca.transform(p.features))) +val test_pca = test.map(p => p.copy(features = pca.transform(p.features))) + +val numIterations = 100 +val model = LinearRegressionWithSGD.train(training, numIterations) +val model_pca = LinearRegressionWithSGD.train(training_pca, numIterations) + +val valuesAndPreds = test.map { point => + val score = model.predict(point.features) + (score, point.label) +} + +val valuesAndPreds_pca = test_pca.map { point => + val score = model_pca.predict(point.features) + (score, point.label) +} + +val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean() +val MSE_pca = valuesAndPreds_pca.map{case(v, p) => math.pow((v - p), 2)}.mean() + +println("Mean Squared Error = " + MSE) +println("PCA Mean Squared Error = " + MSE_pca) +{% endhighlight %} +
    +
    diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md index 9fd9be0dd01b1..bcc066a185526 100644 --- a/docs/mllib-frequent-pattern-mining.md +++ b/docs/mllib-frequent-pattern-mining.md @@ -39,11 +39,11 @@ MLlib's FP-growth implementation takes the following (hyper-)parameters:
    -[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the +[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) implements the FP-growth algorithm. It take a `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type. Calling `FPGrowth.run` with transactions returns an -[`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html) +[`FPGrowthModel`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowthModel) that stores the frequent itemsets with their frequencies. {% highlight scala %} diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index f8e879496c135..d2d1cc93fe006 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -7,7 +7,19 @@ description: MLlib machine learning library overview for Spark SPARK_VERSION_SHO MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative -filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below: +filtering, dimensionality reduction, as well as underlying optimization primitives. +Guides for individual algorithms are listed below. + +The API is divided into 2 parts: + +* [The original `spark.mllib` API](mllib-guide.html#mllib-types-algorithms-and-utilities) is the primary API. +* [The "Pipelines" `spark.ml` API](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) is a higher-level API for constructing ML workflows. + +We list major functionality from both below, with links to detailed guides. + +# MLlib types, algorithms and utilities + +This lists functionality included in `spark.mllib`, the main MLlib API. * [Data types](mllib-data-types.html) * [Basic statistics](mllib-statistics.html) @@ -39,6 +51,7 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv * [Optimization (developer)](mllib-optimization.html) * stochastic gradient descent * limited-memory BFGS (L-BFGS) +* [PMML model export](mllib-pmml-model-export.html) MLlib is under active development. The APIs marked `Experimental`/`DeveloperApi` may change in future releases, @@ -48,8 +61,8 @@ and the migration guide below will explain all changes between releases. Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of high-level APIs that help users create and tune practical machine learning pipelines. -It is currently an alpha component, and we would like to hear back from the community about -how it fits real-world use cases and how it could be improved. + +*Graduated from Alpha!* The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`. Note that we will keep supporting and adding features to `spark.mllib` along with the development of `spark.ml`. @@ -57,7 +70,11 @@ Users should be comfortable using `spark.mllib` features and expect more feature Developers should contribute new algorithms to `spark.mllib` and can optionally contribute to `spark.ml`. -See the **[spark.ml programming guide](ml-guide.html)** for more information on this package. +More detailed guides for `spark.ml` include: + +* **[spark.ml programming guide](ml-guide.html)**: overview of the Pipelines API and major concepts +* [Feature transformers](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API +* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API # Dependencies @@ -89,21 +106,14 @@ version 1.4 or newer. For the `spark.ml` package, please see the [spark.ml Migration Guide](ml-guide.html#migration-guide). -## From 1.2 to 1.3 - -In the `spark.mllib` package, there were several breaking changes. The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental. - -* *(Breaking change)* In [`ALS`](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS), the extraneous method `solveLeastSquares` has been removed. The `DeveloperApi` method `analyzeBlocks` was also removed. -* *(Breaking change)* [`StandardScalerModel`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel) remains an Alpha component. In it, the `variance` method has been replaced with the `std` method. To compute the column variance values returned by the original `variance` method, simply square the standard deviation values returned by `std`. -* *(Breaking change)* [`StreamingLinearRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD) remains an Experimental component. In it, there were two changes: - * The constructor taking arguments was removed in favor of a builder patten using the default constructor plus parameter setter methods. - * Variable `model` is no longer public. -* *(Breaking change)* [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) remains an Experimental component. In it and its associated classes, there were several changes: - * In `DecisionTree`, the deprecated class method `train` has been removed. (The object/static `train` methods remain.) - * In `Strategy`, the `checkpointDir` parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training. -* `PythonMLlibAPI` (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared `private[python]`. This was never meant for external use. -* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2. - So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2. +## From 1.3 to 1.4 + +In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs: + +* Gradient-Boosted Trees + * *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed. This is only an issues for users who wrote their own losses for GBTs. + * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields. This could be an issue for users who use `BoostingStrategy` to set GBT parameters. +* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed. It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`. The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm. ## Previous Spark Versions diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md index b521c2f27cd6e..5732bc4c7e79e 100644 --- a/docs/mllib-isotonic-regression.md +++ b/docs/mllib-isotonic-regression.md @@ -60,7 +60,7 @@ Model is created using the training set and a mean squared error is calculated f labels and real labels in the test set. {% highlight scala %} -import org.apache.spark.mllib.regression.IsotonicRegression +import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel} val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt") @@ -88,6 +88,10 @@ val predictionAndLabel = test.map { point => // Calculate mean squared error between predicted and real labels. val meanSquaredError = predictionAndLabel.map{case(p, l) => math.pow((p - l), 2)}.mean() println("Mean Squared Error = " + meanSquaredError) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = IsotonicRegressionModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -150,6 +154,10 @@ Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map( ).rdd()).mean(); System.out.println("Mean Squared Error = " + meanSquaredError); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath"); {% endhighlight %}
    diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 2b2be4d9d0273..3dc8cc902fa72 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -163,11 +163,8 @@ object, and make predictions with the resulting model to compute the training error. {% highlight scala %} -import org.apache.spark.SparkContext import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD} import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics -import org.apache.spark.mllib.regression.LabeledPoint -import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.MLUtils // Load training data in LIBSVM format. @@ -231,15 +228,13 @@ calling `.rdd()` on your `JavaRDD` object. A self-contained application example that is equivalent to the provided example in Scala is given bellow: {% highlight java %} -import java.util.Random; - import scala.Tuple2; import org.apache.spark.api.java.*; import org.apache.spark.api.java.function.Function; import org.apache.spark.mllib.classification.*; import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics; -import org.apache.spark.mllib.linalg.Vector; + import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.SparkConf; @@ -282,8 +277,8 @@ public class SVMClassifier { System.out.println("Area under ROC = " + auROC); // Save and load model - model.save(sc.sc(), "myModelPath"); - SVMModel sameModel = SVMModel.load(sc.sc(), "myModelPath"); + model.save(sc, "myModelPath"); + SVMModel sameModel = SVMModel.load(sc, "myModelPath"); } } {% endhighlight %} @@ -315,15 +310,12 @@ a dependency.
    -The following example shows how to load a sample dataset, build Logistic Regression model, +The following example shows how to load a sample dataset, build SVM model, and make predictions with the resulting model to compute the training error. -Note that the Python API does not yet support model save/load but will in the future. - {% highlight python %} -from pyspark.mllib.classification import LogisticRegressionWithSGD +from pyspark.mllib.classification import SVMWithSGD, SVMModel from pyspark.mllib.regression import LabeledPoint -from numpy import array # Load and parse the data def parsePoint(line): @@ -334,12 +326,16 @@ data = sc.textFile("data/mllib/sample_svm_data.txt") parsedData = data.map(parsePoint) # Build the model -model = LogisticRegressionWithSGD.train(parsedData) +model = SVMWithSGD.train(parsedData, iterations=100) # Evaluating the model on training data labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features))) trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count()) print("Training Error = " + str(trainErr)) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = SVMModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -785,8 +781,7 @@ gradient descent (`stepSize`, `numIterations`, `miniBatchFraction`). For each o all three possible regularizations (none, L1 or L2). For Logistic Regression, [L-BFGS](api/scala/index.html#org.apache.spark.mllib.optimization.LBFGS) -version is implemented under [LogisticRegressionWithLBFGS] -(api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS), and this +version is implemented under [LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS), and this version supports both binary and multinomial Logistic Regression while SGD version only supports binary Logistic Regression. However, L-BFGS version doesn't support L1 regularization but SGD one supports L1 regularization. When L1 regularization is not required, L-BFGS version is strongly diff --git a/docs/mllib-migration-guides.md b/docs/mllib-migration-guides.md index 4de2d9491ac2b..8df68d81f3c78 100644 --- a/docs/mllib-migration-guides.md +++ b/docs/mllib-migration-guides.md @@ -7,6 +7,22 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT The migration guide for the current Spark version is kept on the [MLlib Programming Guide main page](mllib-guide.html#migration-guide). +## From 1.2 to 1.3 + +In the `spark.mllib` package, there were several breaking changes. The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental. + +* *(Breaking change)* In [`ALS`](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS), the extraneous method `solveLeastSquares` has been removed. The `DeveloperApi` method `analyzeBlocks` was also removed. +* *(Breaking change)* [`StandardScalerModel`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel) remains an Alpha component. In it, the `variance` method has been replaced with the `std` method. To compute the column variance values returned by the original `variance` method, simply square the standard deviation values returned by `std`. +* *(Breaking change)* [`StreamingLinearRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD) remains an Experimental component. In it, there were two changes: + * The constructor taking arguments was removed in favor of a builder pattern using the default constructor plus parameter setter methods. + * Variable `model` is no longer public. +* *(Breaking change)* [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) remains an Experimental component. In it and its associated classes, there were several changes: + * In `DecisionTree`, the deprecated class method `train` has been removed. (The object/static `train` methods remain.) + * In `Strategy`, the `checkpointDir` parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training. +* `PythonMLlibAPI` (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared `private[python]`. This was never meant for external use. +* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2. + So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2. + ## From 1.1 to 1.2 The only API changes in MLlib v1.2 are in diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index 9780ea52c4994..bf6d124fd5d8d 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -14,14 +14,13 @@ and use it for prediction. MLlib supports [multinomial naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes) -and [Bernoulli naive Bayes] (http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). -These models are typically used for [document classification] -(http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html). +and [Bernoulli naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). +These models are typically used for [document classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html). Within that context, each observation is a document and each feature represents a term whose value is the frequency of the term (in multinomial naive Bayes) or a zero or one indicating whether the term was found in the document (in Bernoulli naive Bayes). Feature values must be nonnegative. The model type is selected with an optional parameter -"Multinomial" or "Bernoulli" with "Multinomial" as the default. +"multinomial" or "bernoulli" with "multinomial" as the default. [Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of @@ -35,7 +34,7 @@ sparsity. Since the training data is only used once, it is not necessary to cach [NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements multinomial naive Bayes. It takes an RDD of [LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional -smoothing parameter `lambda` as input, an optional model type parameter (default is Multinomial), and outputs a +smoothing parameter `lambda` as input, an optional model type parameter (default is "multinomial"), and outputs a [NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. @@ -54,7 +53,7 @@ val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) val training = splits(0) val test = splits(1) -val model = NaiveBayes.train(training, lambda = 1.0, model = "Multinomial") +val model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial") val predictionAndLabel = test.map(p => (model.predict(p.features), p.label)) val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() @@ -75,6 +74,8 @@ optionally smoothing parameter `lambda` as input, and output a can be used for evaluation and prediction. {% highlight java %} +import scala.Tuple2; + import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.function.Function; @@ -82,7 +83,6 @@ import org.apache.spark.api.java.function.PairFunction; import org.apache.spark.mllib.classification.NaiveBayes; import org.apache.spark.mllib.classification.NaiveBayesModel; import org.apache.spark.mllib.regression.LabeledPoint; -import scala.Tuple2; JavaRDD training = ... // training set JavaRDD test = ... // test set diff --git a/docs/mllib-pmml-model-export.md b/docs/mllib-pmml-model-export.md new file mode 100644 index 0000000000000..42ea2ca81f80d --- /dev/null +++ b/docs/mllib-pmml-model-export.md @@ -0,0 +1,86 @@ +--- +layout: global +title: PMML model export - MLlib +displayTitle: MLlib - PMML model export +--- + +* Table of contents +{:toc} + +## MLlib supported models + +MLlib supports model export to Predictive Model Markup Language ([PMML](http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language)). + +The table below outlines the MLlib models that can be exported to PMML and their equivalent PMML model. + + + + + + + + + + + + + + + + + + + + + + + + + +
    MLlib modelPMML model
    KMeansModelClusteringModel
    LinearRegressionModelRegressionModel (functionName="regression")
    RidgeRegressionModelRegressionModel (functionName="regression")
    LassoModelRegressionModel (functionName="regression")
    SVMModelRegressionModel (functionName="classification" normalizationMethod="none")
    Binary LogisticRegressionModelRegressionModel (functionName="classification" normalizationMethod="logit")
    + +## Examples +
    + +
    +To export a supported `model` (see table above) to PMML, simply call `model.toPMML`. + +Here a complete example of building a KMeansModel and print it out in PMML format: +{% highlight scala %} +import org.apache.spark.mllib.clustering.KMeans +import org.apache.spark.mllib.linalg.Vectors + +// Load and parse the data +val data = sc.textFile("data/mllib/kmeans_data.txt") +val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble))).cache() + +// Cluster the data into two classes using KMeans +val numClusters = 2 +val numIterations = 20 +val clusters = KMeans.train(parsedData, numClusters, numIterations) + +// Export to PMML +println("PMML Model:\n" + clusters.toPMML) +{% endhighlight %} + +As well as exporting the PMML model to a String (`model.toPMML` as in the example above), you can export the PMML model to other formats: + +{% highlight scala %} +// Export the model to a String in PMML format +clusters.toPMML + +// Export the model to a local file in PMML format +clusters.toPMML("/tmp/kmeans.xml") + +// Export the model to a directory on a distributed file system in PMML format +clusters.toPMML(sc,"/tmp/kmeans") + +// Export the model to the OutputStream in PMML format +clusters.toPMML(System.out) +{% endhighlight %} + +For unsupported models, either you will not find a `.toPMML` method or an `IllegalArgumentException` will be thrown. + +
    + +
    diff --git a/docs/monitoring.md b/docs/monitoring.md index 1e0fc150862fb..bcf885fe4e681 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -178,9 +178,9 @@ Note that the history server only displays completed Spark jobs. One way to sign In addition to viewing the metrics in the UI, they are also available as JSON. This gives developers an easy way to create new visualizations and monitoring tools for Spark. The JSON is available for -both running applications, and in the history server. The endpoints are mounted at `/json/v1`. Eg., -for the history server, they would typically be accessible at `http://:18080/json/v1`, and -for a running application, at `http://localhost:4040/json/v1`. +both running applications, and in the history server. The endpoints are mounted at `/api/v1`. Eg., +for the history server, they would typically be accessible at `http://:18080/api/v1`, and +for a running application, at `http://localhost:4040/api/v1`. @@ -228,6 +228,14 @@ for a running application, at `http://localhost:4040/json/v1`. + + + + + + + +
    EndpointMeaning
    /applications/[app-id]/storage/rdd/[rdd-id] Details for the storage status of a given RDD
    /applications/[app-id]/logsDownload the event logs for all attempts of the given application as a zip file
    /applications/[app-id]/[attempt-id]/logsDownload the event logs for the specified attempt of the given application as a zip file
    When running on Yarn, each application has multiple attempts, so `[app-id]` is actually @@ -240,12 +248,12 @@ These endpoints have been strongly versioned to make it easier to develop applic * Individual fields will never be removed for any given endpoint * New endpoints may be added * New fields may be added to existing endpoints -* New versions of the api may be added in the future at a separate endpoint (eg., `json/v2`). New versions are *not* required to be backwards compatible. +* New versions of the api may be added in the future at a separate endpoint (eg., `api/v2`). New versions are *not* required to be backwards compatible. * Api versions may be dropped, but only after at least one minor release of co-existing with a new api version Note that even when examining the UI of a running applications, the `applications/[app-id]` portion is still required, though there is only one application available. Eg. to see the list of jobs for the -running app, you would go to `http://localhost:4040/json/v1/applications/[app-id]/jobs`. This is to +running app, you would go to `http://localhost:4040/api/v1/applications/[app-id]/jobs`. This is to keep the paths consistent in both modes. # Metrics diff --git a/docs/programming-guide.md b/docs/programming-guide.md index 27816515c5de2..ae712d62746f6 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -41,19 +41,20 @@ In addition, if you wish to access an HDFS cluster, you need to add a dependency artifactId = hadoop-client version = -Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines: +Finally, you need to import some Spark classes into your program. Add the following lines: {% highlight scala %} import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ import org.apache.spark.SparkConf {% endhighlight %} +(Before Spark 1.3.0, you need to explicitly `import org.apache.spark.SparkContext._` to enable essential implicit conversions.) +
    -Spark {{site.SPARK_VERSION}} works with Java 6 and higher. If you are using Java 8, Spark supports +Spark {{site.SPARK_VERSION}} works with Java 7 and higher. If you are using Java 8, Spark supports [lambda expressions](http://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html) for concisely writing functions, otherwise you can use the classes in the [org.apache.spark.api.java.function](api/java/index.html?org/apache/spark/api/java/function/package-summary.html) package. @@ -97,9 +98,9 @@ to your version of HDFS. Some common HDFS version tags are listed on the [Prebuilt packages](http://spark.apache.org/downloads.html) are also available on the Spark homepage for common HDFS versions. -Finally, you need to import some Spark classes into your program. Add the following lines: +Finally, you need to import some Spark classes into your program. Add the following line: -{% highlight scala %} +{% highlight python %} from pyspark import SparkContext, SparkConf {% endhighlight %} @@ -477,7 +478,6 @@ the [Converter examples]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main for examples of using Cassandra / HBase ```InputFormat``` and ```OutputFormat``` with custom converters.
    - ## RDD Operations @@ -821,11 +821,9 @@ by a key. In Scala, these operations are automatically available on RDDs containing [Tuple2](http://www.scala-lang.org/api/{{site.SCALA_VERSION}}/index.html#scala.Tuple2) objects -(the built-in tuples in the language, created by simply writing `(a, b)`), as long as you -import `org.apache.spark.SparkContext._` in your program to enable Spark's implicit -conversions. The key-value pair operations are available in the +(the built-in tuples in the language, created by simply writing `(a, b)`). The key-value pair operations are available in the [PairRDDFunctions](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions) class, -which automatically wraps around an RDD of tuples if you import the conversions. +which automatically wraps around an RDD of tuples. For example, the following code uses the `reduceByKey` operation on key-value pairs to count how many times each line of text occurs in a file: @@ -916,7 +914,8 @@ The following table lists some of the common transformations supported by Spark. RDD API doc ([Scala](api/scala/index.html#org.apache.spark.rdd.RDD), [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html), - [Python](api/python/pyspark.html#pyspark.RDD)) + [Python](api/python/pyspark.html#pyspark.RDD), + [R](api/R/index.html)) and pair RDD functions doc ([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions), [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)) @@ -1029,7 +1028,9 @@ The following table lists some of the common actions supported by Spark. Refer t RDD API doc ([Scala](api/scala/index.html#org.apache.spark.rdd.RDD), [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html), - [Python](api/python/pyspark.html#pyspark.RDD)) + [Python](api/python/pyspark.html#pyspark.RDD), + [R](api/R/index.html)) + and pair RDD functions doc ([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions), [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)) @@ -1071,7 +1072,7 @@ for details. saveAsSequenceFile(path)
    (Java and Scala) - Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is available on RDDs of key-value pairs that either implement Hadoop's Writable interface. In Scala, it is also + Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is available on RDDs of key-value pairs that implement Hadoop's Writable interface. In Scala, it is also available on types that are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc). @@ -1122,7 +1123,7 @@ ordered data following shuffle then it's possible to use: * `sortBy` to make a globally ordered RDD Operations which can cause a shuffle include **repartition** operations like -[`repartition`](#RepartitionLink), and [`coalesce`](#CoalesceLink), **'ByKey** operations +[`repartition`](#RepartitionLink) and [`coalesce`](#CoalesceLink), **'ByKey** operations (except for counting) like [`groupByKey`](#GroupByLink) and [`reduceByKey`](#ReduceByLink), and **join** operations like [`cogroup`](#CogroupLink) and [`join`](#JoinLink). @@ -1138,14 +1139,16 @@ read the relevant sorted blocks. Certain shuffle operations can consume significant amounts of heap memory since they employ in-memory data structures to organize records before or after transferring them. Specifically, -`reduceByKey` and `aggregateByKey` create these structures on the map side and `'ByKey` operations +`reduceByKey` and `aggregateByKey` create these structures on the map side, and `'ByKey` operations generate these on the reduce side. When data does not fit in memory Spark will spill these tables to disk, incurring the additional overhead of disk I/O and increased garbage collection. Shuffle also generates a large number of intermediate files on disk. As of Spark 1.3, these files -are not cleaned up from Spark's temporary storage until Spark is stopped, which means that -long-running Spark jobs may consume available disk space. This is done so the shuffle doesn't need -to be re-computed if the lineage is re-computed. The temporary storage directory is specified by the +are preserved until the corresponding RDDs are no longer used and are garbage collected. +This is done so the shuffle files don't need to be re-created if the lineage is re-computed. +Garbage collection may happen only after a long period time, if the application retains references +to these RDDs or if GC does not kick in frequently. This means that long-running Spark jobs may +consume a large amount of disk space. The temporary storage directory is specified by the `spark.local.dir` configuration parameter when configuring the Spark context. Shuffle behavior can be tuned by adjusting a variety of configuration parameters. See the @@ -1213,9 +1216,11 @@ storage levels is: Compared to MEMORY_ONLY_SER, OFF_HEAP reduces garbage collection overhead and allows executors to be smaller and to share a pool of memory, making it attractive in environments with large heaps or multiple concurrent applications. Furthermore, as the RDDs reside in Tachyon, - the crash of an executor does not lead to losing the in-memory cache. In this mode, the memory + the crash of an executor does not lead to losing the in-memory cache. In this mode, the memory in Tachyon is discardable. Thus, Tachyon does not attempt to reconstruct a block that it evicts - from memory. + from memory. If you plan to use Tachyon as the off heap store, Spark is compatible with Tachyon + out-of-the-box. Please refer to this page + for the suggested version pairings. @@ -1566,7 +1571,8 @@ You can see some [example Spark programs](http://spark.apache.org/examples.html) In addition, Spark includes several samples in the `examples` directory ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples), [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples), - [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python)). + [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python), + [R]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/r)). You can run Java and Scala examples by passing the class name to Spark's `bin/run-example` script; for instance: ./bin/run-example SparkPi @@ -1575,6 +1581,10 @@ For Python examples, use `spark-submit` instead: ./bin/spark-submit examples/src/main/python/pi.py +For R examples, use `spark-submit` instead: + + ./bin/spark-submit examples/src/main/r/dataframe.R + For help on optimizing your programs, the [configuration](configuration.html) and [tuning](tuning.html) guides provide information on best practices. They are especially important for making sure that your data is stored in memory in an efficient format. @@ -1582,4 +1592,4 @@ For help on deploying, the [cluster mode overview](cluster-overview.html) descri in distributed operation and supported cluster managers. Finally, full API documentation is available in -[Scala](api/scala/#org.apache.spark.package), [Java](api/java/) and [Python](api/python/). +[Scala](api/scala/#org.apache.spark.package), [Java](api/java/), [Python](api/python/) and [R](api/R/). diff --git a/docs/quick-start.md b/docs/quick-start.md index 81143da865cf0..bb39e4111f244 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -184,10 +184,10 @@ scala> linesWithSpark.cache() res7: spark.RDD[String] = spark.FilteredRDD@17e51082 scala> linesWithSpark.count() -res8: Long = 15 +res8: Long = 19 scala> linesWithSpark.count() -res9: Long = 15 +res9: Long = 19 {% endhighlight %} It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is @@ -202,10 +202,10 @@ a cluster, as described in the [programming guide](programming-guide.html#initia >>> linesWithSpark.cache() >>> linesWithSpark.count() -15 +19 >>> linesWithSpark.count() -15 +19 {% endhighlight %} It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is @@ -423,14 +423,14 @@ dependencies to `spark-submit` through its `--py-files` argument by packaging th We can run this application using the `bin/spark-submit` script: -{% highlight python %} +{% highlight bash %} # Use spark-submit to run your application $ YOUR_SPARK_HOME/bin/spark-submit \ --master local[4] \ SimpleApp.py ... Lines with a: 46, Lines with b: 23 -{% endhighlight python %} +{% endhighlight %} @@ -444,7 +444,8 @@ Congratulations on running your first Spark application! * Finally, Spark includes several samples in the `examples` directory ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples), [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples), - [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python)). + [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python), + [R]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/r)). You can run them as follows: {% highlight bash %} @@ -453,4 +454,7 @@ You can run them as follows: # For Python examples, use spark-submit directly: ./bin/spark-submit examples/src/main/python/pi.py + +# For R examples, use spark-submit directly: +./bin/spark-submit examples/src/main/r/dataframe.R {% endhighlight %} diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 4fb4a90307ec8..96cf612c54fdd 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -71,9 +71,22 @@ Most of the configs are the same for Spark on YARN as for other deployment modes spark.yarn.scheduler.heartbeat.interval-ms - 5000 + 3000 The interval in ms in which the Spark application master heartbeats into the YARN ResourceManager. + The value is capped at half the value of YARN's configuration for the expiry interval + (yarn.am.liveness-monitor.expiry-interval-ms). + + + + spark.yarn.scheduler.initial-allocation.interval + 200ms + + The initial interval in which the Spark application master eagerly heartbeats to the YARN ResourceManager + when there are pending container allocation requests. It should be no larger than + spark.yarn.scheduler.heartbeat.interval-ms. The allocation interval will doubled on + successive eager heartbeats if pending containers still exist, until + spark.yarn.scheduler.heartbeat.interval-ms is reached. @@ -220,6 +233,31 @@ Most of the configs are the same for Spark on YARN as for other deployment modes Otherwise, the client process will exit after submission. + + spark.yarn.executor.nodeLabelExpression + (none) + + A YARN node label expression that restricts the set of nodes executors will be scheduled on. + Only versions of YARN greater than or equal to 2.6 support node label expressions, so when + running against earlier versions, this property will be ignored. + + + + spark.yarn.keytab + (none) + + The full path to the file that contains the keytab for the principal specified above. + This keytab will be copied to the node running the Application Master via the Secure Distributed Cache, + for renewing the login tickets and the delegation tokens periodically. + + + + spark.yarn.principal + (none) + + Principal to be used to login to KDC, while running on secure HDFS. + + # Launching Spark on YARN diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index 0eed9adacf123..4f71fbc086cd0 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -24,7 +24,7 @@ the master's web UI, which is [http://localhost:8080](http://localhost:8080) by Similarly, you can start one or more workers and connect them to the master via: - ./sbin/start-slave.sh + ./sbin/start-slave.sh Once you have started a worker, look at the master's web UI ([http://localhost:8080](http://localhost:8080) by default). You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS). @@ -77,7 +77,7 @@ Note, the master machine accesses each of the worker machines via ssh. By defaul If you do not have a password-less setup, you can set the environment variable SPARK_SSH_FOREGROUND and serially provide a password for each worker. -Once you've set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/bin`: +Once you've set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/sbin`: - `sbin/start-master.sh` - Starts a master instance on the machine the script is executed on. - `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file. diff --git a/docs/sparkr.md b/docs/sparkr.md new file mode 100644 index 0000000000000..4d82129921a37 --- /dev/null +++ b/docs/sparkr.md @@ -0,0 +1,223 @@ +--- +layout: global +displayTitle: SparkR (R on Spark) +title: SparkR (R on Spark) +--- + +* This will become a table of contents (this text will be scraped). +{:toc} + +# Overview +SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. +In Spark {{site.SPARK_VERSION}}, SparkR provides a distributed data frame implementation that +supports operations like selection, filtering, aggregation etc. (similar to R data frames, +[dplyr](https://github.com/hadley/dplyr)) but on large datasets. + +# SparkR DataFrames + +A DataFrame is a distributed collection of data organized into named columns. It is conceptually +equivalent to a table in a relational database or a data frame in R, but with richer +optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: +structured data files, tables in Hive, external databases, or existing local R data frames. + +All of the examples on this page use sample data included in R or the Spark distribution and can be run using the `./bin/sparkR` shell. + +## Starting Up: SparkContext, SQLContext + +
    +The entry point into SparkR is the `SparkContext` which connects your R program to a Spark cluster. +You can create a `SparkContext` using `sparkR.init` and pass in options such as the application name +etc. Further, to work with DataFrames we will need a `SQLContext`, which can be created from the +SparkContext. If you are working from the SparkR shell, the `SQLContext` and `SparkContext` should +already be created for you. + +{% highlight r %} +sc <- sparkR.init() +sqlContext <- sparkRSQL.init(sc) +{% endhighlight %} + +
    + +## Creating DataFrames +With a `SQLContext`, applications can create `DataFrame`s from a local R data frame, from a [Hive table](sql-programming-guide.html#hive-tables), or from other [data sources](sql-programming-guide.html#data-sources). + +### From local data frames +The simplest way to create a data frame is to convert a local R data frame into a SparkR DataFrame. Specifically we can use `createDataFrame` and pass in the local R data frame to create a SparkR DataFrame. As an example, the following creates a `DataFrame` based using the `faithful` dataset from R. + +
    +{% highlight r %} +df <- createDataFrame(sqlContext, faithful) + +# Displays the content of the DataFrame to stdout +head(df) +## eruptions waiting +##1 3.600 79 +##2 1.800 54 +##3 3.333 74 + +{% endhighlight %} +
    + +### From Data Sources + +SparkR supports operating on a variety of data sources through the `DataFrame` interface. This section describes the general methods for loading and saving data using Data Sources. You can check the Spark SQL programming guide for more [specific options](sql-programming-guide.html#manually-specifying-options) that are available for the built-in data sources. + +The general method for creating DataFrames from data sources is `read.df`. This method takes in the `SQLContext`, the path for the file to load and the type of data source. SparkR supports reading JSON and Parquet files natively and through [Spark Packages](http://spark-packages.org/) you can find data source connectors for popular file formats like [CSV](http://spark-packages.org/package/databricks/spark-csv) and [Avro](http://spark-packages.org/package/databricks/spark-avro). + +We can see how to use data sources using an example JSON input file. Note that the file that is used here is _not_ a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. + +
    + +{% highlight r %} +people <- read.df(sqlContext, "./examples/src/main/resources/people.json", "json") +head(people) +## age name +##1 NA Michael +##2 30 Andy +##3 19 Justin + +# SparkR automatically infers the schema from the JSON file +printSchema(people) +# root +# |-- age: integer (nullable = true) +# |-- name: string (nullable = true) + +{% endhighlight %} +
    + +The data sources API can also be used to save out DataFrames into multiple file formats. For example we can save the DataFrame from the previous example +to a Parquet file using `write.df` + +
    +{% highlight r %} +write.df(people, path="people.parquet", source="parquet", mode="overwrite") +{% endhighlight %} +
    + +### From Hive tables + +You can also create SparkR DataFrames from Hive tables. To do this we will need to create a HiveContext which can access tables in the Hive MetaStore. Note that Spark should have been built with [Hive support](building-spark.html#building-with-hive-and-jdbc-support) and more details on the difference between SQLContext and HiveContext can be found in the [SQL programming guide](sql-programming-guide.html#starting-point-sqlcontext). + +
    +{% highlight r %} +# sc is an existing SparkContext. +hiveContext <- sparkRHive.init(sc) + +sql(hiveContext, "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") +sql(hiveContext, "LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src") + +# Queries can be expressed in HiveQL. +results <- hiveContext.sql("FROM src SELECT key, value") + +# results is now a DataFrame +head(results) +## key value +## 1 238 val_238 +## 2 86 val_86 +## 3 311 val_311 + +{% endhighlight %} +
    + +## DataFrame Operations + +SparkR DataFrames support a number of functions to do structured data processing. +Here we include some basic examples and a complete list can be found in the [API](api/R/index.html) docs: + +### Selecting rows, columns + +
    +{% highlight r %} +# Create the DataFrame +df <- createDataFrame(sqlContext, faithful) + +# Get basic information about the DataFrame +df +## DataFrame[eruptions:double, waiting:double] + +# Select only the "eruptions" column +head(select(df, df$eruptions)) +## eruptions +##1 3.600 +##2 1.800 +##3 3.333 + +# You can also pass in column name as strings +head(select(df, "eruptions")) + +# Filter the DataFrame to only retain rows with wait times shorter than 50 mins +head(filter(df, df$waiting < 50)) +## eruptions waiting +##1 1.750 47 +##2 1.750 47 +##3 1.867 48 + +{% endhighlight %} + +
    + +### Grouping, Aggregation + +SparkR data frames support a number of commonly used functions to aggregate data after grouping. For example we can compute a histogram of the `waiting` time in the `faithful` dataset as shown below + +
    +{% highlight r %} + +# We use the `n` operator to count the number of times each waiting time appears +head(summarize(groupBy(df, df$waiting), count = n(df$waiting))) +## waiting count +##1 81 13 +##2 60 6 +##3 68 1 + +# We can also sort the output from the aggregation to get the most common waiting times +waiting_counts <- summarize(groupBy(df, df$waiting), count = n(df$waiting)) +head(arrange(waiting_counts, desc(waiting_counts$count))) + +## waiting count +##1 78 15 +##2 83 14 +##3 81 13 + +{% endhighlight %} +
    + +### Operating on Columns + +SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions. + +
    +{% highlight r %} + +# Convert waiting time from hours to seconds. +# Note that we can assign this to a new column in the same DataFrame +df$waiting_secs <- df$waiting * 60 +head(df) +## eruptions waiting waiting_secs +##1 3.600 79 4740 +##2 1.800 54 3240 +##3 3.333 74 4440 + +{% endhighlight %} +
    + +## Running SQL Queries from SparkR +A SparkR DataFrame can also be registered as a temporary table in Spark SQL and registering a DataFrame as a table allows you to run SQL queries over its data. +The `sql` function enables applications to run SQL queries programmatically and returns the result as a `DataFrame`. + +
    +{% highlight r %} +# Load a JSON file +people <- read.df(sqlContext, "./examples/src/main/resources/people.json", "json") + +# Register this DataFrame as a table. +registerTempTable(people, "people") + +# SQL statements can be run by using the sql method +teenagers <- sql(sqlContext, "SELECT name FROM people WHERE age >= 13 AND age <= 19") +head(teenagers) +## name +##1 Justin + +{% endhighlight %} +
    diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index df4c123bdd86c..26c036f6648da 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -11,14 +11,15 @@ title: Spark SQL and DataFrames Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine. +For how to enable Hive support, please refer to the [Hive Tables](#hive-tables) section. # DataFrames A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs. -The DataFrame API is available in [Scala](api/scala/index.html#org.apache.spark.sql.DataFrame), [Java](api/java/index.html?org/apache/spark/sql/DataFrame.html), and [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame). +The DataFrame API is available in [Scala](api/scala/index.html#org.apache.spark.sql.DataFrame), [Java](api/java/index.html?org/apache/spark/sql/DataFrame.html), [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame), and [R](api/R/index.html). -All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell` or the `pyspark` shell. +All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`, `pyspark` shell, or `sparkR` shell. ## Starting Point: `SQLContext` @@ -64,6 +65,17 @@ from pyspark.sql import SQLContext sqlContext = SQLContext(sc) {% endhighlight %} + + +
    + +The entry point into all relational functionality in Spark is the +`SQLContext` class, or one of its decedents. To create a basic `SQLContext`, all you need is a SparkContext. + +{% highlight r %} +sqlContext <- sparkRSQL.init(sc) +{% endhighlight %} +
    @@ -97,7 +109,7 @@ As an example, the following creates a `DataFrame` based on the content of a JSO val sc: SparkContext // An existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) -val df = sqlContext.jsonFile("examples/src/main/resources/people.json") +val df = sqlContext.read.json("examples/src/main/resources/people.json") // Displays the content of the DataFrame to stdout df.show() @@ -110,7 +122,7 @@ df.show() JavaSparkContext sc = ...; // An existing JavaSparkContext. SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); -DataFrame df = sqlContext.jsonFile("examples/src/main/resources/people.json"); +DataFrame df = sqlContext.read().json("examples/src/main/resources/people.json"); // Displays the content of the DataFrame to stdout df.show(); @@ -123,13 +135,26 @@ df.show(); from pyspark.sql import SQLContext sqlContext = SQLContext(sc) -df = sqlContext.jsonFile("examples/src/main/resources/people.json") +df = sqlContext.read.json("examples/src/main/resources/people.json") # Displays the content of the DataFrame to stdout df.show() {% endhighlight %} + +
    +{% highlight r %} +sqlContext <- SQLContext(sc) + +df <- jsonFile(sqlContext, "examples/src/main/resources/people.json") + +# Displays the content of the DataFrame to stdout +showDF(df) +{% endhighlight %} + +
    + @@ -146,7 +171,7 @@ val sc: SparkContext // An existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) // Create the DataFrame -val df = sqlContext.jsonFile("examples/src/main/resources/people.json") +val df = sqlContext.read.json("examples/src/main/resources/people.json") // Show the content of the DataFrame df.show() @@ -196,7 +221,7 @@ JavaSparkContext sc // An existing SparkContext. SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc) // Create the DataFrame -DataFrame df = sqlContext.jsonFile("examples/src/main/resources/people.json"); +DataFrame df = sqlContext.read().json("examples/src/main/resources/people.json"); // Show the content of the DataFrame df.show(); @@ -252,7 +277,7 @@ from pyspark.sql import SQLContext sqlContext = SQLContext(sc) # Create the DataFrame -df = sqlContext.jsonFile("examples/src/main/resources/people.json") +df = sqlContext.read.json("examples/src/main/resources/people.json") # Show the content of the DataFrame df.show() @@ -296,6 +321,57 @@ df.groupBy("age").count().show() {% endhighlight %} + +
    +{% highlight r %} +sqlContext <- sparkRSQL.init(sc) + +# Create the DataFrame +df <- jsonFile(sqlContext, "examples/src/main/resources/people.json") + +# Show the content of the DataFrame +showDF(df) +## age name +## null Michael +## 30 Andy +## 19 Justin + +# Print the schema in a tree format +printSchema(df) +## root +## |-- age: long (nullable = true) +## |-- name: string (nullable = true) + +# Select only the "name" column +showDF(select(df, "name")) +## name +## Michael +## Andy +## Justin + +# Select everybody, but increment the age by 1 +showDF(select(df, df$name, df$age + 1)) +## name (age + 1) +## Michael null +## Andy 31 +## Justin 20 + +# Select people older than 21 +showDF(where(df, df$age > 21)) +## age name +## 30 Andy + +# Count people by age +showDF(count(groupBy(df, "age"))) +## age count +## null 1 +## 19 1 +## 30 1 + +{% endhighlight %} + +
    + @@ -325,6 +401,14 @@ sqlContext = SQLContext(sc) df = sqlContext.sql("SELECT * FROM table") {% endhighlight %} + +
    +{% highlight r %} +sqlContext <- sparkRSQL.init(sc) +df <- sql(sqlContext, "SELECT * FROM table") +{% endhighlight %} +
    + @@ -367,11 +451,18 @@ val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(" people.registerTempTable("people") // SQL statements can be run by using the sql methods provided by sqlContext. -val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") +val teenagers = sqlContext.sql("SELECT name, age FROM people WHERE age >= 13 AND age <= 19") // The results of SQL queries are DataFrames and support all the normal RDD operations. -// The columns of a row in the result can be accessed by ordinal. +// The columns of a row in the result can be accessed by field index: teenagers.map(t => "Name: " + t(0)).collect().foreach(println) + +// or by field name: +teenagers.map(t => "Name: " + t.getAs[String]("name")).collect().foreach(println) + +// row.getValuesMap[T] retrieves multiple columns at once into a Map[String, T] +teenagers.map(_.getValuesMap[Any](List("name", "age"))).collect().foreach(println) +// Map("name" -> "Justin", "age" -> 19) {% endhighlight %} @@ -470,7 +561,7 @@ parts = lines.map(lambda l: l.split(",")) people = parts.map(lambda p: Row(name=p[0], age=int(p[1]))) # Infer the schema, and register the DataFrame as a table. -schemaPeople = sqlContext.inferSchema(people) +schemaPeople = sqlContext.createDataFrame(people) schemaPeople.registerTempTable("people") # SQL can be run over DataFrames that have been registered as a table. @@ -538,7 +629,7 @@ peopleDataFrame.registerTempTable("people") val results = sqlContext.sql("SELECT name FROM people") // The results of SQL queries are DataFrames and support all the normal RDD operations. -// The columns of a row in the result can be accessed by ordinal. +// The columns of a row in the result can be accessed by field index or by field name. results.map(t => "Name: " + t(0)).collect().foreach(println) {% endhighlight %} @@ -686,8 +777,8 @@ In the simplest form, the default data source (`parquet` unless otherwise config
    {% highlight scala %} -val df = sqlContext.load("examples/src/main/resources/users.parquet") -df.select("name", "favorite_color").save("namesAndFavColors.parquet") +val df = sqlContext.read.load("examples/src/main/resources/users.parquet") +df.select("name", "favorite_color").write.save("namesAndFavColors.parquet") {% endhighlight %}
    @@ -696,8 +787,8 @@ df.select("name", "favorite_color").save("namesAndFavColors.parquet") {% highlight java %} -DataFrame df = sqlContext.load("examples/src/main/resources/users.parquet"); -df.select("name", "favorite_color").save("namesAndFavColors.parquet"); +DataFrame df = sqlContext.read().load("examples/src/main/resources/users.parquet"); +df.select("name", "favorite_color").write().save("namesAndFavColors.parquet"); {% endhighlight %} @@ -707,11 +798,20 @@ df.select("name", "favorite_color").save("namesAndFavColors.parquet"); {% highlight python %} -df = sqlContext.load("examples/src/main/resources/users.parquet") -df.select("name", "favorite_color").save("namesAndFavColors.parquet") +df = sqlContext.read.load("examples/src/main/resources/users.parquet") +df.select("name", "favorite_color").write.save("namesAndFavColors.parquet") {% endhighlight %} + + +
    + +{% highlight r %} +df <- loadDF(sqlContext, "people.parquet") +saveDF(select(df, "name", "age"), "namesAndAges.parquet") +{% endhighlight %} +
    @@ -719,16 +819,16 @@ df.select("name", "favorite_color").save("namesAndFavColors.parquet") You can also manually specify the data source that will be used along with any extra options that you would like to pass to the data source. Data sources are specified by their fully qualified -name (i.e., `org.apache.spark.sql.parquet`), but for built-in sources you can also use the shorted -name (`json`, `parquet`, `jdbc`). DataFrames of any type can be converted into other types +name (i.e., `org.apache.spark.sql.parquet`), but for built-in sources you can also use their short +names (`json`, `parquet`, `jdbc`). DataFrames of any type can be converted into other types using this syntax.
    {% highlight scala %} -val df = sqlContext.load("examples/src/main/resources/people.json", "json") -df.select("name", "age").save("namesAndAges.parquet", "parquet") +val df = sqlContext.read.format("json").load("examples/src/main/resources/people.json") +df.select("name", "age").write.format("json").save("namesAndAges.json") {% endhighlight %}
    @@ -737,8 +837,8 @@ df.select("name", "age").save("namesAndAges.parquet", "parquet") {% highlight java %} -DataFrame df = sqlContext.load("examples/src/main/resources/people.json", "json"); -df.select("name", "age").save("namesAndAges.parquet", "parquet"); +DataFrame df = sqlContext.read().format("json").load("examples/src/main/resources/people.json"); +df.select("name", "age").write().format("parquet").save("namesAndAges.parquet"); {% endhighlight %} @@ -748,8 +848,18 @@ df.select("name", "age").save("namesAndAges.parquet", "parquet"); {% highlight python %} -df = sqlContext.load("examples/src/main/resources/people.json", "json") -df.select("name", "age").save("namesAndAges.parquet", "parquet") +df = sqlContext.read.load("examples/src/main/resources/people.json", format="json") +df.select("name", "age").write.save("namesAndAges.parquet", format="parquet") + +{% endhighlight %} + +
    +
    + +{% highlight r %} + +df <- loadDF(sqlContext, "people.json", "json") +saveDF(select(df, "name", "age"), "namesAndAges.parquet", "parquet") {% endhighlight %} @@ -797,7 +907,7 @@ new data. Ignore mode means that when saving a DataFrame to a data source, if data already exists, the save operation is expected to not save the contents of the DataFrame and to not - change the existing data. This is similar to a `CREATE TABLE IF NOT EXISTS` in SQL. + change the existing data. This is similar to a CREATE TABLE IF NOT EXISTS in SQL. @@ -837,11 +947,11 @@ import sqlContext.implicits._ val people: RDD[Person] = ... // An RDD of case class objects, from the previous example. // The RDD is implicitly converted to a DataFrame by implicits, allowing it to be stored using Parquet. -people.saveAsParquetFile("people.parquet") +people.write.parquet("people.parquet") // Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. // The result of loading a Parquet file is also a DataFrame. -val parquetFile = sqlContext.parquetFile("people.parquet") +val parquetFile = sqlContext.read.parquet("people.parquet") //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile") @@ -859,13 +969,13 @@ teenagers.map(t => "Name: " + t(0)).collect().foreach(println) DataFrame schemaPeople = ... // The DataFrame from the previous example. // DataFrames can be saved as Parquet files, maintaining the schema information. -schemaPeople.saveAsParquetFile("people.parquet"); +schemaPeople.write().parquet("people.parquet"); // Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a DataFrame. -DataFrame parquetFile = sqlContext.parquetFile("people.parquet"); +DataFrame parquetFile = sqlContext.read().parquet("people.parquet"); -//Parquet files can also be registered as tables and then used in SQL statements. +// Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); DataFrame teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); List teenagerNames = teenagers.javaRDD().map(new Function() { @@ -885,11 +995,11 @@ List teenagerNames = teenagers.javaRDD().map(new Function() schemaPeople # The DataFrame from the previous example. # DataFrames can be saved as Parquet files, maintaining the schema information. -schemaPeople.saveAsParquetFile("people.parquet") +schemaPeople.write.parquet("people.parquet") # Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved. # The result of loading a parquet file is also a DataFrame. -parquetFile = sqlContext.parquetFile("people.parquet") +parquetFile = sqlContext.read.parquet("people.parquet") # Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); @@ -901,6 +1011,31 @@ for teenName in teenNames.collect():
    +
    + +{% highlight r %} +# sqlContext from the previous example is used in this example. + +schemaPeople # The DataFrame from the previous example. + +# DataFrames can be saved as Parquet files, maintaining the schema information. +saveAsParquetFile(schemaPeople, "people.parquet") + +# Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved. +# The result of loading a parquet file is also a DataFrame. +parquetFile <- parquetFile(sqlContext, "people.parquet") + +# Parquet files can also be registered as tables and then used in SQL statements. +registerTempTable(parquetFile, "parquetFile"); +teenagers <- sql(sqlContext, "SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19") +teenNames <- map(teenagers, function(p) { paste("Name:", p$name)}) +for (teenName in collect(teenNames)) { + cat(teenName, "\n") +} +{% endhighlight %} + +
    +
    {% highlight sql %} @@ -924,7 +1059,7 @@ SELECT * FROM parquetTable Table partitioning is a common optimization approach used in systems like Hive. In a partitioned table, data are usually stored in different directories, with partitioning column values encoded in the path of each partition directory. The Parquet data source is now able to discover and infer -partitioning information automatically. For exmaple, we can store all our previously used +partitioning information automatically. For example, we can store all our previously used population data into a partitioned table using the following directory structure, with two extra columns, `gender` and `country` as partitioning columns: @@ -952,9 +1087,9 @@ path {% endhighlight %} -By passing `path/to/table` to either `SQLContext.parquetFile` or `SQLContext.load`, Spark SQL will -automatically extract the partitioning information from the paths. Now the schema of the returned -DataFrame becomes: +By passing `path/to/table` to either `SQLContext.read.parquet` or `SQLContext.read.load`, Spark SQL +will automatically extract the partitioning information from the paths. +Now the schema of the returned DataFrame becomes: {% highlight text %} @@ -967,7 +1102,11 @@ root {% endhighlight %} Notice that the data types of the partitioning columns are automatically inferred. Currently, -numeric data types and string type are supported. +numeric data types and string type are supported. Sometimes users may not want to automatically +infer the data types of the partitioning columns. For these use cases, the automatic type inference +can be configured by `spark.sql.sources.partitionColumnTypeInference.enabled`, which is default to +`true`. When type inference is disabled, string type will be used for the partitioning columns. + ### Schema merging @@ -986,20 +1125,20 @@ source is now able to automatically detect this case and merge schemas of all th import sqlContext.implicits._ // Create a simple DataFrame, stored into a partition directory -val df1 = sparkContext.makeRDD(1 to 5).map(i => (i, i * 2)).toDF("single", "double") -df1.saveAsParquetFile("data/test_table/key=1") +val df1 = sc.makeRDD(1 to 5).map(i => (i, i * 2)).toDF("single", "double") +df1.write.parquet("data/test_table/key=1") // Create another DataFrame in a new partition directory, // adding a new column and dropping an existing column -val df2 = sparkContext.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple") -df2.saveAsParquetFile("data/test_table/key=2") +val df2 = sc.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple") +df2.write.parquet("data/test_table/key=2") // Read the partitioned table -val df3 = sqlContext.parquetFile("data/test_table") +val df3 = sqlContext.read.parquet("data/test_table") df3.printSchema() // The final schema consists of all 3 columns in the Parquet files together -// with the partiioning column appeared in the partition directory paths. +// with the partitioning column appeared in the partition directory paths. // root // |-- single: int (nullable = true) // |-- double: int (nullable = true) @@ -1026,11 +1165,38 @@ df2 = sqlContext.createDataFrame(sc.parallelize(range(6, 11)) df2.save("data/test_table/key=2", "parquet") # Read the partitioned table -df3 = sqlContext.parquetFile("data/test_table") +df3 = sqlContext.load("data/test_table", "parquet") df3.printSchema() # The final schema consists of all 3 columns in the Parquet files together -# with the partiioning column appeared in the partition directory paths. +# with the partitioning column appeared in the partition directory paths. +# root +# |-- single: int (nullable = true) +# |-- double: int (nullable = true) +# |-- triple: int (nullable = true) +# |-- key : int (nullable = true) +{% endhighlight %} + +
    + +
    + +{% highlight r %} +# sqlContext from the previous example is used in this example. + +# Create a simple DataFrame, stored into a partition directory +saveDF(df1, "data/test_table/key=1", "parquet", "overwrite") + +# Create another DataFrame in a new partition directory, +# adding a new column and dropping an existing column +saveDF(df2, "data/test_table/key=2", "parquet", "overwrite") + +# Read the partitioned table +df3 <- loadDF(sqlContext, "data/test_table", "parquet") +printSchema(df3) + +# The final schema consists of all 3 columns in the Parquet files together +# with the partitioning column appeared in the partition directory paths. # root # |-- single: int (nullable = true) # |-- double: int (nullable = true) @@ -1054,7 +1220,7 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext` false Some other Parquet-producing systems, in particular Impala and older versions of Spark SQL, do - not differentiate between binary data and strings when writing out the Parquet schema. This + not differentiate between binary data and strings when writing out the Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems. @@ -1071,7 +1237,7 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext` spark.sql.parquet.cacheMetadata true - Turns on caching of Parquet schema metadata. Can speed up querying of static data. + Turns on caching of Parquet schema metadata. Can speed up querying of static data. @@ -1087,7 +1253,7 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext` false Turn on Parquet filter pushdown optimization. This feature is turned off by default because of a known - bug in Paruet 1.6.0rc3 (PARQUET-136). + bug in Parquet 1.6.0rc3 (PARQUET-136). However, if your table doesn't contain any nullable string or binary columns, it's still safe to turn this feature on. @@ -1107,12 +1273,10 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
    Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. -This conversion can be done using one of two methods in a `SQLContext`: +This conversion can be done using `SQLContext.read.json()` on either an RDD of String, +or a JSON file. -* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. -* `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. - -Note that the file that is offered as _jsonFile_ is not a typical JSON file. Each +Note that the file that is offered as _a json file_ is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. @@ -1123,8 +1287,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc) // A JSON dataset is pointed to by path. // The path can be either a single text file or a directory storing text files. val path = "examples/src/main/resources/people.json" -// Create a DataFrame from the file(s) pointed to by path -val people = sqlContext.jsonFile(path) +val people = sqlContext.read.json(path) // The inferred schema can be visualized using the printSchema() method. people.printSchema() @@ -1142,19 +1305,17 @@ val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age // an RDD[String] storing one JSON object per string. val anotherPeopleRDD = sc.parallelize( """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil) -val anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD) +val anotherPeople = sqlContext.read.json(anotherPeopleRDD) {% endhighlight %}
    Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. -This conversion can be done using one of two methods in a `SQLContext` : - -* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. -* `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. +This conversion can be done using `SQLContext.read().json()` on either an RDD of String, +or a JSON file. -Note that the file that is offered as _jsonFile_ is not a typical JSON file. Each +Note that the file that is offered as _a json file_ is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. @@ -1164,9 +1325,7 @@ SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); // A JSON dataset is pointed to by path. // The path can be either a single text file or a directory storing text files. -String path = "examples/src/main/resources/people.json"; -// Create a DataFrame from the file(s) pointed to by path -DataFrame people = sqlContext.jsonFile(path); +DataFrame people = sqlContext.read().json("examples/src/main/resources/people.json"); // The inferred schema can be visualized using the printSchema() method. people.printSchema(); @@ -1185,18 +1344,15 @@ DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AN List jsonData = Arrays.asList( "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); JavaRDD anotherPeopleRDD = sc.parallelize(jsonData); -DataFrame anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD); +DataFrame anotherPeople = sqlContext.read().json(anotherPeopleRDD); {% endhighlight %}
    Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. -This conversion can be done using one of two methods in a `SQLContext`: - -* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. -* `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. +This conversion can be done using `SQLContext.read.json` on a JSON file. -Note that the file that is offered as _jsonFile_ is not a typical JSON file. Each +Note that the file that is offered as _a json file_ is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. @@ -1207,9 +1363,7 @@ sqlContext = SQLContext(sc) # A JSON dataset is pointed to by path. # The path can be either a single text file or a directory storing text files. -path = "examples/src/main/resources/people.json" -# Create a DataFrame from the file(s) pointed to by path -people = sqlContext.jsonFile(path) +people = sqlContext.read.json("examples/src/main/resources/people.json") # The inferred schema can be visualized using the printSchema() method. people.printSchema() @@ -1231,6 +1385,39 @@ anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD) {% endhighlight %}
    +
    +Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. using +the `jsonFile` function, which loads data from a directory of JSON files where each line of the +files is a JSON object. + +Note that the file that is offered as _a json file_ is not a typical JSON file. Each +line must contain a separate, self-contained valid JSON object. As a consequence, +a regular multi-line JSON file will most often fail. + +{% highlight r %} +# sc is an existing SparkContext. +sqlContext <- sparkRSQL.init(sc) + +# A JSON dataset is pointed to by path. +# The path can be either a single text file or a directory storing text files. +path <- "examples/src/main/resources/people.json" +# Create a DataFrame from the file(s) pointed to by path +people <- jsonFile(sqlContext, path) + +# The inferred schema can be visualized using the printSchema() method. +printSchema(people) +# root +# |-- age: integer (nullable = true) +# |-- name: string (nullable = true) + +# Register this DataFrame as a table. +registerTempTable(people, "people") + +# SQL statements can be run by using the sql methods provided by `sqlContext`. +teenagers <- sql(sqlContext, "SELECT name FROM people WHERE age >= 13 AND age <= 19") +{% endhighlight %} +
    +
    {% highlight sql %} @@ -1258,7 +1445,12 @@ This command builds a new assembly jar that includes Hive. Note that this Hive a on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to access data stored in Hive. -Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. +Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running +the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory +and `hive-site.xml` under `conf/` directory need to be available on the driver and all executors launched by the +YARN cluster. The convenient way to do this is adding them through the `--jars` option and `--file` option of the +`spark-submit` command. +
    @@ -1287,12 +1479,12 @@ sqlContext.sql("FROM src SELECT key, value").collect().foreach(println) When working with Hive one must construct a `HiveContext`, which inherits from `SQLContext`, and adds support for finding tables in the MetaStore and writing queries using HiveQL. In addition to -the `sql` method a `HiveContext` also provides an `hql` methods, which allows queries to be +the `sql` method a `HiveContext` also provides an `hql` method, which allows queries to be expressed in HiveQL. {% highlight java %} // sc is an existing JavaSparkContext. -HiveContext sqlContext = new org.apache.spark.sql.hive.HiveContext(sc); +HiveContext sqlContext = new org.apache.spark.sql.hive.HiveContext(sc.sc); sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"); sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src"); @@ -1307,10 +1499,7 @@ Row[] results = sqlContext.sql("FROM src SELECT key, value").collect();
    When working with Hive one must construct a `HiveContext`, which inherits from `SQLContext`, and -adds support for finding tables in the MetaStore and writing queries using HiveQL. In addition to -the `sql` method a `HiveContext` also provides an `hql` methods, which allows queries to be -expressed in HiveQL. - +adds support for finding tables in the MetaStore and writing queries using HiveQL. {% highlight python %} # sc is an existing SparkContext. from pyspark.sql import HiveContext @@ -1324,9 +1513,91 @@ results = sqlContext.sql("FROM src SELECT key, value").collect() {% endhighlight %} +
    + +
    + +When working with Hive one must construct a `HiveContext`, which inherits from `SQLContext`, and +adds support for finding tables in the MetaStore and writing queries using HiveQL. +{% highlight r %} +# sc is an existing SparkContext. +sqlContext <- sparkRHive.init(sc) + +sql(sqlContext, "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") +sql(sqlContext, "LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src") + +# Queries can be expressed in HiveQL. +results = sqlContext.sql("FROM src SELECT key, value").collect() + +{% endhighlight %} +
    +### Interacting with Different Versions of Hive Metastore + +One of the most important pieces of Spark SQL's Hive support is interaction with Hive metastore, +which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. + +Internally, Spark SQL uses two Hive clients, one for executing native Hive commands like `SET` +and `DESCRIBE`, the other dedicated for communicating with Hive metastore. The former uses Hive +jars of version 0.13.1, which are bundled with Spark 1.4.0. The latter uses Hive jars of the +version specified by users. An isolated classloader is used here to avoid dependency conflicts. + + + + + + + + + + + + + + + + + + + + + + + +
    Property NameDefaultMeaning
    spark.sql.hive.metastore.version0.13.1 + Version of the Hive metastore. Available + options are 0.12.0 and 0.13.1. Support for more versions is coming in the future. +
    spark.sql.hive.metastore.jarsbuiltin + Location of the jars that should be used to instantiate the HiveMetastoreClient. This + property can be one of three options: +
      +
    1. builtin
      2. + Use Hive 0.13.1, which is bundled with the Spark assembly jar when -Phive is + enabled. When this option is chosen, spark.sql.hive.metastore.version must be + either 0.13.1 or not defined. +
    2. maven
      3. + Use Hive jars of specified version downloaded from Maven repositories. +
    3. A classpath in the standard format for both Hive and Hadoop.
      4. +
    +
    spark.sql.hive.metastore.sharedPrefixescom.mysql.jdbc,
    org.postgresql,
    com.microsoft.sqlserver,
    oracle.jdbc    		 +

    + A comma separated list of class prefixes that should be loaded using the classloader that is + shared between Spark SQL and a specific version of Hive. An example of classes that should + be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need + to be shared are those that interact with classes that are already shared. For example, + custom appenders that are used by log4j. +

    +
    spark.sql.hive.metastore.barrierPrefixes(empty) +

    + A comma separated list of class prefixes that should explicitly be reloaded for each version + of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a + prefix that typically would be shared (i.e. org.apache.spark.*). +

    +
    + + ## JDBC To Other Databases Spark SQL also includes a data source that can read data from other databases using JDBC. This @@ -1360,7 +1631,7 @@ the Data Sources API. The following options are supported: dbtable - The JDBC table that should be read. Note that anything that is valid in a `FROM` clause of + The JDBC table that should be read. Note that anything that is valid in a FROM clause of a SQL query can be used. For example, instead of a full table you could also use a subquery in parentheses. @@ -1423,6 +1694,16 @@ df = sqlContext.load(source="jdbc", url="jdbc:postgresql:dbserver", dbtable="sch
    +
    + +{% highlight r %} + +df <- loadDF(sqlContext, source="jdbc", url="jdbc:postgresql:dbserver", dbtable="schema.tablename") + +{% endhighlight %} + +
    +
    {% highlight sql %} @@ -1494,7 +1775,7 @@ that these options will be deprecated in future release as more optimizations ar Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when performing a join. By setting this value to -1 broadcasting can be disabled. Note that currently statistics are only supported for Hive Metastore tables where the command - `ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan` has been run. + ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan has been run. @@ -1513,11 +1794,20 @@ that these options will be deprecated in future release as more optimizations ar Configures the number of partitions to use when shuffling data for joins or aggregations. + + spark.sql.planner.externalSort + false + + When true, performs sorts spilling to disk as needed otherwise sort each partition in memory. + + # Distributed SQL Engine -Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries, without the need to write any code. +Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. +In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries, +without the need to write any code. ## Running the Thrift JDBC/ODBC server @@ -1531,7 +1821,7 @@ To start the JDBC/ODBC server, run the following in the Spark directory: This script accepts all `bin/spark-submit` command line options, plus a `--hiveconf` option to specify Hive properties. You may run `./sbin/start-thriftserver.sh --help` for a complete list of all available options. By default, the server listens on localhost:10000. You may override this -bahaviour via either environment variables, i.e.: +behaviour via either environment variables, i.e.: {% highlight bash %} export HIVE_SERVER2_THRIFT_PORT= @@ -1594,6 +1884,83 @@ options. # Migration Guide +## Upgrading from Spark SQL 1.3 to 1.4 + +#### DataFrame data reader/writer interface + +Based on user feedback, we created a new, more fluid API for reading data in (`SQLContext.read`) +and writing data out (`DataFrame.write`), +and deprecated the old APIs (e.g. `SQLContext.parquetFile`, `SQLContext.jsonFile`). + +See the API docs for `SQLContext.read` ( + Scala, + Java, + Python +) and `DataFrame.write` ( + Scala, + Java, + Python +) more information. + + +#### DataFrame.groupBy retains grouping columns + +Based on user feedback, we changed the default behavior of `DataFrame.groupBy().agg()` to retain the grouping columns in the resulting `DataFrame`. To keep the behavior in 1.3, set `spark.sql.retainGroupColumns` to `false`. + +
    +
    +{% highlight scala %} + +// In 1.3.x, in order for the grouping column "department" to show up, +// it must be included explicitly as part of the agg function call. +df.groupBy("department").agg($"department", max("age"), sum("expense")) + +// In 1.4+, grouping column "department" is included automatically. +df.groupBy("department").agg(max("age"), sum("expense")) + +// Revert to 1.3 behavior (not retaining grouping column) by: +sqlContext.setConf("spark.sql.retainGroupColumns", "false") + +{% endhighlight %} +
    + +
    +{% highlight java %} + +// In 1.3.x, in order for the grouping column "department" to show up, +// it must be included explicitly as part of the agg function call. +df.groupBy("department").agg(col("department"), max("age"), sum("expense")); + +// In 1.4+, grouping column "department" is included automatically. +df.groupBy("department").agg(max("age"), sum("expense")); + +// Revert to 1.3 behavior (not retaining grouping column) by: +sqlContext.setConf("spark.sql.retainGroupColumns", "false"); + +{% endhighlight %} +
    + +
    +{% highlight python %} + +import pyspark.sql.functions as func + +# In 1.3.x, in order for the grouping column "department" to show up, +# it must be included explicitly as part of the agg function call. +df.groupBy("department").agg("department"), func.max("age"), func.sum("expense")) + +# In 1.4+, grouping column "department" is included automatically. +df.groupBy("department").agg(func.max("age"), func.sum("expense")) + +# Revert to 1.3.x behavior (not retaining grouping column) by: +sqlContext.setConf("spark.sql.retainGroupColumns", "false") + +{% endhighlight %} +
    + +
    + + ## Upgrading from Spark SQL 1.0-1.2 to 1.3 In Spark 1.3 we removed the "Alpha" label from Spark SQL and as part of this did a cleanup of the @@ -1651,7 +2018,7 @@ moved into the udf object in `SQLContext`.
    -{% highlight java %} +{% highlight scala %} sqlContext.udf.register("strLen", (s: String) => s.length()) @@ -1661,7 +2028,7 @@ sqlContext.udf.register("strLen", (s: String) => s.length())
    {% highlight java %} -sqlContext.udf().register("strLen", (String s) -> { s.length(); }); +sqlContext.udf().register("strLen", (String s) -> s.length(), DataTypes.IntegerType); {% endhighlight %}
    @@ -2289,5 +2656,151 @@ from pyspark.sql.types import *
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Data typeValue type in RAPI to access or create a data type
    ByteType + integer
    + Note: Numbers will be converted to 1-byte signed integer numbers at runtime. + Please make sure that numbers are within the range of -128 to 127. +     		+ "byte" +
    ShortType + integer
    + Note: Numbers will be converted to 2-byte signed integer numbers at runtime. + Please make sure that numbers are within the range of -32768 to 32767. +     		+ "short" +
    IntegerType integer + "integer" +
    LongType + integer
    + Note: Numbers will be converted to 8-byte signed integer numbers at runtime. + Please make sure that numbers are within the range of + -9223372036854775808 to 9223372036854775807. + Otherwise, please convert data to decimal.Decimal and use DecimalType. +     		+ "long" +
    FloatType + numeric
    + Note: Numbers will be converted to 4-byte single-precision floating + point numbers at runtime. +     		+ "float" +
    DoubleType numeric + "double" +
    DecimalType Not supported + Not supported +
    StringType character + "string" +
    BinaryType raw + "binary" +
    BooleanType logical + "bool" +
    TimestampType POSIXct + "timestamp" +
    DateType Date + "date" +
    ArrayType vector or list + list(type="array", elementType=elementType, containsNull=[containsNull])
    + Note: The default value of containsNull is True. +
    MapType environment + list(type="map", keyType=keyType, valueType=valueType, valueContainsNull=[valueContainsNull])
    + Note: The default value of valueContainsNull is True. +
    StructType named list + list(type="struct", fields=fields)
    + Note: fields is a Seq of StructFields. Also, two fields with the same + name are not allowed. +
    StructField The value type in R of the data type of this field + (For example, integer for a StructField with the data type IntegerType) + list(name=name, type=dataType, nullable=nullable) +
    + +
    +
    diff --git a/docs/streaming-custom-receivers.md b/docs/streaming-custom-receivers.md index 6a2048121f8bf..a75587a92adc7 100644 --- a/docs/streaming-custom-receivers.md +++ b/docs/streaming-custom-receivers.md @@ -4,7 +4,7 @@ title: Spark Streaming Custom Receivers --- Spark Streaming can receive streaming data from any arbitrary data source beyond -the one's for which it has in-built support (that is, beyond Flume, Kafka, Kinesis, files, sockets, etc.). +the ones for which it has built-in support (that is, beyond Flume, Kafka, Kinesis, files, sockets, etc.). This requires the developer to implement a *receiver* that is customized for receiving data from the concerned data source. This guide walks through the process of implementing a custom receiver and using it in a Spark Streaming application. Note that custom receivers can be implemented @@ -21,15 +21,15 @@ A custom receiver must extend this abstract class by implementing two methods - `onStop()`: Things to do to stop receiving data. Both `onStart()` and `onStop()` must not block indefinitely. Typically, `onStart()` would start the threads -that responsible for receiving the data and `onStop()` would ensure that the receiving by those threads +that are responsible for receiving the data, and `onStop()` would ensure that these threads receiving the data are stopped. The receiving threads can also use `isStopped()`, a `Receiver` method, to check whether they should stop receiving data. Once the data is received, that data can be stored inside Spark by calling `store(data)`, which is a method provided by the Receiver class. -There are number of flavours of `store()` which allow you store the received data -record-at-a-time or as whole collection of objects / serialized bytes. Note that the flavour of -`store()` used to implemented a receiver affects its reliability and fault-tolerance semantics. +There are a number of flavors of `store()` which allow one to store the received data +record-at-a-time or as whole collection of objects / serialized bytes. Note that the flavor of +`store()` used to implement a receiver affects its reliability and fault-tolerance semantics. This is discussed [later](#receiver-reliability) in more detail. Any exception in the receiving threads should be caught and handled properly to avoid silent @@ -60,7 +60,7 @@ class CustomReceiver(host: String, port: Int) def onStop() { // There is nothing much to do as the thread calling receive() - // is designed to stop by itself isStopped() returns false + // is designed to stop by itself if isStopped() returns false } /** Create a socket connection and receive data until receiver is stopped */ @@ -123,7 +123,7 @@ public class JavaCustomReceiver extends Receiver { public void onStop() { // There is nothing much to do as the thread calling receive() - // is designed to stop by itself isStopped() returns false + // is designed to stop by itself if isStopped() returns false } /** Create a socket connection and receive data until receiver is stopped */ @@ -167,7 +167,7 @@ public class JavaCustomReceiver extends Receiver { The custom receiver can be used in a Spark Streaming application by using `streamingContext.receiverStream()`. This will create -input DStream using data received by the instance of custom receiver, as shown below +an input DStream using data received by the instance of custom receiver, as shown below:
    @@ -206,22 +206,20 @@ there are two kinds of receivers based on their reliability and fault-tolerance and stored in Spark reliably (that is, replicated successfully). Usually, implementing this receiver involves careful consideration of the semantics of source acknowledgements. -1. *Unreliable Receiver* - These are receivers for unreliable sources that do not support - acknowledging. Even for reliable sources, one may implement an unreliable receiver that - do not go into the complexity of acknowledging correctly. +1. *Unreliable Receiver* - An *unreliable receiver* does *not* send acknowledgement to a source. This can be used for sources that do not support acknowledgement, or even for reliable sources when one does not want or need to go into the complexity of acknowledgement. To implement a *reliable receiver*, you have to use `store(multiple-records)` to store data. -This flavour of `store` is a blocking call which returns only after all the given records have +This flavor of `store` is a blocking call which returns only after all the given records have been stored inside Spark. If the receiver's configured storage level uses replication (enabled by default), then this call returns after replication has completed. Thus it ensures that the data is reliably stored, and the receiver can now acknowledge the -source appropriately. This ensures that no data is caused when the receiver fails in the middle +source appropriately. This ensures that no data is lost when the receiver fails in the middle of replicating data -- the buffered data will not be acknowledged and hence will be later resent by the source. An *unreliable receiver* does not have to implement any of this logic. It can simply receive records from the source and insert them one-at-a-time using `store(single-record)`. While it does -not get the reliability guarantees of `store(multiple-records)`, it has the following advantages. +not get the reliability guarantees of `store(multiple-records)`, it has the following advantages: - The system takes care of chunking that data into appropriate sized blocks (look for block interval in the [Spark Streaming Programming Guide](streaming-programming-guide.html)). diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md index c8ab146bcae0a..8d6e74370918f 100644 --- a/docs/streaming-flume-integration.md +++ b/docs/streaming-flume-integration.md @@ -99,6 +99,12 @@ Configuring Flume on the chosen machine requires the following two steps. artifactId = scala-library version = {{site.SCALA_VERSION}} + (iii) *Commons Lang 3 JAR*: Download the Commons Lang 3 JAR. It can be found with the following artifact detail (or, [direct link](http://search.maven.org/remotecontent?filepath=org/apache/commons/commons-lang3/3.3.2/commons-lang3-3.3.2.jar)). + + groupId = org.apache.commons + artifactId = commons-lang3 + version = 3.3.2 + 2. **Configuration file**: On that machine, configure Flume agent to send data to an Avro sink by having the following in the configuration file. agent.sinks = spark diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md index 64714f0b799fc..775d508d4879b 100644 --- a/docs/streaming-kafka-integration.md +++ b/docs/streaming-kafka-integration.md @@ -2,12 +2,12 @@ layout: global title: Spark Streaming + Kafka Integration Guide --- -[Apache Kafka](http://kafka.apache.org/) is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka. There are two approaches to this - the old approach using Receivers and Kafka's high-level API, and a new experimental approach (introduced in Spark 1.3) without using Receivers. They have different programming models, performance characteristics, and semantics guarantees, so read on for more details. +[Apache Kafka](http://kafka.apache.org/) is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka. There are two approaches to this - the old approach using Receivers and Kafka's high-level API, and a new experimental approach (introduced in Spark 1.3) without using Receivers. They have different programming models, performance characteristics, and semantics guarantees, so read on for more details. ## Approach 1: Receiver-based Approach This approach uses a Receiver to receive the data. The Received is implemented using the Kafka high-level consumer API. As with all receivers, the data received from Kafka through a Receiver is stored in Spark executors, and then jobs launched by Spark Streaming processes the data. -However, under default configuration, this approach can lose data under failures (see [receiver reliability](streaming-programming-guide.html#receiver-reliability). To ensure zero-data loss, you have to additionally enable Write Ahead Logs in Spark Streaming. To ensure zero data loss, enable the Write Ahead Logs (introduced in Spark 1.2). This synchronously saves all the received Kafka data into write ahead logs on a distributed file system (e.g HDFS), so that all the data can be recovered on failure. See [Deploying section](streaming-programming-guide.html#deploying-applications) in the streaming programming guide for more details on Write Ahead Logs. +However, under default configuration, this approach can lose data under failures (see [receiver reliability](streaming-programming-guide.html#receiver-reliability). To ensure zero-data loss, you have to additionally enable Write Ahead Logs in Spark Streaming (introduced in Spark 1.2). This synchronously saves all the received Kafka data into write ahead logs on a distributed file system (e.g HDFS), so that all the data can be recovered on failure. See [Deploying section](streaming-programming-guide.html#deploying-applications) in the streaming programming guide for more details on Write Ahead Logs. Next, we discuss how to use this approach in your streaming application. @@ -29,7 +29,7 @@ Next, we discuss how to use this approach in your streaming application. [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]) You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
    import org.apache.spark.streaming.kafka.*; @@ -39,7 +39,7 @@ Next, we discuss how to use this approach in your streaming application. [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]); You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java).
    @@ -74,15 +74,15 @@ Next, we discuss how to use this approach in your streaming application. [Maven repository](http://search.maven.org/#search|ga|1|a%3A%22spark-streaming-kafka-assembly_2.10%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22) and add it to `spark-submit` with `--jars`. ## Approach 2: Direct Approach (No Receivers) -This is a new receiver-less "direct" approach has been introduced in Spark 1.3 to ensure stronger end-to-end guarantees. Instead of using receivers to receive data, this approach periodically queries Kafka for the latest offsets in each topic+partition, and accordingly defines the offset ranges to process in each batch. When the jobs to process the data are launched, Kafka's simple consumer API is used to read the defined ranges of offsets from Kafka (similar to read files from a file system). Note that this is an experimental feature in Spark 1.3 and is only available in the Scala and Java API. +This new receiver-less "direct" approach has been introduced in Spark 1.3 to ensure stronger end-to-end guarantees. Instead of using receivers to receive data, this approach periodically queries Kafka for the latest offsets in each topic+partition, and accordingly defines the offset ranges to process in each batch. When the jobs to process the data are launched, Kafka's simple consumer API is used to read the defined ranges of offsets from Kafka (similar to read files from a file system). Note that this is an experimental feature introduced in Spark 1.3 for the Scala and Java API. Spark 1.4 added a Python API, but it is not yet at full feature parity. -This approach has the following advantages over the received-based approach (i.e. Approach 1). +This approach has the following advantages over the receiver-based approach (i.e. Approach 1). -- *Simplified Parallelism:* No need to create multiple input Kafka streams and union-ing them. With `directStream`, Spark Streaming will create as many RDD partitions as there is Kafka partitions to consume, which will all read data from Kafka in parallel. So there is one-to-one mapping between Kafka and RDD partitions, which is easier to understand and tune. +- *Simplified Parallelism:* No need to create multiple input Kafka streams and union them. With `directStream`, Spark Streaming will create as many RDD partitions as there are Kafka partitions to consume, which will all read data from Kafka in parallel. So there is a one-to-one mapping between Kafka and RDD partitions, which is easier to understand and tune. -- *Efficiency:* Achieving zero-data loss in the first approach required the data to be stored in a Write Ahead Log, which further replicated the data. This is actually inefficient as the data effectively gets replicated twice - once by Kafka, and a second time by the Write Ahead Log. This second approach eliminate the problem as there is no receiver, and hence no need for Write Ahead Logs. +- *Efficiency:* Achieving zero-data loss in the first approach required the data to be stored in a Write Ahead Log, which further replicated the data. This is actually inefficient as the data effectively gets replicated twice - once by Kafka, and a second time by the Write Ahead Log. This second approach eliminates the problem as there is no receiver, and hence no need for Write Ahead Logs. As long as you have sufficient Kafka retention, messages can be recovered from Kafka. -- *Exactly-once semantics:* The first approach uses Kafka's high level API to store consumed offsets in Zookeeper. This is traditionally the way to consume data from Kafka. While this approach (in combination with write ahead logs) can ensure zero data loss (i.e. at-least once semantics), there is a small chance some records may get consumed twice under some failures. This occurs because of inconsistencies between data reliably received by Spark Streaming and offsets tracked by Zookeeper. Hence, in this second approach, we use simple Kafka API that does not use Zookeeper and offsets tracked only by Spark Streaming within its checkpoints. This eliminates inconsistencies between Spark Streaming and Zookeeper/Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures. +- *Exactly-once semantics:* The first approach uses Kafka's high level API to store consumed offsets in Zookeeper. This is traditionally the way to consume data from Kafka. While this approach (in combination with write ahead logs) can ensure zero data loss (i.e. at-least once semantics), there is a small chance some records may get consumed twice under some failures. This occurs because of inconsistencies between data reliably received by Spark Streaming and offsets tracked by Zookeeper. Hence, in this second approach, we use simple Kafka API that does not use Zookeeper. Offsets are tracked by Spark Streaming within its checkpoints. This eliminates inconsistencies between Spark Streaming and Zookeeper/Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures. In order to achieve exactly-once semantics for output of your results, your output operation that saves the data to an external data store must be either idempotent, or an atomic transaction that saves results and offsets (see [Semanitcs of output operations](streaming-programming-guide.html#semantics-of-output-operations) in the main programming guide for further information). Note that one disadvantage of this approach is that it does not update offsets in Zookeeper, hence Zookeeper-based Kafka monitoring tools will not show progress. However, you can access the offsets processed by this approach in each batch and update Zookeeper yourself (see below). @@ -105,7 +105,7 @@ Next, we discuss how to use this approach in your streaming application. streamingContext, [map of Kafka parameters], [set of topics to consume]) See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala).
    import org.apache.spark.streaming.kafka.*; @@ -116,8 +116,15 @@ Next, we discuss how to use this approach in your streaming application. [map of Kafka parameters], [set of topics to consume]); See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java). +
    +
    + from pyspark.streaming.kafka import KafkaUtils + directKafkaStream = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list": brokers}) + + By default, the Python API will decode Kafka data as UTF8 encoded strings. You can specify your custom decoding function to decode the byte arrays in Kafka records to any arbitrary data type. See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.kafka.KafkaUtils) + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/direct_kafka_wordcount.py).
    @@ -128,29 +135,60 @@ Next, we discuss how to use this approach in your streaming application.
    - directKafkaStream.foreachRDD { rdd => - val offsetRanges = rdd.asInstanceOf[HasOffsetRanges] - // offsetRanges.length = # of Kafka partitions being consumed - ... + // Hold a reference to the current offset ranges, so it can be used downstream + var offsetRanges = Array[OffsetRange]() + + directKafkaStream.transform { rdd => + offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges + rdd + }.map { + ... + }.foreachRDD { rdd => + for (o <- offsetRanges) { + println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}") + } + ... }
    - directKafkaStream.foreachRDD( - new Function, Void>() { - @Override - public Void call(JavaPairRDD rdd) throws IOException { - OffsetRange[] offsetRanges = ((HasOffsetRanges)rdd).offsetRanges - // offsetRanges.length = # of Kafka partitions being consumed - ... - return null; - } + // Hold a reference to the current offset ranges, so it can be used downstream + final AtomicReference offsetRanges = new AtomicReference(); + + directKafkaStream.transformToPair( + new Function, JavaPairRDD>() { + @Override + public JavaPairRDD call(JavaPairRDD rdd) throws Exception { + OffsetRange[] offsets = ((HasOffsetRanges) rdd.rdd()).offsetRanges(); + offsetRanges.set(offsets); + return rdd; + } + } + ).map( + ... + ).foreachRDD( + new Function, Void>() { + @Override + public Void call(JavaPairRDD rdd) throws IOException { + for (OffsetRange o : offsetRanges.get()) { + System.out.println( + o.topic() + " " + o.partition() + " " + o.fromOffset() + " " + o.untilOffset() + ); + } + ... + return null; } + } );
    +
    + Not supported yet
    +
    You can use this to update Zookeeper yourself if you want Zookeeper-based Kafka monitoring tools to show progress of the streaming application. - Another thing to note is that since this approach does not use Receivers, the standard receiver-related (that is, [configurations](configuration.html) of the form `spark.streaming.receiver.*` ) will not apply to the input DStreams created by this approach (will apply to other input DStreams though). Instead, use the [configurations](configuration.html) `spark.streaming.kafka.*`. An important one is `spark.streaming.kafka.maxRatePerPartition` which is the maximum rate at which each Kafka partition will be read by this direct API. + Note that the typecast to HasOffsetRanges will only succeed if it is done in the first method called on the directKafkaStream, not later down a chain of methods. You can use transform() instead of foreachRDD() as your first method call in order to access offsets, then call further Spark methods. However, be aware that the one-to-one mapping between RDD partition and Kafka partition does not remain after any methods that shuffle or repartition, e.g. reduceByKey() or window(). + + Another thing to note is that since this approach does not use Receivers, the standard receiver-related (that is, [configurations](configuration.html) of the form `spark.streaming.receiver.*` ) will not apply to the input DStreams created by this approach (will apply to other input DStreams though). Instead, use the [configurations](configuration.html) `spark.streaming.kafka.*`. An important one is `spark.streaming.kafka.maxRatePerPartition` which is the maximum rate (in messages per second) at which each Kafka partition will be read by this direct API. -3. **Deploying:** Similar to the first approach, you can package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR and the launch the application using `spark-submit`. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation. \ No newline at end of file +3. **Deploying:** This is same as the first approach, for Scala, Java and Python. diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md index 379eb513d521e..aa9749afbc867 100644 --- a/docs/streaming-kinesis-integration.md +++ b/docs/streaming-kinesis-integration.md @@ -32,7 +32,8 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream val kinesisStream = KinesisUtils.createStream( - streamingContext, [Kinesis stream name], [endpoint URL], [checkpoint interval], [initial position]) + streamingContext, [Kinesis app name], [Kinesis stream name], [endpoint URL], + [region name], [initial position], [checkpoint interval], StorageLevel.MEMORY_AND_DISK_2) See the [API docs](api/scala/index.html#org.apache.spark.streaming.kinesis.KinesisUtils$) and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala). Refer to the Running the Example section for instructions on how to run the example. @@ -44,7 +45,8 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream; JavaReceiverInputDStream kinesisStream = KinesisUtils.createStream( - streamingContext, [Kinesis stream name], [endpoint URL], [checkpoint interval], [initial position]); + streamingContext, [Kinesis app name], [Kinesis stream name], [endpoint URL], + [region name], [initial position], [checkpoint interval], StorageLevel.MEMORY_AND_DISK_2); See the [API docs](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisUtils.html) and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java). Refer to the next subsection for instructions to run the example. @@ -54,19 +56,23 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m - `streamingContext`: StreamingContext containg an application name used by Kinesis to tie this Kinesis application to the Kinesis stream - - `[Kinesis stream name]`: The Kinesis stream that this streaming application receives from - - The application name used in the streaming context becomes the Kinesis application name + - `[Kineiss app name]`: The application name that will be used to checkpoint the Kinesis + sequence numbers in DynamoDB table. - The application name must be unique for a given account and region. - - The Kinesis backend automatically associates the application name to the Kinesis stream using a DynamoDB table (always in the us-east-1 region) created during Kinesis Client Library initialization. - - Changing the application name or stream name can lead to Kinesis errors in some cases. If you see errors, you may need to manually delete the DynamoDB table. + - If the table exists but has incorrect checkpoint information (for a different stream, or + old expired sequenced numbers), then there may be temporary errors. + - `[Kinesis stream name]`: The Kinesis stream that this streaming application will pull data from. - `[endpoint URL]`: Valid Kinesis endpoints URL can be found [here](http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region). + - `[region name]`: Valid Kinesis region names can be found [here](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html). + - `[checkpoint interval]`: The interval (e.g., Duration(2000) = 2 seconds) at which the Kinesis Client Library saves its position in the stream. For starters, set it to the same as the batch interval of the streaming application. - `[initial position]`: Can be either `InitialPositionInStream.TRIM_HORIZON` or `InitialPositionInStream.LATEST` (see Kinesis Checkpointing section and Amazon Kinesis API documentation for more details). + In other versions of the API, you can also specify the AWS access key and secret key directly. 3. **Deploying:** Package `spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide). @@ -122,12 +128,12 @@ To run the example,
    - bin/run-example streaming.KinesisWordCountASL [Kinesis stream name] [endpoint URL] + bin/run-example streaming.KinesisWordCountASL [Kinesis app name] [Kinesis stream name] [endpoint URL]
    - bin/run-example streaming.JavaKinesisWordCountASL [Kinesis stream name] [endpoint URL] + bin/run-example streaming.JavaKinesisWordCountASL [Kinesis app name] [Kinesis stream name] [endpoint URL]
    @@ -136,7 +142,7 @@ To run the example, - To generate random string data to put onto the Kinesis stream, in another terminal, run the associated Kinesis data producer. - bin/run-example streaming.KinesisWordCountProducerASL [Kinesis stream name] [endpoint URL] 1000 10 + bin/run-example streaming.KinesisWordProducerASL [Kinesis stream name] [endpoint URL] 1000 10 This will push 1000 lines per second of 10 random numbers per line to the Kinesis stream. This data should then be received and processed by the running example. diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 2f2fea53168a3..b784d59666fec 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -11,7 +11,7 @@ description: Spark Streaming programming guide and tutorial for Spark SPARK_VERS # Overview Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources -like Kafka, Flume, Twitter, ZeroMQ, Kinesis or TCP sockets can be processed using complex +like Kafka, Flume, Twitter, ZeroMQ, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like `map`, `reduce`, `join` and `window`. Finally, processed data can be pushed out to filesystems, databases, and live dashboards. In fact, you can apply Spark's @@ -52,7 +52,7 @@ different languages. **Note:** Python API for Spark Streaming has been introduced in Spark 1.2. It has all the DStream transformations and almost all the output operations available in Scala and Java interfaces. -However, it has only support for basic sources like text files and text data over sockets. +However, it only has support for basic sources like text files and text data over sockets. APIs for additional sources, like Kafka and Flume, will be available in the future. Further information about available features in the Python API are mentioned throughout this document; look out for the tag @@ -69,15 +69,15 @@ do is as follows.
    -First, we import the names of the Spark Streaming classes, and some implicit -conversions from StreamingContext into our environment, to add useful methods to +First, we import the names of the Spark Streaming classes and some implicit +conversions from StreamingContext into our environment in order to add useful methods to other classes we need (like DStream). [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) is the -main entry point for all streaming functionality. We create a local StreamingContext with two execution threads, and batch interval of 1 second. +main entry point for all streaming functionality. We create a local StreamingContext with two execution threads, and a batch interval of 1 second. {% highlight scala %} import org.apache.spark._ import org.apache.spark.streaming._ -import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+ +import org.apache.spark.streaming.StreamingContext._ // not necessary since Spark 1.3 // Create a local StreamingContext with two working thread and batch interval of 1 second. // The master requires 2 cores to prevent from a starvation scenario. @@ -96,7 +96,7 @@ val lines = ssc.socketTextStream("localhost", 9999) This `lines` DStream represents the stream of data that will be received from the data server. Each record in this DStream is a line of text. Next, we want to split the lines by -space into words. +space characters into words. {% highlight scala %} // Split each line into words @@ -109,7 +109,7 @@ each line will be split into multiple words and the stream of words is represent `words` DStream. Next, we want to count these words. {% highlight scala %} -import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+ +import org.apache.spark.streaming.StreamingContext._ // not necessary since Spark 1.3 // Count each word in each batch val pairs = words.map(word => (word, 1)) val wordCounts = pairs.reduceByKey(_ + _) @@ -463,7 +463,7 @@ receive it there. However, for local testing and unit tests, you can pass "local in-process (detects the number of cores in the local system). Note that this internally creates a [SparkContext](api/scala/index.html#org.apache.spark.SparkContext) (starting point of all Spark functionality) which can be accessed as `ssc.sparkContext`. The batch interval must be set based on the latency requirements of your application -and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-size) +and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-interval) section for more details. A `StreamingContext` object can also be created from an existing `SparkContext` object. @@ -498,7 +498,7 @@ receive it there. However, for local testing and unit tests, you can pass "local in-process. Note that this internally creates a [JavaSparkContext](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) (starting point of all Spark functionality) which can be accessed as `ssc.sparkContext`. The batch interval must be set based on the latency requirements of your application -and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-size) +and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-interval) section for more details. A `JavaStreamingContext` object can also be created from an existing `JavaSparkContext`. @@ -531,7 +531,7 @@ receive it there. However, for local testing and unit tests, you can pass "local in-process (detects the number of cores in the local system). The batch interval must be set based on the latency requirements of your application -and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-size) +and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-interval) section for more details.
    @@ -549,7 +549,7 @@ After a context is defined, you have to do the following. - Once a context has been started, no new streaming computations can be set up or added to it. - Once a context has been stopped, it cannot be restarted. - Only one StreamingContext can be active in a JVM at the same time. -- stop() on StreamingContext also stops the SparkContext. To stop only the StreamingContext, set optional parameter of `stop()` called `stopSparkContext` to false. +- stop() on StreamingContext also stops the SparkContext. To stop only the StreamingContext, set the optional parameter of `stop()` called `stopSparkContext` to false. - A SparkContext can be re-used to create multiple StreamingContexts, as long as the previous StreamingContext is stopped (without stopping the SparkContext) before the next StreamingContext is created. *** @@ -583,7 +583,7 @@ the `flatMap` operation is applied on each RDD in the `lines` DStream to generat These underlying RDD transformations are computed by the Spark engine. The DStream operations -hide most of these details and provide the developer with higher-level API for convenience. +hide most of these details and provide the developer with a higher-level API for convenience. These operations are discussed in detail in later sections. *** @@ -600,7 +600,7 @@ data from a source and stores it in Spark's memory for processing. Spark Streaming provides two categories of built-in streaming sources. - *Basic sources*: Sources directly available in the StreamingContext API. - Example: file systems, socket connections, and Akka actors. + Examples: file systems, socket connections, and Akka actors. - *Advanced sources*: Sources like Kafka, Flume, Kinesis, Twitter, etc. are available through extra utility classes. These require linking against extra dependencies as discussed in the [linking](#linking) section. @@ -610,11 +610,11 @@ We are going to discuss some of the sources present in each category later in th Note that, if you want to receive multiple streams of data in parallel in your streaming application, you can create multiple input DStreams (discussed further in the [Performance Tuning](#level-of-parallelism-in-data-receiving) section). This will -create multiple receivers which will simultaneously receive multiple data streams. But note that -Spark worker/executor as a long-running task, hence it occupies one of the cores allocated to the -Spark Streaming application. Hence, it is important to remember that Spark Streaming application +create multiple receivers which will simultaneously receive multiple data streams. But note that a +Spark worker/executor is a long-running task, hence it occupies one of the cores allocated to the +Spark Streaming application. Therefore, it is important to remember that a Spark Streaming application needs to be allocated enough cores (or threads, if running locally) to process the received data, -as well as, to run the receiver(s). +as well as to run the receiver(s). ##### Points to remember {:.no_toc} @@ -623,13 +623,13 @@ as well as, to run the receiver(s). Either of these means that only one thread will be used for running tasks locally. If you are using a input DStream based on a receiver (e.g. sockets, Kafka, Flume, etc.), then the single thread will be used to run the receiver, leaving no thread for processing the received data. Hence, when - running locally, always use "local[*n*]" as the master URL where *n* > number of receivers to run - (see [Spark Properties](configuration.html#spark-properties.html) for information on how to set + running locally, always use "local[*n*]" as the master URL, where *n* > number of receivers to run + (see [Spark Properties](configuration.html#spark-properties) for information on how to set the master). - Extending the logic to running on a cluster, the number of cores allocated to the Spark Streaming - application must be more than the number of receivers. Otherwise the system will receive data, but - not be able to process them. + application must be more than the number of receivers. Otherwise the system will receive data, but + not be able to process it. ### Basic Sources {:.no_toc} @@ -639,7 +639,7 @@ which creates a DStream from text data received over a TCP socket connection. Besides sockets, the StreamingContext API provides methods for creating DStreams from files and Akka actors as input sources. -- **File Streams:** For reading data from files on any file system compatible with the HDFS API (that is, HDFS, S3, NFS, etc.), a DStream can be created as +- **File Streams:** For reading data from files on any file system compatible with the HDFS API (that is, HDFS, S3, NFS, etc.), a DStream can be created as:
    @@ -682,14 +682,14 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea ### Advanced Sources {:.no_toc} -Python API As of Spark 1.3, +Python API As of Spark {{site.SPARK_VERSION_SHORT}}, out of these sources, *only* Kafka is available in the Python API. We will add more advanced sources in the Python API in future. This category of sources require interfacing with external non-Spark libraries, some of them with complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts -of dependencies, the functionality to create DStreams from these sources have been moved to separate -libraries, that can be [linked](#linking) to explicitly when necessary. For example, if you want to -create a DStream using data from Twitter's stream of tweets, you have to do the following. +of dependencies, the functionality to create DStreams from these sources has been moved to separate +libraries that can be [linked](#linking) to explicitly when necessary. For example, if you want to +create a DStream using data from Twitter's stream of tweets, you have to do the following: 1. *Linking*: Add the artifact `spark-streaming-twitter_{{site.SCALA_BINARY_VERSION}}` to the SBT/Maven project dependencies. @@ -719,11 +719,11 @@ TwitterUtils.createStream(jssc); Note that these advanced sources are not available in the Spark shell, hence applications based on these advanced sources cannot be tested in the shell. If you really want to use them in the Spark shell you will have to download the corresponding Maven artifact's JAR along with its dependencies -and it in the classpath. +and add it to the classpath. Some of these advanced sources are as follows. -- **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kafka 0.8.1.1. See the [Kafka Integration Guide](streaming-kafka-integration.html) for more details. +- **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kafka 0.8.2.1. See the [Kafka Integration Guide](streaming-kafka-integration.html) for more details. - **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Flume 1.4.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details. @@ -743,7 +743,7 @@ Some of these advanced sources are as follows. Python API This is not yet supported in Python. -Input DStreams can also be created out of custom data sources. All you have to do is implement an +Input DStreams can also be created out of custom data sources. All you have to do is implement a user-defined **receiver** (see next section to understand what that is) that can receive data from the custom sources and push it into Spark. See the [Custom Receiver Guide](streaming-custom-receivers.html) for details. @@ -753,14 +753,12 @@ Guide](streaming-custom-receivers.html) for details. There can be two kinds of data sources based on their *reliability*. Sources (like Kafka and Flume) allow the transferred data to be acknowledged. If the system receiving -data from these *reliable* sources acknowledge the received data correctly, it can be ensured -that no data gets lost due to any kind of failure. This leads to two kinds of receivers. +data from these *reliable* sources acknowledges the received data correctly, it can be ensured +that no data will be lost due to any kind of failure. This leads to two kinds of receivers: -1. *Reliable Receiver* - A *reliable receiver* correctly acknowledges a reliable - source that the data has been received and stored in Spark with replication. -1. *Unreliable Receiver* - These are receivers for sources that do not support acknowledging. Even - for reliable sources, one may implement an unreliable receiver that do not go into the complexity - of acknowledging correctly. +1. *Reliable Receiver* - A *reliable receiver* correctly sends acknowledgment to a reliable + source when the data has been received and stored in Spark with replication. +1. *Unreliable Receiver* - An *unreliable receiver* does *not* send acknowledgment to a source. This can be used for sources that do not support acknowledgment, or even for reliable sources when one does not want or need to go into the complexity of acknowledgment. The details of how to write a reliable receiver are discussed in the [Custom Receiver Guide](streaming-custom-receivers.html). @@ -828,7 +826,7 @@ Some of the common ones are as follows. cogroup(otherStream, [numTasks]) - When called on DStream of (K, V) and (K, W) pairs, return a new DStream of + When called on a DStream of (K, V) and (K, W) pairs, return a new DStream of (K, Seq[V], Seq[W]) tuples. @@ -852,13 +850,13 @@ A few of these transformations are worth discussing in more detail. The `updateStateByKey` operation allows you to maintain arbitrary state while continuously updating it with new information. To use this, you will have to do two steps. -1. Define the state - The state can be of arbitrary data type. +1. Define the state - The state can be an arbitrary data type. 1. Define the state update function - Specify with a function how to update the state using the -previous state and the new values from input stream. +previous state and the new values from an input stream. Let's illustrate this with an example. Say you want to maintain a running count of each word seen in a text data stream. Here, the running count is the state and it is an integer. We -define the update function as +define the update function as:
    @@ -947,7 +945,7 @@ operation that is not exposed in the DStream API. For example, the functionality of joining every batch in a data stream with another dataset is not directly exposed in the DStream API. However, you can easily use `transform` to do this. This enables very powerful possibilities. For example, -if you want to do real-time data cleaning by joining the input data stream with precomputed +one can do real-time data cleaning by joining the input data stream with precomputed spam information (maybe generated with Spark as well) and then filtering based on it.
    @@ -991,13 +989,14 @@ cleanedDStream = wordCounts.transform(lambda rdd: rdd.join(spamInfoRDD).filter(.
    -In fact, you can also use [machine learning](mllib-guide.html) and -[graph computation](graphx-programming-guide.html) algorithms in the `transform` method. +Note that the supplied function gets called in every batch interval. This allows you to do +time-varying RDD operations, that is, RDD operations, number of partitions, broadcast variables, +etc. can be changed between batches. #### Window Operations {:.no_toc} Spark Streaming also provides *windowed computations*, which allow you to apply -transformations over a sliding window of data. This following figure illustrates this sliding +transformations over a sliding window of data. The following figure illustrates this sliding window.

    @@ -1009,11 +1008,11 @@ window. As shown in the figure, every time the window *slides* over a source DStream, the source RDDs that fall within the window are combined and operated upon to produce the -RDDs of the windowed DStream. In this specific case, the operation is applied over last 3 time +RDDs of the windowed DStream. In this specific case, the operation is applied over the last 3 time units of data, and slides by 2 time units. This shows that any window operation needs to specify two parameters. - * window length - The duration of the window (3 in the figure) + * window length - The duration of the window (3 in the figure). * sliding interval - The interval at which the window operation is performed (2 in the figure). @@ -1021,7 +1020,7 @@ These two parameters must be multiples of the batch interval of the source DStre figure). Let's illustrate the window operations with an example. Say, you want to extend the -[earlier example](#a-quick-example) by generating word counts over last 30 seconds of data, +[earlier example](#a-quick-example) by generating word counts over the last 30 seconds of data, every 10 seconds. To do this, we have to apply the `reduceByKey` operation on the `pairs` DStream of `(word, 1)` pairs over the last 30 seconds of data. This is done using the operation `reduceByKeyAndWindow`. @@ -1096,13 +1095,13 @@ said two parameters - windowLength and slideInterval. reduceByKeyAndWindow(func, invFunc, windowLength, slideInterval, [numTasks]) - A more efficient version of the above reduceByKeyAndWindow() where the reduce + A more efficient version of the above reduceByKeyAndWindow() where the reduce value of each window is calculated incrementally using the reduce values of the previous window. - This is done by reducing the new data that enter the sliding window, and "inverse reducing" the - old data that leave the window. An example would be that of "adding" and "subtracting" counts - of keys as the window slides. However, it is applicable to only "invertible reduce functions", + This is done by reducing the new data that enters the sliding window, and "inverse reducing" the + old data that leaves the window. An example would be that of "adding" and "subtracting" counts + of keys as the window slides. However, it is applicable only to "invertible reduce functions", that is, those reduce functions which have a corresponding "inverse reduce" function (taken as - parameter invFunc. Like in reduceByKeyAndWindow, the number of reduce tasks + parameter invFunc). Like in reduceByKeyAndWindow, the number of reduce tasks is configurable through an optional argument. Note that [checkpointing](#checkpointing) must be enabled for using this operation. @@ -1224,7 +1223,7 @@ For the Python API, see [DStream](api/python/pyspark.streaming.html#pyspark.stre *** ## Output Operations on DStreams -Output operations allow DStream's data to be pushed out external systems like a database or a file systems. +Output operations allow DStream's data to be pushed out to external systems like a database or a file systems. Since the output operations actually allow the transformed data to be consumed by external systems, they trigger the actual execution of all the DStream transformations (similar to actions for RDDs). Currently, the following output operations are defined: @@ -1233,7 +1232,7 @@ Currently, the following output operations are defined: Output OperationMeaning print() - Prints first ten elements of every batch of data in a DStream on the driver node running + Prints the first ten elements of every batch of data in a DStream on the driver node running the streaming application. This is useful for development and debugging.
    Python API This is called @@ -1242,12 +1241,12 @@ Currently, the following output operations are defined: saveAsTextFiles(prefix, [suffix]) - Save this DStream's contents as a text files. The file name at each batch interval is + Save this DStream's contents as text files. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]". saveAsObjectFiles(prefix, [suffix]) - Save this DStream's contents as a SequenceFile of serialized Java objects. The file + Save this DStream's contents as SequenceFiles of serialized Java objects. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".
    @@ -1257,7 +1256,7 @@ Currently, the following output operations are defined: saveAsHadoopFiles(prefix, [suffix]) - Save this DStream's contents as a Hadoop file. The file name at each batch interval is + Save this DStream's contents as Hadoop files. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".
    Python API This is not available in @@ -1267,7 +1266,7 @@ Currently, the following output operations are defined: foreachRDD(func) The most generic output operator that applies a function, func, to each RDD generated from - the stream. This function should push the data in each RDD to a external system, like saving the RDD to + the stream. This function should push the data in each RDD to an external system, such as saving the RDD to files, or writing it over the network to a database. Note that the function func is executed in the driver process running the streaming application, and will usually have RDD actions in it that will force the computation of the streaming RDDs. @@ -1277,14 +1276,14 @@ Currently, the following output operations are defined: ### Design Patterns for using foreachRDD {:.no_toc} -`dstream.foreachRDD` is a powerful primitive that allows data to sent out to external systems. +`dstream.foreachRDD` is a powerful primitive that allows data to be sent out to external systems. However, it is important to understand how to use this primitive correctly and efficiently. Some of the common mistakes to avoid are as follows. Often writing data to external system requires creating a connection object (e.g. TCP connection to a remote server) and using it to send data to a remote system. For this purpose, a developer may inadvertently try creating a connection object at -the Spark driver, but try to use it in a Spark worker to save records in the RDDs. +the Spark driver, and then try to use it in a Spark worker to save records in the RDDs. For example (in Scala),

    @@ -1346,7 +1345,7 @@ dstream.foreachRDD(lambda rdd: rdd.foreach(sendRecord)) Typically, creating a connection object has time and resource overheads. Therefore, creating and destroying a connection object for each record can incur unnecessarily high overheads and can significantly reduce the overall throughput of the system. A better solution is to use -`rdd.foreachPartition` - create a single connection object and send all the records in a RDD +`rdd.foreachPartition` - create a single connection object and send all the records in a RDD partition using that connection.
    @@ -1427,26 +1426,6 @@ You can easily use [DataFrames and SQL](sql-programming-guide.html) operations o
    {% highlight scala %} -/** Lazily instantiated singleton instance of SQLContext */ -object SQLContextSingleton { - @transient private var instance: SQLContext = null - - // Instantiate SQLContext on demand - def getInstance(sparkContext: SparkContext): SQLContext = synchronized { - if (instance == null) { - instance = new SQLContext(sparkContext) - } - instance - } -} - -... - -/** Case class for converting RDD to DataFrame */ -case class Row(word: String) - -... - /** DataFrame operations inside your streaming program */ val words: DStream[String] = ... @@ -1454,11 +1433,11 @@ val words: DStream[String] = ... words.foreachRDD { rdd => // Get the singleton instance of SQLContext - val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext) + val sqlContext = SQLContext.getOrCreate(rdd.sparkContext) import sqlContext.implicits._ - // Convert RDD[String] to RDD[case class] to DataFrame - val wordsDataFrame = rdd.map(w => Row(w)).toDF() + // Convert RDD[String] to DataFrame + val wordsDataFrame = rdd.toDF("word") // Register as table wordsDataFrame.registerTempTable("words") @@ -1476,19 +1455,6 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/ma
    {% highlight java %} -/** Lazily instantiated singleton instance of SQLContext */ -class JavaSQLContextSingleton { - static private transient SQLContext instance = null; - static public SQLContext getInstance(SparkContext sparkContext) { - if (instance == null) { - instance = new SQLContext(sparkContext); - } - return instance; - } -} - -... - /** Java Bean class for converting RDD to DataFrame */ public class JavaRow implements java.io.Serializable { private String word; @@ -1512,7 +1478,9 @@ words.foreachRDD( new Function2, Time, Void>() { @Override public Void call(JavaRDD rdd, Time time) { - SQLContext sqlContext = JavaSQLContextSingleton.getInstance(rdd.context()); + + // Get the singleton instance of SQLContext + SQLContext sqlContext = SQLContext.getOrCreate(rdd.context()); // Convert RDD[String] to RDD[case class] to DataFrame JavaRDD rowRDD = rdd.map(new Function() { @@ -1581,7 +1549,7 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/ma
    -You can also run SQL queries on tables defined on streaming data from a different thread (that is, asynchronous to the running StreamingContext). Just make sure that you set the StreamingContext to remember sufficient amount of streaming data such that query can run. Otherwise the StreamingContext, which is unaware of the any asynchronous SQL queries, will delete off old streaming data before the query can complete. For example, if you want to query the last batch, but your query can take 5 minutes to run, then call `streamingContext.remember(Minutes(5))` (in Scala, or equivalent in other languages). +You can also run SQL queries on tables defined on streaming data from a different thread (that is, asynchronous to the running StreamingContext). Just make sure that you set the StreamingContext to remember a sufficient amount of streaming data such that the query can run. Otherwise the StreamingContext, which is unaware of the any asynchronous SQL queries, will delete off old streaming data before the query can complete. For example, if you want to query the last batch, but your query can take 5 minutes to run, then call `streamingContext.remember(Minutes(5))` (in Scala, or equivalent in other languages). See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more about DataFrames. @@ -1594,7 +1562,7 @@ You can also easily use machine learning algorithms provided by [MLlib](mllib-gu ## Caching / Persistence Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, -using `persist()` method on a DStream will automatically persist every RDD of that DStream in +using the `persist()` method on a DStream will automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. @@ -1606,28 +1574,27 @@ default persistence level is set to replicate the data to two nodes for fault-to Note that, unlike RDDs, the default persistence level of DStreams keeps the data serialized in memory. This is further discussed in the [Performance Tuning](#memory-tuning) section. More -information on different persistence levels can be found in -[Spark Programming Guide](programming-guide.html#rdd-persistence). +information on different persistence levels can be found in the [Spark Programming Guide](programming-guide.html#rdd-persistence). *** ## Checkpointing A streaming application must operate 24/7 and hence must be resilient to failures unrelated to the application logic (e.g., system failures, JVM crashes, etc.). For this to be possible, -Spark Streaming needs to *checkpoints* enough information to a fault- +Spark Streaming needs to *checkpoint* enough information to a fault- tolerant storage system such that it can recover from failures. There are two types of data that are checkpointed. - *Metadata checkpointing* - Saving of the information defining the streaming computation to fault-tolerant storage like HDFS. This is used to recover from failure of the node running the driver of the streaming application (discussed in detail later). Metadata includes: - + *Configuration* - The configuration that were used to create the streaming application. + + *Configuration* - The configuration that was used to create the streaming application. + *DStream operations* - The set of DStream operations that define the streaming application. + *Incomplete batches* - Batches whose jobs are queued but have not completed yet. - *Data checkpointing* - Saving of the generated RDDs to reliable storage. This is necessary in some *stateful* transformations that combine data across multiple batches. In such - transformations, the generated RDDs depends on RDDs of previous batches, which causes the length - of the dependency chain to keep increasing with time. To avoid such unbounded increase in recovery + transformations, the generated RDDs depend on RDDs of previous batches, which causes the length + of the dependency chain to keep increasing with time. To avoid such unbounded increases in recovery time (proportional to dependency chain), intermediate RDDs of stateful transformations are periodically *checkpointed* to reliable storage (e.g. HDFS) to cut off the dependency chains. @@ -1641,10 +1608,10 @@ transformations are used. Checkpointing must be enabled for applications with any of the following requirements: - *Usage of stateful transformations* - If either `updateStateByKey` or `reduceByKeyAndWindow` (with - inverse function) is used in the application, then the checkpoint directory must be provided for - allowing periodic RDD checkpointing. + inverse function) is used in the application, then the checkpoint directory must be provided to + allow for periodic RDD checkpointing. - *Recovering from failures of the driver running the application* - Metadata checkpoints are used - for to recover with progress information. + to recover with progress information. Note that simple streaming applications without the aforementioned stateful transformations can be run without enabling checkpointing. The recovery from driver failures will also be partial in @@ -1659,7 +1626,7 @@ Checkpointing can be enabled by setting a directory in a fault-tolerant, reliable file system (e.g., HDFS, S3, etc.) to which the checkpoint information will be saved. This is done by using `streamingContext.checkpoint(checkpointDirectory)`. This will allow you to use the aforementioned stateful transformations. Additionally, -if you want make the application recover from driver failures, you should rewrite your +if you want to make the application recover from driver failures, you should rewrite your streaming application to have the following behavior. + When the program is being started for the first time, it will create a new StreamingContext, @@ -1780,18 +1747,17 @@ You can also explicitly create a `StreamingContext` from the checkpoint data and In addition to using `getOrCreate` one also needs to ensure that the driver process gets restarted automatically on failure. This can only be done by the deployment infrastructure that is used to run the application. This is further discussed in the -[Deployment](#deploying-applications.html) section. +[Deployment](#deploying-applications) section. Note that checkpointing of RDDs incurs the cost of saving to reliable storage. This may cause an increase in the processing time of those batches where RDDs get checkpointed. Hence, the interval of checkpointing needs to be set carefully. At small batch sizes (say 1 second), checkpointing every batch may significantly reduce operation throughput. Conversely, checkpointing too infrequently -causes the lineage and task sizes to grow which may have detrimental effects. For stateful +causes the lineage and task sizes to grow, which may have detrimental effects. For stateful transformations that require RDD checkpointing, the default interval is a multiple of the batch interval that is at least 10 seconds. It can be set by using -`dstream.checkpoint(checkpointInterval)`. Typically, a checkpoint interval of 5 - 10 times of -sliding interval of a DStream is good setting to try. +`dstream.checkpoint(checkpointInterval)`. Typically, a checkpoint interval of 5 - 10 sliding intervals of a DStream is a good setting to try. *** @@ -1864,17 +1830,17 @@ To run a Spark Streaming applications, you need to have the following. {:.no_toc} If a running Spark Streaming application needs to be upgraded with new -application code, then there are two possible mechanism. +application code, then there are two possible mechanisms. - The upgraded Spark Streaming application is started and run in parallel to the existing application. -Once the new one (receiving the same data as the old one) has been warmed up and ready +Once the new one (receiving the same data as the old one) has been warmed up and is ready for prime time, the old one be can be brought down. Note that this can be done for data sources that support sending the data to two destinations (i.e., the earlier and upgraded applications). - The existing application is shutdown gracefully (see [`StreamingContext.stop(...)`](api/scala/index.html#org.apache.spark.streaming.StreamingContext) or [`JavaStreamingContext.stop(...)`](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) -for graceful shutdown options) which ensure data that have been received is completely +for graceful shutdown options) which ensure data that has been received is completely processed before shutdown. Then the upgraded application can be started, which will start processing from the same point where the earlier application left off. Note that this can be done only with input sources that support source-side buffering @@ -1909,13 +1875,13 @@ The following two metrics in web UI are particularly important: to finish. If the batch processing time is consistently more than the batch interval and/or the queueing -delay keeps increasing, then it indicates the system is -not able to process the batches as fast they are being generated and falling behind. +delay keeps increasing, then it indicates that the system is +not able to process the batches as fast they are being generated and is falling behind. In that case, consider -[reducing](#reducing-the-processing-time-of-each-batch) the batch processing time. +[reducing](#reducing-the-batch-processing-times) the batch processing time. The progress of a Spark Streaming program can also be monitored using the -[StreamingListener](api/scala/index.html#org.apache.spark.scheduler.StreamingListener) interface, +[StreamingListener](api/scala/index.html#org.apache.spark.streaming.scheduler.StreamingListener) interface, which allows you to get receiver status and processing times. Note that this is a developer API and it is likely to be improved upon (i.e., more information reported) in the future. @@ -1923,8 +1889,8 @@ and it is likely to be improved upon (i.e., more information reported) in the fu *************************************************************************************************** # Performance Tuning -Getting the best performance of a Spark Streaming application on a cluster requires a bit of -tuning. This section explains a number of the parameters and configurations that can tuned to +Getting the best performance out of a Spark Streaming application on a cluster requires a bit of +tuning. This section explains a number of the parameters and configurations that can be tuned to improve the performance of you application. At a high level, you need to consider two things: 1. Reducing the processing time of each batch of data by efficiently using cluster resources. @@ -1934,22 +1900,22 @@ improve the performance of you application. At a high level, you need to conside ## Reducing the Batch Processing Times There are a number of optimizations that can be done in Spark to minimize the processing time of -each batch. These have been discussed in detail in [Tuning Guide](tuning.html). This section +each batch. These have been discussed in detail in the [Tuning Guide](tuning.html). This section highlights some of the most important ones. ### Level of Parallelism in Data Receiving {:.no_toc} -Receiving data over the network (like Kafka, Flume, socket, etc.) requires the data to deserialized +Receiving data over the network (like Kafka, Flume, socket, etc.) requires the data to be deserialized and stored in Spark. If the data receiving becomes a bottleneck in the system, then consider parallelizing the data receiving. Note that each input DStream creates a single receiver (running on a worker machine) that receives a single stream of data. Receiving multiple data streams can therefore be achieved by creating multiple input DStreams and configuring them to receive different partitions of the data stream from the source(s). For example, a single Kafka input DStream receiving two topics of data can be split into two -Kafka input streams, each receiving only one topic. This would run two receivers on two workers, -thus allowing data to be received in parallel, and increasing overall throughput. These multiple -DStream can be unioned together to create a single DStream. Then the transformations that was -being applied on the single input DStream can applied on the unified stream. This is done as follows. +Kafka input streams, each receiving only one topic. This would run two receivers, +allowing data to be received in parallel, thus increasing overall throughput. These multiple +DStreams can be unioned together to create a single DStream. Then the transformations that were +being applied on a single input DStream can be applied on the unified stream. This is done as follows.
    @@ -1971,16 +1937,24 @@ JavaPairDStream unifiedStream = streamingContext.union(kafkaStre unifiedStream.print(); {% endhighlight %}
    +
    +{% highlight python %} +numStreams = 5 +kafkaStreams = [KafkaUtils.createStream(...) for _ in range (numStreams)] +unifiedStream = streamingContext.union(kafkaStreams) +unifiedStream.print() +{% endhighlight %} +
    Another parameter that should be considered is the receiver's blocking interval, which is determined by the [configuration parameter](configuration.html#spark-streaming) `spark.streaming.blockInterval`. For most receivers, the received data is coalesced together into blocks of data before storing inside Spark's memory. The number of blocks in each batch -determines the number of tasks that will be used to process those +determines the number of tasks that will be used to process the received data in a map-like transformation. The number of tasks per receiver per batch will be approximately (batch interval / block interval). For example, block interval of 200 ms will -create 10 tasks per 2 second batches. Too low the number of tasks (that is, less than the number +create 10 tasks per 2 second batches. If the number of tasks is too low (that is, less than the number of cores per machine), then it will be inefficient as all available cores will not be used to process the data. To increase the number of tasks for a given batch interval, reduce the block interval. However, the recommended minimum value of block interval is about 50 ms, @@ -1988,7 +1962,7 @@ below which the task launching overheads may be a problem. An alternative to receiving data with multiple input streams / receivers is to explicitly repartition the input data stream (using `inputStream.repartition()`). -This distributes the received batches of data across specified number of machines in the cluster +This distributes the received batches of data across the specified number of machines in the cluster before further processing. ### Level of Parallelism in Data Processing @@ -1996,7 +1970,7 @@ before further processing. Cluster resources can be under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is controlled by -the`spark.default.parallelism` [configuration property](configuration.html#spark-properties). You +the `spark.default.parallelism` [configuration property](configuration.html#spark-properties). You can pass the level of parallelism as an argument (see [`PairDStreamFunctions`](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions) documentation), or set the `spark.default.parallelism` @@ -2004,20 +1978,20 @@ documentation), or set the `spark.default.parallelism` ### Data Serialization {:.no_toc} -The overheads of data serialization can be reduce by tuning the serialization formats. In case of streaming, there are two types of data that are being serialized. +The overheads of data serialization can be reduced by tuning the serialization formats. In the case of streaming, there are two types of data that are being serialized. -* **Input data**: By default, the input data received through Receivers is stored in the executors' memory with [StorageLevel.MEMORY_AND_DISK_SER_2](api/scala/index.html#org.apache.spark.storage.StorageLevel$). That is, the data is serialized into bytes to reduce GC overheads, and replicated for tolerating executor failures. Also, the data is kept first in memory, and spilled over to disk only if the memory is unsufficient to hold all the input data necessary for the streaming computation. This serialization obviously has overheads -- the receiver must deserialize the received data and re-serialize it using Spark's serialization format. +* **Input data**: By default, the input data received through Receivers is stored in the executors' memory with [StorageLevel.MEMORY_AND_DISK_SER_2](api/scala/index.html#org.apache.spark.storage.StorageLevel$). That is, the data is serialized into bytes to reduce GC overheads, and replicated for tolerating executor failures. Also, the data is kept first in memory, and spilled over to disk only if the memory is insufficient to hold all of the input data necessary for the streaming computation. This serialization obviously has overheads -- the receiver must deserialize the received data and re-serialize it using Spark's serialization format. -* **Persisted RDDs generated by Streaming Operations**: RDDs generated by streaming computations may be persisted in memory. For example, window operation persist data in memory as they would be processed multiple times. However, unlike Spark, by default RDDs are persisted with [StorageLevel.MEMORY_ONLY_SER](api/scala/index.html#org.apache.spark.storage.StorageLevel$) (i.e. serialized) to minimize GC overheads. +* **Persisted RDDs generated by Streaming Operations**: RDDs generated by streaming computations may be persisted in memory. For example, window operations persist data in memory as they would be processed multiple times. However, unlike the Spark Core default of [StorageLevel.MEMORY_ONLY](api/scala/index.html#org.apache.spark.storage.StorageLevel$), persisted RDDs generated by streaming computations are persisted with [StorageLevel.MEMORY_ONLY_SER](api/scala/index.html#org.apache.spark.storage.StorageLevel$) (i.e. serialized) by default to minimize GC overheads. -In both cases, using Kryo serialization can reduce both CPU and memory overheads. See the [Spark Tuning Guide](tuning.html#data-serialization)) for more details. Consider registering custom classes, and disabling object reference tracking for Kryo (see Kryo-related configurations in the [Configuration Guide](configuration.html#compression-and-serialization)). +In both cases, using Kryo serialization can reduce both CPU and memory overheads. See the [Spark Tuning Guide](tuning.html#data-serialization) for more details. For Kryo, consider registering custom classes, and disabling object reference tracking (see Kryo-related configurations in the [Configuration Guide](configuration.html#compression-and-serialization)). -In specific cases where the amount of data that needs to be retained for the streaming application is not large, it may be feasible to persist data (both types) as deserialized objects without incurring excessive GC overheads. For example, if you are using batch intervals of few seconds and no window operations, then you can try disabling serialization in persisted data by explicitly setting the storage level accordingly. This would reduce the CPU overheads due to serialization, potentially improving performance without too much GC overheads. +In specific cases where the amount of data that needs to be retained for the streaming application is not large, it may be feasible to persist data (both types) as deserialized objects without incurring excessive GC overheads. For example, if you are using batch intervals of a few seconds and no window operations, then you can try disabling serialization in persisted data by explicitly setting the storage level accordingly. This would reduce the CPU overheads due to serialization, potentially improving performance without too much GC overheads. ### Task Launching Overheads {:.no_toc} If the number of tasks launched per second is high (say, 50 or more per second), then the overhead -of sending out tasks to the slaves maybe significant and will make it hard to achieve sub-second +of sending out tasks to the slaves may be significant and will make it hard to achieve sub-second latencies. The overhead can be reduced by the following changes: * **Task Serialization**: Using Kryo serialization for serializing tasks can reduce the task @@ -2036,7 +2010,7 @@ thus allowing sub-second batch size to be viable. For a Spark Streaming application running on a cluster to be stable, the system should be able to process data as fast as it is being received. In other words, batches of data should be processed as fast as they are being generated. Whether this is true for an application can be found by -[monitoring](#monitoring) the processing times in the streaming web UI, where the batch +[monitoring](#monitoring-applications) the processing times in the streaming web UI, where the batch processing time should be less than the batch interval. Depending on the nature of the streaming @@ -2049,35 +2023,35 @@ production can be sustained. A good approach to figure out the right batch size for your application is to test it with a conservative batch interval (say, 5-10 seconds) and a low data rate. To verify whether the system -is able to keep up with data rate, you can check the value of the end-to-end delay experienced +is able to keep up with the data rate, you can check the value of the end-to-end delay experienced by each processed batch (either look for "Total delay" in Spark driver log4j logs, or use the [StreamingListener](api/scala/index.html#org.apache.spark.streaming.scheduler.StreamingListener) interface). If the delay is maintained to be comparable to the batch size, then system is stable. Otherwise, if the delay is continuously increasing, it means that the system is unable to keep up and it therefore unstable. Once you have an idea of a stable configuration, you can try increasing the -data rate and/or reducing the batch size. Note that momentary increase in the delay due to -temporary data rate increases maybe fine as long as the delay reduces back to a low value +data rate and/or reducing the batch size. Note that a momentary increase in the delay due to +temporary data rate increases may be fine as long as the delay reduces back to a low value (i.e., less than batch size). *** ## Memory Tuning -Tuning the memory usage and GC behavior of Spark applications have been discussed in great detail +Tuning the memory usage and GC behavior of Spark applications has been discussed in great detail in the [Tuning Guide](tuning.html#memory-tuning). It is strongly recommended that you read that. In this section, we discuss a few tuning parameters specifically in the context of Spark Streaming applications. -The amount of cluster memory required by a Spark Streaming application depends heavily on the type of transformations used. For example, if you want to use a window operation on last 10 minutes of data, then your cluster should have sufficient memory to hold 10 minutes of worth of data in memory. Or if you want to use `updateStateByKey` with a large number of keys, then the necessary memory will be high. On the contrary, if you want to do a simple map-filter-store operation, then necessary memory will be low. +The amount of cluster memory required by a Spark Streaming application depends heavily on the type of transformations used. For example, if you want to use a window operation on the last 10 minutes of data, then your cluster should have sufficient memory to hold 10 minutes worth of data in memory. Or if you want to use `updateStateByKey` with a large number of keys, then the necessary memory will be high. On the contrary, if you want to do a simple map-filter-store operation, then the necessary memory will be low. -In general, since the data received through receivers are stored with StorageLevel.MEMORY_AND_DISK_SER_2, the data that does not fit in memory will spill over to the disk. This may reduce the performance of the streaming application, and hence it is advised to provide sufficient memory as required by your streaming application. Its best to try and see the memory usage on a small scale and estimate accordingly. +In general, since the data received through receivers is stored with StorageLevel.MEMORY_AND_DISK_SER_2, the data that does not fit in memory will spill over to the disk. This may reduce the performance of the streaming application, and hence it is advised to provide sufficient memory as required by your streaming application. Its best to try and see the memory usage on a small scale and estimate accordingly. -Another aspect of memory tuning is garbage collection. For a streaming application that require low latency, it is undesirable to have large pauses caused by JVM Garbage Collection. +Another aspect of memory tuning is garbage collection. For a streaming application that requires low latency, it is undesirable to have large pauses caused by JVM Garbage Collection. -There are a few parameters that can help you tune the memory usage and GC overheads. +There are a few parameters that can help you tune the memory usage and GC overheads: -* **Persistence Level of DStreams**: As mentioned earlier in the [Data Serialization](#data-serialization) section, the input data and RDDs are by default persisted as serialized bytes. This reduces both, the memory usage and GC overheads, compared to deserialized persistence. Enabling Kryo serialization further reduces serialized sizes and memory usage. Further reduction in memory usage can be achieved with compression (see the Spark configuration `spark.rdd.compress`), at the cost of CPU time. +* **Persistence Level of DStreams**: As mentioned earlier in the [Data Serialization](#data-serialization) section, the input data and RDDs are by default persisted as serialized bytes. This reduces both the memory usage and GC overheads, compared to deserialized persistence. Enabling Kryo serialization further reduces serialized sizes and memory usage. Further reduction in memory usage can be achieved with compression (see the Spark configuration `spark.rdd.compress`), at the cost of CPU time. -* **Clearing old data**: By default, all input data and persisted RDDs generated by DStream transformations are automatically cleared. Spark Streaming decides when to clear the data based on the transformations that are used. For example, if you are using window operation of 10 minutes, then Spark Streaming will keep around last 10 minutes of data, and actively throw away older data. -Data can be retained for longer duration (e.g. interactively querying older data) by setting `streamingContext.remember`. +* **Clearing old data**: By default, all input data and persisted RDDs generated by DStream transformations are automatically cleared. Spark Streaming decides when to clear the data based on the transformations that are used. For example, if you are using a window operation of 10 minutes, then Spark Streaming will keep around the last 10 minutes of data, and actively throw away older data. +Data can be retained for a longer duration (e.g. interactively querying older data) by setting `streamingContext.remember`. * **CMS Garbage Collector**: Use of the concurrent mark-and-sweep GC is strongly recommended for keeping GC-related pauses consistently low. Even though concurrent GC is known to reduce the overall processing throughput of the system, its use is still recommended to achieve more @@ -2107,18 +2081,18 @@ re-computed from the original fault-tolerant dataset using the lineage of operat 1. Assuming that all of the RDD transformations are deterministic, the data in the final transformed RDD will always be the same irrespective of failures in the Spark cluster. -Spark operates on data on fault-tolerant file systems like HDFS or S3. Hence, +Spark operates on data in fault-tolerant file systems like HDFS or S3. Hence, all of the RDDs generated from the fault-tolerant data are also fault-tolerant. However, this is not the case for Spark Streaming as the data in most cases is received over the network (except when `fileStream` is used). To achieve the same fault-tolerance properties for all of the generated RDDs, the received data is replicated among multiple Spark executors in worker nodes in the cluster (default replication factor is 2). This leads to two kinds of data in the -system that needs to recovered in the event of failures: +system that need to recovered in the event of failures: 1. *Data received and replicated* - This data survives failure of a single worker node as a copy - of it exists on one of the nodes. + of it exists on one of the other nodes. 1. *Data received but buffered for replication* - Since this is not replicated, - the only way to recover that data is to get it again from the source. + the only way to recover this data is to get it again from the source. Furthermore, there are two kinds of failures that we should be concerned about: @@ -2145,13 +2119,13 @@ In any stream processing system, broadly speaking, there are three steps in proc 1. *Receiving the data*: The data is received from sources using Receivers or otherwise. -1. *Transforming the data*: The data received data is transformed using DStream and RDD transformations. +1. *Transforming the data*: The received data is transformed using DStream and RDD transformations. 1. *Pushing out the data*: The final transformed data is pushed out to external systems like file systems, databases, dashboards, etc. -If a streaming application has to achieve end-to-end exactly-once guarantees, then each step has to provide exactly-once guarantee. That is, each record must be received exactly once, transformed exactly once, and pushed to downstream systems exactly once. Let's understand the semantics of these steps in the context of Spark Streaming. +If a streaming application has to achieve end-to-end exactly-once guarantees, then each step has to provide an exactly-once guarantee. That is, each record must be received exactly once, transformed exactly once, and pushed to downstream systems exactly once. Let's understand the semantics of these steps in the context of Spark Streaming. -1. *Receiving the data*: Different input sources provided different guarantees. This is discussed in detail in the next subsection. +1. *Receiving the data*: Different input sources provide different guarantees. This is discussed in detail in the next subsection. 1. *Transforming the data*: All data that has been received will be processed _exactly once_, thanks to the guarantees that RDDs provide. Even if there are failures, as long as the received input data is accessible, the final transformed RDDs will always have the same contents. @@ -2163,9 +2137,9 @@ Different input sources provide different guarantees, ranging from _at-least onc ### With Files {:.no_toc} -If all of the input data is already present in a fault-tolerant files system like -HDFS, Spark Streaming can always recover from any failure and process all the data. This gives -*exactly-once* semantics, that all the data will be processed exactly once no matter what fails. +If all of the input data is already present in a fault-tolerant file system like +HDFS, Spark Streaming can always recover from any failure and process all of the data. This gives +*exactly-once* semantics, meaning all of the data will be processed exactly once no matter what fails. ### With Receiver-based Sources {:.no_toc} @@ -2174,21 +2148,21 @@ scenario and the type of receiver. As we discussed [earlier](#receiver-reliability), there are two types of receivers: 1. *Reliable Receiver* - These receivers acknowledge reliable sources only after ensuring that - the received data has been replicated. If such a receiver fails, - the buffered (unreplicated) data does not get acknowledged to the source. If the receiver is - restarted, the source will resend the data, and therefore no data will be lost due to the failure. -1. *Unreliable Receiver* - Such receivers can lose data when they fail due to worker - or driver failures. + the received data has been replicated. If such a receiver fails, the source will not receive + acknowledgment for the buffered (unreplicated) data. Therefore, if the receiver is + restarted, the source will resend the data, and no data will be lost due to the failure. +1. *Unreliable Receiver* - Such receivers do *not* send acknowledgment and therefore *can* lose + data when they fail due to worker or driver failures. Depending on what type of receivers are used we achieve the following semantics. If a worker node fails, then there is no data loss with reliable receivers. With unreliable receivers, data received but not replicated can get lost. If the driver node fails, -then besides these losses, all the past data that was received and replicated in memory will be +then besides these losses, all of the past data that was received and replicated in memory will be lost. This will affect the results of the stateful transformations. To avoid this loss of past received data, Spark 1.2 introduced _write -ahead logs_ which saves the received data to fault-tolerant storage. With the [write ahead logs -enabled](#deploying-applications) and reliable receivers, there is zero data loss. In terms of semantics, it provides at-least once guarantee. +ahead logs_ which save the received data to fault-tolerant storage. With the [write ahead logs +enabled](#deploying-applications) and reliable receivers, there is zero data loss. In terms of semantics, it provides an at-least once guarantee. The following table summarizes the semantics under failures: @@ -2234,7 +2208,7 @@ The following table summarizes the semantics under failures: ### With Kafka Direct API {:.no_toc} -In Spark 1.3, we have introduced a new Kafka Direct API, which can ensure that all the Kafka data is received by Spark Streaming exactly once. Along with this, if you implement exactly-once output operation, you can achieve end-to-end exactly-once guarantees. This approach (experimental as of Spark 1.3) is further discussed in the [Kafka Integration Guide](streaming-kafka-integration.html). +In Spark 1.3, we have introduced a new Kafka Direct API, which can ensure that all the Kafka data is received by Spark Streaming exactly once. Along with this, if you implement exactly-once output operation, you can achieve end-to-end exactly-once guarantees. This approach (experimental as of Spark {{site.SPARK_VERSION_SHORT}}) is further discussed in the [Kafka Integration Guide](streaming-kafka-integration.html). ## Semantics of output operations {:.no_toc} @@ -2248,9 +2222,16 @@ additional effort may be necessary to achieve exactly-once semantics. There are - *Transactional updates*: All updates are made transactionally so that updates are made exactly once atomically. One way to do this would be the following. - - Use the batch time (available in `foreachRDD`) and the partition index of the transformed RDD to create an identifier. This identifier uniquely identifies a blob data in the streaming application. - - Update external system with this blob transactionally (that is, exactly once, atomically) using the identifier. That is, if the identifier is not already committed, commit the partition data and the identifier atomically. Else if this was already committed, skip the update. + - Use the batch time (available in `foreachRDD`) and the partition index of the RDD to create an identifier. This identifier uniquely identifies a blob data in the streaming application. + - Update external system with this blob transactionally (that is, exactly once, atomically) using the identifier. That is, if the identifier is not already committed, commit the partition data and the identifier atomically. Else, if this was already committed, skip the update. + dstream.foreachRDD { (rdd, time) => + rdd.foreachPartition { partitionIterator => + val partitionId = TaskContext.get.partitionId() + val uniqueId = generateUniqueId(time.milliseconds, partitionId) + // use this uniqueId to transactionally commit the data in partitionIterator + } + } *************************************************************************************************** *************************************************************************************************** @@ -2325,7 +2306,7 @@ package and renamed for better clarity. - Java docs * [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html), [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html) and - [PairJavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/PairJavaDStream.html) + [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html) * [KafkaUtils](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html), [FlumeUtils](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html), [KinesisUtils](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisUtils.html) diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md index 3ecbf2308cd44..e58645274e525 100644 --- a/docs/submitting-applications.md +++ b/docs/submitting-applications.md @@ -59,7 +59,7 @@ for applications that involve the REPL (e.g. Spark shell). Alternatively, if your application is submitted from a machine far from the worker machines (e.g. locally on your laptop), it is common to use `cluster` mode to minimize network latency between the drivers and the executors. Note that `cluster` mode is currently not supported for -Mesos clusters or Python applications. +Mesos clusters. Currently only YARN supports cluster mode for Python applications. For Python applications, simply pass a `.py` file in the place of `` instead of a JAR, and add Python `.zip`, `.egg` or `.py` files to the search path with `--py-files`. diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 87c0818279713..56087499464e0 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -19,8 +19,9 @@ # limitations under the License. # -from __future__ import with_statement, print_function +from __future__ import division, print_function, with_statement +import codecs import hashlib import itertools import logging @@ -47,8 +48,10 @@ else: from urllib.request import urlopen, Request from urllib.error import HTTPError + raw_input = input + xrange = range -SPARK_EC2_VERSION = "1.2.1" +SPARK_EC2_VERSION = "1.4.0" SPARK_EC2_DIR = os.path.dirname(os.path.realpath(__file__)) VALID_SPARK_VERSIONS = set([ @@ -65,6 +68,9 @@ "1.1.1", "1.2.0", "1.2.1", + "1.3.0", + "1.3.1", + "1.4.0", ]) SPARK_TACHYON_MAP = { @@ -75,6 +81,9 @@ "1.1.1": "0.5.0", "1.2.0": "0.5.0", "1.2.1": "0.5.0", + "1.3.0": "0.5.0", + "1.3.1": "0.5.0", + "1.4.0": "0.6.4", } DEFAULT_SPARK_VERSION = SPARK_EC2_VERSION @@ -82,7 +91,7 @@ # Default location to get the spark-ec2 scripts (and ami-list) from DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/mesos/spark-ec2" -DEFAULT_SPARK_EC2_BRANCH = "branch-1.3" +DEFAULT_SPARK_EC2_BRANCH = "branch-1.4" def setup_external_libs(libs): @@ -212,7 +221,8 @@ def parse_args(): "(default: %default).") parser.add_option( "--hadoop-major-version", default="1", - help="Major version of Hadoop (default: %default)") + help="Major version of Hadoop. Valid options are 1 (Hadoop 1.0.4), 2 (CDH 4.2.0), yarn " + + "(Hadoop 2.4.0) (default: %default)") parser.add_option( "-D", metavar="[ADDRESS:]PORT", dest="proxy_port", help="Use SSH dynamic port forwarding to create a SOCKS proxy at " + @@ -264,7 +274,8 @@ def parse_args(): help="Launch fresh slaves, but use an existing stopped master if possible") parser.add_option( "--worker-instances", type="int", default=1, - help="Number of instances per worker: variable SPARK_WORKER_INSTANCES (default: %default)") + help="Number of instances per worker: variable SPARK_WORKER_INSTANCES. Not used if YARN " + + "is used as Hadoop major version (default: %default)") parser.add_option( "--master-opts", type="string", default="", help="Extra options to give to master through SPARK_MASTER_OPTS variable " + @@ -347,46 +358,57 @@ def get_validate_spark_version(version, repo): # Source: http://aws.amazon.com/amazon-linux-ami/instance-type-matrix/ -# Last Updated: 2014-06-20 +# Last Updated: 2015-05-08 # For easy maintainability, please keep this manually-inputted dictionary sorted by key. EC2_INSTANCE_TYPES = { "c1.medium": "pvm", "c1.xlarge": "pvm", + "c3.large": "pvm", + "c3.xlarge": "pvm", "c3.2xlarge": "pvm", "c3.4xlarge": "pvm", "c3.8xlarge": "pvm", - "c3.large": "pvm", - "c3.xlarge": "pvm", + "c4.large": "hvm", + "c4.xlarge": "hvm", + "c4.2xlarge": "hvm", + "c4.4xlarge": "hvm", + "c4.8xlarge": "hvm", "cc1.4xlarge": "hvm", "cc2.8xlarge": "hvm", "cg1.4xlarge": "hvm", "cr1.8xlarge": "hvm", + "d2.xlarge": "hvm", + "d2.2xlarge": "hvm", + "d2.4xlarge": "hvm", + "d2.8xlarge": "hvm", + "g2.2xlarge": "hvm", + "g2.8xlarge": "hvm", "hi1.4xlarge": "pvm", "hs1.8xlarge": "pvm", + "i2.xlarge": "hvm", "i2.2xlarge": "hvm", "i2.4xlarge": "hvm", "i2.8xlarge": "hvm", - "i2.xlarge": "hvm", - "m1.large": "pvm", - "m1.medium": "pvm", "m1.small": "pvm", + "m1.medium": "pvm", + "m1.large": "pvm", "m1.xlarge": "pvm", + "m2.xlarge": "pvm", "m2.2xlarge": "pvm", "m2.4xlarge": "pvm", - "m2.xlarge": "pvm", - "m3.2xlarge": "hvm", - "m3.large": "hvm", "m3.medium": "hvm", + "m3.large": "hvm", "m3.xlarge": "hvm", + "m3.2xlarge": "hvm", + "r3.large": "hvm", + "r3.xlarge": "hvm", "r3.2xlarge": "hvm", "r3.4xlarge": "hvm", "r3.8xlarge": "hvm", - "r3.large": "hvm", - "r3.xlarge": "hvm", "t1.micro": "pvm", - "t2.medium": "hvm", "t2.micro": "hvm", "t2.small": "hvm", + "t2.medium": "hvm", } @@ -408,13 +430,14 @@ def get_spark_ami(opts): b=opts.spark_ec2_git_branch) ami_path = "%s/%s/%s" % (ami_prefix, opts.region, instance_type) + reader = codecs.getreader("ascii") try: - ami = urlopen(ami_path).read().strip() - print("Spark AMI: " + ami) + ami = reader(urlopen(ami_path)).read().strip() except: print("Could not resolve AMI at: " + ami_path, file=stderr) sys.exit(1) + print("Spark AMI: " + ami) return ami @@ -472,6 +495,8 @@ def launch_cluster(conn, opts, cluster_name): master_group.authorize('udp', 2049, 2049, authorized_address) master_group.authorize('tcp', 4242, 4242, authorized_address) master_group.authorize('udp', 4242, 4242, authorized_address) + # RM in YARN mode uses 8088 + master_group.authorize('tcp', 8088, 8088, authorized_address) if opts.ganglia: master_group.authorize('tcp', 5080, 5080, authorized_address) if slave_group.rules == []: # Group was just now created @@ -735,11 +760,15 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): 'mapreduce', 'spark-standalone', 'tachyon'] if opts.hadoop_major_version == "1": - modules = filter(lambda x: x != "mapreduce", modules) + modules = list(filter(lambda x: x != "mapreduce", modules)) if opts.ganglia: modules.append('ganglia') + # Clear SPARK_WORKER_INSTANCES if running on YARN + if opts.hadoop_major_version == "yarn": + opts.worker_instances = "" + # NOTE: We should clone the repository before running deploy_files to # prevent ec2-variables.sh from being overwritten print("Cloning spark-ec2 scripts from {r}/tree/{b} on master...".format( @@ -849,7 +878,11 @@ def wait_for_cluster_state(conn, opts, cluster_instances, cluster_state): for i in cluster_instances: i.update() - statuses = conn.get_all_instance_status(instance_ids=[i.id for i in cluster_instances]) + max_batch = 100 + statuses = [] + for j in xrange(0, len(cluster_instances), max_batch): + batch = [i.id for i in cluster_instances[j:j + max_batch]] + statuses.extend(conn.get_all_instance_status(instance_ids=batch)) if cluster_state == 'ssh-ready': if all(i.state == 'running' for i in cluster_instances) and \ @@ -878,44 +911,57 @@ def wait_for_cluster_state(conn, opts, cluster_instances, cluster_state): # Get number of local disks available for a given EC2 instance type. def get_num_disks(instance_type): # Source: http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html - # Last Updated: 2014-06-20 + # Last Updated: 2015-05-08 # For easy maintainability, please keep this manually-inputted dictionary sorted by key. disks_by_instance = { "c1.medium": 1, "c1.xlarge": 4, + "c3.large": 2, + "c3.xlarge": 2, "c3.2xlarge": 2, "c3.4xlarge": 2, "c3.8xlarge": 2, - "c3.large": 2, - "c3.xlarge": 2, + "c4.large": 0, + "c4.xlarge": 0, + "c4.2xlarge": 0, + "c4.4xlarge": 0, + "c4.8xlarge": 0, "cc1.4xlarge": 2, "cc2.8xlarge": 4, "cg1.4xlarge": 2, "cr1.8xlarge": 2, + "d2.xlarge": 3, + "d2.2xlarge": 6, + "d2.4xlarge": 12, + "d2.8xlarge": 24, "g2.2xlarge": 1, + "g2.8xlarge": 2, "hi1.4xlarge": 2, "hs1.8xlarge": 24, + "i2.xlarge": 1, "i2.2xlarge": 2, "i2.4xlarge": 4, "i2.8xlarge": 8, - "i2.xlarge": 1, - "m1.large": 2, - "m1.medium": 1, "m1.small": 1, + "m1.medium": 1, + "m1.large": 2, "m1.xlarge": 4, + "m2.xlarge": 1, "m2.2xlarge": 1, "m2.4xlarge": 2, - "m2.xlarge": 1, - "m3.2xlarge": 2, - "m3.large": 1, "m3.medium": 1, + "m3.large": 1, "m3.xlarge": 2, + "m3.2xlarge": 2, + "r3.large": 1, + "r3.xlarge": 1, "r3.2xlarge": 1, "r3.4xlarge": 1, "r3.8xlarge": 2, - "r3.large": 1, - "r3.xlarge": 1, "t1.micro": 0, + "t2.micro": 0, + "t2.small": 0, + "t2.medium": 0, } if instance_type in disks_by_instance: return disks_by_instance[instance_type] @@ -960,6 +1006,7 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): master_addresses = [get_dns_name(i, opts.private_ips) for i in master_nodes] slave_addresses = [get_dns_name(i, opts.private_ips) for i in slave_nodes] + worker_instances_str = "%d" % opts.worker_instances if opts.worker_instances else "" template_vars = { "master_list": '\n'.join(master_addresses), "active_master": active_master, @@ -973,7 +1020,7 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): "spark_version": spark_v, "tachyon_version": tachyon_v, "hadoop_major_version": opts.hadoop_major_version, - "spark_worker_instances": "%d" % opts.worker_instances, + "spark_worker_instances": worker_instances_str, "spark_master_opts": opts.master_opts } @@ -1128,7 +1175,7 @@ def get_zones(conn, opts): # Gets the number of items in a partition def get_partition(total, num_partitions, current_partitions): - num_slaves_this_zone = total / num_partitions + num_slaves_this_zone = total // num_partitions if (total % num_partitions) - current_partitions > 0: num_slaves_this_zone += 1 return num_slaves_this_zone diff --git a/examples/pom.xml b/examples/pom.xml index 5b04b4f8d6ca0..e6884b09dca94 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -97,6 +97,11 @@ + + org.apache.spark + spark-streaming-kafka_${scala.binary.version} + ${project.version} + org.apache.hbase hbase-testing-util @@ -392,45 +397,6 @@ - - - scala-2.10 - - !scala-2.11 - - - - org.apache.spark - spark-streaming-kafka_${scala.binary.version} - ${project.version} - - - - - - org.codehaus.mojo - build-helper-maven-plugin - - - add-scala-sources - generate-sources - - add-source - - - - src/main/scala - scala-2.10/src/main/scala - scala-2.10/src/main/java - - - - - - - - diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index eac4f898a475d..9df26ffca5775 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -28,6 +28,7 @@ import org.apache.spark.ml.classification.ClassificationModel; import org.apache.spark.ml.param.IntParam; import org.apache.spark.ml.param.ParamMap; +import org.apache.spark.ml.util.Identifiable$; import org.apache.spark.mllib.linalg.BLAS; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; @@ -103,7 +104,23 @@ public static void main(String[] args) throws Exception { * However, this should still compile and run successfully. */ class MyJavaLogisticRegression - extends Classifier { + extends Classifier { + + public MyJavaLogisticRegression() { + init(); + } + + public MyJavaLogisticRegression(String uid) { + this.uid_ = uid; + init(); + } + + private String uid_ = Identifiable$.MODULE$.randomUID("myJavaLogReg"); + + @Override + public String uid() { + return uid_; + } /** * Param for max number of iterations @@ -117,7 +134,7 @@ class MyJavaLogisticRegression int getMaxIter() { return (Integer) getOrDefault(maxIter); } - public MyJavaLogisticRegression() { + private void init() { setMaxIter(100); } @@ -137,7 +154,12 @@ public MyJavaLogisticRegressionModel train(DataFrame dataset) { Vector weights = Vectors.zeros(numFeatures); // Learning would happen here. // Create a model, and return it. - return new MyJavaLogisticRegressionModel(this, weights); + return new MyJavaLogisticRegressionModel(uid(), weights).setParent(this); + } + + @Override + public MyJavaLogisticRegression copy(ParamMap extra) { + return defaultCopy(extra); } } @@ -149,17 +171,21 @@ public MyJavaLogisticRegressionModel train(DataFrame dataset) { * However, this should still compile and run successfully. */ class MyJavaLogisticRegressionModel - extends ClassificationModel { - - private MyJavaLogisticRegression parent_; - public MyJavaLogisticRegression parent() { return parent_; } + extends ClassificationModel { private Vector weights_; public Vector weights() { return weights_; } - public MyJavaLogisticRegressionModel(MyJavaLogisticRegression parent_, Vector weights_) { - this.parent_ = parent_; - this.weights_ = weights_; + public MyJavaLogisticRegressionModel(String uid, Vector weights) { + this.uid_ = uid; + this.weights_ = weights; + } + + private String uid_ = Identifiable$.MODULE$.randomUID("myJavaLogReg"); + + @Override + public String uid() { + return uid_; } // This uses the default implementation of transform(), which reads column "features" and outputs @@ -204,6 +230,6 @@ public Vector predictRaw(Vector features) { */ @Override public MyJavaLogisticRegressionModel copy(ParamMap extra) { - return copyValues(new MyJavaLogisticRegressionModel(parent_, weights_), extra); + return copyValues(new MyJavaLogisticRegressionModel(uid(), weights_), extra); } } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java new file mode 100644 index 0000000000000..75063dbf800d8 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java @@ -0,0 +1,236 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml; + +import org.apache.commons.cli.*; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.classification.LogisticRegression; +import org.apache.spark.ml.classification.OneVsRest; +import org.apache.spark.ml.classification.OneVsRestModel; +import org.apache.spark.ml.util.MetadataUtils; +import org.apache.spark.mllib.evaluation.MulticlassMetrics; +import org.apache.spark.mllib.linalg.Matrix; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.mllib.util.MLUtils; +import org.apache.spark.rdd.RDD; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.types.StructField; + +/** + * An example runner for Multiclass to Binary Reduction with One Vs Rest. + * The example uses Logistic Regression as the base classifier. All parameters that + * can be specified on the base classifier can be passed in to the runner options. + * Run with + * 
    + * bin/run-example ml.JavaOneVsRestExample [options]
+ *
    + */ +public class JavaOneVsRestExample { + + private static class Params { + String input; + String testInput = null; + Integer maxIter = 100; + double tol = 1E-6; + boolean fitIntercept = true; + Double regParam = null; + Double elasticNetParam = null; + double fracTest = 0.2; + } + + public static void main(String[] args) { + // parse the arguments + Params params = parse(args); + SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext jsql = new SQLContext(jsc); + + // configure the base classifier + LogisticRegression classifier = new LogisticRegression() + .setMaxIter(params.maxIter) + .setTol(params.tol) + .setFitIntercept(params.fitIntercept); + + if (params.regParam != null) { + classifier.setRegParam(params.regParam); + } + if (params.elasticNetParam != null) { + classifier.setElasticNetParam(params.elasticNetParam); + } + + // instantiate the One Vs Rest Classifier + OneVsRest ovr = new OneVsRest().setClassifier(classifier); + + String input = params.input; + RDD inputData = MLUtils.loadLibSVMFile(jsc.sc(), input); + RDD train; + RDD test; + + // compute the train/ test split: if testInput is not provided use part of input + String testInput = params.testInput; + if (testInput != null) { + train = inputData; + // compute the number of features in the training set. + int numFeatures = inputData.first().features().size(); + test = MLUtils.loadLibSVMFile(jsc.sc(), testInput, numFeatures); + } else { + double f = params.fracTest; + RDD[] tmp = inputData.randomSplit(new double[]{1 - f, f}, 12345); + train = tmp[0]; + test = tmp[1]; + } + + // train the multiclass model + DataFrame trainingDataFrame = jsql.createDataFrame(train, LabeledPoint.class); + OneVsRestModel ovrModel = ovr.fit(trainingDataFrame.cache()); + + // score the model on test data + DataFrame testDataFrame = jsql.createDataFrame(test, LabeledPoint.class); + DataFrame predictions = ovrModel.transform(testDataFrame.cache()) + .select("prediction", "label"); + + // obtain metrics + MulticlassMetrics metrics = new MulticlassMetrics(predictions); + StructField predictionColSchema = predictions.schema().apply("prediction"); + Integer numClasses = (Integer) MetadataUtils.getNumClasses(predictionColSchema).get(); + + // compute the false positive rate per label + StringBuilder results = new StringBuilder(); + results.append("label\tfpr\n"); + for (int label = 0; label < numClasses; label++) { + results.append(label); + results.append("\t"); + results.append(metrics.falsePositiveRate((double) label)); + results.append("\n"); + } + + Matrix confusionMatrix = metrics.confusionMatrix(); + // output the Confusion Matrix + System.out.println("Confusion Matrix"); + System.out.println(confusionMatrix); + System.out.println(); + System.out.println(results); + + jsc.stop(); + } + + private static Params parse(String[] args) { + Options options = generateCommandlineOptions(); + CommandLineParser parser = new PosixParser(); + Params params = new Params(); + + try { + CommandLine cmd = parser.parse(options, args); + String value; + if (cmd.hasOption("input")) { + params.input = cmd.getOptionValue("input"); + } + if (cmd.hasOption("maxIter")) { + value = cmd.getOptionValue("maxIter"); + params.maxIter = Integer.parseInt(value); + } + if (cmd.hasOption("tol")) { + value = cmd.getOptionValue("tol"); + params.tol = Double.parseDouble(value); + } + if (cmd.hasOption("fitIntercept")) { + value = cmd.getOptionValue("fitIntercept"); + params.fitIntercept = Boolean.parseBoolean(value); + } + if (cmd.hasOption("regParam")) { + value = cmd.getOptionValue("regParam"); + params.regParam = Double.parseDouble(value); + } + if (cmd.hasOption("elasticNetParam")) { + value = cmd.getOptionValue("elasticNetParam"); + params.elasticNetParam = Double.parseDouble(value); + } + if (cmd.hasOption("testInput")) { + value = cmd.getOptionValue("testInput"); + params.testInput = value; + } + if (cmd.hasOption("fracTest")) { + value = cmd.getOptionValue("fracTest"); + params.fracTest = Double.parseDouble(value); + } + + } catch (ParseException e) { + printHelpAndQuit(options); + } + return params; + } + + private static Options generateCommandlineOptions() { + Option input = OptionBuilder.withArgName("input") + .hasArg() + .isRequired() + .withDescription("input path to labeled examples. This path must be specified") + .create("input"); + Option testInput = OptionBuilder.withArgName("testInput") + .hasArg() + .withDescription("input path to test examples") + .create("testInput"); + Option fracTest = OptionBuilder.withArgName("testInput") + .hasArg() + .withDescription("fraction of data to hold out for testing." + + " If given option testInput, this option is ignored. default: 0.2") + .create("fracTest"); + Option maxIter = OptionBuilder.withArgName("maxIter") + .hasArg() + .withDescription("maximum number of iterations for Logistic Regression. default:100") + .create("maxIter"); + Option tol = OptionBuilder.withArgName("tol") + .hasArg() + .withDescription("the convergence tolerance of iterations " + + "for Logistic Regression. default: 1E-6") + .create("tol"); + Option fitIntercept = OptionBuilder.withArgName("fitIntercept") + .hasArg() + .withDescription("fit intercept for logistic regression. default true") + .create("fitIntercept"); + Option regParam = OptionBuilder.withArgName( "regParam" ) + .hasArg() + .withDescription("the regularization parameter for Logistic Regression.") + .create("regParam"); + Option elasticNetParam = OptionBuilder.withArgName("elasticNetParam" ) + .hasArg() + .withDescription("the ElasticNet mixing parameter for Logistic Regression.") + .create("elasticNetParam"); + + Options options = new Options() + .addOption(input) + .addOption(testInput) + .addOption(fracTest) + .addOption(maxIter) + .addOption(tol) + .addOption(fitIntercept) + .addOption(regParam) + .addOption(elasticNetParam); + + return options; + } + + private static void printHelpAndQuit(Options options) { + HelpFormatter formatter = new HelpFormatter(); + formatter.printHelp("JavaOneVsRestExample", options); + System.exit(-1); + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index 29158d5c85651..dac649d1d5ae6 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -97,7 +97,7 @@ public static void main(String[] args) { DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class); // Make predictions on test documents using the Transformer.transform() method. - // LogisticRegression.transform will only use the 'features' column. + // LogisticRegressionModel.transform will only use the 'features' column. // Note that model2.transform() outputs a 'myProbability' column instead of the usual // 'probability' column since we renamed the lr.probabilityCol parameter previously. DataFrame results = model2.transform(test); diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java index ef1ec103a879f..54738813d0016 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java @@ -66,7 +66,7 @@ public static void main(String[] args) { .setOutputCol("features"); LogisticRegression lr = new LogisticRegression() .setMaxIter(10) - .setRegParam(0.01); + .setRegParam(0.001); Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[] {tokenizer, hashingTF, lr}); @@ -77,7 +77,7 @@ public static void main(String[] args) { List localTest = Lists.newArrayList( new Document(4L, "spark i j k"), new Document(5L, "l m n"), - new Document(6L, "mapreduce spark"), + new Document(6L, "spark hadoop spark"), new Document(7L, "apache hadoop")); DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class); diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java index 8159ffbe2d269..afee279ec32b1 100644 --- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java +++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java @@ -94,12 +94,12 @@ public String call(Row row) { System.out.println("=== Data source: Parquet File ==="); // DataFrames can be saved as parquet files, maintaining the schema information. - schemaPeople.saveAsParquetFile("people.parquet"); + schemaPeople.write().parquet("people.parquet"); // Read in the parquet file created above. // Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a DataFrame. - DataFrame parquetFile = sqlContext.parquetFile("people.parquet"); + DataFrame parquetFile = sqlContext.read().parquet("people.parquet"); //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); @@ -120,7 +120,7 @@ public String call(Row row) { // The path can be either a single text file or a directory storing text files. String path = "examples/src/main/resources/people.json"; // Create a DataFrame from the file(s) pointed by path - DataFrame peopleFromJsonFile = sqlContext.jsonFile(path); + DataFrame peopleFromJsonFile = sqlContext.read().json(path); // Because the schema of a JSON dataset is automatically inferred, to write queries, // it is better to take a look at what is the schema. @@ -151,7 +151,7 @@ public String call(Row row) { List jsonData = Arrays.asList( "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); JavaRDD anotherPeopleRDD = ctx.parallelize(jsonData); - DataFrame peopleFromJsonRDD = sqlContext.jsonRDD(anotherPeopleRDD.rdd()); + DataFrame peopleFromJsonRDD = sqlContext.read().json(anotherPeopleRDD.rdd()); // Take a look at the schema of this new DataFrame. peopleFromJsonRDD.printSchema(); diff --git a/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java similarity index 100% rename from examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java diff --git a/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java similarity index 100% rename from examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java diff --git a/examples/src/main/python/hbase_inputformat.py b/examples/src/main/python/hbase_inputformat.py index 5b82a14fba413..c5ae5d043b8ea 100644 --- a/examples/src/main/python/hbase_inputformat.py +++ b/examples/src/main/python/hbase_inputformat.py @@ -18,6 +18,7 @@ from __future__ import print_function import sys +import json from pyspark import SparkContext @@ -27,24 +28,24 @@ hbase(main):016:0> create 'test', 'f1' 0 row(s) in 1.0430 seconds -hbase(main):017:0> put 'test', 'row1', 'f1', 'value1' +hbase(main):017:0> put 'test', 'row1', 'f1:a', 'value1' 0 row(s) in 0.0130 seconds -hbase(main):018:0> put 'test', 'row2', 'f1', 'value2' +hbase(main):018:0> put 'test', 'row1', 'f1:b', 'value2' 0 row(s) in 0.0030 seconds -hbase(main):019:0> put 'test', 'row3', 'f1', 'value3' +hbase(main):019:0> put 'test', 'row2', 'f1', 'value3' 0 row(s) in 0.0050 seconds -hbase(main):020:0> put 'test', 'row4', 'f1', 'value4' +hbase(main):020:0> put 'test', 'row3', 'f1', 'value4' 0 row(s) in 0.0110 seconds hbase(main):021:0> scan 'test' ROW COLUMN+CELL - row1 column=f1:, timestamp=1401883411986, value=value1 - row2 column=f1:, timestamp=1401883415212, value=value2 - row3 column=f1:, timestamp=1401883417858, value=value3 - row4 column=f1:, timestamp=1401883420805, value=value4 + row1 column=f1:a, timestamp=1401883411986, value=value1 + row1 column=f1:b, timestamp=1401883415212, value=value2 + row2 column=f1:, timestamp=1401883417858, value=value3 + row3 column=f1:, timestamp=1401883420805, value=value4 4 row(s) in 0.0240 seconds """ if __name__ == "__main__": @@ -64,6 +65,8 @@ table = sys.argv[2] sc = SparkContext(appName="HBaseInputFormat") + # Other options for configuring scan behavior are available. More information available at + # https://github.com/apache/hbase/blob/master/hbase-server/src/main/java/org/apache/hadoop/hbase/mapreduce/TableInputFormat.java conf = {"hbase.zookeeper.quorum": host, "hbase.mapreduce.inputtable": table} if len(sys.argv) > 3: conf = {"hbase.zookeeper.quorum": host, "zookeeper.znode.parent": sys.argv[3], @@ -78,6 +81,8 @@ keyConverter=keyConv, valueConverter=valueConv, conf=conf) + hbase_rdd = hbase_rdd.flatMapValues(lambda v: v.split("\n")).mapValues(json.loads) + output = hbase_rdd.collect() for (k, v) in output: print((k, v)) diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index 1456c87312841..0ea7cfb7025a0 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -68,7 +68,7 @@ def closestPoint(p, centers): closest = data.map( lambda p: (closestPoint(p, kPoints), (p, 1))) pointStats = closest.reduceByKey( - lambda (p1, c1), (p2, c2): (p1 + p2, c1 + c2)) + lambda p1_c1, p2_c2: (p1_c1[0] + p2_c2[0], p1_c1[1] + p2_c2[1])) newPoints = pointStats.map( lambda st: (st[0], st[1][0] / st[1][1])).collect() diff --git a/examples/src/main/python/ml/cross_validator.py b/examples/src/main/python/ml/cross_validator.py new file mode 100644 index 0000000000000..f0ca97c724940 --- /dev/null +++ b/examples/src/main/python/ml/cross_validator.py @@ -0,0 +1,96 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import print_function + +from pyspark import SparkContext +from pyspark.ml import Pipeline +from pyspark.ml.classification import LogisticRegression +from pyspark.ml.evaluation import BinaryClassificationEvaluator +from pyspark.ml.feature import HashingTF, Tokenizer +from pyspark.ml.tuning import CrossValidator, ParamGridBuilder +from pyspark.sql import Row, SQLContext + +""" +A simple example demonstrating model selection using CrossValidator. +This example also demonstrates how Pipelines are Estimators. +Run with: + + bin/spark-submit examples/src/main/python/ml/cross_validator.py +""" + +if __name__ == "__main__": + sc = SparkContext(appName="CrossValidatorExample") + sqlContext = SQLContext(sc) + + # Prepare training documents, which are labeled. + LabeledDocument = Row("id", "text", "label") + training = sc.parallelize([(0, "a b c d e spark", 1.0), + (1, "b d", 0.0), + (2, "spark f g h", 1.0), + (3, "hadoop mapreduce", 0.0), + (4, "b spark who", 1.0), + (5, "g d a y", 0.0), + (6, "spark fly", 1.0), + (7, "was mapreduce", 0.0), + (8, "e spark program", 1.0), + (9, "a e c l", 0.0), + (10, "spark compile", 1.0), + (11, "hadoop software", 0.0) + ]) \ + .map(lambda x: LabeledDocument(*x)).toDF() + + # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. + tokenizer = Tokenizer(inputCol="text", outputCol="words") + hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") + lr = LogisticRegression(maxIter=10) + pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) + + # We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. + # This will allow us to jointly choose parameters for all Pipeline stages. + # A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. + # We use a ParamGridBuilder to construct a grid of parameters to search over. + # With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, + # this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. + paramGrid = ParamGridBuilder() \ + .addGrid(hashingTF.numFeatures, [10, 100, 1000]) \ + .addGrid(lr.regParam, [0.1, 0.01]) \ + .build() + + crossval = CrossValidator(estimator=pipeline, + estimatorParamMaps=paramGrid, + evaluator=BinaryClassificationEvaluator(), + numFolds=2) # use 3+ folds in practice + + # Run cross-validation, and choose the best set of parameters. + cvModel = crossval.fit(training) + + # Prepare test documents, which are unlabeled. + Document = Row("id", "text") + test = sc.parallelize([(4L, "spark i j k"), + (5L, "l m n"), + (6L, "mapreduce spark"), + (7L, "apache hadoop")]) \ + .map(lambda x: Document(*x)).toDF() + + # Make predictions on test documents. cvModel uses the best model found (lrModel). + prediction = cvModel.transform(test) + selected = prediction.select("id", "text", "probability", "prediction") + for row in selected.collect(): + print(row) + + sc.stop() diff --git a/examples/src/main/python/ml/gradient_boosted_trees.py b/examples/src/main/python/ml/gradient_boosted_trees.py new file mode 100644 index 0000000000000..6446f0fe5eeab --- /dev/null +++ b/examples/src/main/python/ml/gradient_boosted_trees.py @@ -0,0 +1,83 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import print_function + +import sys + +from pyspark import SparkContext +from pyspark.ml.classification import GBTClassifier +from pyspark.ml.feature import StringIndexer +from pyspark.ml.regression import GBTRegressor +from pyspark.mllib.evaluation import BinaryClassificationMetrics, RegressionMetrics +from pyspark.mllib.util import MLUtils +from pyspark.sql import Row, SQLContext + +""" +A simple example demonstrating a Gradient Boosted Trees Classification/Regression Pipeline. +Note: GBTClassifier only supports binary classification currently +Run with: + bin/spark-submit examples/src/main/python/ml/gradient_boosted_trees.py +""" + + +def testClassification(train, test): + # Train a GradientBoostedTrees model. + + rf = GBTClassifier(maxIter=30, maxDepth=4, labelCol="indexedLabel") + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = BinaryClassificationMetrics(predictionAndLabels) + print("AUC %.3f" % metrics.areaUnderROC) + + +def testRegression(train, test): + # Train a GradientBoostedTrees model. + + rf = GBTRegressor(maxIter=30, maxDepth=4, labelCol="indexedLabel") + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = RegressionMetrics(predictionAndLabels) + print("rmse %.3f" % metrics.rootMeanSquaredError) + print("r2 %.3f" % metrics.r2) + print("mae %.3f" % metrics.meanAbsoluteError) + + +if __name__ == "__main__": + if len(sys.argv) > 1: + print("Usage: gradient_boosted_trees", file=sys.stderr) + exit(1) + sc = SparkContext(appName="PythonGBTExample") + sqlContext = SQLContext(sc) + + # Load and parse the data file into a dataframe. + df = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + # Map labels into an indexed column of labels in [0, numLabels) + stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel") + si_model = stringIndexer.fit(df) + td = si_model.transform(df) + [train, test] = td.randomSplit([0.7, 0.3]) + testClassification(train, test) + testRegression(train, test) + sc.stop() diff --git a/examples/src/main/python/ml/random_forest_example.py b/examples/src/main/python/ml/random_forest_example.py new file mode 100644 index 0000000000000..c7730e1bfacd9 --- /dev/null +++ b/examples/src/main/python/ml/random_forest_example.py @@ -0,0 +1,87 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import print_function + +import sys + +from pyspark import SparkContext +from pyspark.ml.classification import RandomForestClassifier +from pyspark.ml.feature import StringIndexer +from pyspark.ml.regression import RandomForestRegressor +from pyspark.mllib.evaluation import MulticlassMetrics, RegressionMetrics +from pyspark.mllib.util import MLUtils +from pyspark.sql import Row, SQLContext + +""" +A simple example demonstrating a RandomForest Classification/Regression Pipeline. +Run with: + bin/spark-submit examples/src/main/python/ml/random_forest_example.py +""" + + +def testClassification(train, test): + # Train a RandomForest model. + # Setting featureSubsetStrategy="auto" lets the algorithm choose. + # Note: Use larger numTrees in practice. + + rf = RandomForestClassifier(labelCol="indexedLabel", numTrees=3, maxDepth=4) + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = MulticlassMetrics(predictionAndLabels) + print("weighted f-measure %.3f" % metrics.weightedFMeasure()) + print("precision %s" % metrics.precision()) + print("recall %s" % metrics.recall()) + + +def testRegression(train, test): + # Train a RandomForest model. + # Note: Use larger numTrees in practice. + + rf = RandomForestRegressor(labelCol="indexedLabel", numTrees=3, maxDepth=4) + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = RegressionMetrics(predictionAndLabels) + print("rmse %.3f" % metrics.rootMeanSquaredError) + print("r2 %.3f" % metrics.r2) + print("mae %.3f" % metrics.meanAbsoluteError) + + +if __name__ == "__main__": + if len(sys.argv) > 1: + print("Usage: random_forest_example", file=sys.stderr) + exit(1) + sc = SparkContext(appName="PythonRandomForestExample") + sqlContext = SQLContext(sc) + + # Load and parse the data file into a dataframe. + df = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + # Map labels into an indexed column of labels in [0, numLabels) + stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel") + si_model = stringIndexer.fit(df) + td = si_model.transform(df) + [train, test] = td.randomSplit([0.7, 0.3]) + testClassification(train, test) + testRegression(train, test) + sc.stop() diff --git a/examples/src/main/python/ml/simple_params_example.py b/examples/src/main/python/ml/simple_params_example.py new file mode 100644 index 0000000000000..a9f29dab2d602 --- /dev/null +++ b/examples/src/main/python/ml/simple_params_example.py @@ -0,0 +1,98 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import print_function + +import pprint +import sys + +from pyspark import SparkContext +from pyspark.ml.classification import LogisticRegression +from pyspark.mllib.linalg import DenseVector +from pyspark.mllib.regression import LabeledPoint +from pyspark.sql import SQLContext + +""" +A simple example demonstrating ways to specify parameters for Estimators and Transformers. +Run with: + bin/spark-submit examples/src/main/python/ml/simple_params_example.py +""" + +if __name__ == "__main__": + if len(sys.argv) > 1: + print("Usage: simple_params_example", file=sys.stderr) + exit(1) + sc = SparkContext(appName="PythonSimpleParamsExample") + sqlContext = SQLContext(sc) + + # prepare training data. + # We create an RDD of LabeledPoints and convert them into a DataFrame. + # A LabeledPoint is an Object with two fields named label and features + # and Spark SQL identifies these fields and creates the schema appropriately. + training = sc.parallelize([ + LabeledPoint(1.0, DenseVector([0.0, 1.1, 0.1])), + LabeledPoint(0.0, DenseVector([2.0, 1.0, -1.0])), + LabeledPoint(0.0, DenseVector([2.0, 1.3, 1.0])), + LabeledPoint(1.0, DenseVector([0.0, 1.2, -0.5]))]).toDF() + + # Create a LogisticRegression instance with maxIter = 10. + # This instance is an Estimator. + lr = LogisticRegression(maxIter=10) + # Print out the parameters, documentation, and any default values. + print("LogisticRegression parameters:\n" + lr.explainParams() + "\n") + + # We may also set parameters using setter methods. + lr.setRegParam(0.01) + + # Learn a LogisticRegression model. This uses the parameters stored in lr. + model1 = lr.fit(training) + + # Since model1 is a Model (i.e., a Transformer produced by an Estimator), + # we can view the parameters it used during fit(). + # This prints the parameter (name: value) pairs, where names are unique IDs for this + # LogisticRegression instance. + print("Model 1 was fit using parameters:\n") + pprint.pprint(model1.extractParamMap()) + + # We may alternatively specify parameters using a parameter map. + # paramMap overrides all lr parameters set earlier. + paramMap = {lr.maxIter: 20, lr.threshold: 0.55, lr.probabilityCol: "myProbability"} + + # Now learn a new model using the new parameters. + model2 = lr.fit(training, paramMap) + print("Model 2 was fit using parameters:\n") + pprint.pprint(model2.extractParamMap()) + + # prepare test data. + test = sc.parallelize([ + LabeledPoint(1.0, DenseVector([-1.0, 1.5, 1.3])), + LabeledPoint(0.0, DenseVector([3.0, 2.0, -0.1])), + LabeledPoint(0.0, DenseVector([0.0, 2.2, -1.5]))]).toDF() + + # Make predictions on test data using the Transformer.transform() method. + # LogisticRegressionModel.transform will only use the 'features' column. + # Note that model2.transform() outputs a 'myProbability' column instead of the usual + # 'probability' column since we renamed the lr.probabilityCol parameter previously. + result = model2.transform(test) \ + .select("features", "label", "myProbability", "prediction") \ + .collect() + + for row in result: + print("features=%s,label=%s -> prob=%s, prediction=%s" + % (row.features, row.label, row.myProbability, row.prediction)) + + sc.stop() diff --git a/examples/src/main/python/ml/simple_text_classification_pipeline.py b/examples/src/main/python/ml/simple_text_classification_pipeline.py index fab21f003b233..b4f06bf888746 100644 --- a/examples/src/main/python/ml/simple_text_classification_pipeline.py +++ b/examples/src/main/python/ml/simple_text_classification_pipeline.py @@ -48,7 +48,7 @@ # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. tokenizer = Tokenizer(inputCol="text", outputCol="words") hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") - lr = LogisticRegression(maxIter=10, regParam=0.01) + lr = LogisticRegression(maxIter=10, regParam=0.001) pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) # Fit the pipeline to training documents. @@ -58,7 +58,7 @@ Document = Row("id", "text") test = sc.parallelize([(4, "spark i j k"), (5, "l m n"), - (6, "mapreduce spark"), + (6, "spark hadoop spark"), (7, "apache hadoop")]) \ .map(lambda x: Document(*x)).toDF() diff --git a/examples/src/main/python/parquet_inputformat.py b/examples/src/main/python/parquet_inputformat.py index 96ddac761d698..e1fd85b082c08 100644 --- a/examples/src/main/python/parquet_inputformat.py +++ b/examples/src/main/python/parquet_inputformat.py @@ -51,7 +51,7 @@ parquet_rdd = sc.newAPIHadoopFile( path, - 'parquet.avro.AvroParquetInputFormat', + 'org.apache.parquet.avro.AvroParquetInputFormat', 'java.lang.Void', 'org.apache.avro.generic.IndexedRecord', valueConverter='org.apache.spark.examples.pythonconverters.IndexedRecordToJavaConverter') diff --git a/examples/src/main/python/streaming/queue_stream.py b/examples/src/main/python/streaming/queue_stream.py new file mode 100644 index 0000000000000..dcd6a0fc6ff91 --- /dev/null +++ b/examples/src/main/python/streaming/queue_stream.py @@ -0,0 +1,50 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" + Create a queue of RDDs that will be mapped/reduced one at a time in + 1 second intervals. + + To run this example use + `$ bin/spark-submit examples/src/main/python/streaming/queue_stream.py +""" +import sys +import time + +from pyspark import SparkContext +from pyspark.streaming import StreamingContext + +if __name__ == "__main__": + + sc = SparkContext(appName="PythonStreamingQueueStream") + ssc = StreamingContext(sc, 1) + + # Create the queue through which RDDs can be pushed to + # a QueueInputDStream + rddQueue = [] + for i in xrange(5): + rddQueue += [ssc.sparkContext.parallelize([j for j in xrange(1, 1001)], 10)] + + # Create the QueueInputDStream and use it do some processing + inputStream = ssc.queueStream(rddQueue) + mappedStream = inputStream.map(lambda x: (x % 10, 1)) + reducedStream = mappedStream.reduceByKey(lambda a, b: a + b) + reducedStream.pprint() + + ssc.start() + time.sleep(6) + ssc.stop(stopSparkContext=True, stopGraceFully=True) diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala index 11d5c92c5952d..023bb3ee2d108 100644 --- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala @@ -104,8 +104,8 @@ object CassandraCQLTest { val casRdd = sc.newAPIHadoopRDD(job.getConfiguration(), classOf[CqlPagingInputFormat], - classOf[java.util.Map[String,ByteBuffer]], - classOf[java.util.Map[String,ByteBuffer]]) + classOf[java.util.Map[String, ByteBuffer]], + classOf[java.util.Map[String, ByteBuffer]]) println("Count: " + casRdd.count) val productSaleRDD = casRdd.map { @@ -118,7 +118,7 @@ object CassandraCQLTest { case (productId, saleCount) => println(productId + ":" + saleCount) } - val casoutputCF = aggregatedRDD.map { + val casoutputCF = aggregatedRDD.map { case (productId, saleCount) => { val outColFamKey = Map("prod_id" -> ByteBufferUtil.bytes(productId)) val outKey: java.util.Map[String, ByteBuffer] = outColFamKey diff --git a/examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala b/examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala new file mode 100644 index 0000000000000..1f12034ce0f57 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala @@ -0,0 +1,138 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples + +import java.io.File + +import scala.io.Source._ + +import org.apache.spark.{SparkContext, SparkConf} +import org.apache.spark.SparkContext._ + +/** + * Simple test for reading and writing to a distributed + * file system. This example does the following: + * + * 1. Reads local file + * 2. Computes word count on local file + * 3. Writes local file to a DFS + * 4. Reads the file back from the DFS + * 5. Computes word count on the file using Spark + * 6. Compares the word count results + */ +object DFSReadWriteTest { + + private var localFilePath: File = new File(".") + private var dfsDirPath: String = "" + + private val NPARAMS = 2 + + private def readFile(filename: String): List[String] = { + val lineIter: Iterator[String] = fromFile(filename).getLines() + val lineList: List[String] = lineIter.toList + lineList + } + + private def printUsage(): Unit = { + val usage: String = "DFS Read-Write Test\n" + + "\n" + + "Usage: localFile dfsDir\n" + + "\n" + + "localFile - (string) local file to use in test\n" + + "dfsDir - (string) DFS directory for read/write tests\n" + + println(usage) + } + + private def parseArgs(args: Array[String]): Unit = { + if (args.length != NPARAMS) { + printUsage() + System.exit(1) + } + + var i = 0 + + localFilePath = new File(args(i)) + if (!localFilePath.exists) { + System.err.println("Given path (" + args(i) + ") does not exist.\n") + printUsage() + System.exit(1) + } + + if (!localFilePath.isFile) { + System.err.println("Given path (" + args(i) + ") is not a file.\n") + printUsage() + System.exit(1) + } + + i += 1 + dfsDirPath = args(i) + } + + def runLocalWordCount(fileContents: List[String]): Int = { + fileContents.flatMap(_.split(" ")) + .flatMap(_.split("\t")) + .filter(_.size > 0) + .groupBy(w => w) + .mapValues(_.size) + .values + .sum + } + + def main(args: Array[String]): Unit = { + parseArgs(args) + + println("Performing local word count") + val fileContents = readFile(localFilePath.toString()) + val localWordCount = runLocalWordCount(fileContents) + + println("Creating SparkConf") + val conf = new SparkConf().setAppName("DFS Read Write Test") + + println("Creating SparkContext") + val sc = new SparkContext(conf) + + println("Writing local file to DFS") + val dfsFilename = dfsDirPath + "/dfs_read_write_test" + val fileRDD = sc.parallelize(fileContents) + fileRDD.saveAsTextFile(dfsFilename) + + println("Reading file from DFS and running Word Count") + val readFileRDD = sc.textFile(dfsFilename) + + val dfsWordCount = readFileRDD + .flatMap(_.split(" ")) + .flatMap(_.split("\t")) + .filter(_.size > 0) + .map(w => (w, 1)) + .countByKey() + .values + .sum + + sc.stop() + + if (localWordCount == dfsWordCount) { + println(s"Success! Local Word Count ($localWordCount) " + + s"and DFS Word Count ($dfsWordCount) agree.") + } else { + println(s"Failure! Local Word Count ($localWordCount) " + + s"and DFS Word Count ($dfsWordCount) disagree.") + } + + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala index 849887d23c9cf..95c96111c9b1f 100644 --- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala @@ -59,5 +59,6 @@ object HBaseTest { hBaseRDD.count() sc.stop() + admin.close() } } diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala index a55e0dc8d36c2..c3fc74a116c0a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala @@ -39,7 +39,7 @@ object LocalLR { def generateData: Array[DataPoint] = { def generatePoint(i: Int): DataPoint = { - val y = if(i % 2 == 0) -1 else 1 + val y = if (i % 2 == 0) -1 else 1 val x = DenseVector.fill(D){rand.nextGaussian + y * R} DataPoint(x, y) } diff --git a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala index 32e02eab8b031..75c82117cbad2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala @@ -22,7 +22,7 @@ import org.apache.spark.SparkContext._ /** * Executes a roll up-style query against Apache logs. - * + * * Usage: LogQuery [logFile] */ object LogQuery { diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala index 6c0ac8013ce34..30c4261551837 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala @@ -117,7 +117,7 @@ object SparkALS { var us = Array.fill(U)(randomVector(F)) // Iteratively update movies then users - val Rc = sc.broadcast(R) + val Rc = sc.broadcast(R) var msb = sc.broadcast(ms) var usb = sc.broadcast(us) for (iter <- 1 to ITERATIONS) { diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala index 8c01a60844620..1e6b4fb0c7514 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala @@ -44,7 +44,7 @@ object SparkLR { def generateData: Array[DataPoint] = { def generatePoint(i: Int): DataPoint = { - val y = if(i % 2 == 0) -1 else 1 + val y = if (i % 2 == 0) -1 else 1 val x = DenseVector.fill(D){rand.nextGaussian + y * R} DataPoint(x, y) } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala index 8d092b6506d33..bd7894f184c4c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala @@ -51,7 +51,7 @@ object SparkPageRank { showWarning() val sparkConf = new SparkConf().setAppName("PageRank") - val iters = if (args.length > 0) args(1).toInt else 10 + val iters = if (args.length > 1) args(1).toInt else 10 val ctx = new SparkContext(sparkConf) val lines = ctx.textFile(args(0), 1) val links = lines.map{ s => diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala deleted file mode 100644 index ab6e63deb3c95..0000000000000 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala +++ /dev/null @@ -1,112 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.bagel - -import org.apache.spark._ -import org.apache.spark.bagel._ - -class PageRankUtils extends Serializable { - def computeWithCombiner(numVertices: Long, epsilon: Double)( - self: PRVertex, messageSum: Option[Double], superstep: Int - ): (PRVertex, Array[PRMessage]) = { - val newValue = messageSum match { - case Some(msgSum) if msgSum != 0 => - 0.15 / numVertices + 0.85 * msgSum - case _ => self.value - } - - val terminate = superstep >= 10 - - val outbox: Array[PRMessage] = - if (!terminate) { - self.outEdges.map(targetId => new PRMessage(targetId, newValue / self.outEdges.size)) - } else { - Array[PRMessage]() - } - - (new PRVertex(newValue, self.outEdges, !terminate), outbox) - } - - def computeNoCombiner(numVertices: Long, epsilon: Double) - (self: PRVertex, messages: Option[Array[PRMessage]], superstep: Int) - : (PRVertex, Array[PRMessage]) = - computeWithCombiner(numVertices, epsilon)(self, messages match { - case Some(msgs) => Some(msgs.map(_.value).sum) - case None => None - }, superstep) -} - -class PRCombiner extends Combiner[PRMessage, Double] with Serializable { - def createCombiner(msg: PRMessage): Double = - msg.value - def mergeMsg(combiner: Double, msg: PRMessage): Double = - combiner + msg.value - def mergeCombiners(a: Double, b: Double): Double = - a + b -} - -class PRVertex() extends Vertex with Serializable { - var value: Double = _ - var outEdges: Array[String] = _ - var active: Boolean = _ - - def this(value: Double, outEdges: Array[String], active: Boolean = true) { - this() - this.value = value - this.outEdges = outEdges - this.active = active - } - - override def toString(): String = { - "PRVertex(value=%f, outEdges.length=%d, active=%s)" - .format(value, outEdges.length, active.toString) - } -} - -class PRMessage() extends Message[String] with Serializable { - var targetId: String = _ - var value: Double = _ - - def this(targetId: String, value: Double) { - this() - this.targetId = targetId - this.value = value - } -} - -class CustomPartitioner(partitions: Int) extends Partitioner { - def numPartitions: Int = partitions - - def getPartition(key: Any): Int = { - val hash = key match { - case k: Long => (k & 0x00000000FFFFFFFFL).toInt - case _ => key.hashCode - } - - val mod = key.hashCode % partitions - if (mod < 0) mod + partitions else mod - } - - override def equals(other: Any): Boolean = other match { - case c: CustomPartitioner => - c.numPartitions == numPartitions - case _ => false - } - - override def hashCode: Int = numPartitions -} diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala deleted file mode 100644 index 859abedf2a55e..0000000000000 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala +++ /dev/null @@ -1,106 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.bagel - -import org.apache.spark._ -import org.apache.spark.SparkContext._ - -import org.apache.spark.bagel._ - -import scala.xml.{XML,NodeSeq} - -/** - * Run PageRank on XML Wikipedia dumps from http://wiki.freebase.com/wiki/WEX. Uses the "articles" - * files from there, which contains one line per wiki article in a tab-separated format - * (http://wiki.freebase.com/wiki/WEX/Documentation#articles). - */ -object WikipediaPageRank { - def main(args: Array[String]) { - if (args.length < 4) { - System.err.println( - "Usage: WikipediaPageRank ") - System.exit(-1) - } - val sparkConf = new SparkConf() - sparkConf.setAppName("WikipediaPageRank") - sparkConf.registerKryoClasses(Array(classOf[PRVertex], classOf[PRMessage])) - - val inputFile = args(0) - val threshold = args(1).toDouble - val numPartitions = args(2).toInt - val usePartitioner = args(3).toBoolean - - sparkConf.setAppName("WikipediaPageRank") - val sc = new SparkContext(sparkConf) - - // Parse the Wikipedia page data into a graph - val input = sc.textFile(inputFile) - - println("Counting vertices...") - val numVertices = input.count() - println("Done counting vertices.") - - println("Parsing input file...") - var vertices = input.map(line => { - val fields = line.split("\t") - val (title, body) = (fields(1), fields(3).replace("\\n", "\n")) - val links = - if (body == "\\N") { - NodeSeq.Empty - } else { - try { - XML.loadString(body) \\ "link" \ "target" - } catch { - case e: org.xml.sax.SAXParseException => - System.err.println("Article \"" + title + "\" has malformed XML in body:\n" + body) - NodeSeq.Empty - } - } - val outEdges = links.map(link => new String(link.text)).toArray - val id = new String(title) - (id, new PRVertex(1.0 / numVertices, outEdges)) - }) - if (usePartitioner) { - vertices = vertices.partitionBy(new HashPartitioner(sc.defaultParallelism)).cache() - } else { - vertices = vertices.cache() - } - println("Done parsing input file.") - - // Do the computation - val epsilon = 0.01 / numVertices - val messages = sc.parallelize(Array[(String, PRMessage)]()) - val utils = new PageRankUtils - val result = - Bagel.run( - sc, vertices, messages, combiner = new PRCombiner(), - numPartitions = numPartitions)( - utils.computeWithCombiner(numVertices, epsilon)) - - // Print the result - System.err.println("Articles with PageRank >= " + threshold + ":") - val top = - (result - .filter { case (id, vertex) => vertex.value >= threshold } - .map { case (id, vertex) => "%s\t%s\n".format(id, vertex.value) } - .collect().mkString) - println(top) - - sc.stop() - } -} diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala deleted file mode 100644 index 576a3e371b993..0000000000000 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala +++ /dev/null @@ -1,232 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.bagel - -import java.io.{InputStream, OutputStream, DataInputStream, DataOutputStream} -import java.nio.ByteBuffer - -import scala.collection.mutable.ArrayBuffer -import scala.xml.{XML, NodeSeq} - -import org.apache.spark._ -import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} -import org.apache.spark.SparkContext._ -import org.apache.spark.rdd.RDD - -import scala.reflect.ClassTag - -object WikipediaPageRankStandalone { - def main(args: Array[String]) { - if (args.length < 4) { - System.err.println("Usage: WikipediaPageRankStandalone " + - " ") - System.exit(-1) - } - val sparkConf = new SparkConf() - sparkConf.set("spark.serializer", "spark.bagel.examples.WPRSerializer") - - val inputFile = args(0) - val threshold = args(1).toDouble - val numIterations = args(2).toInt - val usePartitioner = args(3).toBoolean - - sparkConf.setAppName("WikipediaPageRankStandalone") - - val sc = new SparkContext(sparkConf) - - val input = sc.textFile(inputFile) - val partitioner = new HashPartitioner(sc.defaultParallelism) - val links = - if (usePartitioner) { - input.map(parseArticle _).partitionBy(partitioner).cache() - } else { - input.map(parseArticle _).cache() - } - val n = links.count() - val defaultRank = 1.0 / n - val a = 0.15 - - // Do the computation - val startTime = System.currentTimeMillis - val ranks = - pageRank(links, numIterations, defaultRank, a, n, partitioner, usePartitioner, - sc.defaultParallelism) - - // Print the result - System.err.println("Articles with PageRank >= " + threshold + ":") - val top = - (ranks - .filter { case (id, rank) => rank >= threshold } - .map { case (id, rank) => "%s\t%s\n".format(id, rank) } - .collect().mkString) - println(top) - - val time = (System.currentTimeMillis - startTime) / 1000.0 - println("Completed %d iterations in %f seconds: %f seconds per iteration" - .format(numIterations, time, time / numIterations)) - sc.stop() - } - - def parseArticle(line: String): (String, Array[String]) = { - val fields = line.split("\t") - val (title, body) = (fields(1), fields(3).replace("\\n", "\n")) - val id = new String(title) - val links = - if (body == "\\N") { - NodeSeq.Empty - } else { - try { - XML.loadString(body) \\ "link" \ "target" - } catch { - case e: org.xml.sax.SAXParseException => - System.err.println("Article \"" + title + "\" has malformed XML in body:\n" + body) - NodeSeq.Empty - } - } - val outEdges = links.map(link => new String(link.text)).toArray - (id, outEdges) - } - - def pageRank( - links: RDD[(String, Array[String])], - numIterations: Int, - defaultRank: Double, - a: Double, - n: Long, - partitioner: Partitioner, - usePartitioner: Boolean, - numPartitions: Int - ): RDD[(String, Double)] = { - var ranks = links.mapValues { edges => defaultRank } - for (i <- 1 to numIterations) { - val contribs = links.groupWith(ranks).flatMap { - case (id, (linksWrapperIterable, rankWrapperIterable)) => - val linksWrapper = linksWrapperIterable.iterator - val rankWrapper = rankWrapperIterable.iterator - if (linksWrapper.hasNext) { - val linksWrapperHead = linksWrapper.next - if (rankWrapper.hasNext) { - val rankWrapperHead = rankWrapper.next - linksWrapperHead.map(dest => (dest, rankWrapperHead / linksWrapperHead.size)) - } else { - linksWrapperHead.map(dest => (dest, defaultRank / linksWrapperHead.size)) - } - } else { - Array[(String, Double)]() - } - } - ranks = (contribs.combineByKey((x: Double) => x, - (x: Double, y: Double) => x + y, - (x: Double, y: Double) => x + y, - partitioner) - .mapValues(sum => a/n + (1-a)*sum)) - } - ranks - } -} - -class WPRSerializer extends org.apache.spark.serializer.Serializer { - def newInstance(): SerializerInstance = new WPRSerializerInstance() -} - -class WPRSerializerInstance extends SerializerInstance { - def serialize[T: ClassTag](t: T): ByteBuffer = { - throw new UnsupportedOperationException() - } - - def deserialize[T: ClassTag](bytes: ByteBuffer): T = { - throw new UnsupportedOperationException() - } - - def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T = { - throw new UnsupportedOperationException() - } - - def serializeStream(s: OutputStream): SerializationStream = { - new WPRSerializationStream(s) - } - - def deserializeStream(s: InputStream): DeserializationStream = { - new WPRDeserializationStream(s) - } -} - -class WPRSerializationStream(os: OutputStream) extends SerializationStream { - val dos = new DataOutputStream(os) - - def writeObject[T: ClassTag](t: T): SerializationStream = t match { - case (id: String, wrapper: ArrayBuffer[_]) => wrapper(0) match { - case links: Array[String] => { - dos.writeInt(0) // links - dos.writeUTF(id) - dos.writeInt(links.length) - for (link <- links) { - dos.writeUTF(link) - } - this - } - case rank: Double => { - dos.writeInt(1) // rank - dos.writeUTF(id) - dos.writeDouble(rank) - this - } - } - case (id: String, rank: Double) => { - dos.writeInt(2) // rank without wrapper - dos.writeUTF(id) - dos.writeDouble(rank) - this - } - } - - def flush() { dos.flush() } - def close() { dos.close() } -} - -class WPRDeserializationStream(is: InputStream) extends DeserializationStream { - val dis = new DataInputStream(is) - - def readObject[T: ClassTag](): T = { - val typeId = dis.readInt() - typeId match { - case 0 => { - val id = dis.readUTF() - val numLinks = dis.readInt() - val links = new Array[String](numLinks) - for (i <- 0 until numLinks) { - val link = dis.readUTF() - links(i) = link - } - (id, ArrayBuffer(links)).asInstanceOf[T] - } - case 1 => { - val id = dis.readUTF() - val rank = dis.readDouble() - (id, ArrayBuffer(rank)).asInstanceOf[T] - } - case 2 => { - val id = dis.readUTF() - val rank = dis.readDouble() - (id, rank).asInstanceOf[T] - } - } - } - - def close() { dis.close() } -} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala index 8340d91101ab3..54e4073941056 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -112,7 +112,7 @@ object DecisionTreeExample { .text(s"input path to test dataset. If given, option fracTest is ignored." + s" default: ${defaultParams.testInput}") .action((x, c) => c.copy(testInput = x)) - opt[String]("") + opt[String]("dataFormat") .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") .action((x, c) => c.copy(dataFormat = x)) arg[String]("") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala index 2a2d0677272a0..7b8cc21ed8982 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala @@ -20,6 +20,7 @@ package org.apache.spark.examples.ml import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams} import org.apache.spark.ml.param.{IntParam, ParamMap} +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.sql.{DataFrame, Row, SQLContext} @@ -106,10 +107,12 @@ private trait MyLogisticRegressionParams extends ClassifierParams { * * NOTE: This is private since it is an example. In practice, you may not want it to be private. */ -private class MyLogisticRegression +private class MyLogisticRegression(override val uid: String) extends Classifier[Vector, MyLogisticRegression, MyLogisticRegressionModel] with MyLogisticRegressionParams { + def this() = this(Identifiable.randomUID("myLogReg")) + setMaxIter(100) // Initialize // The parameter setter is in this class since it should return type MyLogisticRegression. @@ -125,8 +128,10 @@ private class MyLogisticRegression val weights = Vectors.zeros(numFeatures) // Learning would happen here. // Create a model, and return it. - new MyLogisticRegressionModel(this, weights) + new MyLogisticRegressionModel(uid, weights).setParent(this) } + + override def copy(extra: ParamMap): MyLogisticRegression = defaultCopy(extra) } /** @@ -135,7 +140,7 @@ private class MyLogisticRegression * NOTE: This is private since it is an example. In practice, you may not want it to be private. */ private class MyLogisticRegressionModel( - override val parent: MyLogisticRegression, + override val uid: String, val weights: Vector) extends ClassificationModel[Vector, MyLogisticRegressionModel] with MyLogisticRegressionParams { @@ -173,6 +178,6 @@ private class MyLogisticRegressionModel( * This is used for the default implementation of [[transform()]]. */ override def copy(extra: ParamMap): MyLogisticRegressionModel = { - copyValues(new MyLogisticRegressionModel(parent, weights), extra) + copyValues(new MyLogisticRegressionModel(uid, weights), extra) } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala index c5899b6683c79..33905277c7341 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala @@ -111,7 +111,7 @@ object GBTExample { .text(s"input path to test dataset. If given, option fracTest is ignored." + s" default: ${defaultParams.testInput}") .action((x, c) => c.copy(testInput = x)) - opt[String]("") + opt[String]("dataFormat") .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") .action((x, c) => c.copy(dataFormat = x)) arg[String]("") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala new file mode 100644 index 0000000000000..b54466fd48bc5 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala @@ -0,0 +1,142 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import scala.collection.mutable +import scala.language.reflectiveCalls + +import scopt.OptionParser + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.examples.mllib.AbstractParams +import org.apache.spark.ml.{Pipeline, PipelineStage} +import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel} +import org.apache.spark.sql.DataFrame + +/** + * An example runner for linear regression with elastic-net (mixing L1/L2) regularization. + * Run with + * {{{ + * bin/run-example ml.LinearRegressionExample [options] + * }}} + * A synthetic dataset can be found at `data/mllib/sample_linear_regression_data.txt` which can be + * trained by + * {{{ + * bin/run-example ml.LinearRegressionExample --regParam 0.15 --elasticNetParam 1.0 \ + * data/mllib/sample_linear_regression_data.txt + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object LinearRegressionExample { + + case class Params( + input: String = null, + testInput: String = "", + dataFormat: String = "libsvm", + regParam: Double = 0.0, + elasticNetParam: Double = 0.0, + maxIter: Int = 100, + tol: Double = 1E-6, + fracTest: Double = 0.2) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("LinearRegressionExample") { + head("LinearRegressionExample: an example Linear Regression with Elastic-Net app.") + opt[Double]("regParam") + .text(s"regularization parameter, default: ${defaultParams.regParam}") + .action((x, c) => c.copy(regParam = x)) + opt[Double]("elasticNetParam") + .text(s"ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. " + + s"For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the penalty is a combination of " + + s"L1 and L2, default: ${defaultParams.elasticNetParam}") + .action((x, c) => c.copy(elasticNetParam = x)) + opt[Int]("maxIter") + .text(s"maximum number of iterations, default: ${defaultParams.maxIter}") + .action((x, c) => c.copy(maxIter = x)) + opt[Double]("tol") + .text(s"the convergence tolerance of iterations, Smaller value will lead " + + s"to higher accuracy with the cost of more iterations, default: ${defaultParams.tol}") + .action((x, c) => c.copy(tol = x)) + opt[Double]("fracTest") + .text(s"fraction of data to hold out for testing. If given option testInput, " + + s"this option is ignored. default: ${defaultParams.fracTest}") + .action((x, c) => c.copy(fracTest = x)) + opt[String]("testInput") + .text(s"input path to test dataset. If given, option fracTest is ignored." + + s" default: ${defaultParams.testInput}") + .action((x, c) => c.copy(testInput = x)) + opt[String]("dataFormat") + .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") + .action((x, c) => c.copy(dataFormat = x)) + arg[String]("") + .text("input path to labeled examples") + .required() + .action((x, c) => c.copy(input = x)) + checkConfig { params => + if (params.fracTest < 0 || params.fracTest >= 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1).") + } else { + success + } + } + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + def run(params: Params) { + val conf = new SparkConf().setAppName(s"LinearRegressionExample with $params") + val sc = new SparkContext(conf) + + println(s"LinearRegressionExample with parameters:\n$params") + + // Load training and test data and cache it. + val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input, + params.dataFormat, params.testInput, "regression", params.fracTest) + + val lir = new LinearRegression() + .setFeaturesCol("features") + .setLabelCol("label") + .setRegParam(params.regParam) + .setElasticNetParam(params.elasticNetParam) + .setMaxIter(params.maxIter) + .setTol(params.tol) + + // Train the model + val startTime = System.nanoTime() + val lirModel = lir.fit(training) + val elapsedTime = (System.nanoTime() - startTime) / 1e9 + println(s"Training time: $elapsedTime seconds") + + // Print the weights and intercept for linear regression. + println(s"Weights: ${lirModel.weights} Intercept: ${lirModel.intercept}") + + println("Training data results:") + DecisionTreeExample.evaluateRegressionModel(lirModel, training, "label") + println("Test data results:") + DecisionTreeExample.evaluateRegressionModel(lirModel, test, "label") + + sc.stop() + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala new file mode 100644 index 0000000000000..3cf193f353fbc --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala @@ -0,0 +1,159 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import scala.collection.mutable +import scala.language.reflectiveCalls + +import scopt.OptionParser + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.examples.mllib.AbstractParams +import org.apache.spark.ml.{Pipeline, PipelineStage} +import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel} +import org.apache.spark.ml.feature.StringIndexer +import org.apache.spark.sql.DataFrame + +/** + * An example runner for logistic regression with elastic-net (mixing L1/L2) regularization. + * Run with + * {{{ + * bin/run-example ml.LogisticRegressionExample [options] + * }}} + * A synthetic dataset can be found at `data/mllib/sample_libsvm_data.txt` which can be + * trained by + * {{{ + * bin/run-example ml.LogisticRegressionExample --regParam 0.3 --elasticNetParam 0.8 \ + * data/mllib/sample_libsvm_data.txt + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object LogisticRegressionExample { + + case class Params( + input: String = null, + testInput: String = "", + dataFormat: String = "libsvm", + regParam: Double = 0.0, + elasticNetParam: Double = 0.0, + maxIter: Int = 100, + fitIntercept: Boolean = true, + tol: Double = 1E-6, + fracTest: Double = 0.2) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("LogisticRegressionExample") { + head("LogisticRegressionExample: an example Logistic Regression with Elastic-Net app.") + opt[Double]("regParam") + .text(s"regularization parameter, default: ${defaultParams.regParam}") + .action((x, c) => c.copy(regParam = x)) + opt[Double]("elasticNetParam") + .text(s"ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. " + + s"For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the penalty is a combination of " + + s"L1 and L2, default: ${defaultParams.elasticNetParam}") + .action((x, c) => c.copy(elasticNetParam = x)) + opt[Int]("maxIter") + .text(s"maximum number of iterations, default: ${defaultParams.maxIter}") + .action((x, c) => c.copy(maxIter = x)) + opt[Boolean]("fitIntercept") + .text(s"whether to fit an intercept term, default: ${defaultParams.fitIntercept}") + .action((x, c) => c.copy(fitIntercept = x)) + opt[Double]("tol") + .text(s"the convergence tolerance of iterations, Smaller value will lead " + + s"to higher accuracy with the cost of more iterations, default: ${defaultParams.tol}") + .action((x, c) => c.copy(tol = x)) + opt[Double]("fracTest") + .text(s"fraction of data to hold out for testing. If given option testInput, " + + s"this option is ignored. default: ${defaultParams.fracTest}") + .action((x, c) => c.copy(fracTest = x)) + opt[String]("testInput") + .text(s"input path to test dataset. If given, option fracTest is ignored." + + s" default: ${defaultParams.testInput}") + .action((x, c) => c.copy(testInput = x)) + opt[String]("dataFormat") + .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") + .action((x, c) => c.copy(dataFormat = x)) + arg[String]("") + .text("input path to labeled examples") + .required() + .action((x, c) => c.copy(input = x)) + checkConfig { params => + if (params.fracTest < 0 || params.fracTest >= 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1).") + } else { + success + } + } + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + def run(params: Params) { + val conf = new SparkConf().setAppName(s"LogisticRegressionExample with $params") + val sc = new SparkContext(conf) + + println(s"LogisticRegressionExample with parameters:\n$params") + + // Load training and test data and cache it. + val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input, + params.dataFormat, params.testInput, "classification", params.fracTest) + + // Set up Pipeline + val stages = new mutable.ArrayBuffer[PipelineStage]() + + val labelIndexer = new StringIndexer() + .setInputCol("labelString") + .setOutputCol("indexedLabel") + stages += labelIndexer + + val lor = new LogisticRegression() + .setFeaturesCol("features") + .setLabelCol("indexedLabel") + .setRegParam(params.regParam) + .setElasticNetParam(params.elasticNetParam) + .setMaxIter(params.maxIter) + .setTol(params.tol) + + stages += lor + val pipeline = new Pipeline().setStages(stages.toArray) + + // Fit the Pipeline + val startTime = System.nanoTime() + val pipelineModel = pipeline.fit(training) + val elapsedTime = (System.nanoTime() - startTime) / 1e9 + println(s"Training time: $elapsedTime seconds") + + val lorModel = pipelineModel.stages.last.asInstanceOf[LogisticRegressionModel] + // Print the weights and intercept for logistic regression. + println(s"Weights: ${lorModel.weights} Intercept: ${lorModel.intercept}") + + println("Training data results:") + DecisionTreeExample.evaluateClassificationModel(pipelineModel, training, "indexedLabel") + println("Test data results:") + DecisionTreeExample.evaluateClassificationModel(pipelineModel, test, "indexedLabel") + + sc.stop() + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala new file mode 100644 index 0000000000000..6927eb8f275cf --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala @@ -0,0 +1,185 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import java.util.concurrent.TimeUnit.{NANOSECONDS => NANO} + +import scopt.OptionParser + +import org.apache.spark.{SparkContext, SparkConf} +import org.apache.spark.examples.mllib.AbstractParams +import org.apache.spark.ml.classification.{OneVsRest, LogisticRegression} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.evaluation.MulticlassMetrics +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLContext + +/** + * An example runner for Multiclass to Binary Reduction with One Vs Rest. + * The example uses Logistic Regression as the base classifier. All parameters that + * can be specified on the base classifier can be passed in to the runner options. + * Run with + * {{{ + * ./bin/run-example ml.OneVsRestExample [options] + * }}} + * For local mode, run + * {{{ + * ./bin/spark-submit --class org.apache.spark.examples.ml.OneVsRestExample --driver-memory 1g + * [examples JAR path] [options] + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object OneVsRestExample { + + case class Params private[ml] ( + input: String = null, + testInput: Option[String] = None, + maxIter: Int = 100, + tol: Double = 1E-6, + fitIntercept: Boolean = true, + regParam: Option[Double] = None, + elasticNetParam: Option[Double] = None, + fracTest: Double = 0.2) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("OneVsRest Example") { + head("OneVsRest Example: multiclass to binary reduction using OneVsRest") + opt[String]("input") + .text("input path to labeled examples. This path must be specified") + .required() + .action((x, c) => c.copy(input = x)) + opt[Double]("fracTest") + .text(s"fraction of data to hold out for testing. If given option testInput, " + + s"this option is ignored. default: ${defaultParams.fracTest}") + .action((x, c) => c.copy(fracTest = x)) + opt[String]("testInput") + .text("input path to test dataset. If given, option fracTest is ignored") + .action((x, c) => c.copy(testInput = Some(x))) + opt[Int]("maxIter") + .text(s"maximum number of iterations for Logistic Regression." + + s" default: ${defaultParams.maxIter}") + .action((x, c) => c.copy(maxIter = x)) + opt[Double]("tol") + .text(s"the convergence tolerance of iterations for Logistic Regression." + + s" default: ${defaultParams.tol}") + .action((x, c) => c.copy(tol = x)) + opt[Boolean]("fitIntercept") + .text(s"fit intercept for Logistic Regression." + + s" default: ${defaultParams.fitIntercept}") + .action((x, c) => c.copy(fitIntercept = x)) + opt[Double]("regParam") + .text(s"the regularization parameter for Logistic Regression.") + .action((x, c) => c.copy(regParam = Some(x))) + opt[Double]("elasticNetParam") + .text(s"the ElasticNet mixing parameter for Logistic Regression.") + .action((x, c) => c.copy(elasticNetParam = Some(x))) + checkConfig { params => + if (params.fracTest < 0 || params.fracTest >= 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1).") + } else { + success + } + } + } + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + private def run(params: Params) { + val conf = new SparkConf().setAppName(s"OneVsRestExample with $params") + val sc = new SparkContext(conf) + val inputData = MLUtils.loadLibSVMFile(sc, params.input) + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // compute the train/test split: if testInput is not provided use part of input. + val data = params.testInput match { + case Some(t) => { + // compute the number of features in the training set. + val numFeatures = inputData.first().features.size + val testData = MLUtils.loadLibSVMFile(sc, t, numFeatures) + Array[RDD[LabeledPoint]](inputData, testData) + } + case None => { + val f = params.fracTest + inputData.randomSplit(Array(1 - f, f), seed = 12345) + } + } + val Array(train, test) = data.map(_.toDF().cache()) + + // instantiate the base classifier + val classifier = new LogisticRegression() + .setMaxIter(params.maxIter) + .setTol(params.tol) + .setFitIntercept(params.fitIntercept) + + // Set regParam, elasticNetParam if specified in params + params.regParam.foreach(classifier.setRegParam) + params.elasticNetParam.foreach(classifier.setElasticNetParam) + + // instantiate the One Vs Rest Classifier. + + val ovr = new OneVsRest() + ovr.setClassifier(classifier) + + // train the multiclass model. + val (trainingDuration, ovrModel) = time(ovr.fit(train)) + + // score the model on test data. + val (predictionDuration, predictions) = time(ovrModel.transform(test)) + + // evaluate the model + val predictionsAndLabels = predictions.select("prediction", "label") + .map(row => (row.getDouble(0), row.getDouble(1))) + + val metrics = new MulticlassMetrics(predictionsAndLabels) + + val confusionMatrix = metrics.confusionMatrix + + // compute the false positive rate per label + val predictionColSchema = predictions.schema("prediction") + val numClasses = MetadataUtils.getNumClasses(predictionColSchema).get + val fprs = Range(0, numClasses).map(p => (p, metrics.falsePositiveRate(p.toDouble))) + + println(s" Training Time ${trainingDuration} sec\n") + + println(s" Prediction Time ${predictionDuration} sec\n") + + println(s" Confusion Matrix\n ${confusionMatrix.toString}\n") + + println("label\tfpr") + + println(fprs.map {case (label, fpr) => label + "\t" + fpr}.mkString("\n")) + + sc.stop() + } + + private def time[R](block: => R): (Long, R) = { + val t0 = System.nanoTime() + val result = block // call-by-name + val t1 = System.nanoTime() + (NANO.toSeconds(t1 - t0), result) + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala index 7f88d2681bcaa..9f7cad68a4594 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala @@ -117,7 +117,7 @@ object RandomForestExample { .text(s"input path to test dataset. If given, option fracTest is ignored." + s" default: ${defaultParams.testInput}") .action((x, c) => c.copy(testInput = x)) - opt[String]("") + opt[String]("dataFormat") .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") .action((x, c) => c.copy(dataFormat = x)) arg[String]("") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala index e8a991f50e338..a0561e2573fc9 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala @@ -87,7 +87,7 @@ object SimpleParamsExample { LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)))) // Make predictions on test data using the Transformer.transform() method. - // LogisticRegression.transform will only use the 'features' column. + // LogisticRegressionModel.transform will only use the 'features' column. // Note that model2.transform() outputs a 'myProbability' column instead of the usual // 'probability' column since we renamed the lr.probabilityCol parameter previously. model2.transform(test.toDF()) diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala index 6772efd2c581c..1324b066c30c3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala @@ -64,7 +64,7 @@ object SimpleTextClassificationPipeline { .setOutputCol("features") val lr = new LogisticRegression() .setMaxIter(10) - .setRegParam(0.01) + .setRegParam(0.001) val pipeline = new Pipeline() .setStages(Array(tokenizer, hashingTF, lr)) @@ -75,7 +75,7 @@ object SimpleTextClassificationPipeline { val test = sc.parallelize(Seq( Document(4L, "spark i j k"), Document(5L, "l m n"), - Document(6L, "mapreduce spark"), + Document(6L, "spark hadoop spark"), Document(7L, "apache hadoop"))) // Make predictions on test documents. diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala index e943d6c889fab..520893b26d595 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala @@ -103,10 +103,10 @@ object DatasetExample { tmpDir.deleteOnExit() val outputDir = new File(tmpDir, "dataset").toString println(s"Saving to $outputDir as Parquet file.") - df.saveAsParquetFile(outputDir) + df.write.parquet(outputDir) println(s"Loading Parquet file with UDT from $outputDir.") - val newDataset = sqlContext.parquetFile(outputDir) + val newDataset = sqlContext.read.parquet(outputDir) println(s"Schema from Parquet: ${newDataset.schema.prettyJson}") val newFeatures = newDataset.select("features").map { case Row(v: Vector) => v } diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala index 262fd2c9611d0..3381941673db8 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala @@ -22,7 +22,6 @@ import scala.language.reflectiveCalls import scopt.OptionParser import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.SparkContext._ import org.apache.spark.mllib.evaluation.MulticlassMetrics import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint @@ -126,7 +125,7 @@ object DecisionTreeRunner { .text(s"input path to test dataset. If given, option fracTest is ignored." + s" default: ${defaultParams.testInput}") .action((x, c) => c.copy(testInput = x)) - opt[String]("") + opt[String]("dataFormat") .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") .action((x, c) => c.copy(dataFormat = x)) arg[String]("") @@ -354,7 +353,11 @@ object DecisionTreeRunner { /** * Calculates the mean squared error for regression. + * + * This is just for demo purpose. In general, don't copy this code because it is NOT efficient + * due to the use of structural types, which leads to one reflection call per record. */ + // scalastyle:off structural.type private[mllib] def meanSquaredError( model: { def predict(features: Vector): Double }, data: RDD[LabeledPoint]): Double = { @@ -363,4 +366,5 @@ object DecisionTreeRunner { err * err }.mean() } + // scalastyle:on structural.type } diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala index df76b45e50810..f8c71ccabc43b 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala @@ -40,23 +40,23 @@ object DenseGaussianMixture { private def run(inputFile: String, k: Int, convergenceTol: Double, maxIterations: Int) { val conf = new SparkConf().setAppName("Gaussian Mixture Model EM example") - val ctx = new SparkContext(conf) - + val ctx = new SparkContext(conf) + val data = ctx.textFile(inputFile).map { line => Vectors.dense(line.trim.split(' ').map(_.toDouble)) }.cache() - + val clusters = new GaussianMixture() .setK(k) .setConvergenceTol(convergenceTol) .setMaxIterations(maxIterations) .run(data) - + for (i <- 0 until clusters.k) { - println("weight=%f\nmu=%s\nsigma=\n%s\n" format + println("weight=%f\nmu=%s\nsigma=\n%s\n" format (clusters.weights(i), clusters.gaussians(i).mu, clusters.gaussians(i).sigma)) } - + println("Cluster labels (first <= 100):") val clusterLabels = clusters.predict(data) clusterLabels.take(100).foreach { x => diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala index 0763a7736305a..7416fb5a40848 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala @@ -69,7 +69,7 @@ object GradientBoostedTreesRunner { .text(s"input path to test dataset. If given, option fracTest is ignored." + s" default: ${defaultParams.testInput}") .action((x, c) => c.copy(testInput = x)) - opt[String]("") + opt[String]("dataFormat") .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") .action((x, c) => c.copy(dataFormat = x)) arg[String]("") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala index a1850390c0a86..31d629f853161 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala @@ -26,7 +26,7 @@ import scopt.OptionParser import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkContext, SparkConf} -import org.apache.spark.mllib.clustering.{DistributedLDAModel, LDA} +import org.apache.spark.mllib.clustering.{EMLDAOptimizer, OnlineLDAOptimizer, DistributedLDAModel, LDA} import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.rdd.RDD @@ -48,6 +48,7 @@ object LDAExample { topicConcentration: Double = -1, vocabSize: Int = 10000, stopwordFile: String = "", + algorithm: String = "em", checkpointDir: Option[String] = None, checkpointInterval: Int = 10) extends AbstractParams[Params] @@ -78,6 +79,10 @@ object LDAExample { .text(s"filepath for a list of stopwords. Note: This must fit on a single machine." + s" default: ${defaultParams.stopwordFile}") .action((x, c) => c.copy(stopwordFile = x)) + opt[String]("algorithm") + .text(s"inference algorithm to use. em and online are supported." + + s" default: ${defaultParams.algorithm}") + .action((x, c) => c.copy(algorithm = x)) opt[String]("checkpointDir") .text(s"Directory for checkpointing intermediate results." + s" Checkpointing helps with recovery and eliminates temporary shuffle files on disk." + @@ -128,7 +133,17 @@ object LDAExample { // Run LDA. val lda = new LDA() - lda.setK(params.k) + + val optimizer = params.algorithm.toLowerCase match { + case "em" => new EMLDAOptimizer + // add (1.0 / actualCorpusSize) to MiniBatchFraction be more robust on tiny datasets. + case "online" => new OnlineLDAOptimizer().setMiniBatchFraction(0.05 + 1.0 / actualCorpusSize) + case _ => throw new IllegalArgumentException( + s"Only em, online are supported but got ${params.algorithm}.") + } + + lda.setOptimizer(optimizer) + .setK(params.k) .setMaxIterations(params.maxIterations) .setDocConcentration(params.docConcentration) .setTopicConcentration(params.topicConcentration) @@ -137,14 +152,18 @@ object LDAExample { sc.setCheckpointDir(params.checkpointDir.get) } val startTime = System.nanoTime() - val ldaModel = lda.run(corpus).asInstanceOf[DistributedLDAModel] + val ldaModel = lda.run(corpus) val elapsed = (System.nanoTime() - startTime) / 1e9 println(s"Finished training LDA model. Summary:") println(s"\t Training time: $elapsed sec") - val avgLogLikelihood = ldaModel.logLikelihood / actualCorpusSize.toDouble - println(s"\t Training data average log likelihood: $avgLogLikelihood") - println() + + if (ldaModel.isInstanceOf[DistributedLDAModel]) { + val distLDAModel = ldaModel.asInstanceOf[DistributedLDAModel] + val avgLogLikelihood = distLDAModel.logLikelihood / actualCorpusSize.toDouble + println(s"\t Training data average log likelihood: $avgLogLikelihood") + println() + } // Print the topics, showing the top-weighted terms for each topic. val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 10) diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala index a11890d6f2b1c..3ebb112fc069e 100644 --- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala +++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala @@ -36,22 +36,21 @@ object AvroConversionUtil extends Serializable { return null } schema.getType match { - case UNION => unpackUnion(obj, schema) - case ARRAY => unpackArray(obj, schema) - case FIXED => unpackFixed(obj, schema) - case MAP => unpackMap(obj, schema) - case BYTES => unpackBytes(obj) - case RECORD => unpackRecord(obj) - case STRING => obj.toString - case ENUM => obj.toString - case NULL => obj + case UNION => unpackUnion(obj, schema) + case ARRAY => unpackArray(obj, schema) + case FIXED => unpackFixed(obj, schema) + case MAP => unpackMap(obj, schema) + case BYTES => unpackBytes(obj) + case RECORD => unpackRecord(obj) + case STRING => obj.toString + case ENUM => obj.toString + case NULL => obj case BOOLEAN => obj - case DOUBLE => obj - case FLOAT => obj - case INT => obj - case LONG => obj - case other => throw new SparkException( - s"Unknown Avro schema type ${other.getName}") + case DOUBLE => obj + case FLOAT => obj + case INT => obj + case LONG => obj + case other => throw new SparkException(s"Unknown Avro schema type ${other.getName}") } } diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala index 273bee0a8b30f..90d48a64106c7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala +++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala @@ -18,20 +18,34 @@ package org.apache.spark.examples.pythonconverters import scala.collection.JavaConversions._ +import scala.util.parsing.json.JSONObject import org.apache.spark.api.python.Converter import org.apache.hadoop.hbase.client.{Put, Result} import org.apache.hadoop.hbase.io.ImmutableBytesWritable import org.apache.hadoop.hbase.util.Bytes +import org.apache.hadoop.hbase.KeyValue.Type +import org.apache.hadoop.hbase.CellUtil /** - * Implementation of [[org.apache.spark.api.python.Converter]] that converts an - * HBase Result to a String + * Implementation of [[org.apache.spark.api.python.Converter]] that converts all + * the records in an HBase Result to a String */ class HBaseResultToStringConverter extends Converter[Any, String] { override def convert(obj: Any): String = { + import collection.JavaConverters._ val result = obj.asInstanceOf[Result] - Bytes.toStringBinary(result.value()) + val output = result.listCells.asScala.map(cell => + Map( + "row" -> Bytes.toStringBinary(CellUtil.cloneRow(cell)), + "columnFamily" -> Bytes.toStringBinary(CellUtil.cloneFamily(cell)), + "qualifier" -> Bytes.toStringBinary(CellUtil.cloneQualifier(cell)), + "timestamp" -> cell.getTimestamp.toString, + "type" -> Type.codeToType(cell.getTypeByte).toString, + "value" -> Bytes.toStringBinary(CellUtil.cloneValue(cell)) + ) + ) + output.map(JSONObject(_).toString()).mkString("\n") } } diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala index 6331d1c0060f8..b11e32047dc34 100644 --- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala +++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala @@ -58,10 +58,10 @@ object RDDRelation { df.where($"key" === 1).orderBy($"value".asc).select($"key").collect().foreach(println) // Write out an RDD as a parquet file. - df.saveAsParquetFile("pair.parquet") + df.write.parquet("pair.parquet") // Read in parquet file. Parquet files are self-describing so the schmema is preserved. - val parquetFile = sqlContext.parquetFile("pair.parquet") + val parquetFile = sqlContext.read.parquet("pair.parquet") // Queries can be run using the DSL on parequet files just like the original RDD. parquetFile.where($"key" === 1).select($"value".as("a")).collect().foreach(println) diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala index 92867b44be138..016de4c63d1d2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala @@ -104,10 +104,8 @@ extends Actor with ActorHelper { object FeederActor { def main(args: Array[String]) { - if(args.length < 2){ - System.err.println( - "Usage: FeederActor \n" - ) + if (args.length < 2){ + System.err.println("Usage: FeederActor \n") System.exit(1) } val Seq(host, port) = args.toSeq diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala similarity index 97% rename from examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala rename to examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala index 11a8cf09533ce..fbe394de4a179 100644 --- a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala @@ -51,7 +51,7 @@ object DirectKafkaWordCount { // Create context with 2 second batch interval val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount") - val ssc = new StreamingContext(sparkConf, Seconds(2)) + val ssc = new StreamingContext(sparkConf, Seconds(2)) // Create direct kafka stream with brokers and topics val topicsSet = topics.split(",").toSet diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala similarity index 95% rename from examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala rename to examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala index f407367a54f6c..60416ee343544 100644 --- a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala @@ -49,10 +49,10 @@ object KafkaWordCount { val Array(zkQuorum, group, topics, numThreads) = args val sparkConf = new SparkConf().setAppName("KafkaWordCount") - val ssc = new StreamingContext(sparkConf, Seconds(2)) + val ssc = new StreamingContext(sparkConf, Seconds(2)) ssc.checkpoint("checkpoint") - val topicMap = topics.split(",").map((_,numThreads.toInt)).toMap + val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1L)) @@ -96,7 +96,7 @@ object KafkaWordCountProducer { producer.send(message) } - Thread.sleep(100) + Thread.sleep(1000) } } diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala index 85b9a54b40baf..813c8554f5193 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala @@ -40,7 +40,7 @@ object MQTTPublisher { StreamingExamples.setStreamingLogLevels() val Seq(brokerUrl, topic) = args.toSeq - + var client: MqttClient = null try { @@ -49,7 +49,7 @@ object MQTTPublisher { client.connect() - val msgtopic = client.getTopic(topic) + val msgtopic = client.getTopic(topic) val msgContent = "hello mqtt demo for spark streaming" val message = new MqttMessage(msgContent.getBytes("utf-8")) @@ -59,10 +59,10 @@ object MQTTPublisher { println(s"Published data. topic: ${msgtopic.getName()}; Message: $message") } catch { case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT => - Thread.sleep(10) + Thread.sleep(10) println("Queue is full, wait for to consume data from the message queue") - } - } + } + } } catch { case e: MqttException => println("Exception Caught: " + e) } finally { @@ -107,7 +107,7 @@ object MQTTWordCount { val lines = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2) val words = lines.flatMap(x => x.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) - + wordCounts.print() ssc.start() ssc.awaitTermination() diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala index 54d996b8ac990..889f052c70263 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala @@ -57,8 +57,7 @@ object PageViewGenerator { 404 -> .05) val userZipCode = Map(94709 -> .5, 94117 -> .5) - val userID = Map((1 to 100).map(_ -> .01):_*) - + val userID = Map((1 to 100).map(_ -> .01) : _*) def pickFromDistribution[T](inputMap : Map[T, Double]) : T = { val rand = new Random().nextDouble() diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml index 1f3e619d97a24..7a7dccc3d0922 100644 --- a/external/flume-sink/pom.xml +++ b/external/flume-sink/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -42,15 +42,46 @@ org.apache.flume flume-ng-sdk + + + + com.google.guava + guava + + + + org.apache.thrift + libthrift + + org.apache.flume flume-ng-core + + + com.google.guava + guava + + + org.apache.thrift + libthrift + + org.scala-lang scala-library + + + com.google.guava + guava + test + + + + diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala index fd01807fc3ac4..dc2a4ab138e18 100644 --- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala +++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala @@ -21,7 +21,6 @@ import java.util.concurrent.atomic.AtomicLong import scala.collection.mutable -import com.google.common.util.concurrent.ThreadFactoryBuilder import org.apache.flume.Channel import org.apache.commons.lang3.RandomStringUtils @@ -45,8 +44,7 @@ import org.apache.commons.lang3.RandomStringUtils private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Channel, val transactionTimeout: Int, val backOffInterval: Int) extends SparkFlumeProtocol with Logging { val transactionExecutorOpt = Option(Executors.newFixedThreadPool(threads, - new ThreadFactoryBuilder().setDaemon(true) - .setNameFormat("Spark Sink Processor Thread - %d").build())) + new SparkSinkThreadFactory("Spark Sink Processor Thread - %d"))) // Protected by `sequenceNumberToProcessor` private val sequenceNumberToProcessor = mutable.HashMap[CharSequence, TransactionProcessor]() // This sink will not persist sequence numbers and reuses them if it gets restarted. diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSinkThreadFactory.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSinkThreadFactory.scala new file mode 100644 index 0000000000000..845fc8debda75 --- /dev/null +++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSinkThreadFactory.scala @@ -0,0 +1,35 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.streaming.flume.sink + +import java.util.concurrent.ThreadFactory +import java.util.concurrent.atomic.AtomicLong + +/** + * Thread factory that generates daemon threads with a specified name format. + */ +private[sink] class SparkSinkThreadFactory(nameFormat: String) extends ThreadFactory { + + private val threadId = new AtomicLong() + + override def newThread(r: Runnable): Thread = { + val t = new Thread(r, nameFormat.format(threadId.incrementAndGet())) + t.setDaemon(true) + t + } + +} diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala index ea45b14294df9..7ad43b1d7b0a0 100644 --- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala +++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala @@ -143,7 +143,7 @@ private class TransactionProcessor(val channel: Channel, val seqNum: String, eventBatch.setErrorMsg(msg) } else { // At this point, the events are available, so fill them into the event batch - eventBatch = new EventBatch("",seqNum, events) + eventBatch = new EventBatch("", seqNum, events) } }) } catch { diff --git a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala index 650b2fbe1c142..fa43629d49771 100644 --- a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala +++ b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala @@ -24,16 +24,24 @@ import scala.collection.JavaConversions._ import scala.concurrent.{ExecutionContext, Future} import scala.util.{Failure, Success} -import com.google.common.util.concurrent.ThreadFactoryBuilder import org.apache.avro.ipc.NettyTransceiver import org.apache.avro.ipc.specific.SpecificRequestor import org.apache.flume.Context import org.apache.flume.channel.MemoryChannel import org.apache.flume.event.EventBuilder import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory + +// Due to MNG-1378, there is not a way to include test dependencies transitively. +// We cannot include Spark core tests as a dependency here because it depends on +// Spark core main, which has too many dependencies to require here manually. +// For this reason, we continue to use FunSuite and ignore the scalastyle checks +// that fail if this is detected. +//scalastyle:off import org.scalatest.FunSuite class SparkSinkSuite extends FunSuite { +//scalastyle:on + val eventsPerBatch = 1000 val channelCapacity = 5000 @@ -185,9 +193,8 @@ class SparkSinkSuite extends FunSuite { count: Int): Seq[(NettyTransceiver, SparkFlumeProtocol.Callback)] = { (1 to count).map(_ => { - lazy val channelFactoryExecutor = - Executors.newCachedThreadPool(new ThreadFactoryBuilder().setDaemon(true). - setNameFormat("Flume Receiver Channel Thread - %d").build()) + lazy val channelFactoryExecutor = Executors.newCachedThreadPool( + new SparkSinkThreadFactory("Flume Receiver Channel Thread - %d")) lazy val channelFactory = new NioClientSocketChannelFactory(channelFactoryExecutor, channelFactoryExecutor) val transceiver = new NettyTransceiver(address, channelFactory) diff --git a/external/flume/pom.xml b/external/flume/pom.xml index 8df7edbdcad33..14f7daaf417e0 100644 --- a/external/flume/pom.xml +++ b/external/flume/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ ${project.version} provided + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.spark spark-streaming-flume-sink_${scala.binary.version} diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala index dc629df4f4ac2..65c49c131518b 100644 --- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala @@ -60,7 +60,7 @@ private[streaming] object EventTransformer extends Logging { out.write(body) val numHeaders = headers.size() out.writeInt(numHeaders) - for ((k,v) <- headers) { + for ((k, v) <- headers) { val keyBuff = Utils.serialize(k.toString) out.writeInt(keyBuff.length) out.write(keyBuff) diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala index 60e2994431b38..1e32a365a1eee 100644 --- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala @@ -152,9 +152,9 @@ class FlumeReceiver( val channelFactory = new NioServerSocketChannelFactory(Executors.newCachedThreadPool(), Executors.newCachedThreadPool()) val channelPipelineFactory = new CompressionChannelPipelineFactory() - + new NettyServer( - responder, + responder, new InetSocketAddress(host, port), channelFactory, channelPipelineFactory, @@ -188,12 +188,12 @@ class FlumeReceiver( override def preferredLocation: Option[String] = Option(host) - /** A Netty Pipeline factory that will decompress incoming data from + /** A Netty Pipeline factory that will decompress incoming data from * and the Netty client and compress data going back to the client. * * The compression on the return is required because Flume requires - * a successful response to indicate it can remove the event/batch - * from the configured channel + * a successful response to indicate it can remove the event/batch + * from the configured channel */ private[streaming] class CompressionChannelPipelineFactory extends ChannelPipelineFactory { diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala index 92fa5b41be89e..583e7dca317ad 100644 --- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala @@ -110,7 +110,7 @@ private[streaming] class FlumePollingReceiver( } /** - * A wrapper around the transceiver and the Avro IPC API. + * A wrapper around the transceiver and the Avro IPC API. * @param transceiver The transceiver to use for communication with Flume * @param client The client that the callbacks are received on. */ diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala index 43c1b865b64a1..d772b9ca9b570 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala @@ -18,26 +18,29 @@ package org.apache.spark.streaming.flume import java.net.InetSocketAddress -import java.util.concurrent.{Callable, ExecutorCompletionService, Executors} +import java.util.concurrent._ import scala.collection.JavaConversions._ import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer} +import scala.concurrent.duration._ +import scala.language.postfixOps import org.apache.flume.Context import org.apache.flume.channel.MemoryChannel import org.apache.flume.conf.Configurables import org.apache.flume.event.EventBuilder +import org.scalatest.concurrent.Eventually._ -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter -import org.apache.spark.{SparkConf, Logging} +import org.apache.spark.{Logging, SparkConf, SparkFunSuite} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.dstream.ReceiverInputDStream import org.apache.spark.streaming.{Seconds, TestOutputStream, StreamingContext} import org.apache.spark.streaming.flume.sink._ import org.apache.spark.util.{ManualClock, Utils} -class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging { +class FlumePollingStreamSuite extends SparkFunSuite with BeforeAndAfter with Logging { val batchCount = 5 val eventsPerBatch = 100 @@ -57,11 +60,11 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging before(beforeFunction()) - ignore("flume polling test") { + test("flume polling test") { testMultipleTimes(testFlumePolling) } - ignore("flume polling test multiple hosts") { + test("flume polling test multiple hosts") { testMultipleTimes(testFlumePollingMultipleHost) } @@ -100,18 +103,8 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging Configurables.configure(sink, context) sink.setChannel(channel) sink.start() - // Set up the streaming context and input streams - val ssc = new StreamingContext(conf, batchDuration) - val flumeStream: ReceiverInputDStream[SparkFlumeEvent] = - FlumeUtils.createPollingStream(ssc, Seq(new InetSocketAddress("localhost", sink.getPort())), - StorageLevel.MEMORY_AND_DISK, eventsPerBatch, 1) - val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]] - with SynchronizedBuffer[Seq[SparkFlumeEvent]] - val outputStream = new TestOutputStream(flumeStream, outputBuffer) - outputStream.register() - ssc.start() - writeAndVerify(Seq(channel), ssc, outputBuffer) + writeAndVerify(Seq(sink), Seq(channel)) assertChannelIsEmpty(channel) sink.stop() channel.stop() @@ -142,10 +135,22 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging Configurables.configure(sink2, context) sink2.setChannel(channel2) sink2.start() + try { + writeAndVerify(Seq(sink, sink2), Seq(channel, channel2)) + assertChannelIsEmpty(channel) + assertChannelIsEmpty(channel2) + } finally { + sink.stop() + sink2.stop() + channel.stop() + channel2.stop() + } + } + def writeAndVerify(sinks: Seq[SparkSink], channels: Seq[MemoryChannel]) { // Set up the streaming context and input streams val ssc = new StreamingContext(conf, batchDuration) - val addresses = Seq(sink.getPort(), sink2.getPort()).map(new InetSocketAddress("localhost", _)) + val addresses = sinks.map(sink => new InetSocketAddress("localhost", sink.getPort())) val flumeStream: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createPollingStream(ssc, addresses, StorageLevel.MEMORY_AND_DISK, eventsPerBatch, 5) @@ -155,61 +160,49 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging outputStream.register() ssc.start() - try { - writeAndVerify(Seq(channel, channel2), ssc, outputBuffer) - assertChannelIsEmpty(channel) - assertChannelIsEmpty(channel2) - } finally { - sink.stop() - sink2.stop() - channel.stop() - channel2.stop() - } - } - - def writeAndVerify(channels: Seq[MemoryChannel], ssc: StreamingContext, - outputBuffer: ArrayBuffer[Seq[SparkFlumeEvent]]) { val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] val executor = Executors.newCachedThreadPool() val executorCompletion = new ExecutorCompletionService[Void](executor) - channels.map(channel => { + + val latch = new CountDownLatch(batchCount * channels.size) + sinks.foreach(_.countdownWhenBatchReceived(latch)) + + channels.foreach(channel => { executorCompletion.submit(new TxnSubmitter(channel, clock)) }) + for (i <- 0 until channels.size) { executorCompletion.take() } - val startTime = System.currentTimeMillis() - while (outputBuffer.size < batchCount * channels.size && - System.currentTimeMillis() - startTime < 15000) { - logInfo("output.size = " + outputBuffer.size) - Thread.sleep(100) - } - val timeTaken = System.currentTimeMillis() - startTime - assert(timeTaken < 15000, "Operation timed out after " + timeTaken + " ms") - logInfo("Stopping context") - ssc.stop() - val flattenedBuffer = outputBuffer.flatten - assert(flattenedBuffer.size === totalEventsPerChannel * channels.size) - var counter = 0 - for (k <- 0 until channels.size; i <- 0 until totalEventsPerChannel) { - val eventToVerify = EventBuilder.withBody((channels(k).getName + " - " + - String.valueOf(i)).getBytes("utf-8"), - Map[String, String]("test-" + i.toString -> "header")) - var found = false - var j = 0 - while (j < flattenedBuffer.size && !found) { - val strToCompare = new String(flattenedBuffer(j).event.getBody.array(), "utf-8") - if (new String(eventToVerify.getBody, "utf-8") == strToCompare && - eventToVerify.getHeaders.get("test-" + i.toString) - .equals(flattenedBuffer(j).event.getHeaders.get("test-" + i.toString))) { - found = true - counter += 1 + latch.await(15, TimeUnit.SECONDS) // Ensure all data has been received. + clock.advance(batchDuration.milliseconds) + + // The eventually is required to ensure that all data in the batch has been processed. + eventually(timeout(10 seconds), interval(100 milliseconds)) { + val flattenedBuffer = outputBuffer.flatten + assert(flattenedBuffer.size === totalEventsPerChannel * channels.size) + var counter = 0 + for (k <- 0 until channels.size; i <- 0 until totalEventsPerChannel) { + val eventToVerify = EventBuilder.withBody((channels(k).getName + " - " + + String.valueOf(i)).getBytes("utf-8"), + Map[String, String]("test-" + i.toString -> "header")) + var found = false + var j = 0 + while (j < flattenedBuffer.size && !found) { + val strToCompare = new String(flattenedBuffer(j).event.getBody.array(), "utf-8") + if (new String(eventToVerify.getBody, "utf-8") == strToCompare && + eventToVerify.getHeaders.get("test-" + i.toString) + .equals(flattenedBuffer(j).event.getHeaders.get("test-" + i.toString))) { + found = true + counter += 1 + } + j += 1 } - j += 1 } + assert(counter === totalEventsPerChannel * channels.size) } - assert(counter === totalEventsPerChannel * channels.size) + ssc.stop() } def assertChannelIsEmpty(channel: MemoryChannel): Unit = { @@ -234,7 +227,6 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging tx.commit() tx.close() Thread.sleep(500) // Allow some time for the events to reach - clock.advance(batchDuration.milliseconds) } null } diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala index 39e6754c81dbf..c926359987d89 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala @@ -35,15 +35,15 @@ import org.jboss.netty.channel.ChannelPipeline import org.jboss.netty.channel.socket.SocketChannel import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory import org.jboss.netty.handler.codec.compression._ -import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} +import org.scalatest.{BeforeAndAfter, Matchers} import org.scalatest.concurrent.Eventually._ -import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.{Logging, SparkConf, SparkFunSuite} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext, TestOutputStream} import org.apache.spark.util.Utils -class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { +class FlumeStreamSuite extends SparkFunSuite with BeforeAndAfter with Matchers with Logging { val conf = new SparkConf().setMaster("local[4]").setAppName("FlumeStreamSuite") var ssc: StreamingContext = null @@ -138,7 +138,7 @@ class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with L val status = client.appendBatch(inputEvents.toList) status should be (avro.Status.OK) } - + eventually(timeout(10 seconds), interval(100 milliseconds)) { val outputEvents = outputBuffer.flatten.map { _.event } outputEvents.foreach { diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml index 0b79f47647f6b..8059c443827ef 100644 --- a/external/kafka-assembly/pom.xml +++ b/external/kafka-assembly/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml index 243ce6eaca658..ded863bd985e8 100644 --- a/external/kafka/pom.xml +++ b/external/kafka/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ ${project.version} provided + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.kafka kafka_${scala.binary.version} diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala index 6715aede7928a..876456c964770 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala @@ -65,6 +65,9 @@ class DirectKafkaInputDStream[ val maxRetries = context.sparkContext.getConf.getInt( "spark.streaming.kafka.maxRetries", 1) + // Keep this consistent with how other streams are named (e.g. "Flume polling stream [2]") + private[streaming] override def name: String = s"Kafka direct stream [$id]" + protected[streaming] override val checkpointData = new DirectKafkaInputDStreamCheckpointData @@ -117,8 +120,7 @@ class DirectKafkaInputDStream[ context.sparkContext, kafkaParams, currentOffsets, untilOffsets, messageHandler) // Report the record number of this batch interval to InputInfoTracker. - val numRecords = rdd.offsetRanges.map(r => r.untilOffset - r.fromOffset).sum - val inputInfo = InputInfo(id, numRecords) + val inputInfo = InputInfo(id, rdd.count) ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) currentOffsets = untilOffsets.map(kv => kv._1 -> kv._2.offset) @@ -150,10 +152,7 @@ class DirectKafkaInputDStream[ override def restore() { // this is assuming that the topics don't change during execution, which is true currently val topics = fromOffsets.keySet - val leaders = kc.findLeaders(topics).fold( - errs => throw new SparkException(errs.mkString("\n")), - ok => ok - ) + val leaders = KafkaCluster.checkErrors(kc.findLeaders(topics)) batchForTime.toSeq.sortBy(_._1)(Time.ordering).foreach { case (t, b) => logInfo(s"Restoring KafkaRDD for time $t ${b.mkString("[", ", ", "]")}") diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala index 6cf254a7b69cb..3e6b937af57b0 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala @@ -113,7 +113,7 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { r.flatMap { tm: TopicMetadata => tm.partitionsMetadata.map { pm: PartitionMetadata => TopicAndPartition(tm.topic, pm.partitionId) - } + } } } } @@ -360,6 +360,14 @@ private[spark] object KafkaCluster { type Err = ArrayBuffer[Throwable] + /** If the result is right, return it, otherwise throw SparkException */ + def checkErrors[T](result: Either[Err, T]): T = { + result.fold( + errs => throw new SparkException(errs.mkString("\n")), + ok => ok + ) + } + private[spark] case class LeaderOffset(host: String, port: Int, offset: Long) diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala index cca0fac0234e1..04b2dc10d39ea 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala @@ -135,7 +135,7 @@ class KafkaReceiver[ store((msgAndMetadata.key, msgAndMetadata.message)) } } catch { - case e: Throwable => logError("Error handling message; exiting", e) + case e: Throwable => reportError("Error handling message; exiting", e) } } } diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala index a1b4a12e5d6a0..c5cd2154772ac 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala @@ -17,9 +17,11 @@ package org.apache.spark.streaming.kafka +import scala.collection.mutable.ArrayBuffer import scala.reflect.{classTag, ClassTag} import org.apache.spark.{Logging, Partition, SparkContext, SparkException, TaskContext} +import org.apache.spark.partial.{PartialResult, BoundedDouble} import org.apache.spark.rdd.RDD import org.apache.spark.util.NextIterator @@ -60,6 +62,48 @@ class KafkaRDD[ }.toArray } + override def count(): Long = offsetRanges.map(_.count).sum + + override def countApprox( + timeout: Long, + confidence: Double = 0.95 + ): PartialResult[BoundedDouble] = { + val c = count + new PartialResult(new BoundedDouble(c, 1.0, c, c), true) + } + + override def isEmpty(): Boolean = count == 0L + + override def take(num: Int): Array[R] = { + val nonEmptyPartitions = this.partitions + .map(_.asInstanceOf[KafkaRDDPartition]) + .filter(_.count > 0) + + if (num < 1 || nonEmptyPartitions.size < 1) { + return new Array[R](0) + } + + // Determine in advance how many messages need to be taken from each partition + val parts = nonEmptyPartitions.foldLeft(Map[Int, Int]()) { (result, part) => + val remain = num - result.values.sum + if (remain > 0) { + val taken = Math.min(remain, part.count) + result + (part.index -> taken.toInt) + } else { + result + } + } + + val buf = new ArrayBuffer[R] + val res = context.runJob( + this, + (tc: TaskContext, it: Iterator[R]) => it.take(parts(tc.partitionId)).toArray, + parts.keys.toArray, + allowLocal = true) + res.foreach(buf ++= _) + buf.toArray + } + override def getPreferredLocations(thePart: Partition): Seq[String] = { val part = thePart.asInstanceOf[KafkaRDDPartition] // TODO is additional hostname resolution necessary here diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala index a842a6f17766f..a660d2a00c35d 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala @@ -35,4 +35,7 @@ class KafkaRDDPartition( val untilOffset: Long, val host: String, val port: Int -) extends Partition +) extends Partition { + /** Number of messages this partition refers to */ + def count(): Long = untilOffset - fromOffset +} diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala index 6dc4e9517d5a4..b608b75952721 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala @@ -195,6 +195,8 @@ private class KafkaTestUtils extends Logging { val props = new Properties() props.put("metadata.broker.list", brokerAddress) props.put("serializer.class", classOf[StringEncoder].getName) + // wait for all in-sync replicas to ack sends + props.put("request.required.acks", "-1") props } @@ -229,21 +231,6 @@ private class KafkaTestUtils extends Logging { tryAgain(1) } - /** Wait until the leader offset for the given topic/partition equals the specified offset */ - def waitUntilLeaderOffset( - topic: String, - partition: Int, - offset: Long): Unit = { - eventually(Time(10000), Time(100)) { - val kc = new KafkaCluster(Map("metadata.broker.list" -> brokerAddress)) - val tp = TopicAndPartition(topic, partition) - val llo = kc.getLatestLeaderOffsets(Set(tp)).right.get.apply(tp).offset - assert( - llo == offset, - s"$topic $partition $offset not reached after timeout") - } - } - private def waitUntilMetadataIsPropagated(topic: String, partition: Int): Unit = { def isPropagated = server.apis.metadataCache.getPartitionInfo(topic, partition) match { case Some(partitionState) => diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala index d7cf500577c2a..0e33362d34acd 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala @@ -158,15 +158,31 @@ object KafkaUtils { /** get leaders for the given offset ranges, or throw an exception */ private def leadersForRanges( - kafkaParams: Map[String, String], + kc: KafkaCluster, offsetRanges: Array[OffsetRange]): Map[TopicAndPartition, (String, Int)] = { - val kc = new KafkaCluster(kafkaParams) val topics = offsetRanges.map(o => TopicAndPartition(o.topic, o.partition)).toSet - val leaders = kc.findLeaders(topics).fold( - errs => throw new SparkException(errs.mkString("\n")), - ok => ok - ) - leaders + val leaders = kc.findLeaders(topics) + KafkaCluster.checkErrors(leaders) + } + + /** Make sure offsets are available in kafka, or throw an exception */ + private def checkOffsets( + kc: KafkaCluster, + offsetRanges: Array[OffsetRange]): Unit = { + val topics = offsetRanges.map(_.topicAndPartition).toSet + val result = for { + low <- kc.getEarliestLeaderOffsets(topics).right + high <- kc.getLatestLeaderOffsets(topics).right + } yield { + offsetRanges.filterNot { o => + low(o.topicAndPartition).offset <= o.fromOffset && + o.untilOffset <= high(o.topicAndPartition).offset + } + } + val badRanges = KafkaCluster.checkErrors(result) + if (!badRanges.isEmpty) { + throw new SparkException("Offsets not available on leader: " + badRanges.mkString(",")) + } } /** @@ -189,9 +205,11 @@ object KafkaUtils { sc: SparkContext, kafkaParams: Map[String, String], offsetRanges: Array[OffsetRange] - ): RDD[(K, V)] = { + ): RDD[(K, V)] = sc.withScope { val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message) - val leaders = leadersForRanges(kafkaParams, offsetRanges) + val kc = new KafkaCluster(kafkaParams) + val leaders = leadersForRanges(kc, offsetRanges) + checkOffsets(kc, offsetRanges) new KafkaRDD[K, V, KD, VD, (K, V)](sc, kafkaParams, offsetRanges, leaders, messageHandler) } @@ -224,16 +242,19 @@ object KafkaUtils { offsetRanges: Array[OffsetRange], leaders: Map[TopicAndPartition, Broker], messageHandler: MessageAndMetadata[K, V] => R - ): RDD[R] = { + ): RDD[R] = sc.withScope { + val kc = new KafkaCluster(kafkaParams) val leaderMap = if (leaders.isEmpty) { - leadersForRanges(kafkaParams, offsetRanges) + leadersForRanges(kc, offsetRanges) } else { // This could be avoided by refactoring KafkaRDD.leaders and KafkaCluster to use Broker leaders.map { case (tp: TopicAndPartition, Broker(host, port)) => (tp, (host, port)) }.toMap } - new KafkaRDD[K, V, KD, VD, R](sc, kafkaParams, offsetRanges, leaderMap, messageHandler) + val cleanedHandler = sc.clean(messageHandler) + checkOffsets(kc, offsetRanges) + new KafkaRDD[K, V, KD, VD, R](sc, kafkaParams, offsetRanges, leaderMap, cleanedHandler) } /** @@ -256,7 +277,7 @@ object KafkaUtils { valueDecoderClass: Class[VD], kafkaParams: JMap[String, String], offsetRanges: Array[OffsetRange] - ): JavaPairRDD[K, V] = { + ): JavaPairRDD[K, V] = jsc.sc.withScope { implicit val keyCmt: ClassTag[K] = ClassTag(keyClass) implicit val valueCmt: ClassTag[V] = ClassTag(valueClass) implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass) @@ -294,7 +315,7 @@ object KafkaUtils { offsetRanges: Array[OffsetRange], leaders: JMap[TopicAndPartition, Broker], messageHandler: JFunction[MessageAndMetadata[K, V], R] - ): JavaRDD[R] = { + ): JavaRDD[R] = jsc.sc.withScope { implicit val keyCmt: ClassTag[K] = ClassTag(keyClass) implicit val valueCmt: ClassTag[V] = ClassTag(valueClass) implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass) @@ -314,7 +335,7 @@ object KafkaUtils { * Points to note: * - No receivers: This stream does not use any receiver. It directly queries Kafka * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked - * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * by the stream itself. For interoperability with Kafka monitoring tools that depend on * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. * You can access the offsets used in each batch from the generated RDDs (see * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). @@ -348,8 +369,9 @@ object KafkaUtils { fromOffsets: Map[TopicAndPartition, Long], messageHandler: MessageAndMetadata[K, V] => R ): InputDStream[R] = { + val cleanedHandler = ssc.sc.clean(messageHandler) new DirectKafkaInputDStream[K, V, KD, VD, R]( - ssc, kafkaParams, fromOffsets, messageHandler) + ssc, kafkaParams, fromOffsets, cleanedHandler) } /** @@ -361,7 +383,7 @@ object KafkaUtils { * Points to note: * - No receivers: This stream does not use any receiver. It directly queries Kafka * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked - * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * by the stream itself. For interoperability with Kafka monitoring tools that depend on * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. * You can access the offsets used in each batch from the generated RDDs (see * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). @@ -397,7 +419,7 @@ object KafkaUtils { val kc = new KafkaCluster(kafkaParams) val reset = kafkaParams.get("auto.offset.reset").map(_.toLowerCase) - (for { + val result = for { topicPartitions <- kc.getPartitions(topics).right leaderOffsets <- (if (reset == Some("smallest")) { kc.getEarliestLeaderOffsets(topicPartitions) @@ -410,10 +432,8 @@ object KafkaUtils { } new DirectKafkaInputDStream[K, V, KD, VD, (K, V)]( ssc, kafkaParams, fromOffsets, messageHandler) - }).fold( - errs => throw new SparkException(errs.mkString("\n")), - ok => ok - ) + } + KafkaCluster.checkErrors(result) } /** @@ -425,7 +445,7 @@ object KafkaUtils { * Points to note: * - No receivers: This stream does not use any receiver. It directly queries Kafka * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked - * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * by the stream itself. For interoperability with Kafka monitoring tools that depend on * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. * You can access the offsets used in each batch from the generated RDDs (see * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). @@ -469,11 +489,12 @@ object KafkaUtils { implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass) implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass) implicit val recordCmt: ClassTag[R] = ClassTag(recordClass) + val cleanedHandler = jssc.sparkContext.clean(messageHandler.call _) createDirectStream[K, V, KD, VD, R]( jssc.ssc, Map(kafkaParams.toSeq: _*), Map(fromOffsets.mapValues { _.longValue() }.toSeq: _*), - messageHandler.call _ + cleanedHandler ) } @@ -486,7 +507,7 @@ object KafkaUtils { * Points to note: * - No receivers: This stream does not use any receiver. It directly queries Kafka * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked - * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * by the stream itself. For interoperability with Kafka monitoring tools that depend on * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. * You can access the offsets used in each batch from the generated RDDs (see * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala index 9c3dfeb8f5928..2675042666304 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala @@ -55,6 +55,12 @@ final class OffsetRange private( val untilOffset: Long) extends Serializable { import OffsetRange.OffsetRangeTuple + /** Kafka TopicAndPartition object, for convenience */ + def topicAndPartition(): TopicAndPartition = TopicAndPartition(topic, partition) + + /** Number of messages this OffsetRange refers to */ + def count(): Long = untilOffset - fromOffset + override def equals(obj: Any): Boolean = obj match { case that: OffsetRange => this.topic == that.topic && diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala index ea87e960379f1..75f0dfc22b9dc 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala @@ -267,7 +267,7 @@ class ReliableKafkaReceiver[ } } catch { case e: Exception => - logError("Error handling message", e) + reportError("Error handling message", e) } } } diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java index 4c1d6a03eb2b8..02cd24a35906f 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java @@ -18,9 +18,8 @@ package org.apache.spark.streaming.kafka; import java.io.Serializable; -import java.util.HashMap; -import java.util.HashSet; -import java.util.Arrays; +import java.util.*; +import java.util.concurrent.atomic.AtomicReference; import scala.Tuple2; @@ -34,6 +33,7 @@ import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.function.Function; import org.apache.spark.streaming.Durations; import org.apache.spark.streaming.api.java.JavaDStream; @@ -67,8 +67,10 @@ public void tearDown() { @Test public void testKafkaStream() throws InterruptedException { - String topic1 = "topic1"; - String topic2 = "topic2"; + final String topic1 = "topic1"; + final String topic2 = "topic2"; + // hold a reference to the current offset ranges, so it can be used downstream + final AtomicReference offsetRanges = new AtomicReference(); String[] topic1data = createTopicAndSendData(topic1); String[] topic2data = createTopicAndSendData(topic2); @@ -89,6 +91,17 @@ public void testKafkaStream() throws InterruptedException { StringDecoder.class, kafkaParams, topicToSet(topic1) + ).transformToPair( + // Make sure you can get offset ranges from the rdd + new Function, JavaPairRDD>() { + @Override + public JavaPairRDD call(JavaPairRDD rdd) throws Exception { + OffsetRange[] offsets = ((HasOffsetRanges) rdd.rdd()).offsetRanges(); + offsetRanges.set(offsets); + Assert.assertEquals(offsets[0].topic(), topic1); + return rdd; + } + } ).map( new Function, String>() { @Override @@ -116,12 +129,17 @@ public String call(MessageAndMetadata msgAndMd) throws Exception ); JavaDStream unifiedStream = stream1.union(stream2); - final HashSet result = new HashSet(); + final Set result = Collections.synchronizedSet(new HashSet()); unifiedStream.foreachRDD( new Function, Void>() { @Override public Void call(JavaRDD rdd) throws Exception { result.addAll(rdd.collect()); + for (OffsetRange o : offsetRanges.get()) { + System.out.println( + o.topic() + " " + o.partition() + " " + o.fromOffset() + " " + o.untilOffset() + ); + } return null; } } diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java index 5cf379635354f..a9dc6e50613ca 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java @@ -72,9 +72,6 @@ public void testKafkaRDD() throws InterruptedException { HashMap kafkaParams = new HashMap(); kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress()); - kafkaTestUtils.waitUntilLeaderOffset(topic1, 0, topic1data.length); - kafkaTestUtils.waitUntilLeaderOffset(topic2, 0, topic2data.length); - OffsetRange[] offsetRanges = { OffsetRange.create(topic1, 0, 0, 1), OffsetRange.create(topic2, 0, 0, 1) diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java index 540f4ceabab47..e4c659215b767 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java @@ -18,9 +18,7 @@ package org.apache.spark.streaming.kafka; import java.io.Serializable; -import java.util.HashMap; -import java.util.List; -import java.util.Random; +import java.util.*; import scala.Tuple2; @@ -94,7 +92,7 @@ public void testKafkaStream() throws InterruptedException { topics, StorageLevel.MEMORY_ONLY_SER()); - final HashMap result = new HashMap(); + final Map result = Collections.synchronizedMap(new HashMap()); JavaDStream words = stream.map( new Function, String>() { diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala index b6d314dfc7783..8e1715f6dbb95 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala @@ -28,10 +28,10 @@ import scala.language.postfixOps import kafka.common.TopicAndPartition import kafka.message.MessageAndMetadata import kafka.serializer.StringDecoder -import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll} import org.scalatest.concurrent.Eventually -import org.apache.spark.{Logging, SparkConf, SparkContext} +import org.apache.spark.{Logging, SparkConf, SparkContext, SparkFunSuite} import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Milliseconds, StreamingContext, Time} import org.apache.spark.streaming.dstream.DStream @@ -39,7 +39,7 @@ import org.apache.spark.streaming.scheduler._ import org.apache.spark.util.Utils class DirectKafkaStreamSuite - extends FunSuite + extends SparkFunSuite with BeforeAndAfter with BeforeAndAfterAll with Eventually @@ -99,15 +99,24 @@ class DirectKafkaStreamSuite ssc, kafkaParams, topics) } - val allReceived = new ArrayBuffer[(String, String)] + val allReceived = + new ArrayBuffer[(String, String)] with mutable.SynchronizedBuffer[(String, String)] - stream.foreachRDD { rdd => - // Get the offset ranges in the RDD - val offsets = rdd.asInstanceOf[HasOffsetRanges].offsetRanges + // hold a reference to the current offset ranges, so it can be used downstream + var offsetRanges = Array[OffsetRange]() + + stream.transform { rdd => + // Get the offset ranges in the RDD + offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges + rdd + }.foreachRDD { rdd => + for (o <- offsetRanges) { + println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}") + } val collected = rdd.mapPartitionsWithIndex { (i, iter) => // For each partition, get size of the range in the partition, // and the number of items in the partition - val off = offsets(i) + val off = offsetRanges(i) val all = iter.toSeq val partSize = all.size val rangeSize = off.untilOffset - off.fromOffset @@ -162,7 +171,7 @@ class DirectKafkaStreamSuite "Start offset not from latest" ) - val collectedData = new mutable.ArrayBuffer[String]() + val collectedData = new mutable.ArrayBuffer[String]() with mutable.SynchronizedBuffer[String] stream.map { _._2 }.foreachRDD { rdd => collectedData ++= rdd.collect() } ssc.start() val newData = Map("b" -> 10) @@ -208,7 +217,7 @@ class DirectKafkaStreamSuite "Start offset not from latest" ) - val collectedData = new mutable.ArrayBuffer[String]() + val collectedData = new mutable.ArrayBuffer[String]() with mutable.SynchronizedBuffer[String] stream.foreachRDD { rdd => collectedData ++= rdd.collect() } ssc.start() val newData = Map("b" -> 10) @@ -324,7 +333,8 @@ class DirectKafkaStreamSuite ssc, kafkaParams, Set(topic)) } - val allReceived = new ArrayBuffer[(String, String)] + val allReceived = + new ArrayBuffer[(String, String)] with mutable.SynchronizedBuffer[(String, String)] stream.foreachRDD { rdd => allReceived ++= rdd.collect() } ssc.start() @@ -350,8 +360,8 @@ class DirectKafkaStreamSuite } object DirectKafkaStreamSuite { - val collectedData = new mutable.ArrayBuffer[String]() - var total = -1L + val collectedData = new mutable.ArrayBuffer[String]() with mutable.SynchronizedBuffer[String] + @volatile var total = -1L class InputInfoCollector extends StreamingListener { val numRecordsSubmitted = new AtomicLong(0L) diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala index 7fb841b79cb65..d66830cbacdee 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala @@ -20,9 +20,11 @@ package org.apache.spark.streaming.kafka import scala.util.Random import kafka.common.TopicAndPartition -import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.BeforeAndAfterAll -class KafkaClusterSuite extends FunSuite with BeforeAndAfterAll { +import org.apache.spark.SparkFunSuite + +class KafkaClusterSuite extends SparkFunSuite with BeforeAndAfterAll { private val topic = "kcsuitetopic" + Random.nextInt(10000) private val topicAndPartition = TopicAndPartition(topic, 0) private var kc: KafkaCluster = null diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala index 39c3fb448ff57..f52a738afd65b 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala @@ -22,11 +22,11 @@ import scala.util.Random import kafka.serializer.StringDecoder import kafka.common.TopicAndPartition import kafka.message.MessageAndMetadata -import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.BeforeAndAfterAll import org.apache.spark._ -class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { +class KafkaRDDSuite extends SparkFunSuite with BeforeAndAfterAll { private var kafkaTestUtils: KafkaTestUtils = _ @@ -55,21 +55,39 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { test("basic usage") { val topic = s"topicbasic-${Random.nextInt}" kafkaTestUtils.createTopic(topic) - val messages = Set("the", "quick", "brown", "fox") - kafkaTestUtils.sendMessages(topic, messages.toArray) + val messages = Array("the", "quick", "brown", "fox") + kafkaTestUtils.sendMessages(topic, messages) val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, "group.id" -> s"test-consumer-${Random.nextInt}") - kafkaTestUtils.waitUntilLeaderOffset(topic, 0, messages.size) - val offsetRanges = Array(OffsetRange(topic, 0, 0, messages.size)) - val rdd = KafkaUtils.createRDD[String, String, StringDecoder, StringDecoder]( + val rdd = KafkaUtils.createRDD[String, String, StringDecoder, StringDecoder]( sc, kafkaParams, offsetRanges) val received = rdd.map(_._2).collect.toSet - assert(received === messages) + assert(received === messages.toSet) + + // size-related method optimizations return sane results + assert(rdd.count === messages.size) + assert(rdd.countApprox(0).getFinalValue.mean === messages.size) + assert(!rdd.isEmpty) + assert(rdd.take(1).size === 1) + assert(rdd.take(1).head._2 === messages.head) + assert(rdd.take(messages.size + 10).size === messages.size) + + val emptyRdd = KafkaUtils.createRDD[String, String, StringDecoder, StringDecoder]( + sc, kafkaParams, Array(OffsetRange(topic, 0, 0, 0))) + + assert(emptyRdd.isEmpty) + + // invalid offset ranges throw exceptions + val badRanges = Array(OffsetRange(topic, 0, 0, messages.size + 1)) + intercept[SparkException] { + KafkaUtils.createRDD[String, String, StringDecoder, StringDecoder]( + sc, kafkaParams, badRanges) + } } test("iterator boundary conditions") { @@ -86,7 +104,6 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { // this is the "lots of messages" case kafkaTestUtils.sendMessages(topic, sent) val sentCount = sent.values.sum - kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sentCount) // rdd defined from leaders after sending messages, should get the number sent val rdd = getRdd(kc, Set(topic)) @@ -113,7 +130,6 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { val sentOnlyOne = Map("d" -> 1) kafkaTestUtils.sendMessages(topic, sentOnlyOne) - kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sentCount + 1) assert(rdd2.isDefined) assert(rdd2.get.count === 0, "got messages when there shouldn't be any") diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala index 24699dfc33adb..797b07f80d8ee 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala @@ -23,14 +23,14 @@ import scala.language.postfixOps import scala.util.Random import kafka.serializer.StringDecoder -import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.BeforeAndAfterAll import org.scalatest.concurrent.Eventually -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext} -class KafkaStreamSuite extends FunSuite with Eventually with BeforeAndAfterAll { +class KafkaStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfterAll { private var ssc: StreamingContext = _ private var kafkaTestUtils: KafkaTestUtils = _ @@ -65,7 +65,7 @@ class KafkaStreamSuite extends FunSuite with Eventually with BeforeAndAfterAll { val stream = KafkaUtils.createStream[String, String, StringDecoder, StringDecoder]( ssc, kafkaParams, Map(topic -> 1), StorageLevel.MEMORY_ONLY) - val result = new mutable.HashMap[String, Long]() + val result = new mutable.HashMap[String, Long]() with mutable.SynchronizedMap[String, Long] stream.map(_._2).countByValue().foreachRDD { r => val ret = r.collect() ret.toMap.foreach { kv => @@ -77,10 +77,7 @@ class KafkaStreamSuite extends FunSuite with Eventually with BeforeAndAfterAll { ssc.start() eventually(timeout(10000 milliseconds), interval(100 milliseconds)) { - assert(sent.size === result.size) - sent.keys.foreach { k => - assert(sent(k) === result(k).toInt) - } + assert(sent === result) } } } diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala index 38548dd73b82c..80e2df62de3fe 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala @@ -26,15 +26,15 @@ import scala.util.Random import kafka.serializer.StringDecoder import kafka.utils.{ZKGroupTopicDirs, ZkUtils} -import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll} import org.scalatest.concurrent.Eventually -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext} import org.apache.spark.util.Utils -class ReliableKafkaStreamSuite extends FunSuite +class ReliableKafkaStreamSuite extends SparkFunSuite with BeforeAndAfterAll with BeforeAndAfter with Eventually { private val sparkConf = new SparkConf() diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml index 98f95a9a64fa0..0e41e5781784b 100644 --- a/external/mqtt/pom.xml +++ b/external/mqtt/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ ${project.version} provided + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.eclipse.paho org.eclipse.paho.client.mqttv3 diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala index 3c0ef94cb0fab..7c2f18cb35bda 100644 --- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala +++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala @@ -17,25 +17,12 @@ package org.apache.spark.streaming.mqtt -import java.io.IOException -import java.util.concurrent.Executors -import java.util.Properties - -import scala.collection.JavaConversions._ -import scala.collection.Map -import scala.collection.mutable.HashMap -import scala.reflect.ClassTag - import org.eclipse.paho.client.mqttv3.IMqttDeliveryToken import org.eclipse.paho.client.mqttv3.MqttCallback import org.eclipse.paho.client.mqttv3.MqttClient -import org.eclipse.paho.client.mqttv3.MqttClientPersistence -import org.eclipse.paho.client.mqttv3.MqttException import org.eclipse.paho.client.mqttv3.MqttMessage -import org.eclipse.paho.client.mqttv3.MqttTopic import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence -import org.apache.spark.Logging import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.dstream._ @@ -57,6 +44,8 @@ class MQTTInputDStream( storageLevel: StorageLevel ) extends ReceiverInputDStream[String](ssc_) { + private[streaming] override def name: String = s"MQTT stream [$id]" + def getReceiver(): Receiver[String] = { new MQTTReceiver(brokerUrl, topic, storageLevel) } @@ -86,7 +75,7 @@ class MQTTReceiver( // Handles Mqtt message override def messageArrived(topic: String, message: MqttMessage) { - store(new String(message.getPayload(),"utf-8")) + store(new String(message.getPayload(), "utf-8")) } override def deliveryComplete(token: IMqttDeliveryToken) { diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala index a19a72c58a705..c4bf5aa7869bb 100644 --- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala +++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala @@ -29,7 +29,7 @@ import org.apache.commons.lang3.RandomUtils import org.eclipse.paho.client.mqttv3._ import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter import org.scalatest.concurrent.Eventually import org.apache.spark.streaming.{Milliseconds, StreamingContext} @@ -37,10 +37,10 @@ import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.dstream.ReceiverInputDStream import org.apache.spark.streaming.scheduler.StreamingListener import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.util.Utils -class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { +class MQTTStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfter { private val batchDuration = Milliseconds(500) private val master = "local[2]" diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml index 8b6a8959ac4cf..178ae8de13b57 100644 --- a/external/twitter/pom.xml +++ b/external/twitter/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ ${project.version} provided + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.twitter4j twitter4j-stream diff --git a/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala b/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala index 9ee57d7581d85..d9acb568879fe 100644 --- a/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala +++ b/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala @@ -18,16 +18,16 @@ package org.apache.spark.streaming.twitter -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter import twitter4j.Status import twitter4j.auth.{NullAuthorization, Authorization} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkFunSuite} import org.apache.spark.streaming.{Seconds, StreamingContext} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.dstream.ReceiverInputDStream -class TwitterStreamSuite extends FunSuite with BeforeAndAfter with Logging { +class TwitterStreamSuite extends SparkFunSuite with BeforeAndAfter with Logging { val batchDuration = Seconds(1) diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml index a50d378b34335..37bfd10d43663 100644 --- a/external/zeromq/pom.xml +++ b/external/zeromq/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ ${project.version} provided + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + ${akka.group} akka-zeromq_${scala.binary.version} diff --git a/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala b/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala index a7566e733d891..35d2e62c68480 100644 --- a/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala +++ b/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala @@ -20,13 +20,13 @@ package org.apache.spark.streaming.zeromq import akka.actor.SupervisorStrategy import akka.util.ByteString import akka.zeromq.Subscribe -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Seconds, StreamingContext} import org.apache.spark.streaming.dstream.ReceiverInputDStream -class ZeroMQStreamSuite extends FunSuite { +class ZeroMQStreamSuite extends SparkFunSuite { val batchDuration = Seconds(1) diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml index 4351a8a12fe21..f138251748c9e 100644 --- a/extras/java8-tests/pom.xml +++ b/extras/java8-tests/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml index 25847a1b33d9c..c6f60bc907438 100644 --- a/extras/kinesis-asl/pom.xml +++ b/extras/kinesis-asl/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -40,6 +40,13 @@ spark-streaming_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.spark spark-streaming_${scala.binary.version} diff --git a/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java b/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java index b0bff27a61c19..06e0ff28afd95 100644 --- a/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java +++ b/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java @@ -20,6 +20,7 @@ import java.util.List; import java.util.regex.Pattern; +import com.amazonaws.regions.RegionUtils; import org.apache.log4j.Logger; import org.apache.spark.SparkConf; import org.apache.spark.api.java.function.FlatMapFunction; @@ -40,140 +41,146 @@ import com.google.common.collect.Lists; /** - * Java-friendly Kinesis Spark Streaming WordCount example + * Consumes messages from a Amazon Kinesis streams and does wordcount. * - * See http://spark.apache.org/docs/latest/streaming-kinesis.html for more details - * on the Kinesis Spark Streaming integration. + * This example spins up 1 Kinesis Receiver per shard for the given stream. + * It then starts pulling from the last checkpointed sequence number of the given stream. * - * This example spins up 1 Kinesis Worker (Spark Streaming Receiver) per shard - * for the given stream. - * It then starts pulling from the last checkpointed sequence number of the given - * and . + * Usage: JavaKinesisWordCountASL [app-name] [stream-name] [endpoint-url] [region-name] + * [app-name] is the name of the consumer app, used to track the read data in DynamoDB + * [stream-name] name of the Kinesis stream (ie. mySparkStream) + * [endpoint-url] endpoint of the Kinesis service + * (e.g. https://kinesis.us-east-1.amazonaws.com) * - * Valid endpoint urls: http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region - * - * This code uses the DefaultAWSCredentialsProviderChain and searches for credentials - * in the following order of precedence: - * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY - * Java System Properties - aws.accessKeyId and aws.secretKey - * Credential profiles file - default location (~/.aws/credentials) shared by all AWS SDKs - * Instance profile credentials - delivered through the Amazon EC2 metadata service - * - * Usage: JavaKinesisWordCountASL - * is the name of the Kinesis stream (ie. mySparkStream) - * is the endpoint of the Kinesis service - * (ie. https://kinesis.us-east-1.amazonaws.com) * * Example: - * $ export AWS_ACCESS_KEY_ID= + * # export AWS keys if necessary + * $ export AWS_ACCESS_KEY_ID=[your-access-key] * $ export AWS_SECRET_KEY= - * $ $SPARK_HOME/bin/run-example \ - * org.apache.spark.examples.streaming.JavaKinesisWordCountASL mySparkStream \ - * https://kinesis.us-east-1.amazonaws.com * - * Note that number of workers/threads should be 1 more than the number of receivers. - * This leaves one thread available for actually processing the data. + * # run the example + * $ SPARK_HOME/bin/run-example streaming.JavaKinesisWordCountASL myAppName mySparkStream \ + * https://kinesis.us-east-1.amazonaws.com + * + * There is a companion helper class called KinesisWordProducerASL which puts dummy data + * onto the Kinesis stream. * - * There is a companion helper class called KinesisWordCountProducerASL which puts dummy data - * onto the Kinesis stream. - * Usage instructions for KinesisWordCountProducerASL are provided in the class definition. + * This code uses the DefaultAWSCredentialsProviderChain to find credentials + * in the following order: + * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY + * Java System Properties - aws.accessKeyId and aws.secretKey + * Credential profiles file - default location (~/.aws/credentials) shared by all AWS SDKs + * Instance profile credentials - delivered through the Amazon EC2 metadata service + * For more information, see + * http://docs.aws.amazon.com/AWSSdkDocsJava/latest/DeveloperGuide/credentials.html + * + * See http://spark.apache.org/docs/latest/streaming-kinesis-integration.html for more details on + * the Kinesis Spark Streaming integration. */ public final class JavaKinesisWordCountASL { // needs to be public for access from run-example - private static final Pattern WORD_SEPARATOR = Pattern.compile(" "); - private static final Logger logger = Logger.getLogger(JavaKinesisWordCountASL.class); - - /* Make the constructor private to enforce singleton */ - private JavaKinesisWordCountASL() { + private static final Pattern WORD_SEPARATOR = Pattern.compile(" "); + private static final Logger logger = Logger.getLogger(JavaKinesisWordCountASL.class); + + public static void main(String[] args) { + // Check that all required args were passed in. + if (args.length != 3) { + System.err.println( + "Usage: JavaKinesisWordCountASL \n\n" + + " is the name of the app, used to track the read data in DynamoDB\n" + + " is the name of the Kinesis stream\n" + + " is the endpoint of the Kinesis service\n" + + " (e.g. https://kinesis.us-east-1.amazonaws.com)\n" + + "Generate data for the Kinesis stream using the example KinesisWordProducerASL.\n" + + "See http://spark.apache.org/docs/latest/streaming-kinesis-integration.html for more\n" + + "details.\n" + ); + System.exit(1); } - public static void main(String[] args) { - /* Check that all required args were passed in. */ - if (args.length < 2) { - System.err.println( - "Usage: JavaKinesisWordCountASL \n" + - " is the name of the Kinesis stream\n" + - " is the endpoint of the Kinesis service\n" + - " (e.g. https://kinesis.us-east-1.amazonaws.com)\n"); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - - /* Populate the appropriate variables from the given args */ - String streamName = args[0]; - String endpointUrl = args[1]; - /* Set the batch interval to a fixed 2000 millis (2 seconds) */ - Duration batchInterval = new Duration(2000); - - /* Create a Kinesis client in order to determine the number of shards for the given stream */ - AmazonKinesisClient kinesisClient = new AmazonKinesisClient( - new DefaultAWSCredentialsProviderChain()); - kinesisClient.setEndpoint(endpointUrl); - - /* Determine the number of shards from the stream */ - int numShards = kinesisClient.describeStream(streamName) - .getStreamDescription().getShards().size(); - - /* In this example, we're going to create 1 Kinesis Worker/Receiver/DStream for each shard */ - int numStreams = numShards; - - /* Setup the Spark config. */ - SparkConf sparkConfig = new SparkConf().setAppName("KinesisWordCount"); - - /* Kinesis checkpoint interval. Same as batchInterval for this example. */ - Duration checkpointInterval = batchInterval; + // Set default log4j logging level to WARN to hide Spark logs + StreamingExamples.setStreamingLogLevels(); + + // Populate the appropriate variables from the given args + String kinesisAppName = args[0]; + String streamName = args[1]; + String endpointUrl = args[2]; + + // Create a Kinesis client in order to determine the number of shards for the given stream + AmazonKinesisClient kinesisClient = + new AmazonKinesisClient(new DefaultAWSCredentialsProviderChain()); + kinesisClient.setEndpoint(endpointUrl); + int numShards = + kinesisClient.describeStream(streamName).getStreamDescription().getShards().size(); + + + // In this example, we're going to create 1 Kinesis Receiver/input DStream for each shard. + // This is not a necessity; if there are less receivers/DStreams than the number of shards, + // then the shards will be automatically distributed among the receivers and each receiver + // will receive data from multiple shards. + int numStreams = numShards; + + // Spark Streaming batch interval + Duration batchInterval = new Duration(2000); + + // Kinesis checkpoint interval. Same as batchInterval for this example. + Duration kinesisCheckpointInterval = batchInterval; + + // Get the region name from the endpoint URL to save Kinesis Client Library metadata in + // DynamoDB of the same region as the Kinesis stream + String regionName = RegionUtils.getRegionByEndpoint(endpointUrl).getName(); + + // Setup the Spark config and StreamingContext + SparkConf sparkConfig = new SparkConf().setAppName("JavaKinesisWordCountASL"); + JavaStreamingContext jssc = new JavaStreamingContext(sparkConfig, batchInterval); + + // Create the Kinesis DStreams + List> streamsList = new ArrayList>(numStreams); + for (int i = 0; i < numStreams; i++) { + streamsList.add( + KinesisUtils.createStream(jssc, kinesisAppName, streamName, endpointUrl, regionName, + InitialPositionInStream.LATEST, kinesisCheckpointInterval, StorageLevel.MEMORY_AND_DISK_2()) + ); + } - /* Setup the StreamingContext */ - JavaStreamingContext jssc = new JavaStreamingContext(sparkConfig, batchInterval); + // Union all the streams if there is more than 1 stream + JavaDStream unionStreams; + if (streamsList.size() > 1) { + unionStreams = jssc.union(streamsList.get(0), streamsList.subList(1, streamsList.size())); + } else { + // Otherwise, just use the 1 stream + unionStreams = streamsList.get(0); + } - /* Create the same number of Kinesis DStreams/Receivers as Kinesis stream's shards */ - List> streamsList = new ArrayList>(numStreams); - for (int i = 0; i < numStreams; i++) { - streamsList.add( - KinesisUtils.createStream(jssc, streamName, endpointUrl, checkpointInterval, - InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2()) - ); + // Convert each line of Array[Byte] to String, and split into words + JavaDStream words = unionStreams.flatMap(new FlatMapFunction() { + @Override + public Iterable call(byte[] line) { + return Lists.newArrayList(WORD_SEPARATOR.split(new String(line))); + } + }); + + // Map each word to a (word, 1) tuple so we can reduce by key to count the words + JavaPairDStream wordCounts = words.mapToPair( + new PairFunction() { + @Override + public Tuple2 call(String s) { + return new Tuple2(s, 1); + } } - - /* Union all the streams if there is more than 1 stream */ - JavaDStream unionStreams; - if (streamsList.size() > 1) { - unionStreams = jssc.union(streamsList.get(0), streamsList.subList(1, streamsList.size())); - } else { - /* Otherwise, just use the 1 stream */ - unionStreams = streamsList.get(0); + ).reduceByKey( + new Function2() { + @Override + public Integer call(Integer i1, Integer i2) { + return i1 + i2; + } } + ); - /* - * Split each line of the union'd DStreams into multiple words using flatMap to produce the collection. - * Convert lines of byte[] to multiple Strings by first converting to String, then splitting on WORD_SEPARATOR. - */ - JavaDStream words = unionStreams.flatMap(new FlatMapFunction() { - @Override - public Iterable call(byte[] line) { - return Lists.newArrayList(WORD_SEPARATOR.split(new String(line))); - } - }); - - /* Map each word to a (word, 1) tuple, then reduce/aggregate by word. */ - JavaPairDStream wordCounts = words.mapToPair( - new PairFunction() { - @Override - public Tuple2 call(String s) { - return new Tuple2(s, 1); - } - }).reduceByKey(new Function2() { - @Override - public Integer call(Integer i1, Integer i2) { - return i1 + i2; - } - }); - - /* Print the first 10 wordCounts */ - wordCounts.print(); - - /* Start the streaming context and await termination */ - jssc.start(); - jssc.awaitTermination(); - } + // Print the first 10 wordCounts + wordCounts.print(); + + // Start the streaming context and await termination + jssc.start(); + jssc.awaitTermination(); + } } diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala index 32da0858d1a1d..be8b62d3cc6ba 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala @@ -18,223 +18,249 @@ package org.apache.spark.examples.streaming import java.nio.ByteBuffer + import scala.util.Random -import org.apache.spark.Logging -import org.apache.spark.SparkConf -import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.Milliseconds -import org.apache.spark.streaming.StreamingContext -import org.apache.spark.streaming.StreamingContext.toPairDStreamFunctions -import org.apache.spark.streaming.kinesis.KinesisUtils -import com.amazonaws.auth.DefaultAWSCredentialsProviderChain + +import com.amazonaws.auth.{DefaultAWSCredentialsProviderChain, BasicAWSCredentials} +import com.amazonaws.regions.RegionUtils import com.amazonaws.services.kinesis.AmazonKinesisClient import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream import com.amazonaws.services.kinesis.model.PutRecordRequest -import org.apache.log4j.Logger -import org.apache.log4j.Level +import org.apache.log4j.{Level, Logger} + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{Milliseconds, StreamingContext} +import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions +import org.apache.spark.streaming.kinesis.KinesisUtils + /** - * Kinesis Spark Streaming WordCount example. + * Consumes messages from a Amazon Kinesis streams and does wordcount. * - * See http://spark.apache.org/docs/latest/streaming-kinesis.html for more details on - * the Kinesis Spark Streaming integration. + * This example spins up 1 Kinesis Receiver per shard for the given stream. + * It then starts pulling from the last checkpointed sequence number of the given stream. * - * This example spins up 1 Kinesis Worker (Spark Streaming Receiver) per shard - * for the given stream. - * It then starts pulling from the last checkpointed sequence number of the given - * and . + * Usage: KinesisWordCountASL + * is the name of the consumer app, used to track the read data in DynamoDB + * name of the Kinesis stream (ie. mySparkStream) + * endpoint of the Kinesis service + * (e.g. https://kinesis.us-east-1.amazonaws.com) * - * Valid endpoint urls: http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region - * - * This code uses the DefaultAWSCredentialsProviderChain and searches for credentials - * in the following order of precedence: - * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY - * Java System Properties - aws.accessKeyId and aws.secretKey - * Credential profiles file - default location (~/.aws/credentials) shared by all AWS SDKs - * Instance profile credentials - delivered through the Amazon EC2 metadata service - * - * Usage: KinesisWordCountASL - * is the name of the Kinesis stream (ie. mySparkStream) - * is the endpoint of the Kinesis service - * (ie. https://kinesis.us-east-1.amazonaws.com) * * Example: - * $ export AWS_ACCESS_KEY_ID= - * $ export AWS_SECRET_KEY= - * $ $SPARK_HOME/bin/run-example \ - * org.apache.spark.examples.streaming.KinesisWordCountASL mySparkStream \ - * https://kinesis.us-east-1.amazonaws.com + * # export AWS keys if necessary + * $ export AWS_ACCESS_KEY_ID= + * $ export AWS_SECRET_KEY= + * + * # run the example + * $ SPARK_HOME/bin/run-example streaming.KinesisWordCountASL myAppName mySparkStream \ + * https://kinesis.us-east-1.amazonaws.com * - * - * Note that number of workers/threads should be 1 more than the number of receivers. - * This leaves one thread available for actually processing the data. + * There is a companion helper class called KinesisWordProducerASL which puts dummy data + * onto the Kinesis stream. * - * There is a companion helper class below called KinesisWordCountProducerASL which puts - * dummy data onto the Kinesis stream. - * Usage instructions for KinesisWordCountProducerASL are provided in that class definition. + * This code uses the DefaultAWSCredentialsProviderChain to find credentials + * in the following order: + * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY + * Java System Properties - aws.accessKeyId and aws.secretKey + * Credential profiles file - default location (~/.aws/credentials) shared by all AWS SDKs + * Instance profile credentials - delivered through the Amazon EC2 metadata service + * For more information, see + * http://docs.aws.amazon.com/AWSSdkDocsJava/latest/DeveloperGuide/credentials.html + * + * See http://spark.apache.org/docs/latest/streaming-kinesis-integration.html for more details on + * the Kinesis Spark Streaming integration. */ -private object KinesisWordCountASL extends Logging { +object KinesisWordCountASL extends Logging { def main(args: Array[String]) { - /* Check that all required args were passed in. */ - if (args.length < 2) { + // Check that all required args were passed in. + if (args.length != 3) { System.err.println( """ - |Usage: KinesisWordCount + |Usage: KinesisWordCountASL + | + | is the name of the consumer app, used to track the read data in DynamoDB | is the name of the Kinesis stream | is the endpoint of the Kinesis service | (e.g. https://kinesis.us-east-1.amazonaws.com) + | + |Generate input data for Kinesis stream using the example KinesisWordProducerASL. + |See http://spark.apache.org/docs/latest/streaming-kinesis-integration.html for more + |details. """.stripMargin) System.exit(1) } StreamingExamples.setStreamingLogLevels() - /* Populate the appropriate variables from the given args */ - val Array(streamName, endpointUrl) = args + // Populate the appropriate variables from the given args + val Array(appName, streamName, endpointUrl) = args - /* Determine the number of shards from the stream */ - val kinesisClient = new AmazonKinesisClient(new DefaultAWSCredentialsProviderChain()) + + // Determine the number of shards from the stream using the low-level Kinesis Client + // from the AWS Java SDK. + val credentials = new DefaultAWSCredentialsProviderChain().getCredentials() + require(credentials != null, + "No AWS credentials found. Please specify credentials using one of the methods specified " + + "in http://docs.aws.amazon.com/AWSSdkDocsJava/latest/DeveloperGuide/credentials.html") + val kinesisClient = new AmazonKinesisClient(credentials) kinesisClient.setEndpoint(endpointUrl) - val numShards = kinesisClient.describeStream(streamName).getStreamDescription().getShards() - .size() + val numShards = kinesisClient.describeStream(streamName).getStreamDescription().getShards().size + - /* In this example, we're going to create 1 Kinesis Worker/Receiver/DStream for each shard. */ + // In this example, we're going to create 1 Kinesis Receiver/input DStream for each shard. + // This is not a necessity; if there are less receivers/DStreams than the number of shards, + // then the shards will be automatically distributed among the receivers and each receiver + // will receive data from multiple shards. val numStreams = numShards - /* Setup the and SparkConfig and StreamingContext */ - /* Spark Streaming batch interval */ + // Spark Streaming batch interval val batchInterval = Milliseconds(2000) - val sparkConfig = new SparkConf().setAppName("KinesisWordCount") - val ssc = new StreamingContext(sparkConfig, batchInterval) - /* Kinesis checkpoint interval. Same as batchInterval for this example. */ + // Kinesis checkpoint interval is the interval at which the DynamoDB is updated with information + // on sequence number of records that have been received. Same as batchInterval for this + // example. val kinesisCheckpointInterval = batchInterval - /* Create the same number of Kinesis DStreams/Receivers as Kinesis stream's shards */ + // Get the region name from the endpoint URL to save Kinesis Client Library metadata in + // DynamoDB of the same region as the Kinesis stream + val regionName = RegionUtils.getRegionByEndpoint(endpointUrl).getName() + + // Setup the SparkConfig and StreamingContext + val sparkConfig = new SparkConf().setAppName("KinesisWordCountASL") + val ssc = new StreamingContext(sparkConfig, batchInterval) + + // Create the Kinesis DStreams val kinesisStreams = (0 until numStreams).map { i => - KinesisUtils.createStream(ssc, streamName, endpointUrl, kinesisCheckpointInterval, - InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2) + KinesisUtils.createStream(ssc, appName, streamName, endpointUrl, regionName, + InitialPositionInStream.LATEST, kinesisCheckpointInterval, StorageLevel.MEMORY_AND_DISK_2) } - /* Union all the streams */ + // Union all the streams val unionStreams = ssc.union(kinesisStreams) - /* Convert each line of Array[Byte] to String, split into words, and count them */ - val words = unionStreams.flatMap(byteArray => new String(byteArray) - .split(" ")) + // Convert each line of Array[Byte] to String, and split into words + val words = unionStreams.flatMap(byteArray => new String(byteArray).split(" ")) - /* Map each word to a (word, 1) tuple so we can reduce/aggregate by key. */ + // Map each word to a (word, 1) tuple so we can reduce by key to count the words val wordCounts = words.map(word => (word, 1)).reduceByKey(_ + _) - /* Print the first 10 wordCounts */ + // Print the first 10 wordCounts wordCounts.print() - /* Start the streaming context and await termination */ + // Start the streaming context and await termination ssc.start() ssc.awaitTermination() } } /** - * Usage: KinesisWordCountProducerASL - * + * Usage: KinesisWordProducerASL \ + * + * * is the name of the Kinesis stream (ie. mySparkStream) - * is the endpoint of the Kinesis service + * is the endpoint of the Kinesis service * (ie. https://kinesis.us-east-1.amazonaws.com) * is the rate of records per second to put onto the stream * is the rate of records per second to put onto the stream * * Example: - * $ export AWS_ACCESS_KEY_ID= - * $ export AWS_SECRET_KEY= - * $ $SPARK_HOME/bin/run-example \ - * org.apache.spark.examples.streaming.KinesisWordCountProducerASL mySparkStream \ - * https://kinesis.us-east-1.amazonaws.com 10 5 + * $ SPARK_HOME/bin/run-example streaming.KinesisWordProducerASL mySparkStream \ + * https://kinesis.us-east-1.amazonaws.com us-east-1 10 5 */ -private object KinesisWordCountProducerASL { +object KinesisWordProducerASL { def main(args: Array[String]) { - if (args.length < 4) { - System.err.println("Usage: KinesisWordCountProducerASL " + - " ") + if (args.length != 4) { + System.err.println( + """ + |Usage: KinesisWordProducerASL + + | + | is the name of the Kinesis stream + | is the endpoint of the Kinesis service + | (e.g. https://kinesis.us-east-1.amazonaws.com) + | is the rate of records per second to put onto the stream + | is the rate of records per second to put onto the stream + | + """.stripMargin) + System.exit(1) } + // Set default log4j logging level to WARN to hide Spark logs StreamingExamples.setStreamingLogLevels() - /* Populate the appropriate variables from the given args */ + // Populate the appropriate variables from the given args val Array(stream, endpoint, recordsPerSecond, wordsPerRecord) = args - /* Generate the records and return the totals */ - val totals = generate(stream, endpoint, recordsPerSecond.toInt, wordsPerRecord.toInt) + // Generate the records and return the totals + val totals = generate(stream, endpoint, recordsPerSecond.toInt, + wordsPerRecord.toInt) - /* Print the array of (index, total) tuples */ - println("Totals") - totals.foreach(total => println(total.toString())) + // Print the array of (word, total) tuples + println("Totals for the words sent") + totals.foreach(println(_)) } def generate(stream: String, endpoint: String, recordsPerSecond: Int, - wordsPerRecord: Int): Seq[(Int, Int)] = { + wordsPerRecord: Int): Seq[(String, Int)] = { - val MaxRandomInts = 10 + val randomWords = List("spark", "you", "are", "my", "father") + val totals = scala.collection.mutable.Map[String, Int]() - /* Create the Kinesis client */ + // Create the low-level Kinesis Client from the AWS Java SDK. val kinesisClient = new AmazonKinesisClient(new DefaultAWSCredentialsProviderChain()) kinesisClient.setEndpoint(endpoint) println(s"Putting records onto stream $stream and endpoint $endpoint at a rate of" + - s" $recordsPerSecond records per second and $wordsPerRecord words per record"); - - val totals = new Array[Int](MaxRandomInts) - /* Put String records onto the stream per the given recordPerSec and wordsPerRecord */ - for (i <- 1 to 5) { - - /* Generate recordsPerSec records to put onto the stream */ - val records = (1 to recordsPerSecond.toInt).map { recordNum => - /* - * Randomly generate each wordsPerRec words between 0 (inclusive) - * and MAX_RANDOM_INTS (exclusive) - */ + s" $recordsPerSecond records per second and $wordsPerRecord words per record") + + // Iterate and put records onto the stream per the given recordPerSec and wordsPerRecord + for (i <- 1 to 10) { + // Generate recordsPerSec records to put onto the stream + val records = (1 to recordsPerSecond.toInt).foreach { recordNum => + // Randomly generate wordsPerRecord number of words val data = (1 to wordsPerRecord.toInt).map(x => { - /* Generate the random int */ - val randomInt = Random.nextInt(MaxRandomInts) + // Get a random index to a word + val randomWordIdx = Random.nextInt(randomWords.size) + val randomWord = randomWords(randomWordIdx) - /* Keep track of the totals */ - totals(randomInt) += 1 + // Increment total count to compare to server counts later + totals(randomWord) = totals.getOrElse(randomWord, 0) + 1 - randomInt.toString() + randomWord }).mkString(" ") - /* Create a partitionKey based on recordNum */ + // Create a partitionKey based on recordNum val partitionKey = s"partitionKey-$recordNum" - /* Create a PutRecordRequest with an Array[Byte] version of the data */ + // Create a PutRecordRequest with an Array[Byte] version of the data val putRecordRequest = new PutRecordRequest().withStreamName(stream) .withPartitionKey(partitionKey) - .withData(ByteBuffer.wrap(data.getBytes())); + .withData(ByteBuffer.wrap(data.getBytes())) - /* Put the record onto the stream and capture the PutRecordResult */ - val putRecordResult = kinesisClient.putRecord(putRecordRequest); + // Put the record onto the stream and capture the PutRecordResult + val putRecordResult = kinesisClient.putRecord(putRecordRequest) } - /* Sleep for a second */ + // Sleep for a second Thread.sleep(1000) println("Sent " + recordsPerSecond + " records") } - - /* Convert the totals to (index, total) tuple */ - (0 to (MaxRandomInts - 1)).zip(totals) + // Convert the totals to (index, total) tuple + totals.toSeq.sortBy(_._1) } } -/** - * Utility functions for Spark Streaming examples. +/** + * Utility functions for Spark Streaming examples. * This has been lifted from the examples/ project to remove the circular dependency. */ private[streaming] object StreamingExamples extends Logging { - - /** Set reasonable logging levels for streaming if the user has not configured log4j. */ + // Set reasonable logging levels for streaming if the user has not configured log4j. def setStreamingLogLevels() { val log4jInitialized = Logger.getRootLogger.getAllAppenders.hasMoreElements if (!log4jInitialized) { diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala index 588e86a1887ec..83a4537559512 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala @@ -23,20 +23,20 @@ import org.apache.spark.util.{Clock, ManualClock, SystemClock} /** * This is a helper class for managing checkpoint clocks. * - * @param checkpointInterval + * @param checkpointInterval * @param currentClock. Default to current SystemClock if none is passed in (mocking purposes) */ private[kinesis] class KinesisCheckpointState( - checkpointInterval: Duration, + checkpointInterval: Duration, currentClock: Clock = new SystemClock()) extends Logging { - + /* Initialize the checkpoint clock using the given currentClock + checkpointInterval millis */ val checkpointClock = new ManualClock() checkpointClock.setTime(currentClock.getTimeMillis() + checkpointInterval.milliseconds) /** - * Check if it's time to checkpoint based on the current time and the derived time + * Check if it's time to checkpoint based on the current time and the derived time * for the next checkpoint * * @return true if it's time to checkpoint @@ -48,7 +48,7 @@ private[kinesis] class KinesisCheckpointState( /** * Advance the checkpoint clock by the checkpoint interval. */ - def advanceCheckpoint() = { + def advanceCheckpoint(): Unit = { checkpointClock.advance(checkpointInterval.milliseconds) } } diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala index a7fe4476cacb8..1a8a4cecc1141 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala @@ -16,39 +16,45 @@ */ package org.apache.spark.streaming.kinesis -import java.net.InetAddress import java.util.UUID +import scala.util.control.NonFatal + +import com.amazonaws.auth.{AWSCredentials, AWSCredentialsProvider, BasicAWSCredentials, DefaultAWSCredentialsProviderChain} +import com.amazonaws.services.kinesis.clientlibrary.interfaces.{IRecordProcessor, IRecordProcessorFactory} +import com.amazonaws.services.kinesis.clientlibrary.lib.worker.{InitialPositionInStream, KinesisClientLibConfiguration, Worker} + import org.apache.spark.Logging import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.Duration import org.apache.spark.streaming.receiver.Receiver import org.apache.spark.util.Utils -import com.amazonaws.auth.AWSCredentialsProvider -import com.amazonaws.auth.DefaultAWSCredentialsProviderChain -import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessor -import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorFactory -import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream -import com.amazonaws.services.kinesis.clientlibrary.lib.worker.KinesisClientLibConfiguration -import com.amazonaws.services.kinesis.clientlibrary.lib.worker.Worker + +private[kinesis] +case class SerializableAWSCredentials(accessKeyId: String, secretKey: String) + extends AWSCredentials { + override def getAWSAccessKeyId: String = accessKeyId + override def getAWSSecretKey: String = secretKey +} /** * Custom AWS Kinesis-specific implementation of Spark Streaming's Receiver. * This implementation relies on the Kinesis Client Library (KCL) Worker as described here: * https://github.com/awslabs/amazon-kinesis-client - * This is a custom receiver used with StreamingContext.receiverStream(Receiver) - * as described here: - * http://spark.apache.org/docs/latest/streaming-custom-receivers.html - * Instances of this class will get shipped to the Spark Streaming Workers - * to run within a Spark Executor. + * This is a custom receiver used with StreamingContext.receiverStream(Receiver) as described here: + * http://spark.apache.org/docs/latest/streaming-custom-receivers.html + * Instances of this class will get shipped to the Spark Streaming Workers to run within a + * Spark Executor. * * @param appName Kinesis application name. Kinesis Apps are mapped to Kinesis Streams * by the Kinesis Client Library. If you change the App name or Stream name, - * the KCL will throw errors. This usually requires deleting the backing + * the KCL will throw errors. This usually requires deleting the backing * DynamoDB table with the same name this Kinesis application. * @param streamName Kinesis stream name * @param endpointUrl Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com) + * @param regionName Region name used by the Kinesis Client Library for + * DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics) * @param checkpointInterval Checkpoint interval for Kinesis checkpointing. * See the Kinesis Spark Streaming documentation for more * details on the different types of checkpoints. @@ -59,92 +65,121 @@ import com.amazonaws.services.kinesis.clientlibrary.lib.worker.Worker * (InitialPositionInStream.TRIM_HORIZON) or * the tip of the stream (InitialPositionInStream.LATEST). * @param storageLevel Storage level to use for storing the received objects - * - * @return ReceiverInputDStream[Array[Byte]] + * @param awsCredentialsOption Optional AWS credentials, used when user directly specifies + * the credentials */ private[kinesis] class KinesisReceiver( appName: String, streamName: String, endpointUrl: String, - checkpointInterval: Duration, + regionName: String, initialPositionInStream: InitialPositionInStream, - storageLevel: StorageLevel) - extends Receiver[Array[Byte]](storageLevel) with Logging { receiver => - - /* - * The following vars are built in the onStart() method which executes in the Spark Worker after - * this code is serialized and shipped remotely. - */ + checkpointInterval: Duration, + storageLevel: StorageLevel, + awsCredentialsOption: Option[SerializableAWSCredentials] + ) extends Receiver[Array[Byte]](storageLevel) with Logging { receiver => /* - * workerId should be based on the ip address of the actual Spark Worker where this code runs - * (not the Driver's ip address.) + * ================================================================================= + * The following vars are initialize in the onStart() method which executes in the + * Spark worker after this Receiver is serialized and shipped to the worker. + * ================================================================================= */ - var workerId: String = null - /* - * This impl uses the DefaultAWSCredentialsProviderChain and searches for credentials - * in the following order of precedence: - * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY - * Java System Properties - aws.accessKeyId and aws.secretKey - * Credential profiles file at the default location (~/.aws/credentials) shared by all - * AWS SDKs and the AWS CLI - * Instance profile credentials delivered through the Amazon EC2 metadata service + /** + * workerId is used by the KCL should be based on the ip address of the actual Spark Worker + * where this code runs (not the driver's IP address.) */ - var credentialsProvider: AWSCredentialsProvider = null - - /* KCL config instance. */ - var kinesisClientLibConfiguration: KinesisClientLibConfiguration = null + private var workerId: String = null - /* - * RecordProcessorFactory creates impls of IRecordProcessor. - * IRecordProcessor adapts the KCL to our Spark KinesisReceiver via the - * IRecordProcessor.processRecords() method. - * We're using our custom KinesisRecordProcessor in this case. + /** + * Worker is the core client abstraction from the Kinesis Client Library (KCL). + * A worker can process more than one shards from the given stream. + * Each shard is assigned its own IRecordProcessor and the worker run multiple such + * processors. */ - var recordProcessorFactory: IRecordProcessorFactory = null + private var worker: Worker = null - /* - * Create a Kinesis Worker. - * This is the core client abstraction from the Kinesis Client Library (KCL). - * We pass the RecordProcessorFactory from above as well as the KCL config instance. - * A Kinesis Worker can process 1..* shards from the given stream - each with its - * own RecordProcessor. - */ - var worker: Worker = null + /** Thread running the worker */ + private var workerThread: Thread = null /** - * This is called when the KinesisReceiver starts and must be non-blocking. - * The KCL creates and manages the receiving/processing thread pool through the Worker.run() - * method. + * This is called when the KinesisReceiver starts and must be non-blocking. + * The KCL creates and manages the receiving/processing thread pool through Worker.run(). */ override def onStart() { workerId = Utils.localHostName() + ":" + UUID.randomUUID() - credentialsProvider = new DefaultAWSCredentialsProviderChain() - kinesisClientLibConfiguration = new KinesisClientLibConfiguration(appName, streamName, - credentialsProvider, workerId).withKinesisEndpoint(endpointUrl) - .withInitialPositionInStream(initialPositionInStream).withTaskBackoffTimeMillis(500) - recordProcessorFactory = new IRecordProcessorFactory { + + // KCL config instance + val awsCredProvider = resolveAWSCredentialsProvider() + val kinesisClientLibConfiguration = + new KinesisClientLibConfiguration(appName, streamName, awsCredProvider, workerId) + .withKinesisEndpoint(endpointUrl) + .withInitialPositionInStream(initialPositionInStream) + .withTaskBackoffTimeMillis(500) + .withRegionName(regionName) + + /* + * RecordProcessorFactory creates impls of IRecordProcessor. + * IRecordProcessor adapts the KCL to our Spark KinesisReceiver via the + * IRecordProcessor.processRecords() method. + * We're using our custom KinesisRecordProcessor in this case. + */ + val recordProcessorFactory = new IRecordProcessorFactory { override def createProcessor: IRecordProcessor = new KinesisRecordProcessor(receiver, workerId, new KinesisCheckpointState(checkpointInterval)) } + worker = new Worker(recordProcessorFactory, kinesisClientLibConfiguration) - worker.run() + workerThread = new Thread() { + override def run(): Unit = { + try { + worker.run() + } catch { + case NonFatal(e) => + restart("Error running the KCL worker in Receiver", e) + } + } + } + workerThread.setName(s"Kinesis Receiver ${streamId}") + workerThread.setDaemon(true) + workerThread.start() logInfo(s"Started receiver with workerId $workerId") } /** - * This is called when the KinesisReceiver stops. - * The KCL worker.shutdown() method stops the receiving/processing threads. - * The KCL will do its best to drain and checkpoint any in-flight records upon shutdown. + * This is called when the KinesisReceiver stops. + * The KCL worker.shutdown() method stops the receiving/processing threads. + * The KCL will do its best to drain and checkpoint any in-flight records upon shutdown. */ override def onStop() { - worker.shutdown() - logInfo(s"Shut down receiver with workerId $workerId") + if (workerThread != null) { + if (worker != null) { + worker.shutdown() + worker = null + } + workerThread.join() + workerThread = null + logInfo(s"Stopped receiver for workerId $workerId") + } workerId = null - credentialsProvider = null - kinesisClientLibConfiguration = null - recordProcessorFactory = null - worker = null + } + + /** + * If AWS credential is provided, return a AWSCredentialProvider returning that credential. + * Otherwise, return the DefaultAWSCredentialsProviderChain. + */ + private def resolveAWSCredentialsProvider(): AWSCredentialsProvider = { + awsCredentialsOption match { + case Some(awsCredentials) => + logInfo("Using provided AWS credentials") + new AWSCredentialsProvider { + override def getCredentials: AWSCredentials = awsCredentials + override def refresh(): Unit = { } + } + case None => + logInfo("Using DefaultAWSCredentialsProviderChain") + new DefaultAWSCredentialsProviderChain() + } } } diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala index af8cd875b4541..fe9e3a0c793e2 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala @@ -35,7 +35,10 @@ import com.amazonaws.services.kinesis.model.Record /** * Kinesis-specific implementation of the Kinesis Client Library (KCL) IRecordProcessor. * This implementation operates on the Array[Byte] from the KinesisReceiver. - * The Kinesis Worker creates an instance of this KinesisRecordProcessor upon startup. + * The Kinesis Worker creates an instance of this KinesisRecordProcessor for each + * shard in the Kinesis stream upon startup. This is normally done in separate threads, + * but the KCLs within the KinesisReceivers will balance themselves out if you create + * multiple Receivers. * * @param receiver Kinesis receiver * @param workerId for logging purposes @@ -47,8 +50,8 @@ private[kinesis] class KinesisRecordProcessor( workerId: String, checkpointState: KinesisCheckpointState) extends IRecordProcessor with Logging { - /* shardId to be populated during initialize() */ - var shardId: String = _ + // shardId to be populated during initialize() + private var shardId: String = _ /** * The Kinesis Client Library calls this method during IRecordProcessor initialization. @@ -56,8 +59,8 @@ private[kinesis] class KinesisRecordProcessor( * @param shardId assigned by the KCL to this particular RecordProcessor. */ override def initialize(shardId: String) { - logInfo(s"Initialize: Initializing workerId $workerId with shardId $shardId") this.shardId = shardId + logInfo(s"Initialized workerId $workerId with shardId $shardId") } /** @@ -66,29 +69,34 @@ private[kinesis] class KinesisRecordProcessor( * and Spark Streaming's Receiver.store(). * * @param batch list of records from the Kinesis stream shard - * @param checkpointer used to update Kinesis when this batch has been processed/stored + * @param checkpointer used to update Kinesis when this batch has been processed/stored * in the DStream */ override def processRecords(batch: List[Record], checkpointer: IRecordProcessorCheckpointer) { if (!receiver.isStopped()) { try { /* - * Note: If we try to store the raw ByteBuffer from record.getData(), the Spark Streaming - * Receiver.store(ByteBuffer) attempts to deserialize the ByteBuffer using the - * internally-configured Spark serializer (kryo, etc). - * This is not desirable, so we instead store a raw Array[Byte] and decouple - * ourselves from Spark's internal serialization strategy. - */ + * Notes: + * 1) If we try to store the raw ByteBuffer from record.getData(), the Spark Streaming + * Receiver.store(ByteBuffer) attempts to deserialize the ByteBuffer using the + * internally-configured Spark serializer (kryo, etc). + * 2) This is not desirable, so we instead store a raw Array[Byte] and decouple + * ourselves from Spark's internal serialization strategy. + * 3) For performance, the BlockGenerator is asynchronously queuing elements within its + * memory before creating blocks. This prevents the small block scenario, but requires + * that you register callbacks to know when a block has been generated and stored + * (WAL is sufficient for storage) before can checkpoint back to the source. + */ batch.foreach(record => receiver.store(record.getData().array())) - + logDebug(s"Stored: Worker $workerId stored ${batch.size} records for shardId $shardId") /* - * Checkpoint the sequence number of the last record successfully processed/stored + * Checkpoint the sequence number of the last record successfully processed/stored * in the batch. * In this implementation, we're checkpointing after the given checkpointIntervalMillis. - * Note that this logic requires that processRecords() be called AND that it's time to - * checkpoint. I point this out because there is no background thread running the + * Note that this logic requires that processRecords() be called AND that it's time to + * checkpoint. I point this out because there is no background thread running the * checkpointer. Checkpointing is tested and trigger only when a new batch comes in. * If the worker is shutdown cleanly, checkpoint will happen (see shutdown() below). * However, if the worker dies unexpectedly, a checkpoint may not happen. @@ -116,22 +124,22 @@ private[kinesis] class KinesisRecordProcessor( logError(s"Exception: WorkerId $workerId encountered and exception while storing " + " or checkpointing a batch for workerId $workerId and shardId $shardId.", e) - /* Rethrow the exception to the Kinesis Worker that is managing this RecordProcessor.*/ + /* Rethrow the exception to the Kinesis Worker that is managing this RecordProcessor. */ throw e } } } else { /* RecordProcessor has been stopped. */ - logInfo(s"Stopped: The Spark KinesisReceiver has stopped for workerId $workerId" + + logInfo(s"Stopped: The Spark KinesisReceiver has stopped for workerId $workerId" + s" and shardId $shardId. No more records will be processed.") } } /** * Kinesis Client Library is shutting down this Worker for 1 of 2 reasons: - * 1) the stream is resharding by splitting or merging adjacent shards + * 1) the stream is resharding by splitting or merging adjacent shards * (ShutdownReason.TERMINATE) - * 2) the failed or latent Worker has stopped sending heartbeats for whatever reason + * 2) the failed or latent Worker has stopped sending heartbeats for whatever reason * (ShutdownReason.ZOMBIE) * * @param checkpointer used to perform a Kinesis checkpoint for ShutdownReason.TERMINATE @@ -145,7 +153,7 @@ private[kinesis] class KinesisRecordProcessor( * Checkpoint to indicate that all records from the shard have been drained and processed. * It's now OK to read from the new shards that resulted from a resharding event. */ - case ShutdownReason.TERMINATE => + case ShutdownReason.TERMINATE => KinesisRecordProcessor.retryRandom(checkpointer.checkpoint(), 4, 100) /* @@ -190,7 +198,7 @@ private[kinesis] object KinesisRecordProcessor extends Logging { logError(s"Retryable Exception: Random backOffMillis=${backOffMillis}", e) retryRandom(expression, numRetriesLeft - 1, maxBackOffMillis) } - /* Throw: Shutdown has been requested by the Kinesis Client Library.*/ + /* Throw: Shutdown has been requested by the Kinesis Client Library. */ case _: ShutdownException => { logError(s"ShutdownException: Caught shutdown exception, skipping checkpoint.", e) throw e diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala index 96f4399accd3a..e5acab50181e1 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala @@ -16,29 +16,78 @@ */ package org.apache.spark.streaming.kinesis -import org.apache.spark.annotation.Experimental +import com.amazonaws.regions.RegionUtils +import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream + import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.Duration -import org.apache.spark.streaming.StreamingContext -import org.apache.spark.streaming.api.java.JavaReceiverInputDStream -import org.apache.spark.streaming.api.java.JavaStreamingContext +import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext} import org.apache.spark.streaming.dstream.ReceiverInputDStream - -import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream +import org.apache.spark.streaming.{Duration, StreamingContext} -/** - * Helper class to create Amazon Kinesis Input Stream - * :: Experimental :: - */ -@Experimental object KinesisUtils { /** - * Create an InputDStream that pulls messages from a Kinesis stream. - * :: Experimental :: - * @param ssc StreamingContext object + * Create an input stream that pulls messages from a Kinesis stream. + * This uses the Kinesis Client Library (KCL) to pull messages from Kinesis. + * + * Note: The AWS credentials will be discovered using the DefaultAWSCredentialsProviderChain + * on the workers. See AWS documentation to understand how DefaultAWSCredentialsProviderChain + * gets the AWS credentials. + * + * @param ssc StreamingContext object + * @param kinesisAppName Kinesis application name used by the Kinesis Client Library + * (KCL) to update DynamoDB + * @param streamName Kinesis stream name + * @param endpointUrl Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com) + * @param regionName Name of region used by the Kinesis Client Library (KCL) to update + * DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics) + * @param initialPositionInStream In the absence of Kinesis checkpoint info, this is the + * worker's initial starting position in the stream. + * The values are either the beginning of the stream + * per Kinesis' limit of 24 hours + * (InitialPositionInStream.TRIM_HORIZON) or + * the tip of the stream (InitialPositionInStream.LATEST). + * @param checkpointInterval Checkpoint interval for Kinesis checkpointing. + * See the Kinesis Spark Streaming documentation for more + * details on the different types of checkpoints. + * @param storageLevel Storage level to use for storing the received objects. + * StorageLevel.MEMORY_AND_DISK_2 is recommended. + */ + def createStream( + ssc: StreamingContext, + kinesisAppName: String, + streamName: String, + endpointUrl: String, + regionName: String, + initialPositionInStream: InitialPositionInStream, + checkpointInterval: Duration, + storageLevel: StorageLevel + ): ReceiverInputDStream[Array[Byte]] = { + // Setting scope to override receiver stream's scope of "receiver stream" + ssc.withNamedScope("kinesis stream") { + ssc.receiverStream( + new KinesisReceiver(kinesisAppName, streamName, endpointUrl, validateRegion(regionName), + initialPositionInStream, checkpointInterval, storageLevel, None)) + } + } + + /** + * Create an input stream that pulls messages from a Kinesis stream. + * This uses the Kinesis Client Library (KCL) to pull messages from Kinesis. + * + * Note: + * The given AWS credentials will get saved in DStream checkpoints if checkpointing + * is enabled. Make sure that your checkpoint directory is secure. + * + * @param ssc StreamingContext object + * @param kinesisAppName Kinesis application name used by the Kinesis Client Library + * (KCL) to update DynamoDB * @param streamName Kinesis stream name * @param endpointUrl Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com) + * @param regionName Name of region used by the Kinesis Client Library (KCL) to update + * DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics) + * @param awsAccessKeyId AWS AccessKeyId (if null, will use DefaultAWSCredentialsProviderChain) + * @param awsSecretKey AWS SecretKey (if null, will use DefaultAWSCredentialsProviderChain) * @param checkpointInterval Checkpoint interval for Kinesis checkpointing. * See the Kinesis Spark Streaming documentation for more * details on the different types of checkpoints. @@ -48,28 +97,84 @@ object KinesisUtils { * per Kinesis' limit of 24 hours * (InitialPositionInStream.TRIM_HORIZON) or * the tip of the stream (InitialPositionInStream.LATEST). - * @param storageLevel Storage level to use for storing the received objects + * @param storageLevel Storage level to use for storing the received objects. + * StorageLevel.MEMORY_AND_DISK_2 is recommended. + */ + def createStream( + ssc: StreamingContext, + kinesisAppName: String, + streamName: String, + endpointUrl: String, + regionName: String, + initialPositionInStream: InitialPositionInStream, + checkpointInterval: Duration, + storageLevel: StorageLevel, + awsAccessKeyId: String, + awsSecretKey: String + ): ReceiverInputDStream[Array[Byte]] = { + ssc.receiverStream( + new KinesisReceiver(kinesisAppName, streamName, endpointUrl, validateRegion(regionName), + initialPositionInStream, checkpointInterval, storageLevel, + Some(SerializableAWSCredentials(awsAccessKeyId, awsSecretKey)))) + } + + /** + * Create an input stream that pulls messages from a Kinesis stream. + * This uses the Kinesis Client Library (KCL) to pull messages from Kinesis. * - * @return ReceiverInputDStream[Array[Byte]] + * Note: + * - The AWS credentials will be discovered using the DefaultAWSCredentialsProviderChain + * on the workers. See AWS documentation to understand how DefaultAWSCredentialsProviderChain + * gets AWS credentials. + * - The region of the `endpointUrl` will be used for DynamoDB and CloudWatch. + * - The Kinesis application name used by the Kinesis Client Library (KCL) will be the app name in + * [[org.apache.spark.SparkConf]]. + * + * @param ssc Java StreamingContext object + * @param streamName Kinesis stream name + * @param endpointUrl Endpoint url of Kinesis service + * (e.g., https://kinesis.us-east-1.amazonaws.com) + * @param checkpointInterval Checkpoint interval for Kinesis checkpointing. + * See the Kinesis Spark Streaming documentation for more + * details on the different types of checkpoints. + * @param initialPositionInStream In the absence of Kinesis checkpoint info, this is the + * worker's initial starting position in the stream. + * The values are either the beginning of the stream + * per Kinesis' limit of 24 hours + * (InitialPositionInStream.TRIM_HORIZON) or + * the tip of the stream (InitialPositionInStream.LATEST). + * @param storageLevel Storage level to use for storing the received objects + * StorageLevel.MEMORY_AND_DISK_2 is recommended. */ - @Experimental + @deprecated("use other forms of createStream", "1.4.0") def createStream( ssc: StreamingContext, streamName: String, endpointUrl: String, checkpointInterval: Duration, initialPositionInStream: InitialPositionInStream, - storageLevel: StorageLevel): ReceiverInputDStream[Array[Byte]] = { - ssc.receiverStream(new KinesisReceiver(ssc.sc.appName, streamName, endpointUrl, - checkpointInterval, initialPositionInStream, storageLevel)) + storageLevel: StorageLevel + ): ReceiverInputDStream[Array[Byte]] = { + ssc.receiverStream( + new KinesisReceiver(ssc.sc.appName, streamName, endpointUrl, getRegionByEndpoint(endpointUrl), + initialPositionInStream, checkpointInterval, storageLevel, None)) } /** - * Create a Java-friendly InputDStream that pulls messages from a Kinesis stream. - * :: Experimental :: + * Create an input stream that pulls messages from a Kinesis stream. + * This uses the Kinesis Client Library (KCL) to pull messages from Kinesis. + * + * Note: The AWS credentials will be discovered using the DefaultAWSCredentialsProviderChain + * on the workers. See AWS documentation to understand how DefaultAWSCredentialsProviderChain + * gets the AWS credentials. + * * @param jssc Java StreamingContext object + * @param kinesisAppName Kinesis application name used by the Kinesis Client Library + * (KCL) to update DynamoDB * @param streamName Kinesis stream name * @param endpointUrl Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com) + * @param regionName Name of region used by the Kinesis Client Library (KCL) to update + * DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics) * @param checkpointInterval Checkpoint interval for Kinesis checkpointing. * See the Kinesis Spark Streaming documentation for more * details on the different types of checkpoints. @@ -79,19 +184,116 @@ object KinesisUtils { * per Kinesis' limit of 24 hours * (InitialPositionInStream.TRIM_HORIZON) or * the tip of the stream (InitialPositionInStream.LATEST). - * @param storageLevel Storage level to use for storing the received objects + * @param storageLevel Storage level to use for storing the received objects. + * StorageLevel.MEMORY_AND_DISK_2 is recommended. + */ + def createStream( + jssc: JavaStreamingContext, + kinesisAppName: String, + streamName: String, + endpointUrl: String, + regionName: String, + initialPositionInStream: InitialPositionInStream, + checkpointInterval: Duration, + storageLevel: StorageLevel + ): JavaReceiverInputDStream[Array[Byte]] = { + createStream(jssc.ssc, kinesisAppName, streamName, endpointUrl, regionName, + initialPositionInStream, checkpointInterval, storageLevel) + } + + /** + * Create an input stream that pulls messages from a Kinesis stream. + * This uses the Kinesis Client Library (KCL) to pull messages from Kinesis. * - * @return JavaReceiverInputDStream[Array[Byte]] + * Note: + * The given AWS credentials will get saved in DStream checkpoints if checkpointing + * is enabled. Make sure that your checkpoint directory is secure. + * + * @param jssc Java StreamingContext object + * @param kinesisAppName Kinesis application name used by the Kinesis Client Library + * (KCL) to update DynamoDB + * @param streamName Kinesis stream name + * @param endpointUrl Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com) + * @param regionName Name of region used by the Kinesis Client Library (KCL) to update + * DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics) + * @param awsAccessKeyId AWS AccessKeyId (if null, will use DefaultAWSCredentialsProviderChain) + * @param awsSecretKey AWS SecretKey (if null, will use DefaultAWSCredentialsProviderChain) + * @param checkpointInterval Checkpoint interval for Kinesis checkpointing. + * See the Kinesis Spark Streaming documentation for more + * details on the different types of checkpoints. + * @param initialPositionInStream In the absence of Kinesis checkpoint info, this is the + * worker's initial starting position in the stream. + * The values are either the beginning of the stream + * per Kinesis' limit of 24 hours + * (InitialPositionInStream.TRIM_HORIZON) or + * the tip of the stream (InitialPositionInStream.LATEST). + * @param storageLevel Storage level to use for storing the received objects. + * StorageLevel.MEMORY_AND_DISK_2 is recommended. */ - @Experimental def createStream( - jssc: JavaStreamingContext, - streamName: String, - endpointUrl: String, + jssc: JavaStreamingContext, + kinesisAppName: String, + streamName: String, + endpointUrl: String, + regionName: String, + initialPositionInStream: InitialPositionInStream, + checkpointInterval: Duration, + storageLevel: StorageLevel, + awsAccessKeyId: String, + awsSecretKey: String + ): JavaReceiverInputDStream[Array[Byte]] = { + createStream(jssc.ssc, kinesisAppName, streamName, endpointUrl, regionName, + initialPositionInStream, checkpointInterval, storageLevel, awsAccessKeyId, awsSecretKey) + } + + /** + * Create an input stream that pulls messages from a Kinesis stream. + * This uses the Kinesis Client Library (KCL) to pull messages from Kinesis. + * + * Note: + * - The AWS credentials will be discovered using the DefaultAWSCredentialsProviderChain + * on the workers. See AWS documentation to understand how DefaultAWSCredentialsProviderChain + * gets AWS credentials. + * - The region of the `endpointUrl` will be used for DynamoDB and CloudWatch. + * - The Kinesis application name used by the Kinesis Client Library (KCL) will be the app name in + * [[org.apache.spark.SparkConf]]. + * + * @param jssc Java StreamingContext object + * @param streamName Kinesis stream name + * @param endpointUrl Endpoint url of Kinesis service + * (e.g., https://kinesis.us-east-1.amazonaws.com) + * @param checkpointInterval Checkpoint interval for Kinesis checkpointing. + * See the Kinesis Spark Streaming documentation for more + * details on the different types of checkpoints. + * @param initialPositionInStream In the absence of Kinesis checkpoint info, this is the + * worker's initial starting position in the stream. + * The values are either the beginning of the stream + * per Kinesis' limit of 24 hours + * (InitialPositionInStream.TRIM_HORIZON) or + * the tip of the stream (InitialPositionInStream.LATEST). + * @param storageLevel Storage level to use for storing the received objects + * StorageLevel.MEMORY_AND_DISK_2 is recommended. + */ + @deprecated("use other forms of createStream", "1.4.0") + def createStream( + jssc: JavaStreamingContext, + streamName: String, + endpointUrl: String, checkpointInterval: Duration, initialPositionInStream: InitialPositionInStream, - storageLevel: StorageLevel): JavaReceiverInputDStream[Array[Byte]] = { - jssc.receiverStream(new KinesisReceiver(jssc.ssc.sc.appName, streamName, - endpointUrl, checkpointInterval, initialPositionInStream, storageLevel)) + storageLevel: StorageLevel + ): JavaReceiverInputDStream[Array[Byte]] = { + createStream( + jssc.ssc, streamName, endpointUrl, checkpointInterval, initialPositionInStream, storageLevel) + } + + private def getRegionByEndpoint(endpointUrl: String): String = { + RegionUtils.getRegionByEndpoint(endpointUrl).getName() + } + + private def validateRegion(regionName: String): String = { + Option(RegionUtils.getRegion(regionName)).map { _.getName }.getOrElse { + throw new IllegalArgumentException(s"Region name '$regionName' is not valid") + } } } diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala index 255fe65819608..2103dca6b766f 100644 --- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala +++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala @@ -20,26 +20,18 @@ import java.nio.ByteBuffer import scala.collection.JavaConversions.seqAsJavaList -import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.Milliseconds -import org.apache.spark.streaming.Seconds -import org.apache.spark.streaming.StreamingContext -import org.apache.spark.streaming.TestSuiteBase -import org.apache.spark.util.{ManualClock, Clock} - -import org.mockito.Mockito._ -import org.scalatest.BeforeAndAfter -import org.scalatest.Matchers -import org.scalatest.mock.MockitoSugar - -import com.amazonaws.services.kinesis.clientlibrary.exceptions.InvalidStateException -import com.amazonaws.services.kinesis.clientlibrary.exceptions.KinesisClientLibDependencyException -import com.amazonaws.services.kinesis.clientlibrary.exceptions.ShutdownException -import com.amazonaws.services.kinesis.clientlibrary.exceptions.ThrottlingException +import com.amazonaws.services.kinesis.clientlibrary.exceptions.{InvalidStateException, KinesisClientLibDependencyException, ShutdownException, ThrottlingException} import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorCheckpointer import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason import com.amazonaws.services.kinesis.model.Record +import org.mockito.Mockito._ +import org.scalatest.{BeforeAndAfter, Matchers} +import org.scalatest.mock.MockitoSugar + +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext, TestSuiteBase} +import org.apache.spark.util.{Clock, ManualClock, Utils} /** * Suite of Kinesis streaming receiver tests focusing mostly on the KinesisRecordProcessor @@ -65,7 +57,7 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft var checkpointStateMock: KinesisCheckpointState = _ var currentClockMock: Clock = _ - override def beforeFunction() = { + override def beforeFunction(): Unit = { receiverMock = mock[KinesisReceiver] checkpointerMock = mock[IRecordProcessorCheckpointer] checkpointClockMock = mock[ManualClock] @@ -81,15 +73,28 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft checkpointStateMock, currentClockMock) } - test("kinesis utils api") { + test("KinesisUtils API") { val ssc = new StreamingContext(master, framework, batchDuration) // Tests the API, does not actually test data receiving - val kinesisStream = KinesisUtils.createStream(ssc, "mySparkStream", + val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream", "https://kinesis.us-west-2.amazonaws.com", Seconds(2), - InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2); + InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2) + val kinesisStream2 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream", + "https://kinesis.us-west-2.amazonaws.com", "us-west-2", + InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2) + val kinesisStream3 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream", + "https://kinesis.us-west-2.amazonaws.com", "us-west-2", + InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2, + "awsAccessKey", "awsSecretKey") + ssc.stop() } + test("check serializability of SerializableAWSCredentials") { + Utils.deserialize[SerializableAWSCredentials]( + Utils.serialize(new SerializableAWSCredentials("x", "y"))) + } + test("process records including store and checkpoint") { when(receiverMock.isStopped()).thenReturn(false) when(checkpointStateMock.shouldCheckpoint()).thenReturn(true) diff --git a/extras/spark-ganglia-lgpl/pom.xml b/extras/spark-ganglia-lgpl/pom.xml index e14bbae4a9b6e..478d0019a25f0 100644 --- a/extras/spark-ganglia-lgpl/pom.xml +++ b/extras/spark-ganglia-lgpl/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml diff --git a/graphx/pom.xml b/graphx/pom.xml index d38a3aa8256b7..853dea9a7795e 100644 --- a/graphx/pom.xml +++ b/graphx/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -40,6 +40,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + com.google.guava guava diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala index 058c8c8aa1b24..ce1054ed92ba1 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala @@ -26,8 +26,8 @@ class EdgeDirection private (private val name: String) extends Serializable { * out becomes in and both and either remain the same. */ def reverse: EdgeDirection = this match { - case EdgeDirection.In => EdgeDirection.Out - case EdgeDirection.Out => EdgeDirection.In + case EdgeDirection.In => EdgeDirection.Out + case EdgeDirection.Out => EdgeDirection.In case EdgeDirection.Either => EdgeDirection.Either case EdgeDirection.Both => EdgeDirection.Both } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala index cc70b396a8dd4..4611a3ace219b 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala @@ -41,14 +41,16 @@ abstract class EdgeRDD[ED]( @transient sc: SparkContext, @transient deps: Seq[Dependency[_]]) extends RDD[Edge[ED]](sc, deps) { + // scalastyle:off structural.type private[graphx] def partitionsRDD: RDD[(PartitionID, EdgePartition[ED, VD])] forSome { type VD } + // scalastyle:on structural.type override protected def getPartitions: Array[Partition] = partitionsRDD.partitions override def compute(part: Partition, context: TaskContext): Iterator[Edge[ED]] = { val p = firstParent[(PartitionID, EdgePartition[ED, _])].iterator(part, context) if (p.hasNext) { - p.next._2.iterator.map(_.copy()) + p.next()._2.iterator.map(_.copy()) } else { Iterator.empty } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala index c8790cac3d8a0..65f82429d2029 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala @@ -37,7 +37,7 @@ class EdgeTriplet[VD, ED] extends Edge[ED] { /** * Set the edge properties of this triplet. */ - protected[spark] def set(other: Edge[ED]): EdgeTriplet[VD,ED] = { + protected[spark] def set(other: Edge[ED]): EdgeTriplet[VD, ED] = { srcId = other.srcId dstId = other.dstId attr = other.attr diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala index 36dc7b0f86c89..db73a8abc5733 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala @@ -316,7 +316,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab * satisfy the predicates */ def subgraph( - epred: EdgeTriplet[VD,ED] => Boolean = (x => true), + epred: EdgeTriplet[VD, ED] => Boolean = (x => true), vpred: (VertexId, VD) => Boolean = ((v, d) => true)) : Graph[VD, ED] diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala index 7edd627b20918..9451ff1e5c0e2 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala @@ -124,18 +124,18 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[Array[(VertexId, VD)]] = { val nbrs = edgeDirection match { case EdgeDirection.Either => - graph.aggregateMessages[Array[(VertexId,VD)]]( + graph.aggregateMessages[Array[(VertexId, VD)]]( ctx => { ctx.sendToSrc(Array((ctx.dstId, ctx.dstAttr))) ctx.sendToDst(Array((ctx.srcId, ctx.srcAttr))) }, (a, b) => a ++ b, TripletFields.All) case EdgeDirection.In => - graph.aggregateMessages[Array[(VertexId,VD)]]( + graph.aggregateMessages[Array[(VertexId, VD)]]( ctx => ctx.sendToDst(Array((ctx.srcId, ctx.srcAttr))), (a, b) => a ++ b, TripletFields.Src) case EdgeDirection.Out => - graph.aggregateMessages[Array[(VertexId,VD)]]( + graph.aggregateMessages[Array[(VertexId, VD)]]( ctx => ctx.sendToSrc(Array((ctx.dstId, ctx.dstAttr))), (a, b) => a ++ b, TripletFields.Dst) case EdgeDirection.Both => @@ -253,7 +253,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali def filter[VD2: ClassTag, ED2: ClassTag]( preprocess: Graph[VD, ED] => Graph[VD2, ED2], epred: (EdgeTriplet[VD2, ED2]) => Boolean = (x: EdgeTriplet[VD2, ED2]) => true, - vpred: (VertexId, VD2) => Boolean = (v:VertexId, d:VD2) => true): Graph[VD, ED] = { + vpred: (VertexId, VD2) => Boolean = (v: VertexId, d: VD2) => true): Graph[VD, ED] = { graph.mask(preprocess(graph).subgraph(epred, vpred)) } @@ -356,7 +356,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali maxIterations: Int = Int.MaxValue, activeDirection: EdgeDirection = EdgeDirection.Either)( vprog: (VertexId, VD, A) => VD, - sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId,A)], + sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)], mergeMsg: (A, A) => A) : Graph[VD, ED] = { Pregel(graph, initialMsg, maxIterations, activeDirection)(vprog, sendMsg, mergeMsg) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala index 01b013ff716fc..cfcf7244eaed5 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala @@ -147,10 +147,10 @@ object Pregel extends Logging { logInfo("Pregel finished iteration " + i) // Unpersist the RDDs hidden by newly-materialized RDDs - oldMessages.unpersist(blocking=false) - newVerts.unpersist(blocking=false) - prevG.unpersistVertices(blocking=false) - prevG.edges.unpersist(blocking=false) + oldMessages.unpersist(blocking = false) + newVerts.unpersist(blocking = false) + prevG.unpersistVertices(blocking = false) + prevG.edges.unpersist(blocking = false) // count the iteration i += 1 } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala index c561570809253..ab021a252eb8a 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala @@ -156,8 +156,8 @@ class EdgePartition[ val size = data.size var i = 0 while (i < size) { - edge.srcId = srcIds(i) - edge.dstId = dstIds(i) + edge.srcId = srcIds(i) + edge.dstId = dstIds(i) edge.attr = data(i) newData(i) = f(edge) i += 1 diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala index bc974b2f04e70..8c0a461e99fa4 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala @@ -116,7 +116,7 @@ object PageRank extends Logging { val personalized = srcId isDefined val src: VertexId = srcId.getOrElse(-1L) - def delta(u: VertexId, v: VertexId):Double = { if (u == v) 1.0 else 0.0 } + def delta(u: VertexId, v: VertexId): Double = { if (u == v) 1.0 else 0.0 } var iteration = 0 var prevRankGraph: Graph[Double, Double] = null @@ -133,13 +133,13 @@ object PageRank extends Logging { // edge partitions. prevRankGraph = rankGraph val rPrb = if (personalized) { - (src: VertexId ,id: VertexId) => resetProb * delta(src,id) + (src: VertexId , id: VertexId) => resetProb * delta(src, id) } else { (src: VertexId, id: VertexId) => resetProb } rankGraph = rankGraph.joinVertices(rankUpdates) { - (id, oldRank, msgSum) => rPrb(src,id) + (1.0 - resetProb) * msgSum + (id, oldRank, msgSum) => rPrb(src, id) + (1.0 - resetProb) * msgSum }.cache() rankGraph.edges.foreachPartition(x => {}) // also materializes rankGraph.vertices @@ -243,7 +243,7 @@ object PageRank extends Logging { // Execute a dynamic version of Pregel. val vp = if (personalized) { - (id: VertexId, attr: (Double, Double),msgSum: Double) => + (id: VertexId, attr: (Double, Double), msgSum: Double) => personalizedVertexProgram(id, attr, msgSum) } else { (id: VertexId, attr: (Double, Double), msgSum: Double) => diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala index 3b0e1628d86b5..9cb24ed080e1c 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala @@ -210,7 +210,7 @@ object SVDPlusPlus { /** * Forces materialization of a Graph by count()ing its RDDs. */ - private def materialize(g: Graph[_,_]): Unit = { + private def materialize(g: Graph[_, _]): Unit = { g.vertices.count() g.edges.count() } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/TriangleCount.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/TriangleCount.scala index daf162085e3e4..a5d598053f9ca 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/TriangleCount.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/TriangleCount.scala @@ -38,7 +38,7 @@ import org.apache.spark.graphx._ */ object TriangleCount { - def run[VD: ClassTag, ED: ClassTag](graph: Graph[VD,ED]): Graph[Int, ED] = { + def run[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]): Graph[Int, ED] = { // Remove redundant edges val g = graph.groupEdges((a, b) => a).cache() @@ -49,7 +49,7 @@ object TriangleCount { var i = 0 while (i < nbrs.size) { // prevent self cycle - if(nbrs(i) != vid) { + if (nbrs(i) != vid) { set.add(nbrs(i)) } i += 1 diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala index 2d6a825b61726..9591c4e9b8f4e 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala @@ -243,14 +243,15 @@ object GraphGenerators { * @return A graph containing vertices with the row and column ids * as their attributes and edge values as 1.0. */ - def gridGraph(sc: SparkContext, rows: Int, cols: Int): Graph[(Int,Int), Double] = { + def gridGraph(sc: SparkContext, rows: Int, cols: Int): Graph[(Int, Int), Double] = { // Convert row column address into vertex ids (row major order) def sub2ind(r: Int, c: Int): VertexId = r * cols + c - val vertices: RDD[(VertexId, (Int,Int))] = - sc.parallelize(0 until rows).flatMap( r => (0 until cols).map( c => (sub2ind(r,c), (r,c)) ) ) + val vertices: RDD[(VertexId, (Int, Int))] = sc.parallelize(0 until rows).flatMap { r => + (0 until cols).map( c => (sub2ind(r, c), (r, c)) ) + } val edges: RDD[Edge[Double]] = - vertices.flatMap{ case (vid, (r,c)) => + vertices.flatMap{ case (vid, (r, c)) => (if (r + 1 < rows) { Seq( (sub2ind(r, c), sub2ind(r + 1, c))) } else { Seq.empty }) ++ (if (c + 1 < cols) { Seq( (sub2ind(r, c), sub2ind(r, c + 1))) } else { Seq.empty }) }.map{ case (src, dst) => Edge(src, dst, 1.0) } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/EdgeRDDSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/EdgeRDDSuite.scala index eb1dbe52c2fda..f1ecc9e2219d1 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/EdgeRDDSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/EdgeRDDSuite.scala @@ -17,11 +17,10 @@ package org.apache.spark.graphx -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.storage.StorageLevel -class EdgeRDDSuite extends FunSuite with LocalSparkContext { +class EdgeRDDSuite extends SparkFunSuite with LocalSparkContext { test("cache, getStorageLevel") { // test to see if getStorageLevel returns correct value after caching diff --git a/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala index 5a2c73b414279..094a63472eaab 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala @@ -17,21 +17,21 @@ package org.apache.spark.graphx -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite -class EdgeSuite extends FunSuite { +class EdgeSuite extends SparkFunSuite { test ("compare") { // decending order val testEdges: Array[Edge[Int]] = Array( - Edge(0x7FEDCBA987654321L, -0x7FEDCBA987654321L, 1), - Edge(0x2345L, 0x1234L, 1), - Edge(0x1234L, 0x5678L, 1), - Edge(0x1234L, 0x2345L, 1), + Edge(0x7FEDCBA987654321L, -0x7FEDCBA987654321L, 1), + Edge(0x2345L, 0x1234L, 1), + Edge(0x1234L, 0x5678L, 1), + Edge(0x1234L, 0x2345L, 1), Edge(-0x7FEDCBA987654321L, 0x7FEDCBA987654321L, 1) ) // to ascending order val sortedEdges = testEdges.sorted(Edge.lexicographicOrdering[Int]) - + for (i <- 0 until testEdges.length) { assert(sortedEdges(i) == testEdges(testEdges.length - i - 1)) } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala index 9bc8007ce49cd..57a8b95dd12e9 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala @@ -17,13 +17,12 @@ package org.apache.spark.graphx -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.graphx.Graph._ import org.apache.spark.graphx.impl.EdgePartition import org.apache.spark.rdd._ -import org.scalatest.FunSuite -class GraphOpsSuite extends FunSuite with LocalSparkContext { +class GraphOpsSuite extends SparkFunSuite with LocalSparkContext { test("joinVertices") { withSpark { sc => @@ -59,7 +58,7 @@ class GraphOpsSuite extends FunSuite with LocalSparkContext { test ("filter") { withSpark { sc => val n = 5 - val vertices = sc.parallelize((0 to n).map(x => (x:VertexId, x))) + val vertices = sc.parallelize((0 to n).map(x => (x: VertexId, x))) val edges = sc.parallelize((1 to n).map(x => Edge(0, x, x))) val graph: Graph[Int, Int] = Graph(vertices, edges).cache() val filteredGraph = graph.filter( @@ -67,11 +66,11 @@ class GraphOpsSuite extends FunSuite with LocalSparkContext { val degrees: VertexRDD[Int] = graph.outDegrees graph.outerJoinVertices(degrees) {(vid, data, deg) => deg.getOrElse(0)} }, - vpred = (vid: VertexId, deg:Int) => deg > 0 + vpred = (vid: VertexId, deg: Int) => deg > 0 ).cache() val v = filteredGraph.vertices.collect().toSet - assert(v === Set((0,0))) + assert(v === Set((0, 0))) // the map is necessary because of object-reuse in the edge iterator val e = filteredGraph.edges.map(e => Edge(e.srcId, e.dstId, e.attr)).collect().toSet diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala index a570e4ed75fc3..1f5e27d5508b8 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala @@ -17,16 +17,14 @@ package org.apache.spark.graphx -import org.scalatest.FunSuite - -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.graphx.Graph._ import org.apache.spark.graphx.PartitionStrategy._ import org.apache.spark.rdd._ import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils -class GraphSuite extends FunSuite with LocalSparkContext { +class GraphSuite extends SparkFunSuite with LocalSparkContext { def starGraph(sc: SparkContext, n: Int): Graph[String, Int] = { Graph.fromEdgeTuples(sc.parallelize((1 to n).map(x => (0: VertexId, x: VertexId)), 3), "v") @@ -248,7 +246,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { test("mask") { withSpark { sc => val n = 5 - val vertices = sc.parallelize((0 to n).map(x => (x:VertexId, x))) + val vertices = sc.parallelize((0 to n).map(x => (x: VertexId, x))) val edges = sc.parallelize((1 to n).map(x => Edge(0, x, x))) val graph: Graph[Int, Int] = Graph(vertices, edges).cache() @@ -260,11 +258,11 @@ class GraphSuite extends FunSuite with LocalSparkContext { val projectedGraph = graph.mask(subgraph) val v = projectedGraph.vertices.collect().toSet - assert(v === Set((0,0), (1,1), (2,2), (4,4), (5,5))) + assert(v === Set((0, 0), (1, 1), (2, 2), (4, 4), (5, 5))) // the map is necessary because of object-reuse in the edge iterator val e = projectedGraph.edges.map(e => Edge(e.srcId, e.dstId, e.attr)).collect().toSet - assert(e === Set(Edge(0,1,1), Edge(0,2,2), Edge(0,5,5))) + assert(e === Set(Edge(0, 1, 1), Edge(0, 2, 2), Edge(0, 5, 5))) } } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/PregelSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/PregelSuite.scala index 490b94429ea1f..8afa2d403b53f 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/PregelSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/PregelSuite.scala @@ -17,12 +17,10 @@ package org.apache.spark.graphx -import org.scalatest.FunSuite - -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.rdd._ -class PregelSuite extends FunSuite with LocalSparkContext { +class PregelSuite extends SparkFunSuite with LocalSparkContext { test("1 iteration") { withSpark { sc => diff --git a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala index d0a7198d691d7..f1aa685a79c98 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala @@ -17,13 +17,11 @@ package org.apache.spark.graphx -import org.scalatest.FunSuite - -import org.apache.spark.{HashPartitioner, SparkContext} +import org.apache.spark.{HashPartitioner, SparkContext, SparkFunSuite} import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel -class VertexRDDSuite extends FunSuite with LocalSparkContext { +class VertexRDDSuite extends SparkFunSuite with LocalSparkContext { private def vertices(sc: SparkContext, n: Int) = { VertexRDD(sc.parallelize((0 to n).map(x => (x.toLong, x)), 5)) diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala index 515f3a9cd02eb..7435647c6d9ee 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala @@ -20,15 +20,13 @@ package org.apache.spark.graphx.impl import scala.reflect.ClassTag import scala.util.Random -import org.scalatest.FunSuite - -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.serializer.JavaSerializer import org.apache.spark.serializer.KryoSerializer import org.apache.spark.graphx._ -class EdgePartitionSuite extends FunSuite { +class EdgePartitionSuite extends SparkFunSuite { def makeEdgePartition[A: ClassTag](xs: Iterable[(Int, Int, A)]): EdgePartition[A, Int] = { val builder = new EdgePartitionBuilder[A, Int] diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala index fe8304c1cdc32..1203f8959f506 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala @@ -17,15 +17,13 @@ package org.apache.spark.graphx.impl -import org.scalatest.FunSuite - -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.serializer.JavaSerializer import org.apache.spark.serializer.KryoSerializer import org.apache.spark.graphx._ -class VertexPartitionSuite extends FunSuite { +class VertexPartitionSuite extends SparkFunSuite { test("isDefined, filter") { val vp = VertexPartition(Iterator((0L, 1), (1L, 1))).filter { (vid, attr) => vid == 0 } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala index 4cc30a96408f8..c965a6eb8df13 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala @@ -17,16 +17,14 @@ package org.apache.spark.graphx.lib -import org.scalatest.FunSuite - -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.SparkContext._ import org.apache.spark.graphx._ import org.apache.spark.graphx.util.GraphGenerators import org.apache.spark.rdd._ -class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { +class ConnectedComponentsSuite extends SparkFunSuite with LocalSparkContext { test("Grid Connected Components") { withSpark { sc => @@ -52,13 +50,16 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { withSpark { sc => val chain1 = (0 until 9).map(x => (x, x + 1)) val chain2 = (10 until 20).map(x => (x, x + 1)) - val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s,d) => (s.toLong, d.toLong) } + val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s, d) => (s.toLong, d.toLong) } val twoChains = Graph.fromEdgeTuples(rawEdges, 1.0) val ccGraph = twoChains.connectedComponents() val vertices = ccGraph.vertices.collect() for ( (id, cc) <- vertices ) { - if(id < 10) { assert(cc === 0) } - else { assert(cc === 10) } + if (id < 10) { + assert(cc === 0) + } else { + assert(cc === 10) + } } val ccMap = vertices.toMap for (id <- 0 until 20) { @@ -75,7 +76,7 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { withSpark { sc => val chain1 = (0 until 9).map(x => (x, x + 1)) val chain2 = (10 until 20).map(x => (x, x + 1)) - val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s,d) => (s.toLong, d.toLong) } + val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s, d) => (s.toLong, d.toLong) } val twoChains = Graph.fromEdgeTuples(rawEdges, true).reverse val ccGraph = twoChains.connectedComponents() val vertices = ccGraph.vertices.collect() @@ -106,9 +107,9 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { (4L, ("peter", "student")))) // Create an RDD for edges val relationships: RDD[Edge[String]] = - sc.parallelize(Array(Edge(3L, 7L, "collab"), Edge(5L, 3L, "advisor"), + sc.parallelize(Array(Edge(3L, 7L, "collab"), Edge(5L, 3L, "advisor"), Edge(2L, 5L, "colleague"), Edge(5L, 7L, "pi"), - Edge(4L, 0L, "student"), Edge(5L, 0L, "colleague"))) + Edge(4L, 0L, "student"), Edge(5L, 0L, "colleague"))) // Edges are: // 2 ---> 5 ---> 3 // | \ diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala index 61fd0c4605568..808877f0590f8 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala @@ -17,11 +17,10 @@ package org.apache.spark.graphx.lib -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.graphx._ -class LabelPropagationSuite extends FunSuite with LocalSparkContext { +class LabelPropagationSuite extends SparkFunSuite with LocalSparkContext { test("Label Propagation") { withSpark { sc => // Construct a graph with two cliques connected by a single edge diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala index 3f3c9dfd7b3dd..45f1e3011035e 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala @@ -17,8 +17,7 @@ package org.apache.spark.graphx.lib -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.graphx._ import org.apache.spark.graphx.util.GraphGenerators @@ -31,14 +30,14 @@ object GridPageRank { def sub2ind(r: Int, c: Int): Int = r * nCols + c // Make the grid graph for (r <- 0 until nRows; c <- 0 until nCols) { - val ind = sub2ind(r,c) + val ind = sub2ind(r, c) if (r + 1 < nRows) { outDegree(ind) += 1 - inNbrs(sub2ind(r + 1,c)) += ind + inNbrs(sub2ind(r + 1, c)) += ind } if (c + 1 < nCols) { outDegree(ind) += 1 - inNbrs(sub2ind(r,c + 1)) += ind + inNbrs(sub2ind(r, c + 1)) += ind } } // compute the pagerank @@ -57,7 +56,7 @@ object GridPageRank { } -class PageRankSuite extends FunSuite with LocalSparkContext { +class PageRankSuite extends SparkFunSuite with LocalSparkContext { def compareRanks(a: VertexRDD[Double], b: VertexRDD[Double]): Double = { a.leftJoin(b) { case (id, a, bOpt) => (a - bOpt.getOrElse(0.0)) * (a - bOpt.getOrElse(0.0)) } @@ -99,8 +98,8 @@ class PageRankSuite extends FunSuite with LocalSparkContext { val resetProb = 0.15 val errorTol = 1.0e-5 - val staticRanks1 = starGraph.staticPersonalizedPageRank(0,numIter = 1, resetProb).vertices - val staticRanks2 = starGraph.staticPersonalizedPageRank(0,numIter = 2, resetProb) + val staticRanks1 = starGraph.staticPersonalizedPageRank(0, numIter = 1, resetProb).vertices + val staticRanks2 = starGraph.staticPersonalizedPageRank(0, numIter = 2, resetProb) .vertices.cache() // Static PageRank should only take 2 iterations to converge @@ -117,7 +116,7 @@ class PageRankSuite extends FunSuite with LocalSparkContext { } assert(staticErrors.sum === 0) - val dynamicRanks = starGraph.personalizedPageRank(0,0, resetProb).vertices.cache() + val dynamicRanks = starGraph.personalizedPageRank(0, 0, resetProb).vertices.cache() assert(compareRanks(staticRanks2, dynamicRanks) < errorTol) } } // end of test Star PageRank @@ -162,7 +161,7 @@ class PageRankSuite extends FunSuite with LocalSparkContext { test("Chain PersonalizedPageRank") { withSpark { sc => val chain1 = (0 until 9).map(x => (x, x + 1) ) - val rawEdges = sc.parallelize(chain1, 1).map { case (s,d) => (s.toLong, d.toLong) } + val rawEdges = sc.parallelize(chain1, 1).map { case (s, d) => (s.toLong, d.toLong) } val chain = Graph.fromEdgeTuples(rawEdges, 1.0).cache() val resetProb = 0.15 val tol = 0.0001 diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala index 7bd6b7f3c4ab2..2991438f5e57e 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.graphx.lib -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.graphx._ -class SVDPlusPlusSuite extends FunSuite with LocalSparkContext { +class SVDPlusPlusSuite extends SparkFunSuite with LocalSparkContext { test("Test SVD++ with mean square error on training set") { withSpark { sc => diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala index f2c38e79c452c..d7eaa70ce6407 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala @@ -17,16 +17,14 @@ package org.apache.spark.graphx.lib -import org.scalatest.FunSuite - -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.SparkContext._ import org.apache.spark.graphx._ import org.apache.spark.graphx.lib._ import org.apache.spark.graphx.util.GraphGenerators import org.apache.spark.rdd._ -class ShortestPathsSuite extends FunSuite with LocalSparkContext { +class ShortestPathsSuite extends SparkFunSuite with LocalSparkContext { test("Shortest Path Computations") { withSpark { sc => diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala index 1f658c371ffcf..d6b03208180db 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala @@ -17,16 +17,14 @@ package org.apache.spark.graphx.lib -import org.scalatest.FunSuite - -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.SparkContext._ import org.apache.spark.graphx._ import org.apache.spark.graphx.util.GraphGenerators import org.apache.spark.rdd._ -class StronglyConnectedComponentsSuite extends FunSuite with LocalSparkContext { +class StronglyConnectedComponentsSuite extends SparkFunSuite with LocalSparkContext { test("Island Strongly Connected Components") { withSpark { sc => diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/TriangleCountSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/TriangleCountSuite.scala index 293c7f3ba4c21..c47552cf3a3bd 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/TriangleCountSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/TriangleCountSuite.scala @@ -17,13 +17,12 @@ package org.apache.spark.graphx.lib -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.graphx._ import org.apache.spark.graphx.PartitionStrategy.RandomVertexCut -class TriangleCountSuite extends FunSuite with LocalSparkContext { +class TriangleCountSuite extends SparkFunSuite with LocalSparkContext { test("Count a single triangle") { withSpark { sc => @@ -58,7 +57,7 @@ class TriangleCountSuite extends FunSuite with LocalSparkContext { val triangles = Array(0L -> 1L, 1L -> 2L, 2L -> 0L) ++ Array(0L -> -1L, -1L -> -2L, -2L -> 0L) - val revTriangles = triangles.map { case (a,b) => (b,a) } + val revTriangles = triangles.map { case (a, b) => (b, a) } val rawEdges = sc.parallelize(triangles ++ revTriangles, 2) val graph = Graph.fromEdgeTuples(rawEdges, true).cache() val triangleCount = graph.triangleCount() diff --git a/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala index f3b3738db0dad..186d0cc2a977b 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala @@ -17,10 +17,10 @@ package org.apache.spark.graphx.util -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite -class BytecodeUtilsSuite extends FunSuite { +class BytecodeUtilsSuite extends SparkFunSuite { import BytecodeUtilsSuite.TestClass diff --git a/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala index 8d9c8ddccbb3c..32e0c841c6997 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala @@ -17,11 +17,10 @@ package org.apache.spark.graphx.util -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.graphx.LocalSparkContext -class GraphGeneratorsSuite extends FunSuite with LocalSparkContext { +class GraphGeneratorsSuite extends SparkFunSuite with LocalSparkContext { test("GraphGenerators.generateRandomEdges") { val src = 5 diff --git a/launcher/pom.xml b/launcher/pom.xml index ebfa7685eaa18..48dd0d5f9106b 100644 --- a/launcher/pom.xml +++ b/launcher/pom.xml @@ -22,14 +22,14 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml org.apache.spark spark-launcher_2.10 jar - Spark Launcher Project + Spark Project Launcher http://spark.apache.org/ launcher diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java index b8f02b961113d..33d65d13f0d25 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java @@ -121,7 +121,10 @@ List buildJavaCommand(String extraClassPath) throws IOException { * set it. */ void addPermGenSizeOpt(List cmd) { - // Don't set MaxPermSize for Java 8 and later. + // Don't set MaxPermSize for IBM Java, or Oracle Java 8 and later. + if (getJavaVendor() == JavaVendor.IBM) { + return; + } String[] version = System.getProperty("java.version").split("\\."); if (Integer.parseInt(version[0]) > 1 || Integer.parseInt(version[1]) > 7) { return; @@ -293,6 +296,9 @@ Properties loadPropertiesFile() throws IOException { try { fd = new FileInputStream(propsFile); props.load(new InputStreamReader(fd, "UTF-8")); + for (Map.Entry e : props.entrySet()) { + e.setValue(e.getValue().toString().trim()); + } } finally { if (fd != null) { try { diff --git a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java index 261402856ac5e..2665a700fe1f5 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java +++ b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java @@ -32,6 +32,11 @@ class CommandBuilderUtils { static final String ENV_SPARK_HOME = "SPARK_HOME"; static final String ENV_SPARK_ASSEMBLY = "_SPARK_ASSEMBLY"; + /** The set of known JVM vendors. */ + static enum JavaVendor { + Oracle, IBM, OpenJDK, Unknown + }; + /** Returns whether the given string is null or empty. */ static boolean isEmpty(String s) { return s == null || s.isEmpty(); @@ -108,6 +113,21 @@ static boolean isWindows() { return os.startsWith("Windows"); } + /** Returns an enum value indicating whose JVM is being used. */ + static JavaVendor getJavaVendor() { + String vendorString = System.getProperty("java.vendor"); + if (vendorString.contains("Oracle")) { + return JavaVendor.Oracle; + } + if (vendorString.contains("IBM")) { + return JavaVendor.IBM; + } + if (vendorString.contains("OpenJDK")) { + return JavaVendor.OpenJDK; + } + return JavaVendor.Unknown; + } + /** * Updates the user environment, appending the given pathList to the existing value of the given * environment variable (or setting it if it hasn't yet been set). diff --git a/launcher/src/main/java/org/apache/spark/launcher/Main.java b/launcher/src/main/java/org/apache/spark/launcher/Main.java index 929b29a49ed70..62492f9baf3bb 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/Main.java +++ b/launcher/src/main/java/org/apache/spark/launcher/Main.java @@ -53,21 +53,33 @@ public static void main(String[] argsArray) throws Exception { List args = new ArrayList(Arrays.asList(argsArray)); String className = args.remove(0); - boolean printLaunchCommand; - boolean printUsage; + boolean printLaunchCommand = !isEmpty(System.getenv("SPARK_PRINT_LAUNCH_COMMAND")); AbstractCommandBuilder builder; - try { - if (className.equals("org.apache.spark.deploy.SparkSubmit")) { + if (className.equals("org.apache.spark.deploy.SparkSubmit")) { + try { builder = new SparkSubmitCommandBuilder(args); - } else { - builder = new SparkClassCommandBuilder(className, args); + } catch (IllegalArgumentException e) { + printLaunchCommand = false; + System.err.println("Error: " + e.getMessage()); + System.err.println(); + + MainClassOptionParser parser = new MainClassOptionParser(); + try { + parser.parse(args); + } catch (Exception ignored) { + // Ignore parsing exceptions. + } + + List help = new ArrayList(); + if (parser.className != null) { + help.add(parser.CLASS); + help.add(parser.className); + } + help.add(parser.USAGE_ERROR); + builder = new SparkSubmitCommandBuilder(help); } - printLaunchCommand = !isEmpty(System.getenv("SPARK_PRINT_LAUNCH_COMMAND")); - printUsage = false; - } catch (IllegalArgumentException e) { - builder = new UsageCommandBuilder(e.getMessage()); - printLaunchCommand = false; - printUsage = true; + } else { + builder = new SparkClassCommandBuilder(className, args); } Map env = new HashMap(); @@ -78,13 +90,7 @@ public static void main(String[] argsArray) throws Exception { } if (isWindows()) { - // When printing the usage message, we can't use "cmd /v" since that prevents the env - // variable from being seen in the caller script. So do not call prepareWindowsCommand(). - if (printUsage) { - System.out.println(join(" ", cmd)); - } else { - System.out.println(prepareWindowsCommand(cmd, env)); - } + System.out.println(prepareWindowsCommand(cmd, env)); } else { // In bash, use NULL as the arg separator since it cannot be used in an argument. List bashCmd = prepareBashCommand(cmd, env); @@ -135,33 +141,30 @@ private static List prepareBashCommand(List cmd, Map buildCommand(Map env) { - if (isWindows()) { - return Arrays.asList("set", "SPARK_LAUNCHER_USAGE_ERROR=" + message); - } else { - return Arrays.asList("usage", message, "1"); - } + protected boolean handleUnknown(String opt) { + return false; + } + + @Override + protected void handleExtraArgs(List extra) { + } } diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java index d80abf2a8676e..de85720febf23 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java @@ -93,6 +93,9 @@ public List buildCommand(Map env) throws IOException { toolsDir.getAbsolutePath(), className); javaOptsKeys.add("SPARK_JAVA_OPTS"); + } else { + javaOptsKeys.add("SPARK_JAVA_OPTS"); + memKey = "SPARK_DRIVER_MEMORY"; } List cmd = buildJavaCommand(extraClassPath); diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java index 7d387d406edae..3e5a2820b6c11 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java @@ -77,6 +77,7 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder { } private final List sparkArgs; + private final boolean printHelp; /** * Controls whether mixing spark-submit arguments with app arguments is allowed. This is needed @@ -87,10 +88,11 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder { SparkSubmitCommandBuilder() { this.sparkArgs = new ArrayList(); + this.printHelp = false; } SparkSubmitCommandBuilder(List args) { - this(); + this.sparkArgs = new ArrayList(); List submitArgs = args; if (args.size() > 0 && args.get(0).equals(PYSPARK_SHELL)) { this.allowsMixedArguments = true; @@ -104,14 +106,16 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder { this.allowsMixedArguments = false; } - new OptionParser().parse(submitArgs); + OptionParser parser = new OptionParser(); + parser.parse(submitArgs); + this.printHelp = parser.helpRequested; } @Override public List buildCommand(Map env) throws IOException { - if (PYSPARK_SHELL_RESOURCE.equals(appResource)) { + if (PYSPARK_SHELL_RESOURCE.equals(appResource) && !printHelp) { return buildPySparkShellCommand(env); - } else if (SPARKR_SHELL_RESOURCE.equals(appResource)) { + } else if (SPARKR_SHELL_RESOURCE.equals(appResource) && !printHelp) { return buildSparkRCommand(env); } else { return buildSparkSubmitCommand(env); @@ -311,6 +315,8 @@ private boolean isThriftServer(String mainClass) { private class OptionParser extends SparkSubmitOptionParser { + boolean helpRequested = false; + @Override protected boolean handle(String opt, String value) { if (opt.equals(MASTER)) { @@ -341,6 +347,9 @@ protected boolean handle(String opt, String value) { allowsMixedArguments = true; appResource = specialClasses.get(value); } + } else if (opt.equals(HELP) || opt.equals(USAGE_ERROR)) { + helpRequested = true; + sparkArgs.add(opt); } else { sparkArgs.add(opt); if (value != null) { @@ -360,6 +369,7 @@ protected boolean handleUnknown(String opt) { appArgs.add(opt); return true; } else { + checkArgument(!opt.startsWith("-"), "Unrecognized option: %s", opt); sparkArgs.add(opt); return false; } diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java index 229000087688f..b88bba883ac65 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java @@ -61,6 +61,7 @@ class SparkSubmitOptionParser { // Options that do not take arguments. protected final String HELP = "--help"; protected final String SUPERVISE = "--supervise"; + protected final String USAGE_ERROR = "--usage-error"; protected final String VERBOSE = "--verbose"; protected final String VERSION = "--version"; @@ -120,6 +121,7 @@ class SparkSubmitOptionParser { final String[][] switches = { { HELP, "-h" }, { SUPERVISE }, + { USAGE_ERROR }, { VERBOSE, "-v" }, { VERSION }, }; diff --git a/make-distribution.sh b/make-distribution.sh index 1bfa9acb1fe6e..9f063da3a16c0 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -58,7 +58,7 @@ while (( "$#" )); do --hadoop) echo "Error: '--hadoop' is no longer supported:" echo "Error: use Maven profiles and options -Dhadoop.version and -Dyarn.version instead." - echo "Error: Related profiles include hadoop-2.2, hadoop-2.3 and hadoop-2.4." + echo "Error: Related profiles include hadoop-1, hadoop-2.2, hadoop-2.3 and hadoop-2.4." exit_with_usage ;; --with-yarn) @@ -141,22 +141,6 @@ SPARK_HIVE=$("$MVN" help:evaluate -Dexpression=project.activeProfiles -pl sql/hi # because we use "set -o pipefail" echo -n) -JAVA_CMD="$JAVA_HOME"/bin/java -JAVA_VERSION=$("$JAVA_CMD" -version 2>&1) -if [[ ! "$JAVA_VERSION" =~ "1.6" && -z "$SKIP_JAVA_TEST" ]]; then - echo "***NOTE***: JAVA_HOME is not set to a JDK 6 installation. The resulting" - echo " distribution may not work well with PySpark and will not run" - echo " with Java 6 (See SPARK-1703 and SPARK-1911)." - echo " This test can be disabled by adding --skip-java-test." - echo "Output from 'java -version' was:" - echo "$JAVA_VERSION" - read -p "Would you like to continue anyways? [y,n]: " -r - if [[ ! "$REPLY" =~ ^[Yy]$ ]]; then - echo "Okay, exiting." - exit 1 - fi -fi - if [ "$NAME" == "none" ]; then NAME=$SPARK_HADOOP_VERSION fi @@ -231,6 +215,11 @@ cp -r "$SPARK_HOME/bin" "$DISTDIR" cp -r "$SPARK_HOME/python" "$DISTDIR" cp -r "$SPARK_HOME/sbin" "$DISTDIR" cp -r "$SPARK_HOME/ec2" "$DISTDIR" +# Copy SparkR if it exists +if [ -d "$SPARK_HOME"/R/lib/SparkR ]; then + mkdir -p "$DISTDIR"/R/lib + cp -r "$SPARK_HOME/R/lib/SparkR" "$DISTDIR"/R/lib +fi # Download and copy in tachyon, if requested if [ "$SPARK_TACHYON" == "true" ]; then diff --git a/mllib/pom.xml b/mllib/pom.xml index a3c57ae26000b..b16058ddc203a 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -40,6 +40,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.spark spark-streaming_${scala.binary.version} @@ -141,16 +148,5 @@ target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes - - - ../python - - pyspark/mllib/*.py - pyspark/mllib/stat/*.py - pyspark/ml/*.py - pyspark/ml/param/*.py - - - diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala index 7f3f3262a644f..57e416591de69 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala @@ -19,16 +19,16 @@ package org.apache.spark.ml import scala.annotation.varargs -import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.{ParamMap, ParamPair, Params} +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.param.{ParamMap, ParamPair} import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: + * :: DeveloperApi :: * Abstract class for estimators that fit models to data. */ -@AlphaComponent -abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { +@DeveloperApi +abstract class Estimator[M <: Model[M]] extends PipelineStage { /** * Fits a single model to the input data with optional parameters. @@ -78,7 +78,5 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { paramMaps.map(fit(dataset, _)) } - override def copy(extra: ParamMap): Estimator[M] = { - super.copy(extra).asInstanceOf[Estimator[M]] - } + override def copy(extra: ParamMap): Estimator[M] } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala index 9974efe7b1d25..252acc156583f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Model.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Model.scala @@ -17,25 +17,33 @@ package org.apache.spark.ml -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param.ParamMap /** - * :: AlphaComponent :: + * :: DeveloperApi :: * A fitted model, i.e., a [[Transformer]] produced by an [[Estimator]]. * * @tparam M model type */ -@AlphaComponent +@DeveloperApi abstract class Model[M <: Model[M]] extends Transformer { /** * The parent estimator that produced this model. * Note: For ensembles' component Models, this value can be null. */ - val parent: Estimator[M] + @transient var parent: Estimator[M] = _ - override def copy(extra: ParamMap): M = { - // The default implementation of Params.copy doesn't work for models. - throw new NotImplementedError(s"${this.getClass} doesn't implement copy(extra: ParamMap)") + /** + * Sets the parent of this model (Java API). + */ + def setParent(parent: Estimator[M]): M = { + this.parent = parent + this.asInstanceOf[M] } + + /** Indicates whether this [[Model]] has a corresponding parent. */ + def hasParent: Boolean = parent != null + + override def copy(extra: ParamMap): M } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 33d430f5671ee..a1f3851d804ff 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -17,19 +17,23 @@ package org.apache.spark.ml +import java.{util => ju} + +import scala.collection.JavaConverters._ import scala.collection.mutable.ListBuffer import org.apache.spark.Logging -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} +import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.ml.param.{Param, ParamMap, Params} +import org.apache.spark.ml.util.Identifiable import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType /** - * :: AlphaComponent :: + * :: DeveloperApi :: * A stage in a pipeline, either an [[Estimator]] or a [[Transformer]]. */ -@AlphaComponent +@DeveloperApi abstract class PipelineStage extends Params with Logging { /** @@ -62,13 +66,11 @@ abstract class PipelineStage extends Params with Logging { outputSchema } - override def copy(extra: ParamMap): PipelineStage = { - super.copy(extra).asInstanceOf[PipelineStage] - } + override def copy(extra: ParamMap): PipelineStage } /** - * :: AlphaComponent :: + * :: Experimental :: * A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline#fit]] is called, the * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator#fit]] method will @@ -79,8 +81,10 @@ abstract class PipelineStage extends Params with Logging { * transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as * an identity transformer. */ -@AlphaComponent -class Pipeline extends Estimator[PipelineModel] { +@Experimental +class Pipeline(override val uid: String) extends Estimator[PipelineModel] { + + def this() = this(Identifiable.randomUID("pipeline")) /** * param for pipeline stages @@ -94,12 +98,9 @@ class Pipeline extends Estimator[PipelineModel] { /** @group getParam */ def getStages: Array[PipelineStage] = $(stages).clone() - override def validateParams(paramMap: ParamMap): Unit = { - val map = extractParamMap(paramMap) - getStages.foreach { - case pStage: Params => pStage.validateParams(map) - case _ => - } + override def validateParams(): Unit = { + super.validateParams() + $(stages).foreach(_.validateParams()) } /** @@ -148,7 +149,7 @@ class Pipeline extends Estimator[PipelineModel] { } } - new PipelineModel(this, transformers.toArray) + new PipelineModel(uid, transformers.toArray).setParent(this) } override def copy(extra: ParamMap): Pipeline = { @@ -166,15 +167,20 @@ class Pipeline extends Estimator[PipelineModel] { } /** - * :: AlphaComponent :: - * Represents a compiled pipeline. + * :: Experimental :: + * Represents a fitted pipeline. */ -@AlphaComponent +@Experimental class PipelineModel private[ml] ( - override val parent: Pipeline, + override val uid: String, val stages: Array[Transformer]) extends Model[PipelineModel] with Logging { + /** A Java/Python-friendly auxiliary constructor. */ + private[ml] def this(uid: String, stages: ju.List[Transformer]) = { + this(uid, stages.asScala.toArray) + } + override def validateParams(): Unit = { super.validateParams() stages.foreach(_.validateParams()) @@ -190,6 +196,6 @@ class PipelineModel private[ml] ( } override def copy(extra: ParamMap): PipelineModel = { - new PipelineModel(parent, stages) + new PipelineModel(uid, stages.map(_.copy(extra))) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala index 0e53877de92db..edaa2afb790e6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala @@ -58,7 +58,6 @@ private[ml] trait PredictorParams extends Params /** * :: DeveloperApi :: - * * Abstraction for prediction problems (regression and classification). * * @tparam FeaturesType Type of features. @@ -88,12 +87,10 @@ abstract class Predictor[ // This handles a few items such as schema validation. // Developers only need to implement train(). transformSchema(dataset.schema, logging = true) - copyValues(train(dataset)) + copyValues(train(dataset).setParent(this)) } - override def copy(extra: ParamMap): Learner = { - super.copy(extra).asInstanceOf[Learner] - } + override def copy(extra: ParamMap): Learner /** * Train a model using the given dataset and parameters. @@ -113,7 +110,7 @@ abstract class Predictor[ * * The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector. */ - protected def featuresDataType: DataType = new VectorUDT + private[ml] def featuresDataType: DataType = new VectorUDT override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema, fitting = true, featuresDataType) @@ -133,7 +130,6 @@ abstract class Predictor[ /** * :: DeveloperApi :: - * * Abstraction for a model for prediction tasks (regression and classification). * * @tparam FeaturesType Type of features. diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index d96b54e511e9c..3c7bcf7590e6d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml import scala.annotation.varargs import org.apache.spark.Logging -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ import org.apache.spark.sql.DataFrame @@ -28,11 +28,11 @@ import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ /** - * :: AlphaComponent :: + * :: DeveloperApi :: * Abstract class for transformers that transform one dataset into another. */ -@AlphaComponent -abstract class Transformer extends PipelineStage with Params { +@DeveloperApi +abstract class Transformer extends PipelineStage { /** * Transforms the dataset with optional parameters @@ -67,16 +67,16 @@ abstract class Transformer extends PipelineStage with Params { */ def transform(dataset: DataFrame): DataFrame - override def copy(extra: ParamMap): Transformer = { - super.copy(extra).asInstanceOf[Transformer] - } + override def copy(extra: ParamMap): Transformer } /** + * :: DeveloperApi :: * Abstract class for transformers that take one input column, apply transformation, and output the * result as a new column. */ -private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]] +@DeveloperApi +abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]] extends Transformer with HasInputCol with HasOutputCol with Logging { /** @group setParam */ @@ -118,4 +118,6 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O dataset.withColumn($(outputCol), callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol)))) } + + override def copy(extra: ParamMap): T = defaultCopy(extra) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala index d7dee8fed2a55..457c15830fd38 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala @@ -19,10 +19,12 @@ package org.apache.spark.ml.attribute import scala.collection.mutable.ArrayBuffer +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.linalg.VectorUDT import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField} /** + * :: DeveloperApi :: * Attributes that describe a vector ML column. * * @param name name of the attribute group (the ML column name) @@ -31,6 +33,7 @@ import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField} * @param attrs optional array of attributes. Attribute will be copied with their corresponding * indices in the array. */ +@DeveloperApi class AttributeGroup private ( val name: String, val numAttributes: Option[Int], @@ -123,6 +126,7 @@ class AttributeGroup private ( nominalMetadata += nominal.toMetadataImpl(withType = false) case binary: BinaryAttribute => binaryMetadata += binary.toMetadataImpl(withType = false) + case UnresolvedAttribute => } val attrBldr = new MetadataBuilder if (numericMetadata.nonEmpty) { @@ -181,7 +185,11 @@ class AttributeGroup private ( } } -/** Factory methods to create attribute groups. */ +/** + * :: DeveloperApi :: + * Factory methods to create attribute groups. + */ +@DeveloperApi object AttributeGroup { import AttributeKeys._ diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala index 65e7e43d5a5b0..5c7089b491677 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala @@ -17,12 +17,17 @@ package org.apache.spark.ml.attribute +import org.apache.spark.annotation.DeveloperApi + /** + * :: DeveloperApi :: * An enum-like type for attribute types: [[AttributeType$#Numeric]], [[AttributeType$#Nominal]], * and [[AttributeType$#Binary]]. */ +@DeveloperApi sealed abstract class AttributeType(val name: String) +@DeveloperApi object AttributeType { /** Numeric type. */ @@ -43,6 +48,12 @@ object AttributeType { Binary } + /** Unresolved type. */ + val Unresolved: AttributeType = { + case object Unresolved extends AttributeType("unresolved") + Unresolved + } + /** * Gets the [[AttributeType]] object from its name. * @param name attribute type name: "numeric", "nominal", or "binary" @@ -54,6 +65,8 @@ object AttributeType { Nominal } else if (name == Binary.name) { Binary + } else if (name == Unresolved.name) { + Unresolved } else { throw new IllegalArgumentException(s"Cannot recognize type $name.") } diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala index 5717d6ec2eaec..ce43a450daad0 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala @@ -19,11 +19,14 @@ package org.apache.spark.ml.attribute import scala.annotation.varargs +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.types.{DoubleType, Metadata, MetadataBuilder, StructField} /** + * :: DeveloperApi :: * Abstract class for ML attributes. */ +@DeveloperApi sealed abstract class Attribute extends Serializable { name.foreach { n => @@ -125,10 +128,20 @@ private[attribute] trait AttributeFactory { */ def fromStructField(field: StructField): Attribute = { require(field.dataType == DoubleType) - fromMetadata(field.metadata.getMetadata(AttributeKeys.ML_ATTR)).withName(field.name) + val metadata = field.metadata + val mlAttr = AttributeKeys.ML_ATTR + if (metadata.contains(mlAttr)) { + fromMetadata(metadata.getMetadata(mlAttr)).withName(field.name) + } else { + UnresolvedAttribute + } } } +/** + * :: DeveloperApi :: + */ +@DeveloperApi object Attribute extends AttributeFactory { private[attribute] override def fromMetadata(metadata: Metadata): Attribute = { @@ -157,6 +170,7 @@ object Attribute extends AttributeFactory { /** + * :: DeveloperApi :: * A numeric attribute with optional summary statistics. * @param name optional name * @param index optional index @@ -165,6 +179,7 @@ object Attribute extends AttributeFactory { * @param std optional standard deviation * @param sparsity optional sparsity (ratio of zeros) */ +@DeveloperApi class NumericAttribute private[ml] ( override val name: Option[String] = None, override val index: Option[Int] = None, @@ -272,8 +287,10 @@ class NumericAttribute private[ml] ( } /** + * :: DeveloperApi :: * Factory methods for numeric attributes. */ +@DeveloperApi object NumericAttribute extends AttributeFactory { /** The default numeric attribute. */ @@ -292,6 +309,7 @@ object NumericAttribute extends AttributeFactory { } /** + * :: DeveloperApi :: * A nominal attribute. * @param name optional name * @param index optional index @@ -300,6 +318,7 @@ object NumericAttribute extends AttributeFactory { * defined. * @param values optional values. At most one of `numValues` and `values` can be defined. */ +@DeveloperApi class NominalAttribute private[ml] ( override val name: Option[String] = None, override val index: Option[Int] = None, @@ -424,7 +443,11 @@ class NominalAttribute private[ml] ( } } -/** Factory methods for nominal attributes. */ +/** + * :: DeveloperApi :: + * Factory methods for nominal attributes. + */ +@DeveloperApi object NominalAttribute extends AttributeFactory { /** The default nominal attribute. */ @@ -444,11 +467,13 @@ object NominalAttribute extends AttributeFactory { } /** + * :: DeveloperApi :: * A binary attribute. * @param name optional name * @param index optional index * @param values optionla values. If set, its size must be 2. */ +@DeveloperApi class BinaryAttribute private[ml] ( override val name: Option[String] = None, override val index: Option[Int] = None, @@ -520,7 +545,11 @@ class BinaryAttribute private[ml] ( } } -/** Factory methods for binary attributes. */ +/** + * :: DeveloperApi :: + * Factory methods for binary attributes. + */ +@DeveloperApi object BinaryAttribute extends AttributeFactory { /** The default binary attribute. */ @@ -535,3 +564,34 @@ object BinaryAttribute extends AttributeFactory { new BinaryAttribute(name, index, values) } } + +/** + * :: DeveloperApi :: + * An unresolved attribute. + */ +@DeveloperApi +object UnresolvedAttribute extends Attribute { + + override def attrType: AttributeType = AttributeType.Unresolved + + override def withIndex(index: Int): Attribute = this + + override def isNumeric: Boolean = false + + override def withoutIndex: Attribute = this + + override def isNominal: Boolean = false + + override def name: Option[String] = None + + override private[attribute] def toMetadataImpl(withType: Boolean): Metadata = { + Metadata.empty + } + + override def withoutName: Attribute = this + + override def index: Option[Int] = None + + override def withName(name: String): Attribute = this + +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala index 263d580fe2dd3..14c285dbfc54a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala @@ -18,6 +18,7 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor} import org.apache.spark.ml.param.shared.HasRawPredictionCol import org.apache.spark.ml.util.SchemaUtils diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala index dcebea1d4b015..2dc1824964a42 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala @@ -17,11 +17,11 @@ package org.apache.spark.ml.classification -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.tree.{TreeClassifierParams, DecisionTreeParams, DecisionTreeModel, Node} -import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams} +import org.apache.spark.ml.util.{Identifiable, MetadataUtils} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree} @@ -31,18 +31,19 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm * for classification. * It supports both binary and multiclass labels, as well as both continuous and categorical * features. */ -@AlphaComponent -final class DecisionTreeClassifier +@Experimental +final class DecisionTreeClassifier(override val uid: String) extends Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] with DecisionTreeParams with TreeClassifierParams { + def this() = this(Identifiable.randomUID("dtc")) + // Override parameter setters from parent trait for Java API compatibility. override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) @@ -85,23 +86,25 @@ final class DecisionTreeClassifier super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity, subsamplingRate = 1.0) } + + override def copy(extra: ParamMap): DecisionTreeClassifier = defaultCopy(extra) } +@Experimental object DecisionTreeClassifier { /** Accessor for supported impurities: entropy, gini */ final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities } /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for classification. * It supports both binary and multiclass labels, as well as both continuous and categorical * features. */ -@AlphaComponent +@Experimental final class DecisionTreeClassificationModel private[ml] ( - override val parent: DecisionTreeClassifier, + override val uid: String, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeClassificationModel] with DecisionTreeModel with Serializable { @@ -114,7 +117,7 @@ final class DecisionTreeClassificationModel private[ml] ( } override def copy(extra: ParamMap): DecisionTreeClassificationModel = { - copyValues(new DecisionTreeClassificationModel(parent, rootNode), extra) + copyValues(new DecisionTreeClassificationModel(uid, rootNode), extra) } override def toString: String = { @@ -138,6 +141,7 @@ private[ml] object DecisionTreeClassificationModel { s"Cannot convert non-classification DecisionTreeModel (old API) to" + s" DecisionTreeClassificationModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeClassificationModel(parent, rootNode) + val uid = if (parent != null) parent.uid else Identifiable.randomUID("dtc") + new DecisionTreeClassificationModel(uid, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala index ae51b05a0c42d..554e3b8e052b2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala @@ -20,12 +20,12 @@ package org.apache.spark.ml.classification import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.Logging -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.ml.regression.DecisionTreeRegressionModel -import org.apache.spark.ml.tree.{GBTParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel} -import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeClassifierParams, TreeEnsembleModel} +import org.apache.spark.ml.util.{Identifiable, MetadataUtils} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} @@ -36,18 +36,19 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] * learning algorithm for classification. * It supports binary labels, as well as both continuous and categorical features. * Note: Multiclass labels are not currently supported. */ -@AlphaComponent -final class GBTClassifier +@Experimental +final class GBTClassifier(override val uid: String) extends Predictor[Vector, GBTClassifier, GBTClassificationModel] with GBTParams with TreeClassifierParams with Logging { + def this() = this(Identifiable.randomUID("gbtc")) + // Override parameter setters from parent trait for Java API compatibility. // Parameters from TreeClassifierParams: @@ -140,8 +141,11 @@ final class GBTClassifier val oldModel = oldGBT.run(oldDataset) GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures) } + + override def copy(extra: ParamMap): GBTClassifier = defaultCopy(extra) } +@Experimental object GBTClassifier { // The losses below should be lowercase. /** Accessor for supported loss settings: logistic */ @@ -149,8 +153,7 @@ object GBTClassifier { } /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] * model for classification. * It supports binary labels, as well as both continuous and categorical features. @@ -158,9 +161,9 @@ object GBTClassifier { * @param _trees Decision trees in the ensemble. * @param _treeWeights Weights for the decision trees in the ensemble. */ -@AlphaComponent +@Experimental final class GBTClassificationModel( - override val parent: GBTClassifier, + override val uid: String, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTClassificationModel] @@ -184,7 +187,7 @@ final class GBTClassificationModel( } override def copy(extra: ParamMap): GBTClassificationModel = { - copyValues(new GBTClassificationModel(parent, _trees, _treeWeights), extra) + copyValues(new GBTClassificationModel(uid, _trees, _treeWeights), extra) } override def toString: String = { @@ -207,9 +210,10 @@ private[ml] object GBTClassificationModel { require(oldModel.algo == OldAlgo.Classification, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => - // parent, fittingParamMap for each tree is null since there are no good ways to set these. + // parent for each tree is null since there is no good way to set this. DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTClassificationModel(parent, newTrees, oldModel.treeWeights) + val uid = if (parent != null) parent.uid else Identifiable.randomUID("gbtc") + new GBTClassificationModel(parent.uid, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index 550369d18cfec..2e6eedd45ab07 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -17,11 +17,22 @@ package org.apache.spark.ml.classification -import org.apache.spark.annotation.AlphaComponent +import scala.collection.mutable + +import breeze.linalg.{DenseVector => BDV, norm => brzNorm} +import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} + +import org.apache.spark.{Logging, SparkException} +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.linalg._ +import org.apache.spark.mllib.linalg.BLAS._ +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel @@ -29,67 +40,197 @@ import org.apache.spark.storage.StorageLevel * Params for logistic regression. */ private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams - with HasRegParam with HasMaxIter with HasFitIntercept with HasThreshold { - - setDefault(regParam -> 0.1, maxIter -> 100, threshold -> 0.5) -} + with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol + with HasThreshold /** - * :: AlphaComponent :: - * + * :: Experimental :: * Logistic regression. * Currently, this class only supports binary classification. */ -@AlphaComponent -class LogisticRegression +@Experimental +class LogisticRegression(override val uid: String) extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] - with LogisticRegressionParams { + with LogisticRegressionParams with Logging { - /** @group setParam */ + def this() = this(Identifiable.randomUID("logreg")) + + /** + * Set the regularization parameter. + * Default is 0.0. + * @group setParam + */ def setRegParam(value: Double): this.type = set(regParam, value) + setDefault(regParam -> 0.0) - /** @group setParam */ + /** + * Set the ElasticNet mixing parameter. + * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. + * For 0 < alpha < 1, the penalty is a combination of L1 and L2. + * Default is 0.0 which is an L2 penalty. + * @group setParam + */ + def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value) + setDefault(elasticNetParam -> 0.0) + + /** + * Set the maximum number of iterations. + * Default is 100. + * @group setParam + */ def setMaxIter(value: Int): this.type = set(maxIter, value) + setDefault(maxIter -> 100) - /** @group setParam */ + /** + * Set the convergence tolerance of iterations. + * Smaller value will lead to higher accuracy with the cost of more iterations. + * Default is 1E-6. + * @group setParam + */ + def setTol(value: Double): this.type = set(tol, value) + setDefault(tol -> 1E-6) + + /** + * Whether to fit an intercept term. + * Default is true. + * @group setParam + * */ def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) + setDefault(fitIntercept -> true) /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) + setDefault(threshold -> 0.5) override protected def train(dataset: DataFrame): LogisticRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist oldDataset. - val oldDataset = extractLabeledPoints(dataset) + val instances = extractLabeledPoints(dataset).map { + case LabeledPoint(label: Double, features: Vector) => (label, features) + } val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE - if (handlePersistence) { - oldDataset.persist(StorageLevel.MEMORY_AND_DISK) + if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) + + val (summarizer, labelSummarizer) = instances.treeAggregate( + (new MultivariateOnlineSummarizer, new MultiClassSummarizer))( + seqOp = (c, v) => (c, v) match { + case ((summarizer: MultivariateOnlineSummarizer, labelSummarizer: MultiClassSummarizer), + (label: Double, features: Vector)) => + (summarizer.add(features), labelSummarizer.add(label)) + }, + combOp = (c1, c2) => (c1, c2) match { + case ((summarizer1: MultivariateOnlineSummarizer, + classSummarizer1: MultiClassSummarizer), (summarizer2: MultivariateOnlineSummarizer, + classSummarizer2: MultiClassSummarizer)) => + (summarizer1.merge(summarizer2), classSummarizer1.merge(classSummarizer2)) + }) + + val histogram = labelSummarizer.histogram + val numInvalid = labelSummarizer.countInvalid + val numClasses = histogram.length + val numFeatures = summarizer.mean.size + + if (numInvalid != 0) { + val msg = s"Classification labels should be in {0 to ${numClasses - 1} " + + s"Found $numInvalid invalid labels." + logError(msg) + throw new SparkException(msg) + } + + if (numClasses > 2) { + val msg = s"Currently, LogisticRegression with ElasticNet in ML package only supports " + + s"binary classification. Found $numClasses in the input dataset." + logError(msg) + throw new SparkException(msg) + } + + val featuresMean = summarizer.mean.toArray + val featuresStd = summarizer.variance.toArray.map(math.sqrt) + + val regParamL1 = $(elasticNetParam) * $(regParam) + val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam) + + val costFun = new LogisticCostFun(instances, numClasses, $(fitIntercept), + featuresStd, featuresMean, regParamL2) + + val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) == 0.0) { + new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol)) + } else { + // Remove the L1 penalization on the intercept + def regParamL1Fun = (index: Int) => { + if (index == numFeatures) 0.0 else regParamL1 + } + new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun, $(tol)) + } + + val initialWeightsWithIntercept = + Vectors.zeros(if ($(fitIntercept)) numFeatures + 1 else numFeatures) + + if ($(fitIntercept)) { + /** + * For binary logistic regression, when we initialize the weights as zeros, + * it will converge faster if we initialize the intercept such that + * it follows the distribution of the labels. + * + * {{{ + * P(0) = 1 / (1 + \exp(b)), and + * P(1) = \exp(b) / (1 + \exp(b)) + * }}}, hence + * {{{ + * b = \log{P(1) / P(0)} = \log{count_1 / count_0} + * }}} + */ + initialWeightsWithIntercept.toArray(numFeatures) + = math.log(histogram(1).toDouble / histogram(0).toDouble) + } + + val states = optimizer.iterations(new CachedDiffFunction(costFun), + initialWeightsWithIntercept.toBreeze.toDenseVector) + + var state = states.next() + val lossHistory = mutable.ArrayBuilder.make[Double] + + while (states.hasNext) { + lossHistory += state.value + state = states.next() + } + lossHistory += state.value + + // The weights are trained in the scaled space; we're converting them back to + // the original space. + val weightsWithIntercept = { + val rawWeights = state.x.toArray.clone() + var i = 0 + // Note that the intercept in scaled space and original space is the same; + // as a result, no scaling is needed. + while (i < numFeatures) { + rawWeights(i) *= { if (featuresStd(i) != 0.0) 1.0 / featuresStd(i) else 0.0 } + i += 1 + } + Vectors.dense(rawWeights) } - // Train model - val lr = new LogisticRegressionWithLBFGS() - .setIntercept($(fitIntercept)) - lr.optimizer - .setRegParam($(regParam)) - .setNumIterations($(maxIter)) - val oldModel = lr.run(oldDataset) - val lrm = new LogisticRegressionModel(this, oldModel.weights, oldModel.intercept) + if (handlePersistence) instances.unpersist() - if (handlePersistence) { - oldDataset.unpersist() + val (weights, intercept) = if ($(fitIntercept)) { + (Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)), + weightsWithIntercept(weightsWithIntercept.size - 1)) + } else { + (weightsWithIntercept, 0.0) } - copyValues(lrm) + + new LogisticRegressionModel(uid, weights.compressed, intercept) } -} + override def copy(extra: ParamMap): LogisticRegression = defaultCopy(extra) +} /** - * :: AlphaComponent :: - * + * :: Experimental :: * Model produced by [[LogisticRegression]]. */ -@AlphaComponent +@Experimental class LogisticRegressionModel private[ml] ( - override val parent: LogisticRegression, + override val uid: String, val weights: Vector, val intercept: Double) extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] @@ -123,7 +264,8 @@ class LogisticRegressionModel private[ml] ( rawPrediction match { case dv: DenseVector => var i = 0 - while (i < dv.size) { + val size = dv.size + while (i < size) { dv.values(i) = 1.0 / (1.0 + math.exp(-dv.values(i))) i += 1 } @@ -140,7 +282,7 @@ class LogisticRegressionModel private[ml] ( } override def copy(extra: ParamMap): LogisticRegressionModel = { - copyValues(new LogisticRegressionModel(parent, weights, intercept), extra) + copyValues(new LogisticRegressionModel(uid, weights, intercept), extra) } override protected def raw2prediction(rawPrediction: Vector): Double = { @@ -150,7 +292,7 @@ class LogisticRegressionModel private[ml] ( } else if (t == 1.0) { Double.PositiveInfinity } else { - Math.log(t / (1.0 - t)) + math.log(t / (1.0 - t)) } if (rawPrediction(1) > rawThreshold) 1 else 0 } @@ -159,3 +301,260 @@ class LogisticRegressionModel private[ml] ( if (probability(1) > getThreshold) 1 else 0 } } + +/** + * MultiClassSummarizer computes the number of distinct labels and corresponding counts, + * and validates the data to see if the labels used for k class multi-label classification + * are in the range of {0, 1, ..., k - 1} in a online fashion. + * + * Two MultilabelSummarizer can be merged together to have a statistical summary of the + * corresponding joint dataset. + */ +private[classification] class MultiClassSummarizer extends Serializable { + private val distinctMap = new mutable.HashMap[Int, Long] + private var totalInvalidCnt: Long = 0L + + /** + * Add a new label into this MultilabelSummarizer, and update the distinct map. + * @param label The label for this data point. + * @return This MultilabelSummarizer + */ + def add(label: Double): this.type = { + if (label - label.toInt != 0.0 || label < 0) { + totalInvalidCnt += 1 + this + } + else { + val counts: Long = distinctMap.getOrElse(label.toInt, 0L) + distinctMap.put(label.toInt, counts + 1) + this + } + } + + /** + * Merge another MultilabelSummarizer, and update the distinct map. + * (Note that it will merge the smaller distinct map into the larger one using in-place + * merging, so either `this` or `other` object will be modified and returned.) + * + * @param other The other MultilabelSummarizer to be merged. + * @return Merged MultilabelSummarizer object. + */ + def merge(other: MultiClassSummarizer): MultiClassSummarizer = { + val (largeMap, smallMap) = if (this.distinctMap.size > other.distinctMap.size) { + (this, other) + } else { + (other, this) + } + smallMap.distinctMap.foreach { + case (key, value) => + val counts = largeMap.distinctMap.getOrElse(key, 0L) + largeMap.distinctMap.put(key, counts + value) + } + largeMap.totalInvalidCnt += smallMap.totalInvalidCnt + largeMap + } + + /** @return The total invalid input counts. */ + def countInvalid: Long = totalInvalidCnt + + /** @return The number of distinct labels in the input dataset. */ + def numClasses: Int = distinctMap.keySet.max + 1 + + /** @return The counts of each label in the input dataset. */ + def histogram: Array[Long] = { + val result = Array.ofDim[Long](numClasses) + var i = 0 + val len = result.length + while (i < len) { + result(i) = distinctMap.getOrElse(i, 0L) + i += 1 + } + result + } +} + +/** + * LogisticAggregator computes the gradient and loss for binary logistic loss function, as used + * in binary classification for samples in sparse or dense vector in a online fashion. + * + * Note that multinomial logistic loss is not supported yet! + * + * Two LogisticAggregator can be merged together to have a summary of loss and gradient of + * the corresponding joint dataset. + * + * @param weights The weights/coefficients corresponding to the features. + * @param numClasses the number of possible outcomes for k classes classification problem in + * Multinomial Logistic Regression. + * @param fitIntercept Whether to fit an intercept term. + * @param featuresStd The standard deviation values of the features. + * @param featuresMean The mean values of the features. + */ +private class LogisticAggregator( + weights: Vector, + numClasses: Int, + fitIntercept: Boolean, + featuresStd: Array[Double], + featuresMean: Array[Double]) extends Serializable { + + private var totalCnt: Long = 0L + private var lossSum = 0.0 + + private val weightsArray = weights match { + case dv: DenseVector => dv.values + case _ => + throw new IllegalArgumentException( + s"weights only supports dense vector but got type ${weights.getClass}.") + } + + private val dim = if (fitIntercept) weightsArray.length - 1 else weightsArray.length + + private val gradientSumArray = Array.ofDim[Double](weightsArray.length) + + /** + * Add a new training data to this LogisticAggregator, and update the loss and gradient + * of the objective function. + * + * @param label The label for this data point. + * @param data The features for one data point in dense/sparse vector format to be added + * into this aggregator. + * @return This LogisticAggregator object. + */ + def add(label: Double, data: Vector): this.type = { + require(dim == data.size, s"Dimensions mismatch when adding new sample." + + s" Expecting $dim but got ${data.size}.") + + val dataSize = data.size + + val localWeightsArray = weightsArray + val localGradientSumArray = gradientSumArray + + numClasses match { + case 2 => + /** + * For Binary Logistic Regression. + */ + val margin = - { + var sum = 0.0 + data.foreachActive { (index, value) => + if (featuresStd(index) != 0.0 && value != 0.0) { + sum += localWeightsArray(index) * (value / featuresStd(index)) + } + } + sum + { if (fitIntercept) localWeightsArray(dim) else 0.0 } + } + + val multiplier = (1.0 / (1.0 + math.exp(margin))) - label + + data.foreachActive { (index, value) => + if (featuresStd(index) != 0.0 && value != 0.0) { + localGradientSumArray(index) += multiplier * (value / featuresStd(index)) + } + } + + if (fitIntercept) { + localGradientSumArray(dim) += multiplier + } + + if (label > 0) { + // The following is equivalent to log(1 + exp(margin)) but more numerically stable. + lossSum += MLUtils.log1pExp(margin) + } else { + lossSum += MLUtils.log1pExp(margin) - margin + } + case _ => + new NotImplementedError("LogisticRegression with ElasticNet in ML package only supports " + + "binary classification for now.") + } + totalCnt += 1 + this + } + + /** + * Merge another LogisticAggregator, and update the loss and gradient + * of the objective function. + * (Note that it's in place merging; as a result, `this` object will be modified.) + * + * @param other The other LogisticAggregator to be merged. + * @return This LogisticAggregator object. + */ + def merge(other: LogisticAggregator): this.type = { + require(dim == other.dim, s"Dimensions mismatch when merging with another " + + s"LeastSquaresAggregator. Expecting $dim but got ${other.dim}.") + + if (other.totalCnt != 0) { + totalCnt += other.totalCnt + lossSum += other.lossSum + + var i = 0 + val localThisGradientSumArray = this.gradientSumArray + val localOtherGradientSumArray = other.gradientSumArray + val len = localThisGradientSumArray.length + while (i < len) { + localThisGradientSumArray(i) += localOtherGradientSumArray(i) + i += 1 + } + } + this + } + + def count: Long = totalCnt + + def loss: Double = lossSum / totalCnt + + def gradient: Vector = { + val result = Vectors.dense(gradientSumArray.clone()) + scal(1.0 / totalCnt, result) + result + } +} + +/** + * LogisticCostFun implements Breeze's DiffFunction[T] for a multinomial logistic loss function, + * as used in multi-class classification (it is also used in binary logistic regression). + * It returns the loss and gradient with L2 regularization at a particular point (weights). + * It's used in Breeze's convex optimization routines. + */ +private class LogisticCostFun( + data: RDD[(Double, Vector)], + numClasses: Int, + fitIntercept: Boolean, + featuresStd: Array[Double], + featuresMean: Array[Double], + regParamL2: Double) extends DiffFunction[BDV[Double]] { + + override def calculate(weights: BDV[Double]): (Double, BDV[Double]) = { + val w = Vectors.fromBreeze(weights) + + val logisticAggregator = data.treeAggregate(new LogisticAggregator(w, numClasses, fitIntercept, + featuresStd, featuresMean))( + seqOp = (c, v) => (c, v) match { + case (aggregator, (label, features)) => aggregator.add(label, features) + }, + combOp = (c1, c2) => (c1, c2) match { + case (aggregator1, aggregator2) => aggregator1.merge(aggregator2) + }) + + // regVal is the sum of weight squares for L2 regularization + val norm = if (regParamL2 == 0.0) { + 0.0 + } else if (fitIntercept) { + brzNorm(Vectors.dense(weights.toArray.slice(0, weights.size -1)).toBreeze, 2.0) + } else { + brzNorm(weights, 2.0) + } + val regVal = 0.5 * regParamL2 * norm * norm + + val loss = logisticAggregator.loss + regVal + val gradient = logisticAggregator.gradient + + if (fitIntercept) { + val wArray = w.toArray.clone() + wArray(wArray.length - 1) = 0.0 + axpy(regParamL2, Vectors.dense(wArray), gradient) + } else { + axpy(regParamL2, w, gradient) + } + + (loss, gradient.toBreeze.asInstanceOf[BDV[Double]]) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala new file mode 100644 index 0000000000000..b657882f8ad3f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala @@ -0,0 +1,226 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import java.util.UUID + +import scala.language.existentials + +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml._ +import org.apache.spark.ml.attribute._ +import org.apache.spark.ml.param.{Param, ParamMap} +import org.apache.spark.ml.util.{Identifiable, MetadataUtils} +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ +import org.apache.spark.storage.StorageLevel + +/** + * Params for [[OneVsRest]]. + */ +private[ml] trait OneVsRestParams extends PredictorParams { + + // scalastyle:off structural.type + type ClassifierType = Classifier[F, E, M] forSome { + type F + type M <: ClassificationModel[F, M] + type E <: Classifier[F, E, M] + } + // scalastyle:on structural.type + + /** + * param for the base binary classifier that we reduce multiclass classification into. + * @group param + */ + val classifier: Param[ClassifierType] = new Param(this, "classifier", "base binary classifier") + + /** @group getParam */ + def getClassifier: ClassifierType = $(classifier) +} + +/** + * :: Experimental :: + * Model produced by [[OneVsRest]]. + * This stores the models resulting from training k binary classifiers: one for each class. + * Each example is scored against all k models, and the model with the highest score + * is picked to label the example. + * + * @param labelMetadata Metadata of label column if it exists, or Nominal attribute + * representing the number of classes in training dataset otherwise. + * @param models The binary classification models for the reduction. + * The i-th model is produced by testing the i-th class (taking label 1) vs the rest + * (taking label 0). + */ +@Experimental +final class OneVsRestModel private[ml] ( + override val uid: String, + labelMetadata: Metadata, + val models: Array[_ <: ClassificationModel[_, _]]) + extends Model[OneVsRestModel] with OneVsRestParams { + + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = false, getClassifier.featuresDataType) + } + + override def transform(dataset: DataFrame): DataFrame = { + // Check schema + transformSchema(dataset.schema, logging = true) + + // determine the input columns: these need to be passed through + val origCols = dataset.schema.map(f => col(f.name)) + + // add an accumulator column to store predictions of all the models + val accColName = "mbc$acc" + UUID.randomUUID().toString + val init: () => Map[Int, Double] = () => {Map()} + val mapType = MapType(IntegerType, DoubleType, valueContainsNull = false) + val newDataset = dataset.withColumn(accColName, callUDF(init, mapType)) + + // persist if underlying dataset is not persistent. + val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE + if (handlePersistence) { + newDataset.persist(StorageLevel.MEMORY_AND_DISK) + } + + // update the accumulator column with the result of prediction of models + val aggregatedDataset = models.zipWithIndex.foldLeft[DataFrame](newDataset) { + case (df, (model, index)) => + val rawPredictionCol = model.getRawPredictionCol + val columns = origCols ++ List(col(rawPredictionCol), col(accColName)) + + // add temporary column to store intermediate scores and update + val tmpColName = "mbc$tmp" + UUID.randomUUID().toString + val update: (Map[Int, Double], Vector) => Map[Int, Double] = + (predictions: Map[Int, Double], prediction: Vector) => { + predictions + ((index, prediction(1))) + } + val updateUdf = callUDF(update, mapType, col(accColName), col(rawPredictionCol)) + val transformedDataset = model.transform(df).select(columns : _*) + val updatedDataset = transformedDataset.withColumn(tmpColName, updateUdf) + val newColumns = origCols ++ List(col(tmpColName)) + + // switch out the intermediate column with the accumulator column + updatedDataset.select(newColumns : _*).withColumnRenamed(tmpColName, accColName) + } + + if (handlePersistence) { + newDataset.unpersist() + } + + // output the index of the classifier with highest confidence as prediction + val label: Map[Int, Double] => Double = (predictions: Map[Int, Double]) => { + predictions.maxBy(_._2)._1.toDouble + } + + // output label and label metadata as prediction + val labelUdf = callUDF(label, DoubleType, col(accColName)) + aggregatedDataset.withColumn($(predictionCol), labelUdf.as($(predictionCol), labelMetadata)) + .drop(accColName) + } + + override def copy(extra: ParamMap): OneVsRestModel = { + val copied = new OneVsRestModel( + uid, labelMetadata, models.map(_.copy(extra).asInstanceOf[ClassificationModel[_, _]])) + copyValues(copied, extra) + } +} + +/** + * :: Experimental :: + * + * Reduction of Multiclass Classification to Binary Classification. + * Performs reduction using one against all strategy. + * For a multiclass classification with k classes, train k models (one per class). + * Each example is scored against all k models and the model with highest score + * is picked to label the example. + */ +@Experimental +final class OneVsRest(override val uid: String) + extends Estimator[OneVsRestModel] with OneVsRestParams { + + def this() = this(Identifiable.randomUID("oneVsRest")) + + /** @group setParam */ + def setClassifier(value: Classifier[_, _, _]): this.type = { + set(classifier, value.asInstanceOf[ClassifierType]) + } + + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = true, getClassifier.featuresDataType) + } + + override def fit(dataset: DataFrame): OneVsRestModel = { + // determine number of classes either from metadata if provided, or via computation. + val labelSchema = dataset.schema($(labelCol)) + val computeNumClasses: () => Int = () => { + val Row(maxLabelIndex: Double) = dataset.agg(max($(labelCol))).head() + // classes are assumed to be numbered from 0,...,maxLabelIndex + maxLabelIndex.toInt + 1 + } + val numClasses = MetadataUtils.getNumClasses(labelSchema).fold(computeNumClasses())(identity) + + val multiclassLabeled = dataset.select($(labelCol), $(featuresCol)) + + // persist if underlying dataset is not persistent. + val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE + if (handlePersistence) { + multiclassLabeled.persist(StorageLevel.MEMORY_AND_DISK) + } + + // create k columns, one for each binary classifier. + val models = Range(0, numClasses).par.map { index => + + val label: Double => Double = (label: Double) => { + if (label.toInt == index) 1.0 else 0.0 + } + + // generate new label metadata for the binary problem. + // TODO: use when ... otherwise after SPARK-7321 is merged + val labelUDF = callUDF(label, DoubleType, col($(labelCol))) + val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata() + val labelColName = "mc2b$" + index + val labelUDFWithNewMeta = labelUDF.as(labelColName, newLabelMeta) + val trainingDataset = multiclassLabeled.withColumn(labelColName, labelUDFWithNewMeta) + val classifier = getClassifier + classifier.fit(trainingDataset, classifier.labelCol -> labelColName) + }.toArray[ClassificationModel[_, _]] + + if (handlePersistence) { + multiclassLabeled.unpersist() + } + + // extract label metadata from label column if present, or create a nominal attribute + // to output the number of labels + val labelAttribute = Attribute.fromStructField(labelSchema) match { + case _: NumericAttribute | UnresolvedAttribute => + NominalAttribute.defaultAttr.withName("label").withNumValues(numClasses) + case attr: Attribute => attr + } + val model = new OneVsRestModel(uid, labelAttribute.toMetadata(), models).setParent(this) + copyValues(model) + } + + override def copy(extra: ParamMap): OneVsRest = { + val copied = defaultCopy(extra).asInstanceOf[OneVsRest] + if (isDefined(classifier)) { + copied.setClassifier($(classifier).copy(extra)) + } + copied + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala index 9954893f14359..d3c67494a31e4 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala @@ -19,11 +19,11 @@ package org.apache.spark.ml.classification import scala.collection.mutable -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.tree.{RandomForestParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel} -import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel} +import org.apache.spark.ml.util.{Identifiable, MetadataUtils} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} @@ -33,18 +33,19 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning algorithm for * classification. * It supports both binary and multiclass labels, as well as both continuous and categorical * features. */ -@AlphaComponent -final class RandomForestClassifier +@Experimental +final class RandomForestClassifier(override val uid: String) extends Predictor[Vector, RandomForestClassifier, RandomForestClassificationModel] with RandomForestParams with TreeClassifierParams { + def this() = this(Identifiable.randomUID("rfc")) + // Override parameter setters from parent trait for Java API compatibility. // Parameters from TreeClassifierParams: @@ -96,8 +97,11 @@ final class RandomForestClassifier oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) RandomForestClassificationModel.fromOld(oldModel, this, categoricalFeatures) } + + override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra) } +@Experimental object RandomForestClassifier { /** Accessor for supported impurity settings: entropy, gini */ final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities @@ -108,17 +112,16 @@ object RandomForestClassifier { } /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] model for classification. * It supports both binary and multiclass labels, as well as both continuous and categorical * features. * @param _trees Decision trees in the ensemble. * Warning: These have null parents. */ -@AlphaComponent +@Experimental final class RandomForestClassificationModel private[ml] ( - override val parent: RandomForestClassifier, + override val uid: String, private val _trees: Array[DecisionTreeClassificationModel]) extends PredictionModel[Vector, RandomForestClassificationModel] with TreeEnsembleModel with Serializable { @@ -146,7 +149,7 @@ final class RandomForestClassificationModel private[ml] ( } override def copy(extra: ParamMap): RandomForestClassificationModel = { - copyValues(new RandomForestClassificationModel(parent, _trees), extra) + copyValues(new RandomForestClassificationModel(uid, _trees), extra) } override def toString: String = { @@ -169,9 +172,10 @@ private[ml] object RandomForestClassificationModel { require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => - // parent, fittingParamMap for each tree is null since there are no good ways to set these. + // parent for each tree is null since there is no good way to set this. DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestClassificationModel(parent, newTrees) + val uid = if (parent != null) parent.uid else Identifiable.randomUID("rfc") + new RandomForestClassificationModel(uid, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index e5a73c6087a11..4a82b77f0edcb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -17,23 +17,24 @@ package org.apache.spark.ml.evaluation -import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.Evaluator +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.types.DoubleType /** - * :: AlphaComponent :: - * + * :: Experimental :: * Evaluator for binary classification, which expects two input columns: score and label. */ -@AlphaComponent -class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol { +@Experimental +class BinaryClassificationEvaluator(override val uid: String) + extends Evaluator with HasRawPredictionCol with HasLabelCol { + + def this() = this(Identifiable.randomUID("binEval")) /** * param for metric name in evaluation @@ -78,4 +79,6 @@ class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol w metrics.unpersist() metric } + + override def copy(extra: ParamMap): BinaryClassificationEvaluator = defaultCopy(extra) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala similarity index 86% rename from mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala rename to mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala index 5f2f8c94e9ff7..e56c946a063e8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala @@ -15,21 +15,21 @@ * limitations under the License. */ -package org.apache.spark.ml +package org.apache.spark.ml.evaluation -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: + * :: DeveloperApi :: * Abstract class for evaluators that compute metrics from predictions. */ -@AlphaComponent +@DeveloperApi abstract class Evaluator extends Params { /** - * Evaluates the output. + * Evaluates model output and returns a scalar metric (larger is better). * * @param dataset a dataset that contains labels/observations and predictions. * @param paramMap parameter map that specifies the input columns and output metrics @@ -46,7 +46,5 @@ abstract class Evaluator extends Params { */ def evaluate(dataset: DataFrame): Double - override def copy(extra: ParamMap): Evaluator = { - super.copy(extra).asInstanceOf[Evaluator] - } + override def copy(extra: ParamMap): Evaluator } diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala new file mode 100644 index 0000000000000..01c000b47514c --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala @@ -0,0 +1,89 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.evaluation + +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators} +import org.apache.spark.ml.param.shared.{HasLabelCol, HasPredictionCol} +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} +import org.apache.spark.mllib.evaluation.RegressionMetrics +import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.types.DoubleType + +/** + * :: Experimental :: + * Evaluator for regression, which expects two input columns: prediction and label. + */ +@Experimental +final class RegressionEvaluator(override val uid: String) + extends Evaluator with HasPredictionCol with HasLabelCol { + + def this() = this(Identifiable.randomUID("regEval")) + + /** + * param for metric name in evaluation (supports `"rmse"` (default), `"mse"`, `"r2"`, and `"mae"`) + * + * Because we will maximize evaluation value (ref: `CrossValidator`), + * when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`), + * we take and output the negative of this metric. + * @group param + */ + val metricName: Param[String] = { + val allowedParams = ParamValidators.inArray(Array("mse", "rmse", "r2", "mae")) + new Param(this, "metricName", "metric name in evaluation (mse|rmse|r2|mae)", allowedParams) + } + + /** @group getParam */ + def getMetricName: String = $(metricName) + + /** @group setParam */ + def setMetricName(value: String): this.type = set(metricName, value) + + /** @group setParam */ + def setPredictionCol(value: String): this.type = set(predictionCol, value) + + /** @group setParam */ + def setLabelCol(value: String): this.type = set(labelCol, value) + + setDefault(metricName -> "rmse") + + override def evaluate(dataset: DataFrame): Double = { + val schema = dataset.schema + SchemaUtils.checkColumnType(schema, $(predictionCol), DoubleType) + SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType) + + val predictionAndLabels = dataset.select($(predictionCol), $(labelCol)) + .map { case Row(prediction: Double, label: Double) => + (prediction, label) + } + val metrics = new RegressionMetrics(predictionAndLabels) + val metric = $(metricName) match { + case "rmse" => + -metrics.rootMeanSquaredError + case "mse" => + -metrics.meanSquaredError + case "r2" => + metrics.r2 + case "mae" => + -metrics.meanAbsoluteError + } + metric + } + + override def copy(extra: ParamMap): RegressionEvaluator = defaultCopy(extra) +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala index 6eb1db6971111..46314854d5e3a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala @@ -17,22 +17,25 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.Transformer import org.apache.spark.ml.attribute.BinaryAttribute import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} -import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.sql._ import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DoubleType, StructType} /** - * :: AlphaComponent :: + * :: Experimental :: * Binarize a column of continuous features given a threshold. */ -@AlphaComponent -final class Binarizer extends Transformer with HasInputCol with HasOutputCol { +@Experimental +final class Binarizer(override val uid: String) + extends Transformer with HasInputCol with HasOutputCol { + + def this() = this(Identifiable.randomUID("binarizer")) /** * Param for threshold used to binarize continuous features. @@ -80,4 +83,6 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol { val outputFields = inputFields :+ attr.toStructField() StructType(outputFields) } + + override def copy(extra: ParamMap): Binarizer = defaultCopy(extra) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala new file mode 100644 index 0000000000000..67e4785bc3553 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala @@ -0,0 +1,135 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import java.{util => ju} + +import org.apache.spark.SparkException +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml.Model +import org.apache.spark.ml.attribute.NominalAttribute +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} +import org.apache.spark.sql._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{DoubleType, StructField, StructType} + +/** + * :: Experimental :: + * `Bucketizer` maps a column of continuous features to a column of feature buckets. + */ +@Experimental +final class Bucketizer(override val uid: String) + extends Model[Bucketizer] with HasInputCol with HasOutputCol { + + def this() = this(Identifiable.randomUID("bucketizer")) + + /** + * Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. + * A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which + * also includes y. Splits should be strictly increasing. + * Values at -inf, inf must be explicitly provided to cover all Double values; + * otherwise, values outside the splits specified will be treated as errors. + * @group param + */ + val splits: DoubleArrayParam = new DoubleArrayParam(this, "splits", + "Split points for mapping continuous features into buckets. With n+1 splits, there are n " + + "buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last " + + "bucket, which also includes y. The splits should be strictly increasing. " + + "Values at -inf, inf must be explicitly provided to cover all Double values; " + + "otherwise, values outside the splits specified will be treated as errors.", + Bucketizer.checkSplits) + + /** @group getParam */ + def getSplits: Array[Double] = $(splits) + + /** @group setParam */ + def setSplits(value: Array[Double]): this.type = set(splits, value) + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema) + val bucketizer = udf { feature: Double => + Bucketizer.binarySearchForBuckets($(splits), feature) + } + val newCol = bucketizer(dataset($(inputCol))) + val newField = prepOutputField(dataset.schema) + dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata)) + } + + private def prepOutputField(schema: StructType): StructField = { + val buckets = $(splits).sliding(2).map(bucket => bucket.mkString(", ")).toArray + val attr = new NominalAttribute(name = Some($(outputCol)), isOrdinal = Some(true), + values = Some(buckets)) + attr.toStructField() + } + + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) + SchemaUtils.appendColumn(schema, prepOutputField(schema)) + } + + override def copy(extra: ParamMap): Bucketizer = defaultCopy(extra) +} + +private[feature] object Bucketizer { + /** We require splits to be of length >= 3 and to be in strictly increasing order. */ + def checkSplits(splits: Array[Double]): Boolean = { + if (splits.length < 3) { + false + } else { + var i = 0 + val n = splits.length - 1 + while (i < n) { + if (splits(i) >= splits(i + 1)) return false + i += 1 + } + true + } + } + + /** + * Binary searching in several buckets to place each data point. + * @throws SparkException if a feature is < splits.head or > splits.last + */ + def binarySearchForBuckets(splits: Array[Double], feature: Double): Double = { + if (feature == splits.last) { + splits.length - 2 + } else { + val idx = ju.Arrays.binarySearch(splits, feature) + if (idx >= 0) { + idx + } else { + val insertPos = -idx - 1 + if (insertPos == 0 || insertPos == splits.length) { + throw new SparkException(s"Feature value $feature out of Bucketizer bounds" + + s" [${splits.head}, ${splits.last}]. Check your features, or loosen " + + s"the lower/upper bound constraints.") + } else { + insertPos - 1 + } + } + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala index f8b56293e3ccc..a359cb8f37ec3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala @@ -17,27 +17,31 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.Param +import org.apache.spark.ml.param.{ParamMap, Param} +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** - * :: AlphaComponent :: + * :: Experimental :: * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a * provided "weight" vector. In other words, it scales each column of the dataset by a scalar * multiplier. */ -@AlphaComponent -class ElementwiseProduct extends UnaryTransformer[Vector, Vector, ElementwiseProduct] { +@Experimental +class ElementwiseProduct(override val uid: String) + extends UnaryTransformer[Vector, Vector, ElementwiseProduct] { + + def this() = this(Identifiable.randomUID("elemProd")) /** * the vector to multiply with input vectors * @group param */ - val scalingVec: Param[Vector] = new Param(this, "scalingVector", "vector for hadamard product") + val scalingVec: Param[Vector] = new Param(this, "scalingVec", "vector for hadamard product") /** @group setParam */ def setScalingVec(value: Vector): this.type = set(scalingVec, value) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index c305a819a8966..319d23e46cef4 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -17,19 +17,31 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{IntParam, ParamValidators} +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.attribute.AttributeGroup +import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators} +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.{Vector, VectorUDT} -import org.apache.spark.sql.types.DataType +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.types.{ArrayType, StructType} /** - * :: AlphaComponent :: + * :: Experimental :: * Maps a sequence of terms to their term frequencies using the hashing trick. */ -@AlphaComponent -class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { +@Experimental +class HashingTF(override val uid: String) extends Transformer with HasInputCol with HasOutputCol { + + def this() = this(Identifiable.randomUID("hashingTF")) + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) /** * Number of features. Should be > 0. @@ -47,10 +59,21 @@ class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { /** @group setParam */ def setNumFeatures(value: Int): this.type = set(numFeatures, value) - override protected def createTransformFunc: Iterable[_] => Vector = { + override def transform(dataset: DataFrame): DataFrame = { + val outputSchema = transformSchema(dataset.schema) val hashingTF = new feature.HashingTF($(numFeatures)) - hashingTF.transform + val t = udf { terms: Seq[_] => hashingTF.transform(terms) } + val metadata = outputSchema($(outputCol)).metadata + dataset.select(col("*"), t(col($(inputCol))).as($(outputCol), metadata)) + } + + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType + require(inputType.isInstanceOf[ArrayType], + s"The input column must be ArrayType, but got $inputType.") + val attrGroup = new AttributeGroup($(outputCol), $(numFeatures)) + SchemaUtils.appendColumn(schema, attrGroup.toStructField()) } - override protected def outputDataType: DataType = new VectorUDT() + override def copy(extra: ParamMap): HashingTF = defaultCopy(extra) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala index d901a20aed002..ecde80810580c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala @@ -17,11 +17,11 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml._ import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql._ @@ -45,9 +45,6 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol /** @group getParam */ def getMinDocFreq: Int = $(minDocFreq) - /** @group setParam */ - def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) - /** * Validate and transform the input schema. */ @@ -58,11 +55,13 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol } /** - * :: AlphaComponent :: + * :: Experimental :: * Compute the Inverse Document Frequency (IDF) given a collection of documents. */ -@AlphaComponent -final class IDF extends Estimator[IDFModel] with IDFBase { +@Experimental +final class IDF(override val uid: String) extends Estimator[IDFModel] with IDFBase { + + def this() = this(Identifiable.randomUID("idf")) /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) @@ -70,25 +69,30 @@ final class IDF extends Estimator[IDFModel] with IDFBase { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) + /** @group setParam */ + def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) + override def fit(dataset: DataFrame): IDFModel = { transformSchema(dataset.schema, logging = true) val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } val idf = new feature.IDF($(minDocFreq)).fit(input) - copyValues(new IDFModel(this, idf)) + copyValues(new IDFModel(uid, idf).setParent(this)) } override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } + + override def copy(extra: ParamMap): IDF = defaultCopy(extra) } /** - * :: AlphaComponent :: + * :: Experimental :: * Model fitted by [[IDF]]. */ -@AlphaComponent +@Experimental class IDFModel private[ml] ( - override val parent: IDF, + override val uid: String, idfModel: feature.IDFModel) extends Model[IDFModel] with IDFBase { @@ -107,4 +111,9 @@ class IDFModel private[ml] ( override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } + + override def copy(extra: ParamMap): IDFModel = { + val copied = new IDFModel(uid, idfModel) + copyValues(copied, extra) + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala index 755b46a64c7f1..8282e5ffa17f7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -17,19 +17,22 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.UnaryTransformer import org.apache.spark.ml.param.{DoubleParam, ParamValidators} +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** - * :: AlphaComponent :: + * :: Experimental :: * Normalize a vector to have unit norm using the given p-norm. */ -@AlphaComponent -class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { +@Experimental +class Normalizer(override val uid: String) extends UnaryTransformer[Vector, Vector, Normalizer] { + + def this() = this(Identifiable.randomUID("normalizer")) /** * Normalization in L^p^ space. Must be >= 1. diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala index 46514ae5f0e84..3825942795645 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala @@ -17,91 +17,154 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkException -import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.attribute.{Attribute, BinaryAttribute, NominalAttribute} -import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT} +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.attribute._ import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} -import org.apache.spark.ml.util.SchemaUtils -import org.apache.spark.sql.types.{DataType, DoubleType, StructType} +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.types.{DoubleType, StructType} /** - * A one-hot encoder that maps a column of label indices to a column of binary vectors, with - * at most a single one-value. By default, the binary vector has an element for each category, so - * with 5 categories, an input value of 2.0 would map to an output vector of - * (0.0, 0.0, 1.0, 0.0, 0.0). If includeFirst is set to false, the first category is omitted, so the - * output vector for the previous example would be (0.0, 1.0, 0.0, 0.0) and an input value - * of 0.0 would map to a vector of all zeros. Including the first category makes the vector columns - * linearly dependent because they sum up to one. + * :: Experimental :: + * A one-hot encoder that maps a column of category indices to a column of binary vectors, with + * at most a single one-value per row that indicates the input category index. + * For example with 5 categories, an input value of 2.0 would map to an output vector of + * `[0.0, 0.0, 1.0, 0.0]`. + * The last category is not included by default (configurable via [[OneHotEncoder!.dropLast]] + * because it makes the vector entries sum up to one, and hence linearly dependent. + * So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`. + * Note that this is different from scikit-learn's OneHotEncoder, which keeps all categories. + * The output vectors are sparse. + * + * @see [[StringIndexer]] for converting categorical values into category indices */ -@AlphaComponent -class OneHotEncoder extends UnaryTransformer[Double, Vector, OneHotEncoder] +@Experimental +class OneHotEncoder(override val uid: String) extends Transformer with HasInputCol with HasOutputCol { + def this() = this(Identifiable.randomUID("oneHot")) + /** - * Whether to include a component in the encoded vectors for the first category, defaults to true. + * Whether to drop the last category in the encoded vector (default: true) * @group param */ - final val includeFirst: BooleanParam = - new BooleanParam(this, "includeFirst", "include first category") - setDefault(includeFirst -> true) - - private var categories: Array[String] = _ + final val dropLast: BooleanParam = + new BooleanParam(this, "dropLast", "whether to drop the last category") + setDefault(dropLast -> true) /** @group setParam */ - def setIncludeFirst(value: Boolean): this.type = set(includeFirst, value) + def setDropLast(value: Boolean): this.type = set(dropLast, value) /** @group setParam */ - override def setInputCol(value: String): this.type = set(inputCol, value) + def setInputCol(value: String): this.type = set(inputCol, value) /** @group setParam */ - override def setOutputCol(value: String): this.type = set(outputCol, value) + def setOutputCol(value: String): this.type = set(outputCol, value) override def transformSchema(schema: StructType): StructType = { - SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) - val inputFields = schema.fields + val is = "_is_" + val inputColName = $(inputCol) val outputColName = $(outputCol) - require(inputFields.forall(_.name != $(outputCol)), - s"Output column ${$(outputCol)} already exists.") - val inputColAttr = Attribute.fromStructField(schema($(inputCol))) - categories = inputColAttr match { + SchemaUtils.checkColumnType(schema, inputColName, DoubleType) + val inputFields = schema.fields + require(!inputFields.exists(_.name == outputColName), + s"Output column $outputColName already exists.") + + val inputAttr = Attribute.fromStructField(schema(inputColName)) + val outputAttrNames: Option[Array[String]] = inputAttr match { case nominal: NominalAttribute => - nominal.values.getOrElse((0 until nominal.numValues.get).map(_.toString).toArray) - case binary: BinaryAttribute => binary.values.getOrElse(Array("0", "1")) + if (nominal.values.isDefined) { + nominal.values.map(_.map(v => inputColName + is + v)) + } else if (nominal.numValues.isDefined) { + nominal.numValues.map(n => Array.tabulate(n)(i => inputColName + is + i)) + } else { + None + } + case binary: BinaryAttribute => + if (binary.values.isDefined) { + binary.values.map(_.map(v => inputColName + is + v)) + } else { + Some(Array.tabulate(2)(i => inputColName + is + i)) + } + case _: NumericAttribute => + throw new RuntimeException( + s"The input column $inputColName cannot be numeric.") case _ => - throw new SparkException(s"OneHotEncoder input column ${$(inputCol)} is not nominal") + None // optimistic about unknown attributes + } + + val filteredOutputAttrNames = outputAttrNames.map { names => + if ($(dropLast)) { + require(names.length > 1, + s"The input column $inputColName should have at least two distinct values.") + names.dropRight(1) + } else { + names + } } - val attrValues = (if ($(includeFirst)) categories else categories.drop(1)).toArray - val attr = NominalAttribute.defaultAttr.withName(outputColName).withValues(attrValues) - val outputFields = inputFields :+ attr.toStructField() + val outputAttrGroup = if (filteredOutputAttrNames.isDefined) { + val attrs: Array[Attribute] = filteredOutputAttrNames.get.map { name => + BinaryAttribute.defaultAttr.withName(name) + } + new AttributeGroup($(outputCol), attrs) + } else { + new AttributeGroup($(outputCol)) + } + + val outputFields = inputFields :+ outputAttrGroup.toStructField() StructType(outputFields) } - protected override def createTransformFunc(): (Double) => Vector = { - val first = $(includeFirst) - val vecLen = if (first) categories.length else categories.length - 1 + override def transform(dataset: DataFrame): DataFrame = { + // schema transformation + val is = "_is_" + val inputColName = $(inputCol) + val outputColName = $(outputCol) + val shouldDropLast = $(dropLast) + var outputAttrGroup = AttributeGroup.fromStructField( + transformSchema(dataset.schema)(outputColName)) + if (outputAttrGroup.size < 0) { + // If the number of attributes is unknown, we check the values from the input column. + val numAttrs = dataset.select(col(inputColName).cast(DoubleType)).map(_.getDouble(0)) + .aggregate(0.0)( + (m, x) => { + assert(x >=0.0 && x == x.toInt, + s"Values from column $inputColName must be indices, but got $x.") + math.max(m, x) + }, + (m0, m1) => { + math.max(m0, m1) + } + ).toInt + 1 + val outputAttrNames = Array.tabulate(numAttrs)(i => inputColName + is + i) + val filtered = if (shouldDropLast) outputAttrNames.dropRight(1) else outputAttrNames + val outputAttrs: Array[Attribute] = + filtered.map(name => BinaryAttribute.defaultAttr.withName(name)) + outputAttrGroup = new AttributeGroup(outputColName, outputAttrs) + } + val metadata = outputAttrGroup.toMetadata() + + // data transformation + val size = outputAttrGroup.size val oneValue = Array(1.0) val emptyValues = Array[Double]() val emptyIndices = Array[Int]() - label: Double => { - val values = if (first || label != 0.0) oneValue else emptyValues - val indices = if (first) { - Array(label.toInt) - } else if (label != 0.0) { - Array(label.toInt - 1) + val encode = udf { label: Double => + if (label < size) { + Vectors.sparse(size, Array(label.toInt), oneValue) } else { - emptyIndices + Vectors.sparse(size, emptyIndices, emptyValues) } - Vectors.sparse(vecLen, indices, values) } + + dataset.select(col("*"), encode(col(inputColName).cast(DoubleType)).as(outputColName, metadata)) } - /** - * Returns the data type of the output column. - */ - protected def outputDataType: DataType = new VectorUDT + override def copy(extra: ParamMap): OneHotEncoder = defaultCopy(extra) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala index 9e6177ca27e4a..d85e468562d4a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala @@ -19,22 +19,26 @@ package org.apache.spark.ml.feature import scala.collection.mutable -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{IntParam, ParamValidators} +import org.apache.spark.ml.param.{ParamMap, IntParam, ParamValidators} +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.linalg._ import org.apache.spark.sql.types.DataType /** - * :: AlphaComponent :: + * :: Experimental :: * Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion, * which is available at [[http://en.wikipedia.org/wiki/Polynomial_expansion]], "In mathematics, an * expansion of a product of sums expresses it as a sum of products by using the fact that * multiplication distributes over addition". Take a 2-variable feature vector as an example: * `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`. */ -@AlphaComponent -class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion] { +@Experimental +class PolynomialExpansion(override val uid: String) + extends UnaryTransformer[Vector, Vector, PolynomialExpansion] { + + def this() = this(Identifiable.randomUID("poly")) /** * The polynomial degree to expand, which should be >= 1. A value of 1 means no expansion. @@ -57,6 +61,8 @@ class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExp } override protected def outputDataType: DataType = new VectorUDT() + + override def copy(extra: ParamMap): PolynomialExpansion = defaultCopy(extra) } /** @@ -71,7 +77,7 @@ class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExp * To handle sparsity, if c is zero, we can skip all monomials that contain it. We remember the * current index and increment it properly for sparse input. */ -object PolynomialExpansion { +private[feature] object PolynomialExpansion { private def choose(n: Int, k: Int): Int = { Range(n, n - k, -1).product / Range(k, 1, -1).product diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index 7cad59ff3fa37..ca3c1cfb56b7f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -17,10 +17,11 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml._ import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql._ @@ -34,13 +35,13 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with /** * Centers the data with mean before scaling. - * It will build a dense output, so this does not work on sparse input + * It will build a dense output, so this does not work on sparse input * and will raise an exception. * Default: false * @group param */ val withMean: BooleanParam = new BooleanParam(this, "withMean", "Center data with mean") - + /** * Scales the data to unit standard deviation. * Default: true @@ -50,12 +51,15 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with } /** - * :: AlphaComponent :: + * :: Experimental :: * Standardizes features by removing the mean and scaling to unit variance using column summary * statistics on the samples in the training set. */ -@AlphaComponent -class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams { +@Experimental +class StandardScaler(override val uid: String) extends Estimator[StandardScalerModel] + with StandardScalerParams { + + def this() = this(Identifiable.randomUID("stdScal")) setDefault(withMean -> false, withStd -> true) @@ -64,19 +68,19 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - + /** @group setParam */ def setWithMean(value: Boolean): this.type = set(withMean, value) - + /** @group setParam */ def setWithStd(value: Boolean): this.type = set(withStd, value) - + override def fit(dataset: DataFrame): StandardScalerModel = { transformSchema(dataset.schema, logging = true) val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } val scaler = new feature.StandardScaler(withMean = $(withMean), withStd = $(withStd)) val scalerModel = scaler.fit(input) - copyValues(new StandardScalerModel(this, scalerModel)) + copyValues(new StandardScalerModel(uid, scalerModel).setParent(this)) } override def transformSchema(schema: StructType): StructType = { @@ -88,15 +92,17 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } + + override def copy(extra: ParamMap): StandardScaler = defaultCopy(extra) } /** - * :: AlphaComponent :: + * :: Experimental :: * Model fitted by [[StandardScaler]]. */ -@AlphaComponent +@Experimental class StandardScalerModel private[ml] ( - override val parent: StandardScaler, + override val uid: String, scaler: feature.StandardScalerModel) extends Model[StandardScalerModel] with StandardScalerParams { @@ -121,4 +127,9 @@ class StandardScalerModel private[ml] ( val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } + + override def copy(extra: ParamMap): StandardScalerModel = { + val copied = new StandardScalerModel(uid, scaler) + copyValues(copied, extra) + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala index 3d78537ad84cb..bf7be363b8224 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -18,11 +18,12 @@ package org.apache.spark.ml.feature import org.apache.spark.SparkException -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.attribute.NominalAttribute import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.Identifiable import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{NumericType, StringType, StructType} @@ -51,14 +52,17 @@ private[feature] trait StringIndexerBase extends Params with HasInputCol with Ha } /** - * :: AlphaComponent :: + * :: Experimental :: * A label indexer that maps a string column of labels to an ML column of label indices. * If the input column is numeric, we cast it to string and index the string values. * The indices are in [0, numLabels), ordered by label frequencies. * So the most frequent label gets index 0. */ -@AlphaComponent -class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase { +@Experimental +class StringIndexer(override val uid: String) extends Estimator[StringIndexerModel] + with StringIndexerBase { + + def this() = this(Identifiable.randomUID("strIdx")) /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) @@ -73,21 +77,26 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase .map(_.getString(0)) .countByValue() val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray - copyValues(new StringIndexerModel(this, labels)) + copyValues(new StringIndexerModel(uid, labels).setParent(this)) } override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } + + override def copy(extra: ParamMap): StringIndexer = defaultCopy(extra) } /** - * :: AlphaComponent :: + * :: Experimental :: * Model fitted by [[StringIndexer]]. + * NOTE: During transformation, if the input column does not exist, + * [[StringIndexerModel.transform]] would return the input dataset unmodified. + * This is a temporary fix for the case when target labels do not exist during prediction. */ -@AlphaComponent +@Experimental class StringIndexerModel private[ml] ( - override val parent: StringIndexer, + override val uid: String, labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase { private val labelToIndex: OpenHashMap[String, Double] = { @@ -108,6 +117,12 @@ class StringIndexerModel private[ml] ( def setOutputCol(value: String): this.type = set(outputCol, value) override def transform(dataset: DataFrame): DataFrame = { + if (!dataset.schema.fieldNames.contains($(inputCol))) { + logInfo(s"Input column ${$(inputCol)} does not exist during transformation. " + + "Skip StringIndexerModel.") + return dataset + } + val indexer = udf { label: String => if (labelToIndex.contains(label)) { labelToIndex(label) @@ -124,6 +139,16 @@ class StringIndexerModel private[ml] ( } override def transformSchema(schema: StructType): StructType = { - validateAndTransformSchema(schema) + if (schema.fieldNames.contains($(inputCol))) { + validateAndTransformSchema(schema) + } else { + // If the input column does not exist during transformation, we skip StringIndexerModel. + schema + } + } + + override def copy(extra: ParamMap): StringIndexerModel = { + val copied = new StringIndexerModel(uid, labels) + copyValues(copied, extra) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 649c217b16590..5f9f57a2ebcfa 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -17,17 +17,22 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.UnaryTransformer import org.apache.spark.ml.param._ +import org.apache.spark.ml.util.Identifiable import org.apache.spark.sql.types.{ArrayType, DataType, StringType} /** - * :: AlphaComponent :: + * :: Experimental :: * A tokenizer that converts the input string to lowercase and then splits it by white spaces. + * + * @see [[RegexTokenizer]] */ -@AlphaComponent -class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { +@Experimental +class Tokenizer(override val uid: String) extends UnaryTransformer[String, Seq[String], Tokenizer] { + + def this() = this(Identifiable.randomUID("tok")) override protected def createTransformFunc: String => Seq[String] = { _.toLowerCase.split("\\s") @@ -38,24 +43,29 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { } override protected def outputDataType: DataType = new ArrayType(StringType, false) + + override def copy(extra: ParamMap): Tokenizer = defaultCopy(extra) } /** - * :: AlphaComponent :: - * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) - * or using it to split the text (set matching to false). Optional parameters also allow filtering - * tokens using a minimal length. + * :: Experimental :: + * A regex based tokenizer that extracts tokens either by using the provided regex pattern to split + * the text (default) or repeatedly matching the regex (if `gaps` is true). + * Optional parameters also allow filtering tokens using a minimal length. * It returns an array of strings that can be empty. */ -@AlphaComponent -class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenizer] { +@Experimental +class RegexTokenizer(override val uid: String) + extends UnaryTransformer[String, Seq[String], RegexTokenizer] { + + def this() = this(Identifiable.randomUID("regexTok")) /** * Minimum token length, >= 0. * Default: 1, to avoid returning empty strings * @group param */ - val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length (>= 0)", + val minTokenLength: IntParam = new IntParam(this, "minTokenLength", "minimum token length (>= 0)", ParamValidators.gtEq(0)) /** @group setParam */ @@ -65,8 +75,8 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def getMinTokenLength: Int = $(minTokenLength) /** - * Indicates whether regex splits on gaps (true) or matching tokens (false). - * Default: false + * Indicates whether regex splits on gaps (true) or matches tokens (false). + * Default: true * @group param */ val gaps: BooleanParam = new BooleanParam(this, "gaps", "Set regex to match gaps or tokens") @@ -78,8 +88,8 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def getGaps: Boolean = $(gaps) /** - * Regex pattern used by tokenizer. - * Default: `"\\p{L}+|[^\\p{L}\\s]+"` + * Regex pattern used to match delimiters if [[gaps]] is true or tokens if [[gaps]] is false. + * Default: `"\\s+"` * @group param */ val pattern: Param[String] = new Param(this, "pattern", "regex pattern used for tokenizing") @@ -90,7 +100,7 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize /** @group getParam */ def getPattern: String = $(pattern) - setDefault(minTokenLength -> 1, gaps -> false, pattern -> "\\p{L}+|[^\\p{L}\\s]+") + setDefault(minTokenLength -> 1, gaps -> true, pattern -> "\\s+") override protected def createTransformFunc: String => Seq[String] = { str => val re = $(pattern).r @@ -104,4 +114,6 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize } override protected def outputDataType: DataType = new ArrayType(StringType, false) + + override def copy(extra: ParamMap): RegexTokenizer = defaultCopy(extra) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index 796758a70ef18..9f83c2ee16178 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -20,20 +20,26 @@ package org.apache.spark.ml.feature import scala.collection.mutable.ArrayBuilder import org.apache.spark.SparkException -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.Transformer +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute, UnresolvedAttribute} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ /** - * :: AlphaComponent :: + * :: Experimental :: * A feature transformer that merges multiple columns into a vector column. */ -@AlphaComponent -class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { +@Experimental +class VectorAssembler(override val uid: String) + extends Transformer with HasInputCols with HasOutputCol { + + def this() = this(Identifiable.randomUID("vecAssembler")) /** @group setParam */ def setInputCols(value: Array[String]): this.type = set(inputCols, value) @@ -42,19 +48,59 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { def setOutputCol(value: String): this.type = set(outputCol, value) override def transform(dataset: DataFrame): DataFrame = { + // Schema transformation. + val schema = dataset.schema + lazy val first = dataset.first() + val attrs = $(inputCols).flatMap { c => + val field = schema(c) + val index = schema.fieldIndex(c) + field.dataType match { + case DoubleType => + val attr = Attribute.fromStructField(field) + // If the input column doesn't have ML attribute, assume numeric. + if (attr == UnresolvedAttribute) { + Some(NumericAttribute.defaultAttr.withName(c)) + } else { + Some(attr.withName(c)) + } + case _: NumericType | BooleanType => + // If the input column type is a compatible scalar type, assume numeric. + Some(NumericAttribute.defaultAttr.withName(c)) + case _: VectorUDT => + val group = AttributeGroup.fromStructField(field) + if (group.attributes.isDefined) { + // If attributes are defined, copy them with updated names. + group.attributes.get.map { attr => + if (attr.name.isDefined) { + // TODO: Define a rigorous naming scheme. + attr.withName(c + "_" + attr.name.get) + } else { + attr + } + } + } else { + // Otherwise, treat all attributes as numeric. If we cannot get the number of attributes + // from metadata, check the first row. + val numAttrs = group.numAttributes.getOrElse(first.getAs[Vector](index).size) + Array.fill(numAttrs)(NumericAttribute.defaultAttr) + } + } + } + val metadata = new AttributeGroup($(outputCol), attrs).toMetadata() + + // Data transformation. val assembleFunc = udf { r: Row => VectorAssembler.assemble(r.toSeq: _*) } - val schema = dataset.schema - val inputColNames = $(inputCols) - val args = inputColNames.map { c => + val args = $(inputCols).map { c => schema(c).dataType match { case DoubleType => dataset(c) case _: VectorUDT => dataset(c) case _: NumericType | BooleanType => dataset(c).cast(DoubleType).as(s"${c}_double_$uid") } } - dataset.select(col("*"), assembleFunc(struct(args : _*)).as($(outputCol))) + + dataset.select(col("*"), assembleFunc(struct(args : _*)).as($(outputCol), metadata)) } override def transformSchema(schema: StructType): StructType = { @@ -72,10 +118,11 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { } StructType(schema.fields :+ new StructField(outputColName, new VectorUDT, false)) } + + override def copy(extra: ParamMap): VectorAssembler = defaultCopy(extra) } -@AlphaComponent -object VectorAssembler { +private object VectorAssembler { private[feature] def assemble(vv: Any*): Vector = { val indices = ArrayBuilder.make[Int] diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala index 07ea579d69893..f4854a5e4b7b7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -17,12 +17,17 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import java.lang.{Double => JDouble, Integer => JInt} +import java.util.{Map => JMap} + +import scala.collection.JavaConverters._ + +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{Estimator, Model} -import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, NumericAttribute} -import org.apache.spark.ml.param.{IntParam, ParamValidators, Params} +import org.apache.spark.ml.attribute._ +import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators, Params} import org.apache.spark.ml.param.shared._ -import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, VectorUDT} import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions.callUDF @@ -51,8 +56,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu } /** - * :: AlphaComponent :: - * + * :: Experimental :: * Class for indexing categorical feature columns in a dataset of [[Vector]]. * * This has 2 usage modes: @@ -86,8 +90,11 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu * - Add warning if a categorical feature has only 1 category. * - Add option for allowing unknown categories. */ -@AlphaComponent -class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams { +@Experimental +class VectorIndexer(override val uid: String) extends Estimator[VectorIndexerModel] + with VectorIndexerParams { + + def this() = this(Identifiable.randomUID("vecIdx")) /** @group setParam */ def setMaxCategories(value: Int): this.type = set(maxCategories, value) @@ -110,7 +117,9 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara iter.foreach(localCatStats.addVector) Iterator(localCatStats) }.reduce((stats1, stats2) => stats1.merge(stats2)) - copyValues(new VectorIndexerModel(this, numFeatures, categoryStats.getCategoryMaps)) + val model = new VectorIndexerModel(uid, numFeatures, categoryStats.getCategoryMaps) + .setParent(this) + copyValues(model) } override def transformSchema(schema: StructType): StructType = { @@ -122,6 +131,8 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara SchemaUtils.checkColumnType(schema, $(inputCol), dataType) SchemaUtils.appendColumn(schema, $(outputCol), dataType) } + + override def copy(extra: ParamMap): VectorIndexer = defaultCopy(extra) } private object VectorIndexer { @@ -189,7 +200,8 @@ private object VectorIndexer { private def addDenseVector(dv: DenseVector): Unit = { var i = 0 - while (i < dv.size) { + val size = dv.size + while (i < size) { if (featureValueSets(i).size <= maxCategories) { featureValueSets(i).add(dv(i)) } @@ -201,7 +213,8 @@ private object VectorIndexer { // TODO: This might be able to handle 0's more efficiently. var vecIndex = 0 // index into vector var k = 0 // index into non-zero elements - while (vecIndex < sv.size) { + val size = sv.size + while (vecIndex < size) { val featureValue = if (k < sv.indices.length && vecIndex == sv.indices(k)) { k += 1 sv.values(k - 1) @@ -218,8 +231,7 @@ private object VectorIndexer { } /** - * :: AlphaComponent :: - * + * :: Experimental :: * Transform categorical features to use 0-based indices instead of their original values. * - Categorical features are mapped to indices. * - Continuous features (columns) are left unchanged. @@ -234,13 +246,18 @@ private object VectorIndexer { * Values are maps from original features values to 0-based category indices. * If a feature is not in this map, it is treated as continuous. */ -@AlphaComponent +@Experimental class VectorIndexerModel private[ml] ( - override val parent: VectorIndexer, + override val uid: String, val numFeatures: Int, val categoryMaps: Map[Int, Map[Double, Int]]) extends Model[VectorIndexerModel] with VectorIndexerParams { + /** Java-friendly version of [[categoryMaps]] */ + def javaCategoryMaps: JMap[JInt, JMap[JDouble, JInt]] = { + categoryMaps.mapValues(_.asJava).asJava.asInstanceOf[JMap[JInt, JMap[JDouble, JInt]]] + } + /** * Pre-computed feature attributes, with some missing info. * In transform(), set attribute name and other info, if available. @@ -375,6 +392,8 @@ class VectorIndexerModel private[ml] ( } case (origAttr: Attribute, featAttr: NumericAttribute) => origAttr.withIndex(featAttr.index.get) + case (origAttr: Attribute, _) => + origAttr } } else { partialFeatureAttributes @@ -382,4 +401,9 @@ class VectorIndexerModel private[ml] ( val newAttributeGroup = new AttributeGroup($(outputCol), featureAttributes) newAttributeGroup.toStructField() } + + override def copy(extra: ParamMap): VectorIndexerModel = { + val copied = new VectorIndexerModel(uid, numFeatures, categoryMaps) + copyValues(copied, extra) + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala index 34ff92970129f..6ea6590956300 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala @@ -17,11 +17,11 @@ package org.apache.spark.ml.feature -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{VectorUDT, Vectors} import org.apache.spark.mllib.linalg.BLAS._ @@ -37,6 +37,7 @@ private[feature] trait Word2VecBase extends Params /** * The dimension of the code that you want to transform from words. + * @group param */ final val vectorSize = new IntParam( this, "vectorSize", "the dimension of codes after transforming from words") @@ -47,6 +48,7 @@ private[feature] trait Word2VecBase extends Params /** * Number of partitions for sentences of words. + * @group param */ final val numPartitions = new IntParam( this, "numPartitions", "number of partitions for sentences of words") @@ -58,6 +60,7 @@ private[feature] trait Word2VecBase extends Params /** * The minimum number of times a token must appear to be included in the word2vec model's * vocabulary. + * @group param */ final val minCount = new IntParam(this, "minCount", "the minimum number of times a token must " + "appear to be included in the word2vec model's vocabulary") @@ -68,7 +71,6 @@ private[feature] trait Word2VecBase extends Params setDefault(stepSize -> 0.025) setDefault(maxIter -> 1) - setDefault(seed -> 42L) /** * Validate and transform the input schema. @@ -80,12 +82,14 @@ private[feature] trait Word2VecBase extends Params } /** - * :: AlphaComponent :: + * :: Experimental :: * Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further * natural language processing or machine learning process. */ -@AlphaComponent -final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { +@Experimental +final class Word2Vec(override val uid: String) extends Estimator[Word2VecModel] with Word2VecBase { + + def this() = this(Identifiable.randomUID("w2v")) /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) @@ -122,21 +126,23 @@ final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { .setSeed($(seed)) .setVectorSize($(vectorSize)) .fit(input) - copyValues(new Word2VecModel(this, wordVectors)) + copyValues(new Word2VecModel(uid, wordVectors).setParent(this)) } override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } + + override def copy(extra: ParamMap): Word2Vec = defaultCopy(extra) } /** - * :: AlphaComponent :: + * :: Experimental :: * Model fitted by [[Word2Vec]]. */ -@AlphaComponent +@Experimental class Word2VecModel private[ml] ( - override val parent: Word2Vec, + override val uid: String, wordVectors: feature.Word2VecModel) extends Model[Word2VecModel] with Word2VecBase { @@ -176,4 +182,9 @@ class Word2VecModel private[ml] ( override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } + + override def copy(extra: ParamMap): Word2VecModel = { + val copied = new Word2VecModel(uid, wordVectors) + copyValues(copied, extra) + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/package-info.java b/mllib/src/main/scala/org/apache/spark/ml/package-info.java index 00d9c802e930d..87f4223964ada 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/package-info.java +++ b/mllib/src/main/scala/org/apache/spark/ml/package-info.java @@ -16,10 +16,10 @@ */ /** - * Spark ML is an ALPHA component that adds a new set of machine learning APIs to let users quickly + * Spark ML is a BETA component that adds a new set of machine learning APIs to let users quickly * assemble and configure practical machine learning pipelines. */ -@AlphaComponent +@Experimental package org.apache.spark.ml; -import org.apache.spark.annotation.AlphaComponent; +import org.apache.spark.annotation.Experimental; diff --git a/mllib/src/main/scala/org/apache/spark/ml/package.scala b/mllib/src/main/scala/org/apache/spark/ml/package.scala index ac75e9de1a8f2..c589d06d9f7e4 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/package.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/package.scala @@ -18,7 +18,7 @@ package org.apache.spark /** - * Spark ML is an ALPHA component that adds a new set of machine learning APIs to let users quickly + * Spark ML is a BETA component that adds a new set of machine learning APIs to let users quickly * assemble and configure practical machine learning pipelines. * * @groupname param Parameters diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 0e1b60d172e3b..50c0d855066f8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -24,11 +24,11 @@ import scala.annotation.varargs import scala.collection.mutable import scala.collection.JavaConverters._ -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.ml.util.Identifiable /** - * :: AlphaComponent :: + * :: DeveloperApi :: * A param with self-contained documentation and optionally default value. Primitive-typed param * should use the specialized versions, which are more friendly to Java users. * @@ -39,13 +39,18 @@ import org.apache.spark.ml.util.Identifiable * See [[ParamValidators]] for factory methods for common validation functions. * @tparam T param value type */ -@AlphaComponent -class Param[T] (val parent: Params, val name: String, val doc: String, val isValid: T => Boolean) +@DeveloperApi +class Param[T](val parent: String, val name: String, val doc: String, val isValid: T => Boolean) extends Serializable { - def this(parent: Params, name: String, doc: String) = + def this(parent: Identifiable, name: String, doc: String, isValid: T => Boolean) = + this(parent.uid, name, doc, isValid) + + def this(parent: String, name: String, doc: String) = this(parent, name, doc, ParamValidators.alwaysTrue[T]) + def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc) + /** * Assert that the given value is valid for this parameter. * @@ -60,41 +65,34 @@ class Param[T] (val parent: Params, val name: String, val doc: String, val isVal */ private[param] def validate(value: T): Unit = { if (!isValid(value)) { - throw new IllegalArgumentException(s"$parent parameter $name given invalid value $value." + - s" Parameter description: $toString") + throw new IllegalArgumentException(s"$parent parameter $name given invalid value $value.") } } - /** - * Creates a param pair with the given value (for Java). - */ + /** Creates a param pair with the given value (for Java). */ def w(value: T): ParamPair[T] = this -> value - /** - * Creates a param pair with the given value (for Scala). - */ + /** Creates a param pair with the given value (for Scala). */ def ->(value: T): ParamPair[T] = ParamPair(this, value) - /** - * Converts this param's name, doc, and optionally its default value and the user-supplied - * value in its parent to string. - */ - override def toString: String = { - val valueStr = if (parent.isDefined(this)) { - val defaultValueStr = parent.getDefault(this).map("default: " + _) - val currentValueStr = parent.get(this).map("current: " + _) - (defaultValueStr ++ currentValueStr).mkString("(", ", ", ")") - } else { - "(undefined)" + override final def toString: String = s"${parent}__$name" + + override final def hashCode: Int = toString.## + + override final def equals(obj: Any): Boolean = { + obj match { + case p: Param[_] => (p.parent == parent) && (p.name == name) + case _ => false } - s"$name: $doc $valueStr" } } /** + * :: DeveloperApi :: * Factory methods for common validation functions for [[Param.isValid]]. * The numerical methods only support Int, Long, Float, and Double. */ +@DeveloperApi object ParamValidators { /** (private[param]) Default validation always return true */ @@ -172,69 +170,136 @@ object ParamValidators { // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ... -/** Specialized version of [[Param[Double]]] for Java. */ -class DoubleParam(parent: Params, name: String, doc: String, isValid: Double => Boolean) +/** + * :: DeveloperApi :: + * Specialized version of [[Param[Double]]] for Java. + */ +@DeveloperApi +class DoubleParam(parent: String, name: String, doc: String, isValid: Double => Boolean) extends Param[Double](parent, name, doc, isValid) { - def this(parent: Params, name: String, doc: String) = + def this(parent: String, name: String, doc: String) = this(parent, name, doc, ParamValidators.alwaysTrue) + def this(parent: Identifiable, name: String, doc: String, isValid: Double => Boolean) = + this(parent.uid, name, doc, isValid) + + def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc) + + /** Creates a param pair with the given value (for Java). */ override def w(value: Double): ParamPair[Double] = super.w(value) } -/** Specialized version of [[Param[Int]]] for Java. */ -class IntParam(parent: Params, name: String, doc: String, isValid: Int => Boolean) +/** + * :: DeveloperApi :: + * Specialized version of [[Param[Int]]] for Java. + */ +@DeveloperApi +class IntParam(parent: String, name: String, doc: String, isValid: Int => Boolean) extends Param[Int](parent, name, doc, isValid) { - def this(parent: Params, name: String, doc: String) = + def this(parent: String, name: String, doc: String) = this(parent, name, doc, ParamValidators.alwaysTrue) + def this(parent: Identifiable, name: String, doc: String, isValid: Int => Boolean) = + this(parent.uid, name, doc, isValid) + + def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc) + + /** Creates a param pair with the given value (for Java). */ override def w(value: Int): ParamPair[Int] = super.w(value) } -/** Specialized version of [[Param[Float]]] for Java. */ -class FloatParam(parent: Params, name: String, doc: String, isValid: Float => Boolean) +/** + * :: DeveloperApi :: + * Specialized version of [[Param[Float]]] for Java. + */ +@DeveloperApi +class FloatParam(parent: String, name: String, doc: String, isValid: Float => Boolean) extends Param[Float](parent, name, doc, isValid) { - def this(parent: Params, name: String, doc: String) = + def this(parent: String, name: String, doc: String) = this(parent, name, doc, ParamValidators.alwaysTrue) + def this(parent: Identifiable, name: String, doc: String, isValid: Float => Boolean) = + this(parent.uid, name, doc, isValid) + + def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc) + + /** Creates a param pair with the given value (for Java). */ override def w(value: Float): ParamPair[Float] = super.w(value) } -/** Specialized version of [[Param[Long]]] for Java. */ -class LongParam(parent: Params, name: String, doc: String, isValid: Long => Boolean) +/** + * :: DeveloperApi :: + * Specialized version of [[Param[Long]]] for Java. + */ +@DeveloperApi +class LongParam(parent: String, name: String, doc: String, isValid: Long => Boolean) extends Param[Long](parent, name, doc, isValid) { - def this(parent: Params, name: String, doc: String) = + def this(parent: String, name: String, doc: String) = this(parent, name, doc, ParamValidators.alwaysTrue) + def this(parent: Identifiable, name: String, doc: String, isValid: Long => Boolean) = + this(parent.uid, name, doc, isValid) + + def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc) + + /** Creates a param pair with the given value (for Java). */ override def w(value: Long): ParamPair[Long] = super.w(value) } -/** Specialized version of [[Param[Boolean]]] for Java. */ -class BooleanParam(parent: Params, name: String, doc: String) // No need for isValid +/** + * :: DeveloperApi :: + * Specialized version of [[Param[Boolean]]] for Java. + */ +@DeveloperApi +class BooleanParam(parent: String, name: String, doc: String) // No need for isValid extends Param[Boolean](parent, name, doc) { + def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc) + + /** Creates a param pair with the given value (for Java). */ override def w(value: Boolean): ParamPair[Boolean] = super.w(value) } -/** Specialized version of [[Param[Array[T]]]] for Java. */ +/** + * :: DeveloperApi :: + * Specialized version of [[Param[Array[String]]]] for Java. + */ +@DeveloperApi class StringArrayParam(parent: Params, name: String, doc: String, isValid: Array[String] => Boolean) extends Param[Array[String]](parent, name, doc, isValid) { def this(parent: Params, name: String, doc: String) = this(parent, name, doc, ParamValidators.alwaysTrue) - override def w(value: Array[String]): ParamPair[Array[String]] = super.w(value) - /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */ def w(value: java.util.List[String]): ParamPair[Array[String]] = w(value.asScala.toArray) } /** - * A param amd its value. + * :: DeveloperApi :: + * Specialized version of [[Param[Array[Double]]]] for Java. */ +@DeveloperApi +class DoubleArrayParam(parent: Params, name: String, doc: String, isValid: Array[Double] => Boolean) + extends Param[Array[Double]](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) + + /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */ + def w(value: java.util.List[java.lang.Double]): ParamPair[Array[Double]] = + w(value.asScala.map(_.asInstanceOf[Double]).toArray) +} + +/** + * :: Experimental :: + * A param and its value. + */ +@Experimental case class ParamPair[T](param: Param[T], value: T) { // This is *the* place Param.validate is called. Whenever a parameter is specified, we should // always construct a ParamPair so that validate is called. @@ -242,16 +307,19 @@ case class ParamPair[T](param: Param[T], value: T) { } /** - * :: AlphaComponent :: + * :: DeveloperApi :: * Trait for components that take parameters. This also provides an internal param map to store * parameter values attached to the instance. */ -@AlphaComponent +@DeveloperApi trait Params extends Identifiable with Serializable { /** * Returns all params sorted by their names. The default implementation uses Java reflection to * list all public methods that have no arguments and return [[Param]]. + * + * Note: Developer should not use this method in constructor because we cannot guarantee that + * this variable gets initialized before other params. */ lazy val params: Array[Param[_]] = { val methods = this.getClass.getMethods @@ -263,19 +331,6 @@ trait Params extends Identifiable with Serializable { .map(m => m.invoke(this).asInstanceOf[Param[_]]) } - /** - * Validates parameter values stored internally plus the input parameter map. - * Raises an exception if any parameter is invalid. - * - * This only needs to check for interactions between parameters. - * Parameter value checks which do not depend on other parameters are handled by - * [[Param.validate()]]. This method does not handle input/output column parameters; - * those are checked during schema validation. - */ - def validateParams(paramMap: ParamMap): Unit = { - copy(paramMap).validateParams() - } - /** * Validates parameter values stored internally. * Raise an exception if any parameter value is invalid. @@ -286,15 +341,36 @@ trait Params extends Identifiable with Serializable { * those are checked during schema validation. */ def validateParams(): Unit = { - params.filter(isDefined _).foreach { param => + params.filter(isDefined).foreach { param => param.asInstanceOf[Param[Any]].validate($(param)) } } /** - * Returns the documentation of all params. + * Explains a param. + * @param param input param, must belong to this instance. + * @return a string that contains the input param name, doc, and optionally its default value and + * the user-supplied value + */ + def explainParam(param: Param[_]): String = { + shouldOwn(param) + val valueStr = if (isDefined(param)) { + val defaultValueStr = getDefault(param).map("default: " + _) + val currentValueStr = get(param).map("current: " + _) + (defaultValueStr ++ currentValueStr).mkString("(", ", ", ")") + } else { + "(undefined)" + } + s"${param.name}: ${param.doc} $valueStr" + } + + /** + * Explains all params of this instance. + * @see [[explainParam()]] */ - def explainParams(): String = params.mkString("\n") + def explainParams(): String = { + params.map(explainParam).mkString("\n") + } /** Checks whether a param is explicitly set. */ final def isSet(param: Param[_]): Boolean = { @@ -379,8 +455,7 @@ trait Params extends Identifiable with Serializable { * @param value the default value */ protected final def setDefault[T](param: Param[T], value: T): this.type = { - shouldOwn(param) - defaultParamMap.put(param, value) + defaultParamMap.put(param -> value) this } @@ -416,24 +491,30 @@ trait Params extends Identifiable with Serializable { } /** - * Creates a copy of this instance with a randomly generated uid and some extra params. - * The default implementation calls the default constructor to create a new instance, then - * copies the embedded and extra parameters over and returns the new instance. - * Subclasses should override this method if the default approach is not sufficient. + * Creates a copy of this instance with the same UID and some extra params. + * Subclasses should implement this method and set the return type properly. + * + * @see [[defaultCopy()]] + */ + def copy(extra: ParamMap): Params + + /** + * Default implementation of copy with extra params. + * It tries to create a new instance with the same UID. + * Then it copies the embedded and extra parameters over and returns the new instance. */ - def copy(extra: ParamMap): Params = { - val that = this.getClass.newInstance() - copyValues(that, extra) - that + protected final def defaultCopy[T <: Params](extra: ParamMap): T = { + val that = this.getClass.getConstructor(classOf[String]).newInstance(uid) + copyValues(that, extra).asInstanceOf[T] } /** * Extracts the embedded default param values and user-supplied values, and then merges them with * extra values from input into a flat param map, where the latter value is used if there exist - * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap. + * conflicts, i.e., with ordering: default param values < user-supplied values < extra. */ - final def extractParamMap(extraParamMap: ParamMap): ParamMap = { - defaultParamMap ++ paramMap ++ extraParamMap + final def extractParamMap(extra: ParamMap): ParamMap = { + defaultParamMap ++ paramMap ++ extra } /** @@ -451,7 +532,7 @@ trait Params extends Identifiable with Serializable { /** Validates that the input param belongs to this instance. */ private def shouldOwn(param: Param[_]): Unit = { - require(param.parent.eq(this), s"Param $param does not belong to $this.") + require(param.parent == uid && hasParam(param.name), s"Param $param does not belong to $this.") } /** @@ -472,18 +553,20 @@ trait Params extends Identifiable with Serializable { } /** + * :: DeveloperApi :: * Java-friendly wrapper for [[Params]]. * Java developers who need to extend [[Params]] should use this class instead. * If you need to extend a abstract class which already extends [[Params]], then that abstract * class should be Java-friendly as well. */ +@DeveloperApi abstract class JavaParams extends Params /** - * :: AlphaComponent :: + * :: Experimental :: * A param to value map. */ -@AlphaComponent +@Experimental final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) extends Serializable { @@ -501,7 +584,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) /** * Puts a (param, value) pair (overwrites if the input param exists). */ - def put[T](param: Param[T], value: T): this.type = put(ParamPair(param, value)) + def put[T](param: Param[T], value: T): this.type = put(param -> value) /** * Puts a list of param pairs (overwrites if the input params exists). @@ -567,7 +650,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) override def toString: String = { map.toSeq.sortBy(_._1.name).map { case (param, value) => - s"\t${param.parent.uid}-${param.name}: $value" + s"\t${param.parent}-${param.name}: $value" }.mkString("{\n", ",\n", "\n}") } @@ -604,6 +687,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) def size: Int = map.size } +@Experimental object ParamMap { /** diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 5085b798daa17..8ffbcf0d8bc71 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -33,7 +33,7 @@ private[shared] object SharedParamsCodeGen { val params = Seq( ParamDesc[Double]("regParam", "regularization parameter (>= 0)", isValid = "ParamValidators.gtEq(0)"), - ParamDesc[Int]("maxIter", "max number of iterations (>= 0)", + ParamDesc[Int]("maxIter", "maximum number of iterations (>= 0)", isValid = "ParamValidators.gtEq(0)"), ParamDesc[String]("featuresCol", "features column name", Some("\"features\"")), ParamDesc[String]("labelCol", "label column name", Some("\"label\"")), @@ -49,11 +49,11 @@ private[shared] object SharedParamsCodeGen { isValid = "ParamValidators.inRange(0, 1)"), ParamDesc[String]("inputCol", "input column name"), ParamDesc[Array[String]]("inputCols", "input column names"), - ParamDesc[String]("outputCol", "output column name"), + ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")), ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)", isValid = "ParamValidators.gtEq(1)"), ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), - ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()")), + ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")), ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." + " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", isValid = "ParamValidators.inRange(0, 1)"), diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index 7525d37007377..a0c8ccdac9ad9 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -45,10 +45,10 @@ private[ml] trait HasRegParam extends Params { private[ml] trait HasMaxIter extends Params { /** - * Param for max number of iterations (>= 0). + * Param for maximum number of iterations (>= 0). * @group param */ - final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations (>= 0)", ParamValidators.gtEq(0)) + final val maxIter: IntParam = new IntParam(this, "maxIter", "maximum number of iterations (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ final def getMaxIter: Int = $(maxIter) @@ -185,7 +185,7 @@ private[ml] trait HasInputCols extends Params { } /** - * (private[ml]) Trait for shared param outputCol. + * (private[ml]) Trait for shared param outputCol (default: uid + "__output"). */ private[ml] trait HasOutputCol extends Params { @@ -195,6 +195,8 @@ private[ml] trait HasOutputCol extends Params { */ final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name") + setDefault(outputCol, uid + "__output") + /** @group getParam */ final def getOutputCol: String = $(outputCol) } @@ -232,7 +234,7 @@ private[ml] trait HasFitIntercept extends Params { } /** - * (private[ml]) Trait for shared param seed (default: Utils.random.nextLong()). + * (private[ml]) Trait for shared param seed (default: this.getClass.getName.hashCode.toLong). */ private[ml] trait HasSeed extends Params { @@ -242,7 +244,7 @@ private[ml] trait HasSeed extends Params { */ final val seed: LongParam = new LongParam(this, "seed", "random seed") - setDefault(seed, Utils.random.nextLong()) + setDefault(seed, this.getClass.getName.hashCode.toLong) /** @group getParam */ final def getSeed: Long = $(seed) diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index 6cf4b40075281..2e44cd4cc6a22 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -31,25 +31,51 @@ import org.apache.hadoop.fs.{FileSystem, Path} import org.netlib.util.intW import org.apache.spark.{Logging, Partitioner} -import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.mllib.optimization.NNLS import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ -import org.apache.spark.sql.types.{DoubleType, FloatType, IntegerType, StructField, StructType} +import org.apache.spark.sql.types.{DoubleType, FloatType, IntegerType, StructType} import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils import org.apache.spark.util.collection.{OpenHashMap, OpenHashSet, SortDataFormat, Sorter} import org.apache.spark.util.random.XORShiftRandom +/** + * Common params for ALS and ALSModel. + */ +private[recommendation] trait ALSModelParams extends Params with HasPredictionCol { + /** + * Param for the column name for user ids. + * Default: "user" + * @group param + */ + val userCol = new Param[String](this, "userCol", "column name for user ids") + + /** @group getParam */ + def getUserCol: String = $(userCol) + + /** + * Param for the column name for item ids. + * Default: "item" + * @group param + */ + val itemCol = new Param[String](this, "itemCol", "column name for item ids") + + /** @group getParam */ + def getItemCol: String = $(itemCol) +} + /** * Common params for ALS. */ -private[recommendation] trait ALSParams extends Params with HasMaxIter with HasRegParam - with HasPredictionCol with HasCheckpointInterval { +private[recommendation] trait ALSParams extends ALSModelParams with HasMaxIter with HasRegParam + with HasPredictionCol with HasCheckpointInterval with HasSeed { /** * Param for rank of the matrix factorization (>= 1). @@ -104,26 +130,6 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR /** @group getParam */ def getAlpha: Double = $(alpha) - /** - * Param for the column name for user ids. - * Default: "user" - * @group param - */ - val userCol = new Param[String](this, "userCol", "column name for user ids") - - /** @group getParam */ - def getUserCol: String = $(userCol) - - /** - * Param for the column name for item ids. - * Default: "item" - * @group param - */ - val itemCol = new Param[String](this, "itemCol", "column name for item ids") - - /** @group getParam */ - def getItemCol: String = $(itemCol) - /** * Param for the column name for ratings. * Default: "rating" @@ -155,58 +161,71 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR * @return output schema */ protected def validateAndTransformSchema(schema: StructType): StructType = { - require(schema($(userCol)).dataType == IntegerType) - require(schema($(itemCol)).dataType== IntegerType) + SchemaUtils.checkColumnType(schema, $(userCol), IntegerType) + SchemaUtils.checkColumnType(schema, $(itemCol), IntegerType) val ratingType = schema($(ratingCol)).dataType require(ratingType == FloatType || ratingType == DoubleType) - val predictionColName = $(predictionCol) - require(!schema.fieldNames.contains(predictionColName), - s"Prediction column $predictionColName already exists.") - val newFields = schema.fields :+ StructField($(predictionCol), FloatType, nullable = false) - StructType(newFields) + SchemaUtils.appendColumn(schema, $(predictionCol), FloatType) } } /** + * :: Experimental :: * Model fitted by ALS. + * + * @param rank rank of the matrix factorization model + * @param userFactors a DataFrame that stores user factors in two columns: `id` and `features` + * @param itemFactors a DataFrame that stores item factors in two columns: `id` and `features` */ +@Experimental class ALSModel private[ml] ( - override val parent: ALS, - k: Int, - userFactors: RDD[(Int, Array[Float])], - itemFactors: RDD[(Int, Array[Float])]) - extends Model[ALSModel] with ALSParams { + override val uid: String, + val rank: Int, + @transient val userFactors: DataFrame, + @transient val itemFactors: DataFrame) + extends Model[ALSModel] with ALSModelParams { + + /** @group setParam */ + def setUserCol(value: String): this.type = set(userCol, value) + + /** @group setParam */ + def setItemCol(value: String): this.type = set(itemCol, value) /** @group setParam */ def setPredictionCol(value: String): this.type = set(predictionCol, value) override def transform(dataset: DataFrame): DataFrame = { - import dataset.sqlContext.implicits._ - val users = userFactors.toDF("id", "features") - val items = itemFactors.toDF("id", "features") - // Register a UDF for DataFrame, and then // create a new column named map(predictionCol) by running the predict UDF. val predict = udf { (userFeatures: Seq[Float], itemFeatures: Seq[Float]) => if (userFeatures != null && itemFeatures != null) { - blas.sdot(k, userFeatures.toArray, 1, itemFeatures.toArray, 1) + blas.sdot(rank, userFeatures.toArray, 1, itemFeatures.toArray, 1) } else { Float.NaN } } dataset - .join(users, dataset($(userCol)) === users("id"), "left") - .join(items, dataset($(itemCol)) === items("id"), "left") - .select(dataset("*"), predict(users("features"), items("features")).as($(predictionCol))) + .join(userFactors, dataset($(userCol)) === userFactors("id"), "left") + .join(itemFactors, dataset($(itemCol)) === itemFactors("id"), "left") + .select(dataset("*"), + predict(userFactors("features"), itemFactors("features")).as($(predictionCol))) } override def transformSchema(schema: StructType): StructType = { - validateAndTransformSchema(schema) + SchemaUtils.checkColumnType(schema, $(userCol), IntegerType) + SchemaUtils.checkColumnType(schema, $(itemCol), IntegerType) + SchemaUtils.appendColumn(schema, $(predictionCol), FloatType) + } + + override def copy(extra: ParamMap): ALSModel = { + val copied = new ALSModel(uid, rank, userFactors, itemFactors) + copyValues(copied, extra) } } /** + * :: Experimental :: * Alternating Least Squares (ALS) matrix factorization. * * ALS attempts to estimate the ratings matrix `R` as the product of two lower-rank matrices, @@ -235,10 +254,13 @@ class ALSModel private[ml] ( * indicated user * preferences rather than explicit ratings given to items. */ -class ALS extends Estimator[ALSModel] with ALSParams { +@Experimental +class ALS(override val uid: String) extends Estimator[ALSModel] with ALSParams { import org.apache.spark.ml.recommendation.ALS.Rating + def this() = this(Identifiable.randomUID("als")) + /** @group setParam */ def setRank(value: Int): this.type = set(rank, value) @@ -278,6 +300,9 @@ class ALS extends Estimator[ALSModel] with ALSParams { /** @group setParam */ def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) + /** @group setParam */ + def setSeed(value: Long): this.type = set(seed, value) + /** * Sets both numUserBlocks and numItemBlocks to the specific value. * @group setParam @@ -289,8 +314,10 @@ class ALS extends Estimator[ALSModel] with ALSParams { } override def fit(dataset: DataFrame): ALSModel = { + import dataset.sqlContext.implicits._ val ratings = dataset - .select(col($(userCol)), col($(itemCol)), col($(ratingCol)).cast(FloatType)) + .select(col($(userCol)).cast(IntegerType), col($(itemCol)).cast(IntegerType), + col($(ratingCol)).cast(FloatType)) .map { row => Rating(row.getInt(0), row.getInt(1), row.getFloat(2)) } @@ -298,13 +325,18 @@ class ALS extends Estimator[ALSModel] with ALSParams { numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks), maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs), alpha = $(alpha), nonnegative = $(nonnegative), - checkpointInterval = $(checkpointInterval)) - copyValues(new ALSModel(this, $(rank), userFactors, itemFactors)) + checkpointInterval = $(checkpointInterval), seed = $(seed)) + val userDF = userFactors.toDF("id", "features") + val itemDF = itemFactors.toDF("id", "features") + val model = new ALSModel(uid, $(rank), userDF, itemDF).setParent(this) + copyValues(model) } override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } + + override def copy(extra: ParamMap): ALS = defaultCopy(extra) } /** @@ -318,7 +350,11 @@ class ALS extends Estimator[ALSModel] with ALSParams { @DeveloperApi object ALS extends Logging { - /** Rating class for better code readability. */ + /** + * :: DeveloperApi :: + * Rating class for better code readability. + */ + @DeveloperApi case class Rating[@specialized(Int, Long) ID](user: ID, item: ID, rating: Float) /** Trait for least squares solvers applied to the normal equation. */ @@ -479,8 +515,10 @@ object ALS extends Logging { } /** + * :: DeveloperApi :: * Implementation of the ALS algorithm. */ + @DeveloperApi def train[ID: ClassTag]( // scalastyle:ignore ratings: RDD[Rating[ID]], rank: Int = 10, diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala index f8f0b161a4812..be1f8063d41d8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -17,11 +17,11 @@ package org.apache.spark.ml.regression -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.tree.{TreeRegressorParams, DecisionTreeParams, DecisionTreeModel, Node} -import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeRegressorParams} +import org.apache.spark.ml.util.{Identifiable, MetadataUtils} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree} @@ -31,17 +31,18 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm * for regression. * It supports both continuous and categorical features. */ -@AlphaComponent -final class DecisionTreeRegressor +@Experimental +final class DecisionTreeRegressor(override val uid: String) extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] with DecisionTreeParams with TreeRegressorParams { + def this() = this(Identifiable.randomUID("dtr")) + // Override parameter setters from parent trait for Java API compatibility. override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) @@ -75,23 +76,25 @@ final class DecisionTreeRegressor super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity, subsamplingRate = 1.0) } + + override def copy(extra: ParamMap): DecisionTreeRegressor = defaultCopy(extra) } +@Experimental object DecisionTreeRegressor { /** Accessor for supported impurities: variance */ final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities } /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for regression. * It supports both continuous and categorical features. * @param rootNode Root of the decision tree */ -@AlphaComponent +@Experimental final class DecisionTreeRegressionModel private[ml] ( - override val parent: DecisionTreeRegressor, + override val uid: String, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with Serializable { @@ -104,7 +107,7 @@ final class DecisionTreeRegressionModel private[ml] ( } override def copy(extra: ParamMap): DecisionTreeRegressionModel = { - copyValues(new DecisionTreeRegressionModel(parent, rootNode), extra) + copyValues(new DecisionTreeRegressionModel(uid, rootNode), extra) } override def toString: String = { @@ -128,6 +131,7 @@ private[ml] object DecisionTreeRegressionModel { s"Cannot convert non-regression DecisionTreeModel (old API) to" + s" DecisionTreeRegressionModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeRegressionModel(parent, rootNode) + val uid = if (parent != null) parent.uid else Identifiable.randomUID("dtr") + new DecisionTreeRegressionModel(uid, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala index 461905c12701a..036e3acb07412 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala @@ -20,11 +20,11 @@ package org.apache.spark.ml.regression import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.Logging -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.{Param, ParamMap} -import org.apache.spark.ml.tree.{GBTParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel} -import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeEnsembleModel, TreeRegressorParams} +import org.apache.spark.ml.util.{Identifiable, MetadataUtils} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} @@ -35,17 +35,18 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] * learning algorithm for regression. * It supports both continuous and categorical features. */ -@AlphaComponent -final class GBTRegressor +@Experimental +final class GBTRegressor(override val uid: String) extends Predictor[Vector, GBTRegressor, GBTRegressionModel] with GBTParams with TreeRegressorParams with Logging { + def this() = this(Identifiable.randomUID("gbtr")) + // Override parameter setters from parent trait for Java API compatibility. // Parameters from TreeRegressorParams: @@ -130,8 +131,11 @@ final class GBTRegressor val oldModel = oldGBT.run(oldDataset) GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures) } + + override def copy(extra: ParamMap): GBTRegressor = defaultCopy(extra) } +@Experimental object GBTRegressor { // The losses below should be lowercase. /** Accessor for supported loss settings: squared (L2), absolute (L1) */ @@ -139,7 +143,7 @@ object GBTRegressor { } /** - * :: AlphaComponent :: + * :: Experimental :: * * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] * model for regression. @@ -147,9 +151,9 @@ object GBTRegressor { * @param _trees Decision trees in the ensemble. * @param _treeWeights Weights for the decision trees in the ensemble. */ -@AlphaComponent +@Experimental final class GBTRegressionModel( - override val parent: GBTRegressor, + override val uid: String, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTRegressionModel] @@ -173,7 +177,7 @@ final class GBTRegressionModel( } override def copy(extra: ParamMap): GBTRegressionModel = { - copyValues(new GBTRegressionModel(parent, _trees, _treeWeights), extra) + copyValues(new GBTRegressionModel(uid, _trees, _treeWeights), extra) } override def toString: String = { @@ -196,9 +200,10 @@ private[ml] object GBTRegressionModel { require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => - // parent, fittingParamMap for each tree is null since there are no good ways to set these. + // parent for each tree is null since there is no good way to set this. DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTRegressionModel(parent, newTrees, oldModel.treeWeights) + val uid = if (parent != null) parent.uid else Identifiable.randomUID("gbtr") + new GBTRegressionModel(parent.uid, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index e63c9a3eead52..01306545fc7cd 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -20,14 +20,14 @@ package org.apache.spark.ml.regression import scala.collection.mutable import breeze.linalg.{DenseVector => BDV, norm => brzNorm} -import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, - OWLQN => BreezeOWLQN} +import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} import org.apache.spark.Logging -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.PredictorParams import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol} +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.linalg.BLAS._ import org.apache.spark.mllib.regression.LabeledPoint @@ -44,8 +44,7 @@ private[regression] trait LinearRegressionParams extends PredictorParams with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol /** - * :: AlphaComponent :: - * + * :: Experimental :: * Linear regression. * * The learning objective is to minimize the squared error, with regularization. @@ -58,10 +57,13 @@ private[regression] trait LinearRegressionParams extends PredictorParams * - L1 (Lasso) * - L2 + L1 (elastic net) */ -@AlphaComponent -class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] +@Experimental +class LinearRegression(override val uid: String) + extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams with Logging { + def this() = this(Identifiable.randomUID("linReg")) + /** * Set the regularization parameter. * Default is 0.0. @@ -81,7 +83,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress setDefault(elasticNetParam -> 0.0) /** - * Set the maximal number of iterations. + * Set the maximum number of iterations. * Default is 100. * @group setParam */ @@ -106,14 +108,16 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) val (summarizer, statCounter) = instances.treeAggregate( - (new MultivariateOnlineSummarizer, new StatCounter))( { - case ((summarizer: MultivariateOnlineSummarizer, statCounter: StatCounter), - (label: Double, features: Vector)) => - (summarizer.add(features), statCounter.merge(label)) - }, { - case ((summarizer1: MultivariateOnlineSummarizer, statCounter1: StatCounter), - (summarizer2: MultivariateOnlineSummarizer, statCounter2: StatCounter)) => - (summarizer1.merge(summarizer2), statCounter1.merge(statCounter2)) + (new MultivariateOnlineSummarizer, new StatCounter))( + seqOp = (c, v) => (c, v) match { + case ((summarizer: MultivariateOnlineSummarizer, statCounter: StatCounter), + (label: Double, features: Vector)) => + (summarizer.add(features), statCounter.merge(label)) + }, + combOp = (c1, c2) => (c1, c2) match { + case ((summarizer1: MultivariateOnlineSummarizer, statCounter1: StatCounter), + (summarizer2: MultivariateOnlineSummarizer, statCounter2: StatCounter)) => + (summarizer1.merge(summarizer2), statCounter1.merge(statCounter2)) }) val numFeatures = summarizer.mean.size @@ -126,7 +130,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " + s"and the intercept will be the mean of the label; as a result, training is not needed.") if (handlePersistence) instances.unpersist() - return new LinearRegressionModel(this, Vectors.sparse(numFeatures, Seq()), yMean) + return new LinearRegressionModel(uid, Vectors.sparse(numFeatures, Seq()), yMean) } val featuresMean = summarizer.mean.toArray @@ -165,7 +169,8 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress val weights = { val rawWeights = state.x.toArray.clone() var i = 0 - while (i < rawWeights.length) { + val len = rawWeights.length + while (i < len) { rawWeights(i) *= { if (featuresStd(i) != 0.0) yStd / featuresStd(i) else 0.0 } i += 1 } @@ -179,18 +184,19 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress if (handlePersistence) instances.unpersist() // TODO: Converts to sparse format based on the storage, but may base on the scoring speed. - new LinearRegressionModel(this, weights.compressed, intercept) + copyValues(new LinearRegressionModel(uid, weights.compressed, intercept)) } + + override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra) } /** - * :: AlphaComponent :: - * + * :: Experimental :: * Model produced by [[LinearRegression]]. */ -@AlphaComponent +@Experimental class LinearRegressionModel private[ml] ( - override val parent: LinearRegression, + override val uid: String, val weights: Vector, val intercept: Double) extends RegressionModel[Vector, LinearRegressionModel] @@ -201,7 +207,7 @@ class LinearRegressionModel private[ml] ( } override def copy(extra: ParamMap): LinearRegressionModel = { - copyValues(new LinearRegressionModel(parent, weights, intercept), extra) + copyValues(new LinearRegressionModel(uid, weights, intercept), extra) } } @@ -305,7 +311,8 @@ private class LeastSquaresAggregator( val weightsArray = weights.toArray.clone() var sum = 0.0 var i = 0 - while (i < weightsArray.length) { + val len = weightsArray.length + while (i < len) { if (featuresStd(i) != 0.0) { weightsArray(i) /= featuresStd(i) sum += weightsArray(i) * featuresMean(i) @@ -316,7 +323,7 @@ private class LeastSquaresAggregator( } (weightsArray, -sum + labelMean / labelStd, weightsArray.length) } - + private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray) private val gradientSumArray = Array.ofDim[Double](dim) diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala index dbc628927433d..21c59061a02fa 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala @@ -17,11 +17,11 @@ package org.apache.spark.ml.regression -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.tree.{RandomForestParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel} -import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams} +import org.apache.spark.ml.util.{Identifiable, MetadataUtils} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} @@ -31,16 +31,17 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning algorithm for regression. * It supports both continuous and categorical features. */ -@AlphaComponent -final class RandomForestRegressor +@Experimental +final class RandomForestRegressor(override val uid: String) extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel] with RandomForestParams with TreeRegressorParams { + def this() = this(Identifiable.randomUID("rfr")) + // Override parameter setters from parent trait for Java API compatibility. // Parameters from TreeRegressorParams: @@ -85,8 +86,11 @@ final class RandomForestRegressor oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) RandomForestRegressionModel.fromOld(oldModel, this, categoricalFeatures) } + + override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra) } +@Experimental object RandomForestRegressor { /** Accessor for supported impurity settings: variance */ final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities @@ -97,15 +101,14 @@ object RandomForestRegressor { } /** - * :: AlphaComponent :: - * + * :: Experimental :: * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] model for regression. * It supports both continuous and categorical features. * @param _trees Decision trees in the ensemble. */ -@AlphaComponent +@Experimental final class RandomForestRegressionModel private[ml] ( - override val parent: RandomForestRegressor, + override val uid: String, private val _trees: Array[DecisionTreeRegressionModel]) extends PredictionModel[Vector, RandomForestRegressionModel] with TreeEnsembleModel with Serializable { @@ -128,7 +131,7 @@ final class RandomForestRegressionModel private[ml] ( } override def copy(extra: ParamMap): RandomForestRegressionModel = { - copyValues(new RandomForestRegressionModel(parent, _trees), extra) + copyValues(new RandomForestRegressionModel(uid, _trees), extra) } override def toString: String = { @@ -151,9 +154,9 @@ private[ml] object RandomForestRegressionModel { require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => - // parent, fittingParamMap for each tree is null since there are no good ways to set these. + // parent for each tree is null since there is no good way to set this. DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestRegressionModel(parent, newTrees) + new RandomForestRegressionModel(parent.uid, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala index d2dec0c76cb12..4242154be14ce 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala @@ -17,14 +17,16 @@ package org.apache.spark.ml.tree +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.tree.model.{InformationGainStats => OldInformationGainStats, Node => OldNode, Predict => OldPredict} - /** + * :: DeveloperApi :: * Decision tree node interface. */ +@DeveloperApi sealed abstract class Node extends Serializable { // TODO: Add aggregate stats (once available). This will happen after we move the DecisionTree @@ -89,10 +91,12 @@ private[ml] object Node { } /** + * :: DeveloperApi :: * Decision tree leaf node. * @param prediction Prediction this node makes * @param impurity Impurity measure at this node (for training data) */ +@DeveloperApi final class LeafNode private[ml] ( override val prediction: Double, override val impurity: Double) extends Node { @@ -118,6 +122,7 @@ final class LeafNode private[ml] ( } /** + * :: DeveloperApi :: * Internal Decision Tree node. * @param prediction Prediction this node would make if it were a leaf node * @param impurity Impurity measure at this node (for training data) @@ -127,6 +132,7 @@ final class LeafNode private[ml] ( * @param rightChild Right-hand child node * @param split Information about the test used to split to the left or right child. */ +@DeveloperApi final class InternalNode private[ml] ( override val prediction: Double, override val impurity: Double, @@ -153,9 +159,9 @@ final class InternalNode private[ml] ( override private[tree] def subtreeToString(indentFactor: Int = 0): String = { val prefix: String = " " * indentFactor - prefix + s"If (${InternalNode.splitToString(split, left=true)})\n" + + prefix + s"If (${InternalNode.splitToString(split, left = true)})\n" + leftChild.subtreeToString(indentFactor + 1) + - prefix + s"Else (${InternalNode.splitToString(split, left=false)})\n" + + prefix + s"Else (${InternalNode.splitToString(split, left = false)})\n" + rightChild.subtreeToString(indentFactor + 1) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala index 90f1d052764d3..7acdeeee72d23 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala @@ -17,15 +17,18 @@ package org.apache.spark.ml.tree +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.tree.configuration.{FeatureType => OldFeatureType} import org.apache.spark.mllib.tree.model.{Split => OldSplit} /** + * :: DeveloperApi :: * Interface for a "Split," which specifies a test made at a decision tree node * to choose the left or right path. */ +@DeveloperApi sealed trait Split extends Serializable { /** Index of feature which this split tests */ @@ -52,12 +55,14 @@ private[tree] object Split { } /** + * :: DeveloperApi :: * Split which tests a categorical feature. * @param featureIndex Index of the feature to test * @param _leftCategories If the feature value is in this set of categories, then the split goes * left. Otherwise, it goes right. * @param numCategories Number of categories for this feature. */ +@DeveloperApi final class CategoricalSplit private[ml] ( override val featureIndex: Int, _leftCategories: Array[Double], @@ -125,11 +130,13 @@ final class CategoricalSplit private[ml] ( } /** + * :: DeveloperApi :: * Split which tests a continuous feature. * @param featureIndex Index of the feature to test * @param threshold If the feature value is <= this threshold, then the split goes left. * Otherwise, it goes right. */ +@DeveloperApi final class ContinuousSplit private[ml] (override val featureIndex: Int, val threshold: Double) extends Split { diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala index 816fcedf2efb3..a0c5238d966bf 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala @@ -17,7 +17,6 @@ package org.apache.spark.ml.tree -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.PredictorParams import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed} @@ -26,12 +25,10 @@ import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldG import org.apache.spark.mllib.tree.loss.{Loss => OldLoss} /** - * :: DeveloperApi :: * Parameters for Decision Tree-based algorithms. * * Note: Marked as private and DeveloperApi since this may be made public in the future. */ -@DeveloperApi private[ml] trait DecisionTreeParams extends PredictorParams { /** @@ -265,12 +262,10 @@ private[ml] object TreeRegressorParams { } /** - * :: DeveloperApi :: * Parameters for Decision Tree-based ensemble algorithms. * * Note: Marked as private and DeveloperApi since this may be made public in the future. */ -@DeveloperApi private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed { /** @@ -307,12 +302,10 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed { } /** - * :: DeveloperApi :: * Parameters for Random Forest algorithms. * * Note: Marked as private and DeveloperApi since this may be made public in the future. */ -@DeveloperApi private[ml] trait RandomForestParams extends TreeEnsembleParams { /** @@ -377,12 +370,10 @@ private[ml] object RandomForestParams { } /** - * :: DeveloperApi :: * Parameters for Gradient-Boosted Tree algorithms. * * Note: Marked as private and DeveloperApi since this may be made public in the future. */ -@DeveloperApi private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { /** diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index ac0d1fed84b2e..e2444ab65b43b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -20,9 +20,11 @@ package org.apache.spark.ml.tuning import com.github.fommil.netlib.F2jBLAS import org.apache.spark.Logging -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml._ +import org.apache.spark.ml.evaluation.Evaluator import org.apache.spark.ml.param._ +import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.util.MLUtils import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -77,11 +79,14 @@ private[ml] trait CrossValidatorParams extends Params { } /** - * :: AlphaComponent :: + * :: Experimental :: * K-fold cross validation. */ -@AlphaComponent -class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorParams with Logging { +@Experimental +class CrossValidator(override val uid: String) extends Estimator[CrossValidatorModel] + with CrossValidatorParams with Logging { + + def this() = this(Identifiable.randomUID("cv")) private val f2jBLAS = new F2jBLAS @@ -97,12 +102,6 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP /** @group setParam */ def setNumFolds(value: Int): this.type = set(numFolds, value) - override def validateParams(paramMap: ParamMap): Unit = { - getEstimatorParamMaps.foreach { eMap => - getEstimator.validateParams(eMap ++ paramMap) - } - } - override def fit(dataset: DataFrame): CrossValidatorModel = { val schema = dataset.schema transformSchema(schema, logging = true) @@ -136,26 +135,46 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP logInfo(s"Best set of parameters:\n${epm(bestIndex)}") logInfo(s"Best cross-validation metric: $bestMetric.") val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]] - copyValues(new CrossValidatorModel(this, bestModel)) + copyValues(new CrossValidatorModel(uid, bestModel, metrics).setParent(this)) } override def transformSchema(schema: StructType): StructType = { $(estimator).transformSchema(schema) } + + override def validateParams(): Unit = { + super.validateParams() + val est = $(estimator) + for (paramMap <- $(estimatorParamMaps)) { + est.copy(paramMap).validateParams() + } + } + + override def copy(extra: ParamMap): CrossValidator = { + val copied = defaultCopy(extra).asInstanceOf[CrossValidator] + if (copied.isDefined(estimator)) { + copied.setEstimator(copied.getEstimator.copy(extra)) + } + if (copied.isDefined(evaluator)) { + copied.setEvaluator(copied.getEvaluator.copy(extra)) + } + copied + } } /** - * :: AlphaComponent :: + * :: Experimental :: * Model from k-fold cross validation. */ -@AlphaComponent +@Experimental class CrossValidatorModel private[ml] ( - override val parent: CrossValidator, - val bestModel: Model[_]) + override val uid: String, + val bestModel: Model[_], + val avgMetrics: Array[Double]) extends Model[CrossValidatorModel] with CrossValidatorParams { - override def validateParams(paramMap: ParamMap): Unit = { - bestModel.validateParams(paramMap) + override def validateParams(): Unit = { + bestModel.validateParams() } override def transform(dataset: DataFrame): DataFrame = { @@ -166,4 +185,12 @@ class CrossValidatorModel private[ml] ( override def transformSchema(schema: StructType): StructType = { bestModel.transformSchema(schema) } + + override def copy(extra: ParamMap): CrossValidatorModel = { + val copied = new CrossValidatorModel( + uid, + bestModel.copy(extra).asInstanceOf[Model[_]], + avgMetrics.clone()) + copyValues(copied, extra) + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala index dafe73d82c00a..98a8f0330ca45 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala @@ -20,14 +20,14 @@ package org.apache.spark.ml.tuning import scala.annotation.varargs import scala.collection.mutable -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.Experimental import org.apache.spark.ml.param._ /** - * :: AlphaComponent :: + * :: Experimental :: * Builder for a param grid used in grid search-based model selection. */ -@AlphaComponent +@Experimental class ParamGridBuilder { private val paramGrid = mutable.Map.empty[Param[_], Iterable[_]] diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala index 8a56748ab0a02..ddd34a54503a6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala @@ -19,15 +19,26 @@ package org.apache.spark.ml.util import java.util.UUID + /** - * Object with a unique id. + * Trait for an object with an immutable unique ID that identifies itself and its derivatives. */ -private[ml] trait Identifiable extends Serializable { +private[spark] trait Identifiable { + + /** + * An immutable unique ID for the object and its derivatives. + */ + val uid: String + + override def toString: String = uid +} + +private[spark] object Identifiable { /** - * A unique id for the object. The default implementation concatenates the class name, "_", and 8 - * random hex chars. + * Returns a random UID that concatenates the given prefix, "_", and 12 random hex chars. */ - private[ml] val uid: String = - this.getClass.getSimpleName + "_" + UUID.randomUUID().toString.take(8) + def randomUID(prefix: String): String = { + prefix + "_" + UUID.randomUUID().toString.takeRight(12) + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala index c84c8b4eb744f..2a1db90f2ca2b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala @@ -19,19 +19,14 @@ package org.apache.spark.ml.util import scala.collection.immutable.HashMap -import org.apache.spark.annotation.Experimental -import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, - NumericAttribute} +import org.apache.spark.ml.attribute._ import org.apache.spark.sql.types.StructField /** - * :: Experimental :: - * * Helper utilities for tree-based algorithms */ -@Experimental -object MetadataUtils { +private[spark] object MetadataUtils { /** * Examine a schema to identify the number of classes in a label column. @@ -39,9 +34,9 @@ object MetadataUtils { */ def getNumClasses(labelSchema: StructField): Option[Int] = { Attribute.fromStructField(labelSchema) match { - case numAttr: NumericAttribute => None case binAttr: BinaryAttribute => Some(2) case nomAttr: NominalAttribute => nomAttr.getNumValues + case _: NumericAttribute | UnresolvedAttribute => None } } @@ -65,7 +60,7 @@ object MetadataUtils { Iterator() } else { attr match { - case numAttr: NumericAttribute => Iterator() + case _: NumericAttribute | UnresolvedAttribute => Iterator() case binAttr: BinaryAttribute => Iterator(idx -> 2) case nomAttr: NominalAttribute => nomAttr.getNumValues match { diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala index 0383bf0b382b7..7cd53c6d7ef79 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala @@ -17,15 +17,13 @@ package org.apache.spark.ml.util -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.types.{DataType, StructField, StructType} + /** - * :: DeveloperApi :: * Utils for handling schemas. */ -@DeveloperApi -object SchemaUtils { +private[spark] object SchemaUtils { // TODO: Move the utility methods to SQL. @@ -58,4 +56,15 @@ object SchemaUtils { val outputFields = schema.fields :+ StructField(colName, dataType, nullable = false) StructType(outputFields) } + + /** + * Appends a new column to the input schema. This fails if the given output column already exists. + * @param schema input schema + * @param col New column schema + * @return new schema with the input column appended + */ + def appendColumn(schema: StructType, col: StructField): StructType = { + require(!schema.fieldNames.contains(col.name), s"Column ${col.name} already exists.") + StructType(schema.fields :+ col) + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 426306d78c1c3..634d56d08d17e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -32,6 +32,7 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.api.python.SerDeUtil import org.apache.spark.mllib.classification._ import org.apache.spark.mllib.clustering._ +import org.apache.spark.mllib.evaluation.RankingMetrics import org.apache.spark.mllib.feature._ import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel} import org.apache.spark.mllib.linalg._ @@ -42,7 +43,8 @@ import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.stat.correlation.CorrelationNames import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.stat.test.ChiSqTestResult -import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics} +import org.apache.spark.mllib.stat.{ + KernelDensity, MultivariateStatisticalSummary, Statistics} import org.apache.spark.mllib.tree.configuration.{Algo, BoostingStrategy, Strategy} import org.apache.spark.mllib.tree.impurity._ import org.apache.spark.mllib.tree.loss.Losses @@ -50,6 +52,7 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTree import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest} import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils @@ -343,28 +346,40 @@ private[python] class PythonMLLibAPI extends Serializable { * Returns a list containing weights, mean and covariance of each mixture component. */ def trainGaussianMixture( - data: JavaRDD[Vector], - k: Int, - convergenceTol: Double, + data: JavaRDD[Vector], + k: Int, + convergenceTol: Double, maxIterations: Int, - seed: java.lang.Long): JList[Object] = { + seed: java.lang.Long, + initialModelWeights: java.util.ArrayList[Double], + initialModelMu: java.util.ArrayList[Vector], + initialModelSigma: java.util.ArrayList[Matrix]): JList[Object] = { val gmmAlg = new GaussianMixture() .setK(k) .setConvergenceTol(convergenceTol) .setMaxIterations(maxIterations) + if (initialModelWeights != null && initialModelMu != null && initialModelSigma != null) { + val gaussians = initialModelMu.asScala.toSeq.zip(initialModelSigma.asScala.toSeq).map { + case (x, y) => new MultivariateGaussian(x.asInstanceOf[Vector], y.asInstanceOf[Matrix]) + } + val initialModel = new GaussianMixtureModel( + initialModelWeights.asScala.toArray, gaussians.toArray) + gmmAlg.setInitialModel(initialModel) + } + if (seed != null) gmmAlg.setSeed(seed) try { val model = gmmAlg.run(data.rdd.persist(StorageLevel.MEMORY_AND_DISK)) var wt = ArrayBuffer.empty[Double] - var mu = ArrayBuffer.empty[Vector] + var mu = ArrayBuffer.empty[Vector] var sigma = ArrayBuffer.empty[Matrix] for (i <- 0 until model.k) { wt += model.weights(i) mu += model.gaussians(i).mu sigma += model.gaussians(i).sigma - } + } List(Vectors.dense(wt.toArray), mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava } finally { data.rdd.unpersist(blocking = false) @@ -378,14 +393,14 @@ private[python] class PythonMLLibAPI extends Serializable { data: JavaRDD[Vector], wt: Vector, mu: Array[Object], - si: Array[Object]): RDD[Vector] = { + si: Array[Object]): RDD[Vector] = { val weight = wt.toArray val mean = mu.map(_.asInstanceOf[DenseVector]) val sigma = si.map(_.asInstanceOf[DenseMatrix]) val gaussians = Array.tabulate(weight.length){ i => new MultivariateGaussian(mean(i), sigma(i)) - } + } val model = new GaussianMixtureModel(weight, gaussians) model.predictSoft(data).map(Vectors.dense) } @@ -414,7 +429,7 @@ private[python] class PythonMLLibAPI extends Serializable { if (seed != null) als.setSeed(seed) - val model = als.run(ratingsJRDD.rdd) + val model = als.run(ratingsJRDD.rdd) new MatrixFactorizationModelWrapper(model) } @@ -445,7 +460,7 @@ private[python] class PythonMLLibAPI extends Serializable { if (seed != null) als.setSeed(seed) - val model = als.run(ratingsJRDD.rdd) + val model = als.run(ratingsJRDD.rdd) new MatrixFactorizationModelWrapper(model) } @@ -480,7 +495,7 @@ private[python] class PythonMLLibAPI extends Serializable { def normalizeVector(p: Double, rdd: JavaRDD[Vector]): JavaRDD[Vector] = { new Normalizer(p).transform(rdd) } - + /** * Java stub for StandardScaler.fit(). This stub returns a * handle to the Java object instead of the content of the Java object. @@ -494,6 +509,26 @@ private[python] class PythonMLLibAPI extends Serializable { new StandardScaler(withMean, withStd).fit(data.rdd) } + /** + * Java stub for ChiSqSelector.fit(). This stub returns a + * handle to the Java object instead of the content of the Java object. + * Extra care needs to be taken in the Python code to ensure it gets freed on + * exit; see the Py4J documentation. + */ + def fitChiSqSelector(numTopFeatures: Int, data: JavaRDD[LabeledPoint]): ChiSqSelectorModel = { + new ChiSqSelector(numTopFeatures).fit(data.rdd) + } + + /** + * Java stub for PCA.fit(). This stub returns a + * handle to the Java object instead of the content of the Java object. + * Extra care needs to be taken in the Python code to ensure it gets freed on + * exit; see the Py4J documentation. + */ + def fitPCA(k: Int, data: JavaRDD[Vector]): PCAModel = { + new PCA(k).fit(data.rdd) + } + /** * Java stub for IDF.fit(). This stub returns a * handle to the Java object instead of the content of the Java object. @@ -677,6 +712,14 @@ private[python] class PythonMLLibAPI extends Serializable { } } + def elementwiseProductVector(scalingVector: Vector, vector: Vector): Vector = { + new ElementwiseProduct(scalingVector).transform(vector) + } + + def elementwiseProductVector(scalingVector: Vector, vector: JavaRDD[Vector]): JavaRDD[Vector] = { + new ElementwiseProduct(scalingVector).transform(vector) + } + /** * Java stub for mllib Statistics.colStats(X: RDD[Vector]). * TODO figure out return type. @@ -913,6 +956,38 @@ private[python] class PythonMLLibAPI extends Serializable { RG.gammaVectorRDD(jsc.sc, shape, scale, numRows, numCols, parts, s) } + /** + * Java stub for the constructor of Python mllib RankingMetrics + */ + def newRankingMetrics(predictionAndLabels: DataFrame): RankingMetrics[Any] = { + new RankingMetrics(predictionAndLabels.map( + r => (r.getSeq(0).toArray[Any], r.getSeq(1).toArray[Any]))) + } + + /** + * Java stub for the estimate method of KernelDensity + */ + def estimateKernelDensity( + sample: JavaRDD[Double], + bandwidth: Double, points: java.util.ArrayList[Double]): Array[Double] = { + return new KernelDensity().setSample(sample).setBandwidth(bandwidth).estimate( + points.asScala.toArray) + } + + /** + * Java stub for the update method of StreamingKMeansModel. + */ + def updateStreamingKMeansModel( + clusterCenters: JList[Vector], + clusterWeights: JList[Double], + data: JavaRDD[Vector], + decayFactor: Double, + timeUnit: String): JList[Object] = { + val model = new StreamingKMeansModel( + clusterCenters.asScala.toArray, clusterWeights.asScala.toArray) + .update(data, decayFactor, timeUnit) + List[AnyRef](model.clusterCenters, Vectors.dense(model.clusterWeights)).asJava + } } @@ -1210,7 +1285,7 @@ private[spark] object SerDe extends Serializable { } /* convert RDD[Tuple2[,]] to RDD[Array[Any]] */ - def fromTuple2RDD(rdd: RDD[(Any, Any)]): RDD[Array[Any]] = { + def fromTuple2RDD(rdd: RDD[(Any, Any)]): RDD[Array[Any]] = { rdd.map(x => Array(x._1, x._2)) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala index bd2e9079ce1ae..2df4d21e8cd55 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala @@ -163,7 +163,7 @@ class LogisticRegressionModel ( override protected def formatVersion: String = "1.0" override def toString: String = { - s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.get}" + s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.getOrElse("None")}" } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala index c9b3ff0172e2e..f51ee36d0dfcb 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala @@ -21,21 +21,16 @@ import java.lang.{Iterable => JIterable} import scala.collection.JavaConverters._ -import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum, Axis} -import breeze.numerics.{exp => brzExp, log => brzLog} - import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ -import org.json4s.{DefaultFormats, JValue} import org.apache.spark.{Logging, SparkContext, SparkException} -import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector} +import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, SparseVector, Vector} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, SQLContext} - /** * Model for Naive Bayes Classifiers. * @@ -43,7 +38,7 @@ import org.apache.spark.sql.{DataFrame, SQLContext} * @param pi log of class priors, whose dimension is C, number of labels * @param theta log of class conditional probabilities, whose dimension is C-by-D, * where D is number of features - * @param modelType The type of NB model to fit can be "Multinomial" or "Bernoulli" + * @param modelType The type of NB model to fit can be "multinomial" or "bernoulli" */ class NaiveBayesModel private[mllib] ( val labels: Array[Double], @@ -52,8 +47,13 @@ class NaiveBayesModel private[mllib] ( val modelType: String) extends ClassificationModel with Serializable with Saveable { + import NaiveBayes.{Bernoulli, Multinomial, supportedModelTypes} + + private val piVector = new DenseVector(pi) + private val thetaMatrix = new DenseMatrix(labels.length, theta(0).length, theta.flatten, true) + private[mllib] def this(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) = - this(labels, pi, theta, "Multinomial") + this(labels, pi, theta, NaiveBayes.Multinomial) /** A Java-friendly constructor that takes three Iterable parameters. */ private[mllib] def this( @@ -62,20 +62,24 @@ class NaiveBayesModel private[mllib] ( theta: JIterable[JIterable[Double]]) = this(labels.asScala.toArray, pi.asScala.toArray, theta.asScala.toArray.map(_.asScala.toArray)) - private val brzPi = new BDV[Double](pi) - private val brzTheta = new BDM(theta(0).length, theta.length, theta.flatten).t + require(supportedModelTypes.contains(modelType), + s"Invalid modelType $modelType. Supported modelTypes are $supportedModelTypes.") // Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0. - // This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra + // This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra // application of this condition (in predict function). - private val (brzNegTheta, brzNegThetaSum) = modelType match { - case "Multinomial" => (None, None) - case "Bernoulli" => - val negTheta = brzLog((brzExp(brzTheta.copy) :*= (-1.0)) :+= 1.0) // log(1.0 - exp(x)) - (Option(negTheta), Option(brzSum(negTheta, Axis._1))) + private val (thetaMinusNegTheta, negThetaSum) = modelType match { + case Multinomial => (None, None) + case Bernoulli => + val negTheta = thetaMatrix.map(value => math.log(1.0 - math.exp(value))) + val ones = new DenseVector(Array.fill(thetaMatrix.numCols){1.0}) + val thetaMinusNegTheta = thetaMatrix.map { value => + value - math.log(1.0 - math.exp(value)) + } + (Option(thetaMinusNegTheta), Option(negTheta.multiply(ones))) case _ => // This should never happen. - throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType") + throw new UnknownError(s"Invalid modelType: $modelType.") } override def predict(testData: RDD[Vector]): RDD[Double] = { @@ -88,14 +92,24 @@ class NaiveBayesModel private[mllib] ( override def predict(testData: Vector): Double = { modelType match { - case "Multinomial" => - labels (brzArgmax (brzPi + brzTheta * testData.toBreeze) ) - case "Bernoulli" => - labels (brzArgmax (brzPi + - (brzTheta - brzNegTheta.get) * testData.toBreeze + brzNegThetaSum.get)) + case Multinomial => + val prob = thetaMatrix.multiply(testData) + BLAS.axpy(1.0, piVector, prob) + labels(prob.argmax) + case Bernoulli => + testData.foreachActive { (index, value) => + if (value != 0.0 && value != 1.0) { + throw new SparkException( + s"Bernoulli naive Bayes requires 0 or 1 feature values but found $testData.") + } + } + val prob = thetaMinusNegTheta.get.multiply(testData) + BLAS.axpy(1.0, piVector, prob) + BLAS.axpy(1.0, negThetaSum.get, prob) + labels(prob.argmax) case _ => // This should never happen. - throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType") + throw new UnknownError(s"Invalid modelType: $modelType.") } } @@ -137,17 +151,17 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { // Create Parquet data. val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF() - dataRDD.saveAsParquetFile(dataPath(path)) + dataRDD.write.parquet(dataPath(path)) } def load(sc: SparkContext, path: String): NaiveBayesModel = { val sqlContext = new SQLContext(sc) // Load Parquet data. - val dataRDD = sqlContext.parquetFile(dataPath(path)) + val dataRDD = sqlContext.read.parquet(dataPath(path)) // Check schema explicitly since erasure makes it hard to use match-case for checking. checkSchema[Data](dataRDD.schema) val dataArray = dataRDD.select("labels", "pi", "theta", "modelType").take(1) - assert(dataArray.size == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}") + assert(dataArray.length == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}") val data = dataArray(0) val labels = data.getAs[Seq[Double]](0).toArray val pi = data.getAs[Seq[Double]](1).toArray @@ -183,17 +197,17 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { // Create Parquet data. val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF() - dataRDD.saveAsParquetFile(dataPath(path)) + dataRDD.write.parquet(dataPath(path)) } def load(sc: SparkContext, path: String): NaiveBayesModel = { val sqlContext = new SQLContext(sc) // Load Parquet data. - val dataRDD = sqlContext.parquetFile(dataPath(path)) + val dataRDD = sqlContext.read.parquet(dataPath(path)) // Check schema explicitly since erasure makes it hard to use match-case for checking. checkSchema[Data](dataRDD.schema) val dataArray = dataRDD.select("labels", "pi", "theta").take(1) - assert(dataArray.size == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}") + assert(dataArray.length == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}") val data = dataArray(0) val labels = data.getAs[Seq[Double]](0).toArray val pi = data.getAs[Seq[Double]](1).toArray @@ -220,16 +234,16 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { s"($loadedClassName, $version). Supported:\n" + s" ($classNameV1_0, 1.0)") } - assert(model.pi.size == numClasses, + assert(model.pi.length == numClasses, s"NaiveBayesModel.load expected $numClasses classes," + - s" but class priors vector pi had ${model.pi.size} elements") - assert(model.theta.size == numClasses, + s" but class priors vector pi had ${model.pi.length} elements") + assert(model.theta.length == numClasses, s"NaiveBayesModel.load expected $numClasses classes," + - s" but class conditionals array theta had ${model.theta.size} elements") - assert(model.theta.forall(_.size == numFeatures), + s" but class conditionals array theta had ${model.theta.length} elements") + assert(model.theta.forall(_.length == numFeatures), s"NaiveBayesModel.load expected $numFeatures features," + s" but class conditionals array theta had elements of size:" + - s" ${model.theta.map(_.size).mkString(",")}") + s" ${model.theta.map(_.length).mkString(",")}") model } } @@ -247,9 +261,11 @@ class NaiveBayes private ( private var lambda: Double, private var modelType: String) extends Serializable with Logging { - def this(lambda: Double) = this(lambda, "Multinomial") + import NaiveBayes.{Bernoulli, Multinomial} + + def this(lambda: Double) = this(lambda, NaiveBayes.Multinomial) - def this() = this(1.0, "Multinomial") + def this() = this(1.0, NaiveBayes.Multinomial) /** Set the smoothing parameter. Default: 1.0. */ def setLambda(lambda: Double): NaiveBayes = { @@ -262,12 +278,11 @@ class NaiveBayes private ( /** * Set the model type using a string (case-sensitive). - * Supported options: "Multinomial" and "Bernoulli". - * (default: Multinomial) + * Supported options: "multinomial" (default) and "bernoulli". */ - def setModelType(modelType:String): NaiveBayes = { + def setModelType(modelType: String): NaiveBayes = { require(NaiveBayes.supportedModelTypes.contains(modelType), - s"NaiveBayes was created with an unknown ModelType: $modelType") + s"NaiveBayes was created with an unknown modelType: $modelType.") this.modelType = modelType this } @@ -283,30 +298,46 @@ class NaiveBayes private ( def run(data: RDD[LabeledPoint]): NaiveBayesModel = { val requireNonnegativeValues: Vector => Unit = (v: Vector) => { val values = v match { - case SparseVector(size, indices, values) => - values - case DenseVector(values) => - values + case sv: SparseVector => sv.values + case dv: DenseVector => dv.values } if (!values.forall(_ >= 0.0)) { throw new SparkException(s"Naive Bayes requires nonnegative feature values but found $v.") } } + val requireZeroOneBernoulliValues: Vector => Unit = (v: Vector) => { + val values = v match { + case sv: SparseVector => sv.values + case dv: DenseVector => dv.values + } + if (!values.forall(v => v == 0.0 || v == 1.0)) { + throw new SparkException( + s"Bernoulli naive Bayes requires 0 or 1 feature values but found $v.") + } + } + // Aggregates term frequencies per label. // TODO: Calling combineByKey and collect creates two stages, we can implement something // TODO: similar to reduceByKeyLocally to save one stage. - val aggregated = data.map(p => (p.label, p.features)).combineByKey[(Long, BDV[Double])]( + val aggregated = data.map(p => (p.label, p.features)).combineByKey[(Long, DenseVector)]( createCombiner = (v: Vector) => { - requireNonnegativeValues(v) - (1L, v.toBreeze.toDenseVector) + if (modelType == Bernoulli) { + requireZeroOneBernoulliValues(v) + } else { + requireNonnegativeValues(v) + } + (1L, v.copy.toDense) }, - mergeValue = (c: (Long, BDV[Double]), v: Vector) => { + mergeValue = (c: (Long, DenseVector), v: Vector) => { requireNonnegativeValues(v) - (c._1 + 1L, c._2 += v.toBreeze) + BLAS.axpy(1.0, v, c._2) + (c._1 + 1L, c._2) }, - mergeCombiners = (c1: (Long, BDV[Double]), c2: (Long, BDV[Double])) => - (c1._1 + c2._1, c1._2 += c2._2) + mergeCombiners = (c1: (Long, DenseVector), c2: (Long, DenseVector)) => { + BLAS.axpy(1.0, c2._2, c1._2) + (c1._1 + c2._1, c1._2) + } ).collect() val numLabels = aggregated.length @@ -326,11 +357,11 @@ class NaiveBayes private ( labels(i) = label pi(i) = math.log(n + lambda) - piLogDenom val thetaLogDenom = modelType match { - case "Multinomial" => math.log(brzSum(sumTermFreqs) + numFeatures * lambda) - case "Bernoulli" => math.log(n + 2.0 * lambda) + case Multinomial => math.log(sumTermFreqs.values.sum + numFeatures * lambda) + case Bernoulli => math.log(n + 2.0 * lambda) case _ => // This should never happen. - throw new UnknownError(s"NaiveBayes was created with an unknown ModelType: $modelType") + throw new UnknownError(s"Invalid modelType: $modelType.") } var j = 0 while (j < numFeatures) { @@ -349,8 +380,14 @@ class NaiveBayes private ( */ object NaiveBayes { + /** String name for multinomial model type. */ + private[classification] val Multinomial: String = "multinomial" + + /** String name for Bernoulli model type. */ + private[classification] val Bernoulli: String = "bernoulli" + /* Set of modelTypes that NaiveBayes supports */ - private[mllib] val supportedModelTypes = Set("Multinomial", "Bernoulli") + private[classification] val supportedModelTypes = Set(Multinomial, Bernoulli) /** * Trains a Naive Bayes model given an RDD of `(label, features)` pairs. @@ -380,7 +417,7 @@ object NaiveBayes { * @param lambda The smoothing parameter */ def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = { - new NaiveBayes(lambda, "Multinomial").run(input) + new NaiveBayes(lambda, Multinomial).run(input) } /** @@ -403,7 +440,7 @@ object NaiveBayes { */ def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = { require(supportedModelTypes.contains(modelType), - s"NaiveBayes was created with an unknown ModelType: $modelType") + s"NaiveBayes was created with an unknown modelType: $modelType.") new NaiveBayes(lambda, modelType).run(input) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index 33104cf06c6ea..348485560713e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -89,7 +89,7 @@ class SVMModel ( override protected def formatVersion: String = "1.0" override def toString: String = { - s"${super.toString}, numClasses = 2, threshold = ${threshold.get}" + s"${super.toString}, numClasses = 2, threshold = ${threshold.getOrElse("None")}" } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala index 3b6790cce47c6..fe09f6b75d28b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala @@ -62,7 +62,7 @@ private[classification] object GLMClassificationModel { // Create Parquet data. val data = Data(weights, intercept, threshold) - sc.parallelize(Seq(data), 1).toDF().saveAsParquetFile(Loader.dataPath(path)) + sc.parallelize(Seq(data), 1).toDF().write.parquet(Loader.dataPath(path)) } /** @@ -75,7 +75,7 @@ private[classification] object GLMClassificationModel { def loadData(sc: SparkContext, path: String, modelClass: String): Data = { val datapath = Loader.dataPath(path) val sqlContext = new SQLContext(sc) - val dataRDD = sqlContext.parquetFile(datapath) + val dataRDD = sqlContext.read.parquet(datapath) val dataArray = dataRDD.select("weights", "intercept", "threshold").take(1) assert(dataArray.size == 1, s"Unable to load $modelClass data from: $datapath") val data = dataArray(0) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala index 568b65305649f..fc509d2ba1470 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala @@ -22,6 +22,7 @@ import scala.collection.mutable.IndexedSeq import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, Vector => BV} import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, Matrices, Vector, Vectors} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.MLUtils @@ -36,11 +37,11 @@ import org.apache.spark.util.Utils * independent Gaussian distributions with associated "mixing" weights * specifying each's contribution to the composite. * - * Given a set of sample points, this class will maximize the log-likelihood - * for a mixture of k Gaussians, iterating until the log-likelihood changes by + * Given a set of sample points, this class will maximize the log-likelihood + * for a mixture of k Gaussians, iterating until the log-likelihood changes by * less than convergenceTol, or until it has reached the max number of iterations. * While this process is generally guaranteed to converge, it is not guaranteed - * to find a global optimum. + * to find a global optimum. * * Note: For high-dimensional data (with many features), this algorithm may perform poorly. * This is due to high-dimensional data (a) making it difficult to cluster at all (based @@ -53,24 +54,24 @@ import org.apache.spark.util.Utils */ @Experimental class GaussianMixture private ( - private var k: Int, - private var convergenceTol: Double, + private var k: Int, + private var convergenceTol: Double, private var maxIterations: Int, private var seed: Long) extends Serializable { - + /** * Constructs a default instance. The default parameters are {k: 2, convergenceTol: 0.01, * maxIterations: 100, seed: random}. */ def this() = this(2, 0.01, 100, Utils.random.nextLong()) - + // number of samples per cluster to use when initializing Gaussians private val nSamples = 5 - - // an initializing GMM can be provided rather than using the + + // an initializing GMM can be provided rather than using the // default random starting point private var initialModel: Option[GaussianMixtureModel] = None - + /** Set the initial GMM starting point, bypassing the random initialization. * You must call setK() prior to calling this method, and the condition * (model.k == this.k) must be met; failure will result in an IllegalArgumentException @@ -83,37 +84,37 @@ class GaussianMixture private ( } this } - + /** Return the user supplied initial GMM, if supplied */ def getInitialModel: Option[GaussianMixtureModel] = initialModel - + /** Set the number of Gaussians in the mixture model. Default: 2 */ def setK(k: Int): this.type = { this.k = k this } - + /** Return the number of Gaussians in the mixture model */ def getK: Int = k - + /** Set the maximum number of iterations to run. Default: 100 */ def setMaxIterations(maxIterations: Int): this.type = { this.maxIterations = maxIterations this } - + /** Return the maximum number of iterations to run */ def getMaxIterations: Int = maxIterations - + /** - * Set the largest change in log-likelihood at which convergence is + * Set the largest change in log-likelihood at which convergence is * considered to have occurred. */ def setConvergenceTol(convergenceTol: Double): this.type = { this.convergenceTol = convergenceTol this } - + /** * Return the largest change in log-likelihood at which convergence is * considered to have occurred. @@ -132,41 +133,41 @@ class GaussianMixture private ( /** Perform expectation maximization */ def run(data: RDD[Vector]): GaussianMixtureModel = { val sc = data.sparkContext - + // we will operate on the data as breeze data val breezeData = data.map(_.toBreeze).cache() - + // Get length of the input vectors val d = breezeData.first().length - + // Determine initial weights and corresponding Gaussians. // If the user supplied an initial GMM, we use those values, otherwise // we start with uniform weights, a random mean from the data, and // diagonal covariance matrices using component variances - // derived from the samples + // derived from the samples val (weights, gaussians) = initialModel match { case Some(gmm) => (gmm.weights, gmm.gaussians) - + case None => { val samples = breezeData.takeSample(withReplacement = true, k * nSamples, seed) - (Array.fill(k)(1.0 / k), Array.tabulate(k) { i => + (Array.fill(k)(1.0 / k), Array.tabulate(k) { i => val slice = samples.view(i * nSamples, (i + 1) * nSamples) - new MultivariateGaussian(vectorMean(slice), initCovariance(slice)) + new MultivariateGaussian(vectorMean(slice), initCovariance(slice)) }) } } - - var llh = Double.MinValue // current log-likelihood + + var llh = Double.MinValue // current log-likelihood var llhp = 0.0 // previous log-likelihood - + var iter = 0 - while(iter < maxIterations && Math.abs(llh-llhp) > convergenceTol) { + while (iter < maxIterations && math.abs(llh-llhp) > convergenceTol) { // create and broadcast curried cluster contribution function val compute = sc.broadcast(ExpectationSum.add(weights, gaussians)_) - + // aggregate the cluster contribution for all sample points val sums = breezeData.aggregate(ExpectationSum.zero(k, d))(compute.value, _ += _) - + // Create new distributions based on the partial assignments // (often referred to as the "M" step in literature) val sumWeights = sums.weights.sum @@ -179,22 +180,25 @@ class GaussianMixture private ( gaussians(i) = new MultivariateGaussian(mu, sums.sigmas(i) / sums.weights(i)) i = i + 1 } - + llhp = llh // current becomes previous llh = sums.logLikelihood // this is the freshly computed log-likelihood iter += 1 - } - + } + new GaussianMixtureModel(weights, gaussians) } - + + /** Java-friendly version of [[run()]] */ + def run(data: JavaRDD[Vector]): GaussianMixtureModel = run(data.rdd) + /** Average of dense breeze vectors */ private def vectorMean(x: IndexedSeq[BV[Double]]): BDV[Double] = { val v = BDV.zeros[Double](x(0).length) x.foreach(xi => v += xi) - v / x.length.toDouble + v / x.length.toDouble } - + /** * Construct matrix where diagonal entries are element-wise * variance of input vectors (computes biased variance) @@ -210,14 +214,14 @@ class GaussianMixture private ( // companion class to provide zero constructor for ExpectationSum private object ExpectationSum { def zero(k: Int, d: Int): ExpectationSum = { - new ExpectationSum(0.0, Array.fill(k)(0.0), - Array.fill(k)(BDV.zeros(d)), Array.fill(k)(BreezeMatrix.zeros(d,d))) + new ExpectationSum(0.0, Array.fill(k)(0.0), + Array.fill(k)(BDV.zeros(d)), Array.fill(k)(BreezeMatrix.zeros(d, d))) } - + // compute cluster contributions for each input point // (U, T) => U for aggregation def add( - weights: Array[Double], + weights: Array[Double], dists: Array[MultivariateGaussian]) (sums: ExpectationSum, x: BV[Double]): ExpectationSum = { val p = weights.zip(dists).map { @@ -235,7 +239,7 @@ private object ExpectationSum { i = i + 1 } sums - } + } } // Aggregation class for partial expectation results @@ -244,9 +248,9 @@ private class ExpectationSum( val weights: Array[Double], val means: Array[BDV[Double]], val sigmas: Array[BreezeMatrix[Double]]) extends Serializable { - + val k = weights.length - + def +=(x: ExpectationSum): ExpectationSum = { var i = 0 while (i < k) { @@ -257,5 +261,5 @@ private class ExpectationSum( } logLikelihood += x.logLikelihood this - } + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala index ec65a3da689de..cb807c8038101 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala @@ -25,6 +25,7 @@ import org.json4s.jackson.JsonMethods._ import org.apache.spark.SparkContext import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.{Vector, Matrices, Matrix} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.{MLUtils, Loader, Saveable} @@ -34,21 +35,20 @@ import org.apache.spark.sql.{SQLContext, Row} /** * :: Experimental :: * - * Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points - * are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are - * the respective mean and covariance for each Gaussian distribution i=1..k. - * - * @param weight Weights for each Gaussian distribution in the mixture, where weight(i) is - * the weight for Gaussian i, and weight.sum == 1 - * @param mu Means for each Gaussian in the mixture, where mu(i) is the mean for Gaussian i - * @param sigma Covariance maxtrix for each Gaussian in the mixture, where sigma(i) is the - * covariance matrix for Gaussian i + * Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points + * are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are + * the respective mean and covariance for each Gaussian distribution i=1..k. + * + * @param weights Weights for each Gaussian distribution in the mixture, where weights(i) is + * the weight for Gaussian i, and weights.sum == 1 + * @param gaussians Array of MultivariateGaussian where gaussians(i) represents + * the Multivariate Gaussian (Normal) Distribution for Gaussian i */ @Experimental class GaussianMixtureModel( - val weights: Array[Double], - val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable{ - + val weights: Array[Double], + val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable { + require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match") override protected def formatVersion = "1.0" @@ -65,20 +65,24 @@ class GaussianMixtureModel( val responsibilityMatrix = predictSoft(points) responsibilityMatrix.map(r => r.indexOf(r.max)) } - + + /** Java-friendly version of [[predict()]] */ + def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] = + predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]] + /** * Given the input vectors, return the membership value of each vector - * to all mixture components. + * to all mixture components. */ def predictSoft(points: RDD[Vector]): RDD[Array[Double]] = { val sc = points.sparkContext val bcDists = sc.broadcast(gaussians) val bcWeights = sc.broadcast(weights) - points.map { x => + points.map { x => computeSoftAssignments(x.toBreeze.toDenseVector, bcDists.value, bcWeights.value, k) } } - + /** * Compute the partial assignments for each vector */ @@ -90,7 +94,7 @@ class GaussianMixtureModel( val p = weights.zip(dists).map { case (weight, dist) => MLUtils.EPSILON + weight * dist.pdf(pt) } - val pSum = p.sum + val pSum = p.sum for (i <- 0 until k) { p(i) /= pSum } @@ -127,13 +131,13 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] { val dataArray = Array.tabulate(weights.length) { i => Data(weights(i), gaussians(i).mu, gaussians(i).sigma) } - sc.parallelize(dataArray, 1).toDF().saveAsParquetFile(Loader.dataPath(path)) + sc.parallelize(dataArray, 1).toDF().write.parquet(Loader.dataPath(path)) } def load(sc: SparkContext, path: String): GaussianMixtureModel = { val dataPath = Loader.dataPath(path) val sqlContext = new SQLContext(sc) - val dataFrame = sqlContext.parquetFile(dataPath) + val dataFrame = sqlContext.read.parquet(dataPath) val dataArray = dataFrame.select("weight", "mu", "sigma").collect() // Check schema explicitly since erasure makes it hard to use match-case for checking. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala index ba228b11fcec3..8ecb3df11d95e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala @@ -110,7 +110,7 @@ object KMeansModel extends Loader[KMeansModel] { val dataRDD = sc.parallelize(model.clusterCenters.zipWithIndex).map { case (point, id) => Cluster(id, point) }.toDF() - dataRDD.saveAsParquetFile(Loader.dataPath(path)) + dataRDD.write.parquet(Loader.dataPath(path)) } def load(sc: SparkContext, path: String): KMeansModel = { @@ -120,7 +120,7 @@ object KMeansModel extends Loader[KMeansModel] { assert(className == thisClassName) assert(formatVersion == thisFormatVersion) val k = (metadata \ "k").extract[Int] - val centriods = sqlContext.parquetFile(Loader.dataPath(path)) + val centriods = sqlContext.read.parquet(Loader.dataPath(path)) Loader.checkSchema[Cluster](centriods.schema) val localCentriods = centriods.map(Cluster.apply).collect() assert(k == localCentriods.size) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala index 6cf26445f20a0..974b26924dfb8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.clustering import breeze.linalg.{DenseMatrix => BDM, normalize, sum => brzSum} import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaPairRDD import org.apache.spark.graphx.{VertexId, EdgeContext, Graph} import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix} import org.apache.spark.rdd.RDD @@ -345,6 +346,11 @@ class DistributedLDAModel private ( } } + /** Java-friendly version of [[topicDistributions]] */ + def javaTopicDistributions: JavaPairRDD[java.lang.Long, Vector] = { + JavaPairRDD.fromRDD(topicDistributions.asInstanceOf[RDD[(java.lang.Long, Vector)]]) + } + // TODO: // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ??? diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala index 6fa2fe053c6a4..8e5154b902d1d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala @@ -273,7 +273,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer { * Default: 1024, following the original Online LDA paper. */ def setTau0(tau0: Double): this.type = { - require(tau0 > 0, s"LDA tau0 must be positive, but was set to $tau0") + require(tau0 > 0, s"LDA tau0 must be positive, but was set to $tau0") this.tau0 = tau0 this } @@ -339,7 +339,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer { override private[clustering] def initialize( docs: RDD[(Long, Vector)], - lda: LDA): OnlineLDAOptimizer = { + lda: LDA): OnlineLDAOptimizer = { this.k = lda.getK this.corpusSize = docs.count() this.vocabSize = docs.first()._2.size @@ -458,7 +458,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer { * uses digamma which is accurate but expensive. */ private def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = { - val rowSum = sum(alpha(breeze.linalg.*, ::)) + val rowSum = sum(alpha(breeze.linalg.*, ::)) val digAlpha = digamma(alpha) val digRowSum = digamma(rowSum) val result = digAlpha(::, breeze.linalg.*) - digRowSum diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala index aa53e88d59856..e7a243f854e33 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala @@ -74,7 +74,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) val dataRDD = model.assignments.toDF() - dataRDD.saveAsParquetFile(Loader.dataPath(path)) + dataRDD.write.parquet(Loader.dataPath(path)) } def load(sc: SparkContext, path: String): PowerIterationClusteringModel = { @@ -86,7 +86,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode assert(formatVersion == thisFormatVersion) val k = (metadata \ "k").extract[Int] - val assignments = sqlContext.parquetFile(Loader.dataPath(path)) + val assignments = sqlContext.read.parquet(Loader.dataPath(path)) Loader.checkSchema[PowerIterationClustering.Assignment](assignments.schema) val assignmentsRDD = assignments.map { @@ -121,7 +121,7 @@ class PowerIterationClustering private[clustering] ( import org.apache.spark.mllib.clustering.PowerIterationClustering._ /** Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100, - * initMode: "random"}. + * initMode: "random"}. */ def this() = this(k = 2, maxIterations = 100, initMode = "random") @@ -243,7 +243,7 @@ object PowerIterationClustering extends Logging { /** * Generates random vertex properties (v0) to start power iteration. - * + * * @param g a graph representing the normalized affinity matrix (W) * @return a graph with edges representing W and vertices representing a random vector * with unit 1-norm @@ -266,7 +266,7 @@ object PowerIterationClustering extends Logging { * Generates the degree vector as the vertex properties (v0) to start power iteration. * It is not exactly the node degrees but just the normalized sum similarities. Call it * as degree vector because it is used in the PIC paper. - * + * * @param g a graph representing the normalized affinity matrix (W) * @return a graph with edges representing W and vertices representing the degree vector */ @@ -276,7 +276,7 @@ object PowerIterationClustering extends Logging { val v0 = g.vertices.mapValues(_ / sum) GraphImpl.fromExistingRDDs(VertexRDD(v0), g.edges) } - + /** * Runs power iteration. * @param g input graph with edges representing the normalized affinity matrix (W) and vertices diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala index 812014a041719..d9b34cec64894 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala @@ -21,8 +21,10 @@ import scala.reflect.ClassTag import org.apache.spark.Logging import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaSparkContext._ import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors} import org.apache.spark.rdd.RDD +import org.apache.spark.streaming.api.java.{JavaPairDStream, JavaDStream} import org.apache.spark.streaming.dstream.DStream import org.apache.spark.util.Utils import org.apache.spark.util.random.XORShiftRandom @@ -178,7 +180,7 @@ class StreamingKMeans( /** Set the decay factor directly (for forgetful algorithms). */ def setDecayFactor(a: Double): this.type = { - this.decayFactor = decayFactor + this.decayFactor = a this } @@ -234,6 +236,9 @@ class StreamingKMeans( } } + /** Java-friendly version of `trainOn`. */ + def trainOn(data: JavaDStream[Vector]): Unit = trainOn(data.dstream) + /** * Use the clustering model to make predictions on batches of data from a DStream. * @@ -245,6 +250,11 @@ class StreamingKMeans( data.map(model.predict) } + /** Java-friendly version of `predictOn`. */ + def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Integer] = { + JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Integer]]) + } + /** * Use the model to make predictions on the values of a DStream and carry over its keys. * @@ -257,6 +267,14 @@ class StreamingKMeans( data.mapValues(model.predict) } + /** Java-friendly version of `predictOnValues`. */ + def predictOnValues[K]( + data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Integer] = { + implicit val tag = fakeClassTag[K] + JavaPairDStream.fromPairDStream( + predictOnValues(data.dstream).asInstanceOf[DStream[(K, java.lang.Integer)]]) + } + /** Check whether cluster centers have been initialized. */ private[this] def assertInitialized(): Unit = { if (model.clusterCenters == null) { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala index 666362ae6739a..4628dc5690913 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala @@ -23,6 +23,7 @@ import org.apache.spark.SparkContext._ import org.apache.spark.annotation.Experimental import org.apache.spark.mllib.linalg.{Matrices, Matrix} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame /** * ::Experimental:: @@ -33,6 +34,13 @@ import org.apache.spark.rdd.RDD @Experimental class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { + /** + * An auxiliary constructor taking a DataFrame. + * @param predictionAndLabels a DataFrame with two double columns: prediction and label + */ + private[mllib] def this(predictionAndLabels: DataFrame) = + this(predictionAndLabels.map(r => (r.getDouble(0), r.getDouble(1)))) + private lazy val labelCountByClass: Map[Double, Long] = predictionAndLabels.values.countByValue() private lazy val labelCount: Long = labelCountByClass.values.sum private lazy val tpByClass: Map[Double, Int] = predictionAndLabels diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala index a8378a76d20ae..bf6eb1d5bd2ab 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala @@ -19,6 +19,7 @@ package org.apache.spark.mllib.evaluation import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ +import org.apache.spark.sql.DataFrame /** * Evaluator for multilabel classification. @@ -27,6 +28,13 @@ import org.apache.spark.SparkContext._ */ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) { + /** + * An auxiliary constructor taking a DataFrame. + * @param predictionAndLabels a DataFrame with two double array columns: prediction and label + */ + private[mllib] def this(predictionAndLabels: DataFrame) = + this(predictionAndLabels.map(r => (r.getSeq[Double](0).toArray, r.getSeq[Double](1).toArray))) + private lazy val numDocs: Long = predictionAndLabels.count() private lazy val numLabels: Long = predictionAndLabels.flatMap { case (_, labels) => diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala index 93a7353e2c070..5b5a2a1450f7f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala @@ -17,17 +17,22 @@ package org.apache.spark.mllib.evaluation +import java.{lang => jl} + +import scala.collection.JavaConverters._ import scala.reflect.ClassTag import org.apache.spark.Logging -import org.apache.spark.SparkContext._ import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.{JavaSparkContext, JavaRDD} import org.apache.spark.rdd.RDD /** * ::Experimental:: * Evaluator for ranking algorithms. * + * Java users should use [[RankingMetrics$.of]] to create a [[RankingMetrics]] instance. + * * @param predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs. */ @Experimental @@ -71,7 +76,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])] logWarning("Empty ground truth set, check input data") 0.0 } - }.mean + }.mean() } /** @@ -100,7 +105,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])] logWarning("Empty ground truth set, check input data") 0.0 } - }.mean + }.mean() } /** @@ -146,7 +151,23 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])] logWarning("Empty ground truth set, check input data") 0.0 } - }.mean + }.mean() } } + +@Experimental +object RankingMetrics { + + /** + * Creates a [[RankingMetrics]] instance (for Java users). + * @param predictionAndLabels a JavaRDD of (predicted ranking, ground truth set) pairs + */ + def of[E, T <: jl.Iterable[E]](predictionAndLabels: JavaRDD[(T, T)]): RankingMetrics[E] = { + implicit val tag = JavaSparkContext.fakeClassTag[E] + val rdd = predictionAndLabels.rdd.map { case (predictions, labels) => + (predictions.asScala.toArray, labels.asScala.toArray) + } + new RankingMetrics(rdd) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala index c6057c7f837b1..5f8c1dea237b4 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala @@ -38,7 +38,8 @@ class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransf protected def isSorted(array: Array[Int]): Boolean = { var i = 1 - while (i < array.length) { + val len = array.length + while (i < len) { if (array(i) < array(i-1)) return false i += 1 } @@ -107,7 +108,7 @@ class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransf * (ordered by statistic value descending) */ @Experimental -class ChiSqSelector (val numTopFeatures: Int) { +class ChiSqSelector (val numTopFeatures: Int) extends Serializable { /** * Returns a ChiSquared feature selector. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala index b0985baf9b278..d67fe6c3ee4f8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala @@ -25,10 +25,10 @@ import org.apache.spark.mllib.linalg._ * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a * provided "weight" vector. In other words, it scales each column of the dataset by a scalar * multiplier. - * @param scalingVector The values used to scale the reference vector's individual components. + * @param scalingVec The values used to scale the reference vector's individual components. */ @Experimental -class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer { +class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer { /** * Does the hadamard product transformation. @@ -37,15 +37,15 @@ class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer { * @return transformed vector. */ override def transform(vector: Vector): Vector = { - require(vector.size == scalingVector.size, - s"vector sizes do not match: Expected ${scalingVector.size} but found ${vector.size}") + require(vector.size == scalingVec.size, + s"vector sizes do not match: Expected ${scalingVec.size} but found ${vector.size}") vector match { case dv: DenseVector => val values: Array[Double] = dv.values.clone() - val dim = scalingVector.size + val dim = scalingVec.size var i = 0 while (i < dim) { - values(i) *= scalingVector(i) + values(i) *= scalingVec(i) i += 1 } Vectors.dense(values) @@ -54,7 +54,7 @@ class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer { val dim = values.length var i = 0 while (i < dim) { - values(i) *= scalingVector(indices(i)) + values(i) *= scalingVec(indices(i)) i += 1 } Vectors.sparse(size, indices, values) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala index a89eea0e21be2..3fab7ea79befc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala @@ -144,7 +144,7 @@ private object IDF { * Since arrays are initialized to 0 by default, * we just omit changing those entries. */ - if(df(j) >= minDocFreq) { + if (df(j) >= minDocFreq) { inv(j) = math.log((m + 1.0) / (df(j) + 1.0)) } j += 1 @@ -159,7 +159,7 @@ private object IDF { * Represents an IDF model that can transform term frequency vectors. */ @Experimental -class IDFModel private[mllib] (val idf: Vector) extends Serializable { +class IDFModel private[spark] (val idf: Vector) extends Serializable { /** * Transforms term frequency (TF) vectors to TF-IDF vectors. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala new file mode 100644 index 0000000000000..4e01e402b4283 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala @@ -0,0 +1,93 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.feature + +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.linalg._ +import org.apache.spark.mllib.linalg.distributed.RowMatrix +import org.apache.spark.rdd.RDD + +/** + * A feature transformer that projects vectors to a low-dimensional space using PCA. + * + * @param k number of principal components + */ +class PCA(val k: Int) { + require(k >= 1, s"PCA requires a number of principal components k >= 1 but was given $k") + + /** + * Computes a [[PCAModel]] that contains the principal components of the input vectors. + * + * @param sources source vectors + */ + def fit(sources: RDD[Vector]): PCAModel = { + require(k <= sources.first().size, + s"source vector size is ${sources.first().size} must be greater than k=$k") + + val mat = new RowMatrix(sources) + val pc = mat.computePrincipalComponents(k) match { + case dm: DenseMatrix => + dm + case sm: SparseMatrix => + /* Convert a sparse matrix to dense. + * + * RowMatrix.computePrincipalComponents always returns a dense matrix. + * The following code is a safeguard. + */ + sm.toDense + case m => + throw new IllegalArgumentException("Unsupported matrix format. Expected " + + s"SparseMatrix or DenseMatrix. Instead got: ${m.getClass}") + + } + new PCAModel(k, pc) + } + + /** Java-friendly version of [[fit()]] */ + def fit(sources: JavaRDD[Vector]): PCAModel = fit(sources.rdd) +} + +/** + * Model fitted by [[PCA]] that can project vectors to a low-dimensional space using PCA. + * + * @param k number of principal components. + * @param pc a principal components Matrix. Each column is one principal component. + */ +class PCAModel private[mllib] (val k: Int, val pc: DenseMatrix) extends VectorTransformer { + /** + * Transform a vector by computed Principal Components. + * + * @param vector vector to be transformed. + * Vector must be the same length as the source vectors given to [[PCA.fit()]]. + * @return transformed vector. Vector will be of length k. + */ + override def transform(vector: Vector): Vector = { + vector match { + case dv: DenseVector => + pc.transpose.multiply(dv) + case SparseVector(size, indices, values) => + /* SparseVector -> single row SparseMatrix */ + val sm = Matrices.sparse(size, 1, Array(0, indices.length), indices, values).transpose + val projection = sm.multiply(pc) + Vectors.dense(projection.values) + case _ => + throw new IllegalArgumentException("Unsupported vector format. Expected " + + s"SparseVector or DenseVector. Instead got: ${vector.getClass}") + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala index 6ae6917eae595..c73b8f258060d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala @@ -90,7 +90,7 @@ class StandardScalerModel ( @DeveloperApi def setWithMean(withMean: Boolean): this.type = { - require(!(withMean && this.mean == null),"cannot set withMean to true while mean is null") + require(!(withMean && this.mean == null), "cannot set withMean to true while mean is null") this.withMean = withMean this } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala index 98e83112f52ae..f087d06d2a46a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala @@ -42,32 +42,32 @@ import org.apache.spark.util.random.XORShiftRandom import org.apache.spark.sql.{SQLContext, Row} /** - * Entry in vocabulary + * Entry in vocabulary */ private case class VocabWord( var word: String, var cn: Int, var point: Array[Int], var code: Array[Int], - var codeLen:Int + var codeLen: Int ) /** * :: Experimental :: * Word2Vec creates vector representation of words in a text corpus. * The algorithm first constructs a vocabulary from the corpus - * and then learns vector representation of words in the vocabulary. - * The vector representation can be used as features in + * and then learns vector representation of words in the vocabulary. + * The vector representation can be used as features in * natural language processing and machine learning algorithms. - * - * We used skip-gram model in our implementation and hierarchical softmax + * + * We used skip-gram model in our implementation and hierarchical softmax * method to train the model. The variable names in the implementation * matches the original C implementation. * - * For original C implementation, see https://code.google.com/p/word2vec/ - * For research papers, see + * For original C implementation, see https://code.google.com/p/word2vec/ + * For research papers, see * Efficient Estimation of Word Representations in Vector Space - * and + * and * Distributed Representations of Words and Phrases and their Compositionality. */ @Experimental @@ -79,7 +79,7 @@ class Word2Vec extends Serializable with Logging { private var numIterations = 1 private var seed = Utils.random.nextLong() private var minCount = 5 - + /** * Sets vector size (default: 100). */ @@ -122,15 +122,15 @@ class Word2Vec extends Serializable with Logging { this } - /** - * Sets minCount, the minimum number of times a token must appear to be included in the word2vec + /** + * Sets minCount, the minimum number of times a token must appear to be included in the word2vec * model's vocabulary (default: 5). */ def setMinCount(minCount: Int): this.type = { this.minCount = minCount this } - + private val EXP_TABLE_SIZE = 1000 private val MAX_EXP = 6 private val MAX_CODE_LENGTH = 40 @@ -150,14 +150,17 @@ class Word2Vec extends Serializable with Logging { .map(x => VocabWord( x._1, x._2, - new Array[Int](MAX_CODE_LENGTH), - new Array[Int](MAX_CODE_LENGTH), + new Array[Int](MAX_CODE_LENGTH), + new Array[Int](MAX_CODE_LENGTH), 0)) .filter(_.cn >= minCount) .collect() .sortWith((a, b) => a.cn > b.cn) - + vocabSize = vocab.length + require(vocabSize > 0, "The vocabulary size should be > 0. You may need to check " + + "the setting of minCount, which could be large enough to remove all your words in sentences.") + var a = 0 while (a < vocabSize) { vocabHash += vocab(a).word -> a @@ -195,8 +198,8 @@ class Word2Vec extends Serializable with Logging { } var pos1 = vocabSize - 1 var pos2 = vocabSize - - var min1i = 0 + + var min1i = 0 var min2i = 0 a = 0 @@ -265,15 +268,15 @@ class Word2Vec extends Serializable with Logging { val words = dataset.flatMap(x => x) learnVocab(words) - + createBinaryTree() - + val sc = dataset.context val expTable = sc.broadcast(createExpTable()) val bcVocab = sc.broadcast(vocab) val bcVocabHash = sc.broadcast(vocabHash) - + val sentences: RDD[Array[Int]] = words.mapPartitions { iter => new Iterator[Array[Int]] { def hasNext: Boolean = iter.hasNext @@ -294,7 +297,7 @@ class Word2Vec extends Serializable with Logging { } } } - + val newSentences = sentences.repartition(numPartitions).cache() val initRandom = new XORShiftRandom(seed) @@ -399,7 +402,7 @@ class Word2Vec extends Serializable with Logging { } } newSentences.unpersist() - + val word2VecMap = mutable.HashMap.empty[String, Array[Float]] var i = 0 while (i < vocabSize) { @@ -428,7 +431,7 @@ class Word2Vec extends Serializable with Logging { * Word2Vec model */ @Experimental -class Word2VecModel private[mllib] ( +class Word2VecModel private[spark] ( model: Map[String, Array[Float]]) extends Serializable with Saveable { // wordList: Ordered list of words obtained from model. @@ -466,7 +469,7 @@ class Word2VecModel private[mllib] ( val norm1 = blas.snrm2(n, v1, 1) val norm2 = blas.snrm2(n, v2, 1) if (norm1 == 0 || norm2 == 0) return 0.0 - blas.sdot(n, v1, 1, v2,1) / norm1 / norm2 + blas.sdot(n, v1, 1, v2, 1) / norm1 / norm2 } override protected def formatVersion = "1.0" @@ -477,7 +480,7 @@ class Word2VecModel private[mllib] ( /** * Transforms a word to its vector representation - * @param word a word + * @param word a word * @return vector representation of word */ def transform(word: String): Vector = { @@ -492,18 +495,18 @@ class Word2VecModel private[mllib] ( /** * Find synonyms of a word * @param word a word - * @param num number of synonyms to find + * @param num number of synonyms to find * @return array of (word, cosineSimilarity) */ def findSynonyms(word: String, num: Int): Array[(String, Double)] = { val vector = transform(word) - findSynonyms(vector,num) + findSynonyms(vector, num) } /** * Find synonyms of the vector representation of a word * @param vector vector representation of a word - * @param num number of synonyms to find + * @param num number of synonyms to find * @return array of (word, cosineSimilarity) */ def findSynonyms(vector: Vector, num: Int): Array[(String, Double)] = { @@ -556,7 +559,7 @@ object Word2VecModel extends Loader[Word2VecModel] { def load(sc: SparkContext, path: String): Word2VecModel = { val dataPath = Loader.dataPath(path) val sqlContext = new SQLContext(sc) - val dataFrame = sqlContext.parquetFile(dataPath) + val dataFrame = sqlContext.read.parquet(dataPath) val dataArray = dataFrame.select("word", "vector").collect() @@ -580,7 +583,7 @@ object Word2VecModel extends Loader[Word2VecModel] { sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) val dataArray = model.toSeq.map { case (w, v) => Data(w, v) } - sc.parallelize(dataArray.toSeq, 1).toDF().saveAsParquetFile(Loader.dataPath(path)) + sc.parallelize(dataArray.toSeq, 1).toDF().write.parquet(Loader.dataPath(path)) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala index 87052e1ba8539..3523f1804325d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala @@ -213,9 +213,9 @@ private[spark] object BLAS extends Serializable with Logging { def scal(a: Double, x: Vector): Unit = { x match { case sx: SparseVector => - f2jBLAS.dscal(sx.values.size, a, sx.values, 1) + f2jBLAS.dscal(sx.values.length, a, sx.values, 1) case dx: DenseVector => - f2jBLAS.dscal(dx.values.size, a, dx.values, 1) + f2jBLAS.dscal(dx.values.length, a, dx.values, 1) case _ => throw new IllegalArgumentException(s"scal doesn't support vector type ${x.getClass}.") } @@ -228,7 +228,7 @@ private[spark] object BLAS extends Serializable with Logging { } _nativeBLAS } - + /** * A := alpha * x * x^T^ + A * @param alpha a real scalar that will be multiplied to x * x^T^. @@ -264,7 +264,7 @@ private[spark] object BLAS extends Serializable with Logging { j += 1 } i += 1 - } + } } private def syr(alpha: Double, x: SparseVector, A: DenseMatrix) { @@ -463,7 +463,7 @@ private[spark] object BLAS extends Serializable with Logging { def gemv( alpha: Double, A: Matrix, - x: DenseVector, + x: Vector, beta: Double, y: DenseVector): Unit = { require(A.numCols == x.size, @@ -473,27 +473,32 @@ private[spark] object BLAS extends Serializable with Logging { if (alpha == 0.0) { logDebug("gemv: alpha is equal to 0. Returning y.") } else { - A match { - case sparse: SparseMatrix => - gemv(alpha, sparse, x, beta, y) - case dense: DenseMatrix => - gemv(alpha, dense, x, beta, y) + (A, x) match { + case (smA: SparseMatrix, dvx: DenseVector) => + gemv(alpha, smA, dvx, beta, y) + case (smA: SparseMatrix, svx: SparseVector) => + gemv(alpha, smA, svx, beta, y) + case (dmA: DenseMatrix, dvx: DenseVector) => + gemv(alpha, dmA, dvx, beta, y) + case (dmA: DenseMatrix, svx: SparseVector) => + gemv(alpha, dmA, svx, beta, y) case _ => - throw new IllegalArgumentException(s"gemv doesn't support matrix type ${A.getClass}.") + throw new IllegalArgumentException(s"gemv doesn't support running on matrix type " + + s"${A.getClass} and vector type ${x.getClass}.") } } } /** * y := alpha * A * x + beta * y - * For `DenseMatrix` A. + * For `DenseMatrix` A and `DenseVector` x. */ private def gemv( alpha: Double, A: DenseMatrix, x: DenseVector, beta: Double, - y: DenseVector): Unit = { + y: DenseVector): Unit = { val tStrA = if (A.isTransposed) "T" else "N" val mA = if (!A.isTransposed) A.numRows else A.numCols val nA = if (!A.isTransposed) A.numCols else A.numRows @@ -503,14 +508,134 @@ private[spark] object BLAS extends Serializable with Logging { /** * y := alpha * A * x + beta * y - * For `SparseMatrix` A. + * For `DenseMatrix` A and `SparseVector` x. + */ + private def gemv( + alpha: Double, + A: DenseMatrix, + x: SparseVector, + beta: Double, + y: DenseVector): Unit = { + val mA: Int = A.numRows + val nA: Int = A.numCols + + val Avals = A.values + + val xIndices = x.indices + val xNnz = xIndices.length + val xValues = x.values + val yValues = y.values + + if (alpha == 0.0) { + scal(beta, y) + return + } + + if (A.isTransposed) { + var rowCounterForA = 0 + while (rowCounterForA < mA) { + var sum = 0.0 + var k = 0 + while (k < xNnz) { + sum += xValues(k) * Avals(xIndices(k) + rowCounterForA * nA) + k += 1 + } + yValues(rowCounterForA) = sum * alpha + beta * yValues(rowCounterForA) + rowCounterForA += 1 + } + } else { + var rowCounterForA = 0 + while (rowCounterForA < mA) { + var sum = 0.0 + var k = 0 + while (k < xNnz) { + sum += xValues(k) * Avals(xIndices(k) * mA + rowCounterForA) + k += 1 + } + yValues(rowCounterForA) = sum * alpha + beta * yValues(rowCounterForA) + rowCounterForA += 1 + } + } + } + + /** + * y := alpha * A * x + beta * y + * For `SparseMatrix` A and `SparseVector` x. + */ + private def gemv( + alpha: Double, + A: SparseMatrix, + x: SparseVector, + beta: Double, + y: DenseVector): Unit = { + val xValues = x.values + val xIndices = x.indices + val xNnz = xIndices.length + + val yValues = y.values + + val mA: Int = A.numRows + val nA: Int = A.numCols + + val Avals = A.values + val Arows = if (!A.isTransposed) A.rowIndices else A.colPtrs + val Acols = if (!A.isTransposed) A.colPtrs else A.rowIndices + + if (alpha == 0.0) { + scal(beta, y) + return + } + + if (A.isTransposed) { + var rowCounter = 0 + while (rowCounter < mA) { + var i = Arows(rowCounter) + val indEnd = Arows(rowCounter + 1) + var sum = 0.0 + var k = 0 + while (k < xNnz && i < indEnd) { + if (xIndices(k) == Acols(i)) { + sum += Avals(i) * xValues(k) + i += 1 + } + k += 1 + } + yValues(rowCounter) = sum * alpha + beta * yValues(rowCounter) + rowCounter += 1 + } + } else { + scal(beta, y) + + var colCounterForA = 0 + var k = 0 + while (colCounterForA < nA && k < xNnz) { + if (xIndices(k) == colCounterForA) { + var i = Acols(colCounterForA) + val indEnd = Acols(colCounterForA + 1) + + val xTemp = xValues(k) * alpha + while (i < indEnd) { + val rowIndex = Arows(i) + yValues(Arows(i)) += Avals(i) * xTemp + i += 1 + } + k += 1 + } + colCounterForA += 1 + } + } + } + + /** + * y := alpha * A * x + beta * y + * For `SparseMatrix` A and `DenseVector` x. */ private def gemv( alpha: Double, A: SparseMatrix, x: DenseVector, beta: Double, - y: DenseVector): Unit = { + y: DenseVector): Unit = { val xValues = x.values val yValues = y.values val mA: Int = A.numRows @@ -534,10 +659,7 @@ private[spark] object BLAS extends Serializable with Logging { rowCounter += 1 } } else { - // Scale vector first if `beta` is not equal to 0.0 - if (beta != 0.0) { - scal(beta, y) - } + scal(beta, y) // Perform matrix-vector multiplication and add to y var colCounterForA = 0 while (colCounterForA < nA) { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala index 866936aa4f118..ae3ba3099c878 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala @@ -81,7 +81,7 @@ private[mllib] object EigenValueDecomposition { require(n * ncv.toLong <= Integer.MAX_VALUE && ncv * (ncv.toLong + 8) <= Integer.MAX_VALUE, s"k = $k and/or n = $n are too large to compute an eigendecomposition") - + var ido = new intW(0) var info = new intW(0) var resid = new Array[Double](n) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala index 3fa5e068d16d4..85e63b1382b5e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala @@ -77,8 +77,13 @@ sealed trait Matrix extends Serializable { C } - /** Convenience method for `Matrix`-`DenseVector` multiplication. */ + /** Convenience method for `Matrix`-`DenseVector` multiplication. For binary compatibility. */ def multiply(y: DenseVector): DenseVector = { + multiply(y.asInstanceOf[Vector]) + } + + /** Convenience method for `Matrix`-`Vector` multiplication. */ + def multiply(y: Vector): DenseVector = { val output = new DenseVector(new Array[Double](numRows)) BLAS.gemv(1.0, this, y, 0.0, output) output @@ -192,6 +197,8 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] { override def typeName: String = "matrix" + override def pyUDT: String = "pyspark.mllib.linalg.MatrixUDT" + private[spark] override def asNullable: MatrixUDT = this } @@ -273,7 +280,8 @@ class DenseMatrix( override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone()) - private[mllib] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f)) + private[mllib] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f), + isTransposed) private[mllib] def update(f: Double => Double): DenseMatrix = { val len = values.length @@ -535,7 +543,7 @@ class SparseMatrix( } private[mllib] def map(f: Double => Double) = - new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.map(f)) + new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.map(f), isTransposed) private[mllib] def update(f: Double => Double): SparseMatrix = { val len = values.length diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index f6bcdf83cd337..2ffa497a99d93 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -176,27 +176,31 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] { } override def serialize(obj: Any): Row = { - val row = new GenericMutableRow(4) obj match { case SparseVector(size, indices, values) => + val row = new GenericMutableRow(4) row.setByte(0, 0) row.setInt(1, size) row.update(2, indices.toSeq) row.update(3, values.toSeq) + row case DenseVector(values) => + val row = new GenericMutableRow(4) row.setByte(0, 1) row.setNullAt(1) row.setNullAt(2) row.update(3, values.toSeq) + row + // TODO: There are bugs in UDT serialization because we don't have a clear separation between + // TODO: internal SQL types and language specific types (including UDT). UDT serialize and + // TODO: deserialize may get called twice. See SPARK-7186. + case row: Row => + row } - row } override def deserialize(datum: Any): Vector = { datum match { - // TODO: something wrong with UDT serialization - case v: Vector => - v case row: Row => require(row.length == 4, s"VectorUDT.deserialize given row with length ${row.length} but requires length == 4") @@ -211,6 +215,11 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] { val values = row.getAs[Iterable[Double]](3).toArray new DenseVector(values) } + // TODO: There are bugs in UDT serialization because we don't have a clear separation between + // TODO: internal SQL types and language specific types (including UDT). UDT serialize and + // TODO: deserialize may get called twice. See SPARK-7186. + case v: Vector => + v } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala index 9a89a6f3a515f..1626da9c3d2ee 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -219,7 +219,7 @@ class RowMatrix( val computeMode = mode match { case "auto" => - if(k > 5000) { + if (k > 5000) { logWarning(s"computing svd with k=$k and n=$n, please check necessity") } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala index 4b7d0589c973b..06e45e10c5bf4 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala @@ -179,7 +179,7 @@ object GradientDescent extends Logging { * if it's L2 updater; for L1 updater, the same logic is followed. */ var regVal = updater.compute( - weights, Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2 + weights, Vectors.zeros(weights.size), 0, 1, regParam)._2 for (i <- 1 to numIterations) { val bcWeights = data.context.broadcast(weights) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala index 4766f7708295d..64d52bae00907 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala @@ -91,7 +91,7 @@ private[spark] object NNLS { val dir = ws.dir val lastDir = ws.lastDir val res = ws.res - val iterMax = Math.max(400, 20 * n) + val iterMax = math.max(400, 20 * n) var lastNorm = 0.0 var iterno = 0 var lastWall = 0 // Last iteration when we hit a bound constraint. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala index 3ed3a5b9b3843..9f463e0cafb6f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala @@ -116,7 +116,8 @@ class L1Updater extends Updater { // Apply proximal operator (soft thresholding) val shrinkageVal = regParam * thisIterStepSize var i = 0 - while (i < brzWeights.length) { + val len = brzWeights.length + while (i < len) { val wi = brzWeights(i) brzWeights(i) = signum(wi) * max(0.0, abs(wi) - shrinkageVal) i += 1 diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala index 354e90f3eeaa6..5e882d4ebb10b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala @@ -23,13 +23,16 @@ import javax.xml.transform.stream.StreamResult import org.jpmml.model.JAXBUtil import org.apache.spark.SparkContext +import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory /** + * :: DeveloperApi :: * Export model to the PMML format * Predictive Model Markup Language (PMML) is an XML-based file format * developed by the Data Mining Group (www.dmg.org). */ +@DeveloperApi trait PMMLExportable { /** @@ -41,30 +44,38 @@ trait PMMLExportable { } /** + * :: Experimental :: * Export the model to a local file in PMML format */ + @Experimental def toPMML(localPath: String): Unit = { toPMML(new StreamResult(new File(localPath))) } /** + * :: Experimental :: * Export the model to a directory on a distributed file system in PMML format */ + @Experimental def toPMML(sc: SparkContext, path: String): Unit = { val pmml = toPMML() sc.parallelize(Array(pmml), 1).saveAsTextFile(path) } /** + * :: Experimental :: * Export the model to the OutputStream in PMML format */ + @Experimental def toPMML(outputStream: OutputStream): Unit = { toPMML(new StreamResult(outputStream)) } /** + * :: Experimental :: * Export the model to a String in PMML format */ + @Experimental def toPMML(): String = { val writer = new StringWriter toPMML(new StreamResult(writer)) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala index 34b447584e521..622b53a252ac5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala @@ -27,10 +27,10 @@ import org.apache.spark.mllib.regression.GeneralizedLinearModel * PMML Model Export for GeneralizedLinearModel class with binary ClassificationModel */ private[mllib] class BinaryClassificationPMMLModelExport( - model : GeneralizedLinearModel, + model : GeneralizedLinearModel, description : String, normalizationMethod : RegressionNormalizationMethodType, - threshold: Double) + threshold: Double) extends PMMLModelExport { populateBinaryClassificationPMML() @@ -72,7 +72,7 @@ private[mllib] class BinaryClassificationPMMLModelExport( .withUsageType(FieldUsageType.ACTIVE)) regressionTableYES.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i))) } - + // add target field val targetField = FieldName.create("target") dataDictionary @@ -80,9 +80,9 @@ private[mllib] class BinaryClassificationPMMLModelExport( miningSchema .withMiningFields(new MiningField(targetField) .withUsageType(FieldUsageType.TARGET)) - + dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size) - + pmml.setDataDictionary(dataDictionary) pmml.withModels(regressionModel) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala index ebdeae50bb32f..c5fdecd3ca17f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala @@ -25,7 +25,7 @@ import scala.beans.BeanProperty import org.dmg.pmml.{Application, Header, PMML, Timestamp} private[mllib] trait PMMLModelExport { - + /** * Holder of the exported model in PMML format */ @@ -33,7 +33,7 @@ private[mllib] trait PMMLModelExport { val pmml: PMML = new PMML setHeader(pmml) - + private def setHeader(pmml: PMML): Unit = { val version = getClass.getPackage.getImplementationVersion val app = new Application().withName("Apache Spark MLlib").withVersion(version) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala index c16e83d6a067d..29bd689e1185a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala @@ -27,9 +27,9 @@ import org.apache.spark.mllib.regression.LinearRegressionModel import org.apache.spark.mllib.regression.RidgeRegressionModel private[mllib] object PMMLModelExportFactory { - + /** - * Factory object to help creating the necessary PMMLModelExport implementation + * Factory object to help creating the necessary PMMLModelExport implementation * taking as input the machine learning model (for example KMeansModel). */ def createPMMLModelExport(model: Any): PMMLModelExport = { @@ -44,7 +44,7 @@ private[mllib] object PMMLModelExportFactory { new GeneralizedLinearPMMLModelExport(lasso, "lasso regression") case svm: SVMModel => new BinaryClassificationPMMLModelExport( - svm, "linear SVM", RegressionNormalizationMethodType.NONE, + svm, "linear SVM", RegressionNormalizationMethodType.NONE, svm.getThreshold.getOrElse(0.0)) case logistic: LogisticRegressionModel => if (logistic.numClasses == 2) { @@ -60,5 +60,5 @@ private[mllib] object PMMLModelExportFactory { "PMML Export not supported for model: " + model.getClass.getName) } } - + } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala index 8341bb86afd71..174d5e0f6c9f0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala @@ -52,7 +52,7 @@ object RandomRDDs { numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double] = { val uniform = new UniformGenerator() - randomRDD(sc, uniform, size, numPartitionsOrDefault(sc, numPartitions), seed) + randomRDD(sc, uniform, size, numPartitionsOrDefault(sc, numPartitions), seed) } /** @@ -234,7 +234,7 @@ object RandomRDDs { * * @param sc SparkContext used to create the RDD. * @param shape shape parameter (> 0) for the gamma distribution - * @param scale scale parameter (> 0) for the gamma distribution + * @param scale scale parameter (> 0) for the gamma distribution * @param size Size of the RDD. * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`). * @param seed Random seed (default: a random long integer). @@ -293,7 +293,7 @@ object RandomRDDs { * * @param sc SparkContext used to create the RDD. * @param mean mean for the log normal distribution - * @param std standard deviation for the log normal distribution + * @param std standard deviation for the log normal distribution * @param size Size of the RDD. * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`). * @param seed Random seed (default: a random long integer). @@ -671,7 +671,7 @@ object RandomRDDs { * * @param sc SparkContext used to create the RDD. * @param shape shape parameter (> 0) for the gamma distribution. - * @param scale scale parameter (> 0) for the gamma distribution. + * @param scale scale parameter (> 0) for the gamma distribution. * @param numRows Number of Vectors in the RDD. * @param numCols Number of elements in each Vector. * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index dddefe1944e9d..93290e6508529 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -175,7 +175,7 @@ class ALS private ( /** * :: DeveloperApi :: * Sets storage level for final RDDs (user/product used in MatrixFactorizationModel). The default - * value is `MEMORY_AND_DISK`. Users can change it to a serialized storage, e.g. + * value is `MEMORY_AND_DISK`. Users can change it to a serialized storage, e.g. * `MEMORY_AND_DISK_SER` and set `spark.rdd.compress` to `true` to reduce the space requirement, * at the cost of speed. */ diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala index 88c2148403313..93aa41e49961e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -281,8 +281,8 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { val metadata = compact(render( ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("rank" -> model.rank))) sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path)) - model.userFeatures.toDF("id", "features").saveAsParquetFile(userPath(path)) - model.productFeatures.toDF("id", "features").saveAsParquetFile(productPath(path)) + model.userFeatures.toDF("id", "features").write.parquet(userPath(path)) + model.productFeatures.toDF("id", "features").write.parquet(productPath(path)) } def load(sc: SparkContext, path: String): MatrixFactorizationModel = { @@ -292,11 +292,11 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { assert(className == thisClassName) assert(formatVersion == thisFormatVersion) val rank = (metadata \ "rank").extract[Int] - val userFeatures = sqlContext.parquetFile(userPath(path)) + val userFeatures = sqlContext.read.parquet(userPath(path)) .map { case Row(id: Int, features: Seq[_]) => (id, features.asInstanceOf[Seq[Double]].toArray) } - val productFeatures = sqlContext.parquetFile(productPath(path)) + val productFeatures = sqlContext.read.parquet(productPath(path)) .map { case Row(id: Int, features: Seq[_]) => (id, features.asInstanceOf[Seq[Double]].toArray) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 26be30ff9d6fd..6709bd79bc820 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -195,11 +195,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] */ val initialWeights = { if (numOfLinearPredictor == 1) { - Vectors.dense(new Array[Double](numFeatures)) + Vectors.zeros(numFeatures) } else if (addIntercept) { - Vectors.dense(new Array[Double]((numFeatures + 1) * numOfLinearPredictor)) + Vectors.zeros((numFeatures + 1) * numOfLinearPredictor) } else { - Vectors.dense(new Array[Double](numFeatures * numOfLinearPredictor)) + Vectors.zeros(numFeatures * numOfLinearPredictor) } } run(input, initialWeights) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala index be2a00c2dfea4..f3b46c75c05f3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala @@ -69,7 +69,8 @@ class IsotonicRegressionModel ( /** Asserts the input array is monotone with the given ordering. */ private def assertOrdered(xs: Array[Double])(implicit ord: Ordering[Double]): Unit = { var i = 1 - while (i < xs.length) { + val len = xs.length + while (i < len) { require(ord.compare(xs(i - 1), xs(i)) <= 0, s"Elements (${xs(i - 1)}, ${xs(i)}) are not ordered.") i += 1 @@ -169,26 +170,26 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] { case class Data(boundary: Double, prediction: Double) def save( - sc: SparkContext, - path: String, - boundaries: Array[Double], - predictions: Array[Double], + sc: SparkContext, + path: String, + boundaries: Array[Double], + predictions: Array[Double], isotonic: Boolean): Unit = { val sqlContext = new SQLContext(sc) val metadata = compact(render( - ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("isotonic" -> isotonic))) sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path)) sqlContext.createDataFrame( boundaries.toSeq.zip(predictions).map { case (b, p) => Data(b, p) } - ).saveAsParquetFile(dataPath(path)) + ).write.parquet(dataPath(path)) } def load(sc: SparkContext, path: String): (Array[Double], Array[Double]) = { val sqlContext = new SQLContext(sc) - val dataRDD = sqlContext.parquetFile(dataPath(path)) + val dataRDD = sqlContext.read.parquet(dataPath(path)) checkSchema[Data](dataRDD.schema) val dataArray = dataRDD.select("boundary", "prediction").collect() @@ -202,7 +203,7 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] { override def load(sc: SparkContext, path: String): IsotonicRegressionModel = { implicit val formats = DefaultFormats val (loadedClassName, version, metadata) = loadMetadata(sc, path) - val isotonic = (metadata \ "isotonic").extract[Boolean] + val isotonic = (metadata \ "isotonic").extract[Boolean] val classNameV1_0 = SaveLoadV1_0.thisClassName (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => @@ -329,11 +330,12 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali } var i = 0 - while (i < input.length) { + val len = input.length + while (i < len) { var j = i // Find monotonicity violating sequence, if any. - while (j < input.length - 1 && input(j)._1 > input(j + 1)._1) { + while (j < len - 1 && input(j)._1 > input(j + 1)._1) { j = j + 1 } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index e0c03d8180c7a..7d28ffad45c92 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -73,7 +73,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] { /** * Train a regression model with L2-regularization using Stochastic Gradient Descent. - * This solves the l1-regularized least squares regression formulation + * This solves the l2-regularized least squares regression formulation * f(weights) = 1/2n ||A weights-y||^2^ + regParam/2 ||weights||^2^ * Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with * its corresponding right hand side label y. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala index cea8f3f47307b..141052ba813ee 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala @@ -83,21 +83,15 @@ abstract class StreamingLinearAlgorithm[ throw new IllegalArgumentException("Model must be initialized before starting training.") } data.foreachRDD { (rdd, time) => - val initialWeights = - model match { - case Some(m) => - m.weights - case None => - val numFeatures = rdd.first().features.size - Vectors.dense(numFeatures) + if (!rdd.isEmpty) { + model = Some(algorithm.run(rdd, model.get.weights)) + logInfo(s"Model updated at time ${time.toString}") + val display = model.get.weights.size match { + case x if x > 100 => model.get.weights.toArray.take(100).mkString("[", ",", "...") + case _ => model.get.weights.toArray.mkString("[", ",", "]") } - model = Some(algorithm.run(rdd, initialWeights)) - logInfo("Model updated at time %s".format(time.toString)) - val display = model.get.weights.size match { - case x if x > 100 => model.get.weights.toArray.take(100).mkString("[", ",", "...") - case _ => model.get.weights.toArray.mkString("[", ",", "]") + logInfo(s"Current model: weights, ${display}") } - logInfo("Current model: weights, %s".format (display)) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala index a49153bf73c0d..235e043c7754b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala @@ -79,7 +79,7 @@ class StreamingLinearRegressionWithSGD private[mllib] ( this } - /** Set the initial weights. Default: [0.0, 0.0]. */ + /** Set the initial weights. */ def setInitialWeights(initialWeights: Vector): this.type = { this.model = Some(algorithm.createModel(initialWeights, 0.0)) this diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala index b55944f74f623..317d3a5702636 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala @@ -60,7 +60,7 @@ private[regression] object GLMRegressionModel { val data = Data(weights, intercept) val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF() // TODO: repartition with 1 partition after SPARK-5532 gets fixed - dataRDD.saveAsParquetFile(Loader.dataPath(path)) + dataRDD.write.parquet(Loader.dataPath(path)) } /** @@ -72,7 +72,7 @@ private[regression] object GLMRegressionModel { def loadData(sc: SparkContext, path: String, modelClass: String, numFeatures: Int): Data = { val datapath = Loader.dataPath(path) val sqlContext = new SQLContext(sc) - val dataRDD = sqlContext.parquetFile(datapath) + val dataRDD = sqlContext.read.parquet(datapath) val dataArray = dataRDD.select("weights", "intercept").take(1) assert(dataArray.size == 1, s"Unable to load $modelClass data from: $datapath") val data = dataArray(0) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala index 0deef11b4511a..58a50f9c19f14 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala @@ -17,55 +17,104 @@ package org.apache.spark.mllib.stat +import com.github.fommil.netlib.BLAS.{getInstance => blas} + +import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.rdd.RDD -private[stat] object KernelDensity { +/** + * :: Experimental :: + * Kernel density estimation. Given a sample from a population, estimate its probability density + * function at each of the given evaluation points using kernels. Only Gaussian kernel is supported. + * + * Scala example: + * + * {{{ + * val sample = sc.parallelize(Seq(0.0, 1.0, 4.0, 4.0)) + * val kd = new KernelDensity() + * .setSample(sample) + * .setBandwidth(3.0) + * val densities = kd.estimate(Array(-1.0, 2.0, 5.0)) + * }}} + */ +@Experimental +class KernelDensity extends Serializable { + + import KernelDensity._ + + /** Bandwidth of the kernel function. */ + private var bandwidth: Double = 1.0 + + /** A sample from a population. */ + private var sample: RDD[Double] = _ + /** - * Given a set of samples from a distribution, estimates its density at the set of given points. - * Uses a Gaussian kernel with the given standard deviation. + * Sets the bandwidth (standard deviation) of the Gaussian kernel (default: `1.0`). */ - def estimate(samples: RDD[Double], standardDeviation: Double, - evaluationPoints: Array[Double]): Array[Double] = { - if (standardDeviation <= 0.0) { - throw new IllegalArgumentException("Standard deviation must be positive") - } + def setBandwidth(bandwidth: Double): this.type = { + require(bandwidth > 0, s"Bandwidth must be positive, but got $bandwidth.") + this.bandwidth = bandwidth + this + } - // This gets used in each Gaussian PDF computation, so compute it up front - val logStandardDeviationPlusHalfLog2Pi = - Math.log(standardDeviation) + 0.5 * Math.log(2 * Math.PI) + /** + * Sets the sample to use for density estimation. + */ + def setSample(sample: RDD[Double]): this.type = { + this.sample = sample + this + } + + /** + * Sets the sample to use for density estimation (for Java users). + */ + def setSample(sample: JavaRDD[java.lang.Double]): this.type = { + this.sample = sample.rdd.asInstanceOf[RDD[Double]] + this + } + + /** + * Estimates probability density function at the given array of points. + */ + def estimate(points: Array[Double]): Array[Double] = { + val sample = this.sample + val bandwidth = this.bandwidth + + require(sample != null, "Must set sample before calling estimate.") - val (points, count) = samples.aggregate((new Array[Double](evaluationPoints.length), 0))( + val n = points.length + // This gets used in each Gaussian PDF computation, so compute it up front + val logStandardDeviationPlusHalfLog2Pi = math.log(bandwidth) + 0.5 * math.log(2 * math.Pi) + val (densities, count) = sample.aggregate((new Array[Double](n), 0L))( (x, y) => { var i = 0 - while (i < evaluationPoints.length) { - x._1(i) += normPdf(y, standardDeviation, logStandardDeviationPlusHalfLog2Pi, - evaluationPoints(i)) + while (i < n) { + x._1(i) += normPdf(y, bandwidth, logStandardDeviationPlusHalfLog2Pi, points(i)) i += 1 } - (x._1, i) + (x._1, x._2 + 1) }, (x, y) => { - var i = 0 - while (i < evaluationPoints.length) { - x._1(i) += y._1(i) - i += 1 - } + blas.daxpy(n, 1.0, y._1, 1, x._1, 1) (x._1, x._2 + y._2) }) - - var i = 0 - while (i < points.length) { - points(i) /= count - i += 1 - } - points + blas.dscal(n, 1.0 / count, densities, 1) + densities } +} + +private object KernelDensity { - private def normPdf(mean: Double, standardDeviation: Double, - logStandardDeviationPlusHalfLog2Pi: Double, x: Double): Double = { + /** Evaluates the PDF of a normal distribution. */ + def normPdf( + mean: Double, + standardDeviation: Double, + logStandardDeviationPlusHalfLog2Pi: Double, + x: Double): Double = { val x0 = x - mean val x1 = x0 / standardDeviation val logDensity = -0.5 * x1 * x1 - logStandardDeviationPlusHalfLog2Pi - Math.exp(logDensity) + math.exp(logDensity) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala index fcc2a148791bd..d321cc554c1cc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala @@ -70,23 +70,30 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S require(n == sample.size, s"Dimensions mismatch when adding new sample." + s" Expecting $n but got ${sample.size}.") + val localCurrMean = currMean + val localCurrM2n = currM2n + val localCurrM2 = currM2 + val localCurrL1 = currL1 + val localNnz = nnz + val localCurrMax = currMax + val localCurrMin = currMin sample.foreachActive { (index, value) => if (value != 0.0) { - if (currMax(index) < value) { - currMax(index) = value + if (localCurrMax(index) < value) { + localCurrMax(index) = value } - if (currMin(index) > value) { - currMin(index) = value + if (localCurrMin(index) > value) { + localCurrMin(index) = value } - val prevMean = currMean(index) + val prevMean = localCurrMean(index) val diff = value - prevMean - currMean(index) = prevMean + diff / (nnz(index) + 1.0) - currM2n(index) += (value - currMean(index)) * diff - currM2(index) += value * value - currL1(index) += math.abs(value) + localCurrMean(index) = prevMean + diff / (localNnz(index) + 1.0) + localCurrM2n(index) += (value - localCurrMean(index)) * diff + localCurrM2(index) += value * value + localCurrL1(index) += math.abs(value) - nnz(index) += 1.0 + localNnz(index) += 1.0 } } @@ -130,14 +137,14 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S } } else if (totalCnt == 0 && other.totalCnt != 0) { this.n = other.n - this.currMean = other.currMean.clone - this.currM2n = other.currM2n.clone - this.currM2 = other.currM2.clone - this.currL1 = other.currL1.clone + this.currMean = other.currMean.clone() + this.currM2n = other.currM2n.clone() + this.currM2 = other.currM2.clone() + this.currL1 = other.currL1.clone() this.totalCnt = other.totalCnt - this.nnz = other.nnz.clone - this.currMax = other.currMax.clone - this.currMin = other.currMin.clone + this.nnz = other.nnz.clone() + this.currMax = other.currMax.clone() + this.currMin = other.currMin.clone() } this } @@ -165,7 +172,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S if (denominator > 0.0) { val deltaMean = currMean var i = 0 - while (i < currM2n.size) { + val len = currM2n.length + while (i < len) { realVariance(i) = currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt realVariance(i) /= denominator @@ -211,7 +219,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S val realMagnitude = Array.ofDim[Double](n) var i = 0 - while (i < currM2.size) { + val len = currM2.length + while (i < len) { realMagnitude(i) = math.sqrt(currM2(i)) i += 1 } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala index 32561620ac914..900007ec6bc74 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala @@ -18,6 +18,7 @@ package org.apache.spark.mllib.stat import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.distributed.RowMatrix import org.apache.spark.mllib.linalg.{Matrix, Vector} import org.apache.spark.mllib.regression.LabeledPoint @@ -80,6 +81,10 @@ object Statistics { */ def corr(x: RDD[Double], y: RDD[Double]): Double = Correlations.corr(x, y) + /** Java-friendly version of [[corr()]] */ + def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double]): Double = + corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]]) + /** * Compute the correlation for the input RDDs using the specified method. * Methods currently supported: `pearson` (default), `spearman`. @@ -96,6 +101,10 @@ object Statistics { */ def corr(x: RDD[Double], y: RDD[Double], method: String): Double = Correlations.corr(x, y, method) + /** Java-friendly version of [[corr()]] */ + def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double], method: String): Double = + corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]], method) + /** * Conduct Pearson's chi-squared goodness of fit test of the observed data against the * expected distribution. @@ -149,18 +158,4 @@ object Statistics { def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = { ChiSqTest.chiSquaredFeatures(data) } - - /** - * Given an empirical distribution defined by the input RDD of samples, estimate its density at - * each of the given evaluation points using a Gaussian kernel. - * - * @param samples The samples RDD used to define the empirical distribution. - * @param standardDeviation The standard deviation of the kernel Gaussians. - * @param evaluationPoints The points at which to estimate densities. - * @return An array the same size as evaluationPoints with the density at each point. - */ - def kernelDensity(samples: RDD[Double], standardDeviation: Double, - evaluationPoints: Iterable[Double]): Array[Double] = { - KernelDensity.estimate(samples, standardDeviation, evaluationPoints.toArray) - } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala index cd6add9d60b0d..cf51b24ff777f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala @@ -29,102 +29,102 @@ import org.apache.spark.mllib.util.MLUtils * the event that the covariance matrix is singular, the density will be computed in a * reduced dimensional subspace under which the distribution is supported. * (see [[http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Degenerate_case]]) - * + * * @param mu The mean vector of the distribution * @param sigma The covariance matrix of the distribution */ @DeveloperApi class MultivariateGaussian ( - val mu: Vector, + val mu: Vector, val sigma: Matrix) extends Serializable { require(sigma.numCols == sigma.numRows, "Covariance matrix must be square") require(mu.size == sigma.numCols, "Mean vector length must match covariance matrix size") - + private val breezeMu = mu.toBreeze.toDenseVector - + /** * private[mllib] constructor - * + * * @param mu The mean vector of the distribution * @param sigma The covariance matrix of the distribution */ private[mllib] def this(mu: DBV[Double], sigma: DBM[Double]) = { this(Vectors.fromBreeze(mu), Matrices.fromBreeze(sigma)) } - + /** * Compute distribution dependent constants: * rootSigmaInv = D^(-1/2)^ * U, where sigma = U * D * U.t - * u = log((2*pi)^(-k/2)^ * det(sigma)^(-1/2)^) + * u = log((2*pi)^(-k/2)^ * det(sigma)^(-1/2)^) */ private val (rootSigmaInv: DBM[Double], u: Double) = calculateCovarianceConstants - + /** Returns density of this multivariate Gaussian at given point, x */ def pdf(x: Vector): Double = { pdf(x.toBreeze) } - + /** Returns the log-density of this multivariate Gaussian at given point, x */ def logpdf(x: Vector): Double = { logpdf(x.toBreeze) } - + /** Returns density of this multivariate Gaussian at given point, x */ private[mllib] def pdf(x: BV[Double]): Double = { math.exp(logpdf(x)) } - + /** Returns the log-density of this multivariate Gaussian at given point, x */ private[mllib] def logpdf(x: BV[Double]): Double = { val delta = x - breezeMu val v = rootSigmaInv * delta u + v.t * v * -0.5 } - + /** * Calculate distribution dependent components used for the density function: * pdf(x) = (2*pi)^(-k/2)^ * det(sigma)^(-1/2)^ * exp((-1/2) * (x-mu).t * inv(sigma) * (x-mu)) * where k is length of the mean vector. - * - * We here compute distribution-fixed parts + * + * We here compute distribution-fixed parts * log((2*pi)^(-k/2)^ * det(sigma)^(-1/2)^) * and * D^(-1/2)^ * U, where sigma = U * D * U.t - * + * * Both the determinant and the inverse can be computed from the singular value decomposition * of sigma. Noting that covariance matrices are always symmetric and positive semi-definite, * we can use the eigendecomposition. We also do not compute the inverse directly; noting - * that - * + * that + * * sigma = U * D * U.t - * inv(Sigma) = U * inv(D) * U.t + * inv(Sigma) = U * inv(D) * U.t * = (D^{-1/2}^ * U).t * (D^{-1/2}^ * U) - * + * * and thus - * + * * -0.5 * (x-mu).t * inv(Sigma) * (x-mu) = -0.5 * norm(D^{-1/2}^ * U * (x-mu))^2^ - * - * To guard against singular covariance matrices, this method computes both the + * + * To guard against singular covariance matrices, this method computes both the * pseudo-determinant and the pseudo-inverse (Moore-Penrose). Singular values are considered * to be non-zero only if they exceed a tolerance based on machine precision, matrix size, and * relation to the maximum singular value (same tolerance used by, e.g., Octave). */ private def calculateCovarianceConstants: (DBM[Double], Double) = { val eigSym.EigSym(d, u) = eigSym(sigma.toBreeze.toDenseMatrix) // sigma = u * diag(d) * u.t - + // For numerical stability, values are considered to be non-zero only if they exceed tol. // This prevents any inverted value from exceeding (eps * n * max(d))^-1 val tol = MLUtils.EPSILON * max(d) * d.length - + try { // log(pseudo-determinant) is sum of the logs of all non-zero singular values val logPseudoDetSigma = d.activeValuesIterator.filter(_ > tol).map(math.log).sum - - // calculate the root-pseudo-inverse of the diagonal matrix of singular values + + // calculate the root-pseudo-inverse of the diagonal matrix of singular values // by inverting the square root of all non-zero values val pinvS = diag(new DBV(d.map(v => if (v > tol) math.sqrt(1.0 / v) else 0.0).toArray)) - + (pinvS * u, -0.5 * (mu.size * math.log(2.0 * math.Pi) + logPseudoDetSigma)) } catch { case uex: UnsupportedOperationException => diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala index ea82d39b72c03..23c8d7c7c8075 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala @@ -196,7 +196,7 @@ private[stat] object ChiSqTest extends Logging { * Pearson's independence test on the input contingency matrix. * TODO: optimize for SparseMatrix when it becomes supported. */ - def chiSquaredMatrix(counts: Matrix, methodName:String = PEARSON.name): ChiSqTestResult = { + def chiSquaredMatrix(counts: Matrix, methodName: String = PEARSON.name): ChiSqTestResult = { val method = methodFromString(methodName) val numRows = counts.numRows val numCols = counts.numCols @@ -205,8 +205,10 @@ private[stat] object ChiSqTest extends Logging { val colSums = new Array[Double](numCols) val rowSums = new Array[Double](numRows) val colMajorArr = counts.toArray + val colMajorArrLen = colMajorArr.length + var i = 0 - while (i < colMajorArr.size) { + while (i < colMajorArrLen) { val elem = colMajorArr(i) if (elem < 0.0) { throw new IllegalArgumentException("Contingency table cannot contain negative entries.") @@ -220,7 +222,7 @@ private[stat] object ChiSqTest extends Logging { // second pass to collect statistic var statistic = 0.0 var j = 0 - while (j < colMajorArr.size) { + while (j < colMajorArrLen) { val col = j / numRows val colSum = colSums(col) if (colSum == 0.0) { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala index dfe3a0b6913ef..cecd1fed896d5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala @@ -169,7 +169,7 @@ object DecisionTree extends Serializable with Logging { numClasses: Int, maxBins: Int, quantileCalculationStrategy: QuantileStrategy, - categoricalFeaturesInfo: Map[Int,Int]): DecisionTreeModel = { + categoricalFeaturesInfo: Map[Int, Int]): DecisionTreeModel = { val strategy = new Strategy(algo, impurity, maxDepth, numClasses, maxBins, quantileCalculationStrategy, categoricalFeaturesInfo) new DecisionTree(strategy).run(input) @@ -768,7 +768,7 @@ object DecisionTree extends Serializable with Logging { */ private def calculatePredictImpurity( leftImpurityCalculator: ImpurityCalculator, - rightImpurityCalculator: ImpurityCalculator): (Predict, Double) = { + rightImpurityCalculator: ImpurityCalculator): (Predict, Double) = { val parentNodeAgg = leftImpurityCalculator.copy parentNodeAgg.add(rightImpurityCalculator) val predict = calculatePredict(parentNodeAgg) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index 1f779584dcffd..a835f96d5d0e3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -60,12 +60,12 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy) def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = { val algo = boostingStrategy.treeStrategy.algo algo match { - case Regression => GradientBoostedTrees.boost(input, input, boostingStrategy, validate=false) + case Regression => + GradientBoostedTrees.boost(input, input, boostingStrategy, validate = false) case Classification => // Map labels to -1, +1 so binary classification can be treated as regression. val remappedInput = input.map(x => new LabeledPoint((x.label * 2) - 1, x.features)) - GradientBoostedTrees.boost(remappedInput, - remappedInput, boostingStrategy, validate=false) + GradientBoostedTrees.boost(remappedInput, remappedInput, boostingStrategy, validate = false) case _ => throw new IllegalArgumentException(s"$algo is not supported by the gradient boosting.") } @@ -93,8 +93,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy) validationInput: RDD[LabeledPoint]): GradientBoostedTreesModel = { val algo = boostingStrategy.treeStrategy.algo algo match { - case Regression => GradientBoostedTrees.boost( - input, validationInput, boostingStrategy, validate=true) + case Regression => + GradientBoostedTrees.boost(input, validationInput, boostingStrategy, validate = true) case Classification => // Map labels to -1, +1 so binary classification can be treated as regression. val remappedInput = input.map( @@ -102,7 +102,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy) val remappedValidationInput = validationInput.map( x => new LabeledPoint((x.label * 2) - 1, x.features)) GradientBoostedTrees.boost(remappedInput, remappedValidationInput, boostingStrategy, - validate=true) + validate = true) case _ => throw new IllegalArgumentException(s"$algo is not supported by the gradient boosting.") } @@ -270,7 +270,7 @@ object GradientBoostedTrees extends Logging { logInfo(s"$timer") if (persistedInput) input.unpersist() - + if (validate) { new GradientBoostedTreesModel( boostingStrategy.treeStrategy.algo, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala index 055e60c7d9c95..069959976a188 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala @@ -36,6 +36,7 @@ import org.apache.spark.mllib.tree.model._ import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils +import org.apache.spark.util.random.SamplingUtils /** * :: Experimental :: @@ -248,7 +249,7 @@ private class RandomForest ( try { nodeIdCache.get.deleteAllCheckpoints() } catch { - case e:IOException => + case e: IOException => logWarning(s"delete all checkpoints failed. Error reason: ${e.getMessage}") } } @@ -473,9 +474,8 @@ object RandomForest extends Serializable with Logging { val (treeIndex, node) = nodeQueue.head // Choose subset of features for node (if subsampling). val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) { - // TODO: Use more efficient subsampling? (use selection-and-rejection or reservoir) - Some(rng.shuffle(Range(0, metadata.numFeatures).toList) - .take(metadata.numFeaturesPerNode).toArray) + Some(SamplingUtils.reservoirSampleAndCount(Range(0, + metadata.numFeatures).iterator, metadata.numFeaturesPerNode, rng.nextLong)._1) } else { None } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala index 60e2ab2bb829e..72eb24c49264a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala @@ -111,11 +111,12 @@ private[tree] abstract class ImpurityCalculator(val stats: Array[Double]) { * Add the stats from another calculator into this one, modifying and returning this calculator. */ def add(other: ImpurityCalculator): ImpurityCalculator = { - require(stats.size == other.stats.size, + require(stats.length == other.stats.length, s"Two ImpurityCalculator instances cannot be added with different counts sizes." + - s" Sizes are ${stats.size} and ${other.stats.size}.") + s" Sizes are ${stats.length} and ${other.stats.length}.") var i = 0 - while (i < other.stats.size) { + val len = other.stats.length + while (i < len) { stats(i) += other.stats(i) i += 1 } @@ -127,11 +128,12 @@ private[tree] abstract class ImpurityCalculator(val stats: Array[Double]) { * calculator. */ def subtract(other: ImpurityCalculator): ImpurityCalculator = { - require(stats.size == other.stats.size, + require(stats.length == other.stats.length, s"Two ImpurityCalculator instances cannot be subtracted with different counts sizes." + - s" Sizes are ${stats.size} and ${other.stats.size}.") + s" Sizes are ${stats.length} and ${other.stats.length}.") var i = 0 - while (i < other.stats.size) { + val len = other.stats.length + while (i < len) { stats(i) -= other.stats(i) i += 1 } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala index 331af428533de..25bb1453db404 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala @@ -223,14 +223,14 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging { val dataRDD: DataFrame = sc.parallelize(nodes) .map(NodeData.apply(0, _)) .toDF() - dataRDD.saveAsParquetFile(Loader.dataPath(path)) + dataRDD.write.parquet(Loader.dataPath(path)) } def load(sc: SparkContext, path: String, algo: String, numNodes: Int): DecisionTreeModel = { val datapath = Loader.dataPath(path) val sqlContext = new SQLContext(sc) // Load Parquet data. - val dataRDD = sqlContext.parquetFile(datapath) + val dataRDD = sqlContext.read.parquet(datapath) // Check schema explicitly since erasure makes it hard to use match-case for checking. Loader.checkSchema[NodeData](dataRDD.schema) val nodes = dataRDD.map(NodeData.apply) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala index 431a839817eac..a6d1398fc267b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala @@ -83,7 +83,7 @@ class Node ( def predict(features: Vector) : Double = { if (isLeaf) { predict.predict - } else{ + } else { if (split.get.featureType == Continuous) { if (features(split.get.feature) <= split.get.threshold) { leftNode.get.predict(features) @@ -151,9 +151,9 @@ class Node ( s"(feature ${split.feature} > ${split.threshold})" } case Categorical => if (left) { - s"(feature ${split.feature} in ${split.categories.mkString("{",",","}")})" + s"(feature ${split.feature} in ${split.categories.mkString("{", ",", "}")})" } else { - s"(feature ${split.feature} not in ${split.categories.mkString("{",",","}")})" + s"(feature ${split.feature} not in ${split.categories.mkString("{", ",", "}")})" } } } @@ -161,9 +161,9 @@ class Node ( if (isLeaf) { prefix + s"Predict: ${predict.predict}\n" } else { - prefix + s"If ${splitToString(split.get, left=true)}\n" + + prefix + s"If ${splitToString(split.get, left = true)}\n" + leftNode.get.subtreeToString(indentFactor + 1) + - prefix + s"Else ${splitToString(split.get, left=false)}\n" + + prefix + s"Else ${splitToString(split.get, left = false)}\n" + rightNode.get.subtreeToString(indentFactor + 1) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala index 8341219bfa71c..1e3333d8d81d0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala @@ -414,7 +414,7 @@ private[tree] object TreeEnsembleModel extends Logging { val dataRDD = sc.parallelize(model.trees.zipWithIndex).flatMap { case (tree, treeId) => tree.topNode.subtreeIterator.toSeq.map(node => NodeData(treeId, node)) }.toDF() - dataRDD.saveAsParquetFile(Loader.dataPath(path)) + dataRDD.write.parquet(Loader.dataPath(path)) } /** @@ -437,7 +437,7 @@ private[tree] object TreeEnsembleModel extends Logging { treeAlgo: String): Array[DecisionTreeModel] = { val datapath = Loader.dataPath(path) val sqlContext = new SQLContext(sc) - val nodes = sqlContext.parquetFile(datapath).map(NodeData.apply) + val nodes = sqlContext.read.parquet(datapath).map(NodeData.apply) val trees = constructTrees(nodes) trees.map(new DecisionTreeModel(_, Algo.fromString(treeAlgo))) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala index b1a4517344970..b4e33c98ba7e5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala @@ -107,7 +107,8 @@ object LinearDataGenerator { x.foreach { v => var i = 0 - while (i < v.length) { + val len = v.length + while (i < len) { v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) i += 1 } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala index 0c5b4f9d04a74..bd73a866c8a82 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala @@ -82,8 +82,7 @@ object MFDataGenerator { BLAS.gemm(z, A, B, 1.0, fullData) val df = rank * (m + n - rank) - val sampSize = scala.math.min(scala.math.round(trainSampFact * df), - scala.math.round(.99 * m * n)).toInt + val sampSize = math.min(math.round(trainSampFact * df), math.round(.99 * m * n)).toInt val rand = new Random() val mn = m * n val shuffled = rand.shuffle((0 until mn).toList) @@ -102,8 +101,8 @@ object MFDataGenerator { // optionally generate testing data if (test) { - val testSampSize = scala.math - .min(scala.math.round(sampSize * testSampFact),scala.math.round(mn - sampSize)).toInt + val testSampSize = math.min( + math.round(sampSize * testSampFact), math.round(mn - sampSize)).toInt val testOmega = shuffled.slice(sampSize, sampSize + testSampSize) val testOrdered = testOmega.sortWith(_ < _).toArray val testData: RDD[(Int, Int, Double)] = sc.parallelize(testOrdered) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala index 5d6ddd47f67d6..7c5cfa7bd84ce 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala @@ -82,6 +82,18 @@ object MLUtils { val value = indexAndValue(1).toDouble (index, value) }.unzip + + // check if indices are one-based and in ascending order + var previous = -1 + var i = 0 + val indicesLength = indices.length + while (i < indicesLength) { + val current = indices(i) + require(current > previous, "indices should be one-based and in ascending order" ) + previous = current + i += 1 + } + (label, indices.toArray, values.toArray) } @@ -258,14 +270,30 @@ object MLUtils { * Returns a new vector with `1.0` (bias) appended to the input vector. */ def appendBias(vector: Vector): Vector = { - val vector1 = vector.toBreeze match { - case dv: BDV[Double] => BDV.vertcat(dv, new BDV[Double](Array(1.0))) - case sv: BSV[Double] => BSV.vertcat(sv, new BSV[Double](Array(0), Array(1.0), 1)) - case v: Any => throw new IllegalArgumentException("Do not support vector type " + v.getClass) + vector match { + case dv: DenseVector => + val inputValues = dv.values + val inputLength = inputValues.length + val outputValues = Array.ofDim[Double](inputLength + 1) + System.arraycopy(inputValues, 0, outputValues, 0, inputLength) + outputValues(inputLength) = 1.0 + Vectors.dense(outputValues) + case sv: SparseVector => + val inputValues = sv.values + val inputIndices = sv.indices + val inputValuesLength = inputValues.length + val dim = sv.size + val outputValues = Array.ofDim[Double](inputValuesLength + 1) + val outputIndices = Array.ofDim[Int](inputValuesLength + 1) + System.arraycopy(inputValues, 0, outputValues, 0, inputValuesLength) + System.arraycopy(inputIndices, 0, outputIndices, 0, inputValuesLength) + outputValues(inputValuesLength) = 1.0 + outputIndices(inputValuesLength) = dim + Vectors.sparse(dim + 1, outputIndices, outputValues) + case _ => throw new IllegalArgumentException(s"Do not support vector type ${vector.getClass}") } - Vectors.fromBreeze(vector1) } - + /** * Returns the squared Euclidean distance between two vectors. The following formula will be used * if it does not introduce too much numerical error: @@ -331,7 +359,7 @@ object MLUtils { * @param x a floating-point value as input. * @return the result of `math.log(1 + math.exp(x))`. */ - private[mllib] def log1pExp(x: Double): Double = { + private[spark] def log1pExp(x: Double): Double = { if (x > 0) { x + math.log1p(math.exp(-x)) } else { diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index 7e7189a2b1d53..f75e024a713ee 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -84,7 +84,7 @@ public void logisticRegressionWithSetters() { .setThreshold(0.6) .setProbabilityCol("myProbability"); LogisticRegressionModel model = lr.fit(dataset); - LogisticRegression parent = model.parent(); + LogisticRegression parent = (LogisticRegression) model.parent(); assert(parent.getMaxIter() == 10); assert(parent.getRegParam() == 1.0); assert(parent.getThreshold() == 0.6); @@ -110,7 +110,7 @@ public void logisticRegressionWithSetters() { // Call fit() with new params, and check as many params as we can. LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.threshold().w(0.4), lr.probabilityCol().w("theProb")); - LogisticRegression parent2 = model2.parent(); + LogisticRegression parent2 = (LogisticRegression) model2.parent(); assert(parent2.getMaxIter() == 5); assert(parent2.getRegParam() == 0.1); assert(parent2.getThreshold() == 0.4); diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java new file mode 100644 index 0000000000000..a1ee554152372 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification; + +import java.io.Serializable; +import java.util.List; + +import static scala.collection.JavaConversions.seqAsJavaList; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateMultinomialLogisticInput; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; + +public class JavaOneVsRestSuite implements Serializable { + + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + private transient DataFrame dataset; + private transient JavaRDD datasetRDD; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaLOneVsRestSuite"); + jsql = new SQLContext(jsc); + int nPoints = 3; + + // The following weights and xMean/xVariance are computed from iris dataset with lambda=0.2. + // As a result, we are drawing samples from probability distribution of an actual model. + double[] weights = { + -0.57997, 0.912083, -0.371077, -0.819866, 2.688191, + -0.16624, -0.84355, -0.048509, -0.301789, 4.170682 }; + + double[] xMean = {5.843, 3.057, 3.758, 1.199}; + double[] xVariance = {0.6856, 0.1899, 3.116, 0.581}; + List points = seqAsJavaList(generateMultinomialLogisticInput( + weights, xMean, xVariance, true, nPoints, 42)); + datasetRDD = jsc.parallelize(points, 2); + dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void oneVsRestDefaultParams() { + OneVsRest ova = new OneVsRest(); + ova.setClassifier(new LogisticRegression()); + Assert.assertEquals(ova.getLabelCol() , "label"); + Assert.assertEquals(ova.getPredictionCol() , "prediction"); + OneVsRestModel ovaModel = ova.fit(dataset); + DataFrame predictions = ovaModel.transform(dataset).select("label", "prediction"); + predictions.collectAsList(); + Assert.assertEquals(ovaModel.getLabelCol(), "label"); + Assert.assertEquals(ovaModel.getPredictionCol() , "prediction"); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java new file mode 100644 index 0000000000000..d5bd230a957a1 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java @@ -0,0 +1,80 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.Metadata; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; + +public class JavaBucketizerSuite { + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaBucketizerSuite"); + jsql = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void bucketizerTest() { + double[] splits = {-0.5, 0.0, 0.5}; + + JavaRDD data = jsc.parallelize(Lists.newArrayList( + RowFactory.create(-0.5), + RowFactory.create(-0.3), + RowFactory.create(0.0), + RowFactory.create(0.2) + )); + StructType schema = new StructType(new StructField[] { + new StructField("feature", DataTypes.DoubleType, false, Metadata.empty()) + }); + DataFrame dataset = jsql.createDataFrame(data, schema); + + Bucketizer bucketizer = new Bucketizer() + .setInputCol("feature") + .setOutputCol("result") + .setSplits(splits); + + Row[] result = bucketizer.transform(dataset).select("result").collect(); + + for (Row r : result) { + double index = r.getDouble(0); + Assert.assertTrue((index >= 0) && (index <= 1)); + } + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java new file mode 100644 index 0000000000000..599e9cfd23ad4 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java @@ -0,0 +1,86 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.Metadata; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; + + +public class JavaHashingTFSuite { + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaHashingTFSuite"); + jsql = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void hashingTF() { + JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( + RowFactory.create(0.0, "Hi I heard about Spark"), + RowFactory.create(0.0, "I wish Java could use case classes"), + RowFactory.create(1.0, "Logistic regression models are neat") + )); + StructType schema = new StructType(new StructField[]{ + new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), + new StructField("sentence", DataTypes.StringType, false, Metadata.empty()) + }); + + DataFrame sentenceData = jsql.createDataFrame(jrdd, schema); + Tokenizer tokenizer = new Tokenizer() + .setInputCol("sentence") + .setOutputCol("words"); + DataFrame wordsData = tokenizer.transform(sentenceData); + int numFeatures = 20; + HashingTF hashingTF = new HashingTF() + .setInputCol("words") + .setOutputCol("rawFeatures") + .setNumFeatures(numFeatures); + DataFrame featurizedData = hashingTF.transform(wordsData); + IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features"); + IDFModel idfModel = idf.fit(featurizedData); + DataFrame rescaledData = idfModel.transform(featurizedData); + for (Row r : rescaledData.select("features", "label").take(3)) { + Vector features = r.getAs(0); + Assert.assertEquals(features.size(), numFeatures); + } + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java new file mode 100644 index 0000000000000..d82f3b7e8c076 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import java.util.List; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; + +public class JavaNormalizerSuite { + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaNormalizerSuite"); + jsql = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void normalizer() { + // The tests are to check Java compatibility. + List points = Lists.newArrayList( + new VectorIndexerSuite.FeatureData(Vectors.dense(0.0, -2.0)), + new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 3.0)), + new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 4.0)) + ); + DataFrame dataFrame = jsql.createDataFrame(jsc.parallelize(points, 2), + VectorIndexerSuite.FeatureData.class); + Normalizer normalizer = new Normalizer() + .setInputCol("features") + .setOutputCol("normFeatures"); + + // Normalize each Vector using $L^2$ norm. + DataFrame l2NormData = normalizer.transform(dataFrame, normalizer.p().w(2)); + l2NormData.count(); + + // Normalize each Vector using $L^\infty$ norm. + DataFrame lInfNormData = + normalizer.transform(dataFrame, normalizer.p().w(Double.POSITIVE_INFINITY)); + lInfNormData.count(); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java new file mode 100644 index 0000000000000..5e8211c2c5118 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java @@ -0,0 +1,91 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.VectorUDT; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.types.Metadata; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; + +public class JavaPolynomialExpansionSuite { + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaPolynomialExpansionSuite"); + jsql = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void polynomialExpansionTest() { + PolynomialExpansion polyExpansion = new PolynomialExpansion() + .setInputCol("features") + .setOutputCol("polyFeatures") + .setDegree(3); + + JavaRDD data = jsc.parallelize(Lists.newArrayList( + RowFactory.create( + Vectors.dense(-2.0, 2.3), + Vectors.dense(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17) + ), + RowFactory.create(Vectors.dense(0.0, 0.0), Vectors.dense(new double[9])), + RowFactory.create( + Vectors.dense(0.6, -1.1), + Vectors.dense(0.6, 0.36, 0.216, -1.1, -0.66, -0.396, 1.21, 0.726, -1.331) + ) + )); + + StructType schema = new StructType(new StructField[] { + new StructField("features", new VectorUDT(), false, Metadata.empty()), + new StructField("expected", new VectorUDT(), false, Metadata.empty()) + }); + + DataFrame dataset = jsql.createDataFrame(data, schema); + + Row[] pairs = polyExpansion.transform(dataset) + .select("polyFeatures", "expected") + .collect(); + + for (Row r : pairs) { + double[] polyFeatures = ((Vector)r.get(0)).toArray(); + double[] expected = ((Vector)r.get(1)).toArray(); + Assert.assertArrayEquals(polyFeatures, expected, 1e-1); + } + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java new file mode 100644 index 0000000000000..74eb2733f06ef --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import java.util.List; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; + +public class JavaStandardScalerSuite { + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaStandardScalerSuite"); + jsql = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void standardScaler() { + // The tests are to check Java compatibility. + List points = Lists.newArrayList( + new VectorIndexerSuite.FeatureData(Vectors.dense(0.0, -2.0)), + new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 3.0)), + new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 4.0)) + ); + DataFrame dataFrame = jsql.createDataFrame(jsc.parallelize(points, 2), + VectorIndexerSuite.FeatureData.class); + StandardScaler scaler = new StandardScaler() + .setInputCol("features") + .setOutputCol("scaledFeatures") + .setWithStd(true) + .setWithMean(false); + + // Compute summary statistics by fitting the StandardScaler + StandardScalerModel scalerModel = scaler.fit(dataFrame); + + // Normalize each feature to have unit standard deviation. + DataFrame scaledData = scalerModel.transform(dataFrame); + scaledData.count(); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStringIndexerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStringIndexerSuite.java new file mode 100644 index 0000000000000..35b18c5308f61 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStringIndexerSuite.java @@ -0,0 +1,77 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import java.util.Arrays; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import static org.apache.spark.sql.types.DataTypes.*; + +public class JavaStringIndexerSuite { + private transient JavaSparkContext jsc; + private transient SQLContext sqlContext; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaStringIndexerSuite"); + sqlContext = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + sqlContext = null; + } + + @Test + public void testStringIndexer() { + StructType schema = createStructType(new StructField[] { + createStructField("id", IntegerType, false), + createStructField("label", StringType, false) + }); + JavaRDD rdd = jsc.parallelize( + Arrays.asList(c(0, "a"), c(1, "b"), c(2, "c"), c(3, "a"), c(4, "a"), c(5, "c"))); + DataFrame dataset = sqlContext.createDataFrame(rdd, schema); + + StringIndexer indexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("labelIndex"); + DataFrame output = indexer.fit(dataset).transform(dataset); + + Assert.assertArrayEquals( + new Row[] { c(0, 0.0), c(1, 2.0), c(2, 1.0), c(3, 0.0), c(4, 0.0), c(5, 1.0) }, + output.orderBy("id").select("id", "labelIndex").collect()); + } + + /** An alias for RowFactory.create. */ + private Row c(Object... values) { + return RowFactory.create(values); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java new file mode 100644 index 0000000000000..b7c564caad3bd --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java @@ -0,0 +1,78 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import java.util.Arrays; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.VectorUDT; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.types.*; +import static org.apache.spark.sql.types.DataTypes.*; + +public class JavaVectorAssemblerSuite { + private transient JavaSparkContext jsc; + private transient SQLContext sqlContext; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaVectorAssemblerSuite"); + sqlContext = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void testVectorAssembler() { + StructType schema = createStructType(new StructField[] { + createStructField("id", IntegerType, false), + createStructField("x", DoubleType, false), + createStructField("y", new VectorUDT(), false), + createStructField("name", StringType, false), + createStructField("z", new VectorUDT(), false), + createStructField("n", LongType, false) + }); + Row row = RowFactory.create( + 0, 0.0, Vectors.dense(1.0, 2.0), "a", + Vectors.sparse(2, new int[] {1}, new double[] {3.0}), 10L); + JavaRDD rdd = jsc.parallelize(Arrays.asList(row)); + DataFrame dataset = sqlContext.createDataFrame(rdd, schema); + VectorAssembler assembler = new VectorAssembler() + .setInputCols(new String[] {"x", "y", "z", "n"}) + .setOutputCol("features"); + DataFrame output = assembler.transform(dataset); + Assert.assertEquals( + Vectors.sparse(6, new int[] {1, 2, 4, 5}, new double[] {1.0, 2.0, 3.0, 10.0}), + output.select("features").first().getAs(0)); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java index 161100134c92d..c7ae5468b9429 100644 --- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java @@ -19,6 +19,7 @@ import java.io.Serializable; import java.util.List; +import java.util.Map; import org.junit.After; import org.junit.Assert; @@ -64,7 +65,8 @@ public void vectorIndexerAPI() { .setMaxCategories(2); VectorIndexerModel model = indexer.fit(data); Assert.assertEquals(model.numFeatures(), 2); - Assert.assertEquals(model.categoryMaps().size(), 1); + Map> categoryMaps = model.javaCategoryMaps(); + Assert.assertEquals(categoryMaps.size(), 1); DataFrame indexedData = model.transform(data); } } diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java new file mode 100644 index 0000000000000..39c70157f83c0 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java @@ -0,0 +1,76 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.types.*; + +public class JavaWord2VecSuite { + private transient JavaSparkContext jsc; + private transient SQLContext sqlContext; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaWord2VecSuite"); + sqlContext = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void testJavaWord2Vec() { + JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( + RowFactory.create(Lists.newArrayList("Hi I heard about Spark".split(" "))), + RowFactory.create(Lists.newArrayList("I wish Java could use case classes".split(" "))), + RowFactory.create(Lists.newArrayList("Logistic regression models are neat".split(" "))) + )); + StructType schema = new StructType(new StructField[]{ + new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty()) + }); + DataFrame documentDF = sqlContext.createDataFrame(jrdd, schema); + + Word2Vec word2Vec = new Word2Vec() + .setInputCol("text") + .setOutputCol("result") + .setVectorSize(3) + .setMinCount(0); + Word2VecModel model = word2Vec.fit(documentDF); + DataFrame result = model.transform(documentDF); + + for (Row r: result.select("result").collect()) { + double[] polyFeatures = ((Vector)r.get(0)).toArray(); + Assert.assertEquals(polyFeatures.length, 3); + } + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java index e7df10dfa63ac..9890155e9f865 100644 --- a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java @@ -50,6 +50,7 @@ public void testParams() { testParams.setMyIntParam(2).setMyDoubleParam(0.4).setMyStringParam("a"); Assert.assertEquals(testParams.getMyDoubleParam(), 0.4, 0.0); Assert.assertEquals(testParams.getMyStringParam(), "a"); + Assert.assertArrayEquals(testParams.getMyDoubleArrayParam(), new double[] {1.0, 2.0}, 0.0); } @Test diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java index 532eca47918fc..3ae09d39ef500 100644 --- a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java +++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java @@ -21,44 +21,90 @@ import com.google.common.collect.Lists; +import org.apache.spark.ml.util.Identifiable$; + /** * A subclass of Params for testing. */ public class JavaTestParams extends JavaParams { - public IntParam myIntParam; + public JavaTestParams() { + this.uid_ = Identifiable$.MODULE$.randomUID("javaTestParams"); + init(); + } + + public JavaTestParams(String uid) { + this.uid_ = uid; + init(); + } + + private String uid_; + + @Override + public String uid() { + return uid_; + } + + private IntParam myIntParam_; + public IntParam myIntParam() { return myIntParam_; } - public int getMyIntParam() { return (Integer)getOrDefault(myIntParam); } + public int getMyIntParam() { return (Integer)getOrDefault(myIntParam_); } public JavaTestParams setMyIntParam(int value) { - set(myIntParam, value); return this; + set(myIntParam_, value); + return this; } - public DoubleParam myDoubleParam; + private DoubleParam myDoubleParam_; + public DoubleParam myDoubleParam() { return myDoubleParam_; } - public double getMyDoubleParam() { return (Double)getOrDefault(myDoubleParam); } + public double getMyDoubleParam() { return (Double)getOrDefault(myDoubleParam_); } public JavaTestParams setMyDoubleParam(double value) { - set(myDoubleParam, value); return this; + set(myDoubleParam_, value); + return this; } - public Param myStringParam; + private Param myStringParam_; + public Param myStringParam() { return myStringParam_; } - public String getMyStringParam() { return (String)getOrDefault(myStringParam); } + public String getMyStringParam() { return getOrDefault(myStringParam_); } public JavaTestParams setMyStringParam(String value) { - set(myStringParam, value); return this; + set(myStringParam_, value); + return this; } - public JavaTestParams() { - myIntParam = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0)); - myDoubleParam = new DoubleParam(this, "myDoubleParam", "this is a double param", + private DoubleArrayParam myDoubleArrayParam_; + public DoubleArrayParam myDoubleArrayParam() { return myDoubleArrayParam_; } + + public double[] getMyDoubleArrayParam() { return getOrDefault(myDoubleArrayParam_); } + + public JavaTestParams setMyDoubleArrayParam(double[] value) { + set(myDoubleArrayParam_, value); + return this; + } + + private void init() { + myIntParam_ = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0)); + myDoubleParam_ = new DoubleParam(this, "myDoubleParam", "this is a double param", ParamValidators.inRange(0.0, 1.0)); List validStrings = Lists.newArrayList("a", "b"); - myStringParam = new Param(this, "myStringParam", "this is a string param", + myStringParam_ = new Param(this, "myStringParam", "this is a string param", ParamValidators.inArray(validStrings)); - setDefault(myIntParam, 1); - setDefault(myDoubleParam, 0.5); - setDefault(myIntParam.w(1), myDoubleParam.w(0.5)); + myDoubleArrayParam_ = + new DoubleArrayParam(this, "myDoubleArrayParam", "this is a double param"); + + setDefault(myIntParam(), 1); + setDefault(myIntParam().w(1)); + setDefault(myDoubleParam(), 0.5); + setDefault(myIntParam().w(1), myDoubleParam().w(0.5)); + setDefault(myDoubleArrayParam(), new double[] {1.0, 2.0}); + setDefault(myDoubleArrayParam().w(new double[] {1.0, 2.0})); + } + + @Override + public JavaTestParams copy(ParamMap extra) { + return defaultCopy(extra); } } diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java index a82b86d560b6e..d591a456864e4 100644 --- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java @@ -77,14 +77,14 @@ public void linearRegressionWithSetters() { .setMaxIter(10) .setRegParam(1.0); LinearRegressionModel model = lr.fit(dataset); - LinearRegression parent = model.parent(); + LinearRegression parent = (LinearRegression) model.parent(); assertEquals(10, parent.getMaxIter()); assertEquals(1.0, parent.getRegParam(), 0.0); // Call fit() with new params, and check as many params as we can. LinearRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred")); - LinearRegression parent2 = model2.parent(); + LinearRegression parent2 = (LinearRegression) model2.parent(); assertEquals(5, parent2.getMaxIter()); assertEquals(0.1, parent2.getRegParam(), 0.0); assertEquals("thePred", model2.getPredictionCol()); diff --git a/mllib/src/test/java/org/apache/spark/ml/util/IdentifiableSuite.scala b/mllib/src/test/java/org/apache/spark/ml/util/IdentifiableSuite.scala new file mode 100644 index 0000000000000..928301523fba9 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/util/IdentifiableSuite.scala @@ -0,0 +1,40 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.util + +import org.apache.spark.SparkFunSuite + +class IdentifiableSuite extends SparkFunSuite { + + import IdentifiableSuite.Test + + test("Identifiable") { + val test0 = new Test("test_0") + assert(test0.uid === "test_0") + + val test1 = new Test + assert(test1.uid.startsWith("test_")) + } +} + +object IdentifiableSuite { + + class Test(override val uid: String) extends Identifiable { + def this() = this(Identifiable.randomUID("test")) + } +} diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java index 71fb7f13c39c2..3771c0ea7ad83 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java @@ -108,7 +108,7 @@ public Vector call(LabeledPoint v) throws Exception { @Test public void testModelTypeSetters() { NaiveBayes nb = new NaiveBayes() - .setModelType("Bernoulli") - .setModelType("Multinomial"); + .setModelType("bernoulli") + .setModelType("multinomial"); } } diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java similarity index 95% rename from mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java rename to mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java index 640d2ec55e4e7..55787f8606d48 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.ml.classification; +package org.apache.spark.mllib.classification; import java.io.Serializable; import java.util.List; @@ -28,7 +28,6 @@ import org.junit.Test; import org.apache.spark.SparkConf; -import org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java new file mode 100644 index 0000000000000..467a7a69e8f30 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.clustering; + +import java.io.Serializable; +import java.util.List; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; + +public class JavaGaussianMixtureSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaGaussianMixture"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runGaussianMixture() { + List points = Lists.newArrayList( + Vectors.dense(1.0, 2.0, 6.0), + Vectors.dense(1.0, 3.0, 0.0), + Vectors.dense(1.0, 4.0, 6.0) + ); + + JavaRDD data = sc.parallelize(points, 2); + GaussianMixtureModel model = new GaussianMixture().setK(2).setMaxIterations(1).setSeed(1234) + .run(data); + assertEquals(model.gaussians().length, 2); + JavaRDD predictions = model.predict(data); + predictions.first(); + } +} diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java index 96c2da169961f..581c033f08ebe 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java @@ -107,6 +107,10 @@ public void distributedLDAModel() { // Check: log probabilities assert(model.logLikelihood() < 0.0); assert(model.logPrior() < 0.0); + + // Check: topic distributions + JavaPairRDD topicDistributions = model.javaTopicDistributions(); + assertEquals(topicDistributions.count(), corpus.count()); } @Test diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java new file mode 100644 index 0000000000000..3b0e879eec77f --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.clustering; + +import java.io.Serializable; +import java.util.List; + +import scala.Tuple2; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import static org.apache.spark.streaming.JavaTestUtils.*; + +import org.apache.spark.SparkConf; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.streaming.Duration; +import org.apache.spark.streaming.api.java.JavaDStream; +import org.apache.spark.streaming.api.java.JavaPairDStream; +import org.apache.spark.streaming.api.java.JavaStreamingContext; + +public class JavaStreamingKMeansSuite implements Serializable { + + protected transient JavaStreamingContext ssc; + + @Before + public void setUp() { + SparkConf conf = new SparkConf() + .setMaster("local[2]") + .setAppName("test") + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); + ssc = new JavaStreamingContext(conf, new Duration(1000)); + ssc.checkpoint("checkpoint"); + } + + @After + public void tearDown() { + ssc.stop(); + ssc = null; + } + + @Test + @SuppressWarnings("unchecked") + public void javaAPI() { + List trainingBatch = Lists.newArrayList( + Vectors.dense(1.0), + Vectors.dense(0.0)); + JavaDStream training = + attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2); + List> testBatch = Lists.newArrayList( + new Tuple2(10, Vectors.dense(1.0)), + new Tuple2(11, Vectors.dense(0.0))); + JavaPairDStream test = JavaPairDStream.fromJavaDStream( + attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2)); + StreamingKMeans skmeans = new StreamingKMeans() + .setK(1) + .setDecayFactor(1.0) + .setInitialCenters(new Vector[]{Vectors.dense(1.0)}, new double[]{0.0}); + skmeans.trainOn(training); + JavaPairDStream prediction = skmeans.predictOnValues(test); + attachTestOutputStream(prediction.count()); + runStreams(ssc, 2, 2); + } +} diff --git a/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java new file mode 100644 index 0000000000000..effc8a1a6dabc --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.evaluation; + +import java.io.Serializable; +import java.util.ArrayList; + +import scala.Tuple2; +import scala.Tuple2$; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; + +public class JavaRankingMetricsSuite implements Serializable { + private transient JavaSparkContext sc; + private transient JavaRDD, ArrayList>> predictionAndLabels; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaRankingMetricsSuite"); + predictionAndLabels = sc.parallelize(Lists.newArrayList( + Tuple2$.MODULE$.apply( + Lists.newArrayList(1, 6, 2, 7, 8, 3, 9, 10, 4, 5), Lists.newArrayList(1, 2, 3, 4, 5)), + Tuple2$.MODULE$.apply( + Lists.newArrayList(4, 1, 5, 6, 2, 7, 3, 8, 9, 10), Lists.newArrayList(1, 2, 3)), + Tuple2$.MODULE$.apply( + Lists.newArrayList(1, 2, 3, 4, 5), Lists.newArrayList())), 2); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void rankingMetrics() { + @SuppressWarnings("unchecked") + RankingMetrics metrics = RankingMetrics.of(predictionAndLabels); + Assert.assertEquals(0.355026, metrics.meanAveragePrecision(), 1e-5); + Assert.assertEquals(0.75 / 3.0, metrics.precisionAt(4), 1e-5); + } +} diff --git a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java new file mode 100644 index 0000000000000..62f7f26b7c98f --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.stat; + +import java.io.Serializable; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; + +public class JavaStatisticsSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaStatistics"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void testCorr() { + JavaRDD x = sc.parallelize(Lists.newArrayList(1.0, 2.0, 3.0, 4.0)); + JavaRDD y = sc.parallelize(Lists.newArrayList(1.1, 2.2, 3.1, 4.3)); + + Double corr1 = Statistics.corr(x, y); + Double corr2 = Statistics.corr(x, y, "pearson"); + // Check default method + assertEquals(corr1, corr2); + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala index 2b04a3034782e..63d2fa31c7499 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala @@ -17,15 +17,18 @@ package org.apache.spark.ml +import scala.collection.JavaConverters._ + import org.mockito.Matchers.{any, eq => meq} import org.mockito.Mockito.when -import org.scalatest.FunSuite import org.scalatest.mock.MockitoSugar.mock +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.feature.HashingTF import org.apache.spark.ml.param.ParamMap import org.apache.spark.sql.DataFrame -class PipelineSuite extends FunSuite { +class PipelineSuite extends SparkFunSuite { abstract class MyModel extends Model[MyModel] @@ -81,4 +84,28 @@ class PipelineSuite extends FunSuite { pipeline.fit(dataset) } } + + test("PipelineModel.copy") { + val hashingTF = new HashingTF() + .setNumFeatures(100) + val model = new PipelineModel("pipeline", Array[Transformer](hashingTF)) + val copied = model.copy(ParamMap(hashingTF.numFeatures -> 10)) + require(copied.stages(0).asInstanceOf[HashingTF].getNumFeatures === 10, + "copy should handle extra stage params") + } + + test("pipeline model constructors") { + val transform0 = mock[Transformer] + val model1 = mock[MyModel] + + val stages = Array(transform0, model1) + val pipelineModel0 = new PipelineModel("pipeline0", stages) + assert(pipelineModel0.uid === "pipeline0") + assert(pipelineModel0.stages === stages) + + val stagesAsList = stages.toList.asJava + val pipelineModel1 = new PipelineModel("pipeline1", stagesAsList) + assert(pipelineModel1.uid === "pipeline1") + assert(pipelineModel1.stages === stages) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala index 17ddd335deb6d..512cffb1acb66 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala @@ -17,9 +17,9 @@ package org.apache.spark.ml.attribute -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite -class AttributeGroupSuite extends FunSuite { +class AttributeGroupSuite extends SparkFunSuite { test("attribute group") { val attrs = Array( diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala index 3e1a7196e37cb..72b575d022547 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala @@ -17,11 +17,10 @@ package org.apache.spark.ml.attribute -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.types._ -import org.apache.spark.sql.types.{DoubleType, MetadataBuilder, Metadata} - -class AttributeSuite extends FunSuite { +class AttributeSuite extends SparkFunSuite { test("default numeric attribute") { val attr: NumericAttribute = NumericAttribute.defaultAttr @@ -209,4 +208,12 @@ class AttributeSuite extends FunSuite { intercept[IllegalArgumentException](attr.withName("")) intercept[IllegalArgumentException](attr.withIndex(-1)) } + + test("attribute from struct field") { + val metadata = NumericAttribute.defaultAttr.withName("label").toMetadata() + val fldWithoutMeta = new StructField("x", DoubleType, false, Metadata.empty) + assert(Attribute.fromStructField(fldWithoutMeta) == UnresolvedAttribute) + val fldWithMeta = new StructField("x", DoubleType, false, metadata) + assert(Attribute.fromStructField(fldWithMeta).isNumeric) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala index 03af4ecd7a7e0..73b4805c4c597 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -17,19 +17,18 @@ package org.apache.spark.ml.classification -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.tree.LeafNode import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint -import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, - DecisionTreeSuite => OldDecisionTreeSuite} +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, DecisionTreeSuite => OldDecisionTreeSuite} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - -class DecisionTreeClassifierSuite extends FunSuite with MLlibTestSparkContext { +class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkContext { import DecisionTreeClassifierSuite.compareAPIs @@ -56,6 +55,12 @@ class DecisionTreeClassifierSuite extends FunSuite with MLlibTestSparkContext { OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()) } + test("params") { + ParamsSuite.checkParams(new DecisionTreeClassifier) + val model = new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0)) + ParamsSuite.checkParams(model) + } + ///////////////////////////////////////////////////////////////////////////// // Tests calling train() ///////////////////////////////////////////////////////////////////////////// @@ -251,7 +256,7 @@ class DecisionTreeClassifierSuite extends FunSuite with MLlibTestSparkContext { */ } -private[ml] object DecisionTreeClassifierSuite extends FunSuite { +private[ml] object DecisionTreeClassifierSuite extends SparkFunSuite { /** * Train 2 decision trees on the given dataset, one using the old API and one using the new API. @@ -266,9 +271,9 @@ private[ml] object DecisionTreeClassifierSuite extends FunSuite { val oldTree = OldDecisionTree.train(data, oldStrategy) val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newTree = dt.fit(newData) - // Use parent, fittingParamMap from newTree since these are not checked anyways. + // Use parent from newTree since this is not checked anyways. val oldTreeAsNew = DecisionTreeClassificationModel.fromOld( - oldTree, newTree.parent, categoricalFeatures) + oldTree, newTree.parent.asInstanceOf[DecisionTreeClassifier], categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala index 16c758b82c7cd..82c345491bb3c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala @@ -17,9 +17,11 @@ package org.apache.spark.ml.classification -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.regression.DecisionTreeRegressionModel +import org.apache.spark.ml.tree.LeafNode import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} @@ -31,7 +33,7 @@ import org.apache.spark.sql.DataFrame /** * Test suite for [[GBTClassifier]]. */ -class GBTClassifierSuite extends FunSuite with MLlibTestSparkContext { +class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext { import GBTClassifierSuite.compareAPIs @@ -52,6 +54,14 @@ class GBTClassifierSuite extends FunSuite with MLlibTestSparkContext { sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 80), 2) } + test("params") { + ParamsSuite.checkParams(new GBTClassifier) + val model = new GBTClassificationModel("gbtc", + Array(new DecisionTreeRegressionModel("dtr", new LeafNode(0.0, 0.0))), + Array(1.0)) + ParamsSuite.checkParams(model) + } + test("Binary classification with continuous features: Log Loss") { val categoricalFeatures = Map.empty[Int, Int] testCombinations.foreach { @@ -128,9 +138,9 @@ private object GBTClassifierSuite { val oldModel = oldGBT.run(data) val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2) val newModel = gbt.fit(newData) - // Use parent, fittingParamMap from newTree since these are not checked anyways. + // Use parent from newTree since this is not checked anyways. val oldModelAsNew = GBTClassificationModel.fromOld( - oldModel, newModel.parent, categoricalFeatures) + oldModel, newModel.parent.asInstanceOf[GBTClassifier], categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index 6dd1fdf05514e..5a6265ea992c6 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -17,46 +17,77 @@ package org.apache.spark.ml.classification -import org.scalatest.FunSuite - -import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput -import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.mllib.classification.LogisticRegressionSuite._ +import org.apache.spark.mllib.linalg.{Vectors, Vector} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -import org.apache.spark.sql.{DataFrame, Row, SQLContext} - +import org.apache.spark.sql.{DataFrame, Row} -class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { +class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { - @transient var sqlContext: SQLContext = _ @transient var dataset: DataFrame = _ + @transient var binaryDataset: DataFrame = _ private val eps: Double = 1e-5 override def beforeAll(): Unit = { super.beforeAll() - sqlContext = new SQLContext(sc) - dataset = sqlContext.createDataFrame( - sc.parallelize(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42), 2)) + + dataset = sqlContext.createDataFrame(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42)) + + /** + * Here is the instruction describing how to export the test data into CSV format + * so we can validate the training accuracy compared with R's glmnet package. + * + * import org.apache.spark.mllib.classification.LogisticRegressionSuite + * val nPoints = 10000 + * val weights = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191) + * val xMean = Array(5.843, 3.057, 3.758, 1.199) + * val xVariance = Array(0.6856, 0.1899, 3.116, 0.581) + * val data = sc.parallelize(LogisticRegressionSuite.generateMultinomialLogisticInput( + * weights, xMean, xVariance, true, nPoints, 42), 1) + * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1) + ", " + * + x.features(2) + ", " + x.features(3)).saveAsTextFile("path") + */ + binaryDataset = { + val nPoints = 10000 + val weights = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191) + val xMean = Array(5.843, 3.057, 3.758, 1.199) + val xVariance = Array(0.6856, 0.1899, 3.116, 0.581) + + val testData = generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42) + + sqlContext.createDataFrame( + generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)) + } + } + + test("params") { + ParamsSuite.checkParams(new LogisticRegression) + val model = new LogisticRegressionModel("logReg", Vectors.dense(0.0), 0.0) + ParamsSuite.checkParams(model) } test("logistic regression: default params") { val lr = new LogisticRegression - assert(lr.getLabelCol == "label") - assert(lr.getFeaturesCol == "features") - assert(lr.getPredictionCol == "prediction") - assert(lr.getRawPredictionCol == "rawPrediction") - assert(lr.getProbabilityCol == "probability") - assert(lr.getFitIntercept == true) + assert(lr.getLabelCol === "label") + assert(lr.getFeaturesCol === "features") + assert(lr.getPredictionCol === "prediction") + assert(lr.getRawPredictionCol === "rawPrediction") + assert(lr.getProbabilityCol === "probability") + assert(lr.getFitIntercept) val model = lr.fit(dataset) model.transform(dataset) .select("label", "probability", "prediction", "rawPrediction") .collect() assert(model.getThreshold === 0.5) - assert(model.getFeaturesCol == "features") - assert(model.getPredictionCol == "prediction") - assert(model.getRawPredictionCol == "rawPrediction") - assert(model.getProbabilityCol == "probability") + assert(model.getFeaturesCol === "features") + assert(model.getPredictionCol === "prediction") + assert(model.getRawPredictionCol === "rawPrediction") + assert(model.getProbabilityCol === "probability") assert(model.intercept !== 0.0) + assert(model.hasParent) } test("logistic regression doesn't fit intercept when fitIntercept is off") { @@ -74,7 +105,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { .setThreshold(0.6) .setProbabilityCol("myProbability") val model = lr.fit(dataset) - val parent = model.parent + val parent = model.parent.asInstanceOf[LogisticRegression] assert(parent.getMaxIter === 10) assert(parent.getRegParam === 1.0) assert(parent.getThreshold === 0.6) @@ -100,12 +131,12 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { // Call fit() with new params, and check as many params as we can. val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4, lr.probabilityCol -> "theProb") - val parent2 = model2.parent + val parent2 = model2.parent.asInstanceOf[LogisticRegression] assert(parent2.getMaxIter === 5) assert(parent2.getRegParam === 0.1) assert(parent2.getThreshold === 0.4) assert(model2.getThreshold === 0.4) - assert(model2.getProbabilityCol == "theProb") + assert(model2.getProbabilityCol === "theProb") } test("logistic regression: Predictor, Classifier methods") { @@ -135,4 +166,364 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { assert(pred == predFromProb) } } + + test("MultiClassSummarizer") { + val summarizer1 = (new MultiClassSummarizer) + .add(0.0).add(3.0).add(4.0).add(3.0).add(6.0) + assert(summarizer1.histogram.zip(Array[Long](1, 0, 0, 2, 1, 0, 1)).forall(x => x._1 === x._2)) + assert(summarizer1.countInvalid === 0) + assert(summarizer1.numClasses === 7) + + val summarizer2 = (new MultiClassSummarizer) + .add(1.0).add(5.0).add(3.0).add(0.0).add(4.0).add(1.0) + assert(summarizer2.histogram.zip(Array[Long](1, 2, 0, 1, 1, 1)).forall(x => x._1 === x._2)) + assert(summarizer2.countInvalid === 0) + assert(summarizer2.numClasses === 6) + + val summarizer3 = (new MultiClassSummarizer) + .add(0.0).add(1.3).add(5.2).add(2.5).add(2.0).add(4.0).add(4.0).add(4.0).add(1.0) + assert(summarizer3.histogram.zip(Array[Long](1, 1, 1, 0, 3)).forall(x => x._1 === x._2)) + assert(summarizer3.countInvalid === 3) + assert(summarizer3.numClasses === 5) + + val summarizer4 = (new MultiClassSummarizer) + .add(3.1).add(4.3).add(2.0).add(1.0).add(3.0) + assert(summarizer4.histogram.zip(Array[Long](0, 1, 1, 1)).forall(x => x._1 === x._2)) + assert(summarizer4.countInvalid === 2) + assert(summarizer4.numClasses === 4) + + // small map merges large one + val summarizerA = summarizer1.merge(summarizer2) + assert(summarizerA.hashCode() === summarizer2.hashCode()) + assert(summarizerA.histogram.zip(Array[Long](2, 2, 0, 3, 2, 1, 1)).forall(x => x._1 === x._2)) + assert(summarizerA.countInvalid === 0) + assert(summarizerA.numClasses === 7) + + // large map merges small one + val summarizerB = summarizer3.merge(summarizer4) + assert(summarizerB.hashCode() === summarizer3.hashCode()) + assert(summarizerB.histogram.zip(Array[Long](1, 2, 2, 1, 3)).forall(x => x._1 === x._2)) + assert(summarizerB.countInvalid === 5) + assert(summarizerB.numClasses === 5) + } + + test("binary logistic regression with intercept without regularization") { + val trainer = (new LogisticRegression).setFitIntercept(true) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) 2.8366423 + * data.V2 -0.5895848 + * data.V3 0.8931147 + * data.V4 -0.3925051 + * data.V5 -0.7996864 + */ + val interceptR = 2.8366423 + val weightsR = Array(-0.5895848, 0.8931147, -0.3925051, -0.7996864) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + assert(model.weights(2) ~== weightsR(2) relTol 1E-3) + assert(model.weights(3) ~== weightsR(3) relTol 1E-3) + } + + test("binary logistic regression without intercept without regularization") { + val trainer = (new LogisticRegression).setFitIntercept(false) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = + * coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * data.V2 -0.3534996 + * data.V3 1.2964482 + * data.V4 -0.3571741 + * data.V5 -0.7407946 + */ + val interceptR = 0.0 + val weightsR = Array(-0.3534996, 1.2964482, -0.3571741, -0.7407946) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-2) + assert(model.weights(1) ~== weightsR(1) relTol 1E-2) + assert(model.weights(2) ~== weightsR(2) relTol 1E-3) + assert(model.weights(3) ~== weightsR(3) relTol 1E-3) + } + + test("binary logistic regression with intercept with L1 regularization") { + val trainer = (new LogisticRegression).setFitIntercept(true) + .setElasticNetParam(1.0).setRegParam(0.12) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) -0.05627428 + * data.V2 . + * data.V3 . + * data.V4 -0.04325749 + * data.V5 -0.02481551 + */ + val interceptR = -0.05627428 + val weightsR = Array(0.0, 0.0, -0.04325749, -0.02481551) + + assert(model.intercept ~== interceptR relTol 1E-2) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + assert(model.weights(2) ~== weightsR(2) relTol 1E-2) + assert(model.weights(3) ~== weightsR(3) relTol 2E-2) + } + + test("binary logistic regression without intercept with L1 regularization") { + val trainer = (new LogisticRegression).setFitIntercept(false) + .setElasticNetParam(1.0).setRegParam(0.12) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12, + * intercept=FALSE)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * data.V2 . + * data.V3 . + * data.V4 -0.05189203 + * data.V5 -0.03891782 + */ + val interceptR = 0.0 + val weightsR = Array(0.0, 0.0, -0.05189203, -0.03891782) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + assert(model.weights(2) ~== weightsR(2) relTol 1E-2) + assert(model.weights(3) ~== weightsR(3) relTol 1E-2) + } + + test("binary logistic regression with intercept with L2 regularization") { + val trainer = (new LogisticRegression).setFitIntercept(true) + .setElasticNetParam(0.0).setRegParam(1.37) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) 0.15021751 + * data.V2 -0.07251837 + * data.V3 0.10724191 + * data.V4 -0.04865309 + * data.V5 -0.10062872 + */ + val interceptR = 0.15021751 + val weightsR = Array(-0.07251837, 0.10724191, -0.04865309, -0.10062872) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + assert(model.weights(2) ~== weightsR(2) relTol 1E-3) + assert(model.weights(3) ~== weightsR(3) relTol 1E-3) + } + + test("binary logistic regression without intercept with L2 regularization") { + val trainer = (new LogisticRegression).setFitIntercept(false) + .setElasticNetParam(0.0).setRegParam(1.37) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37, + * intercept=FALSE)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * data.V2 -0.06099165 + * data.V3 0.12857058 + * data.V4 -0.04708770 + * data.V5 -0.09799775 + */ + val interceptR = 0.0 + val weightsR = Array(-0.06099165, 0.12857058, -0.04708770, -0.09799775) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-2) + assert(model.weights(1) ~== weightsR(1) relTol 1E-2) + assert(model.weights(2) ~== weightsR(2) relTol 1E-3) + assert(model.weights(3) ~== weightsR(3) relTol 1E-3) + } + + test("binary logistic regression with intercept with ElasticNet regularization") { + val trainer = (new LogisticRegression).setFitIntercept(true) + .setElasticNetParam(0.38).setRegParam(0.21) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) 0.57734851 + * data.V2 -0.05310287 + * data.V3 . + * data.V4 -0.08849250 + * data.V5 -0.15458796 + */ + val interceptR = 0.57734851 + val weightsR = Array(-0.05310287, 0.0, -0.08849250, -0.15458796) + + assert(model.intercept ~== interceptR relTol 6E-3) + assert(model.weights(0) ~== weightsR(0) relTol 5E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + assert(model.weights(2) ~== weightsR(2) relTol 5E-3) + assert(model.weights(3) ~== weightsR(3) relTol 1E-3) + } + + test("binary logistic regression without intercept with ElasticNet regularization") { + val trainer = (new LogisticRegression).setFitIntercept(false) + .setElasticNetParam(0.38).setRegParam(0.21) + val model = trainer.fit(binaryDataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21, + * intercept=FALSE)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * data.V2 -0.001005743 + * data.V3 0.072577857 + * data.V4 -0.081203769 + * data.V5 -0.142534158 + */ + val interceptR = 0.0 + val weightsR = Array(-0.001005743, 0.072577857, -0.081203769, -0.142534158) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) absTol 1E-3) + assert(model.weights(1) ~== weightsR(1) absTol 1E-2) + assert(model.weights(2) ~== weightsR(2) relTol 1E-3) + assert(model.weights(3) ~== weightsR(3) relTol 1E-2) + } + + test("binary logistic regression with intercept with strong L1 regularization") { + val trainer = (new LogisticRegression).setFitIntercept(true) + .setElasticNetParam(1.0).setRegParam(6.0) + val model = trainer.fit(binaryDataset) + + val histogram = binaryDataset.map { case Row(label: Double, features: Vector) => label } + .treeAggregate(new MultiClassSummarizer)( + seqOp = (c, v) => (c, v) match { + case (classSummarizer: MultiClassSummarizer, label: Double) => classSummarizer.add(label) + }, + combOp = (c1, c2) => (c1, c2) match { + case (classSummarizer1: MultiClassSummarizer, classSummarizer2: MultiClassSummarizer) => + classSummarizer1.merge(classSummarizer2) + }).histogram + + /** + * For binary logistic regression with strong L1 regularization, all the weights will be zeros. + * As a result, + * {{{ + * P(0) = 1 / (1 + \exp(b)), and + * P(1) = \exp(b) / (1 + \exp(b)) + * }}}, hence + * {{{ + * b = \log{P(1) / P(0)} = \log{count_1 / count_0} + * }}} + */ + val interceptTheory = math.log(histogram(1).toDouble / histogram(0).toDouble) + val weightsTheory = Array(0.0, 0.0, 0.0, 0.0) + + assert(model.intercept ~== interceptTheory relTol 1E-5) + assert(model.weights(0) ~== weightsTheory(0) absTol 1E-6) + assert(model.weights(1) ~== weightsTheory(1) absTol 1E-6) + assert(model.weights(2) ~== weightsTheory(2) absTol 1E-6) + assert(model.weights(3) ~== weightsTheory(3) absTol 1E-6) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * > library("glmnet") + * > data <- read.csv("path", header=FALSE) + * > label = factor(data$V1) + * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + * > weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0)) + * > weights + * 5 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) -0.2480643 + * data.V2 0.0000000 + * data.V3 . + * data.V4 . + * data.V5 . + */ + val interceptR = -0.248065 + val weightsR = Array(0.0, 0.0, 0.0, 0.0) + + assert(model.intercept ~== interceptR relTol 1E-5) + assert(model.weights(0) ~== weightsR(0) absTol 1E-6) + assert(model.weights(1) ~== weightsR(1) absTol 1E-6) + assert(model.weights(2) ~== weightsR(2) absTol 1E-6) + assert(model.weights(3) ~== weightsR(3) absTol 1E-6) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala new file mode 100644 index 0000000000000..75cf5bd4ead4f --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala @@ -0,0 +1,149 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.attribute.NominalAttribute +import org.apache.spark.ml.param.{ParamMap, ParamsSuite} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS +import org.apache.spark.mllib.classification.LogisticRegressionSuite._ +import org.apache.spark.mllib.evaluation.MulticlassMetrics +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.types.Metadata + +class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext { + + @transient var dataset: DataFrame = _ + @transient var rdd: RDD[LabeledPoint] = _ + + override def beforeAll(): Unit = { + super.beforeAll() + + val nPoints = 1000 + + // The following weights and xMean/xVariance are computed from iris dataset with lambda=0.2. + // As a result, we are drawing samples from probability distribution of an actual model. + val weights = Array( + -0.57997, 0.912083, -0.371077, -0.819866, 2.688191, + -0.16624, -0.84355, -0.048509, -0.301789, 4.170682) + + val xMean = Array(5.843, 3.057, 3.758, 1.199) + val xVariance = Array(0.6856, 0.1899, 3.116, 0.581) + rdd = sc.parallelize(generateMultinomialLogisticInput( + weights, xMean, xVariance, true, nPoints, 42), 2) + dataset = sqlContext.createDataFrame(rdd) + } + + test("params") { + ParamsSuite.checkParams(new OneVsRest) + val lrModel = new LogisticRegressionModel("lr", Vectors.dense(0.0), 0.0) + val model = new OneVsRestModel("ovr", Metadata.empty, Array(lrModel)) + ParamsSuite.checkParams(model) + } + + test("one-vs-rest: default params") { + val numClasses = 3 + val ova = new OneVsRest() + .setClassifier(new LogisticRegression) + assert(ova.getLabelCol === "label") + assert(ova.getPredictionCol === "prediction") + val ovaModel = ova.fit(dataset) + assert(ovaModel.models.size === numClasses) + val transformedDataset = ovaModel.transform(dataset) + + // check for label metadata in prediction col + val predictionColSchema = transformedDataset.schema(ovaModel.getPredictionCol) + assert(MetadataUtils.getNumClasses(predictionColSchema) === Some(3)) + + val ovaResults = transformedDataset + .select("prediction", "label") + .map(row => (row.getDouble(0), row.getDouble(1))) + + val lr = new LogisticRegressionWithLBFGS().setIntercept(true).setNumClasses(numClasses) + lr.optimizer.setRegParam(0.1).setNumIterations(100) + + val model = lr.run(rdd) + val results = model.predict(rdd.map(_.features)).zip(rdd.map(_.label)) + // determine the #confusion matrix in each class. + // bound how much error we allow compared to multinomial logistic regression. + val expectedMetrics = new MulticlassMetrics(results) + val ovaMetrics = new MulticlassMetrics(ovaResults) + assert(expectedMetrics.confusionMatrix ~== ovaMetrics.confusionMatrix absTol 400) + } + + test("one-vs-rest: pass label metadata correctly during train") { + val numClasses = 3 + val ova = new OneVsRest() + ova.setClassifier(new MockLogisticRegression) + + val labelMetadata = NominalAttribute.defaultAttr.withName("label").withNumValues(numClasses) + val labelWithMetadata = dataset("label").as("label", labelMetadata.toMetadata()) + val features = dataset("features").as("features") + val datasetWithLabelMetadata = dataset.select(labelWithMetadata, features) + ova.fit(datasetWithLabelMetadata) + } + + test("SPARK-8049: OneVsRest shouldn't output temp columns") { + val logReg = new LogisticRegression() + .setMaxIter(1) + val ovr = new OneVsRest() + .setClassifier(logReg) + val output = ovr.fit(dataset).transform(dataset) + assert(output.schema.fieldNames.toSet === Set("label", "features", "prediction")) + } + + test("OneVsRest.copy and OneVsRestModel.copy") { + val lr = new LogisticRegression() + .setMaxIter(1) + + val ovr = new OneVsRest() + withClue("copy with classifier unset should work") { + ovr.copy(ParamMap(lr.maxIter -> 10)) + } + ovr.setClassifier(lr) + val ovr1 = ovr.copy(ParamMap(lr.maxIter -> 10)) + require(ovr.getClassifier.getOrDefault(lr.maxIter) === 1, "copy should have no side-effects") + require(ovr1.getClassifier.getOrDefault(lr.maxIter) === 10, + "copy should handle extra classifier params") + + val ovrModel = ovr1.fit(dataset).copy(ParamMap(lr.threshold -> 0.1)) + ovrModel.models.foreach { case m: LogisticRegressionModel => + require(m.getThreshold === 0.1, "copy should handle extra model params") + } + } +} + +private class MockLogisticRegression(uid: String) extends LogisticRegression(uid) { + + def this() = this("mockLogReg") + + setMaxIter(1) + + override protected def train(dataset: DataFrame): LogisticRegressionModel = { + val labelSchema = dataset.schema($(labelCol)) + // check for label attribute propagation. + assert(MetadataUtils.getNumClasses(labelSchema).forall(_ == 2)) + super.train(dataset) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala index c41def9330504..1b6b69c7dc71e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala @@ -17,9 +17,10 @@ package org.apache.spark.ml.classification -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.tree.LeafNode import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest} @@ -28,11 +29,10 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * Test suite for [[RandomForestClassifier]]. */ -class RandomForestClassifierSuite extends FunSuite with MLlibTestSparkContext { +class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkContext { import RandomForestClassifierSuite.compareAPIs @@ -63,6 +63,13 @@ class RandomForestClassifierSuite extends FunSuite with MLlibTestSparkContext { compareAPIs(orderedLabeledPoints50_1000, newRF, categoricalFeatures, numClasses) } + test("params") { + ParamsSuite.checkParams(new RandomForestClassifier) + val model = new RandomForestClassificationModel("rfc", + Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0)))) + ParamsSuite.checkParams(model) + } + test("Binary classification with continuous features:" + " comparing DecisionTree vs. RandomForest(numTrees = 1)") { val rf = new RandomForestClassifier() @@ -158,9 +165,11 @@ private object RandomForestClassifierSuite { data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt) val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newModel = rf.fit(newData) - // Use parent, fittingParamMap from newTree since these are not checked anyways. + // Use parent from newTree since this is not checked anyways. val oldModelAsNew = RandomForestClassificationModel.fromOld( - oldModel, newModel.parent, categoricalFeatures) + oldModel, newModel.parent.asInstanceOf[RandomForestClassifier], categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) + assert(newModel.hasParent) + assert(!newModel.trees.head.asInstanceOf[DecisionTreeClassificationModel].hasParent) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluatorSuite.scala new file mode 100644 index 0000000000000..def869fe66777 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluatorSuite.scala @@ -0,0 +1,28 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.evaluation + +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite + +class BinaryClassificationEvaluatorSuite extends SparkFunSuite { + + test("params") { + ParamsSuite.checkParams(new BinaryClassificationEvaluator) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala new file mode 100644 index 0000000000000..5b203784559e2 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala @@ -0,0 +1,76 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.evaluation + +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.regression.LinearRegression +import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext} +import org.apache.spark.mllib.util.TestingUtils._ + +class RegressionEvaluatorSuite extends SparkFunSuite with MLlibTestSparkContext { + + test("params") { + ParamsSuite.checkParams(new RegressionEvaluator) + } + + test("Regression Evaluator: default params") { + /** + * Here is the instruction describing how to export the test data into CSV format + * so we can validate the metrics compared with R's mmetric package. + * + * import org.apache.spark.mllib.util.LinearDataGenerator + * val data = sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, + * Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1)) + * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)) + * .saveAsTextFile("path") + */ + val dataset = sqlContext.createDataFrame( + sc.parallelize(LinearDataGenerator.generateLinearInput( + 6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2)) + + /** + * Using the following R code to load the data, train the model and evaluate metrics. + * + * > library("glmnet") + * > library("rminer") + * > data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE) + * > features <- as.matrix(data.frame(as.numeric(data$V2), as.numeric(data$V3))) + * > label <- as.numeric(data$V1) + * > model <- glmnet(features, label, family="gaussian", alpha = 0, lambda = 0) + * > rmse <- mmetric(label, predict(model, features), metric='RMSE') + * > mae <- mmetric(label, predict(model, features), metric='MAE') + * > r2 <- mmetric(label, predict(model, features), metric='R2') + */ + val trainer = new LinearRegression + val model = trainer.fit(dataset) + val predictions = model.transform(dataset) + + // default = rmse + val evaluator = new RegressionEvaluator() + assert(evaluator.evaluate(predictions) ~== -0.1019382 absTol 0.001) + + // r2 score + evaluator.setMetricName("r2") + assert(evaluator.evaluate(predictions) ~== 0.9998196 absTol 0.001) + + // mae + evaluator.setMetricName("mae") + assert(evaluator.evaluate(predictions) ~== -0.08036075 absTol 0.001) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala index caf1b759593f3..2086043983661 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala @@ -17,24 +17,24 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.mllib.util.TestingUtils._ -import org.apache.spark.sql.{DataFrame, Row, SQLContext} - +import org.apache.spark.sql.{DataFrame, Row} -class BinarizerSuite extends FunSuite with MLlibTestSparkContext { +class BinarizerSuite extends SparkFunSuite with MLlibTestSparkContext { @transient var data: Array[Double] = _ - @transient var sqlContext: SQLContext = _ override def beforeAll(): Unit = { super.beforeAll() - sqlContext = new SQLContext(sc) data = Array(0.1, -0.5, 0.2, -0.3, 0.8, 0.7, -0.1, -0.4) } + test("params") { + ParamsSuite.checkParams(new Binarizer) + } + test("Binarize continuous features with default parameter") { val defaultBinarized: Array[Double] = data.map(x => if (x > 0.0) 1.0 else 0.0) val dataFrame: DataFrame = sqlContext.createDataFrame( @@ -52,7 +52,7 @@ class BinarizerSuite extends FunSuite with MLlibTestSparkContext { test("Binarize continuous features with setter") { val threshold: Double = 0.2 - val thresholdBinarized: Array[Double] = data.map(x => if (x > threshold) 1.0 else 0.0) + val thresholdBinarized: Array[Double] = data.map(x => if (x > threshold) 1.0 else 0.0) val dataFrame: DataFrame = sqlContext.createDataFrame( data.zip(thresholdBinarized)).toDF("feature", "expected") diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala new file mode 100644 index 0000000000000..ec85e0d151e07 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala @@ -0,0 +1,147 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.util.Random + +import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{DataFrame, Row} + +class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext { + + test("params") { + ParamsSuite.checkParams(new Bucketizer) + } + + test("Bucket continuous features, without -inf,inf") { + // Check a set of valid feature values. + val splits = Array(-0.5, 0.0, 0.5) + val validData = Array(-0.5, -0.3, 0.0, 0.2) + val expectedBuckets = Array(0.0, 0.0, 1.0, 1.0) + val dataFrame: DataFrame = + sqlContext.createDataFrame(validData.zip(expectedBuckets)).toDF("feature", "expected") + + val bucketizer: Bucketizer = new Bucketizer() + .setInputCol("feature") + .setOutputCol("result") + .setSplits(splits) + + bucketizer.transform(dataFrame).select("result", "expected").collect().foreach { + case Row(x: Double, y: Double) => + assert(x === y, + s"The feature value is not correct after bucketing. Expected $y but found $x") + } + + // Check for exceptions when using a set of invalid feature values. + val invalidData1: Array[Double] = Array(-0.9) ++ validData + val invalidData2 = Array(0.51) ++ validData + val badDF1 = sqlContext.createDataFrame(invalidData1.zipWithIndex).toDF("feature", "idx") + withClue("Invalid feature value -0.9 was not caught as an invalid feature!") { + intercept[SparkException] { + bucketizer.transform(badDF1).collect() + } + } + val badDF2 = sqlContext.createDataFrame(invalidData2.zipWithIndex).toDF("feature", "idx") + withClue("Invalid feature value 0.51 was not caught as an invalid feature!") { + intercept[SparkException] { + bucketizer.transform(badDF2).collect() + } + } + } + + test("Bucket continuous features, with -inf,inf") { + val splits = Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity) + val validData = Array(-0.9, -0.5, -0.3, 0.0, 0.2, 0.5, 0.9) + val expectedBuckets = Array(0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0) + val dataFrame: DataFrame = + sqlContext.createDataFrame(validData.zip(expectedBuckets)).toDF("feature", "expected") + + val bucketizer: Bucketizer = new Bucketizer() + .setInputCol("feature") + .setOutputCol("result") + .setSplits(splits) + + bucketizer.transform(dataFrame).select("result", "expected").collect().foreach { + case Row(x: Double, y: Double) => + assert(x === y, + s"The feature value is not correct after bucketing. Expected $y but found $x") + } + } + + test("Binary search correctness on hand-picked examples") { + import BucketizerSuite.checkBinarySearch + // length 3, with -inf + checkBinarySearch(Array(Double.NegativeInfinity, 0.0, 1.0)) + // length 4 + checkBinarySearch(Array(-1.0, -0.5, 0.0, 1.0)) + // length 5 + checkBinarySearch(Array(-1.0, -0.5, 0.0, 1.0, 1.5)) + // length 3, with inf + checkBinarySearch(Array(0.0, 1.0, Double.PositiveInfinity)) + // length 3, with -inf and inf + checkBinarySearch(Array(Double.NegativeInfinity, 1.0, Double.PositiveInfinity)) + // length 4, with -inf and inf + checkBinarySearch(Array(Double.NegativeInfinity, 0.0, 1.0, Double.PositiveInfinity)) + } + + test("Binary search correctness in contrast with linear search, on random data") { + val data = Array.fill(100)(Random.nextDouble()) + val splits: Array[Double] = Double.NegativeInfinity +: + Array.fill(10)(Random.nextDouble()).sorted :+ Double.PositiveInfinity + val bsResult = Vectors.dense(data.map(x => Bucketizer.binarySearchForBuckets(splits, x))) + val lsResult = Vectors.dense(data.map(x => BucketizerSuite.linearSearchForBuckets(splits, x))) + assert(bsResult ~== lsResult absTol 1e-5) + } +} + +private object BucketizerSuite extends SparkFunSuite { + /** Brute force search for buckets. Bucket i is defined by the range [split(i), split(i+1)). */ + def linearSearchForBuckets(splits: Array[Double], feature: Double): Double = { + require(feature >= splits.head) + var i = 0 + val n = splits.length - 1 + while (i < n) { + if (feature < splits(i + 1)) return i + i += 1 + } + throw new RuntimeException( + s"linearSearchForBuckets failed to find bucket for feature value $feature") + } + + /** Check all values in splits, plus values between all splits. */ + def checkBinarySearch(splits: Array[Double]): Unit = { + def testFeature(feature: Double, expectedBucket: Double): Unit = { + assert(Bucketizer.binarySearchForBuckets(splits, feature) === expectedBucket, + s"Expected feature value $feature to be in bucket $expectedBucket with splits:" + + s" ${splits.mkString(", ")}") + } + var i = 0 + val n = splits.length - 1 + while (i < n) { + // Split i should fall in bucket i. + testFeature(splits(i), i) + // Value between splits i,i+1 should be in i, which is also true if the (i+1)-th split is inf. + testFeature((splits(i) + splits(i + 1)) / 2, i) + i += 1 + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala new file mode 100644 index 0000000000000..4157b84b29d01 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.attribute.AttributeGroup +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils + +class HashingTFSuite extends SparkFunSuite with MLlibTestSparkContext { + + test("params") { + ParamsSuite.checkParams(new HashingTF) + } + + test("hashingTF") { + val df = sqlContext.createDataFrame(Seq( + (0, "a a b b c d".split(" ").toSeq) + )).toDF("id", "words") + val n = 100 + val hashingTF = new HashingTF() + .setInputCol("words") + .setOutputCol("features") + .setNumFeatures(n) + val output = hashingTF.transform(df) + val attrGroup = AttributeGroup.fromStructField(output.schema("features")) + require(attrGroup.numAttributes === Some(n)) + val features = output.select("features").first().getAs[Vector](0) + // Assume perfect hash on "a", "b", "c", and "d". + def idx(any: Any): Int = Utils.nonNegativeMod(any.##, n) + val expected = Vectors.sparse(n, + Seq((idx("a"), 2.0), (idx("b"), 2.0), (idx("c"), 1.0), (idx("d"), 1.0))) + assert(features ~== expected absTol 1e-14) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala index eaee3443c1f23..08f80af03429b 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala @@ -17,21 +17,15 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.mllib.feature.{IDFModel => OldIDFModel} import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -import org.apache.spark.sql.{Row, SQLContext} - -class IDFSuite extends FunSuite with MLlibTestSparkContext { - - @transient var sqlContext: SQLContext = _ +import org.apache.spark.sql.Row - override def beforeAll(): Unit = { - super.beforeAll() - sqlContext = new SQLContext(sc) - } +class IDFSuite extends SparkFunSuite with MLlibTestSparkContext { def scaleDataWithIDF(dataSet: Array[Vector], model: Vector): Array[Vector] = { dataSet.map { @@ -46,6 +40,12 @@ class IDFSuite extends FunSuite with MLlibTestSparkContext { } } + test("params") { + ParamsSuite.checkParams(new IDF) + val model = new IDFModel("idf", new OldIDFModel(Vectors.dense(1.0))) + ParamsSuite.checkParams(model) + } + test("compute IDF with default parameter") { val numOfFeatures = 4 val data = Array( diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala index 9d09f24709e23..9f03470b7f328 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.sql.{DataFrame, Row, SQLContext} -class NormalizerSuite extends FunSuite with MLlibTestSparkContext { +class NormalizerSuite extends SparkFunSuite with MLlibTestSparkContext { @transient var data: Array[Vector] = _ @transient var dataFrame: DataFrame = _ diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala index 92ec407b98d69..65846a846b7b4 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala @@ -17,20 +17,15 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.attribute.{AttributeGroup, BinaryAttribute, NominalAttribute} +import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{DataFrame, SQLContext} - - -class OneHotEncoderSuite extends FunSuite with MLlibTestSparkContext { - private var sqlContext: SQLContext = _ +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions.col - override def beforeAll(): Unit = { - super.beforeAll() - sqlContext = new SQLContext(sc) - } +class OneHotEncoderSuite extends SparkFunSuite with MLlibTestSparkContext { def stringIndexed(): DataFrame = { val data = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")), 2) @@ -42,15 +37,20 @@ class OneHotEncoderSuite extends FunSuite with MLlibTestSparkContext { indexer.transform(df) } - test("OneHotEncoder includeFirst = true") { + test("params") { + ParamsSuite.checkParams(new OneHotEncoder) + } + + test("OneHotEncoder dropLast = false") { val transformed = stringIndexed() val encoder = new OneHotEncoder() .setInputCol("labelIndex") .setOutputCol("labelVec") + .setDropLast(false) val encoded = encoder.transform(transformed) val output = encoded.select("id", "labelVec").map { r => - val vec = r.get(1).asInstanceOf[Vector] + val vec = r.getAs[Vector](1) (r.getInt(0), vec(0), vec(1), vec(2)) }.collect().toSet // a -> 0, b -> 2, c -> 1 @@ -59,22 +59,46 @@ class OneHotEncoderSuite extends FunSuite with MLlibTestSparkContext { assert(output === expected) } - test("OneHotEncoder includeFirst = false") { + test("OneHotEncoder dropLast = true") { val transformed = stringIndexed() val encoder = new OneHotEncoder() - .setIncludeFirst(false) .setInputCol("labelIndex") .setOutputCol("labelVec") val encoded = encoder.transform(transformed) val output = encoded.select("id", "labelVec").map { r => - val vec = r.get(1).asInstanceOf[Vector] + val vec = r.getAs[Vector](1) (r.getInt(0), vec(0), vec(1)) }.collect().toSet // a -> 0, b -> 2, c -> 1 - val expected = Set((0, 0.0, 0.0), (1, 0.0, 1.0), (2, 1.0, 0.0), - (3, 0.0, 0.0), (4, 0.0, 0.0), (5, 1.0, 0.0)) + val expected = Set((0, 1.0, 0.0), (1, 0.0, 0.0), (2, 0.0, 1.0), + (3, 1.0, 0.0), (4, 1.0, 0.0), (5, 0.0, 1.0)) assert(output === expected) } + test("input column with ML attribute") { + val attr = NominalAttribute.defaultAttr.withValues("small", "medium", "large") + val df = sqlContext.createDataFrame(Seq(0.0, 1.0, 2.0, 1.0).map(Tuple1.apply)).toDF("size") + .select(col("size").as("size", attr.toMetadata())) + val encoder = new OneHotEncoder() + .setInputCol("size") + .setOutputCol("encoded") + val output = encoder.transform(df) + val group = AttributeGroup.fromStructField(output.schema("encoded")) + assert(group.size === 2) + assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("size_is_small").withIndex(0)) + assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("size_is_medium").withIndex(1)) + } + + test("input column without ML attribute") { + val df = sqlContext.createDataFrame(Seq(0.0, 1.0, 2.0, 1.0).map(Tuple1.apply)).toDF("index") + val encoder = new OneHotEncoder() + .setInputCol("index") + .setOutputCol("encoded") + val output = encoder.transform(df) + val group = AttributeGroup.fromStructField(output.schema("encoded")) + assert(group.size === 2) + assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("index_is_0").withIndex(0)) + assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("index_is_1").withIndex(1)) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala index c1d64fba0aa8f..29eebd8960ebc 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala @@ -17,21 +17,19 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite +import org.apache.spark.ml.param.ParamsSuite +import org.scalatest.exceptions.TestFailedException +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -import org.apache.spark.sql.{Row, SQLContext} -import org.scalatest.exceptions.TestFailedException - -class PolynomialExpansionSuite extends FunSuite with MLlibTestSparkContext { +import org.apache.spark.sql.Row - @transient var sqlContext: SQLContext = _ +class PolynomialExpansionSuite extends SparkFunSuite with MLlibTestSparkContext { - override def beforeAll(): Unit = { - super.beforeAll() - sqlContext = new SQLContext(sc) + test("params") { + ParamsSuite.checkParams(new PolynomialExpansion) } test("Polynomial expansion with default parameter") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala index b6939e5870410..99f82bea42688 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala @@ -17,18 +17,17 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.attribute.{Attribute, NominalAttribute} +import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.SQLContext -class StringIndexerSuite extends FunSuite with MLlibTestSparkContext { - private var sqlContext: SQLContext = _ +class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext { - override def beforeAll(): Unit = { - super.beforeAll() - sqlContext = new SQLContext(sc) + test("params") { + ParamsSuite.checkParams(new StringIndexer) + val model = new StringIndexerModel("indexer", Array("a", "b")) + ParamsSuite.checkParams(model) } test("StringIndexer") { @@ -68,4 +67,12 @@ class StringIndexerSuite extends FunSuite with MLlibTestSparkContext { val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)) assert(output === expected) } + + test("StringIndexerModel should keep silent if the input column does not exist.") { + val indexerModel = new StringIndexerModel("indexer", Array("a", "b", "c")) + .setInputCol("label") + .setOutputCol("labelIndex") + val df = sqlContext.range(0L, 10L) + assert(indexerModel.transform(df).eq(df)) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala index d186ead8f542f..e5fd21c3f6fca 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala @@ -19,64 +19,66 @@ package org.apache.spark.ml.feature import scala.beans.BeanInfo -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.sql.{DataFrame, Row} @BeanInfo case class TokenizerTestData(rawText: String, wantedTokens: Array[String]) -class RegexTokenizerSuite extends FunSuite with MLlibTestSparkContext { +class TokenizerSuite extends SparkFunSuite { + + test("params") { + ParamsSuite.checkParams(new Tokenizer) + } +} + +class RegexTokenizerSuite extends SparkFunSuite with MLlibTestSparkContext { import org.apache.spark.ml.feature.RegexTokenizerSuite._ - - @transient var sqlContext: SQLContext = _ - override def beforeAll(): Unit = { - super.beforeAll() - sqlContext = new SQLContext(sc) + test("params") { + ParamsSuite.checkParams(new RegexTokenizer) } test("RegexTokenizer") { - val tokenizer = new RegexTokenizer() + val tokenizer0 = new RegexTokenizer() + .setGaps(false) + .setPattern("\\w+|\\p{Punct}") .setInputCol("rawText") .setOutputCol("tokens") - val dataset0 = sqlContext.createDataFrame(Seq( TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization", ".")), TokenizerTestData("Te,st. punct", Array("Te", ",", "st", ".", "punct")) )) - testRegexTokenizer(tokenizer, dataset0) + testRegexTokenizer(tokenizer0, dataset0) val dataset1 = sqlContext.createDataFrame(Seq( TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization")), TokenizerTestData("Te,st. punct", Array("punct")) )) + tokenizer0.setMinTokenLength(3) + testRegexTokenizer(tokenizer0, dataset1) - tokenizer.setMinTokenLength(3) - testRegexTokenizer(tokenizer, dataset1) - - tokenizer - .setPattern("\\s") - .setGaps(true) - .setMinTokenLength(0) + val tokenizer2 = new RegexTokenizer() + .setInputCol("rawText") + .setOutputCol("tokens") val dataset2 = sqlContext.createDataFrame(Seq( TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization.")), - TokenizerTestData("Te,st. punct", Array("Te,st.", "", "punct")) + TokenizerTestData("Te,st. punct", Array("Te,st.", "punct")) )) - testRegexTokenizer(tokenizer, dataset2) + testRegexTokenizer(tokenizer2, dataset2) } } -object RegexTokenizerSuite extends FunSuite { +object RegexTokenizerSuite extends SparkFunSuite { def testRegexTokenizer(t: RegexTokenizer, dataset: DataFrame): Unit = { t.transform(dataset) .select("tokens", "wantedTokens") .collect() - .foreach { - case Row(tokens, wantedTokens) => - assert(tokens === wantedTokens) - } + .foreach { case Row(tokens, wantedTokens) => + assert(tokens === wantedTokens) + } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala index 0db27607bc274..bb4d5b983e0d4 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala @@ -17,20 +17,18 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite - -import org.apache.spark.SparkException +import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute, NumericAttribute} +import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{Row, SQLContext} - -class VectorAssemblerSuite extends FunSuite with MLlibTestSparkContext { +import org.apache.spark.sql.Row +import org.apache.spark.sql.functions.col - @transient var sqlContext: SQLContext = _ +class VectorAssemblerSuite extends SparkFunSuite with MLlibTestSparkContext { - override def beforeAll(): Unit = { - super.beforeAll() - sqlContext = new SQLContext(sc) + test("params") { + ParamsSuite.checkParams(new VectorAssembler) } test("assemble") { @@ -68,4 +66,39 @@ class VectorAssemblerSuite extends FunSuite with MLlibTestSparkContext { assert(v === Vectors.sparse(6, Array(1, 2, 4, 5), Array(1.0, 2.0, 3.0, 10.0))) } } + + test("ML attributes") { + val browser = NominalAttribute.defaultAttr.withValues("chrome", "firefox", "safari") + val hour = NumericAttribute.defaultAttr.withMin(0.0).withMax(24.0) + val user = new AttributeGroup("user", Array( + NominalAttribute.defaultAttr.withName("gender").withValues("male", "female"), + NumericAttribute.defaultAttr.withName("salary"))) + val row = (1.0, 0.5, 1, Vectors.dense(1.0, 1000.0), Vectors.sparse(2, Array(1), Array(2.0))) + val df = sqlContext.createDataFrame(Seq(row)).toDF("browser", "hour", "count", "user", "ad") + .select( + col("browser").as("browser", browser.toMetadata()), + col("hour").as("hour", hour.toMetadata()), + col("count"), // "count" is an integer column without ML attribute + col("user").as("user", user.toMetadata()), + col("ad")) // "ad" is a vector column without ML attribute + val assembler = new VectorAssembler() + .setInputCols(Array("browser", "hour", "count", "user", "ad")) + .setOutputCol("features") + val output = assembler.transform(df) + val schema = output.schema + val features = AttributeGroup.fromStructField(schema("features")) + assert(features.size === 7) + val browserOut = features.getAttr(0) + assert(browserOut === browser.withIndex(0).withName("browser")) + val hourOut = features.getAttr(1) + assert(hourOut === hour.withIndex(1).withName("hour")) + val countOut = features.getAttr(2) + assert(countOut === NumericAttribute.defaultAttr.withName("count").withIndex(2)) + val userGenderOut = features.getAttr(3) + assert(userGenderOut === user.getAttr("gender").withName("user_gender").withIndex(3)) + val userSalaryOut = features.getAttr(4) + assert(userSalaryOut === user.getAttr("salary").withName("user_salary").withIndex(4)) + assert(features.getAttr(5) === NumericAttribute.defaultAttr.withIndex(5)) + assert(features.getAttr(6) === NumericAttribute.defaultAttr.withIndex(6)) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala index 38dc83b1241cf..8c85c96d5c6d8 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -19,22 +19,18 @@ package org.apache.spark.ml.feature import scala.beans.{BeanInfo, BeanProperty} -import org.scalatest.FunSuite - -import org.apache.spark.SparkException +import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.ml.attribute._ +import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, SQLContext} - +import org.apache.spark.sql.DataFrame -class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { +class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext { import VectorIndexerSuite.FeatureData - @transient var sqlContext: SQLContext = _ - // identical, of length 3 @transient var densePoints1: DataFrame = _ @transient var sparsePoints1: DataFrame = _ @@ -86,7 +82,6 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { checkPair(densePoints1Seq, sparsePoints1Seq) checkPair(densePoints2Seq, sparsePoints2Seq) - sqlContext = new SQLContext(sc) densePoints1 = sqlContext.createDataFrame(sc.parallelize(densePoints1Seq, 2).map(FeatureData)) sparsePoints1 = sqlContext.createDataFrame(sc.parallelize(sparsePoints1Seq, 2).map(FeatureData)) densePoints2 = sqlContext.createDataFrame(sc.parallelize(densePoints2Seq, 2).map(FeatureData)) @@ -97,6 +92,12 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { private def getIndexer: VectorIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexed") + test("params") { + ParamsSuite.checkParams(new VectorIndexer) + val model = new VectorIndexerModel("indexer", 1, Map.empty) + ParamsSuite.checkParams(model) + } + test("Cannot fit an empty DataFrame") { val rdd = sqlContext.createDataFrame(sc.parallelize(Array.empty[Vector], 2).map(FeatureData)) val vectorIndexer = getIndexer diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala index 03ba86670d453..aa6ce533fd885 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala @@ -17,14 +17,21 @@ package org.apache.spark.ml.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.sql.{Row, SQLContext} +import org.apache.spark.mllib.feature.{Word2VecModel => OldWord2VecModel} + +class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext { -class Word2VecSuite extends FunSuite with MLlibTestSparkContext { + test("params") { + ParamsSuite.checkParams(new Word2Vec) + val model = new Word2VecModel("w2v", new OldWord2VecModel(Map("a" -> Array(0.0f)))) + ParamsSuite.checkParams(model) + } test("Word2Vec") { val sqlContext = new SQLContext(sc) @@ -35,9 +42,9 @@ class Word2VecSuite extends FunSuite with MLlibTestSparkContext { val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" ")) val codes = Map( - "a" -> Array(-0.2811822295188904,-0.6356269121170044,-0.3020961284637451), - "b" -> Array(1.0309048891067505,-1.29472815990448,0.22276712954044342), - "c" -> Array(-0.08456747233867645,0.5137411952018738,0.11731560528278351) + "a" -> Array(-0.2811822295188904, -0.6356269121170044, -0.3020961284637451), + "b" -> Array(1.0309048891067505, -1.29472815990448, 0.22276712954044342), + "c" -> Array(-0.08456747233867645, 0.5137411952018738, 0.11731560528278351) ) val expected = doc.map { sentence => @@ -52,6 +59,7 @@ class Word2VecSuite extends FunSuite with MLlibTestSparkContext { .setVectorSize(3) .setInputCol("text") .setOutputCol("result") + .setSeed(42L) .fit(docDF) model.transform(docDF).select("result", "expected").collect().foreach { diff --git a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala index 1505ad872536b..778abcba22c10 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala @@ -19,8 +19,7 @@ package org.apache.spark.ml.impl import scala.collection.JavaConverters._ -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.api.java.JavaRDD import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute, NumericAttribute} import org.apache.spark.ml.tree._ @@ -29,7 +28,7 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.{SQLContext, DataFrame} -private[ml] object TreeTests extends FunSuite { +private[ml] object TreeTests extends SparkFunSuite { /** * Convert the given data to a DataFrame, and set the features and label metadata. diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index 6056e7d3f6ff8..050d4170ea017 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -17,27 +17,28 @@ package org.apache.spark.ml.param -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite -class ParamsSuite extends FunSuite { +class ParamsSuite extends SparkFunSuite { test("param") { val solver = new TestParams() + val uid = solver.uid import solver.{maxIter, inputCol} assert(maxIter.name === "maxIter") - assert(maxIter.doc === "max number of iterations (>= 0)") - assert(maxIter.parent.eq(solver)) - assert(maxIter.toString === "maxIter: max number of iterations (>= 0) (default: 10)") + assert(maxIter.doc === "maximum number of iterations (>= 0)") + assert(maxIter.parent === uid) + assert(maxIter.toString === s"${uid}__maxIter") assert(!maxIter.isValid(-1)) assert(maxIter.isValid(0)) assert(maxIter.isValid(1)) solver.setMaxIter(5) - assert(maxIter.toString === - "maxIter: max number of iterations (>= 0) (default: 10, current: 5)") + assert(solver.explainParam(maxIter) === + "maxIter: maximum number of iterations (>= 0) (default: 10, current: 5)") - assert(inputCol.toString === "inputCol: input column name (undefined)") + assert(inputCol.toString === s"${uid}__inputCol") intercept[IllegalArgumentException] { solver.setMaxIter(-1) @@ -118,7 +119,10 @@ class ParamsSuite extends FunSuite { assert(!solver.isDefined(inputCol)) intercept[NoSuchElementException](solver.getInputCol) - assert(solver.explainParams() === Seq(inputCol, maxIter).mkString("\n")) + assert(solver.explainParam(maxIter) === + "maxIter: maximum number of iterations (>= 0) (default: 10, current: 100)") + assert(solver.explainParams() === + Seq(inputCol, maxIter).map(solver.explainParam).mkString("\n")) assert(solver.getParam("inputCol").eq(inputCol)) assert(solver.getParam("maxIter").eq(maxIter)) @@ -131,7 +135,7 @@ class ParamsSuite extends FunSuite { intercept[IllegalArgumentException] { solver.validateParams() } - solver.validateParams(ParamMap(inputCol -> "input")) + solver.copy(ParamMap(inputCol -> "input")).validateParams() solver.setInputCol("input") assert(solver.isSet(inputCol)) assert(solver.isDefined(inputCol)) @@ -148,7 +152,7 @@ class ParamsSuite extends FunSuite { assert(!solver.isSet(maxIter)) val copied = solver.copy(ParamMap(solver.maxIter -> 50)) - assert(copied.uid !== solver.uid) + assert(copied.uid === solver.uid) assert(copied.getInputCol === solver.getInputCol) assert(copied.getMaxIter === 50) } @@ -197,3 +201,31 @@ class ParamsSuite extends FunSuite { assert(inArray(1) && inArray(2) && !inArray(0)) } } + +object ParamsSuite extends SparkFunSuite { + + /** + * Checks common requirements for [[Params.params]]: + * - params are ordered by names + * - param parent has the same UID as the object's UID + * - param name is the same as the param method name + * - obj.copy should return the same type as the obj + */ + def checkParams(obj: Params): Unit = { + val clazz = obj.getClass + + val params = obj.params + val paramNames = params.map(_.name) + require(paramNames === paramNames.sorted, "params must be ordered by names") + params.foreach { p => + assert(p.parent === obj.uid) + assert(obj.getParam(p.name) === p) + // TODO: Check that setters return self, which needs special handling for generic types. + } + + val copyMethod = clazz.getMethod("copy", classOf[ParamMap]) + val copyReturnType = copyMethod.getReturnType + require(copyReturnType === obj.getClass, + s"${clazz.getName}.copy should return ${clazz.getName} instead of ${copyReturnType.getName}.") + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index dc16073640407..2759248344531 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -18,9 +18,12 @@ package org.apache.spark.ml.param import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter} +import org.apache.spark.ml.util.Identifiable /** A subclass of Params for testing. */ -class TestParams extends Params with HasMaxIter with HasInputCol { +class TestParams(override val uid: String) extends Params with HasMaxIter with HasInputCol { + + def this() = this(Identifiable.randomUID("testParams")) def setMaxIter(value: Int): this.type = { set(maxIter, value); this } @@ -35,7 +38,5 @@ class TestParams extends Params with HasMaxIter with HasInputCol { require(isDefined(inputCol)) } - override def copy(extra: ParamMap): TestParams = { - super.copy(extra).asInstanceOf[TestParams] - } + override def copy(extra: ParamMap): TestParams = defaultCopy(extra) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/shared/SharedParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/shared/SharedParamsSuite.scala new file mode 100644 index 0000000000000..b3af81a3c60b6 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/param/shared/SharedParamsSuite.scala @@ -0,0 +1,36 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param.shared + +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.{ParamMap, Params} + +class SharedParamsSuite extends SparkFunSuite { + + test("outputCol") { + + class Obj(override val uid: String) extends Params with HasOutputCol { + override def copy(extra: ParamMap): Obj = defaultCopy(extra) + } + + val obj = new Obj("obj") + + assert(obj.hasDefault(obj.outputCol)) + assert(obj.getOrDefault(obj.outputCol) === "obj__output") + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala index fc7349330cf86..2e5cfe7027eb6 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala @@ -25,9 +25,8 @@ import scala.collection.mutable.ArrayBuffer import scala.language.existentials import com.github.fommil.netlib.BLAS.{getInstance => blas} -import org.scalatest.FunSuite -import org.apache.spark.{Logging, SparkException} +import org.apache.spark.{Logging, SparkException, SparkFunSuite} import org.apache.spark.ml.recommendation.ALS._ import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.MLlibTestSparkContext @@ -36,16 +35,14 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.{Row, SQLContext} import org.apache.spark.util.Utils -class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { +class ALSSuite extends SparkFunSuite with MLlibTestSparkContext with Logging { - private var sqlContext: SQLContext = _ private var tempDir: File = _ override def beforeAll(): Unit = { super.beforeAll() tempDir = Utils.createTempDir() sc.setCheckpointDir(tempDir.getAbsolutePath) - sqlContext = new SQLContext(sc) } override def afterAll(): Unit = { @@ -345,6 +342,7 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { .setImplicitPrefs(implicitPrefs) .setNumUserBlocks(numUserBlocks) .setNumItemBlocks(numItemBlocks) + .setSeed(0) val alpha = als.getAlpha val model = als.fit(training.toDF()) val predictions = model.transform(test.toDF()) @@ -425,17 +423,18 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01) val longRatings = ratings.map(r => Rating(r.user.toLong, r.item.toLong, r.rating)) - val (longUserFactors, _) = ALS.train(longRatings, rank = 2, maxIter = 4) + val (longUserFactors, _) = ALS.train(longRatings, rank = 2, maxIter = 4, seed = 0) assert(longUserFactors.first()._1.getClass === classOf[Long]) val strRatings = ratings.map(r => Rating(r.user.toString, r.item.toString, r.rating)) - val (strUserFactors, _) = ALS.train(strRatings, rank = 2, maxIter = 4) + val (strUserFactors, _) = ALS.train(strRatings, rank = 2, maxIter = 4, seed = 0) assert(strUserFactors.first()._1.getClass === classOf[String]) } test("nonnegative constraint") { val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01) - val (userFactors, itemFactors) = ALS.train(ratings, rank = 2, maxIter = 4, nonnegative = true) + val (userFactors, itemFactors) = + ALS.train(ratings, rank = 2, maxIter = 4, nonnegative = true, seed = 0) def isNonnegative(factors: RDD[(Int, Array[Float])]): Boolean = { factors.values.map { _.forall(_ >= 0.0) }.reduce(_ && _) } @@ -459,7 +458,7 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { test("partitioner in returned factors") { val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01) val (userFactors, itemFactors) = ALS.train( - ratings, rank = 2, maxIter = 4, numUserBlocks = 3, numItemBlocks = 4) + ratings, rank = 2, maxIter = 4, numUserBlocks = 3, numItemBlocks = 4, seed = 0) for ((tpe, factors) <- Seq(("User", userFactors), ("Item", itemFactors))) { assert(userFactors.partitioner.isDefined, s"$tpe factors should have partitioner.") val part = userFactors.partitioner.get @@ -476,8 +475,8 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { test("als with large number of iterations") { val (ratings, _) = genExplicitTestData(numUsers = 4, numItems = 4, rank = 1) - ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2) - ALS.train( - ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, implicitPrefs = true) + ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, seed = 0) + ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, + implicitPrefs = true, seed = 0) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala index 5aa81b44ddaf9..33aa9d0d62343 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala @@ -17,8 +17,7 @@ package org.apache.spark.ml.regression -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.impl.TreeTests import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, @@ -28,7 +27,7 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame -class DecisionTreeRegressorSuite extends FunSuite with MLlibTestSparkContext { +class DecisionTreeRegressorSuite extends SparkFunSuite with MLlibTestSparkContext { import DecisionTreeRegressorSuite.compareAPIs @@ -69,7 +68,7 @@ class DecisionTreeRegressorSuite extends FunSuite with MLlibTestSparkContext { // TODO: test("model save/load") SPARK-6725 } -private[ml] object DecisionTreeRegressorSuite extends FunSuite { +private[ml] object DecisionTreeRegressorSuite extends SparkFunSuite { /** * Train 2 decision trees on the given dataset, one using the old API and one using the new API. @@ -83,9 +82,9 @@ private[ml] object DecisionTreeRegressorSuite extends FunSuite { val oldTree = OldDecisionTree.train(data, oldStrategy) val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newTree = dt.fit(newData) - // Use parent, fittingParamMap from newTree since these are not checked anyways. + // Use parent from newTree since this is not checked anyways. val oldTreeAsNew = DecisionTreeRegressionModel.fromOld( - oldTree, newTree.parent, categoricalFeatures) + oldTree, newTree.parent.asInstanceOf[DecisionTreeRegressor], categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala index 25b36ab08b67c..98fb3d3f5f22c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala @@ -17,8 +17,7 @@ package org.apache.spark.ml.regression -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.impl.TreeTests import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT} @@ -31,7 +30,7 @@ import org.apache.spark.sql.DataFrame /** * Test suite for [[GBTRegressor]]. */ -class GBTRegressorSuite extends FunSuite with MLlibTestSparkContext { +class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext { import GBTRegressorSuite.compareAPIs @@ -129,8 +128,9 @@ private object GBTRegressorSuite { val oldModel = oldGBT.run(data) val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = gbt.fit(newData) - // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, categoricalFeatures) + // Use parent from newTree since this is not checked anyways. + val oldModelAsNew = GBTRegressionModel.fromOld( + oldModel, newModel.parent.asInstanceOf[GBTRegressor], categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala index 80323ef5201a6..732e2c42be144 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala @@ -17,16 +17,14 @@ package org.apache.spark.ml.regression -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.DenseVector import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext} import org.apache.spark.mllib.util.TestingUtils._ -import org.apache.spark.sql.{Row, SQLContext, DataFrame} +import org.apache.spark.sql.{DataFrame, Row} -class LinearRegressionSuite extends FunSuite with MLlibTestSparkContext { +class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { - @transient var sqlContext: SQLContext = _ @transient var dataset: DataFrame = _ /** @@ -41,7 +39,6 @@ class LinearRegressionSuite extends FunSuite with MLlibTestSparkContext { */ override def beforeAll(): Unit = { super.beforeAll() - sqlContext = new SQLContext(sc) dataset = sqlContext.createDataFrame( sc.parallelize(LinearDataGenerator.generateLinearInput( 6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)) diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala index 45f09f4fdab81..b24ecaa57c89b 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala @@ -17,8 +17,7 @@ package org.apache.spark.ml.regression -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.impl.TreeTests import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest} @@ -31,7 +30,7 @@ import org.apache.spark.sql.DataFrame /** * Test suite for [[RandomForestRegressor]]. */ -class RandomForestRegressorSuite extends FunSuite with MLlibTestSparkContext { +class RandomForestRegressorSuite extends SparkFunSuite with MLlibTestSparkContext { import RandomForestRegressorSuite.compareAPIs @@ -98,7 +97,7 @@ class RandomForestRegressorSuite extends FunSuite with MLlibTestSparkContext { */ } -private object RandomForestRegressorSuite extends FunSuite { +private object RandomForestRegressorSuite extends SparkFunSuite { /** * Train 2 models on the given dataset, one using the old API and one using the new API. @@ -114,9 +113,9 @@ private object RandomForestRegressorSuite extends FunSuite { data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt) val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = rf.fit(newData) - // Use parent, fittingParamMap from newTree since these are not checked anyways. + // Use parent from newTree since this is not checked anyways. val oldModelAsNew = RandomForestRegressionModel.fromOld( - oldModel, newModel.parent, categoricalFeatures) + oldModel, newModel.parent.asInstanceOf[RandomForestRegressor], categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala index 05313d440fbf6..db64511a76055 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala @@ -17,15 +17,19 @@ package org.apache.spark.ml.tuning -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.classification.LogisticRegression -import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator +import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator} +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.shared.HasInputCol +import org.apache.spark.ml.regression.LinearRegression import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{SQLContext, DataFrame} +import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext} +import org.apache.spark.sql.{DataFrame, SQLContext} +import org.apache.spark.sql.types.StructType -class CrossValidatorSuite extends FunSuite with MLlibTestSparkContext { +class CrossValidatorSuite extends SparkFunSuite with MLlibTestSparkContext { @transient var dataset: DataFrame = _ @@ -52,5 +56,90 @@ class CrossValidatorSuite extends FunSuite with MLlibTestSparkContext { val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression] assert(parent.getRegParam === 0.001) assert(parent.getMaxIter === 10) + assert(cvModel.avgMetrics.length === lrParamMaps.length) + } + + test("cross validation with linear regression") { + val dataset = sqlContext.createDataFrame( + sc.parallelize(LinearDataGenerator.generateLinearInput( + 6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2)) + + val trainer = new LinearRegression + val lrParamMaps = new ParamGridBuilder() + .addGrid(trainer.regParam, Array(1000.0, 0.001)) + .addGrid(trainer.maxIter, Array(0, 10)) + .build() + val eval = new RegressionEvaluator() + val cv = new CrossValidator() + .setEstimator(trainer) + .setEstimatorParamMaps(lrParamMaps) + .setEvaluator(eval) + .setNumFolds(3) + val cvModel = cv.fit(dataset) + val parent = cvModel.bestModel.parent.asInstanceOf[LinearRegression] + assert(parent.getRegParam === 0.001) + assert(parent.getMaxIter === 10) + assert(cvModel.avgMetrics.length === lrParamMaps.length) + + eval.setMetricName("r2") + val cvModel2 = cv.fit(dataset) + val parent2 = cvModel2.bestModel.parent.asInstanceOf[LinearRegression] + assert(parent2.getRegParam === 0.001) + assert(parent2.getMaxIter === 10) + assert(cvModel2.avgMetrics.length === lrParamMaps.length) + } + + test("validateParams should check estimatorParamMaps") { + import CrossValidatorSuite._ + + val est = new MyEstimator("est") + val eval = new MyEvaluator + val paramMaps = new ParamGridBuilder() + .addGrid(est.inputCol, Array("input1", "input2")) + .build() + + val cv = new CrossValidator() + .setEstimator(est) + .setEstimatorParamMaps(paramMaps) + .setEvaluator(eval) + + cv.validateParams() // This should pass. + + val invalidParamMaps = paramMaps :+ ParamMap(est.inputCol -> "") + cv.setEstimatorParamMaps(invalidParamMaps) + intercept[IllegalArgumentException] { + cv.validateParams() + } + } +} + +object CrossValidatorSuite { + + abstract class MyModel extends Model[MyModel] + + class MyEstimator(override val uid: String) extends Estimator[MyModel] with HasInputCol { + + override def validateParams(): Unit = require($(inputCol).nonEmpty) + + override def fit(dataset: DataFrame): MyModel = { + throw new UnsupportedOperationException + } + + override def transformSchema(schema: StructType): StructType = { + throw new UnsupportedOperationException + } + + override def copy(extra: ParamMap): MyEstimator = defaultCopy(extra) + } + + class MyEvaluator extends Evaluator { + + override def evaluate(dataset: DataFrame): Double = { + throw new UnsupportedOperationException + } + + override val uid: String = "eval" + + override def copy(extra: ParamMap): MyEvaluator = defaultCopy(extra) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/ParamGridBuilderSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/ParamGridBuilderSuite.scala index 20aa100112bfe..810b70049ec15 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/tuning/ParamGridBuilderSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/ParamGridBuilderSuite.scala @@ -19,11 +19,10 @@ package org.apache.spark.ml.tuning import scala.collection.mutable -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.ml.param.{ParamMap, TestParams} -class ParamGridBuilderSuite extends FunSuite { +class ParamGridBuilderSuite extends SparkFunSuite { val solver = new TestParams() import solver.{inputCol, maxIter} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala index a629dba8a426f..59944416d96a6 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala @@ -17,13 +17,12 @@ package org.apache.spark.mllib.api.python -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Vectors, SparseMatrix} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.recommendation.Rating -class PythonMLLibAPISuite extends FunSuite { +class PythonMLLibAPISuite extends SparkFunSuite { SerDe.initialize() @@ -84,7 +83,7 @@ class PythonMLLibAPISuite extends FunSuite { val smt = new SparseMatrix( 3, 3, Array(0, 2, 3, 5), Array(0, 2, 1, 0, 2), Array(0.9, 1.2, 3.4, 5.7, 8.9), - isTransposed=true) + isTransposed = true) val nsmt = SerDe.loads(SerDe.dumps(smt)).asInstanceOf[SparseMatrix] assert(smt.toArray === nsmt.toArray) } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala index a26c52852c4d7..e8f3d0c4db20a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala @@ -21,9 +21,9 @@ import scala.collection.JavaConversions._ import scala.util.Random import scala.util.control.Breaks._ -import org.scalatest.FunSuite import org.scalatest.Matchers +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} @@ -91,21 +91,22 @@ object LogisticRegressionSuite { seed: Int): Seq[LabeledPoint] = { val rnd = new Random(seed) - val xDim = xMean.size + val xDim = xMean.length val xWithInterceptsDim = if (addIntercept) xDim + 1 else xDim - val nClasses = weights.size / xWithInterceptsDim + 1 + val nClasses = weights.length / xWithInterceptsDim + 1 val x = Array.fill[Vector](nPoints)(Vectors.dense(Array.fill[Double](xDim)(rnd.nextGaussian()))) - x.map(vector => { + x.foreach { vector => // This doesn't work if `vector` is a sparse vector. val vectorArray = vector.toArray var i = 0 - while (i < vectorArray.size) { + val len = vectorArray.length + while (i < len) { vectorArray(i) = vectorArray(i) * math.sqrt(xVariance(i)) + xMean(i) i += 1 } - }) + } val y = (0 until nPoints).map { idx => val xArray = x(idx).toArray @@ -118,7 +119,7 @@ object LogisticRegressionSuite { } // Preventing the overflow when we compute the probability val maxMargin = margins.max - if (maxMargin > 0) for (i <-0 until nClasses) margins(i) -= maxMargin + if (maxMargin > 0) for (i <- 0 until nClasses) margins(i) -= maxMargin // Computing the probabilities for each class from the margins. val norm = { @@ -129,7 +130,7 @@ object LogisticRegressionSuite { } temp } - for (i <-0 until nClasses) probs(i) /= norm + for (i <- 0 until nClasses) probs(i) /= norm // Compute the cumulative probability so we can generate a random number and assign a label. for (i <- 1 until nClasses) probs(i) += probs(i - 1) @@ -168,7 +169,7 @@ object LogisticRegressionSuite { } -class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext with Matchers { +class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext with Matchers { def validatePrediction( predictions: Seq[Double], input: Seq[LabeledPoint], @@ -540,7 +541,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext with M } -class LogisticRegressionClusterSuite extends FunSuite with LocalClusterSparkContext { +class LogisticRegressionClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small in both training and prediction using SGD optimizer") { val m = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala index ea89b17b7c08f..f7fc8730606af 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala @@ -19,20 +19,19 @@ package org.apache.spark.mllib.classification import scala.util.Random -import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum, Axis} +import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum} import breeze.stats.distributions.{Multinomial => BrzMultinomial} -import org.scalatest.FunSuite - -import org.apache.spark.SparkException +import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} import org.apache.spark.util.Utils - object NaiveBayesSuite { + import NaiveBayes.{Multinomial, Bernoulli} + private def calcLabel(p: Double, pi: Array[Double]): Int = { var sum = 0.0 for (j <- 0 until pi.length) { @@ -48,7 +47,7 @@ object NaiveBayesSuite { theta: Array[Array[Double]], // CXD nPoints: Int, seed: Int, - modelType: String = "Multinomial", + modelType: String = Multinomial, sample: Int = 10): Seq[LabeledPoint] = { val D = theta(0).length val rnd = new Random(seed) @@ -58,10 +57,10 @@ object NaiveBayesSuite { for (i <- 0 until nPoints) yield { val y = calcLabel(rnd.nextDouble(), _pi) val xi = modelType match { - case "Bernoulli" => Array.tabulate[Double] (D) { j => + case Bernoulli => Array.tabulate[Double] (D) { j => if (rnd.nextDouble () < _theta(y)(j) ) 1 else 0 } - case "Multinomial" => + case Multinomial => val mult = BrzMultinomial(BDV(_theta(y))) val emptyMap = (0 until D).map(x => (x, 0.0)).toMap val counts = emptyMap ++ mult.sample(sample).groupBy(x => x).map { @@ -70,7 +69,7 @@ object NaiveBayesSuite { counts.toArray.sortBy(_._1).map(_._2) case _ => // This should never happen. - throw new UnknownError(s"NaiveBayesSuite found unknown ModelType: $modelType") + throw new UnknownError(s"Invalid modelType: $modelType.") } LabeledPoint(y, Vectors.dense(xi)) @@ -79,16 +78,16 @@ object NaiveBayesSuite { /** Bernoulli NaiveBayes with binary labels, 3 features */ private val binaryBernoulliModel = new NaiveBayesModel(labels = Array(0.0, 1.0), - pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), - "Bernoulli") + pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), Bernoulli) /** Multinomial NaiveBayes with binary labels, 3 features */ private val binaryMultinomialModel = new NaiveBayesModel(labels = Array(0.0, 1.0), - pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), - "Multinomial") + pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), Multinomial) } -class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { +class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext { + + import NaiveBayes.{Multinomial, Bernoulli} def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) { val numOfPredictions = predictions.zip(input).count { @@ -117,6 +116,11 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { } } + test("model types") { + assert(Multinomial === "multinomial") + assert(Bernoulli === "bernoulli") + } + test("get, set params") { val nb = new NaiveBayes() nb.setLambda(2.0) @@ -134,16 +138,15 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { Array(0.10, 0.10, 0.70, 0.10) // label 2 ).map(_.map(math.log)) - val testData = NaiveBayesSuite.generateNaiveBayesInput( - pi, theta, nPoints, 42, "Multinomial") + val testData = NaiveBayesSuite.generateNaiveBayesInput(pi, theta, nPoints, 42, Multinomial) val testRDD = sc.parallelize(testData, 2) testRDD.cache() - val model = NaiveBayes.train(testRDD, 1.0, "Multinomial") + val model = NaiveBayes.train(testRDD, 1.0, Multinomial) validateModelFit(pi, theta, model) val validationData = NaiveBayesSuite.generateNaiveBayesInput( - pi, theta, nPoints, 17, "Multinomial") + pi, theta, nPoints, 17, Multinomial) val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. @@ -159,19 +162,19 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { val theta = Array( Array(0.50, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.40), // label 0 Array(0.02, 0.70, 0.10, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02), // label 1 - Array(0.02, 0.02, 0.60, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.30) // label 2 + Array(0.02, 0.02, 0.60, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.30) // label 2 ).map(_.map(math.log)) val testData = NaiveBayesSuite.generateNaiveBayesInput( - pi, theta, nPoints, 45, "Bernoulli") + pi, theta, nPoints, 45, Bernoulli) val testRDD = sc.parallelize(testData, 2) testRDD.cache() - val model = NaiveBayes.train(testRDD, 1.0, "Bernoulli") + val model = NaiveBayes.train(testRDD, 1.0, Bernoulli) validateModelFit(pi, theta, model) val validationData = NaiveBayesSuite.generateNaiveBayesInput( - pi, theta, nPoints, 20, "Bernoulli") + pi, theta, nPoints, 20, Bernoulli) val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. @@ -208,6 +211,39 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { } } + test("detect non zero or one values in Bernoulli") { + val badTrain = Seq( + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(0.0, Vectors.dense(2.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(1.0, Vectors.dense(0.0))) + + intercept[SparkException] { + NaiveBayes.train(sc.makeRDD(badTrain, 2), 1.0, Bernoulli) + } + + val okTrain = Seq( + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(0.0, Vectors.dense(0.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(0.0, Vectors.dense(0.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(1.0, Vectors.dense(1.0)) + ) + + val badPredict = Seq( + Vectors.dense(1.0), + Vectors.dense(2.0), + Vectors.dense(1.0), + Vectors.dense(0.0)) + + val model = NaiveBayes.train(sc.makeRDD(okTrain, 2), 1.0, Bernoulli) + intercept[SparkException] { + model.predict(sc.makeRDD(badPredict, 2)).collect() + } + } + test("model save/load: 2.0 to 2.0") { val tempDir = Utils.createTempDir() val path = tempDir.toURI.toString @@ -242,14 +278,14 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { assert(model.labels === sameModel.labels) assert(model.pi === sameModel.pi) assert(model.theta === sameModel.theta) - assert(model.modelType === "Multinomial") + assert(model.modelType === Multinomial) } finally { Utils.deleteRecursively(tempDir) } } } -class NaiveBayesClusterSuite extends FunSuite with LocalClusterSparkContext { +class NaiveBayesClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small in both training and prediction") { val m = 10 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala index 6de098b383ba3..b1d78cba9e3dc 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala @@ -21,9 +21,8 @@ import scala.collection.JavaConversions._ import scala.util.Random import org.jblas.DoubleMatrix -import org.scalatest.FunSuite -import org.apache.spark.SparkException +import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} @@ -46,7 +45,7 @@ object SVMSuite { nPoints: Int, seed: Int): Seq[LabeledPoint] = { val rnd = new Random(seed) - val weightsMat = new DoubleMatrix(1, weights.length, weights:_*) + val weightsMat = new DoubleMatrix(1, weights.length, weights : _*) val x = Array.fill[Array[Double]](nPoints)( Array.fill[Double](weights.length)(rnd.nextDouble() * 2.0 - 1.0)) val y = x.map { xi => @@ -62,7 +61,7 @@ object SVMSuite { } -class SVMSuite extends FunSuite with MLlibTestSparkContext { +class SVMSuite extends SparkFunSuite with MLlibTestSparkContext { def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) { val numOffPredictions = predictions.zip(input).count { case (prediction, expected) => @@ -91,7 +90,7 @@ class SVMSuite extends FunSuite with MLlibTestSparkContext { val model = svm.run(testRDD) val validationData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 17) - val validationRDD = sc.parallelize(validationData, 2) + val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. @@ -117,7 +116,7 @@ class SVMSuite extends FunSuite with MLlibTestSparkContext { val B = -1.5 val C = 1.0 - val testData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 42) + val testData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 42) val testRDD = sc.parallelize(testData, 2) testRDD.cache() @@ -127,8 +126,8 @@ class SVMSuite extends FunSuite with MLlibTestSparkContext { val model = svm.run(testRDD) - val validationData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 17) - val validationRDD = sc.parallelize(validationData, 2) + val validationData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) @@ -145,7 +144,7 @@ class SVMSuite extends FunSuite with MLlibTestSparkContext { val B = -1.5 val C = 1.0 - val testData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 42) + val testData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 42) val initialB = -1.0 val initialC = -1.0 @@ -159,8 +158,8 @@ class SVMSuite extends FunSuite with MLlibTestSparkContext { val model = svm.run(testRDD, initialWeights) - val validationData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 17) - val validationRDD = sc.parallelize(validationData,2) + val validationData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) @@ -177,7 +176,7 @@ class SVMSuite extends FunSuite with MLlibTestSparkContext { val B = -1.5 val C = 1.0 - val testData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 42) + val testData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 42) val testRDD = sc.parallelize(testData, 2) val testRDDInvalid = testRDD.map { lp => @@ -229,7 +228,7 @@ class SVMSuite extends FunSuite with MLlibTestSparkContext { } } -class SVMClusterSuite extends FunSuite with LocalClusterSparkContext { +class SVMClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small in both training and prediction") { val m = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala index 5683b55e8500a..fd653296c9d97 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala @@ -19,15 +19,14 @@ package org.apache.spark.mllib.classification import scala.collection.mutable.ArrayBuffer -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.TestSuiteBase -class StreamingLogisticRegressionSuite extends FunSuite with TestSuiteBase { +class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase { // use longer wait time to ensure job completion override def maxWaitTimeMillis: Int = 30000 @@ -159,4 +158,21 @@ class StreamingLogisticRegressionSuite extends FunSuite with TestSuiteBase { val error = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints).toList assert(error.head > 0.8 & error.last < 0.2) } + + // Test empty RDDs in a stream + test("handling empty RDDs in a stream") { + val model = new StreamingLogisticRegressionWithSGD() + .setInitialWeights(Vectors.dense(-0.1)) + .setStepSize(0.01) + .setNumIterations(10) + val numBatches = 10 + val emptyInput = Seq.empty[Seq[LabeledPoint]] + val ssc = setupStreams(emptyInput, + (inputDStream: DStream[LabeledPoint]) => { + model.trainOn(inputDStream) + model.predictOnValues(inputDStream.map(x => (x.label, x.features))) + } + ) + val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala index f356ffa3e3a26..b218d72f1268a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.clustering -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{Vectors, Matrices} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.util.Utils -class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { +class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext { test("single cluster") { val data = sc.parallelize(Array( Vectors.dense(6.0, 9.0), @@ -47,7 +46,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { } } - + test("two clusters") { val data = sc.parallelize(GaussianTestData.data) @@ -63,7 +62,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { val Ew = Array(1.0 / 3.0, 2.0 / 3.0) val Emu = Array(Vectors.dense(-4.3673), Vectors.dense(5.1604)) val Esigma = Array(Matrices.dense(1, 1, Array(1.1098)), Matrices.dense(1, 1, Array(0.86644))) - + val gmm = new GaussianMixture() .setK(2) .setInitialModel(initialGmm) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala index 0f2b26d462ad2..0dbbd7127444f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala @@ -19,14 +19,13 @@ package org.apache.spark.mllib.clustering import scala.util.Random -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.util.Utils -class KMeansSuite extends FunSuite with MLlibTestSparkContext { +class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext { import org.apache.spark.mllib.clustering.KMeans.{K_MEANS_PARALLEL, RANDOM} @@ -75,7 +74,7 @@ class KMeansSuite extends FunSuite with MLlibTestSparkContext { val center = Vectors.dense(1.0, 2.0, 3.0) // Make sure code runs. - var model = KMeans.train(data, k=2, maxIterations=1) + var model = KMeans.train(data, k = 2, maxIterations = 1) assert(model.clusterCenters.size === 2) } @@ -87,7 +86,7 @@ class KMeansSuite extends FunSuite with MLlibTestSparkContext { 2) // Make sure code runs. - var model = KMeans.train(data, k=3, maxIterations=1) + var model = KMeans.train(data, k = 3, maxIterations = 1) assert(model.clusterCenters.size === 3) } @@ -281,7 +280,7 @@ class KMeansSuite extends FunSuite with MLlibTestSparkContext { } } -object KMeansSuite extends FunSuite { +object KMeansSuite extends SparkFunSuite { def createModel(dim: Int, k: Int, isSparse: Boolean): KMeansModel = { val singlePoint = isSparse match { case true => @@ -305,7 +304,7 @@ object KMeansSuite extends FunSuite { } } -class KMeansClusterSuite extends FunSuite with LocalClusterSparkContext { +class KMeansClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small in both training and prediction") { val m = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index d5b7d96335744..406affa25539d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -19,13 +19,12 @@ package org.apache.spark.mllib.clustering import breeze.linalg.{DenseMatrix => BDM} -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class LDASuite extends FunSuite with MLlibTestSparkContext { +class LDASuite extends SparkFunSuite with MLlibTestSparkContext { import LDASuite._ diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala index 6d6fe6fe46bab..19e65f1b53ab5 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala @@ -20,15 +20,13 @@ package org.apache.spark.mllib.clustering import scala.collection.mutable import scala.util.Random -import org.scalatest.FunSuite - -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.graphx.{Edge, Graph} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.util.Utils -class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext { +class PowerIterationClusteringSuite extends SparkFunSuite with MLlibTestSparkContext { import org.apache.spark.mllib.clustering.PowerIterationClustering._ @@ -58,7 +56,7 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext predictions(a.cluster) += a.id } assert(predictions.toSet == Set((0 to 3).toSet, (4 to 15).toSet)) - + val model2 = new PowerIterationClustering() .setK(2) .setInitializationMode("degree") @@ -94,11 +92,13 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext */ val similarities = Seq[(Long, Long, Double)]( (0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)) + // scalastyle:off val expected = Array( Array(0.0, 1.0/3.0, 1.0/3.0, 1.0/3.0), Array(1.0/2.0, 0.0, 1.0/2.0, 0.0), Array(1.0/3.0, 1.0/3.0, 0.0, 1.0/3.0), Array(1.0/2.0, 0.0, 1.0/2.0, 0.0)) + // scalastyle:on val w = normalize(sc.parallelize(similarities, 2)) w.edges.collect().foreach { case Edge(i, j, x) => assert(x ~== expected(i.toInt)(j.toInt) absTol 1e-14) @@ -128,7 +128,7 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext } } -object PowerIterationClusteringSuite extends FunSuite { +object PowerIterationClusteringSuite extends SparkFunSuite { def createModel(sc: SparkContext, k: Int, nPoints: Int): PowerIterationClusteringModel = { val assignments = sc.parallelize( (0 until nPoints).map(p => PowerIterationClustering.Assignment(p, Random.nextInt(k)))) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala index f90025d535e45..ac01622b8a089 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.clustering -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.streaming.TestSuiteBase import org.apache.spark.streaming.dstream.DStream import org.apache.spark.util.random.XORShiftRandom -class StreamingKMeansSuite extends FunSuite with TestSuiteBase { +class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase { override def maxWaitTimeMillis: Int = 30000 @@ -133,6 +132,13 @@ class StreamingKMeansSuite extends FunSuite with TestSuiteBase { assert(math.abs(c1) ~== 0.8 absTol 0.6) } + test("SPARK-7946 setDecayFactor") { + val kMeans = new StreamingKMeans() + assert(kMeans.decayFactor === 1.0) + kMeans.setDecayFactor(2.0) + assert(kMeans.decayFactor === 2.0) + } + def StreamingKMeansDataGenerator( numPoints: Int, numBatches: Int, diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala index 79847633ff0dc..87ccc7eda44ea 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.evaluation -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class AreaUnderCurveSuite extends FunSuite with MLlibTestSparkContext { +class AreaUnderCurveSuite extends SparkFunSuite with MLlibTestSparkContext { test("auc computation") { val curve = Seq((0.0, 0.0), (1.0, 1.0), (2.0, 3.0), (3.0, 0.0)) val auc = 4.0 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala index e0224f960cc43..99d52fabc5309 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.evaluation -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class BinaryClassificationMetricsSuite extends FunSuite with MLlibTestSparkContext { +class BinaryClassificationMetricsSuite extends SparkFunSuite with MLlibTestSparkContext { private def areWithinEpsilon(x: (Double, Double)): Boolean = x._1 ~= (x._2) absTol 1E-5 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala index 7dc4f3cfbc4e4..d55bc8c3ec09f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.evaluation -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Matrices import org.apache.spark.mllib.util.MLlibTestSparkContext -class MulticlassMetricsSuite extends FunSuite with MLlibTestSparkContext { +class MulticlassMetricsSuite extends SparkFunSuite with MLlibTestSparkContext { test("Multiclass evaluation metrics") { /* * Confusion matrix for 3-class classification with total 9 instances: diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MultilabelMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MultilabelMetricsSuite.scala index 2537dd62c92f2..f3b19aeb42f84 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MultilabelMetricsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MultilabelMetricsSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.evaluation -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD -class MultilabelMetricsSuite extends FunSuite with MLlibTestSparkContext { +class MultilabelMetricsSuite extends SparkFunSuite with MLlibTestSparkContext { test("Multilabel evaluation metrics") { /* * Documents true labels (5x class0, 3x class1, 4x class2): diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala index 609eed983ff4e..c0924a213a844 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.evaluation -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.mllib.util.MLlibTestSparkContext -class RankingMetricsSuite extends FunSuite with MLlibTestSparkContext { +class RankingMetricsSuite extends SparkFunSuite with MLlibTestSparkContext { test("Ranking metrics: map, ndcg") { val predictionAndLabels = sc.parallelize( Seq( diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala index 670b4c34e6095..9de2bdb6d7246 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala @@ -17,16 +17,15 @@ package org.apache.spark.mllib.evaluation -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class RegressionMetricsSuite extends FunSuite with MLlibTestSparkContext { +class RegressionMetricsSuite extends SparkFunSuite with MLlibTestSparkContext { test("regression metrics") { val predictionAndObservations = sc.parallelize( - Seq((2.5,3.0),(0.0,-0.5),(2.0,2.0),(8.0,7.0)), 2) + Seq((2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)), 2) val metrics = new RegressionMetrics(predictionAndObservations) assert(metrics.explainedVariance ~== 0.95717 absTol 1E-5, "explained variance regression score mismatch") @@ -39,7 +38,7 @@ class RegressionMetricsSuite extends FunSuite with MLlibTestSparkContext { test("regression metrics with complete fitting") { val predictionAndObservations = sc.parallelize( - Seq((3.0,3.0),(0.0,0.0),(2.0,2.0),(8.0,8.0)), 2) + Seq((3.0, 3.0), (0.0, 0.0), (2.0, 2.0), (8.0, 8.0)), 2) val metrics = new RegressionMetrics(predictionAndObservations) assert(metrics.explainedVariance ~== 1.0 absTol 1E-5, "explained variance regression score mismatch") diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/ChiSqSelectorSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/ChiSqSelectorSuite.scala index 747f5914598ec..889727fb55823 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/ChiSqSelectorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/ChiSqSelectorSuite.scala @@ -17,13 +17,12 @@ package org.apache.spark.mllib.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLlibTestSparkContext -class ChiSqSelectorSuite extends FunSuite with MLlibTestSparkContext { +class ChiSqSelectorSuite extends SparkFunSuite with MLlibTestSparkContext { /* * Contingency tables diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala index f3a482abda873..ccbf8a91cdd37 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala @@ -17,13 +17,12 @@ package org.apache.spark.mllib.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class ElementwiseProductSuite extends FunSuite with MLlibTestSparkContext { +class ElementwiseProductSuite extends SparkFunSuite with MLlibTestSparkContext { test("elementwise (hadamard) product should properly apply vector to dense data set") { val denseData = Array( diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/HashingTFSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/HashingTFSuite.scala index 0c4dfb7b97c7f..cf279c02334e9 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/HashingTFSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/HashingTFSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.MLlibTestSparkContext -class HashingTFSuite extends FunSuite with MLlibTestSparkContext { +class HashingTFSuite extends SparkFunSuite with MLlibTestSparkContext { test("hashing tf on a single doc") { val hashingTF = new HashingTF(1000) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/IDFSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/IDFSuite.scala index 0a5cad7caf8e4..21163633051e5 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/IDFSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/IDFSuite.scala @@ -17,13 +17,12 @@ package org.apache.spark.mllib.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors, Vector} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class IDFSuite extends FunSuite with MLlibTestSparkContext { +class IDFSuite extends SparkFunSuite with MLlibTestSparkContext { test("idf") { val n = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala index 85fdd271b5ed1..34122d6ed2e95 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.feature -import org.scalatest.FunSuite - import breeze.linalg.{norm => brzNorm} +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class NormalizerSuite extends FunSuite with MLlibTestSparkContext { +class NormalizerSuite extends SparkFunSuite with MLlibTestSparkContext { val data = Array( Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))), @@ -106,10 +105,10 @@ class NormalizerSuite extends FunSuite with MLlibTestSparkContext { assert((dataInf, dataInfRDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5)) - assert(dataInf(0).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5) - assert(dataInf(2).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5) - assert(dataInf(3).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5) - assert(dataInf(4).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5) + assert(dataInf(0).toArray.map(math.abs).max ~== 1.0 absTol 1E-5) + assert(dataInf(2).toArray.map(math.abs).max ~== 1.0 absTol 1E-5) + assert(dataInf(3).toArray.map(math.abs).max ~== 1.0 absTol 1E-5) + assert(dataInf(4).toArray.map(math.abs).max ~== 1.0 absTol 1E-5) assert(dataInf(0) ~== Vectors.sparse(3, Seq((0, -0.86956522), (1, 1.0))) absTol 1E-5) assert(dataInf(1) ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/PCASuite.scala new file mode 100644 index 0000000000000..e57f49191378f --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/PCASuite.scala @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.feature + +import org.apache.spark.SparkFunSuite +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.linalg.distributed.RowMatrix +import org.apache.spark.mllib.util.MLlibTestSparkContext + +class PCASuite extends SparkFunSuite with MLlibTestSparkContext { + + private val data = Array( + Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))), + Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0), + Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0) + ) + + private lazy val dataRDD = sc.parallelize(data, 2) + + test("Correct computing use a PCA wrapper") { + val k = dataRDD.count().toInt + val pca = new PCA(k).fit(dataRDD) + + val mat = new RowMatrix(dataRDD) + val pc = mat.computePrincipalComponents(k) + + val pca_transform = pca.transform(dataRDD).collect() + val mat_multiply = mat.multiply(pc).rows.collect() + + assert(pca_transform.toSet === mat_multiply.toSet) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala index 7f94564b2a3ae..6ab2fa6770123 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, MultivariateOnlineSummarizer} import org.apache.spark.rdd.RDD -class StandardScalerSuite extends FunSuite with MLlibTestSparkContext { +class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext { // When the input data is all constant, the variance is zero. The standardization against // zero variance is not well-defined, but we decide to just set it into zero here. @@ -360,7 +359,7 @@ class StandardScalerSuite extends FunSuite with MLlibTestSparkContext { } withClue("model needs std and mean vectors to be equal size when both are provided") { intercept[IllegalArgumentException] { - val model = new StandardScalerModel(Vectors.dense(0.0), Vectors.dense(0.0,1.0)) + val model = new StandardScalerModel(Vectors.dense(0.0), Vectors.dense(0.0, 1.0)) } } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala index 98a98a7599bcb..b6818369208d7 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala @@ -17,14 +17,13 @@ package org.apache.spark.mllib.feature -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.util.Utils -class Word2VecSuite extends FunSuite with MLlibTestSparkContext { +class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext { // TODO: add more tests diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala index bd5b9cc3afa10..66ae3543ecc4e 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala @@ -16,11 +16,10 @@ */ package org.apache.spark.mllib.fpm -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext -class FPGrowthSuite extends FunSuite with MLlibTestSparkContext { +class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext { test("FP-Growth using String type") { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPTreeSuite.scala index 04017f67c311d..a56d7b3579213 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPTreeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPTreeSuite.scala @@ -19,11 +19,10 @@ package org.apache.spark.mllib.fpm import scala.language.existentials -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext -class FPTreeSuite extends FunSuite with MLlibTestSparkContext { +class FPTreeSuite extends SparkFunSuite with MLlibTestSparkContext { test("add transaction") { val tree = new FPTree[String] diff --git a/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala index 699f009f0f2ec..d34888af2d73b 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala @@ -17,18 +17,16 @@ package org.apache.spark.mllib.impl -import org.scalatest.FunSuite - import org.apache.hadoop.fs.{FileSystem, Path} -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.graphx.{Edge, Graph} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils -class PeriodicGraphCheckpointerSuite extends FunSuite with MLlibTestSparkContext { +class PeriodicGraphCheckpointerSuite extends SparkFunSuite with MLlibTestSparkContext { import PeriodicGraphCheckpointerSuite._ diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala index 002cb253862b5..b0f3f71113c57 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.linalg -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.mllib.linalg.BLAS._ -class BLASSuite extends FunSuite { +class BLASSuite extends SparkFunSuite { test("copy") { val sx = Vectors.sparse(4, Array(0, 2), Array(1.0, -2.0)) @@ -140,7 +139,7 @@ class BLASSuite extends FunSuite { syr(alpha, x, dA) assert(dA ~== expected absTol 1e-15) - + val dB = new DenseMatrix(3, 4, Array(0.0, 1.2, 2.2, 3.1, 1.2, 3.2, 5.3, 4.6, 2.2, 5.3, 1.8, 3.0)) @@ -149,7 +148,7 @@ class BLASSuite extends FunSuite { syr(alpha, x, dB) } } - + val dC = new DenseMatrix(3, 3, Array(0.0, 1.2, 2.2, 1.2, 3.2, 5.3, 2.2, 5.3, 1.8)) @@ -158,7 +157,7 @@ class BLASSuite extends FunSuite { syr(alpha, x, dC) } } - + val y = new DenseVector(Array(0.0, 2.7, 3.5, 2.1, 1.5)) withClue("Size of vector must match the rank of matrix") { @@ -257,32 +256,96 @@ class BLASSuite extends FunSuite { new DenseMatrix(4, 3, Array(0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 3.0)) val sA = new SparseMatrix(4, 3, Array(0, 1, 3, 4), Array(1, 0, 2, 3), Array(1.0, 2.0, 1.0, 3.0)) - val x = new DenseVector(Array(1.0, 2.0, 3.0)) + val dA2 = + new DenseMatrix(4, 3, Array(0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 3.0), true) + val sA2 = + new SparseMatrix(4, 3, Array(0, 1, 2, 3, 4), Array(1, 0, 1, 2), Array(2.0, 1.0, 1.0, 3.0), + true) + + val dx = new DenseVector(Array(1.0, 2.0, 3.0)) + val sx = dx.toSparse val expected = new DenseVector(Array(4.0, 1.0, 2.0, 9.0)) - assert(dA.multiply(x) ~== expected absTol 1e-15) - assert(sA.multiply(x) ~== expected absTol 1e-15) + assert(dA.multiply(dx) ~== expected absTol 1e-15) + assert(sA.multiply(dx) ~== expected absTol 1e-15) + assert(dA.multiply(sx) ~== expected absTol 1e-15) + assert(sA.multiply(sx) ~== expected absTol 1e-15) val y1 = new DenseVector(Array(1.0, 3.0, 1.0, 0.0)) val y2 = y1.copy val y3 = y1.copy val y4 = y1.copy + val y5 = y1.copy + val y6 = y1.copy + val y7 = y1.copy + val y8 = y1.copy + val y9 = y1.copy + val y10 = y1.copy + val y11 = y1.copy + val y12 = y1.copy + val y13 = y1.copy + val y14 = y1.copy + val y15 = y1.copy + val y16 = y1.copy + val expected2 = new DenseVector(Array(6.0, 7.0, 4.0, 9.0)) val expected3 = new DenseVector(Array(10.0, 8.0, 6.0, 18.0)) - gemv(1.0, dA, x, 2.0, y1) - gemv(1.0, sA, x, 2.0, y2) - gemv(2.0, dA, x, 2.0, y3) - gemv(2.0, sA, x, 2.0, y4) + gemv(1.0, dA, dx, 2.0, y1) + gemv(1.0, sA, dx, 2.0, y2) + gemv(1.0, dA, sx, 2.0, y3) + gemv(1.0, sA, sx, 2.0, y4) + + gemv(1.0, dA2, dx, 2.0, y5) + gemv(1.0, sA2, dx, 2.0, y6) + gemv(1.0, dA2, sx, 2.0, y7) + gemv(1.0, sA2, sx, 2.0, y8) + + gemv(2.0, dA, dx, 2.0, y9) + gemv(2.0, sA, dx, 2.0, y10) + gemv(2.0, dA, sx, 2.0, y11) + gemv(2.0, sA, sx, 2.0, y12) + + gemv(2.0, dA2, dx, 2.0, y13) + gemv(2.0, sA2, dx, 2.0, y14) + gemv(2.0, dA2, sx, 2.0, y15) + gemv(2.0, sA2, sx, 2.0, y16) + assert(y1 ~== expected2 absTol 1e-15) assert(y2 ~== expected2 absTol 1e-15) - assert(y3 ~== expected3 absTol 1e-15) - assert(y4 ~== expected3 absTol 1e-15) + assert(y3 ~== expected2 absTol 1e-15) + assert(y4 ~== expected2 absTol 1e-15) + + assert(y5 ~== expected2 absTol 1e-15) + assert(y6 ~== expected2 absTol 1e-15) + assert(y7 ~== expected2 absTol 1e-15) + assert(y8 ~== expected2 absTol 1e-15) + + assert(y9 ~== expected3 absTol 1e-15) + assert(y10 ~== expected3 absTol 1e-15) + assert(y11 ~== expected3 absTol 1e-15) + assert(y12 ~== expected3 absTol 1e-15) + + assert(y13 ~== expected3 absTol 1e-15) + assert(y14 ~== expected3 absTol 1e-15) + assert(y15 ~== expected3 absTol 1e-15) + assert(y16 ~== expected3 absTol 1e-15) + withClue("columns of A don't match the rows of B") { intercept[Exception] { - gemv(1.0, dA.transpose, x, 2.0, y1) + gemv(1.0, dA.transpose, dx, 2.0, y1) + } + intercept[Exception] { + gemv(1.0, sA.transpose, dx, 2.0, y1) + } + intercept[Exception] { + gemv(1.0, dA.transpose, sx, 2.0, y1) + } + intercept[Exception] { + gemv(1.0, sA.transpose, sx, 2.0, y1) } } + val dAT = new DenseMatrix(3, 4, Array(0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 3.0)) val sAT = @@ -291,7 +354,9 @@ class BLASSuite extends FunSuite { val dATT = dAT.transpose val sATT = sAT.transpose - assert(dATT.multiply(x) ~== expected absTol 1e-15) - assert(sATT.multiply(x) ~== expected absTol 1e-15) + assert(dATT.multiply(dx) ~== expected absTol 1e-15) + assert(sATT.multiply(dx) ~== expected absTol 1e-15) + assert(dATT.multiply(sx) ~== expected absTol 1e-15) + assert(sATT.multiply(sx) ~== expected absTol 1e-15) } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala index 2031032373971..dc04258e41d27 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala @@ -17,11 +17,11 @@ package org.apache.spark.mllib.linalg -import org.scalatest.FunSuite - import breeze.linalg.{DenseMatrix => BDM, CSCMatrix => BSM} -class BreezeMatrixConversionSuite extends FunSuite { +import org.apache.spark.SparkFunSuite + +class BreezeMatrixConversionSuite extends SparkFunSuite { test("dense matrix to breeze") { val mat = Matrices.dense(3, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)) val breeze = mat.toBreeze.asInstanceOf[BDM[Double]] diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala index 8abdac72902c6..3772c9235ad3a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala @@ -17,14 +17,14 @@ package org.apache.spark.mllib.linalg -import org.scalatest.FunSuite - import breeze.linalg.{DenseVector => BDV, SparseVector => BSV} +import org.apache.spark.SparkFunSuite + /** * Test Breeze vector conversions. */ -class BreezeVectorConversionSuite extends FunSuite { +class BreezeVectorConversionSuite extends SparkFunSuite { val arr = Array(0.1, 0.2, 0.3, 0.4) val n = 20 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala index 86119ec38101e..8dbb70f5d1c4c 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala @@ -20,13 +20,13 @@ package org.apache.spark.mllib.linalg import java.util.Random import org.mockito.Mockito.when -import org.scalatest.FunSuite import org.scalatest.mock.MockitoSugar._ import scala.collection.mutable.{Map => MutableMap} +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.TestingUtils._ -class MatricesSuite extends FunSuite { +class MatricesSuite extends SparkFunSuite { test("dense matrix construction") { val m = 3 val n = 2 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala index 24755e9ff46fc..c4ae0a16f7c04 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala @@ -20,12 +20,11 @@ package org.apache.spark.mllib.linalg import scala.util.Random import breeze.linalg.{DenseMatrix => BDM, squaredDistance => breezeSquaredDistance} -import org.scalatest.FunSuite -import org.apache.spark.SparkException +import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.mllib.util.TestingUtils._ -class VectorsSuite extends FunSuite { +class VectorsSuite extends SparkFunSuite { val arr = Array(0.1, 0.0, 0.3, 0.4) val n = 4 @@ -215,13 +214,13 @@ class VectorsSuite extends FunSuite { val squaredDist = breezeSquaredDistance(sparseVector1.toBreeze, sparseVector2.toBreeze) - // SparseVector vs. SparseVector - assert(Vectors.sqdist(sparseVector1, sparseVector2) ~== squaredDist relTol 1E-8) + // SparseVector vs. SparseVector + assert(Vectors.sqdist(sparseVector1, sparseVector2) ~== squaredDist relTol 1E-8) // DenseVector vs. SparseVector assert(Vectors.sqdist(denseVector1, sparseVector2) ~== squaredDist relTol 1E-8) // DenseVector vs. DenseVector assert(Vectors.sqdist(denseVector1, denseVector2) ~== squaredDist relTol 1E-8) - } + } } test("foreachActive") { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala index 949d1c9939570..93fe04c139b9a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala @@ -20,14 +20,13 @@ package org.apache.spark.mllib.linalg.distributed import java.{util => ju} import breeze.linalg.{DenseMatrix => BDM} -import org.scalatest.FunSuite -import org.apache.spark.SparkException +import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.mllib.linalg.{SparseMatrix, DenseMatrix, Matrices, Matrix} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class BlockMatrixSuite extends FunSuite with MLlibTestSparkContext { +class BlockMatrixSuite extends SparkFunSuite with MLlibTestSparkContext { val m = 5 val n = 4 @@ -57,11 +56,13 @@ class BlockMatrixSuite extends FunSuite with MLlibTestSparkContext { val random = new ju.Random() // This should generate a 4x4 grid of 1x2 blocks. val part0 = GridPartitioner(4, 7, suggestedNumPartitions = 12) + // scalastyle:off val expected0 = Array( Array(0, 0, 4, 4, 8, 8, 12), Array(1, 1, 5, 5, 9, 9, 13), Array(2, 2, 6, 6, 10, 10, 14), Array(3, 3, 7, 7, 11, 11, 15)) + // scalastyle:on for (i <- 0 until 4; j <- 0 until 7) { assert(part0.getPartition((i, j)) === expected0(i)(j)) assert(part0.getPartition((i, j, random.nextInt())) === expected0(i)(j)) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala index 04b36a9ef9990..f3728cd036a3f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala @@ -17,14 +17,13 @@ package org.apache.spark.mllib.linalg.distributed -import org.scalatest.FunSuite - import breeze.linalg.{DenseMatrix => BDM} +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.linalg.Vectors -class CoordinateMatrixSuite extends FunSuite with MLlibTestSparkContext { +class CoordinateMatrixSuite extends SparkFunSuite with MLlibTestSparkContext { val m = 5 val n = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala index 2ab53cc13db71..4a7b99a976f0a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.linalg.distributed -import org.scalatest.FunSuite - import breeze.linalg.{diag => brzDiag, DenseMatrix => BDM, DenseVector => BDV} +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD import org.apache.spark.mllib.linalg.{Matrices, Vectors} -class IndexedRowMatrixSuite extends FunSuite with MLlibTestSparkContext { +class IndexedRowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext { val m = 4 val n = 3 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala index 3309713e91f87..b6cb53d0c743e 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala @@ -20,12 +20,12 @@ package org.apache.spark.mllib.linalg.distributed import scala.util.Random import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, norm => brzNorm, svd => brzSvd} -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{Matrices, Vectors, Vector} import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} -class RowMatrixSuite extends FunSuite with MLlibTestSparkContext { +class RowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext { val m = 4 val n = 3 @@ -96,7 +96,7 @@ class RowMatrixSuite extends FunSuite with MLlibTestSparkContext { } test("similar columns") { - val colMags = Vectors.dense(Math.sqrt(126), Math.sqrt(66), Math.sqrt(94)) + val colMags = Vectors.dense(math.sqrt(126), math.sqrt(66), math.sqrt(94)) val expected = BDM( (0.0, 54.0, 72.0), (0.0, 0.0, 78.0), @@ -232,7 +232,7 @@ class RowMatrixSuite extends FunSuite with MLlibTestSparkContext { assert(summary.numNonzeros === Vectors.dense(3.0, 3.0, 4.0), "nnz mismatch") assert(summary.max === Vectors.dense(9.0, 7.0, 8.0), "max mismatch") assert(summary.min === Vectors.dense(0.0, 0.0, 1.0), "column mismatch.") - assert(summary.normL2 === Vectors.dense(Math.sqrt(126), Math.sqrt(66), Math.sqrt(94)), + assert(summary.normL2 === Vectors.dense(math.sqrt(126), math.sqrt(66), math.sqrt(94)), "magnitude mismatch.") assert(summary.normL1 === Vectors.dense(18.0, 12.0, 16.0), "L1 norm mismatch") } @@ -240,7 +240,7 @@ class RowMatrixSuite extends FunSuite with MLlibTestSparkContext { } } -class RowMatrixClusterSuite extends FunSuite with LocalClusterSparkContext { +class RowMatrixClusterSuite extends SparkFunSuite with LocalClusterSparkContext { var mat: RowMatrix = _ diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala index 86481c6e66200..a5a59e9fad5ae 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala @@ -20,8 +20,9 @@ package org.apache.spark.mllib.optimization import scala.collection.JavaConversions._ import scala.util.Random -import org.scalatest.{FunSuite, Matchers} +import org.scalatest.Matchers +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} @@ -42,7 +43,7 @@ object GradientDescentSuite { offset: Double, scale: Double, nPoints: Int, - seed: Int): Seq[LabeledPoint] = { + seed: Int): Seq[LabeledPoint] = { val rnd = new Random(seed) val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian()) @@ -61,7 +62,7 @@ object GradientDescentSuite { } } -class GradientDescentSuite extends FunSuite with MLlibTestSparkContext with Matchers { +class GradientDescentSuite extends SparkFunSuite with MLlibTestSparkContext with Matchers { test("Assert the loss is decreasing.") { val nPoints = 10000 @@ -140,7 +141,7 @@ class GradientDescentSuite extends FunSuite with MLlibTestSparkContext with Matc } } -class GradientDescentClusterSuite extends FunSuite with LocalClusterSparkContext { +class GradientDescentClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small") { val m = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala index 70c64775e4c04..d07b9d5b89227 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala @@ -19,14 +19,15 @@ package org.apache.spark.mllib.optimization import scala.util.Random -import org.scalatest.{FunSuite, Matchers} +import org.scalatest.Matchers +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} import org.apache.spark.mllib.util.TestingUtils._ -class LBFGSSuite extends FunSuite with MLlibTestSparkContext with Matchers { +class LBFGSSuite extends SparkFunSuite with MLlibTestSparkContext with Matchers { val nPoints = 10000 val A = 2.0 @@ -89,7 +90,7 @@ class LBFGSSuite extends FunSuite with MLlibTestSparkContext with Matchers { // it requires 90 iterations in GD. No matter how hard we increase // the number of iterations in GD here, the lossGD will be always // larger than lossLBFGS. This is based on observation, no theoretically guaranteed - assert(Math.abs((lossGD.last - loss.last) / loss.last) < 0.02, + assert(math.abs((lossGD.last - loss.last) / loss.last) < 0.02, "LBFGS should match GD result within 2% difference.") } @@ -229,7 +230,7 @@ class LBFGSSuite extends FunSuite with MLlibTestSparkContext with Matchers { } } -class LBFGSClusterSuite extends FunSuite with LocalClusterSparkContext { +class LBFGSClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small") { val m = 10 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala index 22855e4e8f247..d8f9b8c33963d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala @@ -19,13 +19,12 @@ package org.apache.spark.mllib.optimization import scala.util.Random -import org.scalatest.FunSuite - import org.jblas.{DoubleMatrix, SimpleBlas} +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.TestingUtils._ -class NNLSSuite extends FunSuite { +class NNLSSuite extends SparkFunSuite { /** Generate an NNLS problem whose optimal solution is the all-ones vector. */ def genOnesData(n: Int, rand: Random): (DoubleMatrix, DoubleMatrix) = { val A = new DoubleMatrix(n, n, Array.fill(n*n)(rand.nextDouble()): _*) @@ -68,12 +67,14 @@ class NNLSSuite extends FunSuite { test("NNLS: nonnegativity constraint active") { val n = 5 + // scalastyle:off val ata = new DoubleMatrix(Array( Array( 4.377, -3.531, -1.306, -0.139, 3.418), Array(-3.531, 4.344, 0.934, 0.305, -2.140), Array(-1.306, 0.934, 2.644, -0.203, -0.170), Array(-0.139, 0.305, -0.203, 5.883, 1.428), Array( 3.418, -2.140, -0.170, 1.428, 4.684))) + // scalastyle:on val atb = new DoubleMatrix(Array(-1.632, 2.115, 1.094, -1.025, -0.636)) val goodx = Array(0.13025, 0.54506, 0.2874, 0.0, 0.028628) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala index 0b646cf1ce6c4..4c6e76e47419b 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala @@ -19,13 +19,13 @@ package org.apache.spark.mllib.pmml.export import org.dmg.pmml.RegressionModel import org.dmg.pmml.RegressionNormalizationMethodType -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.classification.LogisticRegressionModel import org.apache.spark.mllib.classification.SVMModel import org.apache.spark.mllib.util.LinearDataGenerator -class BinaryClassificationPMMLModelExportSuite extends FunSuite { +class BinaryClassificationPMMLModelExportSuite extends SparkFunSuite { test("logistic regression PMML export") { val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) @@ -53,13 +53,13 @@ class BinaryClassificationPMMLModelExportSuite extends FunSuite { // ensure logistic regression has normalization method set to LOGIT assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.LOGIT) } - + test("linear SVM PMML export") { val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label) - + val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel) - + // assert that the PMML format is as expected assert(svmModelExport.isInstanceOf[PMMLModelExport]) val pmml = svmModelExport.getPmml @@ -80,5 +80,5 @@ class BinaryClassificationPMMLModelExportSuite extends FunSuite { // ensure linear SVM has normalization method set to NONE assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.NONE) } - + } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala index f9afbd888dfc5..1d32309481787 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala @@ -18,12 +18,12 @@ package org.apache.spark.mllib.pmml.export import org.dmg.pmml.RegressionModel -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel} import org.apache.spark.mllib.util.LinearDataGenerator -class GeneralizedLinearPMMLModelExportSuite extends FunSuite { +class GeneralizedLinearPMMLModelExportSuite extends SparkFunSuite { test("linear regression PMML export") { val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala index b985d0446d7b0..b3f9750afa730 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala @@ -18,12 +18,12 @@ package org.apache.spark.mllib.pmml.export import org.dmg.pmml.ClusteringModel -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.clustering.KMeansModel import org.apache.spark.mllib.linalg.Vectors -class KMeansPMMLModelExportSuite extends FunSuite { +class KMeansPMMLModelExportSuite extends SparkFunSuite { test("KMeansPMMLModelExport generate PMML format") { val clusterCenters = Array( @@ -45,5 +45,5 @@ class KMeansPMMLModelExportSuite extends FunSuite { val pmmlClusteringModel = pmml.getModels.get(0).asInstanceOf[ClusteringModel] assert(pmmlClusteringModel.getNumberOfClusters === clusterCenters.length) } - + } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala index f28a4ac8ad01f..af49450961750 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.pmml.export -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.classification.{LogisticRegressionModel, SVMModel} import org.apache.spark.mllib.clustering.KMeansModel import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel} import org.apache.spark.mllib.util.LinearDataGenerator -class PMMLModelExportFactorySuite extends FunSuite { +class PMMLModelExportFactorySuite extends SparkFunSuite { test("PMMLModelExportFactory create KMeansPMMLModelExport when passing a KMeansModel") { val clusterCenters = Array( @@ -61,25 +60,25 @@ class PMMLModelExportFactorySuite extends FunSuite { test("PMMLModelExportFactory create BinaryClassificationPMMLModelExport " + "when passing a LogisticRegressionModel or SVMModel") { val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) - + val logisticRegressionModel = new LogisticRegressionModel(linearInput(0).features, linearInput(0).label) val logisticRegressionModelExport = PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel) assert(logisticRegressionModelExport.isInstanceOf[BinaryClassificationPMMLModelExport]) - + val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label) val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel) assert(svmModelExport.isInstanceOf[BinaryClassificationPMMLModelExport]) } - + test("PMMLModelExportFactory throw IllegalArgumentException " + "when passing a Multinomial Logistic Regression") { /** 3 classes, 2 features */ val multiclassLogisticRegressionModel = new LogisticRegressionModel( - weights = Vectors.dense(0.1, 0.2, 0.3, 0.4), intercept = 1.0, + weights = Vectors.dense(0.1, 0.2, 0.3, 0.4), intercept = 1.0, numFeatures = 2, numClasses = 3) - + intercept[IllegalArgumentException] { PMMLModelExportFactory.createPMMLModelExport(multiclassLogisticRegressionModel) } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala index b792d819fdabb..a5ca1518f82f5 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala @@ -19,12 +19,11 @@ package org.apache.spark.mllib.random import scala.math -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.util.StatCounter // TODO update tests to use TestingUtils for floating point comparison after PR 1367 is merged -class RandomDataGeneratorSuite extends FunSuite { +class RandomDataGeneratorSuite extends SparkFunSuite { def apiChecks(gen: RandomDataGenerator[Double]) { // resetting seed should generate the same sequence of random numbers diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala index 63f2ea916d457..413db2000d6d7 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala @@ -19,8 +19,7 @@ package org.apache.spark.mllib.random import scala.collection.mutable.ArrayBuffer -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.SparkContext._ import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.rdd.{RandomRDDPartition, RandomRDD} @@ -34,7 +33,7 @@ import org.apache.spark.util.StatCounter * * TODO update tests to use TestingUtils for floating point comparison after PR 1367 is merged */ -class RandomRDDsSuite extends FunSuite with MLlibTestSparkContext with Serializable { +class RandomRDDsSuite extends SparkFunSuite with MLlibTestSparkContext with Serializable { def testGeneratedRDD(rdd: RDD[Double], expectedSize: Long, diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala index 57216e8eb4a55..10f5a2be48f7c 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.rdd -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.rdd.MLPairRDDFunctions._ -class MLPairRDDFunctionsSuite extends FunSuite with MLlibTestSparkContext { +class MLPairRDDFunctionsSuite extends SparkFunSuite with MLlibTestSparkContext { test("topByKey") { val topMap = sc.parallelize(Array((1, 7), (1, 3), (1, 6), (1, 1), (1, 2), (3, 2), (3, 7), (5, 1), (3, 5)), 2) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala index 6d6c0aa5be812..bc64172614830 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.rdd -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.rdd.RDDFunctions._ -class RDDFunctionsSuite extends FunSuite with MLlibTestSparkContext { +class RDDFunctionsSuite extends SparkFunSuite with MLlibTestSparkContext { test("sliding") { val data = 0 until 6 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala index b3798940ddc38..05b87728d6fdb 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala @@ -21,9 +21,9 @@ import scala.collection.JavaConversions._ import scala.math.abs import scala.util.Random -import org.scalatest.FunSuite import org.jblas.DoubleMatrix +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.storage.StorageLevel @@ -84,7 +84,7 @@ object ALSSuite { } -class ALSSuite extends FunSuite with MLlibTestSparkContext { +class ALSSuite extends SparkFunSuite with MLlibTestSparkContext { test("rank-1 matrices") { testALS(50, 100, 1, 15, 0.7, 0.3) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala index 2c92866f3893d..2c8ed057a516a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala @@ -17,14 +17,13 @@ package org.apache.spark.mllib.recommendation -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.rdd.RDD import org.apache.spark.util.Utils -class MatrixFactorizationModelSuite extends FunSuite with MLlibTestSparkContext { +class MatrixFactorizationModelSuite extends SparkFunSuite with MLlibTestSparkContext { val rank = 2 var userFeatures: RDD[(Int, Array[Double])] = _ diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala index 8e12340bbd9d6..ea4f2865757c1 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala @@ -17,16 +17,17 @@ package org.apache.spark.mllib.regression -import org.scalatest.{Matchers, FunSuite} +import org.scalatest.Matchers +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.util.Utils -class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with Matchers { +class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext with Matchers { private def round(d: Double) = { - Math.round(d * 100).toDouble / 100 + math.round(d * 100).toDouble / 100 } private def generateIsotonicInput(labels: Seq[Double]): Seq[(Double, Double, Double)] = { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala index 110c44a7193fd..d8364a06de4da 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala @@ -17,11 +17,10 @@ package org.apache.spark.mllib.regression -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors -class LabeledPointSuite extends FunSuite { +class LabeledPointSuite extends SparkFunSuite { test("parse labeled points") { val points = Seq( diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala index c9f5dc069ef2e..08a152ffc7a23 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala @@ -19,8 +19,7 @@ package org.apache.spark.mllib.regression import scala.util.Random -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.{LocalClusterSparkContext, LinearDataGenerator, MLlibTestSparkContext} @@ -32,7 +31,7 @@ private object LassoSuite { val model = new LassoModel(weights = Vectors.dense(0.1, 0.2, 0.3), intercept = 0.5) } -class LassoSuite extends FunSuite with MLlibTestSparkContext { +class LassoSuite extends SparkFunSuite with MLlibTestSparkContext { def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) { val numOffPredictions = predictions.zip(input).count { case (prediction, expected) => @@ -67,11 +66,12 @@ class LassoSuite extends FunSuite with MLlibTestSparkContext { assert(weight1 >= -1.60 && weight1 <= -1.40, weight1 + " not in [-1.6, -1.4]") assert(weight2 >= -1.0e-3 && weight2 <= 1.0e-3, weight2 + " not in [-0.001, 0.001]") - val validationData = LinearDataGenerator.generateLinearInput(A, Array[Double](B,C), nPoints, 17) + val validationData = LinearDataGenerator + .generateLinearInput(A, Array[Double](B, C), nPoints, 17) .map { case LabeledPoint(label, features) => LabeledPoint(label, Vectors.dense(1.0 +: features.toArray)) } - val validationRDD = sc.parallelize(validationData, 2) + val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) @@ -110,11 +110,12 @@ class LassoSuite extends FunSuite with MLlibTestSparkContext { assert(weight1 >= -1.60 && weight1 <= -1.40, weight1 + " not in [-1.6, -1.4]") assert(weight2 >= -1.0e-3 && weight2 <= 1.0e-3, weight2 + " not in [-0.001, 0.001]") - val validationData = LinearDataGenerator.generateLinearInput(A, Array[Double](B,C), nPoints, 17) + val validationData = LinearDataGenerator + .generateLinearInput(A, Array[Double](B, C), nPoints, 17) .map { case LabeledPoint(label, features) => LabeledPoint(label, Vectors.dense(1.0 +: features.toArray)) } - val validationRDD = sc.parallelize(validationData,2) + val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) @@ -141,7 +142,7 @@ class LassoSuite extends FunSuite with MLlibTestSparkContext { } } -class LassoClusterSuite extends FunSuite with LocalClusterSparkContext { +class LassoClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small in both training and prediction") { val m = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala index 3781931c2f819..f88a1c33c9f7c 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala @@ -19,8 +19,7 @@ package org.apache.spark.mllib.regression import scala.util.Random -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.{LocalClusterSparkContext, LinearDataGenerator, MLlibTestSparkContext} @@ -32,7 +31,7 @@ private object LinearRegressionSuite { val model = new LinearRegressionModel(weights = Vectors.dense(0.1, 0.2, 0.3), intercept = 0.5) } -class LinearRegressionSuite extends FunSuite with MLlibTestSparkContext { +class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) { val numOffPredictions = predictions.zip(input).count { case (prediction, expected) => @@ -150,7 +149,7 @@ class LinearRegressionSuite extends FunSuite with MLlibTestSparkContext { } } -class LinearRegressionClusterSuite extends FunSuite with LocalClusterSparkContext { +class LinearRegressionClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small in both training and prediction") { val m = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala index d6c93cc0e49cd..7a781fee634c8 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala @@ -20,8 +20,8 @@ package org.apache.spark.mllib.regression import scala.util.Random import org.jblas.DoubleMatrix -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.{LocalClusterSparkContext, LinearDataGenerator, MLlibTestSparkContext} @@ -33,7 +33,7 @@ private object RidgeRegressionSuite { val model = new RidgeRegressionModel(weights = Vectors.dense(0.1, 0.2, 0.3), intercept = 0.5) } -class RidgeRegressionSuite extends FunSuite with MLlibTestSparkContext { +class RidgeRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { def predictionError(predictions: Seq[Double], input: Seq[LabeledPoint]): Double = { predictions.zip(input).map { case (prediction, expected) => @@ -101,7 +101,7 @@ class RidgeRegressionSuite extends FunSuite with MLlibTestSparkContext { } } -class RidgeRegressionClusterSuite extends FunSuite with LocalClusterSparkContext { +class RidgeRegressionClusterSuite extends SparkFunSuite with LocalClusterSparkContext { test("task size should be small in both training and prediction") { val m = 4 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala index 26604dbe6c1ef..f5e2d31056cbd 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala @@ -19,14 +19,13 @@ package org.apache.spark.mllib.regression import scala.collection.mutable.ArrayBuffer -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.LinearDataGenerator import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.TestSuiteBase -class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { +class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase { // use longer wait time to ensure job completion override def maxWaitTimeMillis: Int = 20000 @@ -167,4 +166,22 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { val error = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints).toList assert((error.head - error.last) > 2) } + + // Test empty RDDs in a stream + test("handling empty RDDs in a stream") { + val model = new StreamingLinearRegressionWithSGD() + .setInitialWeights(Vectors.dense(0.0, 0.0)) + .setStepSize(0.2) + .setNumIterations(25) + val numBatches = 10 + val nPoints = 100 + val emptyInput = Seq.empty[Seq[LabeledPoint]] + val ssc = setupStreams(emptyInput, + (inputDStream: DStream[LabeledPoint]) => { + model.trainOn(inputDStream) + model.predictOnValues(inputDStream.map(x => (x.label, x.features))) + } + ) + val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala index d20a09b4b4925..c292ced75e870 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala @@ -17,16 +17,15 @@ package org.apache.spark.mllib.stat -import org.scalatest.FunSuite - import breeze.linalg.{DenseMatrix => BDM, Matrix => BM} +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.stat.correlation.{Correlations, PearsonCorrelation, SpearmanCorrelation} import org.apache.spark.mllib.util.MLlibTestSparkContext -class CorrelationSuite extends FunSuite with MLlibTestSparkContext { +class CorrelationSuite extends SparkFunSuite with MLlibTestSparkContext { // test input data val xData = Array(1.0, 0.0, -2.0) @@ -96,11 +95,13 @@ class CorrelationSuite extends FunSuite with MLlibTestSparkContext { val X = sc.parallelize(data) val defaultMat = Statistics.corr(X) val pearsonMat = Statistics.corr(X, "pearson") + // scalastyle:off val expected = BDM( (1.00000000, 0.05564149, Double.NaN, 0.4004714), (0.05564149, 1.00000000, Double.NaN, 0.9135959), (Double.NaN, Double.NaN, 1.00000000, Double.NaN), - (0.40047142, 0.91359586, Double.NaN,1.0000000)) + (0.40047142, 0.91359586, Double.NaN, 1.0000000)) + // scalastyle:on assert(matrixApproxEqual(defaultMat.toBreeze, expected)) assert(matrixApproxEqual(pearsonMat.toBreeze, expected)) } @@ -108,11 +109,13 @@ class CorrelationSuite extends FunSuite with MLlibTestSparkContext { test("corr(X) spearman") { val X = sc.parallelize(data) val spearmanMat = Statistics.corr(X, "spearman") + // scalastyle:off val expected = BDM( (1.0000000, 0.1054093, Double.NaN, 0.4000000), (0.1054093, 1.0000000, Double.NaN, 0.9486833), (Double.NaN, Double.NaN, 1.00000000, Double.NaN), (0.4000000, 0.9486833, Double.NaN, 1.0000000)) + // scalastyle:on assert(matrixApproxEqual(spearmanMat.toBreeze, expected)) } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala index 15418e6035965..b084a5fb4313f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala @@ -19,16 +19,14 @@ package org.apache.spark.mllib.stat import java.util.Random -import org.scalatest.FunSuite - -import org.apache.spark.SparkException +import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.mllib.linalg.{DenseVector, Matrices, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.stat.test.ChiSqTest import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class HypothesisTestSuite extends FunSuite with MLlibTestSparkContext { +class HypothesisTestSuite extends SparkFunSuite with MLlibTestSparkContext { test("chi squared pearson goodness of fit") { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala index 16ecae23dd9d4..5feccdf33681a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala @@ -17,31 +17,32 @@ package org.apache.spark.mllib.stat -import org.scalatest.FunSuite - import org.apache.commons.math3.distribution.NormalDistribution +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext -class KernelDensitySuite extends FunSuite with MLlibTestSparkContext { +class KernelDensitySuite extends SparkFunSuite with MLlibTestSparkContext { test("kernel density single sample") { val rdd = sc.parallelize(Array(5.0)) val evaluationPoints = Array(5.0, 6.0) - val densities = KernelDensity.estimate(rdd, 3.0, evaluationPoints) + val densities = new KernelDensity().setSample(rdd).setBandwidth(3.0).estimate(evaluationPoints) val normal = new NormalDistribution(5.0, 3.0) val acceptableErr = 1e-6 - assert(densities(0) - normal.density(5.0) < acceptableErr) - assert(densities(0) - normal.density(6.0) < acceptableErr) + assert(math.abs(densities(0) - normal.density(5.0)) < acceptableErr) + assert(math.abs(densities(1) - normal.density(6.0)) < acceptableErr) } test("kernel density multiple samples") { val rdd = sc.parallelize(Array(5.0, 10.0)) val evaluationPoints = Array(5.0, 6.0) - val densities = KernelDensity.estimate(rdd, 3.0, evaluationPoints) + val densities = new KernelDensity().setSample(rdd).setBandwidth(3.0).estimate(evaluationPoints) val normal1 = new NormalDistribution(5.0, 3.0) val normal2 = new NormalDistribution(10.0, 3.0) val acceptableErr = 1e-6 - assert(densities(0) - (normal1.density(5.0) + normal2.density(5.0)) / 2 < acceptableErr) - assert(densities(0) - (normal1.density(6.0) + normal2.density(6.0)) / 2 < acceptableErr) + assert(math.abs( + densities(0) - (normal1.density(5.0) + normal2.density(5.0)) / 2) < acceptableErr) + assert(math.abs( + densities(1) - (normal1.density(6.0) + normal2.density(6.0)) / 2) < acceptableErr) } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala index 23b0eec865de6..07efde4f5e6dc 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.mllib.stat -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.TestingUtils._ -class MultivariateOnlineSummarizerSuite extends FunSuite { +class MultivariateOnlineSummarizerSuite extends SparkFunSuite { test("basic error handing") { val summarizer = new MultivariateOnlineSummarizer diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussianSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussianSuite.scala index fac2498e4dcb3..aa60deb665aeb 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussianSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussianSuite.scala @@ -17,49 +17,48 @@ package org.apache.spark.mllib.stat.distribution -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{ Vectors, Matrices } import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -class MultivariateGaussianSuite extends FunSuite with MLlibTestSparkContext { +class MultivariateGaussianSuite extends SparkFunSuite with MLlibTestSparkContext { test("univariate") { val x1 = Vectors.dense(0.0) val x2 = Vectors.dense(1.5) - + val mu = Vectors.dense(0.0) val sigma1 = Matrices.dense(1, 1, Array(1.0)) val dist1 = new MultivariateGaussian(mu, sigma1) assert(dist1.pdf(x1) ~== 0.39894 absTol 1E-5) assert(dist1.pdf(x2) ~== 0.12952 absTol 1E-5) - + val sigma2 = Matrices.dense(1, 1, Array(4.0)) val dist2 = new MultivariateGaussian(mu, sigma2) assert(dist2.pdf(x1) ~== 0.19947 absTol 1E-5) assert(dist2.pdf(x2) ~== 0.15057 absTol 1E-5) } - + test("multivariate") { val x1 = Vectors.dense(0.0, 0.0) val x2 = Vectors.dense(1.0, 1.0) - + val mu = Vectors.dense(0.0, 0.0) val sigma1 = Matrices.dense(2, 2, Array(1.0, 0.0, 0.0, 1.0)) val dist1 = new MultivariateGaussian(mu, sigma1) assert(dist1.pdf(x1) ~== 0.15915 absTol 1E-5) assert(dist1.pdf(x2) ~== 0.05855 absTol 1E-5) - + val sigma2 = Matrices.dense(2, 2, Array(4.0, -1.0, -1.0, 2.0)) val dist2 = new MultivariateGaussian(mu, sigma2) assert(dist2.pdf(x1) ~== 0.060155 absTol 1E-5) assert(dist2.pdf(x2) ~== 0.033971 absTol 1E-5) } - + test("multivariate degenerate") { val x1 = Vectors.dense(0.0, 0.0) val x2 = Vectors.dense(1.0, 1.0) - + val mu = Vectors.dense(0.0, 0.0) val sigma = Matrices.dense(2, 2, Array(1.0, 1.0, 1.0, 1.0)) val dist = new MultivariateGaussian(mu, sigma) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala index ce983eb27fa35..356d957f15909 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala @@ -20,8 +20,7 @@ package org.apache.spark.mllib.tree import scala.collection.JavaConverters._ import scala.collection.mutable -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.configuration.Algo._ @@ -34,7 +33,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.util.Utils -class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { +class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext { ///////////////////////////////////////////////////////////////////////////// // Tests examining individual elements of training @@ -859,7 +858,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } } -object DecisionTreeSuite extends FunSuite { +object DecisionTreeSuite extends SparkFunSuite { def validateClassifier( model: DecisionTreeModel, diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala index 55b0bac7d49fe..84dd3b342d4c0 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala @@ -17,8 +17,7 @@ package org.apache.spark.mllib.tree -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.configuration.Algo._ import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Strategy} @@ -32,7 +31,7 @@ import org.apache.spark.util.Utils /** * Test suite for [[GradientBoostedTrees]]. */ -class GradientBoostedTreesSuite extends FunSuite with MLlibTestSparkContext { +class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext { test("Regression with continuous features: SquaredError") { GradientBoostedTreesSuite.testCombinations.foreach { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/ImpuritySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/ImpuritySuite.scala index 92b498580af03..49aff21fe7914 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/ImpuritySuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/ImpuritySuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.tree -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.tree.impurity.{EntropyAggregator, GiniAggregator} import org.apache.spark.mllib.util.MLlibTestSparkContext /** * Test suites for [[GiniAggregator]] and [[EntropyAggregator]]. */ -class ImpuritySuite extends FunSuite with MLlibTestSparkContext { +class ImpuritySuite extends SparkFunSuite with MLlibTestSparkContext { test("Gini impurity does not support negative labels") { val gini = new GiniAggregator(2) intercept[IllegalArgumentException] { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala index ee3bc98486862..e6df5d974bf36 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala @@ -19,8 +19,7 @@ package org.apache.spark.mllib.tree import scala.collection.mutable -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.configuration.Algo._ @@ -35,7 +34,7 @@ import org.apache.spark.util.Utils /** * Test suite for [[RandomForest]]. */ -class RandomForestSuite extends FunSuite with MLlibTestSparkContext { +class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext { def binaryClassificationTestWithContinuousFeatures(strategy: Strategy) { val arr = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000) val rdd = sc.parallelize(arr) @@ -196,7 +195,6 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext { numClasses = 3, categoricalFeaturesInfo = categoricalFeaturesInfo) val model = RandomForest.trainClassifier(input, strategy, numTrees = 2, featureSubsetStrategy = "sqrt", seed = 12345) - EnsembleTestHelper.validateClassifier(model, arr, 1.0) } test("subsampling rate in RandomForest"){ diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/impl/BaggedPointSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/impl/BaggedPointSuite.scala index b184e936672ca..9d756da410325 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/impl/BaggedPointSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/impl/BaggedPointSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.mllib.tree.impl -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.tree.EnsembleTestHelper import org.apache.spark.mllib.util.MLlibTestSparkContext /** * Test suite for [[BaggedPoint]]. */ -class BaggedPointSuite extends FunSuite with MLlibTestSparkContext { +class BaggedPointSuite extends SparkFunSuite with MLlibTestSparkContext { test("BaggedPoint RDD: without subsampling") { val arr = EnsembleTestHelper.generateOrderedLabeledPoints(1, 1000) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala index 668fc1d43c5d6..70219e9ad9d3e 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala @@ -21,19 +21,19 @@ import java.io.File import scala.io.Source -import org.scalatest.FunSuite - import breeze.linalg.{squaredDistance => breezeSquaredDistance} import com.google.common.base.Charsets import com.google.common.io.Files +import org.apache.spark.SparkException +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils._ import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.util.Utils -class MLUtilsSuite extends FunSuite with MLlibTestSparkContext { +class MLUtilsSuite extends SparkFunSuite with MLlibTestSparkContext { test("epsilon computation") { assert(1.0 + EPSILON > 1.0, s"EPSILON is too small: $EPSILON.") @@ -63,7 +63,7 @@ class MLUtilsSuite extends FunSuite with MLlibTestSparkContext { val fastSquaredDist3 = fastSquaredDistance(v2, norm2, v3, norm3, precision) assert((fastSquaredDist3 - squaredDist2) <= precision * squaredDist2, s"failed with m = $m") - if (m > 10) { + if (m > 10) { val v4 = Vectors.sparse(n, indices.slice(0, m - 10), indices.map(i => a(i) + 0.5).slice(0, m - 10)) val norm4 = Vectors.norm(v4, 2.0) @@ -109,6 +109,40 @@ class MLUtilsSuite extends FunSuite with MLlibTestSparkContext { Utils.deleteRecursively(tempDir) } + test("loadLibSVMFile throws IllegalArgumentException when indices is zero-based") { + val lines = + """ + |0 + |0 0:4.0 4:5.0 6:6.0 + """.stripMargin + val tempDir = Utils.createTempDir() + val file = new File(tempDir.getPath, "part-00000") + Files.write(lines, file, Charsets.US_ASCII) + val path = tempDir.toURI.toString + + intercept[SparkException] { + loadLibSVMFile(sc, path).collect() + } + Utils.deleteRecursively(tempDir) + } + + test("loadLibSVMFile throws IllegalArgumentException when indices is not in ascending order") { + val lines = + """ + |0 + |0 3:4.0 2:5.0 6:6.0 + """.stripMargin + val tempDir = Utils.createTempDir() + val file = new File(tempDir.getPath, "part-00000") + Files.write(lines, file, Charsets.US_ASCII) + val path = tempDir.toURI.toString + + intercept[SparkException] { + loadLibSVMFile(sc, path).collect() + } + Utils.deleteRecursively(tempDir) + } + test("saveAsLibSVMFile") { val examples = sc.parallelize(Seq( LabeledPoint(1.1, Vectors.sparse(3, Seq((0, 1.23), (2, 4.56)))), @@ -168,7 +202,7 @@ class MLUtilsSuite extends FunSuite with MLlibTestSparkContext { "Each training+validation set combined should contain all of the data.") } // K fold cross validation should only have each element in the validation set exactly once - assert(foldedRdds.map(_._2).reduce((x,y) => x.union(y)).collect().sorted === + assert(foldedRdds.map(_._2).reduce((x, y) => x.union(y)).collect().sorted === data.collect().sorted) } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLlibTestSparkContext.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLlibTestSparkContext.scala index b658889476d37..5d1796ef65722 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLlibTestSparkContext.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLlibTestSparkContext.scala @@ -17,13 +17,14 @@ package org.apache.spark.mllib.util -import org.scalatest.Suite -import org.scalatest.BeforeAndAfterAll +import org.scalatest.{BeforeAndAfterAll, Suite} import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.sql.SQLContext trait MLlibTestSparkContext extends BeforeAndAfterAll { self: Suite => @transient var sc: SparkContext = _ + @transient var sqlContext: SQLContext = _ override def beforeAll() { super.beforeAll() @@ -31,12 +32,15 @@ trait MLlibTestSparkContext extends BeforeAndAfterAll { self: Suite => .setMaster("local[2]") .setAppName("MLlibUnitTest") sc = new SparkContext(conf) + sqlContext = new SQLContext(sc) } override def afterAll() { + sqlContext = null if (sc != null) { sc.stop() } + sc = null super.afterAll() } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala index f68fb95eac4e4..8dcb9ba9be108 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala @@ -17,11 +17,9 @@ package org.apache.spark.mllib.util -import org.scalatest.FunSuite +import org.apache.spark.{SparkException, SparkFunSuite} -import org.apache.spark.SparkException - -class NumericParserSuite extends FunSuite { +class NumericParserSuite extends SparkFunSuite { test("parser") { val s = "((1.0,2e3),-4,[5e-6,7.0E8],+9)" diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala index 59e6c778806f4..8f475f30249d6 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala @@ -17,12 +17,12 @@ package org.apache.spark.mllib.util +import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors -import org.scalatest.FunSuite import org.apache.spark.mllib.util.TestingUtils._ import org.scalatest.exceptions.TestFailedException -class TestingUtilsSuite extends FunSuite { +class TestingUtilsSuite extends SparkFunSuite { test("Comparing doubles using relative error.") { diff --git a/network/common/pom.xml b/network/common/pom.xml index 0c3147761cfc5..a85e0a66f4a30 100644 --- a/network/common/pom.xml +++ b/network/common/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java index 941ef95772e16..f4fadb1ee3b8d 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java @@ -31,6 +31,7 @@ import io.netty.channel.ChannelOption; import io.netty.channel.EventLoopGroup; import io.netty.channel.socket.SocketChannel; +import org.apache.spark.network.util.JavaUtils; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -65,7 +66,12 @@ public TransportServer( this.appRpcHandler = appRpcHandler; this.bootstraps = Lists.newArrayList(Preconditions.checkNotNull(bootstraps)); - init(portToBind); + try { + init(portToBind); + } catch (RuntimeException e) { + JavaUtils.closeQuietly(this); + throw e; + } } public int getPort() { @@ -114,7 +120,8 @@ protected void initChannel(SocketChannel ch) throws Exception { } }); - bindRightPort(portToBind); + channelFuture = bootstrap.bind(new InetSocketAddress(portToBind)); + channelFuture.syncUninterruptibly(); port = ((InetSocketAddress) channelFuture.channel().localAddress()).getPort(); logger.debug("Shuffle server started on port :" + port); @@ -135,38 +142,4 @@ public void close() { } bootstrap = null; } - - /** - * Attempt to bind to the specified port up to a fixed number of retries. - * If all attempts fail after the max number of retries, exit. - */ - private void bindRightPort(int portToBind) { - int maxPortRetries = conf.portMaxRetries(); - - for (int i = 0; i <= maxPortRetries; i++) { - int tryPort = -1; - if (0 == portToBind) { - // Do not increment port if tryPort is 0, which is treated as a special port - tryPort = 0; - } else { - // If the new port wraps around, do not try a privilege port - tryPort = ((portToBind + i - 1024) % (65536 - 1024)) + 1024; - } - try { - channelFuture = bootstrap.bind(new InetSocketAddress(tryPort)); - channelFuture.syncUninterruptibly(); - return; - } catch (Exception e) { - logger.warn("Netty service could not bind on port " + tryPort + - ". Attempting the next port."); - if (i >= maxPortRetries) { - logger.error(e.getMessage() + ": Netty server failed after " - + maxPortRetries + " retries."); - - // If it can't find a right port, it should exit directly. - System.exit(-1); - } - } - } - } } diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml index 7dc7c65825e34..4b5bfcb6f04bc 100644 --- a/network/shuffle/pom.xml +++ b/network/shuffle/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java index dd08e24cade23..022ed88a16480 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java @@ -108,7 +108,8 @@ public ManagedBuffer getBlockData(String appId, String execId, String blockId) { if ("org.apache.spark.shuffle.hash.HashShuffleManager".equals(executor.shuffleManager)) { return getHashBasedShuffleBlockData(executor, blockId); - } else if ("org.apache.spark.shuffle.sort.SortShuffleManager".equals(executor.shuffleManager)) { + } else if ("org.apache.spark.shuffle.sort.SortShuffleManager".equals(executor.shuffleManager) + || "org.apache.spark.shuffle.unsafe.UnsafeShuffleManager".equals(executor.shuffleManager)) { return getSortBasedShuffleBlockData(executor, shuffleId, mapId, reduceId); } else { throw new UnsupportedOperationException( diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java index 60485bace643c..ce954b8a289e4 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java @@ -24,6 +24,9 @@ import org.apache.spark.network.protocol.Encoders; +// Needed by ScalaDoc. See SPARK-7726 +import static org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; + /** Request to read a set of blocks. Returns {@link StreamHandle}. */ public class OpenBlocks extends BlockTransferMessage { public final String appId; diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java index 38acae3b31d64..cca8b17c4f129 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java @@ -22,6 +22,9 @@ import org.apache.spark.network.protocol.Encoders; +// Needed by ScalaDoc. See SPARK-7726 +import static org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; + /** * Initial registration message between an executor and its local shuffle server. * Returns nothing (empty bye array). diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java index 9a9220211a50c..1915295aa6cc2 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java @@ -20,6 +20,9 @@ import com.google.common.base.Objects; import io.netty.buffer.ByteBuf; +// Needed by ScalaDoc. See SPARK-7726 +import static org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; + /** * Identifier for a fixed number of chunks to read from a stream created by an "open blocks" * message. This is used by {@link org.apache.spark.network.shuffle.OneForOneBlockFetcher}. diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java index 2ff9aaa650f92..3caed59d508fd 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java @@ -24,6 +24,9 @@ import org.apache.spark.network.protocol.Encoders; +// Needed by ScalaDoc. See SPARK-7726 +import static org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; + /** Request to upload a block with a certain StorageLevel. Returns nothing (empty byte array). */ public class UploadBlock extends BlockTransferMessage { diff --git a/network/yarn/pom.xml b/network/yarn/pom.xml index 1e2e9c80af6cc..a99f7c4392d3d 100644 --- a/network/yarn/pom.xml +++ b/network/yarn/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml diff --git a/pom.xml b/pom.xml index 94cf28332cf47..6d4f717d4931b 100644 --- a/pom.xml +++ b/pom.xml @@ -26,7 +26,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT pom Spark Project Parent POM http://spark.apache.org/ @@ -107,35 +107,37 @@ examples repl launcher + external/kafka + external/kafka-assembly UTF-8 UTF-8 - org.spark-project.akka - 2.3.4-spark - 1.6 + com.typesafe.akka + 2.3.11 + 1.7 spark - 2.0.1 0.21.1 shaded-protobuf 1.7.10 1.2.17 2.2.0 - 2.4.1 + 2.5.0 ${hadoop.version} - 0.98.7-hadoop1 + 0.98.7-hadoop2 hbase 1.4.0 3.4.5 + 2.4.0 org.spark-project.hive 0.13.1a 0.13.1 10.10.1.1 - 1.6.0rc3 - 1.2.3 + 1.7.0 + 1.2.4 8.1.14.v20131031 3.0.0.v201112011016 0.5.0 @@ -143,10 +145,10 @@ 2.0.8 3.1.0 1.7.7 - + hadoop2 0.7.1 - 1.8.3 - 1.1.0 + 1.9.16 + 1.2.1 4.3.2 3.4.1 ${project.build.directory}/spark-test-classpath.txt @@ -155,7 +157,7 @@ ${scala.version} org.scala-lang 3.6.3 - 1.8.8 + 1.9.13 2.4.4 1.1.1.7 1.1.2 @@ -177,7 +179,7 @@ compile ${session.executionRootDirectory} @@ -266,6 +268,18 @@ false + + + spark-1.4-staging + Spark 1.4 RC4 Staging Repository + https://repository.apache.org/content/repositories/orgapachespark-1112 + + true + + + false + + @@ -481,7 +495,7 @@ com.ning compress-lzf - 1.0.0 + 1.0.3 org.xerial.snappy @@ -492,7 +506,7 @@ net.jpountz.lz4 lz4 - 1.2.0 + 1.3.0 com.clearspring.analytics @@ -573,7 +587,7 @@ io.netty netty-all - 4.0.23.Final + 4.0.28.Final org.apache.derby @@ -669,7 +683,7 @@ org.mockito mockito-all - 1.9.0 + 1.9.5 test @@ -684,6 +698,18 @@ 4.10 test + + org.hamcrest + hamcrest-core + 1.3 + test + + + org.hamcrest + hamcrest-library + 1.3 + test + com.novocode junit-interface @@ -693,7 +719,7 @@ org.apache.curator curator-recipes - 2.4.0 + ${curator.version} ${hadoop.deps.scope} @@ -702,6 +728,16 @@ + + org.apache.curator + curator-client + ${curator.version} + + + org.apache.curator + curator-framework + ${curator.version} + org.apache.hadoop hadoop-client @@ -1044,13 +1080,13 @@ - com.twitter + org.apache.parquet parquet-column ${parquet.version} ${parquet.deps.scope} - com.twitter + org.apache.parquet parquet-hadoop ${parquet.version} ${parquet.deps.scope} @@ -1180,15 +1216,6 @@ -target ${java.version} - - - - org.scalamacros - paradise_${scala.version} - ${scala.macros.version} - - @@ -1217,7 +1244,7 @@ **/*Suite.java ${project.build.directory}/surefire-reports - -Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m + -Xmx3g -Xss4096k -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m + false @@ -1517,6 +1550,26 @@ + + + org.apache.maven.plugins + maven-antrun-plugin + + + create-tmp-dir + generate-test-resources + + run + + + + + + + + + + org.apache.maven.plugins @@ -1632,26 +1685,29 @@ --> - hadoop-2.2 + hadoop-1 - 2.2.0 - 2.5.0 - 0.98.7-hadoop2 - hadoop2 - 1.9.13 + 1.0.4 + 2.4.1 + 0.98.7-hadoop1 + hadoop1 + 1.8.8 + org.spark-project.akka + 2.3.4-spark + + hadoop-2.2 + + + hadoop-2.3 2.3.0 - 2.5.0 0.9.3 - 0.98.7-hadoop2 3.1.1 - hadoop2 - 1.9.13 @@ -1659,12 +1715,19 @@ hadoop-2.4 2.4.0 - 2.5.0 0.9.3 - 0.98.7-hadoop2 3.1.1 - hadoop2 - 1.9.13 + +     + + + hadoop-2.6 + + 2.6.0 + 0.9.3 + 3.1.1 + 3.4.6 + 2.6.0 @@ -1698,7 +1761,7 @@ org.apache.curator curator-recipes - 2.4.0 + ${curator.version} org.apache.zookeeper @@ -1720,22 +1783,6 @@ sql/hive-thriftserver - - hive-0.12.0 - - 0.12.0-protobuf-2.5 - 0.12.0 - 10.4.2.0 - - - - hive-0.13.1 - - 0.13.1a - 0.13.1 - 10.10.1.1 - - scala-2.10 @@ -1748,10 +1795,6 @@ ${scala.version} org.scala-lang - - external/kafka - external/kafka-assembly - diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala index dde92949fa175..5812b72f0aa78 100644 --- a/project/MimaBuild.scala +++ b/project/MimaBuild.scala @@ -91,7 +91,8 @@ object MimaBuild { def mimaSettings(sparkHome: File, projectRef: ProjectRef) = { val organization = "org.apache.spark" - val previousSparkVersion = "1.3.0" + // TODO: Change this once Spark 1.4.0 is released + val previousSparkVersion = "1.4.0-rc4" val fullId = "spark-" + projectRef.project + "_2.10" mimaDefaultSettings ++ Seq(previousArtifact := Some(organization % fullId % previousSparkVersion), diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index cfe387faec14b..015d0296dd369 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -34,10 +34,34 @@ import com.typesafe.tools.mima.core.ProblemFilters._ object MimaExcludes { def excludes(version: String) = version match { + case v if v.startsWith("1.5") => + Seq( + MimaBuild.excludeSparkPackage("deploy"), + // These are needed if checking against the sbt build, since they are part of + // the maven-generated artifacts in 1.3. + excludePackage("org.spark-project.jetty"), + MimaBuild.excludeSparkPackage("unused"), + // JavaRDDLike is not meant to be extended by user programs + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.api.java.JavaRDDLike.partitioner"), + // Modification of private static method + ProblemFilters.exclude[IncompatibleMethTypeProblem]( + "org.apache.spark.streaming.kafka.KafkaUtils.org$apache$spark$streaming$kafka$KafkaUtils$$leadersForRanges"), + // Mima false positive (was a private[spark] class) + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.util.collection.PairIterator"), + // Removing a testing method from a private class + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"), + // SQL execution is considered private. + excludePackage("org.apache.spark.sql.execution") + ) case v if v.startsWith("1.4") => Seq( MimaBuild.excludeSparkPackage("deploy"), MimaBuild.excludeSparkPackage("ml"), + // SPARK-7910 Adding a method to get the partioner to JavaRDD, + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitioner"), // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff"), // These are needed if checking against the sbt build, since they are part of @@ -87,25 +111,74 @@ object MimaExcludes { ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.mllib.linalg.Vector.toSparse"), ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.mllib.linalg.Vector.numActives") + "org.apache.spark.mllib.linalg.Vector.numActives"), + // SPARK-7681 add SparseVector support for gemv + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Matrix.multiply"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.DenseMatrix.multiply"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.SparseMatrix.multiply") ) ++ Seq( // Execution should never be included as its always internal. MimaBuild.excludeSparkPackage("sql.execution"), // This `protected[sql]` method was removed in 1.3.1 ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.sql.SQLContext.checkAnalysis"), - // This `private[sql]` class was removed in 1.4.0: + // These `private[sql]` class were removed in 1.4.0: ProblemFilters.exclude[MissingClassProblem]( "org.apache.spark.sql.execution.AddExchange"), ProblemFilters.exclude[MissingClassProblem]( "org.apache.spark.sql.execution.AddExchange$"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.PartitionSpec"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.PartitionSpec$"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.Partition"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.Partition$"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues$"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetRelation2"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetRelation2$"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache"), // These test support classes were moved out of src/main and into src/test: ProblemFilters.exclude[MissingClassProblem]( "org.apache.spark.sql.parquet.ParquetTestData"), ProblemFilters.exclude[MissingClassProblem]( "org.apache.spark.sql.parquet.ParquetTestData$"), ProblemFilters.exclude[MissingClassProblem]( - "org.apache.spark.sql.parquet.TestGroupWriteSupport") + "org.apache.spark.sql.parquet.TestGroupWriteSupport"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData$"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CacheManager"), + // TODO: Remove the following rule once ParquetTest has been moved to src/test. + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetTest") + ) ++ Seq( + // SPARK-7530 Added StreamingContext.getState() + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.streaming.StreamingContext.state_=") + ) ++ Seq( + // SPARK-7081 changed ShuffleWriter from a trait to an abstract class and removed some + // unnecessary type bounds in order to fix some compiler warnings that occurred when + // implementing this interface in Java. Note that ShuffleWriter is private[spark]. + ProblemFilters.exclude[IncompatibleTemplateDefProblem]( + "org.apache.spark.shuffle.ShuffleWriter") + ) ++ Seq( + // SPARK-6888 make jdbc driver handling user definable + // This patch renames some classes to API friendly names. + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks$"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.PostgresQuirks"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.NoQuirks"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.MySQLQuirks") ) case v if v.startsWith("1.3") => diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 186345af0e60e..e01720296fed0 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -23,11 +23,12 @@ import scala.collection.JavaConversions._ import sbt._ import sbt.Classpaths.publishTask import sbt.Keys._ -import sbtunidoc.Plugin.genjavadocSettings import sbtunidoc.Plugin.UnidocKeys.unidocGenjavadocVersion import com.typesafe.sbt.pom.{loadEffectivePom, PomBuild, SbtPomKeys} import net.virtualvoid.sbt.graph.Plugin.graphSettings +import spray.revolver.RevolverPlugin._ + object BuildCommons { private val buildLocation = file(".").getAbsoluteFile.getParentFile @@ -52,6 +53,8 @@ object BuildCommons { // Root project. val spark = ProjectRef(buildLocation, "spark") val sparkHome = buildLocation + + val testTempDir = s"$sparkHome/target/tmp" } object SparkBuild extends PomBuild { @@ -118,7 +121,12 @@ object SparkBuild extends PomBuild { lazy val MavenCompile = config("m2r") extend(Compile) lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy") - lazy val sharedSettings = graphSettings ++ genjavadocSettings ++ Seq ( + lazy val sparkGenjavadocSettings: Seq[sbt.Def.Setting[_]] = Seq( + libraryDependencies += compilerPlugin( + "org.spark-project" %% "genjavadoc-plugin" % unidocGenjavadocVersion.value cross CrossVersion.full), + scalacOptions <+= target.map(t => "-P:genjavadoc:out=" + (t / "java"))) + + lazy val sharedSettings = graphSettings ++ sparkGenjavadocSettings ++ Seq ( javaHome := sys.env.get("JAVA_HOME") .orElse(sys.props.get("java.home").map { p => new File(p).getParentFile().getAbsolutePath() }) .map(file), @@ -126,7 +134,7 @@ object SparkBuild extends PomBuild { retrieveManaged := true, retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]", publishMavenStyle := true, - unidocGenjavadocVersion := "0.8", + unidocGenjavadocVersion := "0.9-spark0", resolvers += Resolver.mavenLocal, otherResolvers <<= SbtPomKeys.mvnLocalRepository(dotM2 => Seq(Resolver.file("dotM2", dotM2))), @@ -140,7 +148,9 @@ object SparkBuild extends PomBuild { javacOptions in (Compile, doc) ++= { val Array(major, minor, _) = System.getProperty("java.version").split("\\.", 3) if (major.toInt >= 1 && minor.toInt >= 8) Seq("-Xdoclint:all", "-Xdoclint:-missing") else Seq.empty - } + }, + + javacOptions in Compile ++= Seq("-encoding", "UTF-8") ) def enable(settings: Seq[Setting[_]])(projectRef: ProjectRef) = { @@ -151,7 +161,7 @@ object SparkBuild extends PomBuild { // Note ordering of these settings matter. /* Enable shared settings on all projects */ (allProjects ++ optionallyEnabledProjects ++ assemblyProjects ++ Seq(spark, tools)) - .foreach(enable(sharedSettings ++ ExludedDependencies.settings)) + .foreach(enable(sharedSettings ++ ExludedDependencies.settings ++ Revolver.settings)) /* Enable tests settings for all projects except examples, assembly and tools */ (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings)) @@ -168,15 +178,12 @@ object SparkBuild extends PomBuild { /* Enable Assembly for all assembly projects */ assemblyProjects.foreach(enable(Assembly.settings)) - /* Package pyspark artifacts in the main assembly. */ + /* Package pyspark artifacts in a separate zip file for YARN. */ enable(PySparkAssembly.settings)(assembly) /* Enable unidoc only for the root spark project */ enable(Unidoc.settings)(spark) - /* Catalyst macro settings */ - enable(Catalyst.settings)(catalyst) - /* Spark SQL Core console settings */ enable(SQL.settings)(sql) @@ -271,14 +278,6 @@ object OldDeps { ) } -object Catalyst { - lazy val settings = Seq( - addCompilerPlugin("org.scalamacros" % "paradise" % "2.0.1" cross CrossVersion.full), - // Quasiquotes break compiling scala doc... - // TODO: Investigate fixing this. - sources in (Compile, doc) ~= (_ filter (_.getName contains "codegen"))) -} - object SQL { lazy val settings = Seq( initialCommands in console := @@ -324,6 +323,7 @@ object Hive { |import org.apache.spark.sql.functions._ |import org.apache.spark.sql.hive._ |import org.apache.spark.sql.hive.test.TestHive._ + |import org.apache.spark.sql.hive.test.TestHive.implicits._ |import org.apache.spark.sql.types._""".stripMargin, cleanupCommands in console := "sparkContext.stop()", // Some of our log4j jars make it impossible to submit jobs from this JVM to Hive Map/Reduce @@ -373,22 +373,15 @@ object PySparkAssembly { import java.util.zip.{ZipOutputStream, ZipEntry} lazy val settings = Seq( - unmanagedJars in Compile += { BuildCommons.sparkHome / "python/lib/py4j-0.8.2.1-src.zip" }, // Use a resource generator to copy all .py files from python/pyspark into a managed directory // to be included in the assembly. We can't just add "python/" to the assembly's resource dir // list since that will copy unneeded / unwanted files. resourceGenerators in Compile <+= resourceManaged in Compile map { outDir: File => val src = new File(BuildCommons.sparkHome, "python/pyspark") - val zipFile = new File(BuildCommons.sparkHome , "python/lib/pyspark.zip") zipFile.delete() zipRecursive(src, zipFile) - - val dst = new File(outDir, "pyspark") - if (!dst.isDirectory()) { - require(dst.mkdirs()) - } - copy(src, dst) + Seq[File]() } ) @@ -416,42 +409,11 @@ object PySparkAssembly { output.write(buf, 0, n) } } + output.closeEntry() in.close() } } - private def copy(src: File, dst: File): Seq[File] = { - src.listFiles().flatMap { f => - val child = new File(dst, f.getName()) - if (f.isDirectory()) { - child.mkdir() - copy(f, child) - } else if (f.getName().endsWith(".py")) { - var in: Option[FileInputStream] = None - var out: Option[FileOutputStream] = None - try { - in = Some(new FileInputStream(f)) - out = Some(new FileOutputStream(child)) - - val bytes = new Array[Byte](1024) - var read = 0 - while (read >= 0) { - read = in.get.read(bytes) - if (read > 0) { - out.get.write(bytes, 0, read) - } - } - - Some(child) - } finally { - in.foreach(_.close()) - out.foreach(_.close()) - } - } else { - None - } - } - } } object Unidoc { @@ -540,6 +502,7 @@ object TestSettings { "SPARK_DIST_CLASSPATH" -> (fullClasspath in Test).value.files.map(_.getAbsolutePath).mkString(":").stripSuffix(":"), "JAVA_HOME" -> sys.env.get("JAVA_HOME").getOrElse(sys.props("java.home"))), + javaOptions in Test += s"-Djava.io.tmpdir=$testTempDir", javaOptions in Test += "-Dspark.test.home=" + sparkHome, javaOptions in Test += "-Dspark.testing=1", javaOptions in Test += "-Dspark.port.maxRetries=100", @@ -548,10 +511,11 @@ object TestSettings { javaOptions in Test += "-Dspark.driver.allowMultipleContexts=true", javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true", javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true", + javaOptions in Test += "-Dderby.system.durability=test", javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark") .map { case (k,v) => s"-D$k=$v" }.toSeq, javaOptions in Test += "-ea", - javaOptions in Test ++= "-Xmx3g -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g" + javaOptions in Test ++= "-Xmx3g -Xss4096k -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g" .split(" ").toSeq, javaOptions += "-Xmx3g", // Show full stack trace and duration in test cases. @@ -561,6 +525,13 @@ object TestSettings { libraryDependencies += "com.novocode" % "junit-interface" % "0.9" % "test", // Only allow one test at a time, even across projects, since they run in the same JVM parallelExecution in Test := false, + // Make sure the test temp directory exists. + resourceGenerators in Test <+= resourceManaged in Test map { outDir: File => + if (!new File(testTempDir).isDirectory()) { + require(new File(testTempDir).mkdirs()) + } + Seq[File]() + }, concurrentRestrictions in Global += Tags.limit(Tags.Test, 1), // Remove certain packages from Scaladoc scalacOptions in (Compile, doc) := Seq( diff --git a/project/plugins.sbt b/project/plugins.sbt index 7096b0d3ee7de..51820460ca1a0 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -25,10 +25,12 @@ addSbtPlugin("com.typesafe" % "sbt-mima-plugin" % "0.1.6") addSbtPlugin("com.alpinenow" % "junit_xml_listener" % "0.5.1") -addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.3.1") +addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.3.3") addSbtPlugin("com.cavorite" % "sbt-avro" % "0.3.2") +addSbtPlugin("io.spray" % "sbt-revolver" % "0.7.2") + libraryDependencies += "org.ow2.asm" % "asm" % "5.0.3" libraryDependencies += "org.ow2.asm" % "asm-commons" % "5.0.3" diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst index 220ea595df903..518b8e774dd5f 100644 --- a/python/docs/pyspark.ml.rst +++ b/python/docs/pyspark.ml.rst @@ -1,14 +1,22 @@ pyspark.ml package -===================== +================== -Module Context --------------- +ML Pipeline APIs +---------------- .. automodule:: pyspark.ml :members: :undoc-members: :inherited-members: +pyspark.ml.param module +----------------------- + +.. automodule:: pyspark.ml.param + :members: + :undoc-members: + :inherited-members: + pyspark.ml.feature module ------------------------- @@ -25,16 +33,32 @@ pyspark.ml.classification module :undoc-members: :inherited-members: -pyspark.ml.tuning module +pyspark.ml.recommendation module -------------------------------- +.. automodule:: pyspark.ml.recommendation + :members: + :undoc-members: + :inherited-members: + +pyspark.ml.regression module +---------------------------- + +.. automodule:: pyspark.ml.regression + :members: + :undoc-members: + :inherited-members: + +pyspark.ml.tuning module +------------------------ + .. automodule:: pyspark.ml.tuning :members: :undoc-members: :inherited-members: pyspark.ml.evaluation module --------------------------------- +---------------------------- .. automodule:: pyspark.ml.evaluation :members: diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py index 0d21a132048a5..adca90ddaf397 100644 --- a/python/pyspark/accumulators.py +++ b/python/pyspark/accumulators.py @@ -261,3 +261,7 @@ def _start_update_server(): thread.daemon = True thread.start() return server + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 31992795a9e45..90b2fffbb9c7c 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -173,6 +173,7 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, self._jvm.PythonAccumulatorParam(host, port)) self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python') + self.pythonVer = "%d.%d" % sys.version_info[:2] # Broadcast's __reduce__ method stores Broadcast instances here. # This allows other code to determine which Broadcast instances have @@ -290,6 +291,11 @@ def version(self): """ return self._jsc.version() + @property + def startTime(self): + """Return the epoch time when the Spark Context was started.""" + return self._jsc.startTime() + @property def defaultParallelism(self): """ @@ -318,6 +324,38 @@ def stop(self): with SparkContext._lock: SparkContext._active_spark_context = None + def emptyRDD(self): + """ + Create an RDD that has no partitions or elements. + """ + return RDD(self._jsc.emptyRDD(), self, NoOpSerializer()) + + def range(self, start, end=None, step=1, numSlices=None): + """ + Create a new RDD of int containing elements from `start` to `end` + (exclusive), increased by `step` every element. Can be called the same + way as python's built-in range() function. If called with a single argument, + the argument is interpreted as `end`, and `start` is set to 0. + + :param start: the start value + :param end: the end value (exclusive) + :param step: the incremental step (default: 1) + :param numSlices: the number of partitions of the new RDD + :return: An RDD of int + + >>> sc.range(5).collect() + [0, 1, 2, 3, 4] + >>> sc.range(2, 4).collect() + [2, 3] + >>> sc.range(1, 7, 2).collect() + [1, 3, 5] + """ + if end is None: + end = start + start = 0 + + return self.parallelize(xrange(start, end, step), numSlices) + def parallelize(self, c, numSlices=None): """ Distribute a local Python collection to form an RDD. Using xrange diff --git a/python/pyspark/ml/__init__.py b/python/pyspark/ml/__init__.py index 47fed80f42e13..327a11b14b5aa 100644 --- a/python/pyspark/ml/__init__.py +++ b/python/pyspark/ml/__init__.py @@ -15,7 +15,6 @@ # limitations under the License. # -from pyspark.ml.param import * -from pyspark.ml.pipeline import * +from pyspark.ml.pipeline import Transformer, Estimator, Model, Pipeline, PipelineModel -__all__ = ["Param", "Params", "Transformer", "Estimator", "Pipeline"] +__all__ = ["Transformer", "Estimator", "Model", "Pipeline", "PipelineModel"] diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 45754bc9d4b10..7abbde8b260eb 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -17,17 +17,19 @@ from pyspark.ml.util import keyword_only from pyspark.ml.wrapper import JavaEstimator, JavaModel -from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\ - HasRegParam +from pyspark.ml.param.shared import * +from pyspark.ml.regression import RandomForestParams from pyspark.mllib.common import inherit_doc -__all__ = ['LogisticRegression', 'LogisticRegressionModel'] +__all__ = ['LogisticRegression', 'LogisticRegressionModel', 'DecisionTreeClassifier', + 'DecisionTreeClassificationModel', 'GBTClassifier', 'GBTClassificationModel', + 'RandomForestClassifier', 'RandomForestClassificationModel'] @inherit_doc class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, - HasRegParam): + HasRegParam, HasTol, HasProbabilityCol): """ Logistic regression. @@ -41,6 +43,10 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF() >>> model.transform(test0).head().prediction 0.0 + >>> model.weights + DenseVector([5.5...]) + >>> model.intercept + -2.68... >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF() >>> model.transform(test1).head().prediction 1.0 @@ -49,26 +55,52 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti ... TypeError: Method setParams forces keyword arguments. """ - _java_class = "org.apache.spark.ml.classification.LogisticRegression" + + # a placeholder to make it appear in the generated doc + elasticNetParam = \ + Param(Params._dummy(), "elasticNetParam", + "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " + + "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.") + fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.") + threshold = Param(Params._dummy(), "threshold", + "threshold in binary classification prediction, in range [0, 1].") @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxIter=100, regParam=0.1): + maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, + threshold=0.5, probabilityCol="probability"): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxIter=100, regParam=0.1) + maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ + threshold=0.5, probabilityCol="probability") """ super(LogisticRegression, self).__init__() - self._setDefault(maxIter=100, regParam=0.1) + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.LogisticRegression", self.uid) + #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty + # is an L2 penalty. For alpha = 1, it is an L1 penalty. + self.elasticNetParam = \ + Param(self, "elasticNetParam", + "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " + + "is an L2 penalty. For alpha = 1, it is an L1 penalty.") + #: param for whether to fit an intercept term. + self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.") + #: param for threshold in binary classification prediction, in range [0, 1]. + self.threshold = Param(self, "threshold", + "threshold in binary classification prediction, in range [0, 1].") + self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6, + fitIntercept=True, threshold=0.5) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxIter=100, regParam=0.1): + maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, + threshold=0.5, probabilityCol="probability"): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxIter=100, regParam=0.1) + maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ + threshold=0.5, probabilityCol="probability") Sets params for logistic regression. """ kwargs = self.setParams._input_kwargs @@ -77,12 +109,460 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre def _create_model(self, java_model): return LogisticRegressionModel(java_model) + def setElasticNetParam(self, value): + """ + Sets the value of :py:attr:`elasticNetParam`. + """ + self._paramMap[self.elasticNetParam] = value + return self + + def getElasticNetParam(self): + """ + Gets the value of elasticNetParam or its default value. + """ + return self.getOrDefault(self.elasticNetParam) + + def setFitIntercept(self, value): + """ + Sets the value of :py:attr:`fitIntercept`. + """ + self._paramMap[self.fitIntercept] = value + return self + + def getFitIntercept(self): + """ + Gets the value of fitIntercept or its default value. + """ + return self.getOrDefault(self.fitIntercept) + + def setThreshold(self, value): + """ + Sets the value of :py:attr:`threshold`. + """ + self._paramMap[self.threshold] = value + return self + + def getThreshold(self): + """ + Gets the value of threshold or its default value. + """ + return self.getOrDefault(self.threshold) + class LogisticRegressionModel(JavaModel): """ Model fitted by LogisticRegression. """ + @property + def weights(self): + """ + Model weights. + """ + return self._call_java("weights") + + @property + def intercept(self): + """ + Model intercept. + """ + return self._call_java("intercept") + + +class TreeClassifierParams(object): + """ + Private class to track supported impurity measures. + """ + supportedImpurities = ["entropy", "gini"] + + +class GBTParams(object): + """ + Private class to track supported GBT params. + """ + supportedLossTypes = ["logistic"] + + +@inherit_doc +class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, + DecisionTreeParams, HasCheckpointInterval): + """ + `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree` + learning algorithm for classification. + It supports both binary and multiclass labels, as well as both continuous and categorical + features. + + >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.feature import StringIndexer + >>> df = sqlContext.createDataFrame([ + ... (1.0, Vectors.dense(1.0)), + ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) + >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed") + >>> si_model = stringIndexer.fit(df) + >>> td = si_model.transform(df) + >>> dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed") + >>> model = dt.fit(td) + >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) + >>> model.transform(test0).head().prediction + 0.0 + >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) + >>> model.transform(test1).head().prediction + 1.0 + """ + + # a placeholder to make it appear in the generated doc + impurity = Param(Params._dummy(), "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities)) + + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini"): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini") + """ + super(DecisionTreeClassifier, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.DecisionTreeClassifier", self.uid) + #: param for Criterion used for information gain calculation (case-insensitive). + self.impurity = \ + Param(self, "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities)) + self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + impurity="gini") + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + impurity="gini"): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini") + Sets params for the DecisionTreeClassifier. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return DecisionTreeClassificationModel(java_model) + + def setImpurity(self, value): + """ + Sets the value of :py:attr:`impurity`. + """ + self._paramMap[self.impurity] = value + return self + + def getImpurity(self): + """ + Gets the value of impurity or its default value. + """ + return self.getOrDefault(self.impurity) + + +class DecisionTreeClassificationModel(JavaModel): + """ + Model fitted by DecisionTreeClassifier. + """ + + +@inherit_doc +class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, + DecisionTreeParams, HasCheckpointInterval): + """ + `http://en.wikipedia.org/wiki/Random_forest Random Forest` + learning algorithm for classification. + It supports both binary and multiclass labels, as well as both continuous and categorical + features. + + >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.feature import StringIndexer + >>> df = sqlContext.createDataFrame([ + ... (1.0, Vectors.dense(1.0)), + ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) + >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed") + >>> si_model = stringIndexer.fit(df) + >>> td = si_model.transform(df) + >>> rf = RandomForestClassifier(numTrees=2, maxDepth=2, labelCol="indexed", seed=42) + >>> model = rf.fit(td) + >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) + >>> model.transform(test0).head().prediction + 0.0 + >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) + >>> model.transform(test1).head().prediction + 1.0 + """ + + # a placeholder to make it appear in the generated doc + impurity = Param(Params._dummy(), "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities)) + subsamplingRate = Param(Params._dummy(), "subsamplingRate", + "Fraction of the training data used for learning each decision tree, " + + "in range (0, 1].") + numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1)") + featureSubsetStrategy = \ + Param(Params._dummy(), "featureSubsetStrategy", + "The number of features to consider for splits at each tree node. Supported " + + "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies)) + + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini", + numTrees=20, featureSubsetStrategy="auto", seed=None): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini", \ + numTrees=20, featureSubsetStrategy="auto", seed=None) + """ + super(RandomForestClassifier, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.RandomForestClassifier", self.uid) + #: param for Criterion used for information gain calculation (case-insensitive). + self.impurity = \ + Param(self, "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities)) + #: param for Fraction of the training data used for learning each decision tree, + # in range (0, 1] + self.subsamplingRate = Param(self, "subsamplingRate", + "Fraction of the training data used for learning each " + + "decision tree, in range (0, 1].") + #: param for Number of trees to train (>= 1) + self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1)") + #: param for The number of features to consider for splits at each tree node + self.featureSubsetStrategy = \ + Param(self, "featureSubsetStrategy", + "The number of features to consider for splits at each tree node. Supported " + + "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies)) + self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, + impurity="gini", numTrees=20, featureSubsetStrategy="auto") + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, + impurity="gini", numTrees=20, featureSubsetStrategy="auto"): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \ + impurity="gini", numTrees=20, featureSubsetStrategy="auto") + Sets params for linear classification. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return RandomForestClassificationModel(java_model) + + def setImpurity(self, value): + """ + Sets the value of :py:attr:`impurity`. + """ + self._paramMap[self.impurity] = value + return self + + def getImpurity(self): + """ + Gets the value of impurity or its default value. + """ + return self.getOrDefault(self.impurity) + + def setSubsamplingRate(self, value): + """ + Sets the value of :py:attr:`subsamplingRate`. + """ + self._paramMap[self.subsamplingRate] = value + return self + + def getSubsamplingRate(self): + """ + Gets the value of subsamplingRate or its default value. + """ + return self.getOrDefault(self.subsamplingRate) + + def setNumTrees(self, value): + """ + Sets the value of :py:attr:`numTrees`. + """ + self._paramMap[self.numTrees] = value + return self + + def getNumTrees(self): + """ + Gets the value of numTrees or its default value. + """ + return self.getOrDefault(self.numTrees) + + def setFeatureSubsetStrategy(self, value): + """ + Sets the value of :py:attr:`featureSubsetStrategy`. + """ + self._paramMap[self.featureSubsetStrategy] = value + return self + + def getFeatureSubsetStrategy(self): + """ + Gets the value of featureSubsetStrategy or its default value. + """ + return self.getOrDefault(self.featureSubsetStrategy) + + +class RandomForestClassificationModel(JavaModel): + """ + Model fitted by RandomForestClassifier. + """ + + +@inherit_doc +class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, + DecisionTreeParams, HasCheckpointInterval): + """ + `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)` + learning algorithm for classification. + It supports binary labels, as well as both continuous and categorical features. + Note: Multiclass labels are not currently supported. + + >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.feature import StringIndexer + >>> df = sqlContext.createDataFrame([ + ... (1.0, Vectors.dense(1.0)), + ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) + >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed") + >>> si_model = stringIndexer.fit(df) + >>> td = si_model.transform(df) + >>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed") + >>> model = gbt.fit(td) + >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) + >>> model.transform(test0).head().prediction + 0.0 + >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) + >>> model.transform(test1).head().prediction + 1.0 + """ + + # a placeholder to make it appear in the generated doc + lossType = Param(Params._dummy(), "lossType", + "Loss function which GBT tries to minimize (case-insensitive). " + + "Supported options: " + ", ".join(GBTParams.supportedLossTypes)) + subsamplingRate = Param(Params._dummy(), "subsamplingRate", + "Fraction of the training data used for learning each decision tree, " + + "in range (0, 1].") + stepSize = Param(Params._dummy(), "stepSize", + "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the " + + "contribution of each estimator") + + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="logistic", + maxIter=20, stepSize=0.1): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ + lossType="logistic", maxIter=20, stepSize=0.1) + """ + super(GBTClassifier, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.GBTClassifier", self.uid) + #: param for Loss function which GBT tries to minimize (case-insensitive). + self.lossType = Param(self, "lossType", + "Loss function which GBT tries to minimize (case-insensitive). " + + "Supported options: " + ", ".join(GBTParams.supportedLossTypes)) + #: Fraction of the training data used for learning each decision tree, in range (0, 1]. + self.subsamplingRate = Param(self, "subsamplingRate", + "Fraction of the training data used for learning each " + + "decision tree, in range (0, 1].") + #: Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of + # each estimator + self.stepSize = Param(self, "stepSize", + "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking " + + "the contribution of each estimator") + self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + lossType="logistic", maxIter=20, stepSize=0.1) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + lossType="logistic", maxIter=20, stepSize=0.1): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ + lossType="logistic", maxIter=20, stepSize=0.1) + Sets params for Gradient Boosted Tree Classification. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return GBTClassificationModel(java_model) + + def setLossType(self, value): + """ + Sets the value of :py:attr:`lossType`. + """ + self._paramMap[self.lossType] = value + return self + + def getLossType(self): + """ + Gets the value of lossType or its default value. + """ + return self.getOrDefault(self.lossType) + + def setSubsamplingRate(self, value): + """ + Sets the value of :py:attr:`subsamplingRate`. + """ + self._paramMap[self.subsamplingRate] = value + return self + + def getSubsamplingRate(self): + """ + Gets the value of subsamplingRate or its default value. + """ + return self.getOrDefault(self.subsamplingRate) + + def setStepSize(self, value): + """ + Sets the value of :py:attr:`stepSize`. + """ + self._paramMap[self.stepSize] = value + return self + + def getStepSize(self): + """ + Gets the value of stepSize or its default value. + """ + return self.getOrDefault(self.stepSize) + + +class GBTClassificationModel(JavaModel): + """ + Model fitted by GBTClassifier. + """ + if __name__ == "__main__": import doctest @@ -91,7 +571,7 @@ class LogisticRegressionModel(JavaModel): globs = globals().copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: - sc = SparkContext("local[2]", "ml.feature tests") + sc = SparkContext("local[2]", "ml.classification tests") sqlContext = SQLContext(sc) globs['sc'] = sc globs['sqlContext'] = sqlContext diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py index 02020ebff94c2..595593a7f2cde 100644 --- a/python/pyspark/ml/evaluation.py +++ b/python/pyspark/ml/evaluation.py @@ -15,13 +15,72 @@ # limitations under the License. # -from pyspark.ml.wrapper import JavaEvaluator +from abc import abstractmethod, ABCMeta + +from pyspark.ml.wrapper import JavaWrapper from pyspark.ml.param import Param, Params -from pyspark.ml.param.shared import HasLabelCol, HasRawPredictionCol +from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredictionCol from pyspark.ml.util import keyword_only from pyspark.mllib.common import inherit_doc -__all__ = ['BinaryClassificationEvaluator'] +__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator'] + + +@inherit_doc +class Evaluator(Params): + """ + Base class for evaluators that compute metrics from predictions. + """ + + __metaclass__ = ABCMeta + + @abstractmethod + def _evaluate(self, dataset): + """ + Evaluates the output. + + :param dataset: a dataset that contains labels/observations and + predictions + :return: metric + """ + raise NotImplementedError() + + def evaluate(self, dataset, params={}): + """ + Evaluates the output with optional parameters. + + :param dataset: a dataset that contains labels/observations and + predictions + :param params: an optional param map that overrides embedded + params + :return: metric + """ + if isinstance(params, dict): + if params: + return self.copy(params)._evaluate(dataset) + else: + return self._evaluate(dataset) + else: + raise ValueError("Params must be a param map but got %s." % type(params)) + + +@inherit_doc +class JavaEvaluator(Evaluator, JavaWrapper): + """ + Base class for :py:class:`Evaluator`s that wrap Java/Scala + implementations. + """ + + __metaclass__ = ABCMeta + + def _evaluate(self, dataset): + """ + Evaluates the output. + :param dataset: a dataset that contains labels/observations and predictions. + :return: evaluation metric + """ + self._transfer_params_to_java() + return self._java_obj.evaluate(dataset._jdf) @inherit_doc @@ -42,8 +101,6 @@ class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPrediction 0.83... """ - _java_class = "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator" - # a placeholder to make it appear in the generated doc metricName = Param(Params._dummy(), "metricName", "metric name in evaluation (areaUnderROC|areaUnderPR)") @@ -56,6 +113,8 @@ def __init__(self, rawPredictionCol="rawPrediction", labelCol="label", metricName="areaUnderROC") """ super(BinaryClassificationEvaluator, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid) #: param for metric name in evaluation (areaUnderROC|areaUnderPR) self.metricName = Param(self, "metricName", "metric name in evaluation (areaUnderROC|areaUnderPR)") @@ -68,7 +127,7 @@ def setMetricName(self, value): """ Sets the value of :py:attr:`metricName`. """ - self.paramMap[self.metricName] = value + self._paramMap[self.metricName] = value return self def getMetricName(self): @@ -89,6 +148,72 @@ def setParams(self, rawPredictionCol="rawPrediction", labelCol="label", return self._set(**kwargs) +@inherit_doc +class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol): + """ + Evaluator for Regression, which expects two input + columns: prediction and label. + + >>> scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5), + ... (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)] + >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"]) + ... + >>> evaluator = RegressionEvaluator(predictionCol="raw") + >>> evaluator.evaluate(dataset) + -2.842... + >>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"}) + 0.993... + >>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"}) + -2.649... + """ + # Because we will maximize evaluation value (ref: `CrossValidator`), + # when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`), + # we take and output the negative of this metric. + metricName = Param(Params._dummy(), "metricName", + "metric name in evaluation (mse|rmse|r2|mae)") + + @keyword_only + def __init__(self, predictionCol="prediction", labelCol="label", + metricName="rmse"): + """ + __init__(self, predictionCol="prediction", labelCol="label", \ + metricName="rmse") + """ + super(RegressionEvaluator, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.evaluation.RegressionEvaluator", self.uid) + #: param for metric name in evaluation (mse|rmse|r2|mae) + self.metricName = Param(self, "metricName", + "metric name in evaluation (mse|rmse|r2|mae)") + self._setDefault(predictionCol="prediction", labelCol="label", + metricName="rmse") + kwargs = self.__init__._input_kwargs + self._set(**kwargs) + + def setMetricName(self, value): + """ + Sets the value of :py:attr:`metricName`. + """ + self._paramMap[self.metricName] = value + return self + + def getMetricName(self): + """ + Gets the value of metricName or its default value. + """ + return self.getOrDefault(self.metricName) + + @keyword_only + def setParams(self, predictionCol="prediction", labelCol="label", + metricName="rmse"): + """ + setParams(self, predictionCol="prediction", labelCol="label", \ + metricName="rmse") + Sets params for regression evaluator. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + if __name__ == "__main__": import doctest from pyspark.context import SparkContext diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index f35bc1463d51b..ddb33f427ac64 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -43,7 +43,6 @@ class Binarizer(JavaTransformer, HasInputCol, HasOutputCol): 1.0 """ - _java_class = "org.apache.spark.ml.feature.Binarizer" # a placeholder to make it appear in the generated doc threshold = Param(Params._dummy(), "threshold", "threshold in binary classification prediction, in range [0, 1]") @@ -54,6 +53,7 @@ def __init__(self, threshold=0.0, inputCol=None, outputCol=None): __init__(self, threshold=0.0, inputCol=None, outputCol=None) """ super(Binarizer, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Binarizer", self.uid) self.threshold = Param(self, "threshold", "threshold in binary classification prediction, in range [0, 1]") self._setDefault(threshold=0.0) @@ -73,7 +73,7 @@ def setThreshold(self, value): """ Sets the value of :py:attr:`threshold`. """ - self.paramMap[self.threshold] = value + self._paramMap[self.threshold] = value return self def getThreshold(self): @@ -83,6 +83,83 @@ def getThreshold(self): return self.getOrDefault(self.threshold) +@inherit_doc +class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol): + """ + Maps a column of continuous features to a column of feature buckets. + + >>> df = sqlContext.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"]) + >>> bucketizer = Bucketizer(splits=[-float("inf"), 0.5, 1.4, float("inf")], + ... inputCol="values", outputCol="buckets") + >>> bucketed = bucketizer.transform(df).collect() + >>> bucketed[0].buckets + 0.0 + >>> bucketed[1].buckets + 0.0 + >>> bucketed[2].buckets + 1.0 + >>> bucketed[3].buckets + 2.0 + >>> bucketizer.setParams(outputCol="b").transform(df).head().b + 0.0 + """ + + # a placeholder to make it appear in the generated doc + splits = \ + Param(Params._dummy(), "splits", + "Split points for mapping continuous features into buckets. With n+1 splits, " + + "there are n buckets. A bucket defined by splits x,y holds values in the " + + "range [x,y) except the last bucket, which also includes y. The splits " + + "should be strictly increasing. Values at -inf, inf must be explicitly " + + "provided to cover all Double values; otherwise, values outside the splits " + + "specified will be treated as errors.") + + @keyword_only + def __init__(self, splits=None, inputCol=None, outputCol=None): + """ + __init__(self, splits=None, inputCol=None, outputCol=None) + """ + super(Bucketizer, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Bucketizer", self.uid) + #: param for Splitting points for mapping continuous features into buckets. With n+1 splits, + # there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) + # except the last bucket, which also includes y. The splits should be strictly increasing. + # Values at -inf, inf must be explicitly provided to cover all Double values; otherwise, + # values outside the splits specified will be treated as errors. + self.splits = \ + Param(self, "splits", + "Split points for mapping continuous features into buckets. With n+1 splits, " + + "there are n buckets. A bucket defined by splits x,y holds values in the " + + "range [x,y) except the last bucket, which also includes y. The splits " + + "should be strictly increasing. Values at -inf, inf must be explicitly " + + "provided to cover all Double values; otherwise, values outside the splits " + + "specified will be treated as errors.") + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, splits=None, inputCol=None, outputCol=None): + """ + setParams(self, splits=None, inputCol=None, outputCol=None) + Sets params for this Bucketizer. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def setSplits(self, value): + """ + Sets the value of :py:attr:`splits`. + """ + self._paramMap[self.splits] = value + return self + + def getSplits(self): + """ + Gets the value of threshold or its default value. + """ + return self.getOrDefault(self.splits) + + @inherit_doc class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): """ @@ -100,14 +177,13 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): SparseVector(5, {2: 1.0, 3: 1.0, 4: 1.0}) """ - _java_class = "org.apache.spark.ml.feature.HashingTF" - @keyword_only def __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None): """ __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None) """ super(HashingTF, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.HashingTF", self.uid) self._setDefault(numFeatures=1 << 18) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @@ -140,8 +216,6 @@ class IDF(JavaEstimator, HasInputCol, HasOutputCol): DenseVector([0.2877, 0.0]) """ - _java_class = "org.apache.spark.ml.feature.IDF" - # a placeholder to make it appear in the generated doc minDocFreq = Param(Params._dummy(), "minDocFreq", "minimum of documents in which a term should appear for filtering") @@ -152,6 +226,7 @@ def __init__(self, minDocFreq=0, inputCol=None, outputCol=None): __init__(self, minDocFreq=0, inputCol=None, outputCol=None) """ super(IDF, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IDF", self.uid) self.minDocFreq = Param(self, "minDocFreq", "minimum of documents in which a term should appear for filtering") self._setDefault(minDocFreq=0) @@ -171,7 +246,7 @@ def setMinDocFreq(self, value): """ Sets the value of :py:attr:`minDocFreq`. """ - self.paramMap[self.minDocFreq] = value + self._paramMap[self.minDocFreq] = value return self def getMinDocFreq(self): @@ -180,6 +255,9 @@ def getMinDocFreq(self): """ return self.getOrDefault(self.minDocFreq) + def _create_model(self, java_model): + return IDFModel(java_model) + class IDFModel(JavaModel): """ @@ -208,14 +286,13 @@ class Normalizer(JavaTransformer, HasInputCol, HasOutputCol): # a placeholder to make it appear in the generated doc p = Param(Params._dummy(), "p", "the p norm value.") - _java_class = "org.apache.spark.ml.feature.Normalizer" - @keyword_only def __init__(self, p=2.0, inputCol=None, outputCol=None): """ __init__(self, p=2.0, inputCol=None, outputCol=None) """ super(Normalizer, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Normalizer", self.uid) self.p = Param(self, "p", "the p norm value.") self._setDefault(p=2.0) kwargs = self.__init__._input_kwargs @@ -234,7 +311,7 @@ def setP(self, value): """ Sets the value of :py:attr:`p`. """ - self.paramMap[self.p] = value + self._paramMap[self.p] = value return self def getP(self): @@ -247,66 +324,73 @@ def getP(self): @inherit_doc class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol): """ - A one-hot encoder that maps a column of label indices to a column of binary vectors, with - at most a single one-value. By default, the binary vector has an element for each category, so - with 5 categories, an input value of 2.0 would map to an output vector of - (0.0, 0.0, 1.0, 0.0, 0.0). If includeFirst is set to false, the first category is omitted, so - the output vector for the previous example would be (0.0, 1.0, 0.0, 0.0) and an input value - of 0.0 would map to a vector of all zeros. Including the first category makes the vector columns - linearly dependent because they sum up to one. + A one-hot encoder that maps a column of category indices to a + column of binary vectors, with at most a single one-value per row + that indicates the input category index. + For example with 5 categories, an input value of 2.0 would map to + an output vector of `[0.0, 0.0, 1.0, 0.0]`. + The last category is not included by default (configurable via + :py:attr:`dropLast`) because it makes the vector entries sum up to + one, and hence linearly dependent. + So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`. + Note that this is different from scikit-learn's OneHotEncoder, + which keeps all categories. + The output vectors are sparse. + + .. seealso:: - TODO: This method requires the use of StringIndexer first. Decouple them. + :py:class:`StringIndexer` for converting categorical values into + category indices >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed") >>> model = stringIndexer.fit(stringIndDf) >>> td = model.transform(stringIndDf) - >>> encoder = OneHotEncoder(includeFirst=False, inputCol="indexed", outputCol="features") + >>> encoder = OneHotEncoder(inputCol="indexed", outputCol="features") >>> encoder.transform(td).head().features - SparseVector(2, {}) + SparseVector(2, {0: 1.0}) >>> encoder.setParams(outputCol="freqs").transform(td).head().freqs - SparseVector(2, {}) - >>> params = {encoder.includeFirst: True, encoder.outputCol: "test"} + SparseVector(2, {0: 1.0}) + >>> params = {encoder.dropLast: False, encoder.outputCol: "test"} >>> encoder.transform(td, params).head().test SparseVector(3, {0: 1.0}) """ - _java_class = "org.apache.spark.ml.feature.OneHotEncoder" - # a placeholder to make it appear in the generated doc - includeFirst = Param(Params._dummy(), "includeFirst", "include first category") + dropLast = Param(Params._dummy(), "dropLast", "whether to drop the last category") @keyword_only - def __init__(self, includeFirst=True, inputCol=None, outputCol=None): + def __init__(self, dropLast=True, inputCol=None, outputCol=None): """ __init__(self, includeFirst=True, inputCol=None, outputCol=None) """ super(OneHotEncoder, self).__init__() - self.includeFirst = Param(self, "includeFirst", "include first category") - self._setDefault(includeFirst=True) + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.OneHotEncoder", self.uid) + self.dropLast = Param(self, "dropLast", "whether to drop the last category") + self._setDefault(dropLast=True) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only - def setParams(self, includeFirst=True, inputCol=None, outputCol=None): + def setParams(self, dropLast=True, inputCol=None, outputCol=None): """ - setParams(self, includeFirst=True, inputCol=None, outputCol=None) + setParams(self, dropLast=True, inputCol=None, outputCol=None) Sets params for this OneHotEncoder. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) - def setIncludeFirst(self, value): + def setDropLast(self, value): """ - Sets the value of :py:attr:`includeFirst`. + Sets the value of :py:attr:`dropLast`. """ - self.paramMap[self.includeFirst] = value + self._paramMap[self.dropLast] = value return self - def getIncludeFirst(self): + def getDropLast(self): """ - Gets the value of includeFirst or its default value. + Gets the value of dropLast or its default value. """ - return self.getOrDefault(self.includeFirst) + return self.getOrDefault(self.dropLast) @inherit_doc @@ -327,8 +411,6 @@ class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol): DenseVector([0.5, 0.25, 2.0, 1.0, 4.0]) """ - _java_class = "org.apache.spark.ml.feature.PolynomialExpansion" - # a placeholder to make it appear in the generated doc degree = Param(Params._dummy(), "degree", "the polynomial degree to expand (>= 1)") @@ -338,6 +420,8 @@ def __init__(self, degree=2, inputCol=None, outputCol=None): __init__(self, degree=2, inputCol=None, outputCol=None) """ super(PolynomialExpansion, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.feature.PolynomialExpansion", self.uid) self.degree = Param(self, "degree", "the polynomial degree to expand (>= 1)") self._setDefault(degree=2) kwargs = self.__init__._input_kwargs @@ -356,7 +440,7 @@ def setDegree(self, value): """ Sets the value of :py:attr:`degree`. """ - self.paramMap[self.degree] = value + self._paramMap[self.degree] = value return self def getDegree(self): @@ -370,23 +454,25 @@ def getDegree(self): @ignore_unicode_prefix class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol): """ - A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) - or using it to split the text (set matching to false). Optional parameters also allow filtering - tokens using a minimal length. + A regex based tokenizer that extracts tokens either by using the + provided regex pattern (in Java dialect) to split the text + (default) or repeatedly matching the regex (if gaps is true). + Optional parameters also allow filtering tokens using a minimal + length. It returns an array of strings that can be empty. - >>> df = sqlContext.createDataFrame([("a b c",)], ["text"]) + >>> df = sqlContext.createDataFrame([("a b c",)], ["text"]) >>> reTokenizer = RegexTokenizer(inputCol="text", outputCol="words") >>> reTokenizer.transform(df).head() - Row(text=u'a b c', words=[u'a', u'b', u'c']) + Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> # Change a parameter. >>> reTokenizer.setParams(outputCol="tokens").transform(df).head() - Row(text=u'a b c', tokens=[u'a', u'b', u'c']) + Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Temporarily modify a parameter. >>> reTokenizer.transform(df, {reTokenizer.outputCol: "words"}).head() - Row(text=u'a b c', words=[u'a', u'b', u'c']) + Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> reTokenizer.transform(df).head() - Row(text=u'a b c', tokens=[u'a', u'b', u'c']) + Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Must use keyword arguments to specify params. >>> reTokenizer.setParams("text") Traceback (most recent call last): @@ -394,33 +480,29 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol): TypeError: Method setParams forces keyword arguments. """ - _java_class = "org.apache.spark.ml.feature.RegexTokenizer" # a placeholder to make it appear in the generated doc minTokenLength = Param(Params._dummy(), "minTokenLength", "minimum token length (>= 0)") - gaps = Param(Params._dummy(), "gaps", "Set regex to match gaps or tokens") - pattern = Param(Params._dummy(), "pattern", "regex pattern used for tokenizing") + gaps = Param(Params._dummy(), "gaps", "whether regex splits on gaps (True) or matches tokens") + pattern = Param(Params._dummy(), "pattern", "regex pattern (Java dialect) used for tokenizing") @keyword_only - def __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", - inputCol=None, outputCol=None): + def __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None): """ - __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", - inputCol=None, outputCol=None) + __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None) """ super(RegexTokenizer, self).__init__() - self.minTokenLength = Param(self, "minLength", "minimum token length (>= 0)") - self.gaps = Param(self, "gaps", "Set regex to match gaps or tokens") - self.pattern = Param(self, "pattern", "regex pattern used for tokenizing") - self._setDefault(minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+") + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RegexTokenizer", self.uid) + self.minTokenLength = Param(self, "minTokenLength", "minimum token length (>= 0)") + self.gaps = Param(self, "gaps", "whether regex splits on gaps (True) or matches tokens") + self.pattern = Param(self, "pattern", "regex pattern (Java dialect) used for tokenizing") + self._setDefault(minTokenLength=1, gaps=True, pattern="\\s+") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only - def setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", - inputCol=None, outputCol=None): + def setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None): """ - setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", - inputCol="input", outputCol="output") + setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None) Sets params for this RegexTokenizer. """ kwargs = self.setParams._input_kwargs @@ -430,7 +512,7 @@ def setMinTokenLength(self, value): """ Sets the value of :py:attr:`minTokenLength`. """ - self.paramMap[self.minTokenLength] = value + self._paramMap[self.minTokenLength] = value return self def getMinTokenLength(self): @@ -443,7 +525,7 @@ def setGaps(self, value): """ Sets the value of :py:attr:`gaps`. """ - self.paramMap[self.gaps] = value + self._paramMap[self.gaps] = value return self def getGaps(self): @@ -456,7 +538,7 @@ def setPattern(self, value): """ Sets the value of :py:attr:`pattern`. """ - self.paramMap[self.pattern] = value + self._paramMap[self.pattern] = value return self def getPattern(self): @@ -480,8 +562,6 @@ class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol): DenseVector([1.4142]) """ - _java_class = "org.apache.spark.ml.feature.StandardScaler" - # a placeholder to make it appear in the generated doc withMean = Param(Params._dummy(), "withMean", "Center data with mean") withStd = Param(Params._dummy(), "withStd", "Scale to unit standard deviation") @@ -492,6 +572,7 @@ def __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None): __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None) """ super(StandardScaler, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StandardScaler", self.uid) self.withMean = Param(self, "withMean", "Center data with mean") self.withStd = Param(self, "withStd", "Scale to unit standard deviation") self._setDefault(withMean=False, withStd=True) @@ -511,7 +592,7 @@ def setWithMean(self, value): """ Sets the value of :py:attr:`withMean`. """ - self.paramMap[self.withMean] = value + self._paramMap[self.withMean] = value return self def getWithMean(self): @@ -524,7 +605,7 @@ def setWithStd(self, value): """ Sets the value of :py:attr:`withStd`. """ - self.paramMap[self.withStd] = value + self._paramMap[self.withStd] = value return self def getWithStd(self): @@ -533,6 +614,9 @@ def getWithStd(self): """ return self.getOrDefault(self.withStd) + def _create_model(self, java_model): + return StandardScalerModel(java_model) + class StandardScalerModel(JavaModel): """ @@ -556,14 +640,13 @@ class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol): [(0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)] """ - _java_class = "org.apache.spark.ml.feature.StringIndexer" - @keyword_only def __init__(self, inputCol=None, outputCol=None): """ __init__(self, inputCol=None, outputCol=None) """ super(StringIndexer, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StringIndexer", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @@ -576,6 +659,9 @@ def setParams(self, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) + def _create_model(self, java_model): + return StringIndexerModel(java_model) + class StringIndexerModel(JavaModel): """ @@ -609,14 +695,13 @@ class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): TypeError: Method setParams forces keyword arguments. """ - _java_class = "org.apache.spark.ml.feature.Tokenizer" - @keyword_only def __init__(self, inputCol=None, outputCol=None): """ __init__(self, inputCol=None, outputCol=None) """ super(Tokenizer, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Tokenizer", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @@ -646,14 +731,13 @@ class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol): DenseVector([0.0, 1.0]) """ - _java_class = "org.apache.spark.ml.feature.VectorAssembler" - @keyword_only def __init__(self, inputCols=None, outputCol=None): """ __init__(self, inputCols=None, outputCol=None) """ super(VectorAssembler, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorAssembler", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @@ -720,7 +804,6 @@ class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol): DenseVector([1.0, 0.0]) """ - _java_class = "org.apache.spark.ml.feature.VectorIndexer" # a placeholder to make it appear in the generated doc maxCategories = Param(Params._dummy(), "maxCategories", "Threshold for the number of values a categorical feature can take " + @@ -733,6 +816,7 @@ def __init__(self, maxCategories=20, inputCol=None, outputCol=None): __init__(self, maxCategories=20, inputCol=None, outputCol=None) """ super(VectorIndexer, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorIndexer", self.uid) self.maxCategories = Param(self, "maxCategories", "Threshold for the number of values a categorical feature " + "can take (>= 2). If a feature is found to have " + @@ -754,7 +838,7 @@ def setMaxCategories(self, value): """ Sets the value of :py:attr:`maxCategories`. """ - self.paramMap[self.maxCategories] = value + self._paramMap[self.maxCategories] = value return self def getMaxCategories(self): @@ -763,6 +847,15 @@ def getMaxCategories(self): """ return self.getOrDefault(self.maxCategories) + def _create_model(self, java_model): + return VectorIndexerModel(java_model) + + +class VectorIndexerModel(JavaModel): + """ + Model fitted by VectorIndexer. + """ + @inherit_doc @ignore_unicode_prefix @@ -778,7 +871,6 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has DenseVector([-0.0422, -0.5138, -0.2546, 0.6885, 0.276]) """ - _java_class = "org.apache.spark.ml.feature.Word2Vec" # a placeholder to make it appear in the generated doc vectorSize = Param(Params._dummy(), "vectorSize", "the dimension of codes after transforming from words") @@ -790,12 +882,13 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has @keyword_only def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, - seed=42, inputCol=None, outputCol=None): + seed=None, inputCol=None, outputCol=None): """ - __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, - seed=42, inputCol=None, outputCol=None) + __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, \ + seed=None, inputCol=None, outputCol=None) """ super(Word2Vec, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Word2Vec", self.uid) self.vectorSize = Param(self, "vectorSize", "the dimension of codes after transforming from words") self.numPartitions = Param(self, "numPartitions", @@ -804,15 +897,15 @@ def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, "the minimum number of times a token must appear to be included " + "in the word2vec model's vocabulary") self._setDefault(vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, - seed=42) + seed=None) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, - seed=42, inputCol=None, outputCol=None): + seed=None, inputCol=None, outputCol=None): """ - setParams(self, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=42, + setParams(self, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, \ inputCol=None, outputCol=None) Sets params for this Word2Vec. """ @@ -823,7 +916,7 @@ def setVectorSize(self, value): """ Sets the value of :py:attr:`vectorSize`. """ - self.paramMap[self.vectorSize] = value + self._paramMap[self.vectorSize] = value return self def getVectorSize(self): @@ -836,7 +929,7 @@ def setNumPartitions(self, value): """ Sets the value of :py:attr:`numPartitions`. """ - self.paramMap[self.numPartitions] = value + self._paramMap[self.numPartitions] = value return self def getNumPartitions(self): @@ -849,7 +942,7 @@ def setMinCount(self, value): """ Sets the value of :py:attr:`minCount`. """ - self.paramMap[self.minCount] = value + self._paramMap[self.minCount] = value return self def getMinCount(self): @@ -858,6 +951,9 @@ def getMinCount(self): """ return self.getOrDefault(self.minCount) + def _create_model(self, java_model): + return Word2VecModel(java_model) + class Word2VecModel(JavaModel): """ diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py index 49c20b4cf70cf..7845536161e07 100644 --- a/python/pyspark/ml/param/__init__.py +++ b/python/pyspark/ml/param/__init__.py @@ -16,6 +16,7 @@ # from abc import ABCMeta +import copy from pyspark.ml.util import Identifiable @@ -29,9 +30,9 @@ class Param(object): """ def __init__(self, parent, name, doc): - if not isinstance(parent, Params): - raise TypeError("Parent must be a Params but got type %s." % type(parent)) - self.parent = parent + if not isinstance(parent, Identifiable): + raise TypeError("Parent must be an Identifiable but got type %s." % type(parent)) + self.parent = parent.uid self.name = str(name) self.doc = str(doc) @@ -41,6 +42,15 @@ def __str__(self): def __repr__(self): return "Param(parent=%r, name=%r, doc=%r)" % (self.parent, self.name, self.doc) + def __hash__(self): + return hash(str(self)) + + def __eq__(self, other): + if isinstance(other, Param): + return self.parent == other.parent and self.name == other.name + else: + return False + class Params(Identifiable): """ @@ -51,10 +61,13 @@ class Params(Identifiable): __metaclass__ = ABCMeta #: internal param map for user-supplied values param map - paramMap = {} + _paramMap = {} #: internal param map for default values - defaultParamMap = {} + _defaultParamMap = {} + + #: value returned by :py:func:`params` + _params = None @property def params(self): @@ -63,10 +76,12 @@ def params(self): uses :py:func:`dir` to get all attributes of type :py:class:`Param`. """ - return list(filter(lambda attr: isinstance(attr, Param), - [getattr(self, x) for x in dir(self) if x != "params"])) + if self._params is None: + self._params = list(filter(lambda attr: isinstance(attr, Param), + [getattr(self, x) for x in dir(self) if x != "params"])) + return self._params - def _explain(self, param): + def explainParam(self, param): """ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string. @@ -74,10 +89,10 @@ def _explain(self, param): param = self._resolveParam(param) values = [] if self.isDefined(param): - if param in self.defaultParamMap: - values.append("default: %s" % self.defaultParamMap[param]) - if param in self.paramMap: - values.append("current: %s" % self.paramMap[param]) + if param in self._defaultParamMap: + values.append("default: %s" % self._defaultParamMap[param]) + if param in self._paramMap: + values.append("current: %s" % self._paramMap[param]) else: values.append("undefined") valueStr = "(" + ", ".join(values) + ")" @@ -88,7 +103,7 @@ def explainParams(self): Returns the documentation of all params with their optionally default values and user-supplied values. """ - return "\n".join([self._explain(param) for param in self.params]) + return "\n".join([self.explainParam(param) for param in self.params]) def getParam(self, paramName): """ @@ -105,56 +120,76 @@ def isSet(self, param): Checks whether a param is explicitly set by user. """ param = self._resolveParam(param) - return param in self.paramMap + return param in self._paramMap def hasDefault(self, param): """ Checks whether a param has a default value. """ param = self._resolveParam(param) - return param in self.defaultParamMap + return param in self._defaultParamMap def isDefined(self, param): """ - Checks whether a param is explicitly set by user or has a default value. + Checks whether a param is explicitly set by user or has + a default value. """ return self.isSet(param) or self.hasDefault(param) + def hasParam(self, paramName): + """ + Tests whether this instance contains a param with a given + (string) name. + """ + param = self._resolveParam(paramName) + return param in self.params + def getOrDefault(self, param): """ Gets the value of a param in the user-supplied param map or its - default value. Raises an error if either is set. + default value. Raises an error if neither is set. """ - if isinstance(param, Param): - if param in self.paramMap: - return self.paramMap[param] - else: - return self.defaultParamMap[param] - elif isinstance(param, str): - return self.getOrDefault(self.getParam(param)) + param = self._resolveParam(param) + if param in self._paramMap: + return self._paramMap[param] else: - raise KeyError("Cannot recognize %r as a param." % param) + return self._defaultParamMap[param] - def extractParamMap(self, extraParamMap={}): + def extractParamMap(self, extra={}): """ Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < - user-supplied values < extraParamMap. - :param extraParamMap: extra param values + user-supplied values < extra. + :param extra: extra param values :return: merged param map """ - paramMap = self.defaultParamMap.copy() - paramMap.update(self.paramMap) - paramMap.update(extraParamMap) + paramMap = self._defaultParamMap.copy() + paramMap.update(self._paramMap) + paramMap.update(extra) return paramMap + def copy(self, extra={}): + """ + Creates a copy of this instance with the same uid and some + extra params. The default implementation creates a + shallow copy using :py:func:`copy.copy`, and then copies the + embedded and extra parameters over and returns the copy. + Subclasses should override this method if the default approach + is not sufficient. + :param extra: Extra parameters to copy to the new instance + :return: Copy of this instance + """ + that = copy.copy(self) + that._paramMap = self.extractParamMap(extra) + return that + def _shouldOwn(self, param): """ Validates that the input param belongs to this Params instance. """ - if param.parent is not self: + if not (self.uid == param.parent and self.hasParam(param.name)): raise ValueError("Param %r does not belong to %r." % (param, self)) def _resolveParam(self, param): @@ -175,7 +210,8 @@ def _resolveParam(self, param): @staticmethod def _dummy(): """ - Returns a dummy Params instance used as a placeholder to generate docs. + Returns a dummy Params instance used as a placeholder to + generate docs. """ dummy = Params() dummy.uid = "undefined" @@ -186,7 +222,7 @@ def _set(self, **kwargs): Sets user-supplied params. """ for param, value in kwargs.items(): - self.paramMap[getattr(self, param)] = value + self._paramMap[getattr(self, param)] = value return self def _setDefault(self, **kwargs): @@ -194,5 +230,19 @@ def _setDefault(self, **kwargs): Sets default params. """ for param, value in kwargs.items(): - self.defaultParamMap[getattr(self, param)] = value + self._defaultParamMap[getattr(self, param)] = value return self + + def _copyValues(self, to, extra={}): + """ + Copies param values from this instance to another instance for + params shared by them. + :param to: the target instance + :param extra: extra params to be copied + :return: the target instance with param values copied + """ + paramMap = self.extractParamMap(extra) + for p in self.params: + if p in paramMap and to.hasParam(p.name): + to._set(**{p.name: paramMap[p]}) + return to diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py index ee901f25847bc..69efc424ec4ef 100644 --- a/python/pyspark/ml/param/_shared_params_code_gen.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -38,16 +38,13 @@ # python _shared_params_code_gen.py > shared.py -def _gen_param_code(name, doc, defaultValueStr): +def _gen_param_header(name, doc, defaultValueStr): """ - Generates Python code for a shared param class. + Generates the header part for shared variables :param name: param name :param doc: param doc - :param defaultValueStr: string representation of the default value - :return: code string """ - # TODO: How to correctly inherit instance attributes? template = '''class Has$Name(Params): """ Mixin for param $name: $doc. @@ -59,15 +56,35 @@ def _gen_param_code(name, doc, defaultValueStr): def __init__(self): super(Has$Name, self).__init__() #: param for $doc - self.$name = Param(self, "$name", "$doc") - if $defaultValueStr is not None: - self._setDefault($name=$defaultValueStr) + self.$name = Param(self, "$name", "$doc")''' + if defaultValueStr is not None: + template += ''' + self._setDefault($name=$defaultValueStr)''' + + Name = name[0].upper() + name[1:] + return template \ + .replace("$name", name) \ + .replace("$Name", Name) \ + .replace("$doc", doc) \ + .replace("$defaultValueStr", str(defaultValueStr)) + +def _gen_param_code(name, doc, defaultValueStr): + """ + Generates Python code for a shared param class. + + :param name: param name + :param doc: param doc + :param defaultValueStr: string representation of the default value + :return: code string + """ + # TODO: How to correctly inherit instance attributes? + template = ''' def set$Name(self, value): """ Sets the value of :py:attr:`$name`. """ - self.paramMap[self.$name] = value + self._paramMap[self.$name] = value return self def get$Name(self): @@ -88,19 +105,64 @@ def get$Name(self): print("\n# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.\n") print("from pyspark.ml.param import Param, Params\n\n") shared = [ - ("maxIter", "max number of iterations", None), - ("regParam", "regularization constant", None), + ("maxIter", "max number of iterations (>= 0)", None), + ("regParam", "regularization parameter (>= 0)", None), ("featuresCol", "features column name", "'features'"), ("labelCol", "label column name", "'label'"), ("predictionCol", "prediction column name", "'prediction'"), - ("rawPredictionCol", "raw prediction column name", "'rawPrediction'"), + ("probabilityCol", "Column name for predicted class conditional probabilities. " + + "Note: Not all models output well-calibrated probability estimates! These probabilities " + + "should be treated as confidences, not precise probabilities.", "'probability'"), + ("rawPredictionCol", "raw prediction (a.k.a. confidence) column name", "'rawPrediction'"), ("inputCol", "input column name", None), ("inputCols", "input column names", None), - ("outputCol", "output column name", None), - ("seed", "random seed", None), + ("outputCol", "output column name", "self.uid + '__output'"), + ("numFeatures", "number of features", None), + ("checkpointInterval", "checkpoint interval (>= 1)", None), + ("seed", "random seed", "hash(type(self).__name__)"), ("tol", "the convergence tolerance for iterative algorithms", None), ("stepSize", "Step size to be used for each iteration of optimization.", None)] code = [] for name, doc, defaultValueStr in shared: - code.append(_gen_param_code(name, doc, defaultValueStr)) + param_code = _gen_param_header(name, doc, defaultValueStr) + code.append(param_code + "\n" + _gen_param_code(name, doc, defaultValueStr)) + + decisionTreeParams = [ + ("maxDepth", "Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; " + + "depth 1 means 1 internal node + 2 leaf nodes."), + ("maxBins", "Max number of bins for" + + " discretizing continuous features. Must be >=2 and >= number of categories for any" + + " categorical feature."), + ("minInstancesPerNode", "Minimum number of instances each child must have after split. " + + "If a split causes the left or right child to have fewer than minInstancesPerNode, the " + + "split will be discarded as invalid. Should be >= 1."), + ("minInfoGain", "Minimum information gain for a split to be considered at a tree node."), + ("maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation."), + ("cacheNodeIds", "If false, the algorithm will pass trees to executors to match " + + "instances with nodes. If true, the algorithm will cache node IDs for each instance. " + + "Caching can speed up training of deeper trees.")] + + decisionTreeCode = '''class DecisionTreeParams(Params): + """ + Mixin for Decision Tree parameters. + """ + + # a placeholder to make it appear in the generated doc + $dummyPlaceHolders + + def __init__(self): + super(DecisionTreeParams, self).__init__() + $realParams''' + dtParamMethods = "" + dummyPlaceholders = "" + realParams = "" + paramTemplate = """$name = Param($owner, "$name", "$doc")""" + for name, doc in decisionTreeParams: + variable = paramTemplate.replace("$name", name).replace("$doc", doc) + dummyPlaceholders += variable.replace("$owner", "Params._dummy()") + "\n " + realParams += "#: param for " + doc + "\n " + realParams += "self." + variable.replace("$owner", "self") + "\n " + dtParamMethods += _gen_param_code(name, doc, None) + "\n" + code.append(decisionTreeCode.replace("$dummyPlaceHolders", dummyPlaceholders) + .replace("$realParams", realParams) + dtParamMethods) print("\n\n\n".join(code)) diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 5e7529c1dcf1e..bc088e4c29e26 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -22,24 +22,22 @@ class HasMaxIter(Params): """ - Mixin for param maxIter: max number of iterations. + Mixin for param maxIter: max number of iterations (>= 0). """ # a placeholder to make it appear in the generated doc - maxIter = Param(Params._dummy(), "maxIter", "max number of iterations") + maxIter = Param(Params._dummy(), "maxIter", "max number of iterations (>= 0)") def __init__(self): super(HasMaxIter, self).__init__() - #: param for max number of iterations - self.maxIter = Param(self, "maxIter", "max number of iterations") - if None is not None: - self._setDefault(maxIter=None) + #: param for max number of iterations (>= 0) + self.maxIter = Param(self, "maxIter", "max number of iterations (>= 0)") def setMaxIter(self, value): """ Sets the value of :py:attr:`maxIter`. """ - self.paramMap[self.maxIter] = value + self._paramMap[self.maxIter] = value return self def getMaxIter(self): @@ -51,24 +49,22 @@ def getMaxIter(self): class HasRegParam(Params): """ - Mixin for param regParam: regularization constant. + Mixin for param regParam: regularization parameter (>= 0). """ # a placeholder to make it appear in the generated doc - regParam = Param(Params._dummy(), "regParam", "regularization constant") + regParam = Param(Params._dummy(), "regParam", "regularization parameter (>= 0)") def __init__(self): super(HasRegParam, self).__init__() - #: param for regularization constant - self.regParam = Param(self, "regParam", "regularization constant") - if None is not None: - self._setDefault(regParam=None) + #: param for regularization parameter (>= 0) + self.regParam = Param(self, "regParam", "regularization parameter (>= 0)") def setRegParam(self, value): """ Sets the value of :py:attr:`regParam`. """ - self.paramMap[self.regParam] = value + self._paramMap[self.regParam] = value return self def getRegParam(self): @@ -90,14 +86,13 @@ def __init__(self): super(HasFeaturesCol, self).__init__() #: param for features column name self.featuresCol = Param(self, "featuresCol", "features column name") - if 'features' is not None: - self._setDefault(featuresCol='features') + self._setDefault(featuresCol='features') def setFeaturesCol(self, value): """ Sets the value of :py:attr:`featuresCol`. """ - self.paramMap[self.featuresCol] = value + self._paramMap[self.featuresCol] = value return self def getFeaturesCol(self): @@ -119,14 +114,13 @@ def __init__(self): super(HasLabelCol, self).__init__() #: param for label column name self.labelCol = Param(self, "labelCol", "label column name") - if 'label' is not None: - self._setDefault(labelCol='label') + self._setDefault(labelCol='label') def setLabelCol(self, value): """ Sets the value of :py:attr:`labelCol`. """ - self.paramMap[self.labelCol] = value + self._paramMap[self.labelCol] = value return self def getLabelCol(self): @@ -148,14 +142,13 @@ def __init__(self): super(HasPredictionCol, self).__init__() #: param for prediction column name self.predictionCol = Param(self, "predictionCol", "prediction column name") - if 'prediction' is not None: - self._setDefault(predictionCol='prediction') + self._setDefault(predictionCol='prediction') def setPredictionCol(self, value): """ Sets the value of :py:attr:`predictionCol`. """ - self.paramMap[self.predictionCol] = value + self._paramMap[self.predictionCol] = value return self def getPredictionCol(self): @@ -165,26 +158,53 @@ def getPredictionCol(self): return self.getOrDefault(self.predictionCol) +class HasProbabilityCol(Params): + """ + Mixin for param probabilityCol: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.. + """ + + # a placeholder to make it appear in the generated doc + probabilityCol = Param(Params._dummy(), "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.") + + def __init__(self): + super(HasProbabilityCol, self).__init__() + #: param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities. + self.probabilityCol = Param(self, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.") + self._setDefault(probabilityCol='probability') + + def setProbabilityCol(self, value): + """ + Sets the value of :py:attr:`probabilityCol`. + """ + self._paramMap[self.probabilityCol] = value + return self + + def getProbabilityCol(self): + """ + Gets the value of probabilityCol or its default value. + """ + return self.getOrDefault(self.probabilityCol) + + class HasRawPredictionCol(Params): """ - Mixin for param rawPredictionCol: raw prediction column name. + Mixin for param rawPredictionCol: raw prediction (a.k.a. confidence) column name. """ # a placeholder to make it appear in the generated doc - rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction column name") + rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction (a.k.a. confidence) column name") def __init__(self): super(HasRawPredictionCol, self).__init__() - #: param for raw prediction column name - self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction column name") - if 'rawPrediction' is not None: - self._setDefault(rawPredictionCol='rawPrediction') + #: param for raw prediction (a.k.a. confidence) column name + self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name") + self._setDefault(rawPredictionCol='rawPrediction') def setRawPredictionCol(self, value): """ Sets the value of :py:attr:`rawPredictionCol`. """ - self.paramMap[self.rawPredictionCol] = value + self._paramMap[self.rawPredictionCol] = value return self def getRawPredictionCol(self): @@ -206,14 +226,12 @@ def __init__(self): super(HasInputCol, self).__init__() #: param for input column name self.inputCol = Param(self, "inputCol", "input column name") - if None is not None: - self._setDefault(inputCol=None) def setInputCol(self, value): """ Sets the value of :py:attr:`inputCol`. """ - self.paramMap[self.inputCol] = value + self._paramMap[self.inputCol] = value return self def getInputCol(self): @@ -235,14 +253,12 @@ def __init__(self): super(HasInputCols, self).__init__() #: param for input column names self.inputCols = Param(self, "inputCols", "input column names") - if None is not None: - self._setDefault(inputCols=None) def setInputCols(self, value): """ Sets the value of :py:attr:`inputCols`. """ - self.paramMap[self.inputCols] = value + self._paramMap[self.inputCols] = value return self def getInputCols(self): @@ -264,14 +280,13 @@ def __init__(self): super(HasOutputCol, self).__init__() #: param for output column name self.outputCol = Param(self, "outputCol", "output column name") - if None is not None: - self._setDefault(outputCol=None) + self._setDefault(outputCol=self.uid + '__output') def setOutputCol(self, value): """ Sets the value of :py:attr:`outputCol`. """ - self.paramMap[self.outputCol] = value + self._paramMap[self.outputCol] = value return self def getOutputCol(self): @@ -293,14 +308,12 @@ def __init__(self): super(HasNumFeatures, self).__init__() #: param for number of features self.numFeatures = Param(self, "numFeatures", "number of features") - if None is not None: - self._setDefault(numFeatures=None) def setNumFeatures(self, value): """ Sets the value of :py:attr:`numFeatures`. """ - self.paramMap[self.numFeatures] = value + self._paramMap[self.numFeatures] = value return self def getNumFeatures(self): @@ -310,6 +323,33 @@ def getNumFeatures(self): return self.getOrDefault(self.numFeatures) +class HasCheckpointInterval(Params): + """ + Mixin for param checkpointInterval: checkpoint interval (>= 1). + """ + + # a placeholder to make it appear in the generated doc + checkpointInterval = Param(Params._dummy(), "checkpointInterval", "checkpoint interval (>= 1)") + + def __init__(self): + super(HasCheckpointInterval, self).__init__() + #: param for checkpoint interval (>= 1) + self.checkpointInterval = Param(self, "checkpointInterval", "checkpoint interval (>= 1)") + + def setCheckpointInterval(self, value): + """ + Sets the value of :py:attr:`checkpointInterval`. + """ + self._paramMap[self.checkpointInterval] = value + return self + + def getCheckpointInterval(self): + """ + Gets the value of checkpointInterval or its default value. + """ + return self.getOrDefault(self.checkpointInterval) + + class HasSeed(Params): """ Mixin for param seed: random seed. @@ -322,14 +362,13 @@ def __init__(self): super(HasSeed, self).__init__() #: param for random seed self.seed = Param(self, "seed", "random seed") - if None is not None: - self._setDefault(seed=None) + self._setDefault(seed=hash(type(self).__name__)) def setSeed(self, value): """ Sets the value of :py:attr:`seed`. """ - self.paramMap[self.seed] = value + self._paramMap[self.seed] = value return self def getSeed(self): @@ -351,14 +390,12 @@ def __init__(self): super(HasTol, self).__init__() #: param for the convergence tolerance for iterative algorithms self.tol = Param(self, "tol", "the convergence tolerance for iterative algorithms") - if None is not None: - self._setDefault(tol=None) def setTol(self, value): """ Sets the value of :py:attr:`tol`. """ - self.paramMap[self.tol] = value + self._paramMap[self.tol] = value return self def getTol(self): @@ -374,22 +411,18 @@ class HasStepSize(Params): """ # a placeholder to make it appear in the generated doc - stepSize = Param(Params._dummy(), "stepSize", - "Step size to be used for each iteration of optimization.") + stepSize = Param(Params._dummy(), "stepSize", "Step size to be used for each iteration of optimization.") def __init__(self): super(HasStepSize, self).__init__() #: param for Step size to be used for each iteration of optimization. - self.stepSize = Param(self, "stepSize", - "Step size to be used for each iteration of optimization.") - if None is not None: - self._setDefault(stepSize=None) + self.stepSize = Param(self, "stepSize", "Step size to be used for each iteration of optimization.") def setStepSize(self, value): """ Sets the value of :py:attr:`stepSize`. """ - self.paramMap[self.stepSize] = value + self._paramMap[self.stepSize] = value return self def getStepSize(self): @@ -397,3 +430,112 @@ def getStepSize(self): Gets the value of stepSize or its default value. """ return self.getOrDefault(self.stepSize) + + +class DecisionTreeParams(Params): + """ + Mixin for Decision Tree parameters. + """ + + # a placeholder to make it appear in the generated doc + maxDepth = Param(Params._dummy(), "maxDepth", "Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.") + maxBins = Param(Params._dummy(), "maxBins", "Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature.") + minInstancesPerNode = Param(Params._dummy(), "minInstancesPerNode", "Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1.") + minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.") + maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.") + cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.") + + + def __init__(self): + super(DecisionTreeParams, self).__init__() + #: param for Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. + self.maxDepth = Param(self, "maxDepth", "Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.") + #: param for Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature. + self.maxBins = Param(self, "maxBins", "Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature.") + #: param for Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1. + self.minInstancesPerNode = Param(self, "minInstancesPerNode", "Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1.") + #: param for Minimum information gain for a split to be considered at a tree node. + self.minInfoGain = Param(self, "minInfoGain", "Minimum information gain for a split to be considered at a tree node.") + #: param for Maximum memory in MB allocated to histogram aggregation. + self.maxMemoryInMB = Param(self, "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.") + #: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. + self.cacheNodeIds = Param(self, "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.") + + def setMaxDepth(self, value): + """ + Sets the value of :py:attr:`maxDepth`. + """ + self._paramMap[self.maxDepth] = value + return self + + def getMaxDepth(self): + """ + Gets the value of maxDepth or its default value. + """ + return self.getOrDefault(self.maxDepth) + + def setMaxBins(self, value): + """ + Sets the value of :py:attr:`maxBins`. + """ + self._paramMap[self.maxBins] = value + return self + + def getMaxBins(self): + """ + Gets the value of maxBins or its default value. + """ + return self.getOrDefault(self.maxBins) + + def setMinInstancesPerNode(self, value): + """ + Sets the value of :py:attr:`minInstancesPerNode`. + """ + self._paramMap[self.minInstancesPerNode] = value + return self + + def getMinInstancesPerNode(self): + """ + Gets the value of minInstancesPerNode or its default value. + """ + return self.getOrDefault(self.minInstancesPerNode) + + def setMinInfoGain(self, value): + """ + Sets the value of :py:attr:`minInfoGain`. + """ + self._paramMap[self.minInfoGain] = value + return self + + def getMinInfoGain(self): + """ + Gets the value of minInfoGain or its default value. + """ + return self.getOrDefault(self.minInfoGain) + + def setMaxMemoryInMB(self, value): + """ + Sets the value of :py:attr:`maxMemoryInMB`. + """ + self._paramMap[self.maxMemoryInMB] = value + return self + + def getMaxMemoryInMB(self): + """ + Gets the value of maxMemoryInMB or its default value. + """ + return self.getOrDefault(self.maxMemoryInMB) + + def setCacheNodeIds(self, value): + """ + Sets the value of :py:attr:`cacheNodeIds`. + """ + self._paramMap[self.cacheNodeIds] = value + return self + + def getCacheNodeIds(self): + """ + Gets the value of cacheNodeIds or its default value. + """ + return self.getOrDefault(self.cacheNodeIds) + diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py index c1b2077c985cf..a563024b2cdcb 100644 --- a/python/pyspark/ml/pipeline.py +++ b/python/pyspark/ml/pipeline.py @@ -22,9 +22,6 @@ from pyspark.mllib.common import inherit_doc -__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel', 'Evaluator', 'Model'] - - @inherit_doc class Estimator(Params): """ @@ -34,18 +31,40 @@ class Estimator(Params): __metaclass__ = ABCMeta @abstractmethod - def fit(self, dataset, params={}): + def _fit(self, dataset): """ - Fits a model to the input dataset with optional parameters. + Fits a model to the input dataset. This is called by the + default implementation of fit. :param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame` - :param params: an optional param map that overwrites embedded - params :returns: fitted model """ raise NotImplementedError() + def fit(self, dataset, params={}): + """ + Fits a model to the input dataset with optional parameters. + + :param dataset: input dataset, which is an instance of + :py:class:`pyspark.sql.DataFrame` + :param params: an optional param map that overrides embedded + params. If a list/tuple of param maps is given, + this calls fit on each param map and returns a + list of models. + :returns: fitted model(s) + """ + if isinstance(params, (list, tuple)): + return [self.fit(dataset, paramMap) for paramMap in params] + elif isinstance(params, dict): + if params: + return self.copy(params)._fit(dataset) + else: + return self._fit(dataset) + else: + raise ValueError("Params must be either a param map or a list/tuple of param maps, " + "but got %s." % type(params)) + @inherit_doc class Transformer(Params): @@ -57,18 +76,34 @@ class Transformer(Params): __metaclass__ = ABCMeta @abstractmethod - def transform(self, dataset, params={}): + def _transform(self, dataset): """ Transforms the input dataset with optional parameters. :param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame` - :param params: an optional param map that overwrites embedded - params :returns: transformed dataset """ raise NotImplementedError() + def transform(self, dataset, params={}): + """ + Transforms the input dataset with optional parameters. + + :param dataset: input dataset, which is an instance of + :py:class:`pyspark.sql.DataFrame` + :param params: an optional param map that overrides embedded + params. + :returns: transformed dataset + """ + if isinstance(params, dict): + if params: + return self.copy(params,)._transform(dataset) + else: + return self._transform(dataset) + else: + raise ValueError("Params must be either a param map but got %s." % type(params)) + @inherit_doc class Model(Transformer): @@ -116,15 +151,15 @@ def setStages(self, value): :param value: a list of transformers or estimators :return: the pipeline instance """ - self.paramMap[self.stages] = value + self._paramMap[self.stages] = value return self def getStages(self): """ Get pipeline stages. """ - if self.stages in self.paramMap: - return self.paramMap[self.stages] + if self.stages in self._paramMap: + return self._paramMap[self.stages] @keyword_only def setParams(self, stages=[]): @@ -135,9 +170,8 @@ def setParams(self, stages=[]): kwargs = self.setParams._input_kwargs return self._set(**kwargs) - def fit(self, dataset, params={}): - paramMap = self.extractParamMap(params) - stages = paramMap[self.stages] + def _fit(self, dataset): + stages = self.getStages() for stage in stages: if not (isinstance(stage, Estimator) or isinstance(stage, Transformer)): raise TypeError( @@ -151,16 +185,21 @@ def fit(self, dataset, params={}): if i <= indexOfLastEstimator: if isinstance(stage, Transformer): transformers.append(stage) - dataset = stage.transform(dataset, paramMap) + dataset = stage.transform(dataset) else: # must be an Estimator - model = stage.fit(dataset, paramMap) + model = stage.fit(dataset) transformers.append(model) if i < indexOfLastEstimator: - dataset = model.transform(dataset, paramMap) + dataset = model.transform(dataset) else: transformers.append(stage) return PipelineModel(transformers) + def copy(self, extra={}): + that = Params.copy(self, extra) + stages = [stage.copy(extra) for stage in that.getStages()] + return that.setStages(stages) + @inherit_doc class PipelineModel(Model): @@ -168,33 +207,15 @@ class PipelineModel(Model): Represents a compiled pipeline with transformers and fitted models. """ - def __init__(self, transformers): + def __init__(self, stages): super(PipelineModel, self).__init__() - self.transformers = transformers + self.stages = stages - def transform(self, dataset, params={}): - paramMap = self.extractParamMap(params) - for t in self.transformers: - dataset = t.transform(dataset, paramMap) + def _transform(self, dataset): + for t in self.stages: + dataset = t.transform(dataset) return dataset - -class Evaluator(object): - """ - Base class for evaluators that compute metrics from predictions. - """ - - __metaclass__ = ABCMeta - - @abstractmethod - def evaluate(self, dataset, params={}): - """ - Evaluates the output. - - :param dataset: a dataset that contains labels/observations and - predictions - :param params: an optional param map that overrides embedded - params - :return: metric - """ - raise NotImplementedError() + def copy(self, extra={}): + stages = [stage.copy(extra) for stage in self.stages] + return PipelineModel(stages) diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py new file mode 100644 index 0000000000000..b06099ac0aee6 --- /dev/null +++ b/python/pyspark/ml/recommendation.py @@ -0,0 +1,306 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark.ml.util import keyword_only +from pyspark.ml.wrapper import JavaEstimator, JavaModel +from pyspark.ml.param.shared import * +from pyspark.mllib.common import inherit_doc + + +__all__ = ['ALS', 'ALSModel'] + + +@inherit_doc +class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, HasRegParam, HasSeed): + """ + Alternating Least Squares (ALS) matrix factorization. + + ALS attempts to estimate the ratings matrix `R` as the product of + two lower-rank matrices, `X` and `Y`, i.e. `X * Yt = R`. Typically + these approximations are called 'factor' matrices. The general + approach is iterative. During each iteration, one of the factor + matrices is held constant, while the other is solved for using least + squares. The newly-solved factor matrix is then held constant while + solving for the other factor matrix. + + This is a blocked implementation of the ALS factorization algorithm + that groups the two sets of factors (referred to as "users" and + "products") into blocks and reduces communication by only sending + one copy of each user vector to each product block on each + iteration, and only for the product blocks that need that user's + feature vector. This is achieved by pre-computing some information + about the ratings matrix to determine the "out-links" of each user + (which blocks of products it will contribute to) and "in-link" + information for each product (which of the feature vectors it + receives from each user block it will depend on). This allows us to + send only an array of feature vectors between each user block and + product block, and have the product block find the users' ratings + and update the products based on these messages. + + For implicit preference data, the algorithm used is based on + "Collaborative Filtering for Implicit Feedback Datasets", available + at `http://dx.doi.org/10.1109/ICDM.2008.22`, adapted for the blocked + approach used here. + + Essentially instead of finding the low-rank approximations to the + rating matrix `R`, this finds the approximations for a preference + matrix `P` where the elements of `P` are 1 if r > 0 and 0 if r <= 0. + The ratings then act as 'confidence' values related to strength of + indicated user preferences rather than explicit ratings given to + items. + + >>> df = sqlContext.createDataFrame( + ... [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)], + ... ["user", "item", "rating"]) + >>> als = ALS(rank=10, maxIter=5) + >>> model = als.fit(df) + >>> model.rank + 10 + >>> model.userFactors.orderBy("id").collect() + [Row(id=0, features=[...]), Row(id=1, ...), Row(id=2, ...)] + >>> test = sqlContext.createDataFrame([(0, 2), (1, 0), (2, 0)], ["user", "item"]) + >>> predictions = sorted(model.transform(test).collect(), key=lambda r: r[0]) + >>> predictions[0] + Row(user=0, item=2, prediction=0.39...) + >>> predictions[1] + Row(user=1, item=0, prediction=3.19...) + >>> predictions[2] + Row(user=2, item=0, prediction=-1.15...) + """ + + # a placeholder to make it appear in the generated doc + rank = Param(Params._dummy(), "rank", "rank of the factorization") + numUserBlocks = Param(Params._dummy(), "numUserBlocks", "number of user blocks") + numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks") + implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference") + alpha = Param(Params._dummy(), "alpha", "alpha for implicit preference") + userCol = Param(Params._dummy(), "userCol", "column name for user ids") + itemCol = Param(Params._dummy(), "itemCol", "column name for item ids") + ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings") + nonnegative = Param(Params._dummy(), "nonnegative", + "whether to use nonnegative constraint for least squares") + + @keyword_only + def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, + ratingCol="rating", nonnegative=False, checkpointInterval=10): + """ + __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \ + implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=None, \ + ratingCol="rating", nonnegative=false, checkpointInterval=10) + """ + super(ALS, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.recommendation.ALS", self.uid) + self.rank = Param(self, "rank", "rank of the factorization") + self.numUserBlocks = Param(self, "numUserBlocks", "number of user blocks") + self.numItemBlocks = Param(self, "numItemBlocks", "number of item blocks") + self.implicitPrefs = Param(self, "implicitPrefs", "whether to use implicit preference") + self.alpha = Param(self, "alpha", "alpha for implicit preference") + self.userCol = Param(self, "userCol", "column name for user ids") + self.itemCol = Param(self, "itemCol", "column name for item ids") + self.ratingCol = Param(self, "ratingCol", "column name for ratings") + self.nonnegative = Param(self, "nonnegative", + "whether to use nonnegative constraint for least squares") + self._setDefault(rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, + ratingCol="rating", nonnegative=False, checkpointInterval=10) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, + ratingCol="rating", nonnegative=False, checkpointInterval=10): + """ + setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \ + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, \ + ratingCol="rating", nonnegative=False, checkpointInterval=10) + Sets params for ALS. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return ALSModel(java_model) + + def setRank(self, value): + """ + Sets the value of :py:attr:`rank`. + """ + self._paramMap[self.rank] = value + return self + + def getRank(self): + """ + Gets the value of rank or its default value. + """ + return self.getOrDefault(self.rank) + + def setNumUserBlocks(self, value): + """ + Sets the value of :py:attr:`numUserBlocks`. + """ + self._paramMap[self.numUserBlocks] = value + return self + + def getNumUserBlocks(self): + """ + Gets the value of numUserBlocks or its default value. + """ + return self.getOrDefault(self.numUserBlocks) + + def setNumItemBlocks(self, value): + """ + Sets the value of :py:attr:`numItemBlocks`. + """ + self._paramMap[self.numItemBlocks] = value + return self + + def getNumItemBlocks(self): + """ + Gets the value of numItemBlocks or its default value. + """ + return self.getOrDefault(self.numItemBlocks) + + def setNumBlocks(self, value): + """ + Sets both :py:attr:`numUserBlocks` and :py:attr:`numItemBlocks` to the specific value. + """ + self._paramMap[self.numUserBlocks] = value + self._paramMap[self.numItemBlocks] = value + + def setImplicitPrefs(self, value): + """ + Sets the value of :py:attr:`implicitPrefs`. + """ + self._paramMap[self.implicitPrefs] = value + return self + + def getImplicitPrefs(self): + """ + Gets the value of implicitPrefs or its default value. + """ + return self.getOrDefault(self.implicitPrefs) + + def setAlpha(self, value): + """ + Sets the value of :py:attr:`alpha`. + """ + self._paramMap[self.alpha] = value + return self + + def getAlpha(self): + """ + Gets the value of alpha or its default value. + """ + return self.getOrDefault(self.alpha) + + def setUserCol(self, value): + """ + Sets the value of :py:attr:`userCol`. + """ + self._paramMap[self.userCol] = value + return self + + def getUserCol(self): + """ + Gets the value of userCol or its default value. + """ + return self.getOrDefault(self.userCol) + + def setItemCol(self, value): + """ + Sets the value of :py:attr:`itemCol`. + """ + self._paramMap[self.itemCol] = value + return self + + def getItemCol(self): + """ + Gets the value of itemCol or its default value. + """ + return self.getOrDefault(self.itemCol) + + def setRatingCol(self, value): + """ + Sets the value of :py:attr:`ratingCol`. + """ + self._paramMap[self.ratingCol] = value + return self + + def getRatingCol(self): + """ + Gets the value of ratingCol or its default value. + """ + return self.getOrDefault(self.ratingCol) + + def setNonnegative(self, value): + """ + Sets the value of :py:attr:`nonnegative`. + """ + self._paramMap[self.nonnegative] = value + return self + + def getNonnegative(self): + """ + Gets the value of nonnegative or its default value. + """ + return self.getOrDefault(self.nonnegative) + + +class ALSModel(JavaModel): + """ + Model fitted by ALS. + """ + + @property + def rank(self): + """rank of the matrix factorization model""" + return self._call_java("rank") + + @property + def userFactors(self): + """ + a DataFrame that stores user factors in two columns: `id` and + `features` + """ + return self._call_java("userFactors") + + @property + def itemFactors(self): + """ + a DataFrame that stores item factors in two columns: `id` and + `features` + """ + return self._call_java("itemFactors") + + +if __name__ == "__main__": + import doctest + from pyspark.context import SparkContext + from pyspark.sql import SQLContext + globs = globals().copy() + # The small batch size here ensures that we see multiple batches, + # even in these small test examples: + sc = SparkContext("local[2]", "ml.recommendation tests") + sqlContext = SQLContext(sc) + globs['sc'] = sc + globs['sqlContext'] = sqlContext + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) + sc.stop() + if failure_count: + exit(-1) diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py new file mode 100644 index 0000000000000..b139e27372d80 --- /dev/null +++ b/python/pyspark/ml/regression.py @@ -0,0 +1,541 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark.ml.util import keyword_only +from pyspark.ml.wrapper import JavaEstimator, JavaModel +from pyspark.ml.param.shared import * +from pyspark.mllib.common import inherit_doc + + +__all__ = ['DecisionTreeRegressor', 'DecisionTreeRegressionModel', 'GBTRegressor', + 'GBTRegressionModel', 'LinearRegression', 'LinearRegressionModel', + 'RandomForestRegressor', 'RandomForestRegressionModel'] + + +@inherit_doc +class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, + HasRegParam, HasTol): + """ + Linear regression. + + The learning objective is to minimize the squared error, with regularization. + The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^ + + This support multiple types of regularization: + - none (a.k.a. ordinary least squares) + - L2 (ridge regression) + - L1 (Lasso) + - L2 + L1 (elastic net) + + >>> from pyspark.mllib.linalg import Vectors + >>> df = sqlContext.createDataFrame([ + ... (1.0, Vectors.dense(1.0)), + ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) + >>> lr = LinearRegression(maxIter=5, regParam=0.0) + >>> model = lr.fit(df) + >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) + >>> model.transform(test0).head().prediction + -1.0 + >>> model.weights + DenseVector([1.0]) + >>> model.intercept + 0.0 + >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) + >>> model.transform(test1).head().prediction + 1.0 + >>> lr.setParams("vector") + Traceback (most recent call last): + ... + TypeError: Method setParams forces keyword arguments. + """ + + # a placeholder to make it appear in the generated doc + elasticNetParam = \ + Param(Params._dummy(), "elasticNetParam", + "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " + + "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.") + + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6) + """ + super(LinearRegression, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.regression.LinearRegression", self.uid) + #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty + # is an L2 penalty. For alpha = 1, it is an L1 penalty. + self.elasticNetParam = \ + Param(self, "elasticNetParam", + "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " + + "is an L2 penalty. For alpha = 1, it is an L1 penalty.") + self._setDefault(maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6) + Sets params for linear regression. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return LinearRegressionModel(java_model) + + def setElasticNetParam(self, value): + """ + Sets the value of :py:attr:`elasticNetParam`. + """ + self._paramMap[self.elasticNetParam] = value + return self + + def getElasticNetParam(self): + """ + Gets the value of elasticNetParam or its default value. + """ + return self.getOrDefault(self.elasticNetParam) + + +class LinearRegressionModel(JavaModel): + """ + Model fitted by LinearRegression. + """ + + @property + def weights(self): + """ + Model weights. + """ + return self._call_java("weights") + + @property + def intercept(self): + """ + Model intercept. + """ + return self._call_java("intercept") + + +class TreeRegressorParams(object): + """ + Private class to track supported impurity measures. + """ + supportedImpurities = ["variance"] + + +class RandomForestParams(object): + """ + Private class to track supported random forest parameters. + """ + supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"] + + +class GBTParams(object): + """ + Private class to track supported GBT params. + """ + supportedLossTypes = ["squared", "absolute"] + + +@inherit_doc +class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, + DecisionTreeParams, HasCheckpointInterval): + """ + `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree` + learning algorithm for regression. + It supports both continuous and categorical features. + + >>> from pyspark.mllib.linalg import Vectors + >>> df = sqlContext.createDataFrame([ + ... (1.0, Vectors.dense(1.0)), + ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) + >>> dt = DecisionTreeRegressor(maxDepth=2) + >>> model = dt.fit(df) + >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) + >>> model.transform(test0).head().prediction + 0.0 + >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) + >>> model.transform(test1).head().prediction + 1.0 + """ + + # a placeholder to make it appear in the generated doc + impurity = Param(Params._dummy(), "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities)) + + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance"): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance") + """ + super(DecisionTreeRegressor, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.regression.DecisionTreeRegressor", self.uid) + #: param for Criterion used for information gain calculation (case-insensitive). + self.impurity = \ + Param(self, "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities)) + self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + impurity="variance") + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + impurity="variance"): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance") + Sets params for the DecisionTreeRegressor. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return DecisionTreeRegressionModel(java_model) + + def setImpurity(self, value): + """ + Sets the value of :py:attr:`impurity`. + """ + self._paramMap[self.impurity] = value + return self + + def getImpurity(self): + """ + Gets the value of impurity or its default value. + """ + return self.getOrDefault(self.impurity) + + +class DecisionTreeRegressionModel(JavaModel): + """ + Model fitted by DecisionTreeRegressor. + """ + + +@inherit_doc +class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, + DecisionTreeParams, HasCheckpointInterval): + """ + `http://en.wikipedia.org/wiki/Random_forest Random Forest` + learning algorithm for regression. + It supports both continuous and categorical features. + + >>> from pyspark.mllib.linalg import Vectors + >>> df = sqlContext.createDataFrame([ + ... (1.0, Vectors.dense(1.0)), + ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) + >>> rf = RandomForestRegressor(numTrees=2, maxDepth=2, seed=42) + >>> model = rf.fit(df) + >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) + >>> model.transform(test0).head().prediction + 0.0 + >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) + >>> model.transform(test1).head().prediction + 0.5 + """ + + # a placeholder to make it appear in the generated doc + impurity = Param(Params._dummy(), "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities)) + subsamplingRate = Param(Params._dummy(), "subsamplingRate", + "Fraction of the training data used for learning each decision tree, " + + "in range (0, 1].") + numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1)") + featureSubsetStrategy = \ + Param(Params._dummy(), "featureSubsetStrategy", + "The number of features to consider for splits at each tree node. Supported " + + "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies)) + + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", + numTrees=20, featureSubsetStrategy="auto", seed=None): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ + impurity="variance", numTrees=20, \ + featureSubsetStrategy="auto", seed=None) + """ + super(RandomForestRegressor, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.regression.RandomForestRegressor", self.uid) + #: param for Criterion used for information gain calculation (case-insensitive). + self.impurity = \ + Param(self, "impurity", + "Criterion used for information gain calculation (case-insensitive). " + + "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities)) + #: param for Fraction of the training data used for learning each decision tree, + # in range (0, 1] + self.subsamplingRate = Param(self, "subsamplingRate", + "Fraction of the training data used for learning each " + + "decision tree, in range (0, 1].") + #: param for Number of trees to train (>= 1) + self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1)") + #: param for The number of features to consider for splits at each tree node + self.featureSubsetStrategy = \ + Param(self, "featureSubsetStrategy", + "The number of features to consider for splits at each tree node. Supported " + + "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies)) + self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, + impurity="variance", numTrees=20, featureSubsetStrategy="auto") + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, + impurity="variance", numTrees=20, featureSubsetStrategy="auto"): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \ + impurity="variance", numTrees=20, featureSubsetStrategy="auto") + Sets params for linear regression. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return RandomForestRegressionModel(java_model) + + def setImpurity(self, value): + """ + Sets the value of :py:attr:`impurity`. + """ + self._paramMap[self.impurity] = value + return self + + def getImpurity(self): + """ + Gets the value of impurity or its default value. + """ + return self.getOrDefault(self.impurity) + + def setSubsamplingRate(self, value): + """ + Sets the value of :py:attr:`subsamplingRate`. + """ + self._paramMap[self.subsamplingRate] = value + return self + + def getSubsamplingRate(self): + """ + Gets the value of subsamplingRate or its default value. + """ + return self.getOrDefault(self.subsamplingRate) + + def setNumTrees(self, value): + """ + Sets the value of :py:attr:`numTrees`. + """ + self._paramMap[self.numTrees] = value + return self + + def getNumTrees(self): + """ + Gets the value of numTrees or its default value. + """ + return self.getOrDefault(self.numTrees) + + def setFeatureSubsetStrategy(self, value): + """ + Sets the value of :py:attr:`featureSubsetStrategy`. + """ + self._paramMap[self.featureSubsetStrategy] = value + return self + + def getFeatureSubsetStrategy(self): + """ + Gets the value of featureSubsetStrategy or its default value. + """ + return self.getOrDefault(self.featureSubsetStrategy) + + +class RandomForestRegressionModel(JavaModel): + """ + Model fitted by RandomForestRegressor. + """ + + +@inherit_doc +class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, + DecisionTreeParams, HasCheckpointInterval): + """ + `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)` + learning algorithm for regression. + It supports both continuous and categorical features. + + >>> from pyspark.mllib.linalg import Vectors + >>> df = sqlContext.createDataFrame([ + ... (1.0, Vectors.dense(1.0)), + ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) + >>> gbt = GBTRegressor(maxIter=5, maxDepth=2) + >>> model = gbt.fit(df) + >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) + >>> model.transform(test0).head().prediction + 0.0 + >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) + >>> model.transform(test1).head().prediction + 1.0 + """ + + # a placeholder to make it appear in the generated doc + lossType = Param(Params._dummy(), "lossType", + "Loss function which GBT tries to minimize (case-insensitive). " + + "Supported options: " + ", ".join(GBTParams.supportedLossTypes)) + subsamplingRate = Param(Params._dummy(), "subsamplingRate", + "Fraction of the training data used for learning each decision tree, " + + "in range (0, 1].") + stepSize = Param(Params._dummy(), "stepSize", + "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the " + + "contribution of each estimator") + + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared", + maxIter=20, stepSize=0.1): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ + lossType="squared", maxIter=20, stepSize=0.1) + """ + super(GBTRegressor, self).__init__() + self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", self.uid) + #: param for Loss function which GBT tries to minimize (case-insensitive). + self.lossType = Param(self, "lossType", + "Loss function which GBT tries to minimize (case-insensitive). " + + "Supported options: " + ", ".join(GBTParams.supportedLossTypes)) + #: Fraction of the training data used for learning each decision tree, in range (0, 1]. + self.subsamplingRate = Param(self, "subsamplingRate", + "Fraction of the training data used for learning each " + + "decision tree, in range (0, 1].") + #: Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of + # each estimator + self.stepSize = Param(self, "stepSize", + "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking " + + "the contribution of each estimator") + self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + lossType="squared", maxIter=20, stepSize=0.1) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + lossType="squared", maxIter=20, stepSize=0.1): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ + lossType="squared", maxIter=20, stepSize=0.1) + Sets params for Gradient Boosted Tree Regression. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return GBTRegressionModel(java_model) + + def setLossType(self, value): + """ + Sets the value of :py:attr:`lossType`. + """ + self._paramMap[self.lossType] = value + return self + + def getLossType(self): + """ + Gets the value of lossType or its default value. + """ + return self.getOrDefault(self.lossType) + + def setSubsamplingRate(self, value): + """ + Sets the value of :py:attr:`subsamplingRate`. + """ + self._paramMap[self.subsamplingRate] = value + return self + + def getSubsamplingRate(self): + """ + Gets the value of subsamplingRate or its default value. + """ + return self.getOrDefault(self.subsamplingRate) + + def setStepSize(self, value): + """ + Sets the value of :py:attr:`stepSize`. + """ + self._paramMap[self.stepSize] = value + return self + + def getStepSize(self): + """ + Gets the value of stepSize or its default value. + """ + return self.getOrDefault(self.stepSize) + + +class GBTRegressionModel(JavaModel): + """ + Model fitted by GBTRegressor. + """ + + +if __name__ == "__main__": + import doctest + from pyspark.context import SparkContext + from pyspark.sql import SQLContext + globs = globals().copy() + # The small batch size here ensures that we see multiple batches, + # even in these small test examples: + sc = SparkContext("local[2]", "ml.regression tests") + sqlContext = SQLContext(sc) + globs['sc'] = sc + globs['sqlContext'] = sqlContext + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) + sc.stop() + if failure_count: + exit(-1) diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py index 3a42bcf723894..6adbf166f34a8 100644 --- a/python/pyspark/ml/tests.py +++ b/python/pyspark/ml/tests.py @@ -31,10 +31,13 @@ import unittest from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase -from pyspark.sql import DataFrame -from pyspark.ml.param import Param -from pyspark.ml.param.shared import HasMaxIter, HasInputCol -from pyspark.ml.pipeline import Transformer, Estimator, Pipeline +from pyspark.sql import DataFrame, SQLContext +from pyspark.ml.param import Param, Params +from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed +from pyspark.ml.util import keyword_only +from pyspark.ml import Estimator, Model, Pipeline, Transformer +from pyspark.ml.feature import * +from pyspark.mllib.linalg import DenseVector class MockDataset(DataFrame): @@ -43,44 +46,43 @@ def __init__(self): self.index = 0 -class MockTransformer(Transformer): +class HasFake(Params): + + def __init__(self): + super(HasFake, self).__init__() + self.fake = Param(self, "fake", "fake param") + + def getFake(self): + return self.getOrDefault(self.fake) + + +class MockTransformer(Transformer, HasFake): def __init__(self): super(MockTransformer, self).__init__() - self.fake = Param(self, "fake", "fake") self.dataset_index = None - self.fake_param_value = None - def transform(self, dataset, params={}): + def _transform(self, dataset): self.dataset_index = dataset.index - if self.fake in params: - self.fake_param_value = params[self.fake] dataset.index += 1 return dataset -class MockEstimator(Estimator): +class MockEstimator(Estimator, HasFake): def __init__(self): super(MockEstimator, self).__init__() - self.fake = Param(self, "fake", "fake") self.dataset_index = None - self.fake_param_value = None - self.model = None - def fit(self, dataset, params={}): + def _fit(self, dataset): self.dataset_index = dataset.index - if self.fake in params: - self.fake_param_value = params[self.fake] model = MockModel() - self.model = model + self._copyValues(model) return model -class MockModel(MockTransformer, Transformer): - - def __init__(self): - super(MockModel, self).__init__() +class MockModel(MockTransformer, Model, HasFake): + pass class PipelineTests(PySparkTestCase): @@ -91,19 +93,17 @@ def test_pipeline(self): transformer1 = MockTransformer() estimator2 = MockEstimator() transformer3 = MockTransformer() - pipeline = Pipeline() \ - .setStages([estimator0, transformer1, estimator2, transformer3]) + pipeline = Pipeline(stages=[estimator0, transformer1, estimator2, transformer3]) pipeline_model = pipeline.fit(dataset, {estimator0.fake: 0, transformer1.fake: 1}) - self.assertEqual(0, estimator0.dataset_index) - self.assertEqual(0, estimator0.fake_param_value) - model0 = estimator0.model + model0, transformer1, model2, transformer3 = pipeline_model.stages self.assertEqual(0, model0.dataset_index) + self.assertEqual(0, model0.getFake()) self.assertEqual(1, transformer1.dataset_index) - self.assertEqual(1, transformer1.fake_param_value) - self.assertEqual(2, estimator2.dataset_index) - model2 = estimator2.model - self.assertIsNone(model2.dataset_index, "The model produced by the last estimator should " - "not be called during fit.") + self.assertEqual(1, transformer1.getFake()) + self.assertEqual(2, dataset.index) + self.assertIsNone(model2.dataset_index, "The last model shouldn't be called in fit.") + self.assertIsNone(transformer3.dataset_index, + "The last transformer shouldn't be called in fit.") dataset = pipeline_model.transform(dataset) self.assertEqual(2, model0.dataset_index) self.assertEqual(3, transformer1.dataset_index) @@ -112,14 +112,46 @@ def test_pipeline(self): self.assertEqual(6, dataset.index) -class TestParams(HasMaxIter, HasInputCol): +class TestParams(HasMaxIter, HasInputCol, HasSeed): """ - A subclass of Params mixed with HasMaxIter and HasInputCol. + A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed. """ - - def __init__(self): + @keyword_only + def __init__(self, seed=None): super(TestParams, self).__init__() self._setDefault(maxIter=10) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, seed=None): + """ + setParams(self, seed=None) + Sets params for this test. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + +class OtherTestParams(HasMaxIter, HasInputCol, HasSeed): + """ + A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed. + """ + @keyword_only + def __init__(self, seed=None): + super(OtherTestParams, self).__init__() + self._setDefault(maxIter=10) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, seed=None): + """ + setParams(self, seed=None) + Sets params for this test. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) class ParamTests(PySparkTestCase): @@ -128,17 +160,19 @@ def test_param(self): testParams = TestParams() maxIter = testParams.maxIter self.assertEqual(maxIter.name, "maxIter") - self.assertEqual(maxIter.doc, "max number of iterations") - self.assertTrue(maxIter.parent is testParams) + self.assertEqual(maxIter.doc, "max number of iterations (>= 0)") + self.assertTrue(maxIter.parent == testParams.uid) def test_params(self): testParams = TestParams() maxIter = testParams.maxIter inputCol = testParams.inputCol + seed = testParams.seed params = testParams.params - self.assertEqual(params, [inputCol, maxIter]) + self.assertEqual(params, [inputCol, maxIter, seed]) + self.assertTrue(testParams.hasParam(maxIter)) self.assertTrue(testParams.hasDefault(maxIter)) self.assertFalse(testParams.isSet(maxIter)) self.assertTrue(testParams.isDefined(maxIter)) @@ -147,16 +181,76 @@ def test_params(self): self.assertTrue(testParams.isSet(maxIter)) self.assertEquals(testParams.getMaxIter(), 100) + self.assertTrue(testParams.hasParam(inputCol)) self.assertFalse(testParams.hasDefault(inputCol)) self.assertFalse(testParams.isSet(inputCol)) self.assertFalse(testParams.isDefined(inputCol)) with self.assertRaises(KeyError): testParams.getInputCol() + # Since the default is normally random, set it to a known number for debug str + testParams._setDefault(seed=41) + testParams.setSeed(43) + self.assertEquals( testParams.explainParams(), "\n".join(["inputCol: input column name (undefined)", - "maxIter: max number of iterations (default: 10, current: 100)"])) + "maxIter: max number of iterations (>= 0) (default: 10, current: 100)", + "seed: random seed (default: 41, current: 43)"])) + + def test_hasseed(self): + noSeedSpecd = TestParams() + withSeedSpecd = TestParams(seed=42) + other = OtherTestParams() + # Check that we no longer use 42 as the magic number + self.assertNotEqual(noSeedSpecd.getSeed(), 42) + origSeed = noSeedSpecd.getSeed() + # Check that we only compute the seed once + self.assertEqual(noSeedSpecd.getSeed(), origSeed) + # Check that a specified seed is honored + self.assertEqual(withSeedSpecd.getSeed(), 42) + # Check that a different class has a different seed + self.assertNotEqual(other.getSeed(), noSeedSpecd.getSeed()) + + +class FeatureTests(PySparkTestCase): + + def test_binarizer(self): + b0 = Binarizer() + self.assertListEqual(b0.params, [b0.inputCol, b0.outputCol, b0.threshold]) + self.assertTrue(all([~b0.isSet(p) for p in b0.params])) + self.assertTrue(b0.hasDefault(b0.threshold)) + self.assertEqual(b0.getThreshold(), 0.0) + b0.setParams(inputCol="input", outputCol="output").setThreshold(1.0) + self.assertTrue(all([b0.isSet(p) for p in b0.params])) + self.assertEqual(b0.getThreshold(), 1.0) + self.assertEqual(b0.getInputCol(), "input") + self.assertEqual(b0.getOutputCol(), "output") + + b0c = b0.copy({b0.threshold: 2.0}) + self.assertEqual(b0c.uid, b0.uid) + self.assertListEqual(b0c.params, b0.params) + self.assertEqual(b0c.getThreshold(), 2.0) + + b1 = Binarizer(threshold=2.0, inputCol="input", outputCol="output") + self.assertNotEqual(b1.uid, b0.uid) + self.assertEqual(b1.getThreshold(), 2.0) + self.assertEqual(b1.getInputCol(), "input") + self.assertEqual(b1.getOutputCol(), "output") + + def test_idf(self): + sqlContext = SQLContext(self.sc) + dataset = sqlContext.createDataFrame([ + (DenseVector([1.0, 2.0]),), + (DenseVector([0.0, 1.0]),), + (DenseVector([3.0, 0.2]),)], ["tf"]) + idf0 = IDF(inputCol="tf") + self.assertListEqual(idf0.params, [idf0.inputCol, idf0.minDocFreq, idf0.outputCol]) + idf0m = idf0.fit(dataset, {idf0.outputCol: "idf"}) + self.assertEqual(idf0m.uid, idf0.uid, + "Model should inherit the UID from its parent estimator.") + output = idf0m.transform(dataset) + self.assertIsNotNone(output.head().idf) if __name__ == "__main__": diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index 28e3727f2c064..0bf988fd72f14 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -91,20 +91,19 @@ class CrossValidator(Estimator): >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator >>> from pyspark.mllib.linalg import Vectors >>> dataset = sqlContext.createDataFrame( - ... [(Vectors.dense([0.0, 1.0]), 0.0), - ... (Vectors.dense([1.0, 2.0]), 1.0), - ... (Vectors.dense([0.55, 3.0]), 0.0), - ... (Vectors.dense([0.45, 4.0]), 1.0), - ... (Vectors.dense([0.51, 5.0]), 1.0)] * 10, + ... [(Vectors.dense([0.0]), 0.0), + ... (Vectors.dense([0.4]), 1.0), + ... (Vectors.dense([0.5]), 0.0), + ... (Vectors.dense([0.6]), 1.0), + ... (Vectors.dense([1.0]), 1.0)] * 10, ... ["features", "label"]) >>> lr = LogisticRegression() - >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1, 5]).build() + >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() >>> evaluator = BinaryClassificationEvaluator() >>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) - >>> # SPARK-7432: The following test is flaky. - >>> # cvModel = cv.fit(dataset) - >>> # expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset) - >>> # cvModel.transform(dataset).collect() == expected.collect() + >>> cvModel = cv.fit(dataset) + >>> evaluator.evaluate(cvModel.transform(dataset)) + 0.8333... """ # a placeholder to make it appear in the generated doc @@ -155,7 +154,7 @@ def setEstimator(self, value): """ Sets the value of :py:attr:`estimator`. """ - self.paramMap[self.estimator] = value + self._paramMap[self.estimator] = value return self def getEstimator(self): @@ -168,7 +167,7 @@ def setEstimatorParamMaps(self, value): """ Sets the value of :py:attr:`estimatorParamMaps`. """ - self.paramMap[self.estimatorParamMaps] = value + self._paramMap[self.estimatorParamMaps] = value return self def getEstimatorParamMaps(self): @@ -181,7 +180,7 @@ def setEvaluator(self, value): """ Sets the value of :py:attr:`evaluator`. """ - self.paramMap[self.evaluator] = value + self._paramMap[self.evaluator] = value return self def getEvaluator(self): @@ -194,7 +193,7 @@ def setNumFolds(self, value): """ Sets the value of :py:attr:`numFolds`. """ - self.paramMap[self.numFolds] = value + self._paramMap[self.numFolds] = value return self def getNumFolds(self): @@ -203,13 +202,12 @@ def getNumFolds(self): """ return self.getOrDefault(self.numFolds) - def fit(self, dataset, params={}): - paramMap = self.extractParamMap(params) - est = paramMap[self.estimator] - epm = paramMap[self.estimatorParamMaps] + def _fit(self, dataset): + est = self.getOrDefault(self.estimator) + epm = self.getOrDefault(self.estimatorParamMaps) numModels = len(epm) - eva = paramMap[self.evaluator] - nFolds = paramMap[self.numFolds] + eva = self.getOrDefault(self.evaluator) + nFolds = self.getOrDefault(self.numFolds) h = 1.0 / nFolds randCol = self.uid + "_rand" df = dataset.select("*", rand(0).alias(randCol)) @@ -229,6 +227,15 @@ def fit(self, dataset, params={}): bestModel = est.fit(dataset, epm[bestIndex]) return CrossValidatorModel(bestModel) + def copy(self, extra={}): + newCV = Params.copy(self, extra) + if self.isSet(self.estimator): + newCV.setEstimator(self.getEstimator().copy(extra)) + # estimatorParamMaps remain the same + if self.isSet(self.evaluator): + newCV.setEvaluator(self.getEvaluator().copy(extra)) + return newCV + class CrossValidatorModel(Model): """ @@ -236,11 +243,23 @@ class CrossValidatorModel(Model): """ def __init__(self, bestModel): + super(CrossValidatorModel, self).__init__() #: best model from cross validation self.bestModel = bestModel - def transform(self, dataset, params={}): - return self.bestModel.transform(dataset, params) + def _transform(self, dataset): + return self.bestModel.transform(dataset) + + def copy(self, extra={}): + """ + Creates a copy of this instance with a randomly generated uid + and some extra params. This copies the underlying bestModel, + creates a deep copy of the embedded paramMap, and + copies the embedded and extra parameters over. + :param extra: Extra parameters to copy to the new instance + :return: Copy of this instance + """ + return CrossValidatorModel(self.bestModel.copy(extra)) if __name__ == "__main__": diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py index d3cb100a9efa5..cee9d67b05325 100644 --- a/python/pyspark/ml/util.py +++ b/python/pyspark/ml/util.py @@ -39,9 +39,16 @@ class Identifiable(object): """ def __init__(self): - #: A unique id for the object. The default implementation - #: concatenates the class name, "_", and 8 random hex chars. - self.uid = type(self).__name__ + "_" + uuid.uuid4().hex[:8] + #: A unique id for the object. + self.uid = self._randomUID() def __repr__(self): return self.uid + + @classmethod + def _randomUID(cls): + """ + Generate a unique id for the object. The default implementation + concatenates the class name, "_", and 12 random hex chars. + """ + return cls.__name__ + "_" + uuid.uuid4().hex[12:] diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py index f5ac2a398642a..7b0893e2cdadc 100644 --- a/python/pyspark/ml/wrapper.py +++ b/python/pyspark/ml/wrapper.py @@ -20,8 +20,8 @@ from pyspark import SparkContext from pyspark.sql import DataFrame from pyspark.ml.param import Params -from pyspark.ml.pipeline import Estimator, Transformer, Evaluator, Model -from pyspark.mllib.common import inherit_doc +from pyspark.ml.pipeline import Estimator, Transformer, Model +from pyspark.mllib.common import inherit_doc, _java2py, _py2java def _jvm(): @@ -45,46 +45,61 @@ class JavaWrapper(Params): __metaclass__ = ABCMeta - #: Fully-qualified class name of the wrapped Java component. - _java_class = None + #: The wrapped Java companion object. Subclasses should initialize + #: it properly. The param values in the Java object should be + #: synced with the Python wrapper in fit/transform/evaluate/copy. + _java_obj = None - def _java_obj(self): + @staticmethod + def _new_java_obj(java_class, *args): """ - Returns or creates a Java object. + Construct a new Java object. """ + sc = SparkContext._active_spark_context java_obj = _jvm() - for name in self._java_class.split("."): + for name in java_class.split("."): java_obj = getattr(java_obj, name) - return java_obj() + java_args = [_py2java(sc, arg) for arg in args] + return java_obj(*java_args) - def _transfer_params_to_java(self, params, java_obj): + def _make_java_param_pair(self, param, value): """ - Transforms the embedded params and additional params to the - input Java object. - :param params: additional params (overwriting embedded values) - :param java_obj: Java object to receive the params + Makes a Java parm pair. + """ + sc = SparkContext._active_spark_context + param = self._resolveParam(param) + java_param = self._java_obj.getParam(param.name) + java_value = _py2java(sc, value) + return java_param.w(java_value) + + def _transfer_params_to_java(self): """ - paramMap = self.extractParamMap(params) + Transforms the embedded params to the companion Java object. + """ + paramMap = self.extractParamMap() for param in self.params: if param in paramMap: - value = paramMap[param] - java_param = java_obj.getParam(param.name) - java_obj.set(java_param.w(value)) + pair = self._make_java_param_pair(param, paramMap[param]) + self._java_obj.set(pair) + + def _transfer_params_from_java(self): + """ + Transforms the embedded params from the companion Java object. + """ + sc = SparkContext._active_spark_context + for param in self.params: + if self._java_obj.hasParam(param.name): + java_param = self._java_obj.getParam(param.name) + value = _java2py(sc, self._java_obj.getOrDefault(java_param)) + self._paramMap[param] = value - def _empty_java_param_map(self): + @staticmethod + def _empty_java_param_map(): """ Returns an empty Java ParamMap reference. """ return _jvm().org.apache.spark.ml.param.ParamMap() - def _create_java_param_map(self, params, java_obj): - paramMap = self._empty_java_param_map() - for param, value in params.items(): - if param.parent is self: - java_param = java_obj.getParam(param.name) - paramMap.put(java_param.w(value)) - return paramMap - @inherit_doc class JavaEstimator(Estimator, JavaWrapper): @@ -99,9 +114,9 @@ def _create_model(self, java_model): """ Creates a model from the input Java model reference. """ - return JavaModel(java_model) + raise NotImplementedError() - def _fit_java(self, dataset, params={}): + def _fit_java(self, dataset): """ Fits a Java model to the input dataset. :param dataset: input dataset, which is an instance of @@ -109,12 +124,11 @@ def _fit_java(self, dataset, params={}): :param params: additional params (overwriting embedded values) :return: fitted Java model """ - java_obj = self._java_obj() - self._transfer_params_to_java(params, java_obj) - return java_obj.fit(dataset._jdf, self._empty_java_param_map()) + self._transfer_params_to_java() + return self._java_obj.fit(dataset._jdf) - def fit(self, dataset, params={}): - java_model = self._fit_java(dataset, params) + def _fit(self, dataset): + java_model = self._fit_java(dataset) return self._create_model(java_model) @@ -127,39 +141,47 @@ class JavaTransformer(Transformer, JavaWrapper): __metaclass__ = ABCMeta - def transform(self, dataset, params={}): - java_obj = self._java_obj() - self._transfer_params_to_java(params, java_obj) - return DataFrame(java_obj.transform(dataset._jdf), dataset.sql_ctx) + def _transform(self, dataset): + self._transfer_params_to_java() + return DataFrame(self._java_obj.transform(dataset._jdf), dataset.sql_ctx) @inherit_doc class JavaModel(Model, JavaTransformer): """ Base class for :py:class:`Model`s that wrap Java/Scala - implementations. + implementations. Subclasses should inherit this class before + param mix-ins, because this sets the UID from the Java model. """ __metaclass__ = ABCMeta def __init__(self, java_model): - super(JavaTransformer, self).__init__() - self._java_model = java_model - - def _java_obj(self): - return self._java_model - - -@inherit_doc -class JavaEvaluator(Evaluator, JavaWrapper): - """ - Base class for :py:class:`Evaluator`s that wrap Java/Scala - implementations. - """ - - __metaclass__ = ABCMeta + """ + Initialize this instance with a Java model object. + Subclasses should call this constructor, initialize params, + and then call _transformer_params_from_java. + """ + super(JavaModel, self).__init__() + self._java_obj = java_model + self.uid = java_model.uid() - def evaluate(self, dataset, params={}): - java_obj = self._java_obj() - self._transfer_params_to_java(params, java_obj) - return java_obj.evaluate(dataset._jdf, self._empty_java_param_map()) + def copy(self, extra={}): + """ + Creates a copy of this instance with the same uid and some + extra params. This implementation first calls Params.copy and + then make a copy of the companion Java model with extra params. + So both the Python wrapper and the Java model get copied. + :param extra: Extra parameters to copy to the new instance + :return: Copy of this instance + """ + that = super(JavaModel, self).copy(extra) + that._java_obj = self._java_obj.copy(self._empty_java_param_map()) + that._transfer_params_to_java() + return that + + def _call_java(self, name, *args): + m = getattr(self._java_obj, name) + sc = SparkContext._active_spark_context + java_args = [_py2java(sc, arg) for arg in args] + return _java2py(sc, m(*java_args)) diff --git a/python/pyspark/mllib/__init__.py b/python/pyspark/mllib/__init__.py index 07507b2ad0d05..acba3a717d21a 100644 --- a/python/pyspark/mllib/__init__.py +++ b/python/pyspark/mllib/__init__.py @@ -23,16 +23,10 @@ # MLlib currently needs NumPy 1.4+, so complain if lower import numpy -if numpy.version.version < '1.4': + +ver = [int(x) for x in numpy.version.version.split('.')[:2]] +if ver < [1, 4]: raise Exception("MLlib requires NumPy 1.4+") __all__ = ['classification', 'clustering', 'feature', 'fpm', 'linalg', 'random', 'recommendation', 'regression', 'stat', 'tree', 'util'] - -import sys -from . import rand as random -modname = __name__ + '.random' -random.__name__ = modname -random.RandomRDDs.__module__ = modname -sys.modules[modname] = random -del modname, sys diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index a70c664a71fdb..42e41397bf4bc 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -33,8 +33,8 @@ class LinearClassificationModel(LinearModel): """ - A private abstract class representing a multiclass classification model. - The categories are represented by int values: 0, 1, 2, etc. + A private abstract class representing a multiclass classification + model. The categories are represented by int values: 0, 1, 2, etc. """ def __init__(self, weights, intercept): super(LinearClassificationModel, self).__init__(weights, intercept) @@ -44,10 +44,11 @@ def setThreshold(self, value): """ .. note:: Experimental - Sets the threshold that separates positive predictions from negative - predictions. An example with prediction score greater than or equal - to this threshold is identified as an positive, and negative otherwise. - It is used for binary classification only. + Sets the threshold that separates positive predictions from + negative predictions. An example with prediction score greater + than or equal to this threshold is identified as an positive, + and negative otherwise. It is used for binary classification + only. """ self._threshold = value @@ -56,8 +57,9 @@ def threshold(self): """ .. note:: Experimental - Returns the threshold (if any) used for converting raw prediction scores - into 0/1 predictions. It is used for binary classification only. + Returns the threshold (if any) used for converting raw + prediction scores into 0/1 predictions. It is used for + binary classification only. """ return self._threshold @@ -65,22 +67,35 @@ def clearThreshold(self): """ .. note:: Experimental - Clears the threshold so that `predict` will output raw prediction scores. - It is used for binary classification only. + Clears the threshold so that `predict` will output raw + prediction scores. It is used for binary classification only. """ self._threshold = None def predict(self, test): """ - Predict values for a single data point or an RDD of points using - the model trained. + Predict values for a single data point or an RDD of points + using the model trained. """ raise NotImplementedError class LogisticRegressionModel(LinearClassificationModel): - """A linear binary classification model derived from logistic regression. + """ + Classification model trained using Multinomial/Binary Logistic + Regression. + + :param weights: Weights computed for every feature. + :param intercept: Intercept computed for this model. (Only used + in Binary Logistic Regression. In Multinomial Logistic + Regression, the intercepts will not be a single value, + so the intercepts will be part of the weights.) + :param numFeatures: the dimension of the features. + :param numClasses: the number of possible outcomes for k classes + classification problem in Multinomial Logistic Regression. + By default, it is binary logistic regression so numClasses + will be set to 2. >>> data = [ ... LabeledPoint(0.0, [0.0, 1.0]), @@ -161,8 +176,8 @@ def numClasses(self): def predict(self, x): """ - Predict values for a single data point or an RDD of points using - the model trained. + Predict values for a single data point or an RDD of points + using the model trained. """ if isinstance(x, RDD): return x.map(lambda v: self.predict(v)) @@ -225,16 +240,19 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, """ Train a logistic regression model on the given data. - :param data: The training data, an RDD of LabeledPoint. - :param iterations: The number of iterations (default: 100). + :param data: The training data, an RDD of + LabeledPoint. + :param iterations: The number of iterations + (default: 100). :param step: The step parameter used in SGD (default: 1.0). - :param miniBatchFraction: Fraction of data to be used for each SGD - iteration. + :param miniBatchFraction: Fraction of data to be used for each + SGD iteration (default: 1.0). :param initialWeights: The initial weights (default: None). - :param regParam: The regularizer parameter (default: 0.01). - :param regType: The type of regularizer used for training - our model. + :param regParam: The regularizer parameter + (default: 0.01). + :param regType: The type of regularizer used for + training our model. :Allowed values: - "l1" for using L1 regularization @@ -243,13 +261,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, (default: "l2") - :param intercept: Boolean parameter which indicates the use - or not of the augmented representation for - training data (i.e. whether bias features - are activated or not). - :param validateData: Boolean parameter which indicates if the - algorithm should validate data before training. - (default: True) + :param intercept: Boolean parameter which indicates the + use or not of the augmented representation + for training data (i.e. whether bias + features are activated or not, + default: False). + :param validateData: Boolean parameter which indicates if + the algorithm should validate data + before training. (default: True) """ def train(rdd, i): return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations), @@ -267,12 +286,15 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType """ Train a logistic regression model on the given data. - :param data: The training data, an RDD of LabeledPoint. - :param iterations: The number of iterations (default: 100). + :param data: The training data, an RDD of + LabeledPoint. + :param iterations: The number of iterations + (default: 100). :param initialWeights: The initial weights (default: None). - :param regParam: The regularizer parameter (default: 0.01). - :param regType: The type of regularizer used for training - our model. + :param regParam: The regularizer parameter + (default: 0.01). + :param regType: The type of regularizer used for + training our model. :Allowed values: - "l1" for using L1 regularization @@ -281,19 +303,21 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType (default: "l2") - :param intercept: Boolean parameter which indicates the use - or not of the augmented representation for - training data (i.e. whether bias features - are activated or not). - :param corrections: The number of corrections used in the LBFGS - update (default: 10). - :param tolerance: The convergence tolerance of iterations for - L-BFGS (default: 1e-4). + :param intercept: Boolean parameter which indicates the + use or not of the augmented representation + for training data (i.e. whether bias + features are activated or not, + default: False). + :param corrections: The number of corrections used in the + LBFGS update (default: 10). + :param tolerance: The convergence tolerance of iterations + for L-BFGS (default: 1e-4). :param validateData: Boolean parameter which indicates if the - algorithm should validate data before training. - (default: True) - :param numClasses: The number of classes (i.e., outcomes) a label can take - in Multinomial Logistic Regression (default: 2). + algorithm should validate data before + training. (default: True) + :param numClasses: The number of classes (i.e., outcomes) a + label can take in Multinomial Logistic + Regression (default: 2). >>> data = [ ... LabeledPoint(0.0, [0.0, 1.0]), @@ -323,7 +347,11 @@ def train(rdd, i): class SVMModel(LinearClassificationModel): - """A support vector machine. + """ + Model for Support Vector Machines (SVMs). + + :param weights: Weights computed for every feature. + :param intercept: Intercept computed for this model. >>> data = [ ... LabeledPoint(0.0, [0.0]), @@ -370,8 +398,8 @@ def __init__(self, weights, intercept): def predict(self, x): """ - Predict values for a single data point or an RDD of points using - the model trained. + Predict values for a single data point or an RDD of points + using the model trained. """ if isinstance(x, RDD): return x.map(lambda v: self.predict(v)) @@ -409,16 +437,19 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01, """ Train a support vector machine on the given data. - :param data: The training data, an RDD of LabeledPoint. - :param iterations: The number of iterations (default: 100). + :param data: The training data, an RDD of + LabeledPoint. + :param iterations: The number of iterations + (default: 100). :param step: The step parameter used in SGD (default: 1.0). - :param regParam: The regularizer parameter (default: 0.01). - :param miniBatchFraction: Fraction of data to be used for each SGD - iteration. + :param regParam: The regularizer parameter + (default: 0.01). + :param miniBatchFraction: Fraction of data to be used for each + SGD iteration (default: 1.0). :param initialWeights: The initial weights (default: None). - :param regType: The type of regularizer used for training - our model. + :param regType: The type of regularizer used for + training our model. :Allowed values: - "l1" for using L1 regularization @@ -427,13 +458,14 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01, (default: "l2") - :param intercept: Boolean parameter which indicates the use - or not of the augmented representation for - training data (i.e. whether bias features - are activated or not). - :param validateData: Boolean parameter which indicates if the - algorithm should validate data before training. - (default: True) + :param intercept: Boolean parameter which indicates the + use or not of the augmented representation + for training data (i.e. whether bias + features are activated or not, + default: False). + :param validateData: Boolean parameter which indicates if + the algorithm should validate data + before training. (default: True) """ def train(rdd, i): return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step), @@ -449,9 +481,11 @@ class NaiveBayesModel(Saveable, Loader): """ Model for Naive Bayes classifiers. - Contains two parameters: - - pi: vector of logs of class priors (dimension C) - - theta: matrix of logs of class conditional probabilities (CxD) + :param labels: list of labels. + :param pi: log of class priors, whose dimension is C, + number of labels. + :param theta: log of class conditional probabilities, whose + dimension is C-by-D, where D is number of features. >>> data = [ ... LabeledPoint(0.0, [0.0, 0.0]), @@ -493,7 +527,10 @@ def __init__(self, labels, pi, theta): self.theta = theta def predict(self, x): - """Return the most likely class for a data vector or an RDD of vectors""" + """ + Return the most likely class for a data vector + or an RDD of vectors + """ if isinstance(x, RDD): return x.map(lambda v: self.predict(v)) x = _convert_to_vector(x) @@ -523,16 +560,18 @@ class NaiveBayes(object): @classmethod def train(cls, data, lambda_=1.0): """ - Train a Naive Bayes model given an RDD of (label, features) vectors. + Train a Naive Bayes model given an RDD of (label, features) + vectors. - This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can - handle all kinds of discrete data. For example, by converting - documents into TF-IDF vectors, it can be used for document - classification. By making every vector a 0-1 vector, it can also be - used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}). + This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which + can handle all kinds of discrete data. For example, by + converting documents into TF-IDF vectors, it can be used for + document classification. By making every vector a 0-1 vector, + it can also be used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}). + The input feature values must be nonnegative. :param data: RDD of LabeledPoint. - :param lambda_: The smoothing parameter + :param lambda_: The smoothing parameter (default: 1.0). """ first = data.first() if not isinstance(first, LabeledPoint): diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index 04e67158514f5..c38229864d3b4 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -21,16 +21,20 @@ if sys.version > '3': xrange = range -from numpy import array +from math import exp, log + +from numpy import array, random, tile -from pyspark import RDD from pyspark import SparkContext +from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.mllib.common import callMLlibFunc, callJavaFunc, _py2java, _java2py -from pyspark.mllib.linalg import SparseVector, _convert_to_vector +from pyspark.mllib.linalg import SparseVector, _convert_to_vector, DenseVector from pyspark.mllib.stat.distribution import MultivariateGaussian from pyspark.mllib.util import Saveable, Loader, inherit_doc +from pyspark.streaming import DStream -__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture'] +__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture', + 'StreamingKMeans', 'StreamingKMeansModel'] @inherit_doc @@ -98,6 +102,9 @@ def predict(self, x): """Find the cluster to which x belongs in this model.""" best = 0 best_distance = float("inf") + if isinstance(x, RDD): + return x.map(self.predict) + x = _convert_to_vector(x) for i in xrange(len(self.centers)): distance = x.squared_distance(self.centers[i]) @@ -142,6 +149,7 @@ class GaussianMixtureModel(object): """A clustering model derived from the Gaussian Mixture Model method. + >>> from pyspark.mllib.linalg import Vectors, DenseMatrix >>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1, ... 0.9,0.8,0.75,0.935, ... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2)) @@ -154,11 +162,12 @@ class GaussianMixtureModel(object): True >>> labels[4]==labels[5] True - >>> clusterdata_2 = sc.parallelize(array([-5.1971, -2.5359, -3.8220, - ... -5.2211, -5.0602, 4.7118, - ... 6.8989, 3.4592, 4.6322, - ... 5.7048, 4.6567, 5.5026, - ... 4.5605, 5.2043, 6.2734]).reshape(5, 3)) + >>> data = array([-5.1971, -2.5359, -3.8220, + ... -5.2211, -5.0602, 4.7118, + ... 6.8989, 3.4592, 4.6322, + ... 5.7048, 4.6567, 5.5026, + ... 4.5605, 5.2043, 6.2734]) + >>> clusterdata_2 = sc.parallelize(data.reshape(5,3)) >>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001, ... maxIterations=150, seed=10) >>> labels = model.predict(clusterdata_2).collect() @@ -166,12 +175,38 @@ class GaussianMixtureModel(object): True >>> labels[3]==labels[4] True + >>> clusterdata_3 = sc.parallelize(data.reshape(15, 1)) + >>> im = GaussianMixtureModel([0.5, 0.5], + ... [MultivariateGaussian(Vectors.dense([-1.0]), DenseMatrix(1, 1, [1.0])), + ... MultivariateGaussian(Vectors.dense([1.0]), DenseMatrix(1, 1, [1.0]))]) + >>> model = GaussianMixture.train(clusterdata_3, 2, initialModel=im) """ def __init__(self, weights, gaussians): - self.weights = weights - self.gaussians = gaussians - self.k = len(self.weights) + self._weights = weights + self._gaussians = gaussians + self._k = len(self._weights) + + @property + def weights(self): + """ + Weights for each Gaussian distribution in the mixture, where weights[i] is + the weight for Gaussian i, and weights.sum == 1. + """ + return self._weights + + @property + def gaussians(self): + """ + Array of MultivariateGaussian where gaussians[i] represents + the Multivariate Gaussian (Normal) Distribution for Gaussian i. + """ + return self._gaussians + + @property + def k(self): + """Number of gaussians in mixture.""" + return self._k def predict(self, x): """ @@ -184,6 +219,9 @@ def predict(self, x): if isinstance(x, RDD): cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z))) return cluster_labels + else: + raise TypeError("x should be represented by an RDD, " + "but got %s." % type(x)) def predictSoft(self, x): """ @@ -193,10 +231,13 @@ def predictSoft(self, x): :return: membership_matrix. RDD of array of double values. """ if isinstance(x, RDD): - means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians]) + means, sigmas = zip(*[(g.mu, g.sigma) for g in self._gaussians]) membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector), - _convert_to_vector(self.weights), means, sigmas) + _convert_to_vector(self._weights), means, sigmas) return membership_matrix.map(lambda x: pyarray.array('d', x)) + else: + raise TypeError("x should be represented by an RDD, " + "but got %s." % type(x)) class GaussianMixture(object): @@ -208,17 +249,220 @@ class GaussianMixture(object): :param convergenceTol: Threshold value to check the convergence criteria. Defaults to 1e-3 :param maxIterations: Number of iterations. Default to 100 :param seed: Random Seed + :param initialModel: GaussianMixtureModel for initializing learning """ @classmethod - def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None): + def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None): """Train a Gaussian Mixture clustering model.""" - weight, mu, sigma = callMLlibFunc("trainGaussianMixture", - rdd.map(_convert_to_vector), k, - convergenceTol, maxIterations, seed) + initialModelWeights = None + initialModelMu = None + initialModelSigma = None + if initialModel is not None: + if initialModel.k != k: + raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s" + % (initialModel.k, k)) + initialModelWeights = initialModel.weights + initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)] + initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)] + weight, mu, sigma = callMLlibFunc("trainGaussianMixture", rdd.map(_convert_to_vector), k, + convergenceTol, maxIterations, seed, initialModelWeights, + initialModelMu, initialModelSigma) mvg_obj = [MultivariateGaussian(mu[i], sigma[i]) for i in range(k)] return GaussianMixtureModel(weight, mvg_obj) +class StreamingKMeansModel(KMeansModel): + """ + .. note:: Experimental + + Clustering model which can perform an online update of the centroids. + + The update formula for each centroid is given by + + * c_t+1 = ((c_t * n_t * a) + (x_t * m_t)) / (n_t + m_t) + * n_t+1 = n_t * a + m_t + + where + + * c_t: Centroid at the n_th iteration. + * n_t: Number of samples (or) weights associated with the centroid + at the n_th iteration. + * x_t: Centroid of the new data closest to c_t. + * m_t: Number of samples (or) weights of the new data closest to c_t + * c_t+1: New centroid. + * n_t+1: New number of weights. + * a: Decay Factor, which gives the forgetfulness. + + Note that if a is set to 1, it is the weighted mean of the previous + and new data. If it set to zero, the old centroids are completely + forgotten. + + :param clusterCenters: Initial cluster centers. + :param clusterWeights: List of weights assigned to each cluster. + + >>> initCenters = [[0.0, 0.0], [1.0, 1.0]] + >>> initWeights = [1.0, 1.0] + >>> stkm = StreamingKMeansModel(initCenters, initWeights) + >>> data = sc.parallelize([[-0.1, -0.1], [0.1, 0.1], + ... [0.9, 0.9], [1.1, 1.1]]) + >>> stkm = stkm.update(data, 1.0, u"batches") + >>> stkm.centers + array([[ 0., 0.], + [ 1., 1.]]) + >>> stkm.predict([-0.1, -0.1]) + 0 + >>> stkm.predict([0.9, 0.9]) + 1 + >>> stkm.clusterWeights + [3.0, 3.0] + >>> decayFactor = 0.0 + >>> data = sc.parallelize([DenseVector([1.5, 1.5]), DenseVector([0.2, 0.2])]) + >>> stkm = stkm.update(data, 0.0, u"batches") + >>> stkm.centers + array([[ 0.2, 0.2], + [ 1.5, 1.5]]) + >>> stkm.clusterWeights + [1.0, 1.0] + >>> stkm.predict([0.2, 0.2]) + 0 + >>> stkm.predict([1.5, 1.5]) + 1 + """ + def __init__(self, clusterCenters, clusterWeights): + super(StreamingKMeansModel, self).__init__(centers=clusterCenters) + self._clusterWeights = list(clusterWeights) + + @property + def clusterWeights(self): + """Return the cluster weights.""" + return self._clusterWeights + + @ignore_unicode_prefix + def update(self, data, decayFactor, timeUnit): + """Update the centroids, according to data + + :param data: Should be a RDD that represents the new data. + :param decayFactor: forgetfulness of the previous centroids. + :param timeUnit: Can be "batches" or "points". If points, then the + decay factor is raised to the power of number of new + points and if batches, it is used as it is. + """ + if not isinstance(data, RDD): + raise TypeError("Data should be of an RDD, got %s." % type(data)) + data = data.map(_convert_to_vector) + decayFactor = float(decayFactor) + if timeUnit not in ["batches", "points"]: + raise ValueError( + "timeUnit should be 'batches' or 'points', got %s." % timeUnit) + vectorCenters = [_convert_to_vector(center) for center in self.centers] + updatedModel = callMLlibFunc( + "updateStreamingKMeansModel", vectorCenters, self._clusterWeights, + data, decayFactor, timeUnit) + self.centers = array(updatedModel[0]) + self._clusterWeights = list(updatedModel[1]) + return self + + +class StreamingKMeans(object): + """ + .. note:: Experimental + + Provides methods to set k, decayFactor, timeUnit to configure the + KMeans algorithm for fitting and predicting on incoming dstreams. + More details on how the centroids are updated are provided under the + docs of StreamingKMeansModel. + + :param k: int, number of clusters + :param decayFactor: float, forgetfulness of the previous centroids. + :param timeUnit: can be "batches" or "points". If points, then the + decayfactor is raised to the power of no. of new points. + """ + def __init__(self, k=2, decayFactor=1.0, timeUnit="batches"): + self._k = k + self._decayFactor = decayFactor + if timeUnit not in ["batches", "points"]: + raise ValueError( + "timeUnit should be 'batches' or 'points', got %s." % timeUnit) + self._timeUnit = timeUnit + self._model = None + + def latestModel(self): + """Return the latest model""" + return self._model + + def _validate(self, dstream): + if self._model is None: + raise ValueError( + "Initial centers should be set either by setInitialCenters " + "or setRandomCenters.") + if not isinstance(dstream, DStream): + raise TypeError( + "Expected dstream to be of type DStream, " + "got type %s" % type(dstream)) + + def setK(self, k): + """Set number of clusters.""" + self._k = k + return self + + def setDecayFactor(self, decayFactor): + """Set decay factor.""" + self._decayFactor = decayFactor + return self + + def setHalfLife(self, halfLife, timeUnit): + """ + Set number of batches after which the centroids of that + particular batch has half the weightage. + """ + self._timeUnit = timeUnit + self._decayFactor = exp(log(0.5) / halfLife) + return self + + def setInitialCenters(self, centers, weights): + """ + Set initial centers. Should be set before calling trainOn. + """ + self._model = StreamingKMeansModel(centers, weights) + return self + + def setRandomCenters(self, dim, weight, seed): + """ + Set the initial centres to be random samples from + a gaussian population with constant weights. + """ + rng = random.RandomState(seed) + clusterCenters = rng.randn(self._k, dim) + clusterWeights = tile(weight, self._k) + self._model = StreamingKMeansModel(clusterCenters, clusterWeights) + return self + + def trainOn(self, dstream): + """Train the model on the incoming dstream.""" + self._validate(dstream) + + def update(rdd): + self._model.update(rdd, self._decayFactor, self._timeUnit) + + dstream.foreachRDD(update) + + def predictOn(self, dstream): + """ + Make predictions on a dstream. + Returns a transformed dstream object + """ + self._validate(dstream) + return dstream.map(lambda x: self._model.predict(x)) + + def predictOnValues(self, dstream): + """ + Make predictions on a keyed dstream. + Returns a transformed dstream object. + """ + self._validate(dstream) + return dstream.mapValues(lambda x: self._model.predict(x)) + + def _test(): import doctest globs = globals().copy() diff --git a/python/pyspark/mllib/common.py b/python/pyspark/mllib/common.py index ba6058978880a..855e85f57155e 100644 --- a/python/pyspark/mllib/common.py +++ b/python/pyspark/mllib/common.py @@ -27,7 +27,7 @@ from pyspark import RDD, SparkContext from pyspark.serializers import PickleSerializer, AutoBatchedSerializer - +from pyspark.sql import DataFrame, SQLContext # Hack for support float('inf') in Py4j _old_smart_decode = py4j.protocol.smart_decode @@ -99,6 +99,9 @@ def _java2py(sc, r, encoding="bytes"): jrdd = sc._jvm.SerDe.javaToPython(r) return RDD(jrdd, sc) + if clsName == 'DataFrame': + return DataFrame(r, SQLContext(sc)) + if clsName in _picklable_classes: r = sc._jvm.SerDe.dumps(r) elif isinstance(r, (JavaArray, JavaList)): diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py index 3e11df09da6b1..c5cf3a4e7ff22 100644 --- a/python/pyspark/mllib/evaluation.py +++ b/python/pyspark/mllib/evaluation.py @@ -15,15 +15,20 @@ # limitations under the License. # -from pyspark.mllib.common import JavaModelWrapper +from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc from pyspark.sql import SQLContext -from pyspark.sql.types import StructField, StructType, DoubleType +from pyspark.sql.types import StructField, StructType, DoubleType, IntegerType, ArrayType + +__all__ = ['BinaryClassificationMetrics', 'RegressionMetrics', + 'MulticlassMetrics', 'RankingMetrics'] class BinaryClassificationMetrics(JavaModelWrapper): """ Evaluator for binary classification. + :param scoreAndLabels: an RDD of (score, label) pairs + >>> scoreAndLabels = sc.parallelize([ ... (0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)], 2) >>> metrics = BinaryClassificationMetrics(scoreAndLabels) @@ -35,9 +40,6 @@ class BinaryClassificationMetrics(JavaModelWrapper): """ def __init__(self, scoreAndLabels): - """ - :param scoreAndLabels: an RDD of (score, label) pairs - """ sc = scoreAndLabels.ctx sql_ctx = SQLContext(sc) df = sql_ctx.createDataFrame(scoreAndLabels, schema=StructType([ @@ -73,6 +75,9 @@ class RegressionMetrics(JavaModelWrapper): """ Evaluator for regression. + :param predictionAndObservations: an RDD of (prediction, + observation) pairs. + >>> predictionAndObservations = sc.parallelize([ ... (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)]) >>> metrics = RegressionMetrics(predictionAndObservations) @@ -89,9 +94,6 @@ class RegressionMetrics(JavaModelWrapper): """ def __init__(self, predictionAndObservations): - """ - :param predictionAndObservations: an RDD of (prediction, observation) pairs. - """ sc = predictionAndObservations.ctx sql_ctx = SQLContext(sc) df = sql_ctx.createDataFrame(predictionAndObservations, schema=StructType([ @@ -141,6 +143,325 @@ def r2(self): return self.call("r2") +class MulticlassMetrics(JavaModelWrapper): + """ + Evaluator for multiclass classification. + + :param predictionAndLabels an RDD of (prediction, label) pairs. + + >>> predictionAndLabels = sc.parallelize([(0.0, 0.0), (0.0, 1.0), (0.0, 0.0), + ... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]) + >>> metrics = MulticlassMetrics(predictionAndLabels) + >>> metrics.falsePositiveRate(0.0) + 0.2... + >>> metrics.precision(1.0) + 0.75... + >>> metrics.recall(2.0) + 1.0... + >>> metrics.fMeasure(0.0, 2.0) + 0.52... + >>> metrics.precision() + 0.66... + >>> metrics.recall() + 0.66... + >>> metrics.weightedFalsePositiveRate + 0.19... + >>> metrics.weightedPrecision + 0.68... + >>> metrics.weightedRecall + 0.66... + >>> metrics.weightedFMeasure() + 0.66... + >>> metrics.weightedFMeasure(2.0) + 0.65... + """ + + def __init__(self, predictionAndLabels): + sc = predictionAndLabels.ctx + sql_ctx = SQLContext(sc) + df = sql_ctx.createDataFrame(predictionAndLabels, schema=StructType([ + StructField("prediction", DoubleType(), nullable=False), + StructField("label", DoubleType(), nullable=False)])) + java_class = sc._jvm.org.apache.spark.mllib.evaluation.MulticlassMetrics + java_model = java_class(df._jdf) + super(MulticlassMetrics, self).__init__(java_model) + + def truePositiveRate(self, label): + """ + Returns true positive rate for a given label (category). + """ + return self.call("truePositiveRate", label) + + def falsePositiveRate(self, label): + """ + Returns false positive rate for a given label (category). + """ + return self.call("falsePositiveRate", label) + + def precision(self, label=None): + """ + Returns precision or precision for a given label (category) if specified. + """ + if label is None: + return self.call("precision") + else: + return self.call("precision", float(label)) + + def recall(self, label=None): + """ + Returns recall or recall for a given label (category) if specified. + """ + if label is None: + return self.call("recall") + else: + return self.call("recall", float(label)) + + def fMeasure(self, label=None, beta=None): + """ + Returns f-measure or f-measure for a given label (category) if specified. + """ + if beta is None: + if label is None: + return self.call("fMeasure") + else: + return self.call("fMeasure", label) + else: + if label is None: + raise Exception("If the beta parameter is specified, label can not be none") + else: + return self.call("fMeasure", label, beta) + + @property + def weightedTruePositiveRate(self): + """ + Returns weighted true positive rate. + (equals to precision, recall and f-measure) + """ + return self.call("weightedTruePositiveRate") + + @property + def weightedFalsePositiveRate(self): + """ + Returns weighted false positive rate. + """ + return self.call("weightedFalsePositiveRate") + + @property + def weightedRecall(self): + """ + Returns weighted averaged recall. + (equals to precision, recall and f-measure) + """ + return self.call("weightedRecall") + + @property + def weightedPrecision(self): + """ + Returns weighted averaged precision. + """ + return self.call("weightedPrecision") + + def weightedFMeasure(self, beta=None): + """ + Returns weighted averaged f-measure. + """ + if beta is None: + return self.call("weightedFMeasure") + else: + return self.call("weightedFMeasure", beta) + + +class RankingMetrics(JavaModelWrapper): + """ + Evaluator for ranking algorithms. + + :param predictionAndLabels: an RDD of (predicted ranking, + ground truth set) pairs. + + >>> predictionAndLabels = sc.parallelize([ + ... ([1, 6, 2, 7, 8, 3, 9, 10, 4, 5], [1, 2, 3, 4, 5]), + ... ([4, 1, 5, 6, 2, 7, 3, 8, 9, 10], [1, 2, 3]), + ... ([1, 2, 3, 4, 5], [])]) + >>> metrics = RankingMetrics(predictionAndLabels) + >>> metrics.precisionAt(1) + 0.33... + >>> metrics.precisionAt(5) + 0.26... + >>> metrics.precisionAt(15) + 0.17... + >>> metrics.meanAveragePrecision + 0.35... + >>> metrics.ndcgAt(3) + 0.33... + >>> metrics.ndcgAt(10) + 0.48... + + """ + + def __init__(self, predictionAndLabels): + sc = predictionAndLabels.ctx + sql_ctx = SQLContext(sc) + df = sql_ctx.createDataFrame(predictionAndLabels, + schema=sql_ctx._inferSchema(predictionAndLabels)) + java_model = callMLlibFunc("newRankingMetrics", df._jdf) + super(RankingMetrics, self).__init__(java_model) + + def precisionAt(self, k): + """ + Compute the average precision of all the queries, truncated at ranking position k. + + If for a query, the ranking algorithm returns n (n < k) results, the precision value + will be computed as #(relevant items retrieved) / k. This formula also applies when + the size of the ground truth set is less than k. + + If a query has an empty ground truth set, zero will be used as precision together + with a log warning. + """ + return self.call("precisionAt", int(k)) + + @property + def meanAveragePrecision(self): + """ + Returns the mean average precision (MAP) of all the queries. + If a query has an empty ground truth set, the average precision will be zero and + a log warining is generated. + """ + return self.call("meanAveragePrecision") + + def ndcgAt(self, k): + """ + Compute the average NDCG value of all the queries, truncated at ranking position k. + The discounted cumulative gain at position k is computed as: + sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1), + and the NDCG is obtained by dividing the DCG value on the ground truth set. + In the current implementation, the relevance value is binary. + If a query has an empty ground truth set, zero will be used as NDCG together with + a log warning. + """ + return self.call("ndcgAt", int(k)) + + +class MultilabelMetrics(JavaModelWrapper): + """ + Evaluator for multilabel classification. + + :param predictionAndLabels: an RDD of (predictions, labels) pairs, + both are non-null Arrays, each with + unique elements. + + >>> predictionAndLabels = sc.parallelize([([0.0, 1.0], [0.0, 2.0]), ([0.0, 2.0], [0.0, 1.0]), + ... ([], [0.0]), ([2.0], [2.0]), ([2.0, 0.0], [2.0, 0.0]), + ... ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])]) + >>> metrics = MultilabelMetrics(predictionAndLabels) + >>> metrics.precision(0.0) + 1.0 + >>> metrics.recall(1.0) + 0.66... + >>> metrics.f1Measure(2.0) + 0.5 + >>> metrics.precision() + 0.66... + >>> metrics.recall() + 0.64... + >>> metrics.f1Measure() + 0.63... + >>> metrics.microPrecision + 0.72... + >>> metrics.microRecall + 0.66... + >>> metrics.microF1Measure + 0.69... + >>> metrics.hammingLoss + 0.33... + >>> metrics.subsetAccuracy + 0.28... + >>> metrics.accuracy + 0.54... + """ + + def __init__(self, predictionAndLabels): + sc = predictionAndLabels.ctx + sql_ctx = SQLContext(sc) + df = sql_ctx.createDataFrame(predictionAndLabels, + schema=sql_ctx._inferSchema(predictionAndLabels)) + java_class = sc._jvm.org.apache.spark.mllib.evaluation.MultilabelMetrics + java_model = java_class(df._jdf) + super(MultilabelMetrics, self).__init__(java_model) + + def precision(self, label=None): + """ + Returns precision or precision for a given label (category) if specified. + """ + if label is None: + return self.call("precision") + else: + return self.call("precision", float(label)) + + def recall(self, label=None): + """ + Returns recall or recall for a given label (category) if specified. + """ + if label is None: + return self.call("recall") + else: + return self.call("recall", float(label)) + + def f1Measure(self, label=None): + """ + Returns f1Measure or f1Measure for a given label (category) if specified. + """ + if label is None: + return self.call("f1Measure") + else: + return self.call("f1Measure", float(label)) + + @property + def microPrecision(self): + """ + Returns micro-averaged label-based precision. + (equals to micro-averaged document-based precision) + """ + return self.call("microPrecision") + + @property + def microRecall(self): + """ + Returns micro-averaged label-based recall. + (equals to micro-averaged document-based recall) + """ + return self.call("microRecall") + + @property + def microF1Measure(self): + """ + Returns micro-averaged label-based f1-measure. + (equals to micro-averaged document-based f1-measure) + """ + return self.call("microF1Measure") + + @property + def hammingLoss(self): + """ + Returns Hamming-loss. + """ + return self.call("hammingLoss") + + @property + def subsetAccuracy(self): + """ + Returns subset accuracy. + (for equal sets of labels) + """ + return self.call("subsetAccuracy") + + @property + def accuracy(self): + """ + Returns accuracy. + """ + return self.call("accuracy") + + def _test(): import doctest from pyspark import SparkContext diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index 1140539a24e95..334f5b86cd392 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -33,10 +33,13 @@ from pyspark import SparkContext from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper -from pyspark.mllib.linalg import Vectors, _convert_to_vector +from pyspark.mllib.linalg import ( + Vector, Vectors, DenseVector, SparseVector, _convert_to_vector) +from pyspark.mllib.regression import LabeledPoint __all__ = ['Normalizer', 'StandardScalerModel', 'StandardScaler', - 'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel'] + 'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel', + 'ChiSqSelector', 'ChiSqSelectorModel', 'ElementwiseProduct'] class VectorTransformer(object): @@ -66,6 +69,8 @@ class Normalizer(VectorTransformer): For `p` = float('inf'), max(abs(vector)) will be used as norm for normalization. + :param p: Normalization in L^p^ space, p = 2 by default. + >>> v = Vectors.dense(range(3)) >>> nor = Normalizer(1) >>> nor.transform(v) @@ -80,9 +85,6 @@ class Normalizer(VectorTransformer): DenseVector([0.0, 0.5, 1.0]) """ def __init__(self, p=2.0): - """ - :param p: Normalization in L^p^ space, p = 2 by default. - """ assert p >= 1.0, "p should be greater than 1.0" self.p = float(p) @@ -92,7 +94,7 @@ def transform(self, vector): :param vector: vector or RDD of vector to be normalized. :return: normalized vector. If the norm of the input is zero, it - will return the input vector. + will return the input vector. """ sc = SparkContext._active_spark_context assert sc is not None, "SparkContext should be initialized first" @@ -162,6 +164,13 @@ class StandardScaler(object): variance using column summary statistics on the samples in the training set. + :param withMean: False by default. Centers the data with mean + before scaling. It will build a dense output, so this + does not work on sparse input and will raise an + exception. + :param withStd: True by default. Scales the data to unit + standard deviation. + >>> vs = [Vectors.dense([-2.0, 2.3, 0]), Vectors.dense([3.8, 0.0, 1.9])] >>> dataset = sc.parallelize(vs) >>> standardizer = StandardScaler(True, True) @@ -172,14 +181,6 @@ class StandardScaler(object): DenseVector([0.7071, -0.7071, 0.7071]) """ def __init__(self, withMean=False, withStd=True): - """ - :param withMean: False by default. Centers the data with mean - before scaling. It will build a dense output, so this - does not work on sparse input and will raise an - exception. - :param withStd: True by default. Scales the data to unit - standard deviation. - """ if not (withMean or withStd): warnings.warn("Both withMean and withStd are false. The model does nothing.") self.withMean = withMean @@ -191,7 +192,7 @@ def fit(self, dataset): for later scaling. :param data: The data used to compute the mean and variance - to build the transformation model. + to build the transformation model. :return: a StandardScalarModel """ dataset = dataset.map(_convert_to_vector) @@ -199,6 +200,93 @@ def fit(self, dataset): return StandardScalerModel(jmodel) +class ChiSqSelectorModel(JavaVectorTransformer): + """ + .. note:: Experimental + + Represents a Chi Squared selector model. + """ + def transform(self, vector): + """ + Applies transformation on a vector. + + :param vector: Vector or RDD of Vector to be transformed. + :return: transformed vector. + """ + return JavaVectorTransformer.transform(self, vector) + + +class ChiSqSelector(object): + """ + .. note:: Experimental + + Creates a ChiSquared feature selector. + + :param numTopFeatures: number of features that selector will select. + + >>> data = [ + ... LabeledPoint(0.0, SparseVector(3, {0: 8.0, 1: 7.0})), + ... LabeledPoint(1.0, SparseVector(3, {1: 9.0, 2: 6.0})), + ... LabeledPoint(1.0, [0.0, 9.0, 8.0]), + ... LabeledPoint(2.0, [8.0, 9.0, 5.0]) + ... ] + >>> model = ChiSqSelector(1).fit(sc.parallelize(data)) + >>> model.transform(SparseVector(3, {1: 9.0, 2: 6.0})) + SparseVector(1, {0: 6.0}) + >>> model.transform(DenseVector([8.0, 9.0, 5.0])) + DenseVector([5.0]) + """ + def __init__(self, numTopFeatures): + self.numTopFeatures = int(numTopFeatures) + + def fit(self, data): + """ + Returns a ChiSquared feature selector. + + :param data: an `RDD[LabeledPoint]` containing the labeled dataset + with categorical features. Real-valued features will be + treated as categorical for each distinct value. + Apply feature discretizer before using this function. + """ + jmodel = callMLlibFunc("fitChiSqSelector", self.numTopFeatures, data) + return ChiSqSelectorModel(jmodel) + + +class PCAModel(JavaVectorTransformer): + """ + Model fitted by [[PCA]] that can project vectors to a low-dimensional space using PCA. + """ + + +class PCA(object): + """ + A feature transformer that projects vectors to a low-dimensional space using PCA. + + >>> data = [Vectors.sparse(5, [(1, 1.0), (3, 7.0)]), + ... Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]), + ... Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0])] + >>> model = PCA(2).fit(sc.parallelize(data)) + >>> pcArray = model.transform(Vectors.sparse(5, [(1, 1.0), (3, 7.0)])).toArray() + >>> pcArray[0] + 1.648... + >>> pcArray[1] + -4.013... + """ + def __init__(self, k): + """ + :param k: number of principal components. + """ + self.k = int(k) + + def fit(self, data): + """ + Computes a [[PCAModel]] that contains the principal components of the input vectors. + :param data: source vectors + """ + jmodel = callMLlibFunc("fitPCA", self.k, data) + return PCAModel(jmodel) + + class HashingTF(object): """ .. note:: Experimental @@ -208,15 +296,14 @@ class HashingTF(object): Note: the terms must be hashable (can not be dict/set/list...). + :param numFeatures: number of features (default: 2^20) + >>> htf = HashingTF(100) >>> doc = "a a b b c d".split(" ") >>> htf.transform(doc) SparseVector(100, {...}) """ def __init__(self, numFeatures=1 << 20): - """ - :param numFeatures: number of features (default: 2^20) - """ self.numFeatures = numFeatures def indexOf(self, term): @@ -256,7 +343,7 @@ def transform(self, x): Call transform directly on the RDD instead. :param x: an RDD of term frequency vectors or a term frequency - vector + vector :return: an RDD of TF-IDF vectors or a TF-IDF vector """ if isinstance(x, RDD): @@ -287,6 +374,9 @@ class IDF(object): `minDocFreq`). For terms that are not in at least `minDocFreq` documents, the IDF is found as 0, resulting in TF-IDFs of 0. + :param minDocFreq: minimum of documents in which a term + should appear for filtering + >>> n = 4 >>> freqs = [Vectors.sparse(n, (1, 3), (1.0, 2.0)), ... Vectors.dense([0.0, 1.0, 2.0, 3.0]), @@ -307,10 +397,6 @@ class IDF(object): SparseVector(4, {1: 0.0, 3: 0.5754}) """ def __init__(self, minDocFreq=0): - """ - :param minDocFreq: minimum of documents in which a term - should appear for filtering - """ self.minDocFreq = minDocFreq def fit(self, dataset): @@ -470,6 +556,38 @@ def fit(self, data): return Word2VecModel(jmodel) +class ElementwiseProduct(VectorTransformer): + """ + .. note:: Experimental + + Scales each column of the vector, with the supplied weight vector. + i.e the elementwise product. + + >>> weight = Vectors.dense([1.0, 2.0, 3.0]) + >>> eprod = ElementwiseProduct(weight) + >>> a = Vectors.dense([2.0, 1.0, 3.0]) + >>> eprod.transform(a) + DenseVector([2.0, 2.0, 9.0]) + >>> b = Vectors.dense([9.0, 3.0, 4.0]) + >>> rdd = sc.parallelize([a, b]) + >>> eprod.transform(rdd).collect() + [DenseVector([2.0, 2.0, 9.0]), DenseVector([9.0, 6.0, 12.0])] + """ + def __init__(self, scalingVector): + self.scalingVector = _convert_to_vector(scalingVector) + + def transform(self, vector): + """ + Computes the Hadamard product of the vector. + """ + if isinstance(vector, RDD): + vector = vector.map(_convert_to_vector) + + else: + vector = _convert_to_vector(vector) + return callMLlibFunc("elementwiseProductVector", self.scalingVector, vector) + + def _test(): import doctest from pyspark import SparkContext diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py index d8df02bdbaba9..bdc4a132b1b18 100644 --- a/python/pyspark/mllib/fpm.py +++ b/python/pyspark/mllib/fpm.py @@ -61,12 +61,12 @@ class FPGrowth(object): def train(cls, data, minSupport=0.3, numPartitions=-1): """ Computes an FP-Growth model that contains frequent itemsets. - :param data: The input data set, each element - contains a transaction. - :param minSupport: The minimal support level - (default: `0.3`). - :param numPartitions: The number of partitions used by parallel - FP-growth (default: same as input data). + + :param data: The input data set, each element contains a + transaction. + :param minSupport: The minimal support level (default: `0.3`). + :param numPartitions: The number of partitions used by + parallel FP-growth (default: same as input data). """ model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions)) return FPGrowthModel(model) diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 23d1a79ffe511..e96c5ef87df86 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -36,7 +36,7 @@ import numpy as np from pyspark.sql.types import UserDefinedType, StructField, StructType, ArrayType, DoubleType, \ - IntegerType, ByteType + IntegerType, ByteType, BooleanType __all__ = ['Vector', 'DenseVector', 'SparseVector', 'Vectors', @@ -163,6 +163,59 @@ def simpleString(self): return "vector" +class MatrixUDT(UserDefinedType): + """ + SQL user-defined type (UDT) for Matrix. + """ + + @classmethod + def sqlType(cls): + return StructType([ + StructField("type", ByteType(), False), + StructField("numRows", IntegerType(), False), + StructField("numCols", IntegerType(), False), + StructField("colPtrs", ArrayType(IntegerType(), False), True), + StructField("rowIndices", ArrayType(IntegerType(), False), True), + StructField("values", ArrayType(DoubleType(), False), True), + StructField("isTransposed", BooleanType(), False)]) + + @classmethod + def module(cls): + return "pyspark.mllib.linalg" + + @classmethod + def scalaUDT(cls): + return "org.apache.spark.mllib.linalg.MatrixUDT" + + def serialize(self, obj): + if isinstance(obj, SparseMatrix): + colPtrs = [int(i) for i in obj.colPtrs] + rowIndices = [int(i) for i in obj.rowIndices] + values = [float(v) for v in obj.values] + return (0, obj.numRows, obj.numCols, colPtrs, + rowIndices, values, bool(obj.isTransposed)) + elif isinstance(obj, DenseMatrix): + values = [float(v) for v in obj.values] + return (1, obj.numRows, obj.numCols, None, None, values, + bool(obj.isTransposed)) + else: + raise TypeError("cannot serialize type %r" % (type(obj))) + + def deserialize(self, datum): + assert len(datum) == 7, \ + "MatrixUDT.deserialize given row with length %d but requires 7" % len(datum) + tpe = datum[0] + if tpe == 0: + return SparseMatrix(*datum[1:]) + elif tpe == 1: + return DenseMatrix(datum[1], datum[2], datum[5], datum[6]) + else: + raise ValueError("do not recognize type %r" % tpe) + + def simpleString(self): + return "matrix" + + class Vector(object): __UDT__ = VectorUDT() @@ -781,10 +834,12 @@ def zeros(size): class Matrix(object): + + __UDT__ = MatrixUDT() + """ Represents a local matrix. """ - def __init__(self, numRows, numCols, isTransposed=False): self.numRows = numRows self.numCols = numCols diff --git a/python/pyspark/mllib/rand.py b/python/pyspark/mllib/random.py similarity index 100% rename from python/pyspark/mllib/rand.py rename to python/pyspark/mllib/random.py diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 41bde2ce3e60b..0c4d7d3bbee02 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -33,12 +33,12 @@ class LabeledPoint(object): """ - The features and labels of a data point. + Class that represents the features and labels of a data point. :param label: Label for this data point. :param features: Vector of features for this point (NumPy array, - list, pyspark.mllib.linalg.SparseVector, or scipy.sparse - column matrix) + list, pyspark.mllib.linalg.SparseVector, or scipy.sparse + column matrix) Note: 'label' and 'features' are accessible as class attributes. """ @@ -59,7 +59,12 @@ def __repr__(self): class LinearModel(object): - """A linear model that has a vector of coefficients and an intercept.""" + """ + A linear model that has a vector of coefficients and an intercept. + + :param weights: Weights computed for every feature. + :param intercept: Intercept computed for this model. + """ def __init__(self, weights, intercept): self._coeff = _convert_to_vector(weights) @@ -193,18 +198,28 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None, regParam=0.0, regType=None, intercept=False, validateData=True): """ - Train a linear regression model on the given data. - - :param data: The training data. - :param iterations: The number of iterations (default: 100). + Train a linear regression model using Stochastic Gradient + Descent (SGD). + This solves the least squares regression formulation + f(weights) = 1/n ||A weights-y||^2^ + (which is the mean squared error). + Here the data matrix has n rows, and the input RDD holds the + set of rows of A, each with its corresponding right hand side + label y. See also the documentation for the precise formulation. + + :param data: The training data, an RDD of + LabeledPoint. + :param iterations: The number of iterations + (default: 100). :param step: The step parameter used in SGD (default: 1.0). - :param miniBatchFraction: Fraction of data to be used for each SGD - iteration. + :param miniBatchFraction: Fraction of data to be used for each + SGD iteration (default: 1.0). :param initialWeights: The initial weights (default: None). - :param regParam: The regularizer parameter (default: 0.0). - :param regType: The type of regularizer used for training - our model. + :param regParam: The regularizer parameter + (default: 0.0). + :param regType: The type of regularizer used for + training our model. :Allowed values: - "l1" for using L1 regularization (lasso), @@ -213,13 +228,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, (default: None) - :param intercept: Boolean parameter which indicates the use - or not of the augmented representation for - training data (i.e. whether bias features - are activated or not). (default: False) - :param validateData: Boolean parameter which indicates if the - algorithm should validate data before training. - (default: True) + :param intercept: Boolean parameter which indicates the + use or not of the augmented representation + for training data (i.e. whether bias + features are activated or not, + default: False). + :param validateData: Boolean parameter which indicates if + the algorithm should validate data + before training. (default: True) """ def train(rdd, i): return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations), @@ -232,8 +248,8 @@ def train(rdd, i): @inherit_doc class LassoModel(LinearRegressionModelBase): - """A linear regression model derived from a least-squares fit with an - l_1 penalty term. + """A linear regression model derived from a least-squares fit with + an l_1 penalty term. >>> from pyspark.mllib.regression import LabeledPoint >>> data = [ @@ -304,7 +320,36 @@ class LassoWithSGD(object): def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None, intercept=False, validateData=True): - """Train a Lasso regression model on the given data.""" + """ + Train a regression model with L1-regularization using + Stochastic Gradient Descent. + This solves the l1-regularized least squares regression + formulation + f(weights) = 1/2n ||A weights-y||^2^ + regParam ||weights||_1 + Here the data matrix has n rows, and the input RDD holds the + set of rows of A, each with its corresponding right hand side + label y. See also the documentation for the precise formulation. + + :param data: The training data, an RDD of + LabeledPoint. + :param iterations: The number of iterations + (default: 100). + :param step: The step parameter used in SGD + (default: 1.0). + :param regParam: The regularizer parameter + (default: 0.01). + :param miniBatchFraction: Fraction of data to be used for each + SGD iteration (default: 1.0). + :param initialWeights: The initial weights (default: None). + :param intercept: Boolean parameter which indicates the + use or not of the augmented representation + for training data (i.e. whether bias + features are activated or not, + default: False). + :param validateData: Boolean parameter which indicates if + the algorithm should validate data + before training. (default: True) + """ def train(rdd, i): return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, bool(intercept), @@ -316,8 +361,8 @@ def train(rdd, i): @inherit_doc class RidgeRegressionModel(LinearRegressionModelBase): - """A linear regression model derived from a least-squares fit with an - l_2 penalty term. + """A linear regression model derived from a least-squares fit with + an l_2 penalty term. >>> from pyspark.mllib.regression import LabeledPoint >>> data = [ @@ -389,7 +434,36 @@ class RidgeRegressionWithSGD(object): def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None, intercept=False, validateData=True): - """Train a ridge regression model on the given data.""" + """ + Train a regression model with L2-regularization using + Stochastic Gradient Descent. + This solves the l2-regularized least squares regression + formulation + f(weights) = 1/2n ||A weights-y||^2^ + regParam/2 ||weights||^2^ + Here the data matrix has n rows, and the input RDD holds the + set of rows of A, each with its corresponding right hand side + label y. See also the documentation for the precise formulation. + + :param data: The training data, an RDD of + LabeledPoint. + :param iterations: The number of iterations + (default: 100). + :param step: The step parameter used in SGD + (default: 1.0). + :param regParam: The regularizer parameter + (default: 0.01). + :param miniBatchFraction: Fraction of data to be used for each + SGD iteration (default: 1.0). + :param initialWeights: The initial weights (default: None). + :param intercept: Boolean parameter which indicates the + use or not of the augmented representation + for training data (i.e. whether bias + features are activated or not, + default: False). + :param validateData: Boolean parameter which indicates if + the algorithm should validate data + before training. (default: True) + """ def train(rdd, i): return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, bool(intercept), @@ -400,7 +474,15 @@ def train(rdd, i): class IsotonicRegressionModel(Saveable, Loader): - """Regression model for isotonic regression. + """ + Regression model for isotonic regression. + + :param boundaries: Array of boundaries for which predictions are + known. Boundaries must be sorted in increasing order. + :param predictions: Array of predictions associated to the + boundaries at the same index. Results of isotonic + regression and therefore monotone. + :param isotonic: indicates whether this is isotonic or antitonic. >>> data = [(1, 0, 1), (2, 1, 1), (3, 2, 1), (1, 3, 1), (6, 4, 1), (17, 5, 1), (16, 6, 1)] >>> irm = IsotonicRegression.train(sc.parallelize(data)) @@ -430,6 +512,25 @@ def __init__(self, boundaries, predictions, isotonic): self.isotonic = isotonic def predict(self, x): + """ + Predict labels for provided features. + Using a piecewise linear function. + 1) If x exactly matches a boundary then associated prediction + is returned. In case there are multiple predictions with the + same boundary then one of them is returned. Which one is + undefined (same as java.util.Arrays.binarySearch). + 2) If x is lower or higher than all boundaries then first or + last prediction is returned respectively. In case there are + multiple predictions with the same boundary then the lowest + or highest is returned respectively. + 3) If x falls between two values in boundary array then + prediction is treated as piecewise linear function and + interpolated value is returned. In case there are multiple + values with the same boundary then the same rules as in 2) + are used. + + :param x: Feature or RDD of Features to be labeled. + """ if isinstance(x, RDD): return x.map(lambda v: self.predict(v)) return np.interp(x, self.boundaries, self.predictions) @@ -451,15 +552,15 @@ def load(cls, sc, path): class IsotonicRegression(object): - """ - Run IsotonicRegression algorithm to obtain isotonic regression model. - :param data: RDD of (label, feature, weight) tuples. - :param isotonic: Whether this is isotonic or antitonic. - """ @classmethod def train(cls, data, isotonic=True): - """Train a isotonic regression model on the given data.""" + """ + Train a isotonic regression model on the given data. + + :param data: RDD of (label, feature, weight) tuples. + :param isotonic: Whether this is isotonic or antitonic. + """ boundaries, predictions = callMLlibFunc("trainIsotonicRegressionModel", data.map(_convert_to_vector), bool(isotonic)) return IsotonicRegressionModel(boundaries.toArray(), predictions.toArray(), isotonic) diff --git a/python/pyspark/mllib/stat/KernelDensity.py b/python/pyspark/mllib/stat/KernelDensity.py new file mode 100644 index 0000000000000..7da921976d4d2 --- /dev/null +++ b/python/pyspark/mllib/stat/KernelDensity.py @@ -0,0 +1,61 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import sys + +if sys.version > '3': + xrange = range + +import numpy as np + +from pyspark.mllib.common import callMLlibFunc +from pyspark.rdd import RDD + + +class KernelDensity(object): + """ + .. note:: Experimental + + Estimate probability density at required points given a RDD of samples + from the population. + + >>> kd = KernelDensity() + >>> sample = sc.parallelize([0.0, 1.0]) + >>> kd.setSample(sample) + >>> kd.estimate([0.0, 1.0]) + array([ 0.12938758, 0.12938758]) + """ + def __init__(self): + self._bandwidth = 1.0 + self._sample = None + + def setBandwidth(self, bandwidth): + """Set bandwidth of each sample. Defaults to 1.0""" + self._bandwidth = bandwidth + + def setSample(self, sample): + """Set sample points from the population. Should be a RDD""" + if not isinstance(sample, RDD): + raise TypeError("samples should be a RDD, received %s" % type(sample)) + self._sample = sample + + def estimate(self, points): + """Estimate the probability density at points""" + points = list(points) + densities = callMLlibFunc( + "estimateKernelDensity", self._sample, self._bandwidth, points) + return np.asarray(densities) diff --git a/python/pyspark/mllib/stat/__init__.py b/python/pyspark/mllib/stat/__init__.py index e3e128513e0d7..c8a721d3fe41c 100644 --- a/python/pyspark/mllib/stat/__init__.py +++ b/python/pyspark/mllib/stat/__init__.py @@ -22,6 +22,7 @@ from pyspark.mllib.stat._statistics import * from pyspark.mllib.stat.distribution import MultivariateGaussian from pyspark.mllib.stat.test import ChiSqTestResult +from pyspark.mllib.stat.KernelDensity import KernelDensity __all__ = ["Statistics", "MultivariateStatisticalSummary", "ChiSqTestResult", - "MultivariateGaussian"] + "MultivariateGaussian", "KernelDensity"] diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 36a4c7a5408c6..744dc112d9209 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -23,8 +23,10 @@ import sys import tempfile import array as pyarray +from time import time, sleep -from numpy import array, array_equal, zeros, inf +from numpy import array, array_equal, zeros, inf, all, random +from numpy import sum as array_sum from py4j.protocol import Py4JJavaError if sys.version_info[:2] <= (2, 6): @@ -38,15 +40,18 @@ from pyspark import SparkContext from pyspark.mllib.common import _to_java_object_rdd +from pyspark.mllib.clustering import StreamingKMeans, StreamingKMeansModel from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\ - DenseMatrix, SparseMatrix, Vectors, Matrices + DenseMatrix, SparseMatrix, Vectors, Matrices, MatrixUDT from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.random import RandomRDDs from pyspark.mllib.stat import Statistics from pyspark.mllib.feature import Word2Vec from pyspark.mllib.feature import IDF from pyspark.mllib.feature import StandardScaler +from pyspark.mllib.feature import ElementwiseProduct from pyspark.serializers import PickleSerializer +from pyspark.streaming import StreamingContext from pyspark.sql import SQLContext _have_scipy = False @@ -66,6 +71,20 @@ def setUp(self): self.sc = sc +class MLLibStreamingTestCase(unittest.TestCase): + def setUp(self): + self.sc = sc + self.ssc = StreamingContext(self.sc, 1.0) + + def tearDown(self): + self.ssc.stop(False) + + @staticmethod + def _ssc_wait(start_time, end_time, sleep_time): + while time() - start_time < end_time: + sleep(0.01) + + def _squared_distance(a, b): if isinstance(a, Vector): return a.squared_distance(b) @@ -507,6 +526,38 @@ def test_infer_schema(self): raise TypeError("expecting a vector but got %r of type %r" % (v, type(v))) +class MatrixUDTTests(MLlibTestCase): + + dm1 = DenseMatrix(3, 2, [0, 1, 4, 5, 9, 10]) + dm2 = DenseMatrix(3, 2, [0, 1, 4, 5, 9, 10], isTransposed=True) + sm1 = SparseMatrix(1, 1, [0, 1], [0], [2.0]) + sm2 = SparseMatrix(2, 1, [0, 0, 1], [0], [5.0], isTransposed=True) + udt = MatrixUDT() + + def test_json_schema(self): + self.assertEqual(MatrixUDT.fromJson(self.udt.jsonValue()), self.udt) + + def test_serialization(self): + for m in [self.dm1, self.dm2, self.sm1, self.sm2]: + self.assertEqual(m, self.udt.deserialize(self.udt.serialize(m))) + + def test_infer_schema(self): + sqlCtx = SQLContext(self.sc) + rdd = self.sc.parallelize([("dense", self.dm1), ("sparse", self.sm1)]) + df = rdd.toDF() + schema = df.schema + self.assertTrue(schema.fields[1].dataType, self.udt) + matrices = df.map(lambda x: x._2).collect() + self.assertEqual(len(matrices), 2) + for m in matrices: + if isinstance(m, DenseMatrix): + self.assertTrue(m, self.dm1) + elif isinstance(m, SparseMatrix): + self.assertTrue(m, self.sm1) + else: + raise ValueError("Expected a matrix but got type %r" % type(m)) + + @unittest.skipIf(not _have_scipy, "SciPy not installed") class SciPyTests(MLlibTestCase): @@ -818,6 +869,148 @@ def test_model_transform(self): self.assertEqual(model.transform([1.0, 2.0, 3.0]), DenseVector([1.0, 2.0, 3.0])) +class ElementwiseProductTests(MLlibTestCase): + def test_model_transform(self): + weight = Vectors.dense([3, 2, 1]) + + densevec = Vectors.dense([4, 5, 6]) + sparsevec = Vectors.sparse(3, [0], [1]) + eprod = ElementwiseProduct(weight) + self.assertEqual(eprod.transform(densevec), DenseVector([12, 10, 6])) + self.assertEqual( + eprod.transform(sparsevec), SparseVector(3, [0], [3])) + + +class StreamingKMeansTest(MLLibStreamingTestCase): + def test_model_params(self): + """Test that the model params are set correctly""" + stkm = StreamingKMeans() + stkm.setK(5).setDecayFactor(0.0) + self.assertEquals(stkm._k, 5) + self.assertEquals(stkm._decayFactor, 0.0) + + # Model not set yet. + self.assertIsNone(stkm.latestModel()) + self.assertRaises(ValueError, stkm.trainOn, [0.0, 1.0]) + + stkm.setInitialCenters( + centers=[[0.0, 0.0], [1.0, 1.0]], weights=[1.0, 1.0]) + self.assertEquals( + stkm.latestModel().centers, [[0.0, 0.0], [1.0, 1.0]]) + self.assertEquals(stkm.latestModel().clusterWeights, [1.0, 1.0]) + + def test_accuracy_for_single_center(self): + """Test that parameters obtained are correct for a single center.""" + centers, batches = self.streamingKMeansDataGenerator( + batches=5, numPoints=5, k=1, d=5, r=0.1, seed=0) + stkm = StreamingKMeans(1) + stkm.setInitialCenters([[0., 0., 0., 0., 0.]], [0.]) + input_stream = self.ssc.queueStream( + [self.sc.parallelize(batch, 1) for batch in batches]) + stkm.trainOn(input_stream) + + t = time() + self.ssc.start() + self._ssc_wait(t, 10.0, 0.01) + self.assertEquals(stkm.latestModel().clusterWeights, [25.0]) + realCenters = array_sum(array(centers), axis=0) + for i in range(5): + modelCenters = stkm.latestModel().centers[0][i] + self.assertAlmostEqual(centers[0][i], modelCenters, 1) + self.assertAlmostEqual(realCenters[i], modelCenters, 1) + + def streamingKMeansDataGenerator(self, batches, numPoints, + k, d, r, seed, centers=None): + rng = random.RandomState(seed) + + # Generate centers. + centers = [rng.randn(d) for i in range(k)] + + return centers, [[Vectors.dense(centers[j % k] + r * rng.randn(d)) + for j in range(numPoints)] + for i in range(batches)] + + def test_trainOn_model(self): + """Test the model on toy data with four clusters.""" + stkm = StreamingKMeans() + initCenters = [[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]] + stkm.setInitialCenters( + centers=initCenters, weights=[1.0, 1.0, 1.0, 1.0]) + + # Create a toy dataset by setting a tiny offest for each point. + offsets = [[0, 0.1], [0, -0.1], [0.1, 0], [-0.1, 0]] + batches = [] + for offset in offsets: + batches.append([[offset[0] + center[0], offset[1] + center[1]] + for center in initCenters]) + + batches = [self.sc.parallelize(batch, 1) for batch in batches] + input_stream = self.ssc.queueStream(batches) + stkm.trainOn(input_stream) + t = time() + self.ssc.start() + + # Give enough time to train the model. + self._ssc_wait(t, 6.0, 0.01) + finalModel = stkm.latestModel() + self.assertTrue(all(finalModel.centers == array(initCenters))) + self.assertEquals(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0]) + + def test_predictOn_model(self): + """Test that the model predicts correctly on toy data.""" + stkm = StreamingKMeans() + stkm._model = StreamingKMeansModel( + clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]], + clusterWeights=[1.0, 1.0, 1.0, 1.0]) + + predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]] + predict_data = [sc.parallelize(batch, 1) for batch in predict_data] + predict_stream = self.ssc.queueStream(predict_data) + predict_val = stkm.predictOn(predict_stream) + + result = [] + + def update(rdd): + rdd_collect = rdd.collect() + if rdd_collect: + result.append(rdd_collect) + + predict_val.foreachRDD(update) + t = time() + self.ssc.start() + self._ssc_wait(t, 6.0, 0.01) + self.assertEquals(result, [[0], [1], [2], [3]]) + + def test_trainOn_predictOn(self): + """Test that prediction happens on the updated model.""" + stkm = StreamingKMeans(decayFactor=0.0, k=2) + stkm.setInitialCenters([[0.0], [1.0]], [1.0, 1.0]) + + # Since decay factor is set to zero, once the first batch + # is passed the clusterCenters are updated to [-0.5, 0.7] + # which causes 0.2 & 0.3 to be classified as 1, even though the + # classification based in the initial model would have been 0 + # proving that the model is updated. + batches = [[[-0.5], [0.6], [0.8]], [[0.2], [-0.1], [0.3]]] + batches = [sc.parallelize(batch) for batch in batches] + input_stream = self.ssc.queueStream(batches) + predict_results = [] + + def collect(rdd): + rdd_collect = rdd.collect() + if rdd_collect: + predict_results.append(rdd_collect) + + stkm.trainOn(input_stream) + predict_stream = stkm.predictOn(input_stream) + predict_stream.foreachRDD(collect) + + t = time() + self.ssc.start() + self._ssc_wait(t, 6.0, 0.01) + self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]]) + + if __name__ == "__main__": if not _have_scipy: print("NOTE: Skipping SciPy tests as it does not seem to be installed") diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index d254deb527d10..20c0bc93f413c 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -813,13 +813,21 @@ def op(x, y): def fold(self, zeroValue, op): """ Aggregate the elements of each partition, and then the results for all - the partitions, using a given associative function and a neutral "zero - value." + the partitions, using a given associative and commutative function and + a neutral "zero value." The function C{op(t1, t2)} is allowed to modify C{t1} and return it as its result value to avoid object allocation; however, it should not modify C{t2}. + This behaves somewhat differently from fold operations implemented + for non-distributed collections in functional languages like Scala. + This fold operation may be applied to partitions individually, and then + fold those results into the final result, rather than apply the fold + to each element sequentially in some defined ordering. For functions + that are not commutative, the result may differ from that of a fold + applied to a non-distributed collection. + >>> from operator import add >>> sc.parallelize([1, 2, 3, 4, 5]).fold(0, add) 15 @@ -952,7 +960,7 @@ def sum(self): >>> sc.parallelize([1.0, 2.0, 3.0]).sum() 6.0 """ - return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add) + return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add) def count(self): """ @@ -2239,8 +2247,6 @@ def countApproxDistinct(self, relativeSD=0.05): """ if relativeSD < 0.000017: raise ValueError("relativeSD should be greater than 0.000017") - if relativeSD > 0.37: - raise ValueError("relativeSD should be smaller than 0.37") # the hash space in Java is 2^32 hashRDD = self.map(lambda x: portable_hash(x) & 0xFFFFFFFF) return hashRDD._to_java_object_rdd().countApproxDistinct(relativeSD) @@ -2262,7 +2268,7 @@ def toLocalIterator(self): def _prepare_for_python_RDD(sc, command, obj=None): # the serialized command will be compressed by broadcast ser = CloudPickleSerializer() - pickled_command = ser.dumps((command, sys.version_info[:2])) + pickled_command = ser.dumps(command) if len(pickled_command) > (1 << 20): # 1M # The broadcast will have same life cycle as created PythonRDD broadcast = sc.broadcast(pickled_command) @@ -2346,7 +2352,7 @@ def _jrdd(self): python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(), bytearray(pickled_cmd), env, includes, self.preservesPartitioning, - self.ctx.pythonExec, + self.ctx.pythonExec, self.ctx.pythonVer, bvars, self.ctx._javaAccumulator) self._jrdd_val = python_rdd.asJavaRDD() diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index d8cdcda3a3783..7f9d0a338d31e 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -272,7 +272,7 @@ def dump_stream(self, iterator, stream): if size < best: batch *= 2 elif size > best * 10 and batch > 1: - batch /= 2 + batch //= 2 def __repr__(self): return "AutoBatchedSerializer(%s)" % self.serializer diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py index 1d0b16cade8bb..67752c0d150b9 100644 --- a/python/pyspark/shuffle.py +++ b/python/pyspark/shuffle.py @@ -362,7 +362,7 @@ def _spill(self): self.spills += 1 gc.collect() # release the memory as much as possible - MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 def items(self): """ Return all merged items as iterator """ @@ -486,7 +486,7 @@ def sorted(self, iterator, key=None, reverse=False): goes above the limit. """ global MemoryBytesSpilled, DiskBytesSpilled - batch, limit = 100, self.memory_limit + batch, limit = 100, self._next_limit() chunks, current_chunk = [], [] iterator = iter(iterator) while True: @@ -512,10 +512,7 @@ def load(f): f.close() chunks.append(load(open(path, 'rb'))) current_chunk = [] - gc.collect() - batch //= 2 - limit = self._next_limit() - MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 DiskBytesSpilled += os.path.getsize(path) os.unlink(path) # data will be deleted after close @@ -630,7 +627,7 @@ def _spill(self): self.values = [] gc.collect() DiskBytesSpilled += self._file.tell() - pos - MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 class ExternalListOfList(ExternalList): @@ -794,7 +791,7 @@ def _spill(self): self.spills += 1 gc.collect() # release the memory as much as possible - MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 def _merged_items(self, index): size = sum(os.path.getsize(os.path.join(self._get_spill_dir(j), str(index))) diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py index b60b991dd4d8b..ad9c891ba1c04 100644 --- a/python/pyspark/sql/__init__.py +++ b/python/pyspark/sql/__init__.py @@ -18,45 +18,58 @@ """ Important classes of Spark SQL and DataFrames: - - L{SQLContext} + - :class:`pyspark.sql.SQLContext` Main entry point for :class:`DataFrame` and SQL functionality. - - L{DataFrame} + - :class:`pyspark.sql.DataFrame` A distributed collection of data grouped into named columns. - - L{Column} + - :class:`pyspark.sql.Column` A column expression in a :class:`DataFrame`. - - L{Row} + - :class:`pyspark.sql.Row` A row of data in a :class:`DataFrame`. - - L{HiveContext} + - :class:`pyspark.sql.HiveContext` Main entry point for accessing data stored in Apache Hive. - - L{GroupedData} + - :class:`pyspark.sql.GroupedData` Aggregation methods, returned by :func:`DataFrame.groupBy`. - - L{DataFrameNaFunctions} + - :class:`pyspark.sql.DataFrameNaFunctions` Methods for handling missing data (null values). - - L{functions} + - :class:`pyspark.sql.DataFrameStatFunctions` + Methods for statistics functionality. + - :class:`pyspark.sql.functions` List of built-in functions available for :class:`DataFrame`. - - L{types} + - :class:`pyspark.sql.types` List of data types available. + - :class:`pyspark.sql.Window` + For working with window functions. """ from __future__ import absolute_import -# fix the module name conflict for Python 3+ -import sys -from . import _types as types -modname = __name__ + '.types' -types.__name__ = modname -# update the __module__ for all objects, make them picklable -for v in types.__dict__.values(): - if hasattr(v, "__module__") and v.__module__.endswith('._types'): - v.__module__ = modname -sys.modules[modname] = types -del modname, sys + +def since(version): + """ + A decorator that annotates a function to append the version of Spark the function was added. + """ + import re + indent_p = re.compile(r'\n( +)') + + def deco(f): + indents = indent_p.findall(f.__doc__) + indent = ' ' * (min(len(m) for m in indents) if indents else 0) + f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version) + return f + return deco + from pyspark.sql.types import Row from pyspark.sql.context import SQLContext, HiveContext -from pyspark.sql.dataframe import DataFrame, GroupedData, Column, SchemaRDD, DataFrameNaFunctions -from pyspark.sql.dataframe import DataFrameStatFunctions +from pyspark.sql.column import Column +from pyspark.sql.dataframe import DataFrame, SchemaRDD, DataFrameNaFunctions, DataFrameStatFunctions +from pyspark.sql.group import GroupedData +from pyspark.sql.readwriter import DataFrameReader, DataFrameWriter +from pyspark.sql.window import Window, WindowSpec + __all__ = [ 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', - 'DataFrameNaFunctions', 'DataFrameStatFunctions' + 'DataFrameNaFunctions', 'DataFrameStatFunctions', 'Window', 'WindowSpec', + 'DataFrameReader', 'DataFrameWriter' ] diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py new file mode 100644 index 0000000000000..1ecec5b126505 --- /dev/null +++ b/python/pyspark/sql/column.py @@ -0,0 +1,425 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import sys + +if sys.version >= '3': + basestring = str + long = int + +from pyspark.context import SparkContext +from pyspark.rdd import ignore_unicode_prefix +from pyspark.sql import since +from pyspark.sql.types import * + +__all__ = ["DataFrame", "Column", "SchemaRDD", "DataFrameNaFunctions", + "DataFrameStatFunctions"] + + +def _create_column_from_literal(literal): + sc = SparkContext._active_spark_context + return sc._jvm.functions.lit(literal) + + +def _create_column_from_name(name): + sc = SparkContext._active_spark_context + return sc._jvm.functions.col(name) + + +def _to_java_column(col): + if isinstance(col, Column): + jcol = col._jc + else: + jcol = _create_column_from_name(col) + return jcol + + +def _to_seq(sc, cols, converter=None): + """ + Convert a list of Column (or names) into a JVM Seq of Column. + + An optional `converter` could be used to convert items in `cols` + into JVM Column objects. + """ + if converter: + cols = [converter(c) for c in cols] + return sc._jvm.PythonUtils.toSeq(cols) + + +def _unary_op(name, doc="unary operator"): + """ Create a method for given unary operator """ + def _(self): + jc = getattr(self._jc, name)() + return Column(jc) + _.__doc__ = doc + return _ + + +def _func_op(name, doc=''): + def _(self): + sc = SparkContext._active_spark_context + jc = getattr(sc._jvm.functions, name)(self._jc) + return Column(jc) + _.__doc__ = doc + return _ + + +def _bin_op(name, doc="binary operator"): + """ Create a method for given binary operator + """ + def _(self, other): + jc = other._jc if isinstance(other, Column) else other + njc = getattr(self._jc, name)(jc) + return Column(njc) + _.__doc__ = doc + return _ + + +def _reverse_op(name, doc="binary operator"): + """ Create a method for binary operator (this object is on right side) + """ + def _(self, other): + jother = _create_column_from_literal(other) + jc = getattr(jother, name)(self._jc) + return Column(jc) + _.__doc__ = doc + return _ + + +class Column(object): + + """ + A column in a DataFrame. + + :class:`Column` instances can be created by:: + + # 1. Select a column out of a DataFrame + + df.colName + df["colName"] + + # 2. Create from an expression + df.colName + 1 + 1 / df.colName + + .. note:: Experimental + + .. versionadded:: 1.3 + """ + + def __init__(self, jc): + self._jc = jc + + # arithmetic operators + __neg__ = _func_op("negate") + __add__ = _bin_op("plus") + __sub__ = _bin_op("minus") + __mul__ = _bin_op("multiply") + __div__ = _bin_op("divide") + __truediv__ = _bin_op("divide") + __mod__ = _bin_op("mod") + __radd__ = _bin_op("plus") + __rsub__ = _reverse_op("minus") + __rmul__ = _bin_op("multiply") + __rdiv__ = _reverse_op("divide") + __rtruediv__ = _reverse_op("divide") + __rmod__ = _reverse_op("mod") + + # logistic operators + __eq__ = _bin_op("equalTo") + __ne__ = _bin_op("notEqual") + __lt__ = _bin_op("lt") + __le__ = _bin_op("leq") + __ge__ = _bin_op("geq") + __gt__ = _bin_op("gt") + + # `and`, `or`, `not` cannot be overloaded in Python, + # so use bitwise operators as boolean operators + __and__ = _bin_op('and') + __or__ = _bin_op('or') + __invert__ = _func_op('not') + __rand__ = _bin_op("and") + __ror__ = _bin_op("or") + + # container operators + __contains__ = _bin_op("contains") + __getitem__ = _bin_op("apply") + + # bitwise operators + bitwiseOR = _bin_op("bitwiseOR") + bitwiseAND = _bin_op("bitwiseAND") + bitwiseXOR = _bin_op("bitwiseXOR") + + @since(1.3) + def getItem(self, key): + """ + An expression that gets an item at position ``ordinal`` out of a list, + or gets an item by key out of a dict. + + >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"]) + >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ + >>> df.select(df.l[0], df.d["key"]).show() + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ + """ + return self[key] + + @since(1.3) + def getField(self, name): + """ + An expression that gets a field by name in a StructField. + + >>> from pyspark.sql import Row + >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF() + >>> df.select(df.r.getField("b")).show() + +----+ + |r[b]| + +----+ + | b| + +----+ + >>> df.select(df.r.a).show() + +----+ + |r[a]| + +----+ + | 1| + +----+ + """ + return self[name] + + def __getattr__(self, item): + if item.startswith("__"): + raise AttributeError(item) + return self.getField(item) + + # string methods + rlike = _bin_op("rlike") + like = _bin_op("like") + startswith = _bin_op("startsWith") + endswith = _bin_op("endsWith") + + @ignore_unicode_prefix + @since(1.3) + def substr(self, startPos, length): + """ + Return a :class:`Column` which is a substring of the column. + + :param startPos: start position (int or Column) + :param length: length of the substring (int or Column) + + >>> df.select(df.name.substr(1, 3).alias("col")).collect() + [Row(col=u'Ali'), Row(col=u'Bob')] + """ + if type(startPos) != type(length): + raise TypeError("Can not mix the type") + if isinstance(startPos, (int, long)): + jc = self._jc.substr(startPos, length) + elif isinstance(startPos, Column): + jc = self._jc.substr(startPos._jc, length._jc) + else: + raise TypeError("Unexpected type: %s" % type(startPos)) + return Column(jc) + + __getslice__ = substr + + @ignore_unicode_prefix + @since(1.3) + def inSet(self, *cols): + """ + A boolean expression that is evaluated to true if the value of this + expression is contained by the evaluated values of the arguments. + + >>> df[df.name.inSet("Bob", "Mike")].collect() + [Row(age=5, name=u'Bob')] + >>> df[df.age.inSet([1, 2, 3])].collect() + [Row(age=2, name=u'Alice')] + """ + if len(cols) == 1 and isinstance(cols[0], (list, set)): + cols = cols[0] + cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols] + sc = SparkContext._active_spark_context + jc = getattr(self._jc, "in")(_to_seq(sc, cols)) + return Column(jc) + + # order + asc = _unary_op("asc", "Returns a sort expression based on the" + " ascending order of the given column name.") + desc = _unary_op("desc", "Returns a sort expression based on the" + " descending order of the given column name.") + + isNull = _unary_op("isNull", "True if the current expression is null.") + isNotNull = _unary_op("isNotNull", "True if the current expression is not null.") + + @since(1.3) + def alias(self, *alias): + """ + Returns this column aliased with a new name or names (in the case of expressions that + return more than one column, such as explode). + + >>> df.select(df.age.alias("age2")).collect() + [Row(age2=2), Row(age2=5)] + """ + + if len(alias) == 1: + return Column(getattr(self._jc, "as")(alias[0])) + else: + sc = SparkContext._active_spark_context + return Column(getattr(self._jc, "as")(_to_seq(sc, list(alias)))) + + @ignore_unicode_prefix + @since(1.3) + def cast(self, dataType): + """ Convert the column into type ``dataType``. + + >>> df.select(df.age.cast("string").alias('ages')).collect() + [Row(ages=u'2'), Row(ages=u'5')] + >>> df.select(df.age.cast(StringType()).alias('ages')).collect() + [Row(ages=u'2'), Row(ages=u'5')] + """ + if isinstance(dataType, basestring): + jc = self._jc.cast(dataType) + elif isinstance(dataType, DataType): + sc = SparkContext._active_spark_context + ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) + jdt = ssql_ctx.parseDataType(dataType.json()) + jc = self._jc.cast(jdt) + else: + raise TypeError("unexpected type: %s" % type(dataType)) + return Column(jc) + + astype = cast + + @since(1.3) + def between(self, lowerBound, upperBound): + """ + A boolean expression that is evaluated to true if the value of this + expression is between the given columns. + + >>> df.select(df.name, df.age.between(2, 4)).show() + +-----+--------------------------+ + | name|((age >= 2) && (age <= 4))| + +-----+--------------------------+ + |Alice| true| + | Bob| false| + +-----+--------------------------+ + """ + return (self >= lowerBound) & (self <= upperBound) + + @since(1.4) + def when(self, condition, value): + """ + Evaluates a list of conditions and returns one of multiple possible result expressions. + If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions. + + See :func:`pyspark.sql.functions.when` for example usage. + + :param condition: a boolean :class:`Column` expression. + :param value: a literal value, or a :class:`Column` expression. + + >>> from pyspark.sql import functions as F + >>> df.select(df.name, F.when(df.age > 4, 1).when(df.age < 3, -1).otherwise(0)).show() + +-----+--------------------------------------------------------+ + | name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0| + +-----+--------------------------------------------------------+ + |Alice| -1| + | Bob| 1| + +-----+--------------------------------------------------------+ + """ + if not isinstance(condition, Column): + raise TypeError("condition should be a Column") + v = value._jc if isinstance(value, Column) else value + jc = self._jc.when(condition._jc, v) + return Column(jc) + + @since(1.4) + def otherwise(self, value): + """ + Evaluates a list of conditions and returns one of multiple possible result expressions. + If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions. + + See :func:`pyspark.sql.functions.when` for example usage. + + :param value: a literal value, or a :class:`Column` expression. + + >>> from pyspark.sql import functions as F + >>> df.select(df.name, F.when(df.age > 3, 1).otherwise(0)).show() + +-----+---------------------------------+ + | name|CASE WHEN (age > 3) THEN 1 ELSE 0| + +-----+---------------------------------+ + |Alice| 0| + | Bob| 1| + +-----+---------------------------------+ + """ + v = value._jc if isinstance(value, Column) else value + jc = self._jc.otherwise(v) + return Column(jc) + + @since(1.4) + def over(self, window): + """ + Define a windowing column. + + :param window: a :class:`WindowSpec` + :return: a Column + + >>> from pyspark.sql import Window + >>> window = Window.partitionBy("name").orderBy("age").rowsBetween(-1, 1) + >>> from pyspark.sql.functions import rank, min + >>> # df.select(rank().over(window), min('age').over(window)) + + .. note:: Window functions is only supported with HiveContext in 1.4 + """ + from pyspark.sql.window import WindowSpec + if not isinstance(window, WindowSpec): + raise TypeError("window should be WindowSpec") + jc = self._jc.over(window._jspec) + return Column(jc) + + def __repr__(self): + return 'Column<%s>' % self._jc.toString().encode('utf8') + + +def _test(): + import doctest + from pyspark.context import SparkContext + from pyspark.sql import SQLContext + import pyspark.sql.column + globs = pyspark.sql.column.__dict__.copy() + sc = SparkContext('local[4]', 'PythonTest') + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) + globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \ + .toDF(StructType([StructField('age', IntegerType()), + StructField('name', StringType())])) + + (failure_count, test_count) = doctest.testmod( + pyspark.sql.column, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index f6f107ca32d2f..599c9ac5794a2 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -28,9 +28,11 @@ from pyspark.rdd import RDD, _prepare_for_python_RDD, ignore_unicode_prefix from pyspark.serializers import AutoBatchedSerializer, PickleSerializer +from pyspark.sql import since from pyspark.sql.types import Row, StringType, StructType, _verify_type, \ _infer_schema, _has_nulltype, _merge_type, _create_converter, _python_to_sql_converter from pyspark.sql.dataframe import DataFrame +from pyspark.sql.readwriter import DataFrameReader try: import pandas @@ -105,11 +107,13 @@ def _ssql_ctx(self): self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc()) return self._scala_SQLContext + @since(1.3) def setConf(self, key, value): """Sets the given Spark SQL configuration property. """ self._ssql_ctx.setConf(key, value) + @since(1.3) def getConf(self, key, defaultValue): """Returns the value of Spark SQL configuration property for the given key. @@ -118,11 +122,47 @@ def getConf(self, key, defaultValue): return self._ssql_ctx.getConf(key, defaultValue) @property + @since("1.3.1") def udf(self): - """Returns a :class:`UDFRegistration` for UDF registration.""" + """Returns a :class:`UDFRegistration` for UDF registration. + + :return: :class:`UDFRegistration` + """ return UDFRegistration(self) + @since(1.4) + def range(self, start, end=None, step=1, numPartitions=None): + """ + Create a :class:`DataFrame` with single LongType column named `id`, + containing elements in a range from `start` to `end` (exclusive) with + step value `step`. + + :param start: the start value + :param end: the end value (exclusive) + :param step: the incremental step (default: 1) + :param numPartitions: the number of partitions of the DataFrame + :return: :class:`DataFrame` + + >>> sqlContext.range(1, 7, 2).collect() + [Row(id=1), Row(id=3), Row(id=5)] + + If only one argument is specified, it will be used as the end value. + + >>> sqlContext.range(3).collect() + [Row(id=0), Row(id=1), Row(id=2)] + """ + if numPartitions is None: + numPartitions = self._sc.defaultParallelism + + if end is None: + jdf = self._ssql_ctx.range(0, int(start), int(step), int(numPartitions)) + else: + jdf = self._ssql_ctx.range(int(start), int(end), int(step), int(numPartitions)) + + return DataFrame(jdf, self) + @ignore_unicode_prefix + @since(1.2) def registerFunction(self, name, f, returnType=StringType()): """Registers a lambda function as a UDF so it can be used in SQL statements. @@ -157,6 +197,7 @@ def registerFunction(self, name, f, returnType=StringType()): env, includes, self._sc.pythonExec, + self._sc.pythonVer, bvars, self._sc._javaAccumulator, returnType.json()) @@ -167,8 +208,8 @@ def _inferSchema(self, rdd, samplingRatio=None): raise ValueError("The first row in RDD is empty, " "can not infer schema") if type(first) is dict: - warnings.warn("Using RDD of dict to inferSchema is deprecated," - "please use pyspark.sql.Row instead") + warnings.warn("Using RDD of dict to inferSchema is deprecated. " + "Use pyspark.sql.Row instead") if samplingRatio is None: schema = _infer_schema(first) @@ -188,9 +229,10 @@ def _inferSchema(self, rdd, samplingRatio=None): @ignore_unicode_prefix def inferSchema(self, rdd, samplingRatio=None): - """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. """ - warnings.warn("inferSchema is deprecated, please use createDataFrame instead") + .. note:: Deprecated in 1.3, use :func:`createDataFrame` instead. + """ + warnings.warn("inferSchema is deprecated, please use createDataFrame instead.") if isinstance(rdd, DataFrame): raise TypeError("Cannot apply schema to DataFrame") @@ -199,7 +241,8 @@ def inferSchema(self, rdd, samplingRatio=None): @ignore_unicode_prefix def applySchema(self, rdd, schema): - """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. + """ + .. note:: Deprecated in 1.3, use :func:`createDataFrame` instead. """ warnings.warn("applySchema is deprecated, please use createDataFrame instead") @@ -211,6 +254,7 @@ def applySchema(self, rdd, schema): return self.createDataFrame(rdd, schema) + @since(1.3) @ignore_unicode_prefix def createDataFrame(self, data, schema=None, samplingRatio=None): """ @@ -231,6 +275,7 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): :class:`list`, or :class:`pandas.DataFrame`. :param schema: a :class:`StructType` or list of column names. default None. :param samplingRatio: the sample ratio of rows used for inferring + :return: :class:`DataFrame` >>> l = [('Alice', 1)] >>> sqlContext.createDataFrame(l).collect() @@ -315,6 +360,7 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) return DataFrame(df, self) + @since(1.3) def registerDataFrameAsTable(self, df, tableName): """Registers the given :class:`DataFrame` as a temporary table in the catalog. @@ -330,14 +376,12 @@ def registerDataFrameAsTable(self, df, tableName): def parquetFile(self, *paths): """Loads a Parquet file, returning the result as a :class:`DataFrame`. - >>> import tempfile, shutil - >>> parquetFile = tempfile.mkdtemp() - >>> shutil.rmtree(parquetFile) - >>> df.saveAsParquetFile(parquetFile) - >>> df2 = sqlContext.parquetFile(parquetFile) - >>> sorted(df.collect()) == sorted(df2.collect()) - True + .. note:: Deprecated in 1.4, use :func:`DataFrameReader.parquet` instead. + + >>> sqlContext.parquetFile('python/test_support/sql/parquet_partitioned').dtypes + [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')] """ + warnings.warn("parquetFile is deprecated. Use read.parquet() instead.") gateway = self._sc._gateway jpaths = gateway.new_array(gateway.jvm.java.lang.String, len(paths)) for i in range(0, len(paths)): @@ -348,35 +392,12 @@ def parquetFile(self, *paths): def jsonFile(self, path, schema=None, samplingRatio=1.0): """Loads a text file storing one JSON object per line as a :class:`DataFrame`. - If the schema is provided, applies the given schema to this JSON dataset. - Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema. + .. note:: Deprecated in 1.4, use :func:`DataFrameReader.json` instead. - >>> import tempfile, shutil - >>> jsonFile = tempfile.mkdtemp() - >>> shutil.rmtree(jsonFile) - >>> with open(jsonFile, 'w') as f: - ... f.writelines(jsonStrings) - >>> df1 = sqlContext.jsonFile(jsonFile) - >>> df1.printSchema() - root - |-- field1: long (nullable = true) - |-- field2: string (nullable = true) - |-- field3: struct (nullable = true) - | |-- field4: long (nullable = true) - - >>> from pyspark.sql.types import * - >>> schema = StructType([ - ... StructField("field2", StringType()), - ... StructField("field3", - ... StructType([StructField("field5", ArrayType(IntegerType()))]))]) - >>> df2 = sqlContext.jsonFile(jsonFile, schema) - >>> df2.printSchema() - root - |-- field2: string (nullable = true) - |-- field3: struct (nullable = true) - | |-- field5: array (nullable = true) - | | |-- element: integer (containsNull = true) + >>> sqlContext.jsonFile('python/test_support/sql/people.json').dtypes + [('age', 'bigint'), ('name', 'string')] """ + warnings.warn("jsonFile is deprecated. Use read.json() instead.") if schema is None: df = self._ssql_ctx.jsonFile(path, samplingRatio) else: @@ -385,6 +406,7 @@ def jsonFile(self, path, schema=None, samplingRatio=1.0): return DataFrame(df, self) @ignore_unicode_prefix + @since(1.0) def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): """Loads an RDD storing one JSON object per string as a :class:`DataFrame`. @@ -430,28 +452,13 @@ def func(iterator): def load(self, path=None, source=None, schema=None, **options): """Returns the dataset in a data source as a :class:`DataFrame`. - The data source is specified by the ``source`` and a set of ``options``. - If ``source`` is not specified, the default data source configured by - ``spark.sql.sources.default`` will be used. - - Optionally, a schema can be provided as the schema of the returned DataFrame. + .. note:: Deprecated in 1.4, use :func:`DataFrameReader.load` instead. """ - if path is not None: - options["path"] = path - if source is None: - source = self.getConf("spark.sql.sources.default", - "org.apache.spark.sql.parquet") - if schema is None: - df = self._ssql_ctx.load(source, options) - else: - if not isinstance(schema, StructType): - raise TypeError("schema should be StructType") - scala_datatype = self._ssql_ctx.parseDataType(schema.json()) - df = self._ssql_ctx.load(source, scala_datatype, options) - return DataFrame(df, self) + warnings.warn("load is deprecated. Use read.load() instead.") + return self.read.load(path, source, schema, **options) - def createExternalTable(self, tableName, path=None, source=None, - schema=None, **options): + @since(1.3) + def createExternalTable(self, tableName, path=None, source=None, schema=None, **options): """Creates an external table based on the dataset in a data source. It returns the DataFrame associated with the external table. @@ -462,6 +469,8 @@ def createExternalTable(self, tableName, path=None, source=None, Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and created external table. + + :return: :class:`DataFrame` """ if path is not None: options["path"] = path @@ -479,9 +488,12 @@ def createExternalTable(self, tableName, path=None, source=None, return DataFrame(df, self) @ignore_unicode_prefix + @since(1.0) def sql(self, sqlQuery): """Returns a :class:`DataFrame` representing the result of the given query. + :return: :class:`DataFrame` + >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> df2.collect() @@ -489,9 +501,12 @@ def sql(self, sqlQuery): """ return DataFrame(self._ssql_ctx.sql(sqlQuery), self) + @since(1.0) def table(self, tableName): """Returns the specified table as a :class:`DataFrame`. + :return: :class:`DataFrame` + >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> df2 = sqlContext.table("table1") >>> sorted(df.collect()) == sorted(df2.collect()) @@ -500,6 +515,7 @@ def table(self, tableName): return DataFrame(self._ssql_ctx.table(tableName), self) @ignore_unicode_prefix + @since(1.3) def tables(self, dbName=None): """Returns a :class:`DataFrame` containing names of tables in the given database. @@ -508,6 +524,9 @@ def tables(self, dbName=None): The returned DataFrame has two columns: ``tableName`` and ``isTemporary`` (a column with :class:`BooleanType` indicating if a table is a temporary one or not). + :param dbName: string, name of the database to use. + :return: :class:`DataFrame` + >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> df2 = sqlContext.tables() >>> df2.filter("tableName = 'table1'").first() @@ -518,10 +537,12 @@ def tables(self, dbName=None): else: return DataFrame(self._ssql_ctx.tables(dbName), self) + @since(1.3) def tableNames(self, dbName=None): """Returns a list of names of tables in the database ``dbName``. - If ``dbName`` is not specified, the current database will be used. + :param dbName: string, name of the database to use. Default to the current database. + :return: list of table names, in string >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> "table1" in sqlContext.tableNames() @@ -534,18 +555,32 @@ def tableNames(self, dbName=None): else: return [name for name in self._ssql_ctx.tableNames(dbName)] + @since(1.0) def cacheTable(self, tableName): """Caches the specified table in-memory.""" self._ssql_ctx.cacheTable(tableName) + @since(1.0) def uncacheTable(self, tableName): """Removes the specified table from the in-memory cache.""" self._ssql_ctx.uncacheTable(tableName) + @since(1.3) def clearCache(self): """Removes all cached tables from the in-memory cache. """ self._ssql_ctx.clearCache() + @property + @since(1.4) + def read(self): + """ + Returns a :class:`DataFrameReader` that can be used to read data + in as a :class:`DataFrame`. + + :return: :class:`DataFrameReader` + """ + return DataFrameReader(self) + class HiveContext(SQLContext): """A variant of Spark SQL that integrates with data stored in Hive. @@ -600,10 +635,14 @@ def register(self, name, f, returnType=StringType()): def _test(): + import os import doctest from pyspark.context import SparkContext from pyspark.sql import Row, SQLContext import pyspark.sql.context + + os.chdir(os.environ["SPARK_HOME"]) + globs = pyspark.sql.context.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index a9697999e82cb..152b87351db31 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -22,20 +22,21 @@ if sys.version >= '3': basestring = unicode = str long = int + from functools import reduce else: from itertools import imap as map -from pyspark.context import SparkContext from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix from pyspark.serializers import BatchedSerializer, PickleSerializer, UTF8Deserializer from pyspark.storagelevel import StorageLevel from pyspark.traceback_utils import SCCallSiteSync -from pyspark.sql.types import * +from pyspark.sql import since from pyspark.sql.types import _create_cls, _parse_datatype_json_string +from pyspark.sql.column import Column, _to_seq, _to_java_column +from pyspark.sql.readwriter import DataFrameWriter +from pyspark.sql.types import * - -__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD", "DataFrameNaFunctions", - "DataFrameStatFunctions"] +__all__ = ["DataFrame", "SchemaRDD", "DataFrameNaFunctions", "DataFrameStatFunctions"] class DataFrame(object): @@ -44,7 +45,7 @@ class DataFrame(object): A :class:`DataFrame` is equivalent to a relational table in Spark SQL, and can be created using various functions in :class:`SQLContext`:: - people = sqlContext.parquetFile("...") + people = sqlContext.read.parquet("...") Once created, it can be manipulated using the various domain-specific-language (DSL) functions defined in: :class:`DataFrame`, :class:`Column`. @@ -56,11 +57,15 @@ class DataFrame(object): A more concrete example:: # To create DataFrame using SQLContext - people = sqlContext.parquetFile("...") - department = sqlContext.parquetFile("...") + people = sqlContext.read.parquet("...") + department = sqlContext.read.parquet("...") people.filter(people.age > 30).join(department, people.deptId == department.id)) \ .groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"}) + + .. note:: Experimental + + .. versionadded:: 1.3 """ def __init__(self, jdf, sql_ctx): @@ -72,6 +77,7 @@ def __init__(self, jdf, sql_ctx): self._lazy_rdd = None @property + @since(1.3) def rdd(self): """Returns the content as an :class:`pyspark.RDD` of :class:`Row`. """ @@ -89,18 +95,21 @@ def applySchema(it): return self._lazy_rdd @property + @since("1.3.1") def na(self): """Returns a :class:`DataFrameNaFunctions` for handling missing values. """ return DataFrameNaFunctions(self) @property + @since(1.4) def stat(self): """Returns a :class:`DataFrameStatFunctions` for statistic functions. """ return DataFrameStatFunctions(self) @ignore_unicode_prefix + @since(1.3) def toJSON(self, use_unicode=True): """Converts a :class:`DataFrame` into a :class:`RDD` of string. @@ -115,19 +124,12 @@ def toJSON(self, use_unicode=True): def saveAsParquetFile(self, path): """Saves the contents as a Parquet file, preserving the schema. - Files that are written out using this method can be read back in as - a :class:`DataFrame` using :func:`SQLContext.parquetFile`. - - >>> import tempfile, shutil - >>> parquetFile = tempfile.mkdtemp() - >>> shutil.rmtree(parquetFile) - >>> df.saveAsParquetFile(parquetFile) - >>> df2 = sqlContext.parquetFile(parquetFile) - >>> sorted(df2.collect()) == sorted(df.collect()) - True + .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.parquet` instead. """ + warnings.warn("saveAsParquetFile is deprecated. Use write.parquet() instead.") self._jdf.saveAsParquetFile(path) + @since(1.3) def registerTempTable(self, name): """Registers this RDD as a temporary table using the given name. @@ -142,81 +144,49 @@ def registerTempTable(self, name): self._jdf.registerTempTable(name) def registerAsTable(self, name): - """DEPRECATED: use :func:`registerTempTable` instead""" - warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning) + """ + .. note:: Deprecated in 1.4, use :func:`registerTempTable` instead. + """ + warnings.warn("Use registerTempTable instead of registerAsTable.") self.registerTempTable(name) def insertInto(self, tableName, overwrite=False): """Inserts the contents of this :class:`DataFrame` into the specified table. - Optionally overwriting any existing data. + .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.insertInto` instead. """ - self._jdf.insertInto(tableName, overwrite) - - def _java_save_mode(self, mode): - """Returns the Java save mode based on the Python save mode represented by a string. - """ - jSaveMode = self._sc._jvm.org.apache.spark.sql.SaveMode - jmode = jSaveMode.ErrorIfExists - mode = mode.lower() - if mode == "append": - jmode = jSaveMode.Append - elif mode == "overwrite": - jmode = jSaveMode.Overwrite - elif mode == "ignore": - jmode = jSaveMode.Ignore - elif mode == "error": - pass - else: - raise ValueError( - "Only 'append', 'overwrite', 'ignore', and 'error' are acceptable save mode.") - return jmode + warnings.warn("insertInto is deprecated. Use write.insertInto() instead.") + self.write.insertInto(tableName, overwrite) def saveAsTable(self, tableName, source=None, mode="error", **options): """Saves the contents of this :class:`DataFrame` to a data source as a table. - The data source is specified by the ``source`` and a set of ``options``. - If ``source`` is not specified, the default data source configured by - ``spark.sql.sources.default`` will be used. - - Additionally, mode is used to specify the behavior of the saveAsTable operation when - table already exists in the data source. There are four modes: - - * `append`: Append contents of this :class:`DataFrame` to existing data. - * `overwrite`: Overwrite existing data. - * `error`: Throw an exception if data already exists. - * `ignore`: Silently ignore this operation if data already exists. + .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.saveAsTable` instead. """ - if source is None: - source = self.sql_ctx.getConf("spark.sql.sources.default", - "org.apache.spark.sql.parquet") - jmode = self._java_save_mode(mode) - self._jdf.saveAsTable(tableName, source, jmode, options) + warnings.warn("insertInto is deprecated. Use write.saveAsTable() instead.") + self.write.saveAsTable(tableName, source, mode, **options) + @since(1.3) def save(self, path=None, source=None, mode="error", **options): """Saves the contents of the :class:`DataFrame` to a data source. - The data source is specified by the ``source`` and a set of ``options``. - If ``source`` is not specified, the default data source configured by - ``spark.sql.sources.default`` will be used. + .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.save` instead. + """ + warnings.warn("insertInto is deprecated. Use write.save() instead.") + return self.write.save(path, source, mode, **options) - Additionally, mode is used to specify the behavior of the save operation when - data already exists in the data source. There are four modes: + @property + @since(1.4) + def write(self): + """ + Interface for saving the content of the :class:`DataFrame` out into external storage. - * `append`: Append contents of this :class:`DataFrame` to existing data. - * `overwrite`: Overwrite existing data. - * `error`: Throw an exception if data already exists. - * `ignore`: Silently ignore this operation if data already exists. + :return: :class:`DataFrameWriter` """ - if path is not None: - options["path"] = path - if source is None: - source = self.sql_ctx.getConf("spark.sql.sources.default", - "org.apache.spark.sql.parquet") - jmode = self._java_save_mode(mode) - self._jdf.save(source, jmode, options) + return DataFrameWriter(self) @property + @since(1.3) def schema(self): """Returns the schema of this :class:`DataFrame` as a :class:`types.StructType`. @@ -224,9 +194,14 @@ def schema(self): StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true))) """ if self._schema is None: - self._schema = _parse_datatype_json_string(self._jdf.schema().json()) + try: + self._schema = _parse_datatype_json_string(self._jdf.schema().json()) + except AttributeError as e: + raise Exception( + "Unable to parse datatype from schema. %s" % e) return self._schema + @since(1.3) def printSchema(self): """Prints out the schema in the tree format. @@ -238,6 +213,7 @@ def printSchema(self): """ print(self._jdf.schema().treeString()) + @since(1.3) def explain(self, extended=False): """Prints the (logical and physical) plans to the console for debugging purpose. @@ -263,12 +239,14 @@ def explain(self, extended=False): else: print(self._jdf.queryExecution().executedPlan().toString()) + @since(1.3) def isLocal(self): """Returns ``True`` if the :func:`collect` and :func:`take` methods can be run locally (without any Spark executors). """ return self._jdf.isLocal() + @since(1.3) def show(self, n=20): """Prints the first ``n`` rows to the console. @@ -287,6 +265,7 @@ def show(self, n=20): def __repr__(self): return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes)) + @since(1.3) def count(self): """Returns the number of rows in this :class:`DataFrame`. @@ -296,6 +275,7 @@ def count(self): return int(self._jdf.count()) @ignore_unicode_prefix + @since(1.3) def collect(self): """Returns all the records as a list of :class:`Row`. @@ -309,6 +289,7 @@ def collect(self): return [cls(r) for r in rs] @ignore_unicode_prefix + @since(1.3) def limit(self, num): """Limits the result count to the number specified. @@ -321,6 +302,7 @@ def limit(self, num): return DataFrame(jdf, self.sql_ctx) @ignore_unicode_prefix + @since(1.3) def take(self, num): """Returns the first ``num`` rows as a :class:`list` of :class:`Row`. @@ -330,6 +312,7 @@ def take(self, num): return self.limit(num).collect() @ignore_unicode_prefix + @since(1.3) def map(self, f): """ Returns a new :class:`RDD` by applying a the ``f`` function to each :class:`Row`. @@ -341,6 +324,7 @@ def map(self, f): return self.rdd.map(f) @ignore_unicode_prefix + @since(1.3) def flatMap(self, f): """ Returns a new :class:`RDD` by first applying the ``f`` function to each :class:`Row`, and then flattening the results. @@ -352,6 +336,7 @@ def flatMap(self, f): """ return self.rdd.flatMap(f) + @since(1.3) def mapPartitions(self, f, preservesPartitioning=False): """Returns a new :class:`RDD` by applying the ``f`` function to each partition. @@ -364,6 +349,7 @@ def mapPartitions(self, f, preservesPartitioning=False): """ return self.rdd.mapPartitions(f, preservesPartitioning) + @since(1.3) def foreach(self, f): """Applies the ``f`` function to all :class:`Row` of this :class:`DataFrame`. @@ -375,6 +361,7 @@ def foreach(self, f): """ return self.rdd.foreach(f) + @since(1.3) def foreachPartition(self, f): """Applies the ``f`` function to each partition of this :class:`DataFrame`. @@ -387,6 +374,7 @@ def foreachPartition(self, f): """ return self.rdd.foreachPartition(f) + @since(1.3) def cache(self): """ Persists with the default storage level (C{MEMORY_ONLY_SER}). """ @@ -394,6 +382,7 @@ def cache(self): self._jdf.cache() return self + @since(1.3) def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): """Sets the storage level to persist its values across operations after the first time it is computed. This can only be used to assign @@ -405,6 +394,7 @@ def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): self._jdf.persist(javaStorageLevel) return self + @since(1.3) def unpersist(self, blocking=True): """Marks the :class:`DataFrame` as non-persistent, and remove all blocks for it from memory and disk. @@ -413,10 +403,22 @@ def unpersist(self, blocking=True): self._jdf.unpersist(blocking) return self - # def coalesce(self, numPartitions, shuffle=False): - # rdd = self._jdf.coalesce(numPartitions, shuffle, None) - # return DataFrame(rdd, self.sql_ctx) + @since(1.4) + def coalesce(self, numPartitions): + """ + Returns a new :class:`DataFrame` that has exactly `numPartitions` partitions. + + Similar to coalesce defined on an :class:`RDD`, this operation results in a + narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, + there will not be a shuffle, instead each of the 100 new partitions will + claim 10 of the current partitions. + + >>> df.coalesce(1).rdd.getNumPartitions() + 1 + """ + return DataFrame(self._jdf.coalesce(numPartitions), self.sql_ctx) + @since(1.3) def repartition(self, numPartitions): """Returns a new :class:`DataFrame` that has exactly ``numPartitions`` partitions. @@ -425,6 +427,7 @@ def repartition(self, numPartitions): """ return DataFrame(self._jdf.repartition(numPartitions), self.sql_ctx) + @since(1.3) def distinct(self): """Returns a new :class:`DataFrame` containing the distinct rows in this :class:`DataFrame`. @@ -433,6 +436,7 @@ def distinct(self): """ return DataFrame(self._jdf.distinct(), self.sql_ctx) + @since(1.3) def sample(self, withReplacement, fraction, seed=None): """Returns a sampled subset of this :class:`DataFrame`. @@ -444,6 +448,7 @@ def sample(self, withReplacement, fraction, seed=None): rdd = self._jdf.sample(withReplacement, fraction, long(seed)) return DataFrame(rdd, self.sql_ctx) + @since(1.4) def randomSplit(self, weights, seed=None): """Randomly splits this :class:`DataFrame` with the provided weights. @@ -466,6 +471,7 @@ def randomSplit(self, weights, seed=None): return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array] @property + @since(1.3) def dtypes(self): """Returns all column names and their data types as a list. @@ -476,6 +482,7 @@ def dtypes(self): @property @ignore_unicode_prefix + @since(1.3) def columns(self): """Returns all column names as a list. @@ -485,6 +492,7 @@ def columns(self): return [f.name for f in self.schema.fields] @ignore_unicode_prefix + @since(1.3) def alias(self, alias): """Returns a new :class:`DataFrame` with an alias set. @@ -499,39 +507,57 @@ def alias(self, alias): return DataFrame(getattr(self._jdf, "as")(alias), self.sql_ctx) @ignore_unicode_prefix - def join(self, other, joinExprs=None, joinType=None): + @since(1.3) + def join(self, other, on=None, how=None): """Joins with another :class:`DataFrame`, using the given join expression. The following performs a full outer join between ``df1`` and ``df2``. :param other: Right side of the join - :param joinExprs: a string for join column name, or a join expression (Column). - If joinExprs is a string indicating the name of the join column, - the column must exist on both sides, and this performs an inner equi-join. - :param joinType: str, default 'inner'. + :param on: a string for join column name, a list of column names, + , a join expression (Column) or a list of Columns. + If `on` is a string or a list of string indicating the name of the join column(s), + the column(s) must exist on both sides, and this performs an inner equi-join. + :param how: str, default 'inner'. One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. >>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect() [Row(name=None, height=80), Row(name=u'Alice', height=None), Row(name=u'Bob', height=85)] + >>> cond = [df.name == df3.name, df.age == df3.age] + >>> df.join(df3, cond, 'outer').select(df.name, df3.age).collect() + [Row(name=u'Bob', age=5), Row(name=u'Alice', age=2)] + >>> df.join(df2, 'name').select(df.name, df2.height).collect() [Row(name=u'Bob', height=85)] + + >>> df.join(df4, ['name', 'age']).select(df.name, df.age).collect() + [Row(name=u'Bob', age=5)] """ - if joinExprs is None: + if on is not None and not isinstance(on, list): + on = [on] + + if on is None or len(on) == 0: jdf = self._jdf.join(other._jdf) - elif isinstance(joinExprs, basestring): - jdf = self._jdf.join(other._jdf, joinExprs) + + if isinstance(on[0], basestring): + jdf = self._jdf.join(other._jdf, self._jseq(on)) else: - assert isinstance(joinExprs, Column), "joinExprs should be Column" - if joinType is None: - jdf = self._jdf.join(other._jdf, joinExprs._jc) + assert isinstance(on[0], Column), "on should be Column or list of Column" + if len(on) > 1: + on = reduce(lambda x, y: x.__and__(y), on) + else: + on = on[0] + if how is None: + jdf = self._jdf.join(other._jdf, on._jc, "inner") else: - assert isinstance(joinType, basestring), "joinType should be basestring" - jdf = self._jdf.join(other._jdf, joinExprs._jc, joinType) + assert isinstance(how, basestring), "how should be basestring" + jdf = self._jdf.join(other._jdf, on._jc, how) return DataFrame(jdf, self.sql_ctx) @ignore_unicode_prefix + @since(1.3) def sort(self, *cols, **kwargs): """Returns a new :class:`DataFrame` sorted by the specified column(s). @@ -578,6 +604,10 @@ def _jseq(self, cols, converter=None): """Return a JVM Seq of Columns from a list of Column or names""" return _to_seq(self.sql_ctx._sc, cols, converter) + def _jmap(self, jm): + """Return a JVM Scala Map from a dict""" + return _to_scala_map(self.sql_ctx._sc, jm) + def _jcols(self, *cols): """Return a JVM Seq of Columns from a list of Column or column names @@ -587,12 +617,16 @@ def _jcols(self, *cols): cols = cols[0] return self._jseq(cols, _to_java_column) + @since("1.3.1") def describe(self, *cols): """Computes statistics for numeric columns. This include count, mean, stddev, min, and max. If no columns are given, this function computes statistics for all numerical columns. + .. note:: This function is meant for exploratory data analysis, as we make no \ + guarantee about the backward compatibility of the schema of the resulting DataFrame. + >>> df.describe().show() +-------+---+ |summary|age| @@ -603,15 +637,30 @@ def describe(self, *cols): | min| 2| | max| 5| +-------+---+ + >>> df.describe(['age', 'name']).show() + +-------+---+-----+ + |summary|age| name| + +-------+---+-----+ + | count| 2| 2| + | mean|3.5| null| + | stddev|1.5| null| + | min| 2|Alice| + | max| 5| Bob| + +-------+---+-----+ """ + if len(cols) == 1 and isinstance(cols[0], list): + cols = cols[0] jdf = self._jdf.describe(self._jseq(cols)) return DataFrame(jdf, self.sql_ctx) @ignore_unicode_prefix + @since(1.3) def head(self, n=None): - """ - Returns the first ``n`` rows as a list of :class:`Row`, - or the first :class:`Row` if ``n`` is ``None.`` + """Returns the first ``n`` rows. + + :param n: int, default 1. Number of rows to return. + :return: If n is greater than 1, return a list of :class:`Row`. + If n is 1, return a single Row. >>> df.head() Row(age=2, name=u'Alice') @@ -624,6 +673,7 @@ def head(self, n=None): return self.take(n) @ignore_unicode_prefix + @since(1.3) def first(self): """Returns the first row as a :class:`Row`. @@ -633,6 +683,7 @@ def first(self): return self.head() @ignore_unicode_prefix + @since(1.3) def __getitem__(self, item): """Returns the column as a :class:`Column`. @@ -660,6 +711,7 @@ def __getitem__(self, item): else: raise TypeError("unexpected item type: %s" % type(item)) + @since(1.3) def __getattr__(self, name): """Returns the :class:`Column` denoted by ``name``. @@ -673,6 +725,7 @@ def __getattr__(self, name): return Column(jc) @ignore_unicode_prefix + @since(1.3) def select(self, *cols): """Projects a set of expressions and returns a new :class:`DataFrame`. @@ -690,13 +743,14 @@ def select(self, *cols): jdf = self._jdf.select(self._jcols(*cols)) return DataFrame(jdf, self.sql_ctx) + @since(1.3) def selectExpr(self, *expr): """Projects a set of SQL expressions and returns a new :class:`DataFrame`. This is a variant of :func:`select` that accepts SQL expressions. >>> df.selectExpr("age * 2", "abs(age)").collect() - [Row((age * 2)=4, Abs(age)=2), Row((age * 2)=10, Abs(age)=5)] + [Row((age * 2)=4, 'abs(age)=2), Row((age * 2)=10, 'abs(age)=5)] """ if len(expr) == 1 and isinstance(expr[0], list): expr = expr[0] @@ -704,6 +758,7 @@ def selectExpr(self, *expr): return DataFrame(jdf, self.sql_ctx) @ignore_unicode_prefix + @since(1.3) def filter(self, condition): """Filters rows using the given condition. @@ -733,6 +788,7 @@ def filter(self, condition): where = filter @ignore_unicode_prefix + @since(1.3) def groupBy(self, *cols): """Groups the :class:`DataFrame` using the specified columns, so we can run aggregation on them. See :class:`GroupedData` @@ -752,11 +808,55 @@ def groupBy(self, *cols): >>> df.groupBy(['name', df.age]).count().collect() [Row(name=u'Bob', age=5, count=1), Row(name=u'Alice', age=2, count=1)] """ - jdf = self._jdf.groupBy(self._jcols(*cols)) - return GroupedData(jdf, self.sql_ctx) - - groupby = groupBy - + jgd = self._jdf.groupBy(self._jcols(*cols)) + from pyspark.sql.group import GroupedData + return GroupedData(jgd, self.sql_ctx) + + @since(1.4) + def rollup(self, *cols): + """ + Create a multi-dimensional rollup for the current :class:`DataFrame` using + the specified columns, so we can run aggregation on them. + + >>> df.rollup('name', df.age).count().show() + +-----+----+-----+ + | name| age|count| + +-----+----+-----+ + |Alice|null| 1| + | Bob| 5| 1| + | Bob|null| 1| + | null|null| 2| + |Alice| 2| 1| + +-----+----+-----+ + """ + jgd = self._jdf.rollup(self._jcols(*cols)) + from pyspark.sql.group import GroupedData + return GroupedData(jgd, self.sql_ctx) + + @since(1.4) + def cube(self, *cols): + """ + Create a multi-dimensional cube for the current :class:`DataFrame` using + the specified columns, so we can run aggregation on them. + + >>> df.cube('name', df.age).count().show() + +-----+----+-----+ + | name| age|count| + +-----+----+-----+ + | null| 2| 1| + |Alice|null| 1| + | Bob| 5| 1| + | Bob|null| 1| + | null| 5| 1| + | null|null| 2| + |Alice| 2| 1| + +-----+----+-----+ + """ + jgd = self._jdf.cube(self._jcols(*cols)) + from pyspark.sql.group import GroupedData + return GroupedData(jgd, self.sql_ctx) + + @since(1.3) def agg(self, *exprs): """ Aggregate on the entire :class:`DataFrame` without groups (shorthand for ``df.groupBy.agg()``). @@ -769,6 +869,7 @@ def agg(self, *exprs): """ return self.groupBy().agg(*exprs) + @since(1.3) def unionAll(self, other): """ Return a new :class:`DataFrame` containing union of rows in this frame and another frame. @@ -777,6 +878,7 @@ def unionAll(self, other): """ return DataFrame(self._jdf.unionAll(other._jdf), self.sql_ctx) + @since(1.3) def intersect(self, other): """ Return a new :class:`DataFrame` containing rows only in both this frame and another frame. @@ -785,6 +887,7 @@ def intersect(self, other): """ return DataFrame(self._jdf.intersect(other._jdf), self.sql_ctx) + @since(1.3) def subtract(self, other): """ Return a new :class:`DataFrame` containing rows in this frame but not in another frame. @@ -793,10 +896,41 @@ def subtract(self, other): """ return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx) + @since(1.4) + def dropDuplicates(self, subset=None): + """Return a new :class:`DataFrame` with duplicate rows removed, + optionally only considering certain columns. + + >>> from pyspark.sql import Row + >>> df = sc.parallelize([ \ + Row(name='Alice', age=5, height=80), \ + Row(name='Alice', age=5, height=80), \ + Row(name='Alice', age=10, height=80)]).toDF() + >>> df.dropDuplicates().show() + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 5| 80|Alice| + | 10| 80|Alice| + +---+------+-----+ + + >>> df.dropDuplicates(['name', 'height']).show() + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 5| 80|Alice| + +---+------+-----+ + """ + if subset is None: + jdf = self._jdf.dropDuplicates() + else: + jdf = self._jdf.dropDuplicates(self._jseq(subset)) + return DataFrame(jdf, self.sql_ctx) + + @since("1.3.1") def dropna(self, how='any', thresh=None, subset=None): """Returns a new :class:`DataFrame` omitting rows with null values. - - This is an alias for ``na.drop()``. + :func:`DataFrame.dropna` and :func:`DataFrameNaFunctions.drop` are aliases of each other. :param how: 'any' or 'all'. If 'any', drop a row if it contains any nulls. @@ -806,13 +940,6 @@ def dropna(self, how='any', thresh=None, subset=None): This overwrites the `how` parameter. :param subset: optional list of column names to consider. - >>> df4.dropna().show() - +---+------+-----+ - |age|height| name| - +---+------+-----+ - | 10| 80|Alice| - +---+------+-----+ - >>> df4.na.drop().show() +---+------+-----+ |age|height| name| @@ -835,8 +962,10 @@ def dropna(self, how='any', thresh=None, subset=None): return DataFrame(self._jdf.na().drop(thresh, self._jseq(subset)), self.sql_ctx) + @since("1.3.1") def fillna(self, value, subset=None): """Replace null values, alias for ``na.fill()``. + :func:`DataFrame.fillna` and :func:`DataFrameNaFunctions.fill` are aliases of each other. :param value: int, long, float, string, or dict. Value to replace null values with. @@ -848,7 +977,7 @@ def fillna(self, value, subset=None): For example, if `value` is a string, and subset contains a non-string column, then the non-string column is simply ignored. - >>> df4.fillna(50).show() + >>> df4.na.fill(50).show() +---+------+-----+ |age|height| name| +---+------+-----+ @@ -858,16 +987,6 @@ def fillna(self, value, subset=None): | 50| 50| null| +---+------+-----+ - >>> df4.fillna({'age': 50, 'name': 'unknown'}).show() - +---+------+-------+ - |age|height| name| - +---+------+-------+ - | 10| 80| Alice| - | 5| null| Bob| - | 50| null| Tom| - | 50| null|unknown| - +---+------+-------+ - >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show() +---+------+-------+ |age|height| name| @@ -896,11 +1015,90 @@ def fillna(self, value, subset=None): return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) + @since(1.4) + def replace(self, to_replace, value, subset=None): + """Returns a new :class:`DataFrame` replacing a value with another value. + :func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are + aliases of each other. + + :param to_replace: int, long, float, string, or list. + Value to be replaced. + If the value is a dict, then `value` is ignored and `to_replace` must be a + mapping from column name (string) to replacement value. The value to be + replaced must be an int, long, float, or string. + :param value: int, long, float, string, or list. + Value to use to replace holes. + The replacement value must be an int, long, float, or string. If `value` is a + list or tuple, `value` should be of the same length with `to_replace`. + :param subset: optional list of column names to consider. + Columns specified in subset that do not have matching data type are ignored. + For example, if `value` is a string, and subset contains a non-string column, + then the non-string column is simply ignored. + + >>> df4.na.replace(10, 20).show() + +----+------+-----+ + | age|height| name| + +----+------+-----+ + | 20| 80|Alice| + | 5| null| Bob| + |null| null| Tom| + |null| null| null| + +----+------+-----+ + + >>> df4.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show() + +----+------+----+ + | age|height|name| + +----+------+----+ + | 10| 80| A| + | 5| null| B| + |null| null| Tom| + |null| null|null| + +----+------+----+ + """ + if not isinstance(to_replace, (float, int, long, basestring, list, tuple, dict)): + raise ValueError( + "to_replace should be a float, int, long, string, list, tuple, or dict") + + if not isinstance(value, (float, int, long, basestring, list, tuple)): + raise ValueError("value should be a float, int, long, string, list, or tuple") + + rep_dict = dict() + + if isinstance(to_replace, (float, int, long, basestring)): + to_replace = [to_replace] + + if isinstance(to_replace, tuple): + to_replace = list(to_replace) + + if isinstance(value, tuple): + value = list(value) + + if isinstance(to_replace, list) and isinstance(value, list): + if len(to_replace) != len(value): + raise ValueError("to_replace and value lists should be of the same length") + rep_dict = dict(zip(to_replace, value)) + elif isinstance(to_replace, list) and isinstance(value, (float, int, long, basestring)): + rep_dict = dict([(tr, value) for tr in to_replace]) + elif isinstance(to_replace, dict): + rep_dict = to_replace + + if subset is None: + return DataFrame(self._jdf.na().replace('*', rep_dict), self.sql_ctx) + elif isinstance(subset, basestring): + subset = [subset] + + if not isinstance(subset, (list, tuple)): + raise ValueError("subset should be a list or tuple of column names") + + return DataFrame( + self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)), self.sql_ctx) + + @since(1.4) def corr(self, col1, col2, method=None): """ - Calculates the correlation of two columns of a DataFrame as a double value. Currently only - supports the Pearson Correlation Coefficient. - :func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases. + Calculates the correlation of two columns of a DataFrame as a double value. + Currently only supports the Pearson Correlation Coefficient. + :func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases of each other. :param col1: The name of the first column :param col2: The name of the second column @@ -917,6 +1115,7 @@ def corr(self, col1, col2, method=None): "coefficient is supported.") return self._jdf.stat().corr(col1, col2, method) + @since(1.4) def cov(self, col1, col2): """ Calculate the sample covariance for the given columns, specified by their names, as a @@ -931,6 +1130,7 @@ def cov(self, col1, col2): raise ValueError("col2 should be a string.") return self._jdf.stat().cov(col1, col2) + @since(1.4) def crosstab(self, col1, col2): """ Computes a pair-wise frequency table of the given columns. Also known as a contingency @@ -952,6 +1152,7 @@ def crosstab(self, col1, col2): raise ValueError("col2 should be a string.") return DataFrame(self._jdf.stat().crosstab(col1, col2), self.sql_ctx) + @since(1.4) def freqItems(self, cols, support=None): """ Finding frequent items for columns, possibly with false positives. Using the @@ -959,6 +1160,9 @@ def freqItems(self, cols, support=None): "http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou". :func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases. + .. note:: This function is meant for exploratory data analysis, as we make no \ + guarantee about the backward compatibility of the schema of the resulting DataFrame. + :param cols: Names of the columns to calculate frequent items for as a list or tuple of strings. :param support: The frequency with which to consider an item 'frequent'. Default is 1%. @@ -973,6 +1177,7 @@ def freqItems(self, cols, support=None): return DataFrame(self._jdf.stat().freqItems(_to_seq(self._sc, cols), support), self.sql_ctx) @ignore_unicode_prefix + @since(1.3) def withColumn(self, colName, col): """Returns a new :class:`DataFrame` by adding a column. @@ -985,8 +1190,9 @@ def withColumn(self, colName, col): return self.select('*', col.alias(colName)) @ignore_unicode_prefix + @since(1.3) def withColumnRenamed(self, existing, new): - """REturns a new :class:`DataFrame` by renaming an existing column. + """Returns a new :class:`DataFrame` by renaming an existing column. :param existing: string, name of the existing column to rename. :param col: string, new name of the column. @@ -999,430 +1205,73 @@ def withColumnRenamed(self, existing, new): for c in self.columns] return self.select(*cols) - def toPandas(self): - """Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``. - - This is only available if Pandas is installed and available. - - >>> df.toPandas() # doctest: +SKIP - age name - 0 2 Alice - 1 5 Bob - """ - import pandas as pd - return pd.DataFrame.from_records(self.collect(), columns=self.columns) - - -# Having SchemaRDD for backward compatibility (for docs) -class SchemaRDD(DataFrame): - """SchemaRDD is deprecated, please use :class:`DataFrame`. - """ - - -def dfapi(f): - def _api(self): - name = f.__name__ - jdf = getattr(self._jdf, name)() - return DataFrame(jdf, self.sql_ctx) - _api.__name__ = f.__name__ - _api.__doc__ = f.__doc__ - return _api - - -def df_varargs_api(f): - def _api(self, *args): - name = f.__name__ - jdf = getattr(self._jdf, name)(_to_seq(self.sql_ctx._sc, args)) - return DataFrame(jdf, self.sql_ctx) - _api.__name__ = f.__name__ - _api.__doc__ = f.__doc__ - return _api - - -class GroupedData(object): - """ - A set of methods for aggregations on a :class:`DataFrame`, - created by :func:`DataFrame.groupBy`. - """ - - def __init__(self, jdf, sql_ctx): - self._jdf = jdf - self.sql_ctx = sql_ctx - + @since(1.4) @ignore_unicode_prefix - def agg(self, *exprs): - """Compute aggregates and returns the result as a :class:`DataFrame`. + def drop(self, col): + """Returns a new :class:`DataFrame` that drops the specified column. - The available aggregate functions are `avg`, `max`, `min`, `sum`, `count`. + :param col: a string name of the column to drop, or a + :class:`Column` to drop. - If ``exprs`` is a single :class:`dict` mapping from string to string, then the key - is the column to perform aggregation on, and the value is the aggregate function. + >>> df.drop('age').collect() + [Row(name=u'Alice'), Row(name=u'Bob')] - Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions. + >>> df.drop(df.age).collect() + [Row(name=u'Alice'), Row(name=u'Bob')] - :param exprs: a dict mapping from column name (string) to aggregate functions (string), - or a list of :class:`Column`. + >>> df.join(df2, df.name == df2.name, 'inner').drop(df.name).collect() + [Row(age=5, height=85, name=u'Bob')] - >>> gdf = df.groupBy(df.name) - >>> gdf.agg({"*": "count"}).collect() - [Row(name=u'Alice', COUNT(1)=1), Row(name=u'Bob', COUNT(1)=1)] - - >>> from pyspark.sql import functions as F - >>> gdf.agg(F.min(df.age)).collect() - [Row(MIN(age)=2), Row(MIN(age)=5)] + >>> df.join(df2, df.name == df2.name, 'inner').drop(df2.name).collect() + [Row(age=5, name=u'Bob', height=85)] """ - assert exprs, "exprs should not be empty" - if len(exprs) == 1 and isinstance(exprs[0], dict): - jdf = self._jdf.agg(exprs[0]) + if isinstance(col, basestring): + jdf = self._jdf.drop(col) + elif isinstance(col, Column): + jdf = self._jdf.drop(col._jc) else: - # Columns - assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column" - jdf = self._jdf.agg(exprs[0]._jc, - _to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]])) + raise TypeError("col should be a string or a Column") return DataFrame(jdf, self.sql_ctx) - @dfapi - def count(self): - """Counts the number of records for each group. - - >>> df.groupBy(df.age).count().collect() - [Row(age=2, count=1), Row(age=5, count=1)] - """ - - @df_varargs_api - def mean(self, *cols): - """Computes average values for each numeric columns for each group. - - :func:`mean` is an alias for :func:`avg`. - - :param cols: list of column names (string). Non-numeric columns are ignored. - - >>> df.groupBy().mean('age').collect() - [Row(AVG(age)=3.5)] - >>> df3.groupBy().mean('age', 'height').collect() - [Row(AVG(age)=3.5, AVG(height)=82.5)] - """ - - @df_varargs_api - def avg(self, *cols): - """Computes average values for each numeric columns for each group. - - :func:`mean` is an alias for :func:`avg`. - - :param cols: list of column names (string). Non-numeric columns are ignored. - - >>> df.groupBy().avg('age').collect() - [Row(AVG(age)=3.5)] - >>> df3.groupBy().avg('age', 'height').collect() - [Row(AVG(age)=3.5, AVG(height)=82.5)] - """ - - @df_varargs_api - def max(self, *cols): - """Computes the max value for each numeric columns for each group. - - >>> df.groupBy().max('age').collect() - [Row(MAX(age)=5)] - >>> df3.groupBy().max('age', 'height').collect() - [Row(MAX(age)=5, MAX(height)=85)] - """ - - @df_varargs_api - def min(self, *cols): - """Computes the min value for each numeric column for each group. - - :param cols: list of column names (string). Non-numeric columns are ignored. - - >>> df.groupBy().min('age').collect() - [Row(MIN(age)=2)] - >>> df3.groupBy().min('age', 'height').collect() - [Row(MIN(age)=2, MIN(height)=80)] - """ - - @df_varargs_api - def sum(self, *cols): - """Compute the sum for each numeric columns for each group. + @since(1.3) + def toPandas(self): + """Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``. - :param cols: list of column names (string). Non-numeric columns are ignored. + This is only available if Pandas is installed and available. - >>> df.groupBy().sum('age').collect() - [Row(SUM(age)=7)] - >>> df3.groupBy().sum('age', 'height').collect() - [Row(SUM(age)=7, SUM(height)=165)] + >>> df.toPandas() # doctest: +SKIP + age name + 0 2 Alice + 1 5 Bob """ + import pandas as pd + return pd.DataFrame.from_records(self.collect(), columns=self.columns) + ########################################################################################## + # Pandas compatibility + ########################################################################################## -def _create_column_from_literal(literal): - sc = SparkContext._active_spark_context - return sc._jvm.functions.lit(literal) - - -def _create_column_from_name(name): - sc = SparkContext._active_spark_context - return sc._jvm.functions.col(name) - - -def _to_java_column(col): - if isinstance(col, Column): - jcol = col._jc - else: - jcol = _create_column_from_name(col) - return jcol - - -def _to_seq(sc, cols, converter=None): - """ - Convert a list of Column (or names) into a JVM Seq of Column. - - An optional `converter` could be used to convert items in `cols` - into JVM Column objects. - """ - if converter: - cols = [converter(c) for c in cols] - return sc._jvm.PythonUtils.toSeq(cols) - - -def _unary_op(name, doc="unary operator"): - """ Create a method for given unary operator """ - def _(self): - jc = getattr(self._jc, name)() - return Column(jc) - _.__doc__ = doc - return _ - - -def _func_op(name, doc=''): - def _(self): - sc = SparkContext._active_spark_context - jc = getattr(sc._jvm.functions, name)(self._jc) - return Column(jc) - _.__doc__ = doc - return _ + groupby = groupBy + drop_duplicates = dropDuplicates -def _bin_op(name, doc="binary operator"): - """ Create a method for given binary operator +# Having SchemaRDD for backward compatibility (for docs) +class SchemaRDD(DataFrame): + """SchemaRDD is deprecated, please use :class:`DataFrame`. """ - def _(self, other): - jc = other._jc if isinstance(other, Column) else other - njc = getattr(self._jc, name)(jc) - return Column(njc) - _.__doc__ = doc - return _ -def _reverse_op(name, doc="binary operator"): - """ Create a method for binary operator (this object is on right side) +def _to_scala_map(sc, jm): """ - def _(self, other): - jother = _create_column_from_literal(other) - jc = getattr(jother, name)(self._jc) - return Column(jc) - _.__doc__ = doc - return _ - - -class Column(object): - + Convert a dict into a JVM Map. """ - A column in a DataFrame. - - :class:`Column` instances can be created by:: - - # 1. Select a column out of a DataFrame - - df.colName - df["colName"] - - # 2. Create from an expression - df.colName + 1 - 1 / df.colName - """ - - def __init__(self, jc): - self._jc = jc - - # arithmetic operators - __neg__ = _func_op("negate") - __add__ = _bin_op("plus") - __sub__ = _bin_op("minus") - __mul__ = _bin_op("multiply") - __div__ = _bin_op("divide") - __truediv__ = _bin_op("divide") - __mod__ = _bin_op("mod") - __radd__ = _bin_op("plus") - __rsub__ = _reverse_op("minus") - __rmul__ = _bin_op("multiply") - __rdiv__ = _reverse_op("divide") - __rtruediv__ = _reverse_op("divide") - __rmod__ = _reverse_op("mod") - - # logistic operators - __eq__ = _bin_op("equalTo") - __ne__ = _bin_op("notEqual") - __lt__ = _bin_op("lt") - __le__ = _bin_op("leq") - __ge__ = _bin_op("geq") - __gt__ = _bin_op("gt") - - # `and`, `or`, `not` cannot be overloaded in Python, - # so use bitwise operators as boolean operators - __and__ = _bin_op('and') - __or__ = _bin_op('or') - __invert__ = _func_op('not') - __rand__ = _bin_op("and") - __ror__ = _bin_op("or") - - # container operators - __contains__ = _bin_op("contains") - __getitem__ = _bin_op("apply") - - # bitwise operators - bitwiseOR = _bin_op("bitwiseOR") - bitwiseAND = _bin_op("bitwiseAND") - bitwiseXOR = _bin_op("bitwiseXOR") - - def getItem(self, key): - """An expression that gets an item at position `ordinal` out of a list, - or gets an item by key out of a dict. - - >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"]) - >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() - +----+------+ - |l[0]|d[key]| - +----+------+ - | 1| value| - +----+------+ - >>> df.select(df.l[0], df.d["key"]).show() - +----+------+ - |l[0]|d[key]| - +----+------+ - | 1| value| - +----+------+ - """ - return self[key] - - def getField(self, name): - """An expression that gets a field by name in a StructField. - - >>> from pyspark.sql import Row - >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF() - >>> df.select(df.r.getField("b")).show() - +----+ - |r[b]| - +----+ - | b| - +----+ - >>> df.select(df.r.a).show() - +----+ - |r[a]| - +----+ - | 1| - +----+ - """ - return self[name] - - def __getattr__(self, item): - if item.startswith("__"): - raise AttributeError(item) - return self.getField(item) - - # string methods - rlike = _bin_op("rlike") - like = _bin_op("like") - startswith = _bin_op("startsWith") - endswith = _bin_op("endsWith") - - @ignore_unicode_prefix - def substr(self, startPos, length): - """ - Return a :class:`Column` which is a substring of the column - - :param startPos: start position (int or Column) - :param length: length of the substring (int or Column) - - >>> df.select(df.name.substr(1, 3).alias("col")).collect() - [Row(col=u'Ali'), Row(col=u'Bob')] - """ - if type(startPos) != type(length): - raise TypeError("Can not mix the type") - if isinstance(startPos, (int, long)): - jc = self._jc.substr(startPos, length) - elif isinstance(startPos, Column): - jc = self._jc.substr(startPos._jc, length._jc) - else: - raise TypeError("Unexpected type: %s" % type(startPos)) - return Column(jc) - - __getslice__ = substr - - @ignore_unicode_prefix - def inSet(self, *cols): - """ A boolean expression that is evaluated to true if the value of this - expression is contained by the evaluated values of the arguments. - - >>> df[df.name.inSet("Bob", "Mike")].collect() - [Row(age=5, name=u'Bob')] - >>> df[df.age.inSet([1, 2, 3])].collect() - [Row(age=2, name=u'Alice')] - """ - if len(cols) == 1 and isinstance(cols[0], (list, set)): - cols = cols[0] - cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols] - sc = SparkContext._active_spark_context - jc = getattr(self._jc, "in")(_to_seq(sc, cols)) - return Column(jc) - - # order - asc = _unary_op("asc", "Returns a sort expression based on the" - " ascending order of the given column name.") - desc = _unary_op("desc", "Returns a sort expression based on the" - " descending order of the given column name.") - - isNull = _unary_op("isNull", "True if the current expression is null.") - isNotNull = _unary_op("isNotNull", "True if the current expression is not null.") - - def alias(self, alias): - """Return a alias for this column - - >>> df.select(df.age.alias("age2")).collect() - [Row(age2=2), Row(age2=5)] - """ - return Column(getattr(self._jc, "as")(alias)) - - @ignore_unicode_prefix - def cast(self, dataType): - """ Convert the column into type `dataType` - - >>> df.select(df.age.cast("string").alias('ages')).collect() - [Row(ages=u'2'), Row(ages=u'5')] - >>> df.select(df.age.cast(StringType()).alias('ages')).collect() - [Row(ages=u'2'), Row(ages=u'5')] - """ - if isinstance(dataType, basestring): - jc = self._jc.cast(dataType) - elif isinstance(dataType, DataType): - sc = SparkContext._active_spark_context - ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) - jdt = ssql_ctx.parseDataType(dataType.json()) - jc = self._jc.cast(jdt) - else: - raise TypeError("unexpected type: %s" % type(dataType)) - return Column(jc) - - @ignore_unicode_prefix - def between(self, lowerBound, upperBound): - """ A boolean expression that is evaluated to true if the value of this - expression is between the given columns. - """ - return (self >= lowerBound) & (self <= upperBound) - - def __repr__(self): - return 'Column<%s>' % self._jc.toString().encode('utf8') + return sc._jvm.PythonUtils.toScalaMap(jm) class DataFrameNaFunctions(object): """Functionality for working with missing data in :class:`DataFrame`. + + .. versionadded:: 1.4 """ def __init__(self, df): @@ -1438,9 +1287,16 @@ def fill(self, value, subset=None): fill.__doc__ = DataFrame.fillna.__doc__ + def replace(self, to_replace, value, subset=None): + return self.df.replace(to_replace, value, subset) + + replace.__doc__ = DataFrame.replace.__doc__ + class DataFrameStatFunctions(object): """Functionality for statistic functions with :class:`DataFrame`. + + .. versionadded:: 1.4 """ def __init__(self, df): @@ -1480,9 +1336,8 @@ def _test(): .toDF(StructType([StructField('age', IntegerType()), StructField('name', StringType())])) globs['df2'] = sc.parallelize([Row(name='Tom', height=80), Row(name='Bob', height=85)]).toDF() - globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80), - Row(name='Bob', age=5, height=85)]).toDF() - + globs['df3'] = sc.parallelize([Row(name='Alice', age=2), + Row(name='Bob', age=5)]).toDF() globs['df4'] = sc.parallelize([Row(name='Alice', age=10, height=80), Row(name='Bob', age=5, height=None), Row(name='Tom', age=None, height=None), diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 38a043a3c59d7..cfa87aeea193a 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -18,6 +18,7 @@ """ A collections of builtin functions """ +import math import sys if sys.version < "3": @@ -26,19 +27,27 @@ from pyspark import SparkContext from pyspark.rdd import _prepare_for_python_RDD, ignore_unicode_prefix from pyspark.serializers import PickleSerializer, AutoBatchedSerializer +from pyspark.sql import since from pyspark.sql.types import StringType -from pyspark.sql.dataframe import Column, _to_java_column, _to_seq +from pyspark.sql.column import Column, _to_java_column, _to_seq __all__ = [ + 'array', 'approxCountDistinct', + 'bin', + 'coalesce', 'countDistinct', + 'explode', 'monotonicallyIncreasingId', 'rand', 'randn', 'sparkPartitionId', - 'coalesce', - 'udf'] + 'struct', + 'udf', + 'when'] + +__all__ += ['lag', 'lead', 'ntile'] def _create_function(name, doc=""): @@ -65,6 +74,17 @@ def _(col1, col2): return _ +def _create_window_function(name, doc=''): + """ Create a window function by name """ + def _(): + sc = SparkContext._active_spark_context + jc = getattr(sc._jvm.functions, name)() + return Column(jc) + _.__name__ = name + _.__doc__ = 'Window function: ' + doc + return _ + + _functions = { 'lit': 'Creates a :class:`Column` of literal value.', 'col': 'Returns a :class:`Column` based on the given column name.', @@ -77,6 +97,18 @@ def _(col1, col2): 'sqrt': 'Computes the square root of the specified float value.', 'abs': 'Computes the absolute value.', + 'max': 'Aggregate function: returns the maximum value of the expression in a group.', + 'min': 'Aggregate function: returns the minimum value of the expression in a group.', + 'first': 'Aggregate function: returns the first value in a group.', + 'last': 'Aggregate function: returns the last value in a group.', + 'count': 'Aggregate function: returns the number of items in a group.', + 'sum': 'Aggregate function: returns the sum of all values in the expression.', + 'avg': 'Aggregate function: returns the average of the values in a group.', + 'mean': 'Aggregate function: returns the average of the values in a group.', + 'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.', +} + +_functions_1_4 = { # unary math functions 'acos': 'Computes the cosine inverse of the given value; the returned angle is in the range' + '0.0 through pi.', @@ -101,21 +133,11 @@ def _(col1, col2): 'tan': 'Computes the tangent of the given value.', 'tanh': 'Computes the hyperbolic tangent of the given value.', 'toDegrees': 'Converts an angle measured in radians to an approximately equivalent angle ' + - 'measured in degrees.', + 'measured in degrees.', 'toRadians': 'Converts an angle measured in degrees to an approximately equivalent angle ' + - 'measured in radians.', + 'measured in radians.', 'bitwiseNOT': 'Computes bitwise not.', - - 'max': 'Aggregate function: returns the maximum value of the expression in a group.', - 'min': 'Aggregate function: returns the minimum value of the expression in a group.', - 'first': 'Aggregate function: returns the first value in a group.', - 'last': 'Aggregate function: returns the last value in a group.', - 'count': 'Aggregate function: returns the number of items in a group.', - 'sum': 'Aggregate function: returns the sum of all values in the expression.', - 'avg': 'Aggregate function: returns the average of the values in a group.', - 'mean': 'Aggregate function: returns the average of the values in a group.', - 'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.', } # math functions that take two arguments as input @@ -123,19 +145,60 @@ def _(col1, col2): 'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' + 'polar coordinates (r, theta).', 'hypot': 'Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.', - 'pow': 'Returns the value of the first argument raised to the power of the second argument.' + 'pow': 'Returns the value of the first argument raised to the power of the second argument.', +} + +_window_functions = { + 'rowNumber': + """returns a sequential number starting at 1 within a window partition. + + This is equivalent to the ROW_NUMBER function in SQL.""", + 'denseRank': + """returns the rank of rows within a window partition, without any gaps. + + The difference between rank and denseRank is that denseRank leaves no gaps in ranking + sequence when there are ties. That is, if you were ranking a competition using denseRank + and had three people tie for second place, you would say that all three were in second + place and that the next person came in third. + + This is equivalent to the DENSE_RANK function in SQL.""", + 'rank': + """returns the rank of rows within a window partition. + + The difference between rank and denseRank is that denseRank leaves no gaps in ranking + sequence when there are ties. That is, if you were ranking a competition using denseRank + and had three people tie for second place, you would say that all three were in second + place and that the next person came in third. + + This is equivalent to the RANK function in SQL.""", + 'cumeDist': + """returns the cumulative distribution of values within a window partition, + i.e. the fraction of rows that are below the current row. + + This is equivalent to the CUME_DIST function in SQL.""", + 'percentRank': + """returns the relative rank (i.e. percentile) of rows within a window partition. + + This is equivalent to the PERCENT_RANK function in SQL.""", } for _name, _doc in _functions.items(): - globals()[_name] = _create_function(_name, _doc) + globals()[_name] = since(1.3)(_create_function(_name, _doc)) +for _name, _doc in _functions_1_4.items(): + globals()[_name] = since(1.4)(_create_function(_name, _doc)) for _name, _doc in _binary_mathfunctions.items(): - globals()[_name] = _create_binary_mathfunction(_name, _doc) + globals()[_name] = since(1.4)(_create_binary_mathfunction(_name, _doc)) +for _name, _doc in _window_functions.items(): + globals()[_name] = since(1.4)(_create_window_function(_name, _doc)) del _name, _doc __all__ += _functions.keys() +__all__ += _functions_1_4.keys() __all__ += _binary_mathfunctions.keys() +__all__ += _window_functions.keys() __all__.sort() +@since(1.4) def array(*cols): """Creates a new array column. @@ -154,6 +217,7 @@ def array(*cols): return Column(jc) +@since(1.3) def approxCountDistinct(col, rsd=None): """Returns a new :class:`Column` for approximate distinct count of ``col``. @@ -168,6 +232,20 @@ def approxCountDistinct(col, rsd=None): return Column(jc) +@ignore_unicode_prefix +@since(1.5) +def bin(col): + """Returns the string representation of the binary value of the given column. + + >>> df.select(bin(df.age).alias('c')).collect() + [Row(c=u'10'), Row(c=u'101')] + """ + sc = SparkContext._active_spark_context + jc = sc._jvm.functions.bin(_to_java_column(col)) + return Column(jc) + + +@since(1.4) def coalesce(*cols): """Returns the first column that is not null. @@ -204,6 +282,7 @@ def coalesce(*cols): return Column(jc) +@since(1.3) def countDistinct(col, *cols): """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``. @@ -218,6 +297,28 @@ def countDistinct(col, *cols): return Column(jc) +@since(1.4) +def explode(col): + """Returns a new row for each element in the given array or map. + + >>> from pyspark.sql import Row + >>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})]) + >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect() + [Row(anInt=1), Row(anInt=2), Row(anInt=3)] + + >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show() + +---+-----+ + |key|value| + +---+-----+ + | a| b| + +---+-----+ + """ + sc = SparkContext._active_spark_context + jc = sc._jvm.functions.explode(_to_java_column(col)) + return Column(jc) + + +@since(1.4) def monotonicallyIncreasingId(): """A column that generates monotonically increasing 64-bit integers. @@ -226,7 +327,7 @@ def monotonicallyIncreasingId(): within each partition in the lower 33 bits. The assumption is that the data frame has less than 1 billion partitions, and each partition has less than 8 billion records. - As an example, consider a [[DataFrame]] with two partitions, each with 3 records. + As an example, consider a :class:`DataFrame` with two partitions, each with 3 records. This expression would return the following IDs: 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594. @@ -238,6 +339,7 @@ def monotonicallyIncreasingId(): return Column(sc._jvm.functions.monotonicallyIncreasingId()) +@since(1.4) def rand(seed=None): """Generates a random column with i.i.d. samples from U[0.0, 1.0]. """ @@ -249,6 +351,7 @@ def rand(seed=None): return Column(jc) +@since(1.4) def randn(seed=None): """Generates a column with i.i.d. samples from the standard normal distribution. """ @@ -260,6 +363,7 @@ def randn(seed=None): return Column(jc) +@since(1.4) def sparkPartitionId(): """A column for partition ID of the Spark task. @@ -273,6 +377,7 @@ def sparkPartitionId(): @ignore_unicode_prefix +@since(1.4) def struct(*cols): """Creates a new struct column. @@ -291,9 +396,102 @@ def struct(*cols): return Column(jc) +@since(1.4) +def when(condition, value): + """Evaluates a list of conditions and returns one of multiple possible result expressions. + If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions. + + :param condition: a boolean :class:`Column` expression. + :param value: a literal value, or a :class:`Column` expression. + + >>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect() + [Row(age=3), Row(age=4)] + + >>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect() + [Row(age=3), Row(age=None)] + """ + sc = SparkContext._active_spark_context + if not isinstance(condition, Column): + raise TypeError("condition should be a Column") + v = value._jc if isinstance(value, Column) else value + jc = sc._jvm.functions.when(condition._jc, v) + return Column(jc) + + +@since(1.5) +def log(arg1, arg2=None): + """Returns the first argument-based logarithm of the second argument. + + If there is only one argument, then this takes the natural logarithm of the argument. + + >>> df.select(log(10.0, df.age).alias('ten')).map(lambda l: str(l.ten)[:7]).collect() + ['0.30102', '0.69897'] + + >>> df.select(log(df.age).alias('e')).map(lambda l: str(l.e)[:7]).collect() + ['0.69314', '1.60943'] + """ + sc = SparkContext._active_spark_context + if arg2 is None: + jc = sc._jvm.functions.log(_to_java_column(arg1)) + else: + jc = sc._jvm.functions.log(arg1, _to_java_column(arg2)) + return Column(jc) + + +@since(1.4) +def lag(col, count=1, default=None): + """ + Window function: returns the value that is `offset` rows before the current row, and + `defaultValue` if there is less than `offset` rows before the current row. For example, + an `offset` of one will return the previous row at any given point in the window partition. + + This is equivalent to the LAG function in SQL. + + :param col: name of column or expression + :param count: number of row to extend + :param default: default value + """ + sc = SparkContext._active_spark_context + return Column(sc._jvm.functions.lag(_to_java_column(col), count, default)) + + +@since(1.4) +def lead(col, count=1, default=None): + """ + Window function: returns the value that is `offset` rows after the current row, and + `defaultValue` if there is less than `offset` rows after the current row. For example, + an `offset` of one will return the next row at any given point in the window partition. + + This is equivalent to the LEAD function in SQL. + + :param col: name of column or expression + :param count: number of row to extend + :param default: default value + """ + sc = SparkContext._active_spark_context + return Column(sc._jvm.functions.lead(_to_java_column(col), count, default)) + + +@since(1.4) +def ntile(n): + """ + Window function: returns a group id from 1 to `n` (inclusive) in a round-robin fashion in + a window partition. Fow example, if `n` is 3, the first row will get 1, the second row will + get 2, the third row will get 3, and the fourth row will get 1... + + This is equivalent to the NTILE function in SQL. + + :param n: an integer + """ + sc = SparkContext._active_spark_context + return Column(sc._jvm.functions.ntile(int(n))) + + class UserDefinedFunction(object): """ User defined function in Python + + .. versionadded:: 1.3 """ def __init__(self, func, returnType): self.func = func @@ -311,8 +509,8 @@ def _create_judf(self): ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) jdt = ssql_ctx.parseDataType(self.returnType.json()) fname = f.__name__ if hasattr(f, '__name__') else f.__class__.__name__ - judf = sc._jvm.UserDefinedPythonFunction(fname, bytearray(pickled_command), env, - includes, sc.pythonExec, broadcast_vars, + judf = sc._jvm.UserDefinedPythonFunction(fname, bytearray(pickled_command), env, includes, + sc.pythonExec, sc.pythonVer, broadcast_vars, sc._javaAccumulator, jdt) return judf @@ -327,6 +525,7 @@ def __call__(self, *cols): return Column(jc) +@since(1.3) def udf(f, returnType=StringType()): """Creates a :class:`Column` expression representing a user defined function (UDF). diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py new file mode 100644 index 0000000000000..5a37a673ee80c --- /dev/null +++ b/python/pyspark/sql/group.py @@ -0,0 +1,195 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark.rdd import ignore_unicode_prefix +from pyspark.sql import since +from pyspark.sql.column import Column, _to_seq +from pyspark.sql.dataframe import DataFrame +from pyspark.sql.types import * + +__all__ = ["GroupedData"] + + +def dfapi(f): + def _api(self): + name = f.__name__ + jdf = getattr(self._jdf, name)() + return DataFrame(jdf, self.sql_ctx) + _api.__name__ = f.__name__ + _api.__doc__ = f.__doc__ + return _api + + +def df_varargs_api(f): + def _api(self, *args): + name = f.__name__ + jdf = getattr(self._jdf, name)(_to_seq(self.sql_ctx._sc, args)) + return DataFrame(jdf, self.sql_ctx) + _api.__name__ = f.__name__ + _api.__doc__ = f.__doc__ + return _api + + +class GroupedData(object): + """ + A set of methods for aggregations on a :class:`DataFrame`, + created by :func:`DataFrame.groupBy`. + + .. note:: Experimental + + .. versionadded:: 1.3 + """ + + def __init__(self, jdf, sql_ctx): + self._jdf = jdf + self.sql_ctx = sql_ctx + + @ignore_unicode_prefix + @since(1.3) + def agg(self, *exprs): + """Compute aggregates and returns the result as a :class:`DataFrame`. + + The available aggregate functions are `avg`, `max`, `min`, `sum`, `count`. + + If ``exprs`` is a single :class:`dict` mapping from string to string, then the key + is the column to perform aggregation on, and the value is the aggregate function. + + Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions. + + :param exprs: a dict mapping from column name (string) to aggregate functions (string), + or a list of :class:`Column`. + + >>> gdf = df.groupBy(df.name) + >>> gdf.agg({"*": "count"}).collect() + [Row(name=u'Alice', COUNT(1)=1), Row(name=u'Bob', COUNT(1)=1)] + + >>> from pyspark.sql import functions as F + >>> gdf.agg(F.min(df.age)).collect() + [Row(name=u'Alice', MIN(age)=2), Row(name=u'Bob', MIN(age)=5)] + """ + assert exprs, "exprs should not be empty" + if len(exprs) == 1 and isinstance(exprs[0], dict): + jdf = self._jdf.agg(exprs[0]) + else: + # Columns + assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column" + jdf = self._jdf.agg(exprs[0]._jc, + _to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]])) + return DataFrame(jdf, self.sql_ctx) + + @dfapi + @since(1.3) + def count(self): + """Counts the number of records for each group. + + >>> df.groupBy(df.age).count().collect() + [Row(age=2, count=1), Row(age=5, count=1)] + """ + + @df_varargs_api + @since(1.3) + def mean(self, *cols): + """Computes average values for each numeric columns for each group. + + :func:`mean` is an alias for :func:`avg`. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().mean('age').collect() + [Row(AVG(age)=3.5)] + >>> df3.groupBy().mean('age', 'height').collect() + [Row(AVG(age)=3.5, AVG(height)=82.5)] + """ + + @df_varargs_api + @since(1.3) + def avg(self, *cols): + """Computes average values for each numeric columns for each group. + + :func:`mean` is an alias for :func:`avg`. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().avg('age').collect() + [Row(AVG(age)=3.5)] + >>> df3.groupBy().avg('age', 'height').collect() + [Row(AVG(age)=3.5, AVG(height)=82.5)] + """ + + @df_varargs_api + @since(1.3) + def max(self, *cols): + """Computes the max value for each numeric columns for each group. + + >>> df.groupBy().max('age').collect() + [Row(MAX(age)=5)] + >>> df3.groupBy().max('age', 'height').collect() + [Row(MAX(age)=5, MAX(height)=85)] + """ + + @df_varargs_api + @since(1.3) + def min(self, *cols): + """Computes the min value for each numeric column for each group. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().min('age').collect() + [Row(MIN(age)=2)] + >>> df3.groupBy().min('age', 'height').collect() + [Row(MIN(age)=2, MIN(height)=80)] + """ + + @df_varargs_api + @since(1.3) + def sum(self, *cols): + """Compute the sum for each numeric columns for each group. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().sum('age').collect() + [Row(SUM(age)=7)] + >>> df3.groupBy().sum('age', 'height').collect() + [Row(SUM(age)=7, SUM(height)=165)] + """ + + +def _test(): + import doctest + from pyspark.context import SparkContext + from pyspark.sql import Row, SQLContext + import pyspark.sql.group + globs = pyspark.sql.group.__dict__.copy() + sc = SparkContext('local[4]', 'PythonTest') + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) + globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \ + .toDF(StructType([StructField('age', IntegerType()), + StructField('name', StringType())])) + globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80), + Row(name='Bob', age=5, height=85)]).toDF() + + (failure_count, test_count) = doctest.testmod( + pyspark.sql.group, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py new file mode 100644 index 0000000000000..f036644acc961 --- /dev/null +++ b/python/pyspark/sql/readwriter.py @@ -0,0 +1,407 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from py4j.java_gateway import JavaClass + +from pyspark.sql import since +from pyspark.sql.column import _to_seq +from pyspark.sql.types import * + +__all__ = ["DataFrameReader", "DataFrameWriter"] + + +class DataFrameReader(object): + """ + Interface used to load a :class:`DataFrame` from external storage systems + (e.g. file systems, key-value stores, etc). Use :func:`SQLContext.read` + to access this. + + ::Note: Experimental + + .. versionadded:: 1.4 + """ + + def __init__(self, sqlContext): + self._jreader = sqlContext._ssql_ctx.read() + self._sqlContext = sqlContext + + def _df(self, jdf): + from pyspark.sql.dataframe import DataFrame + return DataFrame(jdf, self._sqlContext) + + @since(1.4) + def format(self, source): + """Specifies the input data source format. + + :param source: string, name of the data source, e.g. 'json', 'parquet'. + + >>> df = sqlContext.read.format('json').load('python/test_support/sql/people.json') + >>> df.dtypes + [('age', 'bigint'), ('name', 'string')] + + """ + self._jreader = self._jreader.format(source) + return self + + @since(1.4) + def schema(self, schema): + """Specifies the input schema. + + Some data sources (e.g. JSON) can infer the input schema automatically from data. + By specifying the schema here, the underlying data source can skip the schema + inference step, and thus speed up data loading. + + :param schema: a StructType object + """ + if not isinstance(schema, StructType): + raise TypeError("schema should be StructType") + jschema = self._sqlContext._ssql_ctx.parseDataType(schema.json()) + self._jreader = self._jreader.schema(jschema) + return self + + @since(1.4) + def options(self, **options): + """Adds input options for the underlying data source. + """ + for k in options: + self._jreader = self._jreader.option(k, options[k]) + return self + + @since(1.4) + def load(self, path=None, format=None, schema=None, **options): + """Loads data from a data source and returns it as a :class`DataFrame`. + + :param path: optional string for file-system backed data sources. + :param format: optional string for format of the data source. Default to 'parquet'. + :param schema: optional :class:`StructType` for the input schema. + :param options: all other string options + + >>> df = sqlContext.read.load('python/test_support/sql/parquet_partitioned') + >>> df.dtypes + [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')] + """ + if format is not None: + self.format(format) + if schema is not None: + self.schema(schema) + self.options(**options) + if path is not None: + return self._df(self._jreader.load(path)) + else: + return self._df(self._jreader.load()) + + @since(1.4) + def json(self, path, schema=None): + """ + Loads a JSON file (one object per line) and returns the result as + a :class`DataFrame`. + + If the ``schema`` parameter is not specified, this function goes + through the input once to determine the input schema. + + :param path: string, path to the JSON dataset. + :param schema: an optional :class:`StructType` for the input schema. + + >>> df = sqlContext.read.json('python/test_support/sql/people.json') + >>> df.dtypes + [('age', 'bigint'), ('name', 'string')] + + """ + if schema is not None: + self.schema(schema) + return self._df(self._jreader.json(path)) + + @since(1.4) + def table(self, tableName): + """Returns the specified table as a :class:`DataFrame`. + + :param tableName: string, name of the table. + + >>> df = sqlContext.read.parquet('python/test_support/sql/parquet_partitioned') + >>> df.registerTempTable('tmpTable') + >>> sqlContext.read.table('tmpTable').dtypes + [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')] + """ + return self._df(self._jreader.table(tableName)) + + @since(1.4) + def parquet(self, *path): + """Loads a Parquet file, returning the result as a :class:`DataFrame`. + + >>> df = sqlContext.read.parquet('python/test_support/sql/parquet_partitioned') + >>> df.dtypes + [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')] + """ + return self._df(self._jreader.parquet(_to_seq(self._sqlContext._sc, path))) + + @since(1.4) + def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None, + predicates=None, properties={}): + """ + Construct a :class:`DataFrame` representing the database table accessible + via JDBC URL `url` named `table` and connection `properties`. + + The `column` parameter could be used to partition the table, then it will + be retrieved in parallel based on the parameters passed to this function. + + The `predicates` parameter gives a list expressions suitable for inclusion + in WHERE clauses; each one defines one partition of the :class:`DataFrame`. + + ::Note: Don't create too many partitions in parallel on a large cluster; + otherwise Spark might crash your external database systems. + + :param url: a JDBC URL + :param table: name of table + :param column: the column used to partition + :param lowerBound: the lower bound of partition column + :param upperBound: the upper bound of the partition column + :param numPartitions: the number of partitions + :param predicates: a list of expressions + :param properties: JDBC database connection arguments, a list of arbitrary string + tag/value. Normally at least a "user" and "password" property + should be included. + :return: a DataFrame + """ + jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)() + for k in properties: + jprop.setProperty(k, properties[k]) + if column is not None: + if numPartitions is None: + numPartitions = self._sqlContext._sc.defaultParallelism + return self._df(self._jreader.jdbc(url, table, column, int(lowerBound), int(upperBound), + int(numPartitions), jprop)) + if predicates is not None: + arr = self._sqlContext._sc._jvm.PythonUtils.toArray(predicates) + return self._df(self._jreader.jdbc(url, table, arr, jprop)) + return self._df(self._jreader.jdbc(url, table, jprop)) + + +class DataFrameWriter(object): + """ + Interface used to write a [[DataFrame]] to external storage systems + (e.g. file systems, key-value stores, etc). Use :func:`DataFrame.write` + to access this. + + ::Note: Experimental + + .. versionadded:: 1.4 + """ + def __init__(self, df): + self._df = df + self._sqlContext = df.sql_ctx + self._jwrite = df._jdf.write() + + @since(1.4) + def mode(self, saveMode): + """Specifies the behavior when data or table already exists. + + Options include: + + * `append`: Append contents of this :class:`DataFrame` to existing data. + * `overwrite`: Overwrite existing data. + * `error`: Throw an exception if data already exists. + * `ignore`: Silently ignore this operation if data already exists. + + >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data')) + """ + self._jwrite = self._jwrite.mode(saveMode) + return self + + @since(1.4) + def format(self, source): + """Specifies the underlying output data source. + + :param source: string, name of the data source, e.g. 'json', 'parquet'. + + >>> df.write.format('json').save(os.path.join(tempfile.mkdtemp(), 'data')) + """ + self._jwrite = self._jwrite.format(source) + return self + + @since(1.4) + def options(self, **options): + """Adds output options for the underlying data source. + """ + for k in options: + self._jwrite = self._jwrite.option(k, options[k]) + return self + + @since(1.4) + def partitionBy(self, *cols): + """Partitions the output by the given columns on the file system. + + If specified, the output is laid out on the file system similar + to Hive's partitioning scheme. + + :param cols: name of columns + + >>> df.write.partitionBy('year', 'month').parquet(os.path.join(tempfile.mkdtemp(), 'data')) + """ + if len(cols) == 1 and isinstance(cols[0], (list, tuple)): + cols = cols[0] + self._jwrite = self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols)) + return self + + @since(1.4) + def save(self, path=None, format=None, mode="error", **options): + """Saves the contents of the :class:`DataFrame` to a data source. + + The data source is specified by the ``format`` and a set of ``options``. + If ``format`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. + + :param path: the path in a Hadoop supported file system + :param format: the format used to save + :param mode: specifies the behavior of the save operation when data already exists. + + * ``append``: Append contents of this :class:`DataFrame` to existing data. + * ``overwrite``: Overwrite existing data. + * ``ignore``: Silently ignore this operation if data already exists. + * ``error`` (default case): Throw an exception if data already exists. + :param options: all other string options + + >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data')) + """ + self.mode(mode).options(**options) + if format is not None: + self.format(format) + if path is None: + self._jwrite.save() + else: + self._jwrite.save(path) + + @since(1.4) + def insertInto(self, tableName, overwrite=False): + """Inserts the content of the :class:`DataFrame` to the specified table. + + It requires that the schema of the class:`DataFrame` is the same as the + schema of the table. + + Optionally overwriting any existing data. + """ + self._jwrite.mode("overwrite" if overwrite else "append").insertInto(tableName) + + @since(1.4) + def saveAsTable(self, name, format=None, mode="error", **options): + """Saves the content of the :class:`DataFrame` as the specified table. + + In the case the table already exists, behavior of this function depends on the + save mode, specified by the `mode` function (default to throwing an exception). + When `mode` is `Overwrite`, the schema of the [[DataFrame]] does not need to be + the same as that of the existing table. + + * `append`: Append contents of this :class:`DataFrame` to existing data. + * `overwrite`: Overwrite existing data. + * `error`: Throw an exception if data already exists. + * `ignore`: Silently ignore this operation if data already exists. + + :param name: the table name + :param format: the format used to save + :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error) + :param options: all other string options + """ + self.mode(mode).options(**options) + if format is not None: + self.format(format) + self._jwrite.saveAsTable(name) + + @since(1.4) + def json(self, path, mode="error"): + """Saves the content of the :class:`DataFrame` in JSON format at the specified path. + + :param path: the path in any Hadoop supported file system + :param mode: specifies the behavior of the save operation when data already exists. + + * ``append``: Append contents of this :class:`DataFrame` to existing data. + * ``overwrite``: Overwrite existing data. + * ``ignore``: Silently ignore this operation if data already exists. + * ``error`` (default case): Throw an exception if data already exists. + + >>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data')) + """ + self._jwrite.mode(mode).json(path) + + @since(1.4) + def parquet(self, path, mode="error"): + """Saves the content of the :class:`DataFrame` in Parquet format at the specified path. + + :param path: the path in any Hadoop supported file system + :param mode: specifies the behavior of the save operation when data already exists. + + * ``append``: Append contents of this :class:`DataFrame` to existing data. + * ``overwrite``: Overwrite existing data. + * ``ignore``: Silently ignore this operation if data already exists. + * ``error`` (default case): Throw an exception if data already exists. + + >>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data')) + """ + self._jwrite.mode(mode).parquet(path) + + @since(1.4) + def jdbc(self, url, table, mode="error", properties={}): + """Saves the content of the :class:`DataFrame` to a external database table via JDBC. + + .. note:: Don't create too many partitions in parallel on a large cluster;\ + otherwise Spark might crash your external database systems. + + :param url: a JDBC URL of the form ``jdbc:subprotocol:subname`` + :param table: Name of the table in the external database. + :param mode: specifies the behavior of the save operation when data already exists. + + * ``append``: Append contents of this :class:`DataFrame` to existing data. + * ``overwrite``: Overwrite existing data. + * ``ignore``: Silently ignore this operation if data already exists. + * ``error`` (default case): Throw an exception if data already exists. + :param properties: JDBC database connection arguments, a list of + arbitrary string tag/value. Normally at least a + "user" and "password" property should be included. + """ + jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)() + for k in properties: + jprop.setProperty(k, properties[k]) + self._jwrite.mode(mode).jdbc(url, table, jprop) + + +def _test(): + import doctest + import os + import tempfile + from pyspark.context import SparkContext + from pyspark.sql import Row, SQLContext + import pyspark.sql.readwriter + + os.chdir(os.environ["SPARK_HOME"]) + + globs = pyspark.sql.readwriter.__dict__.copy() + sc = SparkContext('local[4]', 'PythonTest') + + globs['tempfile'] = tempfile + globs['os'] = os + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) + globs['df'] = globs['sqlContext'].read.parquet('python/test_support/sql/parquet_partitioned') + + (failure_count, test_count) = doctest.testmod( + pyspark.sql.readwriter, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 7e63f4d6461f6..b5fbb7d098820 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -26,6 +26,7 @@ import tempfile import pickle import functools +import time import datetime import py4j @@ -44,6 +45,21 @@ from pyspark.sql.types import UserDefinedType, _infer_type from pyspark.tests import ReusedPySparkTestCase from pyspark.sql.functions import UserDefinedFunction +from pyspark.sql.window import Window + + +class UTC(datetime.tzinfo): + """UTC""" + ZERO = datetime.timedelta(0) + + def utcoffset(self, dt): + return self.ZERO + + def tzname(self, dt): + return "UTC" + + def dst(self, dt): + return self.ZERO class ExamplePointUDT(UserDefinedType): @@ -99,6 +115,15 @@ def test_data_type_eq(self): lt2 = pickle.loads(pickle.dumps(LongType())) self.assertEquals(lt, lt2) + # regression test for SPARK-7978 + def test_decimal_type(self): + t1 = DecimalType() + t2 = DecimalType(10, 2) + self.assertTrue(t2 is not t1) + self.assertNotEqual(t1, t2) + t3 = DecimalType(8) + self.assertNotEqual(t2, t3) + class SQLTests(ReusedPySparkTestCase): @@ -117,6 +142,28 @@ def tearDownClass(cls): ReusedPySparkTestCase.tearDownClass() shutil.rmtree(cls.tempdir.name, ignore_errors=True) + def test_range(self): + self.assertEqual(self.sqlCtx.range(1, 1).count(), 0) + self.assertEqual(self.sqlCtx.range(1, 0, -1).count(), 1) + self.assertEqual(self.sqlCtx.range(0, 1 << 40, 1 << 39).count(), 2) + self.assertEqual(self.sqlCtx.range(-2).count(), 0) + self.assertEqual(self.sqlCtx.range(3).count(), 3) + + def test_explode(self): + from pyspark.sql.functions import explode + d = [Row(a=1, intlist=[1, 2, 3], mapfield={"a": "b"})] + rdd = self.sc.parallelize(d) + data = self.sqlCtx.createDataFrame(rdd) + + result = data.select(explode(data.intlist).alias("a")).select("a").collect() + self.assertEqual(result[0][0], 1) + self.assertEqual(result[1][0], 2) + self.assertEqual(result[2][0], 3) + + result = data.select(explode(data.mapfield).alias("a", "b")).select("a", "b").collect() + self.assertEqual(result[0][0], "a") + self.assertEqual(result[0][1], "b") + def test_udf_with_callable(self): d = [Row(number=i, squared=i**2) for i in range(10)] rdd = self.sc.parallelize(d) @@ -465,29 +512,29 @@ def test_save_and_load(self): df = self.df tmpPath = tempfile.mkdtemp() shutil.rmtree(tmpPath) - df.save(tmpPath, "org.apache.spark.sql.json", "error") - actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json") - self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + df.write.json(tmpPath) + actual = self.sqlCtx.read.json(tmpPath) + self.assertEqual(sorted(df.collect()), sorted(actual.collect())) schema = StructType([StructField("value", StringType(), True)]) - actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json", schema) - self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect())) + actual = self.sqlCtx.read.json(tmpPath, schema) + self.assertEqual(sorted(df.select("value").collect()), sorted(actual.collect())) - df.save(tmpPath, "org.apache.spark.sql.json", "overwrite") - actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json") - self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + df.write.json(tmpPath, "overwrite") + actual = self.sqlCtx.read.json(tmpPath) + self.assertEqual(sorted(df.collect()), sorted(actual.collect())) - df.save(source="org.apache.spark.sql.json", mode="overwrite", path=tmpPath, - noUse="this options will not be used in save.") - actual = self.sqlCtx.load(source="org.apache.spark.sql.json", path=tmpPath, - noUse="this options will not be used in load.") - self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + df.write.save(format="json", mode="overwrite", path=tmpPath, + noUse="this options will not be used in save.") + actual = self.sqlCtx.read.load(format="json", path=tmpPath, + noUse="this options will not be used in load.") + self.assertEqual(sorted(df.collect()), sorted(actual.collect())) defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default", "org.apache.spark.sql.parquet") self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json") actual = self.sqlCtx.load(path=tmpPath) - self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + self.assertEqual(sorted(df.collect()), sorted(actual.collect())) self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName) shutil.rmtree(tmpPath) @@ -556,6 +603,23 @@ def test_filter_with_datetime(self): self.assertEqual(0, df.filter(df.date > date).count()) self.assertEqual(0, df.filter(df.time > time).count()) + def test_time_with_timezone(self): + day = datetime.date.today() + now = datetime.datetime.now() + ts = time.mktime(now.timetuple()) + now.microsecond / 1e6 + # class in __main__ is not serializable + from pyspark.sql.tests import UTC + utc = UTC() + utcnow = datetime.datetime.fromtimestamp(ts, utc) + df = self.sqlCtx.createDataFrame([(day, now, utcnow)]) + day1, now1, utcnow1 = df.first() + # Pyrolite serialize java.sql.Date as datetime, will be fixed in new version + self.assertEqual(day1.date(), day) + # Pyrolite does not support microsecond, the error should be + # less than 1 millisecond + self.assertTrue(now - now1 < datetime.timedelta(0.001)) + self.assertTrue(now - utcnow1 < datetime.timedelta(0.001)) + def test_dropna(self): schema = StructType([ StructField("name", StringType(), True), @@ -665,6 +729,54 @@ def test_bitwise_operations(self): result = df.select(functions.bitwiseNOT(df.b)).collect()[0].asDict() self.assertEqual(~75, result['~b']) + def test_replace(self): + schema = StructType([ + StructField("name", StringType(), True), + StructField("age", IntegerType(), True), + StructField("height", DoubleType(), True)]) + + # replace with int + row = self.sqlCtx.createDataFrame([(u'Alice', 10, 10.0)], schema).replace(10, 20).first() + self.assertEqual(row.age, 20) + self.assertEqual(row.height, 20.0) + + # replace with double + row = self.sqlCtx.createDataFrame( + [(u'Alice', 80, 80.0)], schema).replace(80.0, 82.1).first() + self.assertEqual(row.age, 82) + self.assertEqual(row.height, 82.1) + + # replace with string + row = self.sqlCtx.createDataFrame( + [(u'Alice', 10, 80.1)], schema).replace(u'Alice', u'Ann').first() + self.assertEqual(row.name, u"Ann") + self.assertEqual(row.age, 10) + + # replace with subset specified by a string of a column name w/ actual change + row = self.sqlCtx.createDataFrame( + [(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='age').first() + self.assertEqual(row.age, 20) + + # replace with subset specified by a string of a column name w/o actual change + row = self.sqlCtx.createDataFrame( + [(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='height').first() + self.assertEqual(row.age, 10) + + # replace with subset specified with one column replaced, another column not in subset + # stays unchanged. + row = self.sqlCtx.createDataFrame( + [(u'Alice', 10, 10.0)], schema).replace(10, 20, subset=['name', 'age']).first() + self.assertEqual(row.name, u'Alice') + self.assertEqual(row.age, 20) + self.assertEqual(row.height, 10.0) + + # replace with subset specified but no column will be replaced + row = self.sqlCtx.createDataFrame( + [(u'Alice', 10, None)], schema).replace(10, 20, subset=['name', 'height']).first() + self.assertEqual(row.name, u'Alice') + self.assertEqual(row.age, 10) + self.assertEqual(row.height, None) + class HiveContextSQLTests(ReusedPySparkTestCase): @@ -675,11 +787,11 @@ def setUpClass(cls): try: cls.sc._jvm.org.apache.hadoop.hive.conf.HiveConf() except py4j.protocol.Py4JError: - cls.sqlCtx = None - return + cls.tearDownClass() + raise unittest.SkipTest("Hive is not available") except TypeError: - cls.sqlCtx = None - return + cls.tearDownClass() + raise unittest.SkipTest("Hive is not available") os.unlink(cls.tempdir.name) _scala_HiveContext =\ cls.sc._jvm.org.apache.spark.sql.hive.test.TestHiveContext(cls.sc._jsc.sc()) @@ -693,57 +805,68 @@ def tearDownClass(cls): shutil.rmtree(cls.tempdir.name, ignore_errors=True) def test_save_and_load_table(self): - if self.sqlCtx is None: - return # no hive available, skipped - df = self.df tmpPath = tempfile.mkdtemp() shutil.rmtree(tmpPath) - df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "append", path=tmpPath) - actual = self.sqlCtx.createExternalTable("externalJsonTable", tmpPath, - "org.apache.spark.sql.json") - self.assertTrue( - sorted(df.collect()) == - sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) - self.assertTrue( - sorted(df.collect()) == - sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) - self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + df.write.saveAsTable("savedJsonTable", "json", "append", path=tmpPath) + actual = self.sqlCtx.createExternalTable("externalJsonTable", tmpPath, "json") + self.assertEqual(sorted(df.collect()), + sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) + self.assertEqual(sorted(df.collect()), + sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) + self.assertEqual(sorted(df.collect()), sorted(actual.collect())) self.sqlCtx.sql("DROP TABLE externalJsonTable") - df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "overwrite", path=tmpPath) + df.write.saveAsTable("savedJsonTable", "json", "overwrite", path=tmpPath) schema = StructType([StructField("value", StringType(), True)]) - actual = self.sqlCtx.createExternalTable("externalJsonTable", - source="org.apache.spark.sql.json", + actual = self.sqlCtx.createExternalTable("externalJsonTable", source="json", schema=schema, path=tmpPath, noUse="this options will not be used") - self.assertTrue( - sorted(df.collect()) == - sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) - self.assertTrue( - sorted(df.select("value").collect()) == - sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) - self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect())) + self.assertEqual(sorted(df.collect()), + sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) + self.assertEqual(sorted(df.select("value").collect()), + sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) + self.assertEqual(sorted(df.select("value").collect()), sorted(actual.collect())) self.sqlCtx.sql("DROP TABLE savedJsonTable") self.sqlCtx.sql("DROP TABLE externalJsonTable") defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default", "org.apache.spark.sql.parquet") self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json") - df.saveAsTable("savedJsonTable", path=tmpPath, mode="overwrite") + df.write.saveAsTable("savedJsonTable", path=tmpPath, mode="overwrite") actual = self.sqlCtx.createExternalTable("externalJsonTable", path=tmpPath) - self.assertTrue( - sorted(df.collect()) == - sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) - self.assertTrue( - sorted(df.collect()) == - sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) - self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + self.assertEqual(sorted(df.collect()), + sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) + self.assertEqual(sorted(df.collect()), + sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) + self.assertEqual(sorted(df.collect()), sorted(actual.collect())) self.sqlCtx.sql("DROP TABLE savedJsonTable") self.sqlCtx.sql("DROP TABLE externalJsonTable") self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName) shutil.rmtree(tmpPath) + def test_window_functions(self): + df = self.sqlCtx.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"]) + w = Window.partitionBy("value").orderBy("key") + from pyspark.sql import functions as F + sel = df.select(df.value, df.key, + F.max("key").over(w.rowsBetween(0, 1)), + F.min("key").over(w.rowsBetween(0, 1)), + F.count("key").over(w.rowsBetween(float('-inf'), float('inf'))), + F.rowNumber().over(w), + F.rank().over(w), + F.denseRank().over(w), + F.ntile(2).over(w)) + rs = sorted(sel.collect()) + expected = [ + ("1", 1, 1, 1, 1, 1, 1, 1, 1), + ("2", 1, 1, 1, 3, 1, 1, 1, 1), + ("2", 1, 2, 1, 3, 2, 1, 1, 1), + ("2", 2, 2, 2, 3, 3, 3, 2, 2) + ] + for r, ex in zip(rs, expected): + self.assertEqual(tuple(r), ex[:len(r)]) + if __name__ == "__main__": unittest.main() diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/types.py similarity index 93% rename from python/pyspark/sql/_types.py rename to python/pyspark/sql/types.py index fd98e116d2cf1..23d9adb0daea1 100644 --- a/python/pyspark/sql/_types.py +++ b/python/pyspark/sql/types.py @@ -19,6 +19,7 @@ import decimal import time import datetime +import calendar import keyword import warnings import json @@ -73,56 +74,84 @@ def json(self): # This singleton pattern does not work with pickle, you will get # another object after pickle and unpickle -class PrimitiveTypeSingleton(type): - """Metaclass for PrimitiveType""" +class DataTypeSingleton(type): + """Metaclass for DataType""" _instances = {} def __call__(cls): if cls not in cls._instances: - cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__() + cls._instances[cls] = super(DataTypeSingleton, cls).__call__() return cls._instances[cls] -class PrimitiveType(DataType): - """Spark SQL PrimitiveType""" +class NullType(DataType): + """Null type. - __metaclass__ = PrimitiveTypeSingleton + The data type representing None, used for the types that cannot be inferred. + """ + __metaclass__ = DataTypeSingleton -class NullType(PrimitiveType): - """Null type. - The data type representing None, used for the types that cannot be inferred. +class AtomicType(DataType): + """An internal type used to represent everything that is not + null, UDTs, arrays, structs, and maps.""" + + +class NumericType(AtomicType): + """Numeric data types. + """ + + +class IntegralType(NumericType): + """Integral data types. + """ + + __metaclass__ = DataTypeSingleton + + +class FractionalType(NumericType): + """Fractional data types. """ -class StringType(PrimitiveType): +class StringType(AtomicType): """String data type. """ + __metaclass__ = DataTypeSingleton -class BinaryType(PrimitiveType): + +class BinaryType(AtomicType): """Binary (byte array) data type. """ + __metaclass__ = DataTypeSingleton + -class BooleanType(PrimitiveType): +class BooleanType(AtomicType): """Boolean data type. """ + __metaclass__ = DataTypeSingleton -class DateType(PrimitiveType): + +class DateType(AtomicType): """Date (datetime.date) data type. """ + __metaclass__ = DataTypeSingleton + -class TimestampType(PrimitiveType): +class TimestampType(AtomicType): """Timestamp (datetime.datetime) data type. """ + __metaclass__ = DataTypeSingleton -class DecimalType(DataType): + +class DecimalType(FractionalType): """Decimal (decimal.Decimal) data type. """ @@ -150,31 +179,35 @@ def __repr__(self): return "DecimalType()" -class DoubleType(PrimitiveType): +class DoubleType(FractionalType): """Double data type, representing double precision floats. """ + __metaclass__ = DataTypeSingleton + -class FloatType(PrimitiveType): +class FloatType(FractionalType): """Float data type, representing single precision floats. """ + __metaclass__ = DataTypeSingleton -class ByteType(PrimitiveType): + +class ByteType(IntegralType): """Byte data type, i.e. a signed integer in a single byte. """ def simpleString(self): return 'tinyint' -class IntegerType(PrimitiveType): +class IntegerType(IntegralType): """Int data type, i.e. a signed 32-bit integer. """ def simpleString(self): return 'int' -class LongType(PrimitiveType): +class LongType(IntegralType): """Long data type, i.e. a signed 64-bit integer. If the values are beyond the range of [-9223372036854775808, 9223372036854775807], @@ -184,7 +217,7 @@ def simpleString(self): return 'bigint' -class ShortType(PrimitiveType): +class ShortType(IntegralType): """Short data type, i.e. a signed 16-bit integer. """ def simpleString(self): @@ -426,11 +459,9 @@ def __eq__(self, other): return type(self) == type(other) -_all_primitive_types = dict((v.typeName(), v) - for v in list(globals().values()) - if (type(v) is type or type(v) is PrimitiveTypeSingleton) - and v.__base__ == PrimitiveType) - +_atomic_types = [StringType, BinaryType, BooleanType, DecimalType, FloatType, DoubleType, + ByteType, ShortType, IntegerType, LongType, DateType, TimestampType] +_all_atomic_types = dict((t.typeName(), t) for t in _atomic_types) _all_complex_types = dict((v.typeName(), v) for v in [ArrayType, MapType, StructType]) @@ -444,7 +475,7 @@ def _parse_datatype_json_string(json_string): ... scala_datatype = sqlContext._ssql_ctx.parseDataType(datatype.json()) ... python_datatype = _parse_datatype_json_string(scala_datatype.json()) ... assert datatype == python_datatype - >>> for cls in _all_primitive_types.values(): + >>> for cls in _all_atomic_types.values(): ... check_datatype(cls()) >>> # Simple ArrayType. @@ -494,8 +525,8 @@ def _parse_datatype_json_string(json_string): def _parse_datatype_json_value(json_value): if not isinstance(json_value, dict): - if json_value in _all_primitive_types.keys(): - return _all_primitive_types[json_value]() + if json_value in _all_atomic_types.keys(): + return _all_atomic_types[json_value]() elif json_value == 'decimal': return DecimalType() elif _FIXED_DECIMAL.match(json_value): @@ -624,10 +655,15 @@ def _need_python_to_sql_conversion(dataType): _need_python_to_sql_conversion(dataType.valueType) elif isinstance(dataType, UserDefinedType): return True + elif isinstance(dataType, (DateType, TimestampType)): + return True else: return False +EPOCH_ORDINAL = datetime.datetime(1970, 1, 1).toordinal() + + def _python_to_sql_converter(dataType): """ Returns a converter that converts a Python object into a SQL datum for the given type. @@ -665,18 +701,32 @@ def converter(obj): return tuple(c(d.get(n)) for n, c in zip(names, converters)) else: return tuple(c(v) for c, v in zip(converters, obj)) - else: + elif obj is not None: raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType)) return converter elif isinstance(dataType, ArrayType): element_converter = _python_to_sql_converter(dataType.elementType) - return lambda a: [element_converter(v) for v in a] + return lambda a: a and [element_converter(v) for v in a] elif isinstance(dataType, MapType): key_converter = _python_to_sql_converter(dataType.keyType) value_converter = _python_to_sql_converter(dataType.valueType) - return lambda m: dict([(key_converter(k), value_converter(v)) for k, v in m.items()]) + return lambda m: m and dict([(key_converter(k), value_converter(v)) for k, v in m.items()]) + elif isinstance(dataType, UserDefinedType): - return lambda obj: dataType.serialize(obj) + return lambda obj: obj and dataType.serialize(obj) + + elif isinstance(dataType, DateType): + return lambda d: d and d.toordinal() - EPOCH_ORDINAL + + elif isinstance(dataType, TimestampType): + + def to_posix_timstamp(dt): + if dt: + seconds = (calendar.timegm(dt.utctimetuple()) if dt.tzinfo + else time.mktime(dt.timetuple())) + return int(seconds * 1e7 + dt.microsecond * 10) + return to_posix_timstamp + else: raise ValueError("Unexpected type %r" % dataType) @@ -930,7 +980,7 @@ def _infer_schema_type(obj, dataType): DecimalType: (decimal.Decimal,), StringType: (str, unicode), BinaryType: (bytearray,), - DateType: (datetime.date,), + DateType: (datetime.date, datetime.datetime), TimestampType: (datetime.datetime,), ArrayType: (list, tuple, array), MapType: (dict,), @@ -1125,7 +1175,7 @@ def Dict(d): return lambda datum: dataType.deserialize(datum) elif not isinstance(dataType, StructType): - # no wrapper for primitive types + # no wrapper for atomic types return lambda x: x class Row(tuple): @@ -1228,12 +1278,14 @@ def __getattr__(self, item): raise AttributeError(item) def __reduce__(self): + """Returns a tuple so Python knows how to pickle Row.""" if hasattr(self, "__fields__"): return (_create_row, (self.__fields__, tuple(self))) else: return tuple.__reduce__(self) def __repr__(self): + """Printable representation of Row used in Python REPL.""" if hasattr(self, "__fields__"): return "Row(%s)" % ", ".join("%s=%r" % (k, v) for k, v in zip(self.__fields__, tuple(self))) diff --git a/python/pyspark/sql/window.py b/python/pyspark/sql/window.py new file mode 100644 index 0000000000000..c74745c726a0c --- /dev/null +++ b/python/pyspark/sql/window.py @@ -0,0 +1,157 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import sys + +from pyspark import SparkContext +from pyspark.sql import since +from pyspark.sql.column import _to_seq, _to_java_column + +__all__ = ["Window", "WindowSpec"] + + +def _to_java_cols(cols): + sc = SparkContext._active_spark_context + if len(cols) == 1 and isinstance(cols[0], list): + cols = cols[0] + return _to_seq(sc, cols, _to_java_column) + + +class Window(object): + """ + Utility functions for defining window in DataFrames. + + For example: + + >>> # PARTITION BY country ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW + >>> window = Window.partitionBy("country").orderBy("date").rowsBetween(-sys.maxsize, 0) + + >>> # PARTITION BY country ORDER BY date RANGE BETWEEN 3 PRECEDING AND 3 FOLLOWING + >>> window = Window.orderBy("date").partitionBy("country").rangeBetween(-3, 3) + + .. note:: Experimental + + .. versionadded:: 1.4 + """ + @staticmethod + @since(1.4) + def partitionBy(*cols): + """ + Creates a :class:`WindowSpec` with the partitioning defined. + """ + sc = SparkContext._active_spark_context + jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols)) + return WindowSpec(jspec) + + @staticmethod + @since(1.4) + def orderBy(*cols): + """ + Creates a :class:`WindowSpec` with the partitioning defined. + """ + sc = SparkContext._active_spark_context + jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols)) + return WindowSpec(jspec) + + +class WindowSpec(object): + """ + A window specification that defines the partitioning, ordering, + and frame boundaries. + + Use the static methods in :class:`Window` to create a :class:`WindowSpec`. + + .. note:: Experimental + + .. versionadded:: 1.4 + """ + + _JAVA_MAX_LONG = (1 << 63) - 1 + _JAVA_MIN_LONG = - (1 << 63) + + def __init__(self, jspec): + self._jspec = jspec + + @since(1.4) + def partitionBy(self, *cols): + """ + Defines the partitioning columns in a :class:`WindowSpec`. + + :param cols: names of columns or expressions + """ + return WindowSpec(self._jspec.partitionBy(_to_java_cols(cols))) + + @since(1.4) + def orderBy(self, *cols): + """ + Defines the ordering columns in a :class:`WindowSpec`. + + :param cols: names of columns or expressions + """ + return WindowSpec(self._jspec.orderBy(_to_java_cols(cols))) + + @since(1.4) + def rowsBetween(self, start, end): + """ + Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive). + + Both `start` and `end` are relative positions from the current row. + For example, "0" means "current row", while "-1" means the row before + the current row, and "5" means the fifth row after the current row. + + :param start: boundary start, inclusive. + The frame is unbounded if this is ``-sys.maxsize`` (or lower). + :param end: boundary end, inclusive. + The frame is unbounded if this is ``sys.maxsize`` (or higher). + """ + if start <= -sys.maxsize: + start = self._JAVA_MIN_LONG + if end >= sys.maxsize: + end = self._JAVA_MAX_LONG + return WindowSpec(self._jspec.rowsBetween(start, end)) + + @since(1.4) + def rangeBetween(self, start, end): + """ + Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive). + + Both `start` and `end` are relative from the current row. For example, + "0" means "current row", while "-1" means one off before the current row, + and "5" means the five off after the current row. + + :param start: boundary start, inclusive. + The frame is unbounded if this is ``-sys.maxsize`` (or lower). + :param end: boundary end, inclusive. + The frame is unbounded if this is ``sys.maxsize`` (or higher). + """ + if start <= -sys.maxsize: + start = self._JAVA_MIN_LONG + if end >= sys.maxsize: + end = self._JAVA_MAX_LONG + return WindowSpec(self._jspec.rangeBetween(start, end)) + + +def _test(): + import doctest + SparkContext('local[4]', 'PythonTest') + (failure_count, test_count) = doctest.testmod() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py index ff097985fae3e..8dcb9645cdc6b 100644 --- a/python/pyspark/streaming/dstream.py +++ b/python/pyspark/streaming/dstream.py @@ -176,7 +176,7 @@ def takeAndPrint(time, rdd): print(record) if len(taken) > num: print("...") - print() + print("") self.foreachRDD(takeAndPrint) diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py index e278b29003f69..10a859a532e28 100644 --- a/python/pyspark/streaming/kafka.py +++ b/python/pyspark/streaming/kafka.py @@ -132,11 +132,12 @@ def createRDD(sc, kafkaParams, offsetRanges, leaders={}, .. note:: Experimental Create a RDD from Kafka using offset ranges for each topic and partition. + :param sc: SparkContext object :param kafkaParams: Additional params for Kafka :param offsetRanges: list of offsetRange to specify topic:partition:[start, end) to consume :param leaders: Kafka brokers for each TopicAndPartition in offsetRanges. May be an empty - map, in which case leaders will be looked up on the driver. + map, in which case leaders will be looked up on the driver. :param keyDecoder: A function used to decode key (default is utf8_decoder) :param valueDecoder: A function used to decode value (default is utf8_decoder) :return: A RDD object diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 33ea8c9293d74..57049beea4dba 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -41,8 +41,8 @@ class PySparkStreamingTestCase(unittest.TestCase): - timeout = 4 # seconds - duration = .2 + timeout = 10 # seconds + duration = .5 @classmethod def setUpClass(cls): @@ -379,13 +379,13 @@ def func(dstream): class WindowFunctionTests(PySparkStreamingTestCase): - timeout = 5 + timeout = 15 def test_window(self): input = [range(1), range(2), range(3), range(4), range(5)] def func(dstream): - return dstream.window(.6, .2).count() + return dstream.window(1.5, .5).count() expected = [[1], [3], [6], [9], [12], [9], [5]] self._test_func(input, func, expected) @@ -394,7 +394,7 @@ def test_count_by_window(self): input = [range(1), range(2), range(3), range(4), range(5)] def func(dstream): - return dstream.countByWindow(.6, .2) + return dstream.countByWindow(1.5, .5) expected = [[1], [3], [6], [9], [12], [9], [5]] self._test_func(input, func, expected) @@ -403,7 +403,7 @@ def test_count_by_window_large(self): input = [range(1), range(2), range(3), range(4), range(5), range(6)] def func(dstream): - return dstream.countByWindow(1, .2) + return dstream.countByWindow(2.5, .5) expected = [[1], [3], [6], [10], [15], [20], [18], [15], [11], [6]] self._test_func(input, func, expected) @@ -412,7 +412,7 @@ def test_count_by_value_and_window(self): input = [range(1), range(2), range(3), range(4), range(5), range(6)] def func(dstream): - return dstream.countByValueAndWindow(1, .2) + return dstream.countByValueAndWindow(2.5, .5) expected = [[1], [2], [3], [4], [5], [6], [6], [6], [6], [6]] self._test_func(input, func, expected) @@ -421,7 +421,7 @@ def test_group_by_key_and_window(self): input = [[('a', i)] for i in range(5)] def func(dstream): - return dstream.groupByKeyAndWindow(.6, .2).mapValues(list) + return dstream.groupByKeyAndWindow(1.5, .5).mapValues(list) expected = [[('a', [0])], [('a', [0, 1])], [('a', [0, 1, 2])], [('a', [1, 2, 3])], [('a', [2, 3, 4])], [('a', [3, 4])], [('a', [4])]] @@ -615,7 +615,6 @@ def test_kafka_stream(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), "test-streaming-consumer", {topic: 1}, @@ -631,7 +630,6 @@ def test_kafka_direct_stream(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams) self._validateStreamResult(sendData, stream) @@ -646,7 +644,6 @@ def test_kafka_direct_stream_from_offset(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams, fromOffsets) self._validateStreamResult(sendData, stream) @@ -661,7 +658,6 @@ def test_kafka_rdd(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges) self._validateRddResult(sendData, rdd) @@ -677,7 +673,6 @@ def test_kafka_rdd_with_leaders(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, leaders) self._validateRddResult(sendData, rdd) diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index ea63a396da5b8..78265423682b0 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -179,9 +179,12 @@ def test_in_memory_sort(self): list(sorter.sorted(l, key=lambda x: -x, reverse=True))) def test_external_sort(self): + class CustomizedSorter(ExternalSorter): + def _next_limit(self): + return self.memory_limit l = list(range(1024)) random.shuffle(l) - sorter = ExternalSorter(1) + sorter = CustomizedSorter(1) self.assertEqual(sorted(l), list(sorter.sorted(l))) self.assertGreater(shuffle.DiskBytesSpilled, 0) last = shuffle.DiskBytesSpilled @@ -444,6 +447,11 @@ def func(x): class RDDTests(ReusedPySparkTestCase): + def test_range(self): + self.assertEqual(self.sc.range(1, 1).count(), 0) + self.assertEqual(self.sc.range(1, 0, -1).count(), 1) + self.assertEqual(self.sc.range(0, 1 << 40, 1 << 39).count(), 2) + def test_id(self): rdd = self.sc.parallelize(range(10)) id = rdd.id() @@ -453,6 +461,14 @@ def test_id(self): self.assertEqual(id + 1, id2) self.assertEqual(id2, rdd2.id()) + def test_empty_rdd(self): + rdd = self.sc.emptyRDD() + self.assertTrue(rdd.isEmpty()) + + def test_sum(self): + self.assertEqual(0, self.sc.emptyRDD().sum()) + self.assertEqual(6, self.sc.parallelize([1, 2, 3]).sum()) + def test_save_as_textfile_with_unicode(self): # Regression test for SPARK-970 x = u"\u00A1Hola, mundo!" @@ -644,7 +660,6 @@ def test_count_approx_distinct(self): self.assertTrue(18 < rdd.map(lambda x: (x, -x)).countApproxDistinct() < 22) self.assertRaises(ValueError, lambda: rdd.countApproxDistinct(0.00000001)) - self.assertRaises(ValueError, lambda: rdd.countApproxDistinct(0.5)) def test_histogram(self): # empty @@ -1544,13 +1559,13 @@ def count(): def test_with_different_versions_of_python(self): rdd = self.sc.parallelize(range(10)) rdd.count() - version = sys.version_info - sys.version_info = (2, 0, 0) + version = self.sc.pythonVer + self.sc.pythonVer = "2.0" try: with QuietTest(self.sc): self.assertRaises(Py4JJavaError, lambda: rdd.count()) finally: - sys.version_info = version + self.sc.pythonVer = version class SparkSubmitTests(unittest.TestCase): @@ -1805,6 +1820,10 @@ def run(): sc.stop() + def test_startTime(self): + with SparkContext() as sc: + self.assertGreater(sc.startTime, 0) + @unittest.skipIf(not _have_scipy, "SciPy not installed") class SciPyTests(PySparkTestCase): diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index fbdaf3a5814cd..93df9002be377 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -57,6 +57,12 @@ def main(infile, outfile): if split_index == -1: # for unit tests exit(-1) + version = utf8_deserializer.loads(infile) + if version != "%d.%d" % sys.version_info[:2]: + raise Exception(("Python in worker has different version %s than that in " + + "driver %s, PySpark cannot run with different minor versions") % + ("%d.%d" % sys.version_info[:2], version)) + # initialize global state shuffle.MemoryBytesSpilled = 0 shuffle.DiskBytesSpilled = 0 @@ -92,11 +98,7 @@ def main(infile, outfile): command = pickleSer._read_with_length(infile) if isinstance(command, Broadcast): command = pickleSer.loads(command.value) - (func, profiler, deserializer, serializer), version = command - if version != sys.version_info[:2]: - raise Exception(("Python in worker has different version %s than that in " + - "driver %s, PySpark cannot run with different minor versions") % - (sys.version_info[:2], version)) + func, profiler, deserializer, serializer = command init_time = time.time() def process(): diff --git a/python/run-tests b/python/run-tests index f9ca26467f17e..4468fdb3f267e 100755 --- a/python/run-tests +++ b/python/run-tests @@ -57,50 +57,57 @@ function run_test() { function run_core_tests() { echo "Run core tests ..." - run_test "pyspark/rdd.py" - run_test "pyspark/context.py" - run_test "pyspark/conf.py" - PYSPARK_DOC_TEST=1 run_test "pyspark/broadcast.py" - PYSPARK_DOC_TEST=1 run_test "pyspark/accumulators.py" - run_test "pyspark/serializers.py" - run_test "pyspark/profiler.py" - run_test "pyspark/shuffle.py" - run_test "pyspark/tests.py" + run_test "pyspark.rdd" + run_test "pyspark.context" + run_test "pyspark.conf" + run_test "pyspark.broadcast" + run_test "pyspark.accumulators" + run_test "pyspark.serializers" + run_test "pyspark.profiler" + run_test "pyspark.shuffle" + run_test "pyspark.tests" } function run_sql_tests() { echo "Run sql tests ..." - run_test "pyspark/sql/_types.py" - run_test "pyspark/sql/context.py" - run_test "pyspark/sql/dataframe.py" - run_test "pyspark/sql/functions.py" - run_test "pyspark/sql/tests.py" + run_test "pyspark.sql.types" + run_test "pyspark.sql.context" + run_test "pyspark.sql.column" + run_test "pyspark.sql.dataframe" + run_test "pyspark.sql.group" + run_test "pyspark.sql.functions" + run_test "pyspark.sql.readwriter" + run_test "pyspark.sql.window" + run_test "pyspark.sql.tests" } function run_mllib_tests() { echo "Run mllib tests ..." - run_test "pyspark/mllib/classification.py" - run_test "pyspark/mllib/clustering.py" - run_test "pyspark/mllib/evaluation.py" - run_test "pyspark/mllib/feature.py" - run_test "pyspark/mllib/fpm.py" - run_test "pyspark/mllib/linalg.py" - run_test "pyspark/mllib/rand.py" - run_test "pyspark/mllib/recommendation.py" - run_test "pyspark/mllib/regression.py" - run_test "pyspark/mllib/stat/_statistics.py" - run_test "pyspark/mllib/tree.py" - run_test "pyspark/mllib/util.py" - run_test "pyspark/mllib/tests.py" + run_test "pyspark.mllib.classification" + run_test "pyspark.mllib.clustering" + run_test "pyspark.mllib.evaluation" + run_test "pyspark.mllib.feature" + run_test "pyspark.mllib.fpm" + run_test "pyspark.mllib.linalg" + run_test "pyspark.mllib.random" + run_test "pyspark.mllib.recommendation" + run_test "pyspark.mllib.regression" + run_test "pyspark.mllib.stat._statistics" + run_test "pyspark.mllib.stat.KernelDensity" + run_test "pyspark.mllib.tree" + run_test "pyspark.mllib.util" + run_test "pyspark.mllib.tests" } function run_ml_tests() { echo "Run ml tests ..." - run_test "pyspark/ml/feature.py" - run_test "pyspark/ml/classification.py" - run_test "pyspark/ml/tuning.py" - run_test "pyspark/ml/tests.py" - run_test "pyspark/ml/evaluation.py" + run_test "pyspark.ml.feature" + run_test "pyspark.ml.classification" + run_test "pyspark.ml.recommendation" + run_test "pyspark.ml.regression" + run_test "pyspark.ml.tuning" + run_test "pyspark.ml.tests" + run_test "pyspark.ml.evaluation" } function run_streaming_tests() { @@ -120,8 +127,8 @@ function run_streaming_tests() { done export PYSPARK_SUBMIT_ARGS="--jars ${KAFKA_ASSEMBLY_JAR} pyspark-shell" - run_test "pyspark/streaming/util.py" - run_test "pyspark/streaming/tests.py" + run_test "pyspark.streaming.util" + run_test "pyspark.streaming.tests" } echo "Running PySpark tests. Output is in python/$LOG_FILE." diff --git a/python/test_support/sql/parquet_partitioned/_SUCCESS b/python/test_support/sql/parquet_partitioned/_SUCCESS new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/python/test_support/sql/parquet_partitioned/_common_metadata b/python/test_support/sql/parquet_partitioned/_common_metadata new file mode 100644 index 0000000000000..7ef2320651dee Binary files /dev/null and b/python/test_support/sql/parquet_partitioned/_common_metadata differ diff --git a/python/test_support/sql/parquet_partitioned/_metadata b/python/test_support/sql/parquet_partitioned/_metadata new file mode 100644 index 0000000000000..78a1ca7d38279 Binary files /dev/null and b/python/test_support/sql/parquet_partitioned/_metadata differ diff --git a/python/test_support/sql/parquet_partitioned/year=2014/month=9/day=1/.part-r-00008.gz.parquet.crc b/python/test_support/sql/parquet_partitioned/year=2014/month=9/day=1/.part-r-00008.gz.parquet.crc new file mode 100644 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a/python/test_support/sql/parquet_partitioned/year=2015/month=9/day=1/.part-r-00007.gz.parquet.crc b/python/test_support/sql/parquet_partitioned/year=2015/month=9/day=1/.part-r-00007.gz.parquet.crc new file mode 100644 index 0000000000000..58d9bb5fc5883 Binary files /dev/null and b/python/test_support/sql/parquet_partitioned/year=2015/month=9/day=1/.part-r-00007.gz.parquet.crc differ diff --git a/python/test_support/sql/parquet_partitioned/year=2015/month=9/day=1/part-r-00007.gz.parquet b/python/test_support/sql/parquet_partitioned/year=2015/month=9/day=1/part-r-00007.gz.parquet new file mode 100644 index 0000000000000..9b00805481e7b Binary files /dev/null and b/python/test_support/sql/parquet_partitioned/year=2015/month=9/day=1/part-r-00007.gz.parquet differ diff --git a/python/test_support/sql/people.json b/python/test_support/sql/people.json new file mode 100644 index 0000000000000..50a859cbd7ee8 --- /dev/null +++ b/python/test_support/sql/people.json @@ -0,0 +1,3 @@ +{"name":"Michael"} +{"name":"Andy", "age":30} +{"name":"Justin", "age":19} diff --git a/repl/pom.xml b/repl/pom.xml index 03053b4c3b287..85f7bc8ac1024 100644 --- a/repl/pom.xml +++ b/repl/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -48,6 +48,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.spark spark-bagel_${scala.binary.version} diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 488f3a9f33256..2b235525250c2 100644 --- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -206,7 +206,8 @@ class SparkILoop( // e.g. file:/C:/my/path.jar -> C:/my/path.jar SparkILoop.getAddedJars.map { jar => new URI(jar).getPath.stripPrefix("/") } } else { - SparkILoop.getAddedJars + // We need new URI(jar).getPath here for the case that `jar` includes encoded white space (%20). + SparkILoop.getAddedJars.map { jar => new URI(jar).getPath } } // work around for Scala bug val totalClassPath = addedJars.foldLeft( @@ -1109,7 +1110,7 @@ object SparkILoop extends Logging { if (settings.classpath.isDefault) settings.classpath.value = sys.props("java.class.path") - getAddedJars.foreach(settings.classpath.append(_)) + getAddedJars.map(jar => new URI(jar).getPath).foreach(settings.classpath.append(_)) repl process settings } diff --git a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala index 934daaeaafca1..f150fec7db945 100644 --- a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -22,13 +22,12 @@ import java.net.URLClassLoader import scala.collection.mutable.ArrayBuffer -import org.scalatest.FunSuite -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.commons.lang3.StringEscapeUtils import org.apache.spark.util.Utils -class ReplSuite extends FunSuite { +class ReplSuite extends SparkFunSuite { def runInterpreter(master: String, input: String): String = { val CONF_EXECUTOR_CLASSPATH = "spark.executor.extraClassPath" @@ -268,6 +267,17 @@ class ReplSuite extends FunSuite { assertDoesNotContain("Exception", output) } + test("SPARK-8461 SQL with codegen") { + val output = runInterpreter("local", + """ + |val sqlContext = new org.apache.spark.sql.SQLContext(sc) + |sqlContext.setConf("spark.sql.codegen", "true") + |sqlContext.range(0, 100).filter('id > 50).count() + """.stripMargin) + assertContains("Long = 49", output) + assertDoesNotContain("java.lang.ClassNotFoundException", output) + } + test("SPARK-2632 importing a method from non serializable class and not using it.") { val output = runInterpreter("local", """ diff --git a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala index 14f5e9ed4f25e..9ecc7c229e38a 100644 --- a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -24,14 +24,13 @@ import scala.collection.mutable.ArrayBuffer import scala.concurrent.duration._ import scala.tools.nsc.interpreter.SparkILoop -import org.scalatest.FunSuite import org.apache.commons.lang3.StringEscapeUtils -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkFunSuite} import org.apache.spark.util.Utils -class ReplSuite extends FunSuite { +class ReplSuite extends SparkFunSuite { def runInterpreter(master: String, input: String): String = { val CONF_EXECUTOR_CLASSPATH = "spark.executor.extraClassPath" diff --git a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala index c709cde740748..a58eda12b1120 100644 --- a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala +++ b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala @@ -25,7 +25,6 @@ import scala.language.implicitConversions import scala.language.postfixOps import org.scalatest.BeforeAndAfterAll -import org.scalatest.FunSuite import org.scalatest.concurrent.Interruptor import org.scalatest.concurrent.Timeouts._ import org.scalatest.mock.MockitoSugar @@ -35,7 +34,7 @@ import org.apache.spark._ import org.apache.spark.util.Utils class ExecutorClassLoaderSuite - extends FunSuite + extends SparkFunSuite with BeforeAndAfterAll with MockitoSugar with Logging { diff --git a/sbin/start-master.sh b/sbin/start-master.sh index 17fff58f4f768..a7f5d5702fd80 100755 --- a/sbin/start-master.sh +++ b/sbin/start-master.sh @@ -22,6 +22,8 @@ sbin="`dirname "$0"`" sbin="`cd "$sbin"; pwd`" +ORIGINAL_ARGS="$@" + START_TACHYON=false while (( "$#" )); do @@ -53,7 +55,9 @@ if [ "$SPARK_MASTER_WEBUI_PORT" = "" ]; then SPARK_MASTER_WEBUI_PORT=8080 fi -"$sbin"/spark-daemon.sh start org.apache.spark.deploy.master.Master 1 --ip $SPARK_MASTER_IP --port $SPARK_MASTER_PORT --webui-port $SPARK_MASTER_WEBUI_PORT +"$sbin"/spark-daemon.sh start org.apache.spark.deploy.master.Master 1 \ + --ip $SPARK_MASTER_IP --port $SPARK_MASTER_PORT --webui-port $SPARK_MASTER_WEBUI_PORT \ + $ORIGINAL_ARGS if [ "$START_TACHYON" == "true" ]; then "$sbin"/../tachyon/bin/tachyon bootstrap-conf $SPARK_MASTER_IP diff --git a/scalastyle-config.xml b/scalastyle-config.xml index 7168d5b2a8e26..d6f927b6fa803 100644 --- a/scalastyle-config.xml +++ b/scalastyle-config.xml @@ -14,25 +14,41 @@ ~ See the License for the specific language governing permissions and ~ limitations under the License. --> - - - - - - + - Scalastyle standard configuration - - - - - - - - - Scalastyle standard configuration + + + + + + + + + + - - - - - - - - - - true - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ARROW, EQUALS, ELSE, TRY, CATCH, FINALLY, LARROW, RARROW + + + + + + ARROW, EQUALS, COMMA, COLON, IF, ELSE, DO, WHILE, FOR, MATCH, TRY, CATCH, FINALLY, LARROW, RARROW + + + + + + + + + ^FunSuite[A-Za-z]*$ + Tests must extend org.apache.spark.SparkFunSuite instead. + + + + + + + + + ^println$ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 800> + + + + + 30 + + + + + 10 + + + + + 50 + + + + + + + + + + + -1,0,1,2,3 + + diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml index 5c322d032d474..f4b1cc3a4ffe7 100644 --- a/sql/catalyst/pom.xml +++ b/sql/catalyst/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -36,10 +36,6 @@ - - org.scala-lang - scala-compiler - org.scala-lang scala-reflect @@ -50,6 +46,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.spark spark-unsafe_${scala.binary.version} @@ -60,6 +63,11 @@ scalacheck_${scala.binary.version} test + + org.codehaus.janino + janino + 2.7.8 + target/scala-${scala.binary.version}/classes @@ -101,13 +109,6 @@ !scala-2.11 - - - org.scalamacros - quasiquotes_${scala.binary.version} - ${scala.macros.version} - - diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseMutableRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseMutableRow.java new file mode 100644 index 0000000000000..acec2bf4520f2 --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseMutableRow.java @@ -0,0 +1,68 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql; + +import org.apache.spark.sql.catalyst.expressions.MutableRow; + +public abstract class BaseMutableRow extends BaseRow implements MutableRow { + + @Override + public void update(int ordinal, Object value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setInt(int ordinal, int value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setLong(int ordinal, long value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setDouble(int ordinal, double value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setBoolean(int ordinal, boolean value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setShort(int ordinal, short value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setByte(int ordinal, byte value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setFloat(int ordinal, float value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setString(int ordinal, String value) { + throw new UnsupportedOperationException(); + } +} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java new file mode 100644 index 0000000000000..611e02d8fb666 --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java @@ -0,0 +1,218 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql; + +import java.math.BigDecimal; +import java.sql.Date; +import java.sql.Timestamp; +import java.util.List; + +import scala.collection.Seq; +import scala.collection.mutable.ArraySeq; + +import org.apache.spark.sql.catalyst.InternalRow; +import org.apache.spark.sql.catalyst.expressions.GenericRow; +import org.apache.spark.sql.types.StructType; + +public abstract class BaseRow extends InternalRow { + + @Override + final public int length() { + return size(); + } + + @Override + public boolean anyNull() { + final int n = size(); + for (int i=0; i < n; i++) { + if (isNullAt(i)) { + return true; + } + } + return false; + } + + @Override + public StructType schema() { throw new UnsupportedOperationException(); } + + @Override + final public Object apply(int i) { + return get(i); + } + + @Override + public int getInt(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public long getLong(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public float getFloat(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public double getDouble(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public byte getByte(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public short getShort(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public boolean getBoolean(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public String getString(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public BigDecimal getDecimal(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Date getDate(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Timestamp getTimestamp(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Seq getSeq(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public List getList(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public scala.collection.Map getMap(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public scala.collection.immutable.Map getValuesMap(Seq fieldNames) { + throw new UnsupportedOperationException(); + } + + @Override + public java.util.Map getJavaMap(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Row getStruct(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public T getAs(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public T getAs(String fieldName) { + throw new UnsupportedOperationException(); + } + + @Override + public int fieldIndex(String name) { + throw new UnsupportedOperationException(); + } + + /** + * A generic version of Row.equals(Row), which is used for tests. + */ + @Override + public boolean equals(Object other) { + if (other instanceof Row) { + Row row = (Row) other; + int n = size(); + if (n != row.size()) { + return false; + } + for (int i = 0; i < n; i ++) { + if (isNullAt(i) != row.isNullAt(i) || (!isNullAt(i) && !get(i).equals(row.get(i)))) { + return false; + } + } + return true; + } + return false; + } + + @Override + public InternalRow copy() { + final int n = size(); + Object[] arr = new Object[n]; + for (int i = 0; i < n; i++) { + arr[i] = get(i); + } + return new GenericRow(arr); + } + + @Override + public Seq toSeq() { + final int n = size(); + final ArraySeq values = new ArraySeq(n); + for (int i = 0; i < n; i++) { + values.update(i, get(i)); + } + return values; + } + + @Override + public String toString() { + return mkString("[", ",", "]"); + } + + @Override + public String mkString() { + return toSeq().mkString(); + } + + @Override + public String mkString(String sep) { + return toSeq().mkString(sep); + } + + @Override + public String mkString(String start, String sep, String end) { + return toSeq().mkString(start, sep, end); + } +} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java index 299ff3728a6d9..f7849ebebc573 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java @@ -20,7 +20,7 @@ import java.util.Arrays; import java.util.Iterator; -import org.apache.spark.sql.Row; +import org.apache.spark.sql.catalyst.InternalRow; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType; import org.apache.spark.unsafe.PlatformDependent; @@ -39,7 +39,7 @@ public final class UnsafeFixedWidthAggregationMap { * An empty aggregation buffer, encoded in UnsafeRow format. When inserting a new key into the * map, we copy this buffer and use it as the value. */ - private final long[] emptyAggregationBuffer; + private final byte[] emptyAggregationBuffer; private final StructType aggregationBufferSchema; @@ -63,10 +63,10 @@ public final class UnsafeFixedWidthAggregationMap { /** * Scratch space that is used when encoding grouping keys into UnsafeRow format. * - * By default, this is a 1MB array, but it will grow as necessary in case larger keys are + * By default, this is a 8 kb array, but it will grow as necessary in case larger keys are * encountered. */ - private long[] groupingKeyConversionScratchSpace = new long[1024 / 8]; + private byte[] groupingKeyConversionScratchSpace = new byte[1024 * 8]; private final boolean enablePerfMetrics; @@ -107,7 +107,7 @@ public static boolean supportsAggregationBufferSchema(StructType schema) { * @param enablePerfMetrics if true, performance metrics will be recorded (has minor perf impact) */ public UnsafeFixedWidthAggregationMap( - Row emptyAggregationBuffer, + InternalRow emptyAggregationBuffer, StructType aggregationBufferSchema, StructType groupingKeySchema, TaskMemoryManager memoryManager, @@ -123,13 +123,13 @@ public UnsafeFixedWidthAggregationMap( } /** - * Convert a Java object row into an UnsafeRow, allocating it into a new long array. + * Convert a Java object row into an UnsafeRow, allocating it into a new byte array. */ - private static long[] convertToUnsafeRow(Row javaRow, StructType schema) { + private static byte[] convertToUnsafeRow(InternalRow javaRow, StructType schema) { final UnsafeRowConverter converter = new UnsafeRowConverter(schema); - final long[] unsafeRow = new long[converter.getSizeRequirement(javaRow)]; - final long writtenLength = - converter.writeRow(javaRow, unsafeRow, PlatformDependent.LONG_ARRAY_OFFSET); + final byte[] unsafeRow = new byte[converter.getSizeRequirement(javaRow)]; + final int writtenLength = + converter.writeRow(javaRow, unsafeRow, PlatformDependent.BYTE_ARRAY_OFFSET); assert (writtenLength == unsafeRow.length): "Size requirement calculation was wrong!"; return unsafeRow; } @@ -138,39 +138,39 @@ private static long[] convertToUnsafeRow(Row javaRow, StructType schema) { * Return the aggregation buffer for the current group. For efficiency, all calls to this method * return the same object. */ - public UnsafeRow getAggregationBuffer(Row groupingKey) { + public UnsafeRow getAggregationBuffer(InternalRow groupingKey) { final int groupingKeySize = groupingKeyToUnsafeRowConverter.getSizeRequirement(groupingKey); // Make sure that the buffer is large enough to hold the key. If it's not, grow it: if (groupingKeySize > groupingKeyConversionScratchSpace.length) { // This new array will be initially zero, so there's no need to zero it out here - groupingKeyConversionScratchSpace = new long[groupingKeySize]; + groupingKeyConversionScratchSpace = new byte[groupingKeySize]; } else { // Zero out the buffer that's used to hold the current row. This is necessary in order // to ensure that rows hash properly, since garbage data from the previous row could // otherwise end up as padding in this row. As a performance optimization, we only zero out // the portion of the buffer that we'll actually write to. - Arrays.fill(groupingKeyConversionScratchSpace, 0, groupingKeySize, 0); + Arrays.fill(groupingKeyConversionScratchSpace, 0, groupingKeySize, (byte) 0); } - final long actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow( + final int actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow( groupingKey, groupingKeyConversionScratchSpace, - PlatformDependent.LONG_ARRAY_OFFSET); + PlatformDependent.BYTE_ARRAY_OFFSET); assert (groupingKeySize == actualGroupingKeySize) : "Size requirement calculation was wrong!"; // Probe our map using the serialized key final BytesToBytesMap.Location loc = map.lookup( groupingKeyConversionScratchSpace, - PlatformDependent.LONG_ARRAY_OFFSET, + PlatformDependent.BYTE_ARRAY_OFFSET, groupingKeySize); if (!loc.isDefined()) { // This is the first time that we've seen this grouping key, so we'll insert a copy of the // empty aggregation buffer into the map: loc.putNewKey( groupingKeyConversionScratchSpace, - PlatformDependent.LONG_ARRAY_OFFSET, + PlatformDependent.BYTE_ARRAY_OFFSET, groupingKeySize, emptyAggregationBuffer, - PlatformDependent.LONG_ARRAY_OFFSET, + PlatformDependent.BYTE_ARRAY_OFFSET, emptyAggregationBuffer.length ); } diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java index bb546b3086b33..ed04d2e50ec84 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java @@ -17,23 +17,25 @@ package org.apache.spark.sql.catalyst.expressions; -import scala.collection.Map; +import javax.annotation.Nullable; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashSet; +import java.util.Set; + import scala.collection.Seq; import scala.collection.mutable.ArraySeq; -import javax.annotation.Nullable; -import java.math.BigDecimal; -import java.sql.Date; -import java.util.*; - -import org.apache.spark.sql.Row; +import org.apache.spark.sql.catalyst.InternalRow; +import org.apache.spark.sql.BaseMutableRow; import org.apache.spark.sql.types.DataType; -import static org.apache.spark.sql.types.DataTypes.*; import org.apache.spark.sql.types.StructType; -import org.apache.spark.sql.types.UTF8String; +import org.apache.spark.unsafe.types.UTF8String; import org.apache.spark.unsafe.PlatformDependent; import org.apache.spark.unsafe.bitset.BitSetMethods; +import static org.apache.spark.sql.types.DataTypes.*; + /** * An Unsafe implementation of Row which is backed by raw memory instead of Java objects. * @@ -49,7 +51,7 @@ * * Instances of `UnsafeRow` act as pointers to row data stored in this format. */ -public final class UnsafeRow implements MutableRow { +public final class UnsafeRow extends BaseMutableRow { private Object baseObject; private long baseOffset; @@ -101,7 +103,9 @@ public static int calculateBitSetWidthInBytes(int numFields) { IntegerType, LongType, FloatType, - DoubleType + DoubleType, + DateType, + TimestampType }))); // We support get() on a superset of the types for which we support set(): @@ -227,21 +231,11 @@ public int size() { return numFields; } - @Override - public int length() { - return size(); - } - @Override public StructType schema() { return schema; } - @Override - public Object apply(int i) { - return get(i); - } - @Override public Object get(int i) { assertIndexIsValid(i); @@ -318,7 +312,6 @@ public double getDouble(int i) { public UTF8String getUTF8String(int i) { assertIndexIsValid(i); - final UTF8String str = new UTF8String(); final long offsetToStringSize = getLong(i); final int stringSizeInBytes = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offsetToStringSize); @@ -330,8 +323,7 @@ public UTF8String getUTF8String(int i) { PlatformDependent.BYTE_ARRAY_OFFSET, stringSizeInBytes ); - str.set(strBytes); - return str; + return UTF8String.fromBytes(strBytes); } @Override @@ -340,62 +332,7 @@ public String getString(int i) { } @Override - public BigDecimal getDecimal(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public Date getDate(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public Seq getSeq(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public List getList(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public Map getMap(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public scala.collection.immutable.Map getValuesMap(Seq fieldNames) { - throw new UnsupportedOperationException(); - } - - @Override - public java.util.Map getJavaMap(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public Row getStruct(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public T getAs(int i) { - throw new UnsupportedOperationException(); - } - - @Override - public T getAs(String fieldName) { - throw new UnsupportedOperationException(); - } - - @Override - public int fieldIndex(String name) { - throw new UnsupportedOperationException(); - } - - @Override - public Row copy() { + public InternalRow copy() { throw new UnsupportedOperationException(); } @@ -412,24 +349,4 @@ public Seq toSeq() { } return values; } - - @Override - public String toString() { - return mkString("[", ",", "]"); - } - - @Override - public String mkString() { - return toSeq().mkString(); - } - - @Override - public String mkString(String sep) { - return toSeq().mkString(sep); - } - - @Override - public String mkString(String start, String sep, String end) { - return toSeq().mkString(start, sep, end); - } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala index 4190b7ffe1c8f..8aaf5d7d89154 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala @@ -55,6 +55,9 @@ object Row { // TODO: Improve the performance of this if used in performance critical part. new GenericRow(rows.flatMap(_.toSeq).toArray) } + + /** Returns an empty row. */ + val empty = apply() } @@ -257,9 +260,15 @@ trait Row extends Serializable { * * @throws ClassCastException when data type does not match. */ - // TODO(davies): This is not the right default implementation, we use Int as Date internally def getDate(i: Int): java.sql.Date = apply(i).asInstanceOf[java.sql.Date] + /** + * Returns the value at position i of date type as java.sql.Timestamp. + * + * @throws ClassCastException when data type does not match. + */ + def getTimestamp(i: Int): java.sql.Timestamp = apply(i).asInstanceOf[java.sql.Timestamp] + /** * Returns the value at position i of array type as a Scala Seq. * diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 2eb3e167baad5..ef7b3ad9432cf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -103,7 +103,7 @@ class SqlLexical extends StdLexical { ( identChar ~ (identChar | digit).* ^^ { case first ~ rest => processIdent((first :: rest).mkString) } | rep1(digit) ~ ('.' ~> digit.*).? ^^ { - case i ~ None => NumericLit(i.mkString) + case i ~ None => NumericLit(i.mkString) case i ~ Some(d) => FloatLit(i.mkString + "." + d.mkString) } | '\'' ~> chrExcept('\'', '\n', EofCh).* <~ '\'' ^^ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala similarity index 62% rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala index 977003493d471..3f351b07b37df 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala @@ -17,17 +17,19 @@ package org.apache.spark.sql.catalyst -import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +private[spark] trait CatalystConf { + def caseSensitiveAnalysis: Boolean +} /** - * Root class of SQL Parser Dialect, and we don't guarantee the binary - * compatibility for the future release, let's keep it as the internal - * interface for advanced user. - * + * A trivial conf that is empty. Used for testing when all + * relations are already filled in and the analyser needs only to resolve attribute references. */ -@DeveloperApi -abstract class Dialect { - // this is the main function that will be implemented by sql parser. - def parse(sqlText: String): LogicalPlan +object EmptyConf extends CatalystConf { + override def caseSensitiveAnalysis: Boolean = { + throw new UnsupportedOperationException + } } + +/** A CatalystConf that can be used for local testing. */ +case class SimpleCatalystConf(caseSensitiveAnalysis: Boolean) extends CatalystConf diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala index a13e2f36a1a1f..620e8de83a96c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala @@ -18,12 +18,18 @@ package org.apache.spark.sql.catalyst import java.lang.{Iterable => JavaIterable} +import java.math.{BigDecimal => JavaBigDecimal} +import java.sql.{Timestamp, Date} import java.util.{Map => JavaMap} +import javax.annotation.Nullable import scala.collection.mutable.HashMap import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.util.DateUtils +import org.apache.spark.sql.Row import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /** * Functions to convert Scala types to Catalyst types and vice versa. @@ -33,237 +39,357 @@ object CatalystTypeConverters { // Since the map values can be mutable, we explicitly import scala.collection.Map at here. import scala.collection.Map + private def isPrimitive(dataType: DataType): Boolean = { + dataType match { + case BooleanType => true + case ByteType => true + case ShortType => true + case IntegerType => true + case LongType => true + case FloatType => true + case DoubleType => true + case _ => false + } + } + + private def getConverterForType(dataType: DataType): CatalystTypeConverter[Any, Any, Any] = { + val converter = dataType match { + case udt: UserDefinedType[_] => UDTConverter(udt) + case arrayType: ArrayType => ArrayConverter(arrayType.elementType) + case mapType: MapType => MapConverter(mapType.keyType, mapType.valueType) + case structType: StructType => StructConverter(structType) + case StringType => StringConverter + case DateType => DateConverter + case TimestampType => TimestampConverter + case dt: DecimalType => BigDecimalConverter + case BooleanType => BooleanConverter + case ByteType => ByteConverter + case ShortType => ShortConverter + case IntegerType => IntConverter + case LongType => LongConverter + case FloatType => FloatConverter + case DoubleType => DoubleConverter + case _ => IdentityConverter + } + converter.asInstanceOf[CatalystTypeConverter[Any, Any, Any]] + } + /** - * Converts Scala objects to catalyst rows / types. This method is slow, and for batch - * conversion you should be using converter produced by createToCatalystConverter. - * Note: This is always called after schemaFor has been called. - * This ordering is important for UDT registration. + * Converts a Scala type to its Catalyst equivalent (and vice versa). + * + * @tparam ScalaInputType The type of Scala values that can be converted to Catalyst. + * @tparam ScalaOutputType The type of Scala values returned when converting Catalyst to Scala. + * @tparam CatalystType The internal Catalyst type used to represent values of this Scala type. */ - def convertToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { - // Check UDT first since UDTs can override other types - case (obj, udt: UserDefinedType[_]) => - udt.serialize(obj) - - case (o: Option[_], _) => - o.map(convertToCatalyst(_, dataType)).orNull - - case (s: Seq[_], arrayType: ArrayType) => - s.map(convertToCatalyst(_, arrayType.elementType)) - - case (jit: JavaIterable[_], arrayType: ArrayType) => { - val iter = jit.iterator - var listOfItems: List[Any] = List() - while (iter.hasNext) { - val item = iter.next() - listOfItems :+= convertToCatalyst(item, arrayType.elementType) + private abstract class CatalystTypeConverter[ScalaInputType, ScalaOutputType, CatalystType] + extends Serializable { + + /** + * Converts a Scala type to its Catalyst equivalent while automatically handling nulls + * and Options. + */ + final def toCatalyst(@Nullable maybeScalaValue: Any): CatalystType = { + if (maybeScalaValue == null) { + null.asInstanceOf[CatalystType] + } else if (maybeScalaValue.isInstanceOf[Option[ScalaInputType]]) { + val opt = maybeScalaValue.asInstanceOf[Option[ScalaInputType]] + if (opt.isDefined) { + toCatalystImpl(opt.get) + } else { + null.asInstanceOf[CatalystType] + } + } else { + toCatalystImpl(maybeScalaValue.asInstanceOf[ScalaInputType]) } - listOfItems } - case (s: Array[_], arrayType: ArrayType) => - s.toSeq.map(convertToCatalyst(_, arrayType.elementType)) + /** + * Given a Catalyst row, convert the value at column `column` to its Scala equivalent. + */ + final def toScala(row: InternalRow, column: Int): ScalaOutputType = { + if (row.isNullAt(column)) null.asInstanceOf[ScalaOutputType] else toScalaImpl(row, column) + } - case (m: Map[_, _], mapType: MapType) => - m.map { case (k, v) => - convertToCatalyst(k, mapType.keyType) -> convertToCatalyst(v, mapType.valueType) - } + /** + * Convert a Catalyst value to its Scala equivalent. + */ + def toScala(@Nullable catalystValue: CatalystType): ScalaOutputType + + /** + * Converts a Scala value to its Catalyst equivalent. + * @param scalaValue the Scala value, guaranteed not to be null. + * @return the Catalyst value. + */ + protected def toCatalystImpl(scalaValue: ScalaInputType): CatalystType + + /** + * Given a Catalyst row, convert the value at column `column` to its Scala equivalent. + * This method will only be called on non-null columns. + */ + protected def toScalaImpl(row: InternalRow, column: Int): ScalaOutputType + } + + private object IdentityConverter extends CatalystTypeConverter[Any, Any, Any] { + override def toCatalystImpl(scalaValue: Any): Any = scalaValue + override def toScala(catalystValue: Any): Any = catalystValue + override def toScalaImpl(row: InternalRow, column: Int): Any = row(column) + } + + private case class UDTConverter( + udt: UserDefinedType[_]) extends CatalystTypeConverter[Any, Any, Any] { + override def toCatalystImpl(scalaValue: Any): Any = udt.serialize(scalaValue) + override def toScala(catalystValue: Any): Any = udt.deserialize(catalystValue) + override def toScalaImpl(row: InternalRow, column: Int): Any = toScala(row(column)) + } - case (jmap: JavaMap[_, _], mapType: MapType) => - val iter = jmap.entrySet.iterator - var listOfEntries: List[(Any, Any)] = List() - while (iter.hasNext) { - val entry = iter.next() - listOfEntries :+= (convertToCatalyst(entry.getKey, mapType.keyType), - convertToCatalyst(entry.getValue, mapType.valueType)) + /** Converter for arrays, sequences, and Java iterables. */ + private case class ArrayConverter( + elementType: DataType) extends CatalystTypeConverter[Any, Seq[Any], Seq[Any]] { + + private[this] val elementConverter = getConverterForType(elementType) + + override def toCatalystImpl(scalaValue: Any): Seq[Any] = { + scalaValue match { + case a: Array[_] => a.toSeq.map(elementConverter.toCatalyst) + case s: Seq[_] => s.map(elementConverter.toCatalyst) + case i: JavaIterable[_] => + val iter = i.iterator + var convertedIterable: List[Any] = List() + while (iter.hasNext) { + val item = iter.next() + convertedIterable :+= elementConverter.toCatalyst(item) + } + convertedIterable } - listOfEntries.toMap - - case (p: Product, structType: StructType) => - val ar = new Array[Any](structType.size) - val iter = p.productIterator - var idx = 0 - while (idx < structType.size) { - ar(idx) = convertToCatalyst(iter.next(), structType.fields(idx).dataType) - idx += 1 + } + + override def toScala(catalystValue: Seq[Any]): Seq[Any] = { + if (catalystValue == null) { + null + } else { + catalystValue.asInstanceOf[Seq[_]].map(elementConverter.toScala) } - new GenericRowWithSchema(ar, structType) + } - case (d: String, _) => - UTF8String(d) + override def toScalaImpl(row: InternalRow, column: Int): Seq[Any] = + toScala(row(column).asInstanceOf[Seq[Any]]) + } - case (d: BigDecimal, _) => - Decimal(d) + private case class MapConverter( + keyType: DataType, + valueType: DataType) + extends CatalystTypeConverter[Any, Map[Any, Any], Map[Any, Any]] { - case (d: java.math.BigDecimal, _) => - Decimal(d) + private[this] val keyConverter = getConverterForType(keyType) + private[this] val valueConverter = getConverterForType(valueType) - case (d: java.sql.Date, _) => - DateUtils.fromJavaDate(d) + override def toCatalystImpl(scalaValue: Any): Map[Any, Any] = scalaValue match { + case m: Map[_, _] => + m.map { case (k, v) => + keyConverter.toCatalyst(k) -> valueConverter.toCatalyst(v) + } - case (r: Row, structType: StructType) => - val converters = structType.fields.map { - f => (item: Any) => convertToCatalyst(item, f.dataType) + case jmap: JavaMap[_, _] => + val iter = jmap.entrySet.iterator + val convertedMap: HashMap[Any, Any] = HashMap() + while (iter.hasNext) { + val entry = iter.next() + val key = keyConverter.toCatalyst(entry.getKey) + convertedMap(key) = valueConverter.toCatalyst(entry.getValue) + } + convertedMap + } + + override def toScala(catalystValue: Map[Any, Any]): Map[Any, Any] = { + if (catalystValue == null) { + null + } else { + catalystValue.map { case (k, v) => + keyConverter.toScala(k) -> valueConverter.toScala(v) + } } - convertRowWithConverters(r, structType, converters) + } - case (other, _) => - other + override def toScalaImpl(row: InternalRow, column: Int): Map[Any, Any] = + toScala(row(column).asInstanceOf[Map[Any, Any]]) } - /** - * Creates a converter function that will convert Scala objects to the specified catalyst type. - * Typical use case would be converting a collection of rows that have the same schema. You will - * call this function once to get a converter, and apply it to every row. - */ - private[sql] def createToCatalystConverter(dataType: DataType): Any => Any = { - def extractOption(item: Any): Any = item match { - case opt: Option[_] => opt.orNull - case other => other - } + private case class StructConverter( + structType: StructType) extends CatalystTypeConverter[Any, Row, InternalRow] { - dataType match { - // Check UDT first since UDTs can override other types - case udt: UserDefinedType[_] => - (item) => extractOption(item) match { - case null => null - case other => udt.serialize(other) - } + private[this] val converters = structType.fields.map { f => getConverterForType(f.dataType) } - case arrayType: ArrayType => - val elementConverter = createToCatalystConverter(arrayType.elementType) - (item: Any) => { - extractOption(item) match { - case a: Array[_] => a.toSeq.map(elementConverter) - case s: Seq[_] => s.map(elementConverter) - case i: JavaIterable[_] => { - val iter = i.iterator - var convertedIterable: List[Any] = List() - while (iter.hasNext) { - val item = iter.next() - convertedIterable :+= elementConverter(item) - } - convertedIterable - } - case null => null - } + override def toCatalystImpl(scalaValue: Any): InternalRow = scalaValue match { + case row: Row => + val ar = new Array[Any](row.size) + var idx = 0 + while (idx < row.size) { + ar(idx) = converters(idx).toCatalyst(row(idx)) + idx += 1 } - - case mapType: MapType => - val keyConverter = createToCatalystConverter(mapType.keyType) - val valueConverter = createToCatalystConverter(mapType.valueType) - (item: Any) => { - extractOption(item) match { - case m: Map[_, _] => - m.map { case (k, v) => - keyConverter(k) -> valueConverter(v) - } - - case jmap: JavaMap[_, _] => - val iter = jmap.entrySet.iterator - val convertedMap: HashMap[Any, Any] = HashMap() - while (iter.hasNext) { - val entry = iter.next() - convertedMap(keyConverter(entry.getKey)) = valueConverter(entry.getValue) - } - convertedMap - - case null => null - } + new GenericRowWithSchema(ar, structType) + + case p: Product => + val ar = new Array[Any](structType.size) + val iter = p.productIterator + var idx = 0 + while (idx < structType.size) { + ar(idx) = converters(idx).toCatalyst(iter.next()) + idx += 1 } + new GenericRowWithSchema(ar, structType) + } - case structType: StructType => - val converters = structType.fields.map(f => createToCatalystConverter(f.dataType)) - (item: Any) => { - extractOption(item) match { - case r: Row => - convertRowWithConverters(r, structType, converters) - - case p: Product => - val ar = new Array[Any](structType.size) - val iter = p.productIterator - var idx = 0 - while (idx < structType.size) { - ar(idx) = converters(idx)(iter.next()) - idx += 1 - } - new GenericRowWithSchema(ar, structType) - - case null => - null - } + override def toScala(row: InternalRow): Row = { + if (row == null) { + null + } else { + val ar = new Array[Any](row.size) + var idx = 0 + while (idx < row.size) { + ar(idx) = converters(idx).toScala(row, idx) + idx += 1 } - - case dateType: DateType => (item: Any) => extractOption(item) match { - case d: java.sql.Date => DateUtils.fromJavaDate(d) - case other => other + new GenericRowWithSchema(ar, structType) } + } + + override def toScalaImpl(row: InternalRow, column: Int): Row = + toScala(row(column).asInstanceOf[InternalRow]) + } + + private object StringConverter extends CatalystTypeConverter[Any, String, Any] { + override def toCatalystImpl(scalaValue: Any): UTF8String = scalaValue match { + case str: String => UTF8String.fromString(str) + case utf8: UTF8String => utf8 + } + override def toScala(catalystValue: Any): String = catalystValue match { + case null => null + case str: String => str + case utf8: UTF8String => utf8.toString() + } + override def toScalaImpl(row: InternalRow, column: Int): String = row(column).toString + } + + private object DateConverter extends CatalystTypeConverter[Date, Date, Any] { + override def toCatalystImpl(scalaValue: Date): Int = DateUtils.fromJavaDate(scalaValue) + override def toScala(catalystValue: Any): Date = + if (catalystValue == null) null else DateUtils.toJavaDate(catalystValue.asInstanceOf[Int]) + override def toScalaImpl(row: InternalRow, column: Int): Date = toScala(row.getInt(column)) + } + + private object TimestampConverter extends CatalystTypeConverter[Timestamp, Timestamp, Any] { + override def toCatalystImpl(scalaValue: Timestamp): Long = + DateUtils.fromJavaTimestamp(scalaValue) + override def toScala(catalystValue: Any): Timestamp = + if (catalystValue == null) null + else DateUtils.toJavaTimestamp(catalystValue.asInstanceOf[Long]) + override def toScalaImpl(row: InternalRow, column: Int): Timestamp = + toScala(row.getLong(column)) + } - case dataType: StringType => (item: Any) => extractOption(item) match { - case s: String => UTF8String(s) - case other => other + private object BigDecimalConverter extends CatalystTypeConverter[Any, JavaBigDecimal, Decimal] { + override def toCatalystImpl(scalaValue: Any): Decimal = scalaValue match { + case d: BigDecimal => Decimal(d) + case d: JavaBigDecimal => Decimal(d) + case d: Decimal => d + } + override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal + override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal = + row.get(column) match { + case d: JavaBigDecimal => d + case d: Decimal => d.toJavaBigDecimal } + } + + private abstract class PrimitiveConverter[T] extends CatalystTypeConverter[T, Any, Any] { + final override def toScala(catalystValue: Any): Any = catalystValue + final override def toCatalystImpl(scalaValue: T): Any = scalaValue + } + + private object BooleanConverter extends PrimitiveConverter[Boolean] { + override def toScalaImpl(row: InternalRow, column: Int): Boolean = row.getBoolean(column) + } + + private object ByteConverter extends PrimitiveConverter[Byte] { + override def toScalaImpl(row: InternalRow, column: Int): Byte = row.getByte(column) + } + + private object ShortConverter extends PrimitiveConverter[Short] { + override def toScalaImpl(row: InternalRow, column: Int): Short = row.getShort(column) + } + + private object IntConverter extends PrimitiveConverter[Int] { + override def toScalaImpl(row: InternalRow, column: Int): Int = row.getInt(column) + } + + private object LongConverter extends PrimitiveConverter[Long] { + override def toScalaImpl(row: InternalRow, column: Int): Long = row.getLong(column) + } - case _ => - (item: Any) => extractOption(item) match { - case d: BigDecimal => Decimal(d) - case d: java.math.BigDecimal => Decimal(d) - case other => other + private object FloatConverter extends PrimitiveConverter[Float] { + override def toScalaImpl(row: InternalRow, column: Int): Float = row.getFloat(column) + } + + private object DoubleConverter extends PrimitiveConverter[Double] { + override def toScalaImpl(row: InternalRow, column: Int): Double = row.getDouble(column) + } + + /** + * Creates a converter function that will convert Scala objects to the specified Catalyst type. + * Typical use case would be converting a collection of rows that have the same schema. You will + * call this function once to get a converter, and apply it to every row. + */ + private[sql] def createToCatalystConverter(dataType: DataType): Any => Any = { + if (isPrimitive(dataType)) { + // Although the `else` branch here is capable of handling inbound conversion of primitives, + // we add some special-case handling for those types here. The motivation for this relates to + // Java method invocation costs: if we have rows that consist entirely of primitive columns, + // then returning the same conversion function for all of the columns means that the call site + // will be monomorphic instead of polymorphic. In microbenchmarks, this actually resulted in + // a measurable performance impact. Note that this optimization will be unnecessary if we + // use code generation to construct Scala Row -> Catalyst Row converters. + def convert(maybeScalaValue: Any): Any = { + if (maybeScalaValue.isInstanceOf[Option[Any]]) { + maybeScalaValue.asInstanceOf[Option[Any]].orNull + } else { + maybeScalaValue } + } + convert + } else { + getConverterForType(dataType).toCatalyst } } /** - * Converts Scala objects to catalyst rows / types. + * Converts Scala objects to Catalyst rows / types. * * Note: This should be called before do evaluation on Row * (It does not support UDT) * This is used to create an RDD or test results with correct types for Catalyst. */ def convertToCatalyst(a: Any): Any = a match { - case s: String => UTF8String(s) - case d: java.sql.Date => DateUtils.fromJavaDate(d) - case d: BigDecimal => Decimal(d) - case d: java.math.BigDecimal => Decimal(d) + case s: String => StringConverter.toCatalyst(s) + case d: Date => DateConverter.toCatalyst(d) + case t: Timestamp => TimestampConverter.toCatalyst(t) + case d: BigDecimal => BigDecimalConverter.toCatalyst(d) + case d: JavaBigDecimal => BigDecimalConverter.toCatalyst(d) case seq: Seq[Any] => seq.map(convertToCatalyst) - case r: Row => Row(r.toSeq.map(convertToCatalyst): _*) + case r: Row => InternalRow(r.toSeq.map(convertToCatalyst): _*) case arr: Array[Any] => arr.toSeq.map(convertToCatalyst).toArray case m: Map[Any, Any] => m.map { case (k, v) => (convertToCatalyst(k), convertToCatalyst(v)) }.toMap case other => other } - /** + /** * Converts Catalyst types used internally in rows to standard Scala types * This method is slow, and for batch conversion you should be using converter * produced by createToScalaConverter. */ - def convertToScala(a: Any, dataType: DataType): Any = (a, dataType) match { - // Check UDT first since UDTs can override other types - case (d, udt: UserDefinedType[_]) => - udt.deserialize(d) - - case (s: Seq[_], arrayType: ArrayType) => - s.map(convertToScala(_, arrayType.elementType)) - - case (m: Map[_, _], mapType: MapType) => - m.map { case (k, v) => - convertToScala(k, mapType.keyType) -> convertToScala(v, mapType.valueType) - } - - case (r: Row, s: StructType) => - convertRowToScala(r, s) - - case (d: Decimal, _: DecimalType) => - d.toJavaBigDecimal - - case (i: Int, DateType) => - DateUtils.toJavaDate(i) - - case (s: UTF8String, StringType) => - s.toString() - - case (other, _) => - other + def convertToScala(catalystValue: Any, dataType: DataType): Any = { + getConverterForType(dataType).toScala(catalystValue) } /** @@ -271,82 +397,7 @@ object CatalystTypeConverters { * Typical use case would be converting a collection of rows that have the same schema. You will * call this function once to get a converter, and apply it to every row. */ - private[sql] def createToScalaConverter(dataType: DataType): Any => Any = dataType match { - // Check UDT first since UDTs can override other types - case udt: UserDefinedType[_] => - (item: Any) => if (item == null) null else udt.deserialize(item) - - case arrayType: ArrayType => - val elementConverter = createToScalaConverter(arrayType.elementType) - (item: Any) => if (item == null) null else item.asInstanceOf[Seq[_]].map(elementConverter) - - case mapType: MapType => - val keyConverter = createToScalaConverter(mapType.keyType) - val valueConverter = createToScalaConverter(mapType.valueType) - (item: Any) => if (item == null) { - null - } else { - item.asInstanceOf[Map[_, _]].map { case (k, v) => - keyConverter(k) -> valueConverter(v) - } - } - - case s: StructType => - val converters = s.fields.map(f => createToScalaConverter(f.dataType)) - (item: Any) => { - if (item == null) { - null - } else { - convertRowWithConverters(item.asInstanceOf[Row], s, converters) - } - } - - case _: DecimalType => - (item: Any) => item match { - case d: Decimal => d.toJavaBigDecimal - case other => other - } - - case DateType => - (item: Any) => item match { - case i: Int => DateUtils.toJavaDate(i) - case other => other - } - - case StringType => - (item: Any) => item match { - case s: UTF8String => s.toString() - case other => other - } - - case other => - (item: Any) => item - } - - def convertRowToScala(r: Row, schema: StructType): Row = { - val ar = new Array[Any](r.size) - var idx = 0 - while (idx < r.size) { - ar(idx) = convertToScala(r(idx), schema.fields(idx).dataType) - idx += 1 - } - new GenericRowWithSchema(ar, schema) - } - - /** - * Converts a row by applying the provided set of converter functions. It is used for both - * toScala and toCatalyst conversions. - */ - private[sql] def convertRowWithConverters( - row: Row, - schema: StructType, - converters: Array[Any => Any]): Row = { - val ar = new Array[Any](row.size) - var idx = 0 - while (idx < row.size) { - ar(idx) = converters(idx)(row(idx)) - idx += 1 - } - new GenericRowWithSchema(ar, schema) + private[sql] def createToScalaConverter(dataType: DataType): Any => Any = { + getConverterForType(dataType).toScala } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala new file mode 100644 index 0000000000000..e3c2cc243310b --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala @@ -0,0 +1,57 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions.GenericRow + +/** + * An abstract class for row used internal in Spark SQL, which only contain the columns as + * internal types. + */ +abstract class InternalRow extends Row { + // A default implementation to change the return type + override def copy(): InternalRow = {this} +} + +object InternalRow { + def unapplySeq(row: InternalRow): Some[Seq[Any]] = Some(row.toSeq) + + /** + * This method can be used to construct a [[Row]] with the given values. + */ + def apply(values: Any*): InternalRow = new GenericRow(values.toArray) + + /** + * This method can be used to construct a [[Row]] from a [[Seq]] of values. + */ + def fromSeq(values: Seq[Any]): InternalRow = new GenericRow(values.toArray) + + def fromTuple(tuple: Product): InternalRow = fromSeq(tuple.productIterator.toSeq) + + /** + * Merge multiple rows into a single row, one after another. + */ + def merge(rows: InternalRow*): InternalRow = { + // TODO: Improve the performance of this if used in performance critical part. + new GenericRow(rows.flatMap(_.toSeq).toArray) + } + + /** Returns an empty row. */ + val empty = apply() +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala similarity index 93% rename from sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala index db484c5f50074..9a3f9694e4c48 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala @@ -15,18 +15,17 @@ * limitations under the License. */ -package org.apache.spark.sql +package org.apache.spark.sql.catalyst import java.beans.Introspector import java.lang.{Iterable => JIterable} import java.util.{Iterator => JIterator, Map => JMap} -import com.google.common.reflect.TypeToken +import scala.language.existentials +import com.google.common.reflect.TypeToken import org.apache.spark.sql.types._ -import scala.language.existentials - /** * Type-inference utilities for POJOs and Java collections. */ @@ -39,12 +38,21 @@ private [sql] object JavaTypeInference { private val keySetReturnType = classOf[JMap[_, _]].getMethod("keySet").getGenericReturnType private val valuesReturnType = classOf[JMap[_, _]].getMethod("values").getGenericReturnType + /** + * Infers the corresponding SQL data type of a JavaClean class. + * @param beanClass Java type + * @return (SQL data type, nullable) + */ + def inferDataType(beanClass: Class[_]): (DataType, Boolean) = { + inferDataType(TypeToken.of(beanClass)) + } + /** * Infers the corresponding SQL data type of a Java type. * @param typeToken Java type * @return (SQL data type, nullable) */ - private [sql] def inferDataType(typeToken: TypeToken[_]): (DataType, Boolean) = { + private def inferDataType(typeToken: TypeToken[_]): (DataType, Boolean) = { // TODO: All of this could probably be moved to Catalyst as it is mostly not Spark specific. typeToken.getRawType match { case c: Class[_] if c.isAnnotationPresent(classOf[SQLUserDefinedType]) => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ParserDialect.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ParserDialect.scala new file mode 100644 index 0000000000000..554fb4eb25eb1 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ParserDialect.scala @@ -0,0 +1,69 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan + +/** + * Root class of SQL Parser Dialect, and we don't guarantee the binary + * compatibility for the future release, let's keep it as the internal + * interface for advanced user. + * + */ +@DeveloperApi +abstract class ParserDialect { + // this is the main function that will be implemented by sql parser. + def parse(sqlText: String): LogicalPlan +} + +/** + * Currently we support the default dialect named "sql", associated with the class + * [[DefaultParserDialect]] + * + * And we can also provide custom SQL Dialect, for example in Spark SQL CLI: + * {{{ + *-- switch to "hiveql" dialect + * spark-sql>SET spark.sql.dialect=hiveql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- switch to "sql" dialect + * spark-sql>SET spark.sql.dialect=sql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- register the new SQL dialect + * spark-sql> SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- register the non-exist SQL dialect + * spark-sql> SET spark.sql.dialect=NotExistedClass; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- Exception will be thrown and switch to dialect + *-- "sql" (for SQLContext) or + *-- "hiveql" (for HiveContext) + * }}} + */ +private[spark] class DefaultParserDialect extends ParserDialect { + @transient + protected val sqlParser = new SqlParser + + override def parse(sqlText: String): LogicalPlan = { + sqlParser.parse(sqlText) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 6998cc8d9666d..90698cd572de4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.catalyst +import org.apache.spark.unsafe.types.UTF8String import org.apache.spark.util.Utils import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.LocalRelation diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index fc36b9f1f20d2..da3a717f90058 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst import scala.language.implicitConversions +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ @@ -48,31 +49,25 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword` // properties via reflection the class in runtime for constructing the SqlLexical object - protected val ABS = Keyword("ABS") protected val ALL = Keyword("ALL") protected val AND = Keyword("AND") protected val APPROXIMATE = Keyword("APPROXIMATE") protected val AS = Keyword("AS") protected val ASC = Keyword("ASC") - protected val AVG = Keyword("AVG") protected val BETWEEN = Keyword("BETWEEN") protected val BY = Keyword("BY") protected val CASE = Keyword("CASE") protected val CAST = Keyword("CAST") - protected val COALESCE = Keyword("COALESCE") - protected val COUNT = Keyword("COUNT") protected val DESC = Keyword("DESC") protected val DISTINCT = Keyword("DISTINCT") protected val ELSE = Keyword("ELSE") protected val END = Keyword("END") protected val EXCEPT = Keyword("EXCEPT") protected val FALSE = Keyword("FALSE") - protected val FIRST = Keyword("FIRST") protected val FROM = Keyword("FROM") protected val FULL = Keyword("FULL") protected val GROUP = Keyword("GROUP") protected val HAVING = Keyword("HAVING") - protected val IF = Keyword("IF") protected val IN = Keyword("IN") protected val INNER = Keyword("INNER") protected val INSERT = Keyword("INSERT") @@ -80,13 +75,9 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected val INTO = Keyword("INTO") protected val IS = Keyword("IS") protected val JOIN = Keyword("JOIN") - protected val LAST = Keyword("LAST") protected val LEFT = Keyword("LEFT") protected val LIKE = Keyword("LIKE") protected val LIMIT = Keyword("LIMIT") - protected val LOWER = Keyword("LOWER") - protected val MAX = Keyword("MAX") - protected val MIN = Keyword("MIN") protected val NOT = Keyword("NOT") protected val NULL = Keyword("NULL") protected val ON = Keyword("ON") @@ -100,15 +91,10 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected val RLIKE = Keyword("RLIKE") protected val SELECT = Keyword("SELECT") protected val SEMI = Keyword("SEMI") - protected val SQRT = Keyword("SQRT") - protected val SUBSTR = Keyword("SUBSTR") - protected val SUBSTRING = Keyword("SUBSTRING") - protected val SUM = Keyword("SUM") protected val TABLE = Keyword("TABLE") protected val THEN = Keyword("THEN") protected val TRUE = Keyword("TRUE") protected val UNION = Keyword("UNION") - protected val UPPER = Keyword("UPPER") protected val WHEN = Keyword("WHEN") protected val WHERE = Keyword("WHERE") protected val WITH = Keyword("WITH") @@ -140,7 +126,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { (HAVING ~> expression).? ~ sortType.? ~ (LIMIT ~> expression).? ^^ { - case d ~ p ~ r ~ f ~ g ~ h ~ o ~ l => + case d ~ p ~ r ~ f ~ g ~ h ~ o ~ l => val base = r.getOrElse(OneRowRelation) val withFilter = f.map(Filter(_, base)).getOrElse(base) val withProjection = g @@ -212,7 +198,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected lazy val ordering: Parser[Seq[SortOrder]] = ( rep1sep(expression ~ direction.? , ",") ^^ { - case exps => exps.map(pair => SortOrder(pair._1, pair._2.getOrElse(Ascending))) + case exps => exps.map(pair => SortOrder(pair._1, pair._2.getOrElse(Ascending))) } ) @@ -242,7 +228,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { | termExpression ~ NOT.? ~ (BETWEEN ~> termExpression) ~ (AND ~> termExpression) ^^ { case e ~ not ~ el ~ eu => val betweenExpr: Expression = And(GreaterThanOrEqual(e, el), LessThanOrEqual(e, eu)) - not.fold(betweenExpr)(f=> Not(betweenExpr)) + not.fold(betweenExpr)(f => Not(betweenExpr)) } | termExpression ~ (RLIKE ~> termExpression) ^^ { case e1 ~ e2 => RLike(e1, e2) } | termExpression ~ (REGEXP ~> termExpression) ^^ { case e1 ~ e2 => RLike(e1, e2) } @@ -277,25 +263,37 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { ) protected lazy val function: Parser[Expression] = - ( SUM ~> "(" ~> expression <~ ")" ^^ { case exp => Sum(exp) } - | SUM ~> "(" ~> DISTINCT ~> expression <~ ")" ^^ { case exp => SumDistinct(exp) } - | COUNT ~ "(" ~> "*" <~ ")" ^^ { case _ => Count(Literal(1)) } - | COUNT ~ "(" ~> expression <~ ")" ^^ { case exp => Count(exp) } - | COUNT ~> "(" ~> DISTINCT ~> repsep(expression, ",") <~ ")" ^^ - { case exps => CountDistinct(exps) } - | APPROXIMATE ~ COUNT ~ "(" ~ DISTINCT ~> expression <~ ")" ^^ - { case exp => ApproxCountDistinct(exp) } - | APPROXIMATE ~> "(" ~> floatLit ~ ")" ~ COUNT ~ "(" ~ DISTINCT ~ expression <~ ")" ^^ - { case s ~ _ ~ _ ~ _ ~ _ ~ e => ApproxCountDistinct(e, s.toDouble) } - | FIRST ~ "(" ~> expression <~ ")" ^^ { case exp => First(exp) } - | LAST ~ "(" ~> expression <~ ")" ^^ { case exp => Last(exp) } - | AVG ~ "(" ~> expression <~ ")" ^^ { case exp => Average(exp) } - | MIN ~ "(" ~> expression <~ ")" ^^ { case exp => Min(exp) } - | MAX ~ "(" ~> expression <~ ")" ^^ { case exp => Max(exp) } - | UPPER ~ "(" ~> expression <~ ")" ^^ { case exp => Upper(exp) } - | LOWER ~ "(" ~> expression <~ ")" ^^ { case exp => Lower(exp) } - | IF ~ "(" ~> expression ~ ("," ~> expression) ~ ("," ~> expression) <~ ")" ^^ - { case c ~ t ~ f => If(c, t, f) } + ( ident <~ ("(" ~ "*" ~ ")") ^^ { case udfName => + if (lexical.normalizeKeyword(udfName) == "count") { + Count(Literal(1)) + } else { + throw new AnalysisException(s"invalid expression $udfName(*)") + } + } + | ident ~ ("(" ~> repsep(expression, ",")) <~ ")" ^^ + { case udfName ~ exprs => UnresolvedFunction(udfName, exprs) } + | ident ~ ("(" ~ DISTINCT ~> repsep(expression, ",")) <~ ")" ^^ { case udfName ~ exprs => + lexical.normalizeKeyword(udfName) match { + case "sum" => SumDistinct(exprs.head) + case "count" => CountDistinct(exprs) + case _ => throw new AnalysisException(s"function $udfName does not support DISTINCT") + } + } + | APPROXIMATE ~> ident ~ ("(" ~ DISTINCT ~> expression <~ ")") ^^ { case udfName ~ exp => + if (lexical.normalizeKeyword(udfName) == "count") { + ApproxCountDistinct(exp) + } else { + throw new AnalysisException(s"invalid function approximate $udfName") + } + } + | APPROXIMATE ~> "(" ~> floatLit ~ ")" ~ ident ~ "(" ~ DISTINCT ~ expression <~ ")" ^^ + { case s ~ _ ~ udfName ~ _ ~ _ ~ exp => + if (lexical.normalizeKeyword(udfName) == "count") { + ApproxCountDistinct(exp, s.toDouble) + } else { + throw new AnalysisException(s"invalid function approximate($floatLit) $udfName") + } + } | CASE ~> expression.? ~ rep1(WHEN ~> expression ~ (THEN ~> expression)) ~ (ELSE ~> expression).? <~ END ^^ { case casePart ~ altPart ~ elsePart => @@ -304,16 +302,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { } ++ elsePart casePart.map(CaseKeyWhen(_, branches)).getOrElse(CaseWhen(branches)) } - | (SUBSTR | SUBSTRING) ~ "(" ~> expression ~ ("," ~> expression) <~ ")" ^^ - { case s ~ p => Substring(s, p, Literal(Integer.MAX_VALUE)) } - | (SUBSTR | SUBSTRING) ~ "(" ~> expression ~ ("," ~> expression) ~ ("," ~> expression) <~ ")" ^^ - { case s ~ p ~ l => Substring(s, p, l) } - | COALESCE ~ "(" ~> repsep(expression, ",") <~ ")" ^^ { case exprs => Coalesce(exprs) } - | SQRT ~ "(" ~> expression <~ ")" ^^ { case exp => Sqrt(exp) } - | ABS ~ "(" ~> expression <~ ")" ^^ { case exp => Abs(exp) } - | ident ~ ("(" ~> repsep(expression, ",")) <~ ")" ^^ - { case udfName ~ exprs => UnresolvedFunction(udfName, exprs) } - ) + ) protected lazy val cast: Parser[Expression] = CAST ~ "(" ~> expression ~ (AS ~> dataType) <~ ")" ^^ { @@ -365,7 +354,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected lazy val baseExpression: Parser[Expression] = ( "*" ^^^ UnresolvedStar(None) - | ident <~ "." ~ "*" ^^ { case tableName => UnresolvedStar(Option(tableName)) } + | ident <~ "." ~ "*" ^^ { case tableName => UnresolvedStar(Option(tableName)) } | primary ) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index ecbac57ea4d62..21b05760256b4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -19,19 +19,21 @@ package org.apache.spark.sql.catalyst.analysis import scala.collection.mutable.ArrayBuffer -import org.apache.spark.util.collection.OpenHashSet import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.{SimpleCatalystConf, CatalystConf} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules._ import org.apache.spark.sql.types._ +import scala.collection.mutable.ArrayBuffer /** * A trivial [[Analyzer]] with an [[EmptyCatalog]] and [[EmptyFunctionRegistry]]. Used for testing * when all relations are already filled in and the analyzer needs only to resolve attribute * references. */ -object SimpleAnalyzer extends Analyzer(EmptyCatalog, EmptyFunctionRegistry, true) +object SimpleAnalyzer + extends Analyzer(EmptyCatalog, EmptyFunctionRegistry, new SimpleCatalystConf(true)) /** * Provides a logical query plan analyzer, which translates [[UnresolvedAttribute]]s and @@ -41,11 +43,17 @@ object SimpleAnalyzer extends Analyzer(EmptyCatalog, EmptyFunctionRegistry, true class Analyzer( catalog: Catalog, registry: FunctionRegistry, - caseSensitive: Boolean, + conf: CatalystConf, maxIterations: Int = 100) extends RuleExecutor[LogicalPlan] with HiveTypeCoercion with CheckAnalysis { - val resolver = if (caseSensitive) caseSensitiveResolution else caseInsensitiveResolution + def resolver: Resolver = { + if (conf.caseSensitiveAnalysis) { + caseSensitiveResolution + } else { + caseInsensitiveResolution + } + } val fixedPoint = FixedPoint(maxIterations) @@ -65,7 +73,6 @@ class Analyzer( ResolveGroupingAnalytics :: ResolveSortReferences :: ResolveGenerate :: - ImplicitGenerate :: ResolveFunctions :: ExtractWindowExpressions :: GlobalAggregates :: @@ -135,25 +142,6 @@ class Analyzer( } object ResolveGroupingAnalytics extends Rule[LogicalPlan] { - /** - * Extract attribute set according to the grouping id - * @param bitmask bitmask to represent the selected of the attribute sequence - * @param exprs the attributes in sequence - * @return the attributes of non selected specified via bitmask (with the bit set to 1) - */ - private def buildNonSelectExprSet(bitmask: Int, exprs: Seq[Expression]) - : OpenHashSet[Expression] = { - val set = new OpenHashSet[Expression](2) - - var bit = exprs.length - 1 - while (bit >= 0) { - if (((bitmask >> bit) & 1) == 0) set.add(exprs(bit)) - bit -= 1 - } - - set - } - /* * GROUP BY a, b, c WITH ROLLUP * is equivalent to @@ -185,15 +173,20 @@ class Analyzer( * expressions which equal GroupBy expressions with Literal(null), if those expressions * are not set for this grouping set (according to the bit mask). */ - private[this] def expand(g: GroupingSets): Seq[GroupExpression] = { - val result = new scala.collection.mutable.ArrayBuffer[GroupExpression] + private[this] def expand(g: GroupingSets): Seq[Seq[Expression]] = { + val result = new scala.collection.mutable.ArrayBuffer[Seq[Expression]] g.bitmasks.foreach { bitmask => // get the non selected grouping attributes according to the bit mask - val nonSelectedGroupExprSet = buildNonSelectExprSet(bitmask, g.groupByExprs) + val nonSelectedGroupExprs = ArrayBuffer.empty[Expression] + var bit = g.groupByExprs.length - 1 + while (bit >= 0) { + if (((bitmask >> bit) & 1) == 0) nonSelectedGroupExprs += g.groupByExprs(bit) + bit -= 1 + } val substitution = (g.child.output :+ g.gid).map(expr => expr transformDown { - case x: Expression if nonSelectedGroupExprSet.contains(x) => + case x: Expression if nonSelectedGroupExprs.find(_ semanticEquals x).isDefined => // if the input attribute in the Invalid Grouping Expression set of for this group // replace it with constant null Literal.create(null, expr.dataType) @@ -202,7 +195,7 @@ class Analyzer( Literal.create(bitmask, IntegerType) }) - result += GroupExpression(substitution) + result += substitution } result.toSeq @@ -243,9 +236,8 @@ class Analyzer( } /** - * Replaces [[UnresolvedAttribute]]s with concrete - * [[catalyst.expressions.AttributeReference AttributeReferences]] from a logical plan node's - * children. + * Replaces [[UnresolvedAttribute]]s with concrete [[AttributeReference]]s from + * a logical plan node's children. */ object ResolveReferences extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { @@ -314,6 +306,16 @@ class Analyzer( case oldVersion @ Aggregate(_, aggregateExpressions, _) if findAliases(aggregateExpressions).intersect(conflictingAttributes).nonEmpty => (oldVersion, oldVersion.copy(aggregateExpressions = newAliases(aggregateExpressions))) + + case oldVersion: Generate + if oldVersion.generatedSet.intersect(conflictingAttributes).nonEmpty => + val newOutput = oldVersion.generatorOutput.map(_.newInstance()) + (oldVersion, oldVersion.copy(generatorOutput = newOutput)) + + case oldVersion @ Window(_, windowExpressions, _, child) + if AttributeSet(windowExpressions.map(_.toAttribute)).intersect(conflictingAttributes) + .nonEmpty => + (oldVersion, oldVersion.copy(windowExpressions = newAliases(windowExpressions))) }.headOption.getOrElse { // Only handle first case, others will be fixed on the next pass. sys.error( s""" @@ -334,9 +336,15 @@ class Analyzer( } j.copy(right = newRight) + // When resolve `SortOrder`s in Sort based on child, don't report errors as + // we still have chance to resolve it based on grandchild + case s @ Sort(ordering, global, child) if child.resolved && !s.resolved => + val newOrdering = resolveSortOrders(ordering, child, throws = false) + Sort(newOrdering, global, child) + case q: LogicalPlan => logTrace(s"Attempting to resolve ${q.simpleString}") - q transformExpressionsUp { + q transformExpressionsUp { case u @ UnresolvedAttribute(nameParts) if nameParts.length == 1 && resolver(nameParts(0), VirtualColumn.groupingIdName) && q.isInstanceOf[GroupingAnalytics] => @@ -371,6 +379,26 @@ class Analyzer( exprs.exists(_.collect { case _: Star => true }.nonEmpty) } + private def resolveSortOrders(ordering: Seq[SortOrder], plan: LogicalPlan, throws: Boolean) = { + ordering.map { order => + // Resolve SortOrder in one round. + // If throws == false or the desired attribute doesn't exist + // (like try to resolve `a.b` but `a` doesn't exist), fail and return the origin one. + // Else, throw exception. + try { + val newOrder = order transformUp { + case u @ UnresolvedAttribute(nameParts) => + plan.resolve(nameParts, resolver).getOrElse(u) + case UnresolvedExtractValue(child, fieldName) if child.resolved => + ExtractValue(child, fieldName, resolver) + } + newOrder.asInstanceOf[SortOrder] + } catch { + case a: AnalysisException if !throws => order + } + } + } + /** * In many dialects of SQL it is valid to sort by attributes that are not present in the SELECT * clause. This rule detects such queries and adds the required attributes to the original @@ -381,13 +409,13 @@ class Analyzer( def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { case s @ Sort(ordering, global, p @ Project(projectList, child)) if !s.resolved && p.resolved => - val (resolvedOrdering, missing) = resolveAndFindMissing(ordering, p, child) + val (newOrdering, missing) = resolveAndFindMissing(ordering, p, child) // If this rule was not a no-op, return the transformed plan, otherwise return the original. if (missing.nonEmpty) { // Add missing attributes and then project them away after the sort. Project(p.output, - Sort(resolvedOrdering, global, + Sort(newOrdering, global, Project(projectList ++ missing, child))) } else { logDebug(s"Failed to find $missing in ${p.output.mkString(", ")}") @@ -395,19 +423,31 @@ class Analyzer( } case s @ Sort(ordering, global, a @ Aggregate(grouping, aggs, child)) if !s.resolved && a.resolved => - val unresolved = ordering.flatMap(_.collect { case UnresolvedAttribute(name) => name }) // A small hack to create an object that will allow us to resolve any references that // refer to named expressions that are present in the grouping expressions. val groupingRelation = LocalRelation( grouping.collect { case ne: NamedExpression => ne.toAttribute } ) - val (resolvedOrdering, missing) = resolveAndFindMissing(ordering, a, groupingRelation) + // Find sort attributes that are projected away so we can temporarily add them back in. + val (newOrdering, missingAttr) = resolveAndFindMissing(ordering, a, groupingRelation) + + // Find aggregate expressions and evaluate them early, since they can't be evaluated in a + // Sort. + val (withAggsRemoved, aliasedAggregateList) = newOrdering.map { + case aggOrdering if aggOrdering.collect { case a: AggregateExpression => a }.nonEmpty => + val aliased = Alias(aggOrdering.child, "_aggOrdering")() + (aggOrdering.copy(child = aliased.toAttribute), Some(aliased)) + + case other => (other, None) + }.unzip + + val missing = missingAttr ++ aliasedAggregateList.flatten if (missing.nonEmpty) { // Add missing grouping exprs and then project them away after the sort. Project(a.output, - Sort(resolvedOrdering, global, + Sort(withAggsRemoved, global, Aggregate(grouping, aggs ++ missing, child))) } else { s // Nothing we can do here. Return original plan. @@ -415,52 +455,39 @@ class Analyzer( } /** - * Given a child and a grandchild that are present beneath a sort operator, returns - * a resolved sort ordering and a list of attributes that are missing from the child - * but are present in the grandchild. + * Given a child and a grandchild that are present beneath a sort operator, try to resolve + * the sort ordering and returns it with a list of attributes that are missing from the + * child but are present in the grandchild. */ def resolveAndFindMissing( ordering: Seq[SortOrder], child: LogicalPlan, grandchild: LogicalPlan): (Seq[SortOrder], Seq[Attribute]) = { - // Find any attributes that remain unresolved in the sort. - val unresolved: Seq[Seq[String]] = - ordering.flatMap(_.collect { case UnresolvedAttribute(nameParts) => nameParts }) - - // Create a map from name, to resolved attributes, when the desired name can be found - // prior to the projection. - val resolved: Map[Seq[String], NamedExpression] = - unresolved.flatMap(u => grandchild.resolve(u, resolver).map(a => u -> a)).toMap - + val newOrdering = resolveSortOrders(ordering, grandchild, throws = true) // Construct a set that contains all of the attributes that we need to evaluate the // ordering. - val requiredAttributes = AttributeSet(resolved.values) - + val requiredAttributes = AttributeSet(newOrdering.filter(_.resolved)) // Figure out which ones are missing from the projection, so that we can add them and // remove them after the sort. val missingInProject = requiredAttributes -- child.output - - // Now that we have all the attributes we need, reconstruct a resolved ordering. - // It is important to do it here, instead of waiting for the standard resolved as adding - // attributes to the project below can actually introduce ambiquity that was not present - // before. - val resolvedOrdering = ordering.map(_ transform { - case u @ UnresolvedAttribute(name) => resolved.getOrElse(name, u) - }).asInstanceOf[Seq[SortOrder]] - - (resolvedOrdering, missingInProject.toSeq) + // It is important to return the new SortOrders here, instead of waiting for the standard + // resolving process as adding attributes to the project below can actually introduce + // ambiguity that was not present before. + (newOrdering, missingInProject.toSeq) } } /** - * Replaces [[UnresolvedFunction]]s with concrete [[catalyst.expressions.Expression Expressions]]. + * Replaces [[UnresolvedFunction]]s with concrete [[Expression]]s. */ object ResolveFunctions extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressions { - case u @ UnresolvedFunction(name, children) if u.childrenResolved => - registry.lookupFunction(name, children) + case u @ UnresolvedFunction(name, children) => + withPosition(u) { + registry.lookupFunction(name, children) + } } } } @@ -491,83 +518,123 @@ class Analyzer( object UnresolvedHavingClauseAttributes extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { case filter @ Filter(havingCondition, aggregate @ Aggregate(_, originalAggExprs, _)) - if aggregate.resolved && containsAggregate(havingCondition) => { - val evaluatedCondition = Alias(havingCondition, "havingCondition")() + if aggregate.resolved && containsAggregate(havingCondition) => + + val evaluatedCondition = Alias(havingCondition, "havingCondition")() val aggExprsWithHaving = evaluatedCondition +: originalAggExprs Project(aggregate.output, Filter(evaluatedCondition.toAttribute, aggregate.copy(aggregateExpressions = aggExprsWithHaving))) - } } - protected def containsAggregate(condition: Expression): Boolean = + protected def containsAggregate(condition: Expression): Boolean = { condition .collect { case ae: AggregateExpression => ae } .nonEmpty + } } /** - * When a SELECT clause has only a single expression and that expression is a - * [[catalyst.expressions.Generator Generator]] we convert the - * [[catalyst.plans.logical.Project Project]] to a [[catalyst.plans.logical.Generate Generate]]. + * Rewrites table generating expressions that either need one or more of the following in order + * to be resolved: + * - concrete attribute references for their output. + * - to be relocated from a SELECT clause (i.e. from a [[Project]]) into a [[Generate]]). + * + * Names for the output [[Attribute]]s are extracted from [[Alias]] or [[MultiAlias]] expressions + * that wrap the [[Generator]]. If more than one [[Generator]] is found in a Project, an + * [[AnalysisException]] is throw. */ - object ImplicitGenerate extends Rule[LogicalPlan] { + object ResolveGenerate extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case Project(Seq(Alias(g: Generator, name)), child) => - Generate(g, join = false, outer = false, - qualifier = None, UnresolvedAttribute(name) :: Nil, child) - case Project(Seq(MultiAlias(g: Generator, names)), child) => - Generate(g, join = false, outer = false, - qualifier = None, names.map(UnresolvedAttribute(_)), child) + case p: Generate if !p.child.resolved || !p.generator.resolved => p + case g: Generate if !g.resolved => + g.copy(generatorOutput = makeGeneratorOutput(g.generator, g.generatorOutput.map(_.name))) + + case p @ Project(projectList, child) => + // Holds the resolved generator, if one exists in the project list. + var resolvedGenerator: Generate = null + + val newProjectList = projectList.flatMap { + case AliasedGenerator(generator, names) if generator.childrenResolved => + if (resolvedGenerator != null) { + failAnalysis( + s"Only one generator allowed per select but ${resolvedGenerator.nodeName} and " + + s"and ${generator.nodeName} found.") + } + + resolvedGenerator = + Generate( + generator, + join = projectList.size > 1, // Only join if there are other expressions in SELECT. + outer = false, + qualifier = None, + generatorOutput = makeGeneratorOutput(generator, names), + child) + + resolvedGenerator.generatorOutput + case other => other :: Nil + } + + if (resolvedGenerator != null) { + Project(newProjectList, resolvedGenerator) + } else { + p + } } - } - /** - * Resolve the Generate, if the output names specified, we will take them, otherwise - * we will try to provide the default names, which follow the same rule with Hive. - */ - object ResolveGenerate extends Rule[LogicalPlan] { - // Construct the output attributes for the generator, - // The output attribute names can be either specified or - // auto generated. + /** Extracts a [[Generator]] expression and any names assigned by aliases to their output. */ + private object AliasedGenerator { + def unapply(e: Expression): Option[(Generator, Seq[String])] = e match { + case Alias(g: Generator, name) + if g.resolved && + g.elementTypes.size > 1 && + java.util.regex.Pattern.matches("_c[0-9]+", name) => { + // Assume the default name given by parser is "_c[0-9]+", + // TODO in long term, move the naming logic from Parser to Analyzer. + // In projection, Parser gave default name for TGF as does for normal UDF, + // but the TGF probably have multiple output columns/names. + // e.g. SELECT explode(map(key, value)) FROM src; + // Let's simply ignore the default given name for this case. + Some((g, Nil)) + } + case Alias(g: Generator, name) if g.resolved && g.elementTypes.size > 1 => + // If not given the default names, and the TGF with multiple output columns + failAnalysis( + s"""Expect multiple names given for ${g.getClass.getName}, + |but only single name '${name}' specified""".stripMargin) + case Alias(g: Generator, name) => Some((g, name :: Nil)) + case MultiAlias(g: Generator, names) => Some(g, names) + case _ => None + } + } + + /** + * Construct the output attributes for a [[Generator]], given a list of names. If the list of + * names is empty names are assigned by ordinal (i.e., _c0, _c1, ...) to match Hive's defaults. + */ private def makeGeneratorOutput( generator: Generator, - generatorOutput: Seq[Attribute]): Seq[Attribute] = { + names: Seq[String]): Seq[Attribute] = { val elementTypes = generator.elementTypes - if (generatorOutput.length == elementTypes.length) { - generatorOutput.zip(elementTypes).map { - case (a, (t, nullable)) if !a.resolved => - AttributeReference(a.name, t, nullable)() - case (a, _) => a + if (names.length == elementTypes.length) { + names.zip(elementTypes).map { + case (name, (t, nullable)) => + AttributeReference(name, t, nullable)() } - } else if (generatorOutput.length == 0) { + } else if (names.isEmpty) { elementTypes.zipWithIndex.map { // keep the default column names as Hive does _c0, _c1, _cN case ((t, nullable), i) => AttributeReference(s"_c$i", t, nullable)() } } else { - throw new AnalysisException( - s""" - |The number of aliases supplied in the AS clause does not match - |the number of columns output by the UDTF expected - |${elementTypes.size} aliases but got ${generatorOutput.size} - """.stripMargin) + failAnalysis( + "The number of aliases supplied in the AS clause does not match the number of columns " + + s"output by the UDTF expected ${elementTypes.size} aliases but got " + + s"${names.mkString(",")} ") } } - - def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case p: Generate if !p.child.resolved || !p.generator.resolved => p - case p: Generate if p.resolved == false => - // if the generator output names are not specified, we will use the default ones. - Generate( - p.generator, - join = p.join, - outer = p.outer, - p.qualifier, - makeGeneratorOutput(p.generator, p.generatorOutput), p.child) - } } /** @@ -594,10 +661,10 @@ class Analyzer( * it into the plan tree. */ object ExtractWindowExpressions extends Rule[LogicalPlan] { - def hasWindowFunction(projectList: Seq[NamedExpression]): Boolean = + private def hasWindowFunction(projectList: Seq[NamedExpression]): Boolean = projectList.exists(hasWindowFunction) - def hasWindowFunction(expr: NamedExpression): Boolean = { + private def hasWindowFunction(expr: NamedExpression): Boolean = { expr.find { case window: WindowExpression => true case _ => false @@ -605,14 +672,24 @@ class Analyzer( } /** - * From a Seq of [[NamedExpression]]s, extract window expressions and - * other regular expressions. + * From a Seq of [[NamedExpression]]s, extract expressions containing window expressions and + * other regular expressions that do not contain any window expression. For example, for + * `col1, Sum(col2 + col3) OVER (PARTITION BY col4 ORDER BY col5)`, we will extract + * `col1`, `col2 + col3`, `col4`, and `col5` out and replace their appearances in + * the window expression as attribute references. So, the first returned value will be + * `[Sum(_w0) OVER (PARTITION BY _w1 ORDER BY _w2)]` and the second returned value will be + * [col1, col2 + col3 as _w0, col4 as _w1, col5 as _w2]. + * + * @return (seq of expressions containing at lease one window expressions, + * seq of non-window expressions) */ - def extract( + private def extract( expressions: Seq[NamedExpression]): (Seq[NamedExpression], Seq[NamedExpression]) = { - // First, we simple partition the input expressions to two part, one having - // WindowExpressions and another one without WindowExpressions. - val (windowExpressions, regularExpressions) = expressions.partition(hasWindowFunction) + // First, we partition the input expressions to two part. For the first part, + // every expression in it contain at least one WindowExpression. + // Expressions in the second part do not have any WindowExpression. + val (expressionsWithWindowFunctions, regularExpressions) = + expressions.partition(hasWindowFunction) // Then, we need to extract those regular expressions used in the WindowExpression. // For example, when we have col1 - Sum(col2 + col3) OVER (PARTITION BY col4 ORDER BY col5), @@ -621,8 +698,8 @@ class Analyzer( val extractedExprBuffer = new ArrayBuffer[NamedExpression]() def extractExpr(expr: Expression): Expression = expr match { case ne: NamedExpression => - // If a named expression is not in regularExpressions, add extract it and replace it - // with an AttributeReference. + // If a named expression is not in regularExpressions, add it to + // extractedExprBuffer and replace it with an AttributeReference. val missingExpr = AttributeSet(Seq(expr)) -- (regularExpressions ++ extractedExprBuffer) if (missingExpr.nonEmpty) { @@ -639,8 +716,9 @@ class Analyzer( withName.toAttribute } - // Now, we extract expressions from windowExpressions by using extractExpr. - val newWindowExpressions = windowExpressions.map { + // Now, we extract regular expressions from expressionsWithWindowFunctions + // by using extractExpr. + val newExpressionsWithWindowFunctions = expressionsWithWindowFunctions.map { _.transform { // Extracts children expressions of a WindowFunction (input parameters of // a WindowFunction). @@ -666,37 +744,80 @@ class Analyzer( }.asInstanceOf[NamedExpression] } - (newWindowExpressions, regularExpressions ++ extractedExprBuffer) - } + (newExpressionsWithWindowFunctions, regularExpressions ++ extractedExprBuffer) + } // end of extract /** * Adds operators for Window Expressions. Every Window operator handles a single Window Spec. */ - def addWindow(windowExpressions: Seq[NamedExpression], child: LogicalPlan): LogicalPlan = { - // First, we group window expressions based on their Window Spec. - val groupedWindowExpression = windowExpressions.groupBy { expr => - val windowSpec = expr.collectFirst { + private def addWindow( + expressionsWithWindowFunctions: Seq[NamedExpression], + child: LogicalPlan): LogicalPlan = { + // First, we need to extract all WindowExpressions from expressionsWithWindowFunctions + // and put those extracted WindowExpressions to extractedWindowExprBuffer. + // This step is needed because it is possible that an expression contains multiple + // WindowExpressions with different Window Specs. + // After extracting WindowExpressions, we need to construct a project list to generate + // expressionsWithWindowFunctions based on extractedWindowExprBuffer. + // For example, for "sum(a) over (...) / sum(b) over (...)", we will first extract + // "sum(a) over (...)" and "sum(b) over (...)" out, and assign "_we0" as the alias to + // "sum(a) over (...)" and "_we1" as the alias to "sum(b) over (...)". + // Then, the projectList will be [_we0/_we1]. + val extractedWindowExprBuffer = new ArrayBuffer[NamedExpression]() + val newExpressionsWithWindowFunctions = expressionsWithWindowFunctions.map { + // We need to use transformDown because we want to trigger + // "case alias @ Alias(window: WindowExpression, _)" first. + _.transformDown { + case alias @ Alias(window: WindowExpression, _) => + // If a WindowExpression has an assigned alias, just use it. + extractedWindowExprBuffer += alias + alias.toAttribute + case window: WindowExpression => + // If there is no alias assigned to the WindowExpressions. We create an + // internal column. + val withName = Alias(window, s"_we${extractedWindowExprBuffer.length}")() + extractedWindowExprBuffer += withName + withName.toAttribute + }.asInstanceOf[NamedExpression] + } + + // Second, we group extractedWindowExprBuffer based on their Window Spec. + val groupedWindowExpressions = extractedWindowExprBuffer.groupBy { expr => + val distinctWindowSpec = expr.collect { case window: WindowExpression => window.windowSpec + }.distinct + + // We do a final check and see if we only have a single Window Spec defined in an + // expressions. + if (distinctWindowSpec.length == 0 ) { + failAnalysis(s"$expr does not have any WindowExpression.") + } else if (distinctWindowSpec.length > 1) { + // newExpressionsWithWindowFunctions only have expressions with a single + // WindowExpression. If we reach here, we have a bug. + failAnalysis(s"$expr has multiple Window Specifications ($distinctWindowSpec)." + + s"Please file a bug report with this error message, stack trace, and the query.") + } else { + distinctWindowSpec.head } - windowSpec.getOrElse( - failAnalysis(s"$windowExpressions does not have any WindowExpression.")) }.toSeq - // For every Window Spec, we add a Window operator and set currentChild as the child of it. + // Third, for every Window Spec, we add a Window operator and set currentChild as the + // child of it. var currentChild = child var i = 0 - while (i < groupedWindowExpression.size) { - val (windowSpec, windowExpressions) = groupedWindowExpression(i) + while (i < groupedWindowExpressions.size) { + val (windowSpec, windowExpressions) = groupedWindowExpressions(i) // Set currentChild to the newly created Window operator. currentChild = Window(currentChild.output, windowExpressions, windowSpec, currentChild) - // Move to next WindowExpression. + // Move to next Window Spec. i += 1 } - // We return the top operator. - currentChild - } + // Finally, we create a Project to output currentChild's output + // newExpressionsWithWindowFunctions. + Project(currentChild.output ++ newExpressionsWithWindowFunctions, currentChild) + } // end of addWindow // We have to use transformDown at here to make sure the rule of // "Aggregate with Having clause" will be triggered. @@ -753,9 +874,8 @@ class Analyzer( } /** - * Removes [[catalyst.plans.logical.Subquery Subquery]] operators from the plan. Subqueries are - * only required to provide scoping information for attributes and can be removed once analysis is - * complete. + * Removes [[Subquery]] operators from the plan. Subqueries are only required to provide + * scoping information for attributes and can be removed once analysis is complete. */ object EliminateSubQueries extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala index 18c24b651921a..1541491608b24 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala @@ -17,8 +17,14 @@ package org.apache.spark.sql.catalyst.analysis +import java.util.concurrent.ConcurrentHashMap + +import scala.collection.JavaConversions._ import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import org.apache.spark.sql.catalyst.CatalystConf +import org.apache.spark.sql.catalyst.EmptyConf import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery} /** @@ -34,7 +40,7 @@ class NoSuchDatabaseException extends Exception */ trait Catalog { - def caseSensitive: Boolean + val conf: CatalystConf def tableExists(tableIdentifier: Seq[String]): Boolean @@ -57,10 +63,10 @@ trait Catalog { def unregisterAllTables(): Unit protected def processTableIdentifier(tableIdentifier: Seq[String]): Seq[String] = { - if (!caseSensitive) { - tableIdentifier.map(_.toLowerCase) - } else { + if (conf.caseSensitiveAnalysis) { tableIdentifier + } else { + tableIdentifier.map(_.toLowerCase) } } @@ -78,19 +84,19 @@ trait Catalog { } } -class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { - val tables = new mutable.HashMap[String, LogicalPlan]() +class SimpleCatalog(val conf: CatalystConf) extends Catalog { + val tables = new ConcurrentHashMap[String, LogicalPlan] override def registerTable( tableIdentifier: Seq[String], plan: LogicalPlan): Unit = { val tableIdent = processTableIdentifier(tableIdentifier) - tables += ((getDbTableName(tableIdent), plan)) + tables.put(getDbTableName(tableIdent), plan) } override def unregisterTable(tableIdentifier: Seq[String]): Unit = { val tableIdent = processTableIdentifier(tableIdentifier) - tables -= getDbTableName(tableIdent) + tables.remove(getDbTableName(tableIdent)) } override def unregisterAllTables(): Unit = { @@ -99,10 +105,7 @@ class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { override def tableExists(tableIdentifier: Seq[String]): Boolean = { val tableIdent = processTableIdentifier(tableIdentifier) - tables.get(getDbTableName(tableIdent)) match { - case Some(_) => true - case None => false - } + tables.containsKey(getDbTableName(tableIdent)) } override def lookupRelation( @@ -110,7 +113,10 @@ class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { alias: Option[String] = None): LogicalPlan = { val tableIdent = processTableIdentifier(tableIdentifier) val tableFullName = getDbTableName(tableIdent) - val table = tables.getOrElse(tableFullName, sys.error(s"Table Not Found: $tableFullName")) + val table = tables.get(tableFullName) + if (table == null) { + sys.error(s"Table Not Found: $tableFullName") + } val tableWithQualifiers = Subquery(tableIdent.last, table) // If an alias was specified by the lookup, wrap the plan in a subquery so that attributes are @@ -119,9 +125,11 @@ class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { } override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = { - tables.map { - case (name, _) => (name, true) - }.toSeq + val result = ArrayBuffer.empty[(String, Boolean)] + for (name <- tables.keySet()) { + result += ((name, true)) + } + result } override def refreshTable(databaseName: String, tableName: String): Unit = { @@ -138,7 +146,7 @@ class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { trait OverrideCatalog extends Catalog { // TODO: This doesn't work when the database changes... - val overrides = new mutable.HashMap[(Option[String],String), LogicalPlan]() + val overrides = new mutable.HashMap[(Option[String], String), LogicalPlan]() abstract override def tableExists(tableIdentifier: Seq[String]): Boolean = { val tableIdent = processTableIdentifier(tableIdentifier) @@ -164,10 +172,10 @@ trait OverrideCatalog extends Catalog { } abstract override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = { - val dbName = if (!caseSensitive) { - if (databaseName.isDefined) Some(databaseName.get.toLowerCase) else None - } else { + val dbName = if (conf.caseSensitiveAnalysis) { databaseName + } else { + if (databaseName.isDefined) Some(databaseName.get.toLowerCase) else None } val temporaryTables = overrides.filter { @@ -207,7 +215,7 @@ trait OverrideCatalog extends Catalog { */ object EmptyCatalog extends Catalog { - override val caseSensitive: Boolean = true + override val conf: CatalystConf = EmptyConf override def tableExists(tableIdentifier: Seq[String]): Boolean = { throw new UnsupportedOperationException diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index c8288c6767004..7fabd2bfc80ab 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -51,25 +51,24 @@ trait CheckAnalysis { case operator: LogicalPlan => operator transformExpressionsUp { case a: Attribute if !a.resolved => - if (operator.childrenResolved) { - a match { - case UnresolvedAttribute(nameParts) => - // Throw errors for specific problems with get field. - operator.resolveChildren(nameParts, resolver, throwErrors = true) - } - } - val from = operator.inputSet.map(_.name).mkString(", ") a.failAnalysis(s"cannot resolve '${a.prettyString}' given input columns $from") + case e: Expression if e.checkInputDataTypes().isFailure => + e.checkInputDataTypes() match { + case TypeCheckResult.TypeCheckFailure(message) => + e.failAnalysis( + s"cannot resolve '${e.prettyString}' due to data type mismatch: $message") + } + case c: Cast if !c.resolved => failAnalysis( s"invalid cast from ${c.child.dataType.simpleString} to ${c.dataType.simpleString}") - case b: BinaryExpression if !b.resolved => + case WindowExpression(UnresolvedWindowFunction(name, _), _) => failAnalysis( - s"invalid expression ${b.prettyString} " + - s"between ${b.left.simpleString} and ${b.right.simpleString}") + s"Could not resolve window function '$name'. " + + "Note that, using window functions currently requires a HiveContext") case w @ WindowExpression(windowFunction, windowSpec) if windowSpec.validate.nonEmpty => // The window spec is not valid. @@ -86,12 +85,12 @@ trait CheckAnalysis { case Aggregate(groupingExprs, aggregateExprs, child) => def checkValidAggregateExpression(expr: Expression): Unit = expr match { case _: AggregateExpression => // OK - case e: Attribute if !groupingExprs.contains(e) => + case e: Attribute if groupingExprs.find(_ semanticEquals e).isEmpty => failAnalysis( s"expression '${e.prettyString}' is neither present in the group by, " + s"nor is it an aggregate function. " + "Add to group by or wrap in first() if you don't care which value you get.") - case e if groupingExprs.contains(e) => // OK + case e if groupingExprs.find(_ semanticEquals e).isDefined => // OK case e if e.references.isEmpty => // OK case e => e.children.foreach(checkValidAggregateExpression) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala index 16ca5bcd57a72..5fb3369f85d12 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala @@ -17,42 +17,50 @@ package org.apache.spark.sql.catalyst.analysis -import org.apache.spark.sql.catalyst.expressions.Expression -import scala.collection.mutable +import scala.reflect.ClassTag +import scala.util.{Failure, Success, Try} + +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.FunctionBuilder +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.util.StringKeyHashMap + /** A catalog for looking up user defined functions, used by an [[Analyzer]]. */ trait FunctionRegistry { - type FunctionBuilder = Seq[Expression] => Expression def registerFunction(name: String, builder: FunctionBuilder): Unit + @throws[AnalysisException]("If function does not exist") def lookupFunction(name: String, children: Seq[Expression]): Expression - - def caseSensitive: Boolean } -trait OverrideFunctionRegistry extends FunctionRegistry { +class OverrideFunctionRegistry(underlying: FunctionRegistry) extends FunctionRegistry { - val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive) + private val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive = false) override def registerFunction(name: String, builder: FunctionBuilder): Unit = { functionBuilders.put(name, builder) } - abstract override def lookupFunction(name: String, children: Seq[Expression]): Expression = { - functionBuilders.get(name).map(_(children)).getOrElse(super.lookupFunction(name, children)) + override def lookupFunction(name: String, children: Seq[Expression]): Expression = { + functionBuilders.get(name).map(_(children)).getOrElse(underlying.lookupFunction(name, children)) } } -class SimpleFunctionRegistry(val caseSensitive: Boolean) extends FunctionRegistry { - val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive) +class SimpleFunctionRegistry extends FunctionRegistry { + + private val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive = false) override def registerFunction(name: String, builder: FunctionBuilder): Unit = { functionBuilders.put(name, builder) } override def lookupFunction(name: String, children: Seq[Expression]): Expression = { - functionBuilders(name)(children) + val func = functionBuilders.get(name).getOrElse { + throw new AnalysisException(s"undefined function $name") + } + func(children) } } @@ -68,30 +76,113 @@ object EmptyFunctionRegistry extends FunctionRegistry { override def lookupFunction(name: String, children: Seq[Expression]): Expression = { throw new UnsupportedOperationException } - - override def caseSensitive: Boolean = throw new UnsupportedOperationException } -/** - * Build a map with String type of key, and it also supports either key case - * sensitive or insensitive. - * TODO move this into util folder? - */ -object StringKeyHashMap { - def apply[T](caseSensitive: Boolean): StringKeyHashMap[T] = caseSensitive match { - case false => new StringKeyHashMap[T](_.toLowerCase) - case true => new StringKeyHashMap[T](identity) - } -} -class StringKeyHashMap[T](normalizer: (String) => String) { - private val base = new collection.mutable.HashMap[String, T]() +object FunctionRegistry { + + type FunctionBuilder = Seq[Expression] => Expression - def apply(key: String): T = base(normalizer(key)) + val expressions: Map[String, FunctionBuilder] = Map( + // misc non-aggregate functions + expression[Abs]("abs"), + expression[CreateArray]("array"), + expression[Coalesce]("coalesce"), + expression[Explode]("explode"), + expression[If]("if"), + expression[IsNull]("isnull"), + expression[IsNotNull]("isnotnull"), + expression[Coalesce]("nvl"), + expression[Rand]("rand"), + expression[Randn]("randn"), + expression[CreateStruct]("struct"), + expression[Sqrt]("sqrt"), + + // math functions + expression[Acos]("acos"), + expression[Asin]("asin"), + expression[Atan]("atan"), + expression[Atan2]("atan2"), + expression[Bin]("bin"), + expression[Cbrt]("cbrt"), + expression[Ceil]("ceil"), + expression[Ceil]("ceiling"), + expression[Cos]("cos"), + expression[EulerNumber]("e"), + expression[Exp]("exp"), + expression[Expm1]("expm1"), + expression[Floor]("floor"), + expression[Hypot]("hypot"), + expression[Logarithm]("log"), + expression[Log]("ln"), + expression[Log10]("log10"), + expression[Log1p]("log1p"), + expression[UnaryMinus]("negative"), + expression[Pi]("pi"), + expression[Log2]("log2"), + expression[Pow]("pow"), + expression[Pow]("power"), + expression[UnaryPositive]("positive"), + expression[Rint]("rint"), + expression[Signum]("sign"), + expression[Signum]("signum"), + expression[Sin]("sin"), + expression[Sinh]("sinh"), + expression[Tan]("tan"), + expression[Tanh]("tanh"), + expression[ToDegrees]("degrees"), + expression[ToRadians]("radians"), + + // misc functions + expression[Md5]("md5"), + + // aggregate functions + expression[Average]("avg"), + expression[Count]("count"), + expression[First]("first"), + expression[Last]("last"), + expression[Max]("max"), + expression[Min]("min"), + expression[Sum]("sum"), + + // string functions + expression[Lower]("lcase"), + expression[Lower]("lower"), + expression[StringLength]("length"), + expression[Substring]("substr"), + expression[Substring]("substring"), + expression[Upper]("ucase"), + expression[Upper]("upper") + ) + + val builtin: FunctionRegistry = { + val fr = new SimpleFunctionRegistry + expressions.foreach { case (name, builder) => fr.registerFunction(name, builder) } + fr + } - def get(key: String): Option[T] = base.get(normalizer(key)) - def put(key: String, value: T): Option[T] = base.put(normalizer(key), value) - def remove(key: String): Option[T] = base.remove(normalizer(key)) - def iterator: Iterator[(String, T)] = base.toIterator + /** See usage above. */ + private def expression[T <: Expression](name: String) + (implicit tag: ClassTag[T]): (String, FunctionBuilder) = { + + // See if we can find a constructor that accepts Seq[Expression] + val varargCtor = Try(tag.runtimeClass.getDeclaredConstructor(classOf[Seq[_]])).toOption + val builder = (expressions: Seq[Expression]) => { + if (varargCtor.isDefined) { + // If there is an apply method that accepts Seq[Expression], use that one. + varargCtor.get.newInstance(expressions).asInstanceOf[Expression] + } else { + // Otherwise, find an ctor method that matches the number of arguments, and use that. + val params = Seq.fill(expressions.size)(classOf[Expression]) + val f = Try(tag.runtimeClass.getDeclaredConstructor(params : _*)) match { + case Success(e) => + e + case Failure(e) => + throw new AnalysisException(s"Invalid number of arguments for function $name") + } + f.newInstance(expressions : _*).asInstanceOf[Expression] + } + } + (name, builder) + } } - diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 168a4e30eab86..d4ab1fc643c33 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -41,7 +41,7 @@ object HiveTypeCoercion { * with primitive types, because in that case the precision and scale of the result depends on * the operation. Those rules are implemented in [[HiveTypeCoercion.DecimalPrecision]]. */ - val findTightestCommonType: (DataType, DataType) => Option[DataType] = { + val findTightestCommonTypeOfTwo: (DataType, DataType) => Option[DataType] = { case (t1, t2) if t1 == t2 => Some(t1) case (NullType, t1) => Some(t1) case (t1, NullType) => Some(t1) @@ -57,6 +57,39 @@ object HiveTypeCoercion { case _ => None } + + /** Similar to [[findTightestCommonType]], but can promote all the way to StringType. */ + private def findTightestCommonTypeToString(left: DataType, right: DataType): Option[DataType] = { + findTightestCommonTypeOfTwo(left, right).orElse((left, right) match { + case (StringType, t2: AtomicType) if t2 != BinaryType && t2 != BooleanType => Some(StringType) + case (t1: AtomicType, StringType) if t1 != BinaryType && t1 != BooleanType => Some(StringType) + case _ => None + }) + } + + /** + * Similar to [[findTightestCommonType]], if can not find the TightestCommonType, try to use + * [[findTightestCommonTypeToString]] to find the TightestCommonType. + */ + private def findTightestCommonTypeAndPromoteToString(types: Seq[DataType]): Option[DataType] = { + types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match { + case None => None + case Some(d) => + findTightestCommonTypeOfTwo(d, c).orElse(findTightestCommonTypeToString(d, c)) + }) + } + + + /** + * Find the tightest common type of a set of types by continuously applying + * `findTightestCommonTypeOfTwo` on these types. + */ + private def findTightestCommonType(types: Seq[DataType]) = { + types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match { + case None => None + case Some(d) => findTightestCommonTypeOfTwo(d, c) + }) + } } /** @@ -76,11 +109,11 @@ trait HiveTypeCoercion { WidenTypes :: PromoteStrings :: DecimalPrecision :: - BooleanComparisons :: - BooleanCasts :: + BooleanEquality :: StringToIntegralCasts :: FunctionArgumentConversion :: CaseWhenCoercion :: + IfCoercion :: Division :: PropagateTypes :: ExpectedInputConversion :: @@ -120,7 +153,7 @@ trait HiveTypeCoercion { * the appropriate numeric equivalent. */ object ConvertNaNs extends Rule[LogicalPlan] { - val stringNaN = Literal("NaN") + private val StringNaN = Literal("NaN") def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressions { @@ -128,20 +161,20 @@ trait HiveTypeCoercion { case e if !e.childrenResolved => e /* Double Conversions */ - case b: BinaryExpression if b.left == stringNaN && b.right.dataType == DoubleType => - b.makeCopy(Array(b.right, Literal(Double.NaN))) - case b: BinaryExpression if b.left.dataType == DoubleType && b.right == stringNaN => - b.makeCopy(Array(Literal(Double.NaN), b.left)) - case b: BinaryExpression if b.left == stringNaN && b.right == stringNaN => - b.makeCopy(Array(Literal(Double.NaN), b.left)) + case b @ BinaryExpression(StringNaN, right @ DoubleType()) => + b.makeCopy(Array(Literal(Double.NaN), right)) + case b @ BinaryExpression(left @ DoubleType(), StringNaN) => + b.makeCopy(Array(left, Literal(Double.NaN))) /* Float Conversions */ - case b: BinaryExpression if b.left == stringNaN && b.right.dataType == FloatType => - b.makeCopy(Array(b.right, Literal(Float.NaN))) - case b: BinaryExpression if b.left.dataType == FloatType && b.right == stringNaN => - b.makeCopy(Array(Literal(Float.NaN), b.left)) - case b: BinaryExpression if b.left == stringNaN && b.right == stringNaN => - b.makeCopy(Array(Literal(Float.NaN), b.left)) + case b @ BinaryExpression(StringNaN, right @ FloatType()) => + b.makeCopy(Array(Literal(Float.NaN), right)) + case b @ BinaryExpression(left @ FloatType(), StringNaN) => + b.makeCopy(Array(left, Literal(Float.NaN))) + + /* Use float NaN by default to avoid unnecessary type widening */ + case b @ BinaryExpression(left @ StringNaN, StringNaN) => + b.makeCopy(Array(left, Literal(Float.NaN))) } } } @@ -174,21 +207,25 @@ trait HiveTypeCoercion { case u @ Union(left, right) if u.childrenResolved && !u.resolved => val castedInput = left.output.zip(right.output).map { // When a string is found on one side, make the other side a string too. - case (l, r) if l.dataType == StringType && r.dataType != StringType => - (l, Alias(Cast(r, StringType), r.name)()) - case (l, r) if l.dataType != StringType && r.dataType == StringType => - (Alias(Cast(l, StringType), l.name)(), r) - - case (l, r) if l.dataType != r.dataType => - logDebug(s"Resolving mismatched union input ${l.dataType}, ${r.dataType}") - findTightestCommonType(l.dataType, r.dataType).map { widestType => + case (lhs, rhs) if lhs.dataType == StringType && rhs.dataType != StringType => + (lhs, Alias(Cast(rhs, StringType), rhs.name)()) + case (lhs, rhs) if lhs.dataType != StringType && rhs.dataType == StringType => + (Alias(Cast(lhs, StringType), lhs.name)(), rhs) + + case (lhs, rhs) if lhs.dataType != rhs.dataType => + logDebug(s"Resolving mismatched union input ${lhs.dataType}, ${rhs.dataType}") + findTightestCommonTypeOfTwo(lhs.dataType, rhs.dataType).map { widestType => val newLeft = - if (l.dataType == widestType) l else Alias(Cast(l, widestType), l.name)() + if (lhs.dataType == widestType) lhs else Alias(Cast(lhs, widestType), lhs.name)() val newRight = - if (r.dataType == widestType) r else Alias(Cast(r, widestType), r.name)() + if (rhs.dataType == widestType) rhs else Alias(Cast(rhs, widestType), rhs.name)() (newLeft, newRight) - }.getOrElse((l, r)) // If there is no applicable conversion, leave expression unchanged. + }.getOrElse { + // If there is no applicable conversion, leave expression unchanged. + (lhs, rhs) + } + case other => other } @@ -217,12 +254,10 @@ trait HiveTypeCoercion { // Skip nodes who's children have not been resolved yet. case e if !e.childrenResolved => e - case b: BinaryExpression if b.left.dataType != b.right.dataType => - findTightestCommonType(b.left.dataType, b.right.dataType).map { widestType => - val newLeft = - if (b.left.dataType == widestType) b.left else Cast(b.left, widestType) - val newRight = - if (b.right.dataType == widestType) b.right else Cast(b.right, widestType) + case b @ BinaryExpression(left, right) if left.dataType != right.dataType => + findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { widestType => + val newLeft = if (left.dataType == widestType) left else Cast(left, widestType) + val newRight = if (right.dataType == widestType) right else Cast(right, widestType) b.makeCopy(Array(newLeft, newRight)) }.getOrElse(b) // If there is no applicable conversion, leave expression unchanged. } @@ -237,57 +272,52 @@ trait HiveTypeCoercion { // Skip nodes who's children have not been resolved yet. case e if !e.childrenResolved => e - case a: BinaryArithmetic if a.left.dataType == StringType => - a.makeCopy(Array(Cast(a.left, DoubleType), a.right)) - case a: BinaryArithmetic if a.right.dataType == StringType => - a.makeCopy(Array(a.left, Cast(a.right, DoubleType))) - - // we should cast all timestamp/date/string compare into string compare - case p: BinaryComparison if p.left.dataType == StringType && - p.right.dataType == DateType => - p.makeCopy(Array(p.left, Cast(p.right, StringType))) - case p: BinaryComparison if p.left.dataType == DateType && - p.right.dataType == StringType => - p.makeCopy(Array(Cast(p.left, StringType), p.right)) - case p: BinaryComparison if p.left.dataType == StringType && - p.right.dataType == TimestampType => - p.makeCopy(Array(p.left, Cast(p.right, StringType))) - case p: BinaryComparison if p.left.dataType == TimestampType && - p.right.dataType == StringType => - p.makeCopy(Array(Cast(p.left, StringType), p.right)) - case p: BinaryComparison if p.left.dataType == TimestampType && - p.right.dataType == DateType => - p.makeCopy(Array(Cast(p.left, StringType), Cast(p.right, StringType))) - case p: BinaryComparison if p.left.dataType == DateType && - p.right.dataType == TimestampType => - p.makeCopy(Array(Cast(p.left, StringType), Cast(p.right, StringType))) - - case p: BinaryComparison if p.left.dataType == StringType && - p.right.dataType != StringType => - p.makeCopy(Array(Cast(p.left, DoubleType), p.right)) - case p: BinaryComparison if p.left.dataType != StringType && - p.right.dataType == StringType => - p.makeCopy(Array(p.left, Cast(p.right, DoubleType))) - - case i @ In(a, b) if a.dataType == DateType && - b.forall(_.dataType == StringType) => - i.makeCopy(Array(Cast(a, StringType), b)) - case i @ In(a, b) if a.dataType == TimestampType && - b.forall(_.dataType == StringType) => + case a @ BinaryArithmetic(left @ StringType(), r) => + a.makeCopy(Array(Cast(left, DoubleType), r)) + case a @ BinaryArithmetic(left, right @ StringType()) => + a.makeCopy(Array(left, Cast(right, DoubleType))) + + // For equality between string and timestamp we cast the string to a timestamp + // so that things like rounding of subsecond precision does not affect the comparison. + case p @ Equality(left @ StringType(), right @ TimestampType()) => + p.makeCopy(Array(Cast(left, TimestampType), right)) + case p @ Equality(left @ TimestampType(), right @ StringType()) => + p.makeCopy(Array(left, Cast(right, TimestampType))) + + // We should cast all relative timestamp/date/string comparison into string comparisions + // This behaves as a user would expect because timestamp strings sort lexicographically. + // i.e. TimeStamp(2013-01-01 00:00 ...) < "2014" = true + case p @ BinaryComparison(left @ StringType(), right @ DateType()) => + p.makeCopy(Array(left, Cast(right, StringType))) + case p @ BinaryComparison(left @ DateType(), right @ StringType()) => + p.makeCopy(Array(Cast(left, StringType), right)) + case p @ BinaryComparison(left @ StringType(), right @ TimestampType()) => + p.makeCopy(Array(left, Cast(right, StringType))) + case p @ BinaryComparison(left @ TimestampType(), right @ StringType()) => + p.makeCopy(Array(Cast(left, StringType), right)) + + // Comparisons between dates and timestamps. + case p @ BinaryComparison(left @ TimestampType(), right @ DateType()) => + p.makeCopy(Array(Cast(left, StringType), Cast(right, StringType))) + case p @ BinaryComparison(left @ DateType(), right @ TimestampType()) => + p.makeCopy(Array(Cast(left, StringType), Cast(right, StringType))) + + case p @ BinaryComparison(left @ StringType(), right) if right.dataType != StringType => + p.makeCopy(Array(Cast(left, DoubleType), right)) + case p @ BinaryComparison(left, right @ StringType()) if left.dataType != StringType => + p.makeCopy(Array(left, Cast(right, DoubleType))) + + case i @ In(a @ DateType(), b) if b.forall(_.dataType == StringType) => i.makeCopy(Array(Cast(a, StringType), b)) - case i @ In(a, b) if a.dataType == DateType && - b.forall(_.dataType == TimestampType) => + case i @ In(a @ TimestampType(), b) if b.forall(_.dataType == StringType) => + i.makeCopy(Array(a, b.map(Cast(_, TimestampType)))) + case i @ In(a @ DateType(), b) if b.forall(_.dataType == TimestampType) => i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType)))) - case i @ In(a, b) if a.dataType == TimestampType && - b.forall(_.dataType == DateType) => + case i @ In(a @ TimestampType(), b) if b.forall(_.dataType == DateType) => i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType)))) - case Sum(e) if e.dataType == StringType => - Sum(Cast(e, DoubleType)) - case Average(e) if e.dataType == StringType => - Average(Cast(e, DoubleType)) - case Sqrt(e) if e.dataType == StringType => - Sqrt(Cast(e, DoubleType)) + case Sum(e @ StringType()) => Sum(Cast(e, DoubleType)) + case Average(e @ StringType()) => Average(Cast(e, DoubleType)) } } @@ -296,6 +326,9 @@ trait HiveTypeCoercion { */ object InConversion extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { + // Skip nodes who's children have not been resolved yet. + case e if !e.childrenResolved => e + case i @ In(a, b) if b.exists(_.dataType != a.dataType) => i.makeCopy(Array(a, b.map(Cast(_, a.dataType)))) } @@ -347,17 +380,17 @@ trait HiveTypeCoercion { import scala.math.{max, min} // Conversion rules for integer types into fixed-precision decimals - val intTypeToFixed: Map[DataType, DecimalType] = Map( + private val intTypeToFixed: Map[DataType, DecimalType] = Map( ByteType -> DecimalType(3, 0), ShortType -> DecimalType(5, 0), IntegerType -> DecimalType(10, 0), LongType -> DecimalType(20, 0) ) - def isFloat(t: DataType): Boolean = t == FloatType || t == DoubleType + private def isFloat(t: DataType): Boolean = t == FloatType || t == DoubleType // Conversion rules for float and double into fixed-precision decimals - val floatTypeToFixed: Map[DataType, DecimalType] = Map( + private val floatTypeToFixed: Map[DataType, DecimalType] = Map( FloatType -> DecimalType(7, 7), DoubleType -> DecimalType(15, 15) ) @@ -366,22 +399,22 @@ trait HiveTypeCoercion { // fix decimal precision for union case u @ Union(left, right) if u.childrenResolved && !u.resolved => val castedInput = left.output.zip(right.output).map { - case (l, r) if l.dataType != r.dataType => - (l.dataType, r.dataType) match { + case (lhs, rhs) if lhs.dataType != rhs.dataType => + (lhs.dataType, rhs.dataType) match { case (DecimalType.Fixed(p1, s1), DecimalType.Fixed(p2, s2)) => // Union decimals with precision/scale p1/s2 and p2/s2 will be promoted to // DecimalType(max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2)) val fixedType = DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2), max(s1, s2)) - (Alias(Cast(l, fixedType), l.name)(), Alias(Cast(r, fixedType), r.name)()) + (Alias(Cast(lhs, fixedType), lhs.name)(), Alias(Cast(rhs, fixedType), rhs.name)()) case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => - (Alias(Cast(l, intTypeToFixed(t)), l.name)(), r) + (Alias(Cast(lhs, intTypeToFixed(t)), lhs.name)(), rhs) case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => - (l, Alias(Cast(r, intTypeToFixed(t)), r.name)()) + (lhs, Alias(Cast(rhs, intTypeToFixed(t)), rhs.name)()) case (t, DecimalType.Fixed(p, s)) if floatTypeToFixed.contains(t) => - (Alias(Cast(l, floatTypeToFixed(t)), l.name)(), r) + (Alias(Cast(lhs, floatTypeToFixed(t)), lhs.name)(), rhs) case (DecimalType.Fixed(p, s), t) if floatTypeToFixed.contains(t) => - (l, Alias(Cast(r, floatTypeToFixed(t)), r.name)()) - case _ => (l, r) + (lhs, Alias(Cast(rhs, floatTypeToFixed(t)), rhs.name)()) + case _ => (lhs, rhs) } case other => other } @@ -439,34 +472,25 @@ trait HiveTypeCoercion { DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) ) - case LessThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - LessThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case LessThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - LessThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case GreaterThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - GreaterThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case GreaterThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - GreaterThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + // When we compare 2 decimal types with different precisions, cast them to the smallest + // common precision. + case b @ BinaryComparison(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + val resultType = DecimalType(max(p1, p2), max(s1, s2)) + b.makeCopy(Array(Cast(e1, resultType), Cast(e2, resultType))) // Promote integers inside a binary expression with fixed-precision decimals to decimals, // and fixed-precision decimals in an expression with floats / doubles to doubles - case b: BinaryExpression if b.left.dataType != b.right.dataType => - (b.left.dataType, b.right.dataType) match { + case b @ BinaryExpression(left, right) if left.dataType != right.dataType => + (left.dataType, right.dataType) match { case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => - b.makeCopy(Array(Cast(b.left, intTypeToFixed(t)), b.right)) + b.makeCopy(Array(Cast(left, intTypeToFixed(t)), right)) case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => - b.makeCopy(Array(b.left, Cast(b.right, intTypeToFixed(t)))) + b.makeCopy(Array(left, Cast(right, intTypeToFixed(t)))) case (t, DecimalType.Fixed(p, s)) if isFloat(t) => - b.makeCopy(Array(b.left, Cast(b.right, DoubleType))) + b.makeCopy(Array(left, Cast(right, DoubleType))) case (DecimalType.Fixed(p, s), t) if isFloat(t) => - b.makeCopy(Array(Cast(b.left, DoubleType), b.right)) + b.makeCopy(Array(Cast(left, DoubleType), right)) case _ => b } @@ -480,56 +504,66 @@ trait HiveTypeCoercion { } /** - * Changes Boolean values to Bytes so that expressions like true < false can be Evaluated. + * Changes numeric values to booleans so that expressions like true = 1 can be evaluated. */ - object BooleanComparisons extends Rule[LogicalPlan] { - val trueValues = Seq(1, 1L, 1.toByte, 1.toShort, new java.math.BigDecimal(1)).map(Literal(_)) - val falseValues = Seq(0, 0L, 0.toByte, 0.toShort, new java.math.BigDecimal(0)).map(Literal(_)) + object BooleanEquality extends Rule[LogicalPlan] { + private val trueValues = Seq(1.toByte, 1.toShort, 1, 1L, Decimal(1)) + private val falseValues = Seq(0.toByte, 0.toShort, 0, 0L, Decimal(0)) + + private def buildCaseKeyWhen(booleanExpr: Expression, numericExpr: Expression) = { + CaseKeyWhen(numericExpr, Seq( + Literal(trueValues.head), booleanExpr, + Literal(falseValues.head), Not(booleanExpr), + Literal(false))) + } - def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { - // Skip nodes who's children have not been resolved yet. - case e if !e.childrenResolved => e + private def transform(booleanExpr: Expression, numericExpr: Expression) = { + If(Or(IsNull(booleanExpr), IsNull(numericExpr)), + Literal.create(null, BooleanType), + buildCaseKeyWhen(booleanExpr, numericExpr)) + } - // Hive treats (true = 1) as true and (false = 0) as true. - case EqualTo(l @ BooleanType(), r) if trueValues.contains(r) => l - case EqualTo(l, r @ BooleanType()) if trueValues.contains(l) => r - case EqualTo(l @ BooleanType(), r) if falseValues.contains(r) => Not(l) - case EqualTo(l, r @ BooleanType()) if falseValues.contains(l) => Not(r) - - // No need to change other EqualTo operators as that actually makes sense for boolean types. - case e: EqualTo => e - // No need to change the EqualNullSafe operators, too - case e: EqualNullSafe => e - // Otherwise turn them to Byte types so that there exists and ordering. - case p: BinaryComparison if p.left.dataType == BooleanType && - p.right.dataType == BooleanType => - p.makeCopy(Array(Cast(p.left, ByteType), Cast(p.right, ByteType))) + private def transformNullSafe(booleanExpr: Expression, numericExpr: Expression) = { + CaseWhen(Seq( + And(IsNull(booleanExpr), IsNull(numericExpr)), Literal(true), + Or(IsNull(booleanExpr), IsNull(numericExpr)), Literal(false), + buildCaseKeyWhen(booleanExpr, numericExpr) + )) } - } - /** - * Casts to/from [[BooleanType]] are transformed into comparisons since - * the JVM does not consider Booleans to be numeric types. - */ - object BooleanCasts extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { // Skip nodes who's children have not been resolved yet. case e if !e.childrenResolved => e - // Skip if the type is boolean type already. Note that this extra cast should be removed - // by optimizer.SimplifyCasts. - case Cast(e, BooleanType) if e.dataType == BooleanType => e - // DateType should be null if be cast to boolean. - case Cast(e, BooleanType) if e.dataType == DateType => Cast(e, BooleanType) - // If the data type is not boolean and is being cast boolean, turn it into a comparison - // with the numeric value, i.e. x != 0. This will coerce the type into numeric type. - case Cast(e, BooleanType) if e.dataType != BooleanType => Not(EqualTo(e, Literal(0))) - // Stringify boolean if casting to StringType. - // TODO Ensure true/false string letter casing is consistent with Hive in all cases. - case Cast(e, StringType) if e.dataType == BooleanType => - If(e, Literal("true"), Literal("false")) - // Turn true into 1, and false into 0 if casting boolean into other types. - case Cast(e, dataType) if e.dataType == BooleanType => - Cast(If(e, Literal(1), Literal(0)), dataType) + + // Hive treats (true = 1) as true and (false = 0) as true, + // all other cases are considered as false. + + // We may simplify the expression if one side is literal numeric values + case EqualTo(bool @ BooleanType(), Literal(value, _: NumericType)) + if trueValues.contains(value) => bool + case EqualTo(bool @ BooleanType(), Literal(value, _: NumericType)) + if falseValues.contains(value) => Not(bool) + case EqualTo(Literal(value, _: NumericType), bool @ BooleanType()) + if trueValues.contains(value) => bool + case EqualTo(Literal(value, _: NumericType), bool @ BooleanType()) + if falseValues.contains(value) => Not(bool) + case EqualNullSafe(bool @ BooleanType(), Literal(value, _: NumericType)) + if trueValues.contains(value) => And(IsNotNull(bool), bool) + case EqualNullSafe(bool @ BooleanType(), Literal(value, _: NumericType)) + if falseValues.contains(value) => And(IsNotNull(bool), Not(bool)) + case EqualNullSafe(Literal(value, _: NumericType), bool @ BooleanType()) + if trueValues.contains(value) => And(IsNotNull(bool), bool) + case EqualNullSafe(Literal(value, _: NumericType), bool @ BooleanType()) + if falseValues.contains(value) => And(IsNotNull(bool), Not(bool)) + + case EqualTo(left @ BooleanType(), right @ NumericType()) => + transform(left , right) + case EqualTo(left @ NumericType(), right @ BooleanType()) => + transform(right, left) + case EqualNullSafe(left @ BooleanType(), right @ NumericType()) => + transformNullSafe(left, right) + case EqualNullSafe(left @ NumericType(), right @ BooleanType()) => + transformNullSafe(right, left) } } @@ -558,8 +592,7 @@ trait HiveTypeCoercion { case a @ CreateArray(children) if !a.resolved => val commonType = a.childTypes.reduce( - (a,b) => - findTightestCommonType(a,b).getOrElse(StringType)) + (a, b) => findTightestCommonTypeOfTwo(a, b).getOrElse(StringType)) CreateArray( children.map(c => if (c.dataType == commonType) c else Cast(c, commonType))) @@ -588,14 +621,9 @@ trait HiveTypeCoercion { // from the list. So we need to make sure the return type is deterministic and // compatible with every child column. case Coalesce(es) if es.map(_.dataType).distinct.size > 1 => - val dt: Option[DataType] = Some(NullType) val types = es.map(_.dataType) - val rt = types.foldLeft(dt)((r, c) => r match { - case None => None - case Some(d) => findTightestCommonType(d, c) - }) - rt match { - case Some(finaldt) => Coalesce(es.map(Cast(_, finaldt))) + findTightestCommonTypeAndPromoteToString(types) match { + case Some(finalDataType) => Coalesce(es.map(Cast(_, finalDataType))) case None => sys.error(s"Could not determine return type of Coalesce for ${types.mkString(",")}") } @@ -608,19 +636,15 @@ trait HiveTypeCoercion { */ object Division extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { - // Skip nodes who's children have not been resolved yet. - case e if !e.childrenResolved => e + // Skip nodes who has not been resolved yet, + // as this is an extra rule which should be applied at last. + case e if !e.resolved => e // Decimal and Double remain the same - case d: Divide if d.resolved && d.dataType == DoubleType => d - case d: Divide if d.resolved && d.dataType.isInstanceOf[DecimalType] => d + case d: Divide if d.dataType == DoubleType => d + case d: Divide if d.dataType.isInstanceOf[DecimalType] => d - case Divide(l, r) if l.dataType.isInstanceOf[DecimalType] => - Divide(l, Cast(r, DecimalType.Unlimited)) - case Divide(l, r) if r.dataType.isInstanceOf[DecimalType] => - Divide(Cast(l, DecimalType.Unlimited), r) - - case Divide(l, r) => Divide(Cast(l, DoubleType), Cast(r, DoubleType)) + case Divide(left, right) => Divide(Cast(left, DoubleType), Cast(right, DoubleType)) } } @@ -631,25 +655,54 @@ trait HiveTypeCoercion { import HiveTypeCoercion._ def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { - case cw: CaseWhenLike if !cw.resolved && cw.childrenResolved && !cw.valueTypesEqual => - logDebug(s"Input values for null casting ${cw.valueTypes.mkString(",")}") - val commonType = cw.valueTypes.reduce { (v1, v2) => - findTightestCommonType(v1, v2).getOrElse(sys.error( - s"Types in CASE WHEN must be the same or coercible to a common type: $v1 != $v2")) - } - val transformedBranches = cw.branches.sliding(2, 2).map { - case Seq(when, value) if value.dataType != commonType => - Seq(when, Cast(value, commonType)) - case Seq(elseVal) if elseVal.dataType != commonType => - Seq(Cast(elseVal, commonType)) - case s => s - }.reduce(_ ++ _) - cw match { - case _: CaseWhen => - CaseWhen(transformedBranches) - case CaseKeyWhen(key, _) => - CaseKeyWhen(key, transformedBranches) - } + case c: CaseWhenLike if c.childrenResolved && !c.valueTypesEqual => + logDebug(s"Input values for null casting ${c.valueTypes.mkString(",")}") + val maybeCommonType = findTightestCommonTypeAndPromoteToString(c.valueTypes) + maybeCommonType.map { commonType => + val castedBranches = c.branches.grouped(2).map { + case Seq(when, value) if value.dataType != commonType => + Seq(when, Cast(value, commonType)) + case Seq(elseVal) if elseVal.dataType != commonType => + Seq(Cast(elseVal, commonType)) + case other => other + }.reduce(_ ++ _) + c match { + case _: CaseWhen => CaseWhen(castedBranches) + case CaseKeyWhen(key, _) => CaseKeyWhen(key, castedBranches) + } + }.getOrElse(c) + + case c: CaseKeyWhen if c.childrenResolved && !c.resolved => + val maybeCommonType = + findTightestCommonTypeAndPromoteToString((c.key +: c.whenList).map(_.dataType)) + maybeCommonType.map { commonType => + val castedBranches = c.branches.grouped(2).map { + case Seq(when, then) if when.dataType != commonType => + Seq(Cast(when, commonType), then) + case other => other + }.reduce(_ ++ _) + CaseKeyWhen(Cast(c.key, commonType), castedBranches) + }.getOrElse(c) + } + } + + /** + * Coerces the type of different branches of If statement to a common type. + */ + object IfCoercion extends Rule[LogicalPlan] { + def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { + // Find tightest common type for If, if the true value and false value have different types. + case i @ If(pred, left, right) if left.dataType != right.dataType => + findTightestCommonTypeToString(left.dataType, right.dataType).map { widestType => + val newLeft = if (left.dataType == widestType) left else Cast(left, widestType) + val newRight = if (right.dataType == widestType) right else Cast(right, widestType) + If(pred, newLeft, newRight) + }.getOrElse(i) // If there is no applicable conversion, leave expression unchanged. + + // Convert If(null literal, _, _) into boolean type. + // In the optimizer, we should short-circuit this directly into false value. + case If(pred, left, right) if pred.dataType == NullType => + If(Literal.create(null, BooleanType), left, right) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCheckResult.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCheckResult.scala new file mode 100644 index 0000000000000..79c3528a522d3 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCheckResult.scala @@ -0,0 +1,45 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +/** + * Represents the result of `Expression.checkInputDataTypes`. + * We will throw `AnalysisException` in `CheckAnalysis` if `isFailure` is true. + */ +trait TypeCheckResult { + def isFailure: Boolean = !isSuccess + def isSuccess: Boolean +} + +object TypeCheckResult { + + /** + * Represents the successful result of `Expression.checkInputDataTypes`. + */ + object TypeCheckSuccess extends TypeCheckResult { + def isSuccess: Boolean = true + } + + /** + * Represents the failing result of `Expression.checkInputDataTypes`, + * with a error message to show the reason of failure. + */ + case class TypeCheckFailure(message: String) extends TypeCheckResult { + def isSuccess: Boolean = false + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala index 2999c2ef3efe1..c9d91425788a8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.catalyst.analysis +import org.apache.spark.sql.catalyst import org.apache.spark.sql.catalyst.{errors, trees} import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions._ @@ -67,7 +68,7 @@ case class UnresolvedAttribute(nameParts: Seq[String]) override def withName(newName: String): UnresolvedAttribute = UnresolvedAttribute.quoted(newName) // Unresolved attributes are transient at compile time and don't get evaluated during execution. - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def toString: String = s"'$name" @@ -85,7 +86,7 @@ case class UnresolvedFunction(name: String, children: Seq[Expression]) extends E override lazy val resolved = false // Unresolved functions are transient at compile time and don't get evaluated during execution. - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def toString: String = s"'$name(${children.mkString(",")})" @@ -107,7 +108,7 @@ trait Star extends NamedExpression with trees.LeafNode[Expression] { override lazy val resolved = false // Star gets expanded at runtime so we never evaluate a Star. - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") def expand(input: Seq[Attribute], resolver: Resolver): Seq[NamedExpression] @@ -166,7 +167,7 @@ case class MultiAlias(child: Expression, names: Seq[String]) override lazy val resolved = false - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def toString: String = s"$child AS $names" @@ -200,7 +201,7 @@ case class UnresolvedExtractValue(child: Expression, extraction: Expression) override def nullable: Boolean = throw new UnresolvedException(this, "nullable") override lazy val resolved = false - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def toString: String = s"$child[$extraction]" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 4c0d70203c6f5..51821757967d2 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst import java.sql.{Date, Timestamp} import scala.language.implicitConversions -import scala.reflect.runtime.universe.{TypeTag, typeTag} import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedExtractValue, UnresolvedAttribute} import org.apache.spark.sql.catalyst.expressions._ @@ -61,7 +60,7 @@ package object dsl { trait ImplicitOperators { def expr: Expression - def unary_- : Expression= UnaryMinus(expr) + def unary_- : Expression = UnaryMinus(expr) def unary_! : Predicate = Not(expr) def unary_~ : Expression = BitwiseNot(expr) @@ -141,7 +140,7 @@ package object dsl { // Note that if we make ExpressionConversions an object rather than a trait, we can // then make this a value class to avoid the small penalty of runtime instantiation. def $(args: Any*): analysis.UnresolvedAttribute = { - analysis.UnresolvedAttribute(sc.s(args :_*)) + analysis.UnresolvedAttribute(sc.s(args : _*)) } } @@ -234,129 +233,59 @@ package object dsl { implicit class DslAttribute(a: AttributeReference) { def notNull: AttributeReference = a.withNullability(false) def nullable: AttributeReference = a.withNullability(true) - - // Protobuf terminology - def required: AttributeReference = a.withNullability(false) - def at(ordinal: Int): BoundReference = BoundReference(ordinal, a.dataType, a.nullable) } } - object expressions extends ExpressionConversions // scalastyle:ignore - abstract class LogicalPlanFunctions { - def logicalPlan: LogicalPlan - - def select(exprs: NamedExpression*): LogicalPlan = Project(exprs, logicalPlan) + object plans { // scalastyle:ignore + implicit class DslLogicalPlan(val logicalPlan: LogicalPlan) { + def select(exprs: NamedExpression*): LogicalPlan = Project(exprs, logicalPlan) - def where(condition: Expression): LogicalPlan = Filter(condition, logicalPlan) + def where(condition: Expression): LogicalPlan = Filter(condition, logicalPlan) - def limit(limitExpr: Expression): LogicalPlan = Limit(limitExpr, logicalPlan) + def limit(limitExpr: Expression): LogicalPlan = Limit(limitExpr, logicalPlan) - def join( + def join( otherPlan: LogicalPlan, joinType: JoinType = Inner, condition: Option[Expression] = None): LogicalPlan = - Join(logicalPlan, otherPlan, joinType, condition) + Join(logicalPlan, otherPlan, joinType, condition) - def orderBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, true, logicalPlan) + def orderBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, true, logicalPlan) - def sortBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, false, logicalPlan) + def sortBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, false, logicalPlan) - def groupBy(groupingExprs: Expression*)(aggregateExprs: Expression*): LogicalPlan = { - val aliasedExprs = aggregateExprs.map { - case ne: NamedExpression => ne - case e => Alias(e, e.toString)() + def groupBy(groupingExprs: Expression*)(aggregateExprs: Expression*): LogicalPlan = { + val aliasedExprs = aggregateExprs.map { + case ne: NamedExpression => ne + case e => Alias(e, e.toString)() + } + Aggregate(groupingExprs, aliasedExprs, logicalPlan) } - Aggregate(groupingExprs, aliasedExprs, logicalPlan) - } - def subquery(alias: Symbol): LogicalPlan = Subquery(alias.name, logicalPlan) + def subquery(alias: Symbol): LogicalPlan = Subquery(alias.name, logicalPlan) + + def except(otherPlan: LogicalPlan): LogicalPlan = Except(logicalPlan, otherPlan) - def unionAll(otherPlan: LogicalPlan): LogicalPlan = Union(logicalPlan, otherPlan) + def intersect(otherPlan: LogicalPlan): LogicalPlan = Intersect(logicalPlan, otherPlan) - def sfilter[T1](arg1: Symbol)(udf: (T1) => Boolean): LogicalPlan = - Filter(ScalaUdf(udf, BooleanType, Seq(UnresolvedAttribute(arg1.name))), logicalPlan) + def unionAll(otherPlan: LogicalPlan): LogicalPlan = Union(logicalPlan, otherPlan) - // TODO specify the output column names - def generate( + // TODO specify the output column names + def generate( generator: Generator, join: Boolean = false, outer: Boolean = false, alias: Option[String] = None): LogicalPlan = - Generate(generator, join = join, outer = outer, alias, Nil, logicalPlan) + Generate(generator, join = join, outer = outer, alias, Nil, logicalPlan) - def insertInto(tableName: String, overwrite: Boolean = false): LogicalPlan = - InsertIntoTable( - analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite, false) + def insertInto(tableName: String, overwrite: Boolean = false): LogicalPlan = + InsertIntoTable( + analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite, false) - def analyze: LogicalPlan = EliminateSubQueries(analysis.SimpleAnalyzer.execute(logicalPlan)) - } - - object plans { // scalastyle:ignore - implicit class DslLogicalPlan(val logicalPlan: LogicalPlan) extends LogicalPlanFunctions { - def writeToFile(path: String): LogicalPlan = WriteToFile(path, logicalPlan) + def analyze: LogicalPlan = EliminateSubQueries(analysis.SimpleAnalyzer.execute(logicalPlan)) } } - - case class ScalaUdfBuilder[T: TypeTag](f: AnyRef) { - def call(args: Expression*): ScalaUdf = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[T]).dataType, args) - } - } - - // scalastyle:off - /** functionToUdfBuilder 1-22 were generated by this script - - (1 to 22).map { x => - val argTypes = Seq.fill(x)("_").mkString(", ") - s"implicit def functionToUdfBuilder[T: TypeTag](func: Function$x[$argTypes, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func)" - } - */ - - implicit def functionToUdfBuilder[T: TypeTag](func: Function1[_, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function2[_, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function3[_, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function4[_, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function5[_, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function6[_, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function7[_, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function8[_, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function9[_, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function10[_, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function11[_, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function12[_, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function13[_, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function14[_, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function15[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function16[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function17[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function18[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function19[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function20[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function21[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - - implicit def functionToUdfBuilder[T: TypeTag](func: Function22[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) - // scalastyle:on } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala index 0fd4f9b374ee0..d2a90a50c89f4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala @@ -49,11 +49,4 @@ package object errors { case e: Exception => throw new TreeNodeException(tree, msg, e) } } - - /** - * Executes `f` which is expected to throw a - * [[catalyst.errors.TreeNodeException TreeNodeException]]. The first tree encountered in - * the stack of exceptions of type `TreeType` is returned. - */ - def getTree[TreeType <: TreeNode[_]](f: => Unit): TreeType = ??? // TODO: Implement } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala index c6217f07c452d..5db2fcfcb267b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala @@ -19,8 +19,9 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.Logging import org.apache.spark.sql.catalyst.errors.attachTree -import org.apache.spark.sql.types._ +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.types._ /** * A bound reference points to a specific slot in the input tuple, allowing the actual value @@ -30,11 +31,9 @@ import org.apache.spark.sql.catalyst.trees case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean) extends NamedExpression with trees.LeafNode[Expression] { - type EvaluatedType = Any - override def toString: String = s"input[$ordinal]" - override def eval(input: Row): Any = input(ordinal) + override def eval(input: InternalRow): Any = input(ordinal) override def name: String = s"i[$ordinal]" @@ -43,6 +42,14 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean) override def qualifiers: Seq[String] = throw new UnsupportedOperationException override def exprId: ExprId = throw new UnsupportedOperationException + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + s""" + boolean ${ev.isNull} = i.isNullAt($ordinal); + ${ctx.javaType(dataType)} ${ev.primitive} = ${ev.isNull} ? + ${ctx.defaultValue(dataType)} : (${ctx.getColumn(dataType, ordinal)}); + """ + } } object BindReferences extends Logging { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index adf941ab2a45f..ad920f287820c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -17,11 +17,16 @@ package org.apache.spark.sql.catalyst.expressions +import java.math.{BigDecimal => JavaBigDecimal} import java.sql.{Date, Timestamp} import java.text.{DateFormat, SimpleDateFormat} import org.apache.spark.Logging +import org.apache.spark.sql.catalyst +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /** Cast the child expression to the target data type. */ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression with Logging { @@ -34,48 +39,48 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w private[this] def forceNullable(from: DataType, to: DataType) = (from, to) match { case (StringType, _: NumericType) => true - case (StringType, TimestampType) => true - case (DoubleType, TimestampType) => true - case (FloatType, TimestampType) => true - case (StringType, DateType) => true - case (_: NumericType, DateType) => true - case (BooleanType, DateType) => true - case (DateType, _: NumericType) => true - case (DateType, BooleanType) => true + case (StringType, TimestampType) => true + case (DoubleType, TimestampType) => true + case (FloatType, TimestampType) => true + case (StringType, DateType) => true + case (_: NumericType, DateType) => true + case (BooleanType, DateType) => true + case (DateType, _: NumericType) => true + case (DateType, BooleanType) => true case (DoubleType, _: DecimalType) => true - case (FloatType, _: DecimalType) => true + case (FloatType, _: DecimalType) => true case (_, DecimalType.Fixed(_, _)) => true // TODO: not all upcasts here can really give null - case _ => false + case _ => false } private[this] def resolvableNullability(from: Boolean, to: Boolean) = !from || to private[this] def resolve(from: DataType, to: DataType): Boolean = { (from, to) match { - case (from, to) if from == to => true + case (from, to) if from == to => true - case (NullType, _) => true + case (NullType, _) => true - case (_, StringType) => true + case (_, StringType) => true - case (StringType, BinaryType) => true + case (StringType, BinaryType) => true - case (StringType, BooleanType) => true - case (DateType, BooleanType) => true - case (TimestampType, BooleanType) => true - case (_: NumericType, BooleanType) => true + case (StringType, BooleanType) => true + case (DateType, BooleanType) => true + case (TimestampType, BooleanType) => true + case (_: NumericType, BooleanType) => true - case (StringType, TimestampType) => true - case (BooleanType, TimestampType) => true - case (DateType, TimestampType) => true - case (_: NumericType, TimestampType) => true + case (StringType, TimestampType) => true + case (BooleanType, TimestampType) => true + case (DateType, TimestampType) => true + case (_: NumericType, TimestampType) => true - case (_, DateType) => true + case (_, DateType) => true - case (StringType, _: NumericType) => true - case (BooleanType, _: NumericType) => true - case (DateType, _: NumericType) => true - case (TimestampType, _: NumericType) => true + case (StringType, _: NumericType) => true + case (BooleanType, _: NumericType) => true + case (DateType, _: NumericType) => true + case (TimestampType, _: NumericType) => true case (_: NumericType, _: NumericType) => true case (ArrayType(from, fn), ArrayType(to, tn)) => @@ -104,17 +109,16 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w override def toString: String = s"CAST($child, $dataType)" - type EvaluatedType = Any - // [[func]] assumes the input is no longer null because eval already does the null check. @inline private[this] def buildCast[T](a: Any, func: T => Any): Any = func(a.asInstanceOf[T]) // UDFToString private[this] def castToString(from: DataType): Any => Any = from match { - case BinaryType => buildCast[Array[Byte]](_, UTF8String(_)) - case DateType => buildCast[Int](_, d => UTF8String(DateUtils.toString(d))) - case TimestampType => buildCast[Timestamp](_, t => UTF8String(timestampToString(t))) - case _ => buildCast[Any](_, o => UTF8String(o.toString)) + case BinaryType => buildCast[Array[Byte]](_, UTF8String.fromBytes) + case DateType => buildCast[Int](_, d => UTF8String.fromString(DateUtils.toString(d))) + case TimestampType => buildCast[Long](_, + t => UTF8String.fromString(timestampToString(DateUtils.toJavaTimestamp(t)))) + case _ => buildCast[Any](_, o => UTF8String.fromString(o.toString)) } // BinaryConverter @@ -127,7 +131,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case StringType => buildCast[UTF8String](_, _.length() != 0) case TimestampType => - buildCast[Timestamp](_, t => t.getTime() != 0 || t.getNanos() != 0) + buildCast[Long](_, t => t != 0) case DateType => // Hive would return null when cast from date to boolean buildCast[Int](_, d => null) @@ -140,7 +144,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case ByteType => buildCast[Byte](_, _ != 0) case DecimalType() => - buildCast[Decimal](_, _ != 0) + buildCast[Decimal](_, _ != Decimal(0)) case DoubleType => buildCast[Double](_, _ != 0) case FloatType => @@ -158,20 +162,21 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w if (periodIdx != -1 && n.length() - periodIdx > 9) { n = n.substring(0, periodIdx + 10) } - try Timestamp.valueOf(n) catch { case _: java.lang.IllegalArgumentException => null } + try DateUtils.fromJavaTimestamp(Timestamp.valueOf(n)) + catch { case _: java.lang.IllegalArgumentException => null } }) case BooleanType => - buildCast[Boolean](_, b => new Timestamp((if (b) 1 else 0))) + buildCast[Boolean](_, b => (if (b) 1L else 0)) case LongType => - buildCast[Long](_, l => new Timestamp(l)) + buildCast[Long](_, l => longToTimestamp(l)) case IntegerType => - buildCast[Int](_, i => new Timestamp(i)) + buildCast[Int](_, i => longToTimestamp(i.toLong)) case ShortType => - buildCast[Short](_, s => new Timestamp(s)) + buildCast[Short](_, s => longToTimestamp(s.toLong)) case ByteType => - buildCast[Byte](_, b => new Timestamp(b)) + buildCast[Byte](_, b => longToTimestamp(b.toLong)) case DateType => - buildCast[Int](_, d => new Timestamp(DateUtils.toJavaDate(d).getTime)) + buildCast[Int](_, d => DateUtils.toMillisSinceEpoch(d) * 10000) // TimestampWritable.decimalToTimestamp case DecimalType() => buildCast[Decimal](_, d => decimalToTimestamp(d)) @@ -191,25 +196,17 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w }) } - private[this] def decimalToTimestamp(d: Decimal) = { - val seconds = Math.floor(d.toDouble).toLong - val bd = (d.toBigDecimal - seconds) * 1000000000 - val nanos = bd.intValue() - - val millis = seconds * 1000 - val t = new Timestamp(millis) - - // remaining fractional portion as nanos - t.setNanos(nanos) - t + private[this] def decimalToTimestamp(d: Decimal): Long = { + (d.toBigDecimal * 10000000L).longValue() } - // Timestamp to long, converting milliseconds to seconds - private[this] def timestampToLong(ts: Timestamp) = Math.floor(ts.getTime / 1000.0).toLong - - private[this] def timestampToDouble(ts: Timestamp) = { - // First part is the seconds since the beginning of time, followed by nanosecs. - Math.floor(ts.getTime / 1000.0).toLong + ts.getNanos.toDouble / 1000000000 + // converting milliseconds to 100ns + private[this] def longToTimestamp(t: Long): Long = t * 10000L + // converting 100ns to seconds + private[this] def timestampToLong(ts: Long): Long = math.floor(ts.toDouble / 10000000L).toLong + // converting 100ns to seconds in double + private[this] def timestampToDouble(ts: Long): Double = { + ts / 10000000.0 } // Converts Timestamp to string according to Hive TimestampWritable convention @@ -234,7 +231,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case TimestampType => // throw valid precision more than seconds, according to Hive. // Timestamp.nanos is in 0 to 999,999,999, no more than a second. - buildCast[Timestamp](_, t => DateUtils.millisToDays(t.getTime)) + buildCast[Long](_, t => DateUtils.millisToDays(t / 10000L)) // Hive throws this exception as a Semantic Exception // It is never possible to compare result when hive return with exception, // so we can return null @@ -253,7 +250,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case DateType => buildCast[Int](_, d => null) case TimestampType => - buildCast[Timestamp](_, t => timestampToLong(t)) + buildCast[Long](_, t => timestampToLong(t)) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b) } @@ -269,7 +266,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case DateType => buildCast[Int](_, d => null) case TimestampType => - buildCast[Timestamp](_, t => timestampToLong(t).toInt) + buildCast[Long](_, t => timestampToLong(t).toInt) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b) } @@ -285,7 +282,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case DateType => buildCast[Int](_, d => null) case TimestampType => - buildCast[Timestamp](_, t => timestampToLong(t).toShort) + buildCast[Long](_, t => timestampToLong(t).toShort) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toShort } @@ -301,7 +298,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case DateType => buildCast[Int](_, d => null) case TimestampType => - buildCast[Timestamp](_, t => timestampToLong(t).toByte) + buildCast[Long](_, t => timestampToLong(t).toByte) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toByte } @@ -324,7 +321,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w private[this] def castToDecimal(from: DataType, target: DecimalType): Any => Any = from match { case StringType => buildCast[UTF8String](_, s => try { - changePrecision(Decimal(s.toString.toDouble), target) + changePrecision(Decimal(new JavaBigDecimal(s.toString)), target) } catch { case _: NumberFormatException => null }) @@ -334,7 +331,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w buildCast[Int](_, d => null) // date can't cast to decimal in Hive case TimestampType => // Note that we lose precision here. - buildCast[Timestamp](_, t => changePrecision(Decimal(timestampToDouble(t)), target)) + buildCast[Long](_, t => changePrecision(Decimal(timestampToDouble(t)), target)) case DecimalType() => b => changePrecision(b.asInstanceOf[Decimal].clone(), target) case LongType => @@ -358,7 +355,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case DateType => buildCast[Int](_, d => null) case TimestampType => - buildCast[Timestamp](_, t => timestampToDouble(t)) + buildCast[Long](_, t => timestampToDouble(t)) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toDouble(b) } @@ -374,7 +371,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w case DateType => buildCast[Int](_, d => null) case TimestampType => - buildCast[Timestamp](_, t => timestampToDouble(t).toFloat) + buildCast[Long](_, t => timestampToDouble(t).toFloat) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toFloat(b) } @@ -398,7 +395,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w } // TODO: Could be faster? val newRow = new GenericMutableRow(from.fields.size) - buildCast[Row](_, row => { + buildCast[InternalRow](_, row => { var i = 0 while (i < row.length) { val v = row(i) @@ -411,29 +408,70 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w private[this] def cast(from: DataType, to: DataType): Any => Any = to match { case dt if dt == child.dataType => identity[Any] - case StringType => castToString(from) - case BinaryType => castToBinary(from) - case DateType => castToDate(from) - case decimal: DecimalType => castToDecimal(from, decimal) - case TimestampType => castToTimestamp(from) - case BooleanType => castToBoolean(from) - case ByteType => castToByte(from) - case ShortType => castToShort(from) - case IntegerType => castToInt(from) - case FloatType => castToFloat(from) - case LongType => castToLong(from) - case DoubleType => castToDouble(from) - case array: ArrayType => castArray(from.asInstanceOf[ArrayType], array) - case map: MapType => castMap(from.asInstanceOf[MapType], map) - case struct: StructType => castStruct(from.asInstanceOf[StructType], struct) + case StringType => castToString(from) + case BinaryType => castToBinary(from) + case DateType => castToDate(from) + case decimal: DecimalType => castToDecimal(from, decimal) + case TimestampType => castToTimestamp(from) + case BooleanType => castToBoolean(from) + case ByteType => castToByte(from) + case ShortType => castToShort(from) + case IntegerType => castToInt(from) + case FloatType => castToFloat(from) + case LongType => castToLong(from) + case DoubleType => castToDouble(from) + case array: ArrayType => castArray(from.asInstanceOf[ArrayType], array) + case map: MapType => castMap(from.asInstanceOf[MapType], map) + case struct: StructType => castStruct(from.asInstanceOf[StructType], struct) } private[this] lazy val cast: Any => Any = cast(child.dataType, dataType) - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evaluated = child.eval(input) if (evaluated == null) null else cast(evaluated) } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + // TODO(cg): Add support for more data types. + (child.dataType, dataType) match { + + case (BinaryType, StringType) => + defineCodeGen (ctx, ev, c => + s"${ctx.stringType}.fromBytes($c)") + case (DateType, StringType) => + defineCodeGen(ctx, ev, c => + s"""${ctx.stringType}.fromString( + org.apache.spark.sql.catalyst.util.DateUtils.toString($c))""") + // Special handling required for timestamps in hive test cases since the toString function + // does not match the expected output. + case (TimestampType, StringType) => + super.genCode(ctx, ev) + case (_, StringType) => + defineCodeGen(ctx, ev, c => s"${ctx.stringType}.fromString(String.valueOf($c))") + + // fallback for DecimalType, this must be before other numeric types + case (_, dt: DecimalType) => + super.genCode(ctx, ev) + + case (BooleanType, dt: NumericType) => + defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})($c ? 1 : 0)") + case (dt: DecimalType, BooleanType) => + defineCodeGen(ctx, ev, c => s"!$c.isZero()") + case (dt: NumericType, BooleanType) => + defineCodeGen(ctx, ev, c => s"$c != 0") + + case (_: DecimalType, IntegerType) => + defineCodeGen(ctx, ev, c => s"($c).toInt()") + case (_: DecimalType, dt: NumericType) => + defineCodeGen(ctx, ev, c => s"($c).to${ctx.boxedType(dt)}()") + case (_: NumericType, dt: NumericType) => + defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})($c)") + + case other => + super.genCode(ctx, ev) + } + } } object Cast { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala index 0837a3179d897..a10a959ae766f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala @@ -17,17 +17,23 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, UnresolvedAttribute} +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.trees.TreeNode import org.apache.spark.sql.types._ + +/** + * If an expression wants to be exposed in the function registry (so users can call it with + * "name(arguments...)", the concrete implementation must be a case class whose constructor + * arguments are all Expressions types. + * + * See [[Substring]] for an example. + */ abstract class Expression extends TreeNode[Expression] { self: Product => - /** The narrowest possible type that is produced when this expression is evaluated. */ - type EvaluatedType <: Any - /** * Returns true when an expression is a candidate for static evaluation before the query is * executed. @@ -40,19 +46,77 @@ abstract class Expression extends TreeNode[Expression] { * - A [[Cast]] or [[UnaryMinus]] is foldable if its child is foldable */ def foldable: Boolean = false + + /** + * Returns true when the current expression always return the same result for fixed input values. + */ + // TODO: Need to define explicit input values vs implicit input values. + def deterministic: Boolean = true + def nullable: Boolean + def references: AttributeSet = AttributeSet(children.flatMap(_.references.iterator)) /** Returns the result of evaluating this expression on a given input Row */ - def eval(input: Row = null): EvaluatedType + def eval(input: InternalRow = null): Any + + /** + * Return true if this expression is thread-safe, which means it could be used by multiple + * threads in the same time. + * + * An expression that is not thread-safe can not be cached and re-used, especially for codegen. + */ + def isThreadSafe: Boolean = true + + /** + * Returns an [[GeneratedExpressionCode]], which contains Java source code that + * can be used to generate the result of evaluating the expression on an input row. + * + * @param ctx a [[CodeGenContext]] + * @return [[GeneratedExpressionCode]] + */ + def gen(ctx: CodeGenContext): GeneratedExpressionCode = { + if (!isThreadSafe) { + throw new Exception(s"$this is not thread-safe, can not be used in codegen") + } + val isNull = ctx.freshName("isNull") + val primitive = ctx.freshName("primitive") + val ve = GeneratedExpressionCode("", isNull, primitive) + ve.code = genCode(ctx, ve) + ve + } + + /** + * Returns Java source code that can be compiled to evaluate this expression. + * The default behavior is to call the eval method of the expression. Concrete expression + * implementations should override this to do actual code generation. + * + * @param ctx a [[CodeGenContext]] + * @param ev an [[GeneratedExpressionCode]] with unique terms. + * @return Java source code + */ + protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + ctx.references += this + val objectTerm = ctx.freshName("obj") + s""" + /* expression: ${this} */ + Object $objectTerm = expressions[${ctx.references.size - 1}].eval(i); + boolean ${ev.isNull} = $objectTerm == null; + ${ctx.javaType(this.dataType)} ${ev.primitive} = ${ctx.defaultValue(this.dataType)}; + if (!${ev.isNull}) { + ${ev.primitive} = (${ctx.boxedType(this.dataType)}) $objectTerm; + } + """ + } /** * Returns `true` if this expression and all its children have been resolved to a specific schema - * and `false` if it still contains any unresolved placeholders. Implementations of expressions - * should override this if the resolution of this type of expression involves more than just - * the resolution of its children. + * and input data types checking passed, and `false` if it still contains any unresolved + * placeholders or has data types mismatch. + * Implementations of expressions should override this if the resolution of this type of + * expression involves more than just the resolution of its children and type checking. */ - lazy val resolved: Boolean = childrenResolved + lazy val resolved: Boolean = childrenResolved && checkInputDataTypes().isSuccess /** * Returns the [[DataType]] of the result of evaluating this expression. It is @@ -76,18 +140,85 @@ abstract class Expression extends TreeNode[Expression] { case u: UnresolvedAttribute => PrettyAttribute(u.name) }.toString } + + /** + * Returns true when two expressions will always compute the same result, even if they differ + * cosmetically (i.e. capitalization of names in attributes may be different). + */ + def semanticEquals(other: Expression): Boolean = this.getClass == other.getClass && { + def checkSemantic(elements1: Seq[Any], elements2: Seq[Any]): Boolean = { + elements1.length == elements2.length && elements1.zip(elements2).forall { + case (e1: Expression, e2: Expression) => e1 semanticEquals e2 + case (Some(e1: Expression), Some(e2: Expression)) => e1 semanticEquals e2 + case (t1: Traversable[_], t2: Traversable[_]) => checkSemantic(t1.toSeq, t2.toSeq) + case (i1, i2) => i1 == i2 + } + } + val elements1 = this.productIterator.toSeq + val elements2 = other.asInstanceOf[Product].productIterator.toSeq + checkSemantic(elements1, elements2) + } + + /** + * Checks the input data types, returns `TypeCheckResult.success` if it's valid, + * or returns a `TypeCheckResult` with an error message if invalid. + * Note: it's not valid to call this method until `childrenResolved == true` + * TODO: we should remove the default implementation and implement it for all + * expressions with proper error message. + */ + def checkInputDataTypes(): TypeCheckResult = TypeCheckResult.TypeCheckSuccess } abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] { self: Product => - def symbol: String + def symbol: String = sys.error(s"BinaryExpressions must override either toString or symbol") override def foldable: Boolean = left.foldable && right.foldable override def nullable: Boolean = left.nullable || right.nullable override def toString: String = s"($left $symbol $right)" + + override def isThreadSafe: Boolean = left.isThreadSafe && right.isThreadSafe + /** + * Short hand for generating binary evaluation code, which depends on two sub-evaluations of + * the same type. If either of the sub-expressions is null, the result of this computation + * is assumed to be null. + * + * @param f accepts two variable names and returns Java code to compute the output. + */ + protected def defineCodeGen( + ctx: CodeGenContext, + ev: GeneratedExpressionCode, + f: (String, String) => String): String = { + // TODO: Right now some timestamp tests fail if we enforce this... + if (left.dataType != right.dataType) { + // log.warn(s"${left.dataType} != ${right.dataType}") + } + + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) + val resultCode = f(eval1.primitive, eval2.primitive) + + s""" + ${eval1.code} + boolean ${ev.isNull} = ${eval1.isNull}; + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + if (!${ev.isNull}) { + ${eval2.code} + if (!${eval2.isNull}) { + ${ev.primitive} = $resultCode; + } else { + ${ev.isNull} = true; + } + } + """ + } +} + +private[sql] object BinaryExpression { + def unapply(e: BinaryExpression): Option[(Expression, Expression)] = Some((e.left, e.right)) } abstract class LeafExpression extends Expression with trees.LeafNode[Expression] { @@ -96,19 +227,36 @@ abstract class LeafExpression extends Expression with trees.LeafNode[Expression] abstract class UnaryExpression extends Expression with trees.UnaryNode[Expression] { self: Product => -} -// TODO Semantically we probably not need GroupExpression -// All we need is holding the Seq[Expression], and ONLY used in doing the -// expressions transformation correctly. Probably will be removed since it's -// not like a real expressions. -case class GroupExpression(children: Seq[Expression]) extends Expression { - self: Product => - type EvaluatedType = Seq[Any] - override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException - override def nullable: Boolean = false - override def foldable: Boolean = false - override def dataType: DataType = throw new UnsupportedOperationException + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def isThreadSafe: Boolean = child.isThreadSafe + + /** + * Called by unary expressions to generate a code block that returns null if its parent returns + * null, and if not not null, use `f` to generate the expression. + * + * As an example, the following does a boolean inversion (i.e. NOT). + * {{{ + * defineCodeGen(ctx, ev, c => s"!($c)") + * }}} + * + * @param f function that accepts a variable name and returns Java code to compute the output. + */ + protected def defineCodeGen( + ctx: CodeGenContext, + ev: GeneratedExpressionCode, + f: String => String): String = { + val eval = child.gen(ctx) + // reuse the previous isNull + ev.isNull = eval.isNull + eval.code + s""" + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + if (!${ev.isNull}) { + ${ev.primitive} = ${f(eval.primitive)}; + } + """ + } } /** @@ -116,7 +264,13 @@ case class GroupExpression(children: Seq[Expression]) extends Expression { * so that the proper type conversions can be performed in the analyzer. */ trait ExpectsInputTypes { + self: Expression => def expectedChildTypes: Seq[DataType] + override def checkInputDataTypes(): TypeCheckResult = { + // We will always do type casting for `ExpectsInputTypes` in `HiveTypeCoercion`, + // so type mismatch error won't be reported here, but for underling `Cast`s. + TypeCheckResult.TypeCheckSuccess + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala index e05926cbfe74b..4aaabff15b6ee 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import scala.collection.Map -import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.{catalyst, AnalysisException} import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.types._ @@ -47,7 +47,7 @@ object ExtractValue { case (ArrayType(StructType(fields), containsNull), Literal(fieldName, StringType)) => val ordinal = findField(fields, fieldName.toString, resolver) GetArrayStructFields(child, fields(ordinal), ordinal, containsNull) - case (_: ArrayType, _) if extraction.dataType.isInstanceOf[IntegralType] => + case (_: ArrayType, _) if extraction.dataType.isInstanceOf[IntegralType] => GetArrayItem(child, extraction) case (_: MapType, _) => GetMapValue(child, extraction) @@ -92,8 +92,6 @@ object ExtractValue { trait ExtractValue extends UnaryExpression { self: Product => - - type EvaluatedType = Any } /** @@ -107,8 +105,8 @@ case class GetStructField(child: Expression, field: StructField, ordinal: Int) override def foldable: Boolean = child.foldable override def toString: String = s"$child.${field.name}" - override def eval(input: Row): Any = { - val baseValue = child.eval(input).asInstanceOf[Row] + override def eval(input: InternalRow): Any = { + val baseValue = child.eval(input).asInstanceOf[InternalRow] if (baseValue == null) null else baseValue(ordinal) } } @@ -127,8 +125,8 @@ case class GetArrayStructFields( override def foldable: Boolean = child.foldable override def toString: String = s"$child.${field.name}" - override def eval(input: Row): Any = { - val baseValue = child.eval(input).asInstanceOf[Seq[Row]] + override def eval(input: InternalRow): Any = { + val baseValue = child.eval(input).asInstanceOf[Seq[InternalRow]] if (baseValue == null) null else { baseValue.map { row => if (row == null) null else row(ordinal) @@ -148,7 +146,7 @@ abstract class ExtractValueWithOrdinal extends ExtractValue { override def toString: String = s"$child[$ordinal]" override def children: Seq[Expression] = child :: ordinal :: Nil - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val value = child.eval(input) if (value == null) { null diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala index 8cae548279eb1..d5967438ccb5a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala @@ -17,7 +17,6 @@ package org.apache.spark.sql.catalyst.expressions - /** * A [[Projection]] that is calculated by calling the `eval` of each of the specified expressions. * @param expressions a sequence of expressions that determine the value of each column of the @@ -30,7 +29,7 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection { // null check is required for when Kryo invokes the no-arg constructor. protected val exprArray = if (expressions != null) expressions.toArray else null - def apply(input: Row): Row = { + def apply(input: InternalRow): InternalRow = { val outputArray = new Array[Any](exprArray.length) var i = 0 while (i < exprArray.length) { @@ -55,14 +54,14 @@ case class InterpretedMutableProjection(expressions: Seq[Expression]) extends Mu private[this] val exprArray = expressions.toArray private[this] var mutableRow: MutableRow = new GenericMutableRow(exprArray.size) - def currentValue: Row = mutableRow + def currentValue: InternalRow = mutableRow override def target(row: MutableRow): MutableProjection = { mutableRow = row this } - override def apply(input: Row): Row = { + override def apply(input: InternalRow): InternalRow = { var i = 0 while (i < exprArray.length) { mutableRow(i) = exprArray(i).eval(input) @@ -76,31 +75,31 @@ case class InterpretedMutableProjection(expressions: Seq[Expression]) extends Mu * A mutable wrapper that makes two rows appear as a single concatenated row. Designed to * be instantiated once per thread and reused. */ -class JoinedRow extends Row { - private[this] var row1: Row = _ - private[this] var row2: Row = _ +class JoinedRow extends InternalRow { + private[this] var row1: InternalRow = _ + private[this] var row2: InternalRow = _ - def this(left: Row, right: Row) = { + def this(left: InternalRow, right: InternalRow) = { this() row1 = left row2 = right } /** Updates this JoinedRow to used point at two new base rows. Returns itself. */ - def apply(r1: Row, r2: Row): Row = { + def apply(r1: InternalRow, r2: InternalRow): InternalRow = { row1 = r1 row2 = r2 this } /** Updates this JoinedRow by updating its left base row. Returns itself. */ - def withLeft(newLeft: Row): Row = { + def withLeft(newLeft: InternalRow): InternalRow = { row1 = newLeft this } /** Updates this JoinedRow by updating its right base row. Returns itself. */ - def withRight(newRight: Row): Row = { + def withRight(newRight: InternalRow): InternalRow = { row2 = newRight this } @@ -142,7 +141,7 @@ class JoinedRow extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy(): Row = { + override def copy(): InternalRow = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -176,31 +175,31 @@ class JoinedRow extends Row { * Row will be referenced, increasing the opportunity for the JIT to play tricks. This sounds * crazy but in benchmarks it had noticeable effects. */ -class JoinedRow2 extends Row { - private[this] var row1: Row = _ - private[this] var row2: Row = _ +class JoinedRow2 extends InternalRow { + private[this] var row1: InternalRow = _ + private[this] var row2: InternalRow = _ - def this(left: Row, right: Row) = { + def this(left: InternalRow, right: InternalRow) = { this() row1 = left row2 = right } /** Updates this JoinedRow to used point at two new base rows. Returns itself. */ - def apply(r1: Row, r2: Row): Row = { + def apply(r1: InternalRow, r2: InternalRow): InternalRow = { row1 = r1 row2 = r2 this } /** Updates this JoinedRow by updating its left base row. Returns itself. */ - def withLeft(newLeft: Row): Row = { + def withLeft(newLeft: InternalRow): InternalRow = { row1 = newLeft this } /** Updates this JoinedRow by updating its right base row. Returns itself. */ - def withRight(newRight: Row): Row = { + def withRight(newRight: InternalRow): InternalRow = { row2 = newRight this } @@ -242,7 +241,7 @@ class JoinedRow2 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy(): Row = { + override def copy(): InternalRow = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -270,31 +269,31 @@ class JoinedRow2 extends Row { /** * JIT HACK: Replace with macros */ -class JoinedRow3 extends Row { - private[this] var row1: Row = _ - private[this] var row2: Row = _ +class JoinedRow3 extends InternalRow { + private[this] var row1: InternalRow = _ + private[this] var row2: InternalRow = _ - def this(left: Row, right: Row) = { + def this(left: InternalRow, right: InternalRow) = { this() row1 = left row2 = right } /** Updates this JoinedRow to used point at two new base rows. Returns itself. */ - def apply(r1: Row, r2: Row): Row = { + def apply(r1: InternalRow, r2: InternalRow): InternalRow = { row1 = r1 row2 = r2 this } /** Updates this JoinedRow by updating its left base row. Returns itself. */ - def withLeft(newLeft: Row): Row = { + def withLeft(newLeft: InternalRow): InternalRow = { row1 = newLeft this } /** Updates this JoinedRow by updating its right base row. Returns itself. */ - def withRight(newRight: Row): Row = { + def withRight(newRight: InternalRow): InternalRow = { row2 = newRight this } @@ -336,7 +335,7 @@ class JoinedRow3 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy(): Row = { + override def copy(): InternalRow = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -364,31 +363,31 @@ class JoinedRow3 extends Row { /** * JIT HACK: Replace with macros */ -class JoinedRow4 extends Row { - private[this] var row1: Row = _ - private[this] var row2: Row = _ +class JoinedRow4 extends InternalRow { + private[this] var row1: InternalRow = _ + private[this] var row2: InternalRow = _ - def this(left: Row, right: Row) = { + def this(left: InternalRow, right: InternalRow) = { this() row1 = left row2 = right } /** Updates this JoinedRow to used point at two new base rows. Returns itself. */ - def apply(r1: Row, r2: Row): Row = { + def apply(r1: InternalRow, r2: InternalRow): InternalRow = { row1 = r1 row2 = r2 this } /** Updates this JoinedRow by updating its left base row. Returns itself. */ - def withLeft(newLeft: Row): Row = { + def withLeft(newLeft: InternalRow): InternalRow = { row1 = newLeft this } /** Updates this JoinedRow by updating its right base row. Returns itself. */ - def withRight(newRight: Row): Row = { + def withRight(newRight: InternalRow): InternalRow = { row2 = newRight this } @@ -430,7 +429,7 @@ class JoinedRow4 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy(): Row = { + override def copy(): InternalRow = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -458,31 +457,31 @@ class JoinedRow4 extends Row { /** * JIT HACK: Replace with macros */ -class JoinedRow5 extends Row { - private[this] var row1: Row = _ - private[this] var row2: Row = _ +class JoinedRow5 extends InternalRow { + private[this] var row1: InternalRow = _ + private[this] var row2: InternalRow = _ - def this(left: Row, right: Row) = { + def this(left: InternalRow, right: InternalRow) = { this() row1 = left row2 = right } /** Updates this JoinedRow to used point at two new base rows. Returns itself. */ - def apply(r1: Row, r2: Row): Row = { + def apply(r1: InternalRow, r2: InternalRow): InternalRow = { row1 = r1 row2 = r2 this } /** Updates this JoinedRow by updating its left base row. Returns itself. */ - def withLeft(newLeft: Row): Row = { + def withLeft(newLeft: InternalRow): InternalRow = { row1 = newLeft this } /** Updates this JoinedRow by updating its right base row. Returns itself. */ - def withRight(newRight: Row): Row = { + def withRight(newRight: InternalRow): InternalRow = { row2 = newRight this } @@ -524,7 +523,7 @@ class JoinedRow5 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy(): Row = { + override def copy(): InternalRow = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -552,31 +551,31 @@ class JoinedRow5 extends Row { /** * JIT HACK: Replace with macros */ -class JoinedRow6 extends Row { - private[this] var row1: Row = _ - private[this] var row2: Row = _ +class JoinedRow6 extends InternalRow { + private[this] var row1: InternalRow = _ + private[this] var row2: InternalRow = _ - def this(left: Row, right: Row) = { + def this(left: InternalRow, right: InternalRow) = { this() row1 = left row2 = right } /** Updates this JoinedRow to used point at two new base rows. Returns itself. */ - def apply(r1: Row, r2: Row): Row = { + def apply(r1: InternalRow, r2: InternalRow): InternalRow = { row1 = r1 row2 = r2 this } /** Updates this JoinedRow by updating its left base row. Returns itself. */ - def withLeft(newLeft: Row): Row = { + def withLeft(newLeft: InternalRow): InternalRow = { row1 = newLeft this } /** Updates this JoinedRow by updating its right base row. Returns itself. */ - def withRight(newRight: Row): Row = { + def withRight(newRight: InternalRow): InternalRow = { row2 = newRight this } @@ -618,7 +617,7 @@ class JoinedRow6 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy(): Row = { + override def copy(): InternalRow = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala index 9a77ca624ebe2..3992f1f59dad8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.sql.catalyst import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.types.DataType @@ -27,8 +28,6 @@ import org.apache.spark.sql.types.DataType case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expression]) extends Expression { - type EvaluatedType = Any - override def nullable: Boolean = true override def toString: String = s"scalaUDF(${children.mkString(",")})" @@ -47,7 +46,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val func = function.asInstanceOf[($anys) => Any] $childs $converters - (input: Row) => { + (input: InternalRow) => { func( $evals) } @@ -55,11 +54,11 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi }.foreach(println) */ - - val f = children.size match { - case 0 => + + private[this] val f = children.size match { + case 0 => val func = function.asInstanceOf[() => Any] - (input: Row) => { + (input: InternalRow) => { func() } @@ -67,7 +66,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val func = function.asInstanceOf[(Any) => Any] val child0 = children(0) lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input))) } @@ -78,7 +77,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child1 = children(1) lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input))) @@ -92,7 +91,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -109,7 +108,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -129,7 +128,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -152,7 +151,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -178,7 +177,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -207,7 +206,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -239,7 +238,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -274,7 +273,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -312,7 +311,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -353,7 +352,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -397,7 +396,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -444,7 +443,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -494,7 +493,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -547,7 +546,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -603,7 +602,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -662,7 +661,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -724,7 +723,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -789,7 +788,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -857,7 +856,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) lazy val converter20 = CatalystTypeConverters.createToScalaConverter(child20.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -928,7 +927,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) lazy val converter20 = CatalystTypeConverters.createToScalaConverter(child20.dataType) lazy val converter21 = CatalystTypeConverters.createToScalaConverter(child21.dataType) - (input: Row) => { + (input: InternalRow) => { func( converter0(child0.eval(input)), converter1(child1.eval(input)), @@ -956,7 +955,9 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi } // scalastyle:on + private[this] val converter = CatalystTypeConverters.createToCatalystConverter(dataType) + override def eval(input: InternalRow): Any = converter(f(input)) - override def eval(input: Row): Any = CatalystTypeConverters.convertToCatalyst(f(input), dataType) - + // TODO(davies): make ScalaUdf work with codegen + override def isThreadSafe: Boolean = false } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala index 83074eb1e6310..4baae03b3a224 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala @@ -29,14 +29,14 @@ case object Descending extends SortDirection * An expression that can be used to sort a tuple. This class extends expression primarily so that * transformations over expression will descend into its child. */ -case class SortOrder(child: Expression, direction: SortDirection) extends Expression +case class SortOrder(child: Expression, direction: SortDirection) extends Expression with trees.UnaryNode[Expression] { override def dataType: DataType = child.dataType override def nullable: Boolean = child.nullable // SortOrder itself is never evaluated. - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def toString: String = s"$child ${if (direction == Ascending) "ASC" else "DESC"}" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala index aa4099e4d7bf9..05aab34559985 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /** * A parent class for mutable container objects that are reused when the values are changed, @@ -203,6 +204,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR case BooleanType => new MutableBoolean case LongType => new MutableLong case DateType => new MutableInt // We use INT for DATE internally + case TimestampType => new MutableLong // We use Long for Timestamp internally case _ => new MutableAny }.toArray) @@ -220,7 +222,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR override def isNullAt(i: Int): Boolean = values(i).isNull - override def copy(): Row = { + override def copy(): InternalRow = { val newValues = new Array[Any](values.length) var i = 0 while (i < values.length) { @@ -239,7 +241,8 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR } } - override def setString(ordinal: Int, value: String): Unit = update(ordinal, UTF8String(value)) + override def setString(ordinal: Int, value: String): Unit = + update(ordinal, UTF8String.fromString(value)) override def getString(ordinal: Int): String = apply(ordinal).toString diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala index 5b2c8572784bd..72f740ecaead3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala @@ -17,9 +17,12 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.sql.catalyst.util.DateUtils +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.types._ import org.apache.spark.unsafe.PlatformDependent import org.apache.spark.unsafe.array.ByteArrayMethods +import org.apache.spark.unsafe.types.UTF8String /** * Converts Rows into UnsafeRow format. This class is NOT thread-safe. @@ -47,7 +50,7 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) { /** * Compute the amount of space, in bytes, required to encode the given row. */ - def getSizeRequirement(row: Row): Int = { + def getSizeRequirement(row: InternalRow): Int = { var fieldNumber = 0 var variableLengthFieldSize: Int = 0 while (fieldNumber < writers.length) { @@ -67,7 +70,7 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) { * @param baseOffset the base offset of the destination address * @return the number of bytes written. This should be equal to `getSizeRequirement(row)`. */ - def writeRow(row: Row, baseObject: Object, baseOffset: Long): Long = { + def writeRow(row: InternalRow, baseObject: Object, baseOffset: Long): Int = { unsafeRow.pointTo(baseObject, baseOffset, writers.length, null) var fieldNumber = 0 var appendCursor: Int = fixedLengthSize @@ -98,12 +101,12 @@ private abstract class UnsafeColumnWriter { * used for calculating where variable-length data should be written * @return the number of variable-length bytes written */ - def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int + def write(source: InternalRow, target: UnsafeRow, column: Int, appendCursor: Int): Int /** * Return the number of bytes that are needed to write this variable-length value. */ - def getSize(source: Row, column: Int): Int + def getSize(source: InternalRow, column: Int): Int } private object UnsafeColumnWriter { @@ -119,6 +122,8 @@ private object UnsafeColumnWriter { case FloatType => FloatUnsafeColumnWriter case DoubleType => DoubleUnsafeColumnWriter case StringType => StringUnsafeColumnWriter + case DateType => IntUnsafeColumnWriter + case TimestampType => LongUnsafeColumnWriter case t => throw new UnsupportedOperationException(s"Do not know how to write columns of type $t") } @@ -139,72 +144,108 @@ private object StringUnsafeColumnWriter extends StringUnsafeColumnWriter private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter { // Primitives don't write to the variable-length region: - def getSize(sourceRow: Row, column: Int): Int = 0 + def getSize(sourceRow: InternalRow, column: Int): Int = 0 } private class NullUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setNullAt(column) 0 } } private class BooleanUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setBoolean(column, source.getBoolean(column)) 0 } } private class ByteUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setByte(column, source.getByte(column)) 0 } } private class ShortUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setShort(column, source.getShort(column)) 0 } } private class IntUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setInt(column, source.getInt(column)) 0 } } private class LongUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setLong(column, source.getLong(column)) 0 } } private class FloatUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setFloat(column, source.getFloat(column)) 0 } } private class DoubleUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { target.setDouble(column, source.getDouble(column)) 0 } } private class StringUnsafeColumnWriter private() extends UnsafeColumnWriter { - def getSize(source: Row, column: Int): Int = { + def getSize(source: InternalRow, column: Int): Int = { val numBytes = source.get(column).asInstanceOf[UTF8String].getBytes.length 8 + ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes) } - override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + override def write( + source: InternalRow, + target: UnsafeRow, + column: Int, + appendCursor: Int): Int = { val value = source.get(column).asInstanceOf[UTF8String] val baseObject = target.getBaseObject val baseOffset = target.getBaseOffset diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index f3830c6d3bcf2..00d2e499c5890 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -19,9 +19,10 @@ package org.apache.spark.sql.catalyst.expressions import com.clearspring.analytics.stream.cardinality.HyperLogLog -import org.apache.spark.sql.types._ -import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.catalyst import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.types._ import org.apache.spark.util.collection.OpenHashSet abstract class AggregateExpression extends Expression { @@ -37,7 +38,7 @@ abstract class AggregateExpression extends Expression { * [[AggregateExpression.eval]] should never be invoked because [[AggregateExpression]]'s are * replaced with a physical aggregate operator at runtime. */ - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } @@ -74,15 +75,13 @@ abstract class AggregateFunction extends AggregateExpression with Serializable with trees.LeafNode[Expression] { self: Product => - override type EvaluatedType = Any - /** Base should return the generic aggregate expression that this function is computing */ val base: AggregateExpression override def nullable: Boolean = base.nullable override def dataType: DataType = base.dataType - def update(input: Row): Unit + def update(input: InternalRow): Unit // Do we really need this? override def newInstance(): AggregateFunction = { @@ -110,15 +109,15 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr val currentMin: MutableLiteral = MutableLiteral(null, expr.dataType) val cmp = GreaterThan(currentMin, expr) - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { if (currentMin.value == null) { currentMin.value = expr.eval(input) - } else if(cmp.eval(input) == true) { + } else if (cmp.eval(input) == true) { currentMin.value = expr.eval(input) } } - override def eval(input: Row): Any = currentMin.value + override def eval(input: InternalRow): Any = currentMin.value } case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { @@ -141,15 +140,15 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr val currentMax: MutableLiteral = MutableLiteral(null, expr.dataType) val cmp = LessThan(currentMax, expr) - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { if (currentMax.value == null) { currentMax.value = expr.eval(input) - } else if(cmp.eval(input) == true) { + } else if (cmp.eval(input) == true) { currentMax.value = expr.eval(input) } } - override def eval(input: Row): Any = currentMax.value + override def eval(input: InternalRow): Any = currentMax.value } case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { @@ -207,14 +206,14 @@ case class CollectHashSetFunction( @transient val distinctValue = new InterpretedProjection(expr) - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val evaluatedExpr = distinctValue(input) if (!evaluatedExpr.anyNull) { seen.add(evaluatedExpr) } } - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { seen } } @@ -240,7 +239,7 @@ case class CombineSetsAndCountFunction( val seen = new OpenHashSet[Any]() - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val inputSetEval = inputSet.eval(input).asInstanceOf[OpenHashSet[Any]] val inputIterator = inputSetEval.iterator while (inputIterator.hasNext) { @@ -248,7 +247,7 @@ case class CombineSetsAndCountFunction( } } - override def eval(input: Row): Any = seen.size.toLong + override def eval(input: InternalRow): Any = seen.size.toLong } /** The data type of ApproxCountDistinctPartition since its output is a HyperLogLog object. */ @@ -396,13 +395,13 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ * Combining PartitionLevel InputData * <-- null * Zero <-- Zero <-- null - * + * * <-- null <-- no data - * null <-- null <-- no data + * null <-- null <-- no data */ case class CombineSum(child: Expression) extends AggregateExpression { def this() = this(null) - + override def children: Seq[Expression] = child :: Nil override def nullable: Boolean = true override def dataType: DataType = child.dataType @@ -455,7 +454,7 @@ case class CombineSetsAndSumFunction( val seen = new OpenHashSet[Any]() - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val inputSetEval = inputSet.eval(input).asInstanceOf[OpenHashSet[Any]] val inputIterator = inputSetEval.iterator while (inputIterator.hasNext) { @@ -463,8 +462,8 @@ case class CombineSetsAndSumFunction( } } - override def eval(input: Row): Any = { - val casted = seen.asInstanceOf[OpenHashSet[Row]] + override def eval(input: InternalRow): Any = { + val casted = seen.asInstanceOf[OpenHashSet[InternalRow]] if (casted.size == 0) { null } else { @@ -526,7 +525,7 @@ case class AverageFunction(expr: Expression, base: AggregateExpression) private def addFunction(value: Any) = Add(sum, Cast(Literal.create(value, expr.dataType), calcType)) - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { if (count == 0L) { null } else { @@ -543,7 +542,7 @@ case class AverageFunction(expr: Expression, base: AggregateExpression) } } - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val evaluatedExpr = expr.eval(input) if (evaluatedExpr != null) { count += 1 @@ -557,14 +556,14 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag var count: Long = _ - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val evaluatedExpr = expr.eval(input) if (evaluatedExpr != null) { count += 1L } } - override def eval(input: Row): Any = count + override def eval(input: InternalRow): Any = count } case class ApproxCountDistinctPartitionFunction( @@ -576,14 +575,14 @@ case class ApproxCountDistinctPartitionFunction( private val hyperLogLog = new HyperLogLog(relativeSD) - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val evaluatedExpr = expr.eval(input) if (evaluatedExpr != null) { hyperLogLog.offer(evaluatedExpr) } } - override def eval(input: Row): Any = hyperLogLog + override def eval(input: InternalRow): Any = hyperLogLog } case class ApproxCountDistinctMergeFunction( @@ -595,12 +594,12 @@ case class ApproxCountDistinctMergeFunction( private val hyperLogLog = new HyperLogLog(relativeSD) - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val evaluatedExpr = expr.eval(input) hyperLogLog.addAll(evaluatedExpr.asInstanceOf[HyperLogLog]) } - override def eval(input: Row): Any = hyperLogLog.cardinality() + override def eval(input: InternalRow): Any = hyperLogLog.cardinality() } case class SumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -618,14 +617,14 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr private val sum = MutableLiteral(null, calcType) - private val addFunction = + private val addFunction = Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero)) - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { sum.update(addFunction, input) } - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { expr.dataType match { case DecimalType.Fixed(_, _) => Cast(sum, dataType).eval(null) @@ -636,7 +635,7 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr case class CombineSumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { - + def this() = this(null, null) // Required for serialization. private val calcType = @@ -651,18 +650,18 @@ case class CombineSumFunction(expr: Expression, base: AggregateExpression) private val sum = MutableLiteral(null, calcType) - private val addFunction = + private val addFunction = Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero)) - - override def update(input: Row): Unit = { + + override def update(input: InternalRow): Unit = { val result = expr.eval(input) - // partial sum result can be null only when no input rows present + // partial sum result can be null only when no input rows present if(result != null) { sum.update(addFunction, input) } } - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { expr.dataType match { case DecimalType.Fixed(_, _) => Cast(sum, dataType).eval(null) @@ -678,14 +677,14 @@ case class SumDistinctFunction(expr: Expression, base: AggregateExpression) private val seen = new scala.collection.mutable.HashSet[Any]() - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val evaluatedExpr = expr.eval(input) if (evaluatedExpr != null) { seen += evaluatedExpr } } - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { if (seen.size == 0) { null } else { @@ -709,14 +708,14 @@ case class CountDistinctFunction( @transient val distinctValue = new InterpretedProjection(expr) - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { val evaluatedExpr = distinctValue(input) if (!evaluatedExpr.anyNull) { seen.add(evaluatedExpr) } } - override def eval(input: Row): Any = seen.size.toLong + override def eval(input: InternalRow): Any = seen.size.toLong } case class FirstFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -724,13 +723,13 @@ case class FirstFunction(expr: Expression, base: AggregateExpression) extends Ag var result: Any = null - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { if (result == null) { result = expr.eval(input) } } - override def eval(input: Row): Any = result + override def eval(input: InternalRow): Any = result } case class LastFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -738,11 +737,11 @@ case class LastFunction(expr: Expression, base: AggregateExpression) extends Agg var result: Any = null - override def update(input: Row): Unit = { + override def update(input: InternalRow): Unit = { result = input } - override def eval(input: Row): Any = { - if (result != null) expr.eval(result.asInstanceOf[Row]) else null + override def eval(input: InternalRow): Any = { + if (result != null) expr.eval(result.asInstanceOf[InternalRow]) else null } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala index c7a37ad966df6..ace8427c8ddaf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala @@ -17,77 +17,91 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.analysis.UnresolvedException -import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} +import org.apache.spark.sql.catalyst.util.TypeUtils import org.apache.spark.sql.types._ -case class UnaryMinus(child: Expression) extends UnaryExpression { - type EvaluatedType = Any +abstract class UnaryArithmetic extends UnaryExpression { + self: Product => - override def dataType: DataType = child.dataType override def foldable: Boolean = child.foldable override def nullable: Boolean = child.nullable - override def toString: String = s"-$child" - - lazy val numeric = dataType match { - case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] - case other => sys.error(s"Type $other does not support numeric operations") - } + override def dataType: DataType = child.dataType - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evalE = child.eval(input) if (evalE == null) { null } else { - numeric.negate(evalE) + evalInternal(evalE) } } + + protected def evalInternal(evalE: Any): Any = + sys.error(s"UnaryArithmetics must override either eval or evalInternal") } -case class Sqrt(child: Expression) extends UnaryExpression { - type EvaluatedType = Any +case class UnaryMinus(child: Expression) extends UnaryArithmetic { + override def toString: String = s"-$child" - override def dataType: DataType = DoubleType - override def foldable: Boolean = child.foldable - override def nullable: Boolean = true - override def toString: String = s"SQRT($child)" + override def checkInputDataTypes(): TypeCheckResult = + TypeUtils.checkForNumericExpr(child.dataType, "operator -") - lazy val numeric = child.dataType match { - case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] - case other => sys.error(s"Type $other does not support non-negative numeric operations") - } + private lazy val numeric = TypeUtils.getNumeric(dataType) - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) { - null - } else { - val value = numeric.toDouble(evalE) - if (value < 0) null - else math.sqrt(value) - } + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match { + case dt: DecimalType => defineCodeGen(ctx, ev, c => s"$c.unary_$$minus()") + case dt: NumericType => defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})(-($c))") } + + protected override def evalInternal(evalE: Any) = numeric.negate(evalE) +} + +case class UnaryPositive(child: Expression) extends UnaryArithmetic { + override def toString: String = s"positive($child)" + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = + defineCodeGen(ctx, ev, c => c) + + protected override def evalInternal(evalE: Any) = evalE +} + +/** + * A function that get the absolute value of the numeric value. + */ +case class Abs(child: Expression) extends UnaryArithmetic { + override def toString: String = s"Abs($child)" + + override def checkInputDataTypes(): TypeCheckResult = + TypeUtils.checkForNumericExpr(child.dataType, "function abs") + + private lazy val numeric = TypeUtils.getNumeric(dataType) + + protected override def evalInternal(evalE: Any) = numeric.abs(evalE) } abstract class BinaryArithmetic extends BinaryExpression { self: Product => - type EvaluatedType = Any + /** Name of the function for this expression on a [[Decimal]] type. */ + def decimalMethod: String = "" - override lazy val resolved = - left.resolved && right.resolved && - left.dataType == right.dataType && - !DecimalType.isFixed(left.dataType) - - override def dataType: DataType = { - if (!resolved) { - throw new UnresolvedException(this, - s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") + override def dataType: DataType = left.dataType + + override def checkInputDataTypes(): TypeCheckResult = { + if (left.dataType != right.dataType) { + TypeCheckResult.TypeCheckFailure( + s"differing types in ${this.getClass.getSimpleName} " + + s"(${left.dataType} and ${right.dataType}).") + } else { + checkTypesInternal(dataType) } - left.dataType } - override def eval(input: Row): Any = { + protected def checkTypesInternal(t: DataType): TypeCheckResult + + override def eval(input: InternalRow): Any = { val evalE1 = left.eval(input) if(evalE1 == null) { null @@ -101,91 +115,88 @@ abstract class BinaryArithmetic extends BinaryExpression { } } - def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = - sys.error(s"BinaryExpressions must either override eval or evalInternal") + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match { + case dt: DecimalType => + defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.$decimalMethod($eval2)") + // byte and short are casted into int when add, minus, times or divide + case ByteType | ShortType => + defineCodeGen(ctx, ev, + (eval1, eval2) => s"(${ctx.javaType(dataType)})($eval1 $symbol $eval2)") + case _ => + defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1 $symbol $eval2") + } + + protected def evalInternal(evalE1: Any, evalE2: Any): Any = + sys.error(s"BinaryArithmetics must override either eval or evalInternal") +} + +private[sql] object BinaryArithmetic { + def unapply(e: BinaryArithmetic): Option[(Expression, Expression)] = Some((e.left, e.right)) } case class Add(left: Expression, right: Expression) extends BinaryArithmetic { override def symbol: String = "+" + override def decimalMethod: String = "$plus" - lazy val numeric = dataType match { - case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] - case other => sys.error(s"Type $other does not support numeric operations") - } + override lazy val resolved = + childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType) - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if(evalE1 == null) { - null - } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - numeric.plus(evalE1, evalE2) - } - } - } + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForNumericExpr(t, "operator " + symbol) + + private lazy val numeric = TypeUtils.getNumeric(dataType) + + protected override def evalInternal(evalE1: Any, evalE2: Any) = numeric.plus(evalE1, evalE2) } case class Subtract(left: Expression, right: Expression) extends BinaryArithmetic { override def symbol: String = "-" + override def decimalMethod: String = "$minus" - lazy val numeric = dataType match { - case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] - case other => sys.error(s"Type $other does not support numeric operations") - } + override lazy val resolved = + childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType) - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if(evalE1 == null) { - null - } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - numeric.minus(evalE1, evalE2) - } - } - } + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForNumericExpr(t, "operator " + symbol) + + private lazy val numeric = TypeUtils.getNumeric(dataType) + + protected override def evalInternal(evalE1: Any, evalE2: Any) = numeric.minus(evalE1, evalE2) } case class Multiply(left: Expression, right: Expression) extends BinaryArithmetic { override def symbol: String = "*" + override def decimalMethod: String = "$times" - lazy val numeric = dataType match { - case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] - case other => sys.error(s"Type $other does not support numeric operations") - } + override lazy val resolved = + childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType) - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if(evalE1 == null) { - null - } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - numeric.times(evalE1, evalE2) - } - } - } + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForNumericExpr(t, "operator " + symbol) + + private lazy val numeric = TypeUtils.getNumeric(dataType) + + protected override def evalInternal(evalE1: Any, evalE2: Any) = numeric.times(evalE1, evalE2) } case class Divide(left: Expression, right: Expression) extends BinaryArithmetic { override def symbol: String = "/" + override def decimalMethod: String = "$div" override def nullable: Boolean = true - lazy val div: (Any, Any) => Any = dataType match { + override lazy val resolved = + childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType) + + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForNumericExpr(t, "operator " + symbol) + + private lazy val div: (Any, Any) => Any = dataType match { case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div case it: IntegralType => it.integral.asInstanceOf[Integral[Any]].quot - case other => sys.error(s"Type $other does not support numeric operations") } - - override def eval(input: Row): Any = { + + override def eval(input: InternalRow): Any = { val evalE2 = right.eval(input) if (evalE2 == null || evalE2 == 0) { null @@ -198,20 +209,51 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic } } } + + /** + * Special case handling due to division by 0 => null. + */ + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) + val test = if (left.dataType.isInstanceOf[DecimalType]) { + s"${eval2.primitive}.isZero()" + } else { + s"${eval2.primitive} == 0" + } + val method = if (left.dataType.isInstanceOf[DecimalType]) s".$decimalMethod" else s" $symbol " + val javaType = ctx.javaType(left.dataType) + eval1.code + eval2.code + + s""" + boolean ${ev.isNull} = false; + ${ctx.javaType(left.dataType)} ${ev.primitive} = ${ctx.defaultValue(left.dataType)}; + if (${eval1.isNull} || ${eval2.isNull} || $test) { + ${ev.isNull} = true; + } else { + ${ev.primitive} = ($javaType) (${eval1.primitive}$method(${eval2.primitive})); + } + """ + } } case class Remainder(left: Expression, right: Expression) extends BinaryArithmetic { override def symbol: String = "%" + override def decimalMethod: String = "remainder" override def nullable: Boolean = true - lazy val integral = dataType match { + override lazy val resolved = + childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType) + + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForNumericExpr(t, "operator " + symbol) + + private lazy val integral = dataType match { case i: IntegralType => i.integral.asInstanceOf[Integral[Any]] case i: FractionalType => i.asIntegral.asInstanceOf[Integral[Any]] - case other => sys.error(s"Type $other does not support numeric operations") } - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evalE2 = right.eval(input) if (evalE2 == null || evalE2 == 0) { null @@ -224,131 +266,42 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet } } } -} - -/** - * A function that calculates bitwise and(&) of two numbers. - */ -case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithmetic { - override def symbol: String = "&" - - lazy val and: (Any, Any) => Any = dataType match { - case ByteType => - ((evalE1: Byte, evalE2: Byte) => (evalE1 & evalE2).toByte).asInstanceOf[(Any, Any) => Any] - case ShortType => - ((evalE1: Short, evalE2: Short) => (evalE1 & evalE2).toShort).asInstanceOf[(Any, Any) => Any] - case IntegerType => - ((evalE1: Int, evalE2: Int) => evalE1 & evalE2).asInstanceOf[(Any, Any) => Any] - case LongType => - ((evalE1: Long, evalE2: Long) => evalE1 & evalE2).asInstanceOf[(Any, Any) => Any] - case other => sys.error(s"Unsupported bitwise & operation on $other") - } - - override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = and(evalE1, evalE2) -} - -/** - * A function that calculates bitwise or(|) of two numbers. - */ -case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmetic { - override def symbol: String = "|" - - lazy val or: (Any, Any) => Any = dataType match { - case ByteType => - ((evalE1: Byte, evalE2: Byte) => (evalE1 | evalE2).toByte).asInstanceOf[(Any, Any) => Any] - case ShortType => - ((evalE1: Short, evalE2: Short) => (evalE1 | evalE2).toShort).asInstanceOf[(Any, Any) => Any] - case IntegerType => - ((evalE1: Int, evalE2: Int) => evalE1 | evalE2).asInstanceOf[(Any, Any) => Any] - case LongType => - ((evalE1: Long, evalE2: Long) => evalE1 | evalE2).asInstanceOf[(Any, Any) => Any] - case other => sys.error(s"Unsupported bitwise | operation on $other") - } - - override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = or(evalE1, evalE2) -} -/** - * A function that calculates bitwise xor(^) of two numbers. - */ -case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithmetic { - override def symbol: String = "^" - - lazy val xor: (Any, Any) => Any = dataType match { - case ByteType => - ((evalE1: Byte, evalE2: Byte) => (evalE1 ^ evalE2).toByte).asInstanceOf[(Any, Any) => Any] - case ShortType => - ((evalE1: Short, evalE2: Short) => (evalE1 ^ evalE2).toShort).asInstanceOf[(Any, Any) => Any] - case IntegerType => - ((evalE1: Int, evalE2: Int) => evalE1 ^ evalE2).asInstanceOf[(Any, Any) => Any] - case LongType => - ((evalE1: Long, evalE2: Long) => evalE1 ^ evalE2).asInstanceOf[(Any, Any) => Any] - case other => sys.error(s"Unsupported bitwise ^ operation on $other") - } - - override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = xor(evalE1, evalE2) -} - -/** - * A function that calculates bitwise not(~) of a number. - */ -case class BitwiseNot(child: Expression) extends UnaryExpression { - type EvaluatedType = Any - - override def dataType: DataType = child.dataType - override def foldable: Boolean = child.foldable - override def nullable: Boolean = child.nullable - override def toString: String = s"~$child" - - lazy val not: (Any) => Any = dataType match { - case ByteType => - ((evalE: Byte) => (~evalE).toByte).asInstanceOf[(Any) => Any] - case ShortType => - ((evalE: Short) => (~evalE).toShort).asInstanceOf[(Any) => Any] - case IntegerType => - ((evalE: Int) => ~evalE).asInstanceOf[(Any) => Any] - case LongType => - ((evalE: Long) => ~evalE).asInstanceOf[(Any) => Any] - case other => sys.error(s"Unsupported bitwise ~ operation on $other") - } - - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) { - null + /** + * Special case handling for x % 0 ==> null. + */ + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) + val test = if (left.dataType.isInstanceOf[DecimalType]) { + s"${eval2.primitive}.isZero()" } else { - not(evalE) + s"${eval2.primitive} == 0" } + val method = if (left.dataType.isInstanceOf[DecimalType]) s".$decimalMethod" else s" $symbol " + val javaType = ctx.javaType(left.dataType) + eval1.code + eval2.code + + s""" + boolean ${ev.isNull} = false; + ${ctx.javaType(left.dataType)} ${ev.primitive} = ${ctx.defaultValue(left.dataType)}; + if (${eval1.isNull} || ${eval2.isNull} || $test) { + ${ev.isNull} = true; + } else { + ${ev.primitive} = ($javaType) (${eval1.primitive}$method(${eval2.primitive})); + } + """ } } -case class MaxOf(left: Expression, right: Expression) extends Expression { - type EvaluatedType = Any - - override def foldable: Boolean = left.foldable && right.foldable - +case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic { override def nullable: Boolean = left.nullable && right.nullable - override def children: Seq[Expression] = left :: right :: Nil + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForOrderingExpr(t, "function maxOf") - override lazy val resolved = - left.resolved && right.resolved && - left.dataType == right.dataType + private lazy val ordering = TypeUtils.getOrdering(dataType) - override def dataType: DataType = { - if (!resolved) { - throw new UnresolvedException(this, - s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") - } - left.dataType - } - - lazy val ordering = left.dataType match { - case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] - case other => sys.error(s"Type $other does not support ordered operations") - } - - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evalE1 = left.eval(input) val evalE2 = right.eval(input) if (evalE1 == null) { @@ -364,36 +317,43 @@ case class MaxOf(left: Expression, right: Expression) extends Expression { } } + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) + val compCode = ctx.genComp(dataType, eval1.primitive, eval2.primitive) + + eval1.code + eval2.code + s""" + boolean ${ev.isNull} = false; + ${ctx.javaType(left.dataType)} ${ev.primitive} = + ${ctx.defaultValue(left.dataType)}; + + if (${eval1.isNull}) { + ${ev.isNull} = ${eval2.isNull}; + ${ev.primitive} = ${eval2.primitive}; + } else if (${eval2.isNull}) { + ${ev.isNull} = ${eval1.isNull}; + ${ev.primitive} = ${eval1.primitive}; + } else { + if ($compCode > 0) { + ${ev.primitive} = ${eval1.primitive}; + } else { + ${ev.primitive} = ${eval2.primitive}; + } + } + """ + } override def toString: String = s"MaxOf($left, $right)" } -case class MinOf(left: Expression, right: Expression) extends Expression { - type EvaluatedType = Any - - override def foldable: Boolean = left.foldable && right.foldable - +case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic { override def nullable: Boolean = left.nullable && right.nullable - override def children: Seq[Expression] = left :: right :: Nil - - override lazy val resolved = - left.resolved && right.resolved && - left.dataType == right.dataType + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForOrderingExpr(t, "function minOf") - override def dataType: DataType = { - if (!resolved) { - throw new UnresolvedException(this, - s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") - } - left.dataType - } - - lazy val ordering = left.dataType match { - case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] - case other => sys.error(s"Type $other does not support ordered operations") - } + private lazy val ordering = TypeUtils.getOrdering(dataType) - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evalE1 = left.eval(input) val evalE2 = right.eval(input) if (evalE1 == null) { @@ -409,31 +369,31 @@ case class MinOf(left: Expression, right: Expression) extends Expression { } } - override def toString: String = s"MinOf($left, $right)" -} - -/** - * A function that get the absolute value of the numeric value. - */ -case class Abs(child: Expression) extends UnaryExpression { - type EvaluatedType = Any - - override def dataType: DataType = child.dataType - override def foldable: Boolean = child.foldable - override def nullable: Boolean = child.nullable - override def toString: String = s"Abs($child)" - - lazy val numeric = dataType match { - case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] - case other => sys.error(s"Type $other does not support numeric operations") + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) + val compCode = ctx.genComp(dataType, eval1.primitive, eval2.primitive) + + eval1.code + eval2.code + s""" + boolean ${ev.isNull} = false; + ${ctx.javaType(left.dataType)} ${ev.primitive} = + ${ctx.defaultValue(left.dataType)}; + + if (${eval1.isNull}) { + ${ev.isNull} = ${eval2.isNull}; + ${ev.primitive} = ${eval2.primitive}; + } else if (${eval2.isNull}) { + ${ev.isNull} = ${eval1.isNull}; + ${ev.primitive} = ${eval1.primitive}; + } else { + if ($compCode < 0) { + ${ev.primitive} = ${eval1.primitive}; + } else { + ${ev.primitive} = ${eval2.primitive}; + } + } + """ } - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) { - null - } else { - numeric.abs(evalE) - } - } + override def toString: String = s"MinOf($left, $right)" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala new file mode 100644 index 0000000000000..9002dda7bf4d0 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala @@ -0,0 +1,126 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult +import org.apache.spark.sql.catalyst.expressions.codegen._ +import org.apache.spark.sql.catalyst.util.TypeUtils +import org.apache.spark.sql.types._ + + +/** + * A function that calculates bitwise and(&) of two numbers. + * + * Code generation inherited from BinaryArithmetic. + */ +case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithmetic { + override def symbol: String = "&" + + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForBitwiseExpr(t, "operator " + symbol) + + private lazy val and: (Any, Any) => Any = dataType match { + case ByteType => + ((evalE1: Byte, evalE2: Byte) => (evalE1 & evalE2).toByte).asInstanceOf[(Any, Any) => Any] + case ShortType => + ((evalE1: Short, evalE2: Short) => (evalE1 & evalE2).toShort).asInstanceOf[(Any, Any) => Any] + case IntegerType => + ((evalE1: Int, evalE2: Int) => evalE1 & evalE2).asInstanceOf[(Any, Any) => Any] + case LongType => + ((evalE1: Long, evalE2: Long) => evalE1 & evalE2).asInstanceOf[(Any, Any) => Any] + } + + protected override def evalInternal(evalE1: Any, evalE2: Any) = and(evalE1, evalE2) +} + +/** + * A function that calculates bitwise or(|) of two numbers. + * + * Code generation inherited from BinaryArithmetic. + */ +case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmetic { + override def symbol: String = "|" + + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForBitwiseExpr(t, "operator " + symbol) + + private lazy val or: (Any, Any) => Any = dataType match { + case ByteType => + ((evalE1: Byte, evalE2: Byte) => (evalE1 | evalE2).toByte).asInstanceOf[(Any, Any) => Any] + case ShortType => + ((evalE1: Short, evalE2: Short) => (evalE1 | evalE2).toShort).asInstanceOf[(Any, Any) => Any] + case IntegerType => + ((evalE1: Int, evalE2: Int) => evalE1 | evalE2).asInstanceOf[(Any, Any) => Any] + case LongType => + ((evalE1: Long, evalE2: Long) => evalE1 | evalE2).asInstanceOf[(Any, Any) => Any] + } + + protected override def evalInternal(evalE1: Any, evalE2: Any) = or(evalE1, evalE2) +} + +/** + * A function that calculates bitwise xor of two numbers. + * + * Code generation inherited from BinaryArithmetic. + */ +case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithmetic { + override def symbol: String = "^" + + protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForBitwiseExpr(t, "operator " + symbol) + + private lazy val xor: (Any, Any) => Any = dataType match { + case ByteType => + ((evalE1: Byte, evalE2: Byte) => (evalE1 ^ evalE2).toByte).asInstanceOf[(Any, Any) => Any] + case ShortType => + ((evalE1: Short, evalE2: Short) => (evalE1 ^ evalE2).toShort).asInstanceOf[(Any, Any) => Any] + case IntegerType => + ((evalE1: Int, evalE2: Int) => evalE1 ^ evalE2).asInstanceOf[(Any, Any) => Any] + case LongType => + ((evalE1: Long, evalE2: Long) => evalE1 ^ evalE2).asInstanceOf[(Any, Any) => Any] + } + + protected override def evalInternal(evalE1: Any, evalE2: Any): Any = xor(evalE1, evalE2) +} + +/** + * A function that calculates bitwise not(~) of a number. + */ +case class BitwiseNot(child: Expression) extends UnaryArithmetic { + override def toString: String = s"~$child" + + override def checkInputDataTypes(): TypeCheckResult = + TypeUtils.checkForBitwiseExpr(child.dataType, "operator ~") + + private lazy val not: (Any) => Any = dataType match { + case ByteType => + ((evalE: Byte) => (~evalE).toByte).asInstanceOf[(Any) => Any] + case ShortType => + ((evalE: Short) => (~evalE).toShort).asInstanceOf[(Any) => Any] + case IntegerType => + ((evalE: Int) => ~evalE).asInstanceOf[(Any) => Any] + case LongType => + ((evalE: Long) => ~evalE).asInstanceOf[(Any) => Any] + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dataType)}) ~($c)") + } + + protected override def evalInternal(evalE: Any) = not(evalE) +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index d17af0e7ff87e..bd5475d2066fc 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -17,47 +17,207 @@ package org.apache.spark.sql.catalyst.expressions.codegen -import com.google.common.cache.{CacheLoader, CacheBuilder} - +import scala.collection.mutable import scala.language.existentials +import com.google.common.cache.{CacheBuilder, CacheLoader} +import org.codehaus.janino.ClassBodyEvaluator + import org.apache.spark.Logging -import org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String + // These classes are here to avoid issues with serialization and integration with quasiquotes. class IntegerHashSet extends org.apache.spark.util.collection.OpenHashSet[Int] class LongHashSet extends org.apache.spark.util.collection.OpenHashSet[Long] /** - * A base class for generators of byte code to perform expression evaluation. Includes a set of - * helpers for referring to Catalyst types and building trees that perform evaluation of individual - * expressions. + * Java source for evaluating an [[Expression]] given a [[InternalRow]] of input. + * + * @param code The sequence of statements required to evaluate the expression. + * @param isNull A term that holds a boolean value representing whether the expression evaluated + * to null. + * @param primitive A term for a possible primitive value of the result of the evaluation. Not + * valid if `isNull` is set to `true`. */ -abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Logging { - import scala.reflect.runtime.{universe => ru} - import scala.reflect.runtime.universe._ +case class GeneratedExpressionCode(var code: String, var isNull: String, var primitive: String) - import scala.tools.reflect.ToolBox - - protected val toolBox = runtimeMirror(getClass.getClassLoader).mkToolBox() +/** + * A context for codegen, which is used to bookkeeping the expressions those are not supported + * by codegen, then they are evaluated directly. The unsupported expression is appended at the + * end of `references`, the position of it is kept in the code, used to access and evaluate it. + */ +class CodeGenContext { - protected val rowType = typeOf[Row] - protected val mutableRowType = typeOf[MutableRow] - protected val genericRowType = typeOf[GenericRow] - protected val genericMutableRowType = typeOf[GenericMutableRow] + /** + * Holding all the expressions those do not support codegen, will be evaluated directly. + */ + val references: mutable.ArrayBuffer[Expression] = new mutable.ArrayBuffer[Expression]() - protected val projectionType = typeOf[Projection] - protected val mutableProjectionType = typeOf[MutableProjection] + val stringType: String = classOf[UTF8String].getName + val decimalType: String = classOf[Decimal].getName private val curId = new java.util.concurrent.atomic.AtomicInteger() - private val javaSeparator = "$" /** - * Can be flipped on manually in the console to add (expensive) expression evaluation trace code. + * Returns a term name that is unique within this instance of a `CodeGenerator`. + * + * (Since we aren't in a macro context we do not seem to have access to the built in `freshName` + * function.) + */ + def freshName(prefix: String): String = { + s"$prefix${curId.getAndIncrement}" + } + + /** + * Return the code to access a column for given DataType + */ + def getColumn(dataType: DataType, ordinal: Int): String = { + if (isNativeType(dataType)) { + s"i.${accessorForType(dataType)}($ordinal)" + } else { + s"(${boxedType(dataType)})i.apply($ordinal)" + } + } + + /** + * Return the code to update a column in Row for given DataType + */ + def setColumn(dataType: DataType, ordinal: Int, value: String): String = { + if (isNativeType(dataType)) { + s"${mutatorForType(dataType)}($ordinal, $value)" + } else { + s"update($ordinal, $value)" + } + } + + /** + * Return the name of accessor in Row for a DataType + */ + def accessorForType(dt: DataType): String = dt match { + case IntegerType => "getInt" + case other => s"get${boxedType(dt)}" + } + + /** + * Return the name of mutator in Row for a DataType + */ + def mutatorForType(dt: DataType): String = dt match { + case IntegerType => "setInt" + case other => s"set${boxedType(dt)}" + } + + /** + * Return the Java type for a DataType */ - var debugLogging = false + def javaType(dt: DataType): String = dt match { + case IntegerType => "int" + case LongType => "long" + case ShortType => "short" + case ByteType => "byte" + case DoubleType => "double" + case FloatType => "float" + case BooleanType => "boolean" + case dt: DecimalType => decimalType + case BinaryType => "byte[]" + case StringType => stringType + case DateType => "int" + case TimestampType => "long" + case dt: OpenHashSetUDT if dt.elementType == IntegerType => classOf[IntegerHashSet].getName + case dt: OpenHashSetUDT if dt.elementType == LongType => classOf[LongHashSet].getName + case _ => "Object" + } + + /** + * Return the boxed type in Java + */ + def boxedType(dt: DataType): String = dt match { + case IntegerType => "Integer" + case LongType => "Long" + case ShortType => "Short" + case ByteType => "Byte" + case DoubleType => "Double" + case FloatType => "Float" + case BooleanType => "Boolean" + case DateType => "Integer" + case TimestampType => "Long" + case _ => javaType(dt) + } + + /** + * Return the representation of default value for given DataType + */ + def defaultValue(dt: DataType): String = dt match { + case BooleanType => "false" + case FloatType => "-1.0f" + case ShortType => "(short)-1" + case LongType => "-1L" + case ByteType => "(byte)-1" + case DoubleType => "-1.0" + case IntegerType => "-1" + case DateType => "-1" + case TimestampType => "-1L" + case _ => "null" + } + + /** + * Generate code for equal expression in Java + */ + def genEqual(dataType: DataType, c1: String, c2: String): String = dataType match { + case BinaryType => s"java.util.Arrays.equals($c1, $c2)" + case dt: DataType if isPrimitiveType(dt) => s"$c1 == $c2" + case other => s"$c1.equals($c2)" + } + + /** + * Generate code for compare expression in Java + */ + def genComp(dataType: DataType, c1: String, c2: String): String = dataType match { + // use c1 - c2 may overflow + case dt: DataType if isPrimitiveType(dt) => s"(int)($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)" + case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)" + case other => s"$c1.compare($c2)" + } + + /** + * List of data types that have special accessors and setters in [[InternalRow]]. + */ + val nativeTypes = + Seq(IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType) + + /** + * Returns true if the data type has a special accessor and setter in [[InternalRow]]. + */ + def isNativeType(dt: DataType): Boolean = nativeTypes.contains(dt) + + /** + * List of data types who's Java type is primitive type + */ + val primitiveTypes = nativeTypes ++ Seq(DateType, TimestampType) + + /** + * Returns true if the Java type is primitive type + */ + def isPrimitiveType(dt: DataType): Boolean = primitiveTypes.contains(dt) +} + + +abstract class GeneratedClass { + def generate(expressions: Array[Expression]): Any +} + +/** + * A base class for generators of byte code to perform expression evaluation. Includes a set of + * helpers for referring to Catalyst types and building trees that perform evaluation of individual + * expressions. + */ +abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Logging { + + protected val exprType: String = classOf[Expression].getName + protected val mutableRowType: String = classOf[MutableRow].getName + protected val genericMutableRowType: String = classOf[GenericMutableRow].getName /** * Generates a class for a given input expression. Called when there is not cached code @@ -74,6 +234,30 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin /** Binds an input expression to a given input schema */ protected def bind(in: InType, inputSchema: Seq[Attribute]): InType + /** + * Compile the Java source code into a Java class, using Janino. + * + * It will track the time used to compile + */ + protected def compile(code: String): GeneratedClass = { + val startTime = System.nanoTime() + val evaluator = new ClassBodyEvaluator() + evaluator.setParentClassLoader(getClass.getClassLoader) + evaluator.setDefaultImports(Array("org.apache.spark.sql.catalyst.InternalRow")) + evaluator.setExtendedClass(classOf[GeneratedClass]) + try { + evaluator.cook(code) + } catch { + case e: Exception => + logError(s"failed to compile:\n $code", e) + throw e + } + val endTime = System.nanoTime() + def timeMs: Double = (endTime - startTime).toDouble / 1000000 + logDebug(s"Code (${code.size} bytes) compiled in $timeMs ms") + evaluator.getClazz().newInstance().asInstanceOf[GeneratedClass] + } + /** * A cache of generated classes. * @@ -84,10 +268,10 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin * weak keys/values and thus does not respond to memory pressure. */ protected val cache = CacheBuilder.newBuilder() - .maximumSize(1000) + .maximumSize(100) .build( new CacheLoader[InType, OutType]() { - override def load(in: InType): OutType = globalLock.synchronized { + override def load(in: InType): OutType = { val startTime = System.nanoTime() val result = create(in) val endTime = System.nanoTime() @@ -105,590 +289,10 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin def generate(expressions: InType): OutType = cache.get(canonicalize(expressions)) /** - * Returns a term name that is unique within this instance of a `CodeGenerator`. - * - * (Since we aren't in a macro context we do not seem to have access to the built in `freshName` - * function.) + * Create a new codegen context for expression evaluator, used to store those + * expressions that don't support codegen */ - protected def freshName(prefix: String): TermName = { - newTermName(s"$prefix$javaSeparator${curId.getAndIncrement}") + def newCodeGenContext(): CodeGenContext = { + new CodeGenContext } - - /** - * Scala ASTs for evaluating an [[Expression]] given a [[Row]] of input. - * - * @param code The sequence of statements required to evaluate the expression. - * @param nullTerm A term that holds a boolean value representing whether the expression evaluated - * to null. - * @param primitiveTerm A term for a possible primitive value of the result of the evaluation. Not - * valid if `nullTerm` is set to `true`. - * @param objectTerm A possibly boxed version of the result of evaluating this expression. - */ - protected case class EvaluatedExpression( - code: Seq[Tree], - nullTerm: TermName, - primitiveTerm: TermName, - objectTerm: TermName) - - /** - * Given an expression tree returns an [[EvaluatedExpression]], which contains Scala trees that - * can be used to determine the result of evaluating the expression on an input row. - */ - def expressionEvaluator(e: Expression): EvaluatedExpression = { - val primitiveTerm = freshName("primitiveTerm") - val nullTerm = freshName("nullTerm") - val objectTerm = freshName("objectTerm") - - implicit class Evaluate1(e: Expression) { - def castOrNull(f: TermName => Tree, dataType: DataType): Seq[Tree] = { - val eval = expressionEvaluator(e) - eval.code ++ - q""" - val $nullTerm = ${eval.nullTerm} - val $primitiveTerm = - if($nullTerm) - ${defaultPrimitive(dataType)} - else - ${f(eval.primitiveTerm)} - """.children - } - } - - implicit class Evaluate2(expressions: (Expression, Expression)) { - - /** - * Short hand for generating binary evaluation code, which depends on two sub-evaluations of - * the same type. If either of the sub-expressions is null, the result of this computation - * is assumed to be null. - * - * @param f a function from two primitive term names to a tree that evaluates them. - */ - def evaluate(f: (TermName, TermName) => Tree): Seq[Tree] = - evaluateAs(expressions._1.dataType)(f) - - def evaluateAs(resultType: DataType)(f: (TermName, TermName) => Tree): Seq[Tree] = { - // TODO: Right now some timestamp tests fail if we enforce this... - if (expressions._1.dataType != expressions._2.dataType) { - log.warn(s"${expressions._1.dataType} != ${expressions._2.dataType}") - } - - val eval1 = expressionEvaluator(expressions._1) - val eval2 = expressionEvaluator(expressions._2) - val resultCode = f(eval1.primitiveTerm, eval2.primitiveTerm) - - eval1.code ++ eval2.code ++ - q""" - val $nullTerm = ${eval1.nullTerm} || ${eval2.nullTerm} - val $primitiveTerm: ${termForType(resultType)} = - if($nullTerm) { - ${defaultPrimitive(resultType)} - } else { - $resultCode.asInstanceOf[${termForType(resultType)}] - } - """.children : Seq[Tree] - } - } - - val inputTuple = newTermName(s"i") - - // TODO: Skip generation of null handling code when expression are not nullable. - val primitiveEvaluation: PartialFunction[Expression, Seq[Tree]] = { - case b @ BoundReference(ordinal, dataType, nullable) => - val nullValue = q"$inputTuple.isNullAt($ordinal)" - q""" - val $nullTerm: Boolean = $nullValue - val $primitiveTerm: ${termForType(dataType)} = - if($nullTerm) - ${defaultPrimitive(dataType)} - else - ${getColumn(inputTuple, dataType, ordinal)} - """.children - - case expressions.Literal(null, dataType) => - q""" - val $nullTerm = true - val $primitiveTerm: ${termForType(dataType)} = null.asInstanceOf[${termForType(dataType)}] - """.children - - case expressions.Literal(value: Boolean, dataType) => - q""" - val $nullTerm = ${value == null} - val $primitiveTerm: ${termForType(dataType)} = $value - """.children - - case expressions.Literal(value: UTF8String, dataType) => - q""" - val $nullTerm = ${value == null} - val $primitiveTerm: ${termForType(dataType)} = - org.apache.spark.sql.types.UTF8String(${value.getBytes}) - """.children - - case expressions.Literal(value: Int, dataType) => - q""" - val $nullTerm = ${value == null} - val $primitiveTerm: ${termForType(dataType)} = $value - """.children - - case expressions.Literal(value: Long, dataType) => - q""" - val $nullTerm = ${value == null} - val $primitiveTerm: ${termForType(dataType)} = $value - """.children - - case Cast(e @ BinaryType(), StringType) => - val eval = expressionEvaluator(e) - eval.code ++ - q""" - val $nullTerm = ${eval.nullTerm} - val $primitiveTerm = - if($nullTerm) - ${defaultPrimitive(StringType)} - else - org.apache.spark.sql.types.UTF8String(${eval.primitiveTerm}.asInstanceOf[Array[Byte]]) - """.children - - case Cast(child @ DateType(), StringType) => - child.castOrNull(c => - q"""org.apache.spark.sql.types.UTF8String( - org.apache.spark.sql.types.DateUtils.toString($c))""", - StringType) - - case Cast(child @ NumericType(), IntegerType) => - child.castOrNull(c => q"$c.toInt", IntegerType) - - case Cast(child @ NumericType(), LongType) => - child.castOrNull(c => q"$c.toLong", LongType) - - case Cast(child @ NumericType(), DoubleType) => - child.castOrNull(c => q"$c.toDouble", DoubleType) - - case Cast(child @ NumericType(), FloatType) => - child.castOrNull(c => q"$c.toFloat", FloatType) - - // Special handling required for timestamps in hive test cases since the toString function - // does not match the expected output. - case Cast(e, StringType) if e.dataType != TimestampType => - val eval = expressionEvaluator(e) - eval.code ++ - q""" - val $nullTerm = ${eval.nullTerm} - val $primitiveTerm = - if($nullTerm) - ${defaultPrimitive(StringType)} - else - org.apache.spark.sql.types.UTF8String(${eval.primitiveTerm}.toString) - """.children - - case EqualTo(e1 @ BinaryType(), e2 @ BinaryType()) => - (e1, e2).evaluateAs (BooleanType) { - case (eval1, eval2) => - q""" - java.util.Arrays.equals($eval1.asInstanceOf[Array[Byte]], - $eval2.asInstanceOf[Array[Byte]]) - """ - } - - case EqualTo(e1, e2) => - (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 == $eval2" } - - /* TODO: Fix null semantics. - case In(e1, list) if !list.exists(!_.isInstanceOf[expressions.Literal]) => - val eval = expressionEvaluator(e1) - - val checks = list.map { - case expressions.Literal(v: String, dataType) => - q"if(${eval.primitiveTerm} == $v) return true" - case expressions.Literal(v: Int, dataType) => - q"if(${eval.primitiveTerm} == $v) return true" - } - - val funcName = newTermName(s"isIn${curId.getAndIncrement()}") - - q""" - def $funcName: Boolean = { - ..${eval.code} - if(${eval.nullTerm}) return false - ..$checks - return false - } - val $nullTerm = false - val $primitiveTerm = $funcName - """.children - */ - - case GreaterThan(e1 @ NumericType(), e2 @ NumericType()) => - (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 > $eval2" } - case GreaterThanOrEqual(e1 @ NumericType(), e2 @ NumericType()) => - (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 >= $eval2" } - case LessThan(e1 @ NumericType(), e2 @ NumericType()) => - (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 < $eval2" } - case LessThanOrEqual(e1 @ NumericType(), e2 @ NumericType()) => - (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 <= $eval2" } - - case And(e1, e2) => - val eval1 = expressionEvaluator(e1) - val eval2 = expressionEvaluator(e2) - - q""" - ..${eval1.code} - var $nullTerm = false - var $primitiveTerm: ${termForType(BooleanType)} = false - - if (!${eval1.nullTerm} && ${eval1.primitiveTerm} == false) { - } else { - ..${eval2.code} - if (!${eval2.nullTerm} && ${eval2.primitiveTerm} == false) { - } else if (!${eval1.nullTerm} && !${eval2.nullTerm}) { - $primitiveTerm = true - } else { - $nullTerm = true - } - } - """.children - - case Or(e1, e2) => - val eval1 = expressionEvaluator(e1) - val eval2 = expressionEvaluator(e2) - - q""" - ..${eval1.code} - var $nullTerm = false - var $primitiveTerm: ${termForType(BooleanType)} = false - - if (!${eval1.nullTerm} && ${eval1.primitiveTerm}) { - $primitiveTerm = true - } else { - ..${eval2.code} - if (!${eval2.nullTerm} && ${eval2.primitiveTerm}) { - $primitiveTerm = true - } else if (!${eval1.nullTerm} && !${eval2.nullTerm}) { - $primitiveTerm = false - } else { - $nullTerm = true - } - } - """.children - - case Not(child) => - // Uh, bad function name... - child.castOrNull(c => q"!$c", BooleanType) - - case Add(e1, e2) => (e1, e2) evaluate { case (eval1, eval2) => q"$eval1 + $eval2" } - case Subtract(e1, e2) => (e1, e2) evaluate { case (eval1, eval2) => q"$eval1 - $eval2" } - case Multiply(e1, e2) => (e1, e2) evaluate { case (eval1, eval2) => q"$eval1 * $eval2" } - case Divide(e1, e2) => - val eval1 = expressionEvaluator(e1) - val eval2 = expressionEvaluator(e2) - - eval1.code ++ eval2.code ++ - q""" - var $nullTerm = false - var $primitiveTerm: ${termForType(e1.dataType)} = 0 - - if (${eval1.nullTerm} || ${eval2.nullTerm} ) { - $nullTerm = true - } else if (${eval2.primitiveTerm} == 0) - $nullTerm = true - else { - $primitiveTerm = ${eval1.primitiveTerm} / ${eval2.primitiveTerm} - } - """.children - - case Remainder(e1, e2) => - val eval1 = expressionEvaluator(e1) - val eval2 = expressionEvaluator(e2) - - eval1.code ++ eval2.code ++ - q""" - var $nullTerm = false - var $primitiveTerm: ${termForType(e1.dataType)} = 0 - - if (${eval1.nullTerm} || ${eval2.nullTerm} ) { - $nullTerm = true - } else if (${eval2.primitiveTerm} == 0) - $nullTerm = true - else { - $nullTerm = false - $primitiveTerm = ${eval1.primitiveTerm} % ${eval2.primitiveTerm} - } - """.children - - case IsNotNull(e) => - val eval = expressionEvaluator(e) - q""" - ..${eval.code} - var $nullTerm = false - var $primitiveTerm: ${termForType(BooleanType)} = !${eval.nullTerm} - """.children - - case IsNull(e) => - val eval = expressionEvaluator(e) - q""" - ..${eval.code} - var $nullTerm = false - var $primitiveTerm: ${termForType(BooleanType)} = ${eval.nullTerm} - """.children - - case c @ Coalesce(children) => - q""" - var $nullTerm = true - var $primitiveTerm: ${termForType(c.dataType)} = ${defaultPrimitive(c.dataType)} - """.children ++ - children.map { c => - val eval = expressionEvaluator(c) - q""" - if($nullTerm) { - ..${eval.code} - if(!${eval.nullTerm}) { - $nullTerm = false - $primitiveTerm = ${eval.primitiveTerm} - } - } - """ - } - - case i @ expressions.If(condition, trueValue, falseValue) => - val condEval = expressionEvaluator(condition) - val trueEval = expressionEvaluator(trueValue) - val falseEval = expressionEvaluator(falseValue) - - q""" - var $nullTerm = false - var $primitiveTerm: ${termForType(i.dataType)} = ${defaultPrimitive(i.dataType)} - ..${condEval.code} - if(!${condEval.nullTerm} && ${condEval.primitiveTerm}) { - ..${trueEval.code} - $nullTerm = ${trueEval.nullTerm} - $primitiveTerm = ${trueEval.primitiveTerm} - } else { - ..${falseEval.code} - $nullTerm = ${falseEval.nullTerm} - $primitiveTerm = ${falseEval.primitiveTerm} - } - """.children - - case NewSet(elementType) => - q""" - val $nullTerm = false - val $primitiveTerm = new ${hashSetForType(elementType)}() - """.children - - case AddItemToSet(item, set) => - val itemEval = expressionEvaluator(item) - val setEval = expressionEvaluator(set) - - val elementType = set.dataType.asInstanceOf[OpenHashSetUDT].elementType - - itemEval.code ++ setEval.code ++ - q""" - if (!${itemEval.nullTerm}) { - ${setEval.primitiveTerm} - .asInstanceOf[${hashSetForType(elementType)}] - .add(${itemEval.primitiveTerm}) - } - - val $nullTerm = false - val $primitiveTerm = ${setEval.primitiveTerm} - """.children - - case CombineSets(left, right) => - val leftEval = expressionEvaluator(left) - val rightEval = expressionEvaluator(right) - - val elementType = left.dataType.asInstanceOf[OpenHashSetUDT].elementType - - leftEval.code ++ rightEval.code ++ - q""" - val $nullTerm = false - var $primitiveTerm: ${hashSetForType(elementType)} = null - - { - val leftSet = ${leftEval.primitiveTerm}.asInstanceOf[${hashSetForType(elementType)}] - val rightSet = ${rightEval.primitiveTerm}.asInstanceOf[${hashSetForType(elementType)}] - val iterator = rightSet.iterator - while (iterator.hasNext) { - leftSet.add(iterator.next()) - } - $primitiveTerm = leftSet - } - """.children - - case MaxOf(e1, e2) => - val eval1 = expressionEvaluator(e1) - val eval2 = expressionEvaluator(e2) - - eval1.code ++ eval2.code ++ - q""" - var $nullTerm = false - var $primitiveTerm: ${termForType(e1.dataType)} = ${defaultPrimitive(e1.dataType)} - - if (${eval1.nullTerm}) { - $nullTerm = ${eval2.nullTerm} - $primitiveTerm = ${eval2.primitiveTerm} - } else if (${eval2.nullTerm}) { - $nullTerm = ${eval1.nullTerm} - $primitiveTerm = ${eval1.primitiveTerm} - } else { - if (${eval1.primitiveTerm} > ${eval2.primitiveTerm}) { - $primitiveTerm = ${eval1.primitiveTerm} - } else { - $primitiveTerm = ${eval2.primitiveTerm} - } - } - """.children - - case MinOf(e1, e2) => - val eval1 = expressionEvaluator(e1) - val eval2 = expressionEvaluator(e2) - - eval1.code ++ eval2.code ++ - q""" - var $nullTerm = false - var $primitiveTerm: ${termForType(e1.dataType)} = ${defaultPrimitive(e1.dataType)} - - if (${eval1.nullTerm}) { - $nullTerm = ${eval2.nullTerm} - $primitiveTerm = ${eval2.primitiveTerm} - } else if (${eval2.nullTerm}) { - $nullTerm = ${eval1.nullTerm} - $primitiveTerm = ${eval1.primitiveTerm} - } else { - if (${eval1.primitiveTerm} < ${eval2.primitiveTerm}) { - $primitiveTerm = ${eval1.primitiveTerm} - } else { - $primitiveTerm = ${eval2.primitiveTerm} - } - } - """.children - - case UnscaledValue(child) => - val childEval = expressionEvaluator(child) - - childEval.code ++ - q""" - var $nullTerm = ${childEval.nullTerm} - var $primitiveTerm: Long = if (!$nullTerm) { - ${childEval.primitiveTerm}.toUnscaledLong - } else { - ${defaultPrimitive(LongType)} - } - """.children - - case MakeDecimal(child, precision, scale) => - val childEval = expressionEvaluator(child) - - childEval.code ++ - q""" - var $nullTerm = ${childEval.nullTerm} - var $primitiveTerm: org.apache.spark.sql.types.Decimal = - ${defaultPrimitive(DecimalType())} - - if (!$nullTerm) { - $primitiveTerm = new org.apache.spark.sql.types.Decimal() - $primitiveTerm = $primitiveTerm.setOrNull(${childEval.primitiveTerm}, $precision, $scale) - $nullTerm = $primitiveTerm == null - } - """.children - } - - // If there was no match in the partial function above, we fall back on calling the interpreted - // expression evaluator. - val code: Seq[Tree] = - primitiveEvaluation.lift.apply(e).getOrElse { - log.debug(s"No rules to generate $e") - val tree = reify { e } - q""" - val $objectTerm = $tree.eval(i) - val $nullTerm = $objectTerm == null - val $primitiveTerm = $objectTerm.asInstanceOf[${termForType(e.dataType)}] - """.children - } - - // Only inject debugging code if debugging is turned on. - val debugCode = - if (debugLogging) { - val localLogger = log - val localLoggerTree = reify { localLogger } - q""" - $localLoggerTree.debug( - ${e.toString} + ": " + (if ($nullTerm) "null" else $primitiveTerm.toString)) - """ :: Nil - } else { - Nil - } - - EvaluatedExpression(code ++ debugCode, nullTerm, primitiveTerm, objectTerm) - } - - protected def getColumn(inputRow: TermName, dataType: DataType, ordinal: Int) = { - dataType match { - case StringType => q"$inputRow($ordinal).asInstanceOf[org.apache.spark.sql.types.UTF8String]" - case dt: DataType if isNativeType(dt) => q"$inputRow.${accessorForType(dt)}($ordinal)" - case _ => q"$inputRow.apply($ordinal).asInstanceOf[${termForType(dataType)}]" - } - } - - protected def setColumn( - destinationRow: TermName, - dataType: DataType, - ordinal: Int, - value: TermName) = { - dataType match { - case StringType => q"$destinationRow.update($ordinal, $value)" - case dt: DataType if isNativeType(dt) => - q"$destinationRow.${mutatorForType(dt)}($ordinal, $value)" - case _ => q"$destinationRow.update($ordinal, $value)" - } - } - - protected def accessorForType(dt: DataType) = newTermName(s"get${primitiveForType(dt)}") - protected def mutatorForType(dt: DataType) = newTermName(s"set${primitiveForType(dt)}") - - protected def hashSetForType(dt: DataType) = dt match { - case IntegerType => typeOf[IntegerHashSet] - case LongType => typeOf[LongHashSet] - case unsupportedType => - sys.error(s"Code generation not support for hashset of type $unsupportedType") - } - - protected def primitiveForType(dt: DataType) = dt match { - case IntegerType => "Int" - case LongType => "Long" - case ShortType => "Short" - case ByteType => "Byte" - case DoubleType => "Double" - case FloatType => "Float" - case BooleanType => "Boolean" - case StringType => "org.apache.spark.sql.types.UTF8String" - } - - protected def defaultPrimitive(dt: DataType) = dt match { - case BooleanType => ru.Literal(Constant(false)) - case FloatType => ru.Literal(Constant(-1.0.toFloat)) - case StringType => q"""org.apache.spark.sql.types.UTF8String("")""" - case ShortType => ru.Literal(Constant(-1.toShort)) - case LongType => ru.Literal(Constant(-1L)) - case ByteType => ru.Literal(Constant(-1.toByte)) - case DoubleType => ru.Literal(Constant(-1.toDouble)) - case DecimalType() => q"org.apache.spark.sql.types.Decimal(-1)" - case IntegerType => ru.Literal(Constant(-1)) - case DateType => ru.Literal(Constant(-1)) - case _ => ru.Literal(Constant(null)) - } - - protected def termForType(dt: DataType) = dt match { - case n: AtomicType => n.tag - case _ => typeTag[Any] - } - - /** - * List of data types that have special accessors and setters in [[Row]]. - */ - protected val nativeTypes = - Seq(IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) - - /** - * Returns true if the data type has a special accessor and setter in [[Row]]. - */ - protected def isNativeType(dt: DataType) = nativeTypes.contains(dt) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala index 840260703ab74..e75e82d380541 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala @@ -19,15 +19,14 @@ package org.apache.spark.sql.catalyst.expressions.codegen import org.apache.spark.sql.catalyst.expressions._ +// MutableProjection is not accessible in Java +abstract class BaseMutableProjection extends MutableProjection {} + /** * Generates byte code that produces a [[MutableRow]] object that can update itself based on a new - * input [[Row]] for a fixed set of [[Expression Expressions]]. + * input [[InternalRow]] for a fixed set of [[Expression Expressions]]. */ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => MutableProjection] { - import scala.reflect.runtime.{universe => ru} - import scala.reflect.runtime.universe._ - - val mutableRowName = newTermName("mutableRow") protected def canonicalize(in: Seq[Expression]): Seq[Expression] = in.map(ExpressionCanonicalizer.execute) @@ -36,41 +35,56 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu in.map(BindReferences.bindReference(_, inputSchema)) protected def create(expressions: Seq[Expression]): (() => MutableProjection) = { - val projectionCode = expressions.zipWithIndex.flatMap { case (e, i) => - val evaluationCode = expressionEvaluator(e) - - evaluationCode.code :+ - q""" - if(${evaluationCode.nullTerm}) - mutableRow.setNullAt($i) - else - ${setColumn(mutableRowName, e.dataType, i, evaluationCode.primitiveTerm)} - """ - } + val ctx = newCodeGenContext() + val projectionCode = expressions.zipWithIndex.map { case (e, i) => + val evaluationCode = e.gen(ctx) + evaluationCode.code + + s""" + if(${evaluationCode.isNull}) + mutableRow.setNullAt($i); + else + mutableRow.${ctx.setColumn(e.dataType, i, evaluationCode.primitive)}; + """ + }.mkString("\n") + val code = s""" + public Object generate($exprType[] expr) { + return new SpecificProjection(expr); + } + + class SpecificProjection extends ${classOf[BaseMutableProjection].getName} { - val code = - q""" - () => { new $mutableProjectionType { + private $exprType[] expressions = null; + private $mutableRowType mutableRow = null; - private[this] var $mutableRowName: $mutableRowType = - new $genericMutableRowType(${expressions.size}) + public SpecificProjection($exprType[] expr) { + expressions = expr; + mutableRow = new $genericMutableRowType(${expressions.size}); + } - def target(row: $mutableRowType): $mutableProjectionType = { - $mutableRowName = row - this - } + public ${classOf[BaseMutableProjection].getName} target($mutableRowType row) { + mutableRow = row; + return this; + } - /* Provide immutable access to the last projected row. */ - def currentValue: $rowType = mutableRow + /* Provide immutable access to the last projected row. */ + public InternalRow currentValue() { + return (InternalRow) mutableRow; + } - def apply(i: $rowType): $rowType = { - ..$projectionCode - mutableRow - } - } } - """ + public Object apply(Object _i) { + InternalRow i = (InternalRow) _i; + $projectionCode - log.debug(s"code for ${expressions.mkString(",")}:\n$code") - toolBox.eval(code).asInstanceOf[() => MutableProjection] + return mutableRow; + } + } + """ + + logDebug(s"code for ${expressions.mkString(",")}:\n$code") + + val c = compile(code) + () => { + c.generate(ctx.references.toArray).asInstanceOf[MutableProjection] + } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala index b129c0d898bb7..7ed2c5addec9b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala @@ -18,93 +18,86 @@ package org.apache.spark.sql.catalyst.expressions.codegen import org.apache.spark.Logging +import org.apache.spark.annotation.Private +import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.types.{BinaryType, StringType, NumericType} + +/** + * Inherits some default implementation for Java from `Ordering[Row]` + */ +@Private +class BaseOrdering extends Ordering[InternalRow] { + def compare(a: InternalRow, b: InternalRow): Int = { + throw new UnsupportedOperationException + } +} /** * Generates bytecode for an [[Ordering]] of [[Row Rows]] for a given set of * [[Expression Expressions]]. */ -object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[Row]] with Logging { - import scala.reflect.runtime.{universe => ru} +object GenerateOrdering + extends CodeGenerator[Seq[SortOrder], Ordering[InternalRow]] with Logging { import scala.reflect.runtime.universe._ - protected def canonicalize(in: Seq[SortOrder]): Seq[SortOrder] = + protected def canonicalize(in: Seq[SortOrder]): Seq[SortOrder] = in.map(ExpressionCanonicalizer.execute(_).asInstanceOf[SortOrder]) protected def bind(in: Seq[SortOrder], inputSchema: Seq[Attribute]): Seq[SortOrder] = in.map(BindReferences.bindReference(_, inputSchema)) - protected def create(ordering: Seq[SortOrder]): Ordering[Row] = { + protected def create(ordering: Seq[SortOrder]): Ordering[InternalRow] = { val a = newTermName("a") val b = newTermName("b") - val comparisons = ordering.zipWithIndex.map { case (order, i) => - val evalA = expressionEvaluator(order.child) - val evalB = expressionEvaluator(order.child) + val ctx = newCodeGenContext() - val compare = order.child.dataType match { - case BinaryType => - q""" - val x = ${if (order.direction == Ascending) evalA.primitiveTerm else evalB.primitiveTerm} - val y = ${if (order.direction != Ascending) evalB.primitiveTerm else evalA.primitiveTerm} - var i = 0 - while (i < x.length && i < y.length) { - val res = x(i).compareTo(y(i)) - if (res != 0) return res - i = i+1 - } - return x.length - y.length - """ - case _: NumericType => - q""" - val comp = ${evalA.primitiveTerm} - ${evalB.primitiveTerm} - if(comp != 0) { - return ${if (order.direction == Ascending) q"comp.toInt" else q"-comp.toInt"} - } - """ - case StringType => - if (order.direction == Ascending) { - q"""return ${evalA.primitiveTerm}.compare(${evalB.primitiveTerm})""" + val comparisons = ordering.zipWithIndex.map { case (order, i) => + val evalA = order.child.gen(ctx) + val evalB = order.child.gen(ctx) + val asc = order.direction == Ascending + s""" + i = $a; + ${evalA.code} + i = $b; + ${evalB.code} + if (${evalA.isNull} && ${evalB.isNull}) { + // Nothing + } else if (${evalA.isNull}) { + return ${if (order.direction == Ascending) "-1" else "1"}; + } else if (${evalB.isNull}) { + return ${if (order.direction == Ascending) "1" else "-1"}; } else { - q"""return ${evalB.primitiveTerm}.compare(${evalA.primitiveTerm})""" + int comp = ${ctx.genComp(order.child.dataType, evalA.primitive, evalB.primitive)}; + if (comp != 0) { + return ${if (asc) "comp" else "-comp"}; + } } - } - - q""" - i = $a - ..${evalA.code} - i = $b - ..${evalB.code} - if (${evalA.nullTerm} && ${evalB.nullTerm}) { - // Nothing - } else if (${evalA.nullTerm}) { - return ${if (order.direction == Ascending) q"-1" else q"1"} - } else if (${evalB.nullTerm}) { - return ${if (order.direction == Ascending) q"1" else q"-1"} - } else { - $compare - } """ - } + }.mkString("\n") - val q"class $orderingName extends $orderingType { ..$body }" = reify { - class SpecificOrdering extends Ordering[Row] { - val o = ordering + val code = s""" + public SpecificOrdering generate($exprType[] expr) { + return new SpecificOrdering(expr); } - }.tree.children.head - val code = q""" - class $orderingName extends $orderingType { - ..$body - def compare(a: $rowType, b: $rowType): Int = { - var i: $rowType = null // Holds current row being evaluated. - ..$comparisons - return 0 + class SpecificOrdering extends ${typeOf[BaseOrdering]} { + + private $exprType[] expressions = null; + + public SpecificOrdering($exprType[] expr) { + expressions = expr; } - } - new $orderingName() - """ + + @Override + public int compare(InternalRow a, InternalRow b) { + InternalRow i = null; // Holds current row being evaluated. + $comparisons + return 0; + } + }""" + logDebug(s"Generated Ordering: $code") - toolBox.eval(code).asInstanceOf[Ordering[Row]] + + compile(code).generate(ctx.references.toArray).asInstanceOf[BaseOrdering] } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala index 40e163024360e..3ebc2c147579b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala @@ -20,29 +20,46 @@ package org.apache.spark.sql.catalyst.expressions.codegen import org.apache.spark.sql.catalyst.expressions._ /** - * Generates bytecode that evaluates a boolean [[Expression]] on a given input [[Row]]. + * Interface for generated predicate */ -object GeneratePredicate extends CodeGenerator[Expression, (Row) => Boolean] { - import scala.reflect.runtime.{universe => ru} - import scala.reflect.runtime.universe._ +abstract class Predicate { + def eval(r: InternalRow): Boolean +} + +/** + * Generates bytecode that evaluates a boolean [[Expression]] on a given input [[InternalRow]]. + */ +object GeneratePredicate extends CodeGenerator[Expression, (InternalRow) => Boolean] { protected def canonicalize(in: Expression): Expression = ExpressionCanonicalizer.execute(in) protected def bind(in: Expression, inputSchema: Seq[Attribute]): Expression = BindReferences.bindReference(in, inputSchema) - protected def create(predicate: Expression): ((Row) => Boolean) = { - val cEval = expressionEvaluator(predicate) + protected def create(predicate: Expression): ((InternalRow) => Boolean) = { + val ctx = newCodeGenContext() + val eval = predicate.gen(ctx) + val code = s""" + public SpecificPredicate generate($exprType[] expr) { + return new SpecificPredicate(expr); + } - val code = - q""" - (i: $rowType) => { - ..${cEval.code} - if (${cEval.nullTerm}) false else ${cEval.primitiveTerm} + class SpecificPredicate extends ${classOf[Predicate].getName} { + private final $exprType[] expressions; + public SpecificPredicate($exprType[] expr) { + expressions = expr; } - """ - log.debug(s"Generated predicate '$predicate':\n$code") - toolBox.eval(code).asInstanceOf[Row => Boolean] + @Override + public boolean eval(InternalRow i) { + ${eval.code} + return !${eval.isNull} && ${eval.primitive}; + } + }""" + + logDebug(s"Generated predicate '$predicate':\n$code") + + val p = compile(code).generate(ctx.references.toArray).asInstanceOf[Predicate] + (r: InternalRow) => p.eval(r) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala index 584f938445c8c..2e20eda1a3002 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala @@ -17,17 +17,22 @@ package org.apache.spark.sql.catalyst.expressions.codegen +import org.apache.spark.sql.BaseMutableRow import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types._ +/** + * Java can not access Projection (in package object) + */ +abstract class BaseProject extends Projection {} /** - * Generates bytecode that produces a new [[Row]] object based on a fixed set of input - * [[Expression Expressions]] and a given input [[Row]]. The returned [[Row]] object is custom - * generated based on the output types of the [[Expression]] to avoid boxing of primitive values. + * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input + * [[Expression Expressions]] and a given input [[InternalRow]]. The returned [[InternalRow]] + * object is custom generated based on the output types of the [[Expression]] to avoid boxing of + * primitive values. */ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { - import scala.reflect.runtime.{universe => ru} import scala.reflect.runtime.universe._ protected def canonicalize(in: Seq[Expression]): Seq[Expression] = @@ -38,201 +43,181 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { // Make Mutablility optional... protected def create(expressions: Seq[Expression]): Projection = { - val tupleLength = ru.Literal(Constant(expressions.length)) - val lengthDef = q"final val length = $tupleLength" - - /* TODO: Configurable... - val nullFunctions = - q""" - private final val nullSet = new org.apache.spark.util.collection.BitSet(length) - final def setNullAt(i: Int) = nullSet.set(i) - final def isNullAt(i: Int) = nullSet.get(i) - """ - */ - - val nullFunctions = - q""" - private[this] var nullBits = new Array[Boolean](${expressions.size}) - override def setNullAt(i: Int) = { nullBits(i) = true } - override def isNullAt(i: Int) = nullBits(i) - """.children - - val tupleElements = expressions.zipWithIndex.flatMap { + val ctx = newCodeGenContext() + val columns = expressions.zipWithIndex.map { case (e, i) => - val elementName = newTermName(s"c$i") - val evaluatedExpression = expressionEvaluator(e) - val iLit = ru.Literal(Constant(i)) + s"private ${ctx.javaType(e.dataType)} c$i = ${ctx.defaultValue(e.dataType)};\n" + }.mkString("\n ") - q""" - var ${newTermName(s"c$i")}: ${termForType(e.dataType)} = _ + val initColumns = expressions.zipWithIndex.map { + case (e, i) => + val eval = e.gen(ctx) + s""" { - ..${evaluatedExpression.code} - if(${evaluatedExpression.nullTerm}) - setNullAt($iLit) - else { - nullBits($iLit) = false - $elementName = ${evaluatedExpression.primitiveTerm} + // column$i + ${eval.code} + nullBits[$i] = ${eval.isNull}; + if (!${eval.isNull}) { + c$i = ${eval.primitive}; } } - """.children : Seq[Tree] - } + """ + }.mkString("\n") - val accessorFailure = q"""scala.sys.error("Invalid ordinal:" + i)""" - val applyFunction = { - val cases = (0 until expressions.size).map { i => - val ordinal = ru.Literal(Constant(i)) - val elementName = newTermName(s"c$i") - val iLit = ru.Literal(Constant(i)) + val getCases = (0 until expressions.size).map { i => + s"case $i: return c$i;" + }.mkString("\n ") - q"if(i == $ordinal) { if(isNullAt($i)) return null else return $elementName }" - } - q"override def apply(i: Int): Any = { ..$cases; $accessorFailure }" - } + val updateCases = expressions.zipWithIndex.map { case (e, i) => + s"case $i: { c$i = (${ctx.boxedType(e.dataType)})value; return;}" + }.mkString("\n ") - val updateFunction = { - val cases = expressions.zipWithIndex.map {case (e, i) => - val ordinal = ru.Literal(Constant(i)) - val elementName = newTermName(s"c$i") - val iLit = ru.Literal(Constant(i)) - - q""" - if(i == $ordinal) { - if(value == null) { - setNullAt(i) - } else { - nullBits(i) = false - $elementName = value.asInstanceOf[${termForType(e.dataType)}] - } - return - }""" - } - q"override def update(i: Int, value: Any): Unit = { ..$cases; $accessorFailure }" - } - - val specificAccessorFunctions = nativeTypes.map { dataType => - val ifStatements = expressions.zipWithIndex.flatMap { - // getString() is not used by expressions - case (e, i) if e.dataType == dataType && dataType != StringType => - val elementName = newTermName(s"c$i") - // TODO: The string of ifs gets pretty inefficient as the row grows in size. - // TODO: Optional null checks? - q"if(i == $i) return $elementName" :: Nil + val specificAccessorFunctions = ctx.nativeTypes.map { dataType => + val cases = expressions.zipWithIndex.flatMap { + case (e, i) if ctx.javaType(e.dataType) == ctx.javaType(dataType) => + List(s"case $i: return c$i;") case _ => Nil + }.mkString("\n ") + if (cases.length > 0) { + s""" + @Override + public ${ctx.javaType(dataType)} ${ctx.accessorForType(dataType)}(int i) { + if (isNullAt(i)) { + return ${ctx.defaultValue(dataType)}; + } + switch (i) { + $cases + } + throw new IllegalArgumentException("Invalid index: " + i + + " in ${ctx.accessorForType(dataType)}"); + }""" + } else { + "" } - dataType match { - // Row() need this interface to compile - case StringType => - q""" - override def getString(i: Int): String = { - $accessorFailure - }""" - case other => - q""" - override def ${accessorForType(dataType)}(i: Int): ${termForType(dataType)} = { - ..$ifStatements; - $accessorFailure - }""" - } - } + }.mkString("\n") - val specificMutatorFunctions = nativeTypes.map { dataType => - val ifStatements = expressions.zipWithIndex.flatMap { - // setString() is not used by expressions - case (e, i) if e.dataType == dataType && dataType != StringType => - val elementName = newTermName(s"c$i") - // TODO: The string of ifs gets pretty inefficient as the row grows in size. - // TODO: Optional null checks? - q"if(i == $i) { nullBits($i) = false; $elementName = value; return }" :: Nil + val specificMutatorFunctions = ctx.nativeTypes.map { dataType => + val cases = expressions.zipWithIndex.flatMap { + case (e, i) if ctx.javaType(e.dataType) == ctx.javaType(dataType) => + List(s"case $i: { c$i = value; return; }") case _ => Nil + }.mkString("\n ") + if (cases.length > 0) { + s""" + @Override + public void ${ctx.mutatorForType(dataType)}(int i, ${ctx.javaType(dataType)} value) { + nullBits[i] = false; + switch (i) { + $cases + } + throw new IllegalArgumentException("Invalid index: " + i + + " in ${ctx.mutatorForType(dataType)}"); + }""" + } else { + "" } - dataType match { - case StringType => - // MutableRow() need this interface to compile - q""" - override def setString(i: Int, value: String) { - $accessorFailure - }""" - case other => - q""" - override def ${mutatorForType(dataType)}(i: Int, value: ${termForType(dataType)}) { - ..$ifStatements; - $accessorFailure - }""" - } - } + }.mkString("\n") - val hashValues = expressions.zipWithIndex.map { case (e,i) => - val elementName = newTermName(s"c$i") + val hashValues = expressions.zipWithIndex.map { case (e, i) => + val col = newTermName(s"c$i") val nonNull = e.dataType match { - case BooleanType => q"if ($elementName) 0 else 1" - case ByteType | ShortType | IntegerType => q"$elementName.toInt" - case LongType => q"($elementName ^ ($elementName >>> 32)).toInt" - case FloatType => q"java.lang.Float.floatToIntBits($elementName)" + case BooleanType => s"$col ? 0 : 1" + case ByteType | ShortType | IntegerType | DateType => s"$col" + case LongType | TimestampType => s"$col ^ ($col >>> 32)" + case FloatType => s"Float.floatToIntBits($col)" case DoubleType => - q"{ val b = java.lang.Double.doubleToLongBits($elementName); (b ^ (b >>>32)).toInt }" - case _ => q"$elementName.hashCode" + s"(int)(Double.doubleToLongBits($col) ^ (Double.doubleToLongBits($col) >>> 32))" + case _ => s"$col.hashCode()" } - q"if (isNullAt($i)) 0 else $nonNull" + s"isNullAt($i) ? 0 : ($nonNull)" } - val hashUpdates: Seq[Tree] = hashValues.map(v => q"""result = 37 * result + $v""": Tree) + val hashUpdates: String = hashValues.map( v => + s""" + result *= 37; result += $v;""" + ).mkString("\n") - val hashCodeFunction = - q""" - override def hashCode(): Int = { - var result: Int = 37 - ..$hashUpdates - result + val columnChecks = expressions.zipWithIndex.map { case (e, i) => + s""" + if (nullBits[$i] != row.nullBits[$i] || + (!nullBits[$i] && !(${ctx.genEqual(e.dataType, s"c$i", s"row.c$i")}))) { + return false; } """ + }.mkString("\n") - val columnChecks = (0 until expressions.size).map { i => - val elementName = newTermName(s"c$i") - q"if (this.$elementName != specificType.$elementName) return false" + val code = s""" + public SpecificProjection generate($exprType[] expr) { + return new SpecificProjection(expr); } - val equalsFunction = - q""" - override def equals(other: Any): Boolean = other match { - case specificType: SpecificRow => - ..$columnChecks - return true - case other => super.equals(other) - } - """ + class SpecificProjection extends ${typeOf[BaseProject]} { + private $exprType[] expressions = null; - val allColumns = (0 until expressions.size).map { i => - val iLit = ru.Literal(Constant(i)) - q"if(isNullAt($iLit)) { null } else { ${newTermName(s"c$i")} }" + public SpecificProjection($exprType[] expr) { + expressions = expr; + } + + @Override + public Object apply(Object r) { + return new SpecificRow(expressions, (InternalRow) r); + } } - val copyFunction = - q"override def copy() = new $genericRowType(Array[Any](..$allColumns))" - - val toSeqFunction = - q"override def toSeq: Seq[Any] = Seq(..$allColumns)" - - val classBody = - nullFunctions ++ ( - lengthDef +: - applyFunction +: - updateFunction +: - equalsFunction +: - hashCodeFunction +: - copyFunction +: - toSeqFunction +: - (tupleElements ++ specificAccessorFunctions ++ specificMutatorFunctions)) - - val code = q""" - final class SpecificRow(i: $rowType) extends $mutableRowType { - ..$classBody + final class SpecificRow extends ${typeOf[BaseMutableRow]} { + + $columns + + public SpecificRow($exprType[] expressions, InternalRow i) { + $initColumns } - new $projectionType { def apply(r: $rowType) = new SpecificRow(r) } + public int size() { return ${expressions.length};} + protected boolean[] nullBits = new boolean[${expressions.length}]; + public void setNullAt(int i) { nullBits[i] = true; } + public boolean isNullAt(int i) { return nullBits[i]; } + + public Object get(int i) { + if (isNullAt(i)) return null; + switch (i) { + $getCases + } + return null; + } + public void update(int i, Object value) { + if (value == null) { + setNullAt(i); + return; + } + nullBits[i] = false; + switch (i) { + $updateCases + } + } + $specificAccessorFunctions + $specificMutatorFunctions + + @Override + public int hashCode() { + int result = 37; + $hashUpdates + return result; + } + + @Override + public boolean equals(Object other) { + if (other instanceof SpecificRow) { + SpecificRow row = (SpecificRow) other; + $columnChecks + return true; + } + return super.equals(other); + } + } """ - log.debug( - s"MutableRow, initExprs: ${expressions.mkString(",")} code:\n${toolBox.typeCheck(code)}") - toolBox.eval(code).asInstanceOf[Projection] + logDebug(s"MutableRow, initExprs: ${expressions.mkString(",")} code:\n${code}") + + compile(code).generate(ctx.references.toArray).asInstanceOf[Projection] } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala index 528e38a50a740..7f1b12cdd5800 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala @@ -27,12 +27,6 @@ import org.apache.spark.util.Utils */ package object codegen { - /** - * A lock to protect invoking the scala compiler at runtime, since it is not thread safe in Scala - * 2.10. - */ - protected[codegen] val globalLock = org.apache.spark.sql.catalyst.ScalaReflectionLock - /** Canonicalizes an expression so those that differ only by names can reuse the same code. */ object ExpressionCanonicalizer extends rules.RuleExecutor[Expression] { val batches = diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala index 956a2429b0b61..72fdcebb4cbc8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.sql.catalyst import org.apache.spark.sql.types._ @@ -24,10 +25,9 @@ import org.apache.spark.sql.types._ * Returns an Array containing the evaluation of all children expressions. */ case class CreateArray(children: Seq[Expression]) extends Expression { - override type EvaluatedType = Any - + override def foldable: Boolean = children.forall(_.foldable) - + lazy val childTypes = children.map(_.dataType).distinct override lazy val resolved = @@ -42,7 +42,7 @@ case class CreateArray(children: Seq[Expression]) extends Expression { override def nullable: Boolean = false - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { children.map(_.eval(input)) } @@ -53,8 +53,7 @@ case class CreateArray(children: Seq[Expression]) extends Expression { * Returns a Row containing the evaluation of all children expressions. * TODO: [[CreateStruct]] does not support codegen. */ -case class CreateStruct(children: Seq[NamedExpression]) extends Expression { - override type EvaluatedType = Row +case class CreateStruct(children: Seq[Expression]) extends Expression { override def foldable: Boolean = children.forall(_.foldable) @@ -63,15 +62,20 @@ case class CreateStruct(children: Seq[NamedExpression]) extends Expression { override lazy val dataType: StructType = { assert(resolved, s"CreateStruct contains unresolvable children: ${children.filterNot(_.resolved)}.") - val fields = children.map { child => - StructField(child.name, child.dataType, child.nullable, child.metadata) - } + val fields = children.zipWithIndex.map { case (child, idx) => + child match { + case ne: NamedExpression => + StructField(ne.name, ne.dataType, ne.nullable, ne.metadata) + case _ => + StructField(s"col${idx + 1}", child.dataType, child.nullable, Metadata.empty) + } + } StructType(fields) } override def nullable: Boolean = false - override def eval(input: Row): EvaluatedType = { - Row(children.map(_.eval(input)): _*) + override def eval(input: InternalRow): Any = { + InternalRow(children.map(_.eval(input)): _*) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala new file mode 100644 index 0000000000000..1d7393d3d91f9 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala @@ -0,0 +1,314 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.catalyst +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult +import org.apache.spark.sql.catalyst.expressions.codegen._ +import org.apache.spark.sql.types.{BooleanType, DataType} + + +case class If(predicate: Expression, trueValue: Expression, falseValue: Expression) + extends Expression { + + override def children: Seq[Expression] = predicate :: trueValue :: falseValue :: Nil + override def nullable: Boolean = trueValue.nullable || falseValue.nullable + + override def checkInputDataTypes(): TypeCheckResult = { + if (predicate.dataType != BooleanType) { + TypeCheckResult.TypeCheckFailure( + s"type of predicate expression in If should be boolean, not ${predicate.dataType}") + } else if (trueValue.dataType != falseValue.dataType) { + TypeCheckResult.TypeCheckFailure( + s"differing types in If (${trueValue.dataType} and ${falseValue.dataType}).") + } else { + TypeCheckResult.TypeCheckSuccess + } + } + + override def dataType: DataType = trueValue.dataType + + override def eval(input: InternalRow): Any = { + if (true == predicate.eval(input)) { + trueValue.eval(input) + } else { + falseValue.eval(input) + } + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val condEval = predicate.gen(ctx) + val trueEval = trueValue.gen(ctx) + val falseEval = falseValue.gen(ctx) + + s""" + ${condEval.code} + boolean ${ev.isNull} = false; + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + if (!${condEval.isNull} && ${condEval.primitive}) { + ${trueEval.code} + ${ev.isNull} = ${trueEval.isNull}; + ${ev.primitive} = ${trueEval.primitive}; + } else { + ${falseEval.code} + ${ev.isNull} = ${falseEval.isNull}; + ${ev.primitive} = ${falseEval.primitive}; + } + """ + } + + override def toString: String = s"if ($predicate) $trueValue else $falseValue" +} + +trait CaseWhenLike extends Expression { + self: Product => + + // Note that `branches` are considered in consecutive pairs (cond, val), and the optional last + // element is the value for the default catch-all case (if provided). + // Hence, `branches` consists of at least two elements, and can have an odd or even length. + def branches: Seq[Expression] + + @transient lazy val whenList = + branches.sliding(2, 2).collect { case Seq(whenExpr, _) => whenExpr }.toSeq + @transient lazy val thenList = + branches.sliding(2, 2).collect { case Seq(_, thenExpr) => thenExpr }.toSeq + val elseValue = if (branches.length % 2 == 0) None else Option(branches.last) + + // both then and else expressions should be considered. + def valueTypes: Seq[DataType] = (thenList ++ elseValue).map(_.dataType) + def valueTypesEqual: Boolean = valueTypes.distinct.size == 1 + + override def checkInputDataTypes(): TypeCheckResult = { + if (valueTypesEqual) { + checkTypesInternal() + } else { + TypeCheckResult.TypeCheckFailure( + "THEN and ELSE expressions should all be same type or coercible to a common type") + } + } + + protected def checkTypesInternal(): TypeCheckResult + + override def dataType: DataType = thenList.head.dataType + + override def nullable: Boolean = { + // If no value is nullable and no elseValue is provided, the whole statement defaults to null. + thenList.exists(_.nullable) || (elseValue.map(_.nullable).getOrElse(true)) + } +} + +// scalastyle:off +/** + * Case statements of the form "CASE WHEN a THEN b [WHEN c THEN d]* [ELSE e] END". + * Refer to this link for the corresponding semantics: + * https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions + */ +// scalastyle:on +case class CaseWhen(branches: Seq[Expression]) extends CaseWhenLike { + + // Use private[this] Array to speed up evaluation. + @transient private[this] lazy val branchesArr = branches.toArray + + override def children: Seq[Expression] = branches + + override protected def checkTypesInternal(): TypeCheckResult = { + if (whenList.forall(_.dataType == BooleanType)) { + TypeCheckResult.TypeCheckSuccess + } else { + val index = whenList.indexWhere(_.dataType != BooleanType) + TypeCheckResult.TypeCheckFailure( + s"WHEN expressions in CaseWhen should all be boolean type, " + + s"but the ${index + 1}th when expression's type is ${whenList(index)}") + } + } + + /** Written in imperative fashion for performance considerations. */ + override def eval(input: InternalRow): Any = { + val len = branchesArr.length + var i = 0 + // If all branches fail and an elseVal is not provided, the whole statement + // defaults to null, according to Hive's semantics. + while (i < len - 1) { + if (branchesArr(i).eval(input) == true) { + return branchesArr(i + 1).eval(input) + } + i += 2 + } + var res: Any = null + if (i == len - 1) { + res = branchesArr(i).eval(input) + } + return res + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val len = branchesArr.length + val got = ctx.freshName("got") + + val cases = (0 until len/2).map { i => + val cond = branchesArr(i * 2).gen(ctx) + val res = branchesArr(i * 2 + 1).gen(ctx) + s""" + if (!$got) { + ${cond.code} + if (!${cond.isNull} && ${cond.primitive}) { + $got = true; + ${res.code} + ${ev.isNull} = ${res.isNull}; + ${ev.primitive} = ${res.primitive}; + } + } + """ + }.mkString("\n") + + val other = if (len % 2 == 1) { + val res = branchesArr(len - 1).gen(ctx) + s""" + if (!$got) { + ${res.code} + ${ev.isNull} = ${res.isNull}; + ${ev.primitive} = ${res.primitive}; + } + """ + } else { + "" + } + + s""" + boolean $got = false; + boolean ${ev.isNull} = true; + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + $cases + $other + """ + } + + override def toString: String = { + "CASE" + branches.sliding(2, 2).map { + case Seq(cond, value) => s" WHEN $cond THEN $value" + case Seq(elseValue) => s" ELSE $elseValue" + }.mkString + } +} + +// scalastyle:off +/** + * Case statements of the form "CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END". + * Refer to this link for the corresponding semantics: + * https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions + */ +// scalastyle:on +case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseWhenLike { + + // Use private[this] Array to speed up evaluation. + @transient private[this] lazy val branchesArr = branches.toArray + + override def children: Seq[Expression] = key +: branches + + override protected def checkTypesInternal(): TypeCheckResult = { + if ((key +: whenList).map(_.dataType).distinct.size > 1) { + TypeCheckResult.TypeCheckFailure( + "key and WHEN expressions should all be same type or coercible to a common type") + } else { + TypeCheckResult.TypeCheckSuccess + } + } + + /** Written in imperative fashion for performance considerations. */ + override def eval(input: InternalRow): Any = { + val evaluatedKey = key.eval(input) + val len = branchesArr.length + var i = 0 + // If all branches fail and an elseVal is not provided, the whole statement + // defaults to null, according to Hive's semantics. + while (i < len - 1) { + if (equalNullSafe(evaluatedKey, branchesArr(i).eval(input))) { + return branchesArr(i + 1).eval(input) + } + i += 2 + } + var res: Any = null + if (i == len - 1) { + res = branchesArr(i).eval(input) + } + return res + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val keyEval = key.gen(ctx) + val len = branchesArr.length + val got = ctx.freshName("got") + + val cases = (0 until len/2).map { i => + val cond = branchesArr(i * 2).gen(ctx) + val res = branchesArr(i * 2 + 1).gen(ctx) + s""" + if (!$got) { + ${cond.code} + if (${keyEval.isNull} && ${cond.isNull} || + !${keyEval.isNull} && !${cond.isNull} + && ${ctx.genEqual(key.dataType, keyEval.primitive, cond.primitive)}) { + $got = true; + ${res.code} + ${ev.isNull} = ${res.isNull}; + ${ev.primitive} = ${res.primitive}; + } + } + """ + }.mkString("\n") + + val other = if (len % 2 == 1) { + val res = branchesArr(len - 1).gen(ctx) + s""" + if (!$got) { + ${res.code} + ${ev.isNull} = ${res.isNull}; + ${ev.primitive} = ${res.primitive}; + } + """ + } else { + "" + } + + s""" + boolean $got = false; + boolean ${ev.isNull} = true; + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + ${keyEval.code} + $cases + $other + """ + } + + private def equalNullSafe(l: Any, r: Any) = { + if (l == null && r == null) { + true + } else if (l == null || r == null) { + false + } else { + l == r + } + } + + override def toString: String = { + s"CASE $key" + branches.sliding(2, 2).map { + case Seq(cond, value) => s" WHEN $cond THEN $value" + case Seq(elseValue) => s" ELSE $elseValue" + }.mkString + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala index adb94df7d1c7b..2bc893af02641 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala @@ -17,18 +17,19 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.sql.catalyst +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} import org.apache.spark.sql.types._ /** Return the unscaled Long value of a Decimal, assuming it fits in a Long */ case class UnscaledValue(child: Expression) extends UnaryExpression { - override type EvaluatedType = Any override def dataType: DataType = LongType override def foldable: Boolean = child.foldable override def nullable: Boolean = child.nullable override def toString: String = s"UnscaledValue($child)" - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val childResult = child.eval(input) if (childResult == null) { null @@ -36,18 +37,21 @@ case class UnscaledValue(child: Expression) extends UnaryExpression { childResult.asInstanceOf[Decimal].toUnscaledLong } } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, c => s"$c.toUnscaledLong()") + } } /** Create a Decimal from an unscaled Long value */ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends UnaryExpression { - override type EvaluatedType = Decimal override def dataType: DataType = DecimalType(precision, scale) override def foldable: Boolean = child.foldable override def nullable: Boolean = child.nullable override def toString: String = s"MakeDecimal($child,$precision,$scale)" - override def eval(input: Row): Decimal = { + override def eval(input: InternalRow): Decimal = { val childResult = child.eval(input) if (childResult == null) { null @@ -55,4 +59,18 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un new Decimal().setOrNull(childResult.asInstanceOf[Long], precision, scale) } } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval = child.gen(ctx) + eval.code + s""" + boolean ${ev.isNull} = ${eval.isNull}; + ${ctx.decimalType} ${ev.primitive} = null; + + if (!${ev.isNull}) { + ${ev.primitive} = (new ${ctx.decimalType}()).setOrNull( + ${eval.primitive}, $precision, $scale); + ${ev.isNull} = ${ev.primitive} == null; + } + """ + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala index 9a6cb048af5ad..f30cb42d12b83 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import scala.collection.Map +import org.apache.spark.sql.catalyst import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees} import org.apache.spark.sql.types._ @@ -40,8 +41,6 @@ import org.apache.spark.sql.types._ abstract class Generator extends Expression { self: Product => - override type EvaluatedType = TraversableOnce[Row] - // TODO ideally we should return the type of ArrayType(StructType), // however, we don't keep the output field names in the Generator. override def dataType: DataType = throw new UnsupportedOperationException @@ -55,7 +54,13 @@ abstract class Generator extends Expression { def elementTypes: Seq[(DataType, Boolean)] /** Should be implemented by child classes to perform specific Generators. */ - override def eval(input: Row): TraversableOnce[Row] + override def eval(input: InternalRow): TraversableOnce[InternalRow] + + /** + * Notifies that there are no more rows to process, clean up code, and additional + * rows can be made here. + */ + def terminate(): TraversableOnce[InternalRow] = Nil } /** @@ -63,16 +68,27 @@ abstract class Generator extends Expression { */ case class UserDefinedGenerator( elementTypes: Seq[(DataType, Boolean)], - function: Row => TraversableOnce[Row], + function: InternalRow => TraversableOnce[InternalRow], children: Seq[Expression]) extends Generator { - override def eval(input: Row): TraversableOnce[Row] = { - // TODO(davies): improve this + @transient private[this] var inputRow: InterpretedProjection = _ + @transient private[this] var convertToScala: (InternalRow) => InternalRow = _ + + private def initializeConverters(): Unit = { + inputRow = new InterpretedProjection(children) + convertToScala = { + val inputSchema = StructType(children.map(e => StructField(e.simpleString, e.dataType, true))) + CatalystTypeConverters.createToScalaConverter(inputSchema) + }.asInstanceOf[(InternalRow => InternalRow)] + } + + override def eval(input: InternalRow): TraversableOnce[InternalRow] = { + if (inputRow == null) { + initializeConverters() + } // Convert the objects into Scala Type before calling function, we need schema to support UDT - val inputSchema = StructType(children.map(e => StructField(e.simpleString, e.dataType, true))) - val inputRow = new InterpretedProjection(children) - function(CatalystTypeConverters.convertToScala(inputRow(input), inputSchema).asInstanceOf[Row]) + function(convertToScala(inputRow(input))) } override def toString: String = s"UserDefinedGenerator(${children.mkString(",")})" @@ -93,14 +109,14 @@ case class Explode(child: Expression) case MapType(kt, vt, valueContainsNull) => (kt, false) :: (vt, valueContainsNull) :: Nil } - override def eval(input: Row): TraversableOnce[Row] = { + override def eval(input: InternalRow): TraversableOnce[InternalRow] = { child.dataType match { case ArrayType(_, _) => val inputArray = child.eval(input).asInstanceOf[Seq[Any]] if (inputArray == null) Nil else inputArray.map(v => new GenericRow(Array(v))) case MapType(_, _, _) => - val inputMap = child.eval(input).asInstanceOf[Map[Any,Any]] - if (inputMap == null) Nil else inputMap.map { case (k,v) => new GenericRow(Array(k,v)) } + val inputMap = child.eval(input).asInstanceOf[Map[Any, Any]] + if (inputMap == null) Nil else inputMap.map { case (k, v) => new GenericRow(Array(k, v)) } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index 18cba4cc46707..6c86a47ba200c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -19,8 +19,12 @@ package org.apache.spark.sql.catalyst.expressions import java.sql.{Date, Timestamp} +import org.apache.spark.sql.catalyst import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String object Literal { def apply(v: Any): Literal = v match { @@ -30,12 +34,12 @@ object Literal { case f: Float => Literal(f, FloatType) case b: Byte => Literal(b, ByteType) case s: Short => Literal(s, ShortType) - case s: String => Literal(UTF8String(s), StringType) + case s: String => Literal(UTF8String.fromString(s), StringType) case b: Boolean => Literal(b, BooleanType) case d: BigDecimal => Literal(Decimal(d), DecimalType.Unlimited) case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType.Unlimited) case d: Decimal => Literal(d, DecimalType.Unlimited) - case t: Timestamp => Literal(t, TimestampType) + case t: Timestamp => Literal(DateUtils.fromJavaTimestamp(t), TimestampType) case d: Date => Literal(DateUtils.fromJavaDate(d), DateType) case a: Array[Byte] => Literal(a, BinaryType) case null => Literal(null, NullType) @@ -77,18 +81,71 @@ case class Literal protected (value: Any, dataType: DataType) extends LeafExpres override def toString: String = if (value != null) value.toString else "null" - type EvaluatedType = Any - override def eval(input: Row): Any = value + override def equals(other: Any): Boolean = other match { + case o: Literal => + dataType.equals(o.dataType) && + (value == null && null == o.value || value != null && value.equals(o.value)) + case _ => false + } + + override def eval(input: InternalRow): Any = value + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + // change the isNull and primitive to consts, to inline them + if (value == null) { + ev.isNull = "true" + s"final ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};" + } else { + dataType match { + case BooleanType => + ev.isNull = "false" + ev.primitive = value.toString + "" + case FloatType => + val v = value.asInstanceOf[Float] + if (v.isNaN || v.isInfinite) { + super.genCode(ctx, ev) + } else { + ev.isNull = "false" + ev.primitive = s"${value}f" + "" + } + case DoubleType => + val v = value.asInstanceOf[Double] + if (v.isNaN || v.isInfinite) { + super.genCode(ctx, ev) + } else { + ev.isNull = "false" + ev.primitive = s"${value}" + "" + } + case ByteType | ShortType => + ev.isNull = "false" + ev.primitive = s"(${ctx.javaType(dataType)})$value" + "" + case IntegerType | DateType => + ev.isNull = "false" + ev.primitive = value.toString + "" + case TimestampType | LongType => + ev.isNull = "false" + ev.primitive = s"${value}L" + "" + // eval() version may be faster for non-primitive types + case other => + super.genCode(ctx, ev) + } + } + } } // TODO: Specialize case class MutableLiteral(var value: Any, dataType: DataType, nullable: Boolean = true) extends LeafExpression { - type EvaluatedType = Any - def update(expression: Expression, input: Row): Unit = { + def update(expression: Expression, input: InternalRow): Unit = { value = expression.eval(input) } - override def eval(input: Row): Any = value + override def eval(input: InternalRow): Any = value } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala new file mode 100644 index 0000000000000..250564dc4b818 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala @@ -0,0 +1,314 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import java.lang.{Long => JLong} + +import org.apache.spark.sql.catalyst +import org.apache.spark.sql.catalyst.expressions.codegen._ +import org.apache.spark.sql.types.{DataType, DoubleType, LongType, StringType} +import org.apache.spark.unsafe.types.UTF8String + +/** + * A leaf expression specifically for math constants. Math constants expect no input. + * @param c The math constant. + * @param name The short name of the function + */ +abstract class LeafMathExpression(c: Double, name: String) + extends LeafExpression with Serializable { + self: Product => + + override def dataType: DataType = DoubleType + override def foldable: Boolean = true + override def nullable: Boolean = false + override def toString: String = s"$name()" + + override def eval(input: InternalRow): Any = c + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + s""" + boolean ${ev.isNull} = false; + ${ctx.javaType(dataType)} ${ev.primitive} = java.lang.Math.$name; + """ + } +} + +/** + * A unary expression specifically for math functions. Math Functions expect a specific type of + * input format, therefore these functions extend `ExpectsInputTypes`. + * @param f The math function. + * @param name The short name of the function + */ +abstract class UnaryMathExpression(f: Double => Double, name: String) + extends UnaryExpression with Serializable with ExpectsInputTypes { + self: Product => + + override def expectedChildTypes: Seq[DataType] = Seq(DoubleType) + override def dataType: DataType = DoubleType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = true + override def toString: String = s"$name($child)" + + override def eval(input: InternalRow): Any = { + val evalE = child.eval(input) + if (evalE == null) { + null + } else { + val result = f(evalE.asInstanceOf[Double]) + if (result.isNaN) null else result + } + } + + // name of function in java.lang.Math + def funcName: String = name.toLowerCase + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval = child.gen(ctx) + eval.code + s""" + boolean ${ev.isNull} = ${eval.isNull}; + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + if (!${ev.isNull}) { + ${ev.primitive} = java.lang.Math.${funcName}(${eval.primitive}); + if (Double.valueOf(${ev.primitive}).isNaN()) { + ${ev.isNull} = true; + } + } + """ + } +} + +/** + * A binary expression specifically for math functions that take two `Double`s as input and returns + * a `Double`. + * @param f The math function. + * @param name The short name of the function + */ +abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String) + extends BinaryExpression with Serializable with ExpectsInputTypes { self: Product => + + override def expectedChildTypes: Seq[DataType] = Seq(DoubleType, DoubleType) + + override def toString: String = s"$name($left, $right)" + + override def dataType: DataType = DoubleType + + override def eval(input: InternalRow): Any = { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + val result = f(evalE1.asInstanceOf[Double], evalE2.asInstanceOf[Double]) + if (result.isNaN) null else result + } + } + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.${name.toLowerCase}($c1, $c2)") + } +} + +//////////////////////////////////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////////////////////////////////// +// Leaf math functions +//////////////////////////////////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////////////////////////////////// + +case class EulerNumber() extends LeafMathExpression(math.E, "E") + +case class Pi() extends LeafMathExpression(math.Pi, "PI") + +//////////////////////////////////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////////////////////////////////// +// Unary math functions +//////////////////////////////////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////////////////////////////////// + +case class Acos(child: Expression) extends UnaryMathExpression(math.acos, "ACOS") + +case class Asin(child: Expression) extends UnaryMathExpression(math.asin, "ASIN") + +case class Atan(child: Expression) extends UnaryMathExpression(math.atan, "ATAN") + +case class Cbrt(child: Expression) extends UnaryMathExpression(math.cbrt, "CBRT") + +case class Ceil(child: Expression) extends UnaryMathExpression(math.ceil, "CEIL") + +case class Cos(child: Expression) extends UnaryMathExpression(math.cos, "COS") + +case class Cosh(child: Expression) extends UnaryMathExpression(math.cosh, "COSH") + +case class Exp(child: Expression) extends UnaryMathExpression(math.exp, "EXP") + +case class Expm1(child: Expression) extends UnaryMathExpression(math.expm1, "EXPM1") + +case class Floor(child: Expression) extends UnaryMathExpression(math.floor, "FLOOR") + +case class Log(child: Expression) extends UnaryMathExpression(math.log, "LOG") + +case class Log2(child: Expression) + extends UnaryMathExpression((x: Double) => math.log(x) / math.log(2), "LOG2") { + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval = child.gen(ctx) + eval.code + s""" + boolean ${ev.isNull} = ${eval.isNull}; + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + if (!${ev.isNull}) { + ${ev.primitive} = java.lang.Math.log(${eval.primitive}) / java.lang.Math.log(2); + if (Double.valueOf(${ev.primitive}).isNaN()) { + ${ev.isNull} = true; + } + } + """ + } +} + +case class Log10(child: Expression) extends UnaryMathExpression(math.log10, "LOG10") + +case class Log1p(child: Expression) extends UnaryMathExpression(math.log1p, "LOG1P") + +case class Rint(child: Expression) extends UnaryMathExpression(math.rint, "ROUND") { + override def funcName: String = "rint" +} + +case class Signum(child: Expression) extends UnaryMathExpression(math.signum, "SIGNUM") + +case class Sin(child: Expression) extends UnaryMathExpression(math.sin, "SIN") + +case class Sinh(child: Expression) extends UnaryMathExpression(math.sinh, "SINH") + +case class Sqrt(child: Expression) extends UnaryMathExpression(math.sqrt, "SQRT") + +case class Tan(child: Expression) extends UnaryMathExpression(math.tan, "TAN") + +case class Tanh(child: Expression) extends UnaryMathExpression(math.tanh, "TANH") + +case class ToDegrees(child: Expression) extends UnaryMathExpression(math.toDegrees, "DEGREES") { + override def funcName: String = "toDegrees" +} + +case class ToRadians(child: Expression) extends UnaryMathExpression(math.toRadians, "RADIANS") { + override def funcName: String = "toRadians" +} + +case class Bin(child: Expression) + extends UnaryExpression with Serializable with ExpectsInputTypes { + + val name: String = "BIN" + + override def foldable: Boolean = child.foldable + override def nullable: Boolean = true + override def toString: String = s"$name($child)" + + override def expectedChildTypes: Seq[DataType] = Seq(LongType) + override def dataType: DataType = StringType + + def funcName: String = name.toLowerCase + + override def eval(input: catalyst.InternalRow): Any = { + val evalE = child.eval(input) + if (evalE == null) { + null + } else { + UTF8String.fromString(JLong.toBinaryString(evalE.asInstanceOf[Long])) + } + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c) => + s"${ctx.stringType}.fromString(java.lang.Long.toBinaryString($c))") + } +} + +//////////////////////////////////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////////////////////////////////// +// Binary math functions +//////////////////////////////////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////////////////////////////////// + + +case class Atan2(left: Expression, right: Expression) + extends BinaryMathExpression(math.atan2, "ATAN2") { + + override def eval(input: InternalRow): Any = { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0 + val result = math.atan2(evalE1.asInstanceOf[Double] + 0.0, + evalE2.asInstanceOf[Double] + 0.0) + if (result.isNaN) null else result + } + } + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.atan2($c1 + 0.0, $c2 + 0.0)") + s""" + if (Double.valueOf(${ev.primitive}).isNaN()) { + ${ev.isNull} = true; + } + """ + } +} + +case class Hypot(left: Expression, right: Expression) + extends BinaryMathExpression(math.hypot, "HYPOT") + +case class Pow(left: Expression, right: Expression) + extends BinaryMathExpression(math.pow, "POWER") { + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.pow($c1, $c2)") + s""" + if (Double.valueOf(${ev.primitive}).isNaN()) { + ${ev.isNull} = true; + } + """ + } +} + +case class Logarithm(left: Expression, right: Expression) + extends BinaryMathExpression((c1, c2) => math.log(c2) / math.log(c1), "LOG") { + + /** + * Natural log, i.e. using e as the base. + */ + def this(child: Expression) = { + this(EulerNumber(), child) + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val logCode = if (left.isInstanceOf[EulerNumber]) { + defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.log($c2)") + } else { + defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.log($c2) / java.lang.Math.log($c1)") + } + logCode + s""" + if (Double.valueOf(${ev.primitive}).isNaN()) { + ${ev.isNull} = true; + } + """ + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala deleted file mode 100644 index fcc06d3aa1036..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst.expressions.mathfuncs - -import org.apache.spark.sql.catalyst.analysis.UnresolvedException -import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, BinaryExpression, Expression, Row} -import org.apache.spark.sql.types._ - -/** - * A binary expression specifically for math functions that take two `Double`s as input and returns - * a `Double`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String) - extends BinaryExpression with Serializable with ExpectsInputTypes { self: Product => - type EvaluatedType = Any - override def symbol: String = null - override def expectedChildTypes: Seq[DataType] = Seq(DoubleType, DoubleType) - - override def nullable: Boolean = left.nullable || right.nullable - override def toString: String = s"$name($left, $right)" - - override lazy val resolved = - left.resolved && right.resolved && - left.dataType == right.dataType && - !DecimalType.isFixed(left.dataType) - - override def dataType: DataType = { - if (!resolved) { - throw new UnresolvedException(this, - s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") - } - left.dataType - } - - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if (evalE1 == null) { - null - } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - val result = f(evalE1.asInstanceOf[Double], evalE2.asInstanceOf[Double]) - if (result.isNaN) null else result - } - } - } -} - -case class Atan2( - left: Expression, - right: Expression) extends BinaryMathExpression(math.atan2, "ATAN2") { - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if (evalE1 == null) { - null - } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0 - val result = math.atan2(evalE1.asInstanceOf[Double] + 0.0, - evalE2.asInstanceOf[Double] + 0.0) - if (result.isNaN) null else result - } - } - } -} - -case class Hypot( - left: Expression, - right: Expression) extends BinaryMathExpression(math.hypot, "HYPOT") - -case class Pow(left: Expression, right: Expression) extends BinaryMathExpression(math.pow, "POWER") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala deleted file mode 100644 index dc68469e060cb..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst.expressions.mathfuncs - -import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, Row, UnaryExpression} -import org.apache.spark.sql.types._ - -/** - * A unary expression specifically for math functions. Math Functions expect a specific type of - * input format, therefore these functions extend `ExpectsInputTypes`. - * @param name The short name of the function - */ -abstract class MathematicalExpression(f: Double => Double, name: String) - extends UnaryExpression with Serializable with ExpectsInputTypes { - self: Product => - type EvaluatedType = Any - - override def expectedChildTypes: Seq[DataType] = Seq(DoubleType) - override def dataType: DataType = DoubleType - override def foldable: Boolean = child.foldable - override def nullable: Boolean = true - override def toString: String = s"$name($child)" - - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) { - null - } else { - val result = f(evalE.asInstanceOf[Double]) - if (result.isNaN) null else result - } - } -} - -case class Acos(child: Expression) extends MathematicalExpression(math.acos, "ACOS") - -case class Asin(child: Expression) extends MathematicalExpression(math.asin, "ASIN") - -case class Atan(child: Expression) extends MathematicalExpression(math.atan, "ATAN") - -case class Cbrt(child: Expression) extends MathematicalExpression(math.cbrt, "CBRT") - -case class Ceil(child: Expression) extends MathematicalExpression(math.ceil, "CEIL") - -case class Cos(child: Expression) extends MathematicalExpression(math.cos, "COS") - -case class Cosh(child: Expression) extends MathematicalExpression(math.cosh, "COSH") - -case class Exp(child: Expression) extends MathematicalExpression(math.exp, "EXP") - -case class Expm1(child: Expression) extends MathematicalExpression(math.expm1, "EXPM1") - -case class Floor(child: Expression) extends MathematicalExpression(math.floor, "FLOOR") - -case class Log(child: Expression) extends MathematicalExpression(math.log, "LOG") - -case class Log10(child: Expression) extends MathematicalExpression(math.log10, "LOG10") - -case class Log1p(child: Expression) extends MathematicalExpression(math.log1p, "LOG1P") - -case class Rint(child: Expression) extends MathematicalExpression(math.rint, "ROUND") - -case class Signum(child: Expression) extends MathematicalExpression(math.signum, "SIGNUM") - -case class Sin(child: Expression) extends MathematicalExpression(math.sin, "SIN") - -case class Sinh(child: Expression) extends MathematicalExpression(math.sinh, "SINH") - -case class Tan(child: Expression) extends MathematicalExpression(math.tan, "TAN") - -case class Tanh(child: Expression) extends MathematicalExpression(math.tanh, "TANH") - -case class ToDegrees(child: Expression) - extends MathematicalExpression(math.toDegrees, "DEGREES") - -case class ToRadians(child: Expression) - extends MathematicalExpression(math.toRadians, "RADIANS") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala new file mode 100644 index 0000000000000..4bee8cb728e5c --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.commons.codec.digest.DigestUtils +import org.apache.spark.sql.catalyst.expressions.codegen._ +import org.apache.spark.sql.types.{BinaryType, StringType, DataType} +import org.apache.spark.unsafe.types.UTF8String + +/** + * A function that calculates an MD5 128-bit checksum and returns it as a hex string + * For input of type [[BinaryType]] + */ +case class Md5(child: Expression) + extends UnaryExpression with ExpectsInputTypes { + + override def dataType: DataType = StringType + + override def expectedChildTypes: Seq[DataType] = Seq(BinaryType) + + override def eval(input: InternalRow): Any = { + val value = child.eval(input) + if (value == null) { + null + } else { + UTF8String.fromString(DigestUtils.md5Hex(value.asInstanceOf[Array[Byte]])) + } + } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, c => + "org.apache.spark.unsafe.types.UTF8String.fromString" + + s"(org.apache.commons.codec.digest.DigestUtils.md5Hex($c))") + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index a9170589f8c6c..58dbeaf89cad5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -17,10 +17,11 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.catalyst import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.trees.LeafNode +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} +import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.types._ object NamedExpression { @@ -111,11 +112,14 @@ case class Alias(child: Expression, name: String)( val explicitMetadata: Option[Metadata] = None) extends NamedExpression with trees.UnaryNode[Expression] { - override type EvaluatedType = Any // Alias(Generator, xx) need to be transformed into Generate(generator, ...) override lazy val resolved = childrenResolved && !child.isInstanceOf[Generator] - override def eval(input: Row): Any = child.eval(input) + override def eval(input: InternalRow): Any = child.eval(input) + + override def isThreadSafe: Boolean = child.isThreadSafe + + override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx) override def dataType: DataType = child.dataType override def nullable: Boolean = child.nullable @@ -181,6 +185,11 @@ case class AttributeReference( case _ => false } + override def semanticEquals(other: Expression): Boolean = other match { + case ar: AttributeReference => sameRef(ar) + case _ => false + } + override def hashCode: Int = { // See http://stackoverflow.com/questions/113511/hash-code-implementation var h = 17 @@ -224,7 +233,7 @@ case class AttributeReference( } // Unresolved attributes are transient at compile time and don't get evaluated during execution. - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def toString: String = s"$name#${exprId.id}$typeSuffix" @@ -235,7 +244,6 @@ case class AttributeReference( * expression id or the unresolved indicator. */ case class PrettyAttribute(name: String) extends Attribute with trees.LeafNode[Expression] { - type EvaluatedType = Any override def toString: String = name @@ -247,7 +255,7 @@ case class PrettyAttribute(name: String) extends Attribute with trees.LeafNode[E override def withName(newName: String): Attribute = throw new UnsupportedOperationException override def qualifiers: Seq[String] = throw new UnsupportedOperationException override def exprId: ExprId = throw new UnsupportedOperationException - override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException + override def eval(input: InternalRow): Any = throw new UnsupportedOperationException override def nullable: Boolean = throw new UnsupportedOperationException override def dataType: DataType = NullType } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala index f9161cf34f0c9..98acaf23c44c1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala @@ -17,12 +17,11 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} import org.apache.spark.sql.types.DataType case class Coalesce(children: Seq[Expression]) extends Expression { - type EvaluatedType = Any /** Coalesce is nullable if all of its children are nullable, or if it has no children. */ override def nullable: Boolean = !children.exists(!_.nullable) @@ -43,7 +42,7 @@ case class Coalesce(children: Seq[Expression]) extends Expression { this, s"Coalesce cannot have children of different types. $childTypes") } - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { var i = 0 var result: Any = null val childIterator = children.iterator @@ -52,27 +51,62 @@ case class Coalesce(children: Seq[Expression]) extends Expression { } result } + + override def isThreadSafe: Boolean = children.forall(_.isThreadSafe) + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + s""" + boolean ${ev.isNull} = true; + ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)}; + """ + + children.map { e => + val eval = e.gen(ctx) + s""" + if (${ev.isNull}) { + ${eval.code} + if (!${eval.isNull}) { + ${ev.isNull} = false; + ${ev.primitive} = ${eval.primitive}; + } + } + """ + }.mkString("\n") + } } -case class IsNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] { +case class IsNull(child: Expression) extends UnaryExpression with Predicate { override def foldable: Boolean = child.foldable override def nullable: Boolean = false - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { child.eval(input) == null } + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval = child.gen(ctx) + ev.isNull = "false" + ev.primitive = eval.isNull + eval.code + } + override def toString: String = s"IS NULL $child" } -case class IsNotNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] { +case class IsNotNull(child: Expression) extends UnaryExpression with Predicate { override def foldable: Boolean = child.foldable override def nullable: Boolean = false override def toString: String = s"IS NOT NULL $child" - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { child.eval(input) != null } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval = child.gen(ctx) + ev.isNull = "false" + ev.primitive = s"(!(${eval.isNull}))" + eval.code + } } /** @@ -85,7 +119,7 @@ case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate private[this] val childrenArray = children.toArray - override def eval(input: Row): Boolean = { + override def eval(input: InternalRow): Boolean = { var numNonNulls = 0 var i = 0 while (i < childrenArray.length && numNonNulls < n) { @@ -96,4 +130,25 @@ case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate } numNonNulls >= n } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val nonnull = ctx.freshName("nonnull") + val code = children.map { e => + val eval = e.gen(ctx) + s""" + if ($nonnull < $n) { + ${eval.code} + if (!${eval.isNull}) { + $nonnull += 1; + } + } + """ + }.mkString("\n") + s""" + int $nonnull = 0; + $code + boolean ${ev.isNull} = false; + boolean ${ev.primitive} = $nonnull >= $n; + """ + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala index fbc97b2e75312..d24d74e7b82ad 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala @@ -49,30 +49,30 @@ package org.apache.spark.sql.catalyst */ package object expressions { - type Row = org.apache.spark.sql.Row + type InternalRow = org.apache.spark.sql.catalyst.InternalRow - val Row = org.apache.spark.sql.Row + val InternalRow = org.apache.spark.sql.catalyst.InternalRow /** - * Converts a [[Row]] to another Row given a sequence of expression that define each column of the - * new row. If the schema of the input row is specified, then the given expression will be bound - * to that schema. + * Converts a [[InternalRow]] to another Row given a sequence of expression that define each + * column of the new row. If the schema of the input row is specified, then the given expression + * will be bound to that schema. */ - abstract class Projection extends (Row => Row) + abstract class Projection extends (InternalRow => InternalRow) /** - * Converts a [[Row]] to another Row given a sequence of expression that define each column of the - * new row. If the schema of the input row is specified, then the given expression will be bound - * to that schema. + * Converts a [[InternalRow]] to another Row given a sequence of expression that define each + * column of the new row. If the schema of the input row is specified, then the given expression + * will be bound to that schema. * * In contrast to a normal projection, a MutableProjection reuses the same underlying row object * each time an input row is added. This significantly reduces the cost of calculating the - * projection, but means that it is not safe to hold on to a reference to a [[Row]] after `next()` - * has been called on the [[Iterator]] that produced it. Instead, the user must call `Row.copy()` - * and hold on to the returned [[Row]] before calling `next()`. + * projection, but means that it is not safe to hold on to a reference to a [[InternalRow]] after + * `next()` has been called on the [[Iterator]] that produced it. Instead, the user must call + * `InternalRow.copy()` and hold on to the returned [[InternalRow]] before calling `next()`. */ abstract class MutableProjection extends Projection { - def currentValue: Row + def currentValue: InternalRow /** Uses the given row to store the output of the projection. */ def target(row: MutableRow): MutableProjection diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index 50b0f3ee5f93f..3a12d03ba6bb9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -17,17 +17,18 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.analysis.UnresolvedException -import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult +import org.apache.spark.sql.catalyst.util.TypeUtils +import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.types.{DataType, BinaryType, BooleanType, AtomicType} +import org.apache.spark.sql.types._ object InterpretedPredicate { - def create(expression: Expression, inputSchema: Seq[Attribute]): (Row => Boolean) = + def create(expression: Expression, inputSchema: Seq[Attribute]): (InternalRow => Boolean) = create(BindReferences.bindReference(expression, inputSchema)) - def create(expression: Expression): (Row => Boolean) = { - (r: Row) => expression.eval(r).asInstanceOf[Boolean] + def create(expression: Expression): (InternalRow => Boolean) = { + (r: InternalRow) => expression.eval(r).asInstanceOf[Boolean] } } @@ -35,8 +36,6 @@ trait Predicate extends Expression { self: Product => override def dataType: DataType = BooleanType - - type EvaluatedType = Any } trait PredicateHelper { @@ -78,12 +77,16 @@ case class Not(child: Expression) extends UnaryExpression with Predicate with Ex override def expectedChildTypes: Seq[DataType] = Seq(BooleanType) - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { child.eval(input) match { case null => null case b: Boolean => !b } } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, c => s"!($c)") + } } /** @@ -95,7 +98,7 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate { override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN. override def toString: String = s"$value IN ${list.mkString("(", ",", ")")}" - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evaluatedValue = value.eval(input) list.exists(e => e.eval(input) == evaluatedValue) } @@ -110,10 +113,11 @@ case class InSet(value: Expression, hset: Set[Any]) override def children: Seq[Expression] = value :: Nil + override def foldable: Boolean = value.foldable override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN. override def toString: String = s"$value INSET ${hset.mkString("(", ",", ")")}" - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { hset.contains(value.eval(input)) } } @@ -125,7 +129,7 @@ case class And(left: Expression, right: Expression) override def symbol: String = "&&" - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val l = left.eval(input) if (l == false) { false @@ -142,6 +146,29 @@ case class And(left: Expression, right: Expression) } } } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) + + // The result should be `false`, if any of them is `false` whenever the other is null or not. + s""" + ${eval1.code} + boolean ${ev.isNull} = false; + boolean ${ev.primitive} = false; + + if (!${eval1.isNull} && !${eval1.primitive}) { + } else { + ${eval2.code} + if (!${eval2.isNull} && !${eval2.primitive}) { + } else if (!${eval1.isNull} && !${eval2.isNull}) { + ${ev.primitive} = true; + } else { + ${ev.isNull} = true; + } + } + """ + } } case class Or(left: Expression, right: Expression) @@ -151,7 +178,7 @@ case class Or(left: Expression, right: Expression) override def symbol: String = "||" - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val l = left.eval(input) if (l == true) { true @@ -168,61 +195,47 @@ case class Or(left: Expression, right: Expression) } } } -} -abstract class BinaryComparison extends BinaryExpression with Predicate { - self: Product => -} + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) -case class EqualTo(left: Expression, right: Expression) extends BinaryComparison { - override def symbol: String = "=" + // The result should be `true`, if any of them is `true` whenever the other is null or not. + s""" + ${eval1.code} + boolean ${ev.isNull} = false; + boolean ${ev.primitive} = true; - override def eval(input: Row): Any = { - val l = left.eval(input) - if (l == null) { - null - } else { - val r = right.eval(input) - if (r == null) null - else if (left.dataType != BinaryType) l == r - else java.util.Arrays.equals(l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]]) - } + if (!${eval1.isNull} && ${eval1.primitive}) { + } else { + ${eval2.code} + if (!${eval2.isNull} && ${eval2.primitive}) { + } else if (!${eval1.isNull} && !${eval2.isNull}) { + ${ev.primitive} = false; + } else { + ${ev.isNull} = true; + } + } + """ } } -case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComparison { - override def symbol: String = "<=>" - - override def nullable: Boolean = false +abstract class BinaryComparison extends BinaryExpression with Predicate { + self: Product => - override def eval(input: Row): Any = { - val l = left.eval(input) - val r = right.eval(input) - if (l == null && r == null) { - true - } else if (l == null || r == null) { - false + override def checkInputDataTypes(): TypeCheckResult = { + if (left.dataType != right.dataType) { + TypeCheckResult.TypeCheckFailure( + s"differing types in ${this.getClass.getSimpleName} " + + s"(${left.dataType} and ${right.dataType}).") } else { - l == r + checkTypesInternal(dataType) } } -} - -case class LessThan(left: Expression, right: Expression) extends BinaryComparison { - override def symbol: String = "<" - lazy val ordering: Ordering[Any] = { - if (left.dataType != right.dataType) { - throw new TreeNodeException(this, - s"Types do not match ${left.dataType} != ${right.dataType}") - } - left.dataType match { - case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] - case other => sys.error(s"Type $other does not support ordered operations") - } - } + protected def checkTypesInternal(t: DataType): TypeCheckResult - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evalE1 = left.eval(input) if (evalE1 == null) { null @@ -231,258 +244,123 @@ case class LessThan(left: Expression, right: Expression) extends BinaryCompariso if (evalE2 == null) { null } else { - ordering.lt(evalE1, evalE2) + evalInternal(evalE1, evalE2) } } } -} -case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { - override def symbol: String = "<=" - - lazy val ordering: Ordering[Any] = { - if (left.dataType != right.dataType) { - throw new TreeNodeException(this, - s"Types do not match ${left.dataType} != ${right.dataType}") - } - left.dataType match { - case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] - case other => sys.error(s"Type $other does not support ordered operations") - } - } - - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if (evalE1 == null) { - null + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + if (ctx.isPrimitiveType(left.dataType)) { + // faster version + defineCodeGen(ctx, ev, (c1, c2) => s"$c1 $symbol $c2") } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - ordering.lteq(evalE1, evalE2) - } + defineCodeGen(ctx, ev, (c1, c2) => s"${ctx.genComp(left.dataType, c1, c2)} $symbol 0") } } -} -case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison { - override def symbol: String = ">" + protected def evalInternal(evalE1: Any, evalE2: Any): Any = + sys.error(s"BinaryComparisons must override either eval or evalInternal") +} - lazy val ordering: Ordering[Any] = { - if (left.dataType != right.dataType) { - throw new TreeNodeException(this, - s"Types do not match ${left.dataType} != ${right.dataType}") - } - left.dataType match { - case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] - case other => sys.error(s"Type $other does not support ordered operations") - } - } +private[sql] object BinaryComparison { + def unapply(e: BinaryComparison): Option[(Expression, Expression)] = Some((e.left, e.right)) +} - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if(evalE1 == null) { - null - } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - ordering.gt(evalE1, evalE2) - } - } +/** An extractor that matches both standard 3VL equality and null-safe equality. */ +private[sql] object Equality { + def unapply(e: BinaryComparison): Option[(Expression, Expression)] = e match { + case EqualTo(l, r) => Some((l, r)) + case EqualNullSafe(l, r) => Some((l, r)) + case _ => None } } -case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { - override def symbol: String = ">=" +case class EqualTo(left: Expression, right: Expression) extends BinaryComparison { + override def symbol: String = "=" - lazy val ordering: Ordering[Any] = { - if (left.dataType != right.dataType) { - throw new TreeNodeException(this, - s"Types do not match ${left.dataType} != ${right.dataType}") - } - left.dataType match { - case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] - case other => sys.error(s"Type $other does not support ordered operations") - } + override protected def checkTypesInternal(t: DataType) = TypeCheckResult.TypeCheckSuccess + + protected override def evalInternal(l: Any, r: Any) = { + if (left.dataType != BinaryType) l == r + else java.util.Arrays.equals(l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]]) } - override def eval(input: Row): Any = { - val evalE1 = left.eval(input) - if (evalE1 == null) { - null - } else { - val evalE2 = right.eval(input) - if (evalE2 == null) { - null - } else { - ordering.gteq(evalE1, evalE2) - } - } + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c1, c2) => ctx.genEqual(left.dataType, c1, c2)) } } -case class If(predicate: Expression, trueValue: Expression, falseValue: Expression) - extends Expression { - - override def children: Seq[Expression] = predicate :: trueValue :: falseValue :: Nil - override def nullable: Boolean = trueValue.nullable || falseValue.nullable +case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComparison { + override def symbol: String = "<=>" - override lazy val resolved = childrenResolved && trueValue.dataType == falseValue.dataType - override def dataType: DataType = { - if (!resolved) { - throw new UnresolvedException( - this, - s"Can not resolve due to differing types ${trueValue.dataType}, ${falseValue.dataType}") - } - trueValue.dataType - } + override def nullable: Boolean = false - type EvaluatedType = Any + override protected def checkTypesInternal(t: DataType) = TypeCheckResult.TypeCheckSuccess - override def eval(input: Row): Any = { - if (true == predicate.eval(input)) { - trueValue.eval(input) + override def eval(input: InternalRow): Any = { + val l = left.eval(input) + val r = right.eval(input) + if (l == null && r == null) { + true + } else if (l == null || r == null) { + false } else { - falseValue.eval(input) + l == r } } - override def toString: String = s"if ($predicate) $trueValue else $falseValue" + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val eval1 = left.gen(ctx) + val eval2 = right.gen(ctx) + val equalCode = ctx.genEqual(left.dataType, eval1.primitive, eval2.primitive) + ev.isNull = "false" + eval1.code + eval2.code + s""" + boolean ${ev.primitive} = (${eval1.isNull} && ${eval2.isNull}) || + (!${eval1.isNull} && $equalCode); + """ + } } -trait CaseWhenLike extends Expression { - self: Product => +case class LessThan(left: Expression, right: Expression) extends BinaryComparison { + override def symbol: String = "<" - type EvaluatedType = Any + override protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol) - // Note that `branches` are considered in consecutive pairs (cond, val), and the optional last - // element is the value for the default catch-all case (if provided). - // Hence, `branches` consists of at least two elements, and can have an odd or even length. - def branches: Seq[Expression] + private lazy val ordering = TypeUtils.getOrdering(left.dataType) - @transient lazy val whenList = - branches.sliding(2, 2).collect { case Seq(whenExpr, _) => whenExpr }.toSeq - @transient lazy val thenList = - branches.sliding(2, 2).collect { case Seq(_, thenExpr) => thenExpr }.toSeq - val elseValue = if (branches.length % 2 == 0) None else Option(branches.last) + protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.lt(evalE1, evalE2) +} + +case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { + override def symbol: String = "<=" - // both then and else val should be considered. - def valueTypes: Seq[DataType] = (thenList ++ elseValue).map(_.dataType) - def valueTypesEqual: Boolean = valueTypes.distinct.size <= 1 + override protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol) - override def dataType: DataType = { - if (!resolved) { - throw new UnresolvedException(this, "cannot resolve due to differing types in some branches") - } - valueTypes.head - } + private lazy val ordering = TypeUtils.getOrdering(left.dataType) - override def nullable: Boolean = { - // If no value is nullable and no elseValue is provided, the whole statement defaults to null. - thenList.exists(_.nullable) || (elseValue.map(_.nullable).getOrElse(true)) - } + protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.lteq(evalE1, evalE2) } -// scalastyle:off -/** - * Case statements of the form "CASE WHEN a THEN b [WHEN c THEN d]* [ELSE e] END". - * Refer to this link for the corresponding semantics: - * https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions - */ -// scalastyle:on -case class CaseWhen(branches: Seq[Expression]) extends CaseWhenLike { - - // Use private[this] Array to speed up evaluation. - @transient private[this] lazy val branchesArr = branches.toArray - - override def children: Seq[Expression] = branches - - override lazy val resolved: Boolean = - childrenResolved && - whenList.forall(_.dataType == BooleanType) && - valueTypesEqual - - /** Written in imperative fashion for performance considerations. */ - override def eval(input: Row): Any = { - val len = branchesArr.length - var i = 0 - // If all branches fail and an elseVal is not provided, the whole statement - // defaults to null, according to Hive's semantics. - while (i < len - 1) { - if (branchesArr(i).eval(input) == true) { - return branchesArr(i + 1).eval(input) - } - i += 2 - } - var res: Any = null - if (i == len - 1) { - res = branchesArr(i).eval(input) - } - return res - } +case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison { + override def symbol: String = ">" - override def toString: String = { - "CASE" + branches.sliding(2, 2).map { - case Seq(cond, value) => s" WHEN $cond THEN $value" - case Seq(elseValue) => s" ELSE $elseValue" - }.mkString - } + override protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol) + + private lazy val ordering = TypeUtils.getOrdering(left.dataType) + + protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.gt(evalE1, evalE2) } -// scalastyle:off -/** - * Case statements of the form "CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END". - * Refer to this link for the corresponding semantics: - * https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions - */ -// scalastyle:on -case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseWhenLike { - - // Use private[this] Array to speed up evaluation. - @transient private[this] lazy val branchesArr = branches.toArray - - override def children: Seq[Expression] = key +: branches - - override lazy val resolved: Boolean = - childrenResolved && valueTypesEqual - - /** Written in imperative fashion for performance considerations. */ - override def eval(input: Row): Any = { - val evaluatedKey = key.eval(input) - val len = branchesArr.length - var i = 0 - // If all branches fail and an elseVal is not provided, the whole statement - // defaults to null, according to Hive's semantics. - while (i < len - 1) { - if (equalNullSafe(evaluatedKey, branchesArr(i).eval(input))) { - return branchesArr(i + 1).eval(input) - } - i += 2 - } - var res: Any = null - if (i == len - 1) { - res = branchesArr(i).eval(input) - } - return res - } +case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { + override def symbol: String = ">=" - private def equalNullSafe(l: Any, r: Any) = { - if (l == null && r == null) { - true - } else if (l == null || r == null) { - false - } else { - l == r - } - } + override protected def checkTypesInternal(t: DataType) = + TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol) - override def toString: String = { - s"CASE $key" + branches.sliding(2, 2).map { - case Seq(cond, value) => s" WHEN $cond THEN $value" - case Seq(elseValue) => s" ELSE $elseValue" - }.mkString - } + private lazy val ordering = TypeUtils.getOrdering(left.dataType) + + protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.gteq(evalE1, evalE2) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala index 66d7c8b07cce8..45588bacd2e45 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala @@ -18,13 +18,14 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.TaskContext +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.types.{DataType, DoubleType} import org.apache.spark.util.Utils import org.apache.spark.util.random.XORShiftRandom /** * A Random distribution generating expression. - * TODO: This can be made generic to generate any type of random distribution, or any type of + * TODO: This can be made generic to generate any type of random distribution, or any type of * StructType. * * Since this expression is stateful, it cannot be a case object. @@ -36,9 +37,13 @@ abstract class RDG(seed: Long) extends LeafExpression with Serializable { * Record ID within each partition. By being transient, the Random Number Generator is * reset every time we serialize and deserialize it. */ - @transient protected lazy val rng = new XORShiftRandom(seed + TaskContext.get().partitionId()) + @transient protected lazy val partitionId = TaskContext.get() match { + case null => 0 + case _ => TaskContext.get().partitionId() + } + @transient protected lazy val rng = new XORShiftRandom(seed + partitionId) - override type EvaluatedType = Double + override def deterministic: Boolean = false override def nullable: Boolean = false @@ -46,11 +51,25 @@ abstract class RDG(seed: Long) extends LeafExpression with Serializable { } /** Generate a random column with i.i.d. uniformly distributed values in [0, 1). */ -case class Rand(seed: Long = Utils.random.nextLong()) extends RDG(seed) { - override def eval(input: Row): Double = rng.nextDouble() +case class Rand(seed: Long) extends RDG(seed) { + override def eval(input: InternalRow): Double = rng.nextDouble() + + def this() = this(Utils.random.nextLong()) + + def this(seed: Expression) = this(seed match { + case IntegerLiteral(s) => s + case _ => throw new AnalysisException("Input argument to rand must be an integer literal.") + }) } /** Generate a random column with i.i.d. gaussian random distribution. */ -case class Randn(seed: Long = Utils.random.nextLong()) extends RDG(seed) { - override def eval(input: Row): Double = rng.nextGaussian() +case class Randn(seed: Long) extends RDG(seed) { + override def eval(input: InternalRow): Double = rng.nextGaussian() + + def this() = this(Utils.random.nextLong()) + + def this(seed: Expression) = this(seed match { + case IntegerLiteral(s) => s + case _ => throw new AnalysisException("Input argument to rand must be an integer literal.") + }) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index 5fd892c42e69c..1098962ddc018 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -17,13 +17,14 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.types.{UTF8String, DataType, StructType, AtomicType} +import org.apache.spark.sql.types.{DataType, StructType, AtomicType} +import org.apache.spark.unsafe.types.UTF8String /** - * An extended interface to [[Row]] that allows the values for each column to be updated. Setting - * a value through a primitive function implicitly marks that column as not null. + * An extended interface to [[InternalRow]] that allows the values for each column to be updated. + * Setting a value through a primitive function implicitly marks that column as not null. */ -trait MutableRow extends Row { +trait MutableRow extends InternalRow { def setNullAt(i: Int): Unit def update(ordinal: Int, value: Any) @@ -36,13 +37,12 @@ trait MutableRow extends Row { def setByte(ordinal: Int, value: Byte) def setFloat(ordinal: Int, value: Float) def setString(ordinal: Int, value: String) - // TODO(davies): add setDate() and setDecimal() } /** * A row with no data. Calling any methods will result in an error. Can be used as a placeholder. */ -object EmptyRow extends Row { +object EmptyRow extends InternalRow { override def apply(i: Int): Any = throw new UnsupportedOperationException override def toSeq: Seq[Any] = Seq.empty override def length: Int = 0 @@ -56,7 +56,7 @@ object EmptyRow extends Row { override def getByte(i: Int): Byte = throw new UnsupportedOperationException override def getString(i: Int): String = throw new UnsupportedOperationException override def getAs[T](i: Int): T = throw new UnsupportedOperationException - override def copy(): Row = this + override def copy(): InternalRow = this } /** @@ -64,7 +64,7 @@ object EmptyRow extends Row { * the array is not copied, and thus could technically be mutated after creation, this is not * allowed. */ -class GenericRow(protected[sql] val values: Array[Any]) extends Row { +class GenericRow(protected[sql] val values: Array[Any]) extends InternalRow { /** No-arg constructor for serialization. */ protected def this() = this(null) @@ -153,7 +153,7 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { } override def equals(o: Any): Boolean = o match { - case other: Row => + case other: InternalRow => if (values.length != other.length) { return false } @@ -173,7 +173,7 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { case _ => false } - override def copy(): Row = this + override def copy(): InternalRow = this } class GenericRowWithSchema(values: Array[Any], override val schema: StructType) @@ -197,22 +197,25 @@ class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow { override def setFloat(ordinal: Int, value: Float): Unit = { values(ordinal) = value } override def setInt(ordinal: Int, value: Int): Unit = { values(ordinal) = value } override def setLong(ordinal: Int, value: Long): Unit = { values(ordinal) = value } - override def setString(ordinal: Int, value: String) { values(ordinal) = UTF8String(value)} + override def setString(ordinal: Int, value: String): Unit = { + values(ordinal) = UTF8String.fromString(value) + } + override def setNullAt(i: Int): Unit = { values(i) = null } override def setShort(ordinal: Int, value: Short): Unit = { values(ordinal) = value } override def update(ordinal: Int, value: Any): Unit = { values(ordinal) = value } - override def copy(): Row = new GenericRow(values.clone()) + override def copy(): InternalRow = new GenericRow(values.clone()) } -class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] { +class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] { def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) = this(ordering.map(BindReferences.bindReference(_, inputSchema))) - def compare(a: Row, b: Row): Int = { + def compare(a: InternalRow, b: InternalRow): Int = { var i = 0 while (i < ordering.size) { val order = ordering(i) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala index 4c44182278207..30e41677b774b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode} import org.apache.spark.sql.types._ import org.apache.spark.util.collection.OpenHashSet @@ -51,16 +52,26 @@ private[sql] class OpenHashSetUDT( * Creates a new set of the specified type */ case class NewSet(elementType: DataType) extends LeafExpression { - type EvaluatedType = Any override def nullable: Boolean = false override def dataType: OpenHashSetUDT = new OpenHashSetUDT(elementType) - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { new OpenHashSet[Any]() } + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + elementType match { + case IntegerType | LongType => + ev.isNull = "false" + s""" + ${ctx.javaType(dataType)} ${ev.primitive} = new ${ctx.javaType(dataType)}(); + """ + case _ => super.genCode(ctx, ev) + } + } + override def toString: String = s"new Set($dataType)" } @@ -69,7 +80,6 @@ case class NewSet(elementType: DataType) extends LeafExpression { * For performance, this expression mutates its input during evaluation. */ case class AddItemToSet(item: Expression, set: Expression) extends Expression { - type EvaluatedType = Any override def children: Seq[Expression] = item :: set :: Nil @@ -77,7 +87,7 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { override def dataType: OpenHashSetUDT = set.dataType.asInstanceOf[OpenHashSetUDT] - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val itemEval = item.eval(input) val setEval = set.eval(input).asInstanceOf[OpenHashSet[Any]] @@ -93,6 +103,25 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { } } + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val elementType = set.dataType.asInstanceOf[OpenHashSetUDT].elementType + elementType match { + case IntegerType | LongType => + val itemEval = item.gen(ctx) + val setEval = set.gen(ctx) + val htype = ctx.javaType(dataType) + + ev.isNull = "false" + ev.primitive = setEval.primitive + itemEval.code + setEval.code + s""" + if (!${itemEval.isNull} && !${setEval.isNull}) { + (($htype)${setEval.primitive}).add(${itemEval.primitive}); + } + """ + case _ => super.genCode(ctx, ev) + } + } + override def toString: String = s"$set += $item" } @@ -101,7 +130,6 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { * For performance, this expression mutates its left input set during evaluation. */ case class CombineSets(left: Expression, right: Expression) extends BinaryExpression { - type EvaluatedType = Any override def nullable: Boolean = left.nullable || right.nullable @@ -109,7 +137,7 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres override def symbol: String = "++=" - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val leftEval = left.eval(input).asInstanceOf[OpenHashSet[Any]] if(leftEval != null) { val rightEval = right.eval(input).asInstanceOf[OpenHashSet[Any]] @@ -119,27 +147,43 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres val rightValue = iterator.next() leftEval.add(rightValue) } - leftEval - } else { - null } + leftEval } else { null } } + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + val elementType = left.dataType.asInstanceOf[OpenHashSetUDT].elementType + elementType match { + case IntegerType | LongType => + val leftEval = left.gen(ctx) + val rightEval = right.gen(ctx) + val htype = ctx.javaType(dataType) + + ev.isNull = leftEval.isNull + ev.primitive = leftEval.primitive + leftEval.code + rightEval.code + s""" + if (!${leftEval.isNull} && !${rightEval.isNull}) { + ${leftEval.primitive}.union((${htype})${rightEval.primitive}); + } + """ + case _ => super.genCode(ctx, ev) + } + } } /** * Returns the number of elements in the input set. */ case class CountSet(child: Expression) extends UnaryExpression { - type EvaluatedType = Any override def nullable: Boolean = child.nullable override def dataType: DataType = LongType - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val childEval = child.eval(input).asInstanceOf[OpenHashSet[Any]] if (childEval != null) { childEval.size.toLong diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala index 7683e0990ce80..315c63e63c635 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala @@ -20,13 +20,14 @@ package org.apache.spark.sql.catalyst.expressions import java.util.regex.Pattern import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.catalyst.expressions.Substring +import org.apache.spark.sql.catalyst.expressions.codegen._ import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String trait StringRegexExpression extends ExpectsInputTypes { self: BinaryExpression => - type EvaluatedType = Any - def escape(v: String): String def matches(regex: Pattern, str: String): Boolean @@ -40,16 +41,16 @@ trait StringRegexExpression extends ExpectsInputTypes { case _ => null } - protected def compile(str: String): Pattern = if(str == null) { + protected def compile(str: String): Pattern = if (str == null) { null } else { // Let it raise exception if couldn't compile the regex string Pattern.compile(escape(str)) } - protected def pattern(str: String) = if(cache == null) compile(str) else cache + protected def pattern(str: String) = if (cache == null) compile(str) else cache - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val l = left.eval(input) if (l == null) { null @@ -114,8 +115,6 @@ case class RLike(left: Expression, right: Expression) trait CaseConversionExpression extends ExpectsInputTypes { self: UnaryExpression => - type EvaluatedType = Any - def convert(v: UTF8String): UTF8String override def foldable: Boolean = child.foldable @@ -123,7 +122,7 @@ trait CaseConversionExpression extends ExpectsInputTypes { override def dataType: DataType = StringType override def expectedChildTypes: Seq[DataType] = Seq(StringType) - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val evaluated = child.eval(input) if (evaluated == null) { null @@ -137,20 +136,28 @@ trait CaseConversionExpression extends ExpectsInputTypes { * A function that converts the characters of a string to uppercase. */ case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression { - + override def convert(v: UTF8String): UTF8String = v.toUpperCase() override def toString: String = s"Upper($child)" + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, c => s"($c).toUpperCase()") + } } /** * A function that converts the characters of a string to lowercase. */ case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression { - + override def convert(v: UTF8String): UTF8String = v.toLowerCase() override def toString: String = s"Lower($child)" + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, c => s"($c).toLowerCase()") + } } /** A base trait for functions that compare two strings, returning a boolean. */ @@ -159,13 +166,11 @@ trait StringComparison extends ExpectsInputTypes { def compare(l: UTF8String, r: UTF8String): Boolean - override type EvaluatedType = Any - override def nullable: Boolean = left.nullable || right.nullable override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType) - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val leftEval = left.eval(input) if(leftEval == null) { null @@ -187,6 +192,9 @@ trait StringComparison extends ExpectsInputTypes { case class Contains(left: Expression, right: Expression) extends BinaryExpression with Predicate with StringComparison { override def compare(l: UTF8String, r: UTF8String): Boolean = l.contains(r) + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c1, c2) => s"($c1).contains($c2)") + } } /** @@ -195,6 +203,9 @@ case class Contains(left: Expression, right: Expression) case class StartsWith(left: Expression, right: Expression) extends BinaryExpression with Predicate with StringComparison { override def compare(l: UTF8String, r: UTF8String): Boolean = l.startsWith(r) + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c1, c2) => s"($c1).startsWith($c2)") + } } /** @@ -203,6 +214,9 @@ case class StartsWith(left: Expression, right: Expression) case class EndsWith(left: Expression, right: Expression) extends BinaryExpression with Predicate with StringComparison { override def compare(l: UTF8String, r: UTF8String): Boolean = l.endsWith(r) + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, (c1, c2) => s"($c1).endsWith($c2)") + } } /** @@ -211,12 +225,15 @@ case class EndsWith(left: Expression, right: Expression) */ case class Substring(str: Expression, pos: Expression, len: Expression) extends Expression with ExpectsInputTypes { - - type EvaluatedType = Any + + def this(str: Expression, pos: Expression) = { + this(str, pos, Literal(Integer.MAX_VALUE)) + } override def foldable: Boolean = str.foldable && pos.foldable && len.foldable override def nullable: Boolean = str.nullable || pos.nullable || len.nullable + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, s"Cannot resolve since $children are not resolved") @@ -231,7 +248,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) @inline def slicePos(startPos: Int, sliceLen: Int, length: () => Int): (Int, Int) = { // Hive and SQL use one-based indexing for SUBSTR arguments but also accept zero and - // negative indices for start positions. If a start index i is greater than 0, it + // negative indices for start positions. If a start index i is greater than 0, it // refers to element i-1 in the sequence. If a start index i is less than 0, it refers // to the -ith element before the end of the sequence. If a start index i is 0, it // refers to the first element. @@ -250,7 +267,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) (start, end) } - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { val string = str.eval(input) val po = pos.eval(input) val ln = len.eval(input) @@ -266,7 +283,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) ba.slice(st, end) case s: UTF8String => val (st, end) = slicePos(start, length, () => s.length()) - s.slice(st, end) + s.substring(st, end) } } } @@ -277,3 +294,22 @@ case class Substring(str: Expression, pos: Expression, len: Expression) case _ => s"SUBSTR($str, $pos, $len)" } } + +/** + * A function that return the length of the given string expression. + */ +case class StringLength(child: Expression) extends UnaryExpression with ExpectsInputTypes { + override def dataType: DataType = IntegerType + override def expectedChildTypes: Seq[DataType] = Seq(StringType) + + override def eval(input: InternalRow): Any = { + val string = child.eval(input) + if (string == null) null else string.asInstanceOf[UTF8String].length + } + + override def toString: String = s"length($child)" + + override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = { + defineCodeGen(ctx, ev, c => s"($c).length()") + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala index 099d67ca7fee3..896e383f50eac 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala @@ -19,8 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.analysis.UnresolvedException import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.types.{NumericType, DataType} +import org.apache.spark.sql.types.{DataType, NumericType} /** * The trait of the Window Specification (specified in the OVER clause or WINDOW clause) for @@ -66,9 +65,7 @@ case class WindowSpecDefinition( } } - type EvaluatedType = Any - - override def children: Seq[Expression] = partitionSpec ++ orderSpec + override def children: Seq[Expression] = partitionSpec ++ orderSpec override lazy val resolved: Boolean = childrenResolved && frameSpecification.isInstanceOf[SpecifiedWindowFrame] @@ -76,7 +73,7 @@ case class WindowSpecDefinition( override def toString: String = simpleString - override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException + override def eval(input: InternalRow): Any = throw new UnsupportedOperationException override def nullable: Boolean = true override def foldable: Boolean = false override def dataType: DataType = throw new UnsupportedOperationException @@ -261,7 +258,7 @@ trait WindowFunction extends Expression { def reset(): Unit - def prepareInputParameters(input: Row): AnyRef + def prepareInputParameters(input: InternalRow): AnyRef def update(input: AnyRef): Unit @@ -288,7 +285,7 @@ case class UnresolvedWindowFunction( throw new UnresolvedException(this, "init") override def reset(): Unit = throw new UnresolvedException(this, "reset") - override def prepareInputParameters(input: Row): AnyRef = + override def prepareInputParameters(input: InternalRow): AnyRef = throw new UnresolvedException(this, "prepareInputParameters") override def update(input: AnyRef): Unit = throw new UnresolvedException(this, "update") @@ -299,7 +296,7 @@ case class UnresolvedWindowFunction( override def get(index: Int): Any = throw new UnresolvedException(this, "get") // Unresolved functions are transient at compile time and don't get evaluated during execution. - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def toString: String = s"'$name(${children.mkString(",")})" @@ -311,25 +308,25 @@ case class UnresolvedWindowFunction( case class UnresolvedWindowExpression( child: UnresolvedWindowFunction, windowSpec: WindowSpecReference) extends UnaryExpression { + override def dataType: DataType = throw new UnresolvedException(this, "dataType") override def foldable: Boolean = throw new UnresolvedException(this, "foldable") override def nullable: Boolean = throw new UnresolvedException(this, "nullable") override lazy val resolved = false // Unresolved functions are transient at compile time and don't get evaluated during execution. - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } case class WindowExpression( windowFunction: WindowFunction, windowSpec: WindowSpecDefinition) extends Expression { - override type EvaluatedType = Any override def children: Seq[Expression] = windowFunction :: windowSpec :: Nil - override def eval(input: Row): EvaluatedType = + override def eval(input: InternalRow): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") override def dataType: DataType = windowFunction.dataType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index d7b2f203a6934..9132a786f77a7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -36,23 +36,26 @@ object DefaultOptimizer extends Optimizer { // SubQueries are only needed for analysis and can be removed before execution. Batch("Remove SubQueries", FixedPoint(100), EliminateSubQueries) :: - Batch("Operator Reordering", FixedPoint(100), + Batch("Distinct", FixedPoint(100), + ReplaceDistinctWithAggregate) :: + Batch("Operator Optimizations", FixedPoint(100), UnionPushdown, CombineFilters, PushPredicateThroughProject, - PushPredicateThroughJoin, PushPredicateThroughGenerate, ColumnPruning, - CombineLimits) :: - Batch("ConstantFolding", FixedPoint(100), + ProjectCollapsing, + CombineLimits, NullPropagation, + OptimizeIn, ConstantFolding, LikeSimplification, BooleanSimplification, + PushPredicateThroughJoin, + RemovePositive, SimplifyFilters, SimplifyCasts, - SimplifyCaseConversionExpressions, - OptimizeIn) :: + SimplifyCaseConversionExpressions) :: Batch("Decimal Optimizations", FixedPoint(100), DecimalAggregates) :: Batch("LocalRelation", FixedPoint(100), @@ -114,7 +117,7 @@ object UnionPushdown extends Rule[LogicalPlan] { * - Aggregate * - Project <- Join * - LeftSemiJoin - * - Collapse adjacent projections, performing alias substitution. + * - Performing alias substitution. */ object ColumnPruning extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { @@ -152,8 +155,43 @@ object ColumnPruning extends Rule[LogicalPlan] { Join(left, prunedChild(right, allReferences), LeftSemi, condition) - // Combine adjacent Projects. - case Project(projectList1, Project(projectList2, child)) => + case Project(projectList, Limit(exp, child)) => + Limit(exp, Project(projectList, child)) + + // push down project if possible when the child is sort + case p @ Project(projectList, s @ Sort(_, _, grandChild)) + if s.references.subsetOf(p.outputSet) => + s.copy(child = Project(projectList, grandChild)) + + // Eliminate no-op Projects + case Project(projectList, child) if child.output == projectList => child + } + + /** Applies a projection only when the child is producing unnecessary attributes */ + private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) = + if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) { + Project(allReferences.filter(c.outputSet.contains).toSeq, c) + } else { + c + } +} + +/** + * Combines two adjacent [[Project]] operators into one, merging the + * expressions into one single expression. + */ +object ProjectCollapsing extends Rule[LogicalPlan] { + + /** Returns true if any expression in projectList is non-deterministic. */ + private def hasNondeterministic(projectList: Seq[NamedExpression]): Boolean = { + projectList.exists(expr => expr.find(!_.deterministic).isDefined) + } + + def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { + // We only collapse these two Projects if the child Project's expressions are all + // deterministic. + case Project(projectList1, Project(projectList2, child)) + if !hasNondeterministic(projectList2) => // Create a map of Aliases to their values from the child projection. // e.g., 'SELECT ... FROM (SELECT a + b AS c, d ...)' produces Map(c -> Alias(a + b, c)). val aliasMap = AttributeMap(projectList2.collect { @@ -169,21 +207,7 @@ object ColumnPruning extends Rule[LogicalPlan] { }).asInstanceOf[Seq[NamedExpression]] Project(substitutedProjection, child) - - case Project(projectList, Limit(exp, child)) => - Limit(exp, Project(projectList, child)) - - // Eliminate no-op Projects - case Project(projectList, child) if child.output == projectList => child } - - /** Applies a projection only when the child is producing unnecessary attributes */ - private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) = - if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) { - Project(allReferences.filter(c.outputSet.contains).toSeq, c) - } else { - c - } } /** @@ -242,7 +266,7 @@ object NullPropagation extends Rule[LogicalPlan] { if (newChildren.length == 0) { Literal.create(null, e.dataType) } else if (newChildren.length == 1) { - newChildren(0) + newChildren.head } else { Coalesce(newChildren) } @@ -251,22 +275,23 @@ object NullPropagation extends Rule[LogicalPlan] { case e @ Substring(_, Literal(null, _), _) => Literal.create(null, e.dataType) case e @ Substring(_, _, Literal(null, _)) => Literal.create(null, e.dataType) + // MaxOf and MinOf can't do null propagation + case e: MaxOf => e + case e: MinOf => e + // Put exceptional cases above if any - case e: BinaryArithmetic => e.children match { - case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) - case _ => e - } - case e: BinaryComparison => e.children match { - case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) - case _ => e - } + case e @ BinaryArithmetic(Literal(null, _), _) => Literal.create(null, e.dataType) + case e @ BinaryArithmetic(_, Literal(null, _)) => Literal.create(null, e.dataType) + + case e @ BinaryComparison(Literal(null, _), _) => Literal.create(null, e.dataType) + case e @ BinaryComparison(_, Literal(null, _)) => Literal.create(null, e.dataType) + case e: StringRegexExpression => e.children match { case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } + case e: StringComparison => e.children match { case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) @@ -608,6 +633,15 @@ object SimplifyCasts extends Rule[LogicalPlan] { } } +/** + * Removes [[UnaryPositive]] identify function + */ +object RemovePositive extends Rule[LogicalPlan] { + def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { + case UnaryPositive(child) => child + } +} + /** * Combines two adjacent [[Limit]] operators into one, merging the * expressions into one single expression. @@ -670,3 +704,15 @@ object ConvertToLocalRelation extends Rule[LogicalPlan] { LocalRelation(projectList.map(_.toAttribute), data.map(projection)) } } + +/** + * Replaces logical [[Distinct]] operator with an [[Aggregate]] operator. + * {{{ + * SELECT DISTINCT f1, f2 FROM t ==> SELECT f1, f2 FROM t GROUP BY f1, f2 + * }}} + */ +object ReplaceDistinctWithAggregate extends Rule[LogicalPlan] { + def apply(plan: LogicalPlan): LogicalPlan = plan transform { + case Distinct(child) => Aggregate(child.output, child.output, child) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala index 51b5699affed5..73a21884a4710 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala @@ -51,9 +51,9 @@ abstract class QueryPlanner[PhysicalPlan <: TreeNode[PhysicalPlan]] { * filled in automatically by the QueryPlanner using the other execution strategies that are * available. */ - protected def planLater(plan: LogicalPlan) = apply(plan).next() + protected def planLater(plan: LogicalPlan) = this.plan(plan).next() - def apply(plan: LogicalPlan): Iterator[PhysicalPlan] = { + def plan(plan: LogicalPlan): Iterator[PhysicalPlan] = { // Obviously a lot to do here still... val iter = strategies.view.flatMap(_(plan)).toIterator assert(iter.hasNext, s"No plan for $plan") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala index cd54d04814ea4..3b6f8bfd9ff9b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala @@ -143,11 +143,11 @@ object PartialAggregation { // We need to pass all grouping expressions though so the grouping can happen a second // time. However some of them might be unnamed so we alias them allowing them to be // referenced in the second aggregation. - val namedGroupingExpressions: Map[Expression, NamedExpression] = + val namedGroupingExpressions: Seq[(Expression, NamedExpression)] = groupingExpressions.filter(!_.isInstanceOf[Literal]).map { case n: NamedExpression => (n, n) case other => (other, Alias(other, "PartialGroup")()) - }.toMap + } // Replace aggregations with a new expression that computes the result from the already // computed partial evaluations and grouping values. @@ -159,17 +159,16 @@ object PartialAggregation { // Should trim aliases around `GetField`s. These aliases are introduced while // resolving struct field accesses, because `GetField` is not a `NamedExpression`. // (Should we just turn `GetField` into a `NamedExpression`?) - namedGroupingExpressions - .get(e.transform { case Alias(g: ExtractValue, _) => g }) - .map(_.toAttribute) - .getOrElse(e) + val trimmed = e.transform { case Alias(g: ExtractValue, _) => g } + namedGroupingExpressions.collectFirst { + case (expr, ne) if expr semanticEquals trimmed => ne.toAttribute + }.getOrElse(e) }).asInstanceOf[Seq[NamedExpression]] - val partialComputation = - (namedGroupingExpressions.values ++ - partialEvaluations.values.flatMap(_.partialEvaluations)).toSeq + val partialComputation = namedGroupingExpressions.map(_._2) ++ + partialEvaluations.values.flatMap(_.partialEvaluations) - val namedGroupingAttributes = namedGroupingExpressions.values.map(_.toAttribute).toSeq + val namedGroupingAttributes = namedGroupingExpressions.map(_._2.toAttribute) Some( (namedGroupingAttributes, diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index 7967189cacb24..2f545bb432165 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -81,17 +81,16 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy } } - val newArgs = productIterator.map { + def recursiveTransform(arg: Any): AnyRef = arg match { case e: Expression => transformExpressionDown(e) case Some(e: Expression) => Some(transformExpressionDown(e)) - case m: Map[_,_] => m + case m: Map[_, _] => m case d: DataType => d // Avoid unpacking Structs - case seq: Traversable[_] => seq.map { - case e: Expression => transformExpressionDown(e) - case other => other - } + case seq: Traversable[_] => seq.map(recursiveTransform) case other: AnyRef => other - }.toArray + } + + val newArgs = productIterator.map(recursiveTransform).toArray if (changed) makeCopy(newArgs) else this } @@ -114,17 +113,16 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy } } - val newArgs = productIterator.map { + def recursiveTransform(arg: Any): AnyRef = arg match { case e: Expression => transformExpressionUp(e) case Some(e: Expression) => Some(transformExpressionUp(e)) - case m: Map[_,_] => m + case m: Map[_, _] => m case d: DataType => d // Avoid unpacking Structs - case seq: Traversable[_] => seq.map { - case e: Expression => transformExpressionUp(e) - case other => other - } + case seq: Traversable[_] => seq.map(recursiveTransform) case other: AnyRef => other - }.toArray + } + + val newArgs = productIterator.map(recursiveTransform).toArray if (changed) makeCopy(newArgs) else this } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala index 5dc0539caec24..77dec7ca6e2b5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala @@ -24,6 +24,16 @@ object JoinType { case "leftouter" | "left" => LeftOuter case "rightouter" | "right" => RightOuter case "leftsemi" => LeftSemi + case _ => + val supported = Seq( + "inner", + "outer", "full", "fullouter", + "leftouter", "left", + "rightouter", "right", + "leftsemi") + + throw new IllegalArgumentException(s"Unsupported join type '$typ'. " + + "Supported join types include: " + supported.mkString("'", "', '", "'") + ".") } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala index e3e070f0ff307..1868f119f0e97 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala @@ -17,10 +17,9 @@ package org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.{CatalystTypeConverters, analysis} import org.apache.spark.sql.catalyst.expressions.Attribute -import org.apache.spark.sql.types.{StructType, StructField} +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, analysis} +import org.apache.spark.sql.types.{StructField, StructType} object LocalRelation { def apply(output: Attribute*): LocalRelation = new LocalRelation(output) @@ -32,11 +31,11 @@ object LocalRelation { def fromProduct(output: Seq[Attribute], data: Seq[Product]): LocalRelation = { val schema = StructType.fromAttributes(output) val converter = CatalystTypeConverters.createToCatalystConverter(schema) - LocalRelation(output, data.map(converter(_).asInstanceOf[Row])) + LocalRelation(output, data.map(converter(_).asInstanceOf[InternalRow])) } } -case class LocalRelation(output: Seq[Attribute], data: Seq[Row] = Nil) +case class LocalRelation(output: Seq[Attribute], data: Seq[InternalRow] = Nil) extends LeafNode with analysis.MultiInstanceRelation { /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index dbb12d56f9497..a853e27c1212d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -50,19 +50,19 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * [[org.apache.spark.sql.catalyst.analysis.UnresolvedRelation UnresolvedRelation]] * should return `false`). */ - lazy val resolved: Boolean = !expressions.exists(!_.resolved) && childrenResolved + lazy val resolved: Boolean = expressions.forall(_.resolved) && childrenResolved override protected def statePrefix = if (!resolved) "'" else super.statePrefix /** * Returns true if all its children of this query plan have been resolved. */ - def childrenResolved: Boolean = !children.exists(!_.resolved) + def childrenResolved: Boolean = children.forall(_.resolved) /** * Returns true when the given logical plan will return the same results as this logical plan. * - * Since its likely undecideable to generally determine if two given plans will produce the same + * Since its likely undecidable to generally determine if two given plans will produce the same * results, it is okay for this function to return false, even if the results are actually * the same. Such behavior will not affect correctness, only the application of performance * enhancements like caching. However, it is not acceptable to return true if the results could @@ -90,7 +90,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { val input = children.flatMap(_.output) productIterator.map { // Children are checked using sameResult above. - case tn: TreeNode[_] if children contains tn => null + case tn: TreeNode[_] if containsChild(tn) => null case e: Expression => BindReferences.bindReference(e, input, allowFailures = true) case s: Option[_] => s.map { case e: Expression => BindReferences.bindReference(e, input, allowFailures = true) @@ -105,26 +105,75 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { } /** - * Optionally resolves the given string to a [[NamedExpression]] using the input from all child + * Optionally resolves the given strings to a [[NamedExpression]] using the input from all child * nodes of this LogicalPlan. The attribute is expressed as * as string in the following form: `[scope].AttributeName.[nested].[fields]...`. */ def resolveChildren( nameParts: Seq[String], - resolver: Resolver, - throwErrors: Boolean = false): Option[NamedExpression] = - resolve(nameParts, children.flatMap(_.output), resolver, throwErrors) + resolver: Resolver): Option[NamedExpression] = + resolve(nameParts, children.flatMap(_.output), resolver) /** - * Optionally resolves the given string to a [[NamedExpression]] based on the output of this + * Optionally resolves the given strings to a [[NamedExpression]] based on the output of this * LogicalPlan. The attribute is expressed as string in the following form: * `[scope].AttributeName.[nested].[fields]...`. */ def resolve( nameParts: Seq[String], - resolver: Resolver, - throwErrors: Boolean = false): Option[NamedExpression] = - resolve(nameParts, output, resolver, throwErrors) + resolver: Resolver): Option[NamedExpression] = + resolve(nameParts, output, resolver) + + /** + * Given an attribute name, split it to name parts by dot, but + * don't split the name parts quoted by backticks, for example, + * `ab.cd`.`efg` should be split into two parts "ab.cd" and "efg". + */ + def resolveQuoted( + name: String, + resolver: Resolver): Option[NamedExpression] = { + resolve(parseAttributeName(name), output, resolver) + } + + /** + * Internal method, used to split attribute name by dot with backticks rule. + * Backticks must appear in pairs, and the quoted string must be a complete name part, + * which means `ab..c`e.f is not allowed. + * Escape character is not supported now, so we can't use backtick inside name part. + */ + private def parseAttributeName(name: String): Seq[String] = { + val e = new AnalysisException(s"syntax error in attribute name: $name") + val nameParts = scala.collection.mutable.ArrayBuffer.empty[String] + val tmp = scala.collection.mutable.ArrayBuffer.empty[Char] + var inBacktick = false + var i = 0 + while (i < name.length) { + val char = name(i) + if (inBacktick) { + if (char == '`') { + inBacktick = false + if (i + 1 < name.length && name(i + 1) != '.') throw e + } else { + tmp += char + } + } else { + if (char == '`') { + if (tmp.nonEmpty) throw e + inBacktick = true + } else if (char == '.') { + if (tmp.isEmpty) throw e + nameParts += tmp.mkString + tmp.clear() + } else { + tmp += char + } + } + i += 1 + } + if (tmp.isEmpty || inBacktick) throw e + nameParts += tmp.mkString + nameParts.toSeq + } /** * Resolve the given `name` string against the given attribute, returning either 0 or 1 match. @@ -168,8 +217,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { protected def resolve( nameParts: Seq[String], input: Seq[Attribute], - resolver: Resolver, - throwErrors: Boolean): Option[NamedExpression] = { + resolver: Resolver): Option[NamedExpression] = { // A sequence of possible candidate matches. // Each candidate is a tuple. The first element is a resolved attribute, followed by a list @@ -203,19 +251,15 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // One match, but we also need to extract the requested nested field. case Seq((a, nestedFields)) => - try { - // The foldLeft adds GetFields for every remaining parts of the identifier, - // and aliases it with the last part of the identifier. - // For example, consider "a.b.c", where "a" is resolved to an existing attribute. - // Then this will add GetField("c", GetField("b", a)), and alias - // the final expression as "c". - val fieldExprs = nestedFields.foldLeft(a: Expression)((expr, fieldName) => - ExtractValue(expr, Literal(fieldName), resolver)) - val aliasName = nestedFields.last - Some(Alias(fieldExprs, aliasName)()) - } catch { - case a: AnalysisException if !throwErrors => None - } + // The foldLeft adds ExtractValues for every remaining parts of the identifier, + // and aliases it with the last part of the identifier. + // For example, consider "a.b.c", where "a" is resolved to an existing attribute. + // Then this will add ExtractValue("c", ExtractValue("b", a)), and alias + // the final expression as "c". + val fieldExprs = nestedFields.foldLeft(a: Expression)((expr, fieldName) => + ExtractValue(expr, Literal(fieldName), resolver)) + val aliasName = nestedFields.last + Some(Alias(fieldExprs, aliasName)()) // No matches. case Seq() => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 0f349f9d11415..963c7820914f3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -59,6 +59,9 @@ case class Generate( child: LogicalPlan) extends UnaryNode { + /** The set of all attributes produced by this node. */ + def generatedSet: AttributeSet = AttributeSet(generatorOutput) + override lazy val resolved: Boolean = { generator.resolved && childrenResolved && @@ -90,7 +93,7 @@ case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { override lazy val resolved: Boolean = childrenResolved && - left.output.zip(right.output).forall { case (l,r) => l.dataType == r.dataType } + left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType } override def statistics: Statistics = { val sizeInBytes = left.statistics.sizeInBytes + right.statistics.sizeInBytes @@ -223,7 +226,7 @@ case class Window( * @param child Child operator */ case class Expand( - projections: Seq[GroupExpression], + projections: Seq[Seq[Expression]], output: Seq[Attribute], child: LogicalPlan) extends UnaryNode { override def statistics: Statistics = { @@ -336,6 +339,9 @@ case class Sample( override def output: Seq[Attribute] = child.output } +/** + * Returns a new logical plan that dedups input rows. + */ case class Distinct(child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala index fb4217a44807b..42dead7c28425 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala @@ -17,8 +17,9 @@ package org.apache.spark.sql.catalyst.plans.physical +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.expressions.{Expression, Row, SortOrder} +import org.apache.spark.sql.catalyst.expressions.{Expression, SortOrder} import org.apache.spark.sql.types.{DataType, IntegerType} /** @@ -169,7 +170,7 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) override def keyExpressions: Seq[Expression] = expressions - override def eval(input: Row = null): EvaluatedType = + override def eval(input: InternalRow = null): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } @@ -213,6 +214,6 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) override def keyExpressions: Seq[Expression] = ordering.map(_.child) - override def eval(input: Row): EvaluatedType = + override def eval(input: InternalRow): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala index bc2ad34523d2c..09f6c6b0ec423 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala @@ -59,9 +59,14 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { val origin: Origin = CurrentOrigin.get - /** Returns a Seq of the children of this node */ + /** + * Returns a Seq of the children of this node. + * Children should not change. Immutability required for containsChild optimization + */ def children: Seq[BaseType] + lazy val containsChild: Set[TreeNode[_]] = children.toSet + /** * Faster version of equality which short-circuits when two treeNodes are the same instance. * We don't just override Object.equals, as doing so prevents the scala compiler from @@ -147,7 +152,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { def mapChildren(f: BaseType => BaseType): this.type = { var changed = false val newArgs = productIterator.map { - case arg: TreeNode[_] if children contains arg => + case arg: TreeNode[_] if containsChild(arg) => val newChild = f(arg.asInstanceOf[BaseType]) if (newChild fastEquals arg) { arg @@ -173,7 +178,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { val newArgs = productIterator.map { // Handle Seq[TreeNode] in TreeNode parameters. case s: Seq[_] => s.map { - case arg: TreeNode[_] if children contains arg => + case arg: TreeNode[_] if containsChild(arg) => val newChild = remainingNewChildren.remove(0) val oldChild = remainingOldChildren.remove(0) if (newChild fastEquals oldChild) { @@ -185,7 +190,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { case nonChild: AnyRef => nonChild case null => null } - case arg: TreeNode[_] if children contains arg => + case arg: TreeNode[_] if containsChild(arg) => val newChild = remainingNewChildren.remove(0) val oldChild = remainingOldChildren.remove(0) if (newChild fastEquals oldChild) { @@ -238,7 +243,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { def transformChildrenDown(rule: PartialFunction[BaseType, BaseType]): this.type = { var changed = false val newArgs = productIterator.map { - case arg: TreeNode[_] if children contains arg => + case arg: TreeNode[_] if containsChild(arg) => val newChild = arg.asInstanceOf[BaseType].transformDown(rule) if (!(newChild fastEquals arg)) { changed = true @@ -246,7 +251,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { } else { arg } - case Some(arg: TreeNode[_]) if children contains arg => + case Some(arg: TreeNode[_]) if containsChild(arg) => val newChild = arg.asInstanceOf[BaseType].transformDown(rule) if (!(newChild fastEquals arg)) { changed = true @@ -254,10 +259,10 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { } else { Some(arg) } - case m: Map[_,_] => m + case m: Map[_, _] => m case d: DataType => d // Avoid unpacking Structs case args: Traversable[_] => args.map { - case arg: TreeNode[_] if children contains arg => + case arg: TreeNode[_] if containsChild(arg) => val newChild = arg.asInstanceOf[BaseType].transformDown(rule) if (!(newChild fastEquals arg)) { changed = true @@ -280,7 +285,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { * @param rule the function use to transform this nodes children */ def transformUp(rule: PartialFunction[BaseType, BaseType]): BaseType = { - val afterRuleOnChildren = transformChildrenUp(rule); + val afterRuleOnChildren = transformChildrenUp(rule) if (this fastEquals afterRuleOnChildren) { CurrentOrigin.withOrigin(origin) { rule.applyOrElse(this, identity[BaseType]) @@ -295,7 +300,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { def transformChildrenUp(rule: PartialFunction[BaseType, BaseType]): this.type = { var changed = false val newArgs = productIterator.map { - case arg: TreeNode[_] if children contains arg => + case arg: TreeNode[_] if containsChild(arg) => val newChild = arg.asInstanceOf[BaseType].transformUp(rule) if (!(newChild fastEquals arg)) { changed = true @@ -303,7 +308,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { } else { arg } - case Some(arg: TreeNode[_]) if children contains arg => + case Some(arg: TreeNode[_]) if containsChild(arg) => val newChild = arg.asInstanceOf[BaseType].transformUp(rule) if (!(newChild fastEquals arg)) { changed = true @@ -311,10 +316,10 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { } else { Some(arg) } - case m: Map[_,_] => m + case m: Map[_, _] => m case d: DataType => d // Avoid unpacking Structs case args: Traversable[_] => args.map { - case arg: TreeNode[_] if children contains arg => + case arg: TreeNode[_] if containsChild(arg) => val newChild = arg.asInstanceOf[BaseType].transformUp(rule) if (!(newChild fastEquals arg)) { changed = true @@ -344,11 +349,11 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { * @param newArgs the new product arguments. */ def makeCopy(newArgs: Array[AnyRef]): this.type = attachTree(this, "makeCopy") { - val defaultCtor = - getClass.getConstructors - .find(_.getParameterTypes.size != 0) - .headOption - .getOrElse(sys.error(s"No valid constructor for $nodeName")) + val ctors = getClass.getConstructors.filter(_.getParameterTypes.size != 0) + if (ctors.isEmpty) { + sys.error(s"No valid constructor for $nodeName") + } + val defaultCtor = ctors.maxBy(_.getParameterTypes.size) try { CurrentOrigin.withOrigin(origin) { @@ -383,8 +388,9 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { /** Returns a string representing the arguments to this node, minus any children */ def argString: String = productIterator.flatMap { - case tn: TreeNode[_] if children contains tn => Nil + case tn: TreeNode[_] if containsChild(tn) => Nil case tn: TreeNode[_] if tn.toString contains "\n" => s"(${tn.simpleString})" :: Nil + case seq: Seq[BaseType] if seq.toSet.subsetOf(children.toSet) => Nil case seq: Seq[_] => seq.mkString("[", ",", "]") :: Nil case set: Set[_] => set.mkString("{", ",", "}") :: Nil case other => other :: Nil diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateUtils.scala similarity index 68% rename from sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateUtils.scala index d36a49159b87f..5cadc141af1df 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateUtils.scala @@ -15,19 +15,20 @@ * limitations under the License. */ -package org.apache.spark.sql.types +package org.apache.spark.sql.catalyst.util -import java.sql.Date +import java.sql.{Timestamp, Date} import java.text.SimpleDateFormat import java.util.{Calendar, TimeZone} import org.apache.spark.sql.catalyst.expressions.Cast /** - * helper function to convert between Int value of days since 1970-01-01 and java.sql.Date + * Helper function to convert between Int value of days since 1970-01-01 and java.sql.Date */ object DateUtils { private val MILLIS_PER_DAY = 86400000 + private val HUNDRED_NANOS_PER_SECOND = 10000000L // Java TimeZone has no mention of thread safety. Use thread local instance to be safe. private val LOCAL_TIMEZONE = new ThreadLocal[TimeZone] { @@ -45,17 +46,17 @@ object DateUtils { ((millisLocal + LOCAL_TIMEZONE.get().getOffset(millisLocal)) / MILLIS_PER_DAY).toInt } - private def toMillisSinceEpoch(days: Int): Long = { + def toMillisSinceEpoch(days: Int): Long = { val millisUtc = days.toLong * MILLIS_PER_DAY millisUtc - LOCAL_TIMEZONE.get().getOffset(millisUtc) } - def fromJavaDate(date: java.sql.Date): Int = { + def fromJavaDate(date: Date): Int = { javaDateToDays(date) } - def toJavaDate(daysSinceEpoch: Int): java.sql.Date = { - new java.sql.Date(toMillisSinceEpoch(daysSinceEpoch)) + def toJavaDate(daysSinceEpoch: Int): Date = { + new Date(toMillisSinceEpoch(daysSinceEpoch)) } def toString(days: Int): String = Cast.threadLocalDateFormat.get.format(toJavaDate(days)) @@ -64,9 +65,9 @@ object DateUtils { if (!s.contains('T')) { // JDBC escape string if (s.contains(' ')) { - java.sql.Timestamp.valueOf(s) + Timestamp.valueOf(s) } else { - java.sql.Date.valueOf(s) + Date.valueOf(s) } } else if (s.endsWith("Z")) { // this is zero timezone of ISO8601 @@ -87,4 +88,33 @@ object DateUtils { ISO8601GMT.parse(s) } } + + /** + * Return a java.sql.Timestamp from number of 100ns since epoch + */ + def toJavaTimestamp(num100ns: Long): Timestamp = { + // setNanos() will overwrite the millisecond part, so the milliseconds should be + // cut off at seconds + var seconds = num100ns / HUNDRED_NANOS_PER_SECOND + var nanos = num100ns % HUNDRED_NANOS_PER_SECOND + // setNanos() can not accept negative value + if (nanos < 0) { + nanos += HUNDRED_NANOS_PER_SECOND + seconds -= 1 + } + val t = new Timestamp(seconds * 1000) + t.setNanos(nanos.toInt * 100) + t + } + + /** + * Return the number of 100ns since epoch from java.sql.Timestamp. + */ + def fromJavaTimestamp(t: Timestamp): Long = { + if (t != null) { + t.getTime() * 10000L + (t.getNanos().toLong / 100) % 10000L + } else { + 0L + } + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringKeyHashMap.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringKeyHashMap.scala new file mode 100644 index 0000000000000..191d5e6399fc9 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringKeyHashMap.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.util + +/** + * Build a map with String type of key, and it also supports either key case + * sensitive or insensitive. + */ +object StringKeyHashMap { + def apply[T](caseSensitive: Boolean): StringKeyHashMap[T] = caseSensitive match { + case false => new StringKeyHashMap[T](_.toLowerCase) + case true => new StringKeyHashMap[T](identity) + } +} + + +class StringKeyHashMap[T](normalizer: (String) => String) { + private val base = new collection.mutable.HashMap[String, T]() + + def apply(key: String): T = base(normalizer(key)) + + def get(key: String): Option[T] = base.get(normalizer(key)) + + def put(key: String, value: T): Option[T] = base.put(normalizer(key), value) + + def remove(key: String): Option[T] = base.remove(normalizer(key)) + + def iterator: Iterator[(String, T)] = base.toIterator +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala new file mode 100644 index 0000000000000..04857a23f4c1e --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.util + +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult +import org.apache.spark.sql.types._ + +/** + * Helper function to check for valid data types + */ +object TypeUtils { + def checkForNumericExpr(t: DataType, caller: String): TypeCheckResult = { + if (t.isInstanceOf[NumericType] || t == NullType) { + TypeCheckResult.TypeCheckSuccess + } else { + TypeCheckResult.TypeCheckFailure(s"$caller accepts numeric types, not $t") + } + } + + def checkForBitwiseExpr(t: DataType, caller: String): TypeCheckResult = { + if (t.isInstanceOf[IntegralType] || t == NullType) { + TypeCheckResult.TypeCheckSuccess + } else { + TypeCheckResult.TypeCheckFailure(s"$caller accepts integral types, not $t") + } + } + + def checkForOrderingExpr(t: DataType, caller: String): TypeCheckResult = { + if (t.isInstanceOf[AtomicType] || t == NullType) { + TypeCheckResult.TypeCheckSuccess + } else { + TypeCheckResult.TypeCheckFailure(s"$caller accepts non-complex types, not $t") + } + } + + def getNumeric(t: DataType): Numeric[Any] = + t.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]] + + def getOrdering(t: DataType): Ordering[Any] = + t.asInstanceOf[AtomicType].ordering.asInstanceOf[Ordering[Any]] + + def compareBinary(x: Array[Byte], y: Array[Byte]): Int = { + for (i <- 0 until x.length; if i < y.length) { + val res = x(i).compareTo(y(i)) + if (res != 0) return res + } + x.length - y.length + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala index 9d613a940ee86..07054166a5e88 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala @@ -83,7 +83,7 @@ package object util { } def resourceToString( - resource:String, + resource: String, encoding: String = "UTF-8", classLoader: ClassLoader = Utils.getSparkClassLoader): String = { new String(resourceToBytes(resource, classLoader), encoding) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala index a581a9e9468ef..9b58601e5e6ec 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala @@ -22,6 +22,7 @@ import scala.reflect.runtime.universe.typeTag import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.catalyst.ScalaReflectionLock +import org.apache.spark.sql.catalyst.util.TypeUtils /** @@ -43,11 +44,7 @@ class BinaryType private() extends AtomicType { private[sql] val ordering = new Ordering[InternalType] { def compare(x: Array[Byte], y: Array[Byte]): Int = { - for (i <- 0 until x.length; if i < y.length) { - val res = x(i).compareTo(y(i)) - if (res != 0) return res - } - x.length - y.length + TypeUtils.compareBinary(x, y) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala index 0992a7c311ee2..74677ddfcad65 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala @@ -47,10 +47,7 @@ abstract class DataType { * ... * }}} */ - private[sql] def unapply(a: Expression): Boolean = a match { - case e: Expression if e.dataType == this => true - case _ => false - } + private[sql] def unapply(e: Expression): Boolean = e.dataType == this /** * The default size of a value of this data type, used internally for size estimation. @@ -110,7 +107,7 @@ protected[sql] abstract class AtomicType extends DataType { abstract class NumericType extends AtomicType { // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a - // type parameter and and add a numeric annotation (i.e., [JvmType : Numeric]). This gets + // type parameter and add a numeric annotation (i.e., [JvmType : Numeric]). This gets // desugared by the compiler into an argument to the objects constructor. This means there is no // longer an no argument constructor and thus the JVM cannot serialize the object anymore. private[sql] val numeric: Numeric[InternalType] @@ -137,10 +134,7 @@ private[sql] object IntegralType { * ... * }}} */ - def unapply(a: Expression): Boolean = a match { - case e: Expression if e.dataType.isInstanceOf[IntegralType] => true - case _ => false - } + def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[IntegralType] } @@ -157,10 +151,7 @@ private[sql] object FractionalType { * ... * }}} */ - def unapply(a: Expression): Boolean = a match { - case e: Expression if e.dataType.isInstanceOf[FractionalType] => true - case _ => false - } + def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[FractionalType] } @@ -174,6 +165,9 @@ object DataType { def fromJson(json: String): DataType = parseDataType(parse(json)) + /** + * @deprecated As of 1.2.0, replaced by `DataType.fromJson()` + */ @deprecated("Use DataType.fromJson instead", "1.2.0") def fromCaseClassString(string: String): DataType = CaseClassStringParser(string) @@ -280,7 +274,7 @@ object DataType { protected lazy val structField: Parser[StructField] = ("StructField(" ~> "[a-zA-Z0-9_]*".r) ~ ("," ~> dataType) ~ ("," ~> boolVal <~ ")") ^^ { - case name ~ tpe ~ nullable => + case name ~ tpe ~ nullable => StructField(name, tpe, nullable = nullable) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala index 994c5202c15dc..a85af9e04aedb 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala @@ -86,7 +86,7 @@ final class Decimal extends Ordered[Decimal] with Serializable { if (precision < 19) { return null // Requested precision is too low to represent this value } - this.decimalVal = BigDecimal(longVal) + this.decimalVal = BigDecimal(unscaled) this.longVal = 0L } else { val p = POW_10(math.min(precision, MAX_LONG_DIGITS)) @@ -313,7 +313,7 @@ object Decimal { // See scala.math's Numeric.scala for examples for Scala's built-in types. /** Common methods for Decimal evidence parameters */ - trait DecimalIsConflicted extends Numeric[Decimal] { + private[sql] trait DecimalIsConflicted extends Numeric[Decimal] { override def plus(x: Decimal, y: Decimal): Decimal = x + y override def times(x: Decimal, y: Decimal): Decimal = x * y override def minus(x: Decimal, y: Decimal): Decimal = x - y @@ -327,12 +327,12 @@ object Decimal { } /** A [[scala.math.Fractional]] evidence parameter for Decimals. */ - object DecimalIsFractional extends DecimalIsConflicted with Fractional[Decimal] { + private[sql] object DecimalIsFractional extends DecimalIsConflicted with Fractional[Decimal] { override def div(x: Decimal, y: Decimal): Decimal = x / y } /** A [[scala.math.Integral]] evidence parameter for Decimals. */ - object DecimalAsIfIntegral extends DecimalIsConflicted with Integral[Decimal] { + private[sql] object DecimalAsIfIntegral extends DecimalIsConflicted with Integral[Decimal] { override def quot(x: Decimal, y: Decimal): Decimal = x / y override def rem(x: Decimal, y: Decimal): Decimal = x % y } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala index 0f8cecd28f7df..407dc27326c2e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala @@ -82,12 +82,12 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT object DecimalType { val Unlimited: DecimalType = DecimalType(None) - object Fixed { + private[sql] object Fixed { def unapply(t: DecimalType): Option[(Int, Int)] = t.precisionInfo.map(p => (p.precision, p.scale)) } - object Expression { + private[sql] object Expression { def unapply(e: Expression): Option[(Int, Int)] = e.dataType match { case t: DecimalType => t.precisionInfo.map(p => (p.precision, p.scale)) case _ => None diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java index a64d2bb7cde37..df64a878b6b36 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java @@ -24,11 +24,11 @@ /** * ::DeveloperApi:: * A user-defined type which can be automatically recognized by a SQLContext and registered. - * + *

    * WARNING: This annotation will only work if both Java and Scala reflection return the same class * names (after erasure) for the UDT. This will NOT be the case when, e.g., the UDT class * is enclosed in an object (a singleton). - * + *

    * WARNING: UDTs are currently only supported from Scala. */ // TODO: Should I used @Documented ? diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala index 134ab0af4e0de..1e9476ad06656 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala @@ -22,6 +22,7 @@ import scala.reflect.runtime.universe.typeTag import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.catalyst.ScalaReflectionLock +import org.apache.spark.unsafe.types.UTF8String /** * :: DeveloperApi :: diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala index 7e00a27dfe724..193c08a4d0df7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala @@ -230,10 +230,10 @@ object StructType { case (StructType(leftFields), StructType(rightFields)) => val newFields = ArrayBuffer.empty[StructField] + val rightMapped = fieldsMap(rightFields) leftFields.foreach { case leftField @ StructField(leftName, leftType, leftNullable, _) => - rightFields - .find(_.name == leftName) + rightMapped.get(leftName) .map { case rightField @ StructField(_, rightType, rightNullable, _) => leftField.copy( dataType = merge(leftType, rightType), @@ -243,8 +243,9 @@ object StructType { .foreach(newFields += _) } + val leftMapped = fieldsMap(leftFields) rightFields - .filterNot(f => leftFields.map(_.name).contains(f.name)) + .filterNot(f => leftMapped.get(f.name).nonEmpty) .foreach(newFields += _) StructType(newFields) @@ -264,4 +265,9 @@ object StructType { case _ => throw new SparkException(s"Failed to merge incompatible data types $left and $right") } + + private[sql] def fieldsMap(fields: Array[StructField]): Map[String, StructField] = { + import scala.collection.breakOut + fields.map(s => (s.name, s))(breakOut) + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala index aebabfc475925..a558641fcfed7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala @@ -17,8 +17,6 @@ package org.apache.spark.sql.types -import java.sql.Timestamp - import scala.math.Ordering import scala.reflect.runtime.universe.typeTag @@ -38,18 +36,16 @@ class TimestampType private() extends AtomicType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "TimestampType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Timestamp + private[sql] type InternalType = Long @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val ordering = new Ordering[InternalType] { - def compare(x: Timestamp, y: Timestamp): Int = x.compareTo(y) - } + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the TimestampType is 12 bytes. */ - override def defaultSize: Int = 12 + override def defaultSize: Int = 8 private[spark] override def asNullable: TimestampType = this } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala deleted file mode 100644 index fc02ba6c9c43e..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala +++ /dev/null @@ -1,214 +0,0 @@ -/* -* Licensed to the Apache Software Foundation (ASF) under one or more -* contributor license agreements. See the NOTICE file distributed with -* this work for additional information regarding copyright ownership. -* The ASF licenses this file to You under the Apache License, Version 2.0 -* (the "License"); you may not use this file except in compliance with -* the License. You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -*/ - -package org.apache.spark.sql.types - -import java.util.Arrays - -/** - * A UTF-8 String, as internal representation of StringType in SparkSQL - * - * A String encoded in UTF-8 as an Array[Byte], which can be used for comparison, - * search, see http://en.wikipedia.org/wiki/UTF-8 for details. - * - * Note: This is not designed for general use cases, should not be used outside SQL. - */ - -final class UTF8String extends Ordered[UTF8String] with Serializable { - - private[this] var bytes: Array[Byte] = _ - - /** - * Update the UTF8String with String. - */ - def set(str: String): UTF8String = { - bytes = str.getBytes("utf-8") - this - } - - /** - * Update the UTF8String with Array[Byte], which should be encoded in UTF-8 - */ - def set(bytes: Array[Byte]): UTF8String = { - this.bytes = bytes - this - } - - /** - * Return the number of bytes for a code point with the first byte as `b` - * @param b The first byte of a code point - */ - @inline - private[this] def numOfBytes(b: Byte): Int = { - val offset = (b & 0xFF) - 192 - if (offset >= 0) UTF8String.bytesOfCodePointInUTF8(offset) else 1 - } - - /** - * Return the number of code points in it. - * - * This is only used by Substring() when `start` is negative. - */ - def length(): Int = { - var len = 0 - var i: Int = 0 - while (i < bytes.length) { - i += numOfBytes(bytes(i)) - len += 1 - } - len - } - - def getBytes: Array[Byte] = { - bytes - } - - /** - * Return a substring of this, - * @param start the position of first code point - * @param until the position after last code point - */ - def slice(start: Int, until: Int): UTF8String = { - if (until <= start || start >= bytes.length || bytes == null) { - new UTF8String - } - - var c = 0 - var i: Int = 0 - while (c < start && i < bytes.length) { - i += numOfBytes(bytes(i)) - c += 1 - } - var j = i - while (c < until && j < bytes.length) { - j += numOfBytes(bytes(j)) - c += 1 - } - UTF8String(Arrays.copyOfRange(bytes, i, j)) - } - - def contains(sub: UTF8String): Boolean = { - val b = sub.getBytes - if (b.length == 0) { - return true - } - var i: Int = 0 - while (i <= bytes.length - b.length) { - // In worst case, it's O(N*K), but should works fine with SQL - if (bytes(i) == b(0) && Arrays.equals(Arrays.copyOfRange(bytes, i, i + b.length), b)) { - return true - } - i += 1 - } - false - } - - def startsWith(prefix: UTF8String): Boolean = { - val b = prefix.getBytes - if (b.length > bytes.length) { - return false - } - Arrays.equals(Arrays.copyOfRange(bytes, 0, b.length), b) - } - - def endsWith(suffix: UTF8String): Boolean = { - val b = suffix.getBytes - if (b.length > bytes.length) { - return false - } - Arrays.equals(Arrays.copyOfRange(bytes, bytes.length - b.length, bytes.length), b) - } - - def toUpperCase(): UTF8String = { - // upper case depends on locale, fallback to String. - UTF8String(toString().toUpperCase) - } - - def toLowerCase(): UTF8String = { - // lower case depends on locale, fallback to String. - UTF8String(toString().toLowerCase) - } - - override def toString(): String = { - new String(bytes, "utf-8") - } - - override def clone(): UTF8String = new UTF8String().set(this.bytes) - - override def compare(other: UTF8String): Int = { - var i: Int = 0 - val b = other.getBytes - while (i < bytes.length && i < b.length) { - val res = bytes(i).compareTo(b(i)) - if (res != 0) return res - i += 1 - } - bytes.length - b.length - } - - override def compareTo(other: UTF8String): Int = { - compare(other) - } - - override def equals(other: Any): Boolean = other match { - case s: UTF8String => - Arrays.equals(bytes, s.getBytes) - case s: String => - // This is only used for Catalyst unit tests - // fail fast - bytes.length >= s.length && length() == s.length && toString() == s - case _ => - false - } - - override def hashCode(): Int = { - Arrays.hashCode(bytes) - } -} - -object UTF8String { - // number of tailing bytes in a UTF8 sequence for a code point - // see http://en.wikipedia.org/wiki/UTF-8, 192-256 of Byte 1 - private[types] val bytesOfCodePointInUTF8: Array[Int] = Array(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, - 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, - 4, 4, 4, 4, 4, 4, 4, 4, - 5, 5, 5, 5, - 6, 6, 6, 6) - - /** - * Create a UTF-8 String from String - */ - def apply(s: String): UTF8String = { - if (s != null) { - new UTF8String().set(s) - } else{ - null - } - } - - /** - * Create a UTF-8 String from Array[Byte], which should be encoded in UTF-8 - */ - def apply(bytes: Array[Byte]): UTF8String = { - if (bytes != null) { - new UTF8String().set(bytes) - } else { - null - } - } -} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala new file mode 100644 index 0000000000000..df0f04563edcf --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala @@ -0,0 +1,62 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.Row +import org.apache.spark.sql.types._ + +class CatalystTypeConvertersSuite extends SparkFunSuite { + + private val simpleTypes: Seq[DataType] = Seq( + StringType, + DateType, + BooleanType, + ByteType, + ShortType, + IntegerType, + LongType, + FloatType, + DoubleType) + + test("null handling in rows") { + val schema = StructType(simpleTypes.map(t => StructField(t.getClass.getName, t))) + val convertToCatalyst = CatalystTypeConverters.createToCatalystConverter(schema) + val convertToScala = CatalystTypeConverters.createToScalaConverter(schema) + + val scalaRow = Row.fromSeq(Seq.fill(simpleTypes.length)(null)) + assert(convertToScala(convertToCatalyst(scalaRow)) === scalaRow) + } + + test("null handling for individual values") { + for (dataType <- simpleTypes) { + assert(CatalystTypeConverters.createToScalaConverter(dataType)(null) === null) + } + } + + test("option handling in convertToCatalyst") { + // convertToCatalyst doesn't handle unboxing from Options. This is inconsistent with + // createToCatalystConverter but it may not actually matter as this is only called internally + // in a handful of places where we don't expect to receive Options. + assert(CatalystTypeConverters.convertToCatalyst(Some(123)) === Some(123)) + } + + test("option handling in createToCatalystConverter") { + assert(CatalystTypeConverters.createToCatalystConverter(IntegerType)(Some(123)) === 123) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala index ea82cd2622de9..c046dbf4dc2c9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala @@ -17,14 +17,13 @@ package org.apache.spark.sql.catalyst -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.plans.physical._ /* Implicit conversions */ import org.apache.spark.sql.catalyst.dsl.expressions._ -class DistributionSuite extends FunSuite { +class DistributionSuite extends SparkFunSuite { protected def checkSatisfied( inputPartitioning: Partitioning, diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala index bbc0b661a0c0c..b4b00f558463f 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala @@ -20,9 +20,7 @@ package org.apache.spark.sql.catalyst import java.math.BigInteger import java.sql.{Date, Timestamp} -import org.scalatest.FunSuite - -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.types._ case class PrimitiveData( @@ -75,8 +73,8 @@ case class MultipleConstructorsData(a: Int, b: String, c: Double) { def this(b: String, a: Int) = this(a, b, c = 1.0) } -class ScalaReflectionSuite extends FunSuite { - import ScalaReflection._ +class ScalaReflectionSuite extends SparkFunSuite { + import org.apache.spark.sql.catalyst.ScalaReflection._ test("primitive data") { val schema = schemaFor[PrimitiveData] @@ -253,14 +251,14 @@ class ScalaReflectionSuite extends FunSuite { } assert(ArrayType(IntegerType) === typeOfObject3(Seq(1, 2, 3))) - assert(ArrayType(ArrayType(IntegerType)) === typeOfObject3(Seq(Seq(1,2,3)))) + assert(ArrayType(ArrayType(IntegerType)) === typeOfObject3(Seq(Seq(1, 2, 3)))) } test("convert PrimitiveData to catalyst") { val data = PrimitiveData(1, 1, 1, 1, 1, 1, true) - val convertedData = Row(1, 1.toLong, 1.toDouble, 1.toFloat, 1.toShort, 1.toByte, true) + val convertedData = InternalRow(1, 1.toLong, 1.toDouble, 1.toFloat, 1.toShort, 1.toByte, true) val dataType = schemaFor[PrimitiveData].dataType - assert(CatalystTypeConverters.convertToCatalyst(data, dataType) === convertedData) + assert(CatalystTypeConverters.createToCatalystConverter(dataType)(data) === convertedData) } test("convert Option[Product] to catalyst") { @@ -268,9 +266,9 @@ class ScalaReflectionSuite extends FunSuite { val data = OptionalData(Some(2), Some(2), Some(2), Some(2), Some(2), Some(2), Some(true), Some(primitiveData)) val dataType = schemaFor[OptionalData].dataType - val convertedData = Row(2, 2.toLong, 2.toDouble, 2.toFloat, 2.toShort, 2.toByte, true, - Row(1, 1, 1, 1, 1, 1, true)) - assert(CatalystTypeConverters.convertToCatalyst(data, dataType) === convertedData) + val convertedData = InternalRow(2, 2.toLong, 2.toDouble, 2.toFloat, 2.toShort, 2.toByte, true, + InternalRow(1, 1, 1, 1, 1, 1, true)) + assert(CatalystTypeConverters.createToCatalystConverter(dataType)(data) === convertedData) } test("infer schema from case class with multiple constructors") { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala index 890ea2a84b82e..b93a3abc6ebd2 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala @@ -17,10 +17,10 @@ package org.apache.spark.sql.catalyst +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.catalyst.plans.logical.Command -import org.scalatest.FunSuite private[sql] case class TestCommand(cmd: String) extends LogicalPlan with Command { override def output: Seq[Attribute] = Seq.empty @@ -28,7 +28,7 @@ private[sql] case class TestCommand(cmd: String) extends LogicalPlan with Comman } private[sql] class SuperLongKeywordTestParser extends AbstractSparkSQLParser { - protected val EXECUTE = Keyword("THISISASUPERLONGKEYWORDTEST") + protected val EXECUTE = Keyword("THISISASUPERLONGKEYWORDTEST") override protected lazy val start: Parser[LogicalPlan] = set @@ -39,7 +39,7 @@ private[sql] class SuperLongKeywordTestParser extends AbstractSparkSQLParser { } private[sql] class CaseInsensitiveTestParser extends AbstractSparkSQLParser { - protected val EXECUTE = Keyword("EXECUTE") + protected val EXECUTE = Keyword("EXECUTE") override protected lazy val start: Parser[LogicalPlan] = set @@ -49,7 +49,7 @@ private[sql] class CaseInsensitiveTestParser extends AbstractSparkSQLParser { } } -class SqlParserSuite extends FunSuite { +class SqlParserSuite extends SparkFunSuite { test("test long keyword") { val parser = new SuperLongKeywordTestParser diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index 971e1ff5ec2b8..e09cd790a7187 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -17,30 +17,33 @@ package org.apache.spark.sql.catalyst.analysis -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.types._ - +import org.apache.spark.sql.catalyst.SimpleCatalystConf import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.dsl.plans._ -class AnalysisSuite extends FunSuite with BeforeAndAfter { - val caseSensitiveCatalog = new SimpleCatalog(true) - val caseInsensitiveCatalog = new SimpleCatalog(false) +class AnalysisSuite extends SparkFunSuite with BeforeAndAfter { + val caseSensitiveConf = new SimpleCatalystConf(true) + val caseInsensitiveConf = new SimpleCatalystConf(false) + + val caseSensitiveCatalog = new SimpleCatalog(caseSensitiveConf) + val caseInsensitiveCatalog = new SimpleCatalog(caseInsensitiveConf) val caseSensitiveAnalyzer = - new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitive = true) { + new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitiveConf) { override val extendedResolutionRules = EliminateSubQueries :: Nil } val caseInsensitiveAnalyzer = - new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseSensitive = false) { + new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseInsensitiveConf) { override val extendedResolutionRules = EliminateSubQueries :: Nil } - def caseSensitiveAnalyze(plan: LogicalPlan): Unit = caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer.execute(plan)) @@ -70,6 +73,9 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { StructField("cField", StringType) :: Nil ))()) + val listRelation = LocalRelation( + AttributeReference("list", ArrayType(IntegerType))()) + before { caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation) caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation) @@ -150,17 +156,35 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { caseSensitive: Boolean = true): Unit = { test(name) { val error = intercept[AnalysisException] { - if(caseSensitive) { + if (caseSensitive) { caseSensitiveAnalyze(plan) } else { caseInsensitiveAnalyze(plan) } } - errorMessages.foreach(m => assert(error.getMessage contains m)) + errorMessages.foreach(m => assert(error.getMessage.toLowerCase contains m.toLowerCase)) } } + errorTest( + "unresolved window function", + testRelation2.select( + WindowExpression( + UnresolvedWindowFunction( + "lead", + UnresolvedAttribute("c") :: Nil), + WindowSpecDefinition( + UnresolvedAttribute("a") :: Nil, + SortOrder(UnresolvedAttribute("b"), Ascending) :: Nil, + UnspecifiedFrame)).as('window)), + "lead" :: "window functions currently requires a HiveContext" :: Nil) + + errorTest( + "too many generators", + listRelation.select(Explode('list).as('a), Explode('list).as('b)), + "only one generator" :: "explode" :: Nil) + errorTest( "unresolved attributes", testRelation.select('abcd), diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala index 36b03d1c65e28..7bac97b7894f5 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala @@ -17,14 +17,18 @@ package org.apache.spark.sql.catalyst.analysis +import org.scalatest.BeforeAndAfter + +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.{Union, Project, LocalRelation} import org.apache.spark.sql.types._ -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.apache.spark.sql.catalyst.SimpleCatalystConf -class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { - val catalog = new SimpleCatalog(false) - val analyzer = new Analyzer(catalog, EmptyFunctionRegistry, caseSensitive = false) +class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter { + val conf = new SimpleCatalystConf(true) + val catalog = new SimpleCatalog(conf) + val analyzer = new Analyzer(catalog, EmptyFunctionRegistry, conf) val relation = LocalRelation( AttributeReference("i", IntegerType)(), @@ -88,8 +92,10 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { } test("Comparison operations") { - checkComparison(LessThan(i, d1), DecimalType.Unlimited) - checkComparison(LessThanOrEqual(d1, d2), DecimalType.Unlimited) + checkComparison(EqualTo(i, d1), DecimalType(10, 1)) + checkComparison(EqualNullSafe(d2, d1), DecimalType(5, 2)) + checkComparison(LessThan(i, d1), DecimalType(10, 1)) + checkComparison(LessThanOrEqual(d1, d2), DecimalType(5, 2)) checkComparison(GreaterThan(d2, u), DecimalType.Unlimited) checkComparison(GreaterThanOrEqual(d1, f), DoubleType) checkComparison(GreaterThan(d2, d2), DecimalType(5, 2)) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala index fcd745f43cfbf..f7b8e21bed490 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala @@ -20,18 +20,19 @@ package org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.plans.PlanTest import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, Project} +import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LocalRelation, Project} +import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.sql.types._ class HiveTypeCoercionSuite extends PlanTest { test("tightest common bound for types") { def widenTest(t1: DataType, t2: DataType, tightestCommon: Option[DataType]) { - var found = HiveTypeCoercion.findTightestCommonType(t1, t2) + var found = HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2) assert(found == tightestCommon, s"Expected $tightestCommon as tightest common type for $t1 and $t2, found $found") // Test both directions to make sure the widening is symmetric. - found = HiveTypeCoercion.findTightestCommonType(t2, t1) + found = HiveTypeCoercion.findTightestCommonTypeOfTwo(t2, t1) assert(found == tightestCommon, s"Expected $tightestCommon as tightest common type for $t2 and $t1, found $found") } @@ -104,31 +105,16 @@ class HiveTypeCoercionSuite extends PlanTest { widenTest(ArrayType(IntegerType), StructType(Seq()), None) } - test("boolean casts") { - val booleanCasts = new HiveTypeCoercion { }.BooleanCasts - def ruleTest(initial: Expression, transformed: Expression) { - val testRelation = LocalRelation(AttributeReference("a", IntegerType)()) - comparePlans( - booleanCasts(Project(Seq(Alias(initial, "a")()), testRelation)), - Project(Seq(Alias(transformed, "a")()), testRelation)) - } - // Remove superflous boolean -> boolean casts. - ruleTest(Cast(Literal(true), BooleanType), Literal(true)) - // Stringify boolean when casting to string. - ruleTest( - Cast(Literal(false), StringType), - If(Literal(false), Literal("true"), Literal("false"))) + private def ruleTest(rule: Rule[LogicalPlan], initial: Expression, transformed: Expression) { + val testRelation = LocalRelation(AttributeReference("a", IntegerType)()) + comparePlans( + rule(Project(Seq(Alias(initial, "a")()), testRelation)), + Project(Seq(Alias(transformed, "a")()), testRelation)) } test("coalesce casts") { val fac = new HiveTypeCoercion { }.FunctionArgumentConversion - def ruleTest(initial: Expression, transformed: Expression) { - val testRelation = LocalRelation(AttributeReference("a", IntegerType)()) - comparePlans( - fac(Project(Seq(Alias(initial, "a")()), testRelation)), - Project(Seq(Alias(transformed, "a")()), testRelation)) - } - ruleTest( + ruleTest(fac, Coalesce(Literal(1.0) :: Literal(1) :: Literal.create(1.0, FloatType) @@ -137,7 +123,7 @@ class HiveTypeCoercionSuite extends PlanTest { :: Cast(Literal(1), DoubleType) :: Cast(Literal.create(1.0, FloatType), DoubleType) :: Nil)) - ruleTest( + ruleTest(fac, Coalesce(Literal(1L) :: Literal(1) :: Literal(new java.math.BigDecimal("1000000000000000000000")) @@ -147,4 +133,71 @@ class HiveTypeCoercionSuite extends PlanTest { :: Cast(Literal(new java.math.BigDecimal("1000000000000000000000")), DecimalType()) :: Nil)) } + + test("type coercion for If") { + val rule = new HiveTypeCoercion { }.IfCoercion + ruleTest(rule, + If(Literal(true), Literal(1), Literal(1L)), + If(Literal(true), Cast(Literal(1), LongType), Literal(1L)) + ) + + ruleTest(rule, + If(Literal.create(null, NullType), Literal(1), Literal(1)), + If(Literal.create(null, BooleanType), Literal(1), Literal(1)) + ) + } + + test("type coercion for CaseKeyWhen") { + val cwc = new HiveTypeCoercion {}.CaseWhenCoercion + ruleTest(cwc, + CaseKeyWhen(Literal(1.toShort), Seq(Literal(1), Literal("a"))), + CaseKeyWhen(Cast(Literal(1.toShort), IntegerType), Seq(Literal(1), Literal("a"))) + ) + ruleTest(cwc, + CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a"))), + CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a"))) + ) + } + + test("type coercion simplification for equal to") { + val be = new HiveTypeCoercion {}.BooleanEquality + + ruleTest(be, + EqualTo(Literal(true), Literal(1)), + Literal(true) + ) + ruleTest(be, + EqualTo(Literal(true), Literal(0)), + Not(Literal(true)) + ) + ruleTest(be, + EqualNullSafe(Literal(true), Literal(1)), + And(IsNotNull(Literal(true)), Literal(true)) + ) + ruleTest(be, + EqualNullSafe(Literal(true), Literal(0)), + And(IsNotNull(Literal(true)), Not(Literal(true))) + ) + + ruleTest(be, + EqualTo(Literal(true), Literal(1L)), + Literal(true) + ) + ruleTest(be, + EqualTo(Literal(new java.math.BigDecimal(1)), Literal(true)), + Literal(true) + ) + ruleTest(be, + EqualTo(Literal(BigDecimal(0)), Literal(true)), + Not(Literal(true)) + ) + ruleTest(be, + EqualTo(Literal(Decimal(1)), Literal(true)), + Literal(true) + ) + ruleTest(be, + EqualTo(Literal.create(Decimal(1), DecimalType(8, 0)), Literal(true)), + Literal(true) + ) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala new file mode 100644 index 0000000000000..4bbbbe6c7f091 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala @@ -0,0 +1,145 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types.{Decimal, DoubleType, IntegerType} + + +class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper { + + /** + * Runs through the testFunc for all numeric data types. + * + * @param testFunc a test function that accepts a conversion function to convert an integer + * into another data type. + */ + private def testNumericDataTypes(testFunc: (Int => Any) => Unit): Unit = { + testFunc(_.toByte) + testFunc(_.toShort) + testFunc(identity) + testFunc(_.toLong) + testFunc(_.toFloat) + testFunc(_.toDouble) + testFunc(Decimal(_)) + } + + test("+ (Add)") { + testNumericDataTypes { convert => + val left = Literal(convert(1)) + val right = Literal(convert(2)) + checkEvaluation(Add(left, right), convert(3)) + checkEvaluation(Add(Literal.create(null, left.dataType), right), null) + checkEvaluation(Add(left, Literal.create(null, right.dataType)), null) + } + } + + test("- (UnaryMinus)") { + testNumericDataTypes { convert => + val input = Literal(convert(1)) + val dataType = input.dataType + checkEvaluation(UnaryMinus(input), convert(-1)) + checkEvaluation(UnaryMinus(Literal.create(null, dataType)), null) + } + } + + test("- (Minus)") { + testNumericDataTypes { convert => + val left = Literal(convert(1)) + val right = Literal(convert(2)) + checkEvaluation(Subtract(left, right), convert(-1)) + checkEvaluation(Subtract(Literal.create(null, left.dataType), right), null) + checkEvaluation(Subtract(left, Literal.create(null, right.dataType)), null) + } + } + + test("* (Multiply)") { + testNumericDataTypes { convert => + val left = Literal(convert(1)) + val right = Literal(convert(2)) + checkEvaluation(Multiply(left, right), convert(2)) + checkEvaluation(Multiply(Literal.create(null, left.dataType), right), null) + checkEvaluation(Multiply(left, Literal.create(null, right.dataType)), null) + } + } + + test("/ (Divide) basic") { + testNumericDataTypes { convert => + val left = Literal(convert(2)) + val right = Literal(convert(1)) + val dataType = left.dataType + checkEvaluation(Divide(left, right), convert(2)) + checkEvaluation(Divide(Literal.create(null, dataType), right), null) + checkEvaluation(Divide(left, Literal.create(null, right.dataType)), null) + checkEvaluation(Divide(left, Literal(convert(0))), null) // divide by zero + } + } + + test("/ (Divide) for integral type") { + checkEvaluation(Divide(Literal(1.toByte), Literal(2.toByte)), 0.toByte) + checkEvaluation(Divide(Literal(1.toShort), Literal(2.toShort)), 0.toShort) + checkEvaluation(Divide(Literal(1), Literal(2)), 0) + checkEvaluation(Divide(Literal(1.toLong), Literal(2.toLong)), 0.toLong) + } + + test("/ (Divide) for floating point") { + checkEvaluation(Divide(Literal(1.0f), Literal(2.0f)), 0.5f) + checkEvaluation(Divide(Literal(1.0), Literal(2.0)), 0.5) + checkEvaluation(Divide(Literal(Decimal(1.0)), Literal(Decimal(2.0))), Decimal(0.5)) + } + + test("% (Remainder)") { + testNumericDataTypes { convert => + val left = Literal(convert(1)) + val right = Literal(convert(2)) + checkEvaluation(Remainder(left, right), convert(1)) + checkEvaluation(Remainder(Literal.create(null, left.dataType), right), null) + checkEvaluation(Remainder(left, Literal.create(null, right.dataType)), null) + checkEvaluation(Remainder(left, Literal(convert(0))), null) // mod by 0 + } + } + + test("Abs") { + testNumericDataTypes { convert => + checkEvaluation(Abs(Literal(convert(0))), convert(0)) + checkEvaluation(Abs(Literal(convert(1))), convert(1)) + checkEvaluation(Abs(Literal(convert(-1))), convert(1)) + } + } + + test("MaxOf") { + checkEvaluation(MaxOf(1, 2), 2) + checkEvaluation(MaxOf(2, 1), 2) + checkEvaluation(MaxOf(1L, 2L), 2L) + checkEvaluation(MaxOf(2L, 1L), 2L) + + checkEvaluation(MaxOf(Literal.create(null, IntegerType), 2), 2) + checkEvaluation(MaxOf(2, Literal.create(null, IntegerType)), 2) + } + + test("MinOf") { + checkEvaluation(MinOf(1, 2), 1) + checkEvaluation(MinOf(2, 1), 1) + checkEvaluation(MinOf(1L, 2L), 1L) + checkEvaluation(MinOf(2L, 1L), 1L) + + checkEvaluation(MinOf(Literal.create(null, IntegerType), 1), 1) + checkEvaluation(MinOf(1, Literal.create(null, IntegerType)), 1) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala index f2f3a84d19380..97cfb5f06dd73 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala @@ -17,11 +17,10 @@ package org.apache.spark.sql.catalyst.expressions -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.types.IntegerType -class AttributeSetSuite extends FunSuite { +class AttributeSetSuite extends SparkFunSuite { val aUpper = AttributeReference("A", IntegerType)(exprId = ExprId(1)) val aLower = AttributeReference("a", IntegerType)(exprId = ExprId(1)) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala new file mode 100644 index 0000000000000..c9bbc7a8b8c14 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala @@ -0,0 +1,80 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types._ + + +class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper { + + test("Bitwise operations") { + val row = create_row(1, 2, 3, null) + val c1 = 'a.int.at(0) + val c2 = 'a.int.at(1) + val c3 = 'a.int.at(2) + val c4 = 'a.int.at(3) + + checkEvaluation(BitwiseAnd(c1, c4), null, row) + checkEvaluation(BitwiseAnd(c1, c2), 0, row) + checkEvaluation(BitwiseAnd(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseAnd(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + + checkEvaluation(BitwiseOr(c1, c4), null, row) + checkEvaluation(BitwiseOr(c1, c2), 3, row) + checkEvaluation(BitwiseOr(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseOr(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + + checkEvaluation(BitwiseXor(c1, c4), null, row) + checkEvaluation(BitwiseXor(c1, c2), 3, row) + checkEvaluation(BitwiseXor(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseXor(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + + checkEvaluation(BitwiseNot(c4), null, row) + checkEvaluation(BitwiseNot(c1), -2, row) + checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null, row) + + checkEvaluation(c1 & c2, 0, row) + checkEvaluation(c1 | c2, 3, row) + checkEvaluation(c1 ^ c2, 3, row) + checkEvaluation(~c1, -2, row) + } + + test("unary BitwiseNOT") { + checkEvaluation(BitwiseNot(1), -2) + assert(BitwiseNot(1).dataType === IntegerType) + assert(BitwiseNot(1).eval(EmptyRow).isInstanceOf[Int]) + + checkEvaluation(BitwiseNot(1.toLong), -2.toLong) + assert(BitwiseNot(1.toLong).dataType === LongType) + assert(BitwiseNot(1.toLong).eval(EmptyRow).isInstanceOf[Long]) + + checkEvaluation(BitwiseNot(1.toShort), -2.toShort) + assert(BitwiseNot(1.toShort).dataType === ShortType) + assert(BitwiseNot(1.toShort).eval(EmptyRow).isInstanceOf[Short]) + + checkEvaluation(BitwiseNot(1.toByte), -2.toByte) + assert(BitwiseNot(1.toByte).dataType === ByteType) + assert(BitwiseNot(1.toByte).eval(EmptyRow).isInstanceOf[Byte]) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala new file mode 100644 index 0000000000000..e407f6f166e86 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala @@ -0,0 +1,564 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import java.sql.{Timestamp, Date} + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.util.DateUtils +import org.apache.spark.sql.types._ + +/** + * Test suite for data type casting expression [[Cast]]. + */ +class CastSuite extends SparkFunSuite with ExpressionEvalHelper { + + private def cast(v: Any, targetType: DataType): Cast = { + v match { + case lit: Expression => Cast(lit, targetType) + case _ => Cast(Literal(v), targetType) + } + } + + // expected cannot be null + private def checkCast(v: Any, expected: Any): Unit = { + checkEvaluation(cast(v, Literal(expected).dataType), expected) + } + + test("cast from int") { + checkCast(0, false) + checkCast(1, true) + checkCast(-5, true) + checkCast(1, 1.toByte) + checkCast(1, 1.toShort) + checkCast(1, 1) + checkCast(1, 1.toLong) + checkCast(1, 1.0f) + checkCast(1, 1.0) + checkCast(123, "123") + + checkEvaluation(cast(123, DecimalType.Unlimited), Decimal(123)) + checkEvaluation(cast(123, DecimalType(3, 0)), Decimal(123)) + checkEvaluation(cast(123, DecimalType(3, 1)), null) + checkEvaluation(cast(123, DecimalType(2, 0)), null) + } + + test("cast from long") { + checkCast(0L, false) + checkCast(1L, true) + checkCast(-5L, true) + checkCast(1L, 1.toByte) + checkCast(1L, 1.toShort) + checkCast(1L, 1) + checkCast(1L, 1.toLong) + checkCast(1L, 1.0f) + checkCast(1L, 1.0) + checkCast(123L, "123") + + checkEvaluation(cast(123L, DecimalType.Unlimited), Decimal(123)) + checkEvaluation(cast(123L, DecimalType(3, 0)), Decimal(123)) + checkEvaluation(cast(123L, DecimalType(3, 1)), Decimal(123.0)) + + // TODO: Fix the following bug and re-enable it. + // checkEvaluation(cast(123L, DecimalType(2, 0)), null) + } + + test("cast from boolean") { + checkEvaluation(cast(true, IntegerType), 1) + checkEvaluation(cast(false, IntegerType), 0) + checkEvaluation(cast(true, StringType), "true") + checkEvaluation(cast(false, StringType), "false") + checkEvaluation(cast(cast(1, BooleanType), IntegerType), 1) + checkEvaluation(cast(cast(0, BooleanType), IntegerType), 0) + } + + test("cast from int 2") { + checkEvaluation(cast(1, LongType), 1.toLong) + checkEvaluation(cast(cast(1000, TimestampType), LongType), 1.toLong) + checkEvaluation(cast(cast(-1200, TimestampType), LongType), -2.toLong) + + checkEvaluation(cast(123, DecimalType.Unlimited), Decimal(123)) + checkEvaluation(cast(123, DecimalType(3, 0)), Decimal(123)) + checkEvaluation(cast(123, DecimalType(3, 1)), null) + checkEvaluation(cast(123, DecimalType(2, 0)), null) + } + + test("cast from float") { + checkCast(0.0f, false) + checkCast(0.5f, true) + checkCast(-5.0f, true) + checkCast(1.5f, 1.toByte) + checkCast(1.5f, 1.toShort) + checkCast(1.5f, 1) + checkCast(1.5f, 1.toLong) + checkCast(1.5f, 1.5) + checkCast(1.5f, "1.5") + } + + test("cast from double") { + checkCast(0.0, false) + checkCast(0.5, true) + checkCast(-5.0, true) + checkCast(1.5, 1.toByte) + checkCast(1.5, 1.toShort) + checkCast(1.5, 1) + checkCast(1.5, 1.toLong) + checkCast(1.5, 1.5f) + checkCast(1.5, "1.5") + + checkEvaluation(cast(cast(1.toDouble, TimestampType), DoubleType), 1.toDouble) + checkEvaluation(cast(cast(1.toDouble, TimestampType), DoubleType), 1.toDouble) + } + + test("cast from string") { + assert(cast("abcdef", StringType).nullable === false) + assert(cast("abcdef", BinaryType).nullable === false) + assert(cast("abcdef", BooleanType).nullable === false) + assert(cast("abcdef", TimestampType).nullable === true) + assert(cast("abcdef", LongType).nullable === true) + assert(cast("abcdef", IntegerType).nullable === true) + assert(cast("abcdef", ShortType).nullable === true) + assert(cast("abcdef", ByteType).nullable === true) + assert(cast("abcdef", DecimalType.Unlimited).nullable === true) + assert(cast("abcdef", DecimalType(4, 2)).nullable === true) + assert(cast("abcdef", DoubleType).nullable === true) + assert(cast("abcdef", FloatType).nullable === true) + } + + test("data type casting") { + val sd = "1970-01-01" + val d = Date.valueOf(sd) + val zts = sd + " 00:00:00" + val sts = sd + " 00:00:02" + val nts = sts + ".1" + val ts = Timestamp.valueOf(nts) + + checkEvaluation(cast("abdef", StringType), "abdef") + checkEvaluation(cast("abdef", DecimalType.Unlimited), null) + checkEvaluation(cast("abdef", TimestampType), null) + checkEvaluation(cast("12.65", DecimalType.Unlimited), Decimal(12.65)) + + checkEvaluation(cast(cast(sd, DateType), StringType), sd) + checkEvaluation(cast(cast(d, StringType), DateType), 0) + checkEvaluation(cast(cast(nts, TimestampType), StringType), nts) + checkEvaluation(cast(cast(ts, StringType), TimestampType), DateUtils.fromJavaTimestamp(ts)) + + // all convert to string type to check + checkEvaluation(cast(cast(cast(nts, TimestampType), DateType), StringType), sd) + checkEvaluation(cast(cast(cast(ts, DateType), TimestampType), StringType), zts) + + checkEvaluation(cast(cast("abdef", BinaryType), StringType), "abdef") + + checkEvaluation(cast(cast(cast(cast( + cast(cast("5", ByteType), ShortType), IntegerType), FloatType), DoubleType), LongType), + 5.toLong) + checkEvaluation( + cast(cast(cast(cast(cast(cast("5", ByteType), TimestampType), + DecimalType.Unlimited), LongType), StringType), ShortType), + 0.toShort) + checkEvaluation( + cast(cast(cast(cast(cast(cast("5", TimestampType), ByteType), + DecimalType.Unlimited), LongType), StringType), ShortType), + null) + checkEvaluation(cast(cast(cast(cast(cast(cast("5", DecimalType.Unlimited), + ByteType), TimestampType), LongType), StringType), ShortType), + 0.toShort) + + checkEvaluation(cast("23", DoubleType), 23d) + checkEvaluation(cast("23", IntegerType), 23) + checkEvaluation(cast("23", FloatType), 23f) + checkEvaluation(cast("23", DecimalType.Unlimited), Decimal(23)) + checkEvaluation(cast("23", ByteType), 23.toByte) + checkEvaluation(cast("23", ShortType), 23.toShort) + checkEvaluation(cast("2012-12-11", DoubleType), null) + checkEvaluation(cast(123, IntegerType), 123) + + + checkEvaluation(cast(Literal.create(null, IntegerType), ShortType), null) + } + + test("cast and add") { + checkEvaluation(Add(Literal(23d), cast(true, DoubleType)), 24d) + checkEvaluation(Add(Literal(23), cast(true, IntegerType)), 24) + checkEvaluation(Add(Literal(23f), cast(true, FloatType)), 24f) + checkEvaluation(Add(Literal(Decimal(23)), cast(true, DecimalType.Unlimited)), Decimal(24)) + checkEvaluation(Add(Literal(23.toByte), cast(true, ByteType)), 24.toByte) + checkEvaluation(Add(Literal(23.toShort), cast(true, ShortType)), 24.toShort) + } + + test("from decimal") { + checkCast(Decimal(0.0), false) + checkCast(Decimal(0.5), true) + checkCast(Decimal(-5.0), true) + checkCast(Decimal(1.5), 1.toByte) + checkCast(Decimal(1.5), 1.toShort) + checkCast(Decimal(1.5), 1) + checkCast(Decimal(1.5), 1.toLong) + checkCast(Decimal(1.5), 1.5f) + checkCast(Decimal(1.5), 1.5) + checkCast(Decimal(1.5), "1.5") + } + + test("casting to fixed-precision decimals") { + // Overflow and rounding for casting to fixed-precision decimals: + // - Values should round with HALF_UP mode by default when you lower scale + // - Values that would overflow the target precision should turn into null + // - Because of this, casts to fixed-precision decimals should be nullable + + assert(cast(123, DecimalType.Unlimited).nullable === false) + assert(cast(10.03f, DecimalType.Unlimited).nullable === true) + assert(cast(10.03, DecimalType.Unlimited).nullable === true) + assert(cast(Decimal(10.03), DecimalType.Unlimited).nullable === false) + + assert(cast(123, DecimalType(2, 1)).nullable === true) + assert(cast(10.03f, DecimalType(2, 1)).nullable === true) + assert(cast(10.03, DecimalType(2, 1)).nullable === true) + assert(cast(Decimal(10.03), DecimalType(2, 1)).nullable === true) + + + checkEvaluation(cast(10.03, DecimalType.Unlimited), Decimal(10.03)) + checkEvaluation(cast(10.03, DecimalType(4, 2)), Decimal(10.03)) + checkEvaluation(cast(10.03, DecimalType(3, 1)), Decimal(10.0)) + checkEvaluation(cast(10.03, DecimalType(2, 0)), Decimal(10)) + checkEvaluation(cast(10.03, DecimalType(1, 0)), null) + checkEvaluation(cast(10.03, DecimalType(2, 1)), null) + checkEvaluation(cast(10.03, DecimalType(3, 2)), null) + checkEvaluation(cast(Decimal(10.03), DecimalType(3, 1)), Decimal(10.0)) + checkEvaluation(cast(Decimal(10.03), DecimalType(3, 2)), null) + + checkEvaluation(cast(10.05, DecimalType.Unlimited), Decimal(10.05)) + checkEvaluation(cast(10.05, DecimalType(4, 2)), Decimal(10.05)) + checkEvaluation(cast(10.05, DecimalType(3, 1)), Decimal(10.1)) + checkEvaluation(cast(10.05, DecimalType(2, 0)), Decimal(10)) + checkEvaluation(cast(10.05, DecimalType(1, 0)), null) + checkEvaluation(cast(10.05, DecimalType(2, 1)), null) + checkEvaluation(cast(10.05, DecimalType(3, 2)), null) + checkEvaluation(cast(Decimal(10.05), DecimalType(3, 1)), Decimal(10.1)) + checkEvaluation(cast(Decimal(10.05), DecimalType(3, 2)), null) + + checkEvaluation(cast(9.95, DecimalType(3, 2)), Decimal(9.95)) + checkEvaluation(cast(9.95, DecimalType(3, 1)), Decimal(10.0)) + checkEvaluation(cast(9.95, DecimalType(2, 0)), Decimal(10)) + checkEvaluation(cast(9.95, DecimalType(2, 1)), null) + checkEvaluation(cast(9.95, DecimalType(1, 0)), null) + checkEvaluation(cast(Decimal(9.95), DecimalType(3, 1)), Decimal(10.0)) + checkEvaluation(cast(Decimal(9.95), DecimalType(1, 0)), null) + + checkEvaluation(cast(-9.95, DecimalType(3, 2)), Decimal(-9.95)) + checkEvaluation(cast(-9.95, DecimalType(3, 1)), Decimal(-10.0)) + checkEvaluation(cast(-9.95, DecimalType(2, 0)), Decimal(-10)) + checkEvaluation(cast(-9.95, DecimalType(2, 1)), null) + checkEvaluation(cast(-9.95, DecimalType(1, 0)), null) + checkEvaluation(cast(Decimal(-9.95), DecimalType(3, 1)), Decimal(-10.0)) + checkEvaluation(cast(Decimal(-9.95), DecimalType(1, 0)), null) + + checkEvaluation(cast(Double.NaN, DecimalType.Unlimited), null) + checkEvaluation(cast(1.0 / 0.0, DecimalType.Unlimited), null) + checkEvaluation(cast(Float.NaN, DecimalType.Unlimited), null) + checkEvaluation(cast(1.0f / 0.0f, DecimalType.Unlimited), null) + + checkEvaluation(cast(Double.NaN, DecimalType(2, 1)), null) + checkEvaluation(cast(1.0 / 0.0, DecimalType(2, 1)), null) + checkEvaluation(cast(Float.NaN, DecimalType(2, 1)), null) + checkEvaluation(cast(1.0f / 0.0f, DecimalType(2, 1)), null) + } + + test("cast from date") { + val d = Date.valueOf("1970-01-01") + checkEvaluation(cast(d, ShortType), null) + checkEvaluation(cast(d, IntegerType), null) + checkEvaluation(cast(d, LongType), null) + checkEvaluation(cast(d, FloatType), null) + checkEvaluation(cast(d, DoubleType), null) + checkEvaluation(cast(d, DecimalType.Unlimited), null) + checkEvaluation(cast(d, DecimalType(10, 2)), null) + checkEvaluation(cast(d, StringType), "1970-01-01") + checkEvaluation(cast(cast(d, TimestampType), StringType), "1970-01-01 00:00:00") + } + + test("cast from timestamp") { + val millis = 15 * 1000 + 2 + val seconds = millis * 1000 + 2 + val ts = new Timestamp(millis) + val tss = new Timestamp(seconds) + checkEvaluation(cast(ts, ShortType), 15.toShort) + checkEvaluation(cast(ts, IntegerType), 15) + checkEvaluation(cast(ts, LongType), 15.toLong) + checkEvaluation(cast(ts, FloatType), 15.002f) + checkEvaluation(cast(ts, DoubleType), 15.002) + checkEvaluation(cast(cast(tss, ShortType), TimestampType), DateUtils.fromJavaTimestamp(ts)) + checkEvaluation(cast(cast(tss, IntegerType), TimestampType), DateUtils.fromJavaTimestamp(ts)) + checkEvaluation(cast(cast(tss, LongType), TimestampType), DateUtils.fromJavaTimestamp(ts)) + checkEvaluation( + cast(cast(millis.toFloat / 1000, TimestampType), FloatType), + millis.toFloat / 1000) + checkEvaluation( + cast(cast(millis.toDouble / 1000, TimestampType), DoubleType), + millis.toDouble / 1000) + checkEvaluation( + cast(cast(Decimal(1), TimestampType), DecimalType.Unlimited), + Decimal(1)) + + // A test for higher precision than millis + checkEvaluation(cast(cast(0.0000001, TimestampType), DoubleType), 0.0000001) + + checkEvaluation(cast(Double.NaN, TimestampType), null) + checkEvaluation(cast(1.0 / 0.0, TimestampType), null) + checkEvaluation(cast(Float.NaN, TimestampType), null) + checkEvaluation(cast(1.0f / 0.0f, TimestampType), null) + } + + test("cast from array") { + val array = Literal.create(Seq("123", "abc", "", null), + ArrayType(StringType, containsNull = true)) + val array_notNull = Literal.create(Seq("123", "abc", ""), + ArrayType(StringType, containsNull = false)) + + { + val ret = cast(array, ArrayType(IntegerType, containsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Seq(123, null, null, null)) + } + { + val ret = cast(array, ArrayType(IntegerType, containsNull = false)) + assert(ret.resolved === false) + } + { + val ret = cast(array, ArrayType(BooleanType, containsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Seq(true, true, false, null)) + } + { + val ret = cast(array, ArrayType(BooleanType, containsNull = false)) + assert(ret.resolved === false) + } + + { + val ret = cast(array_notNull, ArrayType(IntegerType, containsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Seq(123, null, null)) + } + { + val ret = cast(array_notNull, ArrayType(IntegerType, containsNull = false)) + assert(ret.resolved === false) + } + { + val ret = cast(array_notNull, ArrayType(BooleanType, containsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Seq(true, true, false)) + } + { + val ret = cast(array_notNull, ArrayType(BooleanType, containsNull = false)) + assert(ret.resolved === true) + checkEvaluation(ret, Seq(true, true, false)) + } + + { + val ret = cast(array, IntegerType) + assert(ret.resolved === false) + } + } + + test("cast from map") { + val map = Literal.create( + Map("a" -> "123", "b" -> "abc", "c" -> "", "d" -> null), + MapType(StringType, StringType, valueContainsNull = true)) + val map_notNull = Literal.create( + Map("a" -> "123", "b" -> "abc", "c" -> ""), + MapType(StringType, StringType, valueContainsNull = false)) + + { + val ret = cast(map, MapType(StringType, IntegerType, valueContainsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Map("a" -> 123, "b" -> null, "c" -> null, "d" -> null)) + } + { + val ret = cast(map, MapType(StringType, IntegerType, valueContainsNull = false)) + assert(ret.resolved === false) + } + { + val ret = cast(map, MapType(StringType, BooleanType, valueContainsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false, "d" -> null)) + } + { + val ret = cast(map, MapType(StringType, BooleanType, valueContainsNull = false)) + assert(ret.resolved === false) + } + { + val ret = cast(map, MapType(IntegerType, StringType, valueContainsNull = true)) + assert(ret.resolved === false) + } + + { + val ret = cast(map_notNull, MapType(StringType, IntegerType, valueContainsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Map("a" -> 123, "b" -> null, "c" -> null)) + } + { + val ret = cast(map_notNull, MapType(StringType, IntegerType, valueContainsNull = false)) + assert(ret.resolved === false) + } + { + val ret = cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = true)) + assert(ret.resolved === true) + checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false)) + } + { + val ret = cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = false)) + assert(ret.resolved === true) + checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false)) + } + { + val ret = cast(map_notNull, MapType(IntegerType, StringType, valueContainsNull = true)) + assert(ret.resolved === false) + } + + { + val ret = cast(map, IntegerType) + assert(ret.resolved === false) + } + } + + test("cast from struct") { + val struct = Literal.create( + InternalRow("123", "abc", "", null), + StructType(Seq( + StructField("a", StringType, nullable = true), + StructField("b", StringType, nullable = true), + StructField("c", StringType, nullable = true), + StructField("d", StringType, nullable = true)))) + val struct_notNull = Literal.create( + InternalRow("123", "abc", ""), + StructType(Seq( + StructField("a", StringType, nullable = false), + StructField("b", StringType, nullable = false), + StructField("c", StringType, nullable = false)))) + + { + val ret = cast(struct, StructType(Seq( + StructField("a", IntegerType, nullable = true), + StructField("b", IntegerType, nullable = true), + StructField("c", IntegerType, nullable = true), + StructField("d", IntegerType, nullable = true)))) + assert(ret.resolved === true) + checkEvaluation(ret, InternalRow(123, null, null, null)) + } + { + val ret = cast(struct, StructType(Seq( + StructField("a", IntegerType, nullable = true), + StructField("b", IntegerType, nullable = true), + StructField("c", IntegerType, nullable = false), + StructField("d", IntegerType, nullable = true)))) + assert(ret.resolved === false) + } + { + val ret = cast(struct, StructType(Seq( + StructField("a", BooleanType, nullable = true), + StructField("b", BooleanType, nullable = true), + StructField("c", BooleanType, nullable = true), + StructField("d", BooleanType, nullable = true)))) + assert(ret.resolved === true) + checkEvaluation(ret, InternalRow(true, true, false, null)) + } + { + val ret = cast(struct, StructType(Seq( + StructField("a", BooleanType, nullable = true), + StructField("b", BooleanType, nullable = true), + StructField("c", BooleanType, nullable = false), + StructField("d", BooleanType, nullable = true)))) + assert(ret.resolved === false) + } + + { + val ret = cast(struct_notNull, StructType(Seq( + StructField("a", IntegerType, nullable = true), + StructField("b", IntegerType, nullable = true), + StructField("c", IntegerType, nullable = true)))) + assert(ret.resolved === true) + checkEvaluation(ret, InternalRow(123, null, null)) + } + { + val ret = cast(struct_notNull, StructType(Seq( + StructField("a", IntegerType, nullable = true), + StructField("b", IntegerType, nullable = true), + StructField("c", IntegerType, nullable = false)))) + assert(ret.resolved === false) + } + { + val ret = cast(struct_notNull, StructType(Seq( + StructField("a", BooleanType, nullable = true), + StructField("b", BooleanType, nullable = true), + StructField("c", BooleanType, nullable = true)))) + assert(ret.resolved === true) + checkEvaluation(ret, InternalRow(true, true, false)) + } + { + val ret = cast(struct_notNull, StructType(Seq( + StructField("a", BooleanType, nullable = true), + StructField("b", BooleanType, nullable = true), + StructField("c", BooleanType, nullable = false)))) + assert(ret.resolved === true) + checkEvaluation(ret, InternalRow(true, true, false)) + } + + { + val ret = cast(struct, StructType(Seq( + StructField("a", StringType, nullable = true), + StructField("b", StringType, nullable = true), + StructField("c", StringType, nullable = true)))) + assert(ret.resolved === false) + } + { + val ret = cast(struct, IntegerType) + assert(ret.resolved === false) + } + } + + test("complex casting") { + val complex = Literal.create( + InternalRow( + Seq("123", "abc", ""), + Map("a" -> "123", "b" -> "abc", "c" -> ""), + InternalRow(0)), + StructType(Seq( + StructField("a", + ArrayType(StringType, containsNull = false), nullable = true), + StructField("m", + MapType(StringType, StringType, valueContainsNull = false), nullable = true), + StructField("s", + StructType(Seq( + StructField("i", IntegerType, nullable = true))))))) + + val ret = cast(complex, StructType(Seq( + StructField("a", + ArrayType(IntegerType, containsNull = true), nullable = true), + StructField("m", + MapType(StringType, BooleanType, valueContainsNull = false), nullable = true), + StructField("s", + StructType(Seq( + StructField("l", LongType, nullable = true))))))) + + assert(ret.resolved === true) + checkEvaluation(ret, InternalRow( + Seq(123, null, null), + Map("a" -> true, "b" -> true, "c" -> false), + InternalRow(0L))) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala similarity index 62% rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala index b5ebe4b38e337..481b335d15dfd 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala @@ -17,37 +17,14 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions.codegen._ /** - * Overrides our expression evaluation tests to use code generation for evaluation. + * Additional tests for code generation. */ -class GeneratedEvaluationSuite extends ExpressionEvaluationSuite { - override def checkEvaluation( - expression: Expression, - expected: Any, - inputRow: Row = EmptyRow): Unit = { - val plan = try { - GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)() - } catch { - case e: Throwable => - val evaluated = GenerateProjection.expressionEvaluator(expression) - fail( - s""" - |Code generation of $expression failed: - |${evaluated.code.mkString("\n")} - |$e - """.stripMargin) - } - - val actual = plan(inputRow).apply(0) - if(actual != expected) { - val input = if(inputRow == EmptyRow) "" else s", input: $inputRow" - fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") - } - } - +class CodeGenerationSuite extends SparkFunSuite { test("multithreaded eval") { import scala.concurrent._ diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala new file mode 100644 index 0000000000000..2b0f4618b21e0 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala @@ -0,0 +1,123 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.analysis.UnresolvedExtractValue +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String + + +class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper { + + test("CreateStruct") { + val row = InternalRow(1, 2, 3) + val c1 = 'a.int.at(0).as("a") + val c3 = 'c.int.at(2).as("c") + checkEvaluation(CreateStruct(Seq(c1, c3)), InternalRow(1, 3), row) + } + + test("complex type") { + val row = create_row( + "^Ba*n", // 0 + null.asInstanceOf[UTF8String], // 1 + create_row("aa", "bb"), // 2 + Map("aa"->"bb"), // 3 + Seq("aa", "bb") // 4 + ) + + val typeS = StructType( + StructField("a", StringType, true) :: StructField("b", StringType, true) :: Nil + ) + val typeMap = MapType(StringType, StringType) + val typeArray = ArrayType(StringType) + + checkEvaluation(GetMapValue(BoundReference(3, typeMap, true), + Literal("aa")), "bb", row) + checkEvaluation(GetMapValue(Literal.create(null, typeMap), Literal("aa")), null, row) + checkEvaluation( + GetMapValue(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row) + checkEvaluation(GetMapValue(BoundReference(3, typeMap, true), + Literal.create(null, StringType)), null, row) + + checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true), + Literal(1)), "bb", row) + checkEvaluation(GetArrayItem(Literal.create(null, typeArray), Literal(1)), null, row) + checkEvaluation( + GetArrayItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row) + checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true), + Literal.create(null, IntegerType)), null, row) + + def getStructField(expr: Expression, fieldName: String): ExtractValue = { + expr.dataType match { + case StructType(fields) => + val field = fields.find(_.name == fieldName).get + GetStructField(expr, field, fields.indexOf(field)) + } + } + + def quickResolve(u: UnresolvedExtractValue): ExtractValue = { + ExtractValue(u.child, u.extraction, _ == _) + } + + checkEvaluation(getStructField(BoundReference(2, typeS, nullable = true), "a"), "aa", row) + checkEvaluation(getStructField(Literal.create(null, typeS), "a"), null, row) + + val typeS_notNullable = StructType( + StructField("a", StringType, nullable = false) + :: StructField("b", StringType, nullable = false) :: Nil + ) + + assert(getStructField(BoundReference(2, typeS, nullable = true), "a").nullable === true) + assert(getStructField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable + === false) + + assert(getStructField(Literal.create(null, typeS), "a").nullable === true) + assert(getStructField(Literal.create(null, typeS_notNullable), "a").nullable === true) + + checkEvaluation(quickResolve('c.map(typeMap).at(3).getItem("aa")), "bb", row) + checkEvaluation(quickResolve('c.array(typeArray.elementType).at(4).getItem(1)), "bb", row) + checkEvaluation(quickResolve('c.struct(typeS).at(2).getField("a")), "aa", row) + } + + test("error message of ExtractValue") { + val structType = StructType(StructField("a", StringType, true) :: Nil) + val arrayStructType = ArrayType(structType) + val arrayType = ArrayType(StringType) + val otherType = StringType + + def checkErrorMessage( + childDataType: DataType, + fieldDataType: DataType, + errorMesage: String): Unit = { + val e = intercept[org.apache.spark.sql.AnalysisException] { + ExtractValue( + Literal.create(null, childDataType), + Literal.create(null, fieldDataType), + _ == _) + } + assert(e.getMessage().contains(errorMesage)) + } + + checkErrorMessage(structType, IntegerType, "Field name should be String Literal") + checkErrorMessage(arrayStructType, BooleanType, "Field name should be String Literal") + checkErrorMessage(arrayType, StringType, "Array index should be integral type") + checkErrorMessage(otherType, StringType, "Can't extract value from") + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala new file mode 100644 index 0000000000000..372848ea9a596 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala @@ -0,0 +1,137 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types._ + + +class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper { + + test("if") { + val testcases = Seq[(java.lang.Boolean, Integer, Integer, Integer)]( + (true, 1, 2, 1), + (false, 1, 2, 2), + (null, 1, 2, 2), + (true, null, 2, null), + (false, 1, null, null), + (null, null, 2, 2), + (null, 1, null, null) + ) + + // dataType must match T. + def testIf(convert: (Integer => Any), dataType: DataType): Unit = { + for ((predicate, trueValue, falseValue, expected) <- testcases) { + val trueValueConverted = if (trueValue == null) null else convert(trueValue) + val falseValueConverted = if (falseValue == null) null else convert(falseValue) + val expectedConverted = if (expected == null) null else convert(expected) + + checkEvaluation( + If(Literal.create(predicate, BooleanType), + Literal.create(trueValueConverted, dataType), + Literal.create(falseValueConverted, dataType)), + expectedConverted) + } + } + + testIf(_ == 1, BooleanType) + testIf(_.toShort, ShortType) + testIf(identity, IntegerType) + testIf(_.toLong, LongType) + + testIf(_.toFloat, FloatType) + testIf(_.toDouble, DoubleType) + testIf(Decimal(_), DecimalType.Unlimited) + + testIf(identity, DateType) + testIf(_.toLong, TimestampType) + + testIf(_.toString, StringType) + } + + test("case when") { + val row = create_row(null, false, true, "a", "b", "c") + val c1 = 'a.boolean.at(0) + val c2 = 'a.boolean.at(1) + val c3 = 'a.boolean.at(2) + val c4 = 'a.string.at(3) + val c5 = 'a.string.at(4) + val c6 = 'a.string.at(5) + + checkEvaluation(CaseWhen(Seq(c1, c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(c2, c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(c3, c4, c6)), "a", row) + checkEvaluation(CaseWhen(Seq(Literal.create(null, BooleanType), c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(Literal.create(false, BooleanType), c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(Literal.create(true, BooleanType), c4, c6)), "a", row) + + checkEvaluation(CaseWhen(Seq(c3, c4, c2, c5, c6)), "a", row) + checkEvaluation(CaseWhen(Seq(c2, c4, c3, c5, c6)), "b", row) + checkEvaluation(CaseWhen(Seq(c1, c4, c2, c5, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(c1, c4, c2, c5)), null, row) + + assert(CaseWhen(Seq(c2, c4, c6)).nullable === true) + assert(CaseWhen(Seq(c2, c4, c3, c5, c6)).nullable === true) + assert(CaseWhen(Seq(c2, c4, c3, c5)).nullable === true) + + val c4_notNull = 'a.boolean.notNull.at(3) + val c5_notNull = 'a.boolean.notNull.at(4) + val c6_notNull = 'a.boolean.notNull.at(5) + + assert(CaseWhen(Seq(c2, c4_notNull, c6_notNull)).nullable === false) + assert(CaseWhen(Seq(c2, c4, c6_notNull)).nullable === true) + assert(CaseWhen(Seq(c2, c4_notNull, c6)).nullable === true) + + assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull, c6_notNull)).nullable === false) + assert(CaseWhen(Seq(c2, c4, c3, c5_notNull, c6_notNull)).nullable === true) + assert(CaseWhen(Seq(c2, c4_notNull, c3, c5, c6_notNull)).nullable === true) + assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull, c6)).nullable === true) + + assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull)).nullable === true) + assert(CaseWhen(Seq(c2, c4, c3, c5_notNull)).nullable === true) + assert(CaseWhen(Seq(c2, c4_notNull, c3, c5)).nullable === true) + } + + test("case key when") { + val row = create_row(null, 1, 2, "a", "b", "c") + val c1 = 'a.int.at(0) + val c2 = 'a.int.at(1) + val c3 = 'a.int.at(2) + val c4 = 'a.string.at(3) + val c5 = 'a.string.at(4) + val c6 = 'a.string.at(5) + + val literalNull = Literal.create(null, IntegerType) + val literalInt = Literal(1) + val literalString = Literal("a") + + checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, c5)), "b", row) + checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, literalNull, c5, c6)), "b", row) + checkEvaluation(CaseKeyWhen(c2, Seq(literalInt, c4, c5)), "a", row) + checkEvaluation(CaseKeyWhen(c2, Seq(c1, c4, c5)), "b", row) + checkEvaluation(CaseKeyWhen(c4, Seq(literalString, c2, c3)), 1, row) + checkEvaluation(CaseKeyWhen(c4, Seq(c6, c3, c5, c2, Literal(3))), 3, row) + + checkEvaluation(CaseKeyWhen(literalInt, Seq(c2, c4, c5)), "a", row) + checkEvaluation(CaseKeyWhen(literalString, Seq(c5, c2, c4, c3)), 2, row) + checkEvaluation(CaseKeyWhen(c6, Seq(c5, c2, c4, c3)), null, row) + checkEvaluation(CaseKeyWhen(literalNull, Seq(c2, c5, c1, c6)), "c", row) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala new file mode 100644 index 0000000000000..12d2da8b33986 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala @@ -0,0 +1,146 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.scalactic.TripleEqualsSupport.Spread +import org.scalatest.Matchers._ + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateProjection, GenerateMutableProjection} +import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer +import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project} + +/** + * A few helper functions for expression evaluation testing. Mixin this trait to use them. + */ +trait ExpressionEvalHelper { + self: SparkFunSuite => + + protected def create_row(values: Any*): InternalRow = { + new GenericRow(values.map(CatalystTypeConverters.convertToCatalyst).toArray) + } + + protected def checkEvaluation( + expression: Expression, expected: Any, inputRow: InternalRow = EmptyRow): Unit = { + checkEvaluationWithoutCodegen(expression, expected, inputRow) + checkEvaluationWithGeneratedMutableProjection(expression, expected, inputRow) + checkEvaluationWithGeneratedProjection(expression, expected, inputRow) + checkEvaluationWithOptimization(expression, expected, inputRow) + } + + protected def evaluate(expression: Expression, inputRow: InternalRow = EmptyRow): Any = { + expression.eval(inputRow) + } + + protected def checkEvaluationWithoutCodegen( + expression: Expression, + expected: Any, + inputRow: InternalRow = EmptyRow): Unit = { + val actual = try evaluate(expression, inputRow) catch { + case e: Exception => fail(s"Exception evaluating $expression", e) + } + if (actual != expected) { + val input = if (inputRow == EmptyRow) "" else s", input: $inputRow" + fail(s"Incorrect evaluation (codegen off): $expression, " + + s"actual: $actual, " + + s"expected: $expected$input") + } + } + + protected def checkEvaluationWithGeneratedMutableProjection( + expression: Expression, + expected: Any, + inputRow: InternalRow = EmptyRow): Unit = { + + val plan = try { + GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)() + } catch { + case e: Throwable => + val ctx = GenerateProjection.newCodeGenContext() + val evaluated = expression.gen(ctx) + fail( + s""" + |Code generation of $expression failed: + |${evaluated.code} + |$e + """.stripMargin) + } + + val actual = plan(inputRow).apply(0) + if (actual != expected) { + val input = if (inputRow == EmptyRow) "" else s", input: $inputRow" + fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") + } + } + + protected def checkEvaluationWithGeneratedProjection( + expression: Expression, + expected: Any, + inputRow: InternalRow = EmptyRow): Unit = { + val ctx = GenerateProjection.newCodeGenContext() + lazy val evaluated = expression.gen(ctx) + + val plan = try { + GenerateProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil) + } catch { + case e: Throwable => + fail( + s""" + |Code generation of $expression failed: + |${evaluated.code} + |$e + """.stripMargin) + } + + val actual = plan(inputRow) + val expectedRow = new GenericRow(Array[Any](CatalystTypeConverters.convertToCatalyst(expected))) + if (actual.hashCode() != expectedRow.hashCode()) { + fail( + s""" + |Mismatched hashCodes for values: $actual, $expectedRow + |Hash Codes: ${actual.hashCode()} != ${expectedRow.hashCode()} + |Expressions: $expression + |Code: $evaluated + """.stripMargin) + } + if (actual != expectedRow) { + val input = if (inputRow == EmptyRow) "" else s", input: $inputRow" + fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") + } + } + + protected def checkEvaluationWithOptimization( + expression: Expression, + expected: Any, + inputRow: InternalRow = EmptyRow): Unit = { + val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation) + val optimizedPlan = DefaultOptimizer.execute(plan) + checkEvaluationWithoutCodegen(optimizedPlan.expressions.head, expected, inputRow) + } + + protected def checkDoubleEvaluation( + expression: Expression, + expected: Spread[Double], + inputRow: InternalRow = EmptyRow): Unit = { + val actual = try evaluate(expression, inputRow) catch { + case e: Exception => fail(s"Exception evaluating $expression", e) + } + actual.asInstanceOf[Double] shouldBe expected + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala deleted file mode 100644 index 04fd261d16aa3..0000000000000 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ /dev/null @@ -1,1370 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst.expressions - -import java.sql.{Date, Timestamp} - -import scala.collection.immutable.HashSet - -import org.scalactic.TripleEqualsSupport.Spread -import org.scalatest.FunSuite -import org.scalatest.Matchers._ - -import org.apache.spark.sql.catalyst.CatalystTypeConverters -import org.apache.spark.sql.catalyst.analysis.UnresolvedExtractValue -import org.apache.spark.sql.catalyst.dsl.expressions._ -import org.apache.spark.sql.catalyst.expressions.mathfuncs._ -import org.apache.spark.sql.types._ - - -class ExpressionEvaluationBaseSuite extends FunSuite { - - def evaluate(expression: Expression, inputRow: Row = EmptyRow): Any = { - expression.eval(inputRow) - } - - def checkEvaluation(expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { - val actual = try evaluate(expression, inputRow) catch { - case e: Exception => fail(s"Exception evaluating $expression", e) - } - if(actual != expected) { - val input = if(inputRow == EmptyRow) "" else s", input: $inputRow" - fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") - } - } - - def checkDoubleEvaluation( - expression: Expression, - expected: Spread[Double], - inputRow: Row = EmptyRow): Unit = { - val actual = try evaluate(expression, inputRow) catch { - case e: Exception => fail(s"Exception evaluating $expression", e) - } - actual.asInstanceOf[Double] shouldBe expected - } -} - -class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { - - def create_row(values: Any*): Row = { - new GenericRow(values.map(CatalystTypeConverters.convertToCatalyst).toArray) - } - - test("literals") { - checkEvaluation(Literal(1), 1) - checkEvaluation(Literal(true), true) - checkEvaluation(Literal(0L), 0L) - checkEvaluation(Literal("test"), "test") - checkEvaluation(Literal(1) + Literal(1), 2) - } - - test("unary BitwiseNOT") { - checkEvaluation(BitwiseNot(1), -2) - assert(BitwiseNot(1).dataType === IntegerType) - assert(BitwiseNot(1).eval(EmptyRow).isInstanceOf[Int]) - checkEvaluation(BitwiseNot(1.toLong), -2.toLong) - assert(BitwiseNot(1.toLong).dataType === LongType) - assert(BitwiseNot(1.toLong).eval(EmptyRow).isInstanceOf[Long]) - checkEvaluation(BitwiseNot(1.toShort), -2.toShort) - assert(BitwiseNot(1.toShort).dataType === ShortType) - assert(BitwiseNot(1.toShort).eval(EmptyRow).isInstanceOf[Short]) - checkEvaluation(BitwiseNot(1.toByte), -2.toByte) - assert(BitwiseNot(1.toByte).dataType === ByteType) - assert(BitwiseNot(1.toByte).eval(EmptyRow).isInstanceOf[Byte]) - } - - // scalastyle:off - /** - * Checks for three-valued-logic. Based on: - * http://en.wikipedia.org/wiki/Null_(SQL)#Comparisons_with_NULL_and_the_three-valued_logic_.283VL.29 - * I.e. in flat cpo "False -> Unknown -> True", - * OR is lowest upper bound, - * AND is greatest lower bound. - * p q p OR q p AND q p = q - * True True True True True - * True False True False False - * True Unknown True Unknown Unknown - * False True True False False - * False False False False True - * False Unknown Unknown False Unknown - * Unknown True True Unknown Unknown - * Unknown False Unknown False Unknown - * Unknown Unknown Unknown Unknown Unknown - * - * p NOT p - * True False - * False True - * Unknown Unknown - */ - // scalastyle:on - val notTrueTable = - (true, false) :: - (false, true) :: - (null, null) :: Nil - - test("3VL Not") { - notTrueTable.foreach { - case (v, answer) => - checkEvaluation(!Literal.create(v, BooleanType), answer) - } - } - - booleanLogicTest("AND", _ && _, - (true, true, true) :: - (true, false, false) :: - (true, null, null) :: - (false, true, false) :: - (false, false, false) :: - (false, null, false) :: - (null, true, null) :: - (null, false, false) :: - (null, null, null) :: Nil) - - booleanLogicTest("OR", _ || _, - (true, true, true) :: - (true, false, true) :: - (true, null, true) :: - (false, true, true) :: - (false, false, false) :: - (false, null, null) :: - (null, true, true) :: - (null, false, null) :: - (null, null, null) :: Nil) - - booleanLogicTest("=", _ === _, - (true, true, true) :: - (true, false, false) :: - (true, null, null) :: - (false, true, false) :: - (false, false, true) :: - (false, null, null) :: - (null, true, null) :: - (null, false, null) :: - (null, null, null) :: Nil) - - def booleanLogicTest( - name: String, - op: (Expression, Expression) => Expression, - truthTable: Seq[(Any, Any, Any)]) { - test(s"3VL $name") { - truthTable.foreach { - case (l,r,answer) => - val expr = op(Literal.create(l, BooleanType), Literal.create(r, BooleanType)) - checkEvaluation(expr, answer) - } - } - } - - test("IN") { - checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true) - checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true) - checkEvaluation(In(Literal(3), Seq(Literal(1), Literal(2))), false) - checkEvaluation( - In(Literal(1), Seq(Literal(1), Literal(2))) && In(Literal(2), Seq(Literal(1), Literal(2))), - true) - } - - test("Divide") { - checkEvaluation(Divide(Literal(2), Literal(1)), 2) - checkEvaluation(Divide(Literal(1.0), Literal(2.0)), 0.5) - checkEvaluation(Divide(Literal(1), Literal(2)), 0) - checkEvaluation(Divide(Literal(1), Literal(0)), null) - checkEvaluation(Divide(Literal(1.0), Literal(0.0)), null) - checkEvaluation(Divide(Literal(0.0), Literal(0.0)), null) - checkEvaluation(Divide(Literal(0), Literal.create(null, IntegerType)), null) - checkEvaluation(Divide(Literal(1), Literal.create(null, IntegerType)), null) - checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(0)), null) - checkEvaluation(Divide(Literal.create(null, DoubleType), Literal(0.0)), null) - checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(1)), null) - checkEvaluation(Divide(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), - null) - } - - test("Remainder") { - checkEvaluation(Remainder(Literal(2), Literal(1)), 0) - checkEvaluation(Remainder(Literal(1.0), Literal(2.0)), 1.0) - checkEvaluation(Remainder(Literal(1), Literal(2)), 1) - checkEvaluation(Remainder(Literal(1), Literal(0)), null) - checkEvaluation(Remainder(Literal(1.0), Literal(0.0)), null) - checkEvaluation(Remainder(Literal(0.0), Literal(0.0)), null) - checkEvaluation(Remainder(Literal(0), Literal.create(null, IntegerType)), null) - checkEvaluation(Remainder(Literal(1), Literal.create(null, IntegerType)), null) - checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(0)), null) - checkEvaluation(Remainder(Literal.create(null, DoubleType), Literal(0.0)), null) - checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(1)), null) - checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), - null) - } - - test("INSET") { - val hS = HashSet[Any]() + 1 + 2 - val nS = HashSet[Any]() + 1 + 2 + null - val one = Literal(1) - val two = Literal(2) - val three = Literal(3) - val nl = Literal(null) - val s = Seq(one, two) - val nullS = Seq(one, two, null) - checkEvaluation(InSet(one, hS), true) - checkEvaluation(InSet(two, hS), true) - checkEvaluation(InSet(two, nS), true) - checkEvaluation(InSet(nl, nS), true) - checkEvaluation(InSet(three, hS), false) - checkEvaluation(InSet(three, nS), false) - checkEvaluation(InSet(one, hS) && InSet(two, hS), true) - } - - test("MaxOf") { - checkEvaluation(MaxOf(1, 2), 2) - checkEvaluation(MaxOf(2, 1), 2) - checkEvaluation(MaxOf(1L, 2L), 2L) - checkEvaluation(MaxOf(2L, 1L), 2L) - - checkEvaluation(MaxOf(Literal.create(null, IntegerType), 2), 2) - checkEvaluation(MaxOf(2, Literal.create(null, IntegerType)), 2) - } - - test("MinOf") { - checkEvaluation(MinOf(1, 2), 1) - checkEvaluation(MinOf(2, 1), 1) - checkEvaluation(MinOf(1L, 2L), 1L) - checkEvaluation(MinOf(2L, 1L), 1L) - - checkEvaluation(MinOf(Literal.create(null, IntegerType), 1), 1) - checkEvaluation(MinOf(1, Literal.create(null, IntegerType)), 1) - } - - test("LIKE literal Regular Expression") { - checkEvaluation(Literal.create(null, StringType).like("a"), null) - checkEvaluation(Literal.create("a", StringType).like(Literal.create(null, StringType)), null) - checkEvaluation(Literal.create(null, StringType).like(Literal.create(null, StringType)), null) - checkEvaluation("abdef" like "abdef", true) - checkEvaluation("a_%b" like "a\\__b", true) - checkEvaluation("addb" like "a_%b", true) - checkEvaluation("addb" like "a\\__b", false) - checkEvaluation("addb" like "a%\\%b", false) - checkEvaluation("a_%b" like "a%\\%b", true) - checkEvaluation("addb" like "a%", true) - checkEvaluation("addb" like "**", false) - checkEvaluation("abc" like "a%", true) - checkEvaluation("abc" like "b%", false) - checkEvaluation("abc" like "bc%", false) - checkEvaluation("a\nb" like "a_b", true) - checkEvaluation("ab" like "a%b", true) - checkEvaluation("a\nb" like "a%b", true) - } - - test("LIKE Non-literal Regular Expression") { - val regEx = 'a.string.at(0) - checkEvaluation("abcd" like regEx, null, create_row(null)) - checkEvaluation("abdef" like regEx, true, create_row("abdef")) - checkEvaluation("a_%b" like regEx, true, create_row("a\\__b")) - checkEvaluation("addb" like regEx, true, create_row("a_%b")) - checkEvaluation("addb" like regEx, false, create_row("a\\__b")) - checkEvaluation("addb" like regEx, false, create_row("a%\\%b")) - checkEvaluation("a_%b" like regEx, true, create_row("a%\\%b")) - checkEvaluation("addb" like regEx, true, create_row("a%")) - checkEvaluation("addb" like regEx, false, create_row("**")) - checkEvaluation("abc" like regEx, true, create_row("a%")) - checkEvaluation("abc" like regEx, false, create_row("b%")) - checkEvaluation("abc" like regEx, false, create_row("bc%")) - checkEvaluation("a\nb" like regEx, true, create_row("a_b")) - checkEvaluation("ab" like regEx, true, create_row("a%b")) - checkEvaluation("a\nb" like regEx, true, create_row("a%b")) - - checkEvaluation(Literal.create(null, StringType) like regEx, null, create_row("bc%")) - } - - test("RLIKE literal Regular Expression") { - checkEvaluation(Literal.create(null, StringType) rlike "abdef", null) - checkEvaluation("abdef" rlike Literal.create(null, StringType), null) - checkEvaluation(Literal.create(null, StringType) rlike Literal.create(null, StringType), null) - checkEvaluation("abdef" rlike "abdef", true) - checkEvaluation("abbbbc" rlike "a.*c", true) - - checkEvaluation("fofo" rlike "^fo", true) - checkEvaluation("fo\no" rlike "^fo\no$", true) - checkEvaluation("Bn" rlike "^Ba*n", true) - checkEvaluation("afofo" rlike "fo", true) - checkEvaluation("afofo" rlike "^fo", false) - checkEvaluation("Baan" rlike "^Ba?n", false) - checkEvaluation("axe" rlike "pi|apa", false) - checkEvaluation("pip" rlike "^(pi)*$", false) - - checkEvaluation("abc" rlike "^ab", true) - checkEvaluation("abc" rlike "^bc", false) - checkEvaluation("abc" rlike "^ab", true) - checkEvaluation("abc" rlike "^bc", false) - - intercept[java.util.regex.PatternSyntaxException] { - evaluate("abbbbc" rlike "**") - } - } - - test("RLIKE Non-literal Regular Expression") { - val regEx = 'a.string.at(0) - checkEvaluation("abdef" rlike regEx, true, create_row("abdef")) - checkEvaluation("abbbbc" rlike regEx, true, create_row("a.*c")) - checkEvaluation("fofo" rlike regEx, true, create_row("^fo")) - checkEvaluation("fo\no" rlike regEx, true, create_row("^fo\no$")) - checkEvaluation("Bn" rlike regEx, true, create_row("^Ba*n")) - - intercept[java.util.regex.PatternSyntaxException] { - evaluate("abbbbc" rlike regEx, create_row("**")) - } - } - - test("data type casting") { - - val sd = "1970-01-01" - val d = Date.valueOf(sd) - val zts = sd + " 00:00:00" - val sts = sd + " 00:00:02" - val nts = sts + ".1" - val ts = Timestamp.valueOf(nts) - - checkEvaluation("abdef" cast StringType, "abdef") - checkEvaluation("abdef" cast DecimalType.Unlimited, null) - checkEvaluation("abdef" cast TimestampType, null) - checkEvaluation("12.65" cast DecimalType.Unlimited, Decimal(12.65)) - - checkEvaluation(Literal(1) cast LongType, 1) - checkEvaluation(Cast(Literal(1000) cast TimestampType, LongType), 1.toLong) - checkEvaluation(Cast(Literal(-1200) cast TimestampType, LongType), -2.toLong) - checkEvaluation(Cast(Literal(1.toDouble) cast TimestampType, DoubleType), 1.toDouble) - checkEvaluation(Cast(Literal(1.toDouble) cast TimestampType, DoubleType), 1.toDouble) - - checkEvaluation(Cast(Literal(sd) cast DateType, StringType), sd) - checkEvaluation(Cast(Literal(d) cast StringType, DateType), 0) - checkEvaluation(Cast(Literal(nts) cast TimestampType, StringType), nts) - checkEvaluation(Cast(Literal(ts) cast StringType, TimestampType), ts) - // all convert to string type to check - checkEvaluation( - Cast(Cast(Literal(nts) cast TimestampType, DateType), StringType), sd) - checkEvaluation( - Cast(Cast(Literal(ts) cast DateType, TimestampType), StringType), zts) - - checkEvaluation(Cast("abdef" cast BinaryType, StringType), "abdef") - - checkEvaluation(Cast(Cast(Cast(Cast( - Cast("5" cast ByteType, ShortType), IntegerType), FloatType), DoubleType), LongType), 5) - checkEvaluation(Cast(Cast(Cast(Cast(Cast("5" cast - ByteType, TimestampType), DecimalType.Unlimited), LongType), StringType), ShortType), 0) - checkEvaluation(Cast(Cast(Cast(Cast(Cast("5" cast - TimestampType, ByteType), DecimalType.Unlimited), LongType), StringType), ShortType), null) - checkEvaluation(Cast(Cast(Cast(Cast(Cast("5" cast - DecimalType.Unlimited, ByteType), TimestampType), LongType), StringType), ShortType), 0) - checkEvaluation(Literal(true) cast IntegerType, 1) - checkEvaluation(Literal(false) cast IntegerType, 0) - checkEvaluation(Cast(Literal(1) cast BooleanType, IntegerType), 1) - checkEvaluation(Cast(Literal(0) cast BooleanType, IntegerType), 0) - checkEvaluation("23" cast DoubleType, 23d) - checkEvaluation("23" cast IntegerType, 23) - checkEvaluation("23" cast FloatType, 23f) - checkEvaluation("23" cast DecimalType.Unlimited, Decimal(23)) - checkEvaluation("23" cast ByteType, 23.toByte) - checkEvaluation("23" cast ShortType, 23.toShort) - checkEvaluation("2012-12-11" cast DoubleType, null) - checkEvaluation(Literal(123) cast IntegerType, 123) - - checkEvaluation(Literal(23d) + Cast(true, DoubleType), 24d) - checkEvaluation(Literal(23) + Cast(true, IntegerType), 24) - checkEvaluation(Literal(23f) + Cast(true, FloatType), 24f) - checkEvaluation(Literal(Decimal(23)) + Cast(true, DecimalType.Unlimited), Decimal(24)) - checkEvaluation(Literal(23.toByte) + Cast(true, ByteType), 24.toByte) - checkEvaluation(Literal(23.toShort) + Cast(true, ShortType), 24.toShort) - - intercept[Exception] {evaluate(Literal(1) cast BinaryType, null)} - - assert(("abcdef" cast StringType).nullable === false) - assert(("abcdef" cast BinaryType).nullable === false) - assert(("abcdef" cast BooleanType).nullable === false) - assert(("abcdef" cast TimestampType).nullable === true) - assert(("abcdef" cast LongType).nullable === true) - assert(("abcdef" cast IntegerType).nullable === true) - assert(("abcdef" cast ShortType).nullable === true) - assert(("abcdef" cast ByteType).nullable === true) - assert(("abcdef" cast DecimalType.Unlimited).nullable === true) - assert(("abcdef" cast DecimalType(4, 2)).nullable === true) - assert(("abcdef" cast DoubleType).nullable === true) - assert(("abcdef" cast FloatType).nullable === true) - - checkEvaluation(Cast(Literal.create(null, IntegerType), ShortType), null) - } - - test("date") { - val d1 = DateUtils.fromJavaDate(Date.valueOf("1970-01-01")) - val d2 = DateUtils.fromJavaDate(Date.valueOf("1970-01-02")) - checkEvaluation(Literal(d1) < Literal(d2), true) - } - - test("casting to fixed-precision decimals") { - // Overflow and rounding for casting to fixed-precision decimals: - // - Values should round with HALF_UP mode by default when you lower scale - // - Values that would overflow the target precision should turn into null - // - Because of this, casts to fixed-precision decimals should be nullable - - assert(Cast(Literal(123), DecimalType.Unlimited).nullable === false) - assert(Cast(Literal(10.03f), DecimalType.Unlimited).nullable === true) - assert(Cast(Literal(10.03), DecimalType.Unlimited).nullable === true) - assert(Cast(Literal(Decimal(10.03)), DecimalType.Unlimited).nullable === false) - - assert(Cast(Literal(123), DecimalType(2, 1)).nullable === true) - assert(Cast(Literal(10.03f), DecimalType(2, 1)).nullable === true) - assert(Cast(Literal(10.03), DecimalType(2, 1)).nullable === true) - assert(Cast(Literal(Decimal(10.03)), DecimalType(2, 1)).nullable === true) - - checkEvaluation(Cast(Literal(123), DecimalType.Unlimited), Decimal(123)) - checkEvaluation(Cast(Literal(123), DecimalType(3, 0)), Decimal(123)) - checkEvaluation(Cast(Literal(123), DecimalType(3, 1)), null) - checkEvaluation(Cast(Literal(123), DecimalType(2, 0)), null) - - checkEvaluation(Cast(Literal(10.03), DecimalType.Unlimited), Decimal(10.03)) - checkEvaluation(Cast(Literal(10.03), DecimalType(4, 2)), Decimal(10.03)) - checkEvaluation(Cast(Literal(10.03), DecimalType(3, 1)), Decimal(10.0)) - checkEvaluation(Cast(Literal(10.03), DecimalType(2, 0)), Decimal(10)) - checkEvaluation(Cast(Literal(10.03), DecimalType(1, 0)), null) - checkEvaluation(Cast(Literal(10.03), DecimalType(2, 1)), null) - checkEvaluation(Cast(Literal(10.03), DecimalType(3, 2)), null) - checkEvaluation(Cast(Literal(Decimal(10.03)), DecimalType(3, 1)), Decimal(10.0)) - checkEvaluation(Cast(Literal(Decimal(10.03)), DecimalType(3, 2)), null) - - checkEvaluation(Cast(Literal(10.05), DecimalType.Unlimited), Decimal(10.05)) - checkEvaluation(Cast(Literal(10.05), DecimalType(4, 2)), Decimal(10.05)) - checkEvaluation(Cast(Literal(10.05), DecimalType(3, 1)), Decimal(10.1)) - checkEvaluation(Cast(Literal(10.05), DecimalType(2, 0)), Decimal(10)) - checkEvaluation(Cast(Literal(10.05), DecimalType(1, 0)), null) - checkEvaluation(Cast(Literal(10.05), DecimalType(2, 1)), null) - checkEvaluation(Cast(Literal(10.05), DecimalType(3, 2)), null) - checkEvaluation(Cast(Literal(Decimal(10.05)), DecimalType(3, 1)), Decimal(10.1)) - checkEvaluation(Cast(Literal(Decimal(10.05)), DecimalType(3, 2)), null) - - checkEvaluation(Cast(Literal(9.95), DecimalType(3, 2)), Decimal(9.95)) - checkEvaluation(Cast(Literal(9.95), DecimalType(3, 1)), Decimal(10.0)) - checkEvaluation(Cast(Literal(9.95), DecimalType(2, 0)), Decimal(10)) - checkEvaluation(Cast(Literal(9.95), DecimalType(2, 1)), null) - checkEvaluation(Cast(Literal(9.95), DecimalType(1, 0)), null) - checkEvaluation(Cast(Literal(Decimal(9.95)), DecimalType(3, 1)), Decimal(10.0)) - checkEvaluation(Cast(Literal(Decimal(9.95)), DecimalType(1, 0)), null) - - checkEvaluation(Cast(Literal(-9.95), DecimalType(3, 2)), Decimal(-9.95)) - checkEvaluation(Cast(Literal(-9.95), DecimalType(3, 1)), Decimal(-10.0)) - checkEvaluation(Cast(Literal(-9.95), DecimalType(2, 0)), Decimal(-10)) - checkEvaluation(Cast(Literal(-9.95), DecimalType(2, 1)), null) - checkEvaluation(Cast(Literal(-9.95), DecimalType(1, 0)), null) - checkEvaluation(Cast(Literal(Decimal(-9.95)), DecimalType(3, 1)), Decimal(-10.0)) - checkEvaluation(Cast(Literal(Decimal(-9.95)), DecimalType(1, 0)), null) - - checkEvaluation(Cast(Literal(Double.NaN), DecimalType.Unlimited), null) - checkEvaluation(Cast(Literal(1.0 / 0.0), DecimalType.Unlimited), null) - checkEvaluation(Cast(Literal(Float.NaN), DecimalType.Unlimited), null) - checkEvaluation(Cast(Literal(1.0f / 0.0f), DecimalType.Unlimited), null) - - checkEvaluation(Cast(Literal(Double.NaN), DecimalType(2, 1)), null) - checkEvaluation(Cast(Literal(1.0 / 0.0), DecimalType(2, 1)), null) - checkEvaluation(Cast(Literal(Float.NaN), DecimalType(2, 1)), null) - checkEvaluation(Cast(Literal(1.0f / 0.0f), DecimalType(2, 1)), null) - } - - test("timestamp") { - val ts1 = new Timestamp(12) - val ts2 = new Timestamp(123) - checkEvaluation(Literal("ab") < Literal("abc"), true) - checkEvaluation(Literal(ts1) < Literal(ts2), true) - } - - test("date casting") { - val d = Date.valueOf("1970-01-01") - checkEvaluation(Cast(Literal(d), ShortType), null) - checkEvaluation(Cast(Literal(d), IntegerType), null) - checkEvaluation(Cast(Literal(d), LongType), null) - checkEvaluation(Cast(Literal(d), FloatType), null) - checkEvaluation(Cast(Literal(d), DoubleType), null) - checkEvaluation(Cast(Literal(d), DecimalType.Unlimited), null) - checkEvaluation(Cast(Literal(d), DecimalType(10, 2)), null) - checkEvaluation(Cast(Literal(d), StringType), "1970-01-01") - checkEvaluation(Cast(Cast(Literal(d), TimestampType), StringType), "1970-01-01 00:00:00") - } - - test("timestamp casting") { - val millis = 15 * 1000 + 2 - val seconds = millis * 1000 + 2 - val ts = new Timestamp(millis) - val tss = new Timestamp(seconds) - checkEvaluation(Cast(ts, ShortType), 15) - checkEvaluation(Cast(ts, IntegerType), 15) - checkEvaluation(Cast(ts, LongType), 15) - checkEvaluation(Cast(ts, FloatType), 15.002f) - checkEvaluation(Cast(ts, DoubleType), 15.002) - checkEvaluation(Cast(Cast(tss, ShortType), TimestampType), ts) - checkEvaluation(Cast(Cast(tss, IntegerType), TimestampType), ts) - checkEvaluation(Cast(Cast(tss, LongType), TimestampType), ts) - checkEvaluation(Cast(Cast(millis.toFloat / 1000, TimestampType), FloatType), - millis.toFloat / 1000) - checkEvaluation(Cast(Cast(millis.toDouble / 1000, TimestampType), DoubleType), - millis.toDouble / 1000) - checkEvaluation(Cast(Literal(Decimal(1)) cast TimestampType, DecimalType.Unlimited), Decimal(1)) - - // A test for higher precision than millis - checkEvaluation(Cast(Cast(0.00000001, TimestampType), DoubleType), 0.00000001) - - checkEvaluation(Cast(Literal(Double.NaN), TimestampType), null) - checkEvaluation(Cast(Literal(1.0 / 0.0), TimestampType), null) - checkEvaluation(Cast(Literal(Float.NaN), TimestampType), null) - checkEvaluation(Cast(Literal(1.0f / 0.0f), TimestampType), null) - } - - test("array casting") { - val array = Literal.create(Seq("123", "abc", "", null), - ArrayType(StringType, containsNull = true)) - val array_notNull = Literal.create(Seq("123", "abc", ""), - ArrayType(StringType, containsNull = false)) - - { - val cast = Cast(array, ArrayType(IntegerType, containsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Seq(123, null, null, null)) - } - { - val cast = Cast(array, ArrayType(IntegerType, containsNull = false)) - assert(cast.resolved === false) - } - { - val cast = Cast(array, ArrayType(BooleanType, containsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Seq(true, true, false, null)) - } - { - val cast = Cast(array, ArrayType(BooleanType, containsNull = false)) - assert(cast.resolved === false) - } - - { - val cast = Cast(array_notNull, ArrayType(IntegerType, containsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Seq(123, null, null)) - } - { - val cast = Cast(array_notNull, ArrayType(IntegerType, containsNull = false)) - assert(cast.resolved === false) - } - { - val cast = Cast(array_notNull, ArrayType(BooleanType, containsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Seq(true, true, false)) - } - { - val cast = Cast(array_notNull, ArrayType(BooleanType, containsNull = false)) - assert(cast.resolved === true) - checkEvaluation(cast, Seq(true, true, false)) - } - - { - val cast = Cast(array, IntegerType) - assert(cast.resolved === false) - } - } - - test("map casting") { - val map = Literal.create( - Map("a" -> "123", "b" -> "abc", "c" -> "", "d" -> null), - MapType(StringType, StringType, valueContainsNull = true)) - val map_notNull = Literal.create( - Map("a" -> "123", "b" -> "abc", "c" -> ""), - MapType(StringType, StringType, valueContainsNull = false)) - - { - val cast = Cast(map, MapType(StringType, IntegerType, valueContainsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Map("a" -> 123, "b" -> null, "c" -> null, "d" -> null)) - } - { - val cast = Cast(map, MapType(StringType, IntegerType, valueContainsNull = false)) - assert(cast.resolved === false) - } - { - val cast = Cast(map, MapType(StringType, BooleanType, valueContainsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Map("a" -> true, "b" -> true, "c" -> false, "d" -> null)) - } - { - val cast = Cast(map, MapType(StringType, BooleanType, valueContainsNull = false)) - assert(cast.resolved === false) - } - { - val cast = Cast(map, MapType(IntegerType, StringType, valueContainsNull = true)) - assert(cast.resolved === false) - } - - { - val cast = Cast(map_notNull, MapType(StringType, IntegerType, valueContainsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Map("a" -> 123, "b" -> null, "c" -> null)) - } - { - val cast = Cast(map_notNull, MapType(StringType, IntegerType, valueContainsNull = false)) - assert(cast.resolved === false) - } - { - val cast = Cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = true)) - assert(cast.resolved === true) - checkEvaluation(cast, Map("a" -> true, "b" -> true, "c" -> false)) - } - { - val cast = Cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = false)) - assert(cast.resolved === true) - checkEvaluation(cast, Map("a" -> true, "b" -> true, "c" -> false)) - } - { - val cast = Cast(map_notNull, MapType(IntegerType, StringType, valueContainsNull = true)) - assert(cast.resolved === false) - } - - { - val cast = Cast(map, IntegerType) - assert(cast.resolved === false) - } - } - - test("struct casting") { - val struct = Literal.create( - Row("123", "abc", "", null), - StructType(Seq( - StructField("a", StringType, nullable = true), - StructField("b", StringType, nullable = true), - StructField("c", StringType, nullable = true), - StructField("d", StringType, nullable = true)))) - val struct_notNull = Literal.create( - Row("123", "abc", ""), - StructType(Seq( - StructField("a", StringType, nullable = false), - StructField("b", StringType, nullable = false), - StructField("c", StringType, nullable = false)))) - - { - val cast = Cast(struct, StructType(Seq( - StructField("a", IntegerType, nullable = true), - StructField("b", IntegerType, nullable = true), - StructField("c", IntegerType, nullable = true), - StructField("d", IntegerType, nullable = true)))) - assert(cast.resolved === true) - checkEvaluation(cast, Row(123, null, null, null)) - } - { - val cast = Cast(struct, StructType(Seq( - StructField("a", IntegerType, nullable = true), - StructField("b", IntegerType, nullable = true), - StructField("c", IntegerType, nullable = false), - StructField("d", IntegerType, nullable = true)))) - assert(cast.resolved === false) - } - { - val cast = Cast(struct, StructType(Seq( - StructField("a", BooleanType, nullable = true), - StructField("b", BooleanType, nullable = true), - StructField("c", BooleanType, nullable = true), - StructField("d", BooleanType, nullable = true)))) - assert(cast.resolved === true) - checkEvaluation(cast, Row(true, true, false, null)) - } - { - val cast = Cast(struct, StructType(Seq( - StructField("a", BooleanType, nullable = true), - StructField("b", BooleanType, nullable = true), - StructField("c", BooleanType, nullable = false), - StructField("d", BooleanType, nullable = true)))) - assert(cast.resolved === false) - } - - { - val cast = Cast(struct_notNull, StructType(Seq( - StructField("a", IntegerType, nullable = true), - StructField("b", IntegerType, nullable = true), - StructField("c", IntegerType, nullable = true)))) - assert(cast.resolved === true) - checkEvaluation(cast, Row(123, null, null)) - } - { - val cast = Cast(struct_notNull, StructType(Seq( - StructField("a", IntegerType, nullable = true), - StructField("b", IntegerType, nullable = true), - StructField("c", IntegerType, nullable = false)))) - assert(cast.resolved === false) - } - { - val cast = Cast(struct_notNull, StructType(Seq( - StructField("a", BooleanType, nullable = true), - StructField("b", BooleanType, nullable = true), - StructField("c", BooleanType, nullable = true)))) - assert(cast.resolved === true) - checkEvaluation(cast, Row(true, true, false)) - } - { - val cast = Cast(struct_notNull, StructType(Seq( - StructField("a", BooleanType, nullable = true), - StructField("b", BooleanType, nullable = true), - StructField("c", BooleanType, nullable = false)))) - assert(cast.resolved === true) - checkEvaluation(cast, Row(true, true, false)) - } - - { - val cast = Cast(struct, StructType(Seq( - StructField("a", StringType, nullable = true), - StructField("b", StringType, nullable = true), - StructField("c", StringType, nullable = true)))) - assert(cast.resolved === false) - } - { - val cast = Cast(struct, IntegerType) - assert(cast.resolved === false) - } - } - - test("complex casting") { - val complex = Literal.create( - Row( - Seq("123", "abc", ""), - Map("a" -> "123", "b" -> "abc", "c" -> ""), - Row(0)), - StructType(Seq( - StructField("a", - ArrayType(StringType, containsNull = false), nullable = true), - StructField("m", - MapType(StringType, StringType, valueContainsNull = false), nullable = true), - StructField("s", - StructType(Seq( - StructField("i", IntegerType, nullable = true))))))) - - val cast = Cast(complex, StructType(Seq( - StructField("a", - ArrayType(IntegerType, containsNull = true), nullable = true), - StructField("m", - MapType(StringType, BooleanType, valueContainsNull = false), nullable = true), - StructField("s", - StructType(Seq( - StructField("l", LongType, nullable = true))))))) - - assert(cast.resolved === true) - checkEvaluation(cast, Row( - Seq(123, null, null), - Map("a" -> true, "b" -> true, "c" -> false), - Row(0L))) - } - - test("null checking") { - val row = create_row("^Ba*n", null, true, null) - val c1 = 'a.string.at(0) - val c2 = 'a.string.at(1) - val c3 = 'a.boolean.at(2) - val c4 = 'a.boolean.at(3) - - checkEvaluation(c1.isNull, false, row) - checkEvaluation(c1.isNotNull, true, row) - - checkEvaluation(c2.isNull, true, row) - checkEvaluation(c2.isNotNull, false, row) - - checkEvaluation(Literal.create(1, ShortType).isNull, false) - checkEvaluation(Literal.create(1, ShortType).isNotNull, true) - - checkEvaluation(Literal.create(null, ShortType).isNull, true) - checkEvaluation(Literal.create(null, ShortType).isNotNull, false) - - checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row) - checkEvaluation(Coalesce(Literal.create(null, StringType) :: Nil), null, row) - checkEvaluation(Coalesce(Literal.create(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row) - - checkEvaluation( - If(c3, Literal.create("a", StringType), Literal.create("b", StringType)), "a", row) - checkEvaluation(If(c3, c1, c2), "^Ba*n", row) - checkEvaluation(If(c4, c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal.create(null, BooleanType), c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal.create(true, BooleanType), c1, c2), "^Ba*n", row) - checkEvaluation(If(Literal.create(false, BooleanType), c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal.create(false, BooleanType), - Literal.create("a", StringType), Literal.create("b", StringType)), "b", row) - - checkEvaluation(c1 in (c1, c2), true, row) - checkEvaluation( - Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType)), true, row) - checkEvaluation( - Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType), c2), true, row) - } - - test("case when") { - val row = create_row(null, false, true, "a", "b", "c") - val c1 = 'a.boolean.at(0) - val c2 = 'a.boolean.at(1) - val c3 = 'a.boolean.at(2) - val c4 = 'a.string.at(3) - val c5 = 'a.string.at(4) - val c6 = 'a.string.at(5) - - checkEvaluation(CaseWhen(Seq(c1, c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(c2, c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(c3, c4, c6)), "a", row) - checkEvaluation(CaseWhen(Seq(Literal.create(null, BooleanType), c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(Literal.create(false, BooleanType), c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(Literal.create(true, BooleanType), c4, c6)), "a", row) - - checkEvaluation(CaseWhen(Seq(c3, c4, c2, c5, c6)), "a", row) - checkEvaluation(CaseWhen(Seq(c2, c4, c3, c5, c6)), "b", row) - checkEvaluation(CaseWhen(Seq(c1, c4, c2, c5, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(c1, c4, c2, c5)), null, row) - - assert(CaseWhen(Seq(c2, c4, c6)).nullable === true) - assert(CaseWhen(Seq(c2, c4, c3, c5, c6)).nullable === true) - assert(CaseWhen(Seq(c2, c4, c3, c5)).nullable === true) - - val c4_notNull = 'a.boolean.notNull.at(3) - val c5_notNull = 'a.boolean.notNull.at(4) - val c6_notNull = 'a.boolean.notNull.at(5) - - assert(CaseWhen(Seq(c2, c4_notNull, c6_notNull)).nullable === false) - assert(CaseWhen(Seq(c2, c4, c6_notNull)).nullable === true) - assert(CaseWhen(Seq(c2, c4_notNull, c6)).nullable === true) - - assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull, c6_notNull)).nullable === false) - assert(CaseWhen(Seq(c2, c4, c3, c5_notNull, c6_notNull)).nullable === true) - assert(CaseWhen(Seq(c2, c4_notNull, c3, c5, c6_notNull)).nullable === true) - assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull, c6)).nullable === true) - - assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull)).nullable === true) - assert(CaseWhen(Seq(c2, c4, c3, c5_notNull)).nullable === true) - assert(CaseWhen(Seq(c2, c4_notNull, c3, c5)).nullable === true) - } - - test("case key when") { - val row = create_row(null, 1, 2, "a", "b", "c") - val c1 = 'a.int.at(0) - val c2 = 'a.int.at(1) - val c3 = 'a.int.at(2) - val c4 = 'a.string.at(3) - val c5 = 'a.string.at(4) - val c6 = 'a.string.at(5) - - val literalNull = Literal.create(null, BooleanType) - val literalInt = Literal(1) - val literalString = Literal("a") - - checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, c5)), "b", row) - checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, literalNull, c5, c6)), "b", row) - checkEvaluation(CaseKeyWhen(c2, Seq(literalInt, c4, c5)), "a", row) - checkEvaluation(CaseKeyWhen(c2, Seq(c1, c4, c5)), "b", row) - checkEvaluation(CaseKeyWhen(c4, Seq(literalString, c2, c3)), 1, row) - checkEvaluation(CaseKeyWhen(c4, Seq(c1, c3, c5, c2, Literal(3))), 3, row) - - checkEvaluation(CaseKeyWhen(literalInt, Seq(c2, c4, c5)), "a", row) - checkEvaluation(CaseKeyWhen(literalString, Seq(c5, c2, c4, c3)), 2, row) - checkEvaluation(CaseKeyWhen(literalInt, Seq(c5, c2, c4, c3)), null, row) - checkEvaluation(CaseKeyWhen(literalNull, Seq(c5, c2, c1, c3)), 2, row) - } - - test("complex type") { - val row = create_row( - "^Ba*n", // 0 - null.asInstanceOf[UTF8String], // 1 - create_row("aa", "bb"), // 2 - Map("aa"->"bb"), // 3 - Seq("aa", "bb") // 4 - ) - - val typeS = StructType( - StructField("a", StringType, true) :: StructField("b", StringType, true) :: Nil - ) - val typeMap = MapType(StringType, StringType) - val typeArray = ArrayType(StringType) - - checkEvaluation(GetMapValue(BoundReference(3, typeMap, true), - Literal("aa")), "bb", row) - checkEvaluation(GetMapValue(Literal.create(null, typeMap), Literal("aa")), null, row) - checkEvaluation( - GetMapValue(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row) - checkEvaluation(GetMapValue(BoundReference(3, typeMap, true), - Literal.create(null, StringType)), null, row) - - checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true), - Literal(1)), "bb", row) - checkEvaluation(GetArrayItem(Literal.create(null, typeArray), Literal(1)), null, row) - checkEvaluation( - GetArrayItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row) - checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true), - Literal.create(null, IntegerType)), null, row) - - def getStructField(expr: Expression, fieldName: String): ExtractValue = { - expr.dataType match { - case StructType(fields) => - val field = fields.find(_.name == fieldName).get - GetStructField(expr, field, fields.indexOf(field)) - } - } - - def quickResolve(u: UnresolvedExtractValue): ExtractValue = { - ExtractValue(u.child, u.extraction, _ == _) - } - - checkEvaluation(getStructField(BoundReference(2, typeS, nullable = true), "a"), "aa", row) - checkEvaluation(getStructField(Literal.create(null, typeS), "a"), null, row) - - val typeS_notNullable = StructType( - StructField("a", StringType, nullable = false) - :: StructField("b", StringType, nullable = false) :: Nil - ) - - assert(getStructField(BoundReference(2,typeS, nullable = true), "a").nullable === true) - assert(getStructField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable - === false) - - assert(getStructField(Literal.create(null, typeS), "a").nullable === true) - assert(getStructField(Literal.create(null, typeS_notNullable), "a").nullable === true) - - checkEvaluation(quickResolve('c.map(typeMap).at(3).getItem("aa")), "bb", row) - checkEvaluation(quickResolve('c.array(typeArray.elementType).at(4).getItem(1)), "bb", row) - checkEvaluation(quickResolve('c.struct(typeS).at(2).getField("a")), "aa", row) - } - - test("error message of ExtractValue") { - val structType = StructType(StructField("a", StringType, true) :: Nil) - val arrayStructType = ArrayType(structType) - val arrayType = ArrayType(StringType) - val otherType = StringType - - def checkErrorMessage( - childDataType: DataType, - fieldDataType: DataType, - errorMesage: String): Unit = { - val e = intercept[org.apache.spark.sql.AnalysisException] { - ExtractValue( - Literal.create(null, childDataType), - Literal.create(null, fieldDataType), - _ == _) - } - assert(e.getMessage().contains(errorMesage)) - } - - checkErrorMessage(structType, IntegerType, "Field name should be String Literal") - checkErrorMessage(arrayStructType, BooleanType, "Field name should be String Literal") - checkErrorMessage(arrayType, StringType, "Array index should be integral type") - checkErrorMessage(otherType, StringType, "Can't extract value from") - } - - test("arithmetic") { - val row = create_row(1, 2, 3, null) - val c1 = 'a.int.at(0) - val c2 = 'a.int.at(1) - val c3 = 'a.int.at(2) - val c4 = 'a.int.at(3) - - checkEvaluation(UnaryMinus(c1), -1, row) - checkEvaluation(UnaryMinus(Literal.create(100, IntegerType)), -100) - - checkEvaluation(Add(c1, c4), null, row) - checkEvaluation(Add(c1, c2), 3, row) - checkEvaluation(Add(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation(Add(Literal.create(null, IntegerType), c2), null, row) - checkEvaluation( - Add(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) - - checkEvaluation(-c1, -1, row) - checkEvaluation(c1 + c2, 3, row) - checkEvaluation(c1 - c2, -1, row) - checkEvaluation(c1 * c2, 2, row) - checkEvaluation(c1 / c2, 0, row) - checkEvaluation(c1 % c2, 1, row) - } - - test("fractional arithmetic") { - val row = create_row(1.1, 2.0, 3.1, null) - val c1 = 'a.double.at(0) - val c2 = 'a.double.at(1) - val c3 = 'a.double.at(2) - val c4 = 'a.double.at(3) - - checkEvaluation(UnaryMinus(c1), -1.1, row) - checkEvaluation(UnaryMinus(Literal.create(100.0, DoubleType)), -100.0) - checkEvaluation(Add(c1, c4), null, row) - checkEvaluation(Add(c1, c2), 3.1, row) - checkEvaluation(Add(c1, Literal.create(null, DoubleType)), null, row) - checkEvaluation(Add(Literal.create(null, DoubleType), c2), null, row) - checkEvaluation( - Add(Literal.create(null, DoubleType), Literal.create(null, DoubleType)), null, row) - - checkEvaluation(-c1, -1.1, row) - checkEvaluation(c1 + c2, 3.1, row) - checkDoubleEvaluation(c1 - c2, (-0.9 +- 0.001), row) - checkDoubleEvaluation(c1 * c2, (2.2 +- 0.001), row) - checkDoubleEvaluation(c1 / c2, (0.55 +- 0.001), row) - checkDoubleEvaluation(c3 % c2, (1.1 +- 0.001), row) - } - - test("BinaryComparison") { - val row = create_row(1, 2, 3, null, 3, null) - val c1 = 'a.int.at(0) - val c2 = 'a.int.at(1) - val c3 = 'a.int.at(2) - val c4 = 'a.int.at(3) - val c5 = 'a.int.at(4) - val c6 = 'a.int.at(5) - - checkEvaluation(LessThan(c1, c4), null, row) - checkEvaluation(LessThan(c1, c2), true, row) - checkEvaluation(LessThan(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation(LessThan(Literal.create(null, IntegerType), c2), null, row) - checkEvaluation( - LessThan(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) - - checkEvaluation(c1 < c2, true, row) - checkEvaluation(c1 <= c2, true, row) - checkEvaluation(c1 > c2, false, row) - checkEvaluation(c1 >= c2, false, row) - checkEvaluation(c1 === c2, false, row) - checkEvaluation(c1 !== c2, true, row) - checkEvaluation(c4 <=> c1, false, row) - checkEvaluation(c1 <=> c4, false, row) - checkEvaluation(c4 <=> c6, true, row) - checkEvaluation(c3 <=> c5, true, row) - checkEvaluation(Literal(true) <=> Literal.create(null, BooleanType), false, row) - checkEvaluation(Literal.create(null, BooleanType) <=> Literal(true), false, row) - } - - test("StringComparison") { - val row = create_row("abc", null) - val c1 = 'a.string.at(0) - val c2 = 'a.string.at(1) - - checkEvaluation(c1 contains "b", true, row) - checkEvaluation(c1 contains "x", false, row) - checkEvaluation(c2 contains "b", null, row) - checkEvaluation(c1 contains Literal.create(null, StringType), null, row) - - checkEvaluation(c1 startsWith "a", true, row) - checkEvaluation(c1 startsWith "b", false, row) - checkEvaluation(c2 startsWith "a", null, row) - checkEvaluation(c1 startsWith Literal.create(null, StringType), null, row) - - checkEvaluation(c1 endsWith "c", true, row) - checkEvaluation(c1 endsWith "b", false, row) - checkEvaluation(c2 endsWith "b", null, row) - checkEvaluation(c1 endsWith Literal.create(null, StringType), null, row) - } - - test("Substring") { - val row = create_row("example", "example".toArray.map(_.toByte)) - - val s = 'a.string.at(0) - - // substring from zero position with less-than-full length - checkEvaluation( - Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)), "ex", row) - checkEvaluation( - Substring(s, Literal.create(1, IntegerType), Literal.create(2, IntegerType)), "ex", row) - - // substring from zero position with full length - checkEvaluation( - Substring(s, Literal.create(0, IntegerType), Literal.create(7, IntegerType)), "example", row) - checkEvaluation( - Substring(s, Literal.create(1, IntegerType), Literal.create(7, IntegerType)), "example", row) - - // substring from zero position with greater-than-full length - checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(100, IntegerType)), - "example", row) - checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(100, IntegerType)), - "example", row) - - // substring from nonzero position with less-than-full length - checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(2, IntegerType)), - "xa", row) - - // substring from nonzero position with full length - checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(6, IntegerType)), - "xample", row) - - // substring from nonzero position with greater-than-full length - checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(100, IntegerType)), - "xample", row) - - // zero-length substring (within string bounds) - checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(0, IntegerType)), - "", row) - - // zero-length substring (beyond string bounds) - checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), - "", row) - - // substring(null, _, _) -> null - checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), - null, create_row(null)) - - // substring(_, null, _) -> null - checkEvaluation(Substring(s, Literal.create(null, IntegerType), Literal.create(4, IntegerType)), - null, row) - - // substring(_, _, null) -> null - checkEvaluation( - Substring(s, Literal.create(100, IntegerType), Literal.create(null, IntegerType)), - null, - row) - - // 2-arg substring from zero position - checkEvaluation( - Substring(s, Literal.create(0, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), - "example", - row) - checkEvaluation( - Substring(s, Literal.create(1, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), - "example", - row) - - // 2-arg substring from nonzero position - checkEvaluation( - Substring(s, Literal.create(2, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), - "xample", - row) - - val s_notNull = 'a.string.notNull.at(0) - - assert(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable - === true) - assert( - Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable - === false) - assert(Substring(s_notNull, - Literal.create(null, IntegerType), Literal.create(2, IntegerType)).nullable === true) - assert(Substring(s_notNull, - Literal.create(0, IntegerType), Literal.create(null, IntegerType)).nullable === true) - - checkEvaluation(s.substr(0, 2), "ex", row) - checkEvaluation(s.substr(0), "example", row) - checkEvaluation(s.substring(0, 2), "ex", row) - checkEvaluation(s.substring(0), "example", row) - } - - test("SQRT") { - val inputSequence = (1 to (1<<24) by 511).map(_ * (1L<<24)) - val expectedResults = inputSequence.map(l => math.sqrt(l.toDouble)) - val rowSequence = inputSequence.map(l => create_row(l.toDouble)) - val d = 'a.double.at(0) - - for ((row, expected) <- rowSequence zip expectedResults) { - checkEvaluation(Sqrt(d), expected, row) - } - - checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, create_row(null)) - checkEvaluation(Sqrt(-1), null, EmptyRow) - checkEvaluation(Sqrt(-1.5), null, EmptyRow) - } - - test("Bitwise operations") { - val row = create_row(1, 2, 3, null) - val c1 = 'a.int.at(0) - val c2 = 'a.int.at(1) - val c3 = 'a.int.at(2) - val c4 = 'a.int.at(3) - - checkEvaluation(BitwiseAnd(c1, c4), null, row) - checkEvaluation(BitwiseAnd(c1, c2), 0, row) - checkEvaluation(BitwiseAnd(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation( - BitwiseAnd(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) - - checkEvaluation(BitwiseOr(c1, c4), null, row) - checkEvaluation(BitwiseOr(c1, c2), 3, row) - checkEvaluation(BitwiseOr(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation( - BitwiseOr(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) - - checkEvaluation(BitwiseXor(c1, c4), null, row) - checkEvaluation(BitwiseXor(c1, c2), 3, row) - checkEvaluation(BitwiseXor(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation( - BitwiseXor(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) - - checkEvaluation(BitwiseNot(c4), null, row) - checkEvaluation(BitwiseNot(c1), -2, row) - checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null, row) - - checkEvaluation(c1 & c2, 0, row) - checkEvaluation(c1 | c2, 3, row) - checkEvaluation(c1 ^ c2, 3, row) - checkEvaluation(~c1, -2, row) - } - - /** - * Used for testing math functions for DataFrames. - * @param c The DataFrame function - * @param f The functions in scala.math - * @param domain The set of values to run the function with - * @param expectNull Whether the given values should return null or not - * @tparam T Generic type for primitives - */ - def unaryMathFunctionEvaluation[@specialized(Int, Double, Float, Long) T]( - c: Expression => Expression, - f: T => T, - domain: Iterable[T] = (-20 to 20).map(_ * 0.1), - expectNull: Boolean = false): Unit = { - if (expectNull) { - domain.foreach { value => - checkEvaluation(c(Literal(value)), null, EmptyRow) - } - } else { - domain.foreach { value => - checkEvaluation(c(Literal(value)), f(value), EmptyRow) - } - } - checkEvaluation(c(Literal.create(null, DoubleType)), null, create_row(null)) - } - - test("sin") { - unaryMathFunctionEvaluation(Sin, math.sin) - } - - test("asin") { - unaryMathFunctionEvaluation(Asin, math.asin, (-10 to 10).map(_ * 0.1)) - unaryMathFunctionEvaluation(Asin, math.asin, (11 to 20).map(_ * 0.1), true) - } - - test("sinh") { - unaryMathFunctionEvaluation(Sinh, math.sinh) - } - - test("cos") { - unaryMathFunctionEvaluation(Cos, math.cos) - } - - test("acos") { - unaryMathFunctionEvaluation(Acos, math.acos, (-10 to 10).map(_ * 0.1)) - unaryMathFunctionEvaluation(Acos, math.acos, (11 to 20).map(_ * 0.1), true) - } - - test("cosh") { - unaryMathFunctionEvaluation(Cosh, math.cosh) - } - - test("tan") { - unaryMathFunctionEvaluation(Tan, math.tan) - } - - test("atan") { - unaryMathFunctionEvaluation(Atan, math.atan) - } - - test("tanh") { - unaryMathFunctionEvaluation(Tanh, math.tanh) - } - - test("toDegrees") { - unaryMathFunctionEvaluation(ToDegrees, math.toDegrees) - } - - test("toRadians") { - unaryMathFunctionEvaluation(ToRadians, math.toRadians) - } - - test("cbrt") { - unaryMathFunctionEvaluation(Cbrt, math.cbrt) - } - - test("ceil") { - unaryMathFunctionEvaluation(Ceil, math.ceil) - } - - test("floor") { - unaryMathFunctionEvaluation(Floor, math.floor) - } - - test("rint") { - unaryMathFunctionEvaluation(Rint, math.rint) - } - - test("exp") { - unaryMathFunctionEvaluation(Exp, math.exp) - } - - test("expm1") { - unaryMathFunctionEvaluation(Expm1, math.expm1) - } - - test("signum") { - unaryMathFunctionEvaluation[Double](Signum, math.signum) - } - - test("log") { - unaryMathFunctionEvaluation(Log, math.log, (0 to 20).map(_ * 0.1)) - unaryMathFunctionEvaluation(Log, math.log, (-5 to -1).map(_ * 0.1), true) - } - - test("log10") { - unaryMathFunctionEvaluation(Log10, math.log10, (0 to 20).map(_ * 0.1)) - unaryMathFunctionEvaluation(Log10, math.log10, (-5 to -1).map(_ * 0.1), true) - } - - test("log1p") { - unaryMathFunctionEvaluation(Log1p, math.log1p, (-1 to 20).map(_ * 0.1)) - unaryMathFunctionEvaluation(Log1p, math.log1p, (-10 to -2).map(_ * 1.0), true) - } - - /** - * Used for testing math functions for DataFrames. - * @param c The DataFrame function - * @param f The functions in scala.math - * @param domain The set of values to run the function with - */ - def binaryMathFunctionEvaluation( - c: (Expression, Expression) => Expression, - f: (Double, Double) => Double, - domain: Iterable[(Double, Double)] = (-20 to 20).map(v => (v * 0.1, v * -0.1)), - expectNull: Boolean = false): Unit = { - if (expectNull) { - domain.foreach { case (v1, v2) => - checkEvaluation(c(v1, v2), null, create_row(null)) - } - } else { - domain.foreach { case (v1, v2) => - checkEvaluation(c(v1, v2), f(v1 + 0.0, v2 + 0.0), EmptyRow) - checkEvaluation(c(v2, v1), f(v2 + 0.0, v1 + 0.0), EmptyRow) - } - } - checkEvaluation(c(Literal.create(null, DoubleType), 1.0), null, create_row(null)) - checkEvaluation(c(1.0, Literal.create(null, DoubleType)), null, create_row(null)) - } - - test("pow") { - binaryMathFunctionEvaluation(Pow, math.pow, (-5 to 5).map(v => (v * 1.0, v * 1.0))) - binaryMathFunctionEvaluation(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), true) - } - - test("hypot") { - binaryMathFunctionEvaluation(Hypot, math.hypot) - } - - test("atan2") { - binaryMathFunctionEvaluation(Atan2, math.atan2) - } -} - -// TODO: Make the tests work with codegen. -class ExpressionEvaluationWithoutCodeGenSuite extends ExpressionEvaluationBaseSuite { - - test("CreateStruct") { - val row = Row(1, 2, 3) - val c1 = 'a.int.at(0).as("a") - val c3 = 'c.int.at(2).as("c") - checkEvaluation(CreateStruct(Seq(c1, c3)), Row(1, 3), row) - } -} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionTypeCheckingSuite.scala new file mode 100644 index 0000000000000..49b111989799b --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionTypeCheckingSuite.scala @@ -0,0 +1,141 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.analysis.SimpleAnalyzer +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.plans.logical.LocalRelation +import org.apache.spark.sql.types.StringType + +class ExpressionTypeCheckingSuite extends SparkFunSuite { + + val testRelation = LocalRelation( + 'intField.int, + 'stringField.string, + 'booleanField.boolean, + 'complexField.array(StringType)) + + def assertError(expr: Expression, errorMessage: String): Unit = { + val e = intercept[AnalysisException] { + assertSuccess(expr) + } + assert(e.getMessage.contains( + s"cannot resolve '${expr.prettyString}' due to data type mismatch:")) + assert(e.getMessage.contains(errorMessage)) + } + + def assertSuccess(expr: Expression): Unit = { + val analyzed = testRelation.select(expr.as("c")).analyze + SimpleAnalyzer.checkAnalysis(analyzed) + } + + def assertErrorForDifferingTypes(expr: Expression): Unit = { + assertError(expr, + s"differing types in ${expr.getClass.getSimpleName} (IntegerType and BooleanType).") + } + + test("check types for unary arithmetic") { + assertError(UnaryMinus('stringField), "operator - accepts numeric type") + assertError(Abs('stringField), "function abs accepts numeric type") + assertError(BitwiseNot('stringField), "operator ~ accepts integral type") + } + + test("check types for binary arithmetic") { + // We will cast String to Double for binary arithmetic + assertSuccess(Add('intField, 'stringField)) + assertSuccess(Subtract('intField, 'stringField)) + assertSuccess(Multiply('intField, 'stringField)) + assertSuccess(Divide('intField, 'stringField)) + assertSuccess(Remainder('intField, 'stringField)) + // checkAnalysis(BitwiseAnd('intField, 'stringField)) + + assertErrorForDifferingTypes(Add('intField, 'booleanField)) + assertErrorForDifferingTypes(Subtract('intField, 'booleanField)) + assertErrorForDifferingTypes(Multiply('intField, 'booleanField)) + assertErrorForDifferingTypes(Divide('intField, 'booleanField)) + assertErrorForDifferingTypes(Remainder('intField, 'booleanField)) + assertErrorForDifferingTypes(BitwiseAnd('intField, 'booleanField)) + assertErrorForDifferingTypes(BitwiseOr('intField, 'booleanField)) + assertErrorForDifferingTypes(BitwiseXor('intField, 'booleanField)) + assertErrorForDifferingTypes(MaxOf('intField, 'booleanField)) + assertErrorForDifferingTypes(MinOf('intField, 'booleanField)) + + assertError(Add('booleanField, 'booleanField), "operator + accepts numeric type") + assertError(Subtract('booleanField, 'booleanField), "operator - accepts numeric type") + assertError(Multiply('booleanField, 'booleanField), "operator * accepts numeric type") + assertError(Divide('booleanField, 'booleanField), "operator / accepts numeric type") + assertError(Remainder('booleanField, 'booleanField), "operator % accepts numeric type") + + assertError(BitwiseAnd('booleanField, 'booleanField), "operator & accepts integral type") + assertError(BitwiseOr('booleanField, 'booleanField), "operator | accepts integral type") + assertError(BitwiseXor('booleanField, 'booleanField), "operator ^ accepts integral type") + + assertError(MaxOf('complexField, 'complexField), "function maxOf accepts non-complex type") + assertError(MinOf('complexField, 'complexField), "function minOf accepts non-complex type") + } + + test("check types for predicates") { + // We will cast String to Double for binary comparison + assertSuccess(EqualTo('intField, 'stringField)) + assertSuccess(EqualNullSafe('intField, 'stringField)) + assertSuccess(LessThan('intField, 'stringField)) + assertSuccess(LessThanOrEqual('intField, 'stringField)) + assertSuccess(GreaterThan('intField, 'stringField)) + assertSuccess(GreaterThanOrEqual('intField, 'stringField)) + + // We will transform EqualTo with numeric and boolean types to CaseKeyWhen + assertSuccess(EqualTo('intField, 'booleanField)) + assertSuccess(EqualNullSafe('intField, 'booleanField)) + + assertError(EqualTo('intField, 'complexField), "differing types") + assertError(EqualNullSafe('intField, 'complexField), "differing types") + + assertErrorForDifferingTypes(LessThan('intField, 'booleanField)) + assertErrorForDifferingTypes(LessThanOrEqual('intField, 'booleanField)) + assertErrorForDifferingTypes(GreaterThan('intField, 'booleanField)) + assertErrorForDifferingTypes(GreaterThanOrEqual('intField, 'booleanField)) + + assertError( + LessThan('complexField, 'complexField), "operator < accepts non-complex type") + assertError( + LessThanOrEqual('complexField, 'complexField), "operator <= accepts non-complex type") + assertError( + GreaterThan('complexField, 'complexField), "operator > accepts non-complex type") + assertError( + GreaterThanOrEqual('complexField, 'complexField), "operator >= accepts non-complex type") + + assertError( + If('intField, 'stringField, 'stringField), + "type of predicate expression in If should be boolean") + assertErrorForDifferingTypes(If('booleanField, 'intField, 'booleanField)) + + assertError( + CaseWhen(Seq('booleanField, 'intField, 'booleanField, 'complexField)), + "THEN and ELSE expressions should all be same type or coercible to a common type") + assertError( + CaseKeyWhen('intField, Seq('intField, 'stringField, 'intField, 'complexField)), + "THEN and ELSE expressions should all be same type or coercible to a common type") + assertError( + CaseWhen(Seq('booleanField, 'intField, 'intField, 'intField)), + "WHEN expressions in CaseWhen should all be boolean type") + + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala deleted file mode 100644 index 97af2e0fd0502..0000000000000 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst.expressions - -import org.apache.spark.sql.catalyst.CatalystTypeConverters -import org.apache.spark.sql.catalyst.expressions.codegen._ - -/** - * Overrides our expression evaluation tests to use generated code on mutable rows. - */ -class GeneratedMutableEvaluationSuite extends ExpressionEvaluationSuite { - override def checkEvaluation( - expression: Expression, - expected: Any, - inputRow: Row = EmptyRow): Unit = { - lazy val evaluated = GenerateProjection.expressionEvaluator(expression) - - val plan = try { - GenerateProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil) - } catch { - case e: Throwable => - fail( - s""" - |Code generation of $expression failed: - |${evaluated.code.mkString("\n")} - |$e - """.stripMargin) - } - - val actual = plan(inputRow) - val expectedRow = new GenericRow(Array[Any](CatalystTypeConverters.convertToCatalyst(expected))) - if (actual.hashCode() != expectedRow.hashCode()) { - fail( - s""" - |Mismatched hashCodes for values: $actual, $expectedRow - |Hash Codes: ${actual.hashCode()} != ${expectedRow.hashCode()} - |${evaluated.code.mkString("\n")} - """.stripMargin) - } - if (actual != expectedRow) { - val input = if(inputRow == EmptyRow) "" else s", input: $inputRow" - fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") - } - } -} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala new file mode 100644 index 0000000000000..f44f55dfb92d1 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala @@ -0,0 +1,55 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.types.StringType + + +class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper { + + // TODO: Add tests for all data types. + + test("boolean literals") { + checkEvaluation(Literal(true), true) + checkEvaluation(Literal(false), false) + } + + test("int literals") { + checkEvaluation(Literal(1), 1) + checkEvaluation(Literal(0L), 0L) + } + + test("double literals") { + List(0.0, -0.0, Double.NegativeInfinity, Double.PositiveInfinity).foreach { + d => { + checkEvaluation(Literal(d), d) + checkEvaluation(Literal(d.toFloat), d.toFloat) + } + } + } + + test("string literals") { + checkEvaluation(Literal("test"), "test") + checkEvaluation(Literal.create(null, StringType), null) + } + + test("sum two literals") { + checkEvaluation(Add(Literal(1), Literal(1)), 2) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala new file mode 100644 index 0000000000000..0d1d5ebdff2d5 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala @@ -0,0 +1,255 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types.{DataType, DoubleType, LongType} + +class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper { + + /** + * Used for testing leaf math expressions. + * + * @param e expression + * @param c The constants in scala.math + * @tparam T Generic type for primitives + */ + private def testLeaf[T]( + e: () => Expression, + c: T): Unit = { + checkEvaluation(e(), c, EmptyRow) + checkEvaluation(e(), c, create_row(null)) + } + + /** + * Used for testing unary math expressions. + * + * @param c expression + * @param f The functions in scala.math or elsewhere used to generate expected results + * @param domain The set of values to run the function with + * @param expectNull Whether the given values should return null or not + * @tparam T Generic type for primitives + * @tparam U Generic type for the output of the given function `f` + */ + private def testUnary[T, U]( + c: Expression => Expression, + f: T => U, + domain: Iterable[T] = (-20 to 20).map(_ * 0.1), + expectNull: Boolean = false, + evalType: DataType = DoubleType): Unit = { + if (expectNull) { + domain.foreach { value => + checkEvaluation(c(Literal(value)), null, EmptyRow) + } + } else { + domain.foreach { value => + checkEvaluation(c(Literal(value)), f(value), EmptyRow) + } + } + checkEvaluation(c(Literal.create(null, evalType)), null, create_row(null)) + } + + /** + * Used for testing binary math expressions. + * + * @param c The DataFrame function + * @param f The functions in scala.math + * @param domain The set of values to run the function with + */ + private def testBinary( + c: (Expression, Expression) => Expression, + f: (Double, Double) => Double, + domain: Iterable[(Double, Double)] = (-20 to 20).map(v => (v * 0.1, v * -0.1)), + expectNull: Boolean = false): Unit = { + if (expectNull) { + domain.foreach { case (v1, v2) => + checkEvaluation(c(Literal(v1), Literal(v2)), null, create_row(null)) + } + } else { + domain.foreach { case (v1, v2) => + checkEvaluation(c(Literal(v1), Literal(v2)), f(v1 + 0.0, v2 + 0.0), EmptyRow) + checkEvaluation(c(Literal(v2), Literal(v1)), f(v2 + 0.0, v1 + 0.0), EmptyRow) + } + } + checkEvaluation(c(Literal.create(null, DoubleType), Literal(1.0)), null, create_row(null)) + checkEvaluation(c(Literal(1.0), Literal.create(null, DoubleType)), null, create_row(null)) + } + + test("e") { + testLeaf(EulerNumber, math.E) + } + + test("pi") { + testLeaf(Pi, math.Pi) + } + + test("sin") { + testUnary(Sin, math.sin) + } + + test("asin") { + testUnary(Asin, math.asin, (-10 to 10).map(_ * 0.1)) + testUnary(Asin, math.asin, (11 to 20).map(_ * 0.1), expectNull = true) + } + + test("sinh") { + testUnary(Sinh, math.sinh) + } + + test("cos") { + testUnary(Cos, math.cos) + } + + test("acos") { + testUnary(Acos, math.acos, (-10 to 10).map(_ * 0.1)) + testUnary(Acos, math.acos, (11 to 20).map(_ * 0.1), expectNull = true) + } + + test("cosh") { + testUnary(Cosh, math.cosh) + } + + test("tan") { + testUnary(Tan, math.tan) + } + + test("atan") { + testUnary(Atan, math.atan) + } + + test("tanh") { + testUnary(Tanh, math.tanh) + } + + test("toDegrees") { + testUnary(ToDegrees, math.toDegrees) + } + + test("toRadians") { + testUnary(ToRadians, math.toRadians) + } + + test("cbrt") { + testUnary(Cbrt, math.cbrt) + } + + test("ceil") { + testUnary(Ceil, math.ceil) + } + + test("floor") { + testUnary(Floor, math.floor) + } + + test("rint") { + testUnary(Rint, math.rint) + } + + test("exp") { + testUnary(Exp, math.exp) + } + + test("expm1") { + testUnary(Expm1, math.expm1) + } + + test("signum") { + testUnary[Double, Double](Signum, math.signum) + } + + test("log") { + testUnary(Log, math.log, (0 to 20).map(_ * 0.1)) + testUnary(Log, math.log, (-5 to -1).map(_ * 0.1), expectNull = true) + } + + test("log10") { + testUnary(Log10, math.log10, (0 to 20).map(_ * 0.1)) + testUnary(Log10, math.log10, (-5 to -1).map(_ * 0.1), expectNull = true) + } + + test("log1p") { + testUnary(Log1p, math.log1p, (-1 to 20).map(_ * 0.1)) + testUnary(Log1p, math.log1p, (-10 to -2).map(_ * 1.0), expectNull = true) + } + + test("bin") { + testUnary(Bin, java.lang.Long.toBinaryString, (-20 to 20).map(_.toLong), evalType = LongType) + + val row = create_row(null, 12L, 123L, 1234L, -123L) + val l1 = 'a.long.at(0) + val l2 = 'a.long.at(1) + val l3 = 'a.long.at(2) + val l4 = 'a.long.at(3) + val l5 = 'a.long.at(4) + + checkEvaluation(Bin(l1), null, row) + checkEvaluation(Bin(l2), java.lang.Long.toBinaryString(12), row) + checkEvaluation(Bin(l3), java.lang.Long.toBinaryString(123), row) + checkEvaluation(Bin(l4), java.lang.Long.toBinaryString(1234), row) + checkEvaluation(Bin(l5), java.lang.Long.toBinaryString(-123), row) + } + + test("log2") { + def f: (Double) => Double = (x: Double) => math.log(x) / math.log(2) + testUnary(Log2, f, (0 to 20).map(_ * 0.1)) + testUnary(Log2, f, (-5 to -1).map(_ * 1.0), expectNull = true) + } + + test("sqrt") { + testUnary(Sqrt, math.sqrt, (0 to 20).map(_ * 0.1)) + testUnary(Sqrt, math.sqrt, (-5 to -1).map(_ * 1.0), expectNull = true) + + checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, create_row(null)) + checkEvaluation(Sqrt(Literal(-1.0)), null, EmptyRow) + checkEvaluation(Sqrt(Literal(-1.5)), null, EmptyRow) + } + + test("pow") { + testBinary(Pow, math.pow, (-5 to 5).map(v => (v * 1.0, v * 1.0))) + testBinary(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), expectNull = true) + } + + test("hypot") { + testBinary(Hypot, math.hypot) + } + + test("atan2") { + testBinary(Atan2, math.atan2) + } + + test("binary log") { + val f = (c1: Double, c2: Double) => math.log(c2) / math.log(c1) + val domain = (1 to 20).map(v => (v * 0.1, v * 0.2)) + + domain.foreach { case (v1, v2) => + checkEvaluation(Logarithm(Literal(v1), Literal(v2)), f(v1 + 0.0, v2 + 0.0), EmptyRow) + checkEvaluation(Logarithm(Literal(v2), Literal(v1)), f(v2 + 0.0, v1 + 0.0), EmptyRow) + checkEvaluation(new Logarithm(Literal(v1)), f(math.E, v1 + 0.0), EmptyRow) + } + checkEvaluation( + Logarithm(Literal.create(null, DoubleType), Literal(1.0)), + null, + create_row(null)) + checkEvaluation( + Logarithm(Literal(1.0), Literal.create(null, DoubleType)), + null, + create_row(null)) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala new file mode 100644 index 0000000000000..48b84130b4556 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala @@ -0,0 +1,32 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.types.{StringType, BinaryType} + +class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper { + + test("md5") { + checkEvaluation(Md5(Literal("ABC".getBytes)), "902fbdd2b1df0c4f70b4a5d23525e932") + checkEvaluation(Md5(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType)), + "6ac1e56bc78f031059be7be854522c4c") + checkEvaluation(Md5(Literal.create(null, BinaryType)), null) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala new file mode 100644 index 0000000000000..ccdada8b56f83 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala @@ -0,0 +1,65 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types.{BooleanType, StringType, ShortType} + +class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper { + + test("null checking") { + val row = create_row("^Ba*n", null, true, null) + val c1 = 'a.string.at(0) + val c2 = 'a.string.at(1) + val c3 = 'a.boolean.at(2) + val c4 = 'a.boolean.at(3) + + checkEvaluation(c1.isNull, false, row) + checkEvaluation(c1.isNotNull, true, row) + + checkEvaluation(c2.isNull, true, row) + checkEvaluation(c2.isNotNull, false, row) + + checkEvaluation(Literal.create(1, ShortType).isNull, false) + checkEvaluation(Literal.create(1, ShortType).isNotNull, true) + + checkEvaluation(Literal.create(null, ShortType).isNull, true) + checkEvaluation(Literal.create(null, ShortType).isNotNull, false) + + checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row) + checkEvaluation(Coalesce(Literal.create(null, StringType) :: Nil), null, row) + checkEvaluation(Coalesce(Literal.create(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row) + + checkEvaluation( + If(c3, Literal.create("a", StringType), Literal.create("b", StringType)), "a", row) + checkEvaluation(If(c3, c1, c2), "^Ba*n", row) + checkEvaluation(If(c4, c2, c1), "^Ba*n", row) + checkEvaluation(If(Literal.create(null, BooleanType), c2, c1), "^Ba*n", row) + checkEvaluation(If(Literal.create(true, BooleanType), c1, c2), "^Ba*n", row) + checkEvaluation(If(Literal.create(false, BooleanType), c2, c1), "^Ba*n", row) + checkEvaluation(If(Literal.create(false, BooleanType), + Literal.create("a", StringType), Literal.create("b", StringType)), "b", row) + + checkEvaluation(c1 in (c1, c2), true, row) + checkEvaluation( + Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType)), true, row) + checkEvaluation( + Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType), c2), true, row) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala new file mode 100644 index 0000000000000..b6261bfba0786 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala @@ -0,0 +1,179 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import java.sql.{Date, Timestamp} + +import scala.collection.immutable.HashSet + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.util.DateUtils +import org.apache.spark.sql.types.{IntegerType, BooleanType} + + +class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper { + + private def booleanLogicTest( + name: String, + op: (Expression, Expression) => Expression, + truthTable: Seq[(Any, Any, Any)]) { + test(s"3VL $name") { + truthTable.foreach { + case (l, r, answer) => + val expr = op(Literal.create(l, BooleanType), Literal.create(r, BooleanType)) + checkEvaluation(expr, answer) + } + } + } + + // scalastyle:off + /** + * Checks for three-valued-logic. Based on: + * http://en.wikipedia.org/wiki/Null_(SQL)#Comparisons_with_NULL_and_the_three-valued_logic_.283VL.29 + * I.e. in flat cpo "False -> Unknown -> True", + * OR is lowest upper bound, + * AND is greatest lower bound. + * p q p OR q p AND q p = q + * True True True True True + * True False True False False + * True Unknown True Unknown Unknown + * False True True False False + * False False False False True + * False Unknown Unknown False Unknown + * Unknown True True Unknown Unknown + * Unknown False Unknown False Unknown + * Unknown Unknown Unknown Unknown Unknown + * + * p NOT p + * True False + * False True + * Unknown Unknown + */ + // scalastyle:on + val notTrueTable = + (true, false) :: + (false, true) :: + (null, null) :: Nil + + test("3VL Not") { + notTrueTable.foreach { case (v, answer) => + checkEvaluation(Not(Literal.create(v, BooleanType)), answer) + } + } + + booleanLogicTest("AND", And, + (true, true, true) :: + (true, false, false) :: + (true, null, null) :: + (false, true, false) :: + (false, false, false) :: + (false, null, false) :: + (null, true, null) :: + (null, false, false) :: + (null, null, null) :: Nil) + + booleanLogicTest("OR", Or, + (true, true, true) :: + (true, false, true) :: + (true, null, true) :: + (false, true, true) :: + (false, false, false) :: + (false, null, null) :: + (null, true, true) :: + (null, false, null) :: + (null, null, null) :: Nil) + + booleanLogicTest("=", EqualTo, + (true, true, true) :: + (true, false, false) :: + (true, null, null) :: + (false, true, false) :: + (false, false, true) :: + (false, null, null) :: + (null, true, null) :: + (null, false, null) :: + (null, null, null) :: Nil) + + test("IN") { + checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true) + checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true) + checkEvaluation(In(Literal(3), Seq(Literal(1), Literal(2))), false) + checkEvaluation( + And(In(Literal(1), Seq(Literal(1), Literal(2))), In(Literal(2), Seq(Literal(1), Literal(2)))), + true) + } + + test("INSET") { + val hS = HashSet[Any]() + 1 + 2 + val nS = HashSet[Any]() + 1 + 2 + null + val one = Literal(1) + val two = Literal(2) + val three = Literal(3) + val nl = Literal(null) + val s = Seq(one, two) + val nullS = Seq(one, two, null) + checkEvaluation(InSet(one, hS), true) + checkEvaluation(InSet(two, hS), true) + checkEvaluation(InSet(two, nS), true) + checkEvaluation(InSet(nl, nS), true) + checkEvaluation(InSet(three, hS), false) + checkEvaluation(InSet(three, nS), false) + checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true) + } + + + test("BinaryComparison") { + val row = create_row(1, 2, 3, null, 3, null) + val c1 = 'a.int.at(0) + val c2 = 'a.int.at(1) + val c3 = 'a.int.at(2) + val c4 = 'a.int.at(3) + val c5 = 'a.int.at(4) + val c6 = 'a.int.at(5) + + checkEvaluation(LessThan(c1, c4), null, row) + checkEvaluation(LessThan(c1, c2), true, row) + checkEvaluation(LessThan(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(LessThan(Literal.create(null, IntegerType), c2), null, row) + checkEvaluation( + LessThan(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + + checkEvaluation(c1 < c2, true, row) + checkEvaluation(c1 <= c2, true, row) + checkEvaluation(c1 > c2, false, row) + checkEvaluation(c1 >= c2, false, row) + checkEvaluation(c1 === c2, false, row) + checkEvaluation(c1 !== c2, true, row) + checkEvaluation(c4 <=> c1, false, row) + checkEvaluation(c1 <=> c4, false, row) + checkEvaluation(c4 <=> c6, true, row) + checkEvaluation(c3 <=> c5, true, row) + checkEvaluation(Literal(true) <=> Literal.create(null, BooleanType), false, row) + checkEvaluation(Literal.create(null, BooleanType) <=> Literal(true), false, row) + + val d1 = DateUtils.fromJavaDate(Date.valueOf("1970-01-01")) + val d2 = DateUtils.fromJavaDate(Date.valueOf("1970-01-02")) + checkEvaluation(Literal(d1) < Literal(d2), true) + + val ts1 = new Timestamp(12) + val ts2 = new Timestamp(123) + checkEvaluation(Literal("ab") < Literal("abc"), true) + checkEvaluation(Literal(ts1) < Literal(ts2), true) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala new file mode 100644 index 0000000000000..9be2b23a53f27 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.scalatest.Matchers._ + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types.{DoubleType, IntegerType} + + +class RandomSuite extends SparkFunSuite with ExpressionEvalHelper { + + test("random") { + val row = create_row(1.1, 2.0, 3.1, null) + checkDoubleEvaluation(Rand(30), (0.7363714192755834 +- 0.001), row) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala new file mode 100644 index 0000000000000..d363e631540d8 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala @@ -0,0 +1,230 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types.{IntegerType, StringType} + + +class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper { + + test("StringComparison") { + val row = create_row("abc", null) + val c1 = 'a.string.at(0) + val c2 = 'a.string.at(1) + + checkEvaluation(c1 contains "b", true, row) + checkEvaluation(c1 contains "x", false, row) + checkEvaluation(c2 contains "b", null, row) + checkEvaluation(c1 contains Literal.create(null, StringType), null, row) + + checkEvaluation(c1 startsWith "a", true, row) + checkEvaluation(c1 startsWith "b", false, row) + checkEvaluation(c2 startsWith "a", null, row) + checkEvaluation(c1 startsWith Literal.create(null, StringType), null, row) + + checkEvaluation(c1 endsWith "c", true, row) + checkEvaluation(c1 endsWith "b", false, row) + checkEvaluation(c2 endsWith "b", null, row) + checkEvaluation(c1 endsWith Literal.create(null, StringType), null, row) + } + + test("Substring") { + val row = create_row("example", "example".toArray.map(_.toByte)) + + val s = 'a.string.at(0) + + // substring from zero position with less-than-full length + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)), "ex", row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(2, IntegerType)), "ex", row) + + // substring from zero position with full length + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(7, IntegerType)), "example", row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(7, IntegerType)), "example", row) + + // substring from zero position with greater-than-full length + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(100, IntegerType)), + "example", row) + checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(100, IntegerType)), + "example", row) + + // substring from nonzero position with less-than-full length + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(2, IntegerType)), + "xa", row) + + // substring from nonzero position with full length + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(6, IntegerType)), + "xample", row) + + // substring from nonzero position with greater-than-full length + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(100, IntegerType)), + "xample", row) + + // zero-length substring (within string bounds) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(0, IntegerType)), + "", row) + + // zero-length substring (beyond string bounds) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), + "", row) + + // substring(null, _, _) -> null + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), + null, create_row(null)) + + // substring(_, null, _) -> null + checkEvaluation(Substring(s, Literal.create(null, IntegerType), Literal.create(4, IntegerType)), + null, row) + + // substring(_, _, null) -> null + checkEvaluation( + Substring(s, Literal.create(100, IntegerType), Literal.create(null, IntegerType)), + null, + row) + + // 2-arg substring from zero position + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "example", + row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "example", + row) + + // 2-arg substring from nonzero position + checkEvaluation( + Substring(s, Literal.create(2, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "xample", + row) + + val s_notNull = 'a.string.notNull.at(0) + + assert(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable + === true) + assert( + Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable + === false) + assert(Substring(s_notNull, + Literal.create(null, IntegerType), Literal.create(2, IntegerType)).nullable === true) + assert(Substring(s_notNull, + Literal.create(0, IntegerType), Literal.create(null, IntegerType)).nullable === true) + + checkEvaluation(s.substr(0, 2), "ex", row) + checkEvaluation(s.substr(0), "example", row) + checkEvaluation(s.substring(0, 2), "ex", row) + checkEvaluation(s.substring(0), "example", row) + } + + test("LIKE literal Regular Expression") { + checkEvaluation(Literal.create(null, StringType).like("a"), null) + checkEvaluation(Literal.create("a", StringType).like(Literal.create(null, StringType)), null) + checkEvaluation(Literal.create(null, StringType).like(Literal.create(null, StringType)), null) + checkEvaluation("abdef" like "abdef", true) + checkEvaluation("a_%b" like "a\\__b", true) + checkEvaluation("addb" like "a_%b", true) + checkEvaluation("addb" like "a\\__b", false) + checkEvaluation("addb" like "a%\\%b", false) + checkEvaluation("a_%b" like "a%\\%b", true) + checkEvaluation("addb" like "a%", true) + checkEvaluation("addb" like "**", false) + checkEvaluation("abc" like "a%", true) + checkEvaluation("abc" like "b%", false) + checkEvaluation("abc" like "bc%", false) + checkEvaluation("a\nb" like "a_b", true) + checkEvaluation("ab" like "a%b", true) + checkEvaluation("a\nb" like "a%b", true) + } + + test("LIKE Non-literal Regular Expression") { + val regEx = 'a.string.at(0) + checkEvaluation("abcd" like regEx, null, create_row(null)) + checkEvaluation("abdef" like regEx, true, create_row("abdef")) + checkEvaluation("a_%b" like regEx, true, create_row("a\\__b")) + checkEvaluation("addb" like regEx, true, create_row("a_%b")) + checkEvaluation("addb" like regEx, false, create_row("a\\__b")) + checkEvaluation("addb" like regEx, false, create_row("a%\\%b")) + checkEvaluation("a_%b" like regEx, true, create_row("a%\\%b")) + checkEvaluation("addb" like regEx, true, create_row("a%")) + checkEvaluation("addb" like regEx, false, create_row("**")) + checkEvaluation("abc" like regEx, true, create_row("a%")) + checkEvaluation("abc" like regEx, false, create_row("b%")) + checkEvaluation("abc" like regEx, false, create_row("bc%")) + checkEvaluation("a\nb" like regEx, true, create_row("a_b")) + checkEvaluation("ab" like regEx, true, create_row("a%b")) + checkEvaluation("a\nb" like regEx, true, create_row("a%b")) + + checkEvaluation(Literal.create(null, StringType) like regEx, null, create_row("bc%")) + } + + test("RLIKE literal Regular Expression") { + checkEvaluation(Literal.create(null, StringType) rlike "abdef", null) + checkEvaluation("abdef" rlike Literal.create(null, StringType), null) + checkEvaluation(Literal.create(null, StringType) rlike Literal.create(null, StringType), null) + checkEvaluation("abdef" rlike "abdef", true) + checkEvaluation("abbbbc" rlike "a.*c", true) + + checkEvaluation("fofo" rlike "^fo", true) + checkEvaluation("fo\no" rlike "^fo\no$", true) + checkEvaluation("Bn" rlike "^Ba*n", true) + checkEvaluation("afofo" rlike "fo", true) + checkEvaluation("afofo" rlike "^fo", false) + checkEvaluation("Baan" rlike "^Ba?n", false) + checkEvaluation("axe" rlike "pi|apa", false) + checkEvaluation("pip" rlike "^(pi)*$", false) + + checkEvaluation("abc" rlike "^ab", true) + checkEvaluation("abc" rlike "^bc", false) + checkEvaluation("abc" rlike "^ab", true) + checkEvaluation("abc" rlike "^bc", false) + + intercept[java.util.regex.PatternSyntaxException] { + evaluate("abbbbc" rlike "**") + } + } + + test("RLIKE Non-literal Regular Expression") { + val regEx = 'a.string.at(0) + checkEvaluation("abdef" rlike regEx, true, create_row("abdef")) + checkEvaluation("abbbbc" rlike regEx, true, create_row("a.*c")) + checkEvaluation("fofo" rlike regEx, true, create_row("^fo")) + checkEvaluation("fo\no" rlike regEx, true, create_row("^fo\no$")) + checkEvaluation("Bn" rlike regEx, true, create_row("^Ba*n")) + + intercept[java.util.regex.PatternSyntaxException] { + evaluate("abbbbc" rlike regEx, create_row("**")) + } + } + + test("length for string") { + val regEx = 'a.string.at(0) + checkEvaluation(StringLength(Literal("abc")), 3, create_row("abdef")) + checkEvaluation(StringLength(regEx), 5, create_row("abdef")) + checkEvaluation(StringLength(regEx), 0, create_row("")) + checkEvaluation(StringLength(regEx), null, create_row(null)) + // TODO currently bug in codegen, let's temporally disable this + // checkEvaluation(StringLength(Literal.create(null, StringType)), null, create_row("abdef")) + } + + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala index 7a19e511eb8b5..7aae2bbd8a0b8 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala @@ -20,18 +20,24 @@ package org.apache.spark.sql.catalyst.expressions import scala.collection.JavaConverters._ import scala.util.Random -import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, TaskMemoryManager, MemoryAllocator} -import org.scalatest.{BeforeAndAfterEach, FunSuite, Matchers} +import org.scalatest.{BeforeAndAfterEach, Matchers} +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, TaskMemoryManager, MemoryAllocator} +import org.apache.spark.unsafe.types.UTF8String + -class UnsafeFixedWidthAggregationMapSuite extends FunSuite with Matchers with BeforeAndAfterEach { +class UnsafeFixedWidthAggregationMapSuite + extends SparkFunSuite + with Matchers + with BeforeAndAfterEach { import UnsafeFixedWidthAggregationMap._ private val groupKeySchema = StructType(StructField("product", StringType) :: Nil) private val aggBufferSchema = StructType(StructField("salePrice", IntegerType) :: Nil) - private def emptyAggregationBuffer: Row = new GenericRow(Array[Any](0)) + private def emptyAggregationBuffer: InternalRow = new GenericRow(Array[Any](0)) private var memoryManager: TaskMemoryManager = null @@ -78,7 +84,7 @@ class UnsafeFixedWidthAggregationMapSuite extends FunSuite with Matchers with Be 1024, // initial capacity false // disable perf metrics ) - val groupKey = new GenericRow(Array[Any](UTF8String("cats"))) + val groupKey = new GenericRow(Array[Any](UTF8String.fromString("cats"))) // Looking up a key stores a zero-entry in the map (like Python Counters or DefaultDicts) map.getAggregationBuffer(groupKey) @@ -107,7 +113,7 @@ class UnsafeFixedWidthAggregationMapSuite extends FunSuite with Matchers with Be val rand = new Random(42) val groupKeys: Set[String] = Seq.fill(512)(rand.nextString(1024)).toSet groupKeys.foreach { keyString => - map.getAggregationBuffer(new GenericRow(Array[Any](UTF8String(keyString)))) + map.getAggregationBuffer(new GenericRow(Array[Any](UTF8String.fromString(keyString)))) } val seenKeys: Set[String] = map.iterator().asScala.map { entry => entry.key.getString(0) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala index 3a60c7fd32675..721ef8a22608c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala @@ -17,15 +17,18 @@ package org.apache.spark.sql.catalyst.expressions +import java.sql.{Date, Timestamp} import java.util.Arrays -import org.scalatest.{FunSuite, Matchers} +import org.scalatest.Matchers +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.types._ +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.unsafe.PlatformDependent import org.apache.spark.unsafe.array.ByteArrayMethods -class UnsafeRowConverterSuite extends FunSuite with Matchers { +class UnsafeRowConverterSuite extends SparkFunSuite with Matchers { test("basic conversion with only primitive types") { val fieldTypes: Array[DataType] = Array(LongType, LongType, IntegerType) @@ -73,6 +76,34 @@ class UnsafeRowConverterSuite extends FunSuite with Matchers { unsafeRow.getString(2) should be ("World") } + test("basic conversion with primitive, string, date and timestamp types") { + val fieldTypes: Array[DataType] = Array(LongType, StringType, DateType, TimestampType) + val converter = new UnsafeRowConverter(fieldTypes) + + val row = new SpecificMutableRow(fieldTypes) + row.setLong(0, 0) + row.setString(1, "Hello") + row.update(2, DateUtils.fromJavaDate(Date.valueOf("1970-01-01"))) + row.update(3, DateUtils.fromJavaTimestamp(Timestamp.valueOf("2015-05-08 08:10:25"))) + + val sizeRequired: Int = converter.getSizeRequirement(row) + sizeRequired should be (8 + (8 * 4) + + ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length + 8)) + val buffer: Array[Long] = new Array[Long](sizeRequired / 8) + val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET) + numBytesWritten should be (sizeRequired) + + val unsafeRow = new UnsafeRow() + unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + unsafeRow.getLong(0) should be (0) + unsafeRow.getString(1) should be ("Hello") + // Date is represented as Int in unsafeRow + DateUtils.toJavaDate(unsafeRow.getInt(2)) should be (Date.valueOf("1970-01-01")) + // Timestamp is represented as Long in unsafeRow + DateUtils.toJavaTimestamp(unsafeRow.getLong(3)) should be + (Timestamp.valueOf("2015-05-08 08:10:25")) + } + test("null handling") { val fieldTypes: Array[DataType] = Array( NullType, @@ -85,7 +116,7 @@ class UnsafeRowConverterSuite extends FunSuite with Matchers { DoubleType) val converter = new UnsafeRowConverter(fieldTypes) - val rowWithAllNullColumns: Row = { + val rowWithAllNullColumns: InternalRow = { val r = new SpecificMutableRow(fieldTypes) for (i <- 0 to fieldTypes.length - 1) { r.setNullAt(i) @@ -116,7 +147,7 @@ class UnsafeRowConverterSuite extends FunSuite with Matchers { // If we have an UnsafeRow with columns that are initially non-null and we null out those // columns, then the serialized row representation should be identical to what we would get by // creating an entirely null row via the converter - val rowWithNoNullColumns: Row = { + val rowWithNoNullColumns: InternalRow = { val r = new SpecificMutableRow(fieldTypes) r.setNullAt(0) r.setBoolean(1, false) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala index 6255578d7fa57..465a5e6914204 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala @@ -78,9 +78,9 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper { test("(a && b && c && ...) || (a && b && d && ...) || (a && b && e && ...) ...") { checkCondition('b > 3 || 'c > 5, 'b > 3 || 'c > 5) - checkCondition(('a < 2 && 'a > 3 && 'b > 5) || 'a < 2, 'a < 2) + checkCondition(('a < 2 && 'a > 3 && 'b > 5) || 'a < 2, 'a < 2) - checkCondition('a < 2 || ('a < 2 && 'a > 3 && 'b > 5), 'a < 2) + checkCondition('a < 2 || ('a < 2 && 'a > 3 && 'b > 5), 'a < 2) val input = ('a === 'b && 'b > 3 && 'c > 2) || ('a === 'b && 'c < 1 && 'a === 5) || diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala index a30052b38fc11..06c592f4905a3 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala @@ -71,7 +71,7 @@ class CombiningLimitsSuite extends PlanTest { comparePlans(optimized, correctAnswer) } - + test("limits: combines two limits after ColumnPruning") { val originalQuery = testRelation @@ -79,7 +79,7 @@ class CombiningLimitsSuite extends PlanTest { .limit(2) .select('a) .limit(5) - + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala index 6b7d9a85c341b..ec3b2f1edfa05 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala @@ -35,6 +35,7 @@ class ConstantFoldingSuite extends PlanTest { Batch("AnalysisNodes", Once, EliminateSubQueries) :: Batch("ConstantFolding", Once, + OptimizeIn, ConstantFolding, BooleanSimplification) :: Nil } @@ -247,4 +248,36 @@ class ConstantFoldingSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + + test("Constant folding test: Fold In(v, list) into true or false") { + var originalQuery = + testRelation + .select('a) + .where(In(Literal(1), Seq(Literal(1), Literal(2)))) + + var optimized = Optimize.execute(originalQuery.analyze) + + var correctAnswer = + testRelation + .select('a) + .where(Literal(true)) + .analyze + + comparePlans(optimized, correctAnswer) + + originalQuery = + testRelation + .select('a) + .where(In(Literal(1), Seq(Literal(1), 'a.attr))) + + optimized = Optimize.execute(originalQuery.analyze) + + correctAnswer = + testRelation + .select('a) + .where(Literal(true)) + .analyze + + comparePlans(optimized, correctAnswer) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala index 6841bd9890c97..54e8c6462e962 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala @@ -17,10 +17,10 @@ package org.apache.spark.sql.catalyst.optimizer -import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute -import org.apache.spark.sql.catalyst.dsl.plans._ import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.PlanTest import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor @@ -37,13 +37,11 @@ class ConvertToLocalRelationSuite extends PlanTest { test("Project on LocalRelation should be turned into a single LocalRelation") { val testRelation = LocalRelation( LocalRelation('a.int, 'b.int).output, - Row(1, 2) :: - Row(4, 5) :: Nil) + InternalRow(1, 2) :: InternalRow(4, 5) :: Nil) val correctAnswer = LocalRelation( LocalRelation('a1.int, 'b1.int).output, - Row(1, 3) :: - Row(4, 6) :: Nil) + InternalRow(1, 3) :: InternalRow(4, 6) :: Nil) val projectOnLocal = testRelation.select( UnresolvedAttribute("a").as("a1"), diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index 58d415d9011e1..ffdc673cdc455 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.optimizer import org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries -import org.apache.spark.sql.catalyst.expressions.{Count, Explode} +import org.apache.spark.sql.catalyst.expressions.{SortOrder, Ascending, Count, Explode} import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.plans.{LeftSemi, PlanTest, LeftOuter, RightOuter} import org.apache.spark.sql.catalyst.rules._ @@ -36,9 +36,11 @@ class FilterPushdownSuite extends PlanTest { Batch("Filter Pushdown", Once, CombineFilters, PushPredicateThroughProject, + BooleanSimplification, PushPredicateThroughJoin, PushPredicateThroughGenerate, - ColumnPruning) :: Nil + ColumnPruning, + ProjectCollapsing) :: Nil } val testRelation = LocalRelation('a.int, 'b.int, 'c.int) @@ -92,11 +94,11 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } - + test("column pruning for Project(ne, Limit)") { val originalQuery = testRelation - .select('a,'b) + .select('a, 'b) .limit(2) .select('a) @@ -108,7 +110,7 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } - + // After this line is unimplemented. test("simple push down") { val originalQuery = @@ -155,11 +157,9 @@ class FilterPushdownSuite extends PlanTest { .where('a === 1 && 'a === 2) .select('a).analyze - comparePlans(optimized, correctAnswer) } - test("joins: push to either side") { val x = testRelation.subquery('x) val y = testRelation.subquery('y) @@ -197,6 +197,25 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("joins: push to one side after transformCondition") { + val x = testRelation.subquery('x) + val y = testRelation1.subquery('y) + + val originalQuery = { + x.join(y) + .where(("x.a".attr === 1 && "y.d".attr === "x.b".attr) || + ("x.a".attr === 1 && "y.d".attr === "x.c".attr)) + } + + val optimized = Optimize.execute(originalQuery.analyze) + val left = testRelation.where('a === 1) + val right = testRelation1 + val correctAnswer = + left.join(right, condition = Some("d".attr === "b".attr || "d".attr === "c".attr)).analyze + + comparePlans(optimized, correctAnswer) + } + test("joins: rewrite filter to push to either side") { val x = testRelation.subquery('x) val y = testRelation.subquery('y) @@ -541,4 +560,37 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, originalQuery) } + + test("push down project past sort") { + val x = testRelation.subquery('x) + + // push down valid + val originalQuery = { + x.select('a, 'b) + .sortBy(SortOrder('a, Ascending)) + .select('a) + } + + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = + x.select('a) + .sortBy(SortOrder('a, Ascending)).analyze + + comparePlans(optimized, analysis.EliminateSubQueries(correctAnswer)) + + // push down invalid + val originalQuery1 = { + x.select('a, 'b) + .sortBy(SortOrder('a, Ascending)) + .select('b) + } + + val optimized1 = Optimize.execute(originalQuery1.analyze) + val correctAnswer1 = + x.select('a, 'b) + .sortBy(SortOrder('a, Ascending)) + .select('b).analyze + + comparePlans(optimized1, analysis.EliminateSubQueries(correctAnswer1)) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala index 3eb399e68e70c..1d433275fed2e 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala @@ -46,7 +46,7 @@ class OptimizeInSuite extends PlanTest { test("OptimizedIn test: In clause optimized to InSet") { val originalQuery = testRelation - .where(In(UnresolvedAttribute("a"), Seq(Literal(1),Literal(2)))) + .where(In(UnresolvedAttribute("a"), Seq(Literal(1), Literal(2)))) .analyze val optimized = Optimize.execute(originalQuery.analyze) @@ -57,17 +57,17 @@ class OptimizeInSuite extends PlanTest { comparePlans(optimized, correctAnswer) } - + test("OptimizedIn test: In clause not optimized in case filter has attributes") { val originalQuery = testRelation - .where(In(UnresolvedAttribute("a"), Seq(Literal(1),Literal(2), UnresolvedAttribute("b")))) + .where(In(UnresolvedAttribute("a"), Seq(Literal(1), Literal(2), UnresolvedAttribute("b")))) .analyze val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation - .where(In(UnresolvedAttribute("a"), Seq(Literal(1),Literal(2), UnresolvedAttribute("b")))) + .where(In(UnresolvedAttribute("a"), Seq(Literal(1), Literal(2), UnresolvedAttribute("b")))) .analyze comparePlans(optimized, correctAnswer) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ProjectCollapsingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ProjectCollapsingSuite.scala new file mode 100644 index 0000000000000..151654bffbd66 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ProjectCollapsingSuite.scala @@ -0,0 +1,73 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.optimizer + +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.expressions.Rand +import org.apache.spark.sql.catalyst.plans.PlanTest +import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.RuleExecutor + + +class ProjectCollapsingSuite extends PlanTest { + object Optimize extends RuleExecutor[LogicalPlan] { + val batches = + Batch("Subqueries", FixedPoint(10), EliminateSubQueries) :: + Batch("ProjectCollapsing", Once, ProjectCollapsing) :: Nil + } + + val testRelation = LocalRelation('a.int, 'b.int) + + test("collapse two deterministic, independent projects into one") { + val query = testRelation + .select(('a + 1).as('a_plus_1), 'b) + .select('a_plus_1, ('b + 1).as('b_plus_1)) + + val optimized = Optimize.execute(query.analyze) + val correctAnswer = testRelation.select(('a + 1).as('a_plus_1), ('b + 1).as('b_plus_1)).analyze + + comparePlans(optimized, correctAnswer) + } + + test("collapse two deterministic, dependent projects into one") { + val query = testRelation + .select(('a + 1).as('a_plus_1), 'b) + .select(('a_plus_1 + 1).as('a_plus_2), 'b) + + val optimized = Optimize.execute(query.analyze) + + val correctAnswer = testRelation.select( + (('a + 1).as('a_plus_1) + 1).as('a_plus_2), + 'b).analyze + + comparePlans(optimized, correctAnswer) + } + + test("do not collapse nondeterministic projects") { + val query = testRelation + .select(Rand(10).as('rand)) + .select(('rand + 1).as('rand1), ('rand + 2).as('rand2)) + + val optimized = Optimize.execute(query.analyze) + val correctAnswer = query.analyze + + comparePlans(optimized, correctAnswer) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceDistinctWithAggregateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceDistinctWithAggregateSuite.scala new file mode 100644 index 0000000000000..df29a62ff0e15 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceDistinctWithAggregateSuite.scala @@ -0,0 +1,42 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.optimizer + +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.plans.PlanTest +import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, Distinct, LocalRelation, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.RuleExecutor + +class ReplaceDistinctWithAggregateSuite extends PlanTest { + + object Optimize extends RuleExecutor[LogicalPlan] { + val batches = Batch("ProjectCollapsing", Once, ReplaceDistinctWithAggregate) :: Nil + } + + test("replace distinct with aggregate") { + val input = LocalRelation('a.int, 'b.int) + + val query = Distinct(input) + val optimized = Optimize.execute(query.analyze) + + val correctAnswer = Aggregate(input.output, input.output, input) + + comparePlans(optimized, correctAnswer) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala index a3ad200800b02..35f50be46b76f 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala @@ -33,8 +33,8 @@ class UnionPushdownSuite extends PlanTest { UnionPushdown) :: Nil } - val testRelation = LocalRelation('a.int, 'b.int, 'c.int) - val testRelation2 = LocalRelation('d.int, 'e.int, 'f.int) + val testRelation = LocalRelation('a.int, 'b.int, 'c.int) + val testRelation2 = LocalRelation('d.int, 'e.int, 'f.int) val testUnion = Union(testRelation, testRelation2) test("union: filter to each side") { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala index e7cafcc96de87..765c1e2dda99f 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala @@ -17,8 +17,7 @@ package org.apache.spark.sql.catalyst.plans -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Filter, LogicalPlan} import org.apache.spark.sql.catalyst.util._ @@ -26,7 +25,7 @@ import org.apache.spark.sql.catalyst.util._ /** * Provides helper methods for comparing plans. */ -class PlanTest extends FunSuite { +class PlanTest extends SparkFunSuite { /** * Since attribute references are given globally unique ids during analysis, diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala index 1273921f6394c..62d5f6ac74885 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala @@ -17,8 +17,7 @@ package org.apache.spark.sql.catalyst.plans -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.dsl.plans._ import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions.{ExprId, AttributeReference} @@ -28,7 +27,7 @@ import org.apache.spark.sql.catalyst.util._ /** * Tests for the sameResult function of [[LogicalPlan]]. */ -class SameResultSuite extends FunSuite { +class SameResultSuite extends SparkFunSuite { val testRelation = LocalRelation('a.int, 'b.int, 'c.int) val testRelation2 = LocalRelation('a.int, 'b.int, 'c.int) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala index 2a641c63f87bb..a7de7b052bdc3 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.sql.catalyst.trees -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions.{Expression, IntegerLiteral, Literal} import org.apache.spark.sql.catalyst.rules.{Rule, RuleExecutor} -class RuleExecutorSuite extends FunSuite { +class RuleExecutorSuite extends SparkFunSuite { object DecrementLiterals extends Rule[Expression] { def apply(e: Expression): Expression = e transform { case IntegerLiteral(i) if i > 0 => Literal(i - 1) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala index 3d10dab5ba34c..bda217935cb05 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala @@ -19,21 +19,24 @@ package org.apache.spark.sql.catalyst.trees import scala.collection.mutable.ArrayBuffer -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types.{IntegerType, StringType, NullType} case class Dummy(optKey: Option[Expression]) extends Expression { - def children: Seq[Expression] = optKey.toSeq - def nullable: Boolean = true - def dataType: NullType = NullType + override def children: Seq[Expression] = optKey.toSeq + override def nullable: Boolean = true + override def dataType: NullType = NullType override lazy val resolved = true - type EvaluatedType = Any - def eval(input: Row): Any = null.asInstanceOf[Any] + override def eval(input: InternalRow): Any = null.asInstanceOf[Any] +} + +case class ComplexPlan(exprs: Seq[Seq[Expression]]) + extends org.apache.spark.sql.catalyst.plans.logical.LeafNode { + override def output: Seq[Attribute] = Nil } -class TreeNodeSuite extends FunSuite { +class TreeNodeSuite extends SparkFunSuite { test("top node changed") { val after = Literal(1) transform { case Literal(1, _) => Literal(2) } assert(after === Literal(2)) @@ -92,7 +95,7 @@ class TreeNodeSuite extends FunSuite { test("transform works on nodes with Option children") { val dummy1 = Dummy(Some(Literal.create("1", StringType))) val dummy2 = Dummy(None) - val toZero: PartialFunction[Expression, Expression] = { case Literal(_, _) => Literal(0) } + val toZero: PartialFunction[Expression, Expression] = { case Literal(_, _) => Literal(0) } var actual = dummy1 transformDown toZero assert(actual === Dummy(Some(Literal(0)))) @@ -105,7 +108,7 @@ class TreeNodeSuite extends FunSuite { } test("preserves origin") { - CurrentOrigin.setPosition(1,1) + CurrentOrigin.setPosition(1, 1) val add = Add(Literal(1), Literal(1)) CurrentOrigin.reset() @@ -222,4 +225,13 @@ class TreeNodeSuite extends FunSuite { assert(expected === actual) } } + + test("transformExpressions on nested expression sequence") { + val plan = ComplexPlan(Seq(Seq(Literal(1)), Seq(Literal(2)))) + val actual = plan.transformExpressions { + case Literal(value, _) => Literal(value.toString) + } + val expected = ComplexPlan(Seq(Seq(Literal("1")), Seq(Literal("2")))) + assert(expected === actual) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateUtilsSuite.scala new file mode 100644 index 0000000000000..4d8fe4ac5e78f --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateUtilsSuite.scala @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.util + +import java.sql.Timestamp + +import org.apache.spark.SparkFunSuite + +class DateUtilsSuite extends SparkFunSuite { + + test("timestamp") { + val now = new Timestamp(System.currentTimeMillis()) + now.setNanos(100) + val ns = DateUtils.fromJavaTimestamp(now) + assert(ns % 10000000L == 1) + assert(DateUtils.toJavaTimestamp(ns) == now) + + List(-111111111111L, -1L, 0, 1L, 111111111111L).foreach { t => + val ts = DateUtils.toJavaTimestamp(t) + assert(DateUtils.fromJavaTimestamp(ts) == t) + assert(DateUtils.toJavaTimestamp(DateUtils.fromJavaTimestamp(ts)) == ts) + } + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/MetadataSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/MetadataSuite.scala index d7d60efee50fa..4030a1b1df358 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/MetadataSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/MetadataSuite.scala @@ -18,11 +18,11 @@ package org.apache.spark.sql.catalyst.util import org.json4s.jackson.JsonMethods.parse -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.types.{MetadataBuilder, Metadata} -class MetadataSuite extends FunSuite { +class MetadataSuite extends SparkFunSuite { val baseMetadata = new MetadataBuilder() .putString("purpose", "ml") diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala index 3e7cf7cbb5e63..c6171b7b6916d 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala @@ -17,9 +17,9 @@ package org.apache.spark.sql.types -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite -class DataTypeParserSuite extends FunSuite { +class DataTypeParserSuite extends SparkFunSuite { def checkDataType(dataTypeString: String, expectedDataType: DataType): Unit = { test(s"parse ${dataTypeString.replace("\n", "")}") { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala index d797510f36685..077c0ad70ac4f 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala @@ -17,9 +17,9 @@ package org.apache.spark.sql.types -import org.scalatest.FunSuite +import org.apache.spark.{SparkException, SparkFunSuite} -class DataTypeSuite extends FunSuite { +class DataTypeSuite extends SparkFunSuite { test("construct an ArrayType") { val array = ArrayType(StringType) @@ -69,6 +69,76 @@ class DataTypeSuite extends FunSuite { } } + test("fieldsMap returns map of name to StructField") { + val struct = StructType( + StructField("a", LongType) :: + StructField("b", FloatType) :: Nil) + + val mapped = StructType.fieldsMap(struct.fields) + + val expected = Map( + "a" -> StructField("a", LongType), + "b" -> StructField("b", FloatType)) + + assert(mapped === expected) + } + + test("merge where right is empty") { + val left = StructType( + StructField("a", LongType) :: + StructField("b", FloatType) :: Nil) + + val right = StructType(List()) + val merged = left.merge(right) + + assert(merged === left) + } + + test("merge where left is empty") { + + val left = StructType(List()) + + val right = StructType( + StructField("a", LongType) :: + StructField("b", FloatType) :: Nil) + + val merged = left.merge(right) + + assert(right === merged) + + } + + test("merge where both are non-empty") { + val left = StructType( + StructField("a", LongType) :: + StructField("b", FloatType) :: Nil) + + val right = StructType( + StructField("c", LongType) :: Nil) + + val expected = StructType( + StructField("a", LongType) :: + StructField("b", FloatType) :: + StructField("c", LongType) :: Nil) + + val merged = left.merge(right) + + assert(merged === expected) + } + + test("merge where right contains type conflict") { + val left = StructType( + StructField("a", LongType) :: + StructField("b", FloatType) :: Nil) + + val right = StructType( + StructField("b", LongType) :: Nil) + + intercept[SparkException] { + left.merge(right) + } + } + def checkDataTypeJsonRepr(dataType: DataType): Unit = { test(s"JSON - $dataType") { assert(DataType.fromJson(dataType.json) === dataType) @@ -120,7 +190,7 @@ class DataTypeSuite extends FunSuite { checkDefaultSize(DecimalType(10, 5), 4096) checkDefaultSize(DecimalType.Unlimited, 4096) checkDefaultSize(DateType, 4) - checkDefaultSize(TimestampType,12) + checkDefaultSize(TimestampType, 8) checkDefaultSize(StringType, 4096) checkDefaultSize(BinaryType, 4096) checkDefaultSize(ArrayType(DoubleType, true), 800) @@ -179,11 +249,11 @@ class DataTypeSuite extends FunSuite { expected = false) checkEqualsIgnoreCompatibleNullability( from = MapType(StringType, ArrayType(IntegerType, true), valueContainsNull = true), - to = MapType(StringType, ArrayType(IntegerType, false), valueContainsNull = true), + to = MapType(StringType, ArrayType(IntegerType, false), valueContainsNull = true), expected = false) checkEqualsIgnoreCompatibleNullability( from = MapType(StringType, ArrayType(IntegerType, false), valueContainsNull = true), - to = MapType(StringType, ArrayType(IntegerType, true), valueContainsNull = true), + to = MapType(StringType, ArrayType(IntegerType, true), valueContainsNull = true), expected = true) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala deleted file mode 100644 index a22aa6f244c48..0000000000000 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala +++ /dev/null @@ -1,70 +0,0 @@ -/* -* Licensed to the Apache Software Foundation (ASF) under one or more -* contributor license agreements. See the NOTICE file distributed with -* this work for additional information regarding copyright ownership. -* The ASF licenses this file to You under the Apache License, Version 2.0 -* (the "License"); you may not use this file except in compliance with -* the License. You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -*/ - -package org.apache.spark.sql.types - -import org.scalatest.FunSuite - -// scalastyle:off -class UTF8StringSuite extends FunSuite { - test("basic") { - def check(str: String, len: Int) { - - assert(UTF8String(str).length == len) - assert(UTF8String(str.getBytes("utf8")).length() == len) - - assert(UTF8String(str) == str) - assert(UTF8String(str.getBytes("utf8")) == str) - assert(UTF8String(str).toString == str) - assert(UTF8String(str.getBytes("utf8")).toString == str) - assert(UTF8String(str.getBytes("utf8")) == UTF8String(str)) - - assert(UTF8String(str).hashCode() == UTF8String(str.getBytes("utf8")).hashCode()) - } - - check("hello", 5) - check("世 界", 3) - } - - test("contains") { - assert(UTF8String("hello").contains(UTF8String("ello"))) - assert(!UTF8String("hello").contains(UTF8String("vello"))) - assert(UTF8String("大千世界").contains(UTF8String("千世"))) - assert(!UTF8String("大千世界").contains(UTF8String("世千"))) - } - - test("prefix") { - assert(UTF8String("hello").startsWith(UTF8String("hell"))) - assert(!UTF8String("hello").startsWith(UTF8String("ell"))) - assert(UTF8String("大千世界").startsWith(UTF8String("大千"))) - assert(!UTF8String("大千世界").startsWith(UTF8String("千"))) - } - - test("suffix") { - assert(UTF8String("hello").endsWith(UTF8String("ello"))) - assert(!UTF8String("hello").endsWith(UTF8String("ellov"))) - assert(UTF8String("大千世界").endsWith(UTF8String("世界"))) - assert(!UTF8String("大千世界").endsWith(UTF8String("世"))) - } - - test("slice") { - assert(UTF8String("hello").slice(1, 3) == UTF8String("el")) - assert(UTF8String("大千世界").slice(0, 1) == UTF8String("大")) - assert(UTF8String("大千世界").slice(1, 3) == UTF8String("千世")) - assert(UTF8String("大千世界").slice(3, 5) == UTF8String("界")) - } -} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala index de6a2cd448c47..4c0365cf1b6f9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala @@ -17,12 +17,13 @@ package org.apache.spark.sql.types.decimal +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.types.Decimal -import org.scalatest.{PrivateMethodTester, FunSuite} +import org.scalatest.PrivateMethodTester import scala.language.postfixOps -class DecimalSuite extends FunSuite with PrivateMethodTester { +class DecimalSuite extends SparkFunSuite with PrivateMethodTester { test("creating decimals") { /** Check that a Decimal has the given string representation, precision and scale */ def checkDecimal(d: Decimal, string: String, precision: Int, scale: Int): Unit = { @@ -155,4 +156,10 @@ class DecimalSuite extends FunSuite with PrivateMethodTester { assert(Decimal(-100) % Decimal(3) === Decimal(-1)) assert(Decimal(100) % Decimal(0) === null) } + + test("set/setOrNull") { + assert(new Decimal().set(10L, 10, 0).toUnscaledLong === 10L) + assert(new Decimal().set(100L, 10, 0).toUnscaledLong === 100L) + assert(Decimal(Long.MaxValue, 100, 0).toUnscaledLong === Long.MaxValue) + } } diff --git a/sql/core/pom.xml b/sql/core/pom.xml index e3a6b1fe72435..ed75475a87067 100644 --- a/sql/core/pom.xml +++ b/sql/core/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.spark spark-catalyst_${scala.binary.version} @@ -54,17 +61,17 @@ test - com.twitter + org.apache.parquet parquet-column - com.twitter + org.apache.parquet parquet-hadoop com.fasterxml.jackson.core jackson-databind - 2.3.0 + ${fasterxml.jackson.version} org.jodd @@ -103,13 +110,5 @@ target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes - - - ../../python - - pyspark/sql/*.py - - - diff --git a/sql/core/src/main/java/org/apache/spark/sql/SaveMode.java b/sql/core/src/main/java/org/apache/spark/sql/SaveMode.java index a40be526d0d11..9665c3c46f901 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/SaveMode.java +++ b/sql/core/src/main/java/org/apache/spark/sql/SaveMode.java @@ -18,28 +18,38 @@ /** * SaveMode is used to specify the expected behavior of saving a DataFrame to a data source. + * + * @since 1.3.0 */ public enum SaveMode { /** * Append mode means that when saving a DataFrame to a data source, if data/table already exists, * contents of the DataFrame are expected to be appended to existing data. + * + * @since 1.3.0 */ Append, /** * Overwrite mode means that when saving a DataFrame to a data source, * if data/table already exists, existing data is expected to be overwritten by the contents of * the DataFrame. + * + * @since 1.3.0 */ Overwrite, /** * ErrorIfExists mode means that when saving a DataFrame to a data source, if data already exists, * an exception is expected to be thrown. + * + * @since 1.3.0 */ ErrorIfExists, /** * Ignore mode means that when saving a DataFrame to a data source, if data already exists, * the save operation is expected to not save the contents of the DataFrame and to not * change the existing data. + * + * @since 1.3.0 */ Ignore } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index e6e475bb82f82..b4e008a6e8480 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -21,9 +21,10 @@ import scala.language.implicitConversions import org.apache.spark.annotation.Experimental import org.apache.spark.Logging +import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions.lit import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedStar, UnresolvedExtractValue} +import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.types._ @@ -45,6 +46,8 @@ private[sql] object Column { * @groupname expr_ops Expression operators. * @groupname df_ops DataFrame functions. * @groupname Ungrouped Support functions for DataFrames. + * + * @since 1.3.0 */ @Experimental class Column(protected[sql] val expr: Expression) extends Logging { @@ -77,8 +80,9 @@ class Column(protected[sql] val expr: Expression) extends Logging { * of every struct in that array, and return an Array of fields * * @group expr_ops + * @since 1.4.0 */ - def apply(field: Any): Column = UnresolvedExtractValue(expr, Literal(field)) + def apply(extraction: Any): Column = UnresolvedExtractValue(expr, lit(extraction).expr) /** * Unary minus, i.e. negate the expression. @@ -92,6 +96,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def unary_- : Column = UnaryMinus(expr) @@ -107,6 +112,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def unary_! : Column = Not(expr) @@ -122,6 +128,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def === (other: Any): Column = { val right = lit(other).expr @@ -145,6 +152,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def equalTo(other: Any): Column = this === other @@ -161,6 +169,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def !== (other: Any): Column = Not(EqualTo(expr, lit(other).expr)) @@ -177,6 +186,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def notEqual(other: Any): Column = Not(EqualTo(expr, lit(other).expr)) @@ -192,6 +202,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def > (other: Any): Column = GreaterThan(expr, lit(other).expr) @@ -207,6 +218,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def gt(other: Any): Column = this > other @@ -221,6 +233,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def < (other: Any): Column = LessThan(expr, lit(other).expr) @@ -235,6 +248,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def lt(other: Any): Column = this < other @@ -249,6 +263,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def <= (other: Any): Column = LessThanOrEqual(expr, lit(other).expr) @@ -263,6 +278,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def leq(other: Any): Column = this <= other @@ -277,6 +293,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def >= (other: Any): Column = GreaterThanOrEqual(expr, lit(other).expr) @@ -291,6 +308,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def geq(other: Any): Column = this >= other @@ -298,6 +316,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * Equality test that is safe for null values. * * @group expr_ops + * @since 1.3.0 */ def <=> (other: Any): Column = EqualNullSafe(expr, lit(other).expr) @@ -305,13 +324,78 @@ class Column(protected[sql] val expr: Expression) extends Logging { * Equality test that is safe for null values. * * @group java_expr_ops + * @since 1.3.0 */ def eqNullSafe(other: Any): Column = this <=> other + /** + * Evaluates a list of conditions and returns one of multiple possible result expressions. + * If otherwise is not defined at the end, null is returned for unmatched conditions. + * + * {{{ + * // Example: encoding gender string column into integer. + * + * // Scala: + * people.select(when(people("gender") === "male", 0) + * .when(people("gender") === "female", 1) + * .otherwise(2)) + * + * // Java: + * people.select(when(col("gender").equalTo("male"), 0) + * .when(col("gender").equalTo("female"), 1) + * .otherwise(2)) + * }}} + * + * @group expr_ops + * @since 1.4.0 + */ + def when(condition: Column, value: Any): Column = this.expr match { + case CaseWhen(branches: Seq[Expression]) => + CaseWhen(branches ++ Seq(lit(condition).expr, lit(value).expr)) + case _ => + throw new IllegalArgumentException( + "when() can only be applied on a Column previously generated by when() function") + } + + /** + * Evaluates a list of conditions and returns one of multiple possible result expressions. + * If otherwise is not defined at the end, null is returned for unmatched conditions. + * + * {{{ + * // Example: encoding gender string column into integer. + * + * // Scala: + * people.select(when(people("gender") === "male", 0) + * .when(people("gender") === "female", 1) + * .otherwise(2)) + * + * // Java: + * people.select(when(col("gender").equalTo("male"), 0) + * .when(col("gender").equalTo("female"), 1) + * .otherwise(2)) + * }}} + * + * @group expr_ops + * @since 1.4.0 + */ + def otherwise(value: Any): Column = this.expr match { + case CaseWhen(branches: Seq[Expression]) => + if (branches.size % 2 == 0) { + CaseWhen(branches :+ lit(value).expr) + } else { + throw new IllegalArgumentException( + "otherwise() can only be applied once on a Column previously generated by when()") + } + case _ => + throw new IllegalArgumentException( + "otherwise() can only be applied on a Column previously generated by when()") + } + /** * True if the current column is between the lower bound and upper bound, inclusive. * * @group java_expr_ops + * @since 1.4.0 */ def between(lowerBound: Any, upperBound: Any): Column = { (this >= lowerBound) && (this <= upperBound) @@ -321,6 +405,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * True if the current expression is null. * * @group expr_ops + * @since 1.3.0 */ def isNull: Column = IsNull(expr) @@ -328,6 +413,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * True if the current expression is NOT null. * * @group expr_ops + * @since 1.3.0 */ def isNotNull: Column = IsNotNull(expr) @@ -342,6 +428,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def || (other: Any): Column = Or(expr, lit(other).expr) @@ -356,6 +443,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def or(other: Column): Column = this || other @@ -370,6 +458,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def && (other: Any): Column = And(expr, lit(other).expr) @@ -384,6 +473,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def and(other: Column): Column = this && other @@ -398,6 +488,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def + (other: Any): Column = Add(expr, lit(other).expr) @@ -412,6 +503,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def plus(other: Any): Column = this + other @@ -426,6 +518,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def - (other: Any): Column = Subtract(expr, lit(other).expr) @@ -440,6 +533,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def minus(other: Any): Column = this - other @@ -454,6 +548,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def * (other: Any): Column = Multiply(expr, lit(other).expr) @@ -468,6 +563,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def multiply(other: Any): Column = this * other @@ -482,6 +578,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def / (other: Any): Column = Divide(expr, lit(other).expr) @@ -496,6 +593,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group java_expr_ops + * @since 1.3.0 */ def divide(other: Any): Column = this / other @@ -503,6 +601,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * Modulo (a.k.a. remainder) expression. * * @group expr_ops + * @since 1.3.0 */ def % (other: Any): Column = Remainder(expr, lit(other).expr) @@ -510,6 +609,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * Modulo (a.k.a. remainder) expression. * * @group java_expr_ops + * @since 1.3.0 */ def mod(other: Any): Column = this % other @@ -518,14 +618,16 @@ class Column(protected[sql] val expr: Expression) extends Logging { * by the evaluated values of the arguments. * * @group expr_ops + * @since 1.3.0 */ @scala.annotation.varargs - def in(list: Column*): Column = In(expr, list.map(_.expr)) + def in(list: Any*): Column = In(expr, list.map(lit(_).expr)) /** * SQL like expression. * * @group expr_ops + * @since 1.3.0 */ def like(literal: String): Column = Like(expr, lit(literal).expr) @@ -533,6 +635,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * SQL RLIKE expression (LIKE with Regex). * * @group expr_ops + * @since 1.3.0 */ def rlike(literal: String): Column = RLike(expr, lit(literal).expr) @@ -541,6 +644,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * or gets a value by key `key` in a [[MapType]]. * * @group expr_ops + * @since 1.3.0 */ def getItem(key: Any): Column = UnresolvedExtractValue(expr, Literal(key)) @@ -548,6 +652,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * An expression that gets a field by name in a [[StructType]]. * * @group expr_ops + * @since 1.3.0 */ def getField(fieldName: String): Column = UnresolvedExtractValue(expr, Literal(fieldName)) @@ -557,6 +662,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * @param len expression for the length of the substring. * * @group expr_ops + * @since 1.3.0 */ def substr(startPos: Column, len: Column): Column = Substring(expr, startPos.expr, len.expr) @@ -566,6 +672,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * @param len length of the substring. * * @group expr_ops + * @since 1.3.0 */ def substr(startPos: Int, len: Int): Column = Substring(expr, lit(startPos).expr, lit(len).expr) @@ -573,6 +680,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * Contains the other element. * * @group expr_ops + * @since 1.3.0 */ def contains(other: Any): Column = Contains(expr, lit(other).expr) @@ -580,6 +688,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * String starts with. * * @group expr_ops + * @since 1.3.0 */ def startsWith(other: Column): Column = StartsWith(expr, lit(other).expr) @@ -587,6 +696,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * String starts with another string literal. * * @group expr_ops + * @since 1.3.0 */ def startsWith(literal: String): Column = this.startsWith(lit(literal)) @@ -594,6 +704,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * String ends with. * * @group expr_ops + * @since 1.3.0 */ def endsWith(other: Column): Column = EndsWith(expr, lit(other).expr) @@ -601,9 +712,22 @@ class Column(protected[sql] val expr: Expression) extends Logging { * String ends with another string literal. * * @group expr_ops + * @since 1.3.0 */ def endsWith(literal: String): Column = this.endsWith(lit(literal)) + /** + * Gives the column an alias. Same as `as`. + * {{{ + * // Renames colA to colB in select output. + * df.select($"colA".alias("colB")) + * }}} + * + * @group expr_ops + * @since 1.4.0 + */ + def alias(alias: String): Column = as(alias) + /** * Gives the column an alias. * {{{ @@ -612,9 +736,34 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def as(alias: String): Column = Alias(expr, alias)() + /** + * (Scala-specific) Assigns the given aliases to the results of a table generating function. + * {{{ + * // Renames colA to colB in select output. + * df.select(explode($"myMap").as("key" :: "value" :: Nil)) + * }}} + * + * @group expr_ops + * @since 1.4.0 + */ + def as(aliases: Seq[String]): Column = MultiAlias(expr, aliases) + + /** + * Assigns the given aliases to the results of a table generating function. + * {{{ + * // Renames colA to colB in select output. + * df.select(explode($"myMap").as("key" :: "value" :: Nil)) + * }}} + * + * @group expr_ops + * @since 1.4.0 + */ + def as(aliases: Array[String]): Column = MultiAlias(expr, aliases) + /** * Gives the column an alias. * {{{ @@ -623,6 +772,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def as(alias: Symbol): Column = Alias(expr, alias.name)() @@ -634,6 +784,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def as(alias: String, metadata: Metadata): Column = { Alias(expr, alias)(explicitMetadata = Some(metadata)) @@ -651,6 +802,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def cast(to: DataType): Column = expr match { // Lift alias out of cast so we can support col.as("name").cast(IntegerType) @@ -668,6 +820,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def cast(to: String): Column = cast(DataTypeParser.parse(to)) @@ -682,6 +835,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def desc: Column = SortOrder(expr, Descending) @@ -696,6 +850,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.3.0 */ def asc: Column = SortOrder(expr, Ascending) @@ -703,6 +858,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * Prints the expression to the console for debugging purpose. * * @group df_ops + * @since 1.3.0 */ def explain(extended: Boolean): Unit = { if (extended) { @@ -719,6 +875,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.4.0 */ def bitwiseOR(other: Any): Column = BitwiseOr(expr, lit(other).expr) @@ -729,6 +886,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.4.0 */ def bitwiseAND(other: Any): Column = BitwiseAnd(expr, lit(other).expr) @@ -739,70 +897,141 @@ class Column(protected[sql] val expr: Expression) extends Logging { * }}} * * @group expr_ops + * @since 1.4.0 */ def bitwiseXOR(other: Any): Column = BitwiseXor(expr, lit(other).expr) + /** + * Define a windowing column. + * + * {{{ + * val w = Window.partitionBy("name").orderBy("id") + * df.select( + * sum("price").over(w.rangeBetween(Long.MinValue, 2)), + * avg("price").over(w.rowsBetween(0, 4)) + * ) + * }}} + * + * @group expr_ops + * @since 1.4.0 + */ + def over(window: expressions.WindowSpec): Column = window.withAggregate(this) + } /** * :: Experimental :: * A convenient class used for constructing schema. + * + * @since 1.3.0 */ @Experimental class ColumnName(name: String) extends Column(name) { - /** Creates a new AttributeReference of type boolean */ + /** + * Creates a new [[StructField]] of type boolean. + * @since 1.3.0 + */ def boolean: StructField = StructField(name, BooleanType) - /** Creates a new AttributeReference of type byte */ + /** + * Creates a new [[StructField]] of type byte. + * @since 1.3.0 + */ def byte: StructField = StructField(name, ByteType) - /** Creates a new AttributeReference of type short */ + /** + * Creates a new [[StructField]] of type short. + * @since 1.3.0 + */ def short: StructField = StructField(name, ShortType) - /** Creates a new AttributeReference of type int */ + /** + * Creates a new [[StructField]] of type int. + * @since 1.3.0 + */ def int: StructField = StructField(name, IntegerType) - /** Creates a new AttributeReference of type long */ + /** + * Creates a new [[StructField]] of type long. + * @since 1.3.0 + */ def long: StructField = StructField(name, LongType) - /** Creates a new AttributeReference of type float */ + /** + * Creates a new [[StructField]] of type float. + * @since 1.3.0 + */ def float: StructField = StructField(name, FloatType) - /** Creates a new AttributeReference of type double */ + /** + * Creates a new [[StructField]] of type double. + * @since 1.3.0 + */ def double: StructField = StructField(name, DoubleType) - /** Creates a new AttributeReference of type string */ + /** + * Creates a new [[StructField]] of type string. + * @since 1.3.0 + */ def string: StructField = StructField(name, StringType) - /** Creates a new AttributeReference of type date */ + /** + * Creates a new [[StructField]] of type date. + * @since 1.3.0 + */ def date: StructField = StructField(name, DateType) - /** Creates a new AttributeReference of type decimal */ + /** + * Creates a new [[StructField]] of type decimal. + * @since 1.3.0 + */ def decimal: StructField = StructField(name, DecimalType.Unlimited) - /** Creates a new AttributeReference of type decimal */ + /** + * Creates a new [[StructField]] of type decimal. + * @since 1.3.0 + */ def decimal(precision: Int, scale: Int): StructField = StructField(name, DecimalType(precision, scale)) - /** Creates a new AttributeReference of type timestamp */ + /** + * Creates a new [[StructField]] of type timestamp. + * @since 1.3.0 + */ def timestamp: StructField = StructField(name, TimestampType) - /** Creates a new AttributeReference of type binary */ + /** + * Creates a new [[StructField]] of type binary. + * @since 1.3.0 + */ def binary: StructField = StructField(name, BinaryType) - /** Creates a new AttributeReference of type array */ + /** + * Creates a new [[StructField]] of type array. + * @since 1.3.0 + */ def array(dataType: DataType): StructField = StructField(name, ArrayType(dataType)) - /** Creates a new AttributeReference of type map */ + /** + * Creates a new [[StructField]] of type map. + * @since 1.3.0 + */ def map(keyType: DataType, valueType: DataType): StructField = map(MapType(keyType, valueType)) def map(mapType: MapType): StructField = StructField(name, mapType) - /** Creates a new AttributeReference of type struct */ + /** + * Creates a new [[StructField]] of type struct. + * @since 1.3.0 + */ def struct(fields: StructField*): StructField = struct(StructType(fields)) + /** + * Creates a new [[StructField]] of type struct. + * @since 1.3.0 + */ def struct(structType: StructType): StructField = StructField(name, structType) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 7947042c14299..466258e76f9f6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -18,33 +18,30 @@ package org.apache.spark.sql import java.io.CharArrayWriter -import java.sql.DriverManager +import java.util.Properties - -import scala.collection.JavaConversions._ import scala.language.implicitConversions import scala.reflect.ClassTag import scala.reflect.runtime.universe.TypeTag import scala.util.control.NonFatal import com.fasterxml.jackson.core.JsonFactory - import org.apache.commons.lang3.StringUtils + import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.python.SerDeUtil import org.apache.spark.rdd.RDD -import org.apache.spark.storage.StorageLevel -import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser} -import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedRelation, ResolvedStar} +import org.apache.spark.sql.catalyst.analysis.{MultiAlias, ResolvedStar, UnresolvedAttribute} import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.{JoinType, Inner} -import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.sql.catalyst.plans.logical.{Filter, _} +import org.apache.spark.sql.catalyst.plans.{Inner, JoinType} +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser} import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD} -import org.apache.spark.sql.jdbc.JDBCWriteDetails -import org.apache.spark.sql.json.{JacksonGenerator, JsonRDD} +import org.apache.spark.sql.json.JacksonGenerator +import org.apache.spark.sql.sources.CreateTableUsingAsSelect import org.apache.spark.sql.types._ -import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect} +import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils @@ -59,14 +56,11 @@ private[sql] object DataFrame { * :: Experimental :: * A distributed collection of data organized into named columns. * - * A [[DataFrame]] is equivalent to a relational table in Spark SQL. There are multiple ways - * to create a [[DataFrame]]: + * A [[DataFrame]] is equivalent to a relational table in Spark SQL. The following example creates + * a [[DataFrame]] by pointing Spark SQL to a Parquet data set. * {{{ - * // Create a DataFrame from Parquet files - * val people = sqlContext.parquetFile("...") - * - * // Create a DataFrame from data sources - * val df = sqlContext.load("...", "json") + * val people = sqlContext.read.parquet("...") // in Scala + * DataFrame people = sqlContext.read().parquet("...") // in Java * }}} * * Once created, it can be manipulated using the various domain-specific-language (DSL) functions @@ -88,8 +82,8 @@ private[sql] object DataFrame { * A more concrete example in Scala: * {{{ * // To create DataFrame using SQLContext - * val people = sqlContext.parquetFile("...") - * val department = sqlContext.parquetFile("...") + * val people = sqlContext.read.parquet("...") + * val department = sqlContext.read.parquet("...") * * people.filter("age > 30") * .join(department, people("deptId") === department("id")) @@ -100,8 +94,8 @@ private[sql] object DataFrame { * and in Java: * {{{ * // To create DataFrame using SQLContext - * DataFrame people = sqlContext.parquetFile("..."); - * DataFrame department = sqlContext.parquetFile("..."); + * DataFrame people = sqlContext.read().parquet("..."); + * DataFrame department = sqlContext.read().parquet("..."); * * people.filter("age".gt(30)) * .join(department, people.col("deptId").equalTo(department("id"))) @@ -114,6 +108,7 @@ private[sql] object DataFrame { * @groupname rdd RDD Operations * @groupname output Output Operations * @groupname action Actions + * @since 1.3.0 */ // TODO: Improve documentation. @Experimental @@ -159,7 +154,7 @@ class DataFrame private[sql]( } protected[sql] def resolve(colName: String): NamedExpression = { - queryExecution.analyzed.resolve(colName.split("\\."), sqlContext.analyzer.resolver).getOrElse { + queryExecution.analyzed.resolveQuoted(colName, sqlContext.analyzer.resolver).getOrElse { throw new AnalysisException( s"""Cannot resolve column name "$colName" among (${schema.fieldNames.mkString(", ")})""") } @@ -167,29 +162,40 @@ class DataFrame private[sql]( protected[sql] def numericColumns: Seq[Expression] = { schema.fields.filter(_.dataType.isInstanceOf[NumericType]).map { n => - queryExecution.analyzed.resolve(n.name.split("\\."), sqlContext.analyzer.resolver).get + queryExecution.analyzed.resolveQuoted(n.name, sqlContext.analyzer.resolver).get } } /** * Internal API for Python - * @param numRows Number of rows to show + * @param _numRows Number of rows to show */ - private[sql] def showString(numRows: Int): String = { + private[sql] def showString(_numRows: Int): String = { + val numRows = _numRows.max(0) val sb = new StringBuilder - val data = take(numRows) + val takeResult = take(numRows + 1) + val hasMoreData = takeResult.length > numRows + val data = takeResult.take(numRows) val numCols = schema.fieldNames.length + // For array values, replace Seq and Array with square brackets // For cells that are beyond 20 characters, replace it with the first 17 and "..." val rows: Seq[Seq[String]] = schema.fieldNames.toSeq +: data.map { row => row.toSeq.map { cell => - val str = if (cell == null) "null" else cell.toString + val str = cell match { + case null => "null" + case array: Array[_] => array.mkString("[", ", ", "]") + case seq: Seq[_] => seq.mkString("[", ", ", "]") + case _ => cell.toString + } if (str.length > 20) str.substring(0, 17) + "..." else str }: Seq[String] } + // Initialise the width of each column to a minimum value of '3' + val colWidths = Array.fill(numCols)(3) + // Compute the width of each column - val colWidths = Array.fill(numCols)(0) for (row <- rows) { for ((cell, i) <- row.zipWithIndex) { colWidths(i) = math.max(colWidths(i), cell.length) @@ -201,7 +207,7 @@ class DataFrame private[sql]( // column names rows.head.zipWithIndex.map { case (cell, i) => - StringUtils.leftPad(cell.toString, colWidths(i)) + StringUtils.leftPad(cell, colWidths(i)) }.addString(sb, "|", "|", "|\n") sb.append(sep) @@ -214,6 +220,13 @@ class DataFrame private[sql]( } sb.append(sep) + + // For Data that has more than "numRows" records + if (hasMoreData) { + val rowsString = if (numRows == 1) "row" else "rows" + sb.append(s"only showing top $numRows ${rowsString}\n") + } + sb.toString() } @@ -226,13 +239,10 @@ class DataFrame private[sql]( } } - /** Left here for backward compatibility. */ - @deprecated("1.3.0", "use toDF") - def toSchemaRDD: DataFrame = this - /** * Returns the object itself. * @group basic + * @since 1.3.0 */ // This is declared with parentheses to prevent the Scala compiler from treating // `rdd.toDF("1")` as invoking this toDF and then apply on the returned DataFrame. @@ -247,6 +257,7 @@ class DataFrame private[sql]( * rdd.toDF("id", "name") // this creates a DataFrame with column name "id" and "name" * }}} * @group basic + * @since 1.3.0 */ @scala.annotation.varargs def toDF(colNames: String*): DataFrame = { @@ -258,18 +269,20 @@ class DataFrame private[sql]( val newCols = logicalPlan.output.zip(colNames).map { case (oldAttribute, newName) => Column(oldAttribute).as(newName) } - select(newCols :_*) + select(newCols : _*) } /** * Returns the schema of this [[DataFrame]]. * @group basic + * @since 1.3.0 */ def schema: StructType = queryExecution.analyzed.schema /** * Returns all column names and their data types as an array. * @group basic + * @since 1.3.0 */ def dtypes: Array[(String, String)] = schema.fields.map { field => (field.name, field.dataType.toString) @@ -278,18 +291,21 @@ class DataFrame private[sql]( /** * Returns all column names as an array. * @group basic + * @since 1.3.0 */ def columns: Array[String] = schema.fields.map(_.name) /** * Prints the schema to the console in a nice tree format. * @group basic + * @since 1.3.0 */ def printSchema(): Unit = println(schema.treeString) /** * Prints the plans (logical and physical) to the console for debugging purposes. * @group basic + * @since 1.3.0 */ def explain(extended: Boolean): Unit = { ExplainCommand( @@ -302,6 +318,7 @@ class DataFrame private[sql]( /** * Only prints the physical plan to the console for debugging purposes. * @group basic + * @since 1.3.0 */ def explain(): Unit = explain(extended = false) @@ -309,6 +326,7 @@ class DataFrame private[sql]( * Returns true if the `collect` and `take` methods can be run locally * (without any Spark executors). * @group basic + * @since 1.3.0 */ def isLocal: Boolean = logicalPlan.isInstanceOf[LocalRelation] @@ -325,12 +343,14 @@ class DataFrame private[sql]( * @param numRows Number of rows to show * * @group action + * @since 1.3.0 */ def show(numRows: Int): Unit = println(showString(numRows)) /** * Displays the top 20 rows of [[DataFrame]] in a tabular form. * @group action + * @since 1.3.0 */ def show(): Unit = show(20) @@ -342,6 +362,7 @@ class DataFrame private[sql]( * }}} * * @group dfops + * @since 1.3.1 */ def na: DataFrameNaFunctions = new DataFrameNaFunctions(this) @@ -353,6 +374,7 @@ class DataFrame private[sql]( * }}} * * @group dfops + * @since 1.4.0 */ def stat: DataFrameStatFunctions = new DataFrameStatFunctions(this) @@ -363,6 +385,7 @@ class DataFrame private[sql]( * * @param right Right side of the join operation. * @group dfops + * @since 1.3.0 */ def join(right: DataFrame): DataFrame = { Join(logicalPlan, right.logicalPlan, joinType = Inner, None) @@ -386,22 +409,53 @@ class DataFrame private[sql]( * @param right Right side of the join operation. * @param usingColumn Name of the column to join on. This column must exist on both sides. * @group dfops + * @since 1.4.0 */ def join(right: DataFrame, usingColumn: String): DataFrame = { + join(right, Seq(usingColumn)) + } + + /** + * Inner equi-join with another [[DataFrame]] using the given columns. + * + * Different from other join functions, the join columns will only appear once in the output, + * i.e. similar to SQL's `JOIN USING` syntax. + * + * {{{ + * // Joining df1 and df2 using the columns "user_id" and "user_name" + * df1.join(df2, Seq("user_id", "user_name")) + * }}} + * + * Note that if you perform a self-join using this function without aliasing the input + * [[DataFrame]]s, you will NOT be able to reference any columns after the join, since + * there is no way to disambiguate which side of the join you would like to reference. + * + * @param right Right side of the join operation. + * @param usingColumns Names of the columns to join on. This columns must exist on both sides. + * @group dfops + * @since 1.4.0 + */ + def join(right: DataFrame, usingColumns: Seq[String]): DataFrame = { // Analyze the self join. The assumption is that the analyzer will disambiguate left vs right // by creating a new instance for one of the branch. val joined = sqlContext.executePlan( Join(logicalPlan, right.logicalPlan, joinType = Inner, None)).analyzed.asInstanceOf[Join] - // Project only one of the join column. - val joinedCol = joined.right.resolve(usingColumn) + // Project only one of the join columns. + val joinedCols = usingColumns.map(col => joined.right.resolve(col)) + val condition = usingColumns.map { col => + catalyst.expressions.EqualTo(joined.left.resolve(col), joined.right.resolve(col)) + }.reduceLeftOption[catalyst.expressions.BinaryExpression] { (cond, eqTo) => + catalyst.expressions.And(cond, eqTo) + } + Project( - joined.output.filterNot(_ == joinedCol), + joined.output.filterNot(joinedCols.contains(_)), Join( joined.left, joined.right, joinType = Inner, - Some(EqualTo(joined.left.resolve(usingColumn), joined.right.resolve(usingColumn)))) + condition) ) } @@ -414,6 +468,7 @@ class DataFrame private[sql]( * df1.join(df2).where($"df1Key" === $"df2Key") * }}} * @group dfops + * @since 1.3.0 */ def join(right: DataFrame, joinExprs: Column): DataFrame = join(right, joinExprs, "inner") @@ -433,8 +488,9 @@ class DataFrame private[sql]( * * @param right Right side of the join. * @param joinExprs Join expression. - * @param joinType One of: `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. + * @param joinType One of: `inner`, `outer`, `left_outer`, `right_outer`, `leftsemi`. * @group dfops + * @since 1.3.0 */ def join(right: DataFrame, joinExprs: Column, joinType: String): DataFrame = { // Note that in this function, we introduce a hack in the case of self-join to automatically @@ -466,8 +522,9 @@ class DataFrame private[sql]( // By the time we get here, since we have already run analysis, all attributes should've been // resolved and become AttributeReference. val cond = plan.condition.map { _.transform { - case EqualTo(a: AttributeReference, b: AttributeReference) if a.sameRef(b) => - EqualTo(plan.left.resolve(a.name), plan.right.resolve(b.name)) + case catalyst.expressions.EqualTo(a: AttributeReference, b: AttributeReference) + if a.sameRef(b) => + catalyst.expressions.EqualTo(plan.left.resolve(a.name), plan.right.resolve(b.name)) }} plan.copy(condition = cond) } @@ -481,10 +538,11 @@ class DataFrame private[sql]( * df.sort($"sortcol".asc) * }}} * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs def sort(sortCol: String, sortCols: String*): DataFrame = { - sort((sortCol +: sortCols).map(apply) :_*) + sort((sortCol +: sortCols).map(apply) : _*) } /** @@ -493,6 +551,7 @@ class DataFrame private[sql]( * df.sort($"col1", $"col2".desc) * }}} * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs def sort(sortExprs: Column*): DataFrame = { @@ -511,27 +570,31 @@ class DataFrame private[sql]( * Returns a new [[DataFrame]] sorted by the given expressions. * This is an alias of the `sort` function. * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs - def orderBy(sortCol: String, sortCols: String*): DataFrame = sort(sortCol, sortCols :_*) + def orderBy(sortCol: String, sortCols: String*): DataFrame = sort(sortCol, sortCols : _*) /** * Returns a new [[DataFrame]] sorted by the given expressions. * This is an alias of the `sort` function. * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs - def orderBy(sortExprs: Column*): DataFrame = sort(sortExprs :_*) + def orderBy(sortExprs: Column*): DataFrame = sort(sortExprs : _*) /** * Selects column based on the column name and return it as a [[Column]]. * @group dfops + * @since 1.3.0 */ def apply(colName: String): Column = col(colName) /** * Selects column based on the column name and return it as a [[Column]]. * @group dfops + * @since 1.3.0 */ def col(colName: String): Column = colName match { case "*" => @@ -544,29 +607,37 @@ class DataFrame private[sql]( /** * Returns a new [[DataFrame]] with an alias set. * @group dfops + * @since 1.3.0 */ def as(alias: String): DataFrame = Subquery(alias, logicalPlan) /** * (Scala-specific) Returns a new [[DataFrame]] with an alias set. * @group dfops + * @since 1.3.0 */ def as(alias: Symbol): DataFrame = as(alias.name) /** - * Selects a set of expressions. + * Selects a set of column based expressions. * {{{ * df.select($"colA", $"colB" + 1) * }}} * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs def select(cols: Column*): DataFrame = { val namedExpressions = cols.map { case Column(expr: NamedExpression) => expr + // Leave an unaliased explode with an empty list of names since the analzyer will generate the + // correct defaults after the nested expression's type has been resolved. + case Column(explode: Explode) => MultiAlias(explode, Nil) case Column(expr: Expression) => Alias(expr, expr.prettyString)() } - Project(namedExpressions.toSeq, logicalPlan) + // When user continuously call `select`, speed up analysis by collapsing `Project` + import org.apache.spark.sql.catalyst.optimizer.ProjectCollapsing + Project(namedExpressions.toSeq, ProjectCollapsing(logicalPlan)) } /** @@ -579,9 +650,10 @@ class DataFrame private[sql]( * df.select($"colA", $"colB") * }}} * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs - def select(col: String, cols: String*): DataFrame = select((col +: cols).map(Column(_)) :_*) + def select(col: String, cols: String*): DataFrame = select((col +: cols).map(Column(_)) : _*) /** * Selects a set of SQL expressions. This is a variant of `select` that accepts @@ -591,6 +663,7 @@ class DataFrame private[sql]( * df.selectExpr("colA", "colB as newName", "abs(colC)") * }}} * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs def selectExpr(exprs: String*): DataFrame = { @@ -608,6 +681,7 @@ class DataFrame private[sql]( * peopleDf($"age" > 15) * }}} * @group dfops + * @since 1.3.0 */ def filter(condition: Column): DataFrame = Filter(condition.expr, logicalPlan) @@ -617,6 +691,7 @@ class DataFrame private[sql]( * peopleDf.filter("age > 15") * }}} * @group dfops + * @since 1.3.0 */ def filter(conditionExpr: String): DataFrame = { filter(Column(new SqlParser().parseExpression(conditionExpr))) @@ -631,6 +706,7 @@ class DataFrame private[sql]( * peopleDf($"age" > 15) * }}} * @group dfops + * @since 1.3.0 */ def where(condition: Column): DataFrame = filter(condition) @@ -649,9 +725,56 @@ class DataFrame private[sql]( * )) * }}} * @group dfops + * @since 1.3.0 + */ + @scala.annotation.varargs + def groupBy(cols: Column*): GroupedData = { + GroupedData(this, cols.map(_.expr), GroupedData.GroupByType) + } + + /** + * Create a multi-dimensional rollup for the current [[DataFrame]] using the specified columns, + * so we can run aggregation on them. + * See [[GroupedData]] for all the available aggregate functions. + * + * {{{ + * // Compute the average for all numeric columns rolluped by department and group. + * df.rollup($"department", $"group").avg() + * + * // Compute the max age and average salary, rolluped by department and gender. + * df.rollup($"department", $"gender").agg(Map( + * "salary" -> "avg", + * "age" -> "max" + * )) + * }}} + * @group dfops + * @since 1.4.0 */ @scala.annotation.varargs - def groupBy(cols: Column*): GroupedData = new GroupedData(this, cols.map(_.expr)) + def rollup(cols: Column*): GroupedData = { + GroupedData(this, cols.map(_.expr), GroupedData.RollupType) + } + + /** + * Create a multi-dimensional cube for the current [[DataFrame]] using the specified columns, + * so we can run aggregation on them. + * See [[GroupedData]] for all the available aggregate functions. + * + * {{{ + * // Compute the average for all numeric columns cubed by department and group. + * df.cube($"department", $"group").avg() + * + * // Compute the max age and average salary, cubed by department and gender. + * df.cube($"department", $"gender").agg(Map( + * "salary" -> "avg", + * "age" -> "max" + * )) + * }}} + * @group dfops + * @since 1.4.0 + */ + @scala.annotation.varargs + def cube(cols: Column*): GroupedData = GroupedData(this, cols.map(_.expr), GroupedData.CubeType) /** * Groups the [[DataFrame]] using the specified columns, so we can run aggregation on them. @@ -671,11 +794,66 @@ class DataFrame private[sql]( * )) * }}} * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs def groupBy(col1: String, cols: String*): GroupedData = { val colNames: Seq[String] = col1 +: cols - new GroupedData(this, colNames.map(colName => resolve(colName))) + GroupedData(this, colNames.map(colName => resolve(colName)), GroupedData.GroupByType) + } + + /** + * Create a multi-dimensional rollup for the current [[DataFrame]] using the specified columns, + * so we can run aggregation on them. + * See [[GroupedData]] for all the available aggregate functions. + * + * This is a variant of rollup that can only group by existing columns using column names + * (i.e. cannot construct expressions). + * + * {{{ + * // Compute the average for all numeric columns rolluped by department and group. + * df.rollup("department", "group").avg() + * + * // Compute the max age and average salary, rolluped by department and gender. + * df.rollup($"department", $"gender").agg(Map( + * "salary" -> "avg", + * "age" -> "max" + * )) + * }}} + * @group dfops + * @since 1.4.0 + */ + @scala.annotation.varargs + def rollup(col1: String, cols: String*): GroupedData = { + val colNames: Seq[String] = col1 +: cols + GroupedData(this, colNames.map(colName => resolve(colName)), GroupedData.RollupType) + } + + /** + * Create a multi-dimensional cube for the current [[DataFrame]] using the specified columns, + * so we can run aggregation on them. + * See [[GroupedData]] for all the available aggregate functions. + * + * This is a variant of cube that can only group by existing columns using column names + * (i.e. cannot construct expressions). + * + * {{{ + * // Compute the average for all numeric columns cubed by department and group. + * df.cube("department", "group").avg() + * + * // Compute the max age and average salary, cubed by department and gender. + * df.cube($"department", $"gender").agg(Map( + * "salary" -> "avg", + * "age" -> "max" + * )) + * }}} + * @group dfops + * @since 1.4.0 + */ + @scala.annotation.varargs + def cube(col1: String, cols: String*): GroupedData = { + val colNames: Seq[String] = col1 +: cols + GroupedData(this, colNames.map(colName => resolve(colName)), GroupedData.CubeType) } /** @@ -686,9 +864,10 @@ class DataFrame private[sql]( * df.groupBy().agg("age" -> "max", "salary" -> "avg") * }}} * @group dfops + * @since 1.3.0 */ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = { - groupBy().agg(aggExpr, aggExprs :_*) + groupBy().agg(aggExpr, aggExprs : _*) } /** @@ -699,6 +878,7 @@ class DataFrame private[sql]( * df.groupBy().agg(Map("age" -> "max", "salary" -> "avg")) * }}} * @group dfops + * @since 1.3.0 */ def agg(exprs: Map[String, String]): DataFrame = groupBy().agg(exprs) @@ -710,6 +890,7 @@ class DataFrame private[sql]( * df.groupBy().agg(Map("age" -> "max", "salary" -> "avg")) * }}} * @group dfops + * @since 1.3.0 */ def agg(exprs: java.util.Map[String, String]): DataFrame = groupBy().agg(exprs) @@ -721,14 +902,16 @@ class DataFrame private[sql]( * df.groupBy().agg(max($"age"), avg($"salary")) * }}} * @group dfops + * @since 1.3.0 */ @scala.annotation.varargs - def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs :_*) + def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs : _*) /** * Returns a new [[DataFrame]] by taking the first `n` rows. The difference between this function * and `head` is that `head` returns an array while `limit` returns a new [[DataFrame]]. * @group dfops + * @since 1.3.0 */ def limit(n: Int): DataFrame = Limit(Literal(n), logicalPlan) @@ -736,6 +919,7 @@ class DataFrame private[sql]( * Returns a new [[DataFrame]] containing union of rows in this frame and another frame. * This is equivalent to `UNION ALL` in SQL. * @group dfops + * @since 1.3.0 */ def unionAll(other: DataFrame): DataFrame = Union(logicalPlan, other.logicalPlan) @@ -743,6 +927,7 @@ class DataFrame private[sql]( * Returns a new [[DataFrame]] containing rows only in both this frame and another frame. * This is equivalent to `INTERSECT` in SQL. * @group dfops + * @since 1.3.0 */ def intersect(other: DataFrame): DataFrame = Intersect(logicalPlan, other.logicalPlan) @@ -750,6 +935,7 @@ class DataFrame private[sql]( * Returns a new [[DataFrame]] containing rows in this frame but not in another frame. * This is equivalent to `EXCEPT` in SQL. * @group dfops + * @since 1.3.0 */ def except(other: DataFrame): DataFrame = Except(logicalPlan, other.logicalPlan) @@ -760,6 +946,7 @@ class DataFrame private[sql]( * @param fraction Fraction of rows to generate. * @param seed Seed for sampling. * @group dfops + * @since 1.3.0 */ def sample(withReplacement: Boolean, fraction: Double, seed: Long): DataFrame = { Sample(0.0, fraction, withReplacement, seed, logicalPlan) @@ -771,6 +958,7 @@ class DataFrame private[sql]( * @param withReplacement Sample with replacement or not. * @param fraction Fraction of rows to generate. * @group dfops + * @since 1.3.0 */ def sample(withReplacement: Boolean, fraction: Double): DataFrame = { sample(withReplacement, fraction, Utils.random.nextLong) @@ -782,6 +970,7 @@ class DataFrame private[sql]( * @param weights weights for splits, will be normalized if they don't sum to 1. * @param seed Seed for sampling. * @group dfops + * @since 1.4.0 */ def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] = { val sum = weights.sum @@ -796,6 +985,7 @@ class DataFrame private[sql]( * * @param weights weights for splits, will be normalized if they don't sum to 1. * @group dfops + * @since 1.4.0 */ def randomSplit(weights: Array[Double]): Array[DataFrame] = { randomSplit(weights, Utils.random.nextLong) @@ -832,15 +1022,17 @@ class DataFrame private[sql]( * val bookCountPerWord = allWords.groupBy("word").agg(countDistinct("title")) * }}} * @group dfops + * @since 1.3.0 */ def explode[A <: Product : TypeTag](input: Column*)(f: Row => TraversableOnce[A]): DataFrame = { val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType] val elementTypes = schema.toAttributes.map { attr => (attr.dataType, attr.nullable) } val names = schema.toAttributes.map(_.name) + val convert = CatalystTypeConverters.createToCatalystConverter(schema) val rowFunction = - f.andThen(_.map(CatalystTypeConverters.convertToCatalyst(_, schema).asInstanceOf[Row])) + f.andThen(_.map(convert(_).asInstanceOf[InternalRow])) val generator = UserDefinedGenerator(elementTypes, rowFunction, input.map(_.expr)) Generate(generator, join = true, outer = false, @@ -856,6 +1048,7 @@ class DataFrame private[sql]( * df.explode("words", "word")(words: String => words.split(" ")) * }}} * @group dfops + * @since 1.3.0 */ def explode[A, B : TypeTag](inputColumn: String, outputColumn: String)(f: A => TraversableOnce[B]) : DataFrame = { @@ -865,8 +1058,9 @@ class DataFrame private[sql]( val elementTypes = attributes.map { attr => (attr.dataType, attr.nullable) } val names = attributes.map(_.name) - def rowFunction(row: Row): TraversableOnce[Row] = { - f(row(0).asInstanceOf[A]).map(o => Row(CatalystTypeConverters.convertToCatalyst(o, dataType))) + def rowFunction(row: Row): TraversableOnce[InternalRow] = { + val convert = CatalystTypeConverters.createToCatalystConverter(dataType) + f(row(0).asInstanceOf[A]).map(o => InternalRow(convert(o))) } val generator = UserDefinedGenerator(elementTypes, rowFunction, apply(inputColumn).expr :: Nil) @@ -879,6 +1073,7 @@ class DataFrame private[sql]( /** * Returns a new [[DataFrame]] by adding a column. * @group dfops + * @since 1.3.0 */ def withColumn(colName: String, col: Column): DataFrame = { val resolver = sqlContext.analyzer.resolver @@ -888,7 +1083,7 @@ class DataFrame private[sql]( val name = field.name if (resolver(name, colName)) col.as(colName) else Column(name) } - select(colNames :_*) + select(colNames : _*) } else { select(Column("*"), col.as(colName)) } @@ -898,6 +1093,7 @@ class DataFrame private[sql]( * Returns a new [[DataFrame]] with a column renamed. * This is a no-op if schema doesn't contain existingName. * @group dfops + * @since 1.3.0 */ def withColumnRenamed(existingName: String, newName: String): DataFrame = { val resolver = sqlContext.analyzer.resolver @@ -917,6 +1113,7 @@ class DataFrame private[sql]( * Returns a new [[DataFrame]] with a column dropped. * This is a no-op if schema doesn't contain column name. * @group dfops + * @since 1.4.0 */ def drop(colName: String): DataFrame = { val resolver = sqlContext.analyzer.resolver @@ -932,6 +1129,59 @@ class DataFrame private[sql]( } } + /** + * Returns a new [[DataFrame]] with a column dropped. + * This version of drop accepts a Column rather than a name. + * This is a no-op if the DataFrame doesn't have a column + * with an equivalent expression. + * @group dfops + * @since 1.4.1 + */ + def drop(col: Column): DataFrame = { + val attrs = this.logicalPlan.output + val colsAfterDrop = attrs.filter { attr => + attr != col.expr + }.map(attr => Column(attr)) + select(colsAfterDrop : _*) + } + + /** + * Returns a new [[DataFrame]] that contains only the unique rows from this [[DataFrame]]. + * This is an alias for `distinct`. + * @group dfops + * @since 1.4.0 + */ + def dropDuplicates(): DataFrame = dropDuplicates(this.columns) + + /** + * (Scala-specific) Returns a new [[DataFrame]] with duplicate rows removed, considering only + * the subset of columns. + * + * @group dfops + * @since 1.4.0 + */ + def dropDuplicates(colNames: Seq[String]): DataFrame = { + val groupCols = colNames.map(resolve) + val groupColExprIds = groupCols.map(_.exprId) + val aggCols = logicalPlan.output.map { attr => + if (groupColExprIds.contains(attr.exprId)) { + attr + } else { + Alias(First(attr), attr.name)() + } + } + Aggregate(groupCols, aggCols, logicalPlan) + } + + /** + * Returns a new [[DataFrame]] with duplicate rows removed, considering only + * the subset of columns. + * + * @group dfops + * @since 1.4.0 + */ + def dropDuplicates(colNames: Array[String]): DataFrame = dropDuplicates(colNames.toSeq) + /** * Computes statistics for numeric columns, including count, mean, stddev, min, and max. * If no columns are given, this function computes statistics for all numerical columns. @@ -953,6 +1203,7 @@ class DataFrame private[sql]( * }}} * * @group action + * @since 1.3.1 */ @scala.annotation.varargs def describe(cols: String*): DataFrame = { @@ -971,49 +1222,54 @@ class DataFrame private[sql]( val outputCols = (if (cols.isEmpty) numericColumns.map(_.prettyString) else cols).toList - val ret: Seq[Row] = if (outputCols.nonEmpty) { + val ret: Seq[InternalRow] = if (outputCols.nonEmpty) { val aggExprs = statistics.flatMap { case (_, colToAgg) => - outputCols.map(c => Column(colToAgg(Column(c).expr)).as(c)) + outputCols.map(c => Column(Cast(colToAgg(Column(c).expr), StringType)).as(c)) } val row = agg(aggExprs.head, aggExprs.tail: _*).head().toSeq // Pivot the data so each summary is one row row.grouped(outputCols.size).toSeq.zip(statistics).map { - case (aggregation, (statistic, _)) => Row(statistic :: aggregation.toList: _*) + case (aggregation, (statistic, _)) => + InternalRow(statistic :: aggregation.toList: _*) } } else { // If there are no output columns, just output a single column that contains the stats. - statistics.map { case (name, _) => Row(name) } + statistics.map { case (name, _) => InternalRow(name) } } - // The first column is string type, and the rest are double type. + // All columns are string type val schema = StructType( - StructField("summary", StringType) :: outputCols.map(StructField(_, DoubleType))).toAttributes + StructField("summary", StringType) :: outputCols.map(StructField(_, StringType))).toAttributes LocalRelation(schema, ret) } /** * Returns the first `n` rows. * @group action + * @since 1.3.0 */ def head(n: Int): Array[Row] = limit(n).collect() /** * Returns the first row. * @group action + * @since 1.3.0 */ def head(): Row = head(1).head /** * Returns the first row. Alias for head(). * @group action + * @since 1.3.0 */ override def first(): Row = head() /** * Returns a new RDD by applying a function to all rows of this DataFrame. * @group rdd + * @since 1.3.0 */ override def map[R: ClassTag](f: Row => R): RDD[R] = rdd.map(f) @@ -1021,12 +1277,14 @@ class DataFrame private[sql]( * Returns a new RDD by first applying a function to all rows of this [[DataFrame]], * and then flattening the results. * @group rdd + * @since 1.3.0 */ override def flatMap[R: ClassTag](f: Row => TraversableOnce[R]): RDD[R] = rdd.flatMap(f) /** * Returns a new RDD by applying a function to each partition of this DataFrame. * @group rdd + * @since 1.3.0 */ override def mapPartitions[R: ClassTag](f: Iterator[Row] => Iterator[R]): RDD[R] = { rdd.mapPartitions(f) @@ -1035,42 +1293,49 @@ class DataFrame private[sql]( /** * Applies a function `f` to all rows. * @group rdd + * @since 1.3.0 */ override def foreach(f: Row => Unit): Unit = rdd.foreach(f) /** * Applies a function f to each partition of this [[DataFrame]]. * @group rdd + * @since 1.3.0 */ override def foreachPartition(f: Iterator[Row] => Unit): Unit = rdd.foreachPartition(f) /** * Returns the first `n` rows in the [[DataFrame]]. * @group action + * @since 1.3.0 */ override def take(n: Int): Array[Row] = head(n) /** * Returns an array that contains all of [[Row]]s in this [[DataFrame]]. * @group action + * @since 1.3.0 */ override def collect(): Array[Row] = queryExecution.executedPlan.executeCollect() /** * Returns a Java list that contains all of [[Row]]s in this [[DataFrame]]. * @group action + * @since 1.3.0 */ - override def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() :_*) + override def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() : _*) /** * Returns the number of rows in the [[DataFrame]]. * @group action + * @since 1.3.0 */ override def count(): Long = groupBy().count().collect().head.getLong(0) /** * Returns a new [[DataFrame]] that has exactly `numPartitions` partitions. * @group rdd + * @since 1.3.0 */ override def repartition(numPartitions: Int): DataFrame = { Repartition(numPartitions, shuffle = true, logicalPlan) @@ -1082,6 +1347,7 @@ class DataFrame private[sql]( * if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of * the 100 new partitions will claim 10 of the current partitions. * @group rdd + * @since 1.4.0 */ override def coalesce(numPartitions: Int): DataFrame = { Repartition(numPartitions, shuffle = false, logicalPlan) @@ -1089,12 +1355,15 @@ class DataFrame private[sql]( /** * Returns a new [[DataFrame]] that contains only the unique rows from this [[DataFrame]]. + * This is an alias for `dropDuplicates`. * @group dfops + * @since 1.3.0 */ - override def distinct: DataFrame = Distinct(logicalPlan) + override def distinct: DataFrame = dropDuplicates() /** * @group basic + * @since 1.3.0 */ override def persist(): this.type = { sqlContext.cacheManager.cacheQuery(this) @@ -1103,11 +1372,13 @@ class DataFrame private[sql]( /** * @group basic + * @since 1.3.0 */ override def cache(): this.type = persist() /** * @group basic + * @since 1.3.0 */ override def persist(newLevel: StorageLevel): this.type = { sqlContext.cacheManager.cacheQuery(this, None, newLevel) @@ -1116,6 +1387,7 @@ class DataFrame private[sql]( /** * @group basic + * @since 1.3.0 */ override def unpersist(blocking: Boolean): this.type = { sqlContext.cacheManager.tryUncacheQuery(this, blocking) @@ -1124,6 +1396,7 @@ class DataFrame private[sql]( /** * @group basic + * @since 1.3.0 */ override def unpersist(): this.type = unpersist(blocking = false) @@ -1136,6 +1409,7 @@ class DataFrame private[sql]( * memoized. Once called, it won't change even if you change any query planning related Spark SQL * configurations (e.g. `spark.sql.shuffle.partitions`). * @group rdd + * @since 1.3.0 */ lazy val rdd: RDD[Row] = { // use a local variable to make sure the map closure doesn't capture the whole DataFrame @@ -1149,12 +1423,14 @@ class DataFrame private[sql]( /** * Returns the content of the [[DataFrame]] as a [[JavaRDD]] of [[Row]]s. * @group rdd + * @since 1.3.0 */ def toJavaRDD: JavaRDD[Row] = rdd.toJavaRDD() /** * Returns the content of the [[DataFrame]] as a [[JavaRDD]] of [[Row]]s. * @group rdd + * @since 1.3.0 */ def javaRDD: JavaRDD[Row] = toJavaRDD @@ -1163,27 +1439,129 @@ class DataFrame private[sql]( * temporary table is tied to the [[SQLContext]] that was used to create this DataFrame. * * @group basic + * @since 1.3.0 */ def registerTempTable(tableName: String): Unit = { sqlContext.registerDataFrameAsTable(this, tableName) } + /** + * :: Experimental :: + * Interface for saving the content of the [[DataFrame]] out into external storage. + * + * @group output + * @since 1.4.0 + */ + @Experimental + def write: DataFrameWriter = new DataFrameWriter(this) + + /** + * Returns the content of the [[DataFrame]] as a RDD of JSON strings. + * @group rdd + * @since 1.3.0 + */ + def toJSON: RDD[String] = { + val rowSchema = this.schema + this.mapPartitions { iter => + val writer = new CharArrayWriter() + // create the Generator without separator inserted between 2 records + val gen = new JsonFactory().createGenerator(writer).setRootValueSeparator(null) + + new Iterator[String] { + override def hasNext: Boolean = iter.hasNext + override def next(): String = { + JacksonGenerator(rowSchema, gen)(iter.next()) + gen.flush() + + val json = writer.toString + if (hasNext) { + writer.reset() + } else { + gen.close() + } + + json + } + } + } + } + + //////////////////////////////////////////////////////////////////////////// + // for Python API + //////////////////////////////////////////////////////////////////////////// + + /** + * Converts a JavaRDD to a PythonRDD. + */ + protected[sql] def javaToPython: JavaRDD[Array[Byte]] = { + val fieldTypes = schema.fields.map(_.dataType) + val jrdd = rdd.map(EvaluatePython.rowToArray(_, fieldTypes)).toJavaRDD() + SerDeUtil.javaToPython(jrdd) + } + + //////////////////////////////////////////////////////////////////////////// + //////////////////////////////////////////////////////////////////////////// + // Deprecated methods + //////////////////////////////////////////////////////////////////////////// + //////////////////////////////////////////////////////////////////////////// + + /** + * @deprecated As of 1.3.0, replaced by `toDF()`. + */ + @deprecated("use toDF", "1.3.0") + def toSchemaRDD: DataFrame = this + + /** + * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. + * This will run a `CREATE TABLE` and a bunch of `INSERT INTO` statements. + * If you pass `true` for `allowExisting`, it will drop any table with the + * given name; if you pass `false`, it will throw if the table already + * exists. + * @group output + * @deprecated As of 1.340, replaced by `write().jdbc()`. + */ + @deprecated("Use write.jdbc()", "1.4.0") + def createJDBCTable(url: String, table: String, allowExisting: Boolean): Unit = { + val w = if (allowExisting) write.mode(SaveMode.Overwrite) else write + w.jdbc(url, table, new Properties) + } + + /** + * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. + * Assumes the table already exists and has a compatible schema. If you + * pass `true` for `overwrite`, it will `TRUNCATE` the table before + * performing the `INSERT`s. + * + * The table must already exist on the database. It must have a schema + * that is compatible with the schema of this RDD; inserting the rows of + * the RDD in order via the simple statement + * `INSERT INTO table VALUES (?, ?, ..., ?)` should not fail. + * @group output + * @deprecated As of 1.4.0, replaced by `write().jdbc()`. + */ + @deprecated("Use write.jdbc()", "1.4.0") + def insertIntoJDBC(url: String, table: String, overwrite: Boolean): Unit = { + val w = if (overwrite) write.mode(SaveMode.Overwrite) else write + w.jdbc(url, table, new Properties) + } + /** * Saves the contents of this [[DataFrame]] as a parquet file, preserving the schema. * Files that are written out using this method can be read back in as a [[DataFrame]] * using the `parquetFile` function in [[SQLContext]]. * @group output + * @deprecated As of 1.4.0, replaced by `write().parquet()`. */ + @deprecated("Use write.parquet(path)", "1.4.0") def saveAsParquetFile(path: String): Unit = { if (sqlContext.conf.parquetUseDataSourceApi) { - save("org.apache.spark.sql.parquet", SaveMode.ErrorIfExists, Map("path" -> path)) + write.format("parquet").mode(SaveMode.ErrorIfExists).save(path) } else { sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd } } /** - * :: Experimental :: * Creates a table from the the contents of this DataFrame. * It will use the default data source configured by spark.sql.sources.default. * This will fail if the table already exists. @@ -1192,15 +1570,18 @@ class DataFrame private[sql]( * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * + * Also note that while this function can persist the table metadata into Hive's metastore, + * the table will NOT be accessible from Hive, until SPARK-7550 is resolved. * @group output + * @deprecated As of 1.4.0, replaced by `write().saveAsTable(tableName)`. */ - @Experimental + @deprecated("Use write.saveAsTable(tableName)", "1.4.0") def saveAsTable(tableName: String): Unit = { - saveAsTable(tableName, SaveMode.ErrorIfExists) + write.mode(SaveMode.ErrorIfExists).saveAsTable(tableName) } /** - * :: Experimental :: * Creates a table from the the contents of this DataFrame, using the default data source * configured by spark.sql.sources.default and [[SaveMode.ErrorIfExists]] as the save mode. * @@ -1208,22 +1589,18 @@ class DataFrame private[sql]( * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * + * Also note that while this function can persist the table metadata into Hive's metastore, + * the table will NOT be accessible from Hive, until SPARK-7550 is resolved. * @group output + * @deprecated As of 1.4.0, replaced by `write().mode(mode).saveAsTable(tableName)`. */ - @Experimental + @deprecated("Use write.mode(mode).saveAsTable(tableName)", "1.4.0") def saveAsTable(tableName: String, mode: SaveMode): Unit = { - if (sqlContext.catalog.tableExists(Seq(tableName)) && mode == SaveMode.Append) { - // If table already exists and the save mode is Append, - // we will just call insertInto to append the contents of this DataFrame. - insertInto(tableName, overwrite = false) - } else { - val dataSourceName = sqlContext.conf.defaultDataSourceName - saveAsTable(tableName, dataSourceName, mode) - } + write.mode(mode).saveAsTable(tableName) } /** - * :: Experimental :: * Creates a table at the given path from the the contents of this DataFrame * based on a given data source and a set of options, * using [[SaveMode.ErrorIfExists]] as the save mode. @@ -1232,11 +1609,15 @@ class DataFrame private[sql]( * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * + * Also note that while this function can persist the table metadata into Hive's metastore, + * the table will NOT be accessible from Hive, until SPARK-7550 is resolved. * @group output + * @deprecated As of 1.4.0, replaced by `write().format(source).saveAsTable(tableName)`. */ - @Experimental + @deprecated("Use write.format(source).saveAsTable(tableName)", "1.4.0") def saveAsTable(tableName: String, source: String): Unit = { - saveAsTable(tableName, source, SaveMode.ErrorIfExists) + write.format(source).saveAsTable(tableName) } /** @@ -1248,15 +1629,18 @@ class DataFrame private[sql]( * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * + * Also note that while this function can persist the table metadata into Hive's metastore, + * the table will NOT be accessible from Hive, until SPARK-7550 is resolved. * @group output + * @deprecated As of 1.4.0, replaced by `write().mode(mode).saveAsTable(tableName)`. */ - @Experimental + @deprecated("Use write.format(source).mode(mode).saveAsTable(tableName)", "1.4.0") def saveAsTable(tableName: String, source: String, mode: SaveMode): Unit = { - saveAsTable(tableName, source, mode, Map.empty[String, String]) + write.format(source).mode(mode).saveAsTable(tableName) } /** - * :: Experimental :: * Creates a table at the given path from the the contents of this DataFrame * based on a given data source, [[SaveMode]] specified by mode, and a set of options. * @@ -1264,19 +1648,24 @@ class DataFrame private[sql]( * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * + * Also note that while this function can persist the table metadata into Hive's metastore, + * the table will NOT be accessible from Hive, until SPARK-7550 is resolved. * @group output + * @deprecated As of 1.4.0, replaced by + * `write().format(source).mode(mode).options(options).saveAsTable(tableName)`. */ - @Experimental + @deprecated("Use write.format(source).mode(mode).options(options).saveAsTable(tableName)", + "1.4.0") def saveAsTable( tableName: String, source: String, mode: SaveMode, options: java.util.Map[String, String]): Unit = { - saveAsTable(tableName, source, mode, options.toMap) + write.format(source).mode(mode).options(options).saveAsTable(tableName) } /** - * :: Experimental :: * (Scala-specific) * Creates a table from the the contents of this DataFrame based on a given data source, * [[SaveMode]] specified by mode, and a set of options. @@ -1285,214 +1674,127 @@ class DataFrame private[sql]( * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * + * Also note that while this function can persist the table metadata into Hive's metastore, + * the table will NOT be accessible from Hive, until SPARK-7550 is resolved. * @group output + * @deprecated As of 1.4.0, replaced by + * `write().format(source).mode(mode).options(options).saveAsTable(tableName)`. */ - @Experimental + @deprecated("Use write.format(source).mode(mode).options(options).saveAsTable(tableName)", + "1.4.0") def saveAsTable( tableName: String, source: String, mode: SaveMode, options: Map[String, String]): Unit = { - val cmd = - CreateTableUsingAsSelect( - tableName, - source, - temporary = false, - mode, - options, - logicalPlan) - - sqlContext.executePlan(cmd).toRdd + write.format(source).mode(mode).options(options).saveAsTable(tableName) } /** - * :: Experimental :: * Saves the contents of this DataFrame to the given path, * using the default data source configured by spark.sql.sources.default and * [[SaveMode.ErrorIfExists]] as the save mode. * @group output + * @deprecated As of 1.4.0, replaced by `write().save(path)`. */ - @Experimental + @deprecated("Use write.save(path)", "1.4.0") def save(path: String): Unit = { - save(path, SaveMode.ErrorIfExists) + write.save(path) } /** - * :: Experimental :: * Saves the contents of this DataFrame to the given path and [[SaveMode]] specified by mode, * using the default data source configured by spark.sql.sources.default. * @group output + * @deprecated As of 1.4.0, replaced by `write().mode(mode).save(path)`. */ - @Experimental + @deprecated("Use write.mode(mode).save(path)", "1.4.0") def save(path: String, mode: SaveMode): Unit = { - val dataSourceName = sqlContext.conf.defaultDataSourceName - save(path, dataSourceName, mode) + write.mode(mode).save(path) } /** - * :: Experimental :: * Saves the contents of this DataFrame to the given path based on the given data source, * using [[SaveMode.ErrorIfExists]] as the save mode. * @group output + * @deprecated As of 1.4.0, replaced by `write().format(source).save(path)`. */ - @Experimental + @deprecated("Use write.format(source).save(path)", "1.4.0") def save(path: String, source: String): Unit = { - save(source, SaveMode.ErrorIfExists, Map("path" -> path)) + write.format(source).save(path) } /** - * :: Experimental :: * Saves the contents of this DataFrame to the given path based on the given data source and * [[SaveMode]] specified by mode. * @group output + * @deprecated As of 1.4.0, replaced by `write().format(source).mode(mode).save(path)`. */ - @Experimental + @deprecated("Use write.format(source).mode(mode).save(path)", "1.4.0") def save(path: String, source: String, mode: SaveMode): Unit = { - save(source, mode, Map("path" -> path)) + write.format(source).mode(mode).save(path) } /** - * :: Experimental :: * Saves the contents of this DataFrame based on the given data source, * [[SaveMode]] specified by mode, and a set of options. * @group output + * @deprecated As of 1.4.0, replaced by + * `write().format(source).mode(mode).options(options).save(path)`. */ - @Experimental + @deprecated("Use write.format(source).mode(mode).options(options).save()", "1.4.0") def save( source: String, mode: SaveMode, options: java.util.Map[String, String]): Unit = { - save(source, mode, options.toMap) + write.format(source).mode(mode).options(options).save() } /** - * :: Experimental :: * (Scala-specific) * Saves the contents of this DataFrame based on the given data source, * [[SaveMode]] specified by mode, and a set of options * @group output + * @deprecated As of 1.4.0, replaced by + * `write().format(source).mode(mode).options(options).save(path)`. */ - @Experimental + @deprecated("Use write.format(source).mode(mode).options(options).save()", "1.4.0") def save( source: String, mode: SaveMode, options: Map[String, String]): Unit = { - ResolvedDataSource(sqlContext, source, mode, options, this) + write.format(source).mode(mode).options(options).save() } + /** - * :: Experimental :: * Adds the rows from this RDD to the specified table, optionally overwriting the existing data. * @group output + * @deprecated As of 1.4.0, replaced by + * `write().mode(SaveMode.Append|SaveMode.Overwrite).saveAsTable(tableName)`. */ - @Experimental + @deprecated("Use write.mode(SaveMode.Append|SaveMode.Overwrite).saveAsTable(tableName)", "1.4.0") def insertInto(tableName: String, overwrite: Boolean): Unit = { - sqlContext.executePlan(InsertIntoTable(UnresolvedRelation(Seq(tableName)), - Map.empty, logicalPlan, overwrite, ifNotExists = false)).toRdd + write.mode(if (overwrite) SaveMode.Overwrite else SaveMode.Append).insertInto(tableName) } /** - * :: Experimental :: * Adds the rows from this RDD to the specified table. * Throws an exception if the table already exists. * @group output + * @deprecated As of 1.4.0, replaced by + * `write().mode(SaveMode.Append).saveAsTable(tableName)`. */ - @Experimental - def insertInto(tableName: String): Unit = insertInto(tableName, overwrite = false) - - /** - * Returns the content of the [[DataFrame]] as a RDD of JSON strings. - * @group rdd - */ - def toJSON: RDD[String] = { - val rowSchema = this.schema - this.mapPartitions { iter => - val writer = new CharArrayWriter() - // create the Generator without separator inserted between 2 records - val gen = new JsonFactory().createGenerator(writer).setRootValueSeparator(null) - - new Iterator[String] { - override def hasNext: Boolean = iter.hasNext - override def next(): String = { - JacksonGenerator(rowSchema, gen)(iter.next()) - gen.flush() - - val json = writer.toString - if (hasNext) { - writer.reset() - } else { - gen.close() - } - - json - } - } - } + @deprecated("Use write.mode(SaveMode.Append).saveAsTable(tableName)", "1.4.0") + def insertInto(tableName: String): Unit = { + write.mode(SaveMode.Append).insertInto(tableName) } //////////////////////////////////////////////////////////////////////////// - // JDBC Write Support //////////////////////////////////////////////////////////////////////////// - - /** - * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. - * This will run a `CREATE TABLE` and a bunch of `INSERT INTO` statements. - * If you pass `true` for `allowExisting`, it will drop any table with the - * given name; if you pass `false`, it will throw if the table already - * exists. - * @group output - */ - def createJDBCTable(url: String, table: String, allowExisting: Boolean): Unit = { - val conn = DriverManager.getConnection(url) - try { - if (allowExisting) { - val sql = s"DROP TABLE IF EXISTS $table" - conn.prepareStatement(sql).executeUpdate() - } - val schema = JDBCWriteDetails.schemaString(this, url) - val sql = s"CREATE TABLE $table ($schema)" - conn.prepareStatement(sql).executeUpdate() - } finally { - conn.close() - } - JDBCWriteDetails.saveTable(this, url, table) - } - - /** - * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. - * Assumes the table already exists and has a compatible schema. If you - * pass `true` for `overwrite`, it will `TRUNCATE` the table before - * performing the `INSERT`s. - * - * The table must already exist on the database. It must have a schema - * that is compatible with the schema of this RDD; inserting the rows of - * the RDD in order via the simple statement - * `INSERT INTO table VALUES (?, ?, ..., ?)` should not fail. - * @group output - */ - def insertIntoJDBC(url: String, table: String, overwrite: Boolean): Unit = { - if (overwrite) { - val conn = DriverManager.getConnection(url) - try { - val sql = s"TRUNCATE TABLE $table" - conn.prepareStatement(sql).executeUpdate() - } finally { - conn.close() - } - } - JDBCWriteDetails.saveTable(this, url, table) - } - + // End of deprecated methods //////////////////////////////////////////////////////////////////////////// - // for Python API //////////////////////////////////////////////////////////////////////////// - /** - * Converts a JavaRDD to a PythonRDD. - */ - protected[sql] def javaToPython: JavaRDD[Array[Byte]] = { - val fieldTypes = schema.fields.map(_.dataType) - val jrdd = rdd.map(EvaluatePython.rowToArray(_, fieldTypes)).toJavaRDD() - SerDeUtil.javaToPython(jrdd) - } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala index a3187fe3230fd..2f19ec0403017 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql /** * A container for a [[DataFrame]], used for implicit conversions. + * + * @since 1.3.0 */ private[sql] case class DataFrameHolder(df: DataFrame) { @@ -26,5 +28,5 @@ private[sql] case class DataFrameHolder(df: DataFrame) { // `rdd.toDF("1")` as invoking this toDF and then apply on the returned DataFrame. def toDF(): DataFrame = df - def toDF(colNames: String*): DataFrame = df.toDF(colNames :_*) + def toDF(colNames: String*): DataFrame = df.toDF(colNames : _*) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala index 4a54120ba86f6..b4c2daa055868 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala @@ -30,12 +30,16 @@ import org.apache.spark.sql.types._ /** * :: Experimental :: * Functionality for working with missing data in [[DataFrame]]s. + * + * @since 1.3.1 */ @Experimental final class DataFrameNaFunctions private[sql](df: DataFrame) { /** * Returns a new [[DataFrame]] that drops rows containing any null values. + * + * @since 1.3.1 */ def drop(): DataFrame = drop("any", df.columns) @@ -44,18 +48,24 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * * If `how` is "any", then drop rows containing any null values. * If `how` is "all", then drop rows only if every column is null for that row. + * + * @since 1.3.1 */ def drop(how: String): DataFrame = drop(how, df.columns) /** * Returns a new [[DataFrame]] that drops rows containing any null values * in the specified columns. + * + * @since 1.3.1 */ def drop(cols: Array[String]): DataFrame = drop(cols.toSeq) /** * (Scala-specific) Returns a new [[DataFrame ]] that drops rows containing any null values * in the specified columns. + * + * @since 1.3.1 */ def drop(cols: Seq[String]): DataFrame = drop(cols.size, cols) @@ -65,6 +75,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * * If `how` is "any", then drop rows containing any null values in the specified columns. * If `how` is "all", then drop rows only if every specified column is null for that row. + * + * @since 1.3.1 */ def drop(how: String, cols: Array[String]): DataFrame = drop(how, cols.toSeq) @@ -74,6 +86,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * * If `how` is "any", then drop rows containing any null values in the specified columns. * If `how` is "all", then drop rows only if every specified column is null for that row. + * + * @since 1.3.1 */ def drop(how: String, cols: Seq[String]): DataFrame = { how.toLowerCase match { @@ -85,18 +99,24 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { /** * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null values. + * + * @since 1.3.1 */ def drop(minNonNulls: Int): DataFrame = drop(minNonNulls, df.columns) /** * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null * values in the specified columns. + * + * @since 1.3.1 */ def drop(minNonNulls: Int, cols: Array[String]): DataFrame = drop(minNonNulls, cols.toSeq) /** * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing less than * `minNonNulls` non-null values in the specified columns. + * + * @since 1.3.1 */ def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = { // Filtering condition -- only keep the row if it has at least `minNonNulls` non-null values. @@ -106,23 +126,31 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { /** * Returns a new [[DataFrame]] that replaces null values in numeric columns with `value`. + * + * @since 1.3.1 */ def fill(value: Double): DataFrame = fill(value, df.columns) /** * Returns a new [[DataFrame ]] that replaces null values in string columns with `value`. + * + * @since 1.3.1 */ def fill(value: String): DataFrame = fill(value, df.columns) /** * Returns a new [[DataFrame]] that replaces null values in specified numeric columns. * If a specified column is not a numeric column, it is ignored. + * + * @since 1.3.1 */ def fill(value: Double, cols: Array[String]): DataFrame = fill(value, cols.toSeq) /** * (Scala-specific) Returns a new [[DataFrame]] that replaces null values in specified * numeric columns. If a specified column is not a numeric column, it is ignored. + * + * @since 1.3.1 */ def fill(value: Double, cols: Seq[String]): DataFrame = { val columnEquals = df.sqlContext.analyzer.resolver @@ -140,12 +168,16 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { /** * Returns a new [[DataFrame]] that replaces null values in specified string columns. * If a specified column is not a string column, it is ignored. + * + * @since 1.3.1 */ def fill(value: String, cols: Array[String]): DataFrame = fill(value, cols.toSeq) /** * (Scala-specific) Returns a new [[DataFrame]] that replaces null values in * specified string columns. If a specified column is not a string column, it is ignored. + * + * @since 1.3.1 */ def fill(value: String, cols: Seq[String]): DataFrame = { val columnEquals = df.sqlContext.analyzer.resolver @@ -172,6 +204,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * import com.google.common.collect.ImmutableMap; * df.na.fill(ImmutableMap.of("A", "unknown", "B", 1.0)); * }}} + * + * @since 1.3.1 */ def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.toSeq) @@ -189,6 +223,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * "B" -> 1.0 * )) * }}} + * + * @since 1.3.1 */ def fill(valueMap: Map[String, Any]): DataFrame = fill0(valueMap.toSeq) @@ -212,6 +248,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * * @param col name of the column to apply the value replacement * @param replacement value replacement map, as explained above + * + * @since 1.3.1 */ def replace[T](col: String, replacement: java.util.Map[T, T]): DataFrame = { replace[T](col, replacement.toMap : Map[T, T]) @@ -233,6 +271,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * * @param cols list of columns to apply the value replacement * @param replacement value replacement map, as explained above + * + * @since 1.3.1 */ def replace[T](cols: Array[String], replacement: java.util.Map[T, T]): DataFrame = { replace(cols.toSeq, replacement.toMap) @@ -256,6 +296,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * * @param col name of the column to apply the value replacement * @param replacement value replacement map, as explained above + * + * @since 1.3.1 */ def replace[T](col: String, replacement: Map[T, T]): DataFrame = { if (col == "*") { @@ -279,6 +321,8 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { * * @param cols list of columns to apply the value replacement * @param replacement value replacement map, as explained above + * + * @since 1.3.1 */ def replace[T](cols: Seq[String], replacement: Map[T, T]): DataFrame = replace0(cols, replacement) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala new file mode 100644 index 0000000000000..1828ed1aab50b --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala @@ -0,0 +1,289 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import java.util.Properties + +import org.apache.hadoop.fs.Path +import org.apache.spark.Partition + +import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} +import org.apache.spark.sql.json.{JsonRDD, JSONRelation} +import org.apache.spark.sql.parquet.ParquetRelation2 +import org.apache.spark.sql.sources.{LogicalRelation, ResolvedDataSource} +import org.apache.spark.sql.types.StructType + +/** + * :: Experimental :: + * Interface used to load a [[DataFrame]] from external storage systems (e.g. file systems, + * key-value stores, etc). Use [[SQLContext.read]] to access this. + * + * @since 1.4.0 + */ +@Experimental +class DataFrameReader private[sql](sqlContext: SQLContext) { + + /** + * Specifies the input data source format. + * + * @since 1.4.0 + */ + def format(source: String): DataFrameReader = { + this.source = source + this + } + + /** + * Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema + * automatically from data. By specifying the schema here, the underlying data source can + * skip the schema inference step, and thus speed up data loading. + * + * @since 1.4.0 + */ + def schema(schema: StructType): DataFrameReader = { + this.userSpecifiedSchema = Option(schema) + this + } + + /** + * Adds an input option for the underlying data source. + * + * @since 1.4.0 + */ + def option(key: String, value: String): DataFrameReader = { + this.extraOptions += (key -> value) + this + } + + /** + * (Scala-specific) Adds input options for the underlying data source. + * + * @since 1.4.0 + */ + def options(options: scala.collection.Map[String, String]): DataFrameReader = { + this.extraOptions ++= options + this + } + + /** + * Adds input options for the underlying data source. + * + * @since 1.4.0 + */ + def options(options: java.util.Map[String, String]): DataFrameReader = { + this.options(scala.collection.JavaConversions.mapAsScalaMap(options)) + this + } + + /** + * Loads input in as a [[DataFrame]], for data sources that require a path (e.g. data backed by + * a local or distributed file system). + * + * @since 1.4.0 + */ + def load(path: String): DataFrame = { + option("path", path).load() + } + + /** + * Loads input in as a [[DataFrame]], for data sources that don't require a path (e.g. external + * key-value stores). + * + * @since 1.4.0 + */ + def load(): DataFrame = { + val resolved = ResolvedDataSource( + sqlContext, + userSpecifiedSchema = userSpecifiedSchema, + partitionColumns = Array.empty[String], + provider = source, + options = extraOptions.toMap) + DataFrame(sqlContext, LogicalRelation(resolved.relation)) + } + + /** + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL + * url named table and connection properties. + * + * @since 1.4.0 + */ + def jdbc(url: String, table: String, properties: Properties): DataFrame = { + jdbc(url, table, JDBCRelation.columnPartition(null), properties) + } + + /** + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL + * url named table. Partitions of the table will be retrieved in parallel based on the parameters + * passed to this function. + * + * Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash + * your external database systems. + * + * @param url JDBC database url of the form `jdbc:subprotocol:subname` + * @param table Name of the table in the external database. + * @param columnName the name of a column of integral type that will be used for partitioning. + * @param lowerBound the minimum value of `columnName` used to decide partition stride + * @param upperBound the maximum value of `columnName` used to decide partition stride + * @param numPartitions the number of partitions. the range `minValue`-`maxValue` will be split + * evenly into this many partitions + * @param connectionProperties JDBC database connection arguments, a list of arbitrary string + * tag/value. Normally at least a "user" and "password" property + * should be included. + * + * @since 1.4.0 + */ + def jdbc( + url: String, + table: String, + columnName: String, + lowerBound: Long, + upperBound: Long, + numPartitions: Int, + connectionProperties: Properties): DataFrame = { + val partitioning = JDBCPartitioningInfo(columnName, lowerBound, upperBound, numPartitions) + val parts = JDBCRelation.columnPartition(partitioning) + jdbc(url, table, parts, connectionProperties) + } + + /** + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL + * url named table using connection properties. The `predicates` parameter gives a list + * expressions suitable for inclusion in WHERE clauses; each one defines one partition + * of the [[DataFrame]]. + * + * Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash + * your external database systems. + * + * @param url JDBC database url of the form `jdbc:subprotocol:subname` + * @param table Name of the table in the external database. + * @param predicates Condition in the where clause for each partition. + * @param connectionProperties JDBC database connection arguments, a list of arbitrary string + * tag/value. Normally at least a "user" and "password" property + * should be included. + * @since 1.4.0 + */ + def jdbc( + url: String, + table: String, + predicates: Array[String], + connectionProperties: Properties): DataFrame = { + val parts: Array[Partition] = predicates.zipWithIndex.map { case (part, i) => + JDBCPartition(part, i) : Partition + } + jdbc(url, table, parts, connectionProperties) + } + + private def jdbc( + url: String, + table: String, + parts: Array[Partition], + connectionProperties: Properties): DataFrame = { + val relation = JDBCRelation(url, table, parts, connectionProperties)(sqlContext) + sqlContext.baseRelationToDataFrame(relation) + } + + /** + * Loads a JSON file (one object per line) and returns the result as a [[DataFrame]]. + * + * This function goes through the input once to determine the input schema. If you know the + * schema in advance, use the version that specifies the schema to avoid the extra scan. + * + * @param path input path + * @since 1.4.0 + */ + def json(path: String): DataFrame = format("json").load(path) + + /** + * Loads an `JavaRDD[String]` storing JSON objects (one object per record) and + * returns the result as a [[DataFrame]]. + * + * Unless the schema is specified using [[schema]] function, this function goes through the + * input once to determine the input schema. + * + * @param jsonRDD input RDD with one JSON object per record + * @since 1.4.0 + */ + def json(jsonRDD: JavaRDD[String]): DataFrame = json(jsonRDD.rdd) + + /** + * Loads an `RDD[String]` storing JSON objects (one object per record) and + * returns the result as a [[DataFrame]]. + * + * Unless the schema is specified using [[schema]] function, this function goes through the + * input once to determine the input schema. + * + * @param jsonRDD input RDD with one JSON object per record + * @since 1.4.0 + */ + def json(jsonRDD: RDD[String]): DataFrame = { + val samplingRatio = extraOptions.getOrElse("samplingRatio", "1.0").toDouble + if (sqlContext.conf.useJacksonStreamingAPI) { + sqlContext.baseRelationToDataFrame( + new JSONRelation(() => jsonRDD, None, samplingRatio, userSpecifiedSchema)(sqlContext)) + } else { + val columnNameOfCorruptJsonRecord = sqlContext.conf.columnNameOfCorruptRecord + val appliedSchema = userSpecifiedSchema.getOrElse( + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema(jsonRDD, 1.0, columnNameOfCorruptJsonRecord))) + val rowRDD = JsonRDD.jsonStringToRow(jsonRDD, appliedSchema, columnNameOfCorruptJsonRecord) + sqlContext.internalCreateDataFrame(rowRDD, appliedSchema) + } + } + + /** + * Loads a Parquet file, returning the result as a [[DataFrame]]. This function returns an empty + * [[DataFrame]] if no paths are passed in. + * + * @since 1.4.0 + */ + @scala.annotation.varargs + def parquet(paths: String*): DataFrame = { + if (paths.isEmpty) { + sqlContext.emptyDataFrame + } else { + val globbedPaths = paths.map(new Path(_)).flatMap(SparkHadoopUtil.get.globPath).toArray + sqlContext.baseRelationToDataFrame( + new ParquetRelation2( + globbedPaths.map(_.toString), None, None, Map.empty[String, String])(sqlContext)) + } + } + + /** + * Returns the specified table as a [[DataFrame]]. + * + * @since 1.4.0 + */ + def table(tableName: String): DataFrame = { + DataFrame(sqlContext, sqlContext.catalog.lookupRelation(Seq(tableName))) + } + + /////////////////////////////////////////////////////////////////////////////////////// + // Builder pattern config options + /////////////////////////////////////////////////////////////////////////////////////// + + private var source: String = sqlContext.conf.defaultDataSourceName + + private var userSpecifiedSchema: Option[StructType] = None + + private var extraOptions = new scala.collection.mutable.HashMap[String, String] + +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala index a1e74470afc89..edb9ed7bba56a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -23,6 +23,8 @@ import org.apache.spark.sql.execution.stat._ /** * :: Experimental :: * Statistic functions for [[DataFrame]]s. + * + * @since 1.4.0 */ @Experimental final class DataFrameStatFunctions private[sql](df: DataFrame) { @@ -32,6 +34,8 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * @param col1 the name of the first column * @param col2 the name of the second column * @return the covariance of the two columns. + * + * @since 1.4.0 */ def cov(col1: String, col2: String): Double = { StatFunctions.calculateCov(df, Seq(col1, col2)) @@ -39,12 +43,14 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { /** * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson - * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in + * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in * MLlib's Statistics. * * @param col1 the name of the column * @param col2 the name of the column to calculate the correlation against * @return The Pearson Correlation Coefficient as a Double. + * + * @since 1.4.0 */ def corr(col1: String, col2: String, method: String): Double = { require(method == "pearson", "Currently only the calculation of the Pearson Correlation " + @@ -58,6 +64,8 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * @param col1 the name of the column * @param col2 the name of the column to calculate the correlation against * @return The Pearson Correlation Coefficient as a Double. + * + * @since 1.4.0 */ def corr(col1: String, col2: String): Double = { corr(col1, col2, "pearson") @@ -76,6 +84,8 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * @param col2 The name of the second column. Distinct items will make the column names * of the DataFrame. * @return A DataFrame containing for the contingency table. + * + * @since 1.4.0 */ def crosstab(col1: String, col2: String): DataFrame = { StatFunctions.crossTabulate(df, col1, col2) @@ -87,10 +97,15 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. * The `support` should be greater than 1e-4. * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting [[DataFrame]]. + * * @param cols the names of the columns to search frequent items in. * @param support The minimum frequency for an item to be considered `frequent`. Should be greater * than 1e-4. * @return A Local DataFrame with the Array of frequent items for each column. + * + * @since 1.4.0 */ def freqItems(cols: Array[String], support: Double): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, support) @@ -102,8 +117,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. * Uses a `default` support of 1%. * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting [[DataFrame]]. + * * @param cols the names of the columns to search frequent items in. * @return A Local DataFrame with the Array of frequent items for each column. + * + * @since 1.4.0 */ def freqItems(cols: Array[String]): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, 0.01) @@ -114,8 +134,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * frequent element count algorithm described in * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting [[DataFrame]]. + * * @param cols the names of the columns to search frequent items in. * @return A Local DataFrame with the Array of frequent items for each column. + * + * @since 1.4.0 */ def freqItems(cols: Seq[String], support: Double): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, support) @@ -127,8 +152,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. * Uses a `default` support of 1%. * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting [[DataFrame]]. + * * @param cols the names of the columns to search frequent items in. * @return A Local DataFrame with the Array of frequent items for each column. + * + * @since 1.4.0 */ def freqItems(cols: Seq[String]): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, 0.01) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala new file mode 100644 index 0000000000000..5548b26cb8f80 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala @@ -0,0 +1,295 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import java.util.Properties + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation +import org.apache.spark.sql.catalyst.plans.logical.InsertIntoTable +import org.apache.spark.sql.jdbc.{JDBCWriteDetails, JdbcUtils} +import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect} + + +/** + * :: Experimental :: + * Interface used to write a [[DataFrame]] to external storage systems (e.g. file systems, + * key-value stores, etc). Use [[DataFrame.write]] to access this. + * + * @since 1.4.0 + */ +@Experimental +final class DataFrameWriter private[sql](df: DataFrame) { + + /** + * Specifies the behavior when data or table already exists. Options include: + * - `SaveMode.Overwrite`: overwrite the existing data. + * - `SaveMode.Append`: append the data. + * - `SaveMode.Ignore`: ignore the operation (i.e. no-op). + * - `SaveMode.ErrorIfExists`: default option, throw an exception at runtime. + * + * @since 1.4.0 + */ + def mode(saveMode: SaveMode): DataFrameWriter = { + this.mode = saveMode + this + } + + /** + * Specifies the behavior when data or table already exists. Options include: + * - `overwrite`: overwrite the existing data. + * - `append`: append the data. + * - `ignore`: ignore the operation (i.e. no-op). + * - `error`: default option, throw an exception at runtime. + * + * @since 1.4.0 + */ + def mode(saveMode: String): DataFrameWriter = { + this.mode = saveMode.toLowerCase match { + case "overwrite" => SaveMode.Overwrite + case "append" => SaveMode.Append + case "ignore" => SaveMode.Ignore + case "error" | "default" => SaveMode.ErrorIfExists + case _ => throw new IllegalArgumentException(s"Unknown save mode: $saveMode. " + + "Accepted modes are 'overwrite', 'append', 'ignore', 'error'.") + } + this + } + + /** + * Specifies the underlying output data source. Built-in options include "parquet", "json", etc. + * + * @since 1.4.0 + */ + def format(source: String): DataFrameWriter = { + this.source = source + this + } + + /** + * Adds an output option for the underlying data source. + * + * @since 1.4.0 + */ + def option(key: String, value: String): DataFrameWriter = { + this.extraOptions += (key -> value) + this + } + + /** + * (Scala-specific) Adds output options for the underlying data source. + * + * @since 1.4.0 + */ + def options(options: scala.collection.Map[String, String]): DataFrameWriter = { + this.extraOptions ++= options + this + } + + /** + * Adds output options for the underlying data source. + * + * @since 1.4.0 + */ + def options(options: java.util.Map[String, String]): DataFrameWriter = { + this.options(scala.collection.JavaConversions.mapAsScalaMap(options)) + this + } + + /** + * Partitions the output by the given columns on the file system. If specified, the output is + * laid out on the file system similar to Hive's partitioning scheme. + * + * This is only applicable for Parquet at the moment. + * + * @since 1.4.0 + */ + @scala.annotation.varargs + def partitionBy(colNames: String*): DataFrameWriter = { + this.partitioningColumns = Option(colNames) + this + } + + /** + * Saves the content of the [[DataFrame]] at the specified path. + * + * @since 1.4.0 + */ + def save(path: String): Unit = { + this.extraOptions += ("path" -> path) + save() + } + + /** + * Saves the content of the [[DataFrame]] as the specified table. + * + * @since 1.4.0 + */ + def save(): Unit = { + ResolvedDataSource( + df.sqlContext, + source, + partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]), + mode, + extraOptions.toMap, + df) + } + + /** + * Inserts the content of the [[DataFrame]] to the specified table. It requires that + * the schema of the [[DataFrame]] is the same as the schema of the table. + * + * Because it inserts data to an existing table, format or options will be ignored. + * + * @since 1.4.0 + */ + def insertInto(tableName: String): Unit = { + val partitions = + partitioningColumns.map(_.map(col => col -> (None: Option[String])).toMap) + val overwrite = (mode == SaveMode.Overwrite) + df.sqlContext.executePlan(InsertIntoTable( + UnresolvedRelation(Seq(tableName)), + partitions.getOrElse(Map.empty[String, Option[String]]), + df.logicalPlan, + overwrite, + ifNotExists = false)).toRdd + } + + /** + * Saves the content of the [[DataFrame]] as the specified table. + * + * In the case the table already exists, behavior of this function depends on the + * save mode, specified by the `mode` function (default to throwing an exception). + * When `mode` is `Overwrite`, the schema of the [[DataFrame]] does not need to be + * the same as that of the existing table. + * When `mode` is `Append`, the schema of the [[DataFrame]] need to be + * the same as that of the existing table, and format or options will be ignored. + * + * @since 1.4.0 + */ + def saveAsTable(tableName: String): Unit = { + if (df.sqlContext.catalog.tableExists(tableName :: Nil) && mode != SaveMode.Overwrite) { + mode match { + case SaveMode.Ignore => + // Do nothing + + case SaveMode.ErrorIfExists => + throw new AnalysisException(s"Table $tableName already exists.") + + case SaveMode.Append => + // If it is Append, we just ask insertInto to handle it. We will not use insertInto + // to handle saveAsTable with Overwrite because saveAsTable can change the schema of + // the table. But, insertInto with Overwrite requires the schema of data be the same + // the schema of the table. + insertInto(tableName) + } + } else { + val cmd = + CreateTableUsingAsSelect( + tableName, + source, + temporary = false, + partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]), + mode, + extraOptions.toMap, + df.logicalPlan) + df.sqlContext.executePlan(cmd).toRdd + } + } + + /** + * Saves the content of the [[DataFrame]] to a external database table via JDBC. In the case the + * table already exists in the external database, behavior of this function depends on the + * save mode, specified by the `mode` function (default to throwing an exception). + * + * Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash + * your external database systems. + * + * @param url JDBC database url of the form `jdbc:subprotocol:subname` + * @param table Name of the table in the external database. + * @param connectionProperties JDBC database connection arguments, a list of arbitrary string + * tag/value. Normally at least a "user" and "password" property + * should be included. + */ + def jdbc(url: String, table: String, connectionProperties: Properties): Unit = { + val conn = JdbcUtils.createConnection(url, connectionProperties) + + try { + var tableExists = JdbcUtils.tableExists(conn, table) + + if (mode == SaveMode.Ignore && tableExists) { + return + } + + if (mode == SaveMode.ErrorIfExists && tableExists) { + sys.error(s"Table $table already exists.") + } + + if (mode == SaveMode.Overwrite && tableExists) { + JdbcUtils.dropTable(conn, table) + tableExists = false + } + + // Create the table if the table didn't exist. + if (!tableExists) { + val schema = JDBCWriteDetails.schemaString(df, url) + val sql = s"CREATE TABLE $table ($schema)" + conn.prepareStatement(sql).executeUpdate() + } + } finally { + conn.close() + } + + JDBCWriteDetails.saveTable(df, url, table, connectionProperties) + } + + /** + * Saves the content of the [[DataFrame]] in JSON format at the specified path. + * This is equivalent to: + * {{{ + * format("json").save(path) + * }}} + * + * @since 1.4.0 + */ + def json(path: String): Unit = format("json").save(path) + + /** + * Saves the content of the [[DataFrame]] in Parquet format at the specified path. + * This is equivalent to: + * {{{ + * format("parquet").save(path) + * }}} + * + * @since 1.4.0 + */ + def parquet(path: String): Unit = format("parquet").save(path) + + /////////////////////////////////////////////////////////////////////////////////////// + // Builder pattern config options + /////////////////////////////////////////////////////////////////////////////////////// + + private var source: String = df.sqlContext.conf.defaultDataSourceName + + private var mode: SaveMode = SaveMode.ErrorIfExists + + private var extraOptions = new scala.collection.mutable.HashMap[String, String] + + private var partitioningColumns: Option[Seq[String]] = None + +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala b/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala index d5d7e35a6b35d..717709e4f9312 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala @@ -27,6 +27,8 @@ import org.apache.spark.annotation.Experimental * {{{ * sqlContext.experimental.extraStrategies += ... * }}} + * + * @since 1.3.0 */ @Experimental class ExperimentalMethods protected[sql](sqlContext: SQLContext) { @@ -34,6 +36,8 @@ class ExperimentalMethods protected[sql](sqlContext: SQLContext) { /** * Allows extra strategies to be injected into the query planner at runtime. Note this API * should be consider experimental and is not intended to be stable across releases. + * + * @since 1.3.0 */ @Experimental var extraStrategies: Seq[Strategy] = Nil diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala index 53ad67372e024..45b3e1bc627d5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala @@ -23,28 +23,79 @@ import scala.language.implicitConversions import org.apache.spark.annotation.Experimental import org.apache.spark.sql.catalyst.analysis.Star import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.Aggregate +import org.apache.spark.sql.catalyst.plans.logical.{Rollup, Cube, Aggregate} import org.apache.spark.sql.types.NumericType +/** + * Companion object for GroupedData + */ +private[sql] object GroupedData { + def apply( + df: DataFrame, + groupingExprs: Seq[Expression], + groupType: GroupType): GroupedData = { + new GroupedData(df, groupingExprs, groupType: GroupType) + } + + /** + * The Grouping Type + */ + private[sql] trait GroupType + + /** + * To indicate it's the GroupBy + */ + private[sql] object GroupByType extends GroupType + + /** + * To indicate it's the CUBE + */ + private[sql] object CubeType extends GroupType + + /** + * To indicate it's the ROLLUP + */ + private[sql] object RollupType extends GroupType +} /** * :: Experimental :: * A set of methods for aggregations on a [[DataFrame]], created by [[DataFrame.groupBy]]. + * + * @since 1.3.0 */ @Experimental -class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) { +class GroupedData protected[sql]( + df: DataFrame, + groupingExprs: Seq[Expression], + private val groupType: GroupedData.GroupType) { - private[sql] implicit def toDF(aggExprs: Seq[NamedExpression]): DataFrame = { - val namedGroupingExprs = groupingExprs.map { - case expr: NamedExpression => expr - case expr: Expression => Alias(expr, expr.prettyString)() + private[this] def toDF(aggExprs: Seq[NamedExpression]): DataFrame = { + val aggregates = if (df.sqlContext.conf.dataFrameRetainGroupColumns) { + val retainedExprs = groupingExprs.map { + case expr: NamedExpression => expr + case expr: Expression => Alias(expr, expr.prettyString)() + } + retainedExprs ++ aggExprs + } else { + aggExprs + } + + groupType match { + case GroupedData.GroupByType => + DataFrame( + df.sqlContext, Aggregate(groupingExprs, aggregates, df.logicalPlan)) + case GroupedData.RollupType => + DataFrame( + df.sqlContext, Rollup(groupingExprs, df.logicalPlan, aggregates)) + case GroupedData.CubeType => + DataFrame( + df.sqlContext, Cube(groupingExprs, df.logicalPlan, aggregates)) } - DataFrame( - df.sqlContext, Aggregate(groupingExprs, namedGroupingExprs ++ aggExprs, df.logicalPlan)) } private[this] def aggregateNumericColumns(colNames: String*)(f: Expression => Expression) - : Seq[NamedExpression] = { + : DataFrame = { val columnExprs = if (colNames.isEmpty) { // No columns specified. Use all numeric columns. @@ -61,10 +112,10 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) namedExpr } } - columnExprs.map { c => + toDF(columnExprs.map { c => val a = f(c) Alias(a, a.prettyString)() - } + }) } private[this] def strToExpr(expr: String): (Expression => Expression) = { @@ -94,6 +145,8 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) * "expense" -> "sum" * ) * }}} + * + * @since 1.3.0 */ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = { agg((aggExpr +: aggExprs).toMap) @@ -111,12 +164,14 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) * "expense" -> "sum" * )) * }}} + * + * @since 1.3.0 */ def agg(exprs: Map[String, String]): DataFrame = { - exprs.map { case (colName, expr) => + toDF(exprs.map { case (colName, expr) => val a = strToExpr(expr)(df(colName).expr) Alias(a, a.prettyString)() - }.toSeq + }.toSeq) } /** @@ -129,14 +184,17 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) * import com.google.common.collect.ImmutableMap; * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum")); * }}} + * + * @since 1.3.0 */ def agg(exprs: java.util.Map[String, String]): DataFrame = { agg(exprs.toMap) } /** - * Compute aggregates by specifying a series of aggregate columns. Unlike other methods in this - * class, the resulting [[DataFrame]] won't automatically include the grouping columns. + * Compute aggregates by specifying a series of aggregate columns. Note that this function by + * default retains the grouping columns in its output. To not retain grouping columns, set + * `spark.sql.retainGroupColumns` to false. * * The available aggregate methods are defined in [[org.apache.spark.sql.functions]]. * @@ -145,75 +203,98 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) * * // Scala: * import org.apache.spark.sql.functions._ - * df.groupBy("department").agg($"department", max($"age"), sum($"expense")) + * df.groupBy("department").agg(max("age"), sum("expense")) * * // Java: * import static org.apache.spark.sql.functions.*; - * df.groupBy("department").agg(col("department"), max(col("age")), sum(col("expense"))); + * df.groupBy("department").agg(max("age"), sum("expense")); + * }}} + * + * Note that before Spark 1.4, the default behavior is to NOT retain grouping columns. To change + * to that behavior, set config variable `spark.sql.retainGroupColumns` to `false`. + * {{{ + * // Scala, 1.3.x: + * df.groupBy("department").agg($"department", max("age"), sum("expense")) + * + * // Java, 1.3.x: + * df.groupBy("department").agg(col("department"), max("age"), sum("expense")); * }}} + * + * @since 1.3.0 */ @scala.annotation.varargs def agg(expr: Column, exprs: Column*): DataFrame = { - val aggExprs = (expr +: exprs).map(_.expr).map { + toDF((expr +: exprs).map(_.expr).map { case expr: NamedExpression => expr case expr: Expression => Alias(expr, expr.prettyString)() - } - DataFrame(df.sqlContext, Aggregate(groupingExprs, aggExprs, df.logicalPlan)) + }) } /** * Count the number of rows for each group. * The resulting [[DataFrame]] will also contain the grouping columns. + * + * @since 1.3.0 */ - def count(): DataFrame = Seq(Alias(Count(Literal(1)), "count")()) + def count(): DataFrame = toDF(Seq(Alias(Count(Literal(1)), "count")())) /** * Compute the average value for each numeric columns for each group. This is an alias for `avg`. * The resulting [[DataFrame]] will also contain the grouping columns. * When specified columns are given, only compute the average values for them. + * + * @since 1.3.0 */ @scala.annotation.varargs def mean(colNames: String*): DataFrame = { - aggregateNumericColumns(colNames:_*)(Average) + aggregateNumericColumns(colNames : _*)(Average) } - + /** * Compute the max value for each numeric columns for each group. * The resulting [[DataFrame]] will also contain the grouping columns. * When specified columns are given, only compute the max values for them. + * + * @since 1.3.0 */ @scala.annotation.varargs def max(colNames: String*): DataFrame = { - aggregateNumericColumns(colNames:_*)(Max) + aggregateNumericColumns(colNames : _*)(Max) } /** * Compute the mean value for each numeric columns for each group. * The resulting [[DataFrame]] will also contain the grouping columns. * When specified columns are given, only compute the mean values for them. + * + * @since 1.3.0 */ @scala.annotation.varargs def avg(colNames: String*): DataFrame = { - aggregateNumericColumns(colNames:_*)(Average) + aggregateNumericColumns(colNames : _*)(Average) } /** * Compute the min value for each numeric column for each group. * The resulting [[DataFrame]] will also contain the grouping columns. * When specified columns are given, only compute the min values for them. + * + * @since 1.3.0 */ @scala.annotation.varargs def min(colNames: String*): DataFrame = { - aggregateNumericColumns(colNames:_*)(Min) + aggregateNumericColumns(colNames : _*)(Min) } /** * Compute the sum for each numeric columns for each group. * The resulting [[DataFrame]] will also contain the grouping columns. * When specified columns are given, only compute the sum for them. + * + * @since 1.3.0 */ @scala.annotation.varargs def sum(colNames: String*): DataFrame = { - aggregateNumericColumns(colNames:_*)(Sum) - } + aggregateNumericColumns(colNames : _*)(Sum) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index bfaddd0f2ce1b..16493c3d7c19c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -17,63 +17,341 @@ package org.apache.spark.sql +import java.util.Properties + import scala.collection.immutable import scala.collection.JavaConversions._ -import java.util.Properties +import org.apache.spark.sql.catalyst.CatalystConf private[spark] object SQLConf { - val COMPRESS_CACHED = "spark.sql.inMemoryColumnarStorage.compressed" - val COLUMN_BATCH_SIZE = "spark.sql.inMemoryColumnarStorage.batchSize" - val IN_MEMORY_PARTITION_PRUNING = "spark.sql.inMemoryColumnarStorage.partitionPruning" - val AUTO_BROADCASTJOIN_THRESHOLD = "spark.sql.autoBroadcastJoinThreshold" - val DEFAULT_SIZE_IN_BYTES = "spark.sql.defaultSizeInBytes" - val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions" - val CODEGEN_ENABLED = "spark.sql.codegen" - val UNSAFE_ENABLED = "spark.sql.unsafe.enabled" - val DIALECT = "spark.sql.dialect" - - val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString" - val PARQUET_INT96_AS_TIMESTAMP = "spark.sql.parquet.int96AsTimestamp" - val PARQUET_CACHE_METADATA = "spark.sql.parquet.cacheMetadata" - val PARQUET_COMPRESSION = "spark.sql.parquet.compression.codec" - val PARQUET_FILTER_PUSHDOWN_ENABLED = "spark.sql.parquet.filterPushdown" - val PARQUET_USE_DATA_SOURCE_API = "spark.sql.parquet.useDataSourceApi" - - val HIVE_VERIFY_PARTITIONPATH = "spark.sql.hive.verifyPartitionPath" - - val COLUMN_NAME_OF_CORRUPT_RECORD = "spark.sql.columnNameOfCorruptRecord" - val BROADCAST_TIMEOUT = "spark.sql.broadcastTimeout" + + private val sqlConfEntries = java.util.Collections.synchronizedMap( + new java.util.HashMap[String, SQLConfEntry[_]]()) + + /** + * An entry contains all meta information for a configuration. + * + * @param key the key for the configuration + * @param defaultValue the default value for the configuration + * @param valueConverter how to convert a string to the value. It should throw an exception if the + * string does not have the required format. + * @param stringConverter how to convert a value to a string that the user can use it as a valid + * string value. It's usually `toString`. But sometimes, a custom converter + * is necessary. E.g., if T is List[String], `a, b, c` is better than + * `List(a, b, c)`. + * @param doc the document for the configuration + * @param isPublic if this configuration is public to the user. If it's `false`, this + * configuration is only used internally and we should not expose it to the user. + * @tparam T the value type + */ + private[sql] class SQLConfEntry[T] private( + val key: String, + val defaultValue: Option[T], + val valueConverter: String => T, + val stringConverter: T => String, + val doc: String, + val isPublic: Boolean) { + + def defaultValueString: String = defaultValue.map(stringConverter).getOrElse("") + + override def toString: String = { + s"SQLConfEntry(key = $key, defaultValue=$defaultValueString, doc=$doc, isPublic = $isPublic)" + } + } + + private[sql] object SQLConfEntry { + + private def apply[T]( + key: String, + defaultValue: Option[T], + valueConverter: String => T, + stringConverter: T => String, + doc: String, + isPublic: Boolean): SQLConfEntry[T] = + sqlConfEntries.synchronized { + if (sqlConfEntries.containsKey(key)) { + throw new IllegalArgumentException(s"Duplicate SQLConfEntry. $key has been registered") + } + val entry = + new SQLConfEntry[T](key, defaultValue, valueConverter, stringConverter, doc, isPublic) + sqlConfEntries.put(key, entry) + entry + } + + def intConf( + key: String, + defaultValue: Option[Int] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[Int] = + SQLConfEntry(key, defaultValue, { v => + try { + v.toInt + } catch { + case _: NumberFormatException => + throw new IllegalArgumentException(s"$key should be int, but was $v") + } + }, _.toString, doc, isPublic) + + def longConf( + key: String, + defaultValue: Option[Long] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[Long] = + SQLConfEntry(key, defaultValue, { v => + try { + v.toLong + } catch { + case _: NumberFormatException => + throw new IllegalArgumentException(s"$key should be long, but was $v") + } + }, _.toString, doc, isPublic) + + def doubleConf( + key: String, + defaultValue: Option[Double] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[Double] = + SQLConfEntry(key, defaultValue, { v => + try { + v.toDouble + } catch { + case _: NumberFormatException => + throw new IllegalArgumentException(s"$key should be double, but was $v") + } + }, _.toString, doc, isPublic) + + def booleanConf( + key: String, + defaultValue: Option[Boolean] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[Boolean] = + SQLConfEntry(key, defaultValue, { v => + try { + v.toBoolean + } catch { + case _: IllegalArgumentException => + throw new IllegalArgumentException(s"$key should be boolean, but was $v") + } + }, _.toString, doc, isPublic) + + def stringConf( + key: String, + defaultValue: Option[String] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[String] = + SQLConfEntry(key, defaultValue, v => v, v => v, doc, isPublic) + + def enumConf[T]( + key: String, + valueConverter: String => T, + validValues: Set[T], + defaultValue: Option[T] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[T] = + SQLConfEntry(key, defaultValue, v => { + val _v = valueConverter(v) + if (!validValues.contains(_v)) { + throw new IllegalArgumentException( + s"The value of $key should be one of ${validValues.mkString(", ")}, but was $v") + } + _v + }, _.toString, doc, isPublic) + + def seqConf[T]( + key: String, + valueConverter: String => T, + defaultValue: Option[Seq[T]] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[Seq[T]] = { + SQLConfEntry( + key, defaultValue, _.split(",").map(valueConverter), _.mkString(","), doc, isPublic) + } + + def stringSeqConf( + key: String, + defaultValue: Option[Seq[String]] = None, + doc: String = "", + isPublic: Boolean = true): SQLConfEntry[Seq[String]] = { + seqConf(key, s => s, defaultValue, doc, isPublic) + } + } + + import SQLConfEntry._ + + val COMPRESS_CACHED = booleanConf("spark.sql.inMemoryColumnarStorage.compressed", + defaultValue = Some(true), + doc = "When set to true Spark SQL will automatically select a compression codec for each " + + "column based on statistics of the data.") + + val COLUMN_BATCH_SIZE = intConf("spark.sql.inMemoryColumnarStorage.batchSize", + defaultValue = Some(10000), + doc = "Controls the size of batches for columnar caching. Larger batch sizes can improve " + + "memory utilization and compression, but risk OOMs when caching data.") + + val IN_MEMORY_PARTITION_PRUNING = + booleanConf("spark.sql.inMemoryColumnarStorage.partitionPruning", + defaultValue = Some(false), + doc = "") + + val AUTO_BROADCASTJOIN_THRESHOLD = intConf("spark.sql.autoBroadcastJoinThreshold", + defaultValue = Some(10 * 1024 * 1024), + doc = "Configures the maximum size in bytes for a table that will be broadcast to all worker " + + "nodes when performing a join. By setting this value to -1 broadcasting can be disabled. " + + "Note that currently statistics are only supported for Hive Metastore tables where the " + + "commandANALYZE TABLE <tableName> COMPUTE STATISTICS noscan has been run.") + + val DEFAULT_SIZE_IN_BYTES = longConf("spark.sql.defaultSizeInBytes", isPublic = false) + + val SHUFFLE_PARTITIONS = intConf("spark.sql.shuffle.partitions", + defaultValue = Some(200), + doc = "Configures the number of partitions to use when shuffling data for joins or " + + "aggregations.") + + val CODEGEN_ENABLED = booleanConf("spark.sql.codegen", + defaultValue = Some(true), + doc = "When true, code will be dynamically generated at runtime for expression evaluation in" + + " a specific query. For some queries with complicated expression this option can lead to " + + "significant speed-ups. However, for simple queries this can actually slow down query " + + "execution.") + + val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled", + defaultValue = Some(false), + doc = "") + + val DIALECT = stringConf("spark.sql.dialect", defaultValue = Some("sql"), doc = "") + + val CASE_SENSITIVE = booleanConf("spark.sql.caseSensitive", + defaultValue = Some(true), + doc = "") + + val PARQUET_BINARY_AS_STRING = booleanConf("spark.sql.parquet.binaryAsString", + defaultValue = Some(false), + doc = "Some other Parquet-producing systems, in particular Impala and older versions of " + + "Spark SQL, do not differentiate between binary data and strings when writing out the " + + "Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide " + + "compatibility with these systems.") + + val PARQUET_INT96_AS_TIMESTAMP = booleanConf("spark.sql.parquet.int96AsTimestamp", + defaultValue = Some(true), + doc = "Some Parquet-producing systems, in particular Impala, store Timestamp into INT96. " + + "Spark would also store Timestamp as INT96 because we need to avoid precision lost of the " + + "nanoseconds field. This flag tells Spark SQL to interpret INT96 data as a timestamp to " + + "provide compatibility with these systems.") + + val PARQUET_CACHE_METADATA = booleanConf("spark.sql.parquet.cacheMetadata", + defaultValue = Some(true), + doc = "Turns on caching of Parquet schema metadata. Can speed up querying of static data.") + + val PARQUET_COMPRESSION = enumConf("spark.sql.parquet.compression.codec", + valueConverter = v => v.toLowerCase, + validValues = Set("uncompressed", "snappy", "gzip", "lzo"), + defaultValue = Some("gzip"), + doc = "Sets the compression codec use when writing Parquet files. Acceptable values include: " + + "uncompressed, snappy, gzip, lzo.") + + val PARQUET_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.parquet.filterPushdown", + defaultValue = Some(false), + doc = "Turn on Parquet filter pushdown optimization. This feature is turned off by default" + + " because of a known bug in Paruet 1.6.0rc3 " + + "(PARQUET-136). However, " + + "if your table doesn't contain any nullable string or binary columns, it's still safe to " + + "turn this feature on.") + + val PARQUET_USE_DATA_SOURCE_API = booleanConf("spark.sql.parquet.useDataSourceApi", + defaultValue = Some(true), + doc = "") + + val ORC_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.orc.filterPushdown", + defaultValue = Some(false), + doc = "") + + val HIVE_VERIFY_PARTITIONPATH = booleanConf("spark.sql.hive.verifyPartitionPath", + defaultValue = Some(true), + doc = "") + + val COLUMN_NAME_OF_CORRUPT_RECORD = stringConf("spark.sql.columnNameOfCorruptRecord", + defaultValue = Some("_corrupt_record"), + doc = "") + + val BROADCAST_TIMEOUT = intConf("spark.sql.broadcastTimeout", + defaultValue = Some(5 * 60), + doc = "") // Options that control which operators can be chosen by the query planner. These should be // considered hints and may be ignored by future versions of Spark SQL. - val EXTERNAL_SORT = "spark.sql.planner.externalSort" - val SORTMERGE_JOIN = "spark.sql.planner.sortMergeJoin" + val EXTERNAL_SORT = booleanConf("spark.sql.planner.externalSort", + defaultValue = Some(true), + doc = "When true, performs sorts spilling to disk as needed otherwise sort each partition in" + + " memory.") + + val SORTMERGE_JOIN = booleanConf("spark.sql.planner.sortMergeJoin", + defaultValue = Some(false), + doc = "") // This is only used for the thriftserver - val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool" - val THRIFTSERVER_UI_STATEMENT_LIMIT = "spark.sql.thriftserver.ui.retainedStatements" - val THRIFTSERVER_UI_SESSION_LIMIT = "spark.sql.thriftserver.ui.retainedSessions" + val THRIFTSERVER_POOL = stringConf("spark.sql.thriftserver.scheduler.pool", + doc = "Set a Fair Scheduler pool for a JDBC client session") + + val THRIFTSERVER_UI_STATEMENT_LIMIT = intConf("spark.sql.thriftserver.ui.retainedStatements", + defaultValue = Some(200), + doc = "") + + val THRIFTSERVER_UI_SESSION_LIMIT = intConf("spark.sql.thriftserver.ui.retainedSessions", + defaultValue = Some(200), + doc = "") // This is used to set the default data source - val DEFAULT_DATA_SOURCE_NAME = "spark.sql.sources.default" + val DEFAULT_DATA_SOURCE_NAME = stringConf("spark.sql.sources.default", + defaultValue = Some("org.apache.spark.sql.parquet"), + doc = "") + // This is used to control the when we will split a schema's JSON string to multiple pieces // in order to fit the JSON string in metastore's table property (by default, the value has // a length restriction of 4000 characters). We will split the JSON string of a schema // to its length exceeds the threshold. - val SCHEMA_STRING_LENGTH_THRESHOLD = "spark.sql.sources.schemaStringLengthThreshold" + val SCHEMA_STRING_LENGTH_THRESHOLD = intConf("spark.sql.sources.schemaStringLengthThreshold", + defaultValue = Some(4000), + doc = "") + + // Whether to perform partition discovery when loading external data sources. Default to true. + val PARTITION_DISCOVERY_ENABLED = booleanConf("spark.sql.sources.partitionDiscovery.enabled", + defaultValue = Some(true), + doc = "") + + // Whether to perform partition column type inference. Default to true. + val PARTITION_COLUMN_TYPE_INFERENCE = + booleanConf("spark.sql.sources.partitionColumnTypeInference.enabled", + defaultValue = Some(true), + doc = "") + + // The output committer class used by FSBasedRelation. The specified class needs to be a + // subclass of org.apache.hadoop.mapreduce.OutputCommitter. + // NOTE: This property should be set in Hadoop `Configuration` rather than Spark `SQLConf` + val OUTPUT_COMMITTER_CLASS = + stringConf("spark.sql.sources.outputCommitterClass", isPublic = false) // Whether to perform eager analysis when constructing a dataframe. // Set to false when debugging requires the ability to look at invalid query plans. - val DATAFRAME_EAGER_ANALYSIS = "spark.sql.eagerAnalysis" + val DATAFRAME_EAGER_ANALYSIS = booleanConf("spark.sql.eagerAnalysis", + defaultValue = Some(true), + doc = "") // Whether to automatically resolve ambiguity in join conditions for self-joins. // See SPARK-6231. - val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY = "spark.sql.selfJoinAutoResolveAmbiguity" + val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY = + booleanConf("spark.sql.selfJoinAutoResolveAmbiguity", defaultValue = Some(true), doc = "") + + // Whether to retain group by columns or not in GroupedData.agg. + val DATAFRAME_RETAIN_GROUP_COLUMNS = booleanConf("spark.sql.retainGroupColumns", + defaultValue = Some(true), + doc = "") - val USE_SQL_SERIALIZER2 = "spark.sql.useSerializer2" + val USE_SQL_SERIALIZER2 = booleanConf("spark.sql.useSerializer2", + defaultValue = Some(true), doc = "") - val USE_JACKSON_STREAMING_API = "spark.sql.json.useJacksonStreamingAPI" + val USE_JACKSON_STREAMING_API = booleanConf("spark.sql.json.useJacksonStreamingAPI", + defaultValue = Some(true), doc = "") object Deprecated { val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks" @@ -89,7 +367,8 @@ private[spark] object SQLConf { * * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads). */ -private[sql] class SQLConf extends Serializable { + +private[sql] class SQLConf extends Serializable with CatalystConf { import SQLConf._ /** Only low degree of contention is expected for conf, thus NOT using ConcurrentHashMap. */ @@ -111,52 +390,54 @@ private[sql] class SQLConf extends Serializable { * Note that the choice of dialect does not affect things like what tables are available or * how query execution is performed. */ - private[spark] def dialect: String = getConf(DIALECT, "sql") + private[spark] def dialect: String = getConf(DIALECT) /** When true tables cached using the in-memory columnar caching will be compressed. */ - private[spark] def useCompression: Boolean = getConf(COMPRESS_CACHED, "true").toBoolean + private[spark] def useCompression: Boolean = getConf(COMPRESS_CACHED) /** The compression codec for writing to a Parquetfile */ - private[spark] def parquetCompressionCodec: String = getConf(PARQUET_COMPRESSION, "gzip") + private[spark] def parquetCompressionCodec: String = getConf(PARQUET_COMPRESSION) + + private[spark] def parquetCacheMetadata: Boolean = getConf(PARQUET_CACHE_METADATA) /** The number of rows that will be */ - private[spark] def columnBatchSize: Int = getConf(COLUMN_BATCH_SIZE, "10000").toInt + private[spark] def columnBatchSize: Int = getConf(COLUMN_BATCH_SIZE) /** Number of partitions to use for shuffle operators. */ - private[spark] def numShufflePartitions: Int = getConf(SHUFFLE_PARTITIONS, "200").toInt + private[spark] def numShufflePartitions: Int = getConf(SHUFFLE_PARTITIONS) /** When true predicates will be passed to the parquet record reader when possible. */ - private[spark] def parquetFilterPushDown = - getConf(PARQUET_FILTER_PUSHDOWN_ENABLED, "false").toBoolean + private[spark] def parquetFilterPushDown: Boolean = getConf(PARQUET_FILTER_PUSHDOWN_ENABLED) /** When true uses Parquet implementation based on data source API */ - private[spark] def parquetUseDataSourceApi = - getConf(PARQUET_USE_DATA_SOURCE_API, "true").toBoolean + private[spark] def parquetUseDataSourceApi: Boolean = getConf(PARQUET_USE_DATA_SOURCE_API) + + private[spark] def orcFilterPushDown: Boolean = getConf(ORC_FILTER_PUSHDOWN_ENABLED) /** When true uses verifyPartitionPath to prune the path which is not exists. */ - private[spark] def verifyPartitionPath = - getConf(HIVE_VERIFY_PARTITIONPATH, "true").toBoolean + private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITIONPATH) /** When true the planner will use the external sort, which may spill to disk. */ - private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT, "false").toBoolean + private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT) /** * Sort merge join would sort the two side of join first, and then iterate both sides together * only once to get all matches. Using sort merge join can save a lot of memory usage compared * to HashJoin. */ - private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN, "false").toBoolean + private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN) /** - * When set to true, Spark SQL will use the Scala compiler at runtime to generate custom bytecode + * When set to true, Spark SQL will use the Janino at runtime to generate custom bytecode * that evaluates expressions found in queries. In general this custom code runs much faster - * than interpreted evaluation, but there are significant start-up costs due to compilation. - * As a result codegen is only beneficial when queries run for a long time, or when the same - * expressions are used multiple times. - * - * Defaults to false as this feature is currently experimental. + * than interpreted evaluation, but there are some start-up costs (5-10ms) due to compilation. + */ + private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED) + + /** + * caseSensitive analysis true by default */ - private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, "false").toBoolean + def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE) /** * When set to true, Spark SQL will use managed memory for certain operations. This option only @@ -164,15 +445,14 @@ private[sql] class SQLConf extends Serializable { * * Defaults to false as this feature is currently experimental. */ - private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, "false").toBoolean + private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED) - private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2, "true").toBoolean + private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2) /** * Selects between the new (true) and old (false) JSON handlers, to be removed in Spark 1.5.0 */ - private[spark] def useJacksonStreamingAPI: Boolean = - getConf(USE_JACKSON_STREAMING_API, "true").toBoolean + private[spark] def useJacksonStreamingAPI: Boolean = getConf(USE_JACKSON_STREAMING_API) /** * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to @@ -181,8 +461,7 @@ private[sql] class SQLConf extends Serializable { * * Hive setting: hive.auto.convert.join.noconditionaltask.size, whose default value is 10000. */ - private[spark] def autoBroadcastJoinThreshold: Int = - getConf(AUTO_BROADCASTJOIN_THRESHOLD, (10 * 1024 * 1024).toString).toInt + private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD) /** * The default size in bytes to assign to a logical operator's estimation statistics. By default, @@ -191,73 +470,116 @@ private[sql] class SQLConf extends Serializable { * in joins. */ private[spark] def defaultSizeInBytes: Long = - getConf(DEFAULT_SIZE_IN_BYTES, (autoBroadcastJoinThreshold + 1).toString).toLong + getConf(DEFAULT_SIZE_IN_BYTES, autoBroadcastJoinThreshold + 1L) /** * When set to true, we always treat byte arrays in Parquet files as strings. */ - private[spark] def isParquetBinaryAsString: Boolean = - getConf(PARQUET_BINARY_AS_STRING, "false").toBoolean + private[spark] def isParquetBinaryAsString: Boolean = getConf(PARQUET_BINARY_AS_STRING) /** * When set to true, we always treat INT96Values in Parquet files as timestamp. */ - private[spark] def isParquetINT96AsTimestamp: Boolean = - getConf(PARQUET_INT96_AS_TIMESTAMP, "true").toBoolean + private[spark] def isParquetINT96AsTimestamp: Boolean = getConf(PARQUET_INT96_AS_TIMESTAMP) /** * When set to true, partition pruning for in-memory columnar tables is enabled. */ - private[spark] def inMemoryPartitionPruning: Boolean = - getConf(IN_MEMORY_PARTITION_PRUNING, "false").toBoolean + private[spark] def inMemoryPartitionPruning: Boolean = getConf(IN_MEMORY_PARTITION_PRUNING) - private[spark] def columnNameOfCorruptRecord: String = - getConf(COLUMN_NAME_OF_CORRUPT_RECORD, "_corrupt_record") + private[spark] def columnNameOfCorruptRecord: String = getConf(COLUMN_NAME_OF_CORRUPT_RECORD) /** * Timeout in seconds for the broadcast wait time in hash join */ - private[spark] def broadcastTimeout: Int = - getConf(BROADCAST_TIMEOUT, (5 * 60).toString).toInt + private[spark] def broadcastTimeout: Int = getConf(BROADCAST_TIMEOUT) + + private[spark] def defaultDataSourceName: String = getConf(DEFAULT_DATA_SOURCE_NAME) + + private[spark] def partitionDiscoveryEnabled(): Boolean = + getConf(SQLConf.PARTITION_DISCOVERY_ENABLED) - private[spark] def defaultDataSourceName: String = - getConf(DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.parquet") + private[spark] def partitionColumnTypeInferenceEnabled(): Boolean = + getConf(SQLConf.PARTITION_COLUMN_TYPE_INFERENCE) // Do not use a value larger than 4000 as the default value of this property. // See the comments of SCHEMA_STRING_LENGTH_THRESHOLD above for more information. - private[spark] def schemaStringLengthThreshold: Int = - getConf(SCHEMA_STRING_LENGTH_THRESHOLD, "4000").toInt + private[spark] def schemaStringLengthThreshold: Int = getConf(SCHEMA_STRING_LENGTH_THRESHOLD) - private[spark] def dataFrameEagerAnalysis: Boolean = - getConf(DATAFRAME_EAGER_ANALYSIS, "true").toBoolean + private[spark] def dataFrameEagerAnalysis: Boolean = getConf(DATAFRAME_EAGER_ANALYSIS) private[spark] def dataFrameSelfJoinAutoResolveAmbiguity: Boolean = - getConf(DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY, "true").toBoolean - + getConf(DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY) + + private[spark] def dataFrameRetainGroupColumns: Boolean = getConf(DATAFRAME_RETAIN_GROUP_COLUMNS) + /** ********************** SQLConf functionality methods ************ */ /** Set Spark SQL configuration properties. */ def setConf(props: Properties): Unit = settings.synchronized { - props.foreach { case (k, v) => settings.put(k, v) } + props.foreach { case (k, v) => setConfString(k, v) } } - /** Set the given Spark SQL configuration property. */ - def setConf(key: String, value: String): Unit = { + /** Set the given Spark SQL configuration property using a `string` value. */ + def setConfString(key: String, value: String): Unit = { require(key != null, "key cannot be null") require(value != null, s"value cannot be null for key: $key") + val entry = sqlConfEntries.get(key) + if (entry != null) { + // Only verify configs in the SQLConf object + entry.valueConverter(value) + } settings.put(key, value) } + /** Set the given Spark SQL configuration property. */ + def setConf[T](entry: SQLConfEntry[T], value: T): Unit = { + require(entry != null, "entry cannot be null") + require(value != null, s"value cannot be null for key: ${entry.key}") + require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered") + settings.put(entry.key, entry.stringConverter(value)) + } + /** Return the value of Spark SQL configuration property for the given key. */ - def getConf(key: String): String = { - Option(settings.get(key)).getOrElse(throw new NoSuchElementException(key)) + def getConfString(key: String): String = { + Option(settings.get(key)). + orElse { + // Try to use the default value + Option(sqlConfEntries.get(key)).map(_.defaultValueString) + }. + getOrElse(throw new NoSuchElementException(key)) + } + + /** + * Return the value of Spark SQL configuration property for the given key. If the key is not set + * yet, return `defaultValue`. This is useful when `defaultValue` in SQLConfEntry is not the + * desired one. + */ + def getConf[T](entry: SQLConfEntry[T], defaultValue: T): T = { + require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered") + Option(settings.get(entry.key)).map(entry.valueConverter).getOrElse(defaultValue) } /** * Return the value of Spark SQL configuration property for the given key. If the key is not set - * yet, return `defaultValue`. + * yet, return `defaultValue` in [[SQLConfEntry]]. + */ + def getConf[T](entry: SQLConfEntry[T]): T = { + require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered") + Option(settings.get(entry.key)).map(entry.valueConverter).orElse(entry.defaultValue). + getOrElse(throw new NoSuchElementException(entry.key)) + } + + /** + * Return the `string` value of Spark SQL configuration property for the given key. If the key is + * not set yet, return `defaultValue`. */ - def getConf(key: String, defaultValue: String): String = { + def getConfString(key: String, defaultValue: String): String = { + val entry = sqlConfEntries.get(key) + if (entry != null && defaultValue != "") { + // Only verify configs in the SQLConf object + entry.valueConverter(defaultValue) + } Option(settings.get(key)).getOrElse(defaultValue) } @@ -267,11 +589,25 @@ private[sql] class SQLConf extends Serializable { */ def getAllConfs: immutable.Map[String, String] = settings.synchronized { settings.toMap } - private[spark] def unsetConf(key: String) { + /** + * Return all the configuration definitions that have been defined in [[SQLConf]]. Each + * definition contains key, defaultValue and doc. + */ + def getAllDefinedConfs: Seq[(String, String, String)] = sqlConfEntries.synchronized { + sqlConfEntries.values.filter(_.isPublic).map { entry => + (entry.key, entry.defaultValueString, entry.doc) + }.toSeq + } + + private[spark] def unsetConf(key: String): Unit = { settings -= key } - private[spark] def clear() { + private[spark] def unsetConf(entry: SQLConfEntry[_]): Unit = { + settings -= entry.key + } + + private[spark] def clear(): Unit = { settings.clear() } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 0ac0936f0f592..04fc798bf3738 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql import java.beans.Introspector import java.util.Properties +import java.util.concurrent.atomic.AtomicReference import scala.collection.JavaConversions._ import scala.collection.immutable @@ -26,62 +27,22 @@ import scala.language.implicitConversions import scala.reflect.runtime.universe.TypeTag import scala.util.control.NonFatal -import com.google.common.reflect.TypeToken - +import org.apache.spark.SparkContext import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLConf.SQLConfEntry import org.apache.spark.sql.catalyst.analysis._ -import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.errors.DialectException +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor -import org.apache.spark.sql.catalyst.Dialect -import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, expressions} +import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _} import org.apache.spark.sql.execution.{Filter, _} -import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} -import org.apache.spark.sql.json._ import org.apache.spark.sql.sources._ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils -import org.apache.spark.{Partition, SparkContext} - -/** - * Currently we support the default dialect named "sql", associated with the class - * [[DefaultDialect]] - * - * And we can also provide custom SQL Dialect, for example in Spark SQL CLI: - * {{{ - *-- switch to "hiveql" dialect - * spark-sql>SET spark.sql.dialect=hiveql; - * spark-sql>SELECT * FROM src LIMIT 1; - * - *-- switch to "sql" dialect - * spark-sql>SET spark.sql.dialect=sql; - * spark-sql>SELECT * FROM src LIMIT 1; - * - *-- register the new SQL dialect - * spark-sql> SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect; - * spark-sql> SELECT * FROM src LIMIT 1; - * - *-- register the non-exist SQL dialect - * spark-sql> SET spark.sql.dialect=NotExistedClass; - * spark-sql> SELECT * FROM src LIMIT 1; - * - *-- Exception will be thrown and switch to dialect - *-- "sql" (for SQLContext) or - *-- "hiveql" (for HiveContext) - * }}} - */ -private[spark] class DefaultDialect extends Dialect { - @transient - protected val sqlParser = new catalyst.SqlParser - - override def parse(sqlText: String): LogicalPlan = { - sqlParser.parse(sqlText) - } -} /** * The entry point for working with structured data (rows and columns) in Spark. Allows the @@ -95,6 +56,8 @@ private[spark] class DefaultDialect extends Dialect { * @groupname config Configuration * @groupname dataframes Custom DataFrame Creation * @groupname Ungrouped Support functions for language integrated queries. + * + * @since 1.0.0 */ class SQLContext(@transient val sparkContext: SparkContext) extends org.apache.spark.Logging @@ -113,50 +76,74 @@ class SQLContext(@transient val sparkContext: SparkContext) * Set Spark SQL configuration properties. * * @group config + * @since 1.0.0 */ def setConf(props: Properties): Unit = conf.setConf(props) + /** Set the given Spark SQL configuration property. */ + private[sql] def setConf[T](entry: SQLConfEntry[T], value: T): Unit = conf.setConf(entry, value) + /** * Set the given Spark SQL configuration property. * * @group config + * @since 1.0.0 */ - def setConf(key: String, value: String): Unit = conf.setConf(key, value) + def setConf(key: String, value: String): Unit = conf.setConfString(key, value) /** * Return the value of Spark SQL configuration property for the given key. * * @group config + * @since 1.0.0 + */ + def getConf(key: String): String = conf.getConfString(key) + + /** + * Return the value of Spark SQL configuration property for the given key. If the key is not set + * yet, return `defaultValue` in [[SQLConfEntry]]. + */ + private[sql] def getConf[T](entry: SQLConfEntry[T]): T = conf.getConf(entry) + + /** + * Return the value of Spark SQL configuration property for the given key. If the key is not set + * yet, return `defaultValue`. This is useful when `defaultValue` in SQLConfEntry is not the + * desired one. */ - def getConf(key: String): String = conf.getConf(key) + private[sql] def getConf[T](entry: SQLConfEntry[T], defaultValue: T): T = { + conf.getConf(entry, defaultValue) + } /** * Return the value of Spark SQL configuration property for the given key. If the key is not set * yet, return `defaultValue`. * * @group config + * @since 1.0.0 */ - def getConf(key: String, defaultValue: String): String = conf.getConf(key, defaultValue) + def getConf(key: String, defaultValue: String): String = conf.getConfString(key, defaultValue) /** * Return all the configuration properties that have been set (i.e. not the default). * This creates a new copy of the config properties in the form of a Map. * * @group config + * @since 1.0.0 */ def getAllConfs: immutable.Map[String, String] = conf.getAllConfs // TODO how to handle the temp table per user session? @transient - protected[sql] lazy val catalog: Catalog = new SimpleCatalog(true) + protected[sql] lazy val catalog: Catalog = new SimpleCatalog(conf) // TODO how to handle the temp function per user session? @transient - protected[sql] lazy val functionRegistry: FunctionRegistry = new SimpleFunctionRegistry(true) + protected[sql] lazy val functionRegistry: FunctionRegistry = + new OverrideFunctionRegistry(FunctionRegistry.builtin) @transient protected[sql] lazy val analyzer: Analyzer = - new Analyzer(catalog, functionRegistry, caseSensitive = true) { + new Analyzer(catalog, functionRegistry, conf) { override val extendedResolutionRules = ExtractPythonUdfs :: sources.PreInsertCastAndRename :: @@ -176,10 +163,10 @@ class SQLContext(@transient val sparkContext: SparkContext) @transient protected[sql] val sqlParser = new SparkSQLParser(getSQLDialect().parse(_)) - protected[sql] def getSQLDialect(): Dialect = { + protected[sql] def getSQLDialect(): ParserDialect = { try { val clazz = Utils.classForName(dialectClassName) - clazz.newInstance().asInstanceOf[Dialect] + clazz.newInstance().asInstanceOf[ParserDialect] } catch { case NonFatal(e) => // Since we didn't find the available SQL Dialect, it will fail even for SET command: @@ -209,14 +196,33 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] val defaultSession = createSession() protected[sql] def dialectClassName = if (conf.dialect == "sql") { - classOf[DefaultDialect].getCanonicalName + classOf[DefaultParserDialect].getCanonicalName } else { conf.dialect } - sparkContext.getConf.getAll.foreach { - case (key, value) if key.startsWith("spark.sql") => setConf(key, value) - case _ => + { + // We extract spark sql settings from SparkContext's conf and put them to + // Spark SQL's conf. + // First, we populate the SQLConf (conf). So, we can make sure that other values using + // those settings in their construction can get the correct settings. + // For example, metadataHive in HiveContext may need both spark.sql.hive.metastore.version + // and spark.sql.hive.metastore.jars to get correctly constructed. + val properties = new Properties + sparkContext.getConf.getAll.foreach { + case (key, value) if key.startsWith("spark.sql") => properties.setProperty(key, value) + case _ => + } + // We directly put those settings to conf to avoid of calling setConf, which may have + // side-effects. For example, in HiveContext, setConf may cause executionHive and metadataHive + // get constructed. If we call setConf directly, the constructed metadataHive may have + // wrong settings, or the construction may fail. + conf.setConf(properties) + // After we have populated SQLConf, we call setConf to populate other confs in the subclass + // (e.g. hiveconf in HiveContext). + properties.foreach { + case (key, value) => setConf(key, value) + } } @transient @@ -228,6 +234,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * the query planner for advanced functionality. * * @group basic + * @since 1.3.0 */ @Experimental @transient @@ -238,6 +245,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * Returns a [[DataFrame]] with no rows or columns. * * @group basic + * @since 1.3.0 */ @Experimental @transient @@ -270,6 +278,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * }}} * * @group basic + * @since 1.3.0 * TODO move to SQLSession? */ @transient @@ -278,23 +287,27 @@ class SQLContext(@transient val sparkContext: SparkContext) /** * Returns true if the table is currently cached in-memory. * @group cachemgmt + * @since 1.3.0 */ def isCached(tableName: String): Boolean = cacheManager.isCached(tableName) /** * Caches the specified table in-memory. * @group cachemgmt + * @since 1.3.0 */ def cacheTable(tableName: String): Unit = cacheManager.cacheTable(tableName) /** * Removes the specified table from the in-memory cache. * @group cachemgmt + * @since 1.3.0 */ def uncacheTable(tableName: String): Unit = cacheManager.uncacheTable(tableName) /** * Removes all cached tables from the in-memory cache. + * @since 1.3.0 */ def clearCache(): Unit = cacheManager.clearCache() @@ -311,27 +324,40 @@ class SQLContext(@transient val sparkContext: SparkContext) * }}} * * @group basic + * @since 1.3.0 */ @Experimental object implicits extends Serializable { // scalastyle:on - /** Converts $"col name" into an [[Column]]. */ + /** + * Converts $"col name" into an [[Column]]. + * @since 1.3.0 + */ implicit class StringToColumn(val sc: StringContext) { def $(args: Any*): ColumnName = { - new ColumnName(sc.s(args :_*)) + new ColumnName(sc.s(args : _*)) } } - /** An implicit conversion that turns a Scala `Symbol` into a [[Column]]. */ + /** + * An implicit conversion that turns a Scala `Symbol` into a [[Column]]. + * @since 1.3.0 + */ implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name) - /** Creates a DataFrame from an RDD of case classes or tuples. */ + /** + * Creates a DataFrame from an RDD of case classes or tuples. + * @since 1.3.0 + */ implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = { DataFrameHolder(self.createDataFrame(rdd)) } - /** Creates a DataFrame from a local Seq of Product. */ + /** + * Creates a DataFrame from a local Seq of Product. + * @since 1.3.0 + */ implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = { DataFrameHolder(self.createDataFrame(data)) @@ -341,7 +367,10 @@ class SQLContext(@transient val sparkContext: SparkContext) // making existing implicit conversions ambiguous. In particular, RDD[Double] is dangerous // because of [[DoubleRDDFunctions]]. - /** Creates a single column DataFrame from an RDD[Int]. */ + /** + * Creates a single column DataFrame from an RDD[Int]. + * @since 1.3.0 + */ implicit def intRddToDataFrameHolder(data: RDD[Int]): DataFrameHolder = { val dataType = IntegerType val rows = data.mapPartitions { iter => @@ -354,7 +383,10 @@ class SQLContext(@transient val sparkContext: SparkContext) DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil))) } - /** Creates a single column DataFrame from an RDD[Long]. */ + /** + * Creates a single column DataFrame from an RDD[Long]. + * @since 1.3.0 + */ implicit def longRddToDataFrameHolder(data: RDD[Long]): DataFrameHolder = { val dataType = LongType val rows = data.mapPartitions { iter => @@ -367,7 +399,10 @@ class SQLContext(@transient val sparkContext: SparkContext) DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil))) } - /** Creates a single column DataFrame from an RDD[String]. */ + /** + * Creates a single column DataFrame from an RDD[String]. + * @since 1.3.0 + */ implicit def stringRddToDataFrameHolder(data: RDD[String]): DataFrameHolder = { val dataType = StringType val rows = data.mapPartitions { iter => @@ -386,13 +421,14 @@ class SQLContext(@transient val sparkContext: SparkContext) * Creates a DataFrame from an RDD of case classes. * * @group dataframes + * @since 1.3.0 */ @Experimental def createDataFrame[A <: Product : TypeTag](rdd: RDD[A]): DataFrame = { SparkPlan.currentContext.set(self) val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType] val attributeSeq = schema.toAttributes - val rowRDD = RDDConversions.productToRowRdd(rdd, schema) + val rowRDD = RDDConversions.productToRowRdd(rdd, schema.map(_.dataType)) DataFrame(self, LogicalRDD(attributeSeq, rowRDD)(self)) } @@ -401,6 +437,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * Creates a DataFrame from a local Seq of Product. * * @group dataframes + * @since 1.3.0 */ @Experimental def createDataFrame[A <: Product : TypeTag](data: Seq[A]): DataFrame = { @@ -414,6 +451,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * Convert a [[BaseRelation]] created for external data sources into a [[DataFrame]]. * * @group dataframes + * @since 1.3.0 */ def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame = { DataFrame(this, LogicalRelation(baseRelation)) @@ -449,6 +487,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * }}} * * @group dataframes + * @since 1.3.0 */ @DeveloperApi def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame = { @@ -465,14 +504,26 @@ class SQLContext(@transient val sparkContext: SparkContext) // schema differs from the existing schema on any field data type. val catalystRows = if (needsConversion) { val converter = CatalystTypeConverters.createToCatalystConverter(schema) - rowRDD.map(converter(_).asInstanceOf[Row]) + rowRDD.map(converter(_).asInstanceOf[InternalRow]) } else { - rowRDD + rowRDD.map{r: Row => InternalRow.fromSeq(r.toSeq)} } val logicalPlan = LogicalRDD(schema.toAttributes, catalystRows)(self) DataFrame(this, logicalPlan) } + /** + * Creates a DataFrame from an RDD[Row]. User can specify whether the input rows should be + * converted to Catalyst rows. + */ + private[sql] + def internalCreateDataFrame(catalystRows: RDD[InternalRow], schema: StructType) = { + // TODO: use MutableProjection when rowRDD is another DataFrame and the applied + // schema differs from the existing schema on any field data type. + val logicalPlan = LogicalRDD(schema.toAttributes, catalystRows)(self) + DataFrame(this, logicalPlan) + } + /** * :: DeveloperApi :: * Creates a [[DataFrame]] from an [[JavaRDD]] containing [[Row]]s using the given schema. @@ -480,6 +531,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * the provided schema. Otherwise, there will be runtime exception. * * @group dataframes + * @since 1.3.0 */ @DeveloperApi def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = { @@ -492,6 +544,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, * SELECT * queries will return the columns in an undefined order. * @group dataframes + * @since 1.3.0 */ def createDataFrame(rdd: RDD[_], beanClass: Class[_]): DataFrame = { val attributeSeq = getSchema(beanClass) @@ -502,13 +555,13 @@ class SQLContext(@transient val sparkContext: SparkContext) Class.forName(className, true, Utils.getContextOrSparkClassLoader)) val extractors = localBeanInfo.getPropertyDescriptors.filterNot(_.getName == "class").map(_.getReadMethod) - + val methodsToConverts = extractors.zip(attributeSeq).map { case (e, attr) => + (e, CatalystTypeConverters.createToCatalystConverter(attr.dataType)) + } iter.map { row => new GenericRow( - extractors.zip(attributeSeq).map { case (e, attr) => - CatalystTypeConverters.convertToCatalyst(e.invoke(row), attr.dataType) - }.toArray[Any] - ) : Row + methodsToConverts.map { case (e, convert) => convert(e.invoke(row)) }.toArray[Any] + ) : InternalRow } } DataFrame(this, LogicalRDD(attributeSeq, rowRdd)(this)) @@ -520,490 +573,224 @@ class SQLContext(@transient val sparkContext: SparkContext) * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, * SELECT * queries will return the columns in an undefined order. * @group dataframes + * @since 1.3.0 */ def createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = { createDataFrame(rdd.rdd, beanClass) } /** - * :: DeveloperApi :: - * Creates a [[DataFrame]] from an [[RDD]] containing [[Row]]s by applying a schema to this RDD. - * It is important to make sure that the structure of every [[Row]] of the provided RDD matches - * the provided schema. Otherwise, there will be runtime exception. - * Example: + * :: Experimental :: + * Returns a [[DataFrameReader]] that can be used to read data in as a [[DataFrame]]. * {{{ - * import org.apache.spark.sql._ - * import org.apache.spark.sql.types._ - * val sqlContext = new org.apache.spark.sql.SQLContext(sc) - * - * val schema = - * StructType( - * StructField("name", StringType, false) :: - * StructField("age", IntegerType, true) :: Nil) - * - * val people = - * sc.textFile("examples/src/main/resources/people.txt").map( - * _.split(",")).map(p => Row(p(0), p(1).trim.toInt)) - * val dataFrame = sqlContext. applySchema(people, schema) - * dataFrame.printSchema - * // root - * // |-- name: string (nullable = false) - * // |-- age: integer (nullable = true) - * - * dataFrame.registerTempTable("people") - * sqlContext.sql("select name from people").collect.foreach(println) + * sqlContext.read.parquet("/path/to/file.parquet") + * sqlContext.read.schema(schema).json("/path/to/file.json") * }}} + * + * @group genericdata + * @since 1.4.0 */ - @deprecated("use createDataFrame", "1.3.0") - def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame = { - createDataFrame(rowRDD, schema) - } - - @deprecated("use createDataFrame", "1.3.0") - def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = { - createDataFrame(rowRDD, schema) - } + @Experimental + def read: DataFrameReader = new DataFrameReader(this) /** - * Applies a schema to an RDD of Java Beans. + * :: Experimental :: + * Creates an external table from the given path and returns the corresponding DataFrame. + * It will use the default data source configured by spark.sql.sources.default. * - * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, - * SELECT * queries will return the columns in an undefined order. + * @group ddl_ops + * @since 1.3.0 */ - @deprecated("use createDataFrame", "1.3.0") - def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame = { - createDataFrame(rdd, beanClass) + @Experimental + def createExternalTable(tableName: String, path: String): DataFrame = { + val dataSourceName = conf.defaultDataSourceName + createExternalTable(tableName, path, dataSourceName) } /** - * Applies a schema to an RDD of Java Beans. + * :: Experimental :: + * Creates an external table from the given path based on a data source + * and returns the corresponding DataFrame. * - * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, - * SELECT * queries will return the columns in an undefined order. + * @group ddl_ops + * @since 1.3.0 */ - @deprecated("use createDataFrame", "1.3.0") - def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = { - createDataFrame(rdd, beanClass) + @Experimental + def createExternalTable( + tableName: String, + path: String, + source: String): DataFrame = { + createExternalTable(tableName, source, Map("path" -> path)) } /** - * Loads a Parquet file, returning the result as a [[DataFrame]]. This function returns an empty - * [[DataFrame]] if no paths are passed in. + * :: Experimental :: + * Creates an external table from the given path based on a data source and a set of options. + * Then, returns the corresponding DataFrame. * - * @group specificdata + * @group ddl_ops + * @since 1.3.0 */ - @scala.annotation.varargs - def parquetFile(paths: String*): DataFrame = { - if (paths.isEmpty) { - emptyDataFrame - } else if (conf.parquetUseDataSourceApi) { - baseRelationToDataFrame(parquet.ParquetRelation2(paths, Map.empty)(this)) - } else { - DataFrame(this, parquet.ParquetRelation( - paths.mkString(","), Some(sparkContext.hadoopConfiguration), this)) - } + @Experimental + def createExternalTable( + tableName: String, + source: String, + options: java.util.Map[String, String]): DataFrame = { + createExternalTable(tableName, source, options.toMap) } /** - * Loads a JSON file (one object per line), returning the result as a [[DataFrame]]. - * It goes through the entire dataset once to determine the schema. + * :: Experimental :: + * (Scala-specific) + * Creates an external table from the given path based on a data source and a set of options. + * Then, returns the corresponding DataFrame. * - * @group specificdata + * @group ddl_ops + * @since 1.3.0 */ - def jsonFile(path: String): DataFrame = jsonFile(path, 1.0) + @Experimental + def createExternalTable( + tableName: String, + source: String, + options: Map[String, String]): DataFrame = { + val cmd = + CreateTableUsing( + tableName, + userSpecifiedSchema = None, + source, + temporary = false, + options, + allowExisting = false, + managedIfNoPath = false) + executePlan(cmd).toRdd + table(tableName) + } /** * :: Experimental :: - * Loads a JSON file (one object per line) and applies the given schema, - * returning the result as a [[DataFrame]]. + * Create an external table from the given path based on a data source, a schema and + * a set of options. Then, returns the corresponding DataFrame. * - * @group specificdata + * @group ddl_ops + * @since 1.3.0 */ @Experimental - def jsonFile(path: String, schema: StructType): DataFrame = - load("json", schema, Map("path" -> path)) + def createExternalTable( + tableName: String, + source: String, + schema: StructType, + options: java.util.Map[String, String]): DataFrame = { + createExternalTable(tableName, source, schema, options.toMap) + } /** * :: Experimental :: - * @group specificdata + * (Scala-specific) + * Create an external table from the given path based on a data source, a schema and + * a set of options. Then, returns the corresponding DataFrame. + * + * @group ddl_ops + * @since 1.3.0 */ @Experimental - def jsonFile(path: String, samplingRatio: Double): DataFrame = - load("json", Map("path" -> path, "samplingRatio" -> samplingRatio.toString)) + def createExternalTable( + tableName: String, + source: String, + schema: StructType, + options: Map[String, String]): DataFrame = { + val cmd = + CreateTableUsing( + tableName, + userSpecifiedSchema = Some(schema), + source, + temporary = false, + options, + allowExisting = false, + managedIfNoPath = false) + executePlan(cmd).toRdd + table(tableName) + } /** - * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a - * [[DataFrame]]. - * It goes through the entire dataset once to determine the schema. - * - * @group specificdata + * Registers the given [[DataFrame]] as a temporary table in the catalog. Temporary tables exist + * only during the lifetime of this instance of SQLContext. */ - def jsonRDD(json: RDD[String]): DataFrame = jsonRDD(json, 1.0) - + private[sql] def registerDataFrameAsTable(df: DataFrame, tableName: String): Unit = { + catalog.registerTable(Seq(tableName), df.logicalPlan) + } /** - * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a - * [[DataFrame]]. - * It goes through the entire dataset once to determine the schema. + * Drops the temporary table with the given table name in the catalog. If the table has been + * cached/persisted before, it's also unpersisted. * - * @group specificdata + * @param tableName the name of the table to be unregistered. + * + * @group basic + * @since 1.3.0 */ - def jsonRDD(json: JavaRDD[String]): DataFrame = jsonRDD(json.rdd, 1.0) + def dropTempTable(tableName: String): Unit = { + cacheManager.tryUncacheQuery(table(tableName)) + catalog.unregisterTable(Seq(tableName)) + } /** * :: Experimental :: - * Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, - * returning the result as a [[DataFrame]]. + * Creates a [[DataFrame]] with a single [[LongType]] column named `id`, containing elements + * in an range from 0 to `end` (exclusive) with step value 1. * - * @group specificdata + * @since 1.4.1 + * @group dataframe */ @Experimental - def jsonRDD(json: RDD[String], schema: StructType): DataFrame = { - if (conf.useJacksonStreamingAPI) { - baseRelationToDataFrame(new JSONRelation(() => json, None, 1.0, Some(schema))(this)) - } else { - val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord - val appliedSchema = - Option(schema).getOrElse( - JsonRDD.nullTypeToStringType( - JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord))) - val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - createDataFrame(rowRDD, appliedSchema, needsConversion = false) - } - } + def range(end: Long): DataFrame = range(0, end) /** * :: Experimental :: - * Loads an JavaRDD storing JSON objects (one object per record) and applies the given - * schema, returning the result as a [[DataFrame]]. + * Creates a [[DataFrame]] with a single [[LongType]] column named `id`, containing elements + * in an range from `start` to `end` (exclusive) with step value 1. * - * @group specificdata + * @since 1.4.0 + * @group dataframe */ @Experimental - def jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame = { - jsonRDD(json.rdd, schema) + def range(start: Long, end: Long): DataFrame = { + createDataFrame( + sparkContext.range(start, end).map(Row(_)), + StructType(StructField("id", LongType, nullable = false) :: Nil)) } /** * :: Experimental :: - * Loads an RDD[String] storing JSON objects (one object per record) inferring the - * schema, returning the result as a [[DataFrame]]. + * Creates a [[DataFrame]] with a single [[LongType]] column named `id`, containing elements + * in an range from `start` to `end` (exclusive) with an step value, with partition number + * specified. * - * @group specificdata + * @since 1.4.0 + * @group dataframe */ @Experimental - def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame = { - if (conf.useJacksonStreamingAPI) { - baseRelationToDataFrame(new JSONRelation(() => json, None, samplingRatio, None)(this)) - } else { - val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord - val appliedSchema = - JsonRDD.nullTypeToStringType( - JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord)) - val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - createDataFrame(rowRDD, appliedSchema, needsConversion = false) - } + def range(start: Long, end: Long, step: Long, numPartitions: Int): DataFrame = { + createDataFrame( + sparkContext.range(start, end, step, numPartitions).map(Row(_)), + StructType(StructField("id", LongType, nullable = false) :: Nil)) } /** - * :: Experimental :: - * Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the - * schema, returning the result as a [[DataFrame]]. + * Executes a SQL query using Spark, returning the result as a [[DataFrame]]. The dialect that is + * used for SQL parsing can be configured with 'spark.sql.dialect'. * - * @group specificdata + * @group basic + * @since 1.3.0 */ - @Experimental - def jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame = { - jsonRDD(json.rdd, samplingRatio); - } - - /** - * :: Experimental :: - * Returns the dataset stored at path as a DataFrame, - * using the default data source configured by spark.sql.sources.default. - * - * @group genericdata - */ - @Experimental - def load(path: String): DataFrame = { - val dataSourceName = conf.defaultDataSourceName - load(path, dataSourceName) - } - - /** - * :: Experimental :: - * Returns the dataset stored at path as a DataFrame, using the given data source. - * - * @group genericdata - */ - @Experimental - def load(path: String, source: String): DataFrame = { - load(source, Map("path" -> path)) - } - - /** - * :: Experimental :: - * (Java-specific) Returns the dataset specified by the given data source and - * a set of options as a DataFrame. - * - * @group genericdata - */ - @Experimental - def load(source: String, options: java.util.Map[String, String]): DataFrame = { - load(source, options.toMap) - } - - /** - * :: Experimental :: - * (Scala-specific) Returns the dataset specified by the given data source and - * a set of options as a DataFrame. - * - * @group genericdata - */ - @Experimental - def load(source: String, options: Map[String, String]): DataFrame = { - val resolved = ResolvedDataSource(this, None, source, options) - DataFrame(this, LogicalRelation(resolved.relation)) - } - - /** - * :: Experimental :: - * (Java-specific) Returns the dataset specified by the given data source and - * a set of options as a DataFrame, using the given schema as the schema of the DataFrame. - * - * @group genericdata - */ - @Experimental - def load( - source: String, - schema: StructType, - options: java.util.Map[String, String]): DataFrame = { - load(source, schema, options.toMap) - } - - /** - * :: Experimental :: - * (Scala-specific) Returns the dataset specified by the given data source and - * a set of options as a DataFrame, using the given schema as the schema of the DataFrame. - * @group genericdata - */ - @Experimental - def load( - source: String, - schema: StructType, - options: Map[String, String]): DataFrame = { - val resolved = ResolvedDataSource(this, Some(schema), source, options) - DataFrame(this, LogicalRelation(resolved.relation)) - } - - /** - * :: Experimental :: - * Creates an external table from the given path and returns the corresponding DataFrame. - * It will use the default data source configured by spark.sql.sources.default. - * - * @group ddl_ops - */ - @Experimental - def createExternalTable(tableName: String, path: String): DataFrame = { - val dataSourceName = conf.defaultDataSourceName - createExternalTable(tableName, path, dataSourceName) - } - - /** - * :: Experimental :: - * Creates an external table from the given path based on a data source - * and returns the corresponding DataFrame. - * - * @group ddl_ops - */ - @Experimental - def createExternalTable( - tableName: String, - path: String, - source: String): DataFrame = { - createExternalTable(tableName, source, Map("path" -> path)) - } - - /** - * :: Experimental :: - * Creates an external table from the given path based on a data source and a set of options. - * Then, returns the corresponding DataFrame. - * - * @group ddl_ops - */ - @Experimental - def createExternalTable( - tableName: String, - source: String, - options: java.util.Map[String, String]): DataFrame = { - createExternalTable(tableName, source, options.toMap) - } - - /** - * :: Experimental :: - * (Scala-specific) - * Creates an external table from the given path based on a data source and a set of options. - * Then, returns the corresponding DataFrame. - * - * @group ddl_ops - */ - @Experimental - def createExternalTable( - tableName: String, - source: String, - options: Map[String, String]): DataFrame = { - val cmd = - CreateTableUsing( - tableName, - userSpecifiedSchema = None, - source, - temporary = false, - options, - allowExisting = false, - managedIfNoPath = false) - executePlan(cmd).toRdd - table(tableName) - } - - /** - * :: Experimental :: - * Create an external table from the given path based on a data source, a schema and - * a set of options. Then, returns the corresponding DataFrame. - * - * @group ddl_ops - */ - @Experimental - def createExternalTable( - tableName: String, - source: String, - schema: StructType, - options: java.util.Map[String, String]): DataFrame = { - createExternalTable(tableName, source, schema, options.toMap) - } - - /** - * :: Experimental :: - * (Scala-specific) - * Create an external table from the given path based on a data source, a schema and - * a set of options. Then, returns the corresponding DataFrame. - * - * @group ddl_ops - */ - @Experimental - def createExternalTable( - tableName: String, - source: String, - schema: StructType, - options: Map[String, String]): DataFrame = { - val cmd = - CreateTableUsing( - tableName, - userSpecifiedSchema = Some(schema), - source, - temporary = false, - options, - allowExisting = false, - managedIfNoPath = false) - executePlan(cmd).toRdd - table(tableName) - } - - /** - * :: Experimental :: - * Construct a [[DataFrame]] representing the database table accessible via JDBC URL - * url named table. - * - * @group specificdata - */ - @Experimental - def jdbc(url: String, table: String): DataFrame = { - jdbc(url, table, JDBCRelation.columnPartition(null)) - } - - /** - * :: Experimental :: - * Construct a [[DataFrame]] representing the database table accessible via JDBC URL - * url named table. Partitions of the table will be retrieved in parallel based on the parameters - * passed to this function. - * - * @param columnName the name of a column of integral type that will be used for partitioning. - * @param lowerBound the minimum value of `columnName` used to decide partition stride - * @param upperBound the maximum value of `columnName` used to decide partition stride - * @param numPartitions the number of partitions. the range `minValue`-`maxValue` will be split - * evenly into this many partitions - * - * @group specificdata - */ - @Experimental - def jdbc( - url: String, - table: String, - columnName: String, - lowerBound: Long, - upperBound: Long, - numPartitions: Int): DataFrame = { - val partitioning = JDBCPartitioningInfo(columnName, lowerBound, upperBound, numPartitions) - val parts = JDBCRelation.columnPartition(partitioning) - jdbc(url, table, parts) - } - - /** - * :: Experimental :: - * Construct a [[DataFrame]] representing the database table accessible via JDBC URL - * url named table. The theParts parameter gives a list expressions - * suitable for inclusion in WHERE clauses; each one defines one partition - * of the [[DataFrame]]. - * - * @group specificdata - */ - @Experimental - def jdbc(url: String, table: String, theParts: Array[String]): DataFrame = { - val parts: Array[Partition] = theParts.zipWithIndex.map { case (part, i) => - JDBCPartition(part, i) : Partition - } - jdbc(url, table, parts) - } - - private def jdbc(url: String, table: String, parts: Array[Partition]): DataFrame = { - val relation = JDBCRelation(url, table, parts)(this) - baseRelationToDataFrame(relation) - } - - /** - * Registers the given [[DataFrame]] as a temporary table in the catalog. Temporary tables exist - * only during the lifetime of this instance of SQLContext. - */ - private[sql] def registerDataFrameAsTable(df: DataFrame, tableName: String): Unit = { - catalog.registerTable(Seq(tableName), df.logicalPlan) - } - - /** - * Drops the temporary table with the given table name in the catalog. If the table has been - * cached/persisted before, it's also unpersisted. - * - * @param tableName the name of the table to be unregistered. - * - * @group basic - */ - def dropTempTable(tableName: String): Unit = { - cacheManager.tryUncacheQuery(table(tableName)) - catalog.unregisterTable(Seq(tableName)) - } - - /** - * Executes a SQL query using Spark, returning the result as a [[DataFrame]]. The dialect that is - * used for SQL parsing can be configured with 'spark.sql.dialect'. - * - * @group basic - */ - def sql(sqlText: String): DataFrame = { - DataFrame(this, parseSql(sqlText)) + def sql(sqlText: String): DataFrame = { + DataFrame(this, parseSql(sqlText)) } /** * Returns the specified table as a [[DataFrame]]. * * @group ddl_ops + * @since 1.3.0 */ def table(tableName: String): DataFrame = DataFrame(this, catalog.lookupRelation(Seq(tableName))) @@ -1014,6 +801,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * indicating if a table is a temporary one or not). * * @group ddl_ops + * @since 1.3.0 */ def tables(): DataFrame = { DataFrame(this, ShowTablesCommand(None)) @@ -1025,6 +813,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * indicating if a table is a temporary one or not). * * @group ddl_ops + * @since 1.3.0 */ def tables(databaseName: String): DataFrame = { DataFrame(this, ShowTablesCommand(Some(databaseName))) @@ -1034,6 +823,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * Returns the names of tables in the current database as an array. * * @group ddl_ops + * @since 1.3.0 */ def tableNames(): Array[String] = { catalog.getTables(None).map { @@ -1045,6 +835,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * Returns the names of tables in the given database as an array. * * @group ddl_ops + * @since 1.3.0 */ def tableNames(databaseName: String): Array[String] = { catalog.getTables(Some(databaseName)).map { @@ -1099,7 +890,7 @@ class SQLContext(@transient val sparkContext: SparkContext) val projectSet = AttributeSet(projectList.flatMap(_.references)) val filterSet = AttributeSet(filterPredicates.flatMap(_.references)) val filterCondition = - prunePushedDownFilters(filterPredicates).reduceLeftOption(expressions.And) + prunePushedDownFilters(filterPredicates).reduceLeftOption(catalyst.expressions.And) // Right now we still use a projection even if the only evaluation is applying an alias // to a column. Since this is a no-op, it could be avoided. However, using this @@ -1125,7 +916,7 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] val planner = new SparkPlanner @transient - protected[sql] lazy val emptyResult = sparkContext.parallelize(Seq.empty[Row], 1) + protected[sql] lazy val emptyResult = sparkContext.parallelize(Seq.empty[InternalRow], 1) /** * Prepares a planned SparkPlan for execution by inserting shuffle operations as needed. @@ -1156,6 +947,11 @@ class SQLContext(@transient val sparkContext: SparkContext) tlSession.remove() } + protected[sql] def setSession(session: SQLSession): Unit = { + detachSession() + tlSession.set(session) + } + protected[sql] class SQLSession { // Note that this is a lazy val so we can override the default value in subclasses. protected[sql] lazy val conf: SQLConf = new SQLConf @@ -1180,14 +976,14 @@ class SQLContext(@transient val sparkContext: SparkContext) // TODO: Don't just pick the first one... lazy val sparkPlan: SparkPlan = { SparkPlan.currentContext.set(self) - planner(optimizedPlan).next() + planner.plan(optimizedPlan).next() } // executedPlan should not be used to initialize any SparkPlan. It should be // only used for execution. lazy val executedPlan: SparkPlan = prepareForExecution.execute(sparkPlan) /** Internal version of the RDD. Avoids copies and has no schema */ - lazy val toRdd: RDD[Row] = executedPlan.execute() + lazy val toRdd: RDD[InternalRow] = executedPlan.execute() protected def stringOrError[A](f: => A): String = try f.toString catch { case e: Throwable => e.toString } @@ -1269,7 +1065,7 @@ class SQLContext(@transient val sparkContext: SparkContext) } val rowRdd = convertedRdd.mapPartitions { iter => - iter.map { m => new GenericRow(m): Row} + iter.map { m => new GenericRow(m): InternalRow} } DataFrame(this, LogicalRDD(schema.toAttributes, rowRdd)(self)) @@ -1279,12 +1075,346 @@ class SQLContext(@transient val sparkContext: SparkContext) * Returns a Catalyst Schema for the given java bean class. */ protected def getSchema(beanClass: Class[_]): Seq[AttributeReference] = { - val (dataType, _) = JavaTypeInference.inferDataType(TypeToken.of(beanClass)) + val (dataType, _) = JavaTypeInference.inferDataType(beanClass) dataType.asInstanceOf[StructType].fields.map { f => AttributeReference(f.name, f.dataType, f.nullable)() } } + //////////////////////////////////////////////////////////////////////////// + //////////////////////////////////////////////////////////////////////////// + // Deprecated methods + //////////////////////////////////////////////////////////////////////////// + //////////////////////////////////////////////////////////////////////////// + + /** + * @deprecated As of 1.3.0, replaced by `createDataFrame()`. + */ + @deprecated("use createDataFrame", "1.3.0") + def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame = { + createDataFrame(rowRDD, schema) + } + + /** + * @deprecated As of 1.3.0, replaced by `createDataFrame()`. + */ + @deprecated("use createDataFrame", "1.3.0") + def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = { + createDataFrame(rowRDD, schema) + } + + /** + * @deprecated As of 1.3.0, replaced by `createDataFrame()`. + */ + @deprecated("use createDataFrame", "1.3.0") + def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame = { + createDataFrame(rdd, beanClass) + } + + /** + * @deprecated As of 1.3.0, replaced by `createDataFrame()`. + */ + @deprecated("use createDataFrame", "1.3.0") + def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = { + createDataFrame(rdd, beanClass) + } + + /** + * Loads a Parquet file, returning the result as a [[DataFrame]]. This function returns an empty + * [[DataFrame]] if no paths are passed in. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().parquet()`. + */ + @deprecated("Use read.parquet()", "1.4.0") + @scala.annotation.varargs + def parquetFile(paths: String*): DataFrame = { + if (paths.isEmpty) { + emptyDataFrame + } else if (conf.parquetUseDataSourceApi) { + read.parquet(paths : _*) + } else { + DataFrame(this, parquet.ParquetRelation( + paths.mkString(","), Some(sparkContext.hadoopConfiguration), this)) + } + } + + /** + * Loads a JSON file (one object per line), returning the result as a [[DataFrame]]. + * It goes through the entire dataset once to determine the schema. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonFile(path: String): DataFrame = { + read.json(path) + } + + /** + * Loads a JSON file (one object per line) and applies the given schema, + * returning the result as a [[DataFrame]]. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonFile(path: String, schema: StructType): DataFrame = { + read.schema(schema).json(path) + } + + /** + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonFile(path: String, samplingRatio: Double): DataFrame = { + read.option("samplingRatio", samplingRatio.toString).json(path) + } + + /** + * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a + * [[DataFrame]]. + * It goes through the entire dataset once to determine the schema. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonRDD(json: RDD[String]): DataFrame = read.json(json) + + /** + * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a + * [[DataFrame]]. + * It goes through the entire dataset once to determine the schema. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonRDD(json: JavaRDD[String]): DataFrame = read.json(json) + + /** + * Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, + * returning the result as a [[DataFrame]]. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonRDD(json: RDD[String], schema: StructType): DataFrame = { + read.schema(schema).json(json) + } + + /** + * Loads an JavaRDD storing JSON objects (one object per record) and applies the given + * schema, returning the result as a [[DataFrame]]. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame = { + read.schema(schema).json(json) + } + + /** + * Loads an RDD[String] storing JSON objects (one object per record) inferring the + * schema, returning the result as a [[DataFrame]]. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame = { + read.option("samplingRatio", samplingRatio.toString).json(json) + } + + /** + * Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the + * schema, returning the result as a [[DataFrame]]. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().json()`. + */ + @deprecated("Use read.json()", "1.4.0") + def jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame = { + read.option("samplingRatio", samplingRatio.toString).json(json) + } + + /** + * Returns the dataset stored at path as a DataFrame, + * using the default data source configured by spark.sql.sources.default. + * + * @group genericdata + * @deprecated As of 1.4.0, replaced by `read().load(path)`. + */ + @deprecated("Use read.load(path)", "1.4.0") + def load(path: String): DataFrame = { + read.load(path) + } + + /** + * Returns the dataset stored at path as a DataFrame, using the given data source. + * + * @group genericdata + * @deprecated As of 1.4.0, replaced by `read().format(source).load(path)`. + */ + @deprecated("Use read.format(source).load(path)", "1.4.0") + def load(path: String, source: String): DataFrame = { + read.format(source).load(path) + } + + /** + * (Java-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame. + * + * @group genericdata + * @deprecated As of 1.4.0, replaced by `read().format(source).options(options).load()`. + */ + @deprecated("Use read.format(source).options(options).load()", "1.4.0") + def load(source: String, options: java.util.Map[String, String]): DataFrame = { + read.options(options).format(source).load() + } + + /** + * (Scala-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame. + * + * @group genericdata + * @deprecated As of 1.4.0, replaced by `read().format(source).options(options).load()`. + */ + @deprecated("Use read.format(source).options(options).load()", "1.4.0") + def load(source: String, options: Map[String, String]): DataFrame = { + read.options(options).format(source).load() + } + + /** + * (Java-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame, using the given schema as the schema of the DataFrame. + * + * @group genericdata + * @deprecated As of 1.4.0, replaced by + * `read().format(source).schema(schema).options(options).load()`. + */ + @deprecated("Use read.format(source).schema(schema).options(options).load()", "1.4.0") + def load(source: String, schema: StructType, options: java.util.Map[String, String]): DataFrame = + { + read.format(source).schema(schema).options(options).load() + } + + /** + * (Scala-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame, using the given schema as the schema of the DataFrame. + * + * @group genericdata + * @deprecated As of 1.4.0, replaced by + * `read().format(source).schema(schema).options(options).load()`. + */ + @deprecated("Use read.format(source).schema(schema).options(options).load()", "1.4.0") + def load(source: String, schema: StructType, options: Map[String, String]): DataFrame = { + read.format(source).schema(schema).options(options).load() + } + + /** + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL + * url named table. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().jdbc()`. + */ + @deprecated("use read.jdbc()", "1.4.0") + def jdbc(url: String, table: String): DataFrame = { + read.jdbc(url, table, new Properties) + } + + /** + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL + * url named table. Partitions of the table will be retrieved in parallel based on the parameters + * passed to this function. + * + * @param columnName the name of a column of integral type that will be used for partitioning. + * @param lowerBound the minimum value of `columnName` used to decide partition stride + * @param upperBound the maximum value of `columnName` used to decide partition stride + * @param numPartitions the number of partitions. the range `minValue`-`maxValue` will be split + * evenly into this many partitions + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().jdbc()`. + */ + @deprecated("use read.jdbc()", "1.4.0") + def jdbc( + url: String, + table: String, + columnName: String, + lowerBound: Long, + upperBound: Long, + numPartitions: Int): DataFrame = { + read.jdbc(url, table, columnName, lowerBound, upperBound, numPartitions, new Properties) + } + + /** + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL + * url named table. The theParts parameter gives a list expressions + * suitable for inclusion in WHERE clauses; each one defines one partition + * of the [[DataFrame]]. + * + * @group specificdata + * @deprecated As of 1.4.0, replaced by `read().jdbc()`. + */ + @deprecated("use read.jdbc()", "1.4.0") + def jdbc(url: String, table: String, theParts: Array[String]): DataFrame = { + read.jdbc(url, table, theParts, new Properties) + } + + //////////////////////////////////////////////////////////////////////////// + //////////////////////////////////////////////////////////////////////////// + // End of deprecated methods + //////////////////////////////////////////////////////////////////////////// + //////////////////////////////////////////////////////////////////////////// + + + // Register a succesfully instantiatd context to the singleton. This should be at the end of + // the class definition so that the singleton is updated only if there is no exception in the + // construction of the instance. + SQLContext.setLastInstantiatedContext(self) } +/** + * This SQLContext object contains utility functions to create a singleton SQLContext instance, + * or to get the last created SQLContext instance. + */ +object SQLContext { + + private val INSTANTIATION_LOCK = new Object() + /** + * Reference to the last created SQLContext. + */ + @transient private val lastInstantiatedContext = new AtomicReference[SQLContext]() + + /** + * Get the singleton SQLContext if it exists or create a new one using the given SparkContext. + * This function can be used to create a singleton SQLContext object that can be shared across + * the JVM. + */ + def getOrCreate(sparkContext: SparkContext): SQLContext = { + INSTANTIATION_LOCK.synchronized { + if (lastInstantiatedContext.get() == null) { + new SQLContext(sparkContext) + } + } + lastInstantiatedContext.get() + } + + private[sql] def clearLastInstantiatedContext(): Unit = { + INSTANTIATION_LOCK.synchronized { + lastInstantiatedContext.set(null) + } + } + + private[sql] def setLastInstantiatedContext(sqlContext: SQLContext): Unit = { + INSTANTIATION_LOCK.synchronized { + lastInstantiatedContext.set(sqlContext) + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala index 5921eaf5e63f4..e59fa6e162900 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala @@ -17,7 +17,6 @@ package org.apache.spark.sql - import scala.util.parsing.combinator.RegexParsers import org.apache.spark.sql.catalyst.AbstractSparkSQLParser @@ -45,8 +44,8 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr private val pair: Parser[LogicalPlan] = (key ~ ("=".r ~> value).?).? ^^ { - case None => SetCommand(None, output) - case Some(k ~ v) => SetCommand(Some(k.trim -> v.map(_.trim)), output) + case None => SetCommand(None) + case Some(k ~ v) => SetCommand(Some(k.trim -> v.map(_.trim))) } def apply(input: String): LogicalPlan = parseAll(pair, input) match { @@ -55,15 +54,15 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr } } - protected val AS = Keyword("AS") - protected val CACHE = Keyword("CACHE") - protected val CLEAR = Keyword("CLEAR") - protected val IN = Keyword("IN") - protected val LAZY = Keyword("LAZY") - protected val SET = Keyword("SET") - protected val SHOW = Keyword("SHOW") - protected val TABLE = Keyword("TABLE") - protected val TABLES = Keyword("TABLES") + protected val AS = Keyword("AS") + protected val CACHE = Keyword("CACHE") + protected val CLEAR = Keyword("CLEAR") + protected val IN = Keyword("IN") + protected val LAZY = Keyword("LAZY") + protected val SET = Keyword("SET") + protected val SHOW = Keyword("SHOW") + protected val TABLE = Keyword("TABLE") + protected val TABLES = Keyword("TABLES") protected val UNCACHE = Keyword("UNCACHE") override protected lazy val start: Parser[LogicalPlan] = cache | uncache | set | show | others diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala index b97aaf73529a3..3cc5c2441d8a5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala @@ -33,6 +33,8 @@ import org.apache.spark.sql.types.DataType /** * Functions for registering user-defined functions. Use [[SQLContext.udf]] to access this. + * + * @since 1.3.0 */ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { @@ -44,6 +46,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { envVars: JMap[String, String], pythonIncludes: JList[String], pythonExec: String, + pythonVer: String, broadcastVars: JList[Broadcast[PythonBroadcast]], accumulator: Accumulator[JList[Array[Byte]]], stringDataType: String): Unit = { @@ -68,6 +71,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { envVars, pythonIncludes, pythonExec, + pythonVer, broadcastVars, accumulator, dataType, @@ -87,6 +91,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of ${x} arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[$typeTags](name: String, func: Function$x[$types]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -104,6 +109,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { println(s""" |/** | * Register a user-defined function with ${i} arguments. + | * @since 1.3.0 | */ |def register(name: String, f: UDF$i[$extTypeArgs, _], returnType: DataType) = { | functionRegistry.registerFunction( @@ -116,6 +122,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 0 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag](name: String, func: Function0[RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -127,6 +134,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 1 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag](name: String, func: Function1[A1, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -138,6 +146,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 2 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag](name: String, func: Function2[A1, A2, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -149,6 +158,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 3 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag](name: String, func: Function3[A1, A2, A3, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -160,6 +170,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 4 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag](name: String, func: Function4[A1, A2, A3, A4, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -171,6 +182,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 5 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag](name: String, func: Function5[A1, A2, A3, A4, A5, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -182,6 +194,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 6 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag](name: String, func: Function6[A1, A2, A3, A4, A5, A6, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -193,6 +206,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 7 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag](name: String, func: Function7[A1, A2, A3, A4, A5, A6, A7, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -204,6 +218,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 8 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag](name: String, func: Function8[A1, A2, A3, A4, A5, A6, A7, A8, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -215,6 +230,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 9 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag](name: String, func: Function9[A1, A2, A3, A4, A5, A6, A7, A8, A9, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -226,6 +242,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 10 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](name: String, func: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -237,6 +254,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 11 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag](name: String, func: Function11[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -248,6 +266,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 12 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag](name: String, func: Function12[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -259,6 +278,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 13 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag](name: String, func: Function13[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -270,6 +290,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 14 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag](name: String, func: Function14[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -281,6 +302,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 15 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag](name: String, func: Function15[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -292,6 +314,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 16 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag](name: String, func: Function16[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -303,6 +326,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 17 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag](name: String, func: Function17[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -314,6 +338,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 18 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag](name: String, func: Function18[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -325,6 +350,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 19 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag](name: String, func: Function19[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -336,6 +362,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 20 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag](name: String, func: Function20[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -347,6 +374,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 21 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag](name: String, func: Function21[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -358,6 +386,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a Scala closure of 22 arguments as user-defined function (UDF). * @tparam RT return type of UDF. + * @since 1.3.0 */ def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag, A22: TypeTag](name: String, func: Function22[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, A22, RT]): UserDefinedFunction = { val dataType = ScalaReflection.schemaFor[RT].dataType @@ -371,6 +400,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 1 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF1[_, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -380,6 +410,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 2 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF2[_, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -389,6 +420,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 3 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF3[_, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -398,6 +430,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 4 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF4[_, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -407,6 +440,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 5 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF5[_, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -416,6 +450,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 6 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF6[_, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -425,6 +460,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 7 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF7[_, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -434,6 +470,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 8 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF8[_, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -443,6 +480,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 9 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF9[_, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -452,6 +490,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 10 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -461,6 +500,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 11 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF11[_, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -470,6 +510,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 12 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF12[_, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -479,6 +520,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 13 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF13[_, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -488,6 +530,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 14 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF14[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -497,6 +540,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 15 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF15[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -506,6 +550,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 16 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF16[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -515,6 +560,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 17 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF17[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -524,6 +570,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 18 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF18[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -533,6 +580,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 19 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF19[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -542,6 +590,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 20 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF20[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -551,6 +600,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 21 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF21[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( @@ -560,6 +610,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { /** * Register a user-defined function with 22 arguments. + * @since 1.3.0 */ def register(name: String, f: UDF22[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = { functionRegistry.registerFunction( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala index 295db539adfc4..a02e202d2eebc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql import java.util.{List => JList, Map => JMap} import org.apache.spark.Accumulator +import org.apache.spark.annotation.Experimental import org.apache.spark.api.python.PythonBroadcast import org.apache.spark.broadcast.Broadcast import org.apache.spark.sql.catalyst.expressions.ScalaUdf @@ -36,7 +37,10 @@ import org.apache.spark.sql.types.DataType * // Projects a column that adds a prediction column based on the score column. * df.select( predict(df("score")) ) * }}} + * + * @since 1.3.0 */ +@Experimental case class UserDefinedFunction protected[sql] (f: AnyRef, dataType: DataType) { def apply(exprs: Column*): Column = { @@ -54,14 +58,15 @@ private[sql] case class UserDefinedPythonFunction( envVars: JMap[String, String], pythonIncludes: JList[String], pythonExec: String, + pythonVer: String, broadcastVars: JList[Broadcast[PythonBroadcast]], accumulator: Accumulator[JList[Array[Byte]]], dataType: DataType) { /** Returns a [[Column]] that will evaluate to calling this UDF with the given input. */ def apply(exprs: Column*): Column = { - val udf = PythonUDF(name, command, envVars, pythonIncludes, pythonExec, broadcastVars, - accumulator, dataType, exprs.map(_.expr)) + val udf = PythonUDF(name, command, envVars, pythonIncludes, pythonExec, pythonVer, + broadcastVars, accumulator, dataType, exprs.map(_.expr)) Column(udf) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala index ae77f72998a22..43b62f0e822f8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala @@ -72,17 +72,6 @@ private[r] object SQLUtils { sqlContext.createDataFrame(rowRDD, schema) } - // A helper to include grouping columns in Agg() - def aggWithGrouping(gd: GroupedData, exprs: Column*): DataFrame = { - val aggExprs = exprs.map { col => - col.expr match { - case expr: NamedExpression => expr - case expr: Expression => Alias(expr, expr.simpleString)() - } - } - gd.toDF(aggExprs) - } - def dfToRowRDD(df: DataFrame): JavaRDD[Array[Byte]] = { df.map(r => rowToRBytes(r)) } @@ -117,7 +106,7 @@ private[r] object SQLUtils { dfCols.map { col => colToRBytes(col) - } + } } def convertRowsToColumns(localDF: Array[Row], numCols: Int): Array[Array[Any]] = { @@ -132,7 +121,7 @@ private[r] object SQLUtils { val numRows = col.length val bos = new ByteArrayOutputStream() val dos = new DataOutputStream(bos) - + SerDe.writeInt(dos, numRows) col.map { item => @@ -150,4 +139,19 @@ private[r] object SQLUtils { case "ignore" => SaveMode.Ignore } } + + def loadDF( + sqlContext: SQLContext, + source: String, + options: java.util.Map[String, String]): DataFrame = { + sqlContext.read.format(source).options(options).load() + } + + def loadDF( + sqlContext: SQLContext, + source: String, + schema: StructType, + options: java.util.Map[String, String]): DataFrame = { + sqlContext.read.format(source).schema(schema).options(options).load() + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala index aa10af400c815..1949625699ca8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.columnar import java.nio.{ByteBuffer, ByteOrder} -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.columnar.ColumnBuilder._ import org.apache.spark.sql.columnar.compression.{AllCompressionSchemes, CompressibleColumnBuilder} import org.apache.spark.sql.types._ @@ -33,7 +33,7 @@ private[sql] trait ColumnBuilder { /** * Appends `row(ordinal)` to the column builder. */ - def appendFrom(row: Row, ordinal: Int) + def appendFrom(row: InternalRow, ordinal: Int) /** * Column statistics information @@ -68,7 +68,7 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType]( buffer.order(ByteOrder.nativeOrder()).putInt(columnType.typeId) } - override def appendFrom(row: Row, ordinal: Int): Unit = { + override def appendFrom(row: InternalRow, ordinal: Int): Unit = { buffer = ensureFreeSpace(buffer, columnType.actualSize(row, ordinal)) columnType.append(row, ordinal, buffer) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala index b0f983c180673..1bce214d1d6c3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala @@ -17,11 +17,10 @@ package org.apache.spark.sql.columnar -import java.sql.Timestamp - -import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.expressions.{AttributeMap, Attribute, AttributeReference} +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference} import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String private[sql] class ColumnStatisticsSchema(a: Attribute) extends Serializable { val upperBound = AttributeReference(a.name + ".upperBound", a.dataType, nullable = true)() @@ -54,7 +53,7 @@ private[sql] sealed trait ColumnStats extends Serializable { /** * Gathers statistics information from `row(ordinal)`. */ - def gatherStats(row: Row, ordinal: Int): Unit = { + def gatherStats(row: InternalRow, ordinal: Int): Unit = { if (row.isNullAt(ordinal)) { nullCount += 1 // 4 bytes for null position @@ -67,23 +66,23 @@ private[sql] sealed trait ColumnStats extends Serializable { * Column statistics represented as a single row, currently including closed lower bound, closed * upper bound and null count. */ - def collectedStatistics: Row + def collectedStatistics: InternalRow } /** * A no-op ColumnStats only used for testing purposes. */ private[sql] class NoopColumnStats extends ColumnStats { - override def gatherStats(row: Row, ordinal: Int): Unit = super.gatherStats(row, ordinal) + override def gatherStats(row: InternalRow, ordinal: Int): Unit = super.gatherStats(row, ordinal) - override def collectedStatistics: Row = Row(null, null, nullCount, count, 0L) + override def collectedStatistics: InternalRow = InternalRow(null, null, nullCount, count, 0L) } private[sql] class BooleanColumnStats extends ColumnStats { protected var upper = false protected var lower = true - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row.getBoolean(ordinal) @@ -93,14 +92,15 @@ private[sql] class BooleanColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class ByteColumnStats extends ColumnStats { protected var upper = Byte.MinValue protected var lower = Byte.MaxValue - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row.getByte(ordinal) @@ -110,14 +110,15 @@ private[sql] class ByteColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class ShortColumnStats extends ColumnStats { protected var upper = Short.MinValue protected var lower = Short.MaxValue - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row.getShort(ordinal) @@ -127,14 +128,15 @@ private[sql] class ShortColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class LongColumnStats extends ColumnStats { protected var upper = Long.MinValue protected var lower = Long.MaxValue - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row.getLong(ordinal) @@ -144,14 +146,15 @@ private[sql] class LongColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class DoubleColumnStats extends ColumnStats { protected var upper = Double.MinValue protected var lower = Double.MaxValue - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row.getDouble(ordinal) @@ -161,14 +164,15 @@ private[sql] class DoubleColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class FloatColumnStats extends ColumnStats { protected var upper = Float.MinValue protected var lower = Float.MaxValue - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row.getFloat(ordinal) @@ -178,14 +182,15 @@ private[sql] class FloatColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class FixedDecimalColumnStats extends ColumnStats { protected var upper: Decimal = null protected var lower: Decimal = null - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row(ordinal).asInstanceOf[Decimal] @@ -195,14 +200,15 @@ private[sql] class FixedDecimalColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class IntColumnStats extends ColumnStats { protected var upper = Int.MinValue protected var lower = Int.MaxValue - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row.getInt(ordinal) @@ -212,14 +218,15 @@ private[sql] class IntColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class StringColumnStats extends ColumnStats { protected var upper: UTF8String = null protected var lower: UTF8String = null - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { val value = row(ordinal).asInstanceOf[UTF8String] @@ -229,46 +236,34 @@ private[sql] class StringColumnStats extends ColumnStats { } } - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(lower, upper, nullCount, count, sizeInBytes) } private[sql] class DateColumnStats extends IntColumnStats -private[sql] class TimestampColumnStats extends ColumnStats { - protected var upper: Timestamp = null - protected var lower: Timestamp = null - - override def gatherStats(row: Row, ordinal: Int): Unit = { - super.gatherStats(row, ordinal) - if (!row.isNullAt(ordinal)) { - val value = row(ordinal).asInstanceOf[Timestamp] - if (upper == null || value.compareTo(upper) > 0) upper = value - if (lower == null || value.compareTo(lower) < 0) lower = value - sizeInBytes += TIMESTAMP.defaultSize - } - } - - override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) -} +private[sql] class TimestampColumnStats extends LongColumnStats private[sql] class BinaryColumnStats extends ColumnStats { - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { sizeInBytes += BINARY.actualSize(row, ordinal) } } - override def collectedStatistics: Row = Row(null, null, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(null, null, nullCount, count, sizeInBytes) } private[sql] class GenericColumnStats extends ColumnStats { - override def gatherStats(row: Row, ordinal: Int): Unit = { + override def gatherStats(row: InternalRow, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { sizeInBytes += GENERIC.actualSize(row, ordinal) } } - override def collectedStatistics: Row = Row(null, null, nullCount, count, sizeInBytes) + override def collectedStatistics: InternalRow = + InternalRow(null, null, nullCount, count, sizeInBytes) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala index 20be5ca9d0046..8e21020917768 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala @@ -18,7 +18,6 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import java.sql.Timestamp import scala.reflect.runtime.universe.TypeTag @@ -26,6 +25,7 @@ import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions.MutableRow import org.apache.spark.sql.execution.SparkSqlSerializer import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /** * An abstract class that represents type of a column. Used to append/extract Java objects into/from @@ -321,7 +321,7 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) { val length = buffer.getInt() val stringBytes = new Array[Byte](length) buffer.get(stringBytes, 0, length) - UTF8String(stringBytes) + UTF8String.fromBytes(stringBytes) } override def setField(row: MutableRow, ordinal: Int, value: UTF8String): Unit = { @@ -355,22 +355,20 @@ private[sql] object DATE extends NativeColumnType(DateType, 8, 4) { } } -private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 12) { - override def extract(buffer: ByteBuffer): Timestamp = { - val timestamp = new Timestamp(buffer.getLong()) - timestamp.setNanos(buffer.getInt()) - timestamp +private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 8) { + override def extract(buffer: ByteBuffer): Long = { + buffer.getLong } - override def append(v: Timestamp, buffer: ByteBuffer): Unit = { - buffer.putLong(v.getTime).putInt(v.getNanos) + override def append(v: Long, buffer: ByteBuffer): Unit = { + buffer.putLong(v) } - override def getField(row: Row, ordinal: Int): Timestamp = { - row(ordinal).asInstanceOf[Timestamp] + override def getField(row: Row, ordinal: Int): Long = { + row(ordinal).asInstanceOf[Long] } - override def setField(row: MutableRow, ordinal: Int, value: Timestamp): Unit = { + override def setField(row: MutableRow, ordinal: Int, value: Long): Unit = { row(ordinal) = value } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala index d9b6fb43ab83d..761f427b8cd0d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala @@ -19,21 +19,16 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import org.apache.spark.{Accumulable, Accumulator, Accumulators} -import org.apache.spark.sql.catalyst.expressions - import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap import org.apache.spark.rdd.RDD -import org.apache.spark.sql.Row -import org.apache.spark.SparkContext import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics} import org.apache.spark.sql.execution.{LeafNode, SparkPlan} import org.apache.spark.storage.StorageLevel +import org.apache.spark.{Accumulable, Accumulator, Accumulators} private[sql] object InMemoryRelation { def apply( @@ -45,7 +40,7 @@ private[sql] object InMemoryRelation { new InMemoryRelation(child.output, useCompression, batchSize, storageLevel, child, tableName)() } -private[sql] case class CachedBatch(buffers: Array[Array[Byte]], stats: Row) +private[sql] case class CachedBatch(buffers: Array[Array[Byte]], stats: InternalRow) private[sql] case class InMemoryRelation( output: Seq[Attribute], @@ -56,12 +51,12 @@ private[sql] case class InMemoryRelation( tableName: Option[String])( private var _cachedColumnBuffers: RDD[CachedBatch] = null, private var _statistics: Statistics = null, - private var _batchStats: Accumulable[ArrayBuffer[Row], Row] = null) + private var _batchStats: Accumulable[ArrayBuffer[InternalRow], InternalRow] = null) extends LogicalPlan with MultiInstanceRelation { - private val batchStats: Accumulable[ArrayBuffer[Row], Row] = + private val batchStats: Accumulable[ArrayBuffer[InternalRow], InternalRow] = if (_batchStats == null) { - child.sqlContext.sparkContext.accumulableCollection(ArrayBuffer.empty[Row]) + child.sqlContext.sparkContext.accumulableCollection(ArrayBuffer.empty[InternalRow]) } else { _batchStats } @@ -151,7 +146,7 @@ private[sql] case class InMemoryRelation( rowCount += 1 } - val stats = Row.merge(columnBuilders.map(_.columnStats.collectedStatistics) : _*) + val stats = InternalRow.merge(columnBuilders.map(_.columnStats.collectedStatistics) : _*) batchStats += stats CachedBatch(columnBuilders.map(_.build().array()), stats) @@ -236,7 +231,7 @@ private[sql] case class InMemoryColumnarTableScan( case GreaterThanOrEqual(a: AttributeReference, l: Literal) => l <= statsFor(a).upperBound case GreaterThanOrEqual(l: Literal, a: AttributeReference) => statsFor(a).lowerBound <= l - case IsNull(a: Attribute) => statsFor(a).nullCount > 0 + case IsNull(a: Attribute) => statsFor(a).nullCount > 0 case IsNotNull(a: Attribute) => statsFor(a).count - statsFor(a).nullCount > 0 } @@ -267,7 +262,7 @@ private[sql] case class InMemoryColumnarTableScan( private val inMemoryPartitionPruningEnabled = sqlContext.conf.inMemoryPartitionPruning - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { if (enableAccumulators) { readPartitions.setValue(0) readBatches.setValue(0) @@ -296,7 +291,7 @@ private[sql] case class InMemoryColumnarTableScan( val nextRow = new SpecificMutableRow(requestedColumnDataTypes) - def cachedBatchesToRows(cacheBatches: Iterator[CachedBatch]): Iterator[Row] = { + def cachedBatchesToRows(cacheBatches: Iterator[CachedBatch]): Iterator[InternalRow] = { val rows = cacheBatches.flatMap { cachedBatch => // Build column accessors val columnAccessors = requestedColumnIndices.map { batchColumnIndex => @@ -306,15 +301,15 @@ private[sql] case class InMemoryColumnarTableScan( } // Extract rows via column accessors - new Iterator[Row] { + new Iterator[InternalRow] { private[this] val rowLen = nextRow.length - override def next(): Row = { + override def next(): InternalRow = { var i = 0 while (i < rowLen) { columnAccessors(i).extractTo(nextRow, i) i += 1 } - nextRow + if (attributes.isEmpty) InternalRow.empty else nextRow } override def hasNext: Boolean = columnAccessors(0).hasNext diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala index f1f494ac26d0c..ba47bc783f31e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.columnar import java.nio.{ByteBuffer, ByteOrder} -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow /** * A stackable trait used for building byte buffer for a column containing null values. Memory @@ -52,7 +52,7 @@ private[sql] trait NullableColumnBuilder extends ColumnBuilder { super.initialize(initialSize, columnName, useCompression) } - abstract override def appendFrom(row: Row, ordinal: Int): Unit = { + abstract override def appendFrom(row: InternalRow, ordinal: Int): Unit = { columnStats.gatherStats(row, ordinal) if (row.isNullAt(ordinal)) { nulls = ColumnBuilder.ensureFreeSpace(nulls, 4) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala index 8e2a1af6dae78..39b21ddb47ba4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.columnar.compression import java.nio.{ByteBuffer, ByteOrder} import org.apache.spark.Logging -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.columnar.{ColumnBuilder, NativeColumnBuilder} import org.apache.spark.sql.types.AtomicType @@ -66,7 +66,7 @@ private[sql] trait CompressibleColumnBuilder[T <: AtomicType] encoder.compressionRatio < 0.8 } - private def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = { + private def gatherCompressibilityStats(row: InternalRow, ordinal: Int): Unit = { var i = 0 while (i < compressionEncoders.length) { compressionEncoders(i).gatherCompressibilityStats(row, ordinal) @@ -74,7 +74,7 @@ private[sql] trait CompressibleColumnBuilder[T <: AtomicType] } } - abstract override def appendFrom(row: Row, ordinal: Int): Unit = { + abstract override def appendFrom(row: InternalRow, ordinal: Int): Unit = { super.appendFrom(row, ordinal) if (!row.isNullAt(ordinal)) { gatherCompressibilityStats(row, ordinal) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala index 17c2d9b111188..4eaec6d853d4d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala @@ -18,14 +18,13 @@ package org.apache.spark.sql.columnar.compression import java.nio.{ByteBuffer, ByteOrder} - -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.MutableRow import org.apache.spark.sql.columnar.{ColumnType, NativeColumnType} import org.apache.spark.sql.types.AtomicType private[sql] trait Encoder[T <: AtomicType] { - def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {} + def gatherCompressibilityStats(row: InternalRow, ordinal: Int): Unit = {} def compressedSize: Int diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala index 534ae90ddbc8b..5abc1259a19ab 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala @@ -22,8 +22,7 @@ import java.nio.ByteBuffer import scala.collection.mutable import scala.reflect.ClassTag import scala.reflect.runtime.universe.runtimeMirror - -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.{MutableRow, SpecificMutableRow} import org.apache.spark.sql.columnar._ import org.apache.spark.sql.types._ @@ -96,7 +95,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { override def compressedSize: Int = _compressedSize - override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = { + override def gatherCompressibilityStats(row: InternalRow, ordinal: Int): Unit = { val value = columnType.getField(row, ordinal) val actualSize = columnType.actualSize(row, ordinal) _uncompressedSize += actualSize @@ -217,7 +216,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { // to store dictionary element count. private var dictionarySize = 4 - override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = { + override def gatherCompressibilityStats(row: InternalRow, ordinal: Int): Unit = { val value = columnType.getField(row, ordinal) if (!overflow) { @@ -310,7 +309,7 @@ private[sql] case object BooleanBitSet extends CompressionScheme { class Encoder extends compression.Encoder[BooleanType.type] { private var _uncompressedSize = 0 - override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = { + override def gatherCompressibilityStats(row: InternalRow, ordinal: Int): Unit = { _uncompressedSize += BOOLEAN.defaultSize } @@ -404,7 +403,7 @@ private[sql] case object IntDelta extends CompressionScheme { private var prevValue: Int = _ - override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = { + override def gatherCompressibilityStats(row: InternalRow, ordinal: Int): Unit = { val value = row.getInt(ordinal) val delta = value - prevValue @@ -484,7 +483,7 @@ private[sql] case object LongDelta extends CompressionScheme { private var prevValue: Long = _ - override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = { + override def gatherCompressibilityStats(row: InternalRow, ordinal: Int): Unit = { val value = row.getLong(ordinal) val delta = value - prevValue diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala index 18b1ba4c5c4b9..6e8a5ef18ab62 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala @@ -20,12 +20,10 @@ package org.apache.spark.sql.execution import java.util.HashMap import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ -import org.apache.spark.sql.SQLContext /** * :: DeveloperApi :: @@ -121,11 +119,11 @@ case class Aggregate( } } - override def execute(): RDD[Row] = attachTree(this, "execute") { + protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") { if (groupingExpressions.isEmpty) { child.execute().mapPartitions { iter => val buffer = newAggregateBuffer() - var currentRow: Row = null + var currentRow: InternalRow = null while (iter.hasNext) { currentRow = iter.next() var i = 0 @@ -147,10 +145,10 @@ case class Aggregate( } } else { child.execute().mapPartitions { iter => - val hashTable = new HashMap[Row, Array[AggregateFunction]] + val hashTable = new HashMap[InternalRow, Array[AggregateFunction]] val groupingProjection = new InterpretedMutableProjection(groupingExpressions, child.output) - var currentRow: Row = null + var currentRow: InternalRow = null while (iter.hasNext) { currentRow = iter.next() val currentGroup = groupingProjection(currentRow) @@ -167,7 +165,7 @@ case class Aggregate( } } - new Iterator[Row] { + new Iterator[InternalRow] { private[this] val hashTableIter = hashTable.entrySet().iterator() private[this] val aggregateResults = new GenericMutableRow(computedAggregates.length) private[this] val resultProjection = @@ -177,7 +175,7 @@ case class Aggregate( override final def hasNext: Boolean = hashTableIter.hasNext - override final def next(): Row = { + override final def next(): InternalRow = { val currentEntry = hashTableIter.next() val currentGroup = currentEntry.getKey val currentBuffer = currentEntry.getValue diff --git a/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala similarity index 95% rename from sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala rename to sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala index 18584c2dcf797..a4b38d364d54a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala @@ -15,18 +15,19 @@ * limitations under the License. */ -package org.apache.spark.sql +package org.apache.spark.sql.execution import java.util.concurrent.locks.ReentrantReadWriteLock import org.apache.spark.Logging import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.columnar.InMemoryRelation +import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.spark.storage.StorageLevel import org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK /** Holds a cached logical plan and its data */ -private case class CachedData(plan: LogicalPlan, cachedRepresentation: InMemoryRelation) +private[sql] case class CachedData(plan: LogicalPlan, cachedRepresentation: InMemoryRelation) /** * Provides support in a SQLContext for caching query results and automatically using these cached @@ -102,7 +103,7 @@ private[sql] class CacheManager(sqlContext: SQLContext) extends Logging { sqlContext.conf.useCompression, sqlContext.conf.columnBatchSize, storageLevel, - query.queryExecution.executedPlan, + sqlContext.executePlan(query.logicalPlan).executedPlan, tableName)) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala index f0d54cd6cd94f..edc64a03335d6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala @@ -18,27 +18,19 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.shuffle.sort.SortShuffleManager -import org.apache.spark.{SparkEnv, HashPartitioner, RangePartitioner} import org.apache.spark.rdd.{RDD, ShuffledRDD} import org.apache.spark.serializer.Serializer -import org.apache.spark.sql.{SQLContext, Row} +import org.apache.spark.shuffle.hash.HashShuffleManager +import org.apache.spark.shuffle.sort.SortShuffleManager +import org.apache.spark.shuffle.unsafe.UnsafeShuffleManager +import org.apache.spark.sql.SQLContext import org.apache.spark.sql.catalyst.errors.attachTree import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.sql.types.DataType import org.apache.spark.util.MutablePair - -object Exchange { - /** - * Returns true when the ordering expressions are a subset of the key. - * if true, ShuffledRDD can use `setKeyOrdering(orderingKey)` to sort within [[Exchange]]. - */ - def canSortWithShuffle(partitioning: Partitioning, desiredOrdering: Seq[SortOrder]): Boolean = { - desiredOrdering.map(_.child).toSet.subsetOf(partitioning.keyExpressions.toSet) - } -} +import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEnv} /** * :: DeveloperApi :: @@ -59,11 +51,71 @@ case class Exchange( override def output: Seq[Attribute] = child.output - /** We must copy rows when sort based shuffle is on */ - protected def sortBasedShuffleOn = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager] - - private val bypassMergeThreshold = - child.sqlContext.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) + /** + * Determines whether records must be defensively copied before being sent to the shuffle. + * Several of Spark's shuffle components will buffer deserialized Java objects in memory. The + * shuffle code assumes that objects are immutable and hence does not perform its own defensive + * copying. In Spark SQL, however, operators' iterators return the same mutable `Row` object. In + * order to properly shuffle the output of these operators, we need to perform our own copying + * prior to sending records to the shuffle. This copying is expensive, so we try to avoid it + * whenever possible. This method encapsulates the logic for choosing when to copy. + * + * In the long run, we might want to push this logic into core's shuffle APIs so that we don't + * have to rely on knowledge of core internals here in SQL. + * + * See SPARK-2967, SPARK-4479, and SPARK-7375 for more discussion of this issue. + * + * @param partitioner the partitioner for the shuffle + * @param serializer the serializer that will be used to write rows + * @return true if rows should be copied before being shuffled, false otherwise + */ + private def needToCopyObjectsBeforeShuffle( + partitioner: Partitioner, + serializer: Serializer): Boolean = { + // Note: even though we only use the partitioner's `numPartitions` field, we require it to be + // passed instead of directly passing the number of partitions in order to guard against + // corner-cases where a partitioner constructed with `numPartitions` partitions may output + // fewer partitions (like RangePartitioner, for example). + val conf = child.sqlContext.sparkContext.conf + val shuffleManager = SparkEnv.get.shuffleManager + val sortBasedShuffleOn = shuffleManager.isInstanceOf[SortShuffleManager] || + shuffleManager.isInstanceOf[UnsafeShuffleManager] + val bypassMergeThreshold = conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) + val serializeMapOutputs = conf.getBoolean("spark.shuffle.sort.serializeMapOutputs", true) + if (sortBasedShuffleOn) { + val bypassIsSupported = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager] + if (bypassIsSupported && partitioner.numPartitions <= bypassMergeThreshold) { + // If we're using the original SortShuffleManager and the number of output partitions is + // sufficiently small, then Spark will fall back to the hash-based shuffle write path, which + // doesn't buffer deserialized records. + // Note that we'll have to remove this case if we fix SPARK-6026 and remove this bypass. + false + } else if (serializeMapOutputs && serializer.supportsRelocationOfSerializedObjects) { + // SPARK-4550 extended sort-based shuffle to serialize individual records prior to sorting + // them. This optimization is guarded by a feature-flag and is only applied in cases where + // shuffle dependency does not specify an aggregator or ordering and the record serializer + // has certain properties. If this optimization is enabled, we can safely avoid the copy. + // + // Exchange never configures its ShuffledRDDs with aggregators or key orderings, so we only + // need to check whether the optimization is enabled and supported by our serializer. + // + // This optimization also applies to UnsafeShuffleManager (added in SPARK-7081). + false + } else { + // Spark's SortShuffleManager uses `ExternalSorter` to buffer records in memory. This code + // path is used both when SortShuffleManager is used and when UnsafeShuffleManager falls + // back to SortShuffleManager to perform a shuffle that the new fast path can't handle. In + // both cases, we must copy. + true + } + } else if (shuffleManager.isInstanceOf[HashShuffleManager]) { + // We're using hash-based shuffle, so we don't need to copy. + false + } else { + // Catch-all case to safely handle any future ShuffleManager implementations. + true + } + } private val keyOrdering = { if (newOrdering.nonEmpty) { @@ -81,10 +133,9 @@ case class Exchange( @transient private lazy val sparkConf = child.sqlContext.sparkContext.getConf - def serializer( + private def getSerializer( keySchema: Array[DataType], valueSchema: Array[DataType], - hasKeyOrdering: Boolean, numPartitions: Int): Serializer = { // It is true when there is no field that needs to be write out. // For now, we will not use SparkSqlSerializer2 when noField is true. @@ -100,7 +151,7 @@ case class Exchange( val serializer = if (useSqlSerializer2) { logInfo("Using SparkSqlSerializer2.") - new SparkSqlSerializer2(keySchema, valueSchema, hasKeyOrdering) + new SparkSqlSerializer2(keySchema, valueSchema) } else { logInfo("Using SparkSqlSerializer.") new SparkSqlSerializer(sparkConf) @@ -109,20 +160,15 @@ case class Exchange( serializer } - override def execute(): RDD[Row] = attachTree(this , "execute") { + protected override def doExecute(): RDD[InternalRow] = attachTree(this , "execute") { newPartitioning match { case HashPartitioning(expressions, numPartitions) => - // TODO: Eliminate redundant expressions in grouping key and value. - // This is a workaround for SPARK-4479. When: - // 1. sort based shuffle is on, and - // 2. the partition number is under the merge threshold, and - // 3. no ordering is required - // we can avoid the defensive copies to improve performance. In the long run, we probably - // want to include information in shuffle dependencies to indicate whether elements in the - // source RDD should be copied. - val willMergeSort = sortBasedShuffleOn && numPartitions > bypassMergeThreshold - - val rdd = if (willMergeSort || newOrdering.nonEmpty) { + val keySchema = expressions.map(_.dataType).toArray + val valueSchema = child.output.map(_.dataType).toArray + val serializer = getSerializer(keySchema, valueSchema, numPartitions) + val part = new HashPartitioner(numPartitions) + + val rdd = if (needToCopyObjectsBeforeShuffle(part, serializer)) { child.execute().mapPartitions { iter => val hashExpressions = newMutableProjection(expressions, child.output)() iter.map(r => (hashExpressions(r).copy(), r.copy())) @@ -130,67 +176,61 @@ case class Exchange( } else { child.execute().mapPartitions { iter => val hashExpressions = newMutableProjection(expressions, child.output)() - val mutablePair = new MutablePair[Row, Row]() + val mutablePair = new MutablePair[InternalRow, InternalRow]() iter.map(r => mutablePair.update(hashExpressions(r), r)) } } - val part = new HashPartitioner(numPartitions) - val shuffled = - if (newOrdering.nonEmpty) { - new ShuffledRDD[Row, Row, Row](rdd, part).setKeyOrdering(keyOrdering) - } else { - new ShuffledRDD[Row, Row, Row](rdd, part) - } - val keySchema = expressions.map(_.dataType).toArray - val valueSchema = child.output.map(_.dataType).toArray - shuffled.setSerializer( - serializer(keySchema, valueSchema, newOrdering.nonEmpty, numPartitions)) - + val shuffled = new ShuffledRDD[InternalRow, InternalRow, InternalRow](rdd, part) + shuffled.setSerializer(serializer) shuffled.map(_._2) case RangePartitioning(sortingExpressions, numPartitions) => - val rdd = if (sortBasedShuffleOn || newOrdering.nonEmpty) { - child.execute().mapPartitions { iter => iter.map(row => (row.copy(), null))} - } else { - child.execute().mapPartitions { iter => - val mutablePair = new MutablePair[Row, Null](null, null) - iter.map(row => mutablePair.update(row, null)) + val keySchema = child.output.map(_.dataType).toArray + val serializer = getSerializer(keySchema, null, numPartitions) + + val childRdd = child.execute() + val part: Partitioner = { + // Internally, RangePartitioner runs a job on the RDD that samples keys to compute + // partition bounds. To get accurate samples, we need to copy the mutable keys. + val rddForSampling = childRdd.mapPartitions { iter => + val mutablePair = new MutablePair[InternalRow, Null]() + iter.map(row => mutablePair.update(row.copy(), null)) } + // TODO: RangePartitioner should take an Ordering. + implicit val ordering = new RowOrdering(sortingExpressions, child.output) + new RangePartitioner(numPartitions, rddForSampling, ascending = true) } - // TODO: RangePartitioner should take an Ordering. - implicit val ordering = new RowOrdering(sortingExpressions, child.output) - - val part = new RangePartitioner(numPartitions, rdd, ascending = true) - val shuffled = - if (newOrdering.nonEmpty) { - new ShuffledRDD[Row, Null, Null](rdd, part).setKeyOrdering(keyOrdering) - } else { - new ShuffledRDD[Row, Null, Null](rdd, part) + val rdd = if (needToCopyObjectsBeforeShuffle(part, serializer)) { + childRdd.mapPartitions { iter => iter.map(row => (row.copy(), null))} + } else { + childRdd.mapPartitions { iter => + val mutablePair = new MutablePair[InternalRow, Null]() + iter.map(row => mutablePair.update(row, null)) } - val keySchema = child.output.map(_.dataType).toArray - shuffled.setSerializer( - serializer(keySchema, null, newOrdering.nonEmpty, numPartitions)) + } + val shuffled = new ShuffledRDD[InternalRow, Null, Null](rdd, part) + shuffled.setSerializer(serializer) shuffled.map(_._1) case SinglePartition => - // SPARK-4479: Can't turn off defensive copy as what we do for `HashPartitioning`, since - // operators like `TakeOrdered` may require an ordering within the partition, and currently - // `SinglePartition` doesn't include ordering information. - // TODO Add `SingleOrderedPartition` for operators like `TakeOrdered` - val rdd = if (sortBasedShuffleOn) { - child.execute().mapPartitions { iter => iter.map(r => (null, r.copy())) } + val valueSchema = child.output.map(_.dataType).toArray + val serializer = getSerializer(null, valueSchema, numPartitions = 1) + val partitioner = new HashPartitioner(1) + + val rdd = if (needToCopyObjectsBeforeShuffle(partitioner, serializer)) { + child.execute().mapPartitions { + iter => iter.map(r => (null, r.copy())) + } } else { child.execute().mapPartitions { iter => - val mutablePair = new MutablePair[Null, Row]() + val mutablePair = new MutablePair[Null, InternalRow]() iter.map(r => mutablePair.update(null, r)) } } - val partitioner = new HashPartitioner(1) - val shuffled = new ShuffledRDD[Null, Row, Row](rdd, partitioner) - val valueSchema = child.output.map(_.dataType).toArray - shuffled.setSerializer(serializer(null, valueSchema, false, 1)) + val shuffled = new ShuffledRDD[Null, InternalRow, InternalRow](rdd, partitioner) + shuffled.setSerializer(serializer) shuffled.map(_._2) case _ => sys.error(s"Exchange not implemented for $newPartitioning") @@ -244,7 +284,7 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[ .sliding(2) .map { case Seq(a) => true - case Seq(a,b) => a compatibleWith b + case Seq(a, b) => a.compatibleWith(b) }.exists(!_) // Adds Exchange or Sort operators as required @@ -254,29 +294,24 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[ child: SparkPlan): SparkPlan = { val needSort = rowOrdering.nonEmpty && child.outputOrdering != rowOrdering val needsShuffle = child.outputPartitioning != partitioning - val canSortWithShuffle = Exchange.canSortWithShuffle(partitioning, rowOrdering) - if (needSort && needsShuffle && canSortWithShuffle) { - Exchange(partitioning, rowOrdering, child) + val withShuffle = if (needsShuffle) { + Exchange(partitioning, Nil, child) } else { - val withShuffle = if (needsShuffle) { - Exchange(partitioning, Nil, child) - } else { - child - } + child + } - val withSort = if (needSort) { - if (sqlContext.conf.externalSortEnabled) { - ExternalSort(rowOrdering, global = false, withShuffle) - } else { - Sort(rowOrdering, global = false, withShuffle) - } + val withSort = if (needSort) { + if (sqlContext.conf.externalSortEnabled) { + ExternalSort(rowOrdering, global = false, withShuffle) } else { - withShuffle + Sort(rowOrdering, global = false, withShuffle) } - - withSort + } else { + withShuffle } + + withSort } if (meetsRequirements && compatible && !needsAnySort) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala index 57effbf7ec501..da27a753a710f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala @@ -19,11 +19,11 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters} import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation -import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow, SpecificMutableRow} +import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow} import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics} -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.types.DataType import org.apache.spark.sql.{Row, SQLContext} /** @@ -31,26 +31,19 @@ import org.apache.spark.sql.{Row, SQLContext} */ @DeveloperApi object RDDConversions { - def productToRowRdd[A <: Product](data: RDD[A], schema: StructType): RDD[Row] = { + def productToRowRdd[A <: Product](data: RDD[A], outputTypes: Seq[DataType]): RDD[InternalRow] = { data.mapPartitions { iterator => - if (iterator.isEmpty) { - Iterator.empty - } else { - val bufferedIterator = iterator.buffered - val mutableRow = new SpecificMutableRow(schema.fields.map(_.dataType)) - val schemaFields = schema.fields.toArray - val converters = schemaFields.map { - f => CatalystTypeConverters.createToCatalystConverter(f.dataType) - } - bufferedIterator.map { r => - var i = 0 - while (i < mutableRow.length) { - mutableRow(i) = converters(i)(r.productElement(i)) - i += 1 - } - - mutableRow + val numColumns = outputTypes.length + val mutableRow = new GenericMutableRow(numColumns) + val converters = outputTypes.map(CatalystTypeConverters.createToCatalystConverter) + iterator.map { r => + var i = 0 + while (i < numColumns) { + mutableRow(i) = converters(i)(r.productElement(i)) + i += 1 } + + mutableRow } } } @@ -58,33 +51,28 @@ object RDDConversions { /** * Convert the objects inside Row into the types Catalyst expected. */ - def rowToRowRdd(data: RDD[Row], schema: StructType): RDD[Row] = { + def rowToRowRdd(data: RDD[Row], outputTypes: Seq[DataType]): RDD[InternalRow] = { data.mapPartitions { iterator => - if (iterator.isEmpty) { - Iterator.empty - } else { - val bufferedIterator = iterator.buffered - val mutableRow = new GenericMutableRow(bufferedIterator.head.toSeq.toArray) - val schemaFields = schema.fields.toArray - val converters = schemaFields.map { - f => CatalystTypeConverters.createToCatalystConverter(f.dataType) - } - bufferedIterator.map { r => - var i = 0 - while (i < mutableRow.length) { - mutableRow(i) = converters(i)(r(i)) - i += 1 - } - - mutableRow + val numColumns = outputTypes.length + val mutableRow = new GenericMutableRow(numColumns) + val converters = outputTypes.map(CatalystTypeConverters.createToCatalystConverter) + iterator.map { r => + var i = 0 + while (i < numColumns) { + mutableRow(i) = converters(i)(r(i)) + i += 1 } + + mutableRow } } } } /** Logical plan node for scanning data from an RDD. */ -private[sql] case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLContext) +private[sql] case class LogicalRDD( + output: Seq[Attribute], + rdd: RDD[InternalRow])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { override def children: Seq[LogicalPlan] = Nil @@ -105,13 +93,15 @@ private[sql] case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlCon } /** Physical plan node for scanning data from an RDD. */ -private[sql] case class PhysicalRDD(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode { - override def execute(): RDD[Row] = rdd +private[sql] case class PhysicalRDD( + output: Seq[Attribute], + rdd: RDD[InternalRow]) extends LeafNode { + protected override def doExecute(): RDD[InternalRow] = rdd } /** Logical plan node for scanning data from a local collection. */ private[sql] -case class LogicalLocalTable(output: Seq[Attribute], rows: Seq[Row])(sqlContext: SQLContext) +case class LogicalLocalTable(output: Seq[Attribute], rows: Seq[InternalRow])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { override def children: Seq[LogicalPlan] = Nil diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala index 575849481faad..42a0c1be4f694 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala @@ -19,10 +19,9 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.SQLContext import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.physical.{UnknownPartitioning, Partitioning} +import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning} /** * Apply the all of the GroupExpressions to every input row, hence we will get @@ -34,7 +33,7 @@ import org.apache.spark.sql.catalyst.plans.physical.{UnknownPartitioning, Partit */ @DeveloperApi case class Expand( - projections: Seq[GroupExpression], + projections: Seq[Seq[Expression]], output: Seq[Attribute], child: SparkPlan) extends UnaryNode { @@ -43,22 +42,22 @@ case class Expand( // as UNKNOWN partitioning override def outputPartitioning: Partitioning = UnknownPartitioning(0) - override def execute(): RDD[Row] = attachTree(this, "execute") { + protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") { child.execute().mapPartitions { iter => // TODO Move out projection objects creation and transfer to // workers via closure. However we can't assume the Projection // is serializable because of the code gen, so we have to // create the projections within each of the partition processing. - val groups = projections.map(ee => newProjection(ee.children, child.output)).toArray + val groups = projections.map(ee => newProjection(ee, child.output)).toArray - new Iterator[Row] { - private[this] var result: Row = _ + new Iterator[InternalRow] { + private[this] var result: InternalRow = _ private[this] var idx = -1 // -1 means the initial state - private[this] var input: Row = _ + private[this] var input: InternalRow = _ override final def hasNext: Boolean = (-1 < idx && idx < groups.length) || iter.hasNext - override final def next(): Row = { + override final def next(): InternalRow = { if (idx <= 0) { // in the initial (-1) or beginning(0) of a new input row, fetch the next input tuple input = iter.next() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala index 5201e20a10565..c1665f78a960e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala @@ -21,6 +21,18 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ +/** + * For lazy computing, be sure the generator.terminate() called in the very last + * TODO reusing the CompletionIterator? + */ +private[execution] sealed case class LazyIterator(func: () => TraversableOnce[InternalRow]) + extends Iterator[InternalRow] { + + lazy val results = func().toIterator + override def hasNext: Boolean = results.hasNext + override def next(): InternalRow = results.next() +} + /** * :: DeveloperApi :: * Applies a [[catalyst.expressions.Generator Generator]] to a stream of input rows, combining the @@ -46,28 +58,34 @@ case class Generate( val boundGenerator = BindReferences.bindReference(generator, child.output) - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { + // boundGenerator.terminate() should be triggered after all of the rows in the partition if (join) { child.execute().mapPartitions { iter => - val nullValues = Seq.fill(generator.elementTypes.size)(Literal(null)) - // Used to produce rows with no matches when outer = true. - val outerProjection = - newProjection(child.output ++ nullValues, child.output) - - val joinProjection = newProjection(output, output) + val generatorNullRow = InternalRow.fromSeq(Seq.fill[Any](generator.elementTypes.size)(null)) val joinedRow = new JoinedRow - iter.flatMap {row => + iter.flatMap { row => + // we should always set the left (child output) + joinedRow.withLeft(row) val outputRows = boundGenerator.eval(row) if (outer && outputRows.isEmpty) { - outerProjection(row) :: Nil + joinedRow.withRight(generatorNullRow) :: Nil } else { - outputRows.map(or => joinProjection(joinedRow(row, or))) + outputRows.map(or => joinedRow.withRight(or)) } + } ++ LazyIterator(() => boundGenerator.terminate()).map { row => + // we leave the left side as the last element of its child output + // keep it the same as Hive does + joinedRow.withRight(row) } } } else { - child.execute().mapPartitions(iter => iter.flatMap(row => boundGenerator.eval(row))) + child.execute().mapPartitions { iter => + iter.flatMap(row => boundGenerator.eval(row)) ++ + LazyIterator(() => boundGenerator.terminate()) + } } } } + diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index 5d9f202681045..ba2c8f53d702d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -66,7 +66,7 @@ case class GeneratedAggregate( override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute) - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val aggregatesToCompute = aggregateExpressions.flatMap { a => a.collect { case agg: AggregateExpression => agg} } @@ -118,7 +118,7 @@ case class GeneratedAggregate( AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result) case cs @ CombineSum(expr) => - val calcType = expr.dataType + val calcType = expr.dataType match { case DecimalType.Fixed(_, _) => DecimalType.Unlimited @@ -129,7 +129,7 @@ case class GeneratedAggregate( val currentSum = AttributeReference("currentSum", calcType, nullable = true)() val initialValue = Literal.create(null, calcType) - // Coalasce avoids double calculation... + // Coalesce avoids double calculation... // but really, common sub expression elimination would be better.... val zero = Cast(Literal(0), calcType) // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its @@ -138,15 +138,15 @@ case class GeneratedAggregate( case UnscaledValue(e) => e case _ => expr } - // partial sum result can be null only when no input rows present + // partial sum result can be null only when no input rows present val updateFunction = If( IsNotNull(actualExpr), Coalesce( Add( - Coalesce(currentSum :: zero :: Nil), + Coalesce(currentSum :: zero :: Nil), Cast(expr, calcType)) :: currentSum :: zero :: Nil), currentSum) - + val result = expr.dataType match { case DecimalType.Fixed(_, _) => @@ -155,7 +155,7 @@ case class GeneratedAggregate( } AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result) - + case m @ Max(expr) => val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)() val initialValue = Literal.create(null, expr.dataType) @@ -214,18 +214,18 @@ case class GeneratedAggregate( }.toMap val namedGroups = groupingExpressions.zipWithIndex.map { - case (ne: NamedExpression, _) => (ne, ne) - case (e, i) => (e, Alias(e, s"GroupingExpr$i")()) + case (ne: NamedExpression, _) => (ne, ne.toAttribute) + case (e, i) => (e, Alias(e, s"GroupingExpr$i")().toAttribute) } - val groupMap: Map[Expression, Attribute] = - namedGroups.map { case (k, v) => k -> v.toAttribute}.toMap - // The set of expressions that produce the final output given the aggregation buffer and the // grouping expressions. val resultExpressions = aggregateExpressions.map(_.transform { case e: Expression if resultMap.contains(new TreeNodeRef(e)) => resultMap(new TreeNodeRef(e)) - case e: Expression if groupMap.contains(e) => groupMap(e) + case e: Expression => + namedGroups.collectFirst { + case (expr, attr) if expr semanticEquals e => attr + }.getOrElse(e) }) val aggregationBufferSchema: StructType = StructType.fromAttributes(computationSchema) @@ -265,7 +265,7 @@ case class GeneratedAggregate( val resultProjectionBuilder = newMutableProjection( resultExpressions, - (namedGroups.map(_._2.toAttribute) ++ computationSchema).toSeq) + namedGroups.map(_._2) ++ computationSchema) log.info(s"Result Projection: ${resultExpressions.mkString(",")}") val joinedRow = new JoinedRow3 @@ -273,7 +273,7 @@ case class GeneratedAggregate( if (groupingExpressions.isEmpty) { // TODO: Codegening anything other than the updateProjection is probably over kill. val buffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow] - var currentRow: Row = null + var currentRow: InternalRow = null updateProjection.target(buffer) while (iter.hasNext) { @@ -295,19 +295,19 @@ case class GeneratedAggregate( ) while (iter.hasNext) { - val currentRow: Row = iter.next() - val groupKey: Row = groupProjection(currentRow) + val currentRow: InternalRow = iter.next() + val groupKey: InternalRow = groupProjection(currentRow) val aggregationBuffer = aggregationMap.getAggregationBuffer(groupKey) updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow)) } - new Iterator[Row] { + new Iterator[InternalRow] { private[this] val mapIterator = aggregationMap.iterator() private[this] val resultProjection = resultProjectionBuilder() def hasNext: Boolean = mapIterator.hasNext - def next(): Row = { + def next(): InternalRow = { val entry = mapIterator.next() val result = resultProjection(joinedRow(entry.key, entry.value)) if (hasNext) { @@ -326,9 +326,9 @@ case class GeneratedAggregate( if (unsafeEnabled) { log.info("Not using Unsafe-based aggregator because it is not supported for this schema") } - val buffers = new java.util.HashMap[Row, MutableRow]() + val buffers = new java.util.HashMap[InternalRow, MutableRow]() - var currentRow: Row = null + var currentRow: InternalRow = null while (iter.hasNext) { currentRow = iter.next() val currentGroup = groupProjection(currentRow) @@ -342,13 +342,13 @@ case class GeneratedAggregate( updateProjection.target(currentBuffer)(joinedRow(currentBuffer, currentRow)) } - new Iterator[Row] { + new Iterator[InternalRow] { private[this] val resultIterator = buffers.entrySet.iterator() private[this] val resultProjection = resultProjectionBuilder() def hasNext: Boolean = resultIterator.hasNext - def next(): Row = { + def next(): InternalRow = { val currentGroup = resultIterator.next() resultProjection(joinedRow(currentGroup.getKey, currentGroup.getValue)) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala index ace9af5f384c9..cd341180b6100 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala @@ -19,18 +19,20 @@ package org.apache.spark.sql.execution import org.apache.spark.rdd.RDD import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters} import org.apache.spark.sql.catalyst.expressions.Attribute /** * Physical plan node for scanning data from a local collection. */ -private[sql] case class LocalTableScan(output: Seq[Attribute], rows: Seq[Row]) extends LeafNode { +private[sql] case class LocalTableScan( + output: Seq[Attribute], + rows: Seq[InternalRow]) extends LeafNode { private lazy val rdd = sqlContext.sparkContext.parallelize(rows) - override def execute(): RDD[Row] = rdd + protected override def doExecute(): RDD[InternalRow] = rdd override def executeCollect(): Array[Row] = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala index 59c89800da00f..2b8d30294293c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala @@ -19,10 +19,11 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.Logging -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDD, RDDOperationScope} import org.apache.spark.sql.SQLContext import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees} import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions.codegen._ import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.plans.physical._ @@ -79,14 +80,25 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ def requiredChildOrdering: Seq[Seq[SortOrder]] = Seq.fill(children.size)(Nil) /** - * Runs this query returning the result as an RDD. + * Returns the result of this query as an RDD[InternalRow] by delegating to doExecute + * after adding query plan information to created RDDs for visualization. + * Concrete implementations of SparkPlan should override doExecute instead. */ - def execute(): RDD[Row] + final def execute(): RDD[InternalRow] = { + RDDOperationScope.withScope(sparkContext, nodeName, false, true) { + doExecute() + } + } /** - * Runs this query returning the result as an array. + * Overridden by concrete implementations of SparkPlan. + * Produces the result of the query as an RDD[InternalRow] */ + protected def doExecute(): RDD[InternalRow] + /** + * Runs this query returning the result as an array. + */ def executeCollect(): Array[Row] = { execute().mapPartitions { iter => val converter = CatalystTypeConverters.createToScalaConverter(schema) @@ -106,7 +118,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ val childRDD = execute().map(_.copy()) - val buf = new ArrayBuffer[Row] + val buf = new ArrayBuffer[InternalRow] val totalParts = childRDD.partitions.length var partsScanned = 0 while (buf.size < n && partsScanned < totalParts) { @@ -129,7 +141,8 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) val sc = sqlContext.sparkContext val res = - sc.runJob(childRDD, (it: Iterator[Row]) => it.take(left).toArray, p, allowLocal = false) + sc.runJob(childRDD, (it: Iterator[InternalRow]) => it.take(left).toArray, p, + allowLocal = false) res.foreach(buf ++= _.take(n - buf.size)) partsScanned += numPartsToTry @@ -143,7 +156,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ expressions: Seq[Expression], inputSchema: Seq[Attribute]): Projection = { log.debug( s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled") - if (codegenEnabled) { + if (codegenEnabled && expressions.forall(_.isThreadSafe)) { GenerateProjection.generate(expressions, inputSchema) } else { new InterpretedProjection(expressions, inputSchema) @@ -155,7 +168,8 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ inputSchema: Seq[Attribute]): () => MutableProjection = { log.debug( s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled") - if(codegenEnabled) { + if(codegenEnabled && expressions.forall(_.isThreadSafe)) { + GenerateMutableProjection.generate(expressions, inputSchema) } else { () => new InterpretedMutableProjection(expressions, inputSchema) @@ -164,15 +178,17 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ protected def newPredicate( - expression: Expression, inputSchema: Seq[Attribute]): (Row) => Boolean = { - if (codegenEnabled) { + expression: Expression, inputSchema: Seq[Attribute]): (InternalRow) => Boolean = { + if (codegenEnabled && expression.isThreadSafe) { GeneratePredicate.generate(expression, inputSchema) } else { InterpretedPredicate.create(expression, inputSchema) } } - protected def newOrdering(order: Seq[SortOrder], inputSchema: Seq[Attribute]): Ordering[Row] = { + protected def newOrdering( + order: Seq[SortOrder], + inputSchema: Seq[Attribute]): Ordering[InternalRow] = { if (codegenEnabled) { GenerateOrdering.generate(order, inputSchema) } else { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala index 256d527d7b636..15b6936acd59b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala @@ -20,15 +20,15 @@ package org.apache.spark.sql.execution import java.io._ import java.math.{BigDecimal, BigInteger} import java.nio.ByteBuffer -import java.sql.Timestamp import scala.reflect.ClassTag -import org.apache.spark.serializer._ import org.apache.spark.Logging +import org.apache.spark.serializer._ import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.expressions.{SpecificMutableRow, MutableRow, GenericMutableRow} +import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, MutableRow, SpecificMutableRow} import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /** * The serialization stream for [[SparkSqlSerializer2]]. It assumes that the object passed in @@ -86,7 +86,6 @@ private[sql] class Serializer2SerializationStream( private[sql] class Serializer2DeserializationStream( keySchema: Array[DataType], valueSchema: Array[DataType], - hasKeyOrdering: Boolean, in: InputStream) extends DeserializationStream with Logging { @@ -96,14 +95,9 @@ private[sql] class Serializer2DeserializationStream( if (schema == null) { () => null } else { - if (hasKeyOrdering) { - // We have key ordering specified in a ShuffledRDD, it is not safe to reuse a mutable row. - () => new GenericMutableRow(schema.length) - } else { - // It is safe to reuse the mutable row. - val mutableRow = new SpecificMutableRow(schema) - () => mutableRow - } + // It is safe to reuse the mutable row. + val mutableRow = new SpecificMutableRow(schema) + () => mutableRow } } @@ -133,8 +127,7 @@ private[sql] class Serializer2DeserializationStream( private[sql] class SparkSqlSerializer2Instance( keySchema: Array[DataType], - valueSchema: Array[DataType], - hasKeyOrdering: Boolean) + valueSchema: Array[DataType]) extends SerializerInstance { def serialize[T: ClassTag](t: T): ByteBuffer = @@ -151,7 +144,7 @@ private[sql] class SparkSqlSerializer2Instance( } def deserializeStream(s: InputStream): DeserializationStream = { - new Serializer2DeserializationStream(keySchema, valueSchema, hasKeyOrdering, s) + new Serializer2DeserializationStream(keySchema, valueSchema, s) } } @@ -164,14 +157,13 @@ private[sql] class SparkSqlSerializer2Instance( */ private[sql] class SparkSqlSerializer2( keySchema: Array[DataType], - valueSchema: Array[DataType], - hasKeyOrdering: Boolean) + valueSchema: Array[DataType]) extends Serializer with Logging with Serializable{ def newInstance(): SerializerInstance = - new SparkSqlSerializer2Instance(keySchema, valueSchema, hasKeyOrdering) + new SparkSqlSerializer2Instance(keySchema, valueSchema) override def supportsRelocationOfSerializedObjects: Boolean = { // SparkSqlSerializer2 is stateless and writes no stream headers @@ -304,11 +296,7 @@ private[sql] object SparkSqlSerializer2 { out.writeByte(NULL) } else { out.writeByte(NOT_NULL) - val timestamp = row.getAs[java.sql.Timestamp](i) - val time = timestamp.getTime - val nanos = timestamp.getNanos - out.writeLong(time - (nanos / 1000000)) // Write the milliseconds value. - out.writeInt(nanos) // Write the nanoseconds part. + out.writeLong(row.getAs[Long](i)) } case StringType => @@ -429,11 +417,7 @@ private[sql] object SparkSqlSerializer2 { if (in.readByte() == NULL) { mutableRow.setNullAt(i) } else { - val time = in.readLong() // Read the milliseconds value. - val nanos = in.readInt() // Read the nanoseconds part. - val timestamp = new Timestamp(time) - timestamp.setNanos(nanos) - mutableRow.update(i, timestamp) + mutableRow.update(i, in.readLong()) } case StringType => @@ -443,7 +427,7 @@ private[sql] object SparkSqlSerializer2 { val length = in.readInt() val bytes = new Array[Byte](length) in.readFully(bytes) - mutableRow.update(i, UTF8String(bytes)) + mutableRow.update(i, UTF8String.fromBytes(bytes)) } case BinaryType => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 56a4689eb58f0..422992d019c7b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -203,7 +203,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { } protected lazy val singleRowRdd = - sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): Row), 1) + sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): InternalRow), 1) object TakeOrdered extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { @@ -243,8 +243,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case (predicate, None) => predicate // Filter needs to be applied above when it contains partitioning // columns - case (predicate, _) if(!predicate.references.map(_.name).toSet - .intersect (partitionColNames).isEmpty) => predicate + case (predicate, _) + if !predicate.references.map(_.name).toSet.intersect(partitionColNames).isEmpty => + predicate } } } else { @@ -270,7 +271,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { projectList, filters, identity[Seq[Expression]], // All filters still need to be evaluated. - InMemoryColumnarTableScan(_, filters, mem)) :: Nil + InMemoryColumnarTableScan(_, filters, mem)) :: Nil case _ => Nil } } @@ -283,8 +284,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case r: RunnableCommand => ExecutedCommand(r) :: Nil case logical.Distinct(child) => - execution.Distinct(partial = false, - execution.Distinct(partial = true, planLater(child))) :: Nil + throw new IllegalStateException( + "logical distinct operator should have been replaced by aggregate in the optimizer") case logical.Repartition(numPartitions, shuffle, child) => execution.Repartition(numPartitions, shuffle, planLater(child)) :: Nil case logical.SortPartitions(sortExprs, child) => @@ -343,17 +344,21 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case c: CreateTableUsing if c.temporary && c.allowExisting => sys.error("allowExisting should be set to false when creating a temporary table.") - case CreateTableUsingAsSelect(tableName, provider, true, mode, opts, query) => - val cmd = - CreateTempTableUsingAsSelect(tableName, provider, mode, opts, query) + case CreateTableUsingAsSelect(tableName, provider, true, partitionsCols, mode, opts, query) + if partitionsCols.nonEmpty => + sys.error("Cannot create temporary partitioned table.") + + case CreateTableUsingAsSelect(tableName, provider, true, _, mode, opts, query) => + val cmd = CreateTempTableUsingAsSelect( + tableName, provider, Array.empty[String], mode, opts, query) ExecutedCommand(cmd) :: Nil case c: CreateTableUsingAsSelect if !c.temporary => sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.") - case LogicalDescribeCommand(table, isExtended) => + case describe @ LogicalDescribeCommand(table, isExtended) => val resultPlan = self.sqlContext.executePlan(table).executedPlan ExecutedCommand( - RunnableDescribeCommand(resultPlan, resultPlan.output, isExtended)) :: Nil + RunnableDescribeCommand(resultPlan, describe.output, isExtended)) :: Nil case _ => Nil } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala index 217b559def512..fd6f1d7ae1255 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala @@ -20,9 +20,8 @@ package org.apache.spark.sql.execution import java.util import org.apache.spark.rdd.RDD -import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, Distribution, ClusteredDistribution, Partitioning} +import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, Partitioning} import org.apache.spark.util.collection.CompactBuffer /** @@ -112,16 +111,16 @@ case class Window( } } - def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { child.execute().mapPartitions { iter => - new Iterator[Row] { + new Iterator[InternalRow] { // Although input rows are grouped based on windowSpec.partitionSpec, we need to // know when we have a new partition. // This is to manually construct an ordering that can be used to compare rows. // TODO: We may want to have a newOrdering that takes BoundReferences. // So, we can take advantave of code gen. - private val partitionOrdering: Ordering[Row] = + private val partitionOrdering: Ordering[InternalRow] = RowOrdering.forSchema(windowSpec.partitionSpec.map(_.dataType)) // This is used to project expressions for the partition specification. @@ -137,13 +136,13 @@ case class Window( // The number of buffered rows in the inputRowBuffer (the size of the current partition). var partitionSize: Int = 0 // The buffer used to buffer rows in a partition. - var inputRowBuffer: CompactBuffer[Row] = _ + var inputRowBuffer: CompactBuffer[InternalRow] = _ // The partition key of the current partition. - var currentPartitionKey: Row = _ + var currentPartitionKey: InternalRow = _ // The partition key of next partition. - var nextPartitionKey: Row = _ + var nextPartitionKey: InternalRow = _ // The first row of next partition. - var firstRowInNextPartition: Row = _ + var firstRowInNextPartition: InternalRow = _ // Indicates if this partition is the last one in the iter. var lastPartition: Boolean = false @@ -316,7 +315,7 @@ case class Window( !lastPartition || (rowPosition < partitionSize) } - override final def next(): Row = { + override final def next(): InternalRow = { if (hasNext) { if (rowPosition == partitionSize) { // All rows of this buffer have been consumed. @@ -353,7 +352,7 @@ case class Window( // Fetch the next partition. private def fetchNextPartition(): Unit = { // Create a new buffer for input rows. - inputRowBuffer = new CompactBuffer[Row]() + inputRowBuffer = new CompactBuffer[InternalRow]() // We already have the first row for this partition // (recorded in firstRowInNextPartition). Add it back. inputRowBuffer += firstRowInNextPartition diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 5ca11e67a9434..7aedd630e3871 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -17,16 +17,17 @@ package org.apache.spark.sql.execution -import org.apache.spark.{SparkEnv, HashPartitioner, SparkConf} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.{RDD, ShuffledRDD} import org.apache.spark.shuffle.sort.SortShuffleManager import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.plans.physical._ -import org.apache.spark.util.{CompletionIterator, MutablePair} import org.apache.spark.util.collection.ExternalSorter +import org.apache.spark.util.{CompletionIterator, MutablePair} +import org.apache.spark.{HashPartitioner, SparkEnv} /** * :: DeveloperApi :: @@ -37,7 +38,7 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends @transient lazy val buildProjection = newMutableProjection(projectList, child.output) - override def execute(): RDD[Row] = child.execute().mapPartitions { iter => + protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter => val resuableProjection = buildProjection() iter.map(resuableProjection) } @@ -52,9 +53,10 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output - @transient lazy val conditionEvaluator: (Row) => Boolean = newPredicate(condition, child.output) + @transient lazy val conditionEvaluator: (InternalRow) => Boolean = + newPredicate(condition, child.output) - override def execute(): RDD[Row] = child.execute().mapPartitions { iter => + protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter => iter.filter(conditionEvaluator) } @@ -65,7 +67,7 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { * :: DeveloperApi :: * Sample the dataset. * @param lowerBound Lower-bound of the sampling probability (usually 0.0) - * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled + * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled * will be ub - lb. * @param withReplacement Whether to sample with replacement. * @param seed the random seed @@ -83,7 +85,7 @@ case class Sample( override def output: Seq[Attribute] = child.output // TODO: How to pick seed? - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { if (withReplacement) { child.execute().map(_.copy()).sample(withReplacement, upperBound - lowerBound, seed) } else { @@ -99,7 +101,8 @@ case class Sample( case class Union(children: Seq[SparkPlan]) extends SparkPlan { // TODO: attributes output by union should be distinct for nullability purposes override def output: Seq[Attribute] = children.head.output - override def execute(): RDD[Row] = sparkContext.union(children.map(_.execute())) + protected override def doExecute(): RDD[InternalRow] = + sparkContext.union(children.map(_.execute())) } /** @@ -124,19 +127,19 @@ case class Limit(limit: Int, child: SparkPlan) override def executeCollect(): Array[Row] = child.executeTake(limit) - override def execute(): RDD[Row] = { - val rdd: RDD[_ <: Product2[Boolean, Row]] = if (sortBasedShuffleOn) { + protected override def doExecute(): RDD[InternalRow] = { + val rdd: RDD[_ <: Product2[Boolean, InternalRow]] = if (sortBasedShuffleOn) { child.execute().mapPartitions { iter => iter.take(limit).map(row => (false, row.copy())) } } else { child.execute().mapPartitions { iter => - val mutablePair = new MutablePair[Boolean, Row]() + val mutablePair = new MutablePair[Boolean, InternalRow]() iter.take(limit).map(row => mutablePair.update(false, row)) } } val part = new HashPartitioner(1) - val shuffled = new ShuffledRDD[Boolean, Row, Row](rdd, part) + val shuffled = new ShuffledRDD[Boolean, InternalRow, InternalRow](rdd, part) shuffled.setSerializer(new SparkSqlSerializer(child.sqlContext.sparkContext.getConf)) shuffled.mapPartitions(_.take(limit).map(_._2)) } @@ -157,7 +160,8 @@ case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) private val ord: RowOrdering = new RowOrdering(sortOrder, child.output) - private def collectData(): Array[Row] = child.execute().map(_.copy()).takeOrdered(limit)(ord) + private def collectData(): Array[InternalRow] = + child.execute().map(_.copy()).takeOrdered(limit)(ord) override def executeCollect(): Array[Row] = { val converter = CatalystTypeConverters.createToScalaConverter(schema) @@ -166,7 +170,7 @@ case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) // TODO: Terminal split should be implemented differently from non-terminal split. // TODO: Pick num splits based on |limit|. - override def execute(): RDD[Row] = sparkContext.makeRDD(collectData(), 1) + protected override def doExecute(): RDD[InternalRow] = sparkContext.makeRDD(collectData(), 1) override def outputOrdering: Seq[SortOrder] = sortOrder } @@ -186,7 +190,7 @@ case class Sort( override def requiredChildDistribution: Seq[Distribution] = if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil - override def execute(): RDD[Row] = attachTree(this, "sort") { + protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") { child.execute().mapPartitions( { iterator => val ordering = newOrdering(sortOrder, child.output) iterator.map(_.copy()).toArray.sorted(ordering).iterator @@ -214,14 +218,14 @@ case class ExternalSort( override def requiredChildDistribution: Seq[Distribution] = if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil - override def execute(): RDD[Row] = attachTree(this, "sort") { + protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") { child.execute().mapPartitions( { iterator => val ordering = newOrdering(sortOrder, child.output) - val sorter = new ExternalSorter[Row, Null, Row](ordering = Some(ordering)) + val sorter = new ExternalSorter[InternalRow, Null, InternalRow](ordering = Some(ordering)) sorter.insertAll(iterator.map(r => (r.copy, null))) val baseIterator = sorter.iterator.map(_._1) // TODO(marmbrus): The complex type signature below thwarts inference for no reason. - CompletionIterator[Row, Iterator[Row]](baseIterator, sorter.stop()) + CompletionIterator[InternalRow, Iterator[InternalRow]](baseIterator, sorter.stop()) }, preservesPartitioning = true) } @@ -230,37 +234,6 @@ case class ExternalSort( override def outputOrdering: Seq[SortOrder] = sortOrder } -/** - * :: DeveloperApi :: - * Computes the set of distinct input rows using a HashSet. - * @param partial when true the distinct operation is performed partially, per partition, without - * shuffling the data. - * @param child the input query plan. - */ -@DeveloperApi -case class Distinct(partial: Boolean, child: SparkPlan) extends UnaryNode { - override def output: Seq[Attribute] = child.output - - override def requiredChildDistribution: Seq[Distribution] = - if (partial) UnspecifiedDistribution :: Nil else ClusteredDistribution(child.output) :: Nil - - override def execute(): RDD[Row] = { - child.execute().mapPartitions { iter => - val hashSet = new scala.collection.mutable.HashSet[Row]() - - var currentRow: Row = null - while (iter.hasNext) { - currentRow = iter.next() - if (!hashSet.contains(currentRow)) { - hashSet.add(currentRow.copy()) - } - } - - hashSet.iterator - } - } -} - /** * :: DeveloperApi :: * Return a new RDD that has exactly `numPartitions` partitions. @@ -270,7 +243,7 @@ case class Repartition(numPartitions: Int, shuffle: Boolean, child: SparkPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { child.execute().map(_.copy()).coalesce(numPartitions, shuffle) } } @@ -285,7 +258,7 @@ case class Repartition(numPartitions: Int, shuffle: Boolean, child: SparkPlan) case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode { override def output: Seq[Attribute] = left.output - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { left.execute().map(_.copy()).subtract(right.execute().map(_.copy())) } } @@ -299,7 +272,7 @@ case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode { case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode { override def output: Seq[Attribute] = children.head.output - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { left.execute().map(_.copy()).intersection(right.execute().map(_.copy())) } } @@ -314,5 +287,5 @@ case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode { case class OutputFaker(output: Seq[Attribute], child: SparkPlan) extends SparkPlan { def children: Seq[SparkPlan] = child :: Nil - def execute(): RDD[Row] = child.execute() + protected override def doExecute(): RDD[InternalRow] = child.execute() } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala index 388a8184e4cfe..5e9951f248ff2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala @@ -17,16 +17,18 @@ package org.apache.spark.sql.execution +import java.util.NoSuchElementException + import org.apache.spark.Logging import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters} import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Row} +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference} import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.types._ -import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext} +import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext} /** * A logical command that is executed for its side-effects. `RunnableCommand`s are @@ -64,9 +66,9 @@ private[sql] case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan override def executeTake(limit: Int): Array[Row] = sideEffectResult.take(limit).toArray - override def execute(): RDD[Row] = { - val converted = sideEffectResult.map(r => - CatalystTypeConverters.convertToCatalyst(r, schema).asInstanceOf[Row]) + protected override def doExecute(): RDD[InternalRow] = { + val convert = CatalystTypeConverters.createToCatalystConverter(schema) + val converted = sideEffectResult.map(convert(_).asInstanceOf[InternalRow]) sqlContext.sparkContext.parallelize(converted, 1) } } @@ -75,48 +77,92 @@ private[sql] case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan * :: DeveloperApi :: */ @DeveloperApi -case class SetCommand( - kv: Option[(String, Option[String])], - override val output: Seq[Attribute]) - extends RunnableCommand with Logging { +case class SetCommand(kv: Option[(String, Option[String])]) extends RunnableCommand with Logging { + + private def keyValueOutput: Seq[Attribute] = { + val schema = StructType( + StructField("key", StringType, false) :: + StructField("value", StringType, false) :: Nil) + schema.toAttributes + } - override def run(sqlContext: SQLContext): Seq[Row] = kv match { + private val (_output, runFunc): (Seq[Attribute], SQLContext => Seq[Row]) = kv match { // Configures the deprecated "mapred.reduce.tasks" property. case Some((SQLConf.Deprecated.MAPRED_REDUCE_TASKS, Some(value))) => - logWarning( - s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " + - s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.") - if (value.toInt < 1) { - val msg = s"Setting negative ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} for automatically " + - "determining the number of reducers is not supported." - throw new IllegalArgumentException(msg) - } else { - sqlContext.setConf(SQLConf.SHUFFLE_PARTITIONS, value) - Seq(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=$value")) + val runFunc = (sqlContext: SQLContext) => { + logWarning( + s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " + + s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS.key} instead.") + if (value.toInt < 1) { + val msg = + s"Setting negative ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} for automatically " + + "determining the number of reducers is not supported." + throw new IllegalArgumentException(msg) + } else { + sqlContext.setConf(SQLConf.SHUFFLE_PARTITIONS.key, value) + Seq(Row(SQLConf.SHUFFLE_PARTITIONS.key, value)) + } } + (keyValueOutput, runFunc) // Configures a single property. case Some((key, Some(value))) => - sqlContext.setConf(key, value) - Seq(Row(s"$key=$value")) + val runFunc = (sqlContext: SQLContext) => { + sqlContext.setConf(key, value) + Seq(Row(key, value)) + } + (keyValueOutput, runFunc) - // Queries all key-value pairs that are set in the SQLConf of the sqlContext. - // Notice that different from Hive, here "SET -v" is an alias of "SET". // (In Hive, "SET" returns all changed properties while "SET -v" returns all properties.) - case Some(("-v", None)) | None => - sqlContext.getAllConfs.map { case (k, v) => Row(s"$k=$v") }.toSeq + // Queries all key-value pairs that are set in the SQLConf of the sqlContext. + case None => + val runFunc = (sqlContext: SQLContext) => { + sqlContext.getAllConfs.map { case (k, v) => Row(k, v) }.toSeq + } + (keyValueOutput, runFunc) + + // Queries all properties along with their default values and docs that are defined in the + // SQLConf of the sqlContext. + case Some(("-v", None)) => + val runFunc = (sqlContext: SQLContext) => { + sqlContext.conf.getAllDefinedConfs.map { case (key, defaultValue, doc) => + Row(key, defaultValue, doc) + } + } + val schema = StructType( + StructField("key", StringType, false) :: + StructField("default", StringType, false) :: + StructField("meaning", StringType, false) :: Nil) + (schema.toAttributes, runFunc) // Queries the deprecated "mapred.reduce.tasks" property. case Some((SQLConf.Deprecated.MAPRED_REDUCE_TASKS, None)) => - logWarning( - s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " + - s"showing ${SQLConf.SHUFFLE_PARTITIONS} instead.") - Seq(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=${sqlContext.conf.numShufflePartitions}")) + val runFunc = (sqlContext: SQLContext) => { + logWarning( + s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " + + s"showing ${SQLConf.SHUFFLE_PARTITIONS.key} instead.") + Seq(Row(SQLConf.SHUFFLE_PARTITIONS.key, sqlContext.conf.numShufflePartitions.toString)) + } + (keyValueOutput, runFunc) // Queries a single property. case Some((key, None)) => - Seq(Row(s"$key=${sqlContext.getConf(key, "")}")) + val runFunc = (sqlContext: SQLContext) => { + val value = + try { + sqlContext.getConf(key) + } catch { + case _: NoSuchElementException => "" + } + Seq(Row(key, value)) + } + (keyValueOutput, runFunc) } + + override val output: Seq[Attribute] = _output + + override def run(sqlContext: SQLContext): Seq[Row] = runFunc(sqlContext) + } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala index 710787096e6cb..2964edac1aba2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala @@ -18,13 +18,15 @@ package org.apache.spark.sql.execution import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.Attribute +import org.apache.spark.unsafe.types.UTF8String import scala.collection.mutable.HashSet import org.apache.spark.{AccumulatorParam, Accumulator} import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.{SQLConf, SQLContext, DataFrame, Row} +import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.trees.TreeNodeRef import org.apache.spark.sql.types._ @@ -46,7 +48,7 @@ package object debug { */ implicit class DebugSQLContext(sqlContext: SQLContext) { def debug(): Unit = { - sqlContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, "false") + sqlContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, false) } } @@ -125,11 +127,11 @@ package object debug { } } - def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { child.execute().mapPartitions { iter => - new Iterator[Row] { + new Iterator[InternalRow] { def hasNext: Boolean = iter.hasNext - def next(): Row = { + def next(): InternalRow = { val currentRow = iter.next() tupleCount += 1 var i = 0 @@ -154,7 +156,7 @@ package object debug { def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match { case (null, _) => - case (row: Row, StructType(fields)) => + case (row: InternalRow, StructType(fields)) => row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) } case (s: Seq[_], ArrayType(elemType, _)) => s.foreach(typeCheck(_, elemType)) @@ -170,6 +172,8 @@ package object debug { case (_: Short, ShortType) => case (_: Boolean, BooleanType) => case (_: Double, DoubleType) => + case (_: Int, DateType) => + case (_: Long, TimestampType) => case (v, udt: UserDefinedType[_]) => typeCheck(v, udt.sqlType) case (d, t) => sys.error(s"Invalid data found: got $d (${d.getClass}) expected $t") @@ -193,7 +197,7 @@ package object debug { def children: List[SparkPlan] = child :: Nil - def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { child.execute().map { row => try typeCheck(row, child.schema) catch { case e: Exception => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala index 9ac732b55b188..3b217348b7b7a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.execution.expressions import org.apache.spark.TaskContext -import org.apache.spark.sql.catalyst.expressions.{Row, LeafExpression} +import org.apache.spark.sql.catalyst.expressions.{InternalRow, LeafExpression} import org.apache.spark.sql.types.{LongType, DataType} /** @@ -39,15 +39,15 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression { */ @transient private[this] var count: Long = 0L - override type EvaluatedType = Long - override def nullable: Boolean = false override def dataType: DataType = LongType - override def eval(input: Row): Long = { + override def eval(input: InternalRow): Long = { val currentCount = count count += 1 (TaskContext.get().partitionId().toLong << 33) + currentCount } + + override def isThreadSafe: Boolean = false } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala index c2c6cbd491598..12c2eed0d6b7c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.execution.expressions import org.apache.spark.TaskContext -import org.apache.spark.sql.catalyst.expressions.{LeafExpression, Row} +import org.apache.spark.sql.catalyst.expressions.{LeafExpression, InternalRow} import org.apache.spark.sql.types.{IntegerType, DataType} @@ -27,11 +27,9 @@ import org.apache.spark.sql.types.{IntegerType, DataType} */ private[sql] case object SparkPartitionID extends LeafExpression { - override type EvaluatedType = Int - override def nullable: Boolean = false override def dataType: DataType = IntegerType - override def eval(input: Row): Int = TaskContext.get().partitionId() + override def eval(input: InternalRow): Int = TaskContext.get().partitionId() } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala index 926f5e6c137ee..2d2e1b92b86be 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala @@ -17,16 +17,15 @@ package org.apache.spark.sql.execution.joins -import org.apache.spark.rdd.RDD - import scala.concurrent._ import scala.concurrent.duration._ -import scala.concurrent.ExecutionContext.Implicits.global import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.expressions.{Row, Expression} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions.{Expression, InternalRow} import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution} import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} +import org.apache.spark.util.ThreadUtils /** * :: DeveloperApi :: @@ -61,12 +60,12 @@ case class BroadcastHashJoin( @transient private val broadcastFuture = future { // Note that we use .execute().collect() because we don't want to convert data to Scala types - val input: Array[Row] = buildPlan.execute().map(_.copy()).collect() + val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect() val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.length) sparkContext.broadcast(hashed) - } + }(BroadcastHashJoin.broadcastHashJoinExecutionContext) - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val broadcastRelation = Await.result(broadcastFuture, timeout) streamedPlan.execute().mapPartitions { streamedIter => @@ -74,3 +73,9 @@ case class BroadcastHashJoin( } } } + +object BroadcastHashJoin { + + private val broadcastHashJoinExecutionContext = ExecutionContext.fromExecutorService( + ThreadUtils.newDaemonCachedThreadPool("broadcast-hash-join", 128)) +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala index 3ef1e0d7fbdd4..412a3d4178e12 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} /** @@ -38,10 +38,10 @@ case class BroadcastLeftSemiJoinHash( override def output: Seq[Attribute] = left.output - override def execute(): RDD[Row] = { - val buildIter= buildPlan.execute().map(_.copy()).collect().toIterator - val hashSet = new java.util.HashSet[Row]() - var currentRow: Row = null + protected override def doExecute(): RDD[InternalRow] = { + val buildIter = buildPlan.execute().map(_.copy()).collect().toIterator + val hashSet = new java.util.HashSet[InternalRow]() + var currentRow: InternalRow = null // Create a Hash set of buildKeys while (buildIter.hasNext) { @@ -50,7 +50,8 @@ case class BroadcastLeftSemiJoinHash( if (!rowKey.anyNull) { val keyExists = hashSet.contains(rowKey) if (!keyExists) { - hashSet.add(rowKey) + // rowKey may be not serializable (from codegen) + hashSet.add(rowKey.copy()) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala index 6aaf35fb429e2..0b2cf8e12a6c4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala @@ -61,13 +61,14 @@ case class BroadcastNestedLoopJoin( @transient private lazy val boundCondition = newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output) - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val broadcastedRelation = - sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq) + sparkContext.broadcast(broadcast.execute().map(_.copy()) + .collect().toIndexedSeq) /** All rows that either match both-way, or rows from streamed joined with nulls. */ val matchesOrStreamedRowsWithNulls = streamed.execute().mapPartitions { streamedIter => - val matchedRows = new CompactBuffer[Row] + val matchedRows = new CompactBuffer[InternalRow] // TODO: Use Spark's BitSet. val includedBroadcastTuples = new scala.collection.mutable.BitSet(broadcastedRelation.value.size) @@ -118,8 +119,8 @@ case class BroadcastNestedLoopJoin( val leftNulls = new GenericMutableRow(left.output.size) val rightNulls = new GenericMutableRow(right.output.size) /** Rows from broadcasted joined with nulls. */ - val broadcastRowsWithNulls: Seq[Row] = { - val buf: CompactBuffer[Row] = new CompactBuffer() + val broadcastRowsWithNulls: Seq[InternalRow] = { + val buf: CompactBuffer[InternalRow] = new CompactBuffer() var i = 0 val rel = broadcastedRelation.value while (i < rel.length) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala index 1cbc98354d673..261b4724159fb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.{Attribute, JoinedRow} import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} @@ -30,7 +30,7 @@ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode { override def output: Seq[Attribute] = left.output ++ right.output - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val leftResults = left.execute().map(_.copy()) val rightResults = right.execute().map(_.copy()) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala index 851de1685509a..3a4196a90d14a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala @@ -49,11 +49,13 @@ trait HashJoin { @transient protected lazy val streamSideKeyGenerator: () => MutableProjection = newMutableProjection(streamedKeys, streamedPlan.output) - protected def hashJoin(streamIter: Iterator[Row], hashedRelation: HashedRelation): Iterator[Row] = + protected def hashJoin( + streamIter: Iterator[InternalRow], + hashedRelation: HashedRelation): Iterator[InternalRow] = { - new Iterator[Row] { - private[this] var currentStreamedRow: Row = _ - private[this] var currentHashMatches: CompactBuffer[Row] = _ + new Iterator[InternalRow] { + private[this] var currentStreamedRow: InternalRow = _ + private[this] var currentHashMatches: CompactBuffer[InternalRow] = _ private[this] var currentMatchPosition: Int = -1 // Mutable per row objects. @@ -65,7 +67,7 @@ trait HashJoin { (currentMatchPosition != -1 && currentMatchPosition < currentHashMatches.size) || (streamIter.hasNext && fetchNext()) - override final def next(): Row = { + override final def next(): InternalRow = { val ret = buildSide match { case BuildRight => joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition)) case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala index a396c0f5d56ee..bce0e8d70a57b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala @@ -48,7 +48,8 @@ case class HashOuterJoin( case LeftOuter => left.outputPartitioning case RightOuter => right.outputPartitioning case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions) - case x => throw new Exception(s"HashOuterJoin should not take $x as the JoinType") + case x => + throw new IllegalArgumentException(s"HashOuterJoin should not take $x as the JoinType") } override def requiredChildDistribution: Seq[ClusteredDistribution] = @@ -63,24 +64,27 @@ case class HashOuterJoin( case FullOuter => left.output.map(_.withNullability(true)) ++ right.output.map(_.withNullability(true)) case x => - throw new Exception(s"HashOuterJoin should not take $x as the JoinType") + throw new IllegalArgumentException(s"HashOuterJoin should not take $x as the JoinType") } } - @transient private[this] lazy val DUMMY_LIST = Seq[Row](null) - @transient private[this] lazy val EMPTY_LIST = Seq.empty[Row] + @transient private[this] lazy val DUMMY_LIST = Seq[InternalRow](null) + @transient private[this] lazy val EMPTY_LIST = Seq.empty[InternalRow] @transient private[this] lazy val leftNullRow = new GenericRow(left.output.length) @transient private[this] lazy val rightNullRow = new GenericRow(right.output.length) @transient private[this] lazy val boundCondition = - condition.map(newPredicate(_, left.output ++ right.output)).getOrElse((row: Row) => true) + condition.map( + newPredicate(_, left.output ++ right.output)).getOrElse((row: InternalRow) => true) // TODO we need to rewrite all of the iterators with our own implementation instead of the Scala // iterator for performance purpose. private[this] def leftOuterIterator( - key: Row, joinedRow: JoinedRow, rightIter: Iterable[Row]): Iterator[Row] = { - val ret: Iterable[Row] = { + key: InternalRow, + joinedRow: JoinedRow, + rightIter: Iterable[InternalRow]): Iterator[InternalRow] = { + val ret: Iterable[InternalRow] = { if (!key.anyNull) { val temp = rightIter.collect { case r if boundCondition(joinedRow.withRight(r)) => joinedRow.copy() @@ -98,12 +102,15 @@ case class HashOuterJoin( } private[this] def rightOuterIterator( - key: Row, leftIter: Iterable[Row], joinedRow: JoinedRow): Iterator[Row] = { + key: InternalRow, + leftIter: Iterable[InternalRow], + joinedRow: JoinedRow): Iterator[InternalRow] = { - val ret: Iterable[Row] = { + val ret: Iterable[InternalRow] = { if (!key.anyNull) { val temp = leftIter.collect { - case l if boundCondition(joinedRow.withLeft(l)) => joinedRow.copy + case l if boundCondition(joinedRow.withLeft(l)) => + joinedRow.copy } if (temp.size == 0) { joinedRow.withLeft(leftNullRow).copy :: Nil @@ -118,14 +125,14 @@ case class HashOuterJoin( } private[this] def fullOuterIterator( - key: Row, leftIter: Iterable[Row], rightIter: Iterable[Row], - joinedRow: JoinedRow): Iterator[Row] = { + key: InternalRow, leftIter: Iterable[InternalRow], rightIter: Iterable[InternalRow], + joinedRow: JoinedRow): Iterator[InternalRow] = { if (!key.anyNull) { // Store the positions of records in right, if one of its associated row satisfy // the join condition. val rightMatchedSet = scala.collection.mutable.Set[Int]() - leftIter.iterator.flatMap[Row] { l => + leftIter.iterator.flatMap[InternalRow] { l => joinedRow.withLeft(l) var matched = false rightIter.zipWithIndex.collect { @@ -156,24 +163,25 @@ case class HashOuterJoin( joinedRow(leftNullRow, r).copy() } } else { - leftIter.iterator.map[Row] { l => + leftIter.iterator.map[InternalRow] { l => joinedRow(l, rightNullRow).copy() - } ++ rightIter.iterator.map[Row] { r => + } ++ rightIter.iterator.map[InternalRow] { r => joinedRow(leftNullRow, r).copy() } } } private[this] def buildHashTable( - iter: Iterator[Row], keyGenerator: Projection): JavaHashMap[Row, CompactBuffer[Row]] = { - val hashTable = new JavaHashMap[Row, CompactBuffer[Row]]() + iter: Iterator[InternalRow], + keyGenerator: Projection): JavaHashMap[InternalRow, CompactBuffer[InternalRow]] = { + val hashTable = new JavaHashMap[InternalRow, CompactBuffer[InternalRow]]() while (iter.hasNext) { val currentRow = iter.next() val rowKey = keyGenerator(currentRow) var existingMatchList = hashTable.get(rowKey) if (existingMatchList == null) { - existingMatchList = new CompactBuffer[Row]() + existingMatchList = new CompactBuffer[InternalRow]() hashTable.put(rowKey, existingMatchList) } @@ -183,7 +191,7 @@ case class HashOuterJoin( hashTable } - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val joinedRow = new JoinedRow() left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) => // TODO this probably can be replaced by external sort (sort merged join?) @@ -216,7 +224,8 @@ case class HashOuterJoin( rightHashTable.getOrElse(key, EMPTY_LIST), joinedRow) } - case x => throw new Exception(s"HashOuterJoin should not take $x as the JoinType") + case x => + throw new IllegalArgumentException(s"HashOuterJoin should not take $x as the JoinType") } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala index ab84c123e0c0b..e18c817975134 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.joins import java.io.{ObjectInput, ObjectOutput, Externalizable} import java.util.{HashMap => JavaHashMap} -import org.apache.spark.sql.catalyst.expressions.{Projection, Row} +import org.apache.spark.sql.catalyst.expressions.{Projection, InternalRow} import org.apache.spark.sql.execution.SparkSqlSerializer import org.apache.spark.util.collection.CompactBuffer @@ -30,7 +30,7 @@ import org.apache.spark.util.collection.CompactBuffer * object. */ private[joins] sealed trait HashedRelation { - def get(key: Row): CompactBuffer[Row] + def get(key: InternalRow): CompactBuffer[InternalRow] // This is a helper method to implement Externalizable, and is used by // GeneralHashedRelation and UniqueKeyHashedRelation @@ -54,12 +54,12 @@ private[joins] sealed trait HashedRelation { * A general [[HashedRelation]] backed by a hash map that maps the key into a sequence of values. */ private[joins] final class GeneralHashedRelation( - private var hashTable: JavaHashMap[Row, CompactBuffer[Row]]) + private var hashTable: JavaHashMap[InternalRow, CompactBuffer[InternalRow]]) extends HashedRelation with Externalizable { def this() = this(null) // Needed for serialization - override def get(key: Row): CompactBuffer[Row] = hashTable.get(key) + override def get(key: InternalRow): CompactBuffer[InternalRow] = hashTable.get(key) override def writeExternal(out: ObjectOutput): Unit = { writeBytes(out, SparkSqlSerializer.serialize(hashTable)) @@ -75,17 +75,18 @@ private[joins] final class GeneralHashedRelation( * A specialized [[HashedRelation]] that maps key into a single value. This implementation * assumes the key is unique. */ -private[joins] final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[Row, Row]) +private[joins] +final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalRow, InternalRow]) extends HashedRelation with Externalizable { def this() = this(null) // Needed for serialization - override def get(key: Row): CompactBuffer[Row] = { + override def get(key: InternalRow): CompactBuffer[InternalRow] = { val v = hashTable.get(key) if (v eq null) null else CompactBuffer(v) } - def getValue(key: Row): Row = hashTable.get(key) + def getValue(key: InternalRow): InternalRow = hashTable.get(key) override def writeExternal(out: ObjectOutput): Unit = { writeBytes(out, SparkSqlSerializer.serialize(hashTable)) @@ -103,13 +104,13 @@ private[joins] final class UniqueKeyHashedRelation(private var hashTable: JavaHa private[joins] object HashedRelation { def apply( - input: Iterator[Row], + input: Iterator[InternalRow], keyGenerator: Projection, sizeEstimate: Int = 64): HashedRelation = { // TODO: Use Spark's HashMap implementation. - val hashTable = new JavaHashMap[Row, CompactBuffer[Row]](sizeEstimate) - var currentRow: Row = null + val hashTable = new JavaHashMap[InternalRow, CompactBuffer[InternalRow]](sizeEstimate) + var currentRow: InternalRow = null // Whether the join key is unique. If the key is unique, we can convert the underlying // hash map into one specialized for this. @@ -122,7 +123,7 @@ private[joins] object HashedRelation { if (!rowKey.anyNull) { val existingMatchList = hashTable.get(rowKey) val matchList = if (existingMatchList == null) { - val newMatchList = new CompactBuffer[Row]() + val newMatchList = new CompactBuffer[InternalRow]() hashTable.put(rowKey, newMatchList) newMatchList } else { @@ -134,7 +135,7 @@ private[joins] object HashedRelation { } if (keyIsUnique) { - val uniqHashTable = new JavaHashMap[Row, Row](hashTable.size) + val uniqHashTable = new JavaHashMap[InternalRow, InternalRow](hashTable.size) val iter = hashTable.entrySet().iterator() while (iter.hasNext) { val entry = iter.next() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala index b03af410dca08..2a6d4d1ab08bb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala @@ -47,7 +47,7 @@ case class LeftSemiJoinBNL( @transient private lazy val boundCondition = newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output) - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val broadcastedRelation = sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala index a04f2a63b5a55..20d74270afb48 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row} +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, InternalRow} import org.apache.spark.sql.catalyst.plans.physical.ClusteredDistribution import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} @@ -42,10 +42,10 @@ case class LeftSemiJoinHash( override def output: Seq[Attribute] = left.output - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) => - val hashSet = new java.util.HashSet[Row]() - var currentRow: Row = null + val hashSet = new java.util.HashSet[InternalRow]() + var currentRow: InternalRow = null // Create a Hash set of buildKeys while (buildIter.hasNext) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala index a6cd8337c1c3e..5439e10a60b2a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.Expression import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning} import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} @@ -43,7 +43,7 @@ case class ShuffledHashJoin( override def requiredChildDistribution: Seq[ClusteredDistribution] = ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) => val hashed = HashedRelation(buildIter, buildSideKeyGenerator) hashJoin(streamIter, hashed) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala index b5123668ba11e..2abe65a71813d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala @@ -21,9 +21,7 @@ import java.util.NoSuchElementException import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} import org.apache.spark.util.collection.CompactBuffer @@ -60,29 +58,29 @@ case class SortMergeJoin( private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = keys.map(SortOrder(_, Ascending)) - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val leftResults = left.execute().map(_.copy()) val rightResults = right.execute().map(_.copy()) leftResults.zipPartitions(rightResults) { (leftIter, rightIter) => - new Iterator[Row] { + new Iterator[InternalRow] { // Mutable per row objects. private[this] val joinRow = new JoinedRow5 - private[this] var leftElement: Row = _ - private[this] var rightElement: Row = _ - private[this] var leftKey: Row = _ - private[this] var rightKey: Row = _ - private[this] var rightMatches: CompactBuffer[Row] = _ + private[this] var leftElement: InternalRow = _ + private[this] var rightElement: InternalRow = _ + private[this] var leftKey: InternalRow = _ + private[this] var rightKey: InternalRow = _ + private[this] var rightMatches: CompactBuffer[InternalRow] = _ private[this] var rightPosition: Int = -1 private[this] var stop: Boolean = false - private[this] var matchKey: Row = _ + private[this] var matchKey: InternalRow = _ // initialize iterator initialize() override final def hasNext: Boolean = nextMatchingPair() - override final def next(): Row = { + override final def next(): InternalRow = { if (hasNext) { // we are using the buffered right rows and run down left iterator val joinedRow = joinRow(leftElement, rightMatches(rightPosition)) @@ -145,7 +143,7 @@ case class SortMergeJoin( fetchLeft() } } - rightMatches = new CompactBuffer[Row]() + rightMatches = new CompactBuffer[InternalRow]() if (stop) { stop = false // iterate the right side to buffer all rows that matches diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala index 58cb1980f217e..1ce150ceaf5f9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala @@ -19,22 +19,24 @@ package org.apache.spark.sql.execution import java.util.{List => JList, Map => JMap} -import org.apache.spark.rdd.RDD - import scala.collection.JavaConversions._ import scala.collection.JavaConverters._ import net.razorvine.pickle.{Pickler, Unpickler} + +import org.apache.spark.{Accumulator, Logging => SparkLogging} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.api.python.{PythonBroadcast, PythonRDD} import org.apache.spark.broadcast.Broadcast -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types._ -import org.apache.spark.{Accumulator, Logging => SparkLogging} +import org.apache.spark.unsafe.types.UTF8String /** * A serialized version of a Python lambda function. Suitable for use in a [[PythonRDD]]. @@ -45,6 +47,7 @@ private[spark] case class PythonUDF( envVars: JMap[String, String], pythonIncludes: JList[String], pythonExec: String, + pythonVer: String, broadcastVars: JList[Broadcast[PythonBroadcast]], accumulator: Accumulator[JList[Array[Byte]]], dataType: DataType, @@ -54,8 +57,8 @@ private[spark] case class PythonUDF( def nullable: Boolean = true - override def eval(input: Row): PythonUDF.this.EvaluatedType = { - sys.error("PythonUDFs can not be directly evaluated.") + override def eval(input: InternalRow): Any = { + throw new UnsupportedOperationException("PythonUDFs can not be directly evaluated.") } } @@ -69,43 +72,49 @@ private[spark] case class PythonUDF( private[spark] object ExtractPythonUdfs extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { // Skip EvaluatePython nodes. - case p: EvaluatePython => p + case plan: EvaluatePython => plan - case l: LogicalPlan => + case plan: LogicalPlan => // Extract any PythonUDFs from the current operator. - val udfs = l.expressions.flatMap(_.collect { case udf: PythonUDF => udf}) + val udfs = plan.expressions.flatMap(_.collect { case udf: PythonUDF => udf }) if (udfs.isEmpty) { // If there aren't any, we are done. - l + plan } else { // Pick the UDF we are going to evaluate (TODO: Support evaluating multiple UDFs at a time) // If there is more than one, we will add another evaluation operator in a subsequent pass. - val udf = udfs.head - - var evaluation: EvaluatePython = null - - // Rewrite the child that has the input required for the UDF - val newChildren = l.children.map { child => - // Check to make sure that the UDF can be evaluated with only the input of this child. - // Other cases are disallowed as they are ambiguous or would require a cartisian product. - if (udf.references.subsetOf(child.outputSet)) { - evaluation = EvaluatePython(udf, child) - evaluation - } else if (udf.references.intersect(child.outputSet).nonEmpty) { - sys.error(s"Invalid PythonUDF $udf, requires attributes from more than one child.") - } else { - child - } + udfs.find(_.resolved) match { + case Some(udf) => + var evaluation: EvaluatePython = null + + // Rewrite the child that has the input required for the UDF + val newChildren = plan.children.map { child => + // Check to make sure that the UDF can be evaluated with only the input of this child. + // Other cases are disallowed as they are ambiguous or would require a cartesian + // product. + if (udf.references.subsetOf(child.outputSet)) { + evaluation = EvaluatePython(udf, child) + evaluation + } else if (udf.references.intersect(child.outputSet).nonEmpty) { + sys.error(s"Invalid PythonUDF $udf, requires attributes from more than one child.") + } else { + child + } + } + + assert(evaluation != null, "Unable to evaluate PythonUDF. Missing input attributes.") + + // Trim away the new UDF value if it was only used for filtering or something. + logical.Project( + plan.output, + plan.transformExpressions { + case p: PythonUDF if p.fastEquals(udf) => evaluation.resultAttribute + }.withNewChildren(newChildren)) + + case None => + // If there is no Python UDF that is resolved, skip this round. + plan } - - assert(evaluation != null, "Unable to evaluate PythonUDF. Missing input attributes.") - - // Trim away the new UDF value if it was only used for filtering or something. - logical.Project( - l.output, - l.transformExpressions { - case p: PythonUDF if p.fastEquals(udf) => evaluation.resultAttribute - }.withNewChildren(newChildren)) } } } @@ -140,6 +149,7 @@ object EvaluatePython { case (ud, udt: UserDefinedType[_]) => toJava(udt.serialize(ud), udt.sqlType) case (date: Int, DateType) => DateUtils.toJavaDate(date) + case (t: Long, TimestampType) => DateUtils.toJavaTimestamp(t) case (s: UTF8String, StringType) => s.toString // Pyrolite can handle Timestamp and Decimal @@ -178,10 +188,12 @@ object EvaluatePython { }): Row case (c: java.util.Calendar, DateType) => - DateUtils.fromJavaDate(new java.sql.Date(c.getTime().getTime())) + DateUtils.fromJavaDate(new java.sql.Date(c.getTimeInMillis)) case (c: java.util.Calendar, TimestampType) => - new java.sql.Timestamp(c.getTime().getTime()) + c.getTimeInMillis * 10000L + case (t: java.sql.Timestamp, TimestampType) => + DateUtils.fromJavaTimestamp(t) case (_, udt: UserDefinedType[_]) => fromJava(obj, udt.sqlType) @@ -193,8 +205,10 @@ object EvaluatePython { case (c: Long, IntegerType) => c.toInt case (c: Int, LongType) => c.toLong case (c: Double, FloatType) => c.toFloat - case (c: String, StringType) => UTF8String(c) - case (c, StringType) if !c.isInstanceOf[String] => UTF8String(c.toString) + case (c: String, StringType) => UTF8String.fromString(c) + case (c, StringType) => + // If we get here, c is not a string. Call toString on it. + UTF8String.fromString(c.toString) case (c, _) => c } @@ -228,7 +242,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child: def children: Seq[SparkPlan] = child :: Nil - def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { val childResults = child.execute().map(_.copy()) val parent = childResults.mapPartitions { iter => @@ -250,6 +264,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child: udf.pythonIncludes, false, udf.pythonExec, + udf.pythonVer, udf.broadcastVars, udf.accumulator ).mapPartitions { iter => @@ -262,7 +277,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child: val row = new GenericMutableRow(1) iter.map { result => row(0) = EvaluatePython.fromJava(result, udf.dataType) - row: Row + row: InternalRow } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala index 5ae7e107544f8..8df1da037c434 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala @@ -20,9 +20,10 @@ package org.apache.spark.sql.execution.stat import scala.collection.mutable.{Map => MutableMap} import org.apache.spark.Logging -import org.apache.spark.sql.{Column, DataFrame, Row} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.LocalRelation import org.apache.spark.sql.types.{ArrayType, StructField, StructType} +import org.apache.spark.sql.{Column, DataFrame} private[sql] object FrequentItems extends Logging { @@ -62,7 +63,7 @@ private[sql] object FrequentItems extends Logging { } /** - * Finding frequent items for columns, possibly with false positives. Using the + * Finding frequent items for columns, possibly with false positives. Using the * frequent element count algorithm described in * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. * The `support` should be greater than 1e-4. @@ -75,7 +76,7 @@ private[sql] object FrequentItems extends Logging { * @return A Local DataFrame with the Array of frequent items for each column. */ private[sql] def singlePassFreqItems( - df: DataFrame, + df: DataFrame, cols: Seq[String], support: Double): DataFrame = { require(support >= 1e-4, s"support ($support) must be greater than 1e-4.") @@ -88,8 +89,8 @@ private[sql] object FrequentItems extends Logging { val index = originalSchema.fieldIndex(name) (name, originalSchema.fields(index).dataType) } - - val freqItems = df.select(cols.map(Column(_)):_*).rdd.aggregate(countMaps)( + + val freqItems = df.select(cols.map(Column(_)) : _*).rdd.aggregate(countMaps)( seqOp = (counts, row) => { var i = 0 while (i < numCols) { @@ -110,7 +111,7 @@ private[sql] object FrequentItems extends Logging { } ) val justItems = freqItems.map(m => m.baseMap.keys.toSeq) - val resultRow = Row(justItems:_*) + val resultRow = InternalRow(justItems : _*) // append frequent Items to the column name for easy debugging val outputCols = colInfo.map { v => StructField(v._1 + "_freqItems", ArrayType(v._2, false)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala index 71b7f6c2a6756..93383e5a62f11 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala @@ -18,14 +18,14 @@ package org.apache.spark.sql.execution.stat import org.apache.spark.Logging -import org.apache.spark.sql.{Column, DataFrame} +import org.apache.spark.sql.{Row, Column, DataFrame} import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Cast} import org.apache.spark.sql.catalyst.plans.logical.LocalRelation import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ private[sql] object StatFunctions extends Logging { - + /** Calculate the Pearson Correlation Coefficient for the given columns */ private[sql] def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double = { val counts = collectStatisticalData(df, cols) @@ -104,7 +104,7 @@ private[sql] object StatFunctions extends Logging { /** Generate a table of frequencies for the elements of two columns. */ private[sql] def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = { val tableName = s"${col1}_$col2" - val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).take(1e6.toInt) + val counts = df.groupBy(col1, col2).agg(count("*")).take(1e6.toInt) if (counts.length == 1e6.toInt) { logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " + "the pairs. Please try reducing the amount of distinct items in your columns.") @@ -116,7 +116,10 @@ private[sql] object StatFunctions extends Logging { s"exceed 1e4. Currently $columnSize") val table = counts.groupBy(_.get(0)).map { case (col1Item, rows) => val countsRow = new GenericMutableRow(columnSize + 1) - rows.foreach { row => + rows.foreach { (row: Row) => + // row.get(0) is column 1 + // row.get(1) is column 2 + // row.get(3) is the frequency countsRow.setLong(distinctCol2.get(row.get(1)).get + 1, row.getLong(2)) } // the value of col1 is the first value, the rest are the counts @@ -126,6 +129,6 @@ private[sql] object StatFunctions extends Logging { val headerNames = distinctCol2.map(r => StructField(r._1.toString, LongType)).toSeq val schema = StructType(StructField(tableName, StringType) +: headerNames) - new DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table)) + new DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table)).na.fill(0.0) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/Window.scala new file mode 100644 index 0000000000000..e9b60841fc28c --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/Window.scala @@ -0,0 +1,98 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.expressions + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.Column +import org.apache.spark.sql.catalyst.expressions._ + +/** + * :: Experimental :: + * Utility functions for defining window in DataFrames. + * + * {{{ + * // PARTITION BY country ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW + * Window.partitionBy("country").orderBy("date").rowsBetween(Long.MinValue, 0) + * + * // PARTITION BY country ORDER BY date ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING + * Window.partitionBy("country").orderBy("date").rowsBetween(-3, 3) + * }}} + * + * @since 1.4.0 + */ +@Experimental +object Window { + + /** + * Creates a [[WindowSpec]] with the partitioning defined. + * @since 1.4.0 + */ + @scala.annotation.varargs + def partitionBy(colName: String, colNames: String*): WindowSpec = { + spec.partitionBy(colName, colNames : _*) + } + + /** + * Creates a [[WindowSpec]] with the partitioning defined. + * @since 1.4.0 + */ + @scala.annotation.varargs + def partitionBy(cols: Column*): WindowSpec = { + spec.partitionBy(cols : _*) + } + + /** + * Creates a [[WindowSpec]] with the ordering defined. + * @since 1.4.0 + */ + @scala.annotation.varargs + def orderBy(colName: String, colNames: String*): WindowSpec = { + spec.orderBy(colName, colNames : _*) + } + + /** + * Creates a [[WindowSpec]] with the ordering defined. + * @since 1.4.0 + */ + @scala.annotation.varargs + def orderBy(cols: Column*): WindowSpec = { + spec.orderBy(cols : _*) + } + + private def spec: WindowSpec = { + new WindowSpec(Seq.empty, Seq.empty, UnspecifiedFrame) + } + +} + +/** + * :: Experimental :: + * Utility functions for defining window in DataFrames. + * + * {{{ + * // PARTITION BY country ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW + * Window.partitionBy("country").orderBy("date").rowsBetween(Long.MinValue, 0) + * + * // PARTITION BY country ORDER BY date ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING + * Window.partitionBy("country").orderBy("date").rowsBetween(-3, 3) + * }}} + * + * @since 1.4.0 + */ +@Experimental +class Window private() // So we can see Window in JavaDoc. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/WindowSpec.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/WindowSpec.scala new file mode 100644 index 0000000000000..c3d2246297021 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/WindowSpec.scala @@ -0,0 +1,175 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.expressions + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.{Column, catalyst} +import org.apache.spark.sql.catalyst.expressions._ + + +/** + * :: Experimental :: + * A window specification that defines the partitioning, ordering, and frame boundaries. + * + * Use the static methods in [[Window]] to create a [[WindowSpec]]. + * + * @since 1.4.0 + */ +@Experimental +class WindowSpec private[sql]( + partitionSpec: Seq[Expression], + orderSpec: Seq[SortOrder], + frame: catalyst.expressions.WindowFrame) { + + /** + * Defines the partitioning columns in a [[WindowSpec]]. + * @since 1.4.0 + */ + @scala.annotation.varargs + def partitionBy(colName: String, colNames: String*): WindowSpec = { + partitionBy((colName +: colNames).map(Column(_)): _*) + } + + /** + * Defines the partitioning columns in a [[WindowSpec]]. + * @since 1.4.0 + */ + @scala.annotation.varargs + def partitionBy(cols: Column*): WindowSpec = { + new WindowSpec(cols.map(_.expr), orderSpec, frame) + } + + /** + * Defines the ordering columns in a [[WindowSpec]]. + * @since 1.4.0 + */ + @scala.annotation.varargs + def orderBy(colName: String, colNames: String*): WindowSpec = { + orderBy((colName +: colNames).map(Column(_)): _*) + } + + /** + * Defines the ordering columns in a [[WindowSpec]]. + * @since 1.4.0 + */ + @scala.annotation.varargs + def orderBy(cols: Column*): WindowSpec = { + val sortOrder: Seq[SortOrder] = cols.map { col => + col.expr match { + case expr: SortOrder => + expr + case expr: Expression => + SortOrder(expr, Ascending) + } + } + new WindowSpec(partitionSpec, sortOrder, frame) + } + + /** + * Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive). + * + * Both `start` and `end` are relative positions from the current row. For example, "0" means + * "current row", while "-1" means the row before the current row, and "5" means the fifth row + * after the current row. + * + * @param start boundary start, inclusive. + * The frame is unbounded if this is the minimum long value. + * @param end boundary end, inclusive. + * The frame is unbounded if this is the maximum long value. + * @since 1.4.0 + */ + def rowsBetween(start: Long, end: Long): WindowSpec = { + between(RowFrame, start, end) + } + + /** + * Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive). + * + * Both `start` and `end` are relative from the current row. For example, "0" means "current row", + * while "-1" means one off before the current row, and "5" means the five off after the + * current row. + * + * @param start boundary start, inclusive. + * The frame is unbounded if this is the minimum long value. + * @param end boundary end, inclusive. + * The frame is unbounded if this is the maximum long value. + * @since 1.4.0 + */ + def rangeBetween(start: Long, end: Long): WindowSpec = { + between(RangeFrame, start, end) + } + + private def between(typ: FrameType, start: Long, end: Long): WindowSpec = { + val boundaryStart = start match { + case 0 => CurrentRow + case Long.MinValue => UnboundedPreceding + case x if x < 0 => ValuePreceding(-start.toInt) + case x if x > 0 => ValueFollowing(start.toInt) + } + + val boundaryEnd = end match { + case 0 => CurrentRow + case Long.MaxValue => UnboundedFollowing + case x if x < 0 => ValuePreceding(-end.toInt) + case x if x > 0 => ValueFollowing(end.toInt) + } + + new WindowSpec( + partitionSpec, + orderSpec, + SpecifiedWindowFrame(typ, boundaryStart, boundaryEnd)) + } + + /** + * Converts this [[WindowSpec]] into a [[Column]] with an aggregate expression. + */ + private[sql] def withAggregate(aggregate: Column): Column = { + val windowExpr = aggregate.expr match { + case Average(child) => WindowExpression( + UnresolvedWindowFunction("avg", child :: Nil), + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case Sum(child) => WindowExpression( + UnresolvedWindowFunction("sum", child :: Nil), + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case Count(child) => WindowExpression( + UnresolvedWindowFunction("count", child :: Nil), + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case First(child) => WindowExpression( + // TODO this is a hack for Hive UDAF first_value + UnresolvedWindowFunction("first_value", child :: Nil), + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case Last(child) => WindowExpression( + // TODO this is a hack for Hive UDAF last_value + UnresolvedWindowFunction("last_value", child :: Nil), + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case Min(child) => WindowExpression( + UnresolvedWindowFunction("min", child :: Nil), + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case Max(child) => WindowExpression( + UnresolvedWindowFunction("max", child :: Nil), + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case wf: WindowFunction => WindowExpression( + wf, + WindowSpecDefinition(partitionSpec, orderSpec, frame)) + case x => + throw new UnsupportedOperationException(s"$x is not supported in window operation.") + } + new Column(windowExpr) + } + +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 1728b0b8c910e..7e7a099a8318b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -24,7 +24,6 @@ import org.apache.spark.annotation.Experimental import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star} import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.expressions.mathfuncs._ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils @@ -37,7 +36,11 @@ import org.apache.spark.util.Utils * @groupname sort_funcs Sorting functions * @groupname normal_funcs Non-aggregate functions * @groupname math_funcs Math functions + * @groupname misc_funcs Misc functions + * @groupname window_funcs Window functions + * @groupname string_funcs String functions * @groupname Ungrouped Support functions for DataFrames. + * @since 1.3.0 */ @Experimental // scalastyle:off @@ -50,6 +53,7 @@ object functions { * Returns a [[Column]] based on the given column name. * * @group normal_funcs + * @since 1.3.0 */ def col(colName: String): Column = Column(colName) @@ -57,6 +61,7 @@ object functions { * Returns a [[Column]] based on the given column name. Alias of [[col]]. * * @group normal_funcs + * @since 1.3.0 */ def column(colName: String): Column = Column(colName) @@ -68,6 +73,7 @@ object functions { * Otherwise, a new [[Column]] is created to represent the literal value. * * @group normal_funcs + * @since 1.3.0 */ def lit(literal: Any): Column = { literal match { @@ -92,6 +98,7 @@ object functions { * }}} * * @group sort_funcs + * @since 1.3.0 */ def asc(columnName: String): Column = Column(columnName).asc @@ -103,6 +110,7 @@ object functions { * }}} * * @group sort_funcs + * @since 1.3.0 */ def desc(columnName: String): Column = Column(columnName).desc @@ -114,6 +122,7 @@ object functions { * Aggregate function: returns the sum of all values in the expression. * * @group agg_funcs + * @since 1.3.0 */ def sum(e: Column): Column = Sum(e.expr) @@ -121,6 +130,7 @@ object functions { * Aggregate function: returns the sum of all values in the given column. * * @group agg_funcs + * @since 1.3.0 */ def sum(columnName: String): Column = sum(Column(columnName)) @@ -128,6 +138,7 @@ object functions { * Aggregate function: returns the sum of distinct values in the expression. * * @group agg_funcs + * @since 1.3.0 */ def sumDistinct(e: Column): Column = SumDistinct(e.expr) @@ -135,6 +146,7 @@ object functions { * Aggregate function: returns the sum of distinct values in the expression. * * @group agg_funcs + * @since 1.3.0 */ def sumDistinct(columnName: String): Column = sumDistinct(Column(columnName)) @@ -142,6 +154,7 @@ object functions { * Aggregate function: returns the number of items in a group. * * @group agg_funcs + * @since 1.3.0 */ def count(e: Column): Column = e.expr match { // Turn count(*) into count(1) @@ -153,6 +166,7 @@ object functions { * Aggregate function: returns the number of items in a group. * * @group agg_funcs + * @since 1.3.0 */ def count(columnName: String): Column = count(Column(columnName)) @@ -160,6 +174,7 @@ object functions { * Aggregate function: returns the number of distinct items in a group. * * @group agg_funcs + * @since 1.3.0 */ @scala.annotation.varargs def countDistinct(expr: Column, exprs: Column*): Column = @@ -169,15 +184,17 @@ object functions { * Aggregate function: returns the number of distinct items in a group. * * @group agg_funcs + * @since 1.3.0 */ @scala.annotation.varargs def countDistinct(columnName: String, columnNames: String*): Column = - countDistinct(Column(columnName), columnNames.map(Column.apply) :_*) + countDistinct(Column(columnName), columnNames.map(Column.apply) : _*) /** * Aggregate function: returns the approximate number of distinct items in a group. * * @group agg_funcs + * @since 1.3.0 */ def approxCountDistinct(e: Column): Column = ApproxCountDistinct(e.expr) @@ -185,6 +202,7 @@ object functions { * Aggregate function: returns the approximate number of distinct items in a group. * * @group agg_funcs + * @since 1.3.0 */ def approxCountDistinct(columnName: String): Column = approxCountDistinct(column(columnName)) @@ -192,6 +210,7 @@ object functions { * Aggregate function: returns the approximate number of distinct items in a group. * * @group agg_funcs + * @since 1.3.0 */ def approxCountDistinct(e: Column, rsd: Double): Column = ApproxCountDistinct(e.expr, rsd) @@ -199,6 +218,7 @@ object functions { * Aggregate function: returns the approximate number of distinct items in a group. * * @group agg_funcs + * @since 1.3.0 */ def approxCountDistinct(columnName: String, rsd: Double): Column = { approxCountDistinct(Column(columnName), rsd) @@ -208,6 +228,7 @@ object functions { * Aggregate function: returns the average of the values in a group. * * @group agg_funcs + * @since 1.3.0 */ def avg(e: Column): Column = Average(e.expr) @@ -215,6 +236,7 @@ object functions { * Aggregate function: returns the average of the values in a group. * * @group agg_funcs + * @since 1.3.0 */ def avg(columnName: String): Column = avg(Column(columnName)) @@ -222,6 +244,7 @@ object functions { * Aggregate function: returns the first value in a group. * * @group agg_funcs + * @since 1.3.0 */ def first(e: Column): Column = First(e.expr) @@ -229,6 +252,7 @@ object functions { * Aggregate function: returns the first value of a column in a group. * * @group agg_funcs + * @since 1.3.0 */ def first(columnName: String): Column = first(Column(columnName)) @@ -236,6 +260,7 @@ object functions { * Aggregate function: returns the last value in a group. * * @group agg_funcs + * @since 1.3.0 */ def last(e: Column): Column = Last(e.expr) @@ -243,13 +268,33 @@ object functions { * Aggregate function: returns the last value of the column in a group. * * @group agg_funcs + * @since 1.3.0 */ def last(columnName: String): Column = last(Column(columnName)) + /** + * Aggregate function: returns the average of the values in a group. + * Alias for avg. + * + * @group agg_funcs + * @since 1.4.0 + */ + def mean(e: Column): Column = avg(e) + + /** + * Aggregate function: returns the average of the values in a group. + * Alias for avg. + * + * @group agg_funcs + * @since 1.4.0 + */ + def mean(columnName: String): Column = avg(columnName) + /** * Aggregate function: returns the minimum value of the expression in a group. * * @group agg_funcs + * @since 1.3.0 */ def min(e: Column): Column = Min(e.expr) @@ -257,6 +302,7 @@ object functions { * Aggregate function: returns the minimum value of the column in a group. * * @group agg_funcs + * @since 1.3.0 */ def min(columnName: String): Column = min(Column(columnName)) @@ -264,6 +310,7 @@ object functions { * Aggregate function: returns the maximum value of the expression in a group. * * @group agg_funcs + * @since 1.3.0 */ def max(e: Column): Column = Max(e.expr) @@ -271,9 +318,222 @@ object functions { * Aggregate function: returns the maximum value of the column in a group. * * @group agg_funcs + * @since 1.3.0 */ def max(columnName: String): Column = max(Column(columnName)) + ////////////////////////////////////////////////////////////////////////////////////////////// + // Window functions + ////////////////////////////////////////////////////////////////////////////////////////////// + + /** + * Window function: returns the value that is `offset` rows before the current row, and + * `null` if there is less than `offset` rows before the current row. For example, + * an `offset` of one will return the previous row at any given point in the window partition. + * + * This is equivalent to the LAG function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lag(e: Column, offset: Int): Column = { + lag(e, offset, null) + } + + /** + * Window function: returns the value that is `offset` rows before the current row, and + * `null` if there is less than `offset` rows before the current row. For example, + * an `offset` of one will return the previous row at any given point in the window partition. + * + * This is equivalent to the LAG function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lag(columnName: String, offset: Int): Column = { + lag(columnName, offset, null) + } + + /** + * Window function: returns the value that is `offset` rows before the current row, and + * `defaultValue` if there is less than `offset` rows before the current row. For example, + * an `offset` of one will return the previous row at any given point in the window partition. + * + * This is equivalent to the LAG function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lag(columnName: String, offset: Int, defaultValue: Any): Column = { + lag(Column(columnName), offset, defaultValue) + } + + /** + * Window function: returns the value that is `offset` rows before the current row, and + * `defaultValue` if there is less than `offset` rows before the current row. For example, + * an `offset` of one will return the previous row at any given point in the window partition. + * + * This is equivalent to the LAG function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lag(e: Column, offset: Int, defaultValue: Any): Column = { + UnresolvedWindowFunction("lag", e.expr :: Literal(offset) :: Literal(defaultValue) :: Nil) + } + + /** + * Window function: returns the value that is `offset` rows after the current row, and + * `null` if there is less than `offset` rows after the current row. For example, + * an `offset` of one will return the next row at any given point in the window partition. + * + * This is equivalent to the LEAD function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lead(columnName: String, offset: Int): Column = { + lead(columnName, offset, null) + } + + /** + * Window function: returns the value that is `offset` rows after the current row, and + * `null` if there is less than `offset` rows after the current row. For example, + * an `offset` of one will return the next row at any given point in the window partition. + * + * This is equivalent to the LEAD function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lead(e: Column, offset: Int): Column = { + lead(e, offset, null) + } + + /** + * Window function: returns the value that is `offset` rows after the current row, and + * `defaultValue` if there is less than `offset` rows after the current row. For example, + * an `offset` of one will return the next row at any given point in the window partition. + * + * This is equivalent to the LEAD function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lead(columnName: String, offset: Int, defaultValue: Any): Column = { + lead(Column(columnName), offset, defaultValue) + } + + /** + * Window function: returns the value that is `offset` rows after the current row, and + * `defaultValue` if there is less than `offset` rows after the current row. For example, + * an `offset` of one will return the next row at any given point in the window partition. + * + * This is equivalent to the LEAD function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def lead(e: Column, offset: Int, defaultValue: Any): Column = { + UnresolvedWindowFunction("lead", e.expr :: Literal(offset) :: Literal(defaultValue) :: Nil) + } + + /** + * Window function: returns the ntile group id (from 1 to `n` inclusive) in an ordered window + * partition. Fow example, if `n` is 4, the first quarter of the rows will get value 1, the second + * quarter will get 2, the third quarter will get 3, and the last quarter will get 4. + * + * This is equivalent to the NTILE function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def ntile(n: Int): Column = { + UnresolvedWindowFunction("ntile", lit(n).expr :: Nil) + } + + /** + * Window function: returns a sequential number starting at 1 within a window partition. + * + * This is equivalent to the ROW_NUMBER function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def rowNumber(): Column = { + UnresolvedWindowFunction("row_number", Nil) + } + + /** + * Window function: returns the rank of rows within a window partition, without any gaps. + * + * The difference between rank and denseRank is that denseRank leaves no gaps in ranking + * sequence when there are ties. That is, if you were ranking a competition using denseRank + * and had three people tie for second place, you would say that all three were in second + * place and that the next person came in third. + * + * This is equivalent to the DENSE_RANK function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def denseRank(): Column = { + UnresolvedWindowFunction("dense_rank", Nil) + } + + /** + * Window function: returns the rank of rows within a window partition. + * + * The difference between rank and denseRank is that denseRank leaves no gaps in ranking + * sequence when there are ties. That is, if you were ranking a competition using denseRank + * and had three people tie for second place, you would say that all three were in second + * place and that the next person came in third. + * + * This is equivalent to the RANK function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def rank(): Column = { + UnresolvedWindowFunction("rank", Nil) + } + + /** + * Window function: returns the cumulative distribution of values within a window partition, + * i.e. the fraction of rows that are below the current row. + * + * {{{ + * N = total number of rows in the partition + * cumeDist(x) = number of values before (and including) x / N + * }}} + * + * + * This is equivalent to the CUME_DIST function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def cumeDist(): Column = { + UnresolvedWindowFunction("cume_dist", Nil) + } + + /** + * Window function: returns the relative rank (i.e. percentile) of rows within a window partition. + * + * This is computed by: + * {{{ + * (rank of row in its partition - 1) / (number of rows in the partition - 1) + * }}} + * + * This is equivalent to the PERCENT_RANK function in SQL. + * + * @group window_funcs + * @since 1.4.0 + */ + def percentRank(): Column = { + UnresolvedWindowFunction("percent_rank", Nil) + } + ////////////////////////////////////////////////////////////////////////////////////////////// // Non-aggregate functions ////////////////////////////////////////////////////////////////////////////////////////////// @@ -282,6 +542,7 @@ object functions { * Computes the absolute value. * * @group normal_funcs + * @since 1.3.0 */ def abs(e: Column): Column = Abs(e.expr) @@ -289,6 +550,7 @@ object functions { * Creates a new array column. The input columns must all have the same data type. * * @group normal_funcs + * @since 1.4.0 */ @scala.annotation.varargs def array(cols: Column*): Column = CreateArray(cols.map(_.expr)) @@ -297,6 +559,7 @@ object functions { * Creates a new array column. The input columns must all have the same data type. * * @group normal_funcs + * @since 1.4.0 */ def array(colName: String, colNames: String*): Column = { array((colName +: colNames).map(col) : _*) @@ -309,14 +572,21 @@ object functions { * }}} * * @group normal_funcs + * @since 1.3.0 */ @scala.annotation.varargs def coalesce(e: Column*): Column = Coalesce(e.map(_.expr)) + /** + * Creates a new row for each element in the given array or map column. + */ + def explode(e: Column): Column = Explode(e.expr) + /** * Converts a string exprsesion to lower case. * * @group normal_funcs + * @since 1.3.0 */ def lower(e: Column): Column = Lower(e.expr) @@ -333,6 +603,7 @@ object functions { * 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594. * * @group normal_funcs + * @since 1.4.0 */ def monotonicallyIncreasingId(): Column = execution.expressions.MonotonicallyIncreasingID() @@ -348,6 +619,7 @@ object functions { * }}} * * @group normal_funcs + * @since 1.3.0 */ def negate(e: Column): Column = -e @@ -362,13 +634,40 @@ object functions { * }}} * * @group normal_funcs + * @since 1.3.0 */ def not(e: Column): Column = !e + /** + * Evaluates a list of conditions and returns one of multiple possible result expressions. + * If otherwise is not defined at the end, null is returned for unmatched conditions. + * + * {{{ + * // Example: encoding gender string column into integer. + * + * // Scala: + * people.select(when(people("gender") === "male", 0) + * .when(people("gender") === "female", 1) + * .otherwise(2)) + * + * // Java: + * people.select(when(col("gender").equalTo("male"), 0) + * .when(col("gender").equalTo("female"), 1) + * .otherwise(2)) + * }}} + * + * @group normal_funcs + * @since 1.4.0 + */ + def when(condition: Column, value: Any): Column = { + CaseWhen(Seq(condition.expr, lit(value).expr)) + } + /** * Generate a random column with i.i.d. samples from U[0.0, 1.0]. * * @group normal_funcs + * @since 1.4.0 */ def rand(seed: Long): Column = Rand(seed) @@ -376,6 +675,7 @@ object functions { * Generate a random column with i.i.d. samples from U[0.0, 1.0]. * * @group normal_funcs + * @since 1.4.0 */ def rand(): Column = rand(Utils.random.nextLong) @@ -383,6 +683,7 @@ object functions { * Generate a column with i.i.d. samples from the standard normal distribution. * * @group normal_funcs + * @since 1.4.0 */ def randn(seed: Long): Column = Randn(seed) @@ -390,6 +691,7 @@ object functions { * Generate a column with i.i.d. samples from the standard normal distribution. * * @group normal_funcs + * @since 1.4.0 */ def randn(): Column = randn(Utils.random.nextLong) @@ -399,21 +701,32 @@ object functions { * Note that this is indeterministic because it depends on data partitioning and task scheduling. * * @group normal_funcs + * @since 1.4.0 */ def sparkPartitionId(): Column = execution.expressions.SparkPartitionID /** * Computes the square root of the specified float value. * - * @group normal_funcs + * @group math_funcs + * @since 1.3.0 */ def sqrt(e: Column): Column = Sqrt(e.expr) + /** + * Computes the square root of the specified float value. + * + * @group math_funcs + * @since 1.5.0 + */ + def sqrt(colName: String): Column = sqrt(Column(colName)) + /** * Creates a new struct column. The input column must be a column in a [[DataFrame]], or * a derived column expression that is named (i.e. aliased). * * @group normal_funcs + * @since 1.4.0 */ @scala.annotation.varargs def struct(cols: Column*): Column = { @@ -426,6 +739,7 @@ object functions { * Creates a new struct column that composes multiple input columns. * * @group normal_funcs + * @since 1.4.0 */ def struct(colName: String, colNames: String*): Column = { struct((colName +: colNames).map(col) : _*) @@ -435,14 +749,15 @@ object functions { * Converts a string expression to upper case. * * @group normal_funcs + * @since 1.3.0 */ def upper(e: Column): Column = Upper(e.expr) - /** * Computes bitwise NOT. * * @group normal_funcs + * @since 1.4.0 */ def bitwiseNOT(e: Column): Column = BitwiseNot(e.expr) @@ -455,6 +770,7 @@ object functions { * 0.0 through pi. * * @group math_funcs + * @since 1.4.0 */ def acos(e: Column): Column = Acos(e.expr) @@ -463,6 +779,7 @@ object functions { * 0.0 through pi. * * @group math_funcs + * @since 1.4.0 */ def acos(columnName: String): Column = acos(Column(columnName)) @@ -471,6 +788,7 @@ object functions { * -pi/2 through pi/2. * * @group math_funcs + * @since 1.4.0 */ def asin(e: Column): Column = Asin(e.expr) @@ -479,6 +797,7 @@ object functions { * -pi/2 through pi/2. * * @group math_funcs + * @since 1.4.0 */ def asin(columnName: String): Column = asin(Column(columnName)) @@ -486,6 +805,7 @@ object functions { * Computes the tangent inverse of the given value. * * @group math_funcs + * @since 1.4.0 */ def atan(e: Column): Column = Atan(e.expr) @@ -493,6 +813,7 @@ object functions { * Computes the tangent inverse of the given column. * * @group math_funcs + * @since 1.4.0 */ def atan(columnName: String): Column = atan(Column(columnName)) @@ -501,6 +822,7 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr) @@ -509,6 +831,7 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName)) @@ -517,6 +840,7 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r) @@ -525,6 +849,7 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(leftName: String, rightName: String): Column = atan2(Column(leftName), Column(rightName)) @@ -534,6 +859,7 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr) @@ -542,6 +868,7 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r) @@ -550,6 +877,7 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r) @@ -558,13 +886,33 @@ object functions { * polar coordinates (r, theta). * * @group math_funcs + * @since 1.4.0 */ def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName)) + /** + * An expression that returns the string representation of the binary value of the given long + * column. For example, bin("12") returns "1100". + * + * @group math_funcs + * @since 1.5.0 + */ + def bin(e: Column): Column = Bin(e.expr) + + /** + * An expression that returns the string representation of the binary value of the given long + * column. For example, bin("12") returns "1100". + * + * @group math_funcs + * @since 1.5.0 + */ + def bin(columnName: String): Column = bin(Column(columnName)) + /** * Computes the cube-root of the given value. * * @group math_funcs + * @since 1.4.0 */ def cbrt(e: Column): Column = Cbrt(e.expr) @@ -572,6 +920,7 @@ object functions { * Computes the cube-root of the given column. * * @group math_funcs + * @since 1.4.0 */ def cbrt(columnName: String): Column = cbrt(Column(columnName)) @@ -579,6 +928,7 @@ object functions { * Computes the ceiling of the given value. * * @group math_funcs + * @since 1.4.0 */ def ceil(e: Column): Column = Ceil(e.expr) @@ -586,6 +936,7 @@ object functions { * Computes the ceiling of the given column. * * @group math_funcs + * @since 1.4.0 */ def ceil(columnName: String): Column = ceil(Column(columnName)) @@ -593,6 +944,7 @@ object functions { * Computes the cosine of the given value. * * @group math_funcs + * @since 1.4.0 */ def cos(e: Column): Column = Cos(e.expr) @@ -600,6 +952,7 @@ object functions { * Computes the cosine of the given column. * * @group math_funcs + * @since 1.4.0 */ def cos(columnName: String): Column = cos(Column(columnName)) @@ -607,6 +960,7 @@ object functions { * Computes the hyperbolic cosine of the given value. * * @group math_funcs + * @since 1.4.0 */ def cosh(e: Column): Column = Cosh(e.expr) @@ -614,13 +968,24 @@ object functions { * Computes the hyperbolic cosine of the given column. * * @group math_funcs + * @since 1.4.0 */ def cosh(columnName: String): Column = cosh(Column(columnName)) + /** + * Returns the double value that is closer than any other to e, the base of the natural + * logarithms. + * + * @group math_funcs + * @since 1.5.0 + */ + def e(): Column = EulerNumber() + /** * Computes the exponential of the given value. * * @group math_funcs + * @since 1.4.0 */ def exp(e: Column): Column = Exp(e.expr) @@ -628,6 +993,7 @@ object functions { * Computes the exponential of the given column. * * @group math_funcs + * @since 1.4.0 */ def exp(columnName: String): Column = exp(Column(columnName)) @@ -635,6 +1001,7 @@ object functions { * Computes the exponential of the given value minus one. * * @group math_funcs + * @since 1.4.0 */ def expm1(e: Column): Column = Expm1(e.expr) @@ -642,6 +1009,7 @@ object functions { * Computes the exponential of the given column. * * @group math_funcs + * @since 1.4.0 */ def expm1(columnName: String): Column = expm1(Column(columnName)) @@ -649,6 +1017,7 @@ object functions { * Computes the floor of the given value. * * @group math_funcs + * @since 1.4.0 */ def floor(e: Column): Column = Floor(e.expr) @@ -656,6 +1025,7 @@ object functions { * Computes the floor of the given column. * * @group math_funcs + * @since 1.4.0 */ def floor(columnName: String): Column = floor(Column(columnName)) @@ -663,6 +1033,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr) @@ -670,6 +1041,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName)) @@ -677,6 +1049,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r) @@ -684,6 +1057,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(leftName: String, rightName: String): Column = hypot(Column(leftName), Column(rightName)) @@ -692,6 +1066,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr) @@ -699,6 +1074,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r) @@ -706,6 +1082,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r) @@ -713,6 +1090,7 @@ object functions { * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. * * @group math_funcs + * @since 1.4.0 */ def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName)) @@ -720,6 +1098,7 @@ object functions { * Computes the natural logarithm of the given value. * * @group math_funcs + * @since 1.4.0 */ def log(e: Column): Column = Log(e.expr) @@ -727,20 +1106,39 @@ object functions { * Computes the natural logarithm of the given column. * * @group math_funcs + * @since 1.4.0 */ def log(columnName: String): Column = log(Column(columnName)) /** - * Computes the logarithm of the given value in Base 10. + * Returns the first argument-base logarithm of the second argument. + * + * @group math_funcs + * @since 1.4.0 + */ + def log(base: Double, a: Column): Column = Logarithm(lit(base).expr, a.expr) + + /** + * Returns the first argument-base logarithm of the second argument. + * + * @group math_funcs + * @since 1.4.0 + */ + def log(base: Double, columnName: String): Column = log(base, Column(columnName)) + + /** + * Computes the logarithm of the given value in base 10. * * @group math_funcs + * @since 1.4.0 */ def log10(e: Column): Column = Log10(e.expr) /** - * Computes the logarithm of the given value in Base 10. + * Computes the logarithm of the given value in base 10. * * @group math_funcs + * @since 1.4.0 */ def log10(columnName: String): Column = log10(Column(columnName)) @@ -748,6 +1146,7 @@ object functions { * Computes the natural logarithm of the given value plus one. * * @group math_funcs + * @since 1.4.0 */ def log1p(e: Column): Column = Log1p(e.expr) @@ -755,13 +1154,40 @@ object functions { * Computes the natural logarithm of the given column plus one. * * @group math_funcs + * @since 1.4.0 */ def log1p(columnName: String): Column = log1p(Column(columnName)) + /** + * Returns the double value that is closer than any other to pi, the ratio of the circumference + * of a circle to its diameter. + * + * @group math_funcs + * @since 1.5.0 + */ + def pi(): Column = Pi() + + /** + * Computes the logarithm of the given column in base 2. + * + * @group math_funcs + * @since 1.5.0 + */ + def log2(expr: Column): Column = Log2(expr.expr) + + /** + * Computes the logarithm of the given value in base 2. + * + * @group math_funcs + * @since 1.5.0 + */ + def log2(columnName: String): Column = log2(Column(columnName)) + /** * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(l: Column, r: Column): Column = Pow(l.expr, r.expr) @@ -769,6 +1195,7 @@ object functions { * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(l: Column, rightName: String): Column = pow(l, Column(rightName)) @@ -776,6 +1203,7 @@ object functions { * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(leftName: String, r: Column): Column = pow(Column(leftName), r) @@ -783,6 +1211,7 @@ object functions { * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(leftName: String, rightName: String): Column = pow(Column(leftName), Column(rightName)) @@ -790,6 +1219,7 @@ object functions { * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(l: Column, r: Double): Column = pow(l, lit(r).expr) @@ -797,6 +1227,7 @@ object functions { * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(leftName: String, r: Double): Column = pow(Column(leftName), r) @@ -804,6 +1235,7 @@ object functions { * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(l: Double, r: Column): Column = pow(lit(l).expr, r) @@ -811,6 +1243,7 @@ object functions { * Returns the value of the first argument raised to the power of the second argument. * * @group math_funcs + * @since 1.4.0 */ def pow(l: Double, rightName: String): Column = pow(l, Column(rightName)) @@ -819,6 +1252,7 @@ object functions { * is equal to a mathematical integer. * * @group math_funcs + * @since 1.4.0 */ def rint(e: Column): Column = Rint(e.expr) @@ -827,6 +1261,7 @@ object functions { * is equal to a mathematical integer. * * @group math_funcs + * @since 1.4.0 */ def rint(columnName: String): Column = rint(Column(columnName)) @@ -834,6 +1269,7 @@ object functions { * Computes the signum of the given value. * * @group math_funcs + * @since 1.4.0 */ def signum(e: Column): Column = Signum(e.expr) @@ -841,6 +1277,7 @@ object functions { * Computes the signum of the given column. * * @group math_funcs + * @since 1.4.0 */ def signum(columnName: String): Column = signum(Column(columnName)) @@ -848,6 +1285,7 @@ object functions { * Computes the sine of the given value. * * @group math_funcs + * @since 1.4.0 */ def sin(e: Column): Column = Sin(e.expr) @@ -855,6 +1293,7 @@ object functions { * Computes the sine of the given column. * * @group math_funcs + * @since 1.4.0 */ def sin(columnName: String): Column = sin(Column(columnName)) @@ -862,6 +1301,7 @@ object functions { * Computes the hyperbolic sine of the given value. * * @group math_funcs + * @since 1.4.0 */ def sinh(e: Column): Column = Sinh(e.expr) @@ -869,6 +1309,7 @@ object functions { * Computes the hyperbolic sine of the given column. * * @group math_funcs + * @since 1.4.0 */ def sinh(columnName: String): Column = sinh(Column(columnName)) @@ -876,6 +1317,7 @@ object functions { * Computes the tangent of the given value. * * @group math_funcs + * @since 1.4.0 */ def tan(e: Column): Column = Tan(e.expr) @@ -883,6 +1325,7 @@ object functions { * Computes the tangent of the given column. * * @group math_funcs + * @since 1.4.0 */ def tan(columnName: String): Column = tan(Column(columnName)) @@ -890,6 +1333,7 @@ object functions { * Computes the hyperbolic tangent of the given value. * * @group math_funcs + * @since 1.4.0 */ def tanh(e: Column): Column = Tanh(e.expr) @@ -897,6 +1341,7 @@ object functions { * Computes the hyperbolic tangent of the given column. * * @group math_funcs + * @since 1.4.0 */ def tanh(columnName: String): Column = tanh(Column(columnName)) @@ -904,6 +1349,7 @@ object functions { * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. * * @group math_funcs + * @since 1.4.0 */ def toDegrees(e: Column): Column = ToDegrees(e.expr) @@ -911,6 +1357,7 @@ object functions { * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. * * @group math_funcs + * @since 1.4.0 */ def toDegrees(columnName: String): Column = toDegrees(Column(columnName)) @@ -918,6 +1365,7 @@ object functions { * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. * * @group math_funcs + * @since 1.4.0 */ def toRadians(e: Column): Column = ToRadians(e.expr) @@ -925,9 +1373,47 @@ object functions { * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. * * @group math_funcs + * @since 1.4.0 */ def toRadians(columnName: String): Column = toRadians(Column(columnName)) - + + ////////////////////////////////////////////////////////////////////////////////////////////// + // Misc functions + ////////////////////////////////////////////////////////////////////////////////////////////// + + /** + * Calculates the MD5 digest and returns the value as a 32 character hex string. + * + * @group misc_funcs + * @since 1.5.0 + */ + def md5(e: Column): Column = Md5(e.expr) + + /** + * Calculates the MD5 digest and returns the value as a 32 character hex string. + * + * @group misc_funcs + * @since 1.5.0 + */ + def md5(columnName: String): Column = md5(Column(columnName)) + + ////////////////////////////////////////////////////////////////////////////////////////////// + // String functions + ////////////////////////////////////////////////////////////////////////////////////////////// + + /** + * Computes the length of a given string value + * @group string_funcs + * @since 1.5.0 + */ + def strlen(e: Column): Column = StringLength(e.expr) + + /** + * Computes the length of a given string column + * @group string_funcs + * @since 1.5.0 + */ + def strlen(columnName: String): Column = strlen(Column(columnName)) ////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////// @@ -944,6 +1430,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[$typeTags](f: Function$x[$types]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -960,6 +1447,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function$x[$fTypes], returnType: DataType${if (args.length > 0) ", " + args else ""}): Column = { ScalaUdf(f, returnType, Seq($argsInUdf)) @@ -972,6 +1460,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag](f: Function0[RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -982,6 +1471,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag](f: Function1[A1, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -992,6 +1482,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1002,6 +1493,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag](f: Function3[A1, A2, A3, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1012,6 +1504,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag](f: Function4[A1, A2, A3, A4, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1022,6 +1515,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag](f: Function5[A1, A2, A3, A4, A5, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1032,6 +1526,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag](f: Function6[A1, A2, A3, A4, A5, A6, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1042,6 +1537,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag](f: Function7[A1, A2, A3, A4, A5, A6, A7, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1052,6 +1548,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag](f: Function8[A1, A2, A3, A4, A5, A6, A7, A8, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1062,6 +1559,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag](f: Function9[A1, A2, A3, A4, A5, A6, A7, A8, A9, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1072,6 +1570,7 @@ object functions { * The data types are automatically inferred based on the function's signature. * * @group udf_funcs + * @since 1.3.0 */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](f: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -1084,6 +1583,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function0[_], returnType: DataType): Column = { ScalaUdf(f, returnType, Seq()) @@ -1094,6 +1594,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function1[_, _], returnType: DataType, arg1: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr)) @@ -1104,6 +1605,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function2[_, _, _], returnType: DataType, arg1: Column, arg2: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr)) @@ -1114,6 +1616,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function3[_, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr)) @@ -1124,6 +1627,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function4[_, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr)) @@ -1134,6 +1638,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function5[_, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr)) @@ -1144,6 +1649,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function6[_, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr)) @@ -1154,6 +1660,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function7[_, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr)) @@ -1164,6 +1671,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function8[_, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr)) @@ -1174,6 +1682,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function9[_, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr)) @@ -1184,6 +1693,7 @@ object functions { * you to specify the return data type. * * @group udf_funcs + * @since 1.3.0 */ def callUDF(f: Function10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr)) @@ -1204,6 +1714,7 @@ object functions { * }}} * * @group udf_funcs + * @since 1.4.0 */ def callUdf(udfName: String, cols: Column*): Column = { UnresolvedFunction(udfName, cols.map(_.expr)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala deleted file mode 100644 index 0feabc4282f4a..0000000000000 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala +++ /dev/null @@ -1,99 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.jdbc - -import org.apache.spark.sql.types._ - -import java.sql.Types - - -/** - * Encapsulates workarounds for the extensions, quirks, and bugs in various - * databases. Lots of databases define types that aren't explicitly supported - * by the JDBC spec. Some JDBC drivers also report inaccurate - * information---for instance, BIT(n>1) being reported as a BIT type is quite - * common, even though BIT in JDBC is meant for single-bit values. Also, there - * does not appear to be a standard name for an unbounded string or binary - * type; we use BLOB and CLOB by default but override with database-specific - * alternatives when these are absent or do not behave correctly. - * - * Currently, the only thing DriverQuirks does is handle type mapping. - * `getCatalystType` is used when reading from a JDBC table and `getJDBCType` - * is used when writing to a JDBC table. If `getCatalystType` returns `null`, - * the default type handling is used for the given JDBC type. Similarly, - * if `getJDBCType` returns `(null, None)`, the default type handling is used - * for the given Catalyst type. - */ -private[sql] abstract class DriverQuirks { - def getCatalystType(sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): DataType - def getJDBCType(dt: DataType): (String, Option[Int]) -} - -private[sql] object DriverQuirks { - /** - * Fetch the DriverQuirks class corresponding to a given database url. - */ - def get(url: String): DriverQuirks = { - if (url.startsWith("jdbc:mysql")) { - new MySQLQuirks() - } else if (url.startsWith("jdbc:postgresql")) { - new PostgresQuirks() - } else { - new NoQuirks() - } - } -} - -private[sql] class NoQuirks extends DriverQuirks { - def getCatalystType(sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): DataType = - null - def getJDBCType(dt: DataType): (String, Option[Int]) = (null, None) -} - -private[sql] class PostgresQuirks extends DriverQuirks { - def getCatalystType(sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): DataType = { - if (sqlType == Types.BIT && typeName.equals("bit") && size != 1) { - BinaryType - } else if (sqlType == Types.OTHER && typeName.equals("cidr")) { - StringType - } else if (sqlType == Types.OTHER && typeName.equals("inet")) { - StringType - } else null - } - - def getJDBCType(dt: DataType): (String, Option[Int]) = dt match { - case StringType => ("TEXT", Some(java.sql.Types.CHAR)) - case BinaryType => ("BYTEA", Some(java.sql.Types.BINARY)) - case BooleanType => ("BOOLEAN", Some(java.sql.Types.BOOLEAN)) - case _ => (null, None) - } -} - -private[sql] class MySQLQuirks extends DriverQuirks { - def getCatalystType(sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): DataType = { - if (sqlType == Types.VARBINARY && typeName.equals("BIT") && size != 1) { - // This could instead be a BinaryType if we'd rather return bit-vectors of up to 64 bits as - // byte arrays instead of longs. - md.putLong("binarylong", 1) - LongType - } else if (sqlType == Types.BIT && typeName.equals("TINYINT")) { - BooleanType - } else null - } - def getJDBCType(dt: DataType): (String, Option[Int]) = (null, None) -} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index 1a5083dbe0f61..226b143923df6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -24,25 +24,40 @@ import org.apache.commons.lang3.StringUtils import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.{Row, SpecificMutableRow} +import org.apache.spark.sql.catalyst.expressions.{InternalRow, SpecificMutableRow} +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types._ import org.apache.spark.sql.sources._ -import org.apache.spark.util.Utils +import org.apache.spark.unsafe.types.UTF8String + +/** + * Data corresponding to one partition of a JDBCRDD. + */ +private[sql] case class JDBCPartition(whereClause: String, idx: Int) extends Partition { + override def index: Int = idx +} + private[sql] object JDBCRDD extends Logging { + /** * Maps a JDBC type to a Catalyst type. This function is called only when - * the DriverQuirks class corresponding to your database driver returns null. + * the JdbcDialect class corresponding to your database driver returns null. * * @param sqlType - A field of java.sql.Types * @return The Catalyst type corresponding to sqlType. */ - private def getCatalystType(sqlType: Int, precision: Int, scale: Int): DataType = { + private def getCatalystType( + sqlType: Int, + precision: Int, + scale: Int, + signed: Boolean): DataType = { val answer = sqlType match { + // scalastyle:off case java.sql.Types.ARRAY => null - case java.sql.Types.BIGINT => LongType + case java.sql.Types.BIGINT => if (signed) { LongType } else { DecimalType(20,0) } case java.sql.Types.BINARY => BinaryType - case java.sql.Types.BIT => BooleanType // Per JDBC; Quirks handles quirky drivers. + case java.sql.Types.BIT => BooleanType // @see JdbcDialect for quirks case java.sql.Types.BLOB => BinaryType case java.sql.Types.BOOLEAN => BooleanType case java.sql.Types.CHAR => StringType @@ -55,7 +70,7 @@ private[sql] object JDBCRDD extends Logging { case java.sql.Types.DISTINCT => null case java.sql.Types.DOUBLE => DoubleType case java.sql.Types.FLOAT => FloatType - case java.sql.Types.INTEGER => IntegerType + case java.sql.Types.INTEGER => if (signed) { IntegerType } else { LongType } case java.sql.Types.JAVA_OBJECT => null case java.sql.Types.LONGNVARCHAR => StringType case java.sql.Types.LONGVARBINARY => BinaryType @@ -79,7 +94,8 @@ private[sql] object JDBCRDD extends Logging { case java.sql.Types.TINYINT => IntegerType case java.sql.Types.VARBINARY => BinaryType case java.sql.Types.VARCHAR => StringType - case _ => null + case _ => null + // scalastyle:on } if (answer == null) throw new SQLException("Unsupported type " + sqlType) @@ -99,7 +115,7 @@ private[sql] object JDBCRDD extends Logging { * @throws SQLException if the table contains an unsupported type. */ def resolveTable(url: String, table: String, properties: Properties): StructType = { - val quirks = DriverQuirks.get(url) + val dialect = JdbcDialects.get(url) val conn: Connection = DriverManager.getConnection(url, properties) try { val rs = conn.prepareStatement(s"SELECT * FROM $table WHERE 1=0").executeQuery() @@ -109,15 +125,17 @@ private[sql] object JDBCRDD extends Logging { val fields = new Array[StructField](ncols) var i = 0 while (i < ncols) { - val columnName = rsmd.getColumnName(i + 1) + val columnName = rsmd.getColumnLabel(i + 1) val dataType = rsmd.getColumnType(i + 1) val typeName = rsmd.getColumnTypeName(i + 1) val fieldSize = rsmd.getPrecision(i + 1) val fieldScale = rsmd.getScale(i + 1) + val isSigned = rsmd.isSigned(i + 1) val nullable = rsmd.isNullable(i + 1) != ResultSetMetaData.columnNoNulls val metadata = new MetadataBuilder().putString("name", columnName) - var columnType = quirks.getCatalystType(dataType, typeName, fieldSize, metadata) - if (columnType == null) columnType = getCatalystType(dataType, fieldSize, fieldScale) + val columnType = + dialect.getCatalystType(dataType, typeName, fieldSize, metadata).getOrElse( + getCatalystType(dataType, fieldSize, fieldScale, isSigned)) fields(i) = StructField(columnName, columnType, nullable, metadata.build()) i = i + 1 } @@ -168,6 +186,7 @@ private[sql] object JDBCRDD extends Logging { DriverManager.getConnection(url, properties) } } + /** * Build and return JDBCRDD from the given information. * @@ -192,19 +211,18 @@ private[sql] object JDBCRDD extends Logging { fqTable: String, requiredColumns: Array[String], filters: Array[Filter], - parts: Array[Partition]): RDD[Row] = { - - val prunedSchema = pruneSchema(schema, requiredColumns) - - return new - JDBCRDD( - sc, - getConnector(driver, url, properties), - prunedSchema, - fqTable, - requiredColumns, - filters, - parts) + parts: Array[Partition]): RDD[InternalRow] = { + val dialect = JdbcDialects.get(url) + val quotedColumns = requiredColumns.map(colName => dialect.quoteIdentifier(colName)) + new JDBCRDD( + sc, + getConnector(driver, url, properties), + pruneSchema(schema, requiredColumns), + fqTable, + quotedColumns, + filters, + parts, + properties) } } @@ -220,8 +238,9 @@ private[sql] class JDBCRDD( fqTable: String, columns: Array[String], filters: Array[Filter], - partitions: Array[Partition]) - extends RDD[Row](sc, Nil) { + partitions: Array[Partition], + properties: Properties) + extends RDD[InternalRow](sc, Nil) { /** * Retrieve the list of partitions corresponding to this RDD. @@ -246,7 +265,7 @@ private[sql] class JDBCRDD( } private def escapeSql(value: String): String = - if (value == null) null else StringUtils.replace(value, "'", "''") + if (value == null) null else StringUtils.replace(value, "'", "''") /** * Turns a single Filter into a String representing a SQL expression. @@ -288,13 +307,13 @@ private[sql] class JDBCRDD( // Each JDBC-to-Catalyst conversion corresponds to a tag defined here so that // we don't have to potentially poke around in the Metadata once for every - // row. + // row. // Is there a better way to do this? I'd rather be using a type that // contains only the tags I define. abstract class JDBCConversion case object BooleanConversion extends JDBCConversion case object DateConversion extends JDBCConversion - case object DecimalConversion extends JDBCConversion + case class DecimalConversion(precisionInfo: Option[(Int, Int)]) extends JDBCConversion case object DoubleConversion extends JDBCConversion case object FloatConversion extends JDBCConversion case object IntegerConversion extends JDBCConversion @@ -309,19 +328,19 @@ private[sql] class JDBCRDD( */ def getConversions(schema: StructType): Array[JDBCConversion] = { schema.fields.map(sf => sf.dataType match { - case BooleanType => BooleanConversion - case DateType => DateConversion - case DecimalType.Unlimited => DecimalConversion - case DecimalType.Fixed(d) => DecimalConversion - case DoubleType => DoubleConversion - case FloatType => FloatConversion - case IntegerType => IntegerConversion - case LongType => + case BooleanType => BooleanConversion + case DateType => DateConversion + case DecimalType.Unlimited => DecimalConversion(None) + case DecimalType.Fixed(d) => DecimalConversion(Some(d)) + case DoubleType => DoubleConversion + case FloatType => FloatConversion + case IntegerType => IntegerConversion + case LongType => if (sf.metadata.contains("binarylong")) BinaryLongConversion else LongConversion - case StringType => StringConversion - case TimestampType => TimestampConversion - case BinaryType => BinaryConversion - case _ => throw new IllegalArgumentException(s"Unsupported field $sf") + case StringType => StringConversion + case TimestampType => TimestampConversion + case BinaryType => BinaryConversion + case _ => throw new IllegalArgumentException(s"Unsupported field $sf") }).toArray } @@ -329,12 +348,12 @@ private[sql] class JDBCRDD( /** * Runs the SQL query against the JDBC driver. */ - override def compute(thePart: Partition, context: TaskContext): Iterator[Row] = new Iterator[Row] - { + override def compute(thePart: Partition, context: TaskContext): Iterator[InternalRow] = + new Iterator[InternalRow] { var closed = false var finished = false var gotNext = false - var nextValue: Row = null + var nextValue: InternalRow = null context.addTaskCompletionListener{ context => close() } val part = thePart.asInstanceOf[JDBCPartition] @@ -349,41 +368,64 @@ private[sql] class JDBCRDD( val sqlText = s"SELECT $columnList FROM $fqTable $myWhereClause" val stmt = conn.prepareStatement(sqlText, ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY) + val fetchSize = properties.getProperty("fetchSize", "0").toInt + stmt.setFetchSize(fetchSize) val rs = stmt.executeQuery() val conversions = getConversions(schema) val mutableRow = new SpecificMutableRow(schema.fields.map(x => x.dataType)) - def getNext(): Row = { + def getNext(): InternalRow = { if (rs.next()) { var i = 0 while (i < conversions.length) { val pos = i + 1 conversions(i) match { - case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos)) - case DateConversion => + case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos)) + case DateConversion => // DateUtils.fromJavaDate does not handle null value, so we need to check it. val dateVal = rs.getDate(pos) if (dateVal != null) { - mutableRow.update(i, DateUtils.fromJavaDate(dateVal)) + mutableRow.setInt(i, DateUtils.fromJavaDate(dateVal)) } else { mutableRow.update(i, null) } - case DecimalConversion => + // When connecting with Oracle DB through JDBC, the precision and scale of BigDecimal + // object returned by ResultSet.getBigDecimal is not correctly matched to the table + // schema reported by ResultSetMetaData.getPrecision and ResultSetMetaData.getScale. + // If inserting values like 19999 into a column with NUMBER(12, 2) type, you get through + // a BigDecimal object with scale as 0. But the dataframe schema has correct type as + // DecimalType(12, 2). Thus, after saving the dataframe into parquet file and then + // retrieve it, you will get wrong result 199.99. + // So it is needed to set precision and scale for Decimal based on JDBC metadata. + case DecimalConversion(Some((p, s))) => + val decimalVal = rs.getBigDecimal(pos) + if (decimalVal == null) { + mutableRow.update(i, null) + } else { + mutableRow.update(i, Decimal(decimalVal, p, s)) + } + case DecimalConversion(None) => val decimalVal = rs.getBigDecimal(pos) if (decimalVal == null) { mutableRow.update(i, null) } else { mutableRow.update(i, Decimal(decimalVal)) } - case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos)) - case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos)) - case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos)) - case LongConversion => mutableRow.setLong(i, rs.getLong(pos)) + case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos)) + case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos)) + case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos)) + case LongConversion => mutableRow.setLong(i, rs.getLong(pos)) // TODO(davies): use getBytes for better performance, if the encoding is UTF-8 - case StringConversion => mutableRow.setString(i, rs.getString(pos)) - case TimestampConversion => mutableRow.update(i, rs.getTimestamp(pos)) - case BinaryConversion => mutableRow.update(i, rs.getBytes(pos)) + case StringConversion => mutableRow.setString(i, rs.getString(pos)) + case TimestampConversion => + val t = rs.getTimestamp(pos) + if (t != null) { + mutableRow.setLong(i, DateUtils.fromJavaTimestamp(t)) + } else { + mutableRow.update(i, null) + } + case BinaryConversion => mutableRow.update(i, rs.getBytes(pos)) case BinaryLongConversion => { val bytes = rs.getBytes(pos) var ans = 0L @@ -401,7 +443,7 @@ private[sql] class JDBCRDD( mutableRow } else { finished = true - null.asInstanceOf[Row] + null.asInstanceOf[InternalRow] } } @@ -444,7 +486,7 @@ private[sql] class JDBCRDD( !finished } - override def next(): Row = { + override def next(): InternalRow = { if (!hasNext) { throw new NoSuchElementException("End of stream") } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala index d6b3fb3291a2e..4d3aac464c538 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala @@ -17,25 +17,15 @@ package org.apache.spark.sql.jdbc -import java.sql.DriverManager import java.util.Properties import scala.collection.mutable.ArrayBuffer import org.apache.spark.Partition import org.apache.spark.rdd.RDD -import org.apache.spark.sql.SQLContext -import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.sources._ import org.apache.spark.sql.types.StructType -import org.apache.spark.util.Utils - -/** - * Data corresponding to one partition of a JDBCRDD. - */ -private[sql] case class JDBCPartition(whereClause: String, idx: Int) extends Partition { - override def index: Int = idx -} +import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode} /** * Instructions on how to partition the table among workers. @@ -63,7 +53,7 @@ private[sql] object JDBCRelation { if (numPartitions == 1) return Array[Partition](JDBCPartition(null, 0)) // Overflow and silliness can happen if you subtract then divide. // Here we get a little roundoff, but that's (hopefully) OK. - val stride: Long = (partitioning.upperBound / numPartitions + val stride: Long = (partitioning.upperBound / numPartitions - partitioning.lowerBound / numPartitions) var i: Int = 0 var currentValue: Long = partitioning.lowerBound @@ -129,7 +119,8 @@ private[sql] case class JDBCRelation( parts: Array[Partition], properties: Properties = new Properties())(@transient val sqlContext: SQLContext) extends BaseRelation - with PrunedFilteredScan { + with PrunedFilteredScan + with InsertableRelation { override val needConversion: Boolean = false @@ -146,6 +137,12 @@ private[sql] case class JDBCRelation( table, requiredColumns, filters, - parts) + parts).map(_.asInstanceOf[Row]) + } + + override def insert(data: DataFrame, overwrite: Boolean): Unit = { + data.write + .mode(if (overwrite) SaveMode.Overwrite else SaveMode.Append) + .jdbc(url, table, properties) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala new file mode 100644 index 0000000000000..8849fc2f1f0ef --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala @@ -0,0 +1,224 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.jdbc + +import java.sql.Types + +import org.apache.spark.sql.types._ +import org.apache.spark.annotation.DeveloperApi + +/** + * :: DeveloperApi :: + * A database type definition coupled with the jdbc type needed to send null + * values to the database. + * @param databaseTypeDefinition The database type definition + * @param jdbcNullType The jdbc type (as defined in java.sql.Types) used to + * send a null value to the database. + */ +@DeveloperApi +case class JdbcType(databaseTypeDefinition : String, jdbcNullType : Int) + +/** + * :: DeveloperApi :: + * Encapsulates everything (extensions, workarounds, quirks) to handle the + * SQL dialect of a certain database or jdbc driver. + * Lots of databases define types that aren't explicitly supported + * by the JDBC spec. Some JDBC drivers also report inaccurate + * information---for instance, BIT(n>1) being reported as a BIT type is quite + * common, even though BIT in JDBC is meant for single-bit values. Also, there + * does not appear to be a standard name for an unbounded string or binary + * type; we use BLOB and CLOB by default but override with database-specific + * alternatives when these are absent or do not behave correctly. + * + * Currently, the only thing done by the dialect is type mapping. + * `getCatalystType` is used when reading from a JDBC table and `getJDBCType` + * is used when writing to a JDBC table. If `getCatalystType` returns `null`, + * the default type handling is used for the given JDBC type. Similarly, + * if `getJDBCType` returns `(null, None)`, the default type handling is used + * for the given Catalyst type. + */ +@DeveloperApi +abstract class JdbcDialect { + /** + * Check if this dialect instance can handle a certain jdbc url. + * @param url the jdbc url. + * @return True if the dialect can be applied on the given jdbc url. + * @throws NullPointerException if the url is null. + */ + def canHandle(url : String): Boolean + + /** + * Get the custom datatype mapping for the given jdbc meta information. + * @param sqlType The sql type (see java.sql.Types) + * @param typeName The sql type name (e.g. "BIGINT UNSIGNED") + * @param size The size of the type. + * @param md Result metadata associated with this type. + * @return The actual DataType (subclasses of [[org.apache.spark.sql.types.DataType]]) + * or null if the default type mapping should be used. + */ + def getCatalystType( + sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = None + + /** + * Retrieve the jdbc / sql type for a given datatype. + * @param dt The datatype (e.g. [[org.apache.spark.sql.types.StringType]]) + * @return The new JdbcType if there is an override for this DataType + */ + def getJDBCType(dt: DataType): Option[JdbcType] = None + + /** + * Quotes the identifier. This is used to put quotes around the identifier in case the column + * name is a reserved keyword, or in case it contains characters that require quotes (e.g. space). + */ + def quoteIdentifier(colName: String): String = { + s""""$colName"""" + } +} + +/** + * :: DeveloperApi :: + * Registry of dialects that apply to every new jdbc [[org.apache.spark.sql.DataFrame]]. + * + * If multiple matching dialects are registered then all matching ones will be + * tried in reverse order. A user-added dialect will thus be applied first, + * overwriting the defaults. + * + * Note that all new dialects are applied to new jdbc DataFrames only. Make + * sure to register your dialects first. + */ +@DeveloperApi +object JdbcDialects { + + private var dialects = List[JdbcDialect]() + + /** + * Register a dialect for use on all new matching jdbc [[org.apache.spark.sql.DataFrame]]. + * Readding an existing dialect will cause a move-to-front. + * @param dialect The new dialect. + */ + def registerDialect(dialect: JdbcDialect) : Unit = { + dialects = dialect :: dialects.filterNot(_ == dialect) + } + + /** + * Unregister a dialect. Does nothing if the dialect is not registered. + * @param dialect The jdbc dialect. + */ + def unregisterDialect(dialect : JdbcDialect) : Unit = { + dialects = dialects.filterNot(_ == dialect) + } + + registerDialect(MySQLDialect) + registerDialect(PostgresDialect) + + /** + * Fetch the JdbcDialect class corresponding to a given database url. + */ + private[sql] def get(url: String): JdbcDialect = { + val matchingDialects = dialects.filter(_.canHandle(url)) + matchingDialects.length match { + case 0 => NoopDialect + case 1 => matchingDialects.head + case _ => new AggregatedDialect(matchingDialects) + } + } +} + +/** + * :: DeveloperApi :: + * AggregatedDialect can unify multiple dialects into one virtual Dialect. + * Dialects are tried in order, and the first dialect that does not return a + * neutral element will will. + * @param dialects List of dialects. + */ +@DeveloperApi +class AggregatedDialect(dialects: List[JdbcDialect]) extends JdbcDialect { + + require(dialects.nonEmpty) + + override def canHandle(url : String): Boolean = + dialects.map(_.canHandle(url)).reduce(_ && _) + + override def getCatalystType( + sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = { + dialects.flatMap(_.getCatalystType(sqlType, typeName, size, md)).headOption + } + + override def getJDBCType(dt: DataType): Option[JdbcType] = { + dialects.flatMap(_.getJDBCType(dt)).headOption + } +} + +/** + * :: DeveloperApi :: + * NOOP dialect object, always returning the neutral element. + */ +@DeveloperApi +case object NoopDialect extends JdbcDialect { + override def canHandle(url : String): Boolean = true +} + +/** + * :: DeveloperApi :: + * Default postgres dialect, mapping bit/cidr/inet on read and string/binary/boolean on write. + */ +@DeveloperApi +case object PostgresDialect extends JdbcDialect { + override def canHandle(url: String): Boolean = url.startsWith("jdbc:postgresql") + override def getCatalystType( + sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = { + if (sqlType == Types.BIT && typeName.equals("bit") && size != 1) { + Some(BinaryType) + } else if (sqlType == Types.OTHER && typeName.equals("cidr")) { + Some(StringType) + } else if (sqlType == Types.OTHER && typeName.equals("inet")) { + Some(StringType) + } else None + } + + override def getJDBCType(dt: DataType): Option[JdbcType] = dt match { + case StringType => Some(JdbcType("TEXT", java.sql.Types.CHAR)) + case BinaryType => Some(JdbcType("BYTEA", java.sql.Types.BINARY)) + case BooleanType => Some(JdbcType("BOOLEAN", java.sql.Types.BOOLEAN)) + case _ => None + } +} + +/** + * :: DeveloperApi :: + * Default mysql dialect to read bit/bitsets correctly. + */ +@DeveloperApi +case object MySQLDialect extends JdbcDialect { + override def canHandle(url : String): Boolean = url.startsWith("jdbc:mysql") + override def getCatalystType( + sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = { + if (sqlType == Types.VARBINARY && typeName.equals("BIT") && size != 1) { + // This could instead be a BinaryType if we'd rather return bit-vectors of up to 64 bits as + // byte arrays instead of longs. + md.putLong("binarylong", 1) + Some(LongType) + } else if (sqlType == Types.BIT && typeName.equals("TINYINT")) { + Some(BooleanType) + } else None + } + + override def quoteIdentifier(colName: String): String = { + s"`$colName`" + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala new file mode 100644 index 0000000000000..cc918c237192b --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala @@ -0,0 +1,52 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.jdbc + +import java.sql.{Connection, DriverManager} +import java.util.Properties + +import scala.util.Try + +/** + * Util functions for JDBC tables. + */ +private[sql] object JdbcUtils { + + /** + * Establishes a JDBC connection. + */ + def createConnection(url: String, connectionProperties: Properties): Connection = { + DriverManager.getConnection(url, connectionProperties) + } + + /** + * Returns true if the table already exists in the JDBC database. + */ + def tableExists(conn: Connection, table: String): Boolean = { + // Somewhat hacky, but there isn't a good way to identify whether a table exists for all + // SQL database systems, considering "table" could also include the database name. + Try(conn.prepareStatement(s"SELECT 1 FROM $table LIMIT 1").executeQuery().next()).isSuccess + } + + /** + * Drops a table from the JDBC database. + */ + def dropTable(conn: Connection, table: String): Unit = { + conn.prepareStatement(s"DROP TABLE $table").executeUpdate() + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala index 3a6c2c1e9101f..dd8aaf6474895 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala @@ -57,9 +57,14 @@ package object jdbc { * non-Serializable. Instead, we explicitly close over all variables that * are used. */ - def savePartition(url: String, table: String, iterator: Iterator[Row], - rddSchema: StructType, nullTypes: Array[Int]): Iterator[Byte] = { - val conn = DriverManager.getConnection(url) + def savePartition( + url: String, + table: String, + iterator: Iterator[Row], + rddSchema: StructType, + nullTypes: Array[Int], + properties: Properties): Iterator[Byte] = { + val conn = DriverManager.getConnection(url, properties) var committed = false try { conn.setAutoCommit(false) // Everything in the same db transaction. @@ -124,25 +129,26 @@ package object jdbc { */ def schemaString(df: DataFrame, url: String): String = { val sb = new StringBuilder() - val quirks = DriverQuirks.get(url) + val dialect = JdbcDialects.get(url) df.schema.fields foreach { field => { val name = field.name - var typ: String = quirks.getJDBCType(field.dataType)._1 - if (typ == null) typ = field.dataType match { - case IntegerType => "INTEGER" - case LongType => "BIGINT" - case DoubleType => "DOUBLE PRECISION" - case FloatType => "REAL" - case ShortType => "INTEGER" - case ByteType => "BYTE" - case BooleanType => "BIT(1)" - case StringType => "TEXT" - case BinaryType => "BLOB" - case TimestampType => "TIMESTAMP" - case DateType => "DATE" - case DecimalType.Unlimited => "DECIMAL(40,20)" - case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC") - } + val typ: String = + dialect.getJDBCType(field.dataType).map(_.databaseTypeDefinition).getOrElse( + field.dataType match { + case IntegerType => "INTEGER" + case LongType => "BIGINT" + case DoubleType => "DOUBLE PRECISION" + case FloatType => "REAL" + case ShortType => "INTEGER" + case ByteType => "BYTE" + case BooleanType => "BIT(1)" + case StringType => "TEXT" + case BinaryType => "BLOB" + case TimestampType => "TIMESTAMP" + case DateType => "DATE" + case DecimalType.Unlimited => "DECIMAL(40,20)" + case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC") + }) val nullable = if (field.nullable) "" else "NOT NULL" sb.append(s", $name $typ $nullable") }} @@ -152,11 +158,14 @@ package object jdbc { /** * Saves the RDD to the database in a single transaction. */ - def saveTable(df: DataFrame, url: String, table: String) { - val quirks = DriverQuirks.get(url) - var nullTypes: Array[Int] = df.schema.fields.map(field => { - var nullType: Option[Int] = quirks.getJDBCType(field.dataType)._2 - if (nullType.isEmpty) { + def saveTable( + df: DataFrame, + url: String, + table: String, + properties: Properties = new Properties()) { + val dialect = JdbcDialects.get(url) + val nullTypes: Array[Int] = df.schema.fields.map { field => + dialect.getJDBCType(field.dataType).map(_.jdbcNullType).getOrElse( field.dataType match { case IntegerType => java.sql.Types.INTEGER case LongType => java.sql.Types.BIGINT @@ -172,13 +181,12 @@ package object jdbc { case DecimalType.Unlimited => java.sql.Types.DECIMAL case _ => throw new IllegalArgumentException( s"Can't translate null value for field $field") - } - } else nullType.get - }).toArray + }) + } val rddSchema = df.schema df.foreachPartition { iterator => - JDBCWriteDetails.savePartition(url, table, iterator, rddSchema, nullTypes) + JDBCWriteDetails.savePartition(url, table, iterator, rddSchema, nullTypes, properties) } } @@ -232,10 +240,10 @@ package object jdbc { } } } - + def getDriverClassName(url: String): String = DriverManager.getDriver(url) match { case wrapper: DriverWrapper => wrapper.wrapped.getClass.getCanonicalName - case driver => driver.getClass.getCanonicalName + case driver => driver.getClass.getCanonicalName } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala index 9c58b8e4bb16a..afe2c6c11ac69 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala @@ -43,7 +43,7 @@ private[sql] object InferSchema { } // perform schema inference on each row and merge afterwards - schemaData.mapPartitions { iter => + val rootType = schemaData.mapPartitions { iter => val factory = new JsonFactory() iter.map { row => try { @@ -55,8 +55,13 @@ private[sql] object InferSchema { StructType(Seq(StructField(columnNameOfCorruptRecords, StringType))) } } - }.treeAggregate[DataType](StructType(Seq()))(compatibleRootType, compatibleRootType) match { - case st: StructType => nullTypeToStringType(st) + }.treeAggregate[DataType](StructType(Seq()))(compatibleRootType, compatibleRootType) + + canonicalizeType(rootType) match { + case Some(st: StructType) => st + case _ => + // canonicalizeType erases all empty structs, including the only one we want to keep + StructType(Seq()) } } @@ -116,22 +121,35 @@ private[sql] object InferSchema { } } - private def nullTypeToStringType(struct: StructType): StructType = { - val fields = struct.fields.map { - case StructField(fieldName, dataType, nullable, _) => - val newType = dataType match { - case NullType => StringType - case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull) - case ArrayType(struct: StructType, containsNull) => - ArrayType(nullTypeToStringType(struct), containsNull) - case struct: StructType =>nullTypeToStringType(struct) - case other: DataType => other - } + /** + * Convert NullType to StringType and remove StructTypes with no fields + */ + private def canonicalizeType: DataType => Option[DataType] = { + case at@ArrayType(elementType, _) => + for { + canonicalType <- canonicalizeType(elementType) + } yield { + at.copy(canonicalType) + } - StructField(fieldName, newType, nullable) - } + case StructType(fields) => + val canonicalFields = for { + field <- fields + if field.name.nonEmpty + canonicalType <- canonicalizeType(field.dataType) + } yield { + field.copy(dataType = canonicalType) + } + + if (canonicalFields.nonEmpty) { + Some(StructType(canonicalFields)) + } else { + // per SPARK-8093: empty structs should be deleted + None + } - StructType(fields) + case NullType => Some(StringType) + case other => Some(other) } /** @@ -147,7 +165,7 @@ private[sql] object InferSchema { * Returns the most general data type for two given data types. */ private[json] def compatibleType(t1: DataType, t2: DataType): DataType = { - HiveTypeCoercion.findTightestCommonType(t1, t2).getOrElse { + HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2).getOrElse { // t1 or t2 is a StructType, ArrayType, or an unexpected type. (t1, t2) match { case (other: DataType, NullType) => other diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala index c772cd1f53e53..69bf13e1e5a6a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala @@ -22,10 +22,10 @@ import java.io.IOException import org.apache.hadoop.fs.Path import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.{Expression, Attribute, Row} +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression} import org.apache.spark.sql.sources._ -import org.apache.spark.sql.types.{StructField, StructType} -import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode} +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode} private[sql] class DefaultSource @@ -154,12 +154,12 @@ private[sql] class JSONRelation( JacksonParser( baseRDD(), schema, - sqlContext.conf.columnNameOfCorruptRecord) + sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row]) } else { JsonRDD.jsonStringToRow( baseRDD(), schema, - sqlContext.conf.columnNameOfCorruptRecord) + sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row]) } } @@ -168,12 +168,12 @@ private[sql] class JSONRelation( JacksonParser( baseRDD(), StructType.fromAttributes(requiredColumns), - sqlContext.conf.columnNameOfCorruptRecord) + sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row]) } else { JsonRDD.jsonStringToRow( baseRDD(), StructType.fromAttributes(requiredColumns), - sqlContext.conf.columnNameOfCorruptRecord) + sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row]) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala index 80bf74aa02602..1e6b1198d245b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala @@ -21,7 +21,7 @@ import scala.collection.Map import com.fasterxml.jackson.core._ -import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.Row import org.apache.spark.sql.types._ private[sql] object JacksonGenerator { @@ -33,7 +33,7 @@ private[sql] object JacksonGenerator { */ def apply(rowSchema: StructType, gen: JsonGenerator)(row: Row): Unit = { def valWriter: (DataType, Any) => Unit = { - case (_, null) | (NullType, _) => gen.writeNull() + case (_, null) | (NullType, _) => gen.writeNull() case (StringType, v: String) => gen.writeString(v) case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString) case (IntegerType, v: Int) => gen.writeNumber(v) @@ -48,16 +48,16 @@ private[sql] object JacksonGenerator { case (DateType, v) => gen.writeString(v.toString) case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v)) - case (ArrayType(ty, _), v: Seq[_] ) => + case (ArrayType(ty, _), v: Seq[_]) => gen.writeStartArray() - v.foreach(valWriter(ty,_)) + v.foreach(valWriter(ty, _)) gen.writeEndArray() - case (MapType(kv,vv, _), v: Map[_,_]) => + case (MapType(kv, vv, _), v: Map[_, _]) => gen.writeStartObject() v.foreach { p => gen.writeFieldName(p._1.toString) - valWriter(vv,p._2) + valWriter(vv, p._2) } gen.writeEndObject() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala index a8e69ae61174f..817e8a20b34de 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala @@ -18,7 +18,6 @@ package org.apache.spark.sql.json import java.io.ByteArrayOutputStream -import java.sql.Timestamp import scala.collection.Map @@ -26,14 +25,17 @@ import com.fasterxml.jackson.core._ import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.json.JacksonUtils.nextUntil import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String + private[sql] object JacksonParser { def apply( json: RDD[String], schema: StructType, - columnNameOfCorruptRecords: String): RDD[Row] = { + columnNameOfCorruptRecords: String): RDD[InternalRow] = { parseJson(json, schema, columnNameOfCorruptRecords) } @@ -54,7 +56,7 @@ private[sql] object JacksonParser { convertField(factory, parser, schema) case (VALUE_STRING, StringType) => - UTF8String(parser.getText) + UTF8String.fromString(parser.getText) case (VALUE_STRING, _) if parser.getTextLength < 1 => // guard the non string type @@ -64,17 +66,17 @@ private[sql] object JacksonParser { DateUtils.millisToDays(DateUtils.stringToTime(parser.getText).getTime) case (VALUE_STRING, TimestampType) => - new Timestamp(DateUtils.stringToTime(parser.getText).getTime) + DateUtils.stringToTime(parser.getText).getTime * 10000L case (VALUE_NUMBER_INT, TimestampType) => - new Timestamp(parser.getLongValue) + parser.getLongValue * 10000L case (_, StringType) => val writer = new ByteArrayOutputStream() val generator = factory.createGenerator(writer, JsonEncoding.UTF8) generator.copyCurrentStructure(parser) generator.close() - UTF8String(writer.toByteArray) + UTF8String.fromBytes(writer.toByteArray) case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) => parser.getFloatValue @@ -128,7 +130,10 @@ private[sql] object JacksonParser { * * Fields in the json that are not defined in the requested schema will be dropped. */ - private def convertObject(factory: JsonFactory, parser: JsonParser, schema: StructType): Row = { + private def convertObject( + factory: JsonFactory, + parser: JsonParser, + schema: StructType): InternalRow = { val row = new GenericMutableRow(schema.length) while (nextUntil(parser, JsonToken.END_OBJECT)) { schema.getFieldIndex(parser.getCurrentName) match { @@ -149,10 +154,11 @@ private[sql] object JacksonParser { private def convertMap( factory: JsonFactory, parser: JsonParser, - valueType: DataType): Map[String, Any] = { - val builder = Map.newBuilder[String, Any] + valueType: DataType): Map[UTF8String, Any] = { + val builder = Map.newBuilder[UTF8String, Any] while (nextUntil(parser, JsonToken.END_OBJECT)) { - builder += parser.getCurrentName -> convertField(factory, parser, valueType) + builder += + UTF8String.fromString(parser.getCurrentName) -> convertField(factory, parser, valueType) } builder.result() @@ -173,14 +179,14 @@ private[sql] object JacksonParser { private def parseJson( json: RDD[String], schema: StructType, - columnNameOfCorruptRecords: String): RDD[Row] = { + columnNameOfCorruptRecords: String): RDD[InternalRow] = { - def failedRecord(record: String): Seq[Row] = { + def failedRecord(record: String): Seq[InternalRow] = { // create a row even if no corrupt record column is present val row = new GenericMutableRow(schema.length) for (corruptIndex <- schema.getFieldIndex(columnNameOfCorruptRecords)) { require(schema(corruptIndex).dataType == StringType) - row.update(corruptIndex, record) + row.update(corruptIndex, UTF8String.fromString(record)) } Seq(row) @@ -199,7 +205,7 @@ private[sql] object JacksonParser { // convertField wrap an object into a single value array when necessary. convertField(factory, parser, ArrayType(schema)) match { case null => failedRecord(record) - case list: Seq[Row @unchecked] => list + case list: Seq[InternalRow @unchecked] => list case _ => sys.error( s"Failed to parse record $record. Please make sure that each line of the file " + diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index f62973d5fcfab..44594c5080ff4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -17,27 +17,28 @@ package org.apache.spark.sql.json -import java.sql.Timestamp - import scala.collection.Map -import scala.collection.convert.Wrappers.{JMapWrapper, JListWrapper} +import scala.collection.convert.Wrappers.{JListWrapper, JMapWrapper} -import com.fasterxml.jackson.core.{JsonGenerator, JsonProcessingException} +import com.fasterxml.jackson.core.JsonProcessingException import com.fasterxml.jackson.databind.ObjectMapper +import org.apache.spark.Logging import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types._ -import org.apache.spark.Logging +import org.apache.spark.unsafe.types.UTF8String + private[sql] object JsonRDD extends Logging { private[sql] def jsonStringToRow( json: RDD[String], schema: StructType, - columnNameOfCorruptRecords: String): RDD[Row] = { + columnNameOfCorruptRecords: String): RDD[InternalRow] = { parseJson(json, columnNameOfCorruptRecords).map(parsed => asRow(parsed, schema)) } @@ -140,7 +141,7 @@ private[sql] object JsonRDD extends Logging { case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull) case ArrayType(struct: StructType, containsNull) => ArrayType(nullTypeToStringType(struct), containsNull) - case struct: StructType =>nullTypeToStringType(struct) + case struct: StructType => nullTypeToStringType(struct) case other: DataType => other } StructField(fieldName, newType, nullable) @@ -154,7 +155,7 @@ private[sql] object JsonRDD extends Logging { * Returns the most general data type for two given data types. */ private[json] def compatibleType(t1: DataType, t2: DataType): DataType = { - HiveTypeCoercion.findTightestCommonType(t1, t2) match { + HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2) match { case Some(commonType) => commonType case None => // t1 or t2 is a StructType, ArrayType, or an unexpected type. @@ -215,7 +216,7 @@ private[sql] object JsonRDD extends Logging { case map: Map[_, _] => StructType(Nil) // We have an array of arrays. If those element arrays do not have the same // element types, we will return ArrayType[StringType]. - case seq: Seq[_] => typeOfArray(seq) + case seq: Seq[_] => typeOfArray(seq) case value => typeOfPrimitiveValue(value) } }.reduce((type1: DataType, type2: DataType) => compatibleType(type1, type2)) @@ -317,7 +318,8 @@ private[sql] object JsonRDD extends Logging { parsed } catch { - case e: JsonProcessingException => Map(columnNameOfCorruptRecords -> record) :: Nil + case e: JsonProcessingException => + Map(columnNameOfCorruptRecords -> UTF8String.fromString(record)) :: Nil } } }) @@ -396,20 +398,20 @@ private[sql] object JsonRDD extends Logging { } } - private def toTimestamp(value: Any): Timestamp = { + private def toTimestamp(value: Any): Long = { value match { - case value: java.lang.Integer => new Timestamp(value.asInstanceOf[Int].toLong) - case value: java.lang.Long => new Timestamp(value) - case value: java.lang.String => toTimestamp(DateUtils.stringToTime(value).getTime) + case value: java.lang.Integer => value.asInstanceOf[Int].toLong * 10000L + case value: java.lang.Long => value * 10000L + case value: java.lang.String => DateUtils.stringToTime(value).getTime * 10000L } } - private[json] def enforceCorrectType(value: Any, desiredType: DataType): Any ={ + private[json] def enforceCorrectType(value: Any, desiredType: DataType): Any = { if (value == null) { null } else { desiredType match { - case StringType => UTF8String(toString(value)) + case StringType => UTF8String.fromString(toString(value)) case _ if value == null || value == "" => null // guard the non string type case IntegerType => value.asInstanceOf[IntegerType.InternalType] case LongType => toLong(value) @@ -421,7 +423,10 @@ private[sql] object JsonRDD extends Logging { value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType)) case MapType(StringType, valueType, _) => val map = value.asInstanceOf[Map[String, Any]] - map.mapValues(enforceCorrectType(_, valueType)).map(identity) + map.map { + case (k, v) => + (UTF8String.fromString(k), enforceCorrectType(v, valueType)) + }.map(identity) case struct: StructType => asRow(value.asInstanceOf[Map[String, Any]], struct) case DateType => toDate(value) case TimestampType => toTimestamp(value) @@ -429,7 +434,7 @@ private[sql] object JsonRDD extends Logging { } } - private def asRow(json: Map[String,Any], schema: StructType): Row = { + private def asRow(json: Map[String, Any], schema: StructType): InternalRow = { // TODO: Reuse the row instead of creating a new one for every record. val row = new GenericMutableRow(schema.fields.length) schema.fields.zipWithIndex.foreach { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/package.scala index 3f97a11ceb97d..4e94fd07a8771 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/package.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/package.scala @@ -44,6 +44,7 @@ package object sql { /** * Type alias for [[DataFrame]]. Kept here for backward source compatibility for Scala. + * @deprecated As of 1.3.0, replaced by `DataFrame`. */ @deprecated("1.3.0", "use DataFrame") type SchemaRDD = DataFrame diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala index f5ce2718bec4a..62c4e92ebec68 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala @@ -21,9 +21,9 @@ import org.apache.hadoop.fs.Path import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter -import parquet.Log -import parquet.hadoop.util.ContextUtil -import parquet.hadoop.{ParquetFileReader, ParquetFileWriter, ParquetOutputCommitter, ParquetOutputFormat} +import org.apache.parquet.Log +import org.apache.parquet.hadoop.util.ContextUtil +import org.apache.parquet.hadoop.{ParquetFileReader, ParquetFileWriter, ParquetOutputCommitter, ParquetOutputFormat} private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext) extends ParquetOutputCommitter(outputPath, context) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala index 36cb5e03bbca7..4da5e96b82e3d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala @@ -23,14 +23,16 @@ import java.util.{TimeZone, Calendar} import scala.collection.mutable.{Buffer, ArrayBuffer, HashMap} import jodd.datetime.JDateTime -import parquet.column.Dictionary -import parquet.io.api.{PrimitiveConverter, GroupConverter, Binary, Converter} -import parquet.schema.MessageType +import org.apache.parquet.column.Dictionary +import org.apache.parquet.io.api.{PrimitiveConverter, GroupConverter, Binary, Converter} +import org.apache.parquet.schema.MessageType import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.parquet.CatalystConverter.FieldType import org.apache.spark.sql.types._ import org.apache.spark.sql.parquet.timestamp.NanoTime +import org.apache.spark.unsafe.types.UTF8String /** * Collection of converters of Parquet types (group and primitive types) that @@ -77,7 +79,7 @@ private[sql] object CatalystConverter { // TODO: consider using Array[T] for arrays to avoid boxing of primitive types type ArrayScalaType[T] = Seq[T] - type StructScalaType[T] = Row + type StructScalaType[T] = InternalRow type MapScalaType[K, V] = Map[K, V] protected[parquet] def createConverter( @@ -221,7 +223,7 @@ private[parquet] abstract class CatalystConverter extends GroupConverter { updateField(fieldIndex, value.getBytes) protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = - updateField(fieldIndex, UTF8String(value)) + updateField(fieldIndex, UTF8String.fromBytes(value)) protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit = updateField(fieldIndex, readTimestamp(value)) @@ -238,13 +240,15 @@ private[parquet] abstract class CatalystConverter extends GroupConverter { * * @return */ - def getCurrentRecord: Row = throw new UnsupportedOperationException + def getCurrentRecord: InternalRow = throw new UnsupportedOperationException /** * Read a decimal value from a Parquet Binary into "dest". Only supports decimals that fit in * a long (i.e. precision <= 18) + * + * Returned value is needed by CatalystConverter, which doesn't reuse the Decimal object. */ - protected[parquet] def readDecimal(dest: Decimal, value: Binary, ctype: DecimalType): Unit = { + protected[parquet] def readDecimal(dest: Decimal, value: Binary, ctype: DecimalType): Decimal = { val precision = ctype.precisionInfo.get.precision val scale = ctype.precisionInfo.get.scale val bytes = value.getBytes @@ -264,14 +268,14 @@ private[parquet] abstract class CatalystConverter extends GroupConverter { /** * Read a Timestamp value from a Parquet Int96Value */ - protected[parquet] def readTimestamp(value: Binary): Timestamp = { - CatalystTimestampConverter.convertToTimestamp(value) + protected[parquet] def readTimestamp(value: Binary): Long = { + DateUtils.fromJavaTimestamp(CatalystTimestampConverter.convertToTimestamp(value)) } } /** * A `parquet.io.api.GroupConverter` that is able to convert a Parquet record - * to a [[org.apache.spark.sql.catalyst.expressions.Row]] object. + * to a [[org.apache.spark.sql.catalyst.expressions.InternalRow]] object. * * @param schema The corresponding Catalyst schema in the form of a list of attributes. */ @@ -280,7 +284,7 @@ private[parquet] class CatalystGroupConverter( protected[parquet] val index: Int, protected[parquet] val parent: CatalystConverter, protected[parquet] var current: ArrayBuffer[Any], - protected[parquet] var buffer: ArrayBuffer[Row]) + protected[parquet] var buffer: ArrayBuffer[InternalRow]) extends CatalystConverter { def this(schema: Array[FieldType], index: Int, parent: CatalystConverter) = @@ -289,7 +293,7 @@ private[parquet] class CatalystGroupConverter( index, parent, current = null, - buffer = new ArrayBuffer[Row]( + buffer = new ArrayBuffer[InternalRow]( CatalystArrayConverter.INITIAL_ARRAY_SIZE)) /** @@ -305,7 +309,7 @@ private[parquet] class CatalystGroupConverter( override val size = schema.size - override def getCurrentRecord: Row = { + override def getCurrentRecord: InternalRow = { assert(isRootConverter, "getCurrentRecord should only be called in root group converter!") // TODO: use iterators if possible // Note: this will ever only be called in the root converter when the record has been @@ -343,7 +347,7 @@ private[parquet] class CatalystGroupConverter( /** * A `parquet.io.api.GroupConverter` that is able to convert a Parquet record - * to a [[org.apache.spark.sql.catalyst.expressions.Row]] object. Note that his + * to a [[org.apache.spark.sql.catalyst.expressions.InternalRow]] object. Note that his * converter is optimized for rows of primitive types (non-nested records). */ private[parquet] class CatalystPrimitiveRowConverter( @@ -369,7 +373,7 @@ private[parquet] class CatalystPrimitiveRowConverter( override val parent = null // Should be only called in root group converter! - override def getCurrentRecord: Row = current + override def getCurrentRecord: InternalRow = current override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex) @@ -399,7 +403,7 @@ private[parquet] class CatalystPrimitiveRowConverter( current.setInt(fieldIndex, value) override protected[parquet] def updateDate(fieldIndex: Int, value: Int): Unit = - current.update(fieldIndex, value) + current.setInt(fieldIndex, value) override protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit = current.setLong(fieldIndex, value) @@ -420,10 +424,10 @@ private[parquet] class CatalystPrimitiveRowConverter( current.update(fieldIndex, value.getBytes) override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = - current.update(fieldIndex, UTF8String(value)) + current.update(fieldIndex, UTF8String.fromBytes(value)) override protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit = - current.update(fieldIndex, readTimestamp(value)) + current.setLong(fieldIndex, readTimestamp(value)) override protected[parquet] def updateDecimal( fieldIndex: Int, value: Binary, ctype: DecimalType): Unit = { @@ -480,7 +484,7 @@ private[parquet] class CatalystPrimitiveStringConverter(parent: CatalystConverte override def hasDictionarySupport: Boolean = true - override def setDictionary(dictionary: Dictionary):Unit = + override def setDictionary(dictionary: Dictionary): Unit = dict = Array.tabulate(dictionary.getMaxId + 1) { dictionary.decodeToBinary(_).getBytes } override def addValueFromDictionary(dictionaryId: Int): Unit = @@ -591,8 +595,8 @@ private[parquet] class CatalystArrayConverter( CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, elementType, false), - fieldIndex=0, - parent=this) + fieldIndex = 0, + parent = this) override def getConverter(fieldIndex: Int): Converter = converter @@ -601,7 +605,7 @@ private[parquet] class CatalystArrayConverter( override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = { // fieldIndex is ignored (assumed to be zero but not checked) - if(value == null) { + if (value == null) { throw new IllegalArgumentException("Null values inside Parquet arrays are not supported!") } buffer += value @@ -654,8 +658,8 @@ private[parquet] class CatalystNativeArrayConverter( CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, elementType, false), - fieldIndex=0, - parent=this) + fieldIndex = 0, + parent = this) override def getConverter(fieldIndex: Int): Converter = converter @@ -716,7 +720,7 @@ private[parquet] class CatalystNativeArrayConverter( override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = { checkGrowBuffer() - buffer(elements) = UTF8String(value).asInstanceOf[NativeType] + buffer(elements) = UTF8String.fromBytes(value).asInstanceOf[NativeType] elements += 1 } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala index 5eb1c6abc2432..d57b789f5c1c7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala @@ -17,140 +17,234 @@ package org.apache.spark.sql.parquet +import java.io.Serializable import java.nio.ByteBuffer import com.google.common.io.BaseEncoding import org.apache.hadoop.conf.Configuration -import parquet.filter2.compat.FilterCompat -import parquet.filter2.compat.FilterCompat._ -import parquet.filter2.predicate.FilterApi._ -import parquet.filter2.predicate.{FilterApi, FilterPredicate} -import parquet.io.api.Binary +import org.apache.parquet.filter2.compat.FilterCompat +import org.apache.parquet.filter2.compat.FilterCompat._ +import org.apache.parquet.filter2.predicate.FilterApi._ +import org.apache.parquet.filter2.predicate.{FilterApi, FilterPredicate, Statistics} +import org.apache.parquet.filter2.predicate.UserDefinedPredicate +import org.apache.parquet.io.api.Binary import org.apache.spark.SparkEnv import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.sources import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String private[sql] object ParquetFilters { val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter" def createRecordFilter(filterExpressions: Seq[Expression]): Option[Filter] = { - filterExpressions.flatMap(createFilter).reduceOption(FilterApi.and).map(FilterCompat.get) + filterExpressions.flatMap { filter => + createFilter(filter) + }.reduceOption(FilterApi.and).map(FilterCompat.get) } - def createFilter(predicate: Expression): Option[FilterPredicate] = { - val makeEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { - case BooleanType => - (n: String, v: Any) => FilterApi.eq(booleanColumn(n), v.asInstanceOf[java.lang.Boolean]) - case IntegerType => - (n: String, v: Any) => FilterApi.eq(intColumn(n), v.asInstanceOf[Integer]) - case LongType => - (n: String, v: Any) => FilterApi.eq(longColumn(n), v.asInstanceOf[java.lang.Long]) - case FloatType => - (n: String, v: Any) => FilterApi.eq(floatColumn(n), v.asInstanceOf[java.lang.Float]) - case DoubleType => - (n: String, v: Any) => FilterApi.eq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) - - // Binary.fromString and Binary.fromByteArray don't accept null values - case StringType => - (n: String, v: Any) => FilterApi.eq( - binaryColumn(n), - Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) - case BinaryType => - (n: String, v: Any) => FilterApi.eq( - binaryColumn(n), - Option(v).map(b => Binary.fromByteArray(v.asInstanceOf[Array[Byte]])).orNull) - } + case class SetInFilter[T <: Comparable[T]]( + valueSet: Set[T]) extends UserDefinedPredicate[T] with Serializable { - val makeNotEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { - case BooleanType => - (n: String, v: Any) => FilterApi.notEq(booleanColumn(n), v.asInstanceOf[java.lang.Boolean]) - case IntegerType => - (n: String, v: Any) => FilterApi.notEq(intColumn(n), v.asInstanceOf[Integer]) - case LongType => - (n: String, v: Any) => FilterApi.notEq(longColumn(n), v.asInstanceOf[java.lang.Long]) - case FloatType => - (n: String, v: Any) => FilterApi.notEq(floatColumn(n), v.asInstanceOf[java.lang.Float]) - case DoubleType => - (n: String, v: Any) => FilterApi.notEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) - case StringType => - (n: String, v: Any) => FilterApi.notEq( - binaryColumn(n), - Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) - case BinaryType => - (n: String, v: Any) => FilterApi.notEq( - binaryColumn(n), - Option(v).map(b => Binary.fromByteArray(v.asInstanceOf[Array[Byte]])).orNull) + override def keep(value: T): Boolean = { + value != null && valueSet.contains(value) } - val makeLt: PartialFunction[DataType, (String, Any) => FilterPredicate] = { - case IntegerType => - (n: String, v: Any) => FilterApi.lt(intColumn(n), v.asInstanceOf[Integer]) - case LongType => - (n: String, v: Any) => FilterApi.lt(longColumn(n), v.asInstanceOf[java.lang.Long]) - case FloatType => - (n: String, v: Any) => FilterApi.lt(floatColumn(n), v.asInstanceOf[java.lang.Float]) - case DoubleType => - (n: String, v: Any) => FilterApi.lt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) - case StringType => - (n: String, v: Any) => - FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) - case BinaryType => - (n: String, v: Any) => - FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) - } + override def canDrop(statistics: Statistics[T]): Boolean = false - val makeLtEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { - case IntegerType => - (n: String, v: Any) => FilterApi.ltEq(intColumn(n), v.asInstanceOf[java.lang.Integer]) - case LongType => - (n: String, v: Any) => FilterApi.ltEq(longColumn(n), v.asInstanceOf[java.lang.Long]) - case FloatType => - (n: String, v: Any) => FilterApi.ltEq(floatColumn(n), v.asInstanceOf[java.lang.Float]) - case DoubleType => - (n: String, v: Any) => FilterApi.ltEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) - case StringType => - (n: String, v: Any) => - FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) - case BinaryType => - (n: String, v: Any) => - FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) - } + override def inverseCanDrop(statistics: Statistics[T]): Boolean = false + } - val makeGt: PartialFunction[DataType, (String, Any) => FilterPredicate] = { - case IntegerType => - (n: String, v: Any) => FilterApi.gt(intColumn(n), v.asInstanceOf[java.lang.Integer]) - case LongType => - (n: String, v: Any) => FilterApi.gt(longColumn(n), v.asInstanceOf[java.lang.Long]) - case FloatType => - (n: String, v: Any) => FilterApi.gt(floatColumn(n), v.asInstanceOf[java.lang.Float]) - case DoubleType => - (n: String, v: Any) => FilterApi.gt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) - case StringType => - (n: String, v: Any) => - FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) - case BinaryType => - (n: String, v: Any) => - FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) - } + private val makeEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { + case BooleanType => + (n: String, v: Any) => FilterApi.eq(booleanColumn(n), v.asInstanceOf[java.lang.Boolean]) + case IntegerType => + (n: String, v: Any) => FilterApi.eq(intColumn(n), v.asInstanceOf[Integer]) + case LongType => + (n: String, v: Any) => FilterApi.eq(longColumn(n), v.asInstanceOf[java.lang.Long]) + case FloatType => + (n: String, v: Any) => FilterApi.eq(floatColumn(n), v.asInstanceOf[java.lang.Float]) + case DoubleType => + (n: String, v: Any) => FilterApi.eq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) + + // Binary.fromString and Binary.fromByteArray don't accept null values + case StringType => + (n: String, v: Any) => FilterApi.eq( + binaryColumn(n), + Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) + case BinaryType => + (n: String, v: Any) => FilterApi.eq( + binaryColumn(n), + Option(v).map(b => Binary.fromByteArray(v.asInstanceOf[Array[Byte]])).orNull) + } + + private val makeNotEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { + case BooleanType => + (n: String, v: Any) => FilterApi.notEq(booleanColumn(n), v.asInstanceOf[java.lang.Boolean]) + case IntegerType => + (n: String, v: Any) => FilterApi.notEq(intColumn(n), v.asInstanceOf[Integer]) + case LongType => + (n: String, v: Any) => FilterApi.notEq(longColumn(n), v.asInstanceOf[java.lang.Long]) + case FloatType => + (n: String, v: Any) => FilterApi.notEq(floatColumn(n), v.asInstanceOf[java.lang.Float]) + case DoubleType => + (n: String, v: Any) => FilterApi.notEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) + case StringType => + (n: String, v: Any) => FilterApi.notEq( + binaryColumn(n), + Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) + case BinaryType => + (n: String, v: Any) => FilterApi.notEq( + binaryColumn(n), + Option(v).map(b => Binary.fromByteArray(v.asInstanceOf[Array[Byte]])).orNull) + } + + private val makeLt: PartialFunction[DataType, (String, Any) => FilterPredicate] = { + case IntegerType => + (n: String, v: Any) => FilterApi.lt(intColumn(n), v.asInstanceOf[Integer]) + case LongType => + (n: String, v: Any) => FilterApi.lt(longColumn(n), v.asInstanceOf[java.lang.Long]) + case FloatType => + (n: String, v: Any) => FilterApi.lt(floatColumn(n), v.asInstanceOf[java.lang.Float]) + case DoubleType => + (n: String, v: Any) => FilterApi.lt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) + case StringType => + (n: String, v: Any) => + FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) + case BinaryType => + (n: String, v: Any) => + FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) + } + + private val makeLtEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { + case IntegerType => + (n: String, v: Any) => FilterApi.ltEq(intColumn(n), v.asInstanceOf[java.lang.Integer]) + case LongType => + (n: String, v: Any) => FilterApi.ltEq(longColumn(n), v.asInstanceOf[java.lang.Long]) + case FloatType => + (n: String, v: Any) => FilterApi.ltEq(floatColumn(n), v.asInstanceOf[java.lang.Float]) + case DoubleType => + (n: String, v: Any) => FilterApi.ltEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) + case StringType => + (n: String, v: Any) => + FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) + case BinaryType => + (n: String, v: Any) => + FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) + } + + private val makeGt: PartialFunction[DataType, (String, Any) => FilterPredicate] = { + case IntegerType => + (n: String, v: Any) => FilterApi.gt(intColumn(n), v.asInstanceOf[java.lang.Integer]) + case LongType => + (n: String, v: Any) => FilterApi.gt(longColumn(n), v.asInstanceOf[java.lang.Long]) + case FloatType => + (n: String, v: Any) => FilterApi.gt(floatColumn(n), v.asInstanceOf[java.lang.Float]) + case DoubleType => + (n: String, v: Any) => FilterApi.gt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) + case StringType => + (n: String, v: Any) => + FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) + case BinaryType => + (n: String, v: Any) => + FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) + } + + private val makeGtEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { + case IntegerType => + (n: String, v: Any) => FilterApi.gtEq(intColumn(n), v.asInstanceOf[java.lang.Integer]) + case LongType => + (n: String, v: Any) => FilterApi.gtEq(longColumn(n), v.asInstanceOf[java.lang.Long]) + case FloatType => + (n: String, v: Any) => FilterApi.gtEq(floatColumn(n), v.asInstanceOf[java.lang.Float]) + case DoubleType => + (n: String, v: Any) => FilterApi.gtEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) + case StringType => + (n: String, v: Any) => + FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) + case BinaryType => + (n: String, v: Any) => + FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) + } + + private val makeInSet: PartialFunction[DataType, (String, Set[Any]) => FilterPredicate] = { + case IntegerType => + (n: String, v: Set[Any]) => + FilterApi.userDefined(intColumn(n), SetInFilter(v.asInstanceOf[Set[java.lang.Integer]])) + case LongType => + (n: String, v: Set[Any]) => + FilterApi.userDefined(longColumn(n), SetInFilter(v.asInstanceOf[Set[java.lang.Long]])) + case FloatType => + (n: String, v: Set[Any]) => + FilterApi.userDefined(floatColumn(n), SetInFilter(v.asInstanceOf[Set[java.lang.Float]])) + case DoubleType => + (n: String, v: Set[Any]) => + FilterApi.userDefined(doubleColumn(n), SetInFilter(v.asInstanceOf[Set[java.lang.Double]])) + case StringType => + (n: String, v: Set[Any]) => + FilterApi.userDefined(binaryColumn(n), + SetInFilter(v.map(e => Binary.fromByteArray(e.asInstanceOf[UTF8String].getBytes)))) + case BinaryType => + (n: String, v: Set[Any]) => + FilterApi.userDefined(binaryColumn(n), + SetInFilter(v.map(e => Binary.fromByteArray(e.asInstanceOf[Array[Byte]])))) + } + + /** + * Converts data sources filters to Parquet filter predicates. + */ + def createFilter(schema: StructType, predicate: sources.Filter): Option[FilterPredicate] = { + val dataTypeOf = schema.map(f => f.name -> f.dataType).toMap + + // NOTE: + // + // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`, + // which can be casted to `false` implicitly. Please refer to the `eval` method of these + // operators and the `SimplifyFilters` rule for details. + predicate match { + case sources.IsNull(name) => + makeEq.lift(dataTypeOf(name)).map(_(name, null)) + case sources.IsNotNull(name) => + makeNotEq.lift(dataTypeOf(name)).map(_(name, null)) + + case sources.EqualTo(name, value) => + makeEq.lift(dataTypeOf(name)).map(_(name, value)) + case sources.Not(sources.EqualTo(name, value)) => + makeNotEq.lift(dataTypeOf(name)).map(_(name, value)) + + case sources.LessThan(name, value) => + makeLt.lift(dataTypeOf(name)).map(_(name, value)) + case sources.LessThanOrEqual(name, value) => + makeLtEq.lift(dataTypeOf(name)).map(_(name, value)) + + case sources.GreaterThan(name, value) => + makeGt.lift(dataTypeOf(name)).map(_(name, value)) + case sources.GreaterThanOrEqual(name, value) => + makeGtEq.lift(dataTypeOf(name)).map(_(name, value)) + + case sources.And(lhs, rhs) => + (createFilter(schema, lhs) ++ createFilter(schema, rhs)).reduceOption(FilterApi.and) + + case sources.Or(lhs, rhs) => + for { + lhsFilter <- createFilter(schema, lhs) + rhsFilter <- createFilter(schema, rhs) + } yield FilterApi.or(lhsFilter, rhsFilter) + + case sources.Not(pred) => + createFilter(schema, pred).map(FilterApi.not) - val makeGtEq: PartialFunction[DataType, (String, Any) => FilterPredicate] = { - case IntegerType => - (n: String, v: Any) => FilterApi.gtEq(intColumn(n), v.asInstanceOf[java.lang.Integer]) - case LongType => - (n: String, v: Any) => FilterApi.gtEq(longColumn(n), v.asInstanceOf[java.lang.Long]) - case FloatType => - (n: String, v: Any) => FilterApi.gtEq(floatColumn(n), v.asInstanceOf[java.lang.Float]) - case DoubleType => - (n: String, v: Any) => FilterApi.gtEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) - case StringType => - (n: String, v: Any) => - FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) - case BinaryType => - (n: String, v: Any) => - FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) + case _ => None } + } + /** + * Converts Catalyst predicate expressions to Parquet filter predicates. + * + * @todo This can be removed once we get rid of the old Parquet support. + */ + def createFilter(predicate: Expression): Option[FilterPredicate] = { // NOTE: // // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`, @@ -170,7 +264,7 @@ private[sql] object ParquetFilters { makeEq.lift(dataType).map(_(name, value)) case EqualTo(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) => makeEq.lift(dataType).map(_(name, value)) - + case Not(EqualTo(NamedExpression(name, _), NonNullLiteral(value, dataType))) => makeNotEq.lift(dataType).map(_(name, value)) case Not(EqualTo(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _))) => @@ -192,7 +286,7 @@ private[sql] object ParquetFilters { case LessThanOrEqual(NamedExpression(name, _), NonNullLiteral(value, dataType)) => makeLtEq.lift(dataType).map(_(name, value)) case LessThanOrEqual(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) => - makeLtEq.lift(dataType).map(_(name, value)) + makeLtEq.lift(dataType).map(_(name, value)) case LessThanOrEqual(NonNullLiteral(value, dataType), NamedExpression(name, _)) => makeGtEq.lift(dataType).map(_(name, value)) case LessThanOrEqual(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) => @@ -201,7 +295,7 @@ private[sql] object ParquetFilters { case GreaterThan(NamedExpression(name, _), NonNullLiteral(value, dataType)) => makeGt.lift(dataType).map(_(name, value)) case GreaterThan(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) => - makeGt.lift(dataType).map(_(name, value)) + makeGt.lift(dataType).map(_(name, value)) case GreaterThan(NonNullLiteral(value, dataType), NamedExpression(name, _)) => makeLt.lift(dataType).map(_(name, value)) case GreaterThan(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) => @@ -210,7 +304,7 @@ private[sql] object ParquetFilters { case GreaterThanOrEqual(NamedExpression(name, _), NonNullLiteral(value, dataType)) => makeGtEq.lift(dataType).map(_(name, value)) case GreaterThanOrEqual(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) => - makeGtEq.lift(dataType).map(_(name, value)) + makeGtEq.lift(dataType).map(_(name, value)) case GreaterThanOrEqual(NonNullLiteral(value, dataType), NamedExpression(name, _)) => makeLtEq.lift(dataType).map(_(name, value)) case GreaterThanOrEqual(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) => @@ -228,6 +322,9 @@ private[sql] object ParquetFilters { case Not(pred) => createFilter(pred).map(FilterApi.not) + case InSet(NamedExpression(name, dataType), valueSet) => + makeInSet.lift(dataType).map(_(name, valueSet)) + case _ => None } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala index fcb9513ab66f6..704cf56f38265 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala @@ -18,20 +18,21 @@ package org.apache.spark.sql.parquet import java.io.IOException -import java.util.logging.Level +import java.util.logging.{Level, Logger => JLogger} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.fs.permission.FsAction -import org.apache.spark.sql.types.{StructType, DataType} -import parquet.hadoop.{ParquetOutputCommitter, ParquetOutputFormat} -import parquet.hadoop.metadata.CompressionCodecName -import parquet.schema.MessageType +import org.apache.parquet.hadoop.metadata.CompressionCodecName +import org.apache.parquet.hadoop.{ParquetOutputCommitter, ParquetOutputFormat, ParquetRecordReader} +import org.apache.parquet.schema.MessageType +import org.apache.parquet.{Log => ParquetLog} -import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, UnresolvedException} -import org.apache.spark.sql.catalyst.expressions.{AttributeMap, Attribute} +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap} import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics} +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{DataFrame, SQLContext} /** * Relation that consists of data stored in a Parquet columnar format. @@ -94,40 +95,44 @@ private[sql] case class ParquetRelation( private[sql] object ParquetRelation { def enableLogForwarding() { - // Note: the parquet.Log class has a static initializer that - // sets the java.util.logging Logger for "parquet". This + // Note: the org.apache.parquet.Log class has a static initializer that + // sets the java.util.logging Logger for "org.apache.parquet". This // checks first to see if there's any handlers already set // and if not it creates them. If this method executes prior // to that class being loaded then: // 1) there's no handlers installed so there's none to // remove. But when it IS finally loaded the desired affect // of removing them is circumvented. - // 2) The parquet.Log static initializer calls setUseParentHanders(false) + // 2) The parquet.Log static initializer calls setUseParentHandlers(false) // undoing the attempt to override the logging here. // // Therefore we need to force the class to be loaded. // This should really be resolved by Parquet. - Class.forName(classOf[parquet.Log].getName) + Class.forName(classOf[ParquetLog].getName) // Note: Logger.getLogger("parquet") has a default logger // that appends to Console which needs to be cleared. - val parquetLogger = java.util.logging.Logger.getLogger("parquet") + val parquetLogger = JLogger.getLogger(classOf[ParquetLog].getPackage.getName) parquetLogger.getHandlers.foreach(parquetLogger.removeHandler) - // TODO(witgo): Need to set the log level ? - // if(parquetLogger.getLevel != null) parquetLogger.setLevel(null) - if (!parquetLogger.getUseParentHandlers) parquetLogger.setUseParentHandlers(true) + parquetLogger.setUseParentHandlers(true) - // Disables WARN log message in ParquetOutputCommitter. + // Disables a WARN log message in ParquetOutputCommitter. We first ensure that + // ParquetOutputCommitter is loaded and the static LOG field gets initialized. // See https://issues.apache.org/jira/browse/SPARK-5968 for details Class.forName(classOf[ParquetOutputCommitter].getName) - java.util.logging.Logger.getLogger(classOf[ParquetOutputCommitter].getName).setLevel(Level.OFF) + JLogger.getLogger(classOf[ParquetOutputCommitter].getName).setLevel(Level.OFF) + + // Similar as above, disables a unnecessary WARN log message in ParquetRecordReader. + // See https://issues.apache.org/jira/browse/PARQUET-220 for details + Class.forName(classOf[ParquetRecordReader[_]].getName) + JLogger.getLogger(classOf[ParquetRecordReader[_]].getName).setLevel(Level.OFF) } // The element type for the RDDs that this relation maps to. type RowType = org.apache.spark.sql.catalyst.expressions.GenericMutableRow // The compression type - type CompressionType = parquet.hadoop.metadata.CompressionCodecName + type CompressionType = org.apache.parquet.hadoop.metadata.CompressionCodecName // The parquet compression short names val shortParquetCompressionCodecNames = Map( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index aded126ea0615..b30fc171c0af1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -33,28 +33,29 @@ import org.apache.hadoop.fs.{BlockLocation, FileStatus, Path} import org.apache.hadoop.mapreduce._ import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter, FileOutputFormat => NewFileOutputFormat} -import parquet.hadoop._ -import parquet.hadoop.api.ReadSupport.ReadContext -import parquet.hadoop.api.{InitContext, ReadSupport} -import parquet.hadoop.metadata.GlobalMetaData -import parquet.hadoop.util.ContextUtil -import parquet.io.ParquetDecodingException -import parquet.schema.MessageType +import org.apache.parquet.hadoop._ +import org.apache.parquet.hadoop.api.ReadSupport.ReadContext +import org.apache.parquet.hadoop.api.{InitContext, ReadSupport} +import org.apache.parquet.hadoop.metadata.GlobalMetaData +import org.apache.parquet.hadoop.util.ContextUtil +import org.apache.parquet.io.ParquetDecodingException +import org.apache.parquet.schema.MessageType import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLConf -import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row, _} +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, InternalRow, _} import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode} import org.apache.spark.sql.types.StructType -import org.apache.spark.{Logging, SerializableWritable, TaskContext} +import org.apache.spark.{Logging, TaskContext} +import org.apache.spark.util.SerializableConfiguration /** * :: DeveloperApi :: * Parquet table scan operator. Imports the file that backs the given - * [[org.apache.spark.sql.parquet.ParquetRelation]] as a ``RDD[Row]``. + * [[org.apache.spark.sql.parquet.ParquetRelation]] as a ``RDD[InternalRow]``. */ private[sql] case class ParquetTableScan( attributes: Seq[Attribute], @@ -77,8 +78,8 @@ private[sql] case class ParquetTableScan( } }.toArray - override def execute(): RDD[Row] = { - import parquet.filter2.compat.FilterCompat.FilterPredicateCompat + protected override def doExecute(): RDD[InternalRow] = { + import org.apache.parquet.filter2.compat.FilterCompat.FilterPredicateCompat val sc = sqlContext.sparkContext val job = new Job(sc.hadoopConfiguration) @@ -113,16 +114,19 @@ private[sql] case class ParquetTableScan( .foreach(ParquetInputFormat.setFilterPredicate(conf, _)) // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata - conf.set( - SQLConf.PARQUET_CACHE_METADATA, - sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA, "true")) + conf.setBoolean( + SQLConf.PARQUET_CACHE_METADATA.key, + sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA, true)) + + // Use task side metadata in parquet + conf.setBoolean(ParquetInputFormat.TASK_SIDE_METADATA, true) val baseRDD = new org.apache.spark.rdd.NewHadoopRDD( sc, classOf[FilteringParquetRowInputFormat], classOf[Void], - classOf[Row], + classOf[InternalRow], conf) if (requestedPartitionOrdinals.nonEmpty) { @@ -136,7 +140,7 @@ private[sql] case class ParquetTableScan( baseRDD.mapPartitionsWithInputSplit { case (split, iter) => val partValue = "([^=]+)=([^=]+)".r val partValues = - split.asInstanceOf[parquet.hadoop.ParquetInputSplit] + split.asInstanceOf[org.apache.parquet.hadoop.ParquetInputSplit] .getPath .toString .split("/") @@ -151,9 +155,9 @@ private[sql] case class ParquetTableScan( .map(a => Cast(Literal(partValues(a.name)), a.dataType).eval(EmptyRow)) if (primitiveRow) { - new Iterator[Row] { + new Iterator[InternalRow] { def hasNext: Boolean = iter.hasNext - def next(): Row = { + def next(): InternalRow = { // We are using CatalystPrimitiveRowConverter and it returns a SpecificMutableRow. val row = iter.next()._2.asInstanceOf[SpecificMutableRow] @@ -170,12 +174,12 @@ private[sql] case class ParquetTableScan( } else { // Create a mutable row since we need to fill in values from partition columns. val mutableRow = new GenericMutableRow(outputSize) - new Iterator[Row] { + new Iterator[InternalRow] { def hasNext: Boolean = iter.hasNext - def next(): Row = { + def next(): InternalRow = { // We are using CatalystGroupConverter and it returns a GenericRow. // Since GenericRow is not mutable, we just cast it to a Row. - val row = iter.next()._2.asInstanceOf[Row] + val row = iter.next()._2.asInstanceOf[InternalRow] var i = 0 while (i < row.size) { @@ -255,7 +259,7 @@ private[sql] case class InsertIntoParquetTable( /** * Inserts all rows into the Parquet file. */ - override def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { // TODO: currently we do not check whether the "schema"s are compatible // That means if one first creates a table and then INSERTs data with // and incompatible schema the execution will fail. It would be nice @@ -318,15 +322,15 @@ private[sql] case class InsertIntoParquetTable( * @param conf A [[org.apache.hadoop.conf.Configuration]]. */ private def saveAsHadoopFile( - rdd: RDD[Row], + rdd: RDD[InternalRow], path: String, conf: Configuration) { val job = new Job(conf) val keyType = classOf[Void] job.setOutputKeyClass(keyType) - job.setOutputValueClass(classOf[Row]) + job.setOutputValueClass(classOf[InternalRow]) NewFileOutputFormat.setOutputPath(job, new Path(path)) - val wrappedConf = new SerializableWritable(job.getConfiguration) + val wrappedConf = new SerializableConfiguration(job.getConfiguration) val formatter = new SimpleDateFormat("yyyyMMddHHmm") val jobtrackerID = formatter.format(new Date()) val stageId = sqlContext.sparkContext.newRddId() @@ -339,7 +343,7 @@ private[sql] case class InsertIntoParquetTable( .findMaxTaskId(NewFileOutputFormat.getOutputPath(job).toString, job.getConfiguration) + 1 } - def writeShard(context: TaskContext, iter: Iterator[Row]): Int = { + def writeShard(context: TaskContext, iter: Iterator[InternalRow]): Int = { /* "reduce task" */ val attemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = false, context.partitionId, context.attemptNumber) @@ -378,7 +382,7 @@ private[sql] case class InsertIntoParquetTable( * to imported ones. */ private[parquet] class AppendingParquetOutputFormat(offset: Int) - extends parquet.hadoop.ParquetOutputFormat[Row] { + extends org.apache.parquet.hadoop.ParquetOutputFormat[InternalRow] { // override to accept existing directories as valid output directory override def checkOutputSpecs(job: JobContext): Unit = {} var committer: OutputCommitter = null @@ -431,210 +435,26 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int) * RecordFilter we want to use. */ private[parquet] class FilteringParquetRowInputFormat - extends parquet.hadoop.ParquetInputFormat[Row] with Logging { + extends org.apache.parquet.hadoop.ParquetInputFormat[InternalRow] with Logging { private var fileStatuses = Map.empty[Path, FileStatus] override def createRecordReader( inputSplit: InputSplit, - taskAttemptContext: TaskAttemptContext): RecordReader[Void, Row] = { + taskAttemptContext: TaskAttemptContext): RecordReader[Void, InternalRow] = { - import parquet.filter2.compat.FilterCompat.NoOpFilter + import org.apache.parquet.filter2.compat.FilterCompat.NoOpFilter - val readSupport: ReadSupport[Row] = new RowReadSupport() + val readSupport: ReadSupport[InternalRow] = new RowReadSupport() val filter = ParquetInputFormat.getFilter(ContextUtil.getConfiguration(taskAttemptContext)) if (!filter.isInstanceOf[NoOpFilter]) { - new ParquetRecordReader[Row]( + new ParquetRecordReader[InternalRow]( readSupport, filter) } else { - new ParquetRecordReader[Row](readSupport) - } - } - - // This is only a temporary solution sicne we need to use fileStatuses in - // both getClientSideSplits and getTaskSideSplits. It can be removed once we get rid of these - // two methods. - override def getSplits(jobContext: JobContext): JList[InputSplit] = { - // First set fileStatuses. - val statuses = listStatus(jobContext) - fileStatuses = statuses.map(file => file.getPath -> file).toMap - - super.getSplits(jobContext) - } - - // TODO Remove this method and related code once PARQUET-16 is fixed - // This method together with the `getFooters` method and the `fileStatuses` field are just used - // to mimic this PR: https://github.com/apache/incubator-parquet-mr/pull/17 - override def getSplits( - configuration: Configuration, - footers: JList[Footer]): JList[ParquetInputSplit] = { - - // Use task side strategy by default - val taskSideMetaData = configuration.getBoolean(ParquetInputFormat.TASK_SIDE_METADATA, true) - val maxSplitSize: JLong = configuration.getLong("mapred.max.split.size", Long.MaxValue) - val minSplitSize: JLong = - Math.max(getFormatMinSplitSize, configuration.getLong("mapred.min.split.size", 0L)) - if (maxSplitSize < 0 || minSplitSize < 0) { - throw new ParquetDecodingException( - s"maxSplitSize or minSplitSie should not be negative: maxSplitSize = $maxSplitSize;" + - s" minSplitSize = $minSplitSize") - } - - // Uses strict type checking by default - val getGlobalMetaData = - classOf[ParquetFileWriter].getDeclaredMethod("getGlobalMetaData", classOf[JList[Footer]]) - getGlobalMetaData.setAccessible(true) - var globalMetaData = getGlobalMetaData.invoke(null, footers).asInstanceOf[GlobalMetaData] - - if (globalMetaData == null) { - val splits = mutable.ArrayBuffer.empty[ParquetInputSplit] - return splits - } - - val metadata = configuration.get(RowWriteSupport.SPARK_ROW_SCHEMA) - val mergedMetadata = globalMetaData - .getKeyValueMetaData - .updated(RowReadSupport.SPARK_METADATA_KEY, setAsJavaSet(Set(metadata))) - - globalMetaData = new GlobalMetaData(globalMetaData.getSchema, - mergedMetadata, globalMetaData.getCreatedBy) - - val readContext = getReadSupport(configuration).init( - new InitContext(configuration, - globalMetaData.getKeyValueMetaData, - globalMetaData.getSchema)) - - if (taskSideMetaData){ - logInfo("Using Task Side Metadata Split Strategy") - getTaskSideSplits(configuration, - footers, - maxSplitSize, - minSplitSize, - readContext) - } else { - logInfo("Using Client Side Metadata Split Strategy") - getClientSideSplits(configuration, - footers, - maxSplitSize, - minSplitSize, - readContext) + new ParquetRecordReader[InternalRow](readSupport) } - - } - - def getClientSideSplits( - configuration: Configuration, - footers: JList[Footer], - maxSplitSize: JLong, - minSplitSize: JLong, - readContext: ReadContext): JList[ParquetInputSplit] = { - - import parquet.filter2.compat.FilterCompat.Filter - import parquet.filter2.compat.RowGroupFilter - - import org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.blockLocationCache - - val cacheMetadata = configuration.getBoolean(SQLConf.PARQUET_CACHE_METADATA, true) - - val splits = mutable.ArrayBuffer.empty[ParquetInputSplit] - val filter: Filter = ParquetInputFormat.getFilter(configuration) - var rowGroupsDropped: Long = 0 - var totalRowGroups: Long = 0 - - // Ugly hack, stuck with it until PR: - // https://github.com/apache/incubator-parquet-mr/pull/17 - // is resolved - val generateSplits = - Class.forName("parquet.hadoop.ClientSideMetadataSplitStrategy") - .getDeclaredMethods.find(_.getName == "generateSplits").getOrElse( - sys.error(s"Failed to reflectively invoke ClientSideMetadataSplitStrategy.generateSplits")) - generateSplits.setAccessible(true) - - for (footer <- footers) { - val fs = footer.getFile.getFileSystem(configuration) - val file = footer.getFile - val status = fileStatuses.getOrElse(file, fs.getFileStatus(file)) - val parquetMetaData = footer.getParquetMetadata - val blocks = parquetMetaData.getBlocks - totalRowGroups = totalRowGroups + blocks.size - val filteredBlocks = RowGroupFilter.filterRowGroups( - filter, - blocks, - parquetMetaData.getFileMetaData.getSchema) - rowGroupsDropped = rowGroupsDropped + (blocks.size - filteredBlocks.size) - - if (!filteredBlocks.isEmpty){ - var blockLocations: Array[BlockLocation] = null - if (!cacheMetadata) { - blockLocations = fs.getFileBlockLocations(status, 0, status.getLen) - } else { - blockLocations = blockLocationCache.get(status, new Callable[Array[BlockLocation]] { - def call(): Array[BlockLocation] = fs.getFileBlockLocations(status, 0, status.getLen) - }) - } - splits.addAll( - generateSplits.invoke( - null, - filteredBlocks, - blockLocations, - status, - readContext.getRequestedSchema.toString, - readContext.getReadSupportMetadata, - minSplitSize, - maxSplitSize).asInstanceOf[JList[ParquetInputSplit]]) - } - } - - if (rowGroupsDropped > 0 && totalRowGroups > 0){ - val percentDropped = ((rowGroupsDropped/totalRowGroups.toDouble) * 100).toInt - logInfo(s"Dropping $rowGroupsDropped row groups that do not pass filter predicate " - + s"($percentDropped %) !") - } - else { - logInfo("There were no row groups that could be dropped due to filter predicates") - } - splits - - } - - def getTaskSideSplits( - configuration: Configuration, - footers: JList[Footer], - maxSplitSize: JLong, - minSplitSize: JLong, - readContext: ReadContext): JList[ParquetInputSplit] = { - - val splits = mutable.ArrayBuffer.empty[ParquetInputSplit] - - // Ugly hack, stuck with it until PR: - // https://github.com/apache/incubator-parquet-mr/pull/17 - // is resolved - val generateSplits = - Class.forName("parquet.hadoop.TaskSideMetadataSplitStrategy") - .getDeclaredMethods.find(_.getName == "generateTaskSideMDSplits").getOrElse( - sys.error( - s"Failed to reflectively invoke TaskSideMetadataSplitStrategy.generateTaskSideMDSplits")) - generateSplits.setAccessible(true) - - for (footer <- footers) { - val file = footer.getFile - val fs = file.getFileSystem(configuration) - val status = fileStatuses.getOrElse(file, fs.getFileStatus(file)) - val blockLocations = fs.getFileBlockLocations(status, 0, status.getLen) - splits.addAll( - generateSplits.invoke( - null, - blockLocations, - status, - readContext.getRequestedSchema.toString, - readContext.getReadSupportMetadata, - minSplitSize, - maxSplitSize).asInstanceOf[JList[ParquetInputSplit]]) - } - - splits } } @@ -664,7 +484,7 @@ private[parquet] object FileSystemHelper { s"ParquetTableOperations: path $path does not exist or is not a directory") } fs.globStatus(path) - .flatMap { status => if(status.isDir) fs.listStatus(status.getPath) else List(status) } + .flatMap { status => if (status.isDir) fs.listStatus(status.getPath) else List(status) } .map(_.getPath) } @@ -674,7 +494,7 @@ private[parquet] object FileSystemHelper { def findMaxTaskId(pathStr: String, conf: Configuration): Int = { val files = FileSystemHelper.listFiles(pathStr, conf) // filename pattern is part-r-.parquet - val nameP = new scala.util.matching.Regex("""part-r-(\d{1,}).parquet""", "taskid") + val nameP = new scala.util.matching.Regex("""part-.-(\d{1,}).*""", "taskid") val hiddenFileP = new scala.util.matching.Regex("_.*") files.map(_.getName).map { case nameP(taskid) => taskid.toInt diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala index c45c431438efc..a8775a2a8fd83 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala @@ -20,16 +20,18 @@ package org.apache.spark.sql.parquet import java.util.{HashMap => JHashMap} import org.apache.hadoop.conf.Configuration -import parquet.column.ParquetProperties -import parquet.hadoop.ParquetOutputFormat -import parquet.hadoop.api.ReadSupport.ReadContext -import parquet.hadoop.api.{ReadSupport, WriteSupport} -import parquet.io.api._ -import parquet.schema.MessageType +import org.apache.parquet.column.ParquetProperties +import org.apache.parquet.hadoop.ParquetOutputFormat +import org.apache.parquet.hadoop.api.ReadSupport.ReadContext +import org.apache.parquet.hadoop.api.{ReadSupport, WriteSupport} +import org.apache.parquet.io.api._ +import org.apache.parquet.schema.MessageType import org.apache.spark.Logging -import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} +import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow} +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /** * A `parquet.io.api.RecordMaterializer` for Rows. @@ -37,12 +39,12 @@ import org.apache.spark.sql.types._ *@param root The root group converter for the record. */ private[parquet] class RowRecordMaterializer(root: CatalystConverter) - extends RecordMaterializer[Row] { + extends RecordMaterializer[InternalRow] { def this(parquetSchema: MessageType, attributes: Seq[Attribute]) = this(CatalystConverter.createRootConverter(parquetSchema, attributes)) - override def getCurrentRecord: Row = root.getCurrentRecord + override def getCurrentRecord: InternalRow = root.getCurrentRecord override def getRootConverter: GroupConverter = root.asInstanceOf[GroupConverter] } @@ -50,13 +52,13 @@ private[parquet] class RowRecordMaterializer(root: CatalystConverter) /** * A `parquet.hadoop.api.ReadSupport` for Row objects. */ -private[parquet] class RowReadSupport extends ReadSupport[Row] with Logging { +private[parquet] class RowReadSupport extends ReadSupport[InternalRow] with Logging { override def prepareForRead( conf: Configuration, stringMap: java.util.Map[String, String], fileSchema: MessageType, - readContext: ReadContext): RecordMaterializer[Row] = { + readContext: ReadContext): RecordMaterializer[InternalRow] = { log.debug(s"preparing for read with Parquet file schema $fileSchema") // Note: this very much imitates AvroParquet val parquetSchema = readContext.getRequestedSchema @@ -129,9 +131,9 @@ private[parquet] object RowReadSupport { } /** - * A `parquet.hadoop.api.WriteSupport` for Row ojects. + * A `parquet.hadoop.api.WriteSupport` for Row objects. */ -private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { +private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Logging { private[parquet] var writer: RecordConsumer = null private[parquet] var attributes: Array[Attribute] = null @@ -155,7 +157,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { log.debug(s"preparing for write with schema $attributes") } - override def write(record: Row): Unit = { + override def write(record: InternalRow): Unit = { val attributesSize = attributes.size if (attributesSize > record.size) { throw new IndexOutOfBoundsException( @@ -204,7 +206,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { case IntegerType => writer.addInteger(value.asInstanceOf[Int]) case ShortType => writer.addInteger(value.asInstanceOf[Short]) case LongType => writer.addLong(value.asInstanceOf[Long]) - case TimestampType => writeTimestamp(value.asInstanceOf[java.sql.Timestamp]) + case TimestampType => writeTimestamp(value.asInstanceOf[Long]) case ByteType => writer.addInteger(value.asInstanceOf[Byte]) case DoubleType => writer.addDouble(value.asInstanceOf[Double]) case FloatType => writer.addFloat(value.asInstanceOf[Float]) @@ -311,15 +313,16 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { writer.addBinary(Binary.fromByteArray(scratchBytes, 0, numBytes)) } - private[parquet] def writeTimestamp(ts: java.sql.Timestamp): Unit = { - val binaryNanoTime = CatalystTimestampConverter.convertFromTimestamp(ts) + private[parquet] def writeTimestamp(ts: Long): Unit = { + val binaryNanoTime = CatalystTimestampConverter.convertFromTimestamp( + DateUtils.toJavaTimestamp(ts)) writer.addBinary(binaryNanoTime) } } // Optimized for non-nested rows private[parquet] class MutableRowWriteSupport extends RowWriteSupport { - override def write(record: Row): Unit = { + override def write(record: InternalRow): Unit = { val attributesSize = attributes.size if (attributesSize > record.size) { throw new IndexOutOfBoundsException( @@ -342,7 +345,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport { private def consumeType( ctype: DataType, - record: Row, + record: InternalRow, index: Int): Unit = { ctype match { case StringType => writer.addBinary( @@ -357,7 +360,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport { case FloatType => writer.addFloat(record.getFloat(index)) case BooleanType => writer.addBoolean(record.getBoolean(index)) case DateType => writer.addInteger(record.getInt(index)) - case TimestampType => writeTimestamp(record(index).asInstanceOf[java.sql.Timestamp]) + case TimestampType => writeTimestamp(record(index).asInstanceOf[Long]) case d: DecimalType => if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) { sys.error(s"Unsupported datatype $d, cannot write to consumer") diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala index 1dc819b5d7b9b..ba2a35b74ef82 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala @@ -19,26 +19,25 @@ package org.apache.spark.sql.parquet import java.io.IOException -import scala.collection.mutable.ArrayBuffer +import scala.collection.JavaConversions._ import scala.util.Try import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.mapreduce.Job -import parquet.format.converter.ParquetMetadataConverter -import parquet.hadoop.metadata.{FileMetaData, ParquetMetadata} -import parquet.hadoop.util.ContextUtil -import parquet.hadoop.{Footer, ParquetFileReader, ParquetFileWriter} -import parquet.schema.PrimitiveType.{PrimitiveTypeName => ParquetPrimitiveTypeName} -import parquet.schema.Type.Repetition -import parquet.schema.{ConversionPatterns, DecimalMetadata, GroupType => ParquetGroupType, MessageType, OriginalType => ParquetOriginalType, PrimitiveType => ParquetPrimitiveType, Type => ParquetType, Types => ParquetTypes} +import org.apache.parquet.format.converter.ParquetMetadataConverter +import org.apache.parquet.hadoop.metadata.{FileMetaData, ParquetMetadata} +import org.apache.parquet.hadoop.util.ContextUtil +import org.apache.parquet.hadoop.{Footer, ParquetFileReader, ParquetFileWriter} +import org.apache.parquet.schema.PrimitiveType.{PrimitiveTypeName => ParquetPrimitiveTypeName} +import org.apache.parquet.schema.Type.Repetition +import org.apache.parquet.schema.{ConversionPatterns, DecimalMetadata, GroupType => ParquetGroupType, MessageType, OriginalType => ParquetOriginalType, PrimitiveType => ParquetPrimitiveType, Type => ParquetType, Types => ParquetTypes} +import org.apache.spark.Logging +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference} import org.apache.spark.sql.types._ -import org.apache.spark.{Logging, SparkException} -// Implicits -import scala.collection.JavaConversions._ /** A class representing Parquet info fields we care about, for passing back to Parquet */ private[parquet] case class ParquetTypeInfo( @@ -73,13 +72,12 @@ private[parquet] object ParquetTypesConverter extends Logging { case ParquetPrimitiveTypeName.INT96 if int96AsTimestamp => TimestampType case ParquetPrimitiveTypeName.INT96 => // TODO: add BigInteger type? TODO(andre) use DecimalType instead???? - sys.error("Potential loss of precision: cannot convert INT96") + throw new AnalysisException("Potential loss of precision: cannot convert INT96") case ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY if (originalType == ParquetOriginalType.DECIMAL && decimalInfo.getPrecision <= 18) => // TODO: for now, our reader only supports decimals that fit in a Long DecimalType(decimalInfo.getPrecision, decimalInfo.getScale) - case _ => sys.error( - s"Unsupported parquet datatype $parquetType") + case _ => throw new AnalysisException(s"Unsupported parquet datatype $parquetType") } } @@ -371,7 +369,7 @@ private[parquet] object ParquetTypesConverter extends Logging { parquetKeyType, parquetValueType) } - case _ => sys.error(s"Unsupported datatype $ctype") + case _ => throw new AnalysisException(s"Unsupported datatype $ctype") } } } @@ -403,7 +401,7 @@ private[parquet] object ParquetTypesConverter extends Logging { def convertFromString(string: String): Seq[Attribute] = { Try(DataType.fromJson(string)).getOrElse(DataType.fromCaseClassString(string)) match { case s: StructType => s.toAttributes - case other => sys.error(s"Can convert $string to row") + case other => throw new AnalysisException(s"Can convert $string to row") } } @@ -411,8 +409,8 @@ private[parquet] object ParquetTypesConverter extends Logging { // ,;{}()\n\t= and space character are special characters in Parquet schema schema.map(_.name).foreach { name => if (name.matches(".*[ ,;{}()\n\t=].*")) { - sys.error( - s"""Attribute name "$name" contains invalid character(s) among " ,;{}()\n\t=". + throw new AnalysisException( + s"""Attribute name "$name" contains invalid character(s) among " ,;{}()\\n\\t=". |Please use alias to rename it. """.stripMargin.split("\n").mkString(" ")) } @@ -489,7 +487,7 @@ private[parquet] object ParquetTypesConverter extends Logging { val children = fs .globStatus(path) - .flatMap { status => if(status.isDir) fs.listStatus(status.getPath) else List(status) } + .flatMap { status => if (status.isDir) fs.listStatus(status.getPath) else List(status) } .filterNot { status => val name = status.getPath.getName (name(0) == '.' || name(0) == '_') && name != ParquetFileWriter.PARQUET_METADATA_FILE diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 85e60733bc57a..c9de45e0ddfbb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -14,209 +14,325 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + package org.apache.spark.sql.parquet -import java.io.IOException -import java.lang.{Double => JDouble, Float => JFloat, Long => JLong} -import java.math.{BigDecimal => JBigDecimal} import java.net.URI -import java.text.SimpleDateFormat -import java.util.{Date, List => JList} +import java.util.{List => JList} import scala.collection.JavaConversions._ -import scala.collection.mutable.ArrayBuffer import scala.util.Try -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import com.google.common.base.Objects +import org.apache.hadoop.fs.{FileStatus, Path} import org.apache.hadoop.io.Writable +import org.apache.hadoop.mapreduce._ import org.apache.hadoop.mapreduce.lib.input.FileInputFormat -import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat -import org.apache.hadoop.mapreduce.{InputSplit, Job, JobContext} -import parquet.filter2.predicate.FilterApi -import parquet.format.converter.ParquetMetadataConverter -import parquet.hadoop.metadata.CompressionCodecName -import parquet.hadoop.util.ContextUtil -import parquet.hadoop.{ParquetInputFormat, _} - -import org.apache.spark.annotation.DeveloperApi +import org.apache.parquet.filter2.predicate.FilterApi +import org.apache.parquet.hadoop._ +import org.apache.parquet.hadoop.metadata.CompressionCodecName +import org.apache.parquet.hadoop.util.ContextUtil + +import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.SparkHadoopUtil -import org.apache.spark.mapred.SparkHadoopMapRedUtil -import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil -import org.apache.spark.rdd.{NewHadoopPartition, NewHadoopRDD, RDD} -import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.{CatalystTypeConverters, expressions} -import org.apache.spark.sql.parquet.ParquetTypesConverter._ +import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.RDD._ +import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.sources._ -import org.apache.spark.sql.types.{IntegerType, StructField, StructType, _} -import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext, SaveMode} -import org.apache.spark.{Logging, SerializableWritable, SparkException, TaskContext, Partition => SparkPartition} - -/** - * Allows creation of Parquet based tables using the syntax: - * {{{ - * CREATE TEMPORARY TABLE ... USING org.apache.spark.sql.parquet OPTIONS (...) - * }}} - * - * Supported options include: - * - * - `path`: Required. When reading Parquet files, `path` should point to the location of the - * Parquet file(s). It can be either a single raw Parquet file, or a directory of Parquet files. - * In the latter case, this data source tries to discover partitioning information if the the - * directory is structured in the same style of Hive partitioned tables. When writing Parquet - * file, `path` should point to the destination folder. - * - * - `mergeSchema`: Optional. Indicates whether we should merge potentially different (but - * compatible) schemas stored in all Parquet part-files. - * - * - `partition.defaultName`: Optional. Partition name used when a value of a partition column is - * null or empty string. This is similar to the `hive.exec.default.partition.name` configuration - * in Hive. - */ -private[sql] class DefaultSource - extends RelationProvider - with SchemaRelationProvider - with CreatableRelationProvider { - - private def checkPath(parameters: Map[String, String]): String = { - parameters.getOrElse("path", sys.error("'path' must be specified for parquet tables.")) - } - - /** Returns a new base relation with the given parameters. */ - override def createRelation( - sqlContext: SQLContext, - parameters: Map[String, String]): BaseRelation = { - ParquetRelation2(Seq(checkPath(parameters)), parameters, None)(sqlContext) - } +import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.util.{SerializableConfiguration, Utils} +import org.apache.spark.{Logging, SparkException, Partition => SparkPartition} - /** Returns a new base relation with the given parameters and schema. */ +private[sql] class DefaultSource extends HadoopFsRelationProvider { override def createRelation( sqlContext: SQLContext, - parameters: Map[String, String], - schema: StructType): BaseRelation = { - ParquetRelation2(Seq(checkPath(parameters)), parameters, Some(schema))(sqlContext) + paths: Array[String], + schema: Option[StructType], + partitionColumns: Option[StructType], + parameters: Map[String, String]): HadoopFsRelation = { + new ParquetRelation2(paths, schema, None, partitionColumns, parameters)(sqlContext) } +} - /** Returns a new base relation with the given parameters and save given data into it. */ - override def createRelation( - sqlContext: SQLContext, - mode: SaveMode, - parameters: Map[String, String], - data: DataFrame): BaseRelation = { - val path = checkPath(parameters) - val filesystemPath = new Path(path) - val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) - val doInsertion = (mode, fs.exists(filesystemPath)) match { - case (SaveMode.ErrorIfExists, true) => - sys.error(s"path $path already exists.") - case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) => - true - case (SaveMode.Ignore, exists) => - !exists - } +// NOTE: This class is instantiated and used on executor side only, no need to be serializable. +private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext) + extends OutputWriter { + + private val recordWriter: RecordWriter[Void, InternalRow] = { + val conf = context.getConfiguration + val outputFormat = { + // When appending new Parquet files to an existing Parquet file directory, to avoid + // overwriting existing data files, we need to find out the max task ID encoded in these data + // file names. + // TODO Make this snippet a utility function for other data source developers + val maxExistingTaskId = { + // Note that `path` may point to a temporary location. Here we retrieve the real + // destination path from the configuration + val outputPath = new Path(conf.get("spark.sql.sources.output.path")) + val fs = outputPath.getFileSystem(conf) + + if (fs.exists(outputPath)) { + // Pattern used to match task ID in part file names, e.g.: + // + // part-r-00001.gz.parquet + // ^~~~~ + val partFilePattern = """part-.-(\d{1,}).*""".r + + fs.listStatus(outputPath).map(_.getPath.getName).map { + case partFilePattern(id) => id.toInt + case name if name.startsWith("_") => 0 + case name if name.startsWith(".") => 0 + case name => throw new AnalysisException( + s"Trying to write Parquet files to directory $outputPath, " + + s"but found items with illegal name '$name'.") + }.reduceOption(_ max _).getOrElse(0) + } else { + 0 + } + } - val relation = if (doInsertion) { - // This is a hack. We always set nullable/containsNull/valueContainsNull to true - // for the schema of a parquet data. - val df = - sqlContext.createDataFrame( - data.queryExecution.toRdd, - data.schema.asNullable, - needsConversion = false) - val createdRelation = - createRelation(sqlContext, parameters, df.schema).asInstanceOf[ParquetRelation2] - createdRelation.insert(df, overwrite = mode == SaveMode.Overwrite) - createdRelation - } else { - // If the save mode is Ignore, we will just create the relation based on existing data. - createRelation(sqlContext, parameters) + new ParquetOutputFormat[InternalRow]() { + // Here we override `getDefaultWorkFile` for two reasons: + // + // 1. To allow appending. We need to generate output file name based on the max available + // task ID computed above. + // + // 2. To allow dynamic partitioning. Default `getDefaultWorkFile` uses + // `FileOutputCommitter.getWorkPath()`, which points to the base directory of all + // partitions in the case of dynamic partitioning. + override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = { + val split = context.getTaskAttemptID.getTaskID.getId + maxExistingTaskId + 1 + new Path(path, f"part-r-$split%05d$extension") + } + } } - relation + outputFormat.getRecordWriter(context) } -} -private[sql] case class Partition(values: Row, path: String) + override def write(row: Row): Unit = recordWriter.write(null, row.asInstanceOf[InternalRow]) -private[sql] case class PartitionSpec(partitionColumns: StructType, partitions: Seq[Partition]) + override def close(): Unit = recordWriter.close(context) +} -/** - * An alternative to [[ParquetRelation]] that plugs in using the data sources API. This class is - * intended as a full replacement of the Parquet support in Spark SQL. The old implementation will - * be deprecated and eventually removed once this version is proved to be stable enough. - * - * Compared with the old implementation, this class has the following notable differences: - * - * - Partitioning discovery: Hive style multi-level partitions are auto discovered. - * - Metadata discovery: Parquet is a format comes with schema evolving support. This data source - * can detect and merge schemas from all Parquet part-files as long as they are compatible. - * Also, metadata and [[FileStatus]]es are cached for better performance. - * - Statistics: Statistics for the size of the table are automatically populated during schema - * discovery. - */ -@DeveloperApi -private[sql] case class ParquetRelation2( - paths: Seq[String], - parameters: Map[String, String], - maybeSchema: Option[StructType] = None, - maybePartitionSpec: Option[PartitionSpec] = None)( - @transient val sqlContext: SQLContext) - extends BaseRelation - with CatalystScan - with InsertableRelation - with SparkHadoopMapReduceUtil +private[sql] class ParquetRelation2( + override val paths: Array[String], + private val maybeDataSchema: Option[StructType], + // This is for metastore conversion. + private val maybePartitionSpec: Option[PartitionSpec], + override val userDefinedPartitionColumns: Option[StructType], + parameters: Map[String, String])( + val sqlContext: SQLContext) + extends HadoopFsRelation(maybePartitionSpec) with Logging { + private[sql] def this( + paths: Array[String], + maybeDataSchema: Option[StructType], + maybePartitionSpec: Option[PartitionSpec], + parameters: Map[String, String])( + sqlContext: SQLContext) = { + this( + paths, + maybeDataSchema, + maybePartitionSpec, + maybePartitionSpec.map(_.partitionColumns), + parameters)(sqlContext) + } + // Should we merge schemas from all Parquet part-files? private val shouldMergeSchemas = parameters.getOrElse(ParquetRelation2.MERGE_SCHEMA, "true").toBoolean - // Optional Metastore schema, used when converting Hive Metastore Parquet table - private val maybeMetastoreSchema = - parameters - .get(ParquetRelation2.METASTORE_SCHEMA) - .map(s => DataType.fromJson(s).asInstanceOf[StructType]) + private val maybeMetastoreSchema = parameters + .get(ParquetRelation2.METASTORE_SCHEMA) + .map(DataType.fromJson(_).asInstanceOf[StructType]) - // Hive uses this as part of the default partition name when the partition column value is null - // or empty string - private val defaultPartitionName = parameters.getOrElse( - ParquetRelation2.DEFAULT_PARTITION_NAME, "__HIVE_DEFAULT_PARTITION__") + private lazy val metadataCache: MetadataCache = { + val meta = new MetadataCache + meta.refresh() + meta + } override def equals(other: Any): Boolean = other match { - case relation: ParquetRelation2 => - // If schema merging is required, we don't compare the actual schemas since they may evolve. + case that: ParquetRelation2 => val schemaEquality = if (shouldMergeSchemas) { - shouldMergeSchemas == relation.shouldMergeSchemas + this.shouldMergeSchemas == that.shouldMergeSchemas } else { - schema == relation.schema + this.dataSchema == that.dataSchema && + this.schema == that.schema } - paths.toSet == relation.paths.toSet && + this.paths.toSet == that.paths.toSet && schemaEquality && - maybeMetastoreSchema == relation.maybeMetastoreSchema && - maybePartitionSpec == relation.maybePartitionSpec + this.maybeDataSchema == that.maybeDataSchema && + this.partitionColumns == that.partitionColumns case _ => false } override def hashCode(): Int = { if (shouldMergeSchemas) { - com.google.common.base.Objects.hashCode( - shouldMergeSchemas: java.lang.Boolean, + Objects.hashCode( + Boolean.box(shouldMergeSchemas), paths.toSet, - maybeMetastoreSchema, - maybePartitionSpec) + maybeDataSchema, + partitionColumns) } else { - com.google.common.base.Objects.hashCode( - shouldMergeSchemas: java.lang.Boolean, - schema, + Objects.hashCode( + Boolean.box(shouldMergeSchemas), paths.toSet, - maybeMetastoreSchema, - maybePartitionSpec) + dataSchema, + schema, + maybeDataSchema, + partitionColumns) } } - private[sql] def sparkContext = sqlContext.sparkContext + override def dataSchema: StructType = maybeDataSchema.getOrElse(metadataCache.dataSchema) + + override private[sql] def refresh(): Unit = { + super.refresh() + metadataCache.refresh() + } + + // Parquet data source always uses Catalyst internal representations. + override val needConversion: Boolean = false + + override def sizeInBytes: Long = metadataCache.dataStatuses.map(_.getLen).sum + + override def prepareJobForWrite(job: Job): OutputWriterFactory = { + val conf = ContextUtil.getConfiguration(job) + + val committerClass = + conf.getClass( + "spark.sql.parquet.output.committer.class", + classOf[ParquetOutputCommitter], + classOf[ParquetOutputCommitter]) + + if (conf.get("spark.sql.parquet.output.committer.class") == null) { + logInfo("Using default output committer for Parquet: " + + classOf[ParquetOutputCommitter].getCanonicalName) + } else { + logInfo("Using user defined output committer for Parquet: " + committerClass.getCanonicalName) + } + + conf.setClass( + SQLConf.OUTPUT_COMMITTER_CLASS.key, + committerClass, + classOf[ParquetOutputCommitter]) + + // TODO There's no need to use two kinds of WriteSupport + // We should unify them. `SpecificMutableRow` can process both atomic (primitive) types and + // complex types. + val writeSupportClass = + if (dataSchema.map(_.dataType).forall(ParquetTypesConverter.isPrimitiveType)) { + classOf[MutableRowWriteSupport] + } else { + classOf[RowWriteSupport] + } + + ParquetOutputFormat.setWriteSupportClass(job, writeSupportClass) + RowWriteSupport.setSchema(dataSchema.toAttributes, conf) + + // Sets compression scheme + conf.set( + ParquetOutputFormat.COMPRESSION, + ParquetRelation + .shortParquetCompressionCodecNames + .getOrElse( + sqlContext.conf.parquetCompressionCodec.toUpperCase, + CompressionCodecName.UNCOMPRESSED).name()) + + new OutputWriterFactory { + override def newInstance( + path: String, dataSchema: StructType, context: TaskAttemptContext): OutputWriter = { + new ParquetOutputWriter(path, context) + } + } + } + + override def buildScan( + requiredColumns: Array[String], + filters: Array[Filter], + inputFiles: Array[FileStatus], + broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = { + val useMetadataCache = sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA) + val parquetFilterPushDown = sqlContext.conf.parquetFilterPushDown + // Create the function to set variable Parquet confs at both driver and executor side. + val initLocalJobFuncOpt = + ParquetRelation2.initializeLocalJobFunc( + requiredColumns, + filters, + dataSchema, + useMetadataCache, + parquetFilterPushDown) _ + // Create the function to set input paths at the driver side. + val setInputPaths = ParquetRelation2.initializeDriverSideJobFunc(inputFiles) _ + + val footers = inputFiles.map(f => metadataCache.footers(f.getPath)) + + Utils.withDummyCallSite(sqlContext.sparkContext) { + // TODO Stop using `FilteringParquetRowInputFormat` and overriding `getPartition`. + // After upgrading to Parquet 1.6.0, we should be able to stop caching `FileStatus` objects + // and footers. Especially when a global arbitrative schema (either from metastore or data + // source DDL) is available. + new SqlNewHadoopRDD( + sc = sqlContext.sparkContext, + broadcastedConf = broadcastedConf, + initDriverSideJobFuncOpt = Some(setInputPaths), + initLocalJobFuncOpt = Some(initLocalJobFuncOpt), + inputFormatClass = classOf[FilteringParquetRowInputFormat], + keyClass = classOf[Void], + valueClass = classOf[InternalRow]) { + + val cacheMetadata = useMetadataCache + + @transient val cachedStatuses = inputFiles.map { f => + // In order to encode the authority of a Path containing special characters such as '/' + // (which does happen in some S3N credentials), we need to use the string returned by the + // URI of the path to create a new Path. + val pathWithEscapedAuthority = escapePathUserInfo(f.getPath) + new FileStatus( + f.getLen, f.isDir, f.getReplication, f.getBlockSize, f.getModificationTime, + f.getAccessTime, f.getPermission, f.getOwner, f.getGroup, pathWithEscapedAuthority) + }.toSeq + + @transient val cachedFooters = footers.map { f => + // In order to encode the authority of a Path containing special characters such as /, + // we need to use the string returned by the URI of the path to create a new Path. + new Footer(escapePathUserInfo(f.getFile), f.getParquetMetadata) + }.toSeq + + private def escapePathUserInfo(path: Path): Path = { + val uri = path.toUri + new Path(new URI( + uri.getScheme, uri.getRawUserInfo, uri.getHost, uri.getPort, uri.getPath, + uri.getQuery, uri.getFragment)) + } + + // Overridden so we can inject our own cached files statuses. + override def getPartitions: Array[SparkPartition] = { + val inputFormat = if (cacheMetadata) { + new FilteringParquetRowInputFormat { + override def listStatus(jobContext: JobContext): JList[FileStatus] = cachedStatuses + override def getFooters(jobContext: JobContext): JList[Footer] = cachedFooters + } + } else { + new FilteringParquetRowInputFormat + } + + val jobContext = newJobContext(getConf(isDriverSide = true), jobId) + val rawSplits = inputFormat.getSplits(jobContext) + + Array.tabulate[SparkPartition](rawSplits.size) { i => + new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable]) + } + } + }.values.map(_.asInstanceOf[Row]) + } + } private class MetadataCache { // `FileStatus` objects of all "_metadata" files. @@ -226,127 +342,76 @@ private[sql] case class ParquetRelation2( private var commonMetadataStatuses: Array[FileStatus] = _ // Parquet footer cache. - var footers: Map[FileStatus, Footer] = _ + var footers: Map[Path, Footer] = _ // `FileStatus` objects of all data files (Parquet part-files). var dataStatuses: Array[FileStatus] = _ - // Partition spec of this table, including names, data types, and values of each partition - // column, and paths of each partition. - var partitionSpec: PartitionSpec = _ - // Schema of the actual Parquet files, without partition columns discovered from partition // directory paths. - var parquetSchema: StructType = _ + var dataSchema: StructType = null // Schema of the whole table, including partition columns. var schema: StructType = _ - // Indicates whether partition columns are also included in Parquet data file schema. If not, - // we need to fill in partition column values into read rows when scanning the table. - var partitionKeysIncludedInParquetSchema: Boolean = _ - - def prepareMetadata(path: Path, schema: StructType, conf: Configuration): Unit = { - conf.set( - ParquetOutputFormat.COMPRESSION, - ParquetRelation - .shortParquetCompressionCodecNames - .getOrElse( - sqlContext.conf.parquetCompressionCodec.toUpperCase, - CompressionCodecName.UNCOMPRESSED).name()) - - ParquetRelation.enableLogForwarding() - ParquetTypesConverter.writeMetaData(schema.toAttributes, path, conf) - } - /** * Refreshes `FileStatus`es, footers, partition spec, and table schema. */ def refresh(): Unit = { - // Support either reading a collection of raw Parquet part-files, or a collection of folders - // containing Parquet files (e.g. partitioned Parquet table). - val baseStatuses = paths.distinct.map { p => - val fs = FileSystem.get(URI.create(p), sparkContext.hadoopConfiguration) - val path = new Path(p) - val qualified = path.makeQualified(fs.getUri, fs.getWorkingDirectory) - - if (!fs.exists(qualified) && maybeSchema.isDefined) { - fs.mkdirs(qualified) - prepareMetadata(qualified, maybeSchema.get, sparkContext.hadoopConfiguration) - } - - fs.getFileStatus(qualified) - }.toArray - assert(baseStatuses.forall(!_.isDir) || baseStatuses.forall(_.isDir)) - // Lists `FileStatus`es of all leaf nodes (files) under all base directories. - val leaves = baseStatuses.flatMap { f => - val fs = FileSystem.get(f.getPath.toUri, sparkContext.hadoopConfiguration) - SparkHadoopUtil.get.listLeafStatuses(fs, f.getPath).filter { f => - isSummaryFile(f.getPath) || - !(f.getPath.getName.startsWith("_") || f.getPath.getName.startsWith(".")) - } - } + val leaves = cachedLeafStatuses().filter { f => + isSummaryFile(f.getPath) || + !(f.getPath.getName.startsWith("_") || f.getPath.getName.startsWith(".")) + }.toArray dataStatuses = leaves.filterNot(f => isSummaryFile(f.getPath)) metadataStatuses = leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_METADATA_FILE) commonMetadataStatuses = leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE) - footers = (dataStatuses ++ metadataStatuses ++ commonMetadataStatuses).par.map { f => - val parquetMetadata = ParquetFileReader.readFooter( - sparkContext.hadoopConfiguration, f, ParquetMetadataConverter.NO_FILTER) - f -> new Footer(f.getPath, parquetMetadata) - }.seq.toMap - - partitionSpec = maybePartitionSpec.getOrElse { - val partitionDirs = leaves - .filterNot(baseStatuses.contains) - .map(_.getPath.getParent) - .distinct - - if (partitionDirs.nonEmpty) { - // Parses names and values of partition columns, and infer their data types. - ParquetRelation2.parsePartitions(partitionDirs, defaultPartitionName) + footers = { + val conf = SparkHadoopUtil.get.conf + val taskSideMetaData = conf.getBoolean(ParquetInputFormat.TASK_SIDE_METADATA, true) + val rawFooters = if (shouldMergeSchemas) { + ParquetFileReader.readAllFootersInParallel( + conf, seqAsJavaList(leaves), taskSideMetaData) } else { - // No partition directories found, makes an empty specification - PartitionSpec(StructType(Seq.empty[StructField]), Seq.empty[Partition]) + ParquetFileReader.readAllFootersInParallelUsingSummaryFiles( + conf, seqAsJavaList(leaves), taskSideMetaData) } + + rawFooters.map(footer => footer.getFile -> footer).toMap } - // To get the schema. We first try to get the schema defined in maybeSchema. - // If maybeSchema is not defined, we will try to get the schema from existing parquet data - // (through readSchema). If data does not exist, we will try to get the schema defined in - // maybeMetastoreSchema (defined in the options of the data source). - // Finally, if we still could not get the schema. We throw an error. - parquetSchema = - maybeSchema - .orElse(readSchema()) - .orElse(maybeMetastoreSchema) - .getOrElse(sys.error("Failed to get the schema.")) - - partitionKeysIncludedInParquetSchema = - isPartitioned && - partitionColumns.forall(f => parquetSchema.fieldNames.contains(f.name)) - - schema = { - val fullRelationSchema = if (partitionKeysIncludedInParquetSchema) { - parquetSchema - } else { - StructType(parquetSchema.fields ++ partitionColumns.fields) + // If we already get the schema, don't need to re-compute it since the schema merging is + // time-consuming. + if (dataSchema == null) { + dataSchema = { + val dataSchema0 = maybeDataSchema + .orElse(readSchema()) + .orElse(maybeMetastoreSchema) + .getOrElse(throw new AnalysisException( + s"Failed to discover schema of Parquet file(s) in the following location(s):\n" + + paths.mkString("\n\t"))) + + // If this Parquet relation is converted from a Hive Metastore table, must reconcile case + // case insensitivity issue and possible schema mismatch (probably caused by schema + // evolution). + maybeMetastoreSchema + .map(ParquetRelation2.mergeMetastoreParquetSchema(_, dataSchema0)) + .getOrElse(dataSchema0) } - - // If this Parquet relation is converted from a Hive Metastore table, must reconcile case - // insensitivity issue and possible schema mismatch. - maybeMetastoreSchema - .map(ParquetRelation2.mergeMetastoreParquetSchema(_, fullRelationSchema)) - .getOrElse(fullRelationSchema) } } + private def isSummaryFile(file: Path): Boolean = { + file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE || + file.getName == ParquetFileWriter.PARQUET_METADATA_FILE + } + private def readSchema(): Option[StructType] = { // Sees which file(s) we need to touch in order to figure out the schema. - val filesToTouch = + // // Always tries the summary files first if users don't require a merged schema. In this case, // "_common_metadata" is more preferable than "_metadata" because it doesn't contain row // groups information, and could be much smaller for large Parquet files with lots of row @@ -365,6 +430,7 @@ private[sql] case class ParquetRelation2( // Here we tend to be pessimistic and take the second case into account. Basically this means // we can't trust the summary files if users require a merged schema, and must touch all part- // files to do the merge. + val filesToTouch = if (shouldMergeSchemas) { // Also includes summary files, 'cause there might be empty partition directories. (metadataStatuses ++ commonMetadataStatuses ++ dataStatuses).toSeq @@ -382,355 +448,66 @@ private[sql] case class ParquetRelation2( .toSeq } - ParquetRelation2.readSchema(filesToTouch.map(footers.apply), sqlContext) + assert( + filesToTouch.nonEmpty || maybeDataSchema.isDefined || maybeMetastoreSchema.isDefined, + "No schema defined, " + + s"and no Parquet data file or summary file found under ${paths.mkString(", ")}.") + + ParquetRelation2.readSchema(filesToTouch.map(f => footers.apply(f.getPath)), sqlContext) } } +} - @transient private val metadataCache = new MetadataCache - metadataCache.refresh() - - def partitionSpec: PartitionSpec = metadataCache.partitionSpec - - def partitionColumns: StructType = metadataCache.partitionSpec.partitionColumns - - def partitions: Seq[Partition] = metadataCache.partitionSpec.partitions - - def isPartitioned: Boolean = partitionColumns.nonEmpty - - private def partitionKeysIncludedInDataSchema = metadataCache.partitionKeysIncludedInParquetSchema - - private def parquetSchema = metadataCache.parquetSchema - - override def schema: StructType = metadataCache.schema - - private def isSummaryFile(file: Path): Boolean = { - file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE || - file.getName == ParquetFileWriter.PARQUET_METADATA_FILE - } +private[sql] object ParquetRelation2 extends Logging { + // Whether we should merge schemas collected from all Parquet part-files. + private[sql] val MERGE_SCHEMA = "mergeSchema" - // Skip type conversion - override val needConversion: Boolean = false + // Hive Metastore schema, used when converting Metastore Parquet tables. This option is only used + // internally. + private[sql] val METASTORE_SCHEMA = "metastoreSchema" - // TODO Should calculate per scan size - // It's common that a query only scans a fraction of a large Parquet file. Returning size of the - // whole Parquet file disables some optimizations in this case (e.g. broadcast join). - override val sizeInBytes = metadataCache.dataStatuses.map(_.getLen).sum - - // This is mostly a hack so that we can use the existing parquet filter code. - override def buildScan(output: Seq[Attribute], predicates: Seq[Expression]): RDD[Row] = { - val job = new Job(sparkContext.hadoopConfiguration) - ParquetInputFormat.setReadSupportClass(job, classOf[RowReadSupport]) - val jobConf: Configuration = ContextUtil.getConfiguration(job) - - val selectedPartitions = prunePartitions(predicates, partitions) - val selectedFiles = if (isPartitioned) { - selectedPartitions.flatMap { p => - metadataCache.dataStatuses.filter(_.getPath.getParent.toString == p.path) - } - } else { - metadataCache.dataStatuses.toSeq - } - val selectedFooters = selectedFiles.map(metadataCache.footers) - - // FileInputFormat cannot handle empty lists. - if (selectedFiles.nonEmpty) { - // In order to encode the authority of a Path containning special characters such as /, - // we need to use the string retruned by the URI of the path to create a new Path. - val selectedPaths = selectedFiles.map(status => new Path(status.getPath.toUri.toString)) - FileInputFormat.setInputPaths(job, selectedPaths: _*) - } + /** This closure sets various Parquet configurations at both driver side and executor side. */ + private[parquet] def initializeLocalJobFunc( + requiredColumns: Array[String], + filters: Array[Filter], + dataSchema: StructType, + useMetadataCache: Boolean, + parquetFilterPushDown: Boolean)(job: Job): Unit = { + val conf = job.getConfiguration + conf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[RowReadSupport].getName()) // Try to push down filters when filter push-down is enabled. - if (sqlContext.conf.parquetFilterPushDown) { - val partitionColNames = partitionColumns.map(_.name).toSet - predicates - // Don't push down predicates which reference partition columns - .filter { pred => - val referencedColNames = pred.references.map(_.name).toSet - referencedColNames.intersect(partitionColNames).isEmpty - } + if (parquetFilterPushDown) { + filters // Collects all converted Parquet filter predicates. Notice that not all predicates can be // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap` // is used here. - .flatMap(ParquetFilters.createFilter) + .flatMap(ParquetFilters.createFilter(dataSchema, _)) .reduceOption(FilterApi.and) - .foreach(ParquetInputFormat.setFilterPredicate(jobConf, _)) + .foreach(ParquetInputFormat.setFilterPredicate(conf, _)) } - if (isPartitioned) { - logInfo { - val percentRead = selectedPartitions.size.toDouble / partitions.size.toDouble * 100 - s"Reading $percentRead% of partitions" - } - } - - val requiredColumns = output.map(_.name) - val requestedSchema = StructType(requiredColumns.map(schema(_))) + conf.set(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA, { + val requestedSchema = StructType(requiredColumns.map(dataSchema(_))) + ParquetTypesConverter.convertToString(requestedSchema.toAttributes) + }) - // Store both requested and original schema in `Configuration` - jobConf.set( - RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA, - convertToString(requestedSchema.toAttributes)) - jobConf.set( + conf.set( RowWriteSupport.SPARK_ROW_SCHEMA, - convertToString(schema.toAttributes)) + ParquetTypesConverter.convertToString(dataSchema.toAttributes)) // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata - val useCache = sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA, "true").toBoolean - jobConf.set(SQLConf.PARQUET_CACHE_METADATA, useCache.toString) - - val baseRDD = - new NewHadoopRDD( - sparkContext, - classOf[FilteringParquetRowInputFormat], - classOf[Void], - classOf[Row], - jobConf) { - val cacheMetadata = useCache - - @transient - val cachedStatus = selectedFiles.map { st => - // In order to encode the authority of a Path containning special characters such as /, - // we need to use the string retruned by the URI of the path to create a new Path. - val newPath = new Path(st.getPath.toUri.toString) - - new FileStatus( - st.getLen, - st.isDir, - st.getReplication, - st.getBlockSize, - st.getModificationTime, - st.getAccessTime, - st.getPermission, - st.getOwner, - st.getGroup, - newPath) - } - - @transient - val cachedFooters = selectedFooters.map { f => - // In order to encode the authority of a Path containning special characters such as /, - // we need to use the string retruned by the URI of the path to create a new Path. - new Footer(new Path(f.getFile.toUri.toString), f.getParquetMetadata) - } - - - // Overridden so we can inject our own cached files statuses. - override def getPartitions: Array[SparkPartition] = { - val inputFormat = if (cacheMetadata) { - new FilteringParquetRowInputFormat { - override def listStatus(jobContext: JobContext): JList[FileStatus] = cachedStatus - - override def getFooters(jobContext: JobContext): JList[Footer] = cachedFooters - } - } else { - new FilteringParquetRowInputFormat - } - - val jobContext = newJobContext(getConf, jobId) - val rawSplits = inputFormat.getSplits(jobContext) - - Array.tabulate[SparkPartition](rawSplits.size) { i => - new NewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable]) - } - } - } - - // The ordinals for partition keys in the result row, if requested. - val partitionKeyLocations = partitionColumns.fieldNames.zipWithIndex.map { - case (name, index) => index -> requiredColumns.indexOf(name) - }.toMap.filter { - case (_, index) => index >= 0 - } - - // When the data does not include the key and the key is requested then we must fill it in - // based on information from the input split. - if (!partitionKeysIncludedInDataSchema && partitionKeyLocations.nonEmpty) { - // This check is based on CatalystConverter.createRootConverter. - val primitiveRow = - requestedSchema.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType)) - - baseRDD.mapPartitionsWithInputSplit { case (split: ParquetInputSplit, iterator) => - val partValues = selectedPartitions.collectFirst { - case p if split.getPath.getParent.toString == p.path => - CatalystTypeConverters.convertToCatalyst(p.values).asInstanceOf[Row] - }.get - - val requiredPartOrdinal = partitionKeyLocations.keys.toSeq - - if (primitiveRow) { - iterator.map { pair => - // We are using CatalystPrimitiveRowConverter and it returns a SpecificMutableRow. - val row = pair._2.asInstanceOf[SpecificMutableRow] - var i = 0 - while (i < requiredPartOrdinal.size) { - // TODO Avoids boxing cost here! - val partOrdinal = requiredPartOrdinal(i) - row.update(partitionKeyLocations(partOrdinal), partValues(partOrdinal)) - i += 1 - } - row - } - } else { - // Create a mutable row since we need to fill in values from partition columns. - val mutableRow = new GenericMutableRow(requestedSchema.size) - iterator.map { pair => - // We are using CatalystGroupConverter and it returns a GenericRow. - // Since GenericRow is not mutable, we just cast it to a Row. - val row = pair._2.asInstanceOf[Row] - var i = 0 - while (i < row.size) { - // TODO Avoids boxing cost here! - mutableRow(i) = row(i) - i += 1 - } - - i = 0 - while (i < requiredPartOrdinal.size) { - // TODO Avoids boxing cost here! - val partOrdinal = requiredPartOrdinal(i) - mutableRow.update(partitionKeyLocations(partOrdinal), partValues(partOrdinal)) - i += 1 - } - mutableRow - } - } - } - } else { - baseRDD.map(_._2) - } - } - - private def prunePartitions( - predicates: Seq[Expression], - partitions: Seq[Partition]): Seq[Partition] = { - val partitionColumnNames = partitionColumns.map(_.name).toSet - val partitionPruningPredicates = predicates.filter { - _.references.map(_.name).toSet.subsetOf(partitionColumnNames) - } - - val rawPredicate = - partitionPruningPredicates.reduceOption(expressions.And).getOrElse(Literal(true)) - val boundPredicate = InterpretedPredicate.create(rawPredicate transform { - case a: AttributeReference => - val index = partitionColumns.indexWhere(a.name == _.name) - BoundReference(index, partitionColumns(index).dataType, nullable = true) - }) - - if (isPartitioned && partitionPruningPredicates.nonEmpty) { - partitions.filter(p => boundPredicate(p.values)) - } else { - partitions - } + conf.setBoolean(SQLConf.PARQUET_CACHE_METADATA.key, useMetadataCache) } - override def insert(data: DataFrame, overwrite: Boolean): Unit = { - assert(paths.size == 1, s"Can't write to multiple destinations: ${paths.mkString(",")}") - - // TODO: currently we do not check whether the "schema"s are compatible - // That means if one first creates a table and then INSERTs data with - // and incompatible schema the execution will fail. It would be nice - // to catch this early one, maybe having the planner validate the schema - // before calling execute(). - - val job = new Job(sqlContext.sparkContext.hadoopConfiguration) - val writeSupport = - if (parquetSchema.map(_.dataType).forall(ParquetTypesConverter.isPrimitiveType)) { - log.debug("Initializing MutableRowWriteSupport") - classOf[MutableRowWriteSupport] - } else { - classOf[RowWriteSupport] - } - - ParquetOutputFormat.setWriteSupportClass(job, writeSupport) - - val conf = ContextUtil.getConfiguration(job) - RowWriteSupport.setSchema(data.schema.toAttributes, conf) - - val destinationPath = new Path(paths.head) - - if (overwrite) { - val fs = destinationPath.getFileSystem(conf) - if (fs.exists(destinationPath)) { - var success: Boolean = false - try { - success = fs.delete(destinationPath, true) - } catch { - case e: IOException => - throw new IOException( - s"Unable to clear output directory ${destinationPath.toString} prior" + - s" to writing to Parquet table:\n${e.toString}") - } - if (!success) { - throw new IOException( - s"Unable to clear output directory ${destinationPath.toString} prior" + - s" to writing to Parquet table.") - } - } - } - - job.setOutputKeyClass(classOf[Void]) - job.setOutputValueClass(classOf[Row]) - FileOutputFormat.setOutputPath(job, destinationPath) - - val wrappedConf = new SerializableWritable(job.getConfiguration) - val jobTrackerId = new SimpleDateFormat("yyyyMMddHHmm").format(new Date()) - val stageId = sqlContext.sparkContext.newRddId() - - val taskIdOffset = if (overwrite) { - 1 - } else { - FileSystemHelper.findMaxTaskId( - FileOutputFormat.getOutputPath(job).toString, job.getConfiguration) + 1 - } - - def writeShard(context: TaskContext, iterator: Iterator[Row]): Unit = { - /* "reduce task" */ - val attemptId = newTaskAttemptID( - jobTrackerId, stageId, isMap = false, context.partitionId(), context.attemptNumber()) - val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId) - val format = new AppendingParquetOutputFormat(taskIdOffset) - val committer = format.getOutputCommitter(hadoopContext) - committer.setupTask(hadoopContext) - val writer = format.getRecordWriter(hadoopContext) - try { - while (iterator.hasNext) { - val row = iterator.next() - writer.write(null, row) - } - } finally { - writer.close(hadoopContext) - } - - SparkHadoopMapRedUtil.commitTask(committer, hadoopContext, context) + /** This closure sets input paths at the driver side. */ + private[parquet] def initializeDriverSideJobFunc( + inputFiles: Array[FileStatus])(job: Job): Unit = { + // We side the input paths at the driver side. + if (inputFiles.nonEmpty) { + FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*) } - val jobFormat = new AppendingParquetOutputFormat(taskIdOffset) - /* apparently we need a TaskAttemptID to construct an OutputCommitter; - * however we're only going to use this local OutputCommitter for - * setupJob/commitJob, so we just use a dummy "map" task. - */ - val jobAttemptId = newTaskAttemptID(jobTrackerId, stageId, isMap = true, 0, 0) - val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId) - val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext) - - jobCommitter.setupJob(jobTaskContext) - sqlContext.sparkContext.runJob(data.queryExecution.executedPlan.execute(), writeShard _) - jobCommitter.commitJob(jobTaskContext) - - metadataCache.refresh() } -} - -private[sql] object ParquetRelation2 extends Logging { - // Whether we should merge schemas collected from all Parquet part-files. - val MERGE_SCHEMA = "mergeSchema" - - // Default partition name to use when the partition column value is null or empty string. - val DEFAULT_PARTITION_NAME = "partition.defaultName" - - // Hive Metastore schema, used when converting Metastore Parquet tables. This option is only used - // internally. - private[sql] val METASTORE_SCHEMA = "metastoreSchema" private[parquet] def readSchema( footers: Seq[Footer], sqlContext: SQLContext): Option[StructType] = { @@ -766,7 +543,7 @@ private[sql] object ParquetRelation2 extends Logging { // Falls back to Parquet schema if Spark SQL schema is absent. StructType.fromAttributes( // TODO Really no need to use `Attribute` here, we only need to know the data type. - convertToAttributes( + ParquetTypesConverter.convertToAttributes( parquetSchema, sqlContext.conf.isParquetBinaryAsString, sqlContext.conf.isParquetINT96AsTimestamp)) @@ -805,7 +582,8 @@ private[sql] object ParquetRelation2 extends Logging { val ordinalMap = metastoreSchema.zipWithIndex.map { case (field, index) => field.name.toLowerCase -> index }.toMap - val reorderedParquetSchema = mergedParquetSchema.sortBy(f => + + val reorderedParquetSchema = mergedParquetSchema.sortBy(f => ordinalMap.getOrElse(f.name.toLowerCase, metastoreSchema.size + 1)) StructType(metastoreSchema.zip(reorderedParquetSchema).map { @@ -841,178 +619,4 @@ private[sql] object ParquetRelation2 extends Logging { .filter(_.nullable) StructType(parquetSchema ++ missingFields) } - - - // TODO Data source implementations shouldn't touch Catalyst types (`Literal`). - // However, we are already using Catalyst expressions for partition pruning and predicate - // push-down here... - private[parquet] case class PartitionValues(columnNames: Seq[String], literals: Seq[Literal]) { - require(columnNames.size == literals.size) - } - - /** - * Given a group of qualified paths, tries to parse them and returns a partition specification. - * For example, given: - * {{{ - * hdfs://:/path/to/partition/a=1/b=hello/c=3.14 - * hdfs://:/path/to/partition/a=2/b=world/c=6.28 - * }}} - * it returns: - * {{{ - * PartitionSpec( - * partitionColumns = StructType( - * StructField(name = "a", dataType = IntegerType, nullable = true), - * StructField(name = "b", dataType = StringType, nullable = true), - * StructField(name = "c", dataType = DoubleType, nullable = true)), - * partitions = Seq( - * Partition( - * values = Row(1, "hello", 3.14), - * path = "hdfs://:/path/to/partition/a=1/b=hello/c=3.14"), - * Partition( - * values = Row(2, "world", 6.28), - * path = "hdfs://:/path/to/partition/a=2/b=world/c=6.28"))) - * }}} - */ - private[parquet] def parsePartitions( - paths: Seq[Path], - defaultPartitionName: String): PartitionSpec = { - val partitionValues = resolvePartitions(paths.map(parsePartition(_, defaultPartitionName))) - val fields = { - val (PartitionValues(columnNames, literals)) = partitionValues.head - columnNames.zip(literals).map { case (name, Literal(_, dataType)) => - StructField(name, dataType, nullable = true) - } - } - - val partitions = partitionValues.zip(paths).map { - case (PartitionValues(_, literals), path) => - Partition(Row(literals.map(_.value): _*), path.toString) - } - - PartitionSpec(StructType(fields), partitions) - } - - /** - * Parses a single partition, returns column names and values of each partition column. For - * example, given: - * {{{ - * path = hdfs://:/path/to/partition/a=42/b=hello/c=3.14 - * }}} - * it returns: - * {{{ - * PartitionValues( - * Seq("a", "b", "c"), - * Seq( - * Literal.create(42, IntegerType), - * Literal.create("hello", StringType), - * Literal.create(3.14, FloatType))) - * }}} - */ - private[parquet] def parsePartition( - path: Path, - defaultPartitionName: String): PartitionValues = { - val columns = ArrayBuffer.empty[(String, Literal)] - // Old Hadoop versions don't have `Path.isRoot` - var finished = path.getParent == null - var chopped = path - - while (!finished) { - val maybeColumn = parsePartitionColumn(chopped.getName, defaultPartitionName) - maybeColumn.foreach(columns += _) - chopped = chopped.getParent - finished = maybeColumn.isEmpty || chopped.getParent == null - } - - val (columnNames, values) = columns.reverse.unzip - PartitionValues(columnNames, values) - } - - private def parsePartitionColumn( - columnSpec: String, - defaultPartitionName: String): Option[(String, Literal)] = { - val equalSignIndex = columnSpec.indexOf('=') - if (equalSignIndex == -1) { - None - } else { - val columnName = columnSpec.take(equalSignIndex) - assert(columnName.nonEmpty, s"Empty partition column name in '$columnSpec'") - - val rawColumnValue = columnSpec.drop(equalSignIndex + 1) - assert(rawColumnValue.nonEmpty, s"Empty partition column value in '$columnSpec'") - - val literal = inferPartitionColumnValue(rawColumnValue, defaultPartitionName) - Some(columnName -> literal) - } - } - - /** - * Resolves possible type conflicts between partitions by up-casting "lower" types. The up- - * casting order is: - * {{{ - * NullType -> - * IntegerType -> LongType -> - * FloatType -> DoubleType -> DecimalType.Unlimited -> - * StringType - * }}} - */ - private[parquet] def resolvePartitions(values: Seq[PartitionValues]): Seq[PartitionValues] = { - // Column names of all partitions must match - val distinctPartitionsColNames = values.map(_.columnNames).distinct - assert(distinctPartitionsColNames.size == 1, { - val list = distinctPartitionsColNames.mkString("\t", "\n", "") - s"Conflicting partition column names detected:\n$list" - }) - - // Resolves possible type conflicts for each column - val columnCount = values.head.columnNames.size - val resolvedValues = (0 until columnCount).map { i => - resolveTypeConflicts(values.map(_.literals(i))) - } - - // Fills resolved literals back to each partition - values.zipWithIndex.map { case (d, index) => - d.copy(literals = resolvedValues.map(_(index))) - } - } - - /** - * Converts a string to a `Literal` with automatic type inference. Currently only supports - * [[IntegerType]], [[LongType]], [[FloatType]], [[DoubleType]], [[DecimalType.Unlimited]], and - * [[StringType]]. - */ - private[parquet] def inferPartitionColumnValue( - raw: String, - defaultPartitionName: String): Literal = { - // First tries integral types - Try(Literal.create(Integer.parseInt(raw), IntegerType)) - .orElse(Try(Literal.create(JLong.parseLong(raw), LongType))) - // Then falls back to fractional types - .orElse(Try(Literal.create(JFloat.parseFloat(raw), FloatType))) - .orElse(Try(Literal.create(JDouble.parseDouble(raw), DoubleType))) - .orElse(Try(Literal.create(new JBigDecimal(raw), DecimalType.Unlimited))) - // Then falls back to string - .getOrElse { - if (raw == defaultPartitionName) Literal.create(null, NullType) - else Literal.create(raw, StringType) - } - } - - private val upCastingOrder: Seq[DataType] = - Seq(NullType, IntegerType, LongType, FloatType, DoubleType, DecimalType.Unlimited, StringType) - - /** - * Given a collection of [[Literal]]s, resolves possible type conflicts by up-casting "lower" - * types. - */ - private def resolveTypeConflicts(literals: Seq[Literal]): Seq[Literal] = { - val desiredType = { - val topType = literals.map(_.dataType).maxBy(upCastingOrder.indexOf(_)) - // Falls back to string if all values of this column are null or empty string - if (topType == NullType) StringType else topType - } - - literals.map { case l @ Literal(_, dataType) => - Literal.create(Cast(l, desiredType).eval(), desiredType) - } - } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala index 70bcca7526aae..4d5ed211ad0c0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala @@ -19,8 +19,8 @@ package org.apache.spark.sql.parquet.timestamp import java.nio.{ByteBuffer, ByteOrder} -import parquet.Preconditions -import parquet.io.api.{Binary, RecordConsumer} +import org.apache.parquet.Preconditions +import org.apache.parquet.io.api.{Binary, RecordConsumer} private[parquet] class NanoTime extends Serializable { private var julianDay = 0 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala index b3d71f687a60a..a8f56f4767407 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala @@ -17,75 +17,265 @@ package org.apache.spark.sql.sources -import org.apache.spark.rdd.RDD +import org.apache.spark.{Logging, TaskContext} +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD} +import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.execution.SparkPlan -import org.apache.spark.sql.types.{UTF8String, StringType} -import org.apache.spark.sql.{Row, Strategy, execution, sources} +import org.apache.spark.sql.types.{StringType, StructType} +import org.apache.spark.sql.{SaveMode, Strategy, execution, sources} +import org.apache.spark.util.{SerializableConfiguration, Utils} +import org.apache.spark.unsafe.types.UTF8String /** * A Strategy for planning scans over data sources defined using the sources API. */ -private[sql] object DataSourceStrategy extends Strategy { +private[sql] object DataSourceStrategy extends Strategy with Logging { def apply(plan: LogicalPlan): Seq[execution.SparkPlan] = plan match { - case PhysicalOperation(projectList, filters, l @ LogicalRelation(t: CatalystScan)) => + case PhysicalOperation(projects, filters, l @ LogicalRelation(t: CatalystScan)) => pruneFilterProjectRaw( l, - projectList, + projects, filters, - (a, f) => t.buildScan(a, f)) :: Nil + (a, f) => toCatalystRDD(l, a, t.buildScan(a, f))) :: Nil - case PhysicalOperation(projectList, filters, l @ LogicalRelation(t: PrunedFilteredScan)) => + case PhysicalOperation(projects, filters, l @ LogicalRelation(t: PrunedFilteredScan)) => pruneFilterProject( l, - projectList, + projects, filters, - (a, f) => t.buildScan(a, f)) :: Nil + (a, f) => toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray, f))) :: Nil - case PhysicalOperation(projectList, filters, l @ LogicalRelation(t: PrunedScan)) => + case PhysicalOperation(projects, filters, l @ LogicalRelation(t: PrunedScan)) => pruneFilterProject( l, - projectList, + projects, filters, - (a, _) => t.buildScan(a)) :: Nil + (a, _) => toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray))) :: Nil + + // Scanning partitioned HadoopFsRelation + case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation)) + if t.partitionSpec.partitionColumns.nonEmpty => + val selectedPartitions = prunePartitions(filters, t.partitionSpec).toArray + + logInfo { + val total = t.partitionSpec.partitions.length + val selected = selectedPartitions.length + val percentPruned = (1 - total.toDouble / selected.toDouble) * 100 + s"Selected $selected partitions out of $total, pruned $percentPruned% partitions." + } + + // Only pushes down predicates that do not reference partition columns. + val pushedFilters = { + val partitionColumnNames = t.partitionSpec.partitionColumns.map(_.name).toSet + filters.filter { f => + val referencedColumnNames = f.references.map(_.name).toSet + referencedColumnNames.intersect(partitionColumnNames).isEmpty + } + } + + buildPartitionedTableScan( + l, + projects, + pushedFilters, + t.partitionSpec.partitionColumns, + selectedPartitions) :: Nil + + // Scanning non-partitioned HadoopFsRelation + case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation)) => + // See buildPartitionedTableScan for the reason that we need to create a shard + // broadcast HadoopConf. + val sharedHadoopConf = SparkHadoopUtil.get.conf + val confBroadcast = + t.sqlContext.sparkContext.broadcast(new SerializableConfiguration(sharedHadoopConf)) + pruneFilterProject( + l, + projects, + filters, + (a, f) => + toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray, f, t.paths, confBroadcast))) :: Nil case l @ LogicalRelation(t: TableScan) => - createPhysicalRDD(l.relation, l.output, t.buildScan()) :: Nil + execution.PhysicalRDD(l.output, toCatalystRDD(l, t.buildScan())) :: Nil case i @ logical.InsertIntoTable( l @ LogicalRelation(t: InsertableRelation), part, query, overwrite, false) if part.isEmpty => execution.ExecutedCommand(InsertIntoDataSource(l, query, overwrite)) :: Nil + case i @ logical.InsertIntoTable( + l @ LogicalRelation(t: HadoopFsRelation), part, query, overwrite, false) => + val mode = if (overwrite) SaveMode.Overwrite else SaveMode.Append + execution.ExecutedCommand(InsertIntoHadoopFsRelation(t, query, mode)) :: Nil + case _ => Nil } + private def buildPartitionedTableScan( + logicalRelation: LogicalRelation, + projections: Seq[NamedExpression], + filters: Seq[Expression], + partitionColumns: StructType, + partitions: Array[Partition]) = { + val relation = logicalRelation.relation.asInstanceOf[HadoopFsRelation] + + // Because we are creating one RDD per partition, we need to have a shared HadoopConf. + // Otherwise, the cost of broadcasting HadoopConf in every RDD will be high. + val sharedHadoopConf = SparkHadoopUtil.get.conf + val confBroadcast = + relation.sqlContext.sparkContext.broadcast(new SerializableConfiguration(sharedHadoopConf)) + + // Builds RDD[Row]s for each selected partition. + val perPartitionRows = partitions.map { case Partition(partitionValues, dir) => + // The table scan operator (PhysicalRDD) which retrieves required columns from data files. + // Notice that the schema of data files, represented by `relation.dataSchema`, may contain + // some partition column(s). + val scan = + pruneFilterProject( + logicalRelation, + projections, + filters, + (columns: Seq[Attribute], filters) => { + val partitionColNames = partitionColumns.fieldNames + + // Don't scan any partition columns to save I/O. Here we are being optimistic and + // assuming partition columns data stored in data files are always consistent with those + // partition values encoded in partition directory paths. + val needed = columns.filterNot(a => partitionColNames.contains(a.name)) + val dataRows = + relation.buildScan(needed.map(_.name).toArray, filters, Array(dir), confBroadcast) + + // Merges data values with partition values. + mergeWithPartitionValues( + relation.schema, + columns.map(_.name).toArray, + partitionColNames, + partitionValues, + toCatalystRDD(logicalRelation, needed, dataRows)) + }) + + scan.execute() + } + + val unionedRows = + if (perPartitionRows.length == 0) { + relation.sqlContext.emptyResult + } else { + new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows) + } + + execution.PhysicalRDD(projections.map(_.toAttribute), unionedRows) + } + + private def mergeWithPartitionValues( + schema: StructType, + requiredColumns: Array[String], + partitionColumns: Array[String], + partitionValues: InternalRow, + dataRows: RDD[InternalRow]): RDD[InternalRow] = { + val nonPartitionColumns = requiredColumns.filterNot(partitionColumns.contains) + + // If output columns contain any partition column(s), we need to merge scanned data + // columns and requested partition columns to form the final result. + if (!requiredColumns.sameElements(nonPartitionColumns)) { + val mergers = requiredColumns.zipWithIndex.map { case (name, index) => + // To see whether the `index`-th column is a partition column... + val i = partitionColumns.indexOf(name) + if (i != -1) { + // If yes, gets column value from partition values. + (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => { + mutableRow(ordinal) = partitionValues(i) + } + } else { + // Otherwise, inherits the value from scanned data. + val i = nonPartitionColumns.indexOf(name) + (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => { + mutableRow(ordinal) = dataRow(i) + } + } + } + + // Since we know for sure that this closure is serializable, we can avoid the overhead + // of cleaning a closure for each RDD by creating our own MapPartitionsRDD. Functionally + // this is equivalent to calling `dataRows.mapPartitions(mapPartitionsFunc)` (SPARK-7718). + val mapPartitionsFunc = (_: TaskContext, _: Int, iterator: Iterator[InternalRow]) => { + val dataTypes = requiredColumns.map(schema(_).dataType) + val mutableRow = new SpecificMutableRow(dataTypes) + iterator.map { dataRow => + var i = 0 + while (i < mutableRow.length) { + mergers(i)(mutableRow, dataRow, i) + i += 1 + } + mutableRow.asInstanceOf[InternalRow] + } + } + + // This is an internal RDD whose call site the user should not be concerned with + // Since we create many of these (one per partition), the time spent on computing + // the call site may add up. + Utils.withDummyCallSite(dataRows.sparkContext) { + new MapPartitionsRDD(dataRows, mapPartitionsFunc, preservesPartitioning = false) + } + + } else { + dataRows + } + } + + protected def prunePartitions( + predicates: Seq[Expression], + partitionSpec: PartitionSpec): Seq[Partition] = { + val PartitionSpec(partitionColumns, partitions) = partitionSpec + val partitionColumnNames = partitionColumns.map(_.name).toSet + val partitionPruningPredicates = predicates.filter { + _.references.map(_.name).toSet.subsetOf(partitionColumnNames) + } + + if (partitionPruningPredicates.nonEmpty) { + val predicate = + partitionPruningPredicates + .reduceOption(expressions.And) + .getOrElse(Literal(true)) + + val boundPredicate = InterpretedPredicate.create(predicate.transform { + case a: AttributeReference => + val index = partitionColumns.indexWhere(a.name == _.name) + BoundReference(index, partitionColumns(index).dataType, nullable = true) + }) + + partitions.filter { case Partition(values, _) => boundPredicate(values) } + } else { + partitions + } + } + // Based on Public API. protected def pruneFilterProject( relation: LogicalRelation, - projectList: Seq[NamedExpression], + projects: Seq[NamedExpression], filterPredicates: Seq[Expression], - scanBuilder: (Array[String], Array[Filter]) => RDD[Row]) = { + scanBuilder: (Seq[Attribute], Array[Filter]) => RDD[InternalRow]) = { pruneFilterProjectRaw( relation, - projectList, + projects, filterPredicates, (requestedColumns, pushedFilters) => { - scanBuilder(requestedColumns.map(_.name).toArray, selectFilters(pushedFilters).toArray) + scanBuilder(requestedColumns, selectFilters(pushedFilters).toArray) }) } // Based on Catalyst expressions. protected def pruneFilterProjectRaw( relation: LogicalRelation, - projectList: Seq[NamedExpression], + projects: Seq[NamedExpression], filterPredicates: Seq[Expression], - scanBuilder: (Seq[Attribute], Seq[Expression]) => RDD[Row]) = { + scanBuilder: (Seq[Attribute], Seq[Expression]) => RDD[InternalRow]) = { - val projectSet = AttributeSet(projectList.flatMap(_.references)) + val projectSet = AttributeSet(projects.flatMap(_.references)) val filterSet = AttributeSet(filterPredicates.flatMap(_.references)) val filterCondition = filterPredicates.reduceLeftOption(expressions.And) @@ -93,38 +283,47 @@ private[sql] object DataSourceStrategy extends Strategy { case a: AttributeReference => relation.attributeMap(a) // Match original case of attributes. }} - if (projectList.map(_.toAttribute) == projectList && - projectSet.size == projectList.size && + if (projects.map(_.toAttribute) == projects && + projectSet.size == projects.size && filterSet.subsetOf(projectSet)) { // When it is possible to just use column pruning to get the right projection and // when the columns of this projection are enough to evaluate all filter conditions, // just do a scan followed by a filter, with no extra project. val requestedColumns = - projectList.asInstanceOf[Seq[Attribute]] // Safe due to if above. + projects.asInstanceOf[Seq[Attribute]] // Safe due to if above. .map(relation.attributeMap) // Match original case of attributes. - val scan = createPhysicalRDD(relation.relation, projectList.map(_.toAttribute), - scanBuilder(requestedColumns, pushedFilters)) + val scan = execution.PhysicalRDD(projects.map(_.toAttribute), + scanBuilder(requestedColumns, pushedFilters)) filterCondition.map(execution.Filter(_, scan)).getOrElse(scan) } else { val requestedColumns = (projectSet ++ filterSet).map(relation.attributeMap).toSeq - val scan = createPhysicalRDD(relation.relation, requestedColumns, + val scan = execution.PhysicalRDD(requestedColumns, scanBuilder(requestedColumns, pushedFilters)) - execution.Project(projectList, filterCondition.map(execution.Filter(_, scan)).getOrElse(scan)) + execution.Project(projects, filterCondition.map(execution.Filter(_, scan)).getOrElse(scan)) } } - private[this] def createPhysicalRDD( - relation: BaseRelation, + /** + * Convert RDD of Row into RDD of InternalRow with objects in catalyst types + */ + private[this] def toCatalystRDD( + relation: LogicalRelation, output: Seq[Attribute], - rdd: RDD[Row]): SparkPlan = { - val converted = if (relation.needConversion) { - execution.RDDConversions.rowToRowRdd(rdd, relation.schema) + rdd: RDD[Row]): RDD[InternalRow] = { + if (relation.relation.needConversion) { + execution.RDDConversions.rowToRowRdd(rdd.asInstanceOf[RDD[Row]], output.map(_.dataType)) } else { - rdd + rdd.map(_.asInstanceOf[InternalRow]) } - execution.PhysicalRDD(output, converted) + } + + /** + * Convert RDD of Row into RDD of InternalRow with objects in catalyst types + */ + private[this] def toCatalystRDD(relation: LogicalRelation, rdd: RDD[Row]): RDD[InternalRow] = { + toCatalystRDD(relation, relation.output, rdd) } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala new file mode 100644 index 0000000000000..c6f535dde7676 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala @@ -0,0 +1,333 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources + +import java.lang.{Double => JDouble, Float => JFloat, Integer => JInteger, Long => JLong} +import java.math.{BigDecimal => JBigDecimal} + +import scala.collection.mutable.ArrayBuffer +import scala.util.Try + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.util.Shell +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Cast, Literal} +import org.apache.spark.sql.types._ + +private[sql] case class Partition(values: InternalRow, path: String) + +private[sql] case class PartitionSpec(partitionColumns: StructType, partitions: Seq[Partition]) + +private[sql] object PartitionSpec { + val emptySpec = PartitionSpec(StructType(Seq.empty[StructField]), Seq.empty[Partition]) +} + +private[sql] object PartitioningUtils { + // This duplicates default value of Hive `ConfVars.DEFAULTPARTITIONNAME`, since sql/core doesn't + // depend on Hive. + private[sql] val DEFAULT_PARTITION_NAME = "__HIVE_DEFAULT_PARTITION__" + + private[sql] case class PartitionValues(columnNames: Seq[String], literals: Seq[Literal]) { + require(columnNames.size == literals.size) + } + + /** + * Given a group of qualified paths, tries to parse them and returns a partition specification. + * For example, given: + * {{{ + * hdfs://:/path/to/partition/a=1/b=hello/c=3.14 + * hdfs://:/path/to/partition/a=2/b=world/c=6.28 + * }}} + * it returns: + * {{{ + * PartitionSpec( + * partitionColumns = StructType( + * StructField(name = "a", dataType = IntegerType, nullable = true), + * StructField(name = "b", dataType = StringType, nullable = true), + * StructField(name = "c", dataType = DoubleType, nullable = true)), + * partitions = Seq( + * Partition( + * values = Row(1, "hello", 3.14), + * path = "hdfs://:/path/to/partition/a=1/b=hello/c=3.14"), + * Partition( + * values = Row(2, "world", 6.28), + * path = "hdfs://:/path/to/partition/a=2/b=world/c=6.28"))) + * }}} + */ + private[sql] def parsePartitions( + paths: Seq[Path], + defaultPartitionName: String, + typeInference: Boolean): PartitionSpec = { + // First, we need to parse every partition's path and see if we can find partition values. + val pathsWithPartitionValues = paths.flatMap { path => + parsePartition(path, defaultPartitionName, typeInference).map(path -> _) + } + + if (pathsWithPartitionValues.isEmpty) { + // This dataset is not partitioned. + PartitionSpec.emptySpec + } else { + // This dataset is partitioned. We need to check whether all partitions have the same + // partition columns and resolve potential type conflicts. + val resolvedPartitionValues = resolvePartitions(pathsWithPartitionValues.map(_._2)) + + // Creates the StructType which represents the partition columns. + val fields = { + val PartitionValues(columnNames, literals) = resolvedPartitionValues.head + columnNames.zip(literals).map { case (name, Literal(_, dataType)) => + // We always assume partition columns are nullable since we've no idea whether null values + // will be appended in the future. + StructField(name, dataType, nullable = true) + } + } + + // Finally, we create `Partition`s based on paths and resolved partition values. + val partitions = resolvedPartitionValues.zip(pathsWithPartitionValues).map { + case (PartitionValues(_, literals), (path, _)) => + Partition(InternalRow.fromSeq(literals.map(_.value)), path.toString) + } + + PartitionSpec(StructType(fields), partitions) + } + } + + /** + * Parses a single partition, returns column names and values of each partition column. For + * example, given: + * {{{ + * path = hdfs://:/path/to/partition/a=42/b=hello/c=3.14 + * }}} + * it returns: + * {{{ + * PartitionValues( + * Seq("a", "b", "c"), + * Seq( + * Literal.create(42, IntegerType), + * Literal.create("hello", StringType), + * Literal.create(3.14, FloatType))) + * }}} + */ + private[sql] def parsePartition( + path: Path, + defaultPartitionName: String, + typeInference: Boolean): Option[PartitionValues] = { + val columns = ArrayBuffer.empty[(String, Literal)] + // Old Hadoop versions don't have `Path.isRoot` + var finished = path.getParent == null + var chopped = path + + while (!finished) { + // Sometimes (e.g., when speculative task is enabled), temporary directories may be left + // uncleaned. Here we simply ignore them. + if (chopped.getName.toLowerCase == "_temporary") { + return None + } + + val maybeColumn = parsePartitionColumn(chopped.getName, defaultPartitionName, typeInference) + maybeColumn.foreach(columns += _) + chopped = chopped.getParent + finished = maybeColumn.isEmpty || chopped.getParent == null + } + + if (columns.isEmpty) { + None + } else { + val (columnNames, values) = columns.reverse.unzip + Some(PartitionValues(columnNames, values)) + } + } + + private def parsePartitionColumn( + columnSpec: String, + defaultPartitionName: String, + typeInference: Boolean): Option[(String, Literal)] = { + val equalSignIndex = columnSpec.indexOf('=') + if (equalSignIndex == -1) { + None + } else { + val columnName = columnSpec.take(equalSignIndex) + assert(columnName.nonEmpty, s"Empty partition column name in '$columnSpec'") + + val rawColumnValue = columnSpec.drop(equalSignIndex + 1) + assert(rawColumnValue.nonEmpty, s"Empty partition column value in '$columnSpec'") + + val literal = inferPartitionColumnValue(rawColumnValue, defaultPartitionName, typeInference) + Some(columnName -> literal) + } + } + + /** + * Resolves possible type conflicts between partitions by up-casting "lower" types. The up- + * casting order is: + * {{{ + * NullType -> + * IntegerType -> LongType -> + * DoubleType -> DecimalType.Unlimited -> + * StringType + * }}} + */ + private[sql] def resolvePartitions(values: Seq[PartitionValues]): Seq[PartitionValues] = { + // Column names of all partitions must match + val distinctPartitionsColNames = values.map(_.columnNames).distinct + + if (distinctPartitionsColNames.isEmpty) { + Seq.empty + } else { + assert(distinctPartitionsColNames.size == 1, { + val list = distinctPartitionsColNames.mkString("\t", "\n\t", "") + s"Conflicting partition column names detected:\n$list" + }) + + // Resolves possible type conflicts for each column + val columnCount = values.head.columnNames.size + val resolvedValues = (0 until columnCount).map { i => + resolveTypeConflicts(values.map(_.literals(i))) + } + + // Fills resolved literals back to each partition + values.zipWithIndex.map { case (d, index) => + d.copy(literals = resolvedValues.map(_(index))) + } + } + } + + /** + * Converts a string to a [[Literal]] with automatic type inference. Currently only supports + * [[IntegerType]], [[LongType]], [[DoubleType]], [[DecimalType.Unlimited]], and + * [[StringType]]. + */ + private[sql] def inferPartitionColumnValue( + raw: String, + defaultPartitionName: String, + typeInference: Boolean): Literal = { + if (typeInference) { + // First tries integral types + Try(Literal.create(Integer.parseInt(raw), IntegerType)) + .orElse(Try(Literal.create(JLong.parseLong(raw), LongType))) + // Then falls back to fractional types + .orElse(Try(Literal.create(JDouble.parseDouble(raw), DoubleType))) + .orElse(Try(Literal.create(new JBigDecimal(raw), DecimalType.Unlimited))) + // Then falls back to string + .getOrElse { + if (raw == defaultPartitionName) { + Literal.create(null, NullType) + } else { + Literal.create(unescapePathName(raw), StringType) + } + } + } else { + if (raw == defaultPartitionName) { + Literal.create(null, NullType) + } else { + Literal.create(unescapePathName(raw), StringType) + } + } + } + + private val upCastingOrder: Seq[DataType] = + Seq(NullType, IntegerType, LongType, FloatType, DoubleType, DecimalType.Unlimited, StringType) + + /** + * Given a collection of [[Literal]]s, resolves possible type conflicts by up-casting "lower" + * types. + */ + private def resolveTypeConflicts(literals: Seq[Literal]): Seq[Literal] = { + val desiredType = { + val topType = literals.map(_.dataType).maxBy(upCastingOrder.indexOf(_)) + // Falls back to string if all values of this column are null or empty string + if (topType == NullType) StringType else topType + } + + literals.map { case l @ Literal(_, dataType) => + Literal.create(Cast(l, desiredType).eval(), desiredType) + } + } + + ////////////////////////////////////////////////////////////////////////////////////////////////// + // The following string escaping code is mainly copied from Hive (o.a.h.h.common.FileUtils). + ////////////////////////////////////////////////////////////////////////////////////////////////// + + val charToEscape = { + val bitSet = new java.util.BitSet(128) + + /** + * ASCII 01-1F are HTTP control characters that need to be escaped. + * \u000A and \u000D are \n and \r, respectively. + */ + val clist = Array( + '\u0001', '\u0002', '\u0003', '\u0004', '\u0005', '\u0006', '\u0007', '\u0008', '\u0009', + '\n', '\u000B', '\u000C', '\r', '\u000E', '\u000F', '\u0010', '\u0011', '\u0012', '\u0013', + '\u0014', '\u0015', '\u0016', '\u0017', '\u0018', '\u0019', '\u001A', '\u001B', '\u001C', + '\u001D', '\u001E', '\u001F', '"', '#', '%', '\'', '*', '/', ':', '=', '?', '\\', '\u007F', + '{', '[', ']', '^') + + clist.foreach(bitSet.set(_)) + + if (Shell.WINDOWS) { + Array(' ', '<', '>', '|').foreach(bitSet.set(_)) + } + + bitSet + } + + def needsEscaping(c: Char): Boolean = { + c >= 0 && c < charToEscape.size() && charToEscape.get(c) + } + + def escapePathName(path: String): String = { + val builder = new StringBuilder() + path.foreach { c => + if (needsEscaping(c)) { + builder.append('%') + builder.append(f"${c.asInstanceOf[Int]}%02x") + } else { + builder.append(c) + } + } + + builder.toString() + } + + def unescapePathName(path: String): String = { + val sb = new StringBuilder + var i = 0 + + while (i < path.length) { + val c = path.charAt(i) + if (c == '%' && i + 2 < path.length) { + val code: Int = try { + Integer.valueOf(path.substring(i + 1, i + 3), 16) + } catch { case e: Exception => + -1: Integer + } + if (code >= 0) { + sb.append(code.asInstanceOf[Char]) + i += 3 + } else { + sb.append(c) + i += 1 + } + } else { + sb.append(c) + i += 1 + } + } + + sb.toString() + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/SqlNewHadoopRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/SqlNewHadoopRDD.scala new file mode 100644 index 0000000000000..2bdc341021256 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/SqlNewHadoopRDD.scala @@ -0,0 +1,264 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources + +import java.text.SimpleDateFormat +import java.util.Date + +import org.apache.hadoop.conf.{Configurable, Configuration} +import org.apache.hadoop.io.Writable +import org.apache.hadoop.mapreduce._ +import org.apache.hadoop.mapreduce.lib.input.{CombineFileSplit, FileSplit} +import org.apache.spark.broadcast.Broadcast + +import org.apache.spark.{Partition => SparkPartition, _} +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.executor.DataReadMethod +import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil +import org.apache.spark.rdd.{RDD, HadoopRDD} +import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD +import org.apache.spark.storage.StorageLevel +import org.apache.spark.util.{SerializableConfiguration, Utils} + +import scala.reflect.ClassTag + +private[spark] class SqlNewHadoopPartition( + rddId: Int, + val index: Int, + @transient rawSplit: InputSplit with Writable) + extends SparkPartition { + + val serializableHadoopSplit = new SerializableWritable(rawSplit) + + override def hashCode(): Int = 41 * (41 + rddId) + index +} + +/** + * An RDD that provides core functionality for reading data stored in Hadoop (e.g., files in HDFS, + * sources in HBase, or S3), using the new MapReduce API (`org.apache.hadoop.mapreduce`). + * It is based on [[org.apache.spark.rdd.NewHadoopRDD]]. It has three additions. + * 1. A shared broadcast Hadoop Configuration. + * 2. An optional closure `initDriverSideJobFuncOpt` that set configurations at the driver side + * to the shared Hadoop Configuration. + * 3. An optional closure `initLocalJobFuncOpt` that set configurations at both the driver side + * and the executor side to the shared Hadoop Configuration. + * + * Note: This is RDD is basically a cloned version of [[org.apache.spark.rdd.NewHadoopRDD]] with + * changes based on [[org.apache.spark.rdd.HadoopRDD]]. In future, this functionality will be + * folded into core. + */ +private[sql] class SqlNewHadoopRDD[K, V]( + @transient sc : SparkContext, + broadcastedConf: Broadcast[SerializableConfiguration], + @transient initDriverSideJobFuncOpt: Option[Job => Unit], + initLocalJobFuncOpt: Option[Job => Unit], + inputFormatClass: Class[_ <: InputFormat[K, V]], + keyClass: Class[K], + valueClass: Class[V]) + extends RDD[(K, V)](sc, Nil) + with SparkHadoopMapReduceUtil + with Logging { + + protected def getJob(): Job = { + val conf: Configuration = broadcastedConf.value.value + // "new Job" will make a copy of the conf. Then, it is + // safe to mutate conf properties with initLocalJobFuncOpt + // and initDriverSideJobFuncOpt. + val newJob = new Job(conf) + initLocalJobFuncOpt.map(f => f(newJob)) + newJob + } + + def getConf(isDriverSide: Boolean): Configuration = { + val job = getJob() + if (isDriverSide) { + initDriverSideJobFuncOpt.map(f => f(job)) + } + job.getConfiguration + } + + private val jobTrackerId: String = { + val formatter = new SimpleDateFormat("yyyyMMddHHmm") + formatter.format(new Date()) + } + + @transient protected val jobId = new JobID(jobTrackerId, id) + + override def getPartitions: Array[SparkPartition] = { + val conf = getConf(isDriverSide = true) + val inputFormat = inputFormatClass.newInstance + inputFormat match { + case configurable: Configurable => + configurable.setConf(conf) + case _ => + } + val jobContext = newJobContext(conf, jobId) + val rawSplits = inputFormat.getSplits(jobContext).toArray + val result = new Array[SparkPartition](rawSplits.size) + for (i <- 0 until rawSplits.size) { + result(i) = + new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable]) + } + result + } + + override def compute( + theSplit: SparkPartition, + context: TaskContext): InterruptibleIterator[(K, V)] = { + val iter = new Iterator[(K, V)] { + val split = theSplit.asInstanceOf[SqlNewHadoopPartition] + logInfo("Input split: " + split.serializableHadoopSplit) + val conf = getConf(isDriverSide = false) + + val inputMetrics = context.taskMetrics + .getInputMetricsForReadMethod(DataReadMethod.Hadoop) + + // Find a function that will return the FileSystem bytes read by this thread. Do this before + // creating RecordReader, because RecordReader's constructor might read some bytes + val bytesReadCallback = inputMetrics.bytesReadCallback.orElse { + split.serializableHadoopSplit.value match { + case _: FileSplit | _: CombineFileSplit => + SparkHadoopUtil.get.getFSBytesReadOnThreadCallback() + case _ => None + } + } + inputMetrics.setBytesReadCallback(bytesReadCallback) + + val attemptId = newTaskAttemptID(jobTrackerId, id, isMap = true, split.index, 0) + val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId) + val format = inputFormatClass.newInstance + format match { + case configurable: Configurable => + configurable.setConf(conf) + case _ => + } + val reader = format.createRecordReader( + split.serializableHadoopSplit.value, hadoopAttemptContext) + reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext) + + // Register an on-task-completion callback to close the input stream. + context.addTaskCompletionListener(context => close()) + var havePair = false + var finished = false + var recordsSinceMetricsUpdate = 0 + + override def hasNext: Boolean = { + if (!finished && !havePair) { + finished = !reader.nextKeyValue + havePair = !finished + } + !finished + } + + override def next(): (K, V) = { + if (!hasNext) { + throw new java.util.NoSuchElementException("End of stream") + } + havePair = false + if (!finished) { + inputMetrics.incRecordsRead(1) + } + (reader.getCurrentKey, reader.getCurrentValue) + } + + private def close() { + try { + reader.close() + if (bytesReadCallback.isDefined) { + inputMetrics.updateBytesRead() + } else if (split.serializableHadoopSplit.value.isInstanceOf[FileSplit] || + split.serializableHadoopSplit.value.isInstanceOf[CombineFileSplit]) { + // If we can't get the bytes read from the FS stats, fall back to the split size, + // which may be inaccurate. + try { + inputMetrics.incBytesRead(split.serializableHadoopSplit.value.getLength) + } catch { + case e: java.io.IOException => + logWarning("Unable to get input size to set InputMetrics for task", e) + } + } + } catch { + case e: Exception => { + if (!Utils.inShutdown()) { + logWarning("Exception in RecordReader.close()", e) + } + } + } + } + } + new InterruptibleIterator(context, iter) + } + + /** Maps over a partition, providing the InputSplit that was used as the base of the partition. */ + @DeveloperApi + def mapPartitionsWithInputSplit[U: ClassTag]( + f: (InputSplit, Iterator[(K, V)]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = { + new NewHadoopMapPartitionsWithSplitRDD(this, f, preservesPartitioning) + } + + override def getPreferredLocations(hsplit: SparkPartition): Seq[String] = { + val split = hsplit.asInstanceOf[SqlNewHadoopPartition].serializableHadoopSplit.value + val locs = HadoopRDD.SPLIT_INFO_REFLECTIONS match { + case Some(c) => + try { + val infos = c.newGetLocationInfo.invoke(split).asInstanceOf[Array[AnyRef]] + Some(HadoopRDD.convertSplitLocationInfo(infos)) + } catch { + case e : Exception => + logDebug("Failed to use InputSplit#getLocationInfo.", e) + None + } + case None => None + } + locs.getOrElse(split.getLocations.filter(_ != "localhost")) + } + + override def persist(storageLevel: StorageLevel): this.type = { + if (storageLevel.deserialized) { + logWarning("Caching NewHadoopRDDs as deserialized objects usually leads to undesired" + + " behavior because Hadoop's RecordReader reuses the same Writable object for all records." + + " Use a map transformation to make copies of the records.") + } + super.persist(storageLevel) + } +} + +private[spark] object SqlNewHadoopRDD { + /** + * Analogous to [[org.apache.spark.rdd.MapPartitionsRDD]], but passes in an InputSplit to + * the given function rather than the index of the partition. + */ + private[spark] class NewHadoopMapPartitionsWithSplitRDD[U: ClassTag, T: ClassTag]( + prev: RDD[T], + f: (InputSplit, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false) + extends RDD[U](prev) { + + override val partitioner = if (preservesPartitioning) firstParent[T].partitioner else None + + override def getPartitions: Array[SparkPartition] = firstParent[T].partitions + + override def compute(split: SparkPartition, context: TaskContext): Iterator[U] = { + val partition = split.asInstanceOf[SqlNewHadoopPartition] + val inputSplit = partition.serializableHadoopSplit.value + f(inputSplit, firstParent[T].iterator(split, context)) + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala index dbdb0d39c26a1..c16bd9ae52c81 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala @@ -14,12 +14,30 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + package org.apache.spark.sql.sources -import org.apache.spark.sql.{DataFrame, SQLContext} +import java.util.Date + +import scala.collection.mutable + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.mapreduce._ +import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, FileOutputCommitter => MapReduceFileOutputCommitter} +import org.apache.parquet.hadoop.util.ContextUtil + +import org.apache.spark._ +import org.apache.spark.mapred.SparkHadoopMapRedUtil +import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil +import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection +import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project} import org.apache.spark.sql.execution.RunnableCommand +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext, SaveMode} +import org.apache.spark.util.SerializableConfiguration private[sql] case class InsertIntoDataSource( logicalRelation: LogicalRelation, @@ -27,17 +45,447 @@ private[sql] case class InsertIntoDataSource( overwrite: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val relation = logicalRelation.relation.asInstanceOf[InsertableRelation] val data = DataFrame(sqlContext, query) // Apply the schema of the existing table to the new data. - val df = sqlContext.createDataFrame( - data.queryExecution.toRdd, logicalRelation.schema, needsConversion = false) + val df = sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, logicalRelation.schema) relation.insert(df, overwrite) // Invalidate the cache. sqlContext.cacheManager.invalidateCache(logicalRelation) - Seq.empty[Row] + Seq.empty[InternalRow] + } +} + +private[sql] case class InsertIntoHadoopFsRelation( + @transient relation: HadoopFsRelation, + @transient query: LogicalPlan, + mode: SaveMode) + extends RunnableCommand { + + override def run(sqlContext: SQLContext): Seq[InternalRow] = { + require( + relation.paths.length == 1, + s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}") + + val hadoopConf = sqlContext.sparkContext.hadoopConfiguration + val outputPath = new Path(relation.paths.head) + val fs = outputPath.getFileSystem(hadoopConf) + val qualifiedOutputPath = outputPath.makeQualified(fs.getUri, fs.getWorkingDirectory) + + val doInsertion = (mode, fs.exists(qualifiedOutputPath)) match { + case (SaveMode.ErrorIfExists, true) => + sys.error(s"path $qualifiedOutputPath already exists.") + case (SaveMode.Overwrite, true) => + fs.delete(qualifiedOutputPath, true) + true + case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) => + true + case (SaveMode.Ignore, exists) => + !exists + } + + if (doInsertion) { + val job = new Job(hadoopConf) + job.setOutputKeyClass(classOf[Void]) + job.setOutputValueClass(classOf[InternalRow]) + FileOutputFormat.setOutputPath(job, qualifiedOutputPath) + + // We create a DataFrame by applying the schema of relation to the data to make sure. + // We are writing data based on the expected schema, + val df = { + // For partitioned relation r, r.schema's column ordering can be different from the column + // ordering of data.logicalPlan (partition columns are all moved after data column). We + // need a Project to adjust the ordering, so that inside InsertIntoHadoopFsRelation, we can + // safely apply the schema of r.schema to the data. + val project = Project( + relation.schema.map(field => new UnresolvedAttribute(Seq(field.name))), query) + + sqlContext.internalCreateDataFrame( + DataFrame(sqlContext, project).queryExecution.toRdd, relation.schema) + } + + val partitionColumns = relation.partitionColumns.fieldNames + if (partitionColumns.isEmpty) { + insert(new DefaultWriterContainer(relation, job), df) + } else { + val writerContainer = new DynamicPartitionWriterContainer( + relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME) + insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns) + } + } + + Seq.empty[InternalRow] + } + + private def insert(writerContainer: BaseWriterContainer, df: DataFrame): Unit = { + // Uses local vals for serialization + val needsConversion = relation.needConversion + val dataSchema = relation.dataSchema + + // This call shouldn't be put into the `try` block below because it only initializes and + // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called. + writerContainer.driverSideSetup() + + try { + df.sqlContext.sparkContext.runJob(df.queryExecution.executedPlan.execute(), writeRows _) + writerContainer.commitJob() + relation.refresh() + } catch { case cause: Throwable => + logError("Aborting job.", cause) + writerContainer.abortJob() + throw new SparkException("Job aborted.", cause) + } + + def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = { + // If anything below fails, we should abort the task. + try { + writerContainer.executorSideSetup(taskContext) + + val converter = if (needsConversion) { + CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow => Row] + } else { + r: InternalRow => r.asInstanceOf[Row] + } + while (iterator.hasNext) { + val row = converter(iterator.next()) + writerContainer.outputWriterForRow(row).write(row) + } + + writerContainer.commitTask() + } catch { case cause: Throwable => + logError("Aborting task.", cause) + writerContainer.abortTask() + throw new SparkException("Task failed while writing rows.", cause) + } + } + } + + private def insertWithDynamicPartitions( + sqlContext: SQLContext, + writerContainer: BaseWriterContainer, + df: DataFrame, + partitionColumns: Array[String]): Unit = { + // Uses a local val for serialization + val needsConversion = relation.needConversion + val dataSchema = relation.dataSchema + + require( + df.schema == relation.schema, + s"""DataFrame must have the same schema as the relation to which is inserted. + |DataFrame schema: ${df.schema} + |Relation schema: ${relation.schema} + """.stripMargin) + + val partitionColumnsInSpec = relation.partitionColumns.fieldNames + require( + partitionColumnsInSpec.sameElements(partitionColumns), + s"""Partition columns mismatch. + |Expected: ${partitionColumnsInSpec.mkString(", ")} + |Actual: ${partitionColumns.mkString(", ")} + """.stripMargin) + + val output = df.queryExecution.executedPlan.output + val (partitionOutput, dataOutput) = output.partition(a => partitionColumns.contains(a.name)) + val codegenEnabled = df.sqlContext.conf.codegenEnabled + + // This call shouldn't be put into the `try` block below because it only initializes and + // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called. + writerContainer.driverSideSetup() + + try { + df.sqlContext.sparkContext.runJob(df.queryExecution.executedPlan.execute(), writeRows _) + writerContainer.commitJob() + relation.refresh() + } catch { case cause: Throwable => + logError("Aborting job.", cause) + writerContainer.abortJob() + throw new SparkException("Job aborted.", cause) + } + + def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = { + // If anything below fails, we should abort the task. + try { + writerContainer.executorSideSetup(taskContext) + + val partitionProj = newProjection(codegenEnabled, partitionOutput, output) + val dataProj = newProjection(codegenEnabled, dataOutput, output) + + val dataConverter: InternalRow => Row = if (needsConversion) { + CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow => Row] + } else { + r: InternalRow => r.asInstanceOf[Row] + } + val partitionSchema = StructType.fromAttributes(partitionOutput) + val partConverter: InternalRow => Row = + CatalystTypeConverters.createToScalaConverter(partitionSchema) + .asInstanceOf[InternalRow => Row] + + while (iterator.hasNext) { + val row = iterator.next() + val partitionPart = partConverter(partitionProj(row)) + val dataPart = dataConverter(dataProj(row)) + writerContainer.outputWriterForRow(partitionPart).write(dataPart) + } + + writerContainer.commitTask() + } catch { case cause: Throwable => + logError("Aborting task.", cause) + writerContainer.abortTask() + throw new SparkException("Task failed while writing rows.", cause) + } + } + } + + // This is copied from SparkPlan, probably should move this to a more general place. + private def newProjection( + codegenEnabled: Boolean, + expressions: Seq[Expression], + inputSchema: Seq[Attribute]): Projection = { + log.debug( + s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled") + if (codegenEnabled && expressions.forall(_.isThreadSafe)) { + GenerateProjection.generate(expressions, inputSchema) + } else { + new InterpretedProjection(expressions, inputSchema) + } + } +} + +private[sql] abstract class BaseWriterContainer( + @transient val relation: HadoopFsRelation, + @transient job: Job) + extends SparkHadoopMapReduceUtil + with Logging + with Serializable { + + protected val serializableConf = new SerializableConfiguration(ContextUtil.getConfiguration(job)) + + // This is only used on driver side. + @transient private val jobContext: JobContext = job + + // The following fields are initialized and used on both driver and executor side. + @transient protected var outputCommitter: OutputCommitter = _ + @transient private var jobId: JobID = _ + @transient private var taskId: TaskID = _ + @transient private var taskAttemptId: TaskAttemptID = _ + @transient protected var taskAttemptContext: TaskAttemptContext = _ + + protected val outputPath: String = { + assert( + relation.paths.length == 1, + s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}") + relation.paths.head + } + + protected val dataSchema = relation.dataSchema + + protected var outputWriterFactory: OutputWriterFactory = _ + + private var outputFormatClass: Class[_ <: OutputFormat[_, _]] = _ + + def driverSideSetup(): Unit = { + setupIDs(0, 0, 0) + setupConf() + + // Order of the following two lines is important. For Hadoop 1, TaskAttemptContext constructor + // clones the Configuration object passed in. If we initialize the TaskAttemptContext first, + // configurations made in prepareJobForWrite(job) are not populated into the TaskAttemptContext. + // + // Also, the `prepareJobForWrite` call must happen before initializing output format and output + // committer, since their initialization involve the job configuration, which can be potentially + // decorated in `prepareJobForWrite`. + outputWriterFactory = relation.prepareJobForWrite(job) + taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId) + + outputFormatClass = job.getOutputFormatClass + outputCommitter = newOutputCommitter(taskAttemptContext) + outputCommitter.setupJob(jobContext) + } + + def executorSideSetup(taskContext: TaskContext): Unit = { + setupIDs(taskContext.stageId(), taskContext.partitionId(), taskContext.attemptNumber()) + setupConf() + taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId) + outputCommitter = newOutputCommitter(taskAttemptContext) + outputCommitter.setupTask(taskAttemptContext) + initWriters() + } + + protected def getWorkPath: String = { + outputCommitter match { + // FileOutputCommitter writes to a temporary location returned by `getWorkPath`. + case f: MapReduceFileOutputCommitter => f.getWorkPath.toString + case _ => outputPath + } + } + + private def newOutputCommitter(context: TaskAttemptContext): OutputCommitter = { + val committerClass = context.getConfiguration.getClass( + SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter]) + + Option(committerClass).map { clazz => + logInfo(s"Using user defined output committer class ${clazz.getCanonicalName}") + + // Every output format based on org.apache.hadoop.mapreduce.lib.output.OutputFormat + // has an associated output committer. To override this output committer, + // we will first try to use the output committer set in SQLConf.OUTPUT_COMMITTER_CLASS. + // If a data source needs to override the output committer, it needs to set the + // output committer in prepareForWrite method. + if (classOf[MapReduceFileOutputCommitter].isAssignableFrom(clazz)) { + // The specified output committer is a FileOutputCommitter. + // So, we will use the FileOutputCommitter-specified constructor. + val ctor = clazz.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext]) + ctor.newInstance(new Path(outputPath), context) + } else { + // The specified output committer is just a OutputCommitter. + // So, we will use the no-argument constructor. + val ctor = clazz.getDeclaredConstructor() + ctor.newInstance() + } + }.getOrElse { + // If output committer class is not set, we will use the one associated with the + // file output format. + val outputCommitter = outputFormatClass.newInstance().getOutputCommitter(context) + logInfo(s"Using output committer class ${outputCommitter.getClass.getCanonicalName}") + outputCommitter + } + } + + private def setupIDs(jobId: Int, splitId: Int, attemptId: Int): Unit = { + this.jobId = SparkHadoopWriter.createJobID(new Date, jobId) + this.taskId = new TaskID(this.jobId, true, splitId) + this.taskAttemptId = new TaskAttemptID(taskId, attemptId) + } + + private def setupConf(): Unit = { + serializableConf.value.set("mapred.job.id", jobId.toString) + serializableConf.value.set("mapred.tip.id", taskAttemptId.getTaskID.toString) + serializableConf.value.set("mapred.task.id", taskAttemptId.toString) + serializableConf.value.setBoolean("mapred.task.is.map", true) + serializableConf.value.setInt("mapred.task.partition", 0) + } + + // Called on executor side when writing rows + def outputWriterForRow(row: Row): OutputWriter + + protected def initWriters(): Unit + + def commitTask(): Unit = { + SparkHadoopMapRedUtil.commitTask( + outputCommitter, taskAttemptContext, jobId.getId, taskId.getId, taskAttemptId.getId) + } + + def abortTask(): Unit = { + if (outputCommitter != null) { + outputCommitter.abortTask(taskAttemptContext) + } + logError(s"Task attempt $taskAttemptId aborted.") + } + + def commitJob(): Unit = { + outputCommitter.commitJob(jobContext) + logInfo(s"Job $jobId committed.") + } + + def abortJob(): Unit = { + if (outputCommitter != null) { + outputCommitter.abortJob(jobContext, JobStatus.State.FAILED) + } + logError(s"Job $jobId aborted.") + } +} + +private[sql] class DefaultWriterContainer( + @transient relation: HadoopFsRelation, + @transient job: Job) + extends BaseWriterContainer(relation, job) { + + @transient private var writer: OutputWriter = _ + + override protected def initWriters(): Unit = { + taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path", outputPath) + writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext) + } + + override def outputWriterForRow(row: Row): OutputWriter = writer + + override def commitTask(): Unit = { + try { + assert(writer != null, "OutputWriter instance should have been initialized") + writer.close() + super.commitTask() + } catch { + case cause: Throwable => + super.abortTask() + throw new RuntimeException("Failed to commit task", cause) + } + } + + override def abortTask(): Unit = { + try { + if (writer != null) { + writer.close() + } + } finally { + super.abortTask() + } + } +} + +private[sql] class DynamicPartitionWriterContainer( + @transient relation: HadoopFsRelation, + @transient job: Job, + partitionColumns: Array[String], + defaultPartitionName: String) + extends BaseWriterContainer(relation, job) { + + // All output writers are created on executor side. + @transient protected var outputWriters: mutable.Map[String, OutputWriter] = _ + + override protected def initWriters(): Unit = { + outputWriters = mutable.Map.empty[String, OutputWriter] + } + + override def outputWriterForRow(row: Row): OutputWriter = { + val partitionPath = partitionColumns.zip(row.toSeq).map { case (col, rawValue) => + val string = if (rawValue == null) null else String.valueOf(rawValue) + val valueString = if (string == null || string.isEmpty) { + defaultPartitionName + } else { + PartitioningUtils.escapePathName(string) + } + s"/$col=$valueString" + }.mkString.stripPrefix(Path.SEPARATOR) + + outputWriters.getOrElseUpdate(partitionPath, { + val path = new Path(getWorkPath, partitionPath) + taskAttemptContext.getConfiguration.set( + "spark.sql.sources.output.path", + new Path(outputPath, partitionPath).toString) + outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext) + }) + } + + override def commitTask(): Unit = { + try { + outputWriters.values.foreach(_.close()) + outputWriters.clear() + super.commitTask() + } catch { case cause: Throwable => + super.abortTask() + throw new RuntimeException("Failed to commit task", cause) + } + } + + override def abortTask(): Unit = { + try { + outputWriters.values.foreach(_.close()) + outputWriters.clear() + } finally { + super.abortTask() + } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index 06c64f2bdd59e..b7095c8ead797 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -17,18 +17,20 @@ package org.apache.spark.sql.sources -import scala.language.existentials +import scala.language.{existentials, implicitConversions} import scala.util.matching.Regex -import scala.language.implicitConversions + +import org.apache.hadoop.fs.Path import org.apache.spark.Logging -import org.apache.spark.sql.{AnalysisException, SaveMode, DataFrame, SQLContext} -import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.sql.catalyst.AbstractSparkSQLParser import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Row} +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, InternalRow} +import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.execution.RunnableCommand import org.apache.spark.sql.types._ +import org.apache.spark.sql.{AnalysisException, DataFrame, SQLContext, SaveMode} import org.apache.spark.util.Utils /** @@ -111,6 +113,7 @@ private[sql] class DDLParser( CreateTableUsingAsSelect(tableName, provider, temp.isDefined, + Array.empty[String], mode, options, queryPlan) @@ -127,7 +130,7 @@ private[sql] class DDLParser( } } - protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")" + protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")" /* * describe [extended] table avroTable @@ -135,7 +138,7 @@ private[sql] class DDLParser( */ protected lazy val describeTable: Parser[LogicalPlan] = (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident ^^ { - case e ~ db ~ tbl => + case e ~ db ~ tbl => val tblIdentifier = db match { case Some(dbName) => Seq(dbName, tbl) @@ -157,18 +160,22 @@ private[sql] class DDLParser( protected lazy val className: Parser[String] = repsep(ident, ".") ^^ { case s => s.mkString(".")} override implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch( - s"identifier matching regex ${regex}", { + s"identifier matching regex $regex", { case lexical.Identifier(str) if regex.unapplySeq(str).isDefined => str case lexical.Keyword(str) if regex.unapplySeq(str).isDefined => str } ) - protected lazy val optionName: Parser[String] = "[_a-zA-Z][a-zA-Z0-9]*".r ^^ { + protected lazy val optionPart: Parser[String] = "[_a-zA-Z][_a-zA-Z0-9]*".r ^^ { case name => name } + protected lazy val optionName: Parser[String] = repsep(optionPart, ".") ^^ { + case parts => parts.mkString(".") + } + protected lazy val pair: Parser[(String, String)] = - optionName ~ stringLit ^^ { case k ~ v => (k,v) } + optionName ~ stringLit ^^ { case k ~ v => (k, v) } protected lazy val column: Parser[StructField] = ident ~ dataType ~ (COMMENT ~> stringLit).? ^^ { case columnName ~ typ ~ cm => @@ -185,18 +192,20 @@ private[sql] class DDLParser( private[sql] object ResolvedDataSource { private val builtinSources = Map( - "jdbc" -> classOf[org.apache.spark.sql.jdbc.DefaultSource], - "json" -> classOf[org.apache.spark.sql.json.DefaultSource], - "parquet" -> classOf[org.apache.spark.sql.parquet.DefaultSource] + "jdbc" -> "org.apache.spark.sql.jdbc.DefaultSource", + "json" -> "org.apache.spark.sql.json.DefaultSource", + "parquet" -> "org.apache.spark.sql.parquet.DefaultSource", + "orc" -> "org.apache.spark.sql.hive.orc.DefaultSource" ) /** Given a provider name, look up the data source class definition. */ def lookupDataSource(provider: String): Class[_] = { + val loader = Utils.getContextOrSparkClassLoader + if (builtinSources.contains(provider)) { - return builtinSources(provider) + return loader.loadClass(builtinSources(provider)) } - val loader = Utils.getContextOrSparkClassLoader try { loader.loadClass(provider) } catch { @@ -205,7 +214,11 @@ private[sql] object ResolvedDataSource { loader.loadClass(provider + ".DefaultSource") } catch { case cnf: java.lang.ClassNotFoundException => - sys.error(s"Failed to load class for data source: $provider") + if (provider.startsWith("org.apache.spark.sql.hive.orc")) { + sys.error("The ORC data source must be used with Hive support enabled.") + } else { + sys.error(s"Failed to load class for data source: $provider") + } } } } @@ -214,6 +227,7 @@ private[sql] object ResolvedDataSource { def apply( sqlContext: SQLContext, userSpecifiedSchema: Option[StructType], + partitionColumns: Array[String], provider: String, options: Map[String, String]): ResolvedDataSource = { val clazz: Class[_] = lookupDataSource(provider) @@ -222,6 +236,28 @@ private[sql] object ResolvedDataSource { case Some(schema: StructType) => clazz.newInstance() match { case dataSource: SchemaRelationProvider => dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema) + case dataSource: HadoopFsRelationProvider => + val maybePartitionsSchema = if (partitionColumns.isEmpty) { + None + } else { + Some(partitionColumnsSchema(schema, partitionColumns)) + } + + val caseInsensitiveOptions = new CaseInsensitiveMap(options) + val paths = { + val patternPath = new Path(caseInsensitiveOptions("path")) + SparkHadoopUtil.get.globPath(patternPath).map(_.toString).toArray + } + + val dataSchema = + StructType(schema.filterNot(f => partitionColumns.contains(f.name))).asNullable + + dataSource.createRelation( + sqlContext, + paths, + Some(dataSchema), + maybePartitionsSchema, + caseInsensitiveOptions) case dataSource: org.apache.spark.sql.sources.RelationProvider => throw new AnalysisException(s"$className does not allow user-specified schemas.") case _ => @@ -231,20 +267,39 @@ private[sql] object ResolvedDataSource { case None => clazz.newInstance() match { case dataSource: RelationProvider => dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options)) + case dataSource: HadoopFsRelationProvider => + val caseInsensitiveOptions = new CaseInsensitiveMap(options) + val paths = { + val patternPath = new Path(caseInsensitiveOptions("path")) + SparkHadoopUtil.get.globPath(patternPath).map(_.toString).toArray + } + dataSource.createRelation(sqlContext, paths, None, None, caseInsensitiveOptions) case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider => throw new AnalysisException( s"A schema needs to be specified when using $className.") case _ => - throw new AnalysisException(s"$className is not a RelationProvider.") + throw new AnalysisException( + s"$className is neither a RelationProvider nor a FSBasedRelationProvider.") } } new ResolvedDataSource(clazz, relation) } + private def partitionColumnsSchema( + schema: StructType, + partitionColumns: Array[String]): StructType = { + StructType(partitionColumns.map { col => + schema.find(_.name == col).getOrElse { + throw new RuntimeException(s"Partition column $col not found in schema $schema") + } + }).asNullable + } + /** Create a [[ResolvedDataSource]] for saving the content of the given [[DataFrame]]. */ def apply( sqlContext: SQLContext, provider: String, + partitionColumns: Array[String], mode: SaveMode, options: Map[String, String], data: DataFrame): ResolvedDataSource = { @@ -252,6 +307,34 @@ private[sql] object ResolvedDataSource { val relation = clazz.newInstance() match { case dataSource: CreatableRelationProvider => dataSource.createRelation(sqlContext, mode, options, data) + case dataSource: HadoopFsRelationProvider => + // Don't glob path for the write path. The contracts here are: + // 1. Only one output path can be specified on the write path; + // 2. Output path must be a legal HDFS style file system path; + // 3. It's OK that the output path doesn't exist yet; + val caseInsensitiveOptions = new CaseInsensitiveMap(options) + val outputPath = { + val path = new Path(caseInsensitiveOptions("path")) + val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) + path.makeQualified(fs.getUri, fs.getWorkingDirectory) + } + val dataSchema = StructType(data.schema.filterNot(f => partitionColumns.contains(f.name))) + val r = dataSource.createRelation( + sqlContext, + Array(outputPath.toString), + Some(dataSchema.asNullable), + Some(partitionColumnsSchema(data.schema, partitionColumns)), + caseInsensitiveOptions) + + // For partitioned relation r, r.schema's column ordering can be different from the column + // ordering of data.logicalPlan (partition columns are all moved after data column). This + // will be adjusted within InsertIntoHadoopFsRelation. + sqlContext.executePlan( + InsertIntoHadoopFsRelation( + r, + data.logicalPlan, + mode)).toRdd + r case _ => sys.error(s"${clazz.getCanonicalName} does not allow create table as select.") } @@ -310,6 +393,7 @@ private[sql] case class CreateTableUsingAsSelect( tableName: String, provider: String, temporary: Boolean, + partitionColumns: Array[String], mode: SaveMode, options: Map[String, String], child: LogicalPlan) extends UnaryNode { @@ -324,8 +408,9 @@ private[sql] case class CreateTempTableUsing( provider: String, options: Map[String, String]) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { - val resolved = ResolvedDataSource(sqlContext, userSpecifiedSchema, provider, options) + def run(sqlContext: SQLContext): Seq[InternalRow] = { + val resolved = ResolvedDataSource( + sqlContext, userSpecifiedSchema, Array.empty[String], provider, options) sqlContext.registerDataFrameAsTable( DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName) Seq.empty @@ -335,13 +420,14 @@ private[sql] case class CreateTempTableUsing( private[sql] case class CreateTempTableUsingAsSelect( tableName: String, provider: String, + partitionColumns: Array[String], mode: SaveMode, options: Map[String, String], query: LogicalPlan) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val df = DataFrame(sqlContext, query) - val resolved = ResolvedDataSource(sqlContext, provider, mode, options, df) + val resolved = ResolvedDataSource(sqlContext, provider, partitionColumns, mode, options, df) sqlContext.registerDataFrameAsTable( DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName) @@ -352,7 +438,7 @@ private[sql] case class CreateTempTableUsingAsSelect( private[sql] case class RefreshTable(databaseName: String, tableName: String) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { // Refresh the given table's metadata first. sqlContext.catalog.refreshTable(databaseName, tableName) @@ -371,7 +457,7 @@ private[sql] case class RefreshTable(databaseName: String, tableName: String) sqlContext.cacheManager.cacheQuery(df, Some(tableName)) } - Seq.empty[Row] + Seq.empty[InternalRow] } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala index 791046e0079d6..24e86ca415c51 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala @@ -19,83 +19,113 @@ package org.apache.spark.sql.sources /** * A filter predicate for data sources. + * + * @since 1.3.0 */ abstract class Filter /** * A filter that evaluates to `true` iff the attribute evaluates to a value * equal to `value`. + * + * @since 1.3.0 */ case class EqualTo(attribute: String, value: Any) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to a value * greater than `value`. + * + * @since 1.3.0 */ case class GreaterThan(attribute: String, value: Any) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to a value * greater than or equal to `value`. + * + * @since 1.3.0 */ case class GreaterThanOrEqual(attribute: String, value: Any) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to a value * less than `value`. + * + * @since 1.3.0 */ case class LessThan(attribute: String, value: Any) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to a value * less than or equal to `value`. + * + * @since 1.3.0 */ case class LessThanOrEqual(attribute: String, value: Any) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to one of the values in the array. + * + * @since 1.3.0 */ case class In(attribute: String, values: Array[Any]) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to null. + * + * @since 1.3.0 */ case class IsNull(attribute: String) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to a non-null value. + * + * @since 1.3.0 */ case class IsNotNull(attribute: String) extends Filter /** * A filter that evaluates to `true` iff both `left` or `right` evaluate to `true`. + * + * @since 1.3.0 */ case class And(left: Filter, right: Filter) extends Filter /** * A filter that evaluates to `true` iff at least one of `left` or `right` evaluates to `true`. + * + * @since 1.3.0 */ case class Or(left: Filter, right: Filter) extends Filter /** * A filter that evaluates to `true` iff `child` is evaluated to `false`. + * + * @since 1.3.0 */ case class Not(child: Filter) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to * a string that starts with `value`. + * + * @since 1.3.1 */ case class StringStartsWith(attribute: String, value: String) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to * a string that starts with `value`. + * + * @since 1.3.1 */ case class StringEndsWith(attribute: String, value: String) extends Filter /** * A filter that evaluates to `true` iff the attribute evaluates to * a string that contains the string `value`. + * + * @since 1.3.1 */ case class StringContains(attribute: String, value: String) extends Filter diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala index ca53dcdb92c52..7005c7079af91 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala @@ -17,11 +17,22 @@ package org.apache.spark.sql.sources -import org.apache.spark.annotation.{Experimental, DeveloperApi} +import scala.collection.mutable +import scala.util.Try + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext} + +import org.apache.spark.annotation.{DeveloperApi, Experimental} +import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{SaveMode, DataFrame, Row, SQLContext} -import org.apache.spark.sql.catalyst.expressions.{Expression, Attribute} +import org.apache.spark.sql.execution.RDDConversions +import org.apache.spark.sql.{DataFrame, Row, SaveMode, SQLContext} +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection import org.apache.spark.sql.types.StructType +import org.apache.spark.util.SerializableConfiguration /** * ::DeveloperApi:: @@ -35,6 +46,8 @@ import org.apache.spark.sql.types.StructType * data source 'org.apache.spark.sql.json.DefaultSource' * * A new instance of this class with be instantiated each time a DDL call is made. + * + * @since 1.3.0 */ @DeveloperApi trait RelationProvider { @@ -64,6 +77,8 @@ trait RelationProvider { * users need to provide a schema when using a SchemaRelationProvider. * A relation provider can inherits both [[RelationProvider]] and [[SchemaRelationProvider]] * if it can support both schema inference and user-specified schemas. + * + * @since 1.3.0 */ @DeveloperApi trait SchemaRelationProvider { @@ -78,6 +93,49 @@ trait SchemaRelationProvider { schema: StructType): BaseRelation } +/** + * ::Experimental:: + * Implemented by objects that produce relations for a specific kind of data source + * with a given schema and partitioned columns. When Spark SQL is given a DDL operation with a + * USING clause specified (to specify the implemented [[HadoopFsRelationProvider]]), a user defined + * schema, and an optional list of partition columns, this interface is used to pass in the + * parameters specified by a user. + * + * Users may specify the fully qualified class name of a given data source. When that class is + * not found Spark SQL will append the class name `DefaultSource` to the path, allowing for + * less verbose invocation. For example, 'org.apache.spark.sql.json' would resolve to the + * data source 'org.apache.spark.sql.json.DefaultSource' + * + * A new instance of this class with be instantiated each time a DDL call is made. + * + * The difference between a [[RelationProvider]] and a [[HadoopFsRelationProvider]] is + * that users need to provide a schema and a (possibly empty) list of partition columns when + * using a SchemaRelationProvider. A relation provider can inherits both [[RelationProvider]], + * and [[HadoopFsRelationProvider]] if it can support schema inference, user-specified + * schemas, and accessing partitioned relations. + * + * @since 1.4.0 + */ +@Experimental +trait HadoopFsRelationProvider { + /** + * Returns a new base relation with the given parameters, a user defined schema, and a list of + * partition columns. Note: the parameters' keywords are case insensitive and this insensitivity + * is enforced by the Map that is passed to the function. + * + * @param dataSchema Schema of data columns (i.e., columns that are not partition columns). + */ + def createRelation( + sqlContext: SQLContext, + paths: Array[String], + dataSchema: Option[StructType], + partitionColumns: Option[StructType], + parameters: Map[String, String]): HadoopFsRelation +} + +/** + * @since 1.3.0 + */ @DeveloperApi trait CreatableRelationProvider { /** @@ -91,6 +149,8 @@ trait CreatableRelationProvider { * existing data is expected to be overwritten by the contents of the DataFrame. * ErrorIfExists mode means that when saving a DataFrame to a data source, * if data already exists, an exception is expected to be thrown. + * + * @since 1.3.0 */ def createRelation( sqlContext: SQLContext, @@ -109,6 +169,8 @@ trait CreatableRelationProvider { * BaseRelations must also define a equality function that only returns true when the two * instances will return the same data. This equality function is used when determining when * it is safe to substitute cached results for a given relation. + * + * @since 1.3.0 */ @DeveloperApi abstract class BaseRelation { @@ -124,6 +186,8 @@ abstract class BaseRelation { * * Note that it is always better to overestimate size than underestimate, because underestimation * could lead to execution plans that are suboptimal (i.e. broadcasting a very large table). + * + * @since 1.3.0 */ def sizeInBytes: Long = sqlContext.conf.defaultSizeInBytes @@ -132,8 +196,12 @@ abstract class BaseRelation { * java.lang.String -> UTF8String * java.lang.Decimal -> Decimal * + * If `needConversion` is `false`, buildScan() should return an [[RDD]] of [[InternalRow]] + * * Note: The internal representation is not stable across releases and thus data sources outside * of Spark SQL should leave this as true. + * + * @since 1.4.0 */ def needConversion: Boolean = true } @@ -141,6 +209,8 @@ abstract class BaseRelation { /** * ::DeveloperApi:: * A BaseRelation that can produce all of its tuples as an RDD of Row objects. + * + * @since 1.3.0 */ @DeveloperApi trait TableScan { @@ -151,6 +221,8 @@ trait TableScan { * ::DeveloperApi:: * A BaseRelation that can eliminate unneeded columns before producing an RDD * containing all of its tuples as Row objects. + * + * @since 1.3.0 */ @DeveloperApi trait PrunedScan { @@ -168,6 +240,8 @@ trait PrunedScan { * The pushed down filters are currently purely an optimization as they will all be evaluated * again. This means it is safe to use them with methods that produce false positives such * as filtering partitions based on a bloom filter. + * + * @since 1.3.0 */ @DeveloperApi trait PrunedFilteredScan { @@ -189,6 +263,8 @@ trait PrunedFilteredScan { * 3. It assumes that fields of the data provided in the insert method are nullable. * If a data source needs to check the actual nullability of a field, it needs to do it in the * insert method. + * + * @since 1.3.0 */ @DeveloperApi trait InsertableRelation { @@ -202,8 +278,390 @@ trait InsertableRelation { * [[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]]. Unlike the other APIs this * interface is NOT designed to be binary compatible across releases and thus should only be used * for experimentation. + * + * @since 1.3.0 */ @Experimental trait CatalystScan { def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] } + +/** + * ::Experimental:: + * A factory that produces [[OutputWriter]]s. A new [[OutputWriterFactory]] is created on driver + * side for each write job issued when writing to a [[HadoopFsRelation]], and then gets serialized + * to executor side to create actual [[OutputWriter]]s on the fly. + * + * @since 1.4.0 + */ +@Experimental +abstract class OutputWriterFactory extends Serializable { + /** + * When writing to a [[HadoopFsRelation]], this method gets called by each task on executor side + * to instantiate new [[OutputWriter]]s. + * + * @param path Path of the file to which this [[OutputWriter]] is supposed to write. Note that + * this may not point to the final output file. For example, `FileOutputFormat` writes to + * temporary directories and then merge written files back to the final destination. In + * this case, `path` points to a temporary output file under the temporary directory. + * @param dataSchema Schema of the rows to be written. Partition columns are not included in the + * schema if the relation being written is partitioned. + * @param context The Hadoop MapReduce task context. + * + * @since 1.4.0 + */ + def newInstance(path: String, dataSchema: StructType, context: TaskAttemptContext): OutputWriter +} + +/** + * ::Experimental:: + * [[OutputWriter]] is used together with [[HadoopFsRelation]] for persisting rows to the + * underlying file system. Subclasses of [[OutputWriter]] must provide a zero-argument constructor. + * An [[OutputWriter]] instance is created and initialized when a new output file is opened on + * executor side. This instance is used to persist rows to this single output file. + * + * @since 1.4.0 + */ +@Experimental +abstract class OutputWriter { + /** + * Persists a single row. Invoked on the executor side. When writing to dynamically partitioned + * tables, dynamic partition columns are not included in rows to be written. + * + * @since 1.4.0 + */ + def write(row: Row): Unit + + /** + * Closes the [[OutputWriter]]. Invoked on the executor side after all rows are persisted, before + * the task output is committed. + * + * @since 1.4.0 + */ + def close(): Unit +} + +/** + * ::Experimental:: + * A [[BaseRelation]] that provides much of the common code required for formats that store their + * data to an HDFS compatible filesystem. + * + * For the read path, similar to [[PrunedFilteredScan]], it can eliminate unneeded columns and + * filter using selected predicates before producing an RDD containing all matching tuples as + * [[Row]] objects. In addition, when reading from Hive style partitioned tables stored in file + * systems, it's able to discover partitioning information from the paths of input directories, and + * perform partition pruning before start reading the data. Subclasses of [[HadoopFsRelation()]] + * must override one of the three `buildScan` methods to implement the read path. + * + * For the write path, it provides the ability to write to both non-partitioned and partitioned + * tables. Directory layout of the partitioned tables is compatible with Hive. + * + * @constructor This constructor is for internal uses only. The [[PartitionSpec]] argument is for + * implementing metastore table conversion. + * + * @param maybePartitionSpec An [[HadoopFsRelation]] can be created with an optional + * [[PartitionSpec]], so that partition discovery can be skipped. + * + * @since 1.4.0 + */ +@Experimental +abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[PartitionSpec]) + extends BaseRelation { + + def this() = this(None) + + private val hadoopConf = new Configuration(sqlContext.sparkContext.hadoopConfiguration) + + private val codegenEnabled = sqlContext.conf.codegenEnabled + + private var _partitionSpec: PartitionSpec = _ + + private class FileStatusCache { + var leafFiles = mutable.Map.empty[Path, FileStatus] + + var leafDirToChildrenFiles = mutable.Map.empty[Path, Array[FileStatus]] + + def refresh(): Unit = { + // We don't filter files/directories whose name start with "_" except "_temporary" here, as + // specific data sources may take advantages over them (e.g. Parquet _metadata and + // _common_metadata files). "_temporary" directories are explicitly ignored since failed + // tasks/jobs may leave partial/corrupted data files there. + def listLeafFilesAndDirs(fs: FileSystem, status: FileStatus): Set[FileStatus] = { + if (status.getPath.getName.toLowerCase == "_temporary") { + Set.empty + } else { + val (dirs, files) = fs.listStatus(status.getPath).partition(_.isDir) + val leafDirs = if (dirs.isEmpty) Set(status) else Set.empty[FileStatus] + files.toSet ++ leafDirs ++ dirs.flatMap(dir => listLeafFilesAndDirs(fs, dir)) + } + } + + leafFiles.clear() + + val statuses = paths.flatMap { path => + val hdfsPath = new Path(path) + val fs = hdfsPath.getFileSystem(hadoopConf) + val qualified = hdfsPath.makeQualified(fs.getUri, fs.getWorkingDirectory) + Try(fs.getFileStatus(qualified)).toOption.toArray.flatMap(listLeafFilesAndDirs(fs, _)) + }.filterNot { status => + // SPARK-8037: Ignores files like ".DS_Store" and other hidden files/directories + status.getPath.getName.startsWith(".") + } + + val files = statuses.filterNot(_.isDir) + leafFiles ++= files.map(f => f.getPath -> f).toMap + leafDirToChildrenFiles ++= files.groupBy(_.getPath.getParent) + } + } + + private lazy val fileStatusCache = { + val cache = new FileStatusCache + cache.refresh() + cache + } + + protected def cachedLeafStatuses(): Set[FileStatus] = { + fileStatusCache.leafFiles.values.toSet + } + + final private[sql] def partitionSpec: PartitionSpec = { + if (_partitionSpec == null) { + _partitionSpec = maybePartitionSpec + .flatMap { + case spec if spec.partitions.nonEmpty => + Some(spec.copy(partitionColumns = spec.partitionColumns.asNullable)) + case _ => + None + } + .orElse { + // We only know the partition columns and their data types. We need to discover + // partition values. + userDefinedPartitionColumns.map { partitionSchema => + val spec = discoverPartitions() + val partitionColumnTypes = spec.partitionColumns.map(_.dataType) + val castedPartitions = spec.partitions.map { case p @ Partition(values, path) => + val literals = values.toSeq.zip(partitionColumnTypes).map { + case (value, dataType) => Literal.create(value, dataType) + } + val castedValues = partitionSchema.zip(literals).map { case (field, literal) => + Cast(literal, field.dataType).eval() + } + p.copy(values = InternalRow.fromSeq(castedValues)) + } + PartitionSpec(partitionSchema, castedPartitions) + } + } + .getOrElse { + if (sqlContext.conf.partitionDiscoveryEnabled()) { + discoverPartitions() + } else { + PartitionSpec(StructType(Nil), Array.empty[Partition]) + } + } + } + _partitionSpec + } + + /** + * Base paths of this relation. For partitioned relations, it should be either root directories + * of all partition directories. + * + * @since 1.4.0 + */ + def paths: Array[String] + + /** + * Partition columns. Can be either defined by [[userDefinedPartitionColumns]] or automatically + * discovered. Note that they should always be nullable. + * + * @since 1.4.0 + */ + final def partitionColumns: StructType = + userDefinedPartitionColumns.getOrElse(partitionSpec.partitionColumns) + + /** + * Optional user defined partition columns. + * + * @since 1.4.0 + */ + def userDefinedPartitionColumns: Option[StructType] = None + + private[sql] def refresh(): Unit = { + fileStatusCache.refresh() + if (sqlContext.conf.partitionDiscoveryEnabled()) { + _partitionSpec = discoverPartitions() + } + } + + private def discoverPartitions(): PartitionSpec = { + val typeInference = sqlContext.conf.partitionColumnTypeInferenceEnabled() + // We use leaf dirs containing data files to discover the schema. + val leafDirs = fileStatusCache.leafDirToChildrenFiles.keys.toSeq + PartitioningUtils.parsePartitions(leafDirs, PartitioningUtils.DEFAULT_PARTITION_NAME, + typeInference) + } + + /** + * Schema of this relation. It consists of columns appearing in [[dataSchema]] and all partition + * columns not appearing in [[dataSchema]]. + * + * @since 1.4.0 + */ + override lazy val schema: StructType = { + val dataSchemaColumnNames = dataSchema.map(_.name.toLowerCase).toSet + StructType(dataSchema ++ partitionColumns.filterNot { column => + dataSchemaColumnNames.contains(column.name.toLowerCase) + }) + } + + private[sources] final def buildScan( + requiredColumns: Array[String], + filters: Array[Filter], + inputPaths: Array[String], + broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = { + val inputStatuses = inputPaths.flatMap { input => + val path = new Path(input) + + // First assumes `input` is a directory path, and tries to get all files contained in it. + fileStatusCache.leafDirToChildrenFiles.getOrElse( + path, + // Otherwise, `input` might be a file path + fileStatusCache.leafFiles.get(path).toArray + ).filter { status => + val name = status.getPath.getName + !name.startsWith("_") && !name.startsWith(".") + } + } + + buildScan(requiredColumns, filters, inputStatuses, broadcastedConf) + } + + /** + * Specifies schema of actual data files. For partitioned relations, if one or more partitioned + * columns are contained in the data files, they should also appear in `dataSchema`. + * + * @since 1.4.0 + */ + def dataSchema: StructType + + /** + * For a non-partitioned relation, this method builds an `RDD[Row]` containing all rows within + * this relation. For partitioned relations, this method is called for each selected partition, + * and builds an `RDD[Row]` containing all rows within that single partition. + * + * @param inputFiles For a non-partitioned relation, it contains paths of all data files in the + * relation. For a partitioned relation, it contains paths of all data files in a single + * selected partition. + * + * @since 1.4.0 + */ + def buildScan(inputFiles: Array[FileStatus]): RDD[Row] = { + throw new UnsupportedOperationException( + "At least one buildScan() method should be overridden to read the relation.") + } + + /** + * For a non-partitioned relation, this method builds an `RDD[Row]` containing all rows within + * this relation. For partitioned relations, this method is called for each selected partition, + * and builds an `RDD[Row]` containing all rows within that single partition. + * + * @param requiredColumns Required columns. + * @param inputFiles For a non-partitioned relation, it contains paths of all data files in the + * relation. For a partitioned relation, it contains paths of all data files in a single + * selected partition. + * + * @since 1.4.0 + */ + def buildScan(requiredColumns: Array[String], inputFiles: Array[FileStatus]): RDD[Row] = { + // Yeah, to workaround serialization... + val dataSchema = this.dataSchema + val codegenEnabled = this.codegenEnabled + val needConversion = this.needConversion + + val requiredOutput = requiredColumns.map { col => + val field = dataSchema(col) + BoundReference(dataSchema.fieldIndex(col), field.dataType, field.nullable) + }.toSeq + + val rdd = buildScan(inputFiles) + val converted = + if (needConversion) { + RDDConversions.rowToRowRdd(rdd, dataSchema.fields.map(_.dataType)) + } else { + rdd.map(_.asInstanceOf[InternalRow]) + } + converted.mapPartitions { rows => + val buildProjection = if (codegenEnabled && requiredOutput.forall(_.isThreadSafe)) { + GenerateMutableProjection.generate(requiredOutput, dataSchema.toAttributes) + } else { + () => new InterpretedMutableProjection(requiredOutput, dataSchema.toAttributes) + } + val mutableProjection = buildProjection() + rows.map(r => mutableProjection(r).asInstanceOf[Row]) + } + } + + /** + * For a non-partitioned relation, this method builds an `RDD[Row]` containing all rows within + * this relation. For partitioned relations, this method is called for each selected partition, + * and builds an `RDD[Row]` containing all rows within that single partition. + * + * @param requiredColumns Required columns. + * @param filters Candidate filters to be pushed down. The actual filter should be the conjunction + * of all `filters`. The pushed down filters are currently purely an optimization as they + * will all be evaluated again. This means it is safe to use them with methods that produce + * false positives such as filtering partitions based on a bloom filter. + * @param inputFiles For a non-partitioned relation, it contains paths of all data files in the + * relation. For a partitioned relation, it contains paths of all data files in a single + * selected partition. + * + * @since 1.4.0 + */ + def buildScan( + requiredColumns: Array[String], + filters: Array[Filter], + inputFiles: Array[FileStatus]): RDD[Row] = { + buildScan(requiredColumns, inputFiles) + } + + /** + * For a non-partitioned relation, this method builds an `RDD[Row]` containing all rows within + * this relation. For partitioned relations, this method is called for each selected partition, + * and builds an `RDD[Row]` containing all rows within that single partition. + * + * Note: This interface is subject to change in future. + * + * @param requiredColumns Required columns. + * @param filters Candidate filters to be pushed down. The actual filter should be the conjunction + * of all `filters`. The pushed down filters are currently purely an optimization as they + * will all be evaluated again. This means it is safe to use them with methods that produce + * false positives such as filtering partitions based on a bloom filter. + * @param inputFiles For a non-partitioned relation, it contains paths of all data files in the + * relation. For a partitioned relation, it contains paths of all data files in a single + * selected partition. + * @param broadcastedConf A shared broadcast Hadoop Configuration, which can be used to reduce the + * overhead of broadcasting the Configuration for every Hadoop RDD. + * + * @since 1.4.0 + */ + private[sql] def buildScan( + requiredColumns: Array[String], + filters: Array[Filter], + inputFiles: Array[FileStatus], + broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = { + buildScan(requiredColumns, filters, inputFiles) + } + + /** + * Prepares a write job and returns an [[OutputWriterFactory]]. Client side job preparation can + * be put here. For example, user defined output committer can be configured here + * by setting the output committer class in the conf of spark.sql.sources.outputCommitterClass. + * + * Note that the only side effect expected here is mutating `job` via its setters. Especially, + * Spark SQL caches [[BaseRelation]] instances for performance, mutating relation internal states + * may cause unexpected behaviors. + * + * @since 1.4.0 + */ + def prepareJobForWrite(job: Job): OutputWriterFactory +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala index 6ed68d179edc9..a3fd7f13b3db7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala @@ -35,9 +35,9 @@ private[sql] object PreInsertCastAndRename extends Rule[LogicalPlan] { // Wait until children are resolved. case p: LogicalPlan if !p.childrenResolved => p - // We are inserting into an InsertableRelation. + // We are inserting into an InsertableRelation or HadoopFsRelation. case i @ InsertIntoTable( - l @ LogicalRelation(r: InsertableRelation), partition, child, overwrite, ifNotExists) => { + l @ LogicalRelation(_: InsertableRelation | _: HadoopFsRelation), _, child, _, _) => { // First, make sure the data to be inserted have the same number of fields with the // schema of the relation. if (l.output.size != child.output.size) { @@ -101,13 +101,25 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan => } } - case i @ logical.InsertIntoTable( - l: LogicalRelation, partition, query, overwrite, ifNotExists) - if !l.isInstanceOf[InsertableRelation] => + case logical.InsertIntoTable(LogicalRelation(r: HadoopFsRelation), part, _, _, _) => + // We need to make sure the partition columns specified by users do match partition + // columns of the relation. + val existingPartitionColumns = r.partitionColumns.fieldNames.toSet + val specifiedPartitionColumns = part.keySet + if (existingPartitionColumns != specifiedPartitionColumns) { + failAnalysis(s"Specified partition columns " + + s"(${specifiedPartitionColumns.mkString(", ")}) " + + s"do not match the partition columns of the table. Please use " + + s"(${existingPartitionColumns.mkString(", ")}) as the partition columns.") + } else { + // OK + } + + case logical.InsertIntoTable(l: LogicalRelation, _, _, _, _) => // The relation in l is not an InsertableRelation. failAnalysis(s"$l does not allow insertion.") - case CreateTableUsingAsSelect(tableName, _, _, SaveMode.Overwrite, _, query) => + case CreateTableUsingAsSelect(tableName, _, _, _, SaveMode.Overwrite, _, query) => // When the SaveMode is Overwrite, we need to check if the table is an input table of // the query. If so, we will throw an AnalysisException to let users know it is not allowed. if (catalog.tableExists(Seq(tableName))) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/README.md b/sql/core/src/main/scala/org/apache/spark/sql/test/README.md new file mode 100644 index 0000000000000..d867f181b9728 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/test/README.md @@ -0,0 +1,7 @@ +README +====== + +Please do not add any class in this place unless it is used by `sql/console` or Python tests. +If you need to create any classes or traits that will be used by tests from both `sql/core` and +`sql/hive`, you can add them in the `src/test` of `sql/core` (tests of `sql/hive` +depend on the test jar of `sql/core`). diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala index 356a6100d2cf5..9fa394525d65c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala @@ -38,7 +38,7 @@ class LocalSQLContext protected[sql] class SQLSession extends super.SQLSession { protected[sql] override lazy val conf: SQLConf = new SQLConf { /** Fewer partitions to speed up testing. */ - override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt + override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, 5) } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java index c344a9b095c52..fcb8f5499cf84 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java @@ -187,14 +187,14 @@ public void applySchemaToJSON() { null, "this is another simple string.")); - DataFrame df1 = sqlContext.jsonRDD(jsonRDD); + DataFrame df1 = sqlContext.read().json(jsonRDD); StructType actualSchema1 = df1.schema(); Assert.assertEquals(expectedSchema, actualSchema1); df1.registerTempTable("jsonTable1"); List actual1 = sqlContext.sql("select * from jsonTable1").collectAsList(); Assert.assertEquals(expectedResult, actual1); - DataFrame df2 = sqlContext.jsonRDD(jsonRDD, expectedSchema); + DataFrame df2 = sqlContext.read().schema(expectedSchema).json(jsonRDD); StructType actualSchema2 = df2.schema(); Assert.assertEquals(expectedSchema, actualSchema2); df2.registerTempTable("jsonTable2"); diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java index b76f7d421f643..2706e01bd28af 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java @@ -67,7 +67,7 @@ public void setUp() throws IOException { jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}"); } JavaRDD rdd = sc.parallelize(jsonObjects); - df = sqlContext.jsonRDD(rdd); + df = sqlContext.read().json(rdd); df.registerTempTable("jsonTable"); } @@ -75,10 +75,8 @@ public void setUp() throws IOException { public void saveAndLoad() { Map options = new HashMap(); options.put("path", path.toString()); - df.save("org.apache.spark.sql.json", SaveMode.ErrorIfExists, options); - - DataFrame loadedDF = sqlContext.load("org.apache.spark.sql.json", options); - + df.write().mode(SaveMode.ErrorIfExists).format("json").options(options).save(); + DataFrame loadedDF = sqlContext.read().format("json").options(options).load(); checkAnswer(loadedDF, df.collectAsList()); } @@ -86,12 +84,12 @@ public void saveAndLoad() { public void saveAndLoadWithSchema() { Map options = new HashMap(); options.put("path", path.toString()); - df.save("org.apache.spark.sql.json", SaveMode.ErrorIfExists, options); + df.write().format("json").mode(SaveMode.ErrorIfExists).options(options).save(); List fields = new ArrayList(); fields.add(DataTypes.createStructField("b", DataTypes.StringType, true)); StructType schema = DataTypes.createStructType(fields); - DataFrame loadedDF = sqlContext.load("org.apache.spark.sql.json", schema, options); + DataFrame loadedDF = sqlContext.read().format("json").schema(schema).options(options).load(); checkAnswer(loadedDF, sqlContext.sql("SELECT b FROM jsonTable").collectAsList()); } diff --git a/sql/core/src/test/resources/log4j.properties b/sql/core/src/test/resources/log4j.properties index 28e90b9520b2c..12fb128149d32 100644 --- a/sql/core/src/test/resources/log4j.properties +++ b/sql/core/src/test/resources/log4j.properties @@ -36,11 +36,11 @@ log4j.appender.FA.layout.ConversionPattern=%d{HH:mm:ss.SSS} %t %p %c{1}: %m%n log4j.appender.FA.Threshold = INFO # Some packages are noisy for no good reason. -log4j.additivity.parquet.hadoop.ParquetRecordReader=false -log4j.logger.parquet.hadoop.ParquetRecordReader=OFF +log4j.additivity.org.apache.parquet.hadoop.ParquetRecordReader=false +log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF -log4j.additivity.parquet.hadoop.ParquetOutputCommitter=false -log4j.logger.parquet.hadoop.ParquetOutputCommitter=OFF +log4j.additivity.org.apache.parquet.hadoop.ParquetOutputCommitter=false +log4j.logger.org.apache.parquet.hadoop.ParquetOutputCommitter=OFF log4j.additivity.org.apache.hadoop.hive.serde2.lazy.LazyStruct=false log4j.logger.org.apache.hadoop.hive.serde2.lazy.LazyStruct=OFF @@ -52,5 +52,5 @@ log4j.additivity.hive.ql.metadata.Hive=false log4j.logger.hive.ql.metadata.Hive=OFF # Parquet related logging -log4j.logger.parquet.hadoop=WARN +log4j.logger.org.apache.parquet.hadoop=WARN log4j.logger.org.apache.spark.sql.parquet=INFO diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index 0772e5e187425..eb3e913322062 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -25,17 +25,19 @@ import org.scalatest.concurrent.Eventually._ import org.apache.spark.Accumulators import org.apache.spark.sql.TestData._ import org.apache.spark.sql.columnar._ -import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ -import org.apache.spark.storage.{RDDBlockId, StorageLevel} +import org.apache.spark.storage.{StorageLevel, RDDBlockId} case class BigData(s: String) class CachedTableSuite extends QueryTest { TestData // Load test tables. + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + import ctx.sql + def rddIdOf(tableName: String): Int = { - val executedPlan = table(tableName).queryExecution.executedPlan + val executedPlan = ctx.table(tableName).queryExecution.executedPlan executedPlan.collect { case InMemoryColumnarTableScan(_, _, relation) => relation.cachedColumnBuffers.id @@ -45,151 +47,151 @@ class CachedTableSuite extends QueryTest { } def isMaterialized(rddId: Int): Boolean = { - sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty + ctx.sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty } test("cache temp table") { testData.select('key).registerTempTable("tempTable") assertCached(sql("SELECT COUNT(*) FROM tempTable"), 0) - cacheTable("tempTable") + ctx.cacheTable("tempTable") assertCached(sql("SELECT COUNT(*) FROM tempTable")) - uncacheTable("tempTable") + ctx.uncacheTable("tempTable") } test("unpersist an uncached table will not raise exception") { - assert(None == cacheManager.lookupCachedData(testData)) + assert(None == ctx.cacheManager.lookupCachedData(testData)) testData.unpersist(blocking = true) - assert(None == cacheManager.lookupCachedData(testData)) + assert(None == ctx.cacheManager.lookupCachedData(testData)) testData.unpersist(blocking = false) - assert(None == cacheManager.lookupCachedData(testData)) + assert(None == ctx.cacheManager.lookupCachedData(testData)) testData.persist() - assert(None != cacheManager.lookupCachedData(testData)) + assert(None != ctx.cacheManager.lookupCachedData(testData)) testData.unpersist(blocking = true) - assert(None == cacheManager.lookupCachedData(testData)) + assert(None == ctx.cacheManager.lookupCachedData(testData)) testData.unpersist(blocking = false) - assert(None == cacheManager.lookupCachedData(testData)) + assert(None == ctx.cacheManager.lookupCachedData(testData)) } test("cache table as select") { sql("CACHE TABLE tempTable AS SELECT key FROM testData") assertCached(sql("SELECT COUNT(*) FROM tempTable")) - uncacheTable("tempTable") + ctx.uncacheTable("tempTable") } test("uncaching temp table") { testData.select('key).registerTempTable("tempTable1") testData.select('key).registerTempTable("tempTable2") - cacheTable("tempTable1") + ctx.cacheTable("tempTable1") assertCached(sql("SELECT COUNT(*) FROM tempTable1")) assertCached(sql("SELECT COUNT(*) FROM tempTable2")) // Is this valid? - uncacheTable("tempTable2") + ctx.uncacheTable("tempTable2") // Should this be cached? assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0) } test("too big for memory") { - val data = "*" * 10000 - sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF() + val data = "*" * 1000 + ctx.sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF() .registerTempTable("bigData") - table("bigData").persist(StorageLevel.MEMORY_AND_DISK) - assert(table("bigData").count() === 200000L) - table("bigData").unpersist(blocking = true) + ctx.table("bigData").persist(StorageLevel.MEMORY_AND_DISK) + assert(ctx.table("bigData").count() === 200000L) + ctx.table("bigData").unpersist(blocking = true) } test("calling .cache() should use in-memory columnar caching") { - table("testData").cache() - assertCached(table("testData")) - table("testData").unpersist(blocking = true) + ctx.table("testData").cache() + assertCached(ctx.table("testData")) + ctx.table("testData").unpersist(blocking = true) } test("calling .unpersist() should drop in-memory columnar cache") { - table("testData").cache() - table("testData").count() - table("testData").unpersist(blocking = true) - assertCached(table("testData"), 0) + ctx.table("testData").cache() + ctx.table("testData").count() + ctx.table("testData").unpersist(blocking = true) + assertCached(ctx.table("testData"), 0) } test("isCached") { - cacheTable("testData") + ctx.cacheTable("testData") - assertCached(table("testData")) - assert(table("testData").queryExecution.withCachedData match { + assertCached(ctx.table("testData")) + assert(ctx.table("testData").queryExecution.withCachedData match { case _: InMemoryRelation => true case _ => false }) - uncacheTable("testData") - assert(!isCached("testData")) - assert(table("testData").queryExecution.withCachedData match { + ctx.uncacheTable("testData") + assert(!ctx.isCached("testData")) + assert(ctx.table("testData").queryExecution.withCachedData match { case _: InMemoryRelation => false case _ => true }) } test("SPARK-1669: cacheTable should be idempotent") { - assume(!table("testData").logicalPlan.isInstanceOf[InMemoryRelation]) + assume(!ctx.table("testData").logicalPlan.isInstanceOf[InMemoryRelation]) - cacheTable("testData") - assertCached(table("testData")) + ctx.cacheTable("testData") + assertCached(ctx.table("testData")) assertResult(1, "InMemoryRelation not found, testData should have been cached") { - table("testData").queryExecution.withCachedData.collect { + ctx.table("testData").queryExecution.withCachedData.collect { case r: InMemoryRelation => r }.size } - cacheTable("testData") + ctx.cacheTable("testData") assertResult(0, "Double InMemoryRelations found, cacheTable() is not idempotent") { - table("testData").queryExecution.withCachedData.collect { + ctx.table("testData").queryExecution.withCachedData.collect { case r @ InMemoryRelation(_, _, _, _, _: InMemoryColumnarTableScan, _) => r }.size } - uncacheTable("testData") + ctx.uncacheTable("testData") } test("read from cached table and uncache") { - cacheTable("testData") - checkAnswer(table("testData"), testData.collect().toSeq) - assertCached(table("testData")) + ctx.cacheTable("testData") + checkAnswer(ctx.table("testData"), testData.collect().toSeq) + assertCached(ctx.table("testData")) - uncacheTable("testData") - checkAnswer(table("testData"), testData.collect().toSeq) - assertCached(table("testData"), 0) + ctx.uncacheTable("testData") + checkAnswer(ctx.table("testData"), testData.collect().toSeq) + assertCached(ctx.table("testData"), 0) } test("correct error on uncache of non-cached table") { intercept[IllegalArgumentException] { - uncacheTable("testData") + ctx.uncacheTable("testData") } } test("SELECT star from cached table") { sql("SELECT * FROM testData").registerTempTable("selectStar") - cacheTable("selectStar") + ctx.cacheTable("selectStar") checkAnswer( sql("SELECT * FROM selectStar WHERE key = 1"), Seq(Row(1, "1"))) - uncacheTable("selectStar") + ctx.uncacheTable("selectStar") } test("Self-join cached") { val unCachedAnswer = sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key").collect() - cacheTable("testData") + ctx.cacheTable("testData") checkAnswer( sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key"), unCachedAnswer.toSeq) - uncacheTable("testData") + ctx.uncacheTable("testData") } test("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") { sql("CACHE TABLE testData") - assertCached(table("testData")) + assertCached(ctx.table("testData")) val rddId = rddIdOf("testData") assert( @@ -197,7 +199,7 @@ class CachedTableSuite extends QueryTest { "Eagerly cached in-memory table should have already been materialized") sql("UNCACHE TABLE testData") - assert(!isCached("testData"), "Table 'testData' should not be cached") + assert(!ctx.isCached("testData"), "Table 'testData' should not be cached") eventually(timeout(10 seconds)) { assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted") @@ -206,14 +208,14 @@ class CachedTableSuite extends QueryTest { test("CACHE TABLE tableName AS SELECT * FROM anotherTable") { sql("CACHE TABLE testCacheTable AS SELECT * FROM testData") - assertCached(table("testCacheTable")) + assertCached(ctx.table("testCacheTable")) val rddId = rddIdOf("testCacheTable") assert( isMaterialized(rddId), "Eagerly cached in-memory table should have already been materialized") - uncacheTable("testCacheTable") + ctx.uncacheTable("testCacheTable") eventually(timeout(10 seconds)) { assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted") } @@ -221,14 +223,14 @@ class CachedTableSuite extends QueryTest { test("CACHE TABLE tableName AS SELECT ...") { sql("CACHE TABLE testCacheTable AS SELECT key FROM testData LIMIT 10") - assertCached(table("testCacheTable")) + assertCached(ctx.table("testCacheTable")) val rddId = rddIdOf("testCacheTable") assert( isMaterialized(rddId), "Eagerly cached in-memory table should have already been materialized") - uncacheTable("testCacheTable") + ctx.uncacheTable("testCacheTable") eventually(timeout(10 seconds)) { assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted") } @@ -236,7 +238,7 @@ class CachedTableSuite extends QueryTest { test("CACHE LAZY TABLE tableName") { sql("CACHE LAZY TABLE testData") - assertCached(table("testData")) + assertCached(ctx.table("testData")) val rddId = rddIdOf("testData") assert( @@ -248,7 +250,7 @@ class CachedTableSuite extends QueryTest { isMaterialized(rddId), "Lazily cached in-memory table should have been materialized") - uncacheTable("testData") + ctx.uncacheTable("testData") eventually(timeout(10 seconds)) { assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted") } @@ -256,7 +258,7 @@ class CachedTableSuite extends QueryTest { test("InMemoryRelation statistics") { sql("CACHE TABLE testData") - table("testData").queryExecution.withCachedData.collect { + ctx.table("testData").queryExecution.withCachedData.collect { case cached: InMemoryRelation => val actualSizeInBytes = (1 to 100).map(i => INT.defaultSize + i.toString.length + 4).sum assert(cached.statistics.sizeInBytes === actualSizeInBytes) @@ -265,38 +267,38 @@ class CachedTableSuite extends QueryTest { test("Drops temporary table") { testData.select('key).registerTempTable("t1") - table("t1") - dropTempTable("t1") - assert(intercept[RuntimeException](table("t1")).getMessage.startsWith("Table Not Found")) + ctx.table("t1") + ctx.dropTempTable("t1") + assert(intercept[RuntimeException](ctx.table("t1")).getMessage.startsWith("Table Not Found")) } test("Drops cached temporary table") { testData.select('key).registerTempTable("t1") testData.select('key).registerTempTable("t2") - cacheTable("t1") + ctx.cacheTable("t1") - assert(isCached("t1")) - assert(isCached("t2")) + assert(ctx.isCached("t1")) + assert(ctx.isCached("t2")) - dropTempTable("t1") - assert(intercept[RuntimeException](table("t1")).getMessage.startsWith("Table Not Found")) - assert(!isCached("t2")) + ctx.dropTempTable("t1") + assert(intercept[RuntimeException](ctx.table("t1")).getMessage.startsWith("Table Not Found")) + assert(!ctx.isCached("t2")) } test("Clear all cache") { sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1") sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2") - cacheTable("t1") - cacheTable("t2") - clearCache() - assert(cacheManager.isEmpty) + ctx.cacheTable("t1") + ctx.cacheTable("t2") + ctx.clearCache() + assert(ctx.cacheManager.isEmpty) sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1") sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2") - cacheTable("t1") - cacheTable("t2") + ctx.cacheTable("t1") + ctx.cacheTable("t2") sql("Clear CACHE") - assert(cacheManager.isEmpty) + assert(ctx.cacheManager.isEmpty) } test("Clear accumulators when uncacheTable to prevent memory leaking") { @@ -305,8 +307,8 @@ class CachedTableSuite extends QueryTest { Accumulators.synchronized { val accsSize = Accumulators.originals.size - cacheTable("t1") - cacheTable("t2") + ctx.cacheTable("t1") + ctx.cacheTable("t2") assert((accsSize + 2) == Accumulators.originals.size) } @@ -317,8 +319,8 @@ class CachedTableSuite extends QueryTest { Accumulators.synchronized { val accsSize = Accumulators.originals.size - uncacheTable("t1") - uncacheTable("t2") + ctx.uncacheTable("t1") + ctx.uncacheTable("t2") assert((accsSize - 2) == Accumulators.originals.size) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index d96186c268720..88bb743ab0bc9 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -19,14 +19,82 @@ package org.apache.spark.sql import org.scalatest.Matchers._ +import org.apache.spark.sql.execution.Project import org.apache.spark.sql.functions._ -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ class ColumnExpressionSuite extends QueryTest { import org.apache.spark.sql.TestData._ + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + + test("alias") { + val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList") + assert(df.select(df("a").as("b")).columns.head === "b") + assert(df.select(df("a").alias("b")).columns.head === "b") + } + + test("single explode") { + val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList") + checkAnswer( + df.select(explode('intList)), + Row(1) :: Row(2) :: Row(3) :: Nil) + } + + test("explode and other columns") { + val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList") + + checkAnswer( + df.select($"a", explode('intList)), + Row(1, 1) :: + Row(1, 2) :: + Row(1, 3) :: Nil) + + checkAnswer( + df.select($"*", explode('intList)), + Row(1, Seq(1, 2, 3), 1) :: + Row(1, Seq(1, 2, 3), 2) :: + Row(1, Seq(1, 2, 3), 3) :: Nil) + } + + test("aliased explode") { + val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList") + + checkAnswer( + df.select(explode('intList).as('int)).select('int), + Row(1) :: Row(2) :: Row(3) :: Nil) + + checkAnswer( + df.select(explode('intList).as('int)).select(sum('int)), + Row(6) :: Nil) + } + + test("explode on map") { + val df = Seq((1, Map("a" -> "b"))).toDF("a", "map") + + checkAnswer( + df.select(explode('map)), + Row("a", "b")) + } + + test("explode on map with aliases") { + val df = Seq((1, Map("a" -> "b"))).toDF("a", "map") + + checkAnswer( + df.select(explode('map).as("key1" :: "value1" :: Nil)).select("key1", "value1"), + Row("a", "b")) + } + + test("self join explode") { + val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList") + val exploded = df.select(explode('intList).as('i)) + + checkAnswer( + exploded.join(exploded, exploded("i") === exploded("i")).agg(count("*")), + Row(3) :: Nil) + } + test("collect on column produced by a binary operator") { val df = Seq((1, 2, 3)).toDF("a", "b", "c") checkAnswer(df.select(df("a") + df("b")), Seq(Row(3))) @@ -117,12 +185,20 @@ class ColumnExpressionSuite extends QueryTest { checkAnswer( nullStrings.toDF.where($"s".isNull), nullStrings.collect().toSeq.filter(r => r.getString(1) eq null)) + + checkAnswer( + ctx.sql("select isnull(null), isnull(1)"), + Row(true, false)) } test("isNotNull") { checkAnswer( nullStrings.toDF.where($"s".isNotNull), nullStrings.collect().toSeq.filter(r => r.getString(1) ne null)) + + checkAnswer( + ctx.sql("select isnotnull(null), isnotnull('a')"), + Row(false, true)) } test("===") { @@ -146,7 +222,7 @@ class ColumnExpressionSuite extends QueryTest { } test("!==") { - val nullData = TestSQLContext.createDataFrame(TestSQLContext.sparkContext.parallelize( + val nullData = ctx.createDataFrame(ctx.sparkContext.parallelize( Row(1, 1) :: Row(1, 2) :: Row(1, null) :: @@ -207,7 +283,7 @@ class ColumnExpressionSuite extends QueryTest { } test("between") { - val testData = TestSQLContext.sparkContext.parallelize( + val testData = ctx.sparkContext.parallelize( (0, 1, 2) :: (1, 2, 3) :: (2, 1, 0) :: @@ -220,7 +296,23 @@ class ColumnExpressionSuite extends QueryTest { checkAnswer(testData.filter($"a".between($"b", $"c")), expectAnswer) } - val booleanData = TestSQLContext.createDataFrame(TestSQLContext.sparkContext.parallelize( + test("in") { + val df = Seq((1, "x"), (2, "y"), (3, "z")).toDF("a", "b") + checkAnswer(df.filter($"a".in(1, 2)), + df.collect().toSeq.filter(r => r.getInt(0) == 1 || r.getInt(0) == 2)) + checkAnswer(df.filter($"a".in(3, 2)), + df.collect().toSeq.filter(r => r.getInt(0) == 3 || r.getInt(0) == 2)) + checkAnswer(df.filter($"a".in(3, 1)), + df.collect().toSeq.filter(r => r.getInt(0) == 3 || r.getInt(0) == 1)) + checkAnswer(df.filter($"b".in("y", "x")), + df.collect().toSeq.filter(r => r.getString(1) == "y" || r.getString(1) == "x")) + checkAnswer(df.filter($"b".in("z", "x")), + df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "x")) + checkAnswer(df.filter($"b".in("z", "y")), + df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "y")) + } + + val booleanData = ctx.createDataFrame(ctx.sparkContext.parallelize( Row(false, false) :: Row(false, true) :: Row(true, false) :: @@ -255,36 +347,40 @@ class ColumnExpressionSuite extends QueryTest { Row(false, true) :: Row(true, false) :: Row(true, true) :: Nil) } - test("sqrt") { - checkAnswer( - testData.select(sqrt('key)).orderBy('key.asc), - (1 to 100).map(n => Row(math.sqrt(n))) - ) + test("SPARK-7321 when conditional statements") { + val testData = (1 to 3).map(i => (i, i.toString)).toDF("key", "value") checkAnswer( - testData.select(sqrt('value), 'key).orderBy('key.asc, 'value.asc), - (1 to 100).map(n => Row(math.sqrt(n), n)) + testData.select(when($"key" === 1, -1).when($"key" === 2, -2).otherwise(0)), + Seq(Row(-1), Row(-2), Row(0)) ) + // Without the ending otherwise, return null for unmatched conditions. + // Also test putting a non-literal value in the expression. checkAnswer( - testData.select(sqrt(lit(null))), - (1 to 100).map(_ => Row(null)) + testData.select(when($"key" === 1, lit(0) - $"key").when($"key" === 2, -2)), + Seq(Row(-1), Row(-2), Row(null)) ) + + // Test error handling for invalid expressions. + intercept[IllegalArgumentException] { $"key".when($"key" === 1, -1) } + intercept[IllegalArgumentException] { $"key".otherwise(-1) } + intercept[IllegalArgumentException] { when($"key" === 1, -1).otherwise(-1).otherwise(-1) } } - test("abs") { + test("sqrt") { checkAnswer( - testData.select(abs('key)).orderBy('key.asc), - (1 to 100).map(n => Row(n)) + testData.select(sqrt('key)).orderBy('key.asc), + (1 to 100).map(n => Row(math.sqrt(n))) ) checkAnswer( - negativeData.select(abs('key)).orderBy('key.desc), - (1 to 100).map(n => Row(n)) + testData.select(sqrt('value), 'key).orderBy('key.asc, 'value.asc), + (1 to 100).map(n => Row(math.sqrt(n), n)) ) checkAnswer( - testData.select(abs(lit(null))), + testData.select(sqrt(lit(null))), (1 to 100).map(_ => Row(null)) ) } @@ -304,6 +400,10 @@ class ColumnExpressionSuite extends QueryTest { testData.select(upper(lit(null))), (1 to 100).map(n => Row(null)) ) + + checkAnswer( + ctx.sql("SELECT upper('aB'), ucase('cDe')"), + Row("AB", "CDE")) } test("lower") { @@ -321,11 +421,15 @@ class ColumnExpressionSuite extends QueryTest { testData.select(lower(lit(null))), (1 to 100).map(n => Row(null)) ) + + checkAnswer( + ctx.sql("SELECT lower('aB'), lcase('cDe')"), + Row("ab", "cde")) } test("monotonicallyIncreasingId") { // Make sure we have 2 partitions, each with 2 records. - val df = TestSQLContext.sparkContext.parallelize(1 to 2, 2).mapPartitions { iter => + val df = ctx.sparkContext.parallelize(1 to 2, 2).mapPartitions { iter => Iterator(Tuple1(1), Tuple1(2)) }.toDF("a") checkAnswer( @@ -335,7 +439,7 @@ class ColumnExpressionSuite extends QueryTest { } test("sparkPartitionId") { - val df = TestSQLContext.sparkContext.parallelize(1 to 1, 1).map(i => (i, i)).toDF("a", "b") + val df = ctx.sparkContext.parallelize(1 to 1, 1).map(i => (i, i)).toDF("a", "b") checkAnswer( df.select(sparkPartitionId()), Row(0) @@ -365,13 +469,51 @@ class ColumnExpressionSuite extends QueryTest { } test("rand") { - val randCol = testData.select('key, rand(5L).as("rand")) + val randCol = testData.select($"key", rand(5L).as("rand")) randCol.columns.length should be (2) val rows = randCol.collect() rows.foreach { row => assert(row.getDouble(1) <= 1.0) assert(row.getDouble(1) >= 0.0) } + + def checkNumProjects(df: DataFrame, expectedNumProjects: Int): Unit = { + val projects = df.queryExecution.executedPlan.collect { + case project: Project => project + } + assert(projects.size === expectedNumProjects) + } + + // We first create a plan with two Projects. + // Project [rand + 1 AS rand1, rand - 1 AS rand2] + // Project [key, (Rand 5 + 1) AS rand] + // LogicalRDD [key, value] + // Because Rand function is not deterministic, the column rand is not deterministic. + // So, in the optimizer, we will not collapse Project [rand + 1 AS rand1, rand - 1 AS rand2] + // and Project [key, Rand 5 AS rand]. The final plan still has two Projects. + val dfWithTwoProjects = + testData + .select($"key", (rand(5L) + 1).as("rand")) + .select(($"rand" + 1).as("rand1"), ($"rand" - 1).as("rand2")) + checkNumProjects(dfWithTwoProjects, 2) + + // Now, we add one more project rand1 - rand2 on top of the query plan. + // Since rand1 and rand2 are deterministic (they basically apply +/- to the generated + // rand value), we can collapse rand1 - rand2 to the Project generating rand1 and rand2. + // So, the plan will be optimized from ... + // Project [(rand1 - rand2) AS (rand1 - rand2)] + // Project [rand + 1 AS rand1, rand - 1 AS rand2] + // Project [key, (Rand 5 + 1) AS rand] + // LogicalRDD [key, value] + // to ... + // Project [((rand + 1 AS rand1) - (rand - 1 AS rand2)) AS (rand1 - rand2)] + // Project [key, Rand 5 AS rand] + // LogicalRDD [key, value] + val dfWithThreeProjects = dfWithTwoProjects.select($"rand1" - $"rand2") + checkNumProjects(dfWithThreeProjects, 2) + dfWithThreeProjects.collect().foreach { row => + assert(row.getDouble(0) === 2.0 +- 0.0001) + } } test("randn") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala new file mode 100644 index 0000000000000..b26d3ab253a1d --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala @@ -0,0 +1,194 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.sql.TestData._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.DecimalType + + +class DataFrameAggregateSuite extends QueryTest { + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + + test("groupBy") { + checkAnswer( + testData2.groupBy("a").agg(sum($"b")), + Seq(Row(1, 3), Row(2, 3), Row(3, 3)) + ) + checkAnswer( + testData2.groupBy("a").agg(sum($"b").as("totB")).agg(sum('totB)), + Row(9) + ) + checkAnswer( + testData2.groupBy("a").agg(count("*")), + Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil + ) + checkAnswer( + testData2.groupBy("a").agg(Map("*" -> "count")), + Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil + ) + checkAnswer( + testData2.groupBy("a").agg(Map("b" -> "sum")), + Row(1, 3) :: Row(2, 3) :: Row(3, 3) :: Nil + ) + + val df1 = Seq(("a", 1, 0, "b"), ("b", 2, 4, "c"), ("a", 2, 3, "d")) + .toDF("key", "value1", "value2", "rest") + + checkAnswer( + df1.groupBy("key").min(), + df1.groupBy("key").min("value1", "value2").collect() + ) + checkAnswer( + df1.groupBy("key").min("value2"), + Seq(Row("a", 0), Row("b", 4)) + ) + } + + test("spark.sql.retainGroupColumns config") { + checkAnswer( + testData2.groupBy("a").agg(sum($"b")), + Seq(Row(1, 3), Row(2, 3), Row(3, 3)) + ) + + ctx.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, false) + checkAnswer( + testData2.groupBy("a").agg(sum($"b")), + Seq(Row(3), Row(3), Row(3)) + ) + ctx.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, true) + } + + test("agg without groups") { + checkAnswer( + testData2.agg(sum('b)), + Row(9) + ) + } + + test("average") { + checkAnswer( + testData2.agg(avg('a)), + Row(2.0)) + + // Also check mean + checkAnswer( + testData2.agg(mean('a)), + Row(2.0)) + + checkAnswer( + testData2.agg(avg('a), sumDistinct('a)), // non-partial + Row(2.0, 6.0) :: Nil) + + checkAnswer( + decimalData.agg(avg('a)), + Row(new java.math.BigDecimal(2.0))) + checkAnswer( + decimalData.agg(avg('a), sumDistinct('a)), // non-partial + Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil) + + checkAnswer( + decimalData.agg(avg('a cast DecimalType(10, 2))), + Row(new java.math.BigDecimal(2.0))) + // non-partial + checkAnswer( + decimalData.agg(avg('a cast DecimalType(10, 2)), sumDistinct('a cast DecimalType(10, 2))), + Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil) + } + + test("null average") { + checkAnswer( + testData3.agg(avg('b)), + Row(2.0)) + + checkAnswer( + testData3.agg(avg('b), countDistinct('b)), + Row(2.0, 1)) + + checkAnswer( + testData3.agg(avg('b), sumDistinct('b)), // non-partial + Row(2.0, 2.0)) + } + + test("zero average") { + val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b") + checkAnswer( + emptyTableData.agg(avg('a)), + Row(null)) + + checkAnswer( + emptyTableData.agg(avg('a), sumDistinct('b)), // non-partial + Row(null, null)) + } + + test("count") { + assert(testData2.count() === testData2.map(_ => 1).count()) + + checkAnswer( + testData2.agg(count('a), sumDistinct('a)), // non-partial + Row(6, 6.0)) + } + + test("null count") { + checkAnswer( + testData3.groupBy('a).agg(count('b)), + Seq(Row(1, 0), Row(2, 1)) + ) + + checkAnswer( + testData3.groupBy('a).agg(count('a + 'b)), + Seq(Row(1, 0), Row(2, 1)) + ) + + checkAnswer( + testData3.agg(count('a), count('b), count(lit(1)), countDistinct('a), countDistinct('b)), + Row(2, 1, 2, 2, 1) + ) + + checkAnswer( + testData3.agg(count('b), countDistinct('b), sumDistinct('b)), // non-partial + Row(1, 1, 2) + ) + } + + test("zero count") { + val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b") + assert(emptyTableData.count() === 0) + + checkAnswer( + emptyTableData.agg(count('a), sumDistinct('a)), // non-partial + Row(0, null)) + } + + test("zero sum") { + val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b") + checkAnswer( + emptyTableData.agg(sum('a)), + Row(null)) + } + + test("zero sum distinct") { + val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b") + checkAnswer( + emptyTableData.agg(sumDistinct('a)), + Row(null)) + } + +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameDateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameDateSuite.scala new file mode 100644 index 0000000000000..a4719a38de1d4 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameDateSuite.scala @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.sql.{Date, Timestamp} + +class DataFrameDateTimeSuite extends QueryTest { + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + + test("timestamp comparison with date strings") { + val df = Seq( + (1, Timestamp.valueOf("2015-01-01 00:00:00")), + (2, Timestamp.valueOf("2014-01-01 00:00:00"))).toDF("i", "t") + + checkAnswer( + df.select("t").filter($"t" <= "2014-06-01"), + Row(Timestamp.valueOf("2014-01-01 00:00:00")) :: Nil) + + + checkAnswer( + df.select("t").filter($"t" >= "2014-06-01"), + Row(Timestamp.valueOf("2015-01-01 00:00:00")) :: Nil) + } + + test("date comparison with date strings") { + val df = Seq( + (1, Date.valueOf("2015-01-01")), + (2, Date.valueOf("2014-01-01"))).toDF("i", "t") + + checkAnswer( + df.select("t").filter($"t" <= "2014-06-01"), + Row(Date.valueOf("2014-01-01")) :: Nil) + + + checkAnswer( + df.select("t").filter($"t" >= "2015"), + Row(Date.valueOf("2015-01-01")) :: Nil) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala index b1e0faa310b68..8b53b384a22fd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala @@ -19,7 +19,6 @@ package org.apache.spark.sql import org.apache.spark.sql.TestData._ import org.apache.spark.sql.functions._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ /** @@ -27,6 +26,9 @@ import org.apache.spark.sql.types._ */ class DataFrameFunctionsSuite extends QueryTest { + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + test("array with column name") { val df = Seq((0, 1)).toDF("a", "b") val row = df.select(array("a", "b")).first() @@ -83,9 +85,80 @@ class DataFrameFunctionsSuite extends QueryTest { } } + test("constant functions") { + checkAnswer( + testData2.select(e()).limit(1), + Row(scala.math.E) + ) + checkAnswer( + testData2.select(pi()).limit(1), + Row(scala.math.Pi) + ) + checkAnswer( + ctx.sql("SELECT E()"), + Row(scala.math.E) + ) + checkAnswer( + ctx.sql("SELECT PI()"), + Row(scala.math.Pi) + ) + } + test("bitwiseNOT") { checkAnswer( testData2.select(bitwiseNOT($"a")), testData2.collect().toSeq.map(r => Row(~r.getInt(0)))) } + + test("bin") { + val df = Seq[(Integer, Integer)]((12, null)).toDF("a", "b") + checkAnswer( + df.select(bin("a"), bin("b")), + Row("1100", null)) + checkAnswer( + df.selectExpr("bin(a)", "bin(b)"), + Row("1100", null)) + } + + test("if function") { + val df = Seq((1, 2)).toDF("a", "b") + checkAnswer( + df.selectExpr("if(a = 1, 'one', 'not_one')", "if(b = 1, 'one', 'not_one')"), + Row("one", "not_one")) + } + + test("nvl function") { + checkAnswer( + ctx.sql("SELECT nvl(null, 'x'), nvl('y', 'x'), nvl(null, null)"), + Row("x", "y", null)) + } + + test("misc md5 function") { + val df = Seq(("ABC", Array[Byte](1, 2, 3, 4, 5, 6))).toDF("a", "b") + checkAnswer( + df.select(md5($"a"), md5("b")), + Row("902fbdd2b1df0c4f70b4a5d23525e932", "6ac1e56bc78f031059be7be854522c4c")) + + checkAnswer( + df.selectExpr("md5(a)", "md5(b)"), + Row("902fbdd2b1df0c4f70b4a5d23525e932", "6ac1e56bc78f031059be7be854522c4c")) + } + + test("string length function") { + checkAnswer( + nullStrings.select(strlen($"s"), strlen("s")), + nullStrings.collect().toSeq.map { r => + val v = r.getString(1) + val l = if (v == null) null else v.length + Row(l, l) + }) + + checkAnswer( + nullStrings.selectExpr("length(s)"), + nullStrings.collect().toSeq.map { r => + val v = r.getString(1) + val l = if (v == null) null else v.length + Row(l) + }) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala index 2d2367d6e7292..fbb30706a4943 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.sql -import org.apache.spark.sql.test.TestSQLContext.{sparkContext => sc} -import org.apache.spark.sql.test.TestSQLContext.implicits._ - - class DataFrameImplicitsSuite extends QueryTest { + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + test("RDD of tuples") { checkAnswer( - sc.parallelize(1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"), + ctx.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"), (1 to 10).map(i => Row(i, i.toString))) } @@ -37,19 +36,19 @@ class DataFrameImplicitsSuite extends QueryTest { test("RDD[Int]") { checkAnswer( - sc.parallelize(1 to 10).toDF("intCol"), + ctx.sparkContext.parallelize(1 to 10).toDF("intCol"), (1 to 10).map(i => Row(i))) } test("RDD[Long]") { checkAnswer( - sc.parallelize(1L to 10L).toDF("longCol"), + ctx.sparkContext.parallelize(1L to 10L).toDF("longCol"), (1L to 10L).map(i => Row(i))) } test("RDD[String]") { checkAnswer( - sc.parallelize(1 to 10).map(_.toString).toDF("stringCol"), + ctx.sparkContext.parallelize(1 to 10).map(_.toString).toDF("stringCol"), (1 to 10).map(i => Row(i.toString))) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala index f005f55b6432e..6165764632c29 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala @@ -19,12 +19,12 @@ package org.apache.spark.sql import org.apache.spark.sql.TestData._ import org.apache.spark.sql.functions._ -import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ - class DataFrameJoinSuite extends QueryTest { + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + test("join - join using") { val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") val df2 = Seq(1, 2, 3).map(i => (i, (i + 1).toString)).toDF("int", "str") @@ -34,6 +34,15 @@ class DataFrameJoinSuite extends QueryTest { Row(1, "1", "2") :: Row(2, "2", "3") :: Row(3, "3", "4") :: Nil) } + test("join - join using multiple columns") { + val df = Seq(1, 2, 3).map(i => (i, i + 1, i.toString)).toDF("int", "int2", "str") + val df2 = Seq(1, 2, 3).map(i => (i, i + 1, (i + 1).toString)).toDF("int", "int2", "str") + + checkAnswer( + df.join(df2, Seq("int", "int2")), + Row(1, 2, "1", "2") :: Row(2, 3, "2", "3") :: Row(3, 4, "3", "4") :: Nil) + } + test("join - join using self join") { val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") @@ -49,7 +58,8 @@ class DataFrameJoinSuite extends QueryTest { checkAnswer( df1.join(df2, $"df1.key" === $"df2.key"), - sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq) + ctx.sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key") + .collect().toSeq) } test("join - using aliases after self join") { @@ -77,8 +87,8 @@ class DataFrameJoinSuite extends QueryTest { df.join(df, df("key") === df("key") && df("value") === 1), Row(1, "1", 1, "1") :: Nil) - val left = df.groupBy("key").agg($"key", count("*")) - val right = df.groupBy("key").agg($"key", sum("key")) + val left = df.groupBy("key").agg(count("*")) + val right = df.groupBy("key").agg(sum("key")) checkAnswer( left.join(right, left("key") === right("key")), Row(1, 1, 1, 1) :: Row(2, 1, 2, 2) :: Nil) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala index 41b4f02e6a294..495701d4f616c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala @@ -19,11 +19,12 @@ package org.apache.spark.sql import scala.collection.JavaConversions._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ - class DataFrameNaFunctionsSuite extends QueryTest { + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + def createDF(): DataFrame = { Seq[(String, java.lang.Integer, java.lang.Double)]( ("Bob", 16, 176.5), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala index 46b1845a9180c..0d3ff899dad72 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -17,16 +17,16 @@ package org.apache.spark.sql -import org.scalatest.FunSuite import org.scalatest.Matchers._ -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.SparkFunSuite -class DataFrameStatSuite extends FunSuite { - - val sqlCtx = TestSQLContext - def toLetter(i: Int): String = (i + 97).toChar.toString +class DataFrameStatSuite extends SparkFunSuite { + + private val sqlCtx = org.apache.spark.sql.test.TestSQLContext + import sqlCtx.implicits._ + + private def toLetter(i: Int): String = (i + 97).toChar.toString test("pearson correlation") { val df = Seq.tabulate(10)(i => (i, 2 * i, i * -1.0)).toDF("a", "b", "c") @@ -74,10 +74,10 @@ class DataFrameStatSuite extends FunSuite { val rows: Array[Row] = crosstab.collect().sortBy(_.getString(0)) assert(rows(0).get(0).toString === "0") assert(rows(0).getLong(1) === 2L) - assert(rows(0).get(2) === null) + assert(rows(0).get(2) === 0L) assert(rows(1).get(0).toString === "1") assert(rows(1).getLong(1) === 1L) - assert(rows(1).get(2) === null) + assert(rows(1).get(2) === 0L) assert(rows(2).get(0).toString === "2") assert(rows(2).getLong(1) === 2L) assert(rows(2).getLong(2) === 1L) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index d2ca8dccae574..ba1d020f22f11 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -21,18 +21,19 @@ import scala.language.postfixOps import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, TestSQLContext} -import org.apache.spark.sql.test.TestSQLContext.logicalPlanToSparkQuery -import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint} class DataFrameSuite extends QueryTest { import org.apache.spark.sql.TestData._ + lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + test("analysis error should be eagerly reported") { - val oldSetting = TestSQLContext.conf.dataFrameEagerAnalysis + val oldSetting = ctx.conf.dataFrameEagerAnalysis // Eager analysis. - TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, "true") + ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, true) intercept[Exception] { testData.select('nonExistentName) } intercept[Exception] { @@ -46,11 +47,11 @@ class DataFrameSuite extends QueryTest { } // No more eager analysis once the flag is turned off - TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, "false") + ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, false) testData.select('nonExistentName) // Set the flag back to original value before this test. - TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting.toString) + ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting) } test("dataframe toString") { @@ -60,20 +61,20 @@ class DataFrameSuite extends QueryTest { } test("rename nested groupby") { - val df = Seq((1,(1,1))).toDF() + val df = Seq((1, (1, 1))).toDF() checkAnswer( - df.groupBy("_1").agg(col("_1"), sum("_2._1")).toDF("key", "total"), + df.groupBy("_1").agg(sum("_2._1")).toDF("key", "total"), Row(1, 1) :: Nil) } test("invalid plan toString, debug mode") { - val oldSetting = TestSQLContext.conf.dataFrameEagerAnalysis - TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, "true") + val oldSetting = ctx.conf.dataFrameEagerAnalysis + ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, true) // Turn on debug mode so we can see invalid query plans. import org.apache.spark.sql.execution.debug._ - TestSQLContext.debug() + ctx.debug() val badPlan = testData.select('badColumn) @@ -82,7 +83,7 @@ class DataFrameSuite extends QueryTest { badPlan.toString) // Set the flag back to original value before this test. - TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting.toString) + ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting) } test("access complex data") { @@ -98,8 +99,8 @@ class DataFrameSuite extends QueryTest { } test("empty data frame") { - assert(TestSQLContext.emptyDataFrame.columns.toSeq === Seq.empty[String]) - assert(TestSQLContext.emptyDataFrame.count() === 0) + assert(ctx.emptyDataFrame.columns.toSeq === Seq.empty[String]) + assert(ctx.emptyDataFrame.count() === 0) } test("head and take") { @@ -128,11 +129,19 @@ class DataFrameSuite extends QueryTest { df2 .select('_1 as 'letter, 'number) .groupBy('letter) - .agg('letter, countDistinct('number)), + .agg(countDistinct('number)), Row("a", 3) :: Row("b", 2) :: Row("c", 1) :: Nil ) } + test("explode alias and star") { + val df = Seq((Array("a"), 1)).toDF("a", "b") + + checkAnswer( + df.select(explode($"a").as("a"), $"*"), + Row("a", Seq("a"), 1) :: Nil) + } + test("selectExpr") { checkAnswer( testData.selectExpr("abs(key)", "value"), @@ -165,48 +174,6 @@ class DataFrameSuite extends QueryTest { testData.select('key).collect().toSeq) } - test("groupBy") { - checkAnswer( - testData2.groupBy("a").agg($"a", sum($"b")), - Seq(Row(1, 3), Row(2, 3), Row(3, 3)) - ) - checkAnswer( - testData2.groupBy("a").agg($"a", sum($"b").as("totB")).agg(sum('totB)), - Row(9) - ) - checkAnswer( - testData2.groupBy("a").agg(col("a"), count("*")), - Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil - ) - checkAnswer( - testData2.groupBy("a").agg(Map("*" -> "count")), - Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil - ) - checkAnswer( - testData2.groupBy("a").agg(Map("b" -> "sum")), - Row(1, 3) :: Row(2, 3) :: Row(3, 3) :: Nil - ) - - val df1 = Seq(("a", 1, 0, "b"), ("b", 2, 4, "c"), ("a", 2, 3, "d")) - .toDF("key", "value1", "value2", "rest") - - checkAnswer( - df1.groupBy("key").min(), - df1.groupBy("key").min("value1", "value2").collect() - ) - checkAnswer( - df1.groupBy("key").min("value2"), - Seq(Row("a", 0), Row("b", 4)) - ) - } - - test("agg without groups") { - checkAnswer( - testData2.agg(sum('b)), - Row(9) - ) - } - test("convert $\"attribute name\" into unresolved attribute") { checkAnswer( testData.where($"key" === lit(1)).select($"value"), @@ -254,23 +221,23 @@ class DataFrameSuite extends QueryTest { test("global sorting") { checkAnswer( testData2.orderBy('a.asc, 'b.asc), - Seq(Row(1,1), Row(1,2), Row(2,1), Row(2,2), Row(3,1), Row(3,2))) + Seq(Row(1, 1), Row(1, 2), Row(2, 1), Row(2, 2), Row(3, 1), Row(3, 2))) checkAnswer( testData2.orderBy(asc("a"), desc("b")), - Seq(Row(1,2), Row(1,1), Row(2,2), Row(2,1), Row(3,2), Row(3,1))) + Seq(Row(1, 2), Row(1, 1), Row(2, 2), Row(2, 1), Row(3, 2), Row(3, 1))) checkAnswer( testData2.orderBy('a.asc, 'b.desc), - Seq(Row(1,2), Row(1,1), Row(2,2), Row(2,1), Row(3,2), Row(3,1))) + Seq(Row(1, 2), Row(1, 1), Row(2, 2), Row(2, 1), Row(3, 2), Row(3, 1))) checkAnswer( testData2.orderBy('a.desc, 'b.desc), - Seq(Row(3,2), Row(3,1), Row(2,2), Row(2,1), Row(1,2), Row(1,1))) + Seq(Row(3, 2), Row(3, 1), Row(2, 2), Row(2, 1), Row(1, 2), Row(1, 1))) checkAnswer( testData2.orderBy('a.desc, 'b.asc), - Seq(Row(3,1), Row(3,2), Row(2,1), Row(2,2), Row(1,1), Row(1,2))) + Seq(Row(3, 1), Row(3, 2), Row(2, 1), Row(2, 2), Row(1, 1), Row(1, 2))) checkAnswer( arrayData.toDF().orderBy('data.getItem(0).asc), @@ -303,105 +270,6 @@ class DataFrameSuite extends QueryTest { mapData.take(1).map(r => Row.fromSeq(r.productIterator.toSeq))) } - test("average") { - checkAnswer( - testData2.agg(avg('a)), - Row(2.0)) - - checkAnswer( - testData2.agg(avg('a), sumDistinct('a)), // non-partial - Row(2.0, 6.0) :: Nil) - - checkAnswer( - decimalData.agg(avg('a)), - Row(new java.math.BigDecimal(2.0))) - checkAnswer( - decimalData.agg(avg('a), sumDistinct('a)), // non-partial - Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil) - - checkAnswer( - decimalData.agg(avg('a cast DecimalType(10, 2))), - Row(new java.math.BigDecimal(2.0))) - // non-partial - checkAnswer( - decimalData.agg(avg('a cast DecimalType(10, 2)), sumDistinct('a cast DecimalType(10, 2))), - Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil) - } - - test("null average") { - checkAnswer( - testData3.agg(avg('b)), - Row(2.0)) - - checkAnswer( - testData3.agg(avg('b), countDistinct('b)), - Row(2.0, 1)) - - checkAnswer( - testData3.agg(avg('b), sumDistinct('b)), // non-partial - Row(2.0, 2.0)) - } - - test("zero average") { - checkAnswer( - emptyTableData.agg(avg('a)), - Row(null)) - - checkAnswer( - emptyTableData.agg(avg('a), sumDistinct('b)), // non-partial - Row(null, null)) - } - - test("count") { - assert(testData2.count() === testData2.map(_ => 1).count()) - - checkAnswer( - testData2.agg(count('a), sumDistinct('a)), // non-partial - Row(6, 6.0)) - } - - test("null count") { - checkAnswer( - testData3.groupBy('a).agg('a, count('b)), - Seq(Row(1,0), Row(2, 1)) - ) - - checkAnswer( - testData3.groupBy('a).agg('a, count('a + 'b)), - Seq(Row(1,0), Row(2, 1)) - ) - - checkAnswer( - testData3.agg(count('a), count('b), count(lit(1)), countDistinct('a), countDistinct('b)), - Row(2, 1, 2, 2, 1) - ) - - checkAnswer( - testData3.agg(count('b), countDistinct('b), sumDistinct('b)), // non-partial - Row(1, 1, 2) - ) - } - - test("zero count") { - assert(emptyTableData.count() === 0) - - checkAnswer( - emptyTableData.agg(count('a), sumDistinct('a)), // non-partial - Row(0, null)) - } - - test("zero sum") { - checkAnswer( - emptyTableData.agg(sum('a)), - Row(null)) - } - - test("zero sum distinct") { - checkAnswer( - emptyTableData.agg(sumDistinct('a)), - Row(null)) - } - test("except") { checkAnswer( lowerCaseData.except(upperCaseData), @@ -453,7 +321,7 @@ class DataFrameSuite extends QueryTest { } test("replace column using withColumn") { - val df2 = TestSQLContext.sparkContext.parallelize(Array(1, 2, 3)).toDF("x") + val df2 = ctx.sparkContext.parallelize(Array(1, 2, 3)).toDF("x") val df3 = df2.withColumn("x", df2("x") + 1) checkAnswer( df3.select("x"), @@ -473,7 +341,52 @@ class DataFrameSuite extends QueryTest { checkAnswer( df, testData.collect().toSeq) - assert(df.schema.map(_.name) === Seq("key","value")) + assert(df.schema.map(_.name) === Seq("key", "value")) + } + + test("drop column using drop with column reference") { + val col = testData("key") + val df = testData.drop(col) + checkAnswer( + df, + testData.collect().map(x => Row(x.getString(1))).toSeq) + assert(df.schema.map(_.name) === Seq("value")) + } + + test("drop unknown column (no-op) with column reference") { + val col = Column("random") + val df = testData.drop(col) + checkAnswer( + df, + testData.collect().toSeq) + assert(df.schema.map(_.name) === Seq("key", "value")) + } + + test("drop unknown column with same name (no-op) with column reference") { + val col = Column("key") + val df = testData.drop(col) + checkAnswer( + df, + testData.collect().toSeq) + assert(df.schema.map(_.name) === Seq("key", "value")) + } + + test("drop column after join with duplicate columns using column reference") { + val newSalary = salary.withColumnRenamed("personId", "id") + val col = newSalary("id") + // this join will result in duplicate "id" columns + val joinedDf = person.join(newSalary, + person("id") === newSalary("id"), "inner") + // remove only the "id" column that was associated with newSalary + val df = joinedDf.drop(col) + checkAnswer( + df, + joinedDf.collect().map { + case Row(id: Int, name: String, age: Int, idToDrop: Int, salary: Double) => + Row(id, name, age, salary) + }.toSeq) + assert(df.schema.map(_.name) === Seq("id", "name", "age", "salary")) + assert(df("id") == person("id")) } test("withColumnRenamed") { @@ -489,7 +402,7 @@ class DataFrameSuite extends QueryTest { test("randomSplit") { val n = 600 - val data = TestSQLContext.sparkContext.parallelize(1 to n, 2).toDF("id") + val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id") for (seed <- 1 to 5) { val splits = data.randomSplit(Array[Double](1, 2, 3), seed) assert(splits.length == 3, "wrong number of splits") @@ -506,30 +419,35 @@ class DataFrameSuite extends QueryTest { test("describe") { val describeTestData = Seq( - ("Bob", 16, 176), + ("Bob", 16, 176), ("Alice", 32, 164), ("David", 60, 192), - ("Amy", 24, 180)).toDF("name", "age", "height") + ("Amy", 24, 180)).toDF("name", "age", "height") val describeResult = Seq( - Row("count", 4, 4), - Row("mean", 33.0, 178.0), - Row("stddev", 16.583123951777, 10.0), - Row("min", 16, 164), - Row("max", 60, 192)) + Row("count", "4", "4"), + Row("mean", "33.0", "178.0"), + Row("stddev", "16.583123951777", "10.0"), + Row("min", "16", "164"), + Row("max", "60", "192")) val emptyDescribeResult = Seq( - Row("count", 0, 0), - Row("mean", null, null), - Row("stddev", null, null), - Row("min", null, null), - Row("max", null, null)) + Row("count", "0", "0"), + Row("mean", null, null), + Row("stddev", null, null), + Row("min", null, null), + Row("max", null, null)) def getSchemaAsSeq(df: DataFrame): Seq[String] = df.schema.map(_.name) val describeTwoCols = describeTestData.describe("age", "height") assert(getSchemaAsSeq(describeTwoCols) === Seq("summary", "age", "height")) checkAnswer(describeTwoCols, describeResult) + // All aggregate value should have been cast to string + describeTwoCols.collect().foreach { row => + assert(row.get(1).isInstanceOf[String], "expected string but found " + row.get(1).getClass) + assert(row.get(2).isInstanceOf[String], "expected string but found " + row.get(2).getClass) + } val describeAllCols = describeTestData.describe() assert(getSchemaAsSeq(describeAllCols) === Seq("summary", "age", "height")) @@ -559,12 +477,63 @@ class DataFrameSuite extends QueryTest { testData.select($"*").show(1000) } + test("showString(negative)") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + |+---+-----+ + |only showing top 0 rows + |""".stripMargin + assert(testData.select($"*").showString(-1) === expectedAnswer) + } + + test("showString(0)") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + |+---+-----+ + |only showing top 0 rows + |""".stripMargin + assert(testData.select($"*").showString(0) === expectedAnswer) + } + + test("showString: array") { + val df = Seq( + (Array(1, 2, 3), Array(1, 2, 3)), + (Array(2, 3, 4), Array(2, 3, 4)) + ).toDF() + val expectedAnswer = """+---------+---------+ + || _1| _2| + |+---------+---------+ + ||[1, 2, 3]|[1, 2, 3]| + ||[2, 3, 4]|[2, 3, 4]| + |+---------+---------+ + |""".stripMargin + assert(df.showString(10) === expectedAnswer) + } + + test("showString: minimum column width") { + val df = Seq( + (1, 1), + (2, 2) + ).toDF() + val expectedAnswer = """+---+---+ + || _1| _2| + |+---+---+ + || 1| 1| + || 2| 2| + |+---+---+ + |""".stripMargin + assert(df.showString(10) === expectedAnswer) + } + test("SPARK-7319 showString") { val expectedAnswer = """+---+-----+ ||key|value| |+---+-----+ || 1| 1| |+---+-----+ + |only showing top 1 row |""".stripMargin assert(testData.select($"*").showString(1) === expectedAnswer) } @@ -579,24 +548,150 @@ class DataFrameSuite extends QueryTest { } test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") { - val rowRDD = TestSQLContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0)))) + val rowRDD = ctx.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0)))) val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false))) - val df = TestSQLContext.createDataFrame(rowRDD, schema) + val df = ctx.createDataFrame(rowRDD, schema) df.rdd.collect() } test("SPARK-6899") { - val originalValue = TestSQLContext.conf.codegenEnabled - TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, "true") - checkAnswer( - decimalData.agg(avg('a)), - Row(new java.math.BigDecimal(2.0))) - TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString) + val originalValue = ctx.conf.codegenEnabled + ctx.setConf(SQLConf.CODEGEN_ENABLED, true) + try{ + checkAnswer( + decimalData.agg(avg('a)), + Row(new java.math.BigDecimal(2.0))) + } finally { + ctx.setConf(SQLConf.CODEGEN_ENABLED, originalValue) + } } test("SPARK-7133: Implement struct, array, and map field accessor") { assert(complexData.filter(complexData("a")(0) === 2).count() == 1) assert(complexData.filter(complexData("m")("1") === 1).count() == 1) assert(complexData.filter(complexData("s")("key") === 1).count() == 1) + assert(complexData.filter(complexData("m")(complexData("s")("value")) === 1).count() == 1) + } + + test("SPARK-7551: support backticks for DataFrame attribute resolution") { + val df = ctx.read.json(ctx.sparkContext.makeRDD( + """{"a.b": {"c": {"d..e": {"f": 1}}}}""" :: Nil)) + checkAnswer( + df.select(df("`a.b`.c.`d..e`.`f`")), + Row(1) + ) + + val df2 = ctx.read.json(ctx.sparkContext.makeRDD( + """{"a b": {"c": {"d e": {"f": 1}}}}""" :: Nil)) + checkAnswer( + df2.select(df2("`a b`.c.d e.f")), + Row(1) + ) + + def checkError(testFun: => Unit): Unit = { + val e = intercept[org.apache.spark.sql.AnalysisException] { + testFun + } + assert(e.getMessage.contains("syntax error in attribute name:")) + } + checkError(df("`abc.`c`")) + checkError(df("`abc`..d")) + checkError(df("`a`.b.")) + checkError(df("`a.b`.c.`d")) + } + + test("SPARK-7324 dropDuplicates") { + val testData = ctx.sparkContext.parallelize( + (2, 1, 2) :: (1, 1, 1) :: + (1, 2, 1) :: (2, 1, 2) :: + (2, 2, 2) :: (2, 2, 1) :: + (2, 1, 1) :: (1, 1, 2) :: + (1, 2, 2) :: (1, 2, 1) :: Nil).toDF("key", "value1", "value2") + + checkAnswer( + testData.dropDuplicates(), + Seq(Row(2, 1, 2), Row(1, 1, 1), Row(1, 2, 1), + Row(2, 2, 2), Row(2, 1, 1), Row(2, 2, 1), + Row(1, 1, 2), Row(1, 2, 2))) + + checkAnswer( + testData.dropDuplicates(Seq("key", "value1")), + Seq(Row(2, 1, 2), Row(1, 2, 1), Row(1, 1, 1), Row(2, 2, 2))) + + checkAnswer( + testData.dropDuplicates(Seq("value1", "value2")), + Seq(Row(2, 1, 2), Row(1, 2, 1), Row(1, 1, 1), Row(2, 2, 2))) + + checkAnswer( + testData.dropDuplicates(Seq("key")), + Seq(Row(2, 1, 2), Row(1, 1, 1))) + + checkAnswer( + testData.dropDuplicates(Seq("value1")), + Seq(Row(2, 1, 2), Row(1, 2, 1))) + + checkAnswer( + testData.dropDuplicates(Seq("value2")), + Seq(Row(2, 1, 2), Row(1, 1, 1))) + } + + test("SPARK-7276: Project collapse for continuous select") { + var df = testData + for (i <- 1 to 5) { + df = df.select($"*") + } + + import org.apache.spark.sql.catalyst.plans.logical.Project + // make sure df have at most two Projects + val p = df.logicalPlan.asInstanceOf[Project].child.asInstanceOf[Project] + assert(!p.child.isInstanceOf[Project]) + } + + test("SPARK-7150 range api") { + // numSlice is greater than length + val res1 = ctx.range(0, 10, 1, 15).select("id") + assert(res1.count == 10) + assert(res1.agg(sum("id")).as("sumid").collect() === Seq(Row(45))) + + val res2 = ctx.range(3, 15, 3, 2).select("id") + assert(res2.count == 4) + assert(res2.agg(sum("id")).as("sumid").collect() === Seq(Row(30))) + + val res3 = ctx.range(1, -2).select("id") + assert(res3.count == 0) + + // start is positive, end is negative, step is negative + val res4 = ctx.range(1, -2, -2, 6).select("id") + assert(res4.count == 2) + assert(res4.agg(sum("id")).as("sumid").collect() === Seq(Row(0))) + + // start, end, step are negative + val res5 = ctx.range(-3, -8, -2, 1).select("id") + assert(res5.count == 3) + assert(res5.agg(sum("id")).as("sumid").collect() === Seq(Row(-15))) + + // start, end are negative, step is positive + val res6 = ctx.range(-8, -4, 2, 1).select("id") + assert(res6.count == 2) + assert(res6.agg(sum("id")).as("sumid").collect() === Seq(Row(-14))) + + val res7 = ctx.range(-10, -9, -20, 1).select("id") + assert(res7.count == 0) + + val res8 = ctx.range(Long.MinValue, Long.MaxValue, Long.MaxValue, 100).select("id") + assert(res8.count == 3) + assert(res8.agg(sum("id")).as("sumid").collect() === Seq(Row(-3))) + + val res9 = ctx.range(Long.MaxValue, Long.MinValue, Long.MinValue, 100).select("id") + assert(res9.count == 2) + assert(res9.agg(sum("id")).as("sumid").collect() === Seq(Row(Long.MaxValue - 1))) + + // only end provided as argument + val res10 = ctx.range(10).select("id") + assert(res10.count == 10) + assert(res10.agg(sum("id")).as("sumid").collect() === Seq(Row(45))) + + val res11 = ctx.range(-1).select("id") + assert(res11.count == 0) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala index 037d392c1f929..20390a5544304 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala @@ -20,27 +20,28 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterEach import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.functions._ import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation import org.apache.spark.sql.execution.joins._ -import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ class JoinSuite extends QueryTest with BeforeAndAfterEach { // Ensures tables are loaded. TestData + lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + import ctx.logicalPlanToSparkQuery + test("equi-join is hash-join") { val x = testData2.as("x") val y = testData2.as("y") val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.optimizedPlan - val planned = planner.HashJoin(join) + val planned = ctx.planner.HashJoin(join) assert(planned.size === 1) } def assertJoin(sqlString: String, c: Class[_]): Any = { - val df = sql(sqlString) + val df = ctx.sql(sqlString) val physical = df.queryExecution.sparkPlan val operators = physical.collect { case j: ShuffledHashJoin => j @@ -61,9 +62,9 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { } test("join operator selection") { - cacheManager.clearCache() + ctx.cacheManager.clearCache() - val SORTMERGEJOIN_ENABLED: Boolean = conf.sortMergeJoinEnabled + val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled Seq( ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]), ("SELECT * FROM testData LEFT SEMI JOIN testData2", classOf[LeftSemiJoinBNL]), @@ -94,22 +95,22 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { classOf[BroadcastNestedLoopJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } try { - conf.setConf("spark.sql.planner.sortMergeJoin", "true") + ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true) Seq( ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]), ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]), ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } } finally { - conf.setConf("spark.sql.planner.sortMergeJoin", SORTMERGEJOIN_ENABLED.toString) + ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED) } } test("broadcasted hash join operator selection") { - cacheManager.clearCache() - sql("CACHE TABLE testData") + ctx.cacheManager.clearCache() + ctx.sql("CACHE TABLE testData") - val SORTMERGEJOIN_ENABLED: Boolean = conf.sortMergeJoinEnabled + val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled Seq( ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]), ("SELECT * FROM testData join testData2 ON key = a and key = 2", classOf[BroadcastHashJoin]), @@ -117,7 +118,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { classOf[BroadcastHashJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } try { - conf.setConf("spark.sql.planner.sortMergeJoin", "true") + ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true) Seq( ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]), ("SELECT * FROM testData join testData2 ON key = a and key = 2", @@ -126,17 +127,17 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { classOf[BroadcastHashJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } } finally { - conf.setConf("spark.sql.planner.sortMergeJoin", SORTMERGEJOIN_ENABLED.toString) + ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED) } - sql("UNCACHE TABLE testData") + ctx.sql("UNCACHE TABLE testData") } test("multiple-key equi-join is hash-join") { val x = testData2.as("x") val y = testData2.as("y") val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.optimizedPlan - val planned = planner.HashJoin(join) + val planned = ctx.planner.HashJoin(join) assert(planned.size === 1) } @@ -167,10 +168,10 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { val y = testData2.where($"a" === 1).as("y") checkAnswer( x.join(y).where($"x.a" === $"y.a"), - Row(1,1,1,1) :: - Row(1,1,1,2) :: - Row(1,2,1,1) :: - Row(1,2,1,2) :: Nil + Row(1, 1, 1, 1) :: + Row(1, 1, 1, 2) :: + Row(1, 2, 1, 1) :: + Row(1, 2, 1, 2) :: Nil ) } @@ -241,7 +242,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { // Make sure we are choosing left.outputPartitioning as the // outputPartitioning for the outer join operator. checkAnswer( - sql( + ctx.sql( """ |SELECT l.N, count(*) |FROM upperCaseData l LEFT OUTER JOIN allNulls r ON (l.N = r.a) @@ -255,7 +256,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(6, 1) :: Nil) checkAnswer( - sql( + ctx.sql( """ |SELECT r.a, count(*) |FROM upperCaseData l LEFT OUTER JOIN allNulls r ON (l.N = r.a) @@ -301,7 +302,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { // Make sure we are choosing right.outputPartitioning as the // outputPartitioning for the outer join operator. checkAnswer( - sql( + ctx.sql( """ |SELECT l.a, count(*) |FROM allNulls l RIGHT OUTER JOIN upperCaseData r ON (l.a = r.N) @@ -310,7 +311,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, 6)) checkAnswer( - sql( + ctx.sql( """ |SELECT r.N, count(*) |FROM allNulls l RIGHT OUTER JOIN upperCaseData r ON (l.a = r.N) @@ -362,7 +363,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { // Make sure we are UnknownPartitioning as the outputPartitioning for the outer join operator. checkAnswer( - sql( + ctx.sql( """ |SELECT l.a, count(*) |FROM allNulls l FULL OUTER JOIN upperCaseData r ON (l.a = r.N) @@ -371,7 +372,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, 10)) checkAnswer( - sql( + ctx.sql( """ |SELECT r.N, count(*) |FROM allNulls l FULL OUTER JOIN upperCaseData r ON (l.a = r.N) @@ -386,7 +387,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, 4) :: Nil) checkAnswer( - sql( + ctx.sql( """ |SELECT l.N, count(*) |FROM upperCaseData l FULL OUTER JOIN allNulls r ON (l.N = r.a) @@ -401,7 +402,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, 4) :: Nil) checkAnswer( - sql( + ctx.sql( """ |SELECT r.a, count(*) |FROM upperCaseData l FULL OUTER JOIN allNulls r ON (l.N = r.a) @@ -411,11 +412,11 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { } test("broadcasted left semi join operator selection") { - cacheManager.clearCache() - sql("CACHE TABLE testData") - val tmp = conf.autoBroadcastJoinThreshold + ctx.cacheManager.clearCache() + ctx.sql("CACHE TABLE testData") + val tmp = ctx.conf.autoBroadcastJoinThreshold - sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=1000000000") + ctx.sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=1000000000") Seq( ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[BroadcastLeftSemiJoinHash]) @@ -423,7 +424,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { case (query, joinClass) => assertJoin(query, joinClass) } - sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=-1") + ctx.sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1") Seq( ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]) @@ -431,12 +432,12 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { case (query, joinClass) => assertJoin(query, joinClass) } - setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, tmp.toString) - sql("UNCACHE TABLE testData") + ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, tmp) + ctx.sql("UNCACHE TABLE testData") } test("left semi join") { - val df = sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a") + val df = ctx.sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a") checkAnswer(df, Row(1, 1) :: Row(1, 2) :: diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala index f9f41eb358bd5..2089660c52bf7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala @@ -19,49 +19,47 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfter -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.types.{BooleanType, StringType, StructField, StructType} class ListTablesSuite extends QueryTest with BeforeAndAfter { - import org.apache.spark.sql.test.TestSQLContext.implicits._ + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ - val df = - sparkContext.parallelize((1 to 10).map(i => (i,s"str$i"))).toDF("key", "value") + private lazy val df = (1 to 10).map(i => (i, s"str$i")).toDF("key", "value") before { df.registerTempTable("ListTablesSuiteTable") } after { - catalog.unregisterTable(Seq("ListTablesSuiteTable")) + ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable")) } test("get all tables") { checkAnswer( - tables().filter("tableName = 'ListTablesSuiteTable'"), + ctx.tables().filter("tableName = 'ListTablesSuiteTable'"), Row("ListTablesSuiteTable", true)) checkAnswer( - sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"), + ctx.sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"), Row("ListTablesSuiteTable", true)) - catalog.unregisterTable(Seq("ListTablesSuiteTable")) - assert(tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0) + ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable")) + assert(ctx.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0) } test("getting all Tables with a database name has no impact on returned table names") { checkAnswer( - tables("DB").filter("tableName = 'ListTablesSuiteTable'"), + ctx.tables("DB").filter("tableName = 'ListTablesSuiteTable'"), Row("ListTablesSuiteTable", true)) checkAnswer( - sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"), + ctx.sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"), Row("ListTablesSuiteTable", true)) - catalog.unregisterTable(Seq("ListTablesSuiteTable")) - assert(tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0) + ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable")) + assert(ctx.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0) } test("query the returned DataFrame of tables") { @@ -69,19 +67,20 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter { StructField("tableName", StringType, false) :: StructField("isTemporary", BooleanType, false) :: Nil) - Seq(tables(), sql("SHOW TABLes")).foreach { + Seq(ctx.tables(), ctx.sql("SHOW TABLes")).foreach { case tableDF => assert(expectedSchema === tableDF.schema) tableDF.registerTempTable("tables") checkAnswer( - sql("SELECT isTemporary, tableName from tables WHERE tableName = 'ListTablesSuiteTable'"), + ctx.sql( + "SELECT isTemporary, tableName from tables WHERE tableName = 'ListTablesSuiteTable'"), Row(true, "ListTablesSuiteTable") ) checkAnswer( - tables().filter("tableName = 'tables'").select("tableName", "isTemporary"), + ctx.tables().filter("tableName = 'tables'").select("tableName", "isTemporary"), Row("tables", true)) - dropTempTable("tables") + ctx.dropTempTable("tables") } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala index c4281c4b55c02..2768d7dfc8030 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala @@ -17,36 +17,30 @@ package org.apache.spark.sql -import java.lang.{Double => JavaDouble} - import org.apache.spark.sql.functions._ -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext.implicits._ - -private[this] object MathExpressionsTestData { - - case class DoubleData(a: JavaDouble, b: JavaDouble) - val doubleData = TestSQLContext.sparkContext.parallelize( - (1 to 10).map(i => DoubleData(i * 0.2 - 1, i * -0.2 + 1))).toDF() - - val nnDoubleData = TestSQLContext.sparkContext.parallelize( - (1 to 10).map(i => DoubleData(i * 0.1, i * -0.1))).toDF() - - case class NullDoubles(a: JavaDouble) - val nullDoubles = - TestSQLContext.sparkContext.parallelize( - NullDoubles(1.0) :: - NullDoubles(2.0) :: - NullDoubles(3.0) :: - NullDoubles(null) :: Nil - ).toDF() +import org.apache.spark.sql.functions.{log => logarithm} + + +private object MathExpressionsTestData { + case class DoubleData(a: java.lang.Double, b: java.lang.Double) + case class NullDoubles(a: java.lang.Double) } class MathExpressionsSuite extends QueryTest { import MathExpressionsTestData._ - def testOneToOneMathFunction[@specialized(Int, Long, Float, Double) T]( + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + + private lazy val doubleData = (1 to 10).map(i => DoubleData(i * 0.2 - 1, i * -0.2 + 1)).toDF() + + private lazy val nnDoubleData = (1 to 10).map(i => DoubleData(i * 0.1, i * -0.1)).toDF() + + private lazy val nullDoubles = + Seq(NullDoubles(1.0), NullDoubles(2.0), NullDoubles(3.0), NullDoubles(null)).toDF() + + private def testOneToOneMathFunction[@specialized(Int, Long, Float, Double) T]( c: Column => Column, f: T => T): Unit = { checkAnswer( @@ -65,7 +59,8 @@ class MathExpressionsSuite extends QueryTest { ) } - def testOneToOneNonNegativeMathFunction(c: Column => Column, f: Double => Double): Unit = { + private def testOneToOneNonNegativeMathFunction(c: Column => Column, f: Double => Double): Unit = + { checkAnswer( nnDoubleData.select(c('a)), (1 to 10).map(n => Row(f(n * 0.1))) @@ -89,7 +84,7 @@ class MathExpressionsSuite extends QueryTest { ) } - def testTwoToOneMathFunction( + private def testTwoToOneMathFunction( c: (Column, Column) => Column, d: (Column, Double) => Column, f: (Double, Double) => Double): Unit = { @@ -157,20 +152,31 @@ class MathExpressionsSuite extends QueryTest { testOneToOneMathFunction(tanh, math.tanh) } - test("toDeg") { + test("toDegrees") { testOneToOneMathFunction(toDegrees, math.toDegrees) + checkAnswer( + ctx.sql("SELECT degrees(0), degrees(1), degrees(1.5)"), + Seq((1, 2)).toDF().select(toDegrees(lit(0)), toDegrees(lit(1)), toDegrees(lit(1.5))) + ) } - test("toRad") { + test("toRadians") { testOneToOneMathFunction(toRadians, math.toRadians) + checkAnswer( + ctx.sql("SELECT radians(0), radians(1), radians(1.5)"), + Seq((1, 2)).toDF().select(toRadians(lit(0)), toRadians(lit(1)), toRadians(lit(1.5))) + ) } test("cbrt") { testOneToOneMathFunction(cbrt, math.cbrt) } - test("ceil") { + test("ceil and ceiling") { testOneToOneMathFunction(ceil, math.ceil) + checkAnswer( + ctx.sql("SELECT ceiling(0), ceiling(1), ceiling(1.5)"), + Row(0.0, 1.0, 2.0)) } test("floor") { @@ -189,12 +195,21 @@ class MathExpressionsSuite extends QueryTest { testOneToOneMathFunction(expm1, math.expm1) } - test("signum") { + test("signum / sign") { testOneToOneMathFunction[Double](signum, math.signum) + + checkAnswer( + ctx.sql("SELECT sign(10), signum(-11)"), + Row(1, -1)) } - test("pow") { + test("pow / power") { testTwoToOneMathFunction(pow, pow, math.pow) + + checkAnswer( + ctx.sql("SELECT pow(1, 2), power(2, 1)"), + Seq((1, 2)).toDF().select(pow(lit(1), lit(2)), pow(lit(2), lit(1))) + ) } test("hypot") { @@ -205,8 +220,12 @@ class MathExpressionsSuite extends QueryTest { testTwoToOneMathFunction(atan2, atan2, math.atan2) } - test("log") { - testOneToOneNonNegativeMathFunction(log, math.log) + test("log / ln") { + testOneToOneNonNegativeMathFunction(org.apache.spark.sql.functions.log, math.log) + checkAnswer( + ctx.sql("SELECT ln(0), ln(1), ln(1.5)"), + Seq((1, 2)).toDF().select(logarithm(lit(0)), logarithm(lit(1)), logarithm(lit(1.5))) + ) } test("log10") { @@ -217,4 +236,60 @@ class MathExpressionsSuite extends QueryTest { testOneToOneNonNegativeMathFunction(log1p, math.log1p) } + test("binary log") { + val df = Seq[(Integer, Integer)]((123, null)).toDF("a", "b") + checkAnswer( + df.select(org.apache.spark.sql.functions.log("a"), + org.apache.spark.sql.functions.log(2.0, "a"), + org.apache.spark.sql.functions.log("b")), + Row(math.log(123), math.log(123) / math.log(2), null)) + + checkAnswer( + df.selectExpr("log(a)", "log(2.0, a)", "log(b)"), + Row(math.log(123), math.log(123) / math.log(2), null)) + } + + test("abs") { + val input = + Seq[(java.lang.Double, java.lang.Double)]((null, null), (0.0, 0.0), (1.5, 1.5), (-2.5, 2.5)) + checkAnswer( + input.toDF("key", "value").select(abs($"key").alias("a")).sort("a"), + input.map(pair => Row(pair._2))) + + checkAnswer( + input.toDF("key", "value").selectExpr("abs(key) a").sort("a"), + input.map(pair => Row(pair._2))) + } + + test("log2") { + val df = Seq((1, 2)).toDF("a", "b") + checkAnswer( + df.select(log2("b") + log2("a")), + Row(1)) + + checkAnswer(ctx.sql("SELECT LOG2(8), LOG2(null)"), Row(3, null)) + } + + test("sqrt") { + val df = Seq((1, 4)).toDF("a", "b") + checkAnswer( + df.select(sqrt("a"), sqrt("b")), + Row(1.0, 2.0)) + + checkAnswer(ctx.sql("SELECT SQRT(4.0), SQRT(null)"), Row(2.0, null)) + checkAnswer(df.selectExpr("sqrt(a)", "sqrt(b)", "sqrt(null)"), Row(1.0, 2.0, null)) + } + + test("negative") { + checkAnswer( + ctx.sql("SELECT negative(1), negative(0), negative(-1)"), + Row(-1, 0, 1)) + } + + test("positive") { + val df = Seq((1, -1, "abc")).toDF("a", "b", "c") + checkAnswer(df.selectExpr("positive(a)"), Row(1)) + checkAnswer(df.selectExpr("positive(b)"), Row(-1)) + checkAnswer(df.selectExpr("positive(c)"), Row("abc")) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala index bbf9ab113ca43..98ba3c99283a1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala @@ -67,6 +67,10 @@ class QueryTest extends PlanTest { checkAnswer(df, Seq(expectedAnswer)) } + protected def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = { + checkAnswer(df, expectedAnswer.collect()) + } + def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext) { test(sqlString) { checkAnswer(sqlContext.sql(sqlString), expectedAnswer) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala index fb3ba4bc1b908..d84b57af9c882 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala @@ -17,15 +17,16 @@ package org.apache.spark.sql +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.execution.SparkSqlSerializer -import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, SpecificMutableRow} -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ -class RowSuite extends FunSuite { +class RowSuite extends SparkFunSuite { + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ test("create row") { val expected = new GenericMutableRow(4) @@ -56,7 +57,7 @@ class RowSuite extends FunSuite { test("serialize w/ kryo") { val row = Seq((1, Seq(1), Map(1 -> 1), BigDecimal(1))).toDF().first() - val serializer = new SparkSqlSerializer(TestSQLContext.sparkContext.getConf) + val serializer = new SparkSqlSerializer(ctx.sparkContext.getConf) val instance = serializer.newInstance() val ser = instance.serialize(row) val de = instance.deserialize(ser).asInstanceOf[Row] diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfEntrySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfEntrySuite.scala new file mode 100644 index 0000000000000..2e33777f14adc --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfEntrySuite.scala @@ -0,0 +1,150 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.SQLConf._ + +class SQLConfEntrySuite extends SparkFunSuite { + + val conf = new SQLConf + + test("intConf") { + val key = "spark.sql.SQLConfEntrySuite.int" + val confEntry = SQLConfEntry.intConf(key) + assert(conf.getConf(confEntry, 5) === 5) + + conf.setConf(confEntry, 10) + assert(conf.getConf(confEntry, 5) === 10) + + conf.setConfString(key, "20") + assert(conf.getConfString(key, "5") === "20") + assert(conf.getConfString(key) === "20") + assert(conf.getConf(confEntry, 5) === 20) + + val e = intercept[IllegalArgumentException] { + conf.setConfString(key, "abc") + } + assert(e.getMessage === s"$key should be int, but was abc") + } + + test("longConf") { + val key = "spark.sql.SQLConfEntrySuite.long" + val confEntry = SQLConfEntry.longConf(key) + assert(conf.getConf(confEntry, 5L) === 5L) + + conf.setConf(confEntry, 10L) + assert(conf.getConf(confEntry, 5L) === 10L) + + conf.setConfString(key, "20") + assert(conf.getConfString(key, "5") === "20") + assert(conf.getConfString(key) === "20") + assert(conf.getConf(confEntry, 5L) === 20L) + + val e = intercept[IllegalArgumentException] { + conf.setConfString(key, "abc") + } + assert(e.getMessage === s"$key should be long, but was abc") + } + + test("booleanConf") { + val key = "spark.sql.SQLConfEntrySuite.boolean" + val confEntry = SQLConfEntry.booleanConf(key) + assert(conf.getConf(confEntry, false) === false) + + conf.setConf(confEntry, true) + assert(conf.getConf(confEntry, false) === true) + + conf.setConfString(key, "true") + assert(conf.getConfString(key, "false") === "true") + assert(conf.getConfString(key) === "true") + assert(conf.getConf(confEntry, false) === true) + + val e = intercept[IllegalArgumentException] { + conf.setConfString(key, "abc") + } + assert(e.getMessage === s"$key should be boolean, but was abc") + } + + test("doubleConf") { + val key = "spark.sql.SQLConfEntrySuite.double" + val confEntry = SQLConfEntry.doubleConf(key) + assert(conf.getConf(confEntry, 5.0) === 5.0) + + conf.setConf(confEntry, 10.0) + assert(conf.getConf(confEntry, 5.0) === 10.0) + + conf.setConfString(key, "20.0") + assert(conf.getConfString(key, "5.0") === "20.0") + assert(conf.getConfString(key) === "20.0") + assert(conf.getConf(confEntry, 5.0) === 20.0) + + val e = intercept[IllegalArgumentException] { + conf.setConfString(key, "abc") + } + assert(e.getMessage === s"$key should be double, but was abc") + } + + test("stringConf") { + val key = "spark.sql.SQLConfEntrySuite.string" + val confEntry = SQLConfEntry.stringConf(key) + assert(conf.getConf(confEntry, "abc") === "abc") + + conf.setConf(confEntry, "abcd") + assert(conf.getConf(confEntry, "abc") === "abcd") + + conf.setConfString(key, "abcde") + assert(conf.getConfString(key, "abc") === "abcde") + assert(conf.getConfString(key) === "abcde") + assert(conf.getConf(confEntry, "abc") === "abcde") + } + + test("enumConf") { + val key = "spark.sql.SQLConfEntrySuite.enum" + val confEntry = SQLConfEntry.enumConf(key, v => v, Set("a", "b", "c"), defaultValue = Some("a")) + assert(conf.getConf(confEntry) === "a") + + conf.setConf(confEntry, "b") + assert(conf.getConf(confEntry) === "b") + + conf.setConfString(key, "c") + assert(conf.getConfString(key, "a") === "c") + assert(conf.getConfString(key) === "c") + assert(conf.getConf(confEntry) === "c") + + val e = intercept[IllegalArgumentException] { + conf.setConfString(key, "d") + } + assert(e.getMessage === s"The value of $key should be one of a, b, c, but was d") + } + + test("stringSeqConf") { + val key = "spark.sql.SQLConfEntrySuite.stringSeq" + val confEntry = SQLConfEntry.stringSeqConf("spark.sql.SQLConfEntrySuite.stringSeq", + defaultValue = Some(Nil)) + assert(conf.getConf(confEntry, Seq("a", "b", "c")) === Seq("a", "b", "c")) + + conf.setConf(confEntry, Seq("a", "b", "c", "d")) + assert(conf.getConf(confEntry, Seq("a", "b", "c")) === Seq("a", "b", "c", "d")) + + conf.setConfString(key, "a,b,c,d,e") + assert(conf.getConfString(key, "a,b,c") === "a,b,c,d,e") + assert(conf.getConfString(key) === "a,b,c,d,e") + assert(conf.getConf(confEntry, Seq("a", "b", "c")) === Seq("a", "b", "c", "d", "e")) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala index bf73d0c7074a5..75791e9d53c20 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala @@ -17,68 +17,72 @@ package org.apache.spark.sql -import org.scalatest.FunSuiteLike -import org.apache.spark.sql.test._ +class SQLConfSuite extends QueryTest { -/* Implicits */ -import TestSQLContext._ + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext -class SQLConfSuite extends QueryTest with FunSuiteLike { - - val testKey = "test.key.0" - val testVal = "test.val.0" + private val testKey = "test.key.0" + private val testVal = "test.val.0" test("propagate from spark conf") { // We create a new context here to avoid order dependence with other tests that might call // clear(). - val newContext = new SQLContext(TestSQLContext.sparkContext) - assert(newContext.getConf("spark.sql.testkey", "false") == "true") + val newContext = new SQLContext(ctx.sparkContext) + assert(newContext.getConf("spark.sql.testkey", "false") === "true") } test("programmatic ways of basic setting and getting") { - conf.clear() - assert(getAllConfs.size === 0) + ctx.conf.clear() + assert(ctx.getAllConfs.size === 0) - setConf(testKey, testVal) - assert(getConf(testKey) == testVal) - assert(getConf(testKey, testVal + "_") == testVal) - assert(getAllConfs.contains(testKey)) + ctx.setConf(testKey, testVal) + assert(ctx.getConf(testKey) === testVal) + assert(ctx.getConf(testKey, testVal + "_") === testVal) + assert(ctx.getAllConfs.contains(testKey)) // Tests SQLConf as accessed from a SQLContext is mutable after // the latter is initialized, unlike SparkConf inside a SparkContext. - assert(TestSQLContext.getConf(testKey) == testVal) - assert(TestSQLContext.getConf(testKey, testVal + "_") == testVal) - assert(TestSQLContext.getAllConfs.contains(testKey)) + assert(ctx.getConf(testKey) == testVal) + assert(ctx.getConf(testKey, testVal + "_") === testVal) + assert(ctx.getAllConfs.contains(testKey)) - conf.clear() + ctx.conf.clear() } test("parse SQL set commands") { - conf.clear() - sql(s"set $testKey=$testVal") - assert(getConf(testKey, testVal + "_") == testVal) - assert(TestSQLContext.getConf(testKey, testVal + "_") == testVal) + ctx.conf.clear() + ctx.sql(s"set $testKey=$testVal") + assert(ctx.getConf(testKey, testVal + "_") === testVal) + assert(ctx.getConf(testKey, testVal + "_") === testVal) - sql("set some.property=20") - assert(getConf("some.property", "0") == "20") - sql("set some.property = 40") - assert(getConf("some.property", "0") == "40") + ctx.sql("set some.property=20") + assert(ctx.getConf("some.property", "0") === "20") + ctx.sql("set some.property = 40") + assert(ctx.getConf("some.property", "0") === "40") val key = "spark.sql.key" val vs = "val0,val_1,val2.3,my_table" - sql(s"set $key=$vs") - assert(getConf(key, "0") == vs) + ctx.sql(s"set $key=$vs") + assert(ctx.getConf(key, "0") === vs) - sql(s"set $key=") - assert(getConf(key, "0") == "") + ctx.sql(s"set $key=") + assert(ctx.getConf(key, "0") === "") - conf.clear() + ctx.conf.clear() } test("deprecated property") { - conf.clear() - sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10") - assert(getConf(SQLConf.SHUFFLE_PARTITIONS) == "10") + ctx.conf.clear() + ctx.sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10") + assert(ctx.conf.numShufflePartitions === 10) + } + + test("invalid conf value") { + ctx.conf.clear() + val e = intercept[IllegalArgumentException] { + ctx.sql(s"set ${SQLConf.CASE_SENSITIVE.key}=10") + } + assert(e.getMessage === s"${SQLConf.CASE_SENSITIVE.key} should be boolean, but was 10") } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala new file mode 100644 index 0000000000000..c8d8796568a41 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala @@ -0,0 +1,48 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.SparkFunSuite + +class SQLContextSuite extends SparkFunSuite with BeforeAndAfterAll { + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + + override def afterAll(): Unit = { + SQLContext.setLastInstantiatedContext(ctx) + } + + test("getOrCreate instantiates SQLContext") { + SQLContext.clearLastInstantiatedContext() + val sqlContext = SQLContext.getOrCreate(ctx.sparkContext) + assert(sqlContext != null, "SQLContext.getOrCreate returned null") + assert(SQLContext.getOrCreate(ctx.sparkContext).eq(sqlContext), + "SQLContext created by SQLContext.getOrCreate not returned by SQLContext.getOrCreate") + } + + test("getOrCreate gets last explicitly instantiated SQLContext") { + SQLContext.clearLastInstantiatedContext() + val sqlContext = new SQLContext(ctx.sparkContext) + assert(SQLContext.getOrCreate(ctx.sparkContext) != null, + "SQLContext.getOrCreate after explicitly created SQLContext returned null") + assert(SQLContext.getOrCreate(ctx.sparkContext).eq(sqlContext), + "SQLContext.getOrCreate after explicitly created SQLContext did not return the context") + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 77be3b8b206c0..4441afd6bd811 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -19,27 +19,59 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterAll +import java.sql.Timestamp + +import org.apache.spark.sql.catalyst.DefaultParserDialect import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} - +import org.apache.spark.sql.test.SQLTestUtils import org.apache.spark.sql.types._ /** A SQL Dialect for testing purpose, and it can not be nested type */ -class MyDialect extends DefaultDialect +class MyDialect extends DefaultParserDialect -class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { +class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils { // Make sure the tables are loaded. TestData - import org.apache.spark.sql.test.TestSQLContext.implicits._ - val sqlCtx = TestSQLContext + val sqlContext = org.apache.spark.sql.test.TestSQLContext + import sqlContext.implicits._ + import sqlContext.sql + + test("having clause") { + Seq(("one", 1), ("two", 2), ("three", 3), ("one", 5)).toDF("k", "v").registerTempTable("hav") + checkAnswer( + sql("SELECT k, sum(v) FROM hav GROUP BY k HAVING sum(v) > 2"), + Row("one", 6) :: Row("three", 3) :: Nil) + } + + test("SPARK-8010: promote numeric to string") { + val df = Seq((1, 1)).toDF("key", "value") + df.registerTempTable("src") + val queryCaseWhen = sql("select case when true then 1.0 else '1' end from src ") + val queryCoalesce = sql("select coalesce(null, 1, '1') from src ") + + checkAnswer(queryCaseWhen, Row("1.0") :: Nil) + checkAnswer(queryCoalesce, Row("1") :: Nil) + } + + test("SPARK-6743: no columns from cache") { + Seq( + (83, 0, 38), + (26, 0, 79), + (43, 81, 24) + ).toDF("a", "b", "c").registerTempTable("cachedData") + + sqlContext.cacheTable("cachedData") + checkAnswer( + sql("SELECT t1.b FROM cachedData, cachedData t1 GROUP BY t1.b"), + Row(0) :: Row(81) :: Nil) + } test("self join with aliases") { - Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str").registerTempTable("df") + Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str").registerTempTable("df") checkAnswer( sql( @@ -62,7 +94,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("self join with alias in agg") { - Seq(1,2,3) + Seq(1, 2, 3) .map(i => (i, i.toString)) .toDF("int", "str") .groupBy("str") @@ -80,31 +112,58 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SQL Dialect Switching to a new SQL parser") { - val newContext = new SQLContext(TestSQLContext.sparkContext) + val newContext = new SQLContext(sqlContext.sparkContext) newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) assert(newContext.getSQLDialect().getClass === classOf[MyDialect]) assert(newContext.sql("SELECT 1").collect() === Array(Row(1))) } test("SQL Dialect Switch to an invalid parser with alias") { - val newContext = new SQLContext(TestSQLContext.sparkContext) + val newContext = new SQLContext(sqlContext.sparkContext) newContext.sql("SET spark.sql.dialect=MyTestClass") intercept[DialectException] { newContext.sql("SELECT 1") } // test if the dialect set back to DefaultSQLDialect - assert(newContext.getSQLDialect().getClass === classOf[DefaultDialect]) + assert(newContext.getSQLDialect().getClass === classOf[DefaultParserDialect]) } test("SPARK-4625 support SORT BY in SimpleSQLParser & DSL") { checkAnswer( sql("SELECT a FROM testData2 SORT BY a"), - Seq(1, 1, 2 ,2 ,3 ,3).map(Row(_)) + Seq(1, 1, 2, 2, 3, 3).map(Row(_)) ) } + test("SPARK-7158 collect and take return different results") { + import java.util.UUID + import org.apache.spark.sql.types._ + + val df = Seq(Tuple1(1), Tuple1(2), Tuple1(3)).toDF("index") + // we except the id is materialized once + def id: () => String = () => { UUID.randomUUID().toString() } + + val dfWithId = df.withColumn("id", callUDF(id, StringType)) + // Make a new DataFrame (actually the same reference to the old one) + val cached = dfWithId.cache() + // Trigger the cache + val d0 = dfWithId.collect() + val d1 = cached.collect() + val d2 = cached.collect() + + // Since the ID is only materialized once, then all of the records + // should come from the cache, not by re-computing. Otherwise, the ID + // will be different + assert(d0.map(_(0)) === d2.map(_(0))) + assert(d0.map(_(1)) === d2.map(_(1))) + + assert(d1.map(_(0)) === d2.map(_(0))) + assert(d1.map(_(1)) === d2.map(_(1))) + } + test("grouping on nested fields") { - jsonRDD(sparkContext.parallelize("""{"nested": {"attribute": 1}, "value": 2}""" :: Nil)) + sqlContext.read.json(sqlContext.sparkContext.parallelize( + """{"nested": {"attribute": 1}, "value": 2}""" :: Nil)) .registerTempTable("rows") checkAnswer( @@ -121,7 +180,9 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-6201 IN type conversion") { - jsonRDD(sparkContext.parallelize(Seq("{\"a\": \"1\"}}", "{\"a\": \"2\"}}", "{\"a\": \"3\"}}"))) + sqlContext.read.json( + sqlContext.sparkContext.parallelize( + Seq("{\"a\": \"1\"}}", "{\"a\": \"2\"}}", "{\"a\": \"3\"}}"))) .registerTempTable("d") checkAnswer( @@ -129,25 +190,13 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Seq(Row("1"), Row("2"))) } - test("SPARK-3176 Added Parser of SQL ABS()") { - checkAnswer( - sql("SELECT ABS(-1.3)"), - Row(1.3)) - checkAnswer( - sql("SELECT ABS(0.0)"), - Row(0.0)) - checkAnswer( - sql("SELECT ABS(2.5)"), - Row(2.5)) - } - test("aggregation with codegen") { - val originalValue = conf.codegenEnabled - setConf(SQLConf.CODEGEN_ENABLED, "true") + val originalValue = sqlContext.conf.codegenEnabled + sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true) // Prepare a table that we can group some rows. - table("testData") - .unionAll(table("testData")) - .unionAll(table("testData")) + sqlContext.table("testData") + .unionAll(sqlContext.table("testData")) + .unionAll(sqlContext.table("testData")) .registerTempTable("testData3x") def testCodeGen(sqlText: String, expectedResults: Seq[Row]): Unit = { @@ -169,77 +218,79 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { checkAnswer(df, expectedResults) } - // Just to group rows. - testCodeGen( - "SELECT key FROM testData3x GROUP BY key", - (1 to 100).map(Row(_))) - // COUNT - testCodeGen( - "SELECT key, count(value) FROM testData3x GROUP BY key", - (1 to 100).map(i => Row(i, 3))) - testCodeGen( - "SELECT count(key) FROM testData3x", - Row(300) :: Nil) - // COUNT DISTINCT ON int - testCodeGen( - "SELECT value, count(distinct key) FROM testData3x GROUP BY value", - (1 to 100).map(i => Row(i.toString, 1))) - testCodeGen( - "SELECT count(distinct key) FROM testData3x", - Row(100) :: Nil) - // SUM - testCodeGen( - "SELECT value, sum(key) FROM testData3x GROUP BY value", - (1 to 100).map(i => Row(i.toString, 3 * i))) - testCodeGen( - "SELECT sum(key), SUM(CAST(key as Double)) FROM testData3x", - Row(5050 * 3, 5050 * 3.0) :: Nil) - // AVERAGE - testCodeGen( - "SELECT value, avg(key) FROM testData3x GROUP BY value", - (1 to 100).map(i => Row(i.toString, i))) - testCodeGen( - "SELECT avg(key) FROM testData3x", - Row(50.5) :: Nil) - // MAX - testCodeGen( - "SELECT value, max(key) FROM testData3x GROUP BY value", - (1 to 100).map(i => Row(i.toString, i))) - testCodeGen( - "SELECT max(key) FROM testData3x", - Row(100) :: Nil) - // MIN - testCodeGen( - "SELECT value, min(key) FROM testData3x GROUP BY value", - (1 to 100).map(i => Row(i.toString, i))) - testCodeGen( - "SELECT min(key) FROM testData3x", - Row(1) :: Nil) - // Some combinations. - testCodeGen( - """ - |SELECT - | value, - | sum(key), - | max(key), - | min(key), - | avg(key), - | count(key), - | count(distinct key) - |FROM testData3x - |GROUP BY value - """.stripMargin, - (1 to 100).map(i => Row(i.toString, i*3, i, i, i, 3, 1))) - testCodeGen( - "SELECT max(key), min(key), avg(key), count(key), count(distinct key) FROM testData3x", - Row(100, 1, 50.5, 300, 100) :: Nil) - // Aggregate with Code generation handling all null values - testCodeGen( - "SELECT sum('a'), avg('a'), count(null) FROM testData", - Row(0, null, 0) :: Nil) - - dropTempTable("testData3x") - setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString) + try { + // Just to group rows. + testCodeGen( + "SELECT key FROM testData3x GROUP BY key", + (1 to 100).map(Row(_))) + // COUNT + testCodeGen( + "SELECT key, count(value) FROM testData3x GROUP BY key", + (1 to 100).map(i => Row(i, 3))) + testCodeGen( + "SELECT count(key) FROM testData3x", + Row(300) :: Nil) + // COUNT DISTINCT ON int + testCodeGen( + "SELECT value, count(distinct key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, 1))) + testCodeGen( + "SELECT count(distinct key) FROM testData3x", + Row(100) :: Nil) + // SUM + testCodeGen( + "SELECT value, sum(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, 3 * i))) + testCodeGen( + "SELECT sum(key), SUM(CAST(key as Double)) FROM testData3x", + Row(5050 * 3, 5050 * 3.0) :: Nil) + // AVERAGE + testCodeGen( + "SELECT value, avg(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT avg(key) FROM testData3x", + Row(50.5) :: Nil) + // MAX + testCodeGen( + "SELECT value, max(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT max(key) FROM testData3x", + Row(100) :: Nil) + // MIN + testCodeGen( + "SELECT value, min(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT min(key) FROM testData3x", + Row(1) :: Nil) + // Some combinations. + testCodeGen( + """ + |SELECT + | value, + | sum(key), + | max(key), + | min(key), + | avg(key), + | count(key), + | count(distinct key) + |FROM testData3x + |GROUP BY value + """.stripMargin, + (1 to 100).map(i => Row(i.toString, i*3, i, i, i, 3, 1))) + testCodeGen( + "SELECT max(key), min(key), avg(key), count(key), count(distinct key) FROM testData3x", + Row(100, 1, 50.5, 300, 100) :: Nil) + // Aggregate with Code generation handling all null values + testCodeGen( + "SELECT sum('a'), avg('a'), count(null) FROM testData", + Row(0, null, 0) :: Nil) + } finally { + sqlContext.dropTempTable("testData3x") + sqlContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue) + } } test("Add Parser of SQL COALESCE()") { @@ -296,6 +347,12 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-3173 Timestamp support in the parser") { + (0 to 3).map(i => Tuple1(new Timestamp(i))).toDF("time").registerTempTable("timestamps") + + checkAnswer(sql( + "SELECT time FROM timestamps WHERE time='1969-12-31 16:00:00.0'"), + Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00"))) + checkAnswer(sql( "SELECT time FROM timestamps WHERE time=CAST('1969-12-31 16:00:00.001' AS TIMESTAMP)"), Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00.001"))) @@ -335,7 +392,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("left semi greater than predicate") { checkAnswer( sql("SELECT * FROM testData2 x LEFT SEMI JOIN testData2 y ON x.a >= y.a + 2"), - Seq(Row(3,1), Row(3,2)) + Seq(Row(3, 1), Row(3, 2)) ) } @@ -352,16 +409,16 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("agg") { checkAnswer( sql("SELECT a, SUM(b) FROM testData2 GROUP BY a"), - Seq(Row(1,3), Row(2,3), Row(3,3))) + Seq(Row(1, 3), Row(2, 3), Row(3, 3))) } test("literal in agg grouping expressions") { checkAnswer( sql("SELECT a, count(1) FROM testData2 GROUP BY a, 1"), - Seq(Row(1,2), Row(2,2), Row(3,2))) + Seq(Row(1, 2), Row(2, 2), Row(3, 2))) checkAnswer( sql("SELECT a, count(2) FROM testData2 GROUP BY a, 2"), - Seq(Row(1,2), Row(2,2), Row(3,2))) + Seq(Row(1, 2), Row(2, 2), Row(3, 2))) } test("aggregates with nulls") { @@ -386,19 +443,19 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { def sortTest(): Unit = { checkAnswer( sql("SELECT * FROM testData2 ORDER BY a ASC, b ASC"), - Seq(Row(1,1), Row(1,2), Row(2,1), Row(2,2), Row(3,1), Row(3,2))) + Seq(Row(1, 1), Row(1, 2), Row(2, 1), Row(2, 2), Row(3, 1), Row(3, 2))) checkAnswer( sql("SELECT * FROM testData2 ORDER BY a ASC, b DESC"), - Seq(Row(1,2), Row(1,1), Row(2,2), Row(2,1), Row(3,2), Row(3,1))) + Seq(Row(1, 2), Row(1, 1), Row(2, 2), Row(2, 1), Row(3, 2), Row(3, 1))) checkAnswer( sql("SELECT * FROM testData2 ORDER BY a DESC, b DESC"), - Seq(Row(3,2), Row(3,1), Row(2,2), Row(2,1), Row(1,2), Row(1,1))) + Seq(Row(3, 2), Row(3, 1), Row(2, 2), Row(2, 1), Row(1, 2), Row(1, 1))) checkAnswer( sql("SELECT * FROM testData2 ORDER BY a DESC, b ASC"), - Seq(Row(3,1), Row(3,2), Row(2,1), Row(2,2), Row(1,1), Row(1,2))) + Seq(Row(3, 1), Row(3, 2), Row(2, 1), Row(2, 2), Row(1, 1), Row(1, 2))) checkAnswer( sql("SELECT b FROM binaryData ORDER BY a ASC"), @@ -426,37 +483,43 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("sorting") { - val before = conf.externalSortEnabled - setConf(SQLConf.EXTERNAL_SORT, "false") + val before = sqlContext.conf.externalSortEnabled + sqlContext.setConf(SQLConf.EXTERNAL_SORT, false) sortTest() - setConf(SQLConf.EXTERNAL_SORT, before.toString) + sqlContext.setConf(SQLConf.EXTERNAL_SORT, before) } test("external sorting") { - val before = conf.externalSortEnabled - setConf(SQLConf.EXTERNAL_SORT, "true") + val before = sqlContext.conf.externalSortEnabled + sqlContext.setConf(SQLConf.EXTERNAL_SORT, true) sortTest() - setConf(SQLConf.EXTERNAL_SORT, before.toString) + sqlContext.setConf(SQLConf.EXTERNAL_SORT, before) } test("SPARK-6927 sorting with codegen on") { - val externalbefore = conf.externalSortEnabled - val codegenbefore = conf.codegenEnabled - setConf(SQLConf.EXTERNAL_SORT, "false") - setConf(SQLConf.CODEGEN_ENABLED, "true") - sortTest() - setConf(SQLConf.EXTERNAL_SORT, externalbefore.toString) - setConf(SQLConf.CODEGEN_ENABLED, codegenbefore.toString) + val externalbefore = sqlContext.conf.externalSortEnabled + val codegenbefore = sqlContext.conf.codegenEnabled + sqlContext.setConf(SQLConf.EXTERNAL_SORT, false) + sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true) + try{ + sortTest() + } finally { + sqlContext.setConf(SQLConf.EXTERNAL_SORT, externalbefore) + sqlContext.setConf(SQLConf.CODEGEN_ENABLED, codegenbefore) + } } test("SPARK-6927 external sorting with codegen on") { - val externalbefore = conf.externalSortEnabled - val codegenbefore = conf.codegenEnabled - setConf(SQLConf.CODEGEN_ENABLED, "true") - setConf(SQLConf.EXTERNAL_SORT, "true") - sortTest() - setConf(SQLConf.EXTERNAL_SORT, externalbefore.toString) - setConf(SQLConf.CODEGEN_ENABLED, codegenbefore.toString) + val externalbefore = sqlContext.conf.externalSortEnabled + val codegenbefore = sqlContext.conf.codegenEnabled + sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true) + sqlContext.setConf(SQLConf.EXTERNAL_SORT, true) + try { + sortTest() + } finally { + sqlContext.setConf(SQLConf.EXTERNAL_SORT, externalbefore) + sqlContext.setConf(SQLConf.CODEGEN_ENABLED, codegenbefore) + } } test("limit") { @@ -489,7 +552,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("Allow only a single WITH clause per query") { intercept[RuntimeException] { - sql("with q1 as (select * from testData) with q2 as (select * from q1) select * from q2") + sql( + "with q1 as (select * from testData) with q2 as (select * from q1) select * from q2") } } @@ -533,7 +597,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("average overflow") { checkAnswer( sql("SELECT AVG(a),b FROM largeAndSmallInts group by b"), - Seq(Row(2147483645.0,1), Row(2.0,2))) + Seq(Row(2147483645.0, 1), Row(2.0, 2))) } test("count") { @@ -600,10 +664,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { | (SELECT * FROM testData2 WHERE a = 1) x JOIN | (SELECT * FROM testData2 WHERE a = 1) y |WHERE x.a = y.a""".stripMargin), - Row(1,1,1,1) :: - Row(1,1,1,2) :: - Row(1,2,1,1) :: - Row(1,2,1,2) :: Nil) + Row(1, 1, 1, 1) :: + Row(1, 1, 1, 2) :: + Row(1, 2, 1, 1) :: + Row(1, 2, 1, 2) :: Nil) } test("inner join, no matches") { @@ -635,7 +699,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { row => Seq.fill(16)(Row.merge(row, row))).collect().toSeq) } - ignore("cartesian product join") { + test("cartesian product join") { checkAnswer( testData3.join(testData3), Row(1, null, 1, null) :: @@ -836,7 +900,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SET commands semantics using sql()") { - conf.clear() + sqlContext.conf.clear() val testKey = "test.key.0" val testVal = "test.val.0" val nonexistentKey = "nonexistent" @@ -848,37 +912,37 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { sql(s"SET $testKey=$testVal") checkAnswer( sql("SET"), - Row(s"$testKey=$testVal") + Row(testKey, testVal) ) sql(s"SET ${testKey + testKey}=${testVal + testVal}") checkAnswer( sql("set"), Seq( - Row(s"$testKey=$testVal"), - Row(s"${testKey + testKey}=${testVal + testVal}")) + Row(testKey, testVal), + Row(testKey + testKey, testVal + testVal)) ) // "set key" checkAnswer( sql(s"SET $testKey"), - Row(s"$testKey=$testVal") + Row(testKey, testVal) ) checkAnswer( sql(s"SET $nonexistentKey"), - Row(s"$nonexistentKey=") + Row(nonexistentKey, "") ) - conf.clear() + sqlContext.conf.clear() } test("SET commands with illegal or inappropriate argument") { - conf.clear() + sqlContext.conf.clear() // Set negative mapred.reduce.tasks for automatically determing // the number of reducers is not supported intercept[IllegalArgumentException](sql(s"SET mapred.reduce.tasks=-1")) intercept[IllegalArgumentException](sql(s"SET mapred.reduce.tasks=-01")) intercept[IllegalArgumentException](sql(s"SET mapred.reduce.tasks=-2")) - conf.clear() + sqlContext.conf.clear() } test("apply schema") { @@ -896,7 +960,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(values(0).toInt, values(1), values(2).toBoolean, v4) } - val df1 = sqlCtx.createDataFrame(rowRDD1, schema1) + val df1 = sqlContext.createDataFrame(rowRDD1, schema1) df1.registerTempTable("applySchema1") checkAnswer( sql("SELECT * FROM applySchema1"), @@ -926,7 +990,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(Row(values(0).toInt, values(2).toBoolean), Map(values(1) -> v4)) } - val df2 = sqlCtx.createDataFrame(rowRDD2, schema2) + val df2 = sqlContext.createDataFrame(rowRDD2, schema2) df2.registerTempTable("applySchema2") checkAnswer( sql("SELECT * FROM applySchema2"), @@ -951,7 +1015,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(Row(values(0).toInt, values(2).toBoolean), scala.collection.mutable.Map(values(1) -> v4)) } - val df3 = sqlCtx.createDataFrame(rowRDD3, schema2) + val df3 = sqlContext.createDataFrame(rowRDD3, schema2) df3.registerTempTable("applySchema3") checkAnswer( @@ -996,7 +1060,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { .build() val schemaWithMeta = new StructType(Array( schema("id"), schema("name").copy(metadata = metadata), schema("age"))) - val personWithMeta = sqlCtx.createDataFrame(person.rdd, schemaWithMeta) + val personWithMeta = sqlContext.createDataFrame(person.rdd, schemaWithMeta) def validateMetadata(rdd: DataFrame): Unit = { assert(rdd.schema("name").metadata.getString(docKey) == docValue) } @@ -1011,7 +1075,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-3371 Renaming a function expression with group by gives error") { - TestSQLContext.udf.register("len", (s: String) => s.length) + sqlContext.udf.register("len", (s: String) => s.length) checkAnswer( sql("SELECT len(value) as temp FROM testData WHERE key = 1 group by len(value)"), Row(1)) @@ -1192,9 +1256,9 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-3483 Special chars in column names") { - val data = sparkContext.parallelize( + val data = sqlContext.sparkContext.parallelize( Seq("""{"key?number1": "value1", "key.number2": "value2"}""")) - jsonRDD(data).registerTempTable("records") + sqlContext.read.json(data).registerTempTable("records") sql("SELECT `key?number1`, `key.number2` FROM records") } @@ -1235,35 +1299,37 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-4322 Grouping field with struct field as sub expression") { - jsonRDD(sparkContext.makeRDD("""{"a": {"b": [{"c": 1}]}}""" :: Nil)).registerTempTable("data") + sqlContext.read.json(sqlContext.sparkContext.makeRDD("""{"a": {"b": [{"c": 1}]}}""" :: Nil)) + .registerTempTable("data") checkAnswer(sql("SELECT a.b[0].c FROM data GROUP BY a.b[0].c"), Row(1)) - dropTempTable("data") + sqlContext.dropTempTable("data") - jsonRDD(sparkContext.makeRDD("""{"a": {"b": 1}}""" :: Nil)).registerTempTable("data") + sqlContext.read.json( + sqlContext.sparkContext.makeRDD("""{"a": {"b": 1}}""" :: Nil)).registerTempTable("data") checkAnswer(sql("SELECT a.b + 1 FROM data GROUP BY a.b + 1"), Row(2)) - dropTempTable("data") + sqlContext.dropTempTable("data") } test("SPARK-4432 Fix attribute reference resolution error when using ORDER BY") { checkAnswer( sql("SELECT a + b FROM testData2 ORDER BY a"), - Seq(2, 3, 3 ,4 ,4 ,5).map(Row(_)) + Seq(2, 3, 3, 4, 4, 5).map(Row(_)) ) } test("oder by asc by default when not specify ascending and descending") { checkAnswer( sql("SELECT a, b FROM testData2 ORDER BY a desc, b"), - Seq(Row(3, 1), Row(3, 2), Row(2, 1), Row(2,2), Row(1, 1), Row(1, 2)) + Seq(Row(3, 1), Row(3, 2), Row(2, 1), Row(2, 2), Row(1, 1), Row(1, 2)) ) } test("Supporting relational operator '<=>' in Spark SQL") { - val nullCheckData1 = TestData(1,"1") :: TestData(2,null) :: Nil - val rdd1 = sparkContext.parallelize((0 to 1).map(i => nullCheckData1(i))) + val nullCheckData1 = TestData(1, "1") :: TestData(2, null) :: Nil + val rdd1 = sqlContext.sparkContext.parallelize((0 to 1).map(i => nullCheckData1(i))) rdd1.toDF().registerTempTable("nulldata1") - val nullCheckData2 = TestData(1,"1") :: TestData(2,null) :: Nil - val rdd2 = sparkContext.parallelize((0 to 1).map(i => nullCheckData2(i))) + val nullCheckData2 = TestData(1, "1") :: TestData(2, null) :: Nil + val rdd2 = sqlContext.sparkContext.parallelize((0 to 1).map(i => nullCheckData2(i))) rdd2.toDF().registerTempTable("nulldata2") checkAnswer(sql("SELECT nulldata1.key FROM nulldata1 join " + "nulldata2 on nulldata1.value <=> nulldata2.value"), @@ -1271,14 +1337,24 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("Multi-column COUNT(DISTINCT ...)") { - val data = TestData(1,"val_1") :: TestData(2,"val_2") :: Nil - val rdd = sparkContext.parallelize((0 to 1).map(i => data(i))) + val data = TestData(1, "val_1") :: TestData(2, "val_2") :: Nil + val rdd = sqlContext.sparkContext.parallelize((0 to 1).map(i => data(i))) rdd.toDF().registerTempTable("distinctData") checkAnswer(sql("SELECT COUNT(DISTINCT key,value) FROM distinctData"), Row(2)) } + test("SPARK-4699 case sensitivity SQL query") { + sqlContext.setConf(SQLConf.CASE_SENSITIVE, false) + val data = TestData(1, "val_1") :: TestData(2, "val_2") :: Nil + val rdd = sqlContext.sparkContext.parallelize((0 to 1).map(i => data(i))) + rdd.toDF().registerTempTable("testTable1") + checkAnswer(sql("SELECT VALUE FROM TESTTABLE1 where KEY = 1"), Row("val_1")) + sqlContext.setConf(SQLConf.CASE_SENSITIVE, true) + } + test("SPARK-6145: ORDER BY test for nested fields") { - jsonRDD(sparkContext.makeRDD("""{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil)) + sqlContext.read.json(sqlContext.sparkContext.makeRDD( + """{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil)) .registerTempTable("nestedOrder") checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY a.b"), Row(1)) @@ -1290,17 +1366,90 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-6145: special cases") { - jsonRDD(sparkContext.makeRDD( + sqlContext.read.json(sqlContext.sparkContext.makeRDD( """{"a": {"b": [1]}, "b": [{"a": 1}], "c0": {"a": 1}}""" :: Nil)).registerTempTable("t") checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY c0.a"), Row(1)) checkAnswer(sql("SELECT b[0].a FROM t ORDER BY c0.a"), Row(1)) } test("SPARK-6898: complete support for special chars in column names") { - jsonRDD(sparkContext.makeRDD( + sqlContext.read.json(sqlContext.sparkContext.makeRDD( """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil)) .registerTempTable("t") checkAnswer(sql("SELECT a.`c.b`, `b.$q`[0].`a@!.q`, `q.w`.`w.i&`[0] FROM t"), Row(1, 1, 1)) } + + test("SPARK-6583 order by aggregated function") { + Seq("1" -> 3, "1" -> 4, "2" -> 7, "2" -> 8, "3" -> 5, "3" -> 6, "4" -> 1, "4" -> 2) + .toDF("a", "b").registerTempTable("orderByData") + + checkAnswer( + sql( + """ + |SELECT a + |FROM orderByData + |GROUP BY a + |ORDER BY sum(b) + """.stripMargin), + Row("4") :: Row("1") :: Row("3") :: Row("2") :: Nil) + + checkAnswer( + sql( + """ + |SELECT sum(b) + |FROM orderByData + |GROUP BY a + |ORDER BY sum(b) + """.stripMargin), + Row(3) :: Row(7) :: Row(11) :: Row(15) :: Nil) + + checkAnswer( + sql( + """ + |SELECT a, sum(b) + |FROM orderByData + |GROUP BY a + |ORDER BY sum(b) + """.stripMargin), + Row("4", 3) :: Row("1", 7) :: Row("3", 11) :: Row("2", 15) :: Nil) + + checkAnswer( + sql( + """ + |SELECT a, sum(b) + |FROM orderByData + |GROUP BY a + |ORDER BY sum(b) + 1 + """.stripMargin), + Row("4", 3) :: Row("1", 7) :: Row("3", 11) :: Row("2", 15) :: Nil) + } + + test("SPARK-7952: fix the equality check between boolean and numeric types") { + withTempTable("t") { + // numeric field i, boolean field j, result of i = j, result of i <=> j + Seq[(Integer, java.lang.Boolean, java.lang.Boolean, java.lang.Boolean)]( + (1, true, true, true), + (0, false, true, true), + (2, true, false, false), + (2, false, false, false), + (null, true, null, false), + (null, false, null, false), + (0, null, null, false), + (1, null, null, false), + (null, null, null, true) + ).toDF("i", "b", "r1", "r2").registerTempTable("t") + + checkAnswer(sql("select i = b from t"), sql("select r1 from t")) + checkAnswer(sql("select i <=> b from t"), sql("select r2 from t")) + } + } + + test("SPARK-7067: order by queries for complex ExtractValue chain") { + withTempTable("t") { + sqlContext.read.json(sqlContext.sparkContext.makeRDD( + """{"a": {"b": [{"c": 1}]}, "b": [{"d": 1}]}""" :: Nil)).registerTempTable("t") + checkAnswer(sql("SELECT a.b FROM t ORDER BY b[0].d"), Row(Seq(Row(1)))) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala index 3fa00fd9d0ccb..ece3d6fdf2af5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala @@ -19,10 +19,8 @@ package org.apache.spark.sql import java.sql.{Date, Timestamp} -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.test.TestSQLContext._ case class ReflectData( stringField: String, @@ -74,45 +72,44 @@ case class ComplexReflectData( mapFieldContainsNull: Map[Int, Option[Long]], dataField: Data) -class ScalaReflectionRelationSuite extends FunSuite { +class ScalaReflectionRelationSuite extends SparkFunSuite { - import org.apache.spark.sql.test.TestSQLContext.implicits._ + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ test("query case class RDD") { val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true, - new java.math.BigDecimal(1), new Date(12345), new Timestamp(12345), Seq(1,2,3)) - val rdd = sparkContext.parallelize(data :: Nil) - rdd.toDF().registerTempTable("reflectData") + new java.math.BigDecimal(1), new Date(12345), new Timestamp(12345), Seq(1, 2, 3)) + Seq(data).toDF().registerTempTable("reflectData") - assert(sql("SELECT * FROM reflectData").collect().head === + assert(ctx.sql("SELECT * FROM reflectData").collect().head === Row("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true, new java.math.BigDecimal(1), Date.valueOf("1970-01-01"), - new Timestamp(12345), Seq(1,2,3))) + new Timestamp(12345), Seq(1, 2, 3))) } test("query case class RDD with nulls") { val data = NullReflectData(null, null, null, null, null, null, null) - val rdd = sparkContext.parallelize(data :: Nil) - rdd.toDF().registerTempTable("reflectNullData") + Seq(data).toDF().registerTempTable("reflectNullData") - assert(sql("SELECT * FROM reflectNullData").collect().head === Row.fromSeq(Seq.fill(7)(null))) + assert(ctx.sql("SELECT * FROM reflectNullData").collect().head === + Row.fromSeq(Seq.fill(7)(null))) } test("query case class RDD with Nones") { val data = OptionalReflectData(None, None, None, None, None, None, None) - val rdd = sparkContext.parallelize(data :: Nil) - rdd.toDF().registerTempTable("reflectOptionalData") + Seq(data).toDF().registerTempTable("reflectOptionalData") - assert(sql("SELECT * FROM reflectOptionalData").collect().head === + assert(ctx.sql("SELECT * FROM reflectOptionalData").collect().head === Row.fromSeq(Seq.fill(7)(null))) } // Equality is broken for Arrays, so we test that separately. test("query binary data") { - val rdd = sparkContext.parallelize(ReflectBinary(Array[Byte](1)) :: Nil) - rdd.toDF().registerTempTable("reflectBinary") + Seq(ReflectBinary(Array[Byte](1))).toDF().registerTempTable("reflectBinary") - val result = sql("SELECT data FROM reflectBinary").collect().head(0).asInstanceOf[Array[Byte]] + val result = ctx.sql("SELECT data FROM reflectBinary") + .collect().head(0).asInstanceOf[Array[Byte]] assert(result.toSeq === Seq[Byte](1)) } @@ -128,10 +125,9 @@ class ScalaReflectionRelationSuite extends FunSuite { Map(10 -> 100L, 20 -> 200L), Map(10 -> Some(100L), 20 -> Some(200L), 30 -> None), Nested(None, "abc"))) - val rdd = sparkContext.parallelize(data :: Nil) - rdd.toDF().registerTempTable("reflectComplexData") - assert(sql("SELECT * FROM reflectComplexData").collect().head === + Seq(data).toDF().registerTempTable("reflectComplexData") + assert(ctx.sql("SELECT * FROM reflectComplexData").collect().head === new GenericRow(Array[Any]( Seq(1, 2, 3), Seq(1, 2, null), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala index 6f6d3c9c243d4..e55c9e460b791 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala @@ -17,16 +17,15 @@ package org.apache.spark.sql -import org.scalatest.FunSuite - -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.serializer.JavaSerializer -import org.apache.spark.sql.test.TestSQLContext -class SerializationSuite extends FunSuite { +class SerializationSuite extends SparkFunSuite { + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext test("[SPARK-5235] SQLContext should be serializable") { - val sqlContext = new SQLContext(TestSQLContext.sparkContext) + val sqlContext = new SQLContext(ctx.sparkContext) new JavaSerializer(new SparkConf()).newInstance().serialize(sqlContext) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala index 225b51bd73d6c..520a862ea0838 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala @@ -86,8 +86,6 @@ object TestData { TestData3(2, Some(2)) :: Nil).toDF() testData3.registerTempTable("testData3") - val emptyTableData = logical.LocalRelation($"a".int, $"b".int) - case class UpperCaseData(N: Int, L: String) val upperCaseData = TestSQLContext.sparkContext.parallelize( @@ -111,8 +109,8 @@ object TestData { case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]]) val arrayData = TestSQLContext.sparkContext.parallelize( - ArrayData(Seq(1,2,3), Seq(Seq(1,2,3))) :: - ArrayData(Seq(2,3,4), Seq(Seq(2,3,4))) :: Nil) + ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) :: + ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil) arrayData.toDF().registerTempTable("arrayData") case class MapData(data: scala.collection.Map[Int, String]) @@ -176,12 +174,6 @@ object TestData { "3, C3, true, null" :: "4, D4, true, 2147483644" :: Nil) - case class TimestampField(time: Timestamp) - val timestamps = TestSQLContext.sparkContext.parallelize((1 to 3).map { i => - TimestampField(new Timestamp(i)) - }) - timestamps.toDF().registerTempTable("timestamps") - case class IntField(i: Int) // An RDD with 4 elements and 8 partitions val withEmptyParts = TestSQLContext.sparkContext.parallelize((1 to 4).map(IntField), 8) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala index d615542ab50a7..703a34c47ec20 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala @@ -17,43 +17,83 @@ package org.apache.spark.sql -import org.apache.spark.sql.test._ - -/* Implicits */ -import TestSQLContext._ -import TestSQLContext.implicits._ case class FunctionResult(f1: String, f2: String) class UDFSuite extends QueryTest { + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + + test("built-in fixed arity expressions") { + val df = ctx.emptyDataFrame + df.selectExpr("rand()", "randn()", "rand(5)", "randn(50)") + } + + test("built-in vararg expressions") { + val df = Seq((1, 2)).toDF("a", "b") + df.selectExpr("array(a, b)") + df.selectExpr("struct(a, b)") + } + + test("built-in expressions with multiple constructors") { + val df = Seq(("abcd", 2)).toDF("a", "b") + df.selectExpr("substr(a, 2)", "substr(a, 2, 3)").collect() + } + + test("count") { + val df = Seq(("abcd", 2)).toDF("a", "b") + df.selectExpr("count(a)") + } + + test("count distinct") { + val df = Seq(("abcd", 2)).toDF("a", "b") + df.selectExpr("count(distinct a)") + } + + test("error reporting for incorrect number of arguments") { + val df = ctx.emptyDataFrame + val e = intercept[AnalysisException] { + df.selectExpr("substr('abcd', 2, 3, 4)") + } + assert(e.getMessage.contains("arguments")) + } + + test("error reporting for undefined functions") { + val df = ctx.emptyDataFrame + val e = intercept[AnalysisException] { + df.selectExpr("a_function_that_does_not_exist()") + } + assert(e.getMessage.contains("undefined function")) + } + test("Simple UDF") { - udf.register("strLenScala", (_: String).length) - assert(sql("SELECT strLenScala('test')").head().getInt(0) === 4) + ctx.udf.register("strLenScala", (_: String).length) + assert(ctx.sql("SELECT strLenScala('test')").head().getInt(0) === 4) } test("ZeroArgument UDF") { - udf.register("random0", () => { Math.random()}) - assert(sql("SELECT random0()").head().getDouble(0) >= 0.0) + ctx.udf.register("random0", () => { Math.random()}) + assert(ctx.sql("SELECT random0()").head().getDouble(0) >= 0.0) } test("TwoArgument UDF") { - udf.register("strLenScala", (_: String).length + (_:Int)) - assert(sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5) + ctx.udf.register("strLenScala", (_: String).length + (_: Int)) + assert(ctx.sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5) } test("struct UDF") { - udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2)) + ctx.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2)) val result = - sql("SELECT returnStruct('test', 'test2') as ret") + ctx.sql("SELECT returnStruct('test', 'test2') as ret") .select($"ret.f1").head().getString(0) assert(result === "test") } test("udf that is transformed") { - udf.register("makeStruct", (x: Int, y: Int) => (x, y)) + ctx.udf.register("makeStruct", (x: Int, y: Int) => (x, y)) // 1 + 1 is constant folded causing a transformation. - assert(sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2)) + assert(ctx.sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2)) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala index 2672e20deadc5..45c9f06941c10 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala @@ -17,10 +17,6 @@ package org.apache.spark.sql -import java.io.File - -import org.apache.spark.util.Utils - import scala.beans.{BeanInfo, BeanProperty} import com.clearspring.analytics.stream.cardinality.HyperLogLog @@ -28,12 +24,11 @@ import com.clearspring.analytics.stream.cardinality.HyperLogLog import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.{OpenHashSetUDT, HyperLogLogUDT} import org.apache.spark.sql.functions._ -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext.{sparkContext, sql} -import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils import org.apache.spark.util.collection.OpenHashSet + @SQLUserDefinedType(udt = classOf[MyDenseVectorUDT]) private[sql] class MyDenseVector(val data: Array[Double]) extends Serializable { override def equals(other: Any): Boolean = other match { @@ -72,11 +67,13 @@ private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] { } class UserDefinedTypeSuite extends QueryTest { - val points = Seq( - MyLabeledPoint(1.0, new MyDenseVector(Array(0.1, 1.0))), - MyLabeledPoint(0.0, new MyDenseVector(Array(0.2, 2.0)))) - val pointsRDD = sparkContext.parallelize(points).toDF() + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + + private lazy val pointsRDD = Seq( + MyLabeledPoint(1.0, new MyDenseVector(Array(0.1, 1.0))), + MyLabeledPoint(0.0, new MyDenseVector(Array(0.2, 2.0)))).toDF() test("register user type: MyDenseVector for MyLabeledPoint") { val labels: RDD[Double] = pointsRDD.select('label).rdd.map { case Row(v: Double) => v } @@ -94,10 +91,10 @@ class UserDefinedTypeSuite extends QueryTest { } test("UDTs and UDFs") { - TestSQLContext.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector]) + ctx.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector]) pointsRDD.registerTempTable("points") checkAnswer( - sql("SELECT testType(features) from points"), + ctx.sql("SELECT testType(features) from points"), Seq(Row(true), Row(true))) } @@ -105,13 +102,13 @@ class UserDefinedTypeSuite extends QueryTest { test("UDTs with Parquet") { val tempDir = Utils.createTempDir() tempDir.delete() - pointsRDD.saveAsParquetFile(tempDir.getCanonicalPath) + pointsRDD.write.parquet(tempDir.getCanonicalPath) } test("Repartition UDTs with Parquet") { val tempDir = Utils.createTempDir() tempDir.delete() - pointsRDD.repartition(1).saveAsParquetFile(tempDir.getCanonicalPath) + pointsRDD.repartition(1).write.parquet(tempDir.getCanonicalPath) } // Tests to make sure that all operators correctly convert types on the way out. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala index 7cefcf44061ce..1f37455dd0bc4 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala @@ -17,27 +17,29 @@ package org.apache.spark.sql.columnar -import org.scalatest.FunSuite - -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.expressions.InternalRow import org.apache.spark.sql.types._ -class ColumnStatsSuite extends FunSuite { - testColumnStats(classOf[ByteColumnStats], BYTE, Row(Byte.MaxValue, Byte.MinValue, 0)) - testColumnStats(classOf[ShortColumnStats], SHORT, Row(Short.MaxValue, Short.MinValue, 0)) - testColumnStats(classOf[IntColumnStats], INT, Row(Int.MaxValue, Int.MinValue, 0)) - testColumnStats(classOf[LongColumnStats], LONG, Row(Long.MaxValue, Long.MinValue, 0)) - testColumnStats(classOf[FloatColumnStats], FLOAT, Row(Float.MaxValue, Float.MinValue, 0)) - testColumnStats(classOf[DoubleColumnStats], DOUBLE, Row(Double.MaxValue, Double.MinValue, 0)) - testColumnStats(classOf[FixedDecimalColumnStats], FIXED_DECIMAL(15, 10), Row(null, null, 0)) - testColumnStats(classOf[StringColumnStats], STRING, Row(null, null, 0)) - testColumnStats(classOf[DateColumnStats], DATE, Row(Int.MaxValue, Int.MinValue, 0)) - testColumnStats(classOf[TimestampColumnStats], TIMESTAMP, Row(null, null, 0)) +class ColumnStatsSuite extends SparkFunSuite { + testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0)) + testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0)) + testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0)) + testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0)) + testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0)) + testColumnStats(classOf[DoubleColumnStats], DOUBLE, + InternalRow(Double.MaxValue, Double.MinValue, 0)) + testColumnStats(classOf[FixedDecimalColumnStats], + FIXED_DECIMAL(15, 10), InternalRow(null, null, 0)) + testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0)) + testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0)) + testColumnStats(classOf[TimestampColumnStats], TIMESTAMP, + InternalRow(Long.MaxValue, Long.MinValue, 0)) def testColumnStats[T <: AtomicType, U <: ColumnStats]( columnStatsClass: Class[U], columnType: NativeColumnType[T], - initialStatistics: Row): Unit = { + initialStatistics: InternalRow): Unit = { val columnStatsName = columnStatsClass.getSimpleName diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala index 1e105e259dce7..6daddfb2c4804 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala @@ -18,26 +18,26 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import java.sql.Timestamp -import com.esotericsoftware.kryo.{Serializer, Kryo} import com.esotericsoftware.kryo.io.{Input, Output} -import org.apache.spark.serializer.KryoRegistrator -import org.scalatest.FunSuite +import com.esotericsoftware.kryo.{Kryo, Serializer} -import org.apache.spark.{SparkConf, Logging} +import org.apache.spark.{Logging, SparkConf, SparkFunSuite} +import org.apache.spark.serializer.KryoRegistrator import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar.ColumnarTestUtils._ import org.apache.spark.sql.execution.SparkSqlSerializer import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String + -class ColumnTypeSuite extends FunSuite with Logging { +class ColumnTypeSuite extends SparkFunSuite with Logging { val DEFAULT_BUFFER_SIZE = 512 test("defaultSize") { val checks = Map( INT -> 4, SHORT -> 2, LONG -> 8, BYTE -> 1, DOUBLE -> 8, FLOAT -> 4, - FIXED_DECIMAL(15, 10) -> 8, BOOLEAN -> 1, STRING -> 8, DATE -> 4, TIMESTAMP -> 12, + FIXED_DECIMAL(15, 10) -> 8, BOOLEAN -> 1, STRING -> 8, DATE -> 4, TIMESTAMP -> 8, BINARY -> 16, GENERIC -> 16) checks.foreach { case (columnType, expectedSize) => @@ -68,12 +68,12 @@ class ColumnTypeSuite extends FunSuite with Logging { checkActualSize(FLOAT, Float.MaxValue, 4) checkActualSize(FIXED_DECIMAL(15, 10), Decimal(0, 15, 10), 8) checkActualSize(BOOLEAN, true, 1) - checkActualSize(STRING, UTF8String("hello"), 4 + "hello".getBytes("utf-8").length) + checkActualSize(STRING, UTF8String.fromString("hello"), 4 + "hello".getBytes("utf-8").length) checkActualSize(DATE, 0, 4) - checkActualSize(TIMESTAMP, new Timestamp(0L), 12) + checkActualSize(TIMESTAMP, 0L, 8) val binary = Array.fill[Byte](4)(0: Byte) - checkActualSize(BINARY, binary, 4 + 4) + checkActualSize(BINARY, binary, 4 + 4) val generic = Map(1 -> "a") checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 8) @@ -120,7 +120,7 @@ class ColumnTypeSuite extends FunSuite with Logging { val length = buffer.getInt() val bytes = new Array[Byte](length) buffer.get(bytes) - UTF8String(bytes) + UTF8String.fromBytes(bytes) }) testColumnType[BinaryType.type, Array[Byte]]( @@ -167,7 +167,7 @@ class ColumnTypeSuite extends FunSuite with Logging { val serializer = new SparkSqlSerializer(conf).newInstance() val buffer = ByteBuffer.allocate(512) - val obj = CustomClass(Int.MaxValue,Long.MaxValue) + val obj = CustomClass(Int.MaxValue, Long.MaxValue) val serializedObj = serializer.serialize(obj).array() GENERIC.append(serializer.serialize(obj).array(), buffer) @@ -278,7 +278,7 @@ private[columnar] object CustomerSerializer extends Serializer[CustomClass] { override def read(kryo: Kryo, input: Input, aClass: Class[CustomClass]): CustomClass = { val a = input.readInt() val b = input.readLong() - CustomClass(a,b) + CustomClass(a, b) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala index 75d993e563e06..7c86eae3f77fd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala @@ -17,14 +17,12 @@ package org.apache.spark.sql.columnar -import java.sql.Timestamp - import scala.collection.immutable.HashSet import scala.util.Random - -import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.GenericMutableRow -import org.apache.spark.sql.types.{UTF8String, DataType, Decimal, AtomicType} +import org.apache.spark.sql.types.{DataType, Decimal, AtomicType} +import org.apache.spark.unsafe.types.UTF8String object ColumnarTestUtils { def makeNullRow(length: Int): GenericMutableRow = { @@ -48,14 +46,11 @@ object ColumnarTestUtils { case FLOAT => Random.nextFloat() case DOUBLE => Random.nextDouble() case FIXED_DECIMAL(precision, scale) => Decimal(Random.nextLong() % 100, precision, scale) - case STRING => UTF8String(Random.nextString(Random.nextInt(32))) + case STRING => UTF8String.fromString(Random.nextString(Random.nextInt(32))) case BOOLEAN => Random.nextBoolean() case BINARY => randomBytes(Random.nextInt(32)) case DATE => Random.nextInt() - case TIMESTAMP => - val timestamp = new Timestamp(Random.nextLong()) - timestamp.setNanos(Random.nextInt(999999999)) - timestamp + case TIMESTAMP => Random.nextLong() case _ => // Using a random one-element map instead of an arbitrary object Map(Random.nextInt() -> Random.nextString(Random.nextInt(32))) @@ -81,9 +76,9 @@ object ColumnarTestUtils { def makeRandomRow( head: ColumnType[_ <: DataType, _], - tail: ColumnType[_ <: DataType, _]*): Row = makeRandomRow(Seq(head) ++ tail) + tail: ColumnType[_ <: DataType, _]*): InternalRow = makeRandomRow(Seq(head) ++ tail) - def makeRandomRow(columnTypes: Seq[ColumnType[_ <: DataType, _]]): Row = { + def makeRandomRow(columnTypes: Seq[ColumnType[_ <: DataType, _]]): InternalRow = { val row = new GenericMutableRow(columnTypes.length) makeRandomValues(columnTypes).zipWithIndex.foreach { case (value, index) => row(index) = value diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala index 56591d9dba29e..01bc23277fa88 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala @@ -20,19 +20,20 @@ package org.apache.spark.sql.columnar import java.sql.{Date, Timestamp} import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.catalyst.expressions.Row -import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.{QueryTest, TestData} +import org.apache.spark.sql.{QueryTest, Row, TestData} import org.apache.spark.storage.StorageLevel.MEMORY_ONLY class InMemoryColumnarQuerySuite extends QueryTest { // Make sure the tables are loaded. TestData + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + import ctx.{logicalPlanToSparkQuery, sql} + test("simple columnar query") { - val plan = executePlan(testData.logicalPlan).executedPlan + val plan = ctx.executePlan(testData.logicalPlan).executedPlan val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None) checkAnswer(scan, testData.collect().toSeq) @@ -40,16 +41,16 @@ class InMemoryColumnarQuerySuite extends QueryTest { test("default size avoids broadcast") { // TODO: Improve this test when we have better statistics - sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)) + ctx.sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)) .toDF().registerTempTable("sizeTst") - cacheTable("sizeTst") + ctx.cacheTable("sizeTst") assert( - table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes > - conf.autoBroadcastJoinThreshold) + ctx.table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes > + ctx.conf.autoBroadcastJoinThreshold) } test("projection") { - val plan = executePlan(testData.select('value, 'key).logicalPlan).executedPlan + val plan = ctx.executePlan(testData.select('value, 'key).logicalPlan).executedPlan val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None) checkAnswer(scan, testData.collect().map { @@ -58,7 +59,7 @@ class InMemoryColumnarQuerySuite extends QueryTest { } test("SPARK-1436 regression: in-memory columns must be able to be accessed multiple times") { - val plan = executePlan(testData.logicalPlan).executedPlan + val plan = ctx.executePlan(testData.logicalPlan).executedPlan val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None) checkAnswer(scan, testData.collect().toSeq) @@ -70,7 +71,7 @@ class InMemoryColumnarQuerySuite extends QueryTest { sql("SELECT * FROM repeatedData"), repeatedData.collect().toSeq.map(Row.fromTuple)) - cacheTable("repeatedData") + ctx.cacheTable("repeatedData") checkAnswer( sql("SELECT * FROM repeatedData"), @@ -82,7 +83,7 @@ class InMemoryColumnarQuerySuite extends QueryTest { sql("SELECT * FROM nullableRepeatedData"), nullableRepeatedData.collect().toSeq.map(Row.fromTuple)) - cacheTable("nullableRepeatedData") + ctx.cacheTable("nullableRepeatedData") checkAnswer( sql("SELECT * FROM nullableRepeatedData"), @@ -90,15 +91,18 @@ class InMemoryColumnarQuerySuite extends QueryTest { } test("SPARK-2729 regression: timestamp data type") { + val timestamps = (0 to 3).map(i => Tuple1(new Timestamp(i))).toDF("time") + timestamps.registerTempTable("timestamps") + checkAnswer( sql("SELECT time FROM timestamps"), - timestamps.collect().toSeq.map(Row.fromTuple)) + timestamps.collect().toSeq) - cacheTable("timestamps") + ctx.cacheTable("timestamps") checkAnswer( sql("SELECT time FROM timestamps"), - timestamps.collect().toSeq.map(Row.fromTuple)) + timestamps.collect().toSeq) } test("SPARK-3320 regression: batched column buffer building should work with empty partitions") { @@ -106,7 +110,7 @@ class InMemoryColumnarQuerySuite extends QueryTest { sql("SELECT * FROM withEmptyParts"), withEmptyParts.collect().toSeq.map(Row.fromTuple)) - cacheTable("withEmptyParts") + ctx.cacheTable("withEmptyParts") checkAnswer( sql("SELECT * FROM withEmptyParts"), @@ -155,7 +159,7 @@ class InMemoryColumnarQuerySuite extends QueryTest { // Create a RDD for the schema val rdd = - sparkContext.parallelize((1 to 100), 10).map { i => + ctx.sparkContext.parallelize((1 to 100), 10).map { i => Row( s"str${i}: test cache.", s"binary${i}: test cache.".getBytes("UTF-8"), @@ -173,20 +177,20 @@ class InMemoryColumnarQuerySuite extends QueryTest { new Timestamp(i), (1 to i).toSeq, (0 to i).map(j => s"map_key_$j" -> (Long.MaxValue - j)).toMap, - Row((i - 0.25).toFloat, (1 to i).toSeq)) + Row((i - 0.25).toFloat, Seq(true, false, null))) } - createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types") + ctx.createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types") // Cache the table. sql("cache table InMemoryCache_different_data_types") // Make sure the table is indeed cached. - val tableScan = table("InMemoryCache_different_data_types").queryExecution.executedPlan + val tableScan = ctx.table("InMemoryCache_different_data_types").queryExecution.executedPlan assert( - isCached("InMemoryCache_different_data_types"), + ctx.isCached("InMemoryCache_different_data_types"), "InMemoryCache_different_data_types should be cached.") // Issue a query and check the results. checkAnswer( sql(s"SELECT DISTINCT ${allColumns} FROM InMemoryCache_different_data_types"), - table("InMemoryCache_different_data_types").collect()) - dropTempTable("InMemoryCache_different_data_types") + ctx.table("InMemoryCache_different_data_types").collect()) + ctx.dropTempTable("InMemoryCache_different_data_types") } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala index a0702144f942c..2a6e0c376551a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala @@ -19,8 +19,7 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.types.DataType @@ -39,7 +38,7 @@ object TestNullableColumnAccessor { } } -class NullableColumnAccessorSuite extends FunSuite { +class NullableColumnAccessorSuite extends SparkFunSuite { import ColumnarTestUtils._ Seq( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala index 3a5605d2335d7..cb4e9f1eb7f46 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala @@ -17,8 +17,7 @@ package org.apache.spark.sql.columnar -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.execution.SparkSqlSerializer import org.apache.spark.sql.types._ @@ -35,7 +34,7 @@ object TestNullableColumnBuilder { } } -class NullableColumnBuilderSuite extends FunSuite { +class NullableColumnBuilderSuite extends SparkFunSuite { import ColumnarTestUtils._ Seq( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala index 2a0b701cad7fa..2c0879927a129 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala @@ -17,43 +17,46 @@ package org.apache.spark.sql.columnar -import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll} +import org.apache.spark.SparkFunSuite import org.apache.spark.sql._ -import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ -class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfter { - val originalColumnBatchSize = conf.columnBatchSize - val originalInMemoryPartitionPruning = conf.inMemoryPartitionPruning +class PartitionBatchPruningSuite extends SparkFunSuite with BeforeAndAfterAll with BeforeAndAfter { + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + + private lazy val originalColumnBatchSize = ctx.conf.columnBatchSize + private lazy val originalInMemoryPartitionPruning = ctx.conf.inMemoryPartitionPruning override protected def beforeAll(): Unit = { // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch - setConf(SQLConf.COLUMN_BATCH_SIZE, "10") + ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, 10) - val pruningData = sparkContext.makeRDD((1 to 100).map { key => + val pruningData = ctx.sparkContext.makeRDD((1 to 100).map { key => val string = if (((key - 1) / 10) % 2 == 0) null else key.toString TestData(key, string) }, 5).toDF() pruningData.registerTempTable("pruningData") // Enable in-memory partition pruning - setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, "true") + ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true) // Enable in-memory table scan accumulators - setConf("spark.sql.inMemoryTableScanStatistics.enable", "true") + ctx.setConf("spark.sql.inMemoryTableScanStatistics.enable", "true") } override protected def afterAll(): Unit = { - setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize.toString) - setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning.toString) + ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize) + ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning) } before { - cacheTable("pruningData") + ctx.cacheTable("pruningData") } after { - uncacheTable("pruningData") + ctx.uncacheTable("pruningData") } // Comparisons @@ -107,7 +110,7 @@ class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with Be expectedQueryResult: => Seq[Int]): Unit = { test(query) { - val df = sql(query) + val df = ctx.sql(query) val queryExecution = df.queryExecution assertResult(expectedQueryResult.toArray, s"Wrong query result: $queryExecution") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala index 8b518f094174c..f606e2133bedc 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala @@ -17,14 +17,13 @@ package org.apache.spark.sql.columnar.compression -import org.scalatest.FunSuite - -import org.apache.spark.sql.Row +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.GenericMutableRow -import org.apache.spark.sql.columnar.{NoopColumnStats, BOOLEAN} import org.apache.spark.sql.columnar.ColumnarTestUtils._ +import org.apache.spark.sql.columnar.{BOOLEAN, NoopColumnStats} -class BooleanBitSetSuite extends FunSuite { +class BooleanBitSetSuite extends SparkFunSuite { import BooleanBitSet._ def skeleton(count: Int) { @@ -33,7 +32,7 @@ class BooleanBitSetSuite extends FunSuite { // ------------- val builder = TestCompressibleColumnBuilder(new NoopColumnStats, BOOLEAN, BooleanBitSet) - val rows = Seq.fill[Row](count)(makeRandomRow(BOOLEAN)) + val rows = Seq.fill[InternalRow](count)(makeRandomRow(BOOLEAN)) val values = rows.map(_(0)) rows.foreach(builder.appendFrom(_, 0)) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala index 64b70552eb047..acfab6586c0d1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala @@ -19,16 +19,15 @@ package org.apache.spark.sql.columnar.compression import java.nio.ByteBuffer -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar._ import org.apache.spark.sql.columnar.ColumnarTestUtils._ import org.apache.spark.sql.types.AtomicType -class DictionaryEncodingSuite extends FunSuite { - testDictionaryEncoding(new IntColumnStats, INT) - testDictionaryEncoding(new LongColumnStats, LONG) +class DictionaryEncodingSuite extends SparkFunSuite { + testDictionaryEncoding(new IntColumnStats, INT) + testDictionaryEncoding(new LongColumnStats, LONG) testDictionaryEncoding(new StringColumnStats, STRING) def testDictionaryEncoding[T <: AtomicType]( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala index bfd99f143bedc..2111e9fbe62cb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.sql.columnar.compression -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar._ import org.apache.spark.sql.columnar.ColumnarTestUtils._ import org.apache.spark.sql.types.IntegralType -class IntegralDeltaSuite extends FunSuite { - testIntegralDelta(new IntColumnStats, INT, IntDelta) +class IntegralDeltaSuite extends SparkFunSuite { + testIntegralDelta(new IntColumnStats, INT, IntDelta) testIntegralDelta(new LongColumnStats, LONG, LongDelta) def testIntegralDelta[I <: IntegralType]( @@ -116,7 +115,7 @@ class IntegralDeltaSuite extends FunSuite { test(s"$scheme: simple case") { val input = columnType match { - case INT => Seq(2: Int, 1: Int, 2: Int, 130: Int) + case INT => Seq(2: Int, 1: Int, 2: Int, 130: Int) case LONG => Seq(2: Long, 1: Long, 2: Long, 130: Long) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala index fde7a4595be0e..67ec08f594a43 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala @@ -17,20 +17,19 @@ package org.apache.spark.sql.columnar.compression -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar._ import org.apache.spark.sql.columnar.ColumnarTestUtils._ import org.apache.spark.sql.types.AtomicType -class RunLengthEncodingSuite extends FunSuite { +class RunLengthEncodingSuite extends SparkFunSuite { testRunLengthEncoding(new NoopColumnStats, BOOLEAN) - testRunLengthEncoding(new ByteColumnStats, BYTE) - testRunLengthEncoding(new ShortColumnStats, SHORT) - testRunLengthEncoding(new IntColumnStats, INT) - testRunLengthEncoding(new LongColumnStats, LONG) - testRunLengthEncoding(new StringColumnStats, STRING) + testRunLengthEncoding(new ByteColumnStats, BYTE) + testRunLengthEncoding(new ShortColumnStats, SHORT) + testRunLengthEncoding(new IntColumnStats, INT) + testRunLengthEncoding(new LongColumnStats, LONG) + testRunLengthEncoding(new StringColumnStats, STRING) def testRunLengthEncoding[T <: AtomicType]( columnStats: ColumnStats, diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala index 523be56df65ba..5854ab48db552 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala @@ -17,21 +17,19 @@ package org.apache.spark.sql.execution -import org.scalatest.FunSuite - -import org.apache.spark.sql.{SQLConf, execution} -import org.apache.spark.sql.functions._ +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin} +import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.test.TestSQLContext.planner._ import org.apache.spark.sql.types._ +import org.apache.spark.sql.{Row, SQLConf, execution} -class PlannerSuite extends FunSuite { +class PlannerSuite extends SparkFunSuite { test("unions are collapsed") { val query = testData.unionAll(testData).unionAll(testData).logicalPlan val planned = BasicOperators(query).head @@ -65,7 +63,7 @@ class PlannerSuite extends FunSuite { test("sizeInBytes estimation of limit operator for broadcast hash join optimization") { def checkPlan(fieldTypes: Seq[DataType], newThreshold: Int): Unit = { - setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold.toString) + setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold) val fields = fieldTypes.zipWithIndex.map { case (dataType, index) => StructField(s"c${index}", dataType, true) } :+ StructField("key", IntegerType, true) @@ -121,12 +119,12 @@ class PlannerSuite extends FunSuite { checkPlan(complexTypes, newThreshold = 901617) - setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold.toString) + setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold) } test("InMemoryRelation statistics propagation") { val origThreshold = conf.autoBroadcastJoinThreshold - setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920.toString) + setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920) testData.limit(3).registerTempTable("tiny") sql("CACHE TABLE tiny") @@ -141,6 +139,6 @@ class PlannerSuite extends FunSuite { assert(broadcastHashJoins.size === 1, "Should use broadcast hash join") assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join") - setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold.toString) + setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala new file mode 100644 index 0000000000000..a1e3ca11b1ad9 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution + +import org.apache.spark.sql.catalyst.dsl.expressions._ + +class SortSuite extends SparkPlanTest { + + // This test was originally added as an example of how to use [[SparkPlanTest]]; + // it's not designed to be a comprehensive test of ExternalSort. + test("basic sorting using ExternalSort") { + + val input = Seq( + ("Hello", 4, 2.0), + ("Hello", 1, 1.0), + ("World", 8, 3.0) + ) + + checkAnswer( + input.toDF("a", "b", "c"), + ExternalSort('a.asc :: 'b.asc :: Nil, global = false, _: SparkPlan), + input.sorted) + + checkAnswer( + input.toDF("a", "b", "c"), + ExternalSort('b.asc :: 'a.asc :: Nil, global = false, _: SparkPlan), + input.sortBy(t => (t._2, t._1))) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala new file mode 100644 index 0000000000000..13f3be8ca28d6 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala @@ -0,0 +1,167 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution + +import scala.language.implicitConversions +import scala.reflect.runtime.universe.TypeTag +import scala.util.control.NonFatal + +import org.apache.spark.SparkFunSuite + +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.expressions.BoundReference +import org.apache.spark.sql.catalyst.util._ + +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.{DataFrameHolder, Row, DataFrame} + +/** + * Base class for writing tests for individual physical operators. For an example of how this + * class's test helper methods can be used, see [[SortSuite]]. + */ +class SparkPlanTest extends SparkFunSuite { + + /** + * Creates a DataFrame from a local Seq of Product. + */ + implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = { + TestSQLContext.implicits.localSeqToDataFrameHolder(data) + } + + /** + * Runs the plan and makes sure the answer matches the expected result. + * @param input the input data to be used. + * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate + * the physical operator that's being tested. + * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. + */ + protected def checkAnswer( + input: DataFrame, + planFunction: SparkPlan => SparkPlan, + expectedAnswer: Seq[Row]): Unit = { + SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer) match { + case Some(errorMessage) => fail(errorMessage) + case None => + } + } + + /** + * Runs the plan and makes sure the answer matches the expected result. + * @param input the input data to be used. + * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate + * the physical operator that's being tested. + * @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s. + */ + protected def checkAnswer[A <: Product : TypeTag]( + input: DataFrame, + planFunction: SparkPlan => SparkPlan, + expectedAnswer: Seq[A]): Unit = { + val expectedRows = expectedAnswer.map(Row.fromTuple) + SparkPlanTest.checkAnswer(input, planFunction, expectedRows) match { + case Some(errorMessage) => fail(errorMessage) + case None => + } + } +} + +/** + * Helper methods for writing tests of individual physical operators. + */ +object SparkPlanTest { + + /** + * Runs the plan and makes sure the answer matches the expected result. + * @param input the input data to be used. + * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate + * the physical operator that's being tested. + * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. + */ + def checkAnswer( + input: DataFrame, + planFunction: SparkPlan => SparkPlan, + expectedAnswer: Seq[Row]): Option[String] = { + + val outputPlan = planFunction(input.queryExecution.sparkPlan) + + // A very simple resolver to make writing tests easier. In contrast to the real resolver + // this is always case sensitive and does not try to handle scoping or complex type resolution. + val resolvedPlan = outputPlan transform { + case plan: SparkPlan => + val inputMap = plan.children.flatMap(_.output).zipWithIndex.map { + case (a, i) => + (a.name, BoundReference(i, a.dataType, a.nullable)) + }.toMap + + plan.transformExpressions { + case UnresolvedAttribute(Seq(u)) => + inputMap.getOrElse(u, + sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap")) + } + } + + def prepareAnswer(answer: Seq[Row]): Seq[Row] = { + // Converts data to types that we can do equality comparison using Scala collections. + // For BigDecimal type, the Scala type has a better definition of equality test (similar to + // Java's java.math.BigDecimal.compareTo). + // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for + // equality test. + // This function is copied from Catalyst's QueryTest + val converted: Seq[Row] = answer.map { s => + Row.fromSeq(s.toSeq.map { + case d: java.math.BigDecimal => BigDecimal(d) + case b: Array[Byte] => b.toSeq + case o => o + }) + } + converted.sortBy(_.toString()) + } + + val sparkAnswer: Seq[Row] = try { + resolvedPlan.executeCollect().toSeq + } catch { + case NonFatal(e) => + val errorMessage = + s""" + | Exception thrown while executing Spark plan: + | $outputPlan + | == Exception == + | $e + | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)} + """.stripMargin + return Some(errorMessage) + } + + if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) { + val errorMessage = + s""" + | Results do not match for Spark plan: + | $outputPlan + | == Results == + | ${sideBySide( + s"== Correct Answer - ${expectedAnswer.size} ==" +: + prepareAnswer(expectedAnswer).map(_.toString()), + s"== Spark Answer - ${sparkAnswer.size} ==" +: + prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")} + """.stripMargin + return Some(errorMessage) + } + + None + } +} + diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala index 15337c4045436..8631e247c6c05 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala @@ -19,17 +19,17 @@ package org.apache.spark.sql.execution import java.sql.{Timestamp, Date} -import org.scalatest.{FunSuite, BeforeAndAfterAll} +import org.apache.spark.sql.test.TestSQLContext +import org.scalatest.BeforeAndAfterAll import org.apache.spark.rdd.ShuffledRDD import org.apache.spark.serializer.Serializer -import org.apache.spark.ShuffleDependency +import org.apache.spark.{ShuffleDependency, SparkFunSuite} import org.apache.spark.sql.types._ import org.apache.spark.sql.Row -import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.{MyDenseVectorUDT, QueryTest} -class SparkSqlSerializer2DataTypeSuite extends FunSuite { +class SparkSqlSerializer2DataTypeSuite extends SparkFunSuite { // Make sure that we will not use serializer2 for unsupported data types. def checkSupported(dataType: DataType, isSupported: Boolean): Unit = { val testName = @@ -74,11 +74,13 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll var numShufflePartitions: Int = _ var useSerializer2: Boolean = _ + protected lazy val ctx = TestSQLContext + override def beforeAll(): Unit = { - numShufflePartitions = conf.numShufflePartitions - useSerializer2 = conf.useSqlSerializer2 + numShufflePartitions = ctx.conf.numShufflePartitions + useSerializer2 = ctx.conf.useSqlSerializer2 - sql("set spark.sql.useSerializer2=true") + ctx.sql("set spark.sql.useSerializer2=true") val supportedTypes = Seq(StringType, BinaryType, NullType, BooleanType, @@ -94,7 +96,7 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll // Create a RDD with all data types supported by SparkSqlSerializer2. val rdd = - sparkContext.parallelize((1 to 1000), 10).map { i => + ctx.sparkContext.parallelize((1 to 1000), 10).map { i => Row( s"str${i}: test serializer2.", s"binary${i}: test serializer2.".getBytes("UTF-8"), @@ -112,15 +114,15 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll new Timestamp(i)) } - createDataFrame(rdd, schema).registerTempTable("shuffle") + ctx.createDataFrame(rdd, schema).registerTempTable("shuffle") super.beforeAll() } override def afterAll(): Unit = { - dropTempTable("shuffle") - sql(s"set spark.sql.shuffle.partitions=$numShufflePartitions") - sql(s"set spark.sql.useSerializer2=$useSerializer2") + ctx.dropTempTable("shuffle") + ctx.sql(s"set spark.sql.shuffle.partitions=$numShufflePartitions") + ctx.sql(s"set spark.sql.useSerializer2=$useSerializer2") super.afterAll() } @@ -141,16 +143,16 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll } test("key schema and value schema are not nulls") { - val df = sql(s"SELECT DISTINCT ${allColumns} FROM shuffle") + val df = ctx.sql(s"SELECT DISTINCT ${allColumns} FROM shuffle") checkSerializer(df.queryExecution.executedPlan, serializerClass) checkAnswer( df, - table("shuffle").collect()) + ctx.table("shuffle").collect()) } test("key schema is null") { val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",") - val df = sql(s"SELECT $aggregations FROM shuffle") + val df = ctx.sql(s"SELECT $aggregations FROM shuffle") checkSerializer(df.queryExecution.executedPlan, serializerClass) checkAnswer( df, @@ -158,15 +160,14 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll } test("value schema is null") { - val df = sql(s"SELECT col0 FROM shuffle ORDER BY col0") + val df = ctx.sql(s"SELECT col0 FROM shuffle ORDER BY col0") checkSerializer(df.queryExecution.executedPlan, serializerClass) - assert( - df.map(r => r.getString(0)).collect().toSeq === - table("shuffle").select("col0").map(r => r.getString(0)).collect().sorted.toSeq) + assert(df.map(r => r.getString(0)).collect().toSeq === + ctx.table("shuffle").select("col0").map(r => r.getString(0)).collect().sorted.toSeq) } test("no map output field") { - val df = sql(s"SELECT 1 + 1 FROM shuffle") + val df = ctx.sql(s"SELECT 1 + 1 FROM shuffle") checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer]) } } @@ -177,8 +178,8 @@ class SparkSqlSerializer2SortShuffleSuite extends SparkSqlSerializer2Suite { super.beforeAll() // Sort merge will not be triggered. val bypassMergeThreshold = - sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) - sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold-1}") + ctx.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) + ctx.sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold-1}") } } @@ -189,7 +190,7 @@ class SparkSqlSerializer2SortMergeShuffleSuite extends SparkSqlSerializer2Suite super.beforeAll() // To trigger the sort merge. val bypassMergeThreshold = - sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) - sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold + 1}") + ctx.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) + ctx.sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold + 1}") } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala index 358d8cf06e463..8ec3985e00360 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala @@ -17,12 +17,11 @@ package org.apache.spark.sql.execution.debug -import org.scalatest.FunSuite - +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.TestData._ import org.apache.spark.sql.test.TestSQLContext._ -class DebuggingSuite extends FunSuite { +class DebuggingSuite extends SparkFunSuite { test("DataFrame.debug()") { testData.debug() } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala index 2aad01ded1acf..71db6a2159857 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala @@ -17,47 +17,46 @@ package org.apache.spark.sql.execution.joins -import org.scalatest.FunSuite - -import org.apache.spark.sql.catalyst.expressions.{Projection, Row} +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.expressions.{Projection, InternalRow} import org.apache.spark.util.collection.CompactBuffer -class HashedRelationSuite extends FunSuite { +class HashedRelationSuite extends SparkFunSuite { // Key is simply the record itself private val keyProjection = new Projection { - override def apply(row: Row): Row = row + override def apply(row: InternalRow): InternalRow = row } test("GeneralHashedRelation") { - val data = Array(Row(0), Row(1), Row(2), Row(2)) + val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2)) val hashed = HashedRelation(data.iterator, keyProjection) assert(hashed.isInstanceOf[GeneralHashedRelation]) - assert(hashed.get(data(0)) == CompactBuffer[Row](data(0))) - assert(hashed.get(data(1)) == CompactBuffer[Row](data(1))) - assert(hashed.get(Row(10)) === null) + assert(hashed.get(data(0)) == CompactBuffer[InternalRow](data(0))) + assert(hashed.get(data(1)) == CompactBuffer[InternalRow](data(1))) + assert(hashed.get(InternalRow(10)) === null) - val data2 = CompactBuffer[Row](data(2)) + val data2 = CompactBuffer[InternalRow](data(2)) data2 += data(2) assert(hashed.get(data(2)) == data2) } test("UniqueKeyHashedRelation") { - val data = Array(Row(0), Row(1), Row(2)) + val data = Array(InternalRow(0), InternalRow(1), InternalRow(2)) val hashed = HashedRelation(data.iterator, keyProjection) assert(hashed.isInstanceOf[UniqueKeyHashedRelation]) - assert(hashed.get(data(0)) == CompactBuffer[Row](data(0))) - assert(hashed.get(data(1)) == CompactBuffer[Row](data(1))) - assert(hashed.get(data(2)) == CompactBuffer[Row](data(2))) - assert(hashed.get(Row(10)) === null) + assert(hashed.get(data(0)) == CompactBuffer[InternalRow](data(0))) + assert(hashed.get(data(1)) == CompactBuffer[InternalRow](data(1))) + assert(hashed.get(data(2)) == CompactBuffer[InternalRow](data(2))) + assert(hashed.get(InternalRow(10)) === null) val uniqHashed = hashed.asInstanceOf[UniqueKeyHashedRelation] assert(uniqHashed.getValue(data(0)) == data(0)) assert(uniqHashed.getValue(data(1)) == data(1)) assert(uniqHashed.getValue(data(2)) == data(2)) - assert(uniqHashed.getValue(Row(10)) == null) + assert(uniqHashed.getValue(InternalRow(10)) == null) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index 021affafe36a6..69ab1c292d221 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -21,20 +21,30 @@ import java.math.BigDecimal import java.sql.DriverManager import java.util.{Calendar, GregorianCalendar, Properties} -import org.apache.spark.sql.test._ -import org.apache.spark.sql.types._ import org.h2.jdbc.JdbcSQLException -import org.scalatest.{FunSuite, BeforeAndAfter} -import TestSQLContext._ -import TestSQLContext.implicits._ +import org.scalatest.BeforeAndAfter + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.types._ -class JDBCSuite extends FunSuite with BeforeAndAfter { +class JDBCSuite extends SparkFunSuite with BeforeAndAfter { val url = "jdbc:h2:mem:testdb0" val urlWithUserAndPass = "jdbc:h2:mem:testdb0;user=testUser;password=testPass" var conn: java.sql.Connection = null val testBytes = Array[Byte](99.toByte, 134.toByte, 135.toByte, 200.toByte, 205.toByte) + val testH2Dialect = new JdbcDialect { + override def canHandle(url: String) : Boolean = url.startsWith("jdbc:h2") + override def getCatalystType( + sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = + Some(StringType) + } + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + import ctx.sql + before { Class.forName("org.h2.Driver") // Extra properties that will be specified for our database. We need these to test @@ -61,6 +71,14 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) + sql( + s""" + |CREATE TEMPORARY TABLE fetchtwo + |USING org.apache.spark.sql.jdbc + |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass', + | fetchSize '2') + """.stripMargin.replaceAll("\n", " ")) + sql( s""" |CREATE TEMPORARY TABLE parts @@ -178,6 +196,14 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(names(2).equals("mary")) } + test("SELECT first field when fetchSize is two") { + val names = sql("SELECT NAME FROM fetchtwo").collect().map(x => x.getString(0)).sortWith(_ < _) + assert(names.size === 3) + assert(names(0).equals("fred")) + assert(names(1).equals("joe 'foo' \"bar\"")) + assert(names(2).equals("mary")) + } + test("SELECT second field") { val ids = sql("SELECT THEID FROM foobar").collect().map(x => x.getInt(0)).sortWith(_ < _) assert(ids.size === 3) @@ -186,6 +212,14 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(ids(2) === 3) } + test("SELECT second field when fetchSize is two") { + val ids = sql("SELECT THEID FROM fetchtwo").collect().map(x => x.getInt(0)).sortWith(_ < _) + assert(ids.size === 3) + assert(ids(0) === 1) + assert(ids(1) === 2) + assert(ids(2) === 3) + } + test("SELECT * partitioned") { assert(sql("SELECT * FROM parts").collect().size == 3) } @@ -204,23 +238,49 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(ids(2) === 3) } + test("Register JDBC query with renamed fields") { + // Regression test for bug SPARK-7345 + sql( + s""" + |CREATE TEMPORARY TABLE renamed + |USING org.apache.spark.sql.jdbc + |OPTIONS (url '$url', dbtable '(select NAME as NAME1, NAME as NAME2 from TEST.PEOPLE)', + |user 'testUser', password 'testPass') + """.stripMargin.replaceAll("\n", " ")) + + val df = sql("SELECT * FROM renamed") + assert(df.schema.fields.size == 2) + assert(df.schema.fields(0).name == "NAME1") + assert(df.schema.fields(1).name == "NAME2") + } + test("Basic API") { - assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE").collect().size === 3) + assert(ctx.read.jdbc( + urlWithUserAndPass, "TEST.PEOPLE", new Properties).collect().length === 3) + } + + test("Basic API with FetchSize") { + val properties = new Properties + properties.setProperty("fetchSize", "2") + assert(ctx.read.jdbc( + urlWithUserAndPass, "TEST.PEOPLE", properties).collect().length === 3) } test("Partitioning via JDBCPartitioningInfo API") { - assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3) - .collect.size === 3) + assert( + ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3, new Properties) + .collect().length === 3) } test("Partitioning via list-of-where-clauses API") { val parts = Array[String]("THEID < 2", "THEID >= 2") - assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts).collect().size === 3) + assert(ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties) + .collect().length === 3) } test("H2 integral types") { val rows = sql("SELECT * FROM inttypes WHERE A IS NOT NULL").collect() - assert(rows.size === 1) + assert(rows.length === 1) assert(rows(0).getInt(0) === 1) assert(rows(0).getBoolean(1) === false) assert(rows(0).getInt(2) === 3) @@ -230,7 +290,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { test("H2 null entries") { val rows = sql("SELECT * FROM inttypes WHERE A IS NULL").collect() - assert(rows.size === 1) + assert(rows.length === 1) assert(rows(0).isNullAt(0)) assert(rows(0).isNullAt(1)) assert(rows(0).isNullAt(2)) @@ -266,28 +326,31 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(cal.get(Calendar.HOUR) === 11) assert(cal.get(Calendar.MINUTE) === 22) assert(cal.get(Calendar.SECOND) === 33) - assert(rows(0).getAs[java.sql.Timestamp](2).getNanos === 543543543) + assert(rows(0).getAs[java.sql.Timestamp](2).getNanos === 543543500) } test("test DATE types") { - val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").collect() - val cachedRows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").cache().collect() + val rows = ctx.read.jdbc( + urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect() + val cachedRows = ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties) + .cache().collect() assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) assert(rows(1).getAs[java.sql.Date](1) === null) assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) } test("test DATE types in cache") { - val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").collect() - TestSQLContext - .jdbc(urlWithUserAndPass, "TEST.TIMETYPES").cache().registerTempTable("mycached_date") + val rows = ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect() + ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties) + .cache().registerTempTable("mycached_date") val cachedRows = sql("select * from mycached_date").collect() assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) } test("test types for null value") { - val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.NULLTYPES").collect() + val rows = ctx.read.jdbc( + urlWithUserAndPass, "TEST.NULLTYPES", new Properties).collect() assert((0 to 14).forall(i => rows(0).isNullAt(i))) } @@ -330,4 +393,55 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { """.stripMargin.replaceAll("\n", " ")) } } + + test("Remap types via JdbcDialects") { + JdbcDialects.registerDialect(testH2Dialect) + val df = ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", new Properties) + assert(df.schema.filter(_.dataType != org.apache.spark.sql.types.StringType).isEmpty) + val rows = df.collect() + assert(rows(0).get(0).isInstanceOf[String]) + assert(rows(0).get(1).isInstanceOf[String]) + JdbcDialects.unregisterDialect(testH2Dialect) + } + + test("Default jdbc dialect registration") { + assert(JdbcDialects.get("jdbc:mysql://127.0.0.1/db") == MySQLDialect) + assert(JdbcDialects.get("jdbc:postgresql://127.0.0.1/db") == PostgresDialect) + assert(JdbcDialects.get("test.invalid") == NoopDialect) + } + + test("quote column names by jdbc dialect") { + val MySQL = JdbcDialects.get("jdbc:mysql://127.0.0.1/db") + val Postgres = JdbcDialects.get("jdbc:postgresql://127.0.0.1/db") + + val columns = Seq("abc", "key") + val MySQLColumns = columns.map(MySQL.quoteIdentifier(_)) + val PostgresColumns = columns.map(Postgres.quoteIdentifier(_)) + assert(MySQLColumns === Seq("`abc`", "`key`")) + assert(PostgresColumns === Seq(""""abc"""", """"key"""")) + } + + test("Dialect unregister") { + JdbcDialects.registerDialect(testH2Dialect) + JdbcDialects.unregisterDialect(testH2Dialect) + assert(JdbcDialects.get(urlWithUserAndPass) == NoopDialect) + } + + test("Aggregated dialects") { + val agg = new AggregatedDialect(List(new JdbcDialect { + override def canHandle(url: String) : Boolean = url.startsWith("jdbc:h2:") + override def getCatalystType( + sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = + if (sqlType % 2 == 0) { + Some(LongType) + } else { + None + } + }, testH2Dialect)) + assert(agg.canHandle("jdbc:h2:xxx")) + assert(!agg.canHandle("jdbc:h2")) + assert(agg.getCatalystType(0, "", 1, null) === Some(LongType)) + assert(agg.getCatalystType(1, "", 1, null) === Some(StringType)) + } + } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala index ee5c7620d1a22..d949ef42267ec 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala @@ -18,90 +18,140 @@ package org.apache.spark.sql.jdbc import java.sql.DriverManager +import java.util.Properties -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter -import org.apache.spark.sql.Row -import org.apache.spark.sql.test._ +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.{SaveMode, Row} import org.apache.spark.sql.types._ -class JDBCWriteSuite extends FunSuite with BeforeAndAfter { +class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter { val url = "jdbc:h2:mem:testdb2" var conn: java.sql.Connection = null + val url1 = "jdbc:h2:mem:testdb3" + var conn1: java.sql.Connection = null + val properties = new Properties() + properties.setProperty("user", "testUser") + properties.setProperty("password", "testPass") + properties.setProperty("rowId", "false") + + private lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.implicits._ + import ctx.sql before { Class.forName("org.h2.Driver") conn = DriverManager.getConnection(url) conn.prepareStatement("create schema test").executeUpdate() + + conn1 = DriverManager.getConnection(url1, properties) + conn1.prepareStatement("create schema test").executeUpdate() + conn1.prepareStatement("drop table if exists test.people").executeUpdate() + conn1.prepareStatement( + "create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() + conn1.prepareStatement("insert into test.people values ('fred', 1)").executeUpdate() + conn1.prepareStatement("insert into test.people values ('mary', 2)").executeUpdate() + conn1.prepareStatement("drop table if exists test.people1").executeUpdate() + conn1.prepareStatement( + "create table test.people1 (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() + conn1.commit() + + ctx.sql( + s""" + |CREATE TEMPORARY TABLE PEOPLE + |USING org.apache.spark.sql.jdbc + |OPTIONS (url '$url1', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass') + """.stripMargin.replaceAll("\n", " ")) + + ctx.sql( + s""" + |CREATE TEMPORARY TABLE PEOPLE1 + |USING org.apache.spark.sql.jdbc + |OPTIONS (url '$url1', dbtable 'TEST.PEOPLE1', user 'testUser', password 'testPass') + """.stripMargin.replaceAll("\n", " ")) } after { conn.close() + conn1.close() } - val sc = TestSQLContext.sparkContext + private lazy val sc = ctx.sparkContext - val arr2x2 = Array[Row](Row.apply("dave", 42), Row.apply("mary", 222)) - val arr1x2 = Array[Row](Row.apply("fred", 3)) - val schema2 = StructType( + private lazy val arr2x2 = Array[Row](Row.apply("dave", 42), Row.apply("mary", 222)) + private lazy val arr1x2 = Array[Row](Row.apply("fred", 3)) + private lazy val schema2 = StructType( StructField("name", StringType) :: StructField("id", IntegerType) :: Nil) - val arr2x3 = Array[Row](Row.apply("dave", 42, 1), Row.apply("mary", 222, 2)) - val schema3 = StructType( + private lazy val arr2x3 = Array[Row](Row.apply("dave", 42, 1), Row.apply("mary", 222, 2)) + private lazy val schema3 = StructType( StructField("name", StringType) :: StructField("id", IntegerType) :: StructField("seq", IntegerType) :: Nil) test("Basic CREATE") { - val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) + val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2) - df.createJDBCTable(url, "TEST.BASICCREATETEST", false) - assert(2 == TestSQLContext.jdbc(url, "TEST.BASICCREATETEST").count) - assert(2 == TestSQLContext.jdbc(url, "TEST.BASICCREATETEST").collect()(0).length) + df.write.jdbc(url, "TEST.BASICCREATETEST", new Properties) + assert(2 === ctx.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).count) + assert(2 === ctx.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).collect()(0).length) } test("CREATE with overwrite") { - val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x3), schema3) - val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr1x2), schema2) + val df = ctx.createDataFrame(sc.parallelize(arr2x3), schema3) + val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2) - df.createJDBCTable(url, "TEST.DROPTEST", false) - assert(2 == TestSQLContext.jdbc(url, "TEST.DROPTEST").count) - assert(3 == TestSQLContext.jdbc(url, "TEST.DROPTEST").collect()(0).length) + df.write.jdbc(url1, "TEST.DROPTEST", properties) + assert(2 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).count) + assert(3 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length) - df2.createJDBCTable(url, "TEST.DROPTEST", true) - assert(1 == TestSQLContext.jdbc(url, "TEST.DROPTEST").count) - assert(2 == TestSQLContext.jdbc(url, "TEST.DROPTEST").collect()(0).length) + df2.write.mode(SaveMode.Overwrite).jdbc(url1, "TEST.DROPTEST", properties) + assert(1 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).count) + assert(2 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length) } test("CREATE then INSERT to append") { - val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) - val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr1x2), schema2) + val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2) + val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2) - df.createJDBCTable(url, "TEST.APPENDTEST", false) - df2.insertIntoJDBC(url, "TEST.APPENDTEST", false) - assert(3 == TestSQLContext.jdbc(url, "TEST.APPENDTEST").count) - assert(2 == TestSQLContext.jdbc(url, "TEST.APPENDTEST").collect()(0).length) + df.write.jdbc(url, "TEST.APPENDTEST", new Properties) + df2.write.mode(SaveMode.Append).jdbc(url, "TEST.APPENDTEST", new Properties) + assert(3 === ctx.read.jdbc(url, "TEST.APPENDTEST", new Properties).count) + assert(2 === ctx.read.jdbc(url, "TEST.APPENDTEST", new Properties).collect()(0).length) } test("CREATE then INSERT to truncate") { - val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) - val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr1x2), schema2) + val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2) + val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2) - df.createJDBCTable(url, "TEST.TRUNCATETEST", false) - df2.insertIntoJDBC(url, "TEST.TRUNCATETEST", true) - assert(1 == TestSQLContext.jdbc(url, "TEST.TRUNCATETEST").count) - assert(2 == TestSQLContext.jdbc(url, "TEST.TRUNCATETEST").collect()(0).length) + df.write.jdbc(url1, "TEST.TRUNCATETEST", properties) + df2.write.mode(SaveMode.Overwrite).jdbc(url1, "TEST.TRUNCATETEST", properties) + assert(1 === ctx.read.jdbc(url1, "TEST.TRUNCATETEST", properties).count) + assert(2 === ctx.read.jdbc(url1, "TEST.TRUNCATETEST", properties).collect()(0).length) } test("Incompatible INSERT to append") { - val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) - val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr2x3), schema3) + val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2) + val df2 = ctx.createDataFrame(sc.parallelize(arr2x3), schema3) - df.createJDBCTable(url, "TEST.INCOMPATIBLETEST", false) + df.write.jdbc(url, "TEST.INCOMPATIBLETEST", new Properties) intercept[org.apache.spark.SparkException] { - df2.insertIntoJDBC(url, "TEST.INCOMPATIBLETEST", true) + df2.write.mode(SaveMode.Append).jdbc(url, "TEST.INCOMPATIBLETEST", new Properties) } } + test("INSERT to JDBC Datasource") { + ctx.sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE") + assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count) + assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length) + } + + test("INSERT to JDBC Datasource with overwrite") { + ctx.sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE") + ctx.sql("INSERT OVERWRITE TABLE PEOPLE1 SELECT * FROM PEOPLE") + assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count) + assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala index 263fafba930ce..c32d9f88dd6ee 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala @@ -23,21 +23,19 @@ import java.sql.{Date, Timestamp} import com.fasterxml.jackson.core.JsonFactory import org.scalactic.Tolerance._ +import org.apache.spark.sql.{QueryTest, Row, SQLConf} import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.functions._ +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.json.InferSchema.compatibleType import org.apache.spark.sql.sources.LogicalRelation -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.{QueryTest, Row, SQLConf} import org.apache.spark.util.Utils -class JsonSuite extends QueryTest { - import org.apache.spark.sql.json.TestJsonData._ +class JsonSuite extends QueryTest with TestJsonData { - TestJsonData + protected lazy val ctx = org.apache.spark.sql.test.TestSQLContext + import ctx.sql + import ctx.implicits._ test("Type promotion") { def checkTypePromotion(expected: Any, actual: Any) { @@ -78,21 +76,25 @@ class JsonSuite extends QueryTest { checkTypePromotion( Decimal(doubleNumber), enforceCorrectType(doubleNumber, DecimalType.Unlimited)) - checkTypePromotion(new Timestamp(intNumber), enforceCorrectType(intNumber, TimestampType)) - checkTypePromotion(new Timestamp(intNumber.toLong), + checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(intNumber)), + enforceCorrectType(intNumber, TimestampType)) + checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(intNumber.toLong)), enforceCorrectType(intNumber.toLong, TimestampType)) val strTime = "2014-09-30 12:34:56" - checkTypePromotion(Timestamp.valueOf(strTime), enforceCorrectType(strTime, TimestampType)) + checkTypePromotion(DateUtils.fromJavaTimestamp(Timestamp.valueOf(strTime)), + enforceCorrectType(strTime, TimestampType)) val strDate = "2014-10-15" checkTypePromotion( DateUtils.fromJavaDate(Date.valueOf(strDate)), enforceCorrectType(strDate, DateType)) val ISO8601Time1 = "1970-01-01T01:00:01.0Z" - checkTypePromotion(new Timestamp(3601000), enforceCorrectType(ISO8601Time1, TimestampType)) + checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(3601000)), + enforceCorrectType(ISO8601Time1, TimestampType)) checkTypePromotion(DateUtils.millisToDays(3601000), enforceCorrectType(ISO8601Time1, DateType)) val ISO8601Time2 = "1970-01-01T02:00:01-01:00" - checkTypePromotion(new Timestamp(10801000), enforceCorrectType(ISO8601Time2, TimestampType)) + checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(10801000)), + enforceCorrectType(ISO8601Time2, TimestampType)) checkTypePromotion(DateUtils.millisToDays(10801000), enforceCorrectType(ISO8601Time2, DateType)) } @@ -214,7 +216,7 @@ class JsonSuite extends QueryTest { } test("Complex field and type inferring with null in sampling") { - val jsonDF = jsonRDD(jsonNullStruct) + val jsonDF = ctx.read.json(jsonNullStruct) val expectedSchema = StructType( StructField("headers", StructType( StructField("Charset", StringType, true) :: @@ -233,7 +235,7 @@ class JsonSuite extends QueryTest { } test("Primitive field and type inferring") { - val jsonDF = jsonRDD(primitiveFieldAndType) + val jsonDF = ctx.read.json(primitiveFieldAndType) val expectedSchema = StructType( StructField("bigInteger", DecimalType.Unlimited, true) :: @@ -261,7 +263,7 @@ class JsonSuite extends QueryTest { } test("Complex field and type inferring") { - val jsonDF = jsonRDD(complexFieldAndType1) + val jsonDF = ctx.read.json(complexFieldAndType1) val expectedSchema = StructType( StructField("arrayOfArray1", ArrayType(ArrayType(StringType, true), true), true) :: @@ -360,7 +362,7 @@ class JsonSuite extends QueryTest { } test("GetField operation on complex data type") { - val jsonDF = jsonRDD(complexFieldAndType1) + val jsonDF = ctx.read.json(complexFieldAndType1) jsonDF.registerTempTable("jsonTable") checkAnswer( @@ -376,7 +378,7 @@ class JsonSuite extends QueryTest { } test("Type conflict in primitive field values") { - val jsonDF = jsonRDD(primitiveFieldValueTypeConflict) + val jsonDF = ctx.read.json(primitiveFieldValueTypeConflict) val expectedSchema = StructType( StructField("num_bool", StringType, true) :: @@ -450,7 +452,7 @@ class JsonSuite extends QueryTest { } ignore("Type conflict in primitive field values (Ignored)") { - val jsonDF = jsonRDD(primitiveFieldValueTypeConflict) + val jsonDF = ctx.read.json(primitiveFieldValueTypeConflict) jsonDF.registerTempTable("jsonTable") // Right now, the analyzer does not promote strings in a boolean expression. @@ -503,7 +505,7 @@ class JsonSuite extends QueryTest { } test("Type conflict in complex field values") { - val jsonDF = jsonRDD(complexFieldValueTypeConflict) + val jsonDF = ctx.read.json(complexFieldValueTypeConflict) val expectedSchema = StructType( StructField("array", ArrayType(LongType, true), true) :: @@ -522,12 +524,12 @@ class JsonSuite extends QueryTest { Row(Seq(), "11", "[1,2,3]", Row(null), "[]") :: Row(null, """{"field":false}""", null, null, "{}") :: Row(Seq(4, 5, 6), null, "str", Row(null), "[7,8,9]") :: - Row(Seq(7), "{}","""["str1","str2",33]""", Row("str"), """{"field":true}""") :: Nil + Row(Seq(7), "{}", """["str1","str2",33]""", Row("str"), """{"field":true}""") :: Nil ) } test("Type conflict in array elements") { - val jsonDF = jsonRDD(arrayElementTypeConflict) + val jsonDF = ctx.read.json(arrayElementTypeConflict) val expectedSchema = StructType( StructField("array1", ArrayType(StringType, true), true) :: @@ -555,7 +557,7 @@ class JsonSuite extends QueryTest { } test("Handling missing fields") { - val jsonDF = jsonRDD(missingFields) + val jsonDF = ctx.read.json(missingFields) val expectedSchema = StructType( StructField("a", BooleanType, true) :: @@ -574,8 +576,9 @@ class JsonSuite extends QueryTest { val dir = Utils.createTempDir() dir.delete() val path = dir.getCanonicalPath - sparkContext.parallelize(1 to 100).map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path) - val jsonDF = jsonFile(path, 0.49) + ctx.sparkContext.parallelize(1 to 100) + .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path) + val jsonDF = ctx.read.option("samplingRatio", "0.49").json(path) val analyzed = jsonDF.queryExecution.analyzed assert( @@ -590,7 +593,7 @@ class JsonSuite extends QueryTest { val schema = StructType(StructField("a", LongType, true) :: Nil) val logicalRelation = - jsonFile(path, schema).queryExecution.analyzed.asInstanceOf[LogicalRelation] + ctx.read.schema(schema).json(path).queryExecution.analyzed.asInstanceOf[LogicalRelation] val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation] assert(relationWithSchema.path === Some(path)) assert(relationWithSchema.schema === schema) @@ -602,7 +605,7 @@ class JsonSuite extends QueryTest { dir.delete() val path = dir.getCanonicalPath primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path) - val jsonDF = jsonFile(path) + val jsonDF = ctx.read.json(path) val expectedSchema = StructType( StructField("bigInteger", DecimalType.Unlimited, true) :: @@ -671,7 +674,7 @@ class JsonSuite extends QueryTest { StructField("null", StringType, true) :: StructField("string", StringType, true) :: Nil) - val jsonDF1 = jsonFile(path, schema) + val jsonDF1 = ctx.read.schema(schema).json(path) assert(schema === jsonDF1.schema) @@ -688,7 +691,7 @@ class JsonSuite extends QueryTest { "this is a simple string.") ) - val jsonDF2 = jsonRDD(primitiveFieldAndType, schema) + val jsonDF2 = ctx.read.schema(schema).json(primitiveFieldAndType) assert(schema === jsonDF2.schema) @@ -709,7 +712,7 @@ class JsonSuite extends QueryTest { test("Applying schemas with MapType") { val schemaWithSimpleMap = StructType( StructField("map", MapType(StringType, IntegerType, true), false) :: Nil) - val jsonWithSimpleMap = jsonRDD(mapType1, schemaWithSimpleMap) + val jsonWithSimpleMap = ctx.read.schema(schemaWithSimpleMap).json(mapType1) jsonWithSimpleMap.registerTempTable("jsonWithSimpleMap") @@ -737,7 +740,7 @@ class JsonSuite extends QueryTest { val schemaWithComplexMap = StructType( StructField("map", MapType(StringType, innerStruct, true), false) :: Nil) - val jsonWithComplexMap = jsonRDD(mapType2, schemaWithComplexMap) + val jsonWithComplexMap = ctx.read.schema(schemaWithComplexMap).json(mapType2) jsonWithComplexMap.registerTempTable("jsonWithComplexMap") @@ -763,7 +766,7 @@ class JsonSuite extends QueryTest { } test("SPARK-2096 Correctly parse dot notations") { - val jsonDF = jsonRDD(complexFieldAndType2) + val jsonDF = ctx.read.json(complexFieldAndType2) jsonDF.registerTempTable("jsonTable") checkAnswer( @@ -781,7 +784,7 @@ class JsonSuite extends QueryTest { } test("SPARK-3390 Complex arrays") { - val jsonDF = jsonRDD(complexFieldAndType2) + val jsonDF = ctx.read.json(complexFieldAndType2) jsonDF.registerTempTable("jsonTable") checkAnswer( @@ -804,7 +807,7 @@ class JsonSuite extends QueryTest { } test("SPARK-3308 Read top level JSON arrays") { - val jsonDF = jsonRDD(jsonArray) + val jsonDF = ctx.read.json(jsonArray) jsonDF.registerTempTable("jsonTable") checkAnswer( @@ -822,10 +825,10 @@ class JsonSuite extends QueryTest { test("Corrupt records") { // Test if we can query corrupt records. - val oldColumnNameOfCorruptRecord = TestSQLContext.conf.columnNameOfCorruptRecord - TestSQLContext.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed") + val oldColumnNameOfCorruptRecord = ctx.conf.columnNameOfCorruptRecord + ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed") - val jsonDF = jsonRDD(corruptRecords) + val jsonDF = ctx.read.json(corruptRecords) jsonDF.registerTempTable("jsonTable") val schema = StructType( @@ -875,11 +878,11 @@ class JsonSuite extends QueryTest { Row("]") :: Nil ) - TestSQLContext.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord) + ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord) } test("SPARK-4068: nulls in arrays") { - val jsonDF = jsonRDD(nullsInArrays) + val jsonDF = ctx.read.json(nullsInArrays) jsonDF.registerTempTable("jsonTable") val schema = StructType( @@ -925,7 +928,7 @@ class JsonSuite extends QueryTest { Row(values(0).toInt, values(1), values(2).toBoolean, r.split(",").toList, v5) } - val df1 = createDataFrame(rowRDD1, schema1) + val df1 = ctx.createDataFrame(rowRDD1, schema1) df1.registerTempTable("applySchema1") val df2 = df1.toDF val result = df2.toJSON.collect() @@ -948,7 +951,7 @@ class JsonSuite extends QueryTest { Row(Row(values(0).toInt, values(2).toBoolean), Map(values(1) -> v4)) } - val df3 = createDataFrame(rowRDD2, schema2) + val df3 = ctx.createDataFrame(rowRDD2, schema2) df3.registerTempTable("applySchema2") val df4 = df3.toDF val result2 = df4.toJSON.collect() @@ -956,8 +959,8 @@ class JsonSuite extends QueryTest { assert(result2(1) === "{\"f1\":{\"f11\":2,\"f12\":false},\"f2\":{\"B2\":null}}") assert(result2(3) === "{\"f1\":{\"f11\":4,\"f12\":true},\"f2\":{\"D4\":2147483644}}") - val jsonDF = jsonRDD(primitiveFieldAndType) - val primTable = jsonRDD(jsonDF.toJSON) + val jsonDF = ctx.read.json(primitiveFieldAndType) + val primTable = ctx.read.json(jsonDF.toJSON) primTable.registerTempTable("primativeTable") checkAnswer( sql("select * from primativeTable"), @@ -969,8 +972,8 @@ class JsonSuite extends QueryTest { "this is a simple string.") ) - val complexJsonDF = jsonRDD(complexFieldAndType1) - val compTable = jsonRDD(complexJsonDF.toJSON) + val complexJsonDF = ctx.read.json(complexFieldAndType1) + val compTable = ctx.read.json(complexJsonDF.toJSON) compTable.registerTempTable("complexTable") // Access elements of a primitive array. checkAnswer( @@ -1073,4 +1076,35 @@ class JsonSuite extends QueryTest { assert(StructType(Seq()) === emptySchema) } + test("SPARK-7565 MapType in JsonRDD") { + val useStreaming = ctx.conf.useJacksonStreamingAPI + val oldColumnNameOfCorruptRecord = ctx.conf.columnNameOfCorruptRecord + ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed") + + val schemaWithSimpleMap = StructType( + StructField("map", MapType(StringType, IntegerType, true), false) :: Nil) + try{ + for (useStreaming <- List(true, false)) { + ctx.setConf(SQLConf.USE_JACKSON_STREAMING_API, useStreaming) + val temp = Utils.createTempDir().getPath + + val df = ctx.read.schema(schemaWithSimpleMap).json(mapType1) + df.write.mode("overwrite").parquet(temp) + // order of MapType is not defined + assert(ctx.read.parquet(temp).count() == 5) + + val df2 = ctx.read.json(corruptRecords) + df2.write.mode("overwrite").parquet(temp) + checkAnswer(ctx.read.parquet(temp), df2.collect()) + } + } finally { + ctx.setConf(SQLConf.USE_JACKSON_STREAMING_API, useStreaming) + ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord) + } + } + + test("SPARK-8093 Erase empty structs") { + val emptySchema = InferSchema(emptyRecords, 1.0, "") + assert(StructType(Seq()) === emptySchema) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala index 47a97a49daabb..eb62066ac6430 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala @@ -17,12 +17,15 @@ package org.apache.spark.sql.json -import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLContext -object TestJsonData { +trait TestJsonData { - val primitiveFieldAndType = - TestSQLContext.sparkContext.parallelize( + protected def ctx: SQLContext + + def primitiveFieldAndType: RDD[String] = + ctx.sparkContext.parallelize( """{"string":"this is a simple string.", "integer":10, "long":21474836470, @@ -32,8 +35,8 @@ object TestJsonData { "null":null }""" :: Nil) - val primitiveFieldValueTypeConflict = - TestSQLContext.sparkContext.parallelize( + def primitiveFieldValueTypeConflict: RDD[String] = + ctx.sparkContext.parallelize( """{"num_num_1":11, "num_num_2":null, "num_num_3": 1.1, "num_bool":true, "num_str":13.1, "str_bool":"str1"}""" :: """{"num_num_1":null, "num_num_2":21474836470.9, "num_num_3": null, @@ -43,15 +46,15 @@ object TestJsonData { """{"num_num_1":21474836570, "num_num_2":1.1, "num_num_3": 21474836470, "num_bool":null, "num_str":92233720368547758070, "str_bool":null}""" :: Nil) - val jsonNullStruct = - TestSQLContext.sparkContext.parallelize( + def jsonNullStruct: RDD[String] = + ctx.sparkContext.parallelize( """{"nullstr":"","ip":"27.31.100.29","headers":{"Host":"1.abc.com","Charset":"UTF-8"}}""" :: """{"nullstr":"","ip":"27.31.100.29","headers":{}}""" :: """{"nullstr":"","ip":"27.31.100.29","headers":""}""" :: """{"nullstr":null,"ip":"27.31.100.29","headers":null}""" :: Nil) - val complexFieldValueTypeConflict = - TestSQLContext.sparkContext.parallelize( + def complexFieldValueTypeConflict: RDD[String] = + ctx.sparkContext.parallelize( """{"num_struct":11, "str_array":[1, 2, 3], "array":[], "struct_array":[], "struct": {}}""" :: """{"num_struct":{"field":false}, "str_array":null, @@ -61,23 +64,23 @@ object TestJsonData { """{"num_struct":{}, "str_array":["str1", "str2", 33], "array":[7], "struct_array":{"field": true}, "struct": {"field": "str"}}""" :: Nil) - val arrayElementTypeConflict = - TestSQLContext.sparkContext.parallelize( + def arrayElementTypeConflict: RDD[String] = + ctx.sparkContext.parallelize( """{"array1": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}], "array2": [{"field":214748364700}, {"field":1}]}""" :: """{"array3": [{"field":"str"}, {"field":1}]}""" :: """{"array3": [1, 2, 3]}""" :: Nil) - val missingFields = - TestSQLContext.sparkContext.parallelize( + def missingFields: RDD[String] = + ctx.sparkContext.parallelize( """{"a":true}""" :: """{"b":21474836470}""" :: """{"c":[33, 44]}""" :: """{"d":{"field":true}}""" :: """{"e":"str"}""" :: Nil) - val complexFieldAndType1 = - TestSQLContext.sparkContext.parallelize( + def complexFieldAndType1: RDD[String] = + ctx.sparkContext.parallelize( """{"struct":{"field1": true, "field2": 92233720368547758070}, "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]}, "arrayOfString":["str1", "str2"], @@ -92,8 +95,8 @@ object TestJsonData { "arrayOfArray2":[[1, 2, 3], [1.1, 2.1, 3.1]] }""" :: Nil) - val complexFieldAndType2 = - TestSQLContext.sparkContext.parallelize( + def complexFieldAndType2: RDD[String] = + ctx.sparkContext.parallelize( """{"arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}], "complexArrayOfStruct": [ { @@ -146,16 +149,16 @@ object TestJsonData { ]] }""" :: Nil) - val mapType1 = - TestSQLContext.sparkContext.parallelize( + def mapType1: RDD[String] = + ctx.sparkContext.parallelize( """{"map": {"a": 1}}""" :: """{"map": {"b": 2}}""" :: """{"map": {"c": 3}}""" :: """{"map": {"c": 1, "d": 4}}""" :: """{"map": {"e": null}}""" :: Nil) - val mapType2 = - TestSQLContext.sparkContext.parallelize( + def mapType2: RDD[String] = + ctx.sparkContext.parallelize( """{"map": {"a": {"field1": [1, 2, 3, null]}}}""" :: """{"map": {"b": {"field2": 2}}}""" :: """{"map": {"c": {"field1": [], "field2": 4}}}""" :: @@ -163,22 +166,22 @@ object TestJsonData { """{"map": {"e": null}}""" :: """{"map": {"f": {"field1": null}}}""" :: Nil) - val nullsInArrays = - TestSQLContext.sparkContext.parallelize( + def nullsInArrays: RDD[String] = + ctx.sparkContext.parallelize( """{"field1":[[null], [[["Test"]]]]}""" :: """{"field2":[null, [{"Test":1}]]}""" :: """{"field3":[[null], [{"Test":"2"}]]}""" :: """{"field4":[[null, [1,2,3]]]}""" :: Nil) - val jsonArray = - TestSQLContext.sparkContext.parallelize( + def jsonArray: RDD[String] = + ctx.sparkContext.parallelize( """[{"a":"str_a_1"}]""" :: """[{"a":"str_a_2"}, {"b":"str_b_3"}]""" :: """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" :: """[]""" :: Nil) - val corruptRecords = - TestSQLContext.sparkContext.parallelize( + def corruptRecords: RDD[String] = + ctx.sparkContext.parallelize( """{""" :: """""" :: """{"a":1, b:2}""" :: @@ -186,6 +189,14 @@ object TestJsonData { """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" :: """]""" :: Nil) - val empty = - TestSQLContext.sparkContext.parallelize(Seq[String]()) + def emptyRecords: RDD[String] = + ctx.sparkContext.parallelize( + """{""" :: + """""" :: + """{"a": {}}""" :: + """{"a": {"b": {}}}""" :: + """{"b": [{"c": {}}]}""" :: + """]""" :: Nil) + + def empty: RDD[String] = ctx.sparkContext.parallelize(Seq[String]()) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala index 10d0ede4dc0dc..a2763c78b6450 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala @@ -18,16 +18,15 @@ package org.apache.spark.sql.parquet import org.scalatest.BeforeAndAfterAll -import parquet.filter2.predicate.Operators._ -import parquet.filter2.predicate.{FilterPredicate, Operators} +import org.apache.parquet.filter2.predicate.Operators._ +import org.apache.parquet.filter2.predicate.{FilterPredicate, Operators} import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.sources.LogicalRelation -import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.types._ -import org.apache.spark.sql.{Column, DataFrame, QueryTest, SQLConf} +import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf} /** * A test suite that tests Parquet filter2 API based filter pushdown optimization. @@ -42,7 +41,7 @@ import org.apache.spark.sql.{Column, DataFrame, QueryTest, SQLConf} * data type is nullable. */ class ParquetFilterSuiteBase extends QueryTest with ParquetTest { - val sqlContext = TestSQLContext + lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext private def checkFilterPredicate( df: DataFrame, @@ -52,7 +51,7 @@ class ParquetFilterSuiteBase extends QueryTest with ParquetTest { expected: Seq[Row]): Unit = { val output = predicate.collect { case a: Attribute => a }.distinct - withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") { val query = df .select(output.map(e => Column(e)): _*) .where(Column(predicate)) @@ -312,28 +311,28 @@ class ParquetFilterSuiteBase extends QueryTest with ParquetTest { } class ParquetDataSourceOnFilterSuite extends ParquetFilterSuiteBase with BeforeAndAfterAll { - val originalConf = sqlContext.conf.parquetUseDataSourceApi + lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi override protected def beforeAll(): Unit = { - sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true) } override protected def afterAll(): Unit = { - sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } test("SPARK-6554: don't push down predicates which reference partition columns") { import sqlContext.implicits._ - withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") { withTempPath { dir => val path = s"${dir.getCanonicalPath}/part=1" - (1 to 3).map(i => (i, i.toString)).toDF("a", "b").saveAsParquetFile(path) + (1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(path) // If the "part = 1" filter gets pushed down, this query will throw an exception since // "part" is not a valid column in the actual Parquet file checkAnswer( - sqlContext.parquetFile(path).filter("part = 1"), + sqlContext.read.parquet(path).filter("part = 1"), (1 to 3).map(i => Row(i, i.toString, 1))) } } @@ -341,23 +340,23 @@ class ParquetDataSourceOnFilterSuite extends ParquetFilterSuiteBase with BeforeA } class ParquetDataSourceOffFilterSuite extends ParquetFilterSuiteBase with BeforeAndAfterAll { - val originalConf = sqlContext.conf.parquetUseDataSourceApi + lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi override protected def beforeAll(): Unit = { - sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false) } override protected def afterAll(): Unit = { - sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } - + test("SPARK-6742: don't push down predicates which reference partition columns") { import sqlContext.implicits._ - withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") { withTempPath { dir => val path = s"${dir.getCanonicalPath}/part=1" - (1 to 3).map(i => (i, i.toString)).toDF("a", "b").saveAsParquetFile(path) + (1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(path) // If the "part = 1" filter gets pushed down, this query will throw an exception since // "part" is not a valid column in the actual Parquet file @@ -365,7 +364,7 @@ class ParquetDataSourceOffFilterSuite extends ParquetFilterSuiteBase with Before path, Some(sqlContext.sparkContext.hadoopConfiguration), sqlContext, Seq(AttributeReference("part", IntegerType, false)()) )) - + checkAnswer( df.filter("a = 1 or part = 1"), (1 to 3).map(i => Row(1, i, i.toString))) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index b504842053690..284d99d4938d1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -23,23 +23,22 @@ import scala.reflect.runtime.universe.TypeTag import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} +import org.apache.parquet.example.data.simple.SimpleGroup +import org.apache.parquet.example.data.{Group, GroupWriter} +import org.apache.parquet.hadoop.api.WriteSupport +import org.apache.parquet.hadoop.api.WriteSupport.WriteContext +import org.apache.parquet.hadoop.metadata.{CompressionCodecName, FileMetaData, ParquetMetadata} +import org.apache.parquet.hadoop.{Footer, ParquetFileWriter, ParquetOutputCommitter, ParquetWriter} +import org.apache.parquet.io.api.RecordConsumer +import org.apache.parquet.schema.{MessageType, MessageTypeParser} import org.scalatest.BeforeAndAfterAll -import parquet.example.data.simple.SimpleGroup -import parquet.example.data.{Group, GroupWriter} -import parquet.hadoop.api.WriteSupport -import parquet.hadoop.api.WriteSupport.WriteContext -import parquet.hadoop.metadata.{ParquetMetadata, FileMetaData, CompressionCodecName} -import parquet.hadoop.{Footer, ParquetFileWriter, ParquetWriter} -import parquet.io.api.RecordConsumer -import parquet.schema.{MessageType, MessageTypeParser} +import org.apache.spark.SparkException +import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.catalyst.expressions.Row -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types._ -import org.apache.spark.sql.{DataFrame, QueryTest, SQLConf, SaveMode} // Write support class for nested groups: ParquetWriter initializes GroupWriteSupport // with an empty configuration (it is after all not intended to be used in this way?) @@ -65,9 +64,8 @@ private[parquet] class TestGroupWriteSupport(schema: MessageType) extends WriteS * A test suite that tests basic Parquet I/O. */ class ParquetIOSuiteBase extends QueryTest with ParquetTest { - val sqlContext = TestSQLContext - - import sqlContext.implicits.localSeqToDataFrameHolder + lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext + import sqlContext.implicits._ /** * Writes `data` to a Parquet file, reads it back and check file contents. @@ -96,14 +94,14 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { val data = (1 to 4).map(i => Tuple1(i.toString)) // Property spark.sql.parquet.binaryAsString shouldn't affect Parquet files written by Spark SQL // as we store Spark SQL schema in the extra metadata. - withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING -> "false")(checkParquetFile(data)) - withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING -> "true")(checkParquetFile(data)) + withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "false")(checkParquetFile(data)) + withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true")(checkParquetFile(data)) } test("fixed-length decimals") { def makeDecimalRDD(decimal: DecimalType): DataFrame = - sparkContext + sqlContext.sparkContext .parallelize(0 to 1000) .map(i => Tuple1(i / 100.0)) .toDF() @@ -113,31 +111,31 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { for ((precision, scale) <- Seq((5, 2), (1, 0), (1, 1), (18, 10), (18, 17))) { withTempPath { dir => val data = makeDecimalRDD(DecimalType(precision, scale)) - data.saveAsParquetFile(dir.getCanonicalPath) - checkAnswer(parquetFile(dir.getCanonicalPath), data.collect().toSeq) + data.write.parquet(dir.getCanonicalPath) + checkAnswer(sqlContext.read.parquet(dir.getCanonicalPath), data.collect().toSeq) } } // Decimals with precision above 18 are not yet supported - intercept[RuntimeException] { + intercept[Throwable] { withTempPath { dir => - makeDecimalRDD(DecimalType(19, 10)).saveAsParquetFile(dir.getCanonicalPath) - parquetFile(dir.getCanonicalPath).collect() + makeDecimalRDD(DecimalType(19, 10)).write.parquet(dir.getCanonicalPath) + sqlContext.read.parquet(dir.getCanonicalPath).collect() } } // Unlimited-length decimals are not yet supported - intercept[RuntimeException] { + intercept[Throwable] { withTempPath { dir => - makeDecimalRDD(DecimalType.Unlimited).saveAsParquetFile(dir.getCanonicalPath) - parquetFile(dir.getCanonicalPath).collect() + makeDecimalRDD(DecimalType.Unlimited).write.parquet(dir.getCanonicalPath) + sqlContext.read.parquet(dir.getCanonicalPath).collect() } } } test("date type") { def makeDateRDD(): DataFrame = - sparkContext + sqlContext.sparkContext .parallelize(0 to 1000) .map(i => Tuple1(DateUtils.toJavaDate(i))) .toDF() @@ -145,8 +143,8 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { withTempPath { dir => val data = makeDateRDD() - data.saveAsParquetFile(dir.getCanonicalPath) - checkAnswer(parquetFile(dir.getCanonicalPath), data.collect().toSeq) + data.write.parquet(dir.getCanonicalPath) + checkAnswer(sqlContext.read.parquet(dir.getCanonicalPath), data.collect().toSeq) } } @@ -199,7 +197,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { withParquetDataFrame(allNulls :: Nil) { df => val rows = df.collect() - assert(rows.size === 1) + assert(rows.length === 1) assert(rows.head === Row(Seq.fill(5)(null): _*)) } } @@ -212,7 +210,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { withParquetDataFrame(allNones :: Nil) { df => val rows = df.collect() - assert(rows.size === 1) + assert(rows.length === 1) assert(rows.head === Row(Seq.fill(3)(null): _*)) } } @@ -233,9 +231,9 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { val data = (0 until 10).map(i => (i, i.toString)) def checkCompressionCodec(codec: CompressionCodecName): Unit = { - withSQLConf(SQLConf.PARQUET_COMPRESSION -> codec.name()) { + withSQLConf(SQLConf.PARQUET_COMPRESSION.key -> codec.name()) { withParquetFile(data) { path => - assertResult(conf.parquetCompressionCodec.toUpperCase) { + assertResult(sqlContext.conf.parquetCompressionCodec.toUpperCase) { compressionCodecFor(path) } } @@ -243,7 +241,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { } // Checks default compression codec - checkCompressionCodec(CompressionCodecName.fromConf(conf.parquetCompressionCodec)) + checkCompressionCodec(CompressionCodecName.fromConf(sqlContext.conf.parquetCompressionCodec)) checkCompressionCodec(CompressionCodecName.UNCOMPRESSED) checkCompressionCodec(CompressionCodecName.GZIP) @@ -282,7 +280,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { withTempDir { dir => val path = new Path(dir.toURI.toString, "part-r-0.parquet") makeRawParquetFile(path) - checkAnswer(parquetFile(path.toString), (0 until 10).map { i => + checkAnswer(sqlContext.read.parquet(path.toString), (0 until 10).map { i => Row(i % 2 == 0, i, i.toLong, i.toFloat, i.toDouble) }) } @@ -310,8 +308,8 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { test("save - overwrite") { withParquetFile((1 to 10).map(i => (i, i.toString))) { file => val newData = (11 to 20).map(i => (i, i.toString)) - newData.toDF().save("org.apache.spark.sql.parquet", SaveMode.Overwrite, Map("path" -> file)) - checkAnswer(parquetFile(file), newData.map(Row.fromTuple)) + newData.toDF().write.format("parquet").mode(SaveMode.Overwrite).save(file) + checkAnswer(sqlContext.read.parquet(file), newData.map(Row.fromTuple)) } } @@ -319,8 +317,8 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { val data = (1 to 10).map(i => (i, i.toString)) withParquetFile(data) { file => val newData = (11 to 20).map(i => (i, i.toString)) - newData.toDF().save("org.apache.spark.sql.parquet", SaveMode.Ignore, Map("path" -> file)) - checkAnswer(parquetFile(file), data.map(Row.fromTuple)) + newData.toDF().write.format("parquet").mode(SaveMode.Ignore).save(file) + checkAnswer(sqlContext.read.parquet(file), data.map(Row.fromTuple)) } } @@ -329,8 +327,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { withParquetFile(data) { file => val newData = (11 to 20).map(i => (i, i.toString)) val errorMessage = intercept[Throwable] { - newData.toDF().save( - "org.apache.spark.sql.parquet", SaveMode.ErrorIfExists, Map("path" -> file)) + newData.toDF().write.format("parquet").mode(SaveMode.ErrorIfExists).save(file) }.getMessage assert(errorMessage.contains("already exists")) } @@ -340,8 +337,8 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { val data = (1 to 10).map(i => (i, i.toString)) withParquetFile(data) { file => val newData = (11 to 20).map(i => (i, i.toString)) - newData.toDF().save("org.apache.spark.sql.parquet", SaveMode.Append, Map("path" -> file)) - checkAnswer(parquetFile(file), (data ++ newData).map(Row.fromTuple)) + newData.toDF().write.format("parquet").mode(SaveMode.Append).save(file) + checkAnswer(sqlContext.read.parquet(file), (data ++ newData).map(Row.fromTuple)) } } @@ -369,11 +366,11 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { val path = new Path(location.getCanonicalPath) ParquetFileWriter.writeMetadataFile( - sparkContext.hadoopConfiguration, + sqlContext.sparkContext.hadoopConfiguration, path, new Footer(path, new ParquetMetadata(fileMetadata, Nil)) :: Nil) - assertResult(parquetFile(path.toString).schema) { + assertResult(sqlContext.read.parquet(path.toString).schema) { StructType( StructField("a", BooleanType, nullable = false) :: StructField("b", IntegerType, nullable = false) :: @@ -383,6 +380,8 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { } test("SPARK-6352 DirectParquetOutputCommitter") { + val clonedConf = new Configuration(configuration) + // Write to a parquet file and let it fail. // _temporary should be missing if direct output committer works. try { @@ -391,52 +390,84 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { sqlContext.udf.register("div0", (x: Int) => x / 0) withTempPath { dir => intercept[org.apache.spark.SparkException] { - sqlContext.sql("select div0(1)").saveAsParquetFile(dir.getCanonicalPath) + sqlContext.sql("select div0(1)").write.parquet(dir.getCanonicalPath) } val path = new Path(dir.getCanonicalPath, "_temporary") val fs = path.getFileSystem(configuration) assert(!fs.exists(path)) } + } finally { + // Hadoop 1 doesn't have `Configuration.unset` + configuration.clear() + clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue)) } - finally { - configuration.set("spark.sql.parquet.output.committer.class", - "parquet.hadoop.ParquetOutputCommitter") + } + + test("SPARK-8121: spark.sql.parquet.output.committer.class shouldn't be overriden") { + withTempPath { dir => + val clonedConf = new Configuration(configuration) + + configuration.set( + SQLConf.OUTPUT_COMMITTER_CLASS.key, classOf[ParquetOutputCommitter].getCanonicalName) + + configuration.set( + "spark.sql.parquet.output.committer.class", + classOf[BogusParquetOutputCommitter].getCanonicalName) + + try { + val message = intercept[SparkException] { + sqlContext.range(0, 1).write.parquet(dir.getCanonicalPath) + }.getCause.getMessage + assert(message === "Intentional exception for testing purposes") + } finally { + // Hadoop 1 doesn't have `Configuration.unset` + configuration.clear() + clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue)) + } } } } +class BogusParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext) + extends ParquetOutputCommitter(outputPath, context) { + + override def commitJob(jobContext: JobContext): Unit = { + sys.error("Intentional exception for testing purposes") + } +} + class ParquetDataSourceOnIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll { - val originalConf = sqlContext.conf.parquetUseDataSourceApi + private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi override protected def beforeAll(): Unit = { - sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true) } override protected def afterAll(): Unit = { - sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API.key, originalConf.toString) } test("SPARK-6330 regression test") { // In 1.3.0, save to fs other than file: without configuring core-site.xml would get: // IllegalArgumentException: Wrong FS: hdfs://..., expected: file:/// - intercept[java.io.FileNotFoundException] { - sqlContext.parquetFile("file:///nonexistent") + intercept[Throwable] { + sqlContext.read.parquet("file:///nonexistent") } val errorMessage = intercept[Throwable] { - sqlContext.parquetFile("hdfs://nonexistent") + sqlContext.read.parquet("hdfs://nonexistent") }.toString assert(errorMessage.contains("UnknownHostException")) } } class ParquetDataSourceOffIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll { - val originalConf = sqlContext.conf.parquetUseDataSourceApi + private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi override protected def beforeAll(): Unit = { - sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false) } override protected def afterAll(): Unit = { - sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala index b7561ce7298cb..01df189d1f3be 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala @@ -14,17 +14,25 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + package org.apache.spark.sql.parquet +import java.io.File +import java.math.BigInteger +import java.sql.Timestamp + import scala.collection.mutable.ArrayBuffer +import com.google.common.io.Files import org.apache.hadoop.fs.Path +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.Literal -import org.apache.spark.sql.parquet.ParquetRelation2._ -import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.sources.PartitioningUtils._ +import org.apache.spark.sql.sources.{LogicalRelation, Partition, PartitionSpec} import org.apache.spark.sql.types._ -import org.apache.spark.sql.{QueryTest, Row, SQLContext} +import org.apache.spark.sql._ +import org.apache.spark.unsafe.types.UTF8String // The data where the partitioning key exists only in the directory structure. case class ParquetData(intField: Int, stringField: String) @@ -33,64 +41,65 @@ case class ParquetData(intField: Int, stringField: String) case class ParquetDataWithKey(intField: Int, pi: Int, stringField: String, ps: String) class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { - override val sqlContext: SQLContext = TestSQLContext - import sqlContext._ + override lazy val sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext import sqlContext.implicits._ + import sqlContext.sql - val defaultPartitionName = "__NULL__" + val defaultPartitionName = "__HIVE_DEFAULT_PARTITION__" test("column type inference") { def check(raw: String, literal: Literal): Unit = { - assert(inferPartitionColumnValue(raw, defaultPartitionName) === literal) + assert(inferPartitionColumnValue(raw, defaultPartitionName, true) === literal) } check("10", Literal.create(10, IntegerType)) check("1000000000000000", Literal.create(1000000000000000L, LongType)) - check("1.5", Literal.create(1.5, FloatType)) + check("1.5", Literal.create(1.5, DoubleType)) check("hello", Literal.create("hello", StringType)) check(defaultPartitionName, Literal.create(null, NullType)) } test("parse partition") { - def check(path: String, expected: PartitionValues): Unit = { - assert(expected === parsePartition(new Path(path), defaultPartitionName)) + def check(path: String, expected: Option[PartitionValues]): Unit = { + assert(expected === parsePartition(new Path(path), defaultPartitionName, true)) } def checkThrows[T <: Throwable: Manifest](path: String, expected: String): Unit = { val message = intercept[T] { - parsePartition(new Path(path), defaultPartitionName) + parsePartition(new Path(path), defaultPartitionName, true).get }.getMessage assert(message.contains(expected)) } - check( - "file:///", - PartitionValues( - ArrayBuffer.empty[String], - ArrayBuffer.empty[Literal])) - - check( - "file://path/a=10", + check("file://path/a=10", Some { PartitionValues( ArrayBuffer("a"), - ArrayBuffer(Literal.create(10, IntegerType)))) + ArrayBuffer(Literal.create(10, IntegerType))) + }) - check( - "file://path/a=10/b=hello/c=1.5", + check("file://path/a=10/b=hello/c=1.5", Some { PartitionValues( ArrayBuffer("a", "b", "c"), ArrayBuffer( Literal.create(10, IntegerType), Literal.create("hello", StringType), - Literal.create(1.5, FloatType)))) + Literal.create(1.5, DoubleType))) + }) - check( - "file://path/a=10/b_hello/c=1.5", + check("file://path/a=10/b_hello/c=1.5", Some { PartitionValues( ArrayBuffer("c"), - ArrayBuffer(Literal.create(1.5, FloatType)))) + ArrayBuffer(Literal.create(1.5, DoubleType))) + }) + + check("file:///", None) + check("file:///path/_temporary", None) + check("file:///path/_temporary/c=1.5", None) + check("file:///path/_temporary/path", None) + check("file://path/a=10/_temporary/c=1.5", None) + check("file://path/a=10/c=1.5/_temporary", None) checkThrows[AssertionError]("file://path/=10", "Empty partition column name") checkThrows[AssertionError]("file://path/a=", "Empty partition column value") @@ -98,7 +107,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { test("parse partitions") { def check(paths: Seq[String], spec: PartitionSpec): Unit = { - assert(parsePartitions(paths.map(new Path(_)), defaultPartitionName) === spec) + assert(parsePartitions(paths.map(new Path(_)), defaultPartitionName, true) === spec) } check(Seq( @@ -107,18 +116,42 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { StructType(Seq( StructField("a", IntegerType), StructField("b", StringType))), - Seq(Partition(Row(10, "hello"), "hdfs://host:9000/path/a=10/b=hello")))) + Seq(Partition(InternalRow(10, UTF8String.fromString("hello")), + "hdfs://host:9000/path/a=10/b=hello")))) check(Seq( "hdfs://host:9000/path/a=10/b=20", "hdfs://host:9000/path/a=10.5/b=hello"), PartitionSpec( StructType(Seq( - StructField("a", FloatType), + StructField("a", DoubleType), + StructField("b", StringType))), + Seq( + Partition(InternalRow(10, UTF8String.fromString("20")), + "hdfs://host:9000/path/a=10/b=20"), + Partition(InternalRow(10.5, UTF8String.fromString("hello")), + "hdfs://host:9000/path/a=10.5/b=hello")))) + + check(Seq( + "hdfs://host:9000/path/_temporary", + "hdfs://host:9000/path/a=10/b=20", + "hdfs://host:9000/path/a=10.5/b=hello", + "hdfs://host:9000/path/a=10.5/_temporary", + "hdfs://host:9000/path/a=10.5/_TeMpOrArY", + "hdfs://host:9000/path/a=10.5/b=hello/_temporary", + "hdfs://host:9000/path/a=10.5/b=hello/_TEMPORARY", + "hdfs://host:9000/path/_temporary/path", + "hdfs://host:9000/path/a=11/_temporary/path", + "hdfs://host:9000/path/a=10.5/b=world/_temporary/path"), + PartitionSpec( + StructType(Seq( + StructField("a", DoubleType), StructField("b", StringType))), Seq( - Partition(Row(10, "20"), "hdfs://host:9000/path/a=10/b=20"), - Partition(Row(10.5, "hello"), "hdfs://host:9000/path/a=10.5/b=hello")))) + Partition(InternalRow(10, UTF8String.fromString("20")), + "hdfs://host:9000/path/a=10/b=20"), + Partition(InternalRow(10.5, UTF8String.fromString("hello")), + "hdfs://host:9000/path/a=10.5/b=hello")))) check(Seq( s"hdfs://host:9000/path/a=10/b=20", @@ -128,19 +161,107 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { StructField("a", IntegerType), StructField("b", StringType))), Seq( - Partition(Row(10, "20"), s"hdfs://host:9000/path/a=10/b=20"), - Partition(Row(null, "hello"), s"hdfs://host:9000/path/a=$defaultPartitionName/b=hello")))) + Partition(InternalRow(10, UTF8String.fromString("20")), + s"hdfs://host:9000/path/a=10/b=20"), + Partition(InternalRow(null, UTF8String.fromString("hello")), + s"hdfs://host:9000/path/a=$defaultPartitionName/b=hello")))) check(Seq( s"hdfs://host:9000/path/a=10/b=$defaultPartitionName", s"hdfs://host:9000/path/a=10.5/b=$defaultPartitionName"), PartitionSpec( StructType(Seq( - StructField("a", FloatType), + StructField("a", DoubleType), StructField("b", StringType))), Seq( - Partition(Row(10, null), s"hdfs://host:9000/path/a=10/b=$defaultPartitionName"), - Partition(Row(10.5, null), s"hdfs://host:9000/path/a=10.5/b=$defaultPartitionName")))) + Partition(InternalRow(10, null), s"hdfs://host:9000/path/a=10/b=$defaultPartitionName"), + Partition(InternalRow(10.5, null), + s"hdfs://host:9000/path/a=10.5/b=$defaultPartitionName")))) + + check(Seq( + s"hdfs://host:9000/path1", + s"hdfs://host:9000/path2"), + PartitionSpec.emptySpec) + } + + test("parse partitions with type inference disabled") { + def check(paths: Seq[String], spec: PartitionSpec): Unit = { + assert(parsePartitions(paths.map(new Path(_)), defaultPartitionName, false) === spec) + } + + check(Seq( + "hdfs://host:9000/path/a=10/b=hello"), + PartitionSpec( + StructType(Seq( + StructField("a", StringType), + StructField("b", StringType))), + Seq(Partition(InternalRow(UTF8String.fromString("10"), UTF8String.fromString("hello")), + "hdfs://host:9000/path/a=10/b=hello")))) + + check(Seq( + "hdfs://host:9000/path/a=10/b=20", + "hdfs://host:9000/path/a=10.5/b=hello"), + PartitionSpec( + StructType(Seq( + StructField("a", StringType), + StructField("b", StringType))), + Seq( + Partition(InternalRow(UTF8String.fromString("10"), UTF8String.fromString("20")), + "hdfs://host:9000/path/a=10/b=20"), + Partition(InternalRow(UTF8String.fromString("10.5"), UTF8String.fromString("hello")), + "hdfs://host:9000/path/a=10.5/b=hello")))) + + check(Seq( + "hdfs://host:9000/path/_temporary", + "hdfs://host:9000/path/a=10/b=20", + "hdfs://host:9000/path/a=10.5/b=hello", + "hdfs://host:9000/path/a=10.5/_temporary", + "hdfs://host:9000/path/a=10.5/_TeMpOrArY", + "hdfs://host:9000/path/a=10.5/b=hello/_temporary", + "hdfs://host:9000/path/a=10.5/b=hello/_TEMPORARY", + "hdfs://host:9000/path/_temporary/path", + "hdfs://host:9000/path/a=11/_temporary/path", + "hdfs://host:9000/path/a=10.5/b=world/_temporary/path"), + PartitionSpec( + StructType(Seq( + StructField("a", StringType), + StructField("b", StringType))), + Seq( + Partition(InternalRow(UTF8String.fromString("10"), UTF8String.fromString("20")), + "hdfs://host:9000/path/a=10/b=20"), + Partition(InternalRow(UTF8String.fromString("10.5"), UTF8String.fromString("hello")), + "hdfs://host:9000/path/a=10.5/b=hello")))) + + check(Seq( + s"hdfs://host:9000/path/a=10/b=20", + s"hdfs://host:9000/path/a=$defaultPartitionName/b=hello"), + PartitionSpec( + StructType(Seq( + StructField("a", StringType), + StructField("b", StringType))), + Seq( + Partition(InternalRow(UTF8String.fromString("10"), UTF8String.fromString("20")), + s"hdfs://host:9000/path/a=10/b=20"), + Partition(InternalRow(null, UTF8String.fromString("hello")), + s"hdfs://host:9000/path/a=$defaultPartitionName/b=hello")))) + + check(Seq( + s"hdfs://host:9000/path/a=10/b=$defaultPartitionName", + s"hdfs://host:9000/path/a=10.5/b=$defaultPartitionName"), + PartitionSpec( + StructType(Seq( + StructField("a", StringType), + StructField("b", StringType))), + Seq( + Partition(InternalRow(UTF8String.fromString("10"), null), + s"hdfs://host:9000/path/a=10/b=$defaultPartitionName"), + Partition(InternalRow(UTF8String.fromString("10.5"), null), + s"hdfs://host:9000/path/a=10.5/b=$defaultPartitionName")))) + + check(Seq( + s"hdfs://host:9000/path1", + s"hdfs://host:9000/path2"), + PartitionSpec.emptySpec) } test("read partitioned table - normal case") { @@ -149,12 +270,17 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { pi <- Seq(1, 2) ps <- Seq("foo", "bar") } { + val dir = makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps) makeParquetFile( (1 to 10).map(i => ParquetData(i, i.toString)), - makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + dir) + // Introduce _temporary dir to test the robustness of the schema discovery process. + new File(dir.toString, "_temporary").mkdir() } + // Introduce _temporary dir to the base dir the robustness of the schema discovery process. + new File(base.getCanonicalPath, "_temporary").mkdir() - parquetFile(base.getCanonicalPath).registerTempTable("t") + sqlContext.read.parquet(base.getCanonicalPath).registerTempTable("t") withTempTable("t") { checkAnswer( @@ -201,7 +327,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) } - parquetFile(base.getCanonicalPath).registerTempTable("t") + sqlContext.read.parquet(base.getCanonicalPath).registerTempTable("t") withTempTable("t") { checkAnswer( @@ -249,12 +375,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) } - val parquetRelation = load( - "org.apache.spark.sql.parquet", - Map( - "path" -> base.getCanonicalPath, - ParquetRelation2.DEFAULT_PARTITION_NAME -> defaultPartitionName)) - + val parquetRelation = sqlContext.read.format("parquet").load(base.getCanonicalPath) parquetRelation.registerTempTable("t") withTempTable("t") { @@ -294,12 +415,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) } - val parquetRelation = load( - "org.apache.spark.sql.parquet", - Map( - "path" -> base.getCanonicalPath, - ParquetRelation2.DEFAULT_PARTITION_NAME -> defaultPartitionName)) - + val parquetRelation = sqlContext.read.format("parquet").load(base.getCanonicalPath) parquetRelation.registerTempTable("t") withTempTable("t") { @@ -331,7 +447,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { (1 to 10).map(i => (i, i.toString)).toDF("intField", "stringField"), makePartitionDir(base, defaultPartitionName, "pi" -> 2)) - load(base.getCanonicalPath, "org.apache.spark.sql.parquet").registerTempTable("t") + sqlContext.read.format("parquet").load(base.getCanonicalPath).registerTempTable("t") withTempTable("t") { checkAnswer( @@ -340,4 +456,86 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { } } } + + test("SPARK-7749 Non-partitioned table should have empty partition spec") { + withTempPath { dir => + (1 to 10).map(i => (i, i.toString)).toDF("a", "b").write.parquet(dir.getCanonicalPath) + val queryExecution = sqlContext.read.parquet(dir.getCanonicalPath).queryExecution + queryExecution.analyzed.collectFirst { + case LogicalRelation(relation: ParquetRelation2) => + assert(relation.partitionSpec === PartitionSpec.emptySpec) + }.getOrElse { + fail(s"Expecting a ParquetRelation2, but got:\n$queryExecution") + } + } + } + + test("SPARK-7847: Dynamic partition directory path escaping and unescaping") { + withTempPath { dir => + val df = Seq("/", "[]", "?").zipWithIndex.map(_.swap).toDF("i", "s") + df.write.format("parquet").partitionBy("s").save(dir.getCanonicalPath) + checkAnswer(sqlContext.read.parquet(dir.getCanonicalPath), df.collect()) + } + } + + test("Various partition value types") { + val row = + Row( + 100.toByte, + 40000.toShort, + Int.MaxValue, + Long.MaxValue, + 1.5.toFloat, + 4.5, + new java.math.BigDecimal(new BigInteger("212500"), 5), + new java.math.BigDecimal(2.125), + java.sql.Date.valueOf("2015-05-23"), + new Timestamp(0), + "This is a string, /[]?=:", + "This is not a partition column") + + // BooleanType is not supported yet + val partitionColumnTypes = + Seq( + ByteType, + ShortType, + IntegerType, + LongType, + FloatType, + DoubleType, + DecimalType(10, 5), + DecimalType.Unlimited, + DateType, + TimestampType, + StringType) + + val partitionColumns = partitionColumnTypes.zipWithIndex.map { + case (t, index) => StructField(s"p_$index", t) + } + + val schema = StructType(partitionColumns :+ StructField(s"i", StringType)) + val df = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(row :: Nil), schema) + + withTempPath { dir => + df.write.format("parquet").partitionBy(partitionColumns.map(_.name): _*).save(dir.toString) + val fields = schema.map(f => Column(f.name).cast(f.dataType)) + checkAnswer(sqlContext.read.load(dir.toString).select(fields: _*), row) + } + } + + test("SPARK-8037: Ignores files whose name starts with dot") { + withTempPath { dir => + val df = (1 to 3).map(i => (i, i, i, i)).toDF("a", "b", "c", "d") + + df.write + .format("parquet") + .partitionBy("b", "c", "d") + .save(dir.getCanonicalPath) + + Files.touch(new File(s"${dir.getCanonicalPath}/b=1", ".DS_Store")) + Files.createParentDirs(new File(s"${dir.getCanonicalPath}/b=1/c=1/.foo/bar")) + + checkAnswer(sqlContext.read.format("parquet").load(dir.getCanonicalPath), df) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala index b98ba09ccfc2d..fafad67dde3a7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala @@ -19,16 +19,15 @@ package org.apache.spark.sql.parquet import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.{SQLConf, QueryTest} -import org.apache.spark.sql.catalyst.expressions.Row -import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{QueryTest, Row, SQLConf} /** * A test suite that tests various Parquet queries. */ class ParquetQuerySuiteBase extends QueryTest with ParquetTest { - val sqlContext = TestSQLContext + lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext + import sqlContext.sql test("simple select queries") { withParquetTable((0 until 10).map(i => (i, i.toString)), "t") { @@ -39,22 +38,22 @@ class ParquetQuerySuiteBase extends QueryTest with ParquetTest { test("appending") { val data = (0 until 10).map(i => (i, i.toString)) - createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") + sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") withParquetTable(data, "t") { sql("INSERT INTO TABLE t SELECT * FROM tmp") - checkAnswer(table("t"), (data ++ data).map(Row.fromTuple)) + checkAnswer(sqlContext.table("t"), (data ++ data).map(Row.fromTuple)) } - catalog.unregisterTable(Seq("tmp")) + sqlContext.catalog.unregisterTable(Seq("tmp")) } test("overwriting") { val data = (0 until 10).map(i => (i, i.toString)) - createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") + sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") withParquetTable(data, "t") { sql("INSERT OVERWRITE TABLE t SELECT * FROM tmp") - checkAnswer(table("t"), data.map(Row.fromTuple)) + checkAnswer(sqlContext.table("t"), data.map(Row.fromTuple)) } - catalog.unregisterTable(Seq("tmp")) + sqlContext.catalog.unregisterTable(Seq("tmp")) } test("self-join") { @@ -111,28 +110,40 @@ class ParquetQuerySuiteBase extends QueryTest with ParquetTest { List(Row("same", "run_5", 100))) } } + + test("SPARK-6917 DecimalType should work with non-native types") { + val data = (1 to 10).map(i => Row(Decimal(i, 18, 0), new java.sql.Timestamp(i))) + val schema = StructType(List(StructField("d", DecimalType(18, 0), false), + StructField("time", TimestampType, false)).toArray) + withTempPath { file => + val df = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(data), schema) + df.write.parquet(file.getCanonicalPath) + val df2 = sqlContext.read.parquet(file.getCanonicalPath) + checkAnswer(df2, df.collect().toSeq) + } + } } class ParquetDataSourceOnQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll { - val originalConf = sqlContext.conf.parquetUseDataSourceApi + private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi override protected def beforeAll(): Unit = { - sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true) } override protected def afterAll(): Unit = { - sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } } class ParquetDataSourceOffQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll { - val originalConf = sqlContext.conf.parquetUseDataSourceApi + private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi override protected def beforeAll(): Unit = { - sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false) } override protected def afterAll(): Unit = { - sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala index c964b6d984557..171a656f0e01e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala @@ -20,15 +20,14 @@ package org.apache.spark.sql.parquet import scala.reflect.ClassTag import scala.reflect.runtime.universe.TypeTag -import org.scalatest.FunSuite -import parquet.schema.MessageTypeParser +import org.apache.parquet.schema.MessageTypeParser +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.types._ -class ParquetSchemaSuite extends FunSuite with ParquetTest { - val sqlContext = TestSQLContext +class ParquetSchemaSuite extends SparkFunSuite with ParquetTest { + lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext /** * Checks whether the reflected Parquet message type for product type `T` conforms `messageType`. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala similarity index 57% rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTest.scala rename to sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala index 9d17516e0ef7d..eb15a1609f1d0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala @@ -21,10 +21,9 @@ import java.io.File import scala.reflect.ClassTag import scala.reflect.runtime.universe.TypeTag -import scala.util.Try -import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode} -import org.apache.spark.util.Utils +import org.apache.spark.sql.test.SQLTestUtils +import org.apache.spark.sql.{DataFrame, SaveMode} /** * A helper trait that provides convenient facilities for Parquet testing. @@ -33,54 +32,7 @@ import org.apache.spark.util.Utils * convenient to use tuples rather than special case classes when writing test cases/suites. * Especially, `Tuple1.apply` can be used to easily wrap a single type/value. */ -private[sql] trait ParquetTest { - val sqlContext: SQLContext - - import sqlContext.implicits.{localSeqToDataFrameHolder, rddToDataFrameHolder} - import sqlContext.{conf, sparkContext} - - protected def configuration = sparkContext.hadoopConfiguration - - /** - * Sets all SQL configurations specified in `pairs`, calls `f`, and then restore all SQL - * configurations. - * - * @todo Probably this method should be moved to a more general place - */ - protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = { - val (keys, values) = pairs.unzip - val currentValues = keys.map(key => Try(conf.getConf(key)).toOption) - (keys, values).zipped.foreach(conf.setConf) - try f finally { - keys.zip(currentValues).foreach { - case (key, Some(value)) => conf.setConf(key, value) - case (key, None) => conf.unsetConf(key) - } - } - } - - /** - * Generates a temporary path without creating the actual file/directory, then pass it to `f`. If - * a file/directory is created there by `f`, it will be delete after `f` returns. - * - * @todo Probably this method should be moved to a more general place - */ - protected def withTempPath(f: File => Unit): Unit = { - val path = Utils.createTempDir() - path.delete() - try f(path) finally Utils.deleteRecursively(path) - } - - /** - * Creates a temporary directory, which is then passed to `f` and will be deleted after `f` - * returns. - * - * @todo Probably this method should be moved to a more general place - */ - protected def withTempDir(f: File => Unit): Unit = { - val dir = Utils.createTempDir().getCanonicalFile - try f(dir) finally Utils.deleteRecursively(dir) - } +private[sql] trait ParquetTest extends SQLTestUtils { /** * Writes `data` to a Parquet file, which is then passed to `f` and will be deleted after `f` @@ -90,7 +42,7 @@ private[sql] trait ParquetTest { (data: Seq[T]) (f: String => Unit): Unit = { withTempPath { file => - sparkContext.parallelize(data).toDF().saveAsParquetFile(file.getCanonicalPath) + sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath) f(file.getCanonicalPath) } } @@ -102,14 +54,7 @@ private[sql] trait ParquetTest { protected def withParquetDataFrame[T <: Product: ClassTag: TypeTag] (data: Seq[T]) (f: DataFrame => Unit): Unit = { - withParquetFile(data)(path => f(sqlContext.parquetFile(path))) - } - - /** - * Drops temporary table `tableName` after calling `f`. - */ - protected def withTempTable(tableName: String)(f: => Unit): Unit = { - try f finally sqlContext.dropTempTable(tableName) + withParquetFile(data)(path => f(sqlContext.read.parquet(path))) } /** @@ -128,12 +73,12 @@ private[sql] trait ParquetTest { protected def makeParquetFile[T <: Product: ClassTag: TypeTag]( data: Seq[T], path: File): Unit = { - data.toDF().save(path.getCanonicalPath, "org.apache.spark.sql.parquet", SaveMode.Overwrite) + sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath) } protected def makeParquetFile[T <: Product: ClassTag: TypeTag]( df: DataFrame, path: File): Unit = { - df.save(path.getCanonicalPath, "org.apache.spark.sql.parquet", SaveMode.Overwrite) + df.write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath) } protected def makePartitionDir( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala index 20a23b3bd6aa9..a71088430bfd5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala @@ -17,27 +17,29 @@ package org.apache.spark.sql.sources -import java.io.{IOException, File} +import java.io.{File, IOException} -import org.apache.spark.sql.AnalysisException import org.scalatest.BeforeAndAfterAll +import org.apache.spark.sql.AnalysisException import org.apache.spark.util.Utils class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { - import caseInsensisitiveContext._ + import caseInsensitiveContext.sql + + private lazy val sparkContext = caseInsensitiveContext.sparkContext var path: File = null override def beforeAll(): Unit = { path = Utils.createTempDir() val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) - jsonRDD(rdd).registerTempTable("jt") + caseInsensitiveContext.read.json(rdd).registerTempTable("jt") } override def afterAll(): Unit = { - dropTempTable("jt") + caseInsensitiveContext.dropTempTable("jt") } after { @@ -59,7 +61,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { sql("SELECT a, b FROM jsonTable"), sql("SELECT a, b FROM jt").collect()) - dropTempTable("jsonTable") + caseInsensitiveContext.dropTempTable("jsonTable") } test("CREATE TEMPORARY TABLE AS SELECT based on the file without write permission") { @@ -129,7 +131,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { sql("SELECT * FROM jsonTable"), sql("SELECT a * 4 FROM jt").collect()) - dropTempTable("jsonTable") + caseInsensitiveContext.dropTempTable("jsonTable") // Explicitly delete the data. if (path.exists()) Utils.deleteRecursively(path) @@ -147,7 +149,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { sql("SELECT * FROM jsonTable"), sql("SELECT b FROM jt").collect()) - dropTempTable("jsonTable") + caseInsensitiveContext.dropTempTable("jsonTable") } test("CREATE TEMPORARY TABLE AS SELECT with IF NOT EXISTS is not allowed") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala index ca25751b9583d..5fc53f7012994 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala @@ -19,7 +19,9 @@ package org.apache.spark.sql.sources import org.apache.spark.rdd.RDD import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String class DDLScanSource extends RelationProvider { override def createRelation( @@ -43,7 +45,7 @@ case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlCo StructField("bigintType", LongType, nullable = false), StructField("tinyintType", ByteType, nullable = false), StructField("decimalType", DecimalType.Unlimited, nullable = false), - StructField("fixedDecimalType", DecimalType(5,1), nullable = false), + StructField("fixedDecimalType", DecimalType(5, 1), nullable = false), StructField("binaryType", BinaryType, nullable = false), StructField("booleanType", BooleanType, nullable = false), StructField("smallIntType", ShortType, nullable = false), @@ -51,32 +53,33 @@ case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlCo StructField("mapType", MapType(StringType, StringType)), StructField("arrayType", ArrayType(StringType)), StructField("structType", - StructType(StructField("f1",StringType) :: - (StructField("f2",IntegerType)) :: Nil + StructType(StructField("f1", StringType) :: StructField("f2", IntegerType) :: Nil ) ) )) + override def needConversion: Boolean = false override def buildScan(): RDD[Row] = { - sqlContext.sparkContext.parallelize(from to to).map(e => Row(s"people$e", e * 2)) + sqlContext.sparkContext.parallelize(from to to).map { e => + InternalRow(UTF8String.fromString(s"people$e"), e * 2) + } } } class DDLTestSuite extends DataSourceTest { - import caseInsensisitiveContext._ before { - sql( - """ - |CREATE TEMPORARY TABLE ddlPeople - |USING org.apache.spark.sql.sources.DDLScanSource - |OPTIONS ( - | From '1', - | To '10', - | Table 'test1' - |) - """.stripMargin) + caseInsensitiveContext.sql( + """ + |CREATE TEMPORARY TABLE ddlPeople + |USING org.apache.spark.sql.sources.DDLScanSource + |OPTIONS ( + | From '1', + | To '10', + | Table 'test1' + |) + """.stripMargin) } sqlTest( @@ -99,4 +102,11 @@ class DDLTestSuite extends DataSourceTest { Row("arrayType", "array", ""), Row("structType", "struct", "") )) + + test("SPARK-7686 DescribeCommand should have correct physical plan output attributes") { + val attributes = caseInsensitiveContext.sql("describe ddlPeople") + .queryExecution.executedPlan.output + assert(attributes.map(_.name) === Seq("col_name", "data_type", "comment")) + assert(attributes.map(_.dataType).toSet === Set(StringType)) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala index 33c67355967dd..00cc7d5ea580f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala @@ -17,26 +17,18 @@ package org.apache.spark.sql.sources +import org.scalatest.BeforeAndAfter + import org.apache.spark.sql._ -import org.apache.spark.sql.catalyst.analysis.Analyzer import org.apache.spark.sql.test.TestSQLContext -import org.scalatest.BeforeAndAfter -abstract class DataSourceTest extends QueryTest with BeforeAndAfter { - // Case sensitivity is not configurable yet, but we want to test some edge cases. - // TODO: Remove when it is configurable - implicit val caseInsensisitiveContext = new SQLContext(TestSQLContext.sparkContext) { - @transient - override protected[sql] lazy val analyzer: Analyzer = - new Analyzer(catalog, functionRegistry, caseSensitive = false) { - override val extendedResolutionRules = - PreInsertCastAndRename :: - Nil - override val extendedCheckRules = Seq( - sources.PreWriteCheck(catalog) - ) - } +abstract class DataSourceTest extends QueryTest with BeforeAndAfter { + // We want to test some edge cases. + protected implicit lazy val caseInsensitiveContext = { + val ctx = new SQLContext(TestSQLContext.sparkContext) + ctx.setConf(SQLConf.CASE_SENSITIVE, false) + ctx } -} +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala index cb5e5147ff189..81b3a0f0c5b3a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala @@ -97,7 +97,7 @@ object FiltersPushed { class FilteredScanSuite extends DataSourceTest { - import caseInsensisitiveContext._ + import caseInsensitiveContext.sql before { sql( @@ -154,7 +154,7 @@ class FilteredScanSuite extends DataSourceTest { sqlTest( "SELECT a, b FROM oneToTenFiltered WHERE a IN (1,3,5)", - Seq(1,3,5).map(i => Row(i, i * 2))) + Seq(1, 3, 5).map(i => Row(i, i * 2))) sqlTest( "SELECT a, b FROM oneToTenFiltered WHERE A = 1", diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala index 50629ea4dc066..0b7c46c482c88 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala @@ -26,14 +26,16 @@ import org.apache.spark.util.Utils class InsertSuite extends DataSourceTest with BeforeAndAfterAll { - import caseInsensisitiveContext._ + import caseInsensitiveContext.sql + + private lazy val sparkContext = caseInsensitiveContext.sparkContext var path: File = null override def beforeAll: Unit = { path = Utils.createTempDir() val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) - jsonRDD(rdd).registerTempTable("jt") + caseInsensitiveContext.read.json(rdd).registerTempTable("jt") sql( s""" |CREATE TEMPORARY TABLE jsonTable (a int, b string) @@ -45,8 +47,8 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { } override def afterAll: Unit = { - dropTempTable("jsonTable") - dropTempTable("jt") + caseInsensitiveContext.dropTempTable("jsonTable") + caseInsensitiveContext.dropTempTable("jt") Utils.deleteRecursively(path) } @@ -109,7 +111,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { // Writing the table to less part files. val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 5) - jsonRDD(rdd1).registerTempTable("jt1") + caseInsensitiveContext.read.json(rdd1).registerTempTable("jt1") sql( s""" |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt1 @@ -121,7 +123,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { // Writing the table to more part files. val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 10) - jsonRDD(rdd2).registerTempTable("jt2") + caseInsensitiveContext.read.json(rdd2).registerTempTable("jt2") sql( s""" |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt2 @@ -140,8 +142,8 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { (1 to 10).map(i => Row(i * 10, s"str$i")) ) - dropTempTable("jt1") - dropTempTable("jt2") + caseInsensitiveContext.dropTempTable("jt1") + caseInsensitiveContext.dropTempTable("jt2") } test("INSERT INTO not supported for JSONRelation for now") { @@ -154,13 +156,14 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { } test("save directly to the path of a JSON table") { - table("jt").selectExpr("a * 5 as a", "b").save(path.toString, "json", SaveMode.Overwrite) + caseInsensitiveContext.table("jt").selectExpr("a * 5 as a", "b") + .write.mode(SaveMode.Overwrite).json(path.toString) checkAnswer( sql("SELECT a, b FROM jsonTable"), (1 to 10).map(i => Row(i * 5, s"str$i")) ) - table("jt").save(path.toString, "json", SaveMode.Overwrite) + caseInsensitiveContext.table("jt").write.mode(SaveMode.Overwrite).json(path.toString) checkAnswer( sql("SELECT a, b FROM jsonTable"), (1 to 10).map(i => Row(i, s"str$i")) @@ -181,7 +184,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { test("Caching") { // Cached Query Execution - cacheTable("jsonTable") + caseInsensitiveContext.cacheTable("jsonTable") assertCached(sql("SELECT * FROM jsonTable")) checkAnswer( sql("SELECT * FROM jsonTable"), @@ -220,7 +223,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { sql("SELECT a * 2, b FROM jt").collect()) // Verify uncaching - uncacheTable("jsonTable") + caseInsensitiveContext.uncacheTable("jsonTable") assertCached(sql("SELECT * FROM jsonTable"), 0) } @@ -251,6 +254,6 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { "It is not allowed to insert into a table that is not an InsertableRelation." ) - dropTempTable("oneToTen") + caseInsensitiveContext.dropTempTable("oneToTen") } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala index 6a1ddf2f8e98b..257526feab945 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala @@ -52,10 +52,9 @@ case class SimplePrunedScan(from: Int, to: Int)(@transient val sqlContext: SQLCo } class PrunedScanSuite extends DataSourceTest { - import caseInsensisitiveContext._ before { - sql( + caseInsensitiveContext.sql( """ |CREATE TEMPORARY TABLE oneToTenPruned |USING org.apache.spark.sql.sources.PrunedScanSource @@ -115,7 +114,7 @@ class PrunedScanSuite extends DataSourceTest { def testPruning(sqlString: String, expectedColumns: String*): Unit = { test(s"Columns output ${expectedColumns.mkString(",")}: $sqlString") { - val queryExecution = sql(sqlString).queryExecution + val queryExecution = caseInsensitiveContext.sql(sqlString).queryExecution val rawPlan = queryExecution.executedPlan.collect { case p: execution.PhysicalRDD => p } match { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala index 8331a14c9295c..296b0d6f74a0c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala @@ -17,9 +17,9 @@ package org.apache.spark.sql.sources -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite -class ResolvedDataSourceSuite extends FunSuite { +class ResolvedDataSourceSuite extends SparkFunSuite { test("builtin sources") { assert(ResolvedDataSource.lookupDataSource("jdbc") === diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala index cb287ba85c1f8..b032515a9d28c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala @@ -27,7 +27,9 @@ import org.apache.spark.util.Utils class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll { - import caseInsensisitiveContext._ + import caseInsensitiveContext.sql + + private lazy val sparkContext = caseInsensitiveContext.sparkContext var originalDefaultSource: String = null @@ -36,62 +38,72 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll { var df: DataFrame = null override def beforeAll(): Unit = { - originalDefaultSource = conf.defaultDataSourceName + originalDefaultSource = caseInsensitiveContext.conf.defaultDataSourceName path = Utils.createTempDir() path.delete() val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) - df = jsonRDD(rdd) + df = caseInsensitiveContext.read.json(rdd) df.registerTempTable("jsonTable") } override def afterAll(): Unit = { - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) + caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) } after { - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) + caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) Utils.deleteRecursively(path) } def checkLoad(): Unit = { - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") - checkAnswer(load(path.toString), df.collect()) + caseInsensitiveContext.conf.setConf( + SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") + checkAnswer(caseInsensitiveContext.read.load(path.toString), df.collect()) // Test if we can pick up the data source name passed in load. - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") - checkAnswer(load(path.toString, "org.apache.spark.sql.json"), df.collect()) - checkAnswer(load("org.apache.spark.sql.json", Map("path" -> path.toString)), df.collect()) + caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString), df.collect()) + checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString), df.collect()) val schema = StructType(StructField("b", StringType, true) :: Nil) checkAnswer( - load("org.apache.spark.sql.json", schema, Map("path" -> path.toString)), + caseInsensitiveContext.read.format("json").schema(schema).load(path.toString), sql("SELECT b FROM jsonTable").collect()) } test("save with path and load") { - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") - df.save(path.toString) + caseInsensitiveContext.conf.setConf( + SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") + df.write.save(path.toString) + checkLoad() + } + + test("save with string mode and path, and load") { + caseInsensitiveContext.conf.setConf( + SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") + path.createNewFile() + df.write.mode("overwrite").save(path.toString) checkLoad() } test("save with path and datasource, and load") { - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") - df.save(path.toString, "org.apache.spark.sql.json") + caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + df.write.json(path.toString) checkLoad() } test("save with data source and options, and load") { - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") - df.save("org.apache.spark.sql.json", SaveMode.ErrorIfExists, Map("path" -> path.toString)) + caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + df.write.mode(SaveMode.ErrorIfExists).json(path.toString) checkLoad() } test("save and save again") { - df.save(path.toString, "org.apache.spark.sql.json") + df.write.json(path.toString) var message = intercept[RuntimeException] { - df.save(path.toString, "org.apache.spark.sql.json") + df.write.json(path.toString) }.getMessage assert( @@ -100,14 +112,14 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll { if (path.exists()) Utils.deleteRecursively(path) - df.save(path.toString, "org.apache.spark.sql.json") + df.write.json(path.toString) checkLoad() - df.save("org.apache.spark.sql.json", SaveMode.Overwrite, Map("path" -> path.toString)) + df.write.mode(SaveMode.Overwrite).json(path.toString) checkLoad() message = intercept[RuntimeException] { - df.save("org.apache.spark.sql.json", SaveMode.Append, Map("path" -> path.toString)) + df.write.mode(SaveMode.Append).json(path.toString) }.getMessage assert( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala index 3b47b8adf313b..48875773224c7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala @@ -19,9 +19,13 @@ package org.apache.spark.sql.sources import java.sql.{Timestamp, Date} + import org.apache.spark.rdd.RDD import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.util.DateUtils +import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String class DefaultSource extends SimpleScanSource @@ -47,6 +51,10 @@ class AllDataTypesScanSource extends SchemaRelationProvider { sqlContext: SQLContext, parameters: Map[String, String], schema: StructType): BaseRelation = { + // Check that weird parameters are passed correctly. + parameters("option_with_underscores") + parameters("option.with.dots") + AllDataTypesScan(parameters("from").toInt, parameters("TO").toInt, schema)(sqlContext) } } @@ -60,10 +68,12 @@ case class AllDataTypesScan( override def schema: StructType = userSpecifiedSchema + override def needConversion: Boolean = false + override def buildScan(): RDD[Row] = { sqlContext.sparkContext.parallelize(from to to).map { i => - Row( - s"str_$i", + InternalRow( + UTF8String.fromString(s"str_$i"), s"str_$i".getBytes(), i % 2 == 0, i.toByte, @@ -72,25 +82,26 @@ case class AllDataTypesScan( i.toLong, i.toFloat, i.toDouble, - new java.math.BigDecimal(i), - new java.math.BigDecimal(i), - new Date(1970, 1, 1), - new Timestamp(20000 + i), - s"varchar_$i", + Decimal(new java.math.BigDecimal(i)), + Decimal(new java.math.BigDecimal(i)), + DateUtils.fromJavaDate(new Date(1970, 1, 1)), + DateUtils.fromJavaTimestamp(new Timestamp(20000 + i)), + UTF8String.fromString(s"varchar_$i"), Seq(i, i + 1), - Seq(Map(s"str_$i" -> Row(i.toLong))), + Seq(Map(UTF8String.fromString(s"str_$i") -> InternalRow(i.toLong))), Map(i -> i.toString), - Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)), + Map(Map(UTF8String.fromString(s"str_$i") -> i.toFloat) -> InternalRow(i.toLong)), Row(i, i.toString), - Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date(1970, 1, i + 1))))) + Row(Seq(UTF8String.fromString(s"str_$i"), UTF8String.fromString(s"str_${i + 1}")), + InternalRow(Seq(DateUtils.fromJavaDate(new Date(1970, 1, i + 1)))))) } } } class TableScanSuite extends DataSourceTest { - import caseInsensisitiveContext._ + import caseInsensitiveContext.sql - var tableWithSchemaExpected = (1 to 10).map { i => + private lazy val tableWithSchemaExpected = (1 to 10).map { i => Row( s"str_$i", s"str_$i", @@ -121,7 +132,9 @@ class TableScanSuite extends DataSourceTest { |USING org.apache.spark.sql.sources.SimpleScanSource |OPTIONS ( | From '1', - | To '10' + | To '10', + | option_with_underscores 'someval', + | option.with.dots 'someval' |) """.stripMargin) @@ -152,7 +165,9 @@ class TableScanSuite extends DataSourceTest { |USING org.apache.spark.sql.sources.AllDataTypesScanSource |OPTIONS ( | From '1', - | To '10' + | To '10', + | option_with_underscores 'someval', + | option.with.dots 'someval' |) """.stripMargin) } @@ -215,7 +230,7 @@ class TableScanSuite extends DataSourceTest { Nil ) - assert(expectedSchema == table("tableWithSchema").schema) + assert(expectedSchema == caseInsensitiveContext.table("tableWithSchema").schema) checkAnswer( sql( @@ -270,7 +285,7 @@ class TableScanSuite extends DataSourceTest { test("Caching") { // Cached Query Execution - cacheTable("oneToTen") + caseInsensitiveContext.cacheTable("oneToTen") assertCached(sql("SELECT * FROM oneToTen")) checkAnswer( sql("SELECT * FROM oneToTen"), @@ -297,7 +312,7 @@ class TableScanSuite extends DataSourceTest { (2 to 10).map(i => Row(i, i - 1)).toSeq) // Verify uncaching - uncacheTable("oneToTen") + caseInsensitiveContext.uncacheTable("oneToTen") assertCached(sql("SELECT * FROM oneToTen"), 0) } @@ -354,7 +369,9 @@ class TableScanSuite extends DataSourceTest { |USING org.apache.spark.sql.sources.AllDataTypesScanSource |OPTIONS ( | from '1', - | to '10' + | to '10', + | option_with_underscores 'someval', + | option.with.dots 'someval' |) """.stripMargin) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala new file mode 100644 index 0000000000000..fa01823e9417c --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala @@ -0,0 +1,90 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.test + +import java.io.File + +import scala.util.Try + +import org.apache.spark.sql.SQLContext +import org.apache.spark.util.Utils + +trait SQLTestUtils { + def sqlContext: SQLContext + + protected def configuration = sqlContext.sparkContext.hadoopConfiguration + + /** + * Sets all SQL configurations specified in `pairs`, calls `f`, and then restore all SQL + * configurations. + * + * @todo Probably this method should be moved to a more general place + */ + protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = { + val (keys, values) = pairs.unzip + val currentValues = keys.map(key => Try(sqlContext.conf.getConfString(key)).toOption) + (keys, values).zipped.foreach(sqlContext.conf.setConfString) + try f finally { + keys.zip(currentValues).foreach { + case (key, Some(value)) => sqlContext.conf.setConfString(key, value) + case (key, None) => sqlContext.conf.unsetConf(key) + } + } + } + + /** + * Generates a temporary path without creating the actual file/directory, then pass it to `f`. If + * a file/directory is created there by `f`, it will be delete after `f` returns. + * + * @todo Probably this method should be moved to a more general place + */ + protected def withTempPath(f: File => Unit): Unit = { + val path = Utils.createTempDir() + path.delete() + try f(path) finally Utils.deleteRecursively(path) + } + + /** + * Creates a temporary directory, which is then passed to `f` and will be deleted after `f` + * returns. + * + * @todo Probably this method should be moved to a more general place + */ + protected def withTempDir(f: File => Unit): Unit = { + val dir = Utils.createTempDir().getCanonicalFile + try f(dir) finally Utils.deleteRecursively(dir) + } + + /** + * Drops temporary table `tableName` after calling `f`. + */ + protected def withTempTable(tableNames: String*)(f: => Unit): Unit = { + try f finally tableNames.foreach(sqlContext.dropTempTable) + } + + /** + * Drops table `tableName` after calling `f`. + */ + protected def withTable(tableNames: String*)(f: => Unit): Unit = { + try f finally { + tableNames.foreach { name => + sqlContext.sql(s"DROP TABLE IF EXISTS $name") + } + } + } +} diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml index 437f697d25bf3..73e6ccdb1eaf8 100644 --- a/sql/hive-thriftserver/pom.xml +++ b/sql/hive-thriftserver/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ spark-hive_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + com.google.guava guava diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala index 0be5a92c2546c..700d994bb6a83 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala @@ -17,23 +17,24 @@ package org.apache.spark.sql.hive.thriftserver +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer + import org.apache.commons.logging.LogFactory import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.hive.service.cli.thrift.{ThriftBinaryCLIService, ThriftHttpCLIService} import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor} -import org.apache.spark.sql.SQLConf -import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationEnd, SparkListenerJobStart} +import org.apache.spark.sql.SQLConf import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ -import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListenerApplicationEnd, SparkListener} import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SparkContext} -import scala.collection.mutable -import scala.collection.mutable.ArrayBuffer /** * The main entry point for the Spark SQL port of HiveServer2. Starts up a `SparkSQLContext` and a @@ -51,6 +52,7 @@ object HiveThriftServer2 extends Logging { @DeveloperApi def startWithContext(sqlContext: HiveContext): Unit = { val server = new HiveThriftServer2(sqlContext) + sqlContext.setConf("spark.sql.hive.version", HiveContext.hiveExecutionVersion) server.init(sqlContext.hiveconf) server.start() listener = new HiveThriftServer2Listener(server, sqlContext.conf) @@ -147,13 +149,13 @@ object HiveThriftServer2 extends Logging { override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = { server.stop() } - + var onlineSessionNum: Int = 0 val sessionList = new mutable.LinkedHashMap[String, SessionInfo] val executionList = new mutable.LinkedHashMap[String, ExecutionInfo] val retainedStatements = - conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT, "200").toInt + conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT) val retainedSessions = - conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT, "200").toInt + conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT) var totalRunning = 0 override def onJobStart(jobStart: SparkListenerJobStart): Unit = { @@ -170,11 +172,13 @@ object HiveThriftServer2 extends Logging { def onSessionCreated(ip: String, sessionId: String, userName: String = "UNKNOWN"): Unit = { val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName) sessionList.put(sessionId, info) + onlineSessionNum += 1 trimSessionIfNecessary() } def onSessionClosed(sessionId: String): Unit = { sessionList(sessionId).finishTimestamp = System.currentTimeMillis + onlineSessionNum -= 1 } def onStatementStart( diff --git a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala similarity index 51% rename from sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala rename to sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala index b9d4f1c58c982..e8758887ff3a2 100644 --- a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala @@ -17,78 +17,55 @@ package org.apache.spark.sql.hive.thriftserver +import java.security.PrivilegedExceptionAction import java.sql.{Date, Timestamp} -import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, UUID} - -import org.apache.commons.logging.Log -import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.conf.HiveConf.ConfVars -import org.apache.hive.service.cli.thrift.TProtocolVersion -import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager +import java.util.concurrent.RejectedExecutionException +import java.util.{Map => JMap, UUID} import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, Map => SMap} +import scala.util.control.NonFatal +import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.metastore.api.FieldSchema -import org.apache.hadoop.security.UserGroupInformation import org.apache.hive.service.cli._ +import org.apache.hadoop.hive.ql.metadata.Hive +import org.apache.hadoop.hive.ql.metadata.HiveException +import org.apache.hadoop.hive.ql.session.SessionState +import org.apache.hadoop.hive.shims.ShimLoader +import org.apache.hadoop.security.UserGroupInformation import org.apache.hive.service.cli.operation.ExecuteStatementOperation -import org.apache.hive.service.cli.session.{SessionManager, HiveSession} +import org.apache.hive.service.cli.session.HiveSession -import org.apache.spark.{SparkContext, Logging} -import org.apache.spark.sql.{DataFrame, Row => SparkRow, SQLConf} +import org.apache.spark.Logging import org.apache.spark.sql.execution.SetCommand -import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes} import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, Row => SparkRow, SQLConf} -/** - * A compatibility layer for interacting with Hive version 0.13.1. - */ -private[thriftserver] object HiveThriftServerShim { - val version = "0.13.1" - - def setServerUserName( - sparkServiceUGI: UserGroupInformation, - sparkCliService:SparkSQLCLIService) = { - setSuperField(sparkCliService, "serviceUGI", sparkServiceUGI) - } -} - -private[hive] class SparkSQLDriver(val _context: HiveContext = SparkSQLEnv.hiveContext) - extends AbstractSparkSQLDriver(_context) { - override def getResults(res: JList[_]): Boolean = { - if (hiveResponse == null) { - false - } else { - res.asInstanceOf[JArrayList[String]].addAll(hiveResponse) - hiveResponse = null - true - } - } -} private[hive] class SparkExecuteStatementOperation( parentSession: HiveSession, statement: String, confOverlay: JMap[String, String], - runInBackground: Boolean = true)( - hiveContext: HiveContext, - sessionToActivePool: SMap[SessionHandle, String]) - // NOTE: `runInBackground` is set to `false` intentionally to disable asynchronous execution - extends ExecuteStatementOperation(parentSession, statement, confOverlay, false) with Logging { + runInBackground: Boolean = true) + (hiveContext: HiveContext, sessionToActivePool: SMap[SessionHandle, String]) + extends ExecuteStatementOperation(parentSession, statement, confOverlay, runInBackground) + with Logging { private var result: DataFrame = _ private var iter: Iterator[SparkRow] = _ private var dataTypes: Array[DataType] = _ + private var statementId: String = _ def close(): Unit = { // RDDs will be cleaned automatically upon garbage collection. - logDebug("CLOSING") + hiveContext.sparkContext.clearJobGroup() + logDebug(s"CLOSING $statementId") + cleanup(OperationState.CLOSED) } - def addNonNullColumnValue(from: SparkRow, to: ArrayBuffer[Any], ordinal: Int) { + def addNonNullColumnValue(from: SparkRow, to: ArrayBuffer[Any], ordinal: Int) { dataTypes(ordinal) match { case StringType => to += from.getString(ordinal) @@ -149,10 +126,10 @@ private[hive] class SparkExecuteStatementOperation( } def getResultSetSchema: TableSchema = { - logInfo(s"Result Schema: ${result.queryExecution.analyzed.output}") - if (result.queryExecution.analyzed.output.size == 0) { + if (result == null || result.queryExecution.analyzed.output.size == 0) { new TableSchema(new FieldSchema("Result", "string", "") :: Nil) } else { + logInfo(s"Result Schema: ${result.queryExecution.analyzed.output}") val schema = result.queryExecution.analyzed.output.map { attr => new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "") } @@ -160,9 +137,73 @@ private[hive] class SparkExecuteStatementOperation( } } - def run(): Unit = { - val statementId = UUID.randomUUID().toString - logInfo(s"Running query '$statement'") + override def run(): Unit = { + setState(OperationState.PENDING) + setHasResultSet(true) // avoid no resultset for async run + + if (!runInBackground) { + runInternal() + } else { + val parentSessionState = SessionState.get() + val hiveConf = getConfigForOperation() + val sparkServiceUGI = ShimLoader.getHadoopShims.getUGIForConf(hiveConf) + val sessionHive = getCurrentHive() + val currentSqlSession = hiveContext.currentSession + + // Runnable impl to call runInternal asynchronously, + // from a different thread + val backgroundOperation = new Runnable() { + + override def run(): Unit = { + val doAsAction = new PrivilegedExceptionAction[Object]() { + override def run(): Object = { + + // User information is part of the metastore client member in Hive + hiveContext.setSession(currentSqlSession) + Hive.set(sessionHive) + SessionState.setCurrentSessionState(parentSessionState) + try { + runInternal() + } catch { + case e: HiveSQLException => + setOperationException(e) + log.error("Error running hive query: ", e) + } + return null + } + } + + try { + ShimLoader.getHadoopShims().doAs(sparkServiceUGI, doAsAction) + } catch { + case e: Exception => + setOperationException(new HiveSQLException(e)) + logError("Error running hive query as user : " + + sparkServiceUGI.getShortUserName(), e) + } + } + } + try { + // This submit blocks if no background threads are available to run this operation + val backgroundHandle = + getParentSession().getSessionManager().submitBackgroundOperation(backgroundOperation) + setBackgroundHandle(backgroundHandle) + } catch { + case rejected: RejectedExecutionException => + setState(OperationState.ERROR) + throw new HiveSQLException("The background threadpool cannot accept" + + " new task for execution, please retry the operation", rejected) + case NonFatal(e) => + logError(s"Error executing query in background", e) + setState(OperationState.ERROR) + throw e + } + } + } + + private def runInternal(): Unit = { + statementId = UUID.randomUUID().toString + logInfo(s"Running query '$statement' with $statementId") setState(OperationState.RUNNING) HiveThriftServer2.listener.onStatementStart( statementId, @@ -178,7 +219,7 @@ private[hive] class SparkExecuteStatementOperation( result = hiveContext.sql(statement) logDebug(result.queryExecution.toString()) result.queryExecution.logical match { - case SetCommand(Some((SQLConf.THRIFTSERVER_POOL, Some(value))), _) => + case SetCommand(Some((SQLConf.THRIFTSERVER_POOL.key, Some(value)))) => sessionToActivePool(parentSession.getSessionHandle) = value logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => @@ -194,63 +235,82 @@ private[hive] class SparkExecuteStatementOperation( } } dataTypes = result.queryExecution.analyzed.output.map(_.dataType).toArray - setHasResultSet(true) } catch { + case e: HiveSQLException => + if (getStatus().getState() == OperationState.CANCELED) { + return + } else { + setState(OperationState.ERROR); + throw e + } // Actually do need to catch Throwable as some failures don't inherit from Exception and // HiveServer will silently swallow them. case e: Throwable => + val currentState = getStatus().getState() + logError(s"Error executing query, currentState $currentState, ", e) setState(OperationState.ERROR) HiveThriftServer2.listener.onStatementError( statementId, e.getMessage, e.getStackTraceString) - logError("Error executing query:", e) throw new HiveSQLException(e.toString) } setState(OperationState.FINISHED) HiveThriftServer2.listener.onStatementFinish(statementId) } -} - -private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) - extends SessionManager - with ReflectedCompositeService { - - private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext) - - override def init(hiveConf: HiveConf) { - setSuperField(this, "hiveConf", hiveConf) - - val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS) - setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize)) - getAncestorField[Log](this, 3, "LOG").info( - s"HiveServer2: Async execution pool size $backgroundPoolSize") - setSuperField(this, "operationManager", sparkSqlOperationManager) - addService(sparkSqlOperationManager) - - initCompositeService(hiveConf) + override def cancel(): Unit = { + logInfo(s"Cancel '$statement' with $statementId") + if (statementId != null) { + hiveContext.sparkContext.cancelJobGroup(statementId) + } + cleanup(OperationState.CANCELED) } - override def openSession( - protocol: TProtocolVersion, - username: String, - passwd: String, - sessionConf: java.util.Map[String, String], - withImpersonation: Boolean, - delegationToken: String): SessionHandle = { - hiveContext.openSession() - val sessionHandle = super.openSession( - protocol, username, passwd, sessionConf, withImpersonation, delegationToken) - val session = super.getSession(sessionHandle) - HiveThriftServer2.listener.onSessionCreated( - session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername) - sessionHandle + private def cleanup(state: OperationState) { + setState(state) + if (runInBackground) { + val backgroundHandle = getBackgroundHandle() + if (backgroundHandle != null) { + backgroundHandle.cancel(true) + } + } } - override def closeSession(sessionHandle: SessionHandle) { - HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) - super.closeSession(sessionHandle) - sparkSqlOperationManager.sessionToActivePool -= sessionHandle + /** + * If there are query specific settings to overlay, then create a copy of config + * There are two cases we need to clone the session config that's being passed to hive driver + * 1. Async query - + * If the client changes a config setting, that shouldn't reflect in the execution + * already underway + * 2. confOverlay - + * The query specific settings should only be applied to the query config and not session + * @return new configuration + * @throws HiveSQLException + */ + private def getConfigForOperation(): HiveConf = { + var sqlOperationConf = getParentSession().getHiveConf() + if (!getConfOverlay().isEmpty() || runInBackground) { + // clone the partent session config for this query + sqlOperationConf = new HiveConf(sqlOperationConf) + + // apply overlay query specific settings, if any + getConfOverlay().foreach { case (k, v) => + try { + sqlOperationConf.verifyAndSet(k, v) + } catch { + case e: IllegalArgumentException => + throw new HiveSQLException("Error applying statement specific settings", e) + } + } + } + return sqlOperationConf + } - hiveContext.detachSession() + private def getCurrentHive(): Hive = { + try { + return Hive.get() + } catch { + case e: HiveException => + throw new HiveSQLException("Failed to get current Hive object", e); + } } } diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala index deb1008c468bf..039cfa40d26b3 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -32,18 +32,18 @@ import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils} import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.exec.Utilities -import org.apache.hadoop.hive.ql.processors.{AddResourceProcessor, SetProcessor, CommandProcessor} +import org.apache.hadoop.hive.ql.processors.{AddResourceProcessor, SetProcessor, CommandProcessor, CommandProcessorFactory} import org.apache.hadoop.hive.ql.session.SessionState import org.apache.thrift.transport.TSocket import org.apache.spark.Logging -import org.apache.spark.sql.hive.{HiveContext, HiveShim} +import org.apache.spark.sql.hive.HiveContext import org.apache.spark.util.Utils private[hive] object SparkSQLCLIDriver { private var prompt = "spark-sql" private var continuedPrompt = "".padTo(prompt.length, ' ') - private var transport:TSocket = _ + private var transport: TSocket = _ installSignalHandler() @@ -267,7 +267,7 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging { } else { var ret = 0 val hconf = conf.asInstanceOf[HiveConf] - val proc: CommandProcessor = HiveShim.getCommandProcessor(Array(tokens(0)), hconf) + val proc: CommandProcessor = CommandProcessorFactory.get(Array(tokens(0)), hconf) if (proc != null) { if (proc.isInstanceOf[Driver] || proc.isInstanceOf[SetProcessor] || @@ -276,13 +276,13 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging { driver.init() val out = sessionState.out - val start:Long = System.currentTimeMillis() + val start: Long = System.currentTimeMillis() if (sessionState.getIsVerbose) { out.println(cmd) } val rc = driver.run(cmd) val end = System.currentTimeMillis() - val timeTaken:Double = (end - start) / 1000.0 + val timeTaken: Double = (end - start) / 1000.0 ret = rc.getResponseCode if (ret != 0) { @@ -310,7 +310,7 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging { res.clear() } } catch { - case e:IOException => + case e: IOException => console.printError( s"""Failed with exception ${e.getClass.getName}: ${e.getMessage} |${org.apache.hadoop.util.StringUtils.stringifyException(e)} diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala index 499e077d7294a..41f647d5f8c5a 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala @@ -21,8 +21,6 @@ import java.io.IOException import java.util.{List => JList} import javax.security.auth.login.LoginException -import scala.collection.JavaConversions._ - import org.apache.commons.logging.Log import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.shims.ShimLoader @@ -34,7 +32,8 @@ import org.apache.hive.service.{AbstractService, Service, ServiceException} import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ -import org.apache.spark.util.Utils + +import scala.collection.JavaConversions._ private[hive] class SparkSQLCLIService(hiveContext: HiveContext) extends CLIService @@ -52,7 +51,7 @@ private[hive] class SparkSQLCLIService(hiveContext: HiveContext) try { HiveAuthFactory.loginFromKeytab(hiveConf) sparkServiceUGI = ShimLoader.getHadoopShims.getUGIForConf(hiveConf) - HiveThriftServerShim.setServerUserName(sparkServiceUGI, this) + setSuperField(this, "serviceUGI", sparkServiceUGI) } catch { case e @ (_: IOException | _: LoginException) => throw new ServiceException("Unable to login to kerberos with given principal/keytab", e) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/AbstractSparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala similarity index 86% rename from sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/AbstractSparkSQLDriver.scala rename to sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala index 48ac9062af96a..77272aecf2835 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/AbstractSparkSQLDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.hive.thriftserver -import scala.collection.JavaConversions._ +import java.util.{ArrayList => JArrayList, List => JList} import org.apache.commons.lang3.exception.ExceptionUtils import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema} @@ -27,8 +27,12 @@ import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse import org.apache.spark.Logging import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes} -private[hive] abstract class AbstractSparkSQLDriver( - val context: HiveContext = SparkSQLEnv.hiveContext) extends Driver with Logging { +import scala.collection.JavaConversions._ + +private[hive] class SparkSQLDriver( + val context: HiveContext = SparkSQLEnv.hiveContext) + extends Driver + with Logging { private[hive] var tableSchema: Schema = _ private[hive] var hiveResponse: Seq[String] = _ @@ -71,6 +75,16 @@ private[hive] abstract class AbstractSparkSQLDriver( 0 } + override def getResults(res: JList[_]): Boolean = { + if (hiveResponse == null) { + false + } else { + res.asInstanceOf[JArrayList[String]].addAll(hiveResponse) + hiveResponse = null + true + } + } + override def getSchema: Schema = tableSchema override def destroy() { diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala index 7c0c505e2d61e..79eda1f5123bf 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala @@ -22,7 +22,7 @@ import java.io.PrintStream import scala.collection.JavaConversions._ import org.apache.spark.scheduler.StatsReportListener -import org.apache.spark.sql.hive.{HiveShim, HiveContext} +import org.apache.spark.sql.hive.HiveContext import org.apache.spark.{Logging, SparkConf, SparkContext} import org.apache.spark.util.Utils @@ -56,7 +56,7 @@ private[hive] object SparkSQLEnv extends Logging { hiveContext.metadataHive.setInfo(new PrintStream(System.err, true, "UTF-8")) hiveContext.metadataHive.setError(new PrintStream(System.err, true, "UTF-8")) - hiveContext.setConf("spark.sql.hive.version", HiveShim.version) + hiveContext.setConf("spark.sql.hive.version", HiveContext.hiveExecutionVersion) if (log.isDebugEnabled) { hiveContext.hiveconf.getAllProperties.toSeq.sorted.foreach { case (k, v) => diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala new file mode 100644 index 0000000000000..2d5ee68002286 --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala @@ -0,0 +1,77 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver + +import java.util.concurrent.Executors + +import org.apache.commons.logging.Log +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.hive.service.cli.SessionHandle +import org.apache.hive.service.cli.session.SessionManager +import org.apache.hive.service.cli.thrift.TProtocolVersion + +import org.apache.spark.sql.hive.HiveContext +import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ +import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager + + +private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) + extends SessionManager + with ReflectedCompositeService { + + private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext) + + override def init(hiveConf: HiveConf) { + setSuperField(this, "hiveConf", hiveConf) + + val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS) + setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize)) + getAncestorField[Log](this, 3, "LOG").info( + s"HiveServer2: Async execution pool size $backgroundPoolSize") + + setSuperField(this, "operationManager", sparkSqlOperationManager) + addService(sparkSqlOperationManager) + + initCompositeService(hiveConf) + } + + override def openSession( + protocol: TProtocolVersion, + username: String, + passwd: String, + sessionConf: java.util.Map[String, String], + withImpersonation: Boolean, + delegationToken: String): SessionHandle = { + hiveContext.openSession() + val sessionHandle = super.openSession( + protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + val session = super.getSession(sessionHandle) + HiveThriftServer2.listener.onSessionCreated( + session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername) + sessionHandle + } + + override def closeSession(sessionHandle: SessionHandle) { + HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) + super.closeSession(sessionHandle) + sparkSqlOperationManager.sessionToActivePool -= sessionHandle + + hiveContext.detachSession() + } +} diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala index 9c0bf02391e0e..c8031ed0f3437 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala @@ -44,9 +44,12 @@ private[thriftserver] class SparkSQLOperationManager(hiveContext: HiveContext) confOverlay: JMap[String, String], async: Boolean): ExecuteStatementOperation = synchronized { - val operation = new SparkExecuteStatementOperation(parentSession, statement, confOverlay)( - hiveContext, sessionToActivePool) + val runInBackground = async && hiveContext.hiveThriftServerAsync + val operation = new SparkExecuteStatementOperation(parentSession, statement, confOverlay, + runInBackground)(hiveContext, sessionToActivePool) handleToOperation.put(operation.getHandle, operation) + logDebug(s"Created Operation for $statement with session=$parentSession, " + + s"runInBackground=$runInBackground") operation } } diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala index 71b16b6bebffb..10c83d8b27a2a 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala @@ -29,7 +29,7 @@ import org.apache.spark.ui.UIUtils._ import org.apache.spark.ui._ -/** Page for Spark Web UI that shows statistics of a streaming job */ +/** Page for Spark Web UI that shows statistics of a thrift server */ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("") with Logging { private val listener = parent.listener @@ -42,20 +42,20 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("" generateBasicStats() ++
    ++

    - {listener.sessionList.size} session(s) are online, + {listener.onlineSessionNum} session(s) are online, running {listener.totalRunning} SQL statement(s)

    ++ generateSessionStatsTable() ++ generateSQLStatsTable() - UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + UIUtils.headerSparkPage("JDBC/ODBC Server", content, parent, Some(5000)) } - /** Generate basic stats of the streaming program */ + /** Generate basic stats of the thrift server program */ private def generateBasicStats(): Seq[Node] = { val timeSinceStart = System.currentTimeMillis() - startTime.getTime
    • - Started at: {startTime.toString} + Started at: {formatDate(startTime)}
    • Time since start: {formatDurationVerbose(timeSinceStart)} @@ -77,7 +77,7 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("" [{id}] } - val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + val detail = if (info.state == ExecutionState.FAILED) info.detail else info.executePlan {info.userName} @@ -85,7 +85,7 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("" {info.groupId} {formatDate(info.startTimestamp)} - {if(info.finishTimestamp > 0) formatDate(info.finishTimestamp)} + {if (info.finishTimestamp > 0) formatDate(info.finishTimestamp)} {formatDurationOption(Some(info.totalTime))} {info.statement} {info.state} @@ -143,14 +143,14 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("" val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration", "Total Execute") def generateDataRow(session: SessionInfo): Seq[Node] = { - val sessionLink = "%s/ThriftServer/session?id=%s" + val sessionLink = "%s/sql/session?id=%s" .format(UIUtils.prependBaseUri(parent.basePath), session.sessionId) {session.userName} {session.ip} - {session.sessionId} , + {session.sessionId} {formatDate(session.startTimestamp)} - {if(session.finishTimestamp > 0) formatDate(session.finishTimestamp)} + {if (session.finishTimestamp > 0) formatDate(session.finishTimestamp)} {formatDurationOption(Some(session.totalTime))} {session.totalExecution.toString} diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala index 33ba038ecce73..3b01afa603cea 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala @@ -55,7 +55,7 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab) Total run {sessionStat._2.totalExecution} SQL ++ generateSQLStatsTable(sessionStat._2.sessionId) - UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + UIUtils.headerSparkPage("JDBC/ODBC Session", content, parent, Some(5000)) } /** Generate basic stats of the streaming program */ @@ -87,7 +87,7 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab) [{id}] } - val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + val detail = if (info.state == ExecutionState.FAILED) info.detail else info.executePlan {info.userName} diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala index 343031f10c75c..94fd8a6bb60b9 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala @@ -27,7 +27,9 @@ import org.apache.spark.{SparkContext, Logging, SparkException} * This assumes the given SparkContext has enabled its SparkUI. */ private[thriftserver] class ThriftServerTab(sparkContext: SparkContext) - extends SparkUITab(getSparkUI(sparkContext), "ThriftServer") with Logging { + extends SparkUITab(getSparkUI(sparkContext), "sql") with Logging { + + override val name = "SQL" val parent = getSparkUI(sparkContext) val listener = HiveThriftServer2.listener diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala index b070fa8eaa469..13b0c5951dddc 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala @@ -25,12 +25,16 @@ import scala.concurrent.{Await, Promise} import scala.sys.process.{Process, ProcessLogger} import org.apache.hadoop.hive.conf.HiveConf.ConfVars -import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} +import org.scalatest.BeforeAndAfter -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkFunSuite} import org.apache.spark.util.Utils -class CliSuite extends FunSuite with BeforeAndAfter with Logging { +/** + * A test suite for the `spark-sql` CLI tool. Note that all test cases share the same temporary + * Hive metastore and warehouse. + */ +class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging { val warehousePath = Utils.createTempDir() val metastorePath = Utils.createTempDir() @@ -58,13 +62,13 @@ class CliSuite extends FunSuite with BeforeAndAfter with Logging { | --master local | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$jdbcUrl | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath - | --driver-class-path ${sys.props("java.class.path")} """.stripMargin.split("\\s+").toSeq ++ extraArgs } var next = 0 val foundAllExpectedAnswers = Promise.apply[Unit]() - val queryStream = new ByteArrayInputStream(queries.mkString("\n").getBytes) + // Explicitly adds ENTER for each statement to make sure they are actually entered into the CLI. + val queryStream = new ByteArrayInputStream(queries.map(_ + "\n").mkString.getBytes) val buffer = new ArrayBuffer[String]() val lock = new Object @@ -124,12 +128,12 @@ class CliSuite extends FunSuite with BeforeAndAfter with Logging { "SELECT COUNT(*) FROM hive_test;" -> "5", "DROP TABLE hive_test;" - -> "Time taken: " + -> "OK" ) } test("Single command with -e") { - runCliWithin(1.minute, Seq("-e", "SHOW DATABASES;"))("" -> "OK") + runCliWithin(2.minute, Seq("-e", "SHOW DATABASES;"))("" -> "OK") } test("Single command with --database") { @@ -151,4 +155,33 @@ class CliSuite extends FunSuite with BeforeAndAfter with Logging { -> "hive_test" ) } + + test("Commands using SerDe provided in --jars") { + val jarFile = + "../hive/src/test/resources/hive-hcatalog-core-0.13.1.jar" + .split("/") + .mkString(File.separator) + + val dataFilePath = + Thread.currentThread().getContextClassLoader.getResource("data/files/small_kv.txt") + + runCliWithin(3.minute, Seq("--jars", s"$jarFile"))( + """CREATE TABLE t1(key string, val string) + |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'; + """.stripMargin + -> "OK", + "CREATE TABLE sourceTable (key INT, val STRING);" + -> "OK", + s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE sourceTable;" + -> "OK", + "INSERT INTO TABLE t1 SELECT key, val FROM sourceTable;" + -> "Time taken:", + "SELECT count(key) FROM t1;" + -> "5", + "DROP TABLE t1;" + -> "OK", + "DROP TABLE sourceTable;" + -> "OK" + ) + } } diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala index 1fadea97fd07f..301aa5a6411e2 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala @@ -19,14 +19,18 @@ package org.apache.spark.sql.hive.thriftserver import java.io.File import java.net.URL -import java.sql.{Date, DriverManager, Statement} +import java.nio.charset.StandardCharsets +import java.sql.{Date, DriverManager, SQLException, Statement} import scala.collection.mutable.ArrayBuffer import scala.concurrent.duration._ -import scala.concurrent.{Await, Promise} +import scala.concurrent.{Await, Promise, future} +import scala.concurrent.ExecutionContext.Implicits.global import scala.sys.process.{Process, ProcessLogger} import scala.util.{Random, Try} +import com.google.common.base.Charsets.UTF_8 +import com.google.common.io.Files import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.hive.jdbc.HiveDriver import org.apache.hive.service.auth.PlainSaslHelper @@ -35,10 +39,10 @@ import org.apache.hive.service.cli.thrift.TCLIService.Client import org.apache.hive.service.cli.thrift.ThriftCLIServiceClient import org.apache.thrift.protocol.TBinaryProtocol import org.apache.thrift.transport.TSocket -import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.BeforeAndAfterAll -import org.apache.spark.Logging -import org.apache.spark.sql.hive.HiveShim +import org.apache.spark.{Logging, SparkFunSuite} +import org.apache.spark.sql.hive.HiveContext import org.apache.spark.util.Utils object TestData { @@ -54,7 +58,7 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { override def mode: ServerMode.Value = ServerMode.binary private def withCLIServiceClient(f: ThriftCLIServiceClient => Unit): Unit = { - // Transport creation logics below mimics HiveConnection.createBinaryTransport + // Transport creation logic below mimics HiveConnection.createBinaryTransport val rawTransport = new TSocket("localhost", serverPort) val user = System.getProperty("user.name") val transport = PlainSaslHelper.getPlainTransport(user, "anonymous", rawTransport) @@ -109,7 +113,8 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { withJdbcStatement { statement => val resultSet = statement.executeQuery("SET spark.sql.hive.version") resultSet.next() - assert(resultSet.getString(1) === s"spark.sql.hive.version=${HiveShim.version}") + assert(resultSet.getString(1) === "spark.sql.hive.version") + assert(resultSet.getString(2) === HiveContext.hiveExecutionVersion) } } @@ -233,7 +238,7 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { // first session, we get the default value of the session status { statement => - val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS}") + val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS.key}") rs1.next() defaultV1 = rs1.getString(1) assert(defaultV1 != "200") @@ -251,19 +256,21 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { { statement => val queries = Seq( - s"SET ${SQLConf.SHUFFLE_PARTITIONS}=291", + s"SET ${SQLConf.SHUFFLE_PARTITIONS.key}=291", "SET hive.cli.print.header=true" ) queries.map(statement.execute) - val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS}") + val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS.key}") rs1.next() - assert("spark.sql.shuffle.partitions=291" === rs1.getString(1)) + assert("spark.sql.shuffle.partitions" === rs1.getString(1)) + assert("291" === rs1.getString(2)) rs1.close() val rs2 = statement.executeQuery("SET hive.cli.print.header") rs2.next() - assert("hive.cli.print.header=true" === rs2.getString(1)) + assert("hive.cli.print.header" === rs2.getString(1)) + assert("true" === rs2.getString(2)) rs2.close() }, @@ -271,7 +278,7 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { // default value { statement => - val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS}") + val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS.key}") rs1.next() assert(defaultV1 === rs1.getString(1)) rs1.close() @@ -335,6 +342,42 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { } ) } + + test("test jdbc cancel") { + withJdbcStatement { statement => + val queries = Seq( + "DROP TABLE IF EXISTS test_map", + "CREATE TABLE test_map(key INT, value STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map") + + queries.foreach(statement.execute) + + val largeJoin = "SELECT COUNT(*) FROM test_map " + + List.fill(10)("join test_map").mkString(" ") + val f = future { Thread.sleep(100); statement.cancel(); } + val e = intercept[SQLException] { + statement.executeQuery(largeJoin) + } + assert(e.getMessage contains "cancelled") + Await.result(f, 3.minute) + + // cancel is a noop + statement.executeQuery("SET spark.sql.hive.thriftServer.async=false") + val sf = future { Thread.sleep(100); statement.cancel(); } + val smallJoin = "SELECT COUNT(*) FROM test_map " + + List.fill(4)("join test_map").mkString(" ") + val rs1 = statement.executeQuery(smallJoin) + Await.result(sf, 3.minute) + rs1.next() + assert(rs1.getInt(1) === math.pow(5, 5)) + rs1.close() + + val rs2 = statement.executeQuery("SELECT COUNT(*) FROM test_map") + rs2.next() + assert(rs2.getInt(1) === 5) + rs2.close() + } + } } class HiveThriftHttpServerSuite extends HiveThriftJdbcTest { @@ -363,7 +406,8 @@ class HiveThriftHttpServerSuite extends HiveThriftJdbcTest { withJdbcStatement { statement => val resultSet = statement.executeQuery("SET spark.sql.hive.version") resultSet.next() - assert(resultSet.getString(1) === s"spark.sql.hive.version=${HiveShim.version}") + assert(resultSet.getString(1) === "spark.sql.hive.version") + assert(resultSet.getString(2) === HiveContext.hiveExecutionVersion) } } } @@ -391,10 +435,10 @@ abstract class HiveThriftJdbcTest extends HiveThriftServer2Test { val statements = connections.map(_.createStatement()) try { - statements.zip(fs).map { case (s, f) => f(s) } + statements.zip(fs).foreach { case (s, f) => f(s) } } finally { - statements.map(_.close()) - connections.map(_.close()) + statements.foreach(_.close()) + connections.foreach(_.close()) } } @@ -403,7 +447,7 @@ abstract class HiveThriftJdbcTest extends HiveThriftServer2Test { } } -abstract class HiveThriftServer2Test extends FunSuite with BeforeAndAfterAll with Logging { +abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAll with Logging { def mode: ServerMode.Value private val CLASS_NAME = HiveThriftServer2.getClass.getCanonicalName.stripSuffix("$") @@ -433,15 +477,33 @@ abstract class HiveThriftServer2Test extends FunSuite with BeforeAndAfterAll wit ConfVars.HIVE_SERVER2_THRIFT_HTTP_PORT } + val driverClassPath = { + // Writes a temporary log4j.properties and prepend it to driver classpath, so that it + // overrides all other potential log4j configurations contained in other dependency jar files. + val tempLog4jConf = Utils.createTempDir().getCanonicalPath + + Files.write( + """log4j.rootCategory=INFO, console + |log4j.appender.console=org.apache.log4j.ConsoleAppender + |log4j.appender.console.target=System.err + |log4j.appender.console.layout=org.apache.log4j.PatternLayout + |log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + """.stripMargin, + new File(s"$tempLog4jConf/log4j.properties"), + UTF_8) + + tempLog4jConf + File.pathSeparator + sys.props("java.class.path") + } + s"""$startScript | --master local - | --hiveconf hive.root.logger=INFO,console | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost | --hiveconf ${ConfVars.HIVE_SERVER2_TRANSPORT_MODE}=$mode | --hiveconf $portConf=$port - | --driver-class-path ${sys.props("java.class.path")} + | --driver-class-path $driverClassPath + | --driver-java-options -Dlog4j.debug | --conf spark.ui.enabled=false """.stripMargin.split("\\s+").toSeq } diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala index 47541015a3611..806240e6de458 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala @@ -17,20 +17,18 @@ package org.apache.spark.sql.hive.thriftserver - - import scala.util.Random +import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.openqa.selenium.WebDriver import org.openqa.selenium.htmlunit.HtmlUnitDriver -import org.scalatest.{Matchers, BeforeAndAfterAll} +import org.scalatest.{BeforeAndAfterAll, Matchers} import org.scalatest.concurrent.Eventually._ import org.scalatest.selenium.WebBrowser import org.scalatest.time.SpanSugar._ -import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.spark.sql.hive.HiveContext - +import org.apache.spark.ui.SparkUICssErrorHandler class UISeleniumSuite extends HiveThriftJdbcTest @@ -43,7 +41,9 @@ class UISeleniumSuite override def mode: ServerMode.Value = ServerMode.binary override def beforeAll(): Unit = { - webDriver = new HtmlUnitDriver + webDriver = new HtmlUnitDriver { + getWebClient.setCssErrorHandler(new SparkUICssErrorHandler) + } super.beforeAll() } @@ -75,9 +75,9 @@ class UISeleniumSuite """.stripMargin.split("\\s+").toSeq } - test("thrift server ui test") { - withJdbcStatement(statement =>{ - val baseURL = s"http://localhost:${uiPort}" + ignore("thrift server ui test") { + withJdbcStatement { statement => + val baseURL = s"http://localhost:$uiPort" val queries = Seq( "CREATE TABLE test_map(key INT, value STRING)", @@ -86,20 +86,20 @@ class UISeleniumSuite queries.foreach(statement.execute) eventually(timeout(10 seconds), interval(50 milliseconds)) { - go to (baseURL) - find(cssSelector("""ul li a[href*="ThriftServer"]""")) should not be(None) + go to baseURL + find(cssSelector("""ul li a[href*="sql"]""")) should not be None } eventually(timeout(10 seconds), interval(50 milliseconds)) { - go to (baseURL + "/ThriftServer") - find(id("sessionstat")) should not be(None) - find(id("sqlstat")) should not be(None) + go to (baseURL + "/sql") + find(id("sessionstat")) should not be None + find(id("sqlstat")) should not be None // check whether statements exists queries.foreach { line => findAll(cssSelector("""ul table tbody tr td""")).map(_.text).toList should contain (line) } } - }) + } } } diff --git a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala deleted file mode 100644 index b3a79ba1c7d6b..0000000000000 --- a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala +++ /dev/null @@ -1,278 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive.thriftserver - -import java.sql.{Date, Timestamp} -import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, Map => JMap, UUID} - -import org.apache.commons.logging.Log -import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.conf.HiveConf.ConfVars -import org.apache.hive.service.cli.thrift.TProtocolVersion -import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager - -import scala.collection.JavaConversions._ -import scala.collection.mutable.{ArrayBuffer, Map => SMap} - -import org.apache.hadoop.hive.common.`type`.HiveDecimal -import org.apache.hadoop.hive.metastore.api.FieldSchema -import org.apache.hadoop.hive.shims.ShimLoader -import org.apache.hadoop.security.UserGroupInformation -import org.apache.hive.service.cli._ -import org.apache.hive.service.cli.operation.ExecuteStatementOperation -import org.apache.hive.service.cli.session.{SessionManager, HiveSession} - -import org.apache.spark.Logging -import org.apache.spark.sql.{DataFrame, SQLConf, Row => SparkRow} -import org.apache.spark.sql.execution.SetCommand -import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ -import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes} -import org.apache.spark.sql.types._ - -/** - * A compatibility layer for interacting with Hive version 0.12.0. - */ -private[thriftserver] object HiveThriftServerShim { - val version = "0.12.0" - - def setServerUserName(sparkServiceUGI: UserGroupInformation, sparkCliService:SparkSQLCLIService) = { - val serverUserName = ShimLoader.getHadoopShims.getShortUserName(sparkServiceUGI) - setSuperField(sparkCliService, "serverUserName", serverUserName) - } -} - -private[hive] class SparkSQLDriver(val _context: HiveContext = SparkSQLEnv.hiveContext) - extends AbstractSparkSQLDriver(_context) { - override def getResults(res: JArrayList[String]): Boolean = { - if (hiveResponse == null) { - false - } else { - res.addAll(hiveResponse) - hiveResponse = null - true - } - } -} - -private[hive] class SparkExecuteStatementOperation( - parentSession: HiveSession, - statement: String, - confOverlay: JMap[String, String])( - hiveContext: HiveContext, - sessionToActivePool: SMap[SessionHandle, String]) - extends ExecuteStatementOperation(parentSession, statement, confOverlay) with Logging { - - private var result: DataFrame = _ - private var iter: Iterator[SparkRow] = _ - private var dataTypes: Array[DataType] = _ - - def close(): Unit = { - // RDDs will be cleaned automatically upon garbage collection. - logDebug("CLOSING") - } - - def getNextRowSet(order: FetchOrientation, maxRowsL: Long): RowSet = { - if (!iter.hasNext) { - new RowSet() - } else { - // maxRowsL here typically maps to java.sql.Statement.getFetchSize, which is an int - val maxRows = maxRowsL.toInt - var curRow = 0 - var rowSet = new ArrayBuffer[Row](maxRows.min(1024)) - - while (curRow < maxRows && iter.hasNext) { - val sparkRow = iter.next() - val row = new Row() - var curCol = 0 - - while (curCol < sparkRow.length) { - if (sparkRow.isNullAt(curCol)) { - addNullColumnValue(sparkRow, row, curCol) - } else { - addNonNullColumnValue(sparkRow, row, curCol) - } - curCol += 1 - } - rowSet += row - curRow += 1 - } - new RowSet(rowSet, 0) - } - } - - def addNonNullColumnValue(from: SparkRow, to: Row, ordinal: Int) { - dataTypes(ordinal) match { - case StringType => - to.addString(from(ordinal).asInstanceOf[String]) - case IntegerType => - to.addColumnValue(ColumnValue.intValue(from.getInt(ordinal))) - case BooleanType => - to.addColumnValue(ColumnValue.booleanValue(from.getBoolean(ordinal))) - case DoubleType => - to.addColumnValue(ColumnValue.doubleValue(from.getDouble(ordinal))) - case FloatType => - to.addColumnValue(ColumnValue.floatValue(from.getFloat(ordinal))) - case DecimalType() => - val hiveDecimal = from.getDecimal(ordinal) - to.addColumnValue(ColumnValue.stringValue(new HiveDecimal(hiveDecimal))) - case LongType => - to.addColumnValue(ColumnValue.longValue(from.getLong(ordinal))) - case ByteType => - to.addColumnValue(ColumnValue.byteValue(from.getByte(ordinal))) - case ShortType => - to.addColumnValue(ColumnValue.shortValue(from.getShort(ordinal))) - case DateType => - to.addColumnValue(ColumnValue.dateValue(from(ordinal).asInstanceOf[Date])) - case TimestampType => - to.addColumnValue( - ColumnValue.timestampValue(from.get(ordinal).asInstanceOf[Timestamp])) - case BinaryType | _: ArrayType | _: StructType | _: MapType => - val hiveString = HiveContext.toHiveString((from.get(ordinal), dataTypes(ordinal))) - to.addColumnValue(ColumnValue.stringValue(hiveString)) - } - } - - def addNullColumnValue(from: SparkRow, to: Row, ordinal: Int) { - dataTypes(ordinal) match { - case StringType => - to.addString(null) - case IntegerType => - to.addColumnValue(ColumnValue.intValue(null)) - case BooleanType => - to.addColumnValue(ColumnValue.booleanValue(null)) - case DoubleType => - to.addColumnValue(ColumnValue.doubleValue(null)) - case FloatType => - to.addColumnValue(ColumnValue.floatValue(null)) - case DecimalType() => - to.addColumnValue(ColumnValue.stringValue(null: HiveDecimal)) - case LongType => - to.addColumnValue(ColumnValue.longValue(null)) - case ByteType => - to.addColumnValue(ColumnValue.byteValue(null)) - case ShortType => - to.addColumnValue(ColumnValue.shortValue(null)) - case DateType => - to.addColumnValue(ColumnValue.dateValue(null)) - case TimestampType => - to.addColumnValue(ColumnValue.timestampValue(null)) - case BinaryType | _: ArrayType | _: StructType | _: MapType => - to.addColumnValue(ColumnValue.stringValue(null: String)) - } - } - - def getResultSetSchema: TableSchema = { - logInfo(s"Result Schema: ${result.queryExecution.analyzed.output}") - if (result.queryExecution.analyzed.output.size == 0) { - new TableSchema(new FieldSchema("Result", "string", "") :: Nil) - } else { - val schema = result.queryExecution.analyzed.output.map { attr => - new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "") - } - new TableSchema(schema) - } - } - - def run(): Unit = { - val statementId = UUID.randomUUID().toString - logInfo(s"Running query '$statement'") - setState(OperationState.RUNNING) - HiveThriftServer2.listener.onStatementStart( - statementId, parentSession.getSessionHandle.getSessionId.toString, statement, statementId) - hiveContext.sparkContext.setJobGroup(statementId, statement) - sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => - hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) - } - try { - result = hiveContext.sql(statement) - logDebug(result.queryExecution.toString()) - result.queryExecution.logical match { - case SetCommand(Some((SQLConf.THRIFTSERVER_POOL, Some(value))), _) => - sessionToActivePool(parentSession.getSessionHandle) = value - logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") - case _ => - } - HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString()) - iter = { - val useIncrementalCollect = - hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean - if (useIncrementalCollect) { - result.rdd.toLocalIterator - } else { - result.collect().iterator - } - } - dataTypes = result.queryExecution.analyzed.output.map(_.dataType).toArray - setHasResultSet(true) - } catch { - // Actually do need to catch Throwable as some failures don't inherit from Exception and - // HiveServer will silently swallow them. - case e: Throwable => - setState(OperationState.ERROR) - HiveThriftServer2.listener.onStatementError( - statementId, e.getMessage, e.getStackTraceString) - logError("Error executing query:",e) - throw new HiveSQLException(e.toString) - } - setState(OperationState.FINISHED) - HiveThriftServer2.listener.onStatementFinish(statementId) - } -} - -private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) - extends SessionManager - with ReflectedCompositeService { - - private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext) - - override def init(hiveConf: HiveConf) { - setSuperField(this, "hiveConf", hiveConf) - - val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS) - setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize)) - getAncestorField[Log](this, 3, "LOG").info( - s"HiveServer2: Async execution pool size $backgroundPoolSize") - - setSuperField(this, "operationManager", sparkSqlOperationManager) - addService(sparkSqlOperationManager) - - initCompositeService(hiveConf) - } - - override def openSession( - username: String, - passwd: String, - sessionConf: java.util.Map[String, String], - withImpersonation: Boolean, - delegationToken: String): SessionHandle = { - hiveContext.openSession() - val sessionHandle = super.openSession( - username, passwd, sessionConf, withImpersonation, delegationToken) - HiveThriftServer2.listener.onSessionCreated("UNKNOWN", sessionHandle.getSessionId.toString) - sessionHandle - } - - override def closeSession(sessionHandle: SessionHandle) { - HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) - super.closeSession(sessionHandle) - sparkSqlOperationManager.sessionToActivePool -= sessionHandle - - hiveContext.detachSession() - } -} diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index b6245a57074c8..f88e62763ca70 100644 --- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -23,7 +23,6 @@ import java.util.{Locale, TimeZone} import org.scalatest.BeforeAndAfter import org.apache.spark.sql.SQLConf -import org.apache.spark.sql.hive.HiveShim import org.apache.spark.sql.hive.test.TestHive /** @@ -48,17 +47,17 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { // Add Locale setting Locale.setDefault(Locale.US) // Set a relatively small column batch size for testing purposes - TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, "5") + TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, 5) // Enable in-memory partition pruning for testing purposes - TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, "true") + TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true) } override def afterAll() { TestHive.cacheTables = false TimeZone.setDefault(originalTimeZone) Locale.setDefault(originalLocale) - TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize.toString) - TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning.toString) + TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize) + TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning) } /** A list of tests deemed out of scope currently and thus completely disregarded. */ @@ -250,8 +249,15 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { // The isolated classloader seemed to make some of our test reset mechanisms less robust. "combine1", // This test changes compression settings in a way that breaks all subsequent tests. - "load_dyn_part14.*" // These work alone but fail when run with other tests... - ) ++ HiveShim.compatibilityBlackList + "load_dyn_part14.*", // These work alone but fail when run with other tests... + + // the answer is sensitive for jdk version + "udf_java_method", + + // Spark SQL use Long for TimestampType, lose the precision under 100ns + "timestamp_1", + "timestamp_2" + ) /** * The set of tests that are believed to be working in catalyst. Tests not on whiteList or @@ -793,8 +799,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "stats_publisher_error_1", "subq2", "tablename_with_select", - "timestamp_1", - "timestamp_2", "timestamp_3", "timestamp_comparison", "timestamp_lazy", @@ -815,19 +819,19 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "udf2", "udf5", "udf6", - "udf7", + // "udf7", turn this on after we figure out null vs nan vs infinity "udf8", "udf9", "udf_10_trims", "udf_E", "udf_PI", "udf_abs", - "udf_acos", + // "udf_acos", turn this on after we figure out null vs nan vs infinity "udf_add", "udf_array", "udf_array_contains", "udf_ascii", - "udf_asin", + // "udf_asin", turn this on after we figure out null vs nan vs infinity "udf_atan", "udf_avg", "udf_bigint", @@ -877,7 +881,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "udf_int", "udf_isnotnull", "udf_isnull", - "udf_java_method", "udf_lcase", "udf_length", "udf_lessthan", @@ -916,7 +919,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "udf_repeat", "udf_rlike", "udf_round", - "udf_round_3", + // "udf_round_3", TODO: FIX THIS failed due to cast exception "udf_rpad", "udf_rtrim", "udf_second", diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala index 65d070bd3cbde..f458567e5d7ea 100644 --- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala @@ -26,11 +26,11 @@ import org.apache.spark.sql.hive.test.TestHive class SortMergeCompatibilitySuite extends HiveCompatibilitySuite { override def beforeAll() { super.beforeAll() - TestHive.setConf(SQLConf.SORTMERGE_JOIN, "true") + TestHive.setConf(SQLConf.SORTMERGE_JOIN, true) } override def afterAll() { - TestHive.setConf(SQLConf.SORTMERGE_JOIN, "false") + TestHive.setConf(SQLConf.SORTMERGE_JOIN, false) super.afterAll() } diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index e322340094e6f..a17546d706248 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.spark spark-sql_${scala.binary.version} @@ -136,16 +143,6 @@ - - hive-0.12.0 - - - com.twitter - parquet-hive-bundle - 1.5.0 - - - diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/ExtendedHiveQlParser.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/ExtendedHiveQlParser.scala index 3f20c6142e59a..7f8449cdc282d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/ExtendedHiveQlParser.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/ExtendedHiveQlParser.scala @@ -29,10 +29,10 @@ import org.apache.spark.sql.hive.execution.{AddJar, AddFile, HiveNativeCommand} private[hive] class ExtendedHiveQlParser extends AbstractSparkSQLParser { // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword` // properties via reflection the class in runtime for constructing the SqlLexical object - protected val ADD = Keyword("ADD") - protected val DFS = Keyword("DFS") + protected val ADD = Keyword("ADD") + protected val DFS = Keyword("DFS") protected val FILE = Keyword("FILE") - protected val JAR = Keyword("JAR") + protected val JAR = Keyword("JAR") protected lazy val start: Parser[LogicalPlan] = dfs | addJar | addFile | hiveQl diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index 538c6c7f0a200..cf05c6c989655 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -17,35 +17,35 @@ package org.apache.spark.sql.hive -import java.io.{BufferedReader, File, InputStreamReader, PrintStream} +import java.io.File +import java.net.{URL, URLClassLoader} import java.sql.Timestamp -import java.util.{ArrayList => JArrayList} - -import org.apache.hadoop.hive.ql.parse.VariableSubstitution -import org.apache.spark.sql.catalyst.Dialect import scala.collection.JavaConversions._ +import scala.collection.mutable.HashMap import scala.language.implicitConversions import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.hive.common.StatsSetupConst +import org.apache.hadoop.hive.common.`type`.HiveDecimal import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.hive.ql.parse.VariableSubstitution -import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.ql.session.SessionState import org.apache.hadoop.hive.serde2.io.{DateWritable, TimestampWritable} -import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.SparkContext import org.apache.spark.annotation.Experimental -import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.sql._ -import org.apache.spark.sql.catalyst.analysis.{Analyzer, EliminateSubQueries, OverrideCatalog, OverrideFunctionRegistry} +import org.apache.spark.sql.SQLConf.SQLConfEntry +import org.apache.spark.sql.SQLConf.SQLConfEntry._ +import org.apache.spark.sql.catalyst.ParserDialect +import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUdfs, QueryExecutionException, SetCommand} +import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUdfs, SetCommand} import org.apache.spark.sql.hive.client._ import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNativeCommand} -import org.apache.spark.sql.sources.{DDLParser, DataSourceStrategy} +import org.apache.spark.sql.sources.DataSourceStrategy import org.apache.spark.sql.types._ import org.apache.spark.util.Utils @@ -53,7 +53,7 @@ import org.apache.spark.util.Utils /** * This is the HiveQL Dialect, this dialect is strongly bind with HiveContext */ -private[hive] class HiveQLDialect extends Dialect { +private[hive] class HiveQLDialect extends ParserDialect { override def parse(sqlText: String): LogicalPlan = { HiveQl.parseSql(sqlText) } @@ -62,19 +62,22 @@ private[hive] class HiveQLDialect extends Dialect { /** * An instance of the Spark SQL execution engine that integrates with data stored in Hive. * Configuration for Hive is read from hive-site.xml on the classpath. + * + * @since 1.0.0 */ class HiveContext(sc: SparkContext) extends SQLContext(sc) { self => import HiveContext._ + println("create HiveContext") + /** * When true, enables an experimental feature where metastore tables that use the parquet SerDe * are automatically converted to use the Spark SQL parquet table scan, instead of the Hive * SerDe. */ - protected[sql] def convertMetastoreParquet: Boolean = - getConf("spark.sql.hive.convertMetastoreParquet", "true") == "true" + protected[sql] def convertMetastoreParquet: Boolean = getConf(CONVERT_METASTORE_PARQUET) /** * When true, also tries to merge possibly different but compatible Parquet schemas in different @@ -83,7 +86,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { * This configuration is only effective when "spark.sql.hive.convertMetastoreParquet" is true. */ protected[sql] def convertMetastoreParquetWithSchemaMerging: Boolean = - getConf("spark.sql.hive.convertMetastoreParquet.mergeSchema", "false") == "true" + getConf(CONVERT_METASTORE_PARQUET_WITH_SCHEMA_MERGING) /** * When true, a table created by a Hive CTAS statement (no USING clause) will be @@ -97,8 +100,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { * - The CTAS statement specifies SequenceFile (STORED AS SEQUENCEFILE) as the file format * and no SerDe is specified (no ROW FORMAT SERDE clause). */ - protected[sql] def convertCTAS: Boolean = - getConf("spark.sql.hive.convertCTAS", "false").toBoolean + protected[sql] def convertCTAS: Boolean = getConf(CONVERT_CTAS) /** * The version of the hive client that will be used to communicate with the metastore. Note that @@ -116,8 +118,30 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { * option is only valid when using the execution version of Hive. * - maven - download the correct version of hive on demand from maven. */ - protected[hive] def hiveMetastoreJars: String = - getConf(HIVE_METASTORE_JARS, "builtin") + protected[hive] def hiveMetastoreJars: String = getConf(HIVE_METASTORE_JARS) + + /** + * A comma separated list of class prefixes that should be loaded using the classloader that + * is shared between Spark SQL and a specific version of Hive. An example of classes that should + * be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need + * to be shared are those that interact with classes that are already shared. For example, + * custom appenders that are used by log4j. + */ + protected[hive] def hiveMetastoreSharedPrefixes: Seq[String] = + getConf(HIVE_METASTORE_SHARED_PREFIXES).filterNot(_ == "") + + /** + * A comma separated list of class prefixes that should explicitly be reloaded for each version + * of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a + * prefix that typically would be shared (i.e. org.apache.spark.*) + */ + protected[hive] def hiveMetastoreBarrierPrefixes: Seq[String] = + getConf(HIVE_METASTORE_BARRIER_PREFIXES).filterNot(_ == "") + + /* + * hive thrift server use background spark sql thread pool to execute sql queries + */ + protected[hive] def hiveThriftServerAsync: Boolean = getConf(HIVE_THRIFT_SERVER_ASYNC) @transient protected[sql] lazy val substitutor = new VariableSubstitution() @@ -127,14 +151,15 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { * Hive 13 as this is the version of Hive that is packaged with Spark SQL. This copy of the * client is used for execution related tasks like registering temporary functions or ensuring * that the ThreadLocal SessionState is correctly populated. This copy of Hive is *not* used - * for storing peristent metadata, and only point to a dummy metastore in a temporary directory. + * for storing persistent metadata, and only point to a dummy metastore in a temporary directory. */ @transient protected[hive] lazy val executionHive: ClientWrapper = { - logInfo(s"Initilizing execution hive, version $hiveExecutionVersion") + logInfo(s"Initializing execution hive, version $hiveExecutionVersion") new ClientWrapper( version = IsolatedClientLoader.hiveVersion(hiveExecutionVersion), - config = newTemporaryConfiguration()) + config = newTemporaryConfiguration(), + initClassLoader = Utils.getContextOrSparkClassLoader) } SessionState.setCurrentSessionState(executionHive.state) @@ -161,13 +186,22 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { "Specify a vaild path to the correct hive jars using $HIVE_METASTORE_JARS " + s"or change $HIVE_METASTORE_VERSION to $hiveExecutionVersion.") } - val jars = getClass.getClassLoader match { - case urlClassLoader: java.net.URLClassLoader => urlClassLoader.getURLs - case other => - throw new IllegalArgumentException( - "Unable to locate hive jars to connect to metastore " + - s"using classloader ${other.getClass.getName}. " + - "Please set spark.sql.hive.metastore.jars") + + // We recursively find all jars in the class loader chain, + // starting from the given classLoader. + def allJars(classLoader: ClassLoader): Array[URL] = classLoader match { + case null => Array.empty[URL] + case urlClassLoader: URLClassLoader => + urlClassLoader.getURLs ++ allJars(urlClassLoader.getParent) + case other => allJars(other.getParent) + } + + val classLoader = Utils.getContextOrSparkClassLoader + val jars = allJars(classLoader) + if (jars.length == 0) { + throw new IllegalArgumentException( + "Unable to locate hive jars to connect to metastore. " + + "Please set spark.sql.hive.metastore.jars.") } logInfo( @@ -176,12 +210,14 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { version = metaVersion, execJars = jars.toSeq, config = allConfig, - isolationOn = true) + isolationOn = true, + barrierPrefixes = hiveMetastoreBarrierPrefixes, + sharedPrefixes = hiveMetastoreSharedPrefixes) } else if (hiveMetastoreJars == "maven") { // TODO: Support for loading the jars from an already downloaded location. logInfo( s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using maven.") - IsolatedClientLoader.forVersion(hiveMetastoreVersion, allConfig ) + IsolatedClientLoader.forVersion(hiveMetastoreVersion, allConfig) } else { // Convert to files and expand any directories. val jars = @@ -207,7 +243,9 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { version = metaVersion, execJars = jars.toSeq, config = allConfig, - isolationOn = true) + isolationOn = true, + barrierPrefixes = hiveMetastoreBarrierPrefixes, + sharedPrefixes = hiveMetastoreSharedPrefixes) } isolatedLoader.client } @@ -224,15 +262,15 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { * Spark SQL or the external data source library it uses might cache certain metadata about a * table, such as the location of blocks. When those change outside of Spark SQL, users should * call this function to invalidate the cache. + * + * @since 1.3.0 */ def refreshTable(tableName: String): Unit = { - // TODO: Database support... - catalog.refreshTable("default", tableName) + catalog.refreshTable(catalog.client.currentDatabase, tableName) } protected[hive] def invalidateTable(tableName: String): Unit = { - // TODO: Database support... - catalog.invalidateTable("default", tableName) + catalog.invalidateTable(catalog.client.currentDatabase, tableName) } /** @@ -241,6 +279,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { * * Right now, it only supports Hive tables and it only updates the size of a Hive table * in the Hive metastore. + * + * @since 1.2.0 */ @Experimental def analyze(tableName: String) { @@ -286,7 +326,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { val tableParameters = relation.hiveQlTable.getParameters val oldTotalSize = - Option(tableParameters.get(HiveShim.getStatsSetupConstTotalSize)) + Option(tableParameters.get(StatsSetupConst.TOTAL_SIZE)) .map(_.toLong) .getOrElse(0L) val newTotalSize = getFileSizeForTable(hiveconf, relation.hiveQlTable) @@ -297,7 +337,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { catalog.client.alterTable( relation.table.copy( properties = relation.table.properties + - (HiveShim.getStatsSetupConstTotalSize -> newTotalSize.toString))) + (StatsSetupConst.TOTAL_SIZE -> newTotalSize.toString))) } case otherRelation => throw new UnsupportedOperationException( @@ -309,27 +349,34 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { override def setConf(key: String, value: String): Unit = { super.setConf(key, value) - hiveconf.set(key, value) executionHive.runSqlHive(s"SET $key=$value") metadataHive.runSqlHive(s"SET $key=$value") + // If users put any Spark SQL setting in the spark conf (e.g. spark-defaults.conf), + // this setConf will be called in the constructor of the SQLContext. + // Also, calling hiveconf will create a default session containing a HiveConf, which + // will interfer with the creation of executionHive (which is a lazy val). So, + // we put hiveconf.set at the end of this method. + hiveconf.set(key, value) } - /* A catalyst metadata catalog that points to the Hive Metastore. */ + private[sql] override def setConf[T](entry: SQLConfEntry[T], value: T): Unit = { + setConf(entry.key, entry.stringConverter(value)) + } + + /* A catalyst metadata catalog that points to the Hive Metastore. */ @transient override protected[sql] lazy val catalog = new HiveMetastoreCatalog(metadataHive, this) with OverrideCatalog // Note that HiveUDFs will be overridden by functions registered in this context. @transient - override protected[sql] lazy val functionRegistry = - new HiveFunctionRegistry with OverrideFunctionRegistry { - def caseSensitive: Boolean = false - } + override protected[sql] lazy val functionRegistry: FunctionRegistry = + new OverrideFunctionRegistry(new HiveFunctionRegistry(FunctionRegistry.builtin)) /* An analyzer that uses the Hive metastore. */ @transient - override protected[sql] lazy val analyzer = - new Analyzer(catalog, functionRegistry, caseSensitive = false) { + override protected[sql] lazy val analyzer: Analyzer = + new Analyzer(catalog, functionRegistry, conf) { override val extendedResolutionRules = catalog.ParquetConversions :: catalog.CreateTables :: @@ -338,6 +385,10 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { ResolveHiveWindowFunction :: sources.PreInsertCastAndRename :: Nil + + override val extendedCheckRules = Seq( + sources.PreWriteCheck(catalog) + ) } override protected[sql] def createSession(): SQLSession = { @@ -350,6 +401,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { protected[hive] class SQLSession extends super.SQLSession { protected[sql] override lazy val conf: SQLConf = new SQLConf { override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") + override def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE, false) } /** @@ -465,14 +517,68 @@ private[hive] object HiveContext { val hiveExecutionVersion: String = "0.13.1" val HIVE_METASTORE_VERSION: String = "spark.sql.hive.metastore.version" - val HIVE_METASTORE_JARS: String = "spark.sql.hive.metastore.jars" + val HIVE_METASTORE_JARS = stringConf("spark.sql.hive.metastore.jars", + defaultValue = Some("builtin"), + doc = "Location of the jars that should be used to instantiate the HiveMetastoreClient. This" + + " property can be one of three options: " + + "1. \"builtin\" Use Hive 0.13.1, which is bundled with the Spark assembly jar when " + + "-Phive is enabled. When this option is chosen, " + + "spark.sql.hive.metastore.version must be either 0.13.1 or not defined. " + + "2. \"maven\" Use Hive jars of specified version downloaded from Maven repositories." + + "3. A classpath in the standard format for both Hive and Hadoop.") + + val CONVERT_METASTORE_PARQUET = booleanConf("spark.sql.hive.convertMetastoreParquet", + defaultValue = Some(true), + doc = "When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of " + + "the built in support.") + + val CONVERT_METASTORE_PARQUET_WITH_SCHEMA_MERGING = booleanConf( + "spark.sql.hive.convertMetastoreParquet.mergeSchema", + defaultValue = Some(false), + doc = "TODO") + + val CONVERT_CTAS = booleanConf("spark.sql.hive.convertCTAS", + defaultValue = Some(false), + doc = "TODO") + + val HIVE_METASTORE_SHARED_PREFIXES = stringSeqConf("spark.sql.hive.metastore.sharedPrefixes", + defaultValue = Some(jdbcPrefixes), + doc = "A comma separated list of class prefixes that should be loaded using the classloader " + + "that is shared between Spark SQL and a specific version of Hive. An example of classes " + + "that should be shared is JDBC drivers that are needed to talk to the metastore. Other " + + "classes that need to be shared are those that interact with classes that are already " + + "shared. For example, custom appenders that are used by log4j.") + + private def jdbcPrefixes = Seq( + "com.mysql.jdbc", "org.postgresql", "com.microsoft.sqlserver", "oracle.jdbc") + + val HIVE_METASTORE_BARRIER_PREFIXES = stringSeqConf("spark.sql.hive.metastore.barrierPrefixes", + defaultValue = Some(Seq()), + doc = "A comma separated list of class prefixes that should explicitly be reloaded for each " + + "version of Hive that Spark SQL is communicating with. For example, Hive UDFs that are " + + "declared in a prefix that typically would be shared (i.e. org.apache.spark.*).") + + val HIVE_THRIFT_SERVER_ASYNC = booleanConf("spark.sql.hive.thriftServer.async", + defaultValue = Some(true), + doc = "TODO") /** Constructs a configuration for hive, where the metastore is located in a temp directory. */ def newTemporaryConfiguration(): Map[String, String] = { val tempDir = Utils.createTempDir() val localMetastore = new File(tempDir, "metastore").getAbsolutePath - Map( - "javax.jdo.option.ConnectionURL" -> s"jdbc:derby:;databaseName=$localMetastore;create=true") + val propMap: HashMap[String, String] = HashMap() + // We have to mask all properties in hive-site.xml that relates to metastore data source + // as we used a local metastore here. + HiveConf.ConfVars.values().foreach { confvar => + if (confvar.varname.contains("datanucleus") || confvar.varname.contains("jdo")) { + propMap.put(confvar.varname, confvar.defaultVal) + } + } + propMap.put("javax.jdo.option.ConnectionURL", + s"jdbc:derby:;databaseName=$localMetastore;create=true") + propMap.put("datanucleus.rdbms.datastoreAdapterClassName", + "org.datanucleus.store.rdbms.adapter.DerbyAdapter") + propMap.toMap } protected val primitiveTypes = @@ -486,7 +592,7 @@ private[hive] object HiveContext { }.mkString("{", ",", "}") case (seq: Seq[_], ArrayType(typ, _)) => seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]") - case (map: Map[_,_], MapType(kType, vType, _)) => + case (map: Map[_, _], MapType(kType, vType, _)) => map.map { case (key, value) => toHiveStructString((key, kType)) + ":" + toHiveStructString((value, vType)) @@ -497,7 +603,7 @@ private[hive] object HiveContext { case (bin: Array[Byte], BinaryType) => new String(bin, "UTF-8") case (decimal: java.math.BigDecimal, DecimalType()) => // Hive strips trailing zeros so use its toString - HiveShim.createDecimal(decimal).toString + HiveDecimal.create(decimal).toString case (other, tpe) if primitiveTypes contains tpe => other.toString } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala index 74ae984f34866..d4f1ae8ee01d9 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala @@ -18,14 +18,17 @@ package org.apache.spark.sql.hive import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar} -import org.apache.hadoop.hive.serde2.objectinspector._ import org.apache.hadoop.hive.serde2.objectinspector.primitive._ +import org.apache.hadoop.hive.serde2.objectinspector.{StructField => HiveStructField, _} +import org.apache.hadoop.hive.serde2.typeinfo.{DecimalTypeInfo, TypeInfoFactory} import org.apache.hadoop.hive.serde2.{io => hiveIo} import org.apache.hadoop.{io => hadoopIo} import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.util.DateUtils import org.apache.spark.sql.types import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /* Implicit conversions */ import scala.collection.JavaConversions._ @@ -121,7 +124,7 @@ import scala.collection.JavaConversions._ * even a normal java object (POJO) * UnionObjectInspector: (tag: Int, object data) (TODO: not supported by SparkSQL yet) * - * 3) ConstantObjectInspector: + * 3) ConstantObjectInspector: * Constant object inspector can be either primitive type or Complex type, and it bundles a * constant value as its property, usually the value is created when the constant object inspector * constructed. @@ -132,7 +135,7 @@ import scala.collection.JavaConversions._ } }}} * Hive provides 3 built-in constant object inspectors: - * Primitive Object Inspectors: + * Primitive Object Inspectors: * WritableConstantStringObjectInspector * WritableConstantHiveVarcharObjectInspector * WritableConstantHiveDecimalObjectInspector @@ -146,9 +149,9 @@ import scala.collection.JavaConversions._ * WritableConstantByteObjectInspector * WritableConstantBinaryObjectInspector * WritableConstantDateObjectInspector - * Map Object Inspector: + * Map Object Inspector: * StandardConstantMapObjectInspector - * List Object Inspector: + * List Object Inspector: * StandardConstantListObjectInspector]] * Struct Object Inspector: Hive doesn't provide the built-in constant object inspector for Struct * Union Object Inspector: Hive doesn't provide the built-in constant object inspector for Union @@ -240,18 +243,19 @@ private[hive] trait HiveInspectors { def unwrap(data: Any, oi: ObjectInspector): Any = oi match { case coi: ConstantObjectInspector if coi.getWritableConstantValue == null => null case poi: WritableConstantStringObjectInspector => - UTF8String(poi.getWritableConstantValue.toString) + UTF8String.fromString(poi.getWritableConstantValue.toString) case poi: WritableConstantHiveVarcharObjectInspector => - UTF8String(poi.getWritableConstantValue.getHiveVarchar.getValue) + UTF8String.fromString(poi.getWritableConstantValue.getHiveVarchar.getValue) case poi: WritableConstantHiveDecimalObjectInspector => HiveShim.toCatalystDecimal( PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector, poi.getWritableConstantValue.getHiveDecimal) case poi: WritableConstantTimestampObjectInspector => - poi.getWritableConstantValue.getTimestamp.clone() - case poi: WritableConstantIntObjectInspector => + val t = poi.getWritableConstantValue + t.getSeconds * 10000000L + t.getNanos / 100L + case poi: WritableConstantIntObjectInspector => poi.getWritableConstantValue.get() - case poi: WritableConstantDoubleObjectInspector => + case poi: WritableConstantDoubleObjectInspector => poi.getWritableConstantValue.get() case poi: WritableConstantBooleanObjectInspector => poi.getWritableConstantValue.get() @@ -285,13 +289,13 @@ private[hive] trait HiveInspectors { case pi: PrimitiveObjectInspector => pi match { // We think HiveVarchar is also a String case hvoi: HiveVarcharObjectInspector if hvoi.preferWritable() => - UTF8String(hvoi.getPrimitiveWritableObject(data).getHiveVarchar.getValue) + UTF8String.fromString(hvoi.getPrimitiveWritableObject(data).getHiveVarchar.getValue) case hvoi: HiveVarcharObjectInspector => - UTF8String(hvoi.getPrimitiveJavaObject(data).getValue) + UTF8String.fromString(hvoi.getPrimitiveJavaObject(data).getValue) case x: StringObjectInspector if x.preferWritable() => - UTF8String(x.getPrimitiveWritableObject(data).toString) + UTF8String.fromString(x.getPrimitiveWritableObject(data).toString) case x: StringObjectInspector => - UTF8String(x.getPrimitiveJavaObject(data)) + UTF8String.fromString(x.getPrimitiveJavaObject(data)) case x: IntObjectInspector if x.preferWritable() => x.get(data) case x: BooleanObjectInspector if x.preferWritable() => x.get(data) case x: FloatObjectInspector if x.preferWritable() => x.get(data) @@ -305,17 +309,17 @@ private[hive] trait HiveInspectors { // In order to keep backward-compatible, we have to copy the // bytes with old apis val bw = x.getPrimitiveWritableObject(data) - val result = new Array[Byte](bw.getLength()) + val result = new Array[Byte](bw.getLength()) System.arraycopy(bw.getBytes(), 0, result, 0, bw.getLength()) result case x: DateObjectInspector if x.preferWritable() => DateUtils.fromJavaDate(x.getPrimitiveWritableObject(data).get()) case x: DateObjectInspector => DateUtils.fromJavaDate(x.getPrimitiveJavaObject(data)) - // org.apache.hadoop.hive.serde2.io.TimestampWritable.set will reset current time object - // if next timestamp is null, so Timestamp object is cloned case x: TimestampObjectInspector if x.preferWritable() => - x.getPrimitiveWritableObject(data).getTimestamp.clone() - case ti: TimestampObjectInspector => ti.getPrimitiveJavaObject(data).clone() + val t = x.getPrimitiveWritableObject(data) + t.getSeconds * 10000000L + t.getNanos / 100 + case ti: TimestampObjectInspector => + DateUtils.fromJavaTimestamp(ti.getPrimitiveJavaObject(data)) case _ => pi.getPrimitiveJavaObject(data) } case li: ListObjectInspector => @@ -334,7 +338,7 @@ private[hive] trait HiveInspectors { val allRefs = si.getAllStructFieldRefs new GenericRow( allRefs.map(r => - unwrap(si.getStructFieldData(data,r), r.getFieldObjectInspector)).toArray) + unwrap(si.getStructFieldData(data, r), r.getFieldObjectInspector)).toArray) } @@ -349,17 +353,20 @@ private[hive] trait HiveInspectors { new HiveVarchar(s, s.size) case _: JavaHiveDecimalObjectInspector => - (o: Any) => HiveShim.createDecimal(o.asInstanceOf[Decimal].toJavaBigDecimal) + (o: Any) => HiveDecimal.create(o.asInstanceOf[Decimal].toJavaBigDecimal) case _: JavaDateObjectInspector => (o: Any) => DateUtils.toJavaDate(o.asInstanceOf[Int]) + case _: JavaTimestampObjectInspector => + (o: Any) => DateUtils.toJavaTimestamp(o.asInstanceOf[Long]) + case soi: StandardStructObjectInspector => val wrappers = soi.getAllStructFieldRefs.map(ref => wrapperFor(ref.getFieldObjectInspector)) (o: Any) => { if (o != null) { val struct = soi.create() - (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[Row].toSeq).zipped.foreach { + (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[InternalRow].toSeq).zipped.foreach { (field, wrapper, data) => soi.setStructFieldData(struct, field, wrapper(data)) } struct @@ -393,6 +400,30 @@ private[hive] trait HiveInspectors { identity[Any] } + /** + * Builds specific unwrappers ahead of time according to object inspector + * types to avoid pattern matching and branching costs per row. + */ + def unwrapperFor(field: HiveStructField): (Any, MutableRow, Int) => Unit = + field.getFieldObjectInspector match { + case oi: BooleanObjectInspector => + (value: Any, row: MutableRow, ordinal: Int) => row.setBoolean(ordinal, oi.get(value)) + case oi: ByteObjectInspector => + (value: Any, row: MutableRow, ordinal: Int) => row.setByte(ordinal, oi.get(value)) + case oi: ShortObjectInspector => + (value: Any, row: MutableRow, ordinal: Int) => row.setShort(ordinal, oi.get(value)) + case oi: IntObjectInspector => + (value: Any, row: MutableRow, ordinal: Int) => row.setInt(ordinal, oi.get(value)) + case oi: LongObjectInspector => + (value: Any, row: MutableRow, ordinal: Int) => row.setLong(ordinal, oi.get(value)) + case oi: FloatObjectInspector => + (value: Any, row: MutableRow, ordinal: Int) => row.setFloat(ordinal, oi.get(value)) + case oi: DoubleObjectInspector => + (value: Any, row: MutableRow, ordinal: Int) => row.setDouble(ordinal, oi.get(value)) + case oi => + (value: Any, row: MutableRow, ordinal: Int) => row(ordinal) = unwrap(value, oi) + } + /** * Converts native catalyst types to the types expected by Hive * @param a the value to be wrapped @@ -414,36 +445,36 @@ private[hive] trait HiveInspectors { case _ if a == null => null case x: PrimitiveObjectInspector => x match { // TODO we don't support the HiveVarcharObjectInspector yet. - case _: StringObjectInspector if x.preferWritable() => HiveShim.getStringWritable(a) + case _: StringObjectInspector if x.preferWritable() => getStringWritable(a) case _: StringObjectInspector => a.asInstanceOf[UTF8String].toString() - case _: IntObjectInspector if x.preferWritable() => HiveShim.getIntWritable(a) + case _: IntObjectInspector if x.preferWritable() => getIntWritable(a) case _: IntObjectInspector => a.asInstanceOf[java.lang.Integer] - case _: BooleanObjectInspector if x.preferWritable() => HiveShim.getBooleanWritable(a) + case _: BooleanObjectInspector if x.preferWritable() => getBooleanWritable(a) case _: BooleanObjectInspector => a.asInstanceOf[java.lang.Boolean] - case _: FloatObjectInspector if x.preferWritable() => HiveShim.getFloatWritable(a) + case _: FloatObjectInspector if x.preferWritable() => getFloatWritable(a) case _: FloatObjectInspector => a.asInstanceOf[java.lang.Float] - case _: DoubleObjectInspector if x.preferWritable() => HiveShim.getDoubleWritable(a) + case _: DoubleObjectInspector if x.preferWritable() => getDoubleWritable(a) case _: DoubleObjectInspector => a.asInstanceOf[java.lang.Double] - case _: LongObjectInspector if x.preferWritable() => HiveShim.getLongWritable(a) + case _: LongObjectInspector if x.preferWritable() => getLongWritable(a) case _: LongObjectInspector => a.asInstanceOf[java.lang.Long] - case _: ShortObjectInspector if x.preferWritable() => HiveShim.getShortWritable(a) + case _: ShortObjectInspector if x.preferWritable() => getShortWritable(a) case _: ShortObjectInspector => a.asInstanceOf[java.lang.Short] - case _: ByteObjectInspector if x.preferWritable() => HiveShim.getByteWritable(a) + case _: ByteObjectInspector if x.preferWritable() => getByteWritable(a) case _: ByteObjectInspector => a.asInstanceOf[java.lang.Byte] case _: HiveDecimalObjectInspector if x.preferWritable() => - HiveShim.getDecimalWritable(a.asInstanceOf[Decimal]) + getDecimalWritable(a.asInstanceOf[Decimal]) case _: HiveDecimalObjectInspector => - HiveShim.createDecimal(a.asInstanceOf[Decimal].toJavaBigDecimal) - case _: BinaryObjectInspector if x.preferWritable() => HiveShim.getBinaryWritable(a) + HiveDecimal.create(a.asInstanceOf[Decimal].toJavaBigDecimal) + case _: BinaryObjectInspector if x.preferWritable() => getBinaryWritable(a) case _: BinaryObjectInspector => a.asInstanceOf[Array[Byte]] - case _: DateObjectInspector if x.preferWritable() => HiveShim.getDateWritable(a) + case _: DateObjectInspector if x.preferWritable() => getDateWritable(a) case _: DateObjectInspector => DateUtils.toJavaDate(a.asInstanceOf[Int]) - case _: TimestampObjectInspector if x.preferWritable() => HiveShim.getTimestampWritable(a) - case _: TimestampObjectInspector => a.asInstanceOf[java.sql.Timestamp] + case _: TimestampObjectInspector if x.preferWritable() => getTimestampWritable(a) + case _: TimestampObjectInspector => DateUtils.toJavaTimestamp(a.asInstanceOf[Long]) } case x: SettableStructObjectInspector => val fieldRefs = x.getAllStructFieldRefs - val row = a.asInstanceOf[Row] + val row = a.asInstanceOf[InternalRow] // 1. create the pojo (most likely) object val result = x.create() var i = 0 @@ -459,7 +490,7 @@ private[hive] trait HiveInspectors { result case x: StructObjectInspector => val fieldRefs = x.getAllStructFieldRefs - val row = a.asInstanceOf[Row] + val row = a.asInstanceOf[InternalRow] val result = new java.util.ArrayList[AnyRef](fieldRefs.length) var i = 0 while (i < fieldRefs.length) { @@ -486,7 +517,7 @@ private[hive] trait HiveInspectors { } def wrap( - row: Row, + row: InternalRow, inspectors: Seq[ObjectInspector], cache: Array[AnyRef]): Array[AnyRef] = { var i = 0 @@ -536,8 +567,8 @@ private[hive] trait HiveInspectors { case DecimalType() => PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector case StructType(fields) => ObjectInspectorFactory.getStandardStructObjectInspector( - java.util.Arrays.asList(fields.map(f => f.name) :_*), - java.util.Arrays.asList(fields.map(f => toInspector(f.dataType)) :_*)) + java.util.Arrays.asList(fields.map(f => f.name) : _*), + java.util.Arrays.asList(fields.map(f => toInspector(f.dataType)) : _*)) } /** @@ -549,31 +580,31 @@ private[hive] trait HiveInspectors { */ def toInspector(expr: Expression): ObjectInspector = expr match { case Literal(value, StringType) => - HiveShim.getStringWritableConstantObjectInspector(value) + getStringWritableConstantObjectInspector(value) case Literal(value, IntegerType) => - HiveShim.getIntWritableConstantObjectInspector(value) + getIntWritableConstantObjectInspector(value) case Literal(value, DoubleType) => - HiveShim.getDoubleWritableConstantObjectInspector(value) + getDoubleWritableConstantObjectInspector(value) case Literal(value, BooleanType) => - HiveShim.getBooleanWritableConstantObjectInspector(value) + getBooleanWritableConstantObjectInspector(value) case Literal(value, LongType) => - HiveShim.getLongWritableConstantObjectInspector(value) + getLongWritableConstantObjectInspector(value) case Literal(value, FloatType) => - HiveShim.getFloatWritableConstantObjectInspector(value) + getFloatWritableConstantObjectInspector(value) case Literal(value, ShortType) => - HiveShim.getShortWritableConstantObjectInspector(value) + getShortWritableConstantObjectInspector(value) case Literal(value, ByteType) => - HiveShim.getByteWritableConstantObjectInspector(value) + getByteWritableConstantObjectInspector(value) case Literal(value, BinaryType) => - HiveShim.getBinaryWritableConstantObjectInspector(value) + getBinaryWritableConstantObjectInspector(value) case Literal(value, DateType) => - HiveShim.getDateWritableConstantObjectInspector(value) + getDateWritableConstantObjectInspector(value) case Literal(value, TimestampType) => - HiveShim.getTimestampWritableConstantObjectInspector(value) + getTimestampWritableConstantObjectInspector(value) case Literal(value, DecimalType()) => - HiveShim.getDecimalWritableConstantObjectInspector(value) + getDecimalWritableConstantObjectInspector(value) case Literal(_, NullType) => - HiveShim.getPrimitiveNullWritableConstantObjectInspector + getPrimitiveNullWritableConstantObjectInspector case Literal(value, ArrayType(dt, _)) => val listObjectInspector = toInspector(dt) if (value == null) { @@ -633,8 +664,8 @@ private[hive] trait HiveInspectors { case _: JavaFloatObjectInspector => FloatType case _: WritableBinaryObjectInspector => BinaryType case _: JavaBinaryObjectInspector => BinaryType - case w: WritableHiveDecimalObjectInspector => HiveShim.decimalTypeInfoToCatalyst(w) - case j: JavaHiveDecimalObjectInspector => HiveShim.decimalTypeInfoToCatalyst(j) + case w: WritableHiveDecimalObjectInspector => decimalTypeInfoToCatalyst(w) + case j: JavaHiveDecimalObjectInspector => decimalTypeInfoToCatalyst(j) case _: WritableDateObjectInspector => DateType case _: JavaDateObjectInspector => DateType case _: WritableTimestampObjectInspector => TimestampType @@ -643,17 +674,143 @@ private[hive] trait HiveInspectors { case _: JavaVoidObjectInspector => NullType } + private def decimalTypeInfoToCatalyst(inspector: PrimitiveObjectInspector): DecimalType = { + val info = inspector.getTypeInfo.asInstanceOf[DecimalTypeInfo] + DecimalType(info.precision(), info.scale()) + } + + private def getStringWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.stringTypeInfo, getStringWritable(value)) + + private def getIntWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.intTypeInfo, getIntWritable(value)) + + private def getDoubleWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.doubleTypeInfo, getDoubleWritable(value)) + + private def getBooleanWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.booleanTypeInfo, getBooleanWritable(value)) + + private def getLongWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.longTypeInfo, getLongWritable(value)) + + private def getFloatWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.floatTypeInfo, getFloatWritable(value)) + + private def getShortWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.shortTypeInfo, getShortWritable(value)) + + private def getByteWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.byteTypeInfo, getByteWritable(value)) + + private def getBinaryWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.binaryTypeInfo, getBinaryWritable(value)) + + private def getDateWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.dateTypeInfo, getDateWritable(value)) + + private def getTimestampWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.timestampTypeInfo, getTimestampWritable(value)) + + private def getDecimalWritableConstantObjectInspector(value: Any): ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.decimalTypeInfo, getDecimalWritable(value)) + + private def getPrimitiveNullWritableConstantObjectInspector: ObjectInspector = + PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( + TypeInfoFactory.voidTypeInfo, null) + + private def getStringWritable(value: Any): hadoopIo.Text = + if (value == null) null else new hadoopIo.Text(value.asInstanceOf[UTF8String].getBytes) + + private def getIntWritable(value: Any): hadoopIo.IntWritable = + if (value == null) null else new hadoopIo.IntWritable(value.asInstanceOf[Int]) + + private def getDoubleWritable(value: Any): hiveIo.DoubleWritable = + if (value == null) { + null + } else { + new hiveIo.DoubleWritable(value.asInstanceOf[Double]) + } + + private def getBooleanWritable(value: Any): hadoopIo.BooleanWritable = + if (value == null) { + null + } else { + new hadoopIo.BooleanWritable(value.asInstanceOf[Boolean]) + } + + private def getLongWritable(value: Any): hadoopIo.LongWritable = + if (value == null) null else new hadoopIo.LongWritable(value.asInstanceOf[Long]) + + private def getFloatWritable(value: Any): hadoopIo.FloatWritable = + if (value == null) { + null + } else { + new hadoopIo.FloatWritable(value.asInstanceOf[Float]) + } + + private def getShortWritable(value: Any): hiveIo.ShortWritable = + if (value == null) null else new hiveIo.ShortWritable(value.asInstanceOf[Short]) + + private def getByteWritable(value: Any): hiveIo.ByteWritable = + if (value == null) null else new hiveIo.ByteWritable(value.asInstanceOf[Byte]) + + private def getBinaryWritable(value: Any): hadoopIo.BytesWritable = + if (value == null) { + null + } else { + new hadoopIo.BytesWritable(value.asInstanceOf[Array[Byte]]) + } + + private def getDateWritable(value: Any): hiveIo.DateWritable = + if (value == null) null else new hiveIo.DateWritable(value.asInstanceOf[Int]) + + private def getTimestampWritable(value: Any): hiveIo.TimestampWritable = + if (value == null) { + null + } else { + new hiveIo.TimestampWritable(DateUtils.toJavaTimestamp(value.asInstanceOf[Long])) + } + + private def getDecimalWritable(value: Any): hiveIo.HiveDecimalWritable = + if (value == null) { + null + } else { + // TODO precise, scale? + new hiveIo.HiveDecimalWritable( + HiveDecimal.create(value.asInstanceOf[Decimal].toJavaBigDecimal)) + } + implicit class typeInfoConversions(dt: DataType) { import org.apache.hadoop.hive.serde2.typeinfo._ import TypeInfoFactory._ + private def decimalTypeInfo(decimalType: DecimalType): TypeInfo = decimalType match { + case DecimalType.Fixed(precision, scale) => new DecimalTypeInfo(precision, scale) + case _ => new DecimalTypeInfo( + HiveShim.UNLIMITED_DECIMAL_PRECISION, + HiveShim.UNLIMITED_DECIMAL_SCALE) + } + def toTypeInfo: TypeInfo = dt match { case ArrayType(elemType, _) => getListTypeInfo(elemType.toTypeInfo) case StructType(fields) => getStructTypeInfo( - java.util.Arrays.asList(fields.map(_.name) :_*), - java.util.Arrays.asList(fields.map(_.dataType.toTypeInfo) :_*)) + java.util.Arrays.asList(fields.map(_.name) : _*), + java.util.Arrays.asList(fields.map(_.dataType.toTypeInfo) : _*)) case MapType(keyType, valueType, _) => getMapTypeInfo(keyType.toTypeInfo, valueType.toTypeInfo) case BinaryType => binaryTypeInfo @@ -665,7 +822,7 @@ private[hive] trait HiveInspectors { case LongType => longTypeInfo case ShortType => shortTypeInfo case StringType => stringTypeInfo - case d: DecimalType => HiveShim.decimalTypeInfo(d) + case d: DecimalType => decimalTypeInfo(d) case DateType => dateTypeInfo case TimestampType => timestampTypeInfo case NullType => voidTypeInfo diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 8fcdf3d0ab119..f35ae96ee0b50 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -17,36 +17,28 @@ package org.apache.spark.sql.hive -import java.io.IOException -import java.util.{List => JList} - import com.google.common.base.Objects import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache} import org.apache.hadoop.fs.Path -import org.apache.hadoop.hive.metastore.api.{FieldSchema, Partition => TPartition, Table => TTable} -import org.apache.hadoop.hive.metastore.{TableType, Warehouse} +import org.apache.hadoop.hive.common.StatsSetupConst +import org.apache.hadoop.hive.metastore.Warehouse +import org.apache.hadoop.hive.metastore.api.FieldSchema import org.apache.hadoop.hive.ql.metadata._ -import org.apache.hadoop.hive.ql.plan.CreateTableDesc -import org.apache.hadoop.hive.serde.serdeConstants -import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe -import org.apache.hadoop.hive.serde2.{Deserializer, SerDeException} -import org.apache.hadoop.util.ReflectionUtils +import org.apache.hadoop.hive.ql.plan.TableDesc import org.apache.spark.Logging -import org.apache.spark.sql.hive.client.IsolatedClientLoader -import org.apache.spark.sql.{SaveMode, AnalysisException, SQLContext} -import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, NoSuchTableException, Catalog, OverrideCatalog} +import org.apache.spark.sql.catalyst.analysis.{Catalog, MultiInstanceRelation, OverrideCatalog} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules._ import org.apache.spark.sql.hive.client._ -import org.apache.spark.sql.parquet.{ParquetRelation2, Partition => ParquetPartition, PartitionSpec} -import org.apache.spark.sql.sources.{CreateTableUsingAsSelect, DDLParser, LogicalRelation, ResolvedDataSource} +import org.apache.spark.sql.parquet.ParquetRelation2 +import org.apache.spark.sql.sources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource} import org.apache.spark.sql.types._ -import org.apache.spark.util.Utils +import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode, sources} /* Implicit conversions */ import scala.collection.JavaConversions._ @@ -54,7 +46,7 @@ import scala.collection.JavaConversions._ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: HiveContext) extends Catalog with Logging { - import org.apache.spark.sql.hive.HiveMetastoreTypes._ + val conf = hive.conf /** Usages should lock on `this`. */ protected[hive] lazy val hiveWarehouse = new Warehouse(hive.hiveconf) @@ -75,11 +67,11 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive def schemaStringFromParts: Option[String] = { table.properties.get("spark.sql.sources.schema.numParts").map { numParts => val parts = (0 until numParts.toInt).map { index => - val part = table.properties.get(s"spark.sql.sources.schema.part.${index}").orNull + val part = table.properties.get(s"spark.sql.sources.schema.part.$index").orNull if (part == null) { throw new AnalysisException( - s"Could not read schema from the metastore because it is corrupted " + - s"(missing part ${index} of the schema).") + "Could not read schema from the metastore because it is corrupted " + + s"(missing part $index of the schema, $numParts parts are expected).") } part @@ -98,6 +90,11 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive val userSpecifiedSchema = schemaString.map(s => DataType.fromJson(s).asInstanceOf[StructType]) + // We only need names at here since userSpecifiedSchema we loaded from the metastore + // contains partition columns. We can always get datatypes of partitioning columns + // from userSpecifiedSchema. + val partitionColumns = table.partitionColumns.map(_.name) + // It does not appear that the ql client for the metastore has a way to enumerate all the // SerDe properties directly... val options = table.serdeProperties @@ -106,6 +103,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive ResolvedDataSource( hive, userSpecifiedSchema, + partitionColumns.toArray, table.properties("spark.sql.sources.provider"), options) @@ -119,8 +117,8 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive override def refreshTable(databaseName: String, tableName: String): Unit = { // refreshTable does not eagerly reload the cache. It just invalidate the cache. // Next time when we use the table, it will be populated in the cache. - // Since we also cache ParquetRealtions converted from Hive Parquet tables and - // adding converted ParquetRealtions into the cache is not defined in the load function + // Since we also cache ParquetRelations converted from Hive Parquet tables and + // adding converted ParquetRelations into the cache is not defined in the load function // of the cache (instead, we add the cache entry in convertToParquetRelation), // it is better at here to invalidate the cache to avoid confusing waring logs from the // cache loader (e.g. cannot find data source provider, which is only defined for @@ -141,21 +139,47 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive def createDataSourceTable( tableName: String, userSpecifiedSchema: Option[StructType], + partitionColumns: Array[String], provider: String, options: Map[String, String], isExternal: Boolean): Unit = { - val (dbName, tblName) = processDatabaseAndTableName("default", tableName) + val (dbName, tblName) = processDatabaseAndTableName(client.currentDatabase, tableName) val tableProperties = new scala.collection.mutable.HashMap[String, String] tableProperties.put("spark.sql.sources.provider", provider) + + // Saves optional user specified schema. Serialized JSON schema string may be too long to be + // stored into a single metastore SerDe property. In this case, we split the JSON string and + // store each part as a separate SerDe property. if (userSpecifiedSchema.isDefined) { - val threshold = hive.conf.schemaStringLengthThreshold + val threshold = conf.schemaStringLengthThreshold val schemaJsonString = userSpecifiedSchema.get.json // Split the JSON string. val parts = schemaJsonString.grouped(threshold).toSeq tableProperties.put("spark.sql.sources.schema.numParts", parts.size.toString) parts.zipWithIndex.foreach { case (part, index) => - tableProperties.put(s"spark.sql.sources.schema.part.${index}", part) + tableProperties.put(s"spark.sql.sources.schema.part.$index", part) + } + } + + val metastorePartitionColumns = userSpecifiedSchema.map { schema => + val fields = partitionColumns.map(col => schema(col)) + fields.map { field => + HiveColumn( + name = field.name, + hiveType = HiveMetastoreTypes.toMetastoreType(field.dataType), + comment = "") + }.toSeq + }.getOrElse { + if (partitionColumns.length > 0) { + // The table does not have a specified schema, which means that the schema will be inferred + // when we load the table. So, we are not expecting partition columns and we will discover + // partitions when we load the table. However, if there are specified partition columns, + // we simplily ignore them and provide a warning message.. + logWarning( + s"The schema and partitions of table $tableName will be inferred when it is loaded. " + + s"Specified partition columns (${partitionColumns.mkString(",")}) will be ignored.") } + Seq.empty[HiveColumn] } val tableType = if (isExternal) { @@ -171,7 +195,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive specifiedDatabase = Option(dbName), name = tblName, schema = Seq.empty, - partitionColumns = Seq.empty, + partitionColumns = metastorePartitionColumns, tableType = tableType, properties = tableProperties.toMap, serdeProperties = options)) @@ -207,7 +231,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive val dataSourceTable = cachedDataSourceTables(QualifiedTableName(databaseName, tblName).toLowerCase) // Then, if alias is specified, wrap the table with a Subquery using the alias. - // Othersie, wrap the table with a Subquery using the table name. + // Otherwise, wrap the table with a Subquery using the table name. val withAlias = alias.map(a => Subquery(a, dataSourceTable)).getOrElse( Subquery(tableIdent.last, dataSourceTable)) @@ -252,7 +276,9 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive val useCached = parquetRelation.paths.toSet == pathsInMetastore.toSet && logical.schema.sameType(metastoreSchema) && - parquetRelation.maybePartitionSpec == partitionSpecInMetastore + parquetRelation.partitionSpec == partitionSpecInMetastore.getOrElse { + PartitionSpec(StructType(Nil), Array.empty[sources.Partition]) + } if (useCached) { Some(logical) @@ -264,7 +290,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive case other => logWarning( s"${metastoreRelation.databaseName}.${metastoreRelation.tableName} should be stored " + - s"as Parquet. However, we are getting a ${other} from the metastore cache. " + + s"as Parquet. However, we are getting a $other from the metastore cache. " + s"This cached entry will be invalidated.") cachedDataSourceTables.invalidate(tableIdentifier) None @@ -276,7 +302,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive val partitionColumnDataTypes = partitionSchema.map(_.dataType) val partitions = metastoreRelation.hiveQlPartitions.map { p => val location = p.getLocation - val values = Row.fromSeq(p.getValues.zip(partitionColumnDataTypes).map { + val values = InternalRow.fromSeq(p.getValues.zip(partitionColumnDataTypes).map { case (rawValue, dataType) => Cast(Literal(rawValue), dataType).eval(null) }) ParquetPartition(values, location) @@ -286,8 +312,9 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive val cached = getCached(tableIdentifier, paths, metastoreSchema, Some(partitionSpec)) val parquetRelation = cached.getOrElse { - val created = - LogicalRelation(ParquetRelation2(paths, parquetOptions, None, Some(partitionSpec))(hive)) + val created = LogicalRelation( + new ParquetRelation2( + paths.toArray, None, Some(partitionSpec), parquetOptions)(hive)) cachedDataSourceTables.put(tableIdentifier, created) created } @@ -298,8 +325,8 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive val cached = getCached(tableIdentifier, paths, metastoreSchema, None) val parquetRelation = cached.getOrElse { - val created = - LogicalRelation(ParquetRelation2(paths, parquetOptions)(hive)) + val created = LogicalRelation( + new ParquetRelation2(paths.toArray, None, None, parquetOptions)(hive)) cachedDataSourceTables.put(tableIdentifier, created) created } @@ -355,7 +382,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive // Inserting into partitioned table is not supported in Parquet data source (yet). if !relation.hiveQlTable.isPartitioned && hive.convertMetastoreParquet && - hive.conf.parquetUseDataSourceApi && + conf.parquetUseDataSourceApi && relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) @@ -366,7 +393,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive // Inserting into partitioned table is not supported in Parquet data source (yet). if !relation.hiveQlTable.isPartitioned && hive.convertMetastoreParquet && - hive.conf.parquetUseDataSourceApi && + conf.parquetUseDataSourceApi && relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) @@ -375,7 +402,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive // Read path case p @ PhysicalOperation(_, _, relation: MetastoreRelation) if hive.convertMetastoreParquet && - hive.conf.parquetUseDataSourceApi && + conf.parquetUseDataSourceApi && relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) @@ -415,21 +442,30 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive * For example, because of a CREATE TABLE X AS statement. */ object CreateTables extends Rule[LogicalPlan] { - import org.apache.hadoop.hive.ql.Context - import org.apache.hadoop.hive.ql.parse.{ASTNode, QB, SemanticAnalyzer} - def apply(plan: LogicalPlan): LogicalPlan = plan transform { // Wait until children are resolved. case p: LogicalPlan if !p.childrenResolved => p + case p: LogicalPlan if p.resolved => p + case p @ CreateTableAsSelect(table, child, allowExisting) => + val schema = if (table.schema.size > 0) { + table.schema + } else { + child.output.map { + attr => new HiveColumn( + attr.name, + HiveMetastoreTypes.toMetastoreType(attr.dataType), null) + } + } + + val desc = table.copy(schema = schema) - case CreateTableAsSelect(desc, child, allowExisting) => - if (hive.convertCTAS && !desc.serde.isDefined) { + if (hive.convertCTAS && table.serde.isEmpty) { // Do the conversion when spark.sql.hive.convertCTAS is true and the query // does not specify any storage format (file format and storage handler). - if (desc.specifiedDatabase.isDefined) { + if (table.specifiedDatabase.isDefined) { throw new AnalysisException( "Cannot specify database name in a CTAS statement " + - "when spark.sql.hive.convertCTAS is set to true.") + "when spark.sql.hive.convertCTAS is set to true.") } val mode = if (allowExisting) SaveMode.Ignore else SaveMode.ErrorIfExists @@ -437,42 +473,28 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive desc.name, hive.conf.defaultDataSourceName, temporary = false, + Array.empty[String], mode, options = Map.empty[String, String], child ) } else { - execution.CreateTableAsSelect( - desc.copy( - specifiedDatabase = Option(desc.specifiedDatabase.getOrElse(client.currentDatabase))), - child, - allowExisting) - } - - case p: LogicalPlan if p.resolved => p - - case p @ CreateTableAsSelect(desc, child, allowExisting) => - val (dbName, tblName) = processDatabaseAndTableName(desc.database, desc.name) - - if (hive.convertCTAS) { - if (desc.specifiedDatabase.isDefined) { - throw new AnalysisException( - "Cannot specify database name in a CTAS statement " + - "when spark.sql.hive.convertCTAS is set to true.") + val desc = if (table.serde.isEmpty) { + // add default serde + table.copy( + serde = Some("org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe")) + } else { + table } - val mode = if (allowExisting) SaveMode.Ignore else SaveMode.ErrorIfExists - CreateTableUsingAsSelect( - tblName, - hive.conf.defaultDataSourceName, - temporary = false, - mode, - options = Map.empty[String, String], - child - ) - } else { + val (dbName, tblName) = + processDatabaseAndTableName( + desc.specifiedDatabase.getOrElse(client.currentDatabase), desc.name) + execution.CreateTableAsSelect( - desc, + desc.copy( + specifiedDatabase = Some(dbName), + name = tblName), child, allowExisting) } @@ -495,17 +517,19 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive def castChildOutput(p: InsertIntoTable, table: MetastoreRelation, child: LogicalPlan) : LogicalPlan = { val childOutputDataTypes = child.output.map(_.dataType) + val numDynamicPartitions = p.partition.values.count(_.isEmpty) val tableOutputDataTypes = - (table.attributes ++ table.partitionKeys).take(child.output.length).map(_.dataType) + (table.attributes ++ table.partitionKeys.takeRight(numDynamicPartitions)) + .take(child.output.length).map(_.dataType) if (childOutputDataTypes == tableOutputDataTypes) { - p + InsertIntoHiveTable(table, p.partition, p.child, p.overwrite, p.ifNotExists) } else if (childOutputDataTypes.size == tableOutputDataTypes.size && childOutputDataTypes.zip(tableOutputDataTypes) .forall { case (left, right) => left.sameType(right) }) { // If both types ignoring nullability of ArrayType, MapType, StructType are the same, // use InsertIntoHiveTable instead of InsertIntoTable. - InsertIntoHiveTable(p.table, p.partition, p.child, p.overwrite, p.ifNotExists) + InsertIntoHiveTable(table, p.partition, p.child, p.overwrite, p.ifNotExists) } else { // Only do the casting when child output data types differ from table output data types. val castedChildOutput = child.output.zip(table.output).map { @@ -523,13 +547,17 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive * UNIMPLEMENTED: It needs to be decided how we will persist in-memory tables to the metastore. * For now, if this functionality is desired mix in the in-memory [[OverrideCatalog]]. */ - override def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit = ??? + override def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit = { + throw new UnsupportedOperationException + } /** * UNIMPLEMENTED: It needs to be decided how we will persist in-memory tables to the metastore. * For now, if this functionality is desired mix in the in-memory [[OverrideCatalog]]. */ - override def unregisterTable(tableIdentifier: Seq[String]): Unit = ??? + override def unregisterTable(tableIdentifier: Seq[String]): Unit = { + throw new UnsupportedOperationException + } override def unregisterAllTables(): Unit = {} } @@ -540,7 +568,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive * because Hive table doesn't have nullability for ARRAY, MAP, STRUCT types. */ private[hive] case class InsertIntoHiveTable( - table: LogicalPlan, + table: MetastoreRelation, partition: Map[String, Option[String]], child: LogicalPlan, overwrite: Boolean, @@ -550,7 +578,13 @@ private[hive] case class InsertIntoHiveTable( override def children: Seq[LogicalPlan] = child :: Nil override def output: Seq[Attribute] = child.output - override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall { + val numDynamicPartitions = partition.values.count(_.isEmpty) + + // This is the expected schema of the table prepared to be inserted into, + // including dynamic partition columns. + val tableOutput = table.attributes ++ table.partitionKeys.takeRight(numDynamicPartitions) + + override lazy val resolved: Boolean = childrenResolved && child.output.zip(tableOutput).forall { case (childAttr, tableAttr) => childAttr.dataType.sameType(tableAttr.dataType) } } @@ -563,7 +597,7 @@ private[hive] case class MetastoreRelation self: Product => - override def equals(other: scala.Any): Boolean = other match { + override def equals(other: Any): Boolean = other match { case relation: MetastoreRelation => databaseName == relation.databaseName && tableName == relation.tableName && @@ -637,8 +671,8 @@ private[hive] case class MetastoreRelation @transient override lazy val statistics: Statistics = Statistics( sizeInBytes = { - val totalSize = hiveQlTable.getParameters.get(HiveShim.getStatsSetupConstTotalSize) - val rawDataSize = hiveQlTable.getParameters.get(HiveShim.getStatsSetupConstRawDataSize) + val totalSize = hiveQlTable.getParameters.get(StatsSetupConst.TOTAL_SIZE) + val rawDataSize = hiveQlTable.getParameters.get(StatsSetupConst.RAW_DATA_SIZE) // TODO: check if this estimate is valid for tables after partition pruning. // NOTE: getting `totalSize` directly from params is kind of hacky, but this should be // relatively cheap if parameters for the table are populated into the metastore. An @@ -664,11 +698,7 @@ private[hive] case class MetastoreRelation } } - val tableDesc = HiveShim.getTableDesc( - Class.forName( - hiveQlTable.getSerializationLib, - true, - Utils.getContextOrSparkClassLoader).asInstanceOf[Class[Deserializer]], + val tableDesc = new TableDesc( hiveQlTable.getInputFormatClass, // The class of table should be org.apache.hadoop.hive.ql.metadata.Table because // getOutputFormatClass will use HiveFileFormatUtils.getOutputFormatSubstitute to @@ -678,25 +708,25 @@ private[hive] case class MetastoreRelation hiveQlTable.getMetadata ) - implicit class SchemaAttribute(f: FieldSchema) { + implicit class SchemaAttribute(f: HiveColumn) { def toAttribute: AttributeReference = AttributeReference( - f.getName, - HiveMetastoreTypes.toDataType(f.getType), + f.name, + HiveMetastoreTypes.toDataType(f.hiveType), // Since data can be dumped in randomly with no validation, everything is nullable. nullable = true )(qualifiers = Seq(alias.getOrElse(tableName))) } - // Must be a stable value since new attributes are born here. - val partitionKeys = hiveQlTable.getPartitionKeys.map(_.toAttribute) + /** PartitionKey attributes */ + val partitionKeys = table.partitionColumns.map(_.toAttribute) /** Non-partitionKey attributes */ - val attributes = hiveQlTable.getCols.map(_.toAttribute) + val attributes = table.schema.map(_.toAttribute) val output = attributes ++ partitionKeys /** An attribute map that can be used to lookup original attributes based on expression id. */ - val attributeMap = AttributeMap(output.map(o => (o,o))) + val attributeMap = AttributeMap(output.map(o => (o, o))) /** An attribute map for determining the ordinal for non-partition columns. */ val columnOrdinals = AttributeMap(attributes.zipWithIndex) @@ -710,6 +740,11 @@ private[hive] case class MetastoreRelation private[hive] object HiveMetastoreTypes { def toDataType(metastoreType: String): DataType = DataTypeParser.parse(metastoreType) + def decimalMetastoreString(decimalType: DecimalType): String = decimalType match { + case DecimalType.Fixed(precision, scale) => s"decimal($precision,$scale)" + case _ => s"decimal($HiveShim.UNLIMITED_DECIMAL_PRECISION,$HiveShim.UNLIMITED_DECIMAL_SCALE)" + } + def toMetastoreType(dt: DataType): String = dt match { case ArrayType(elementType, _) => s"array<${toMetastoreType(elementType)}>" case StructType(fields) => @@ -726,7 +761,7 @@ private[hive] object HiveMetastoreTypes { case BinaryType => "binary" case BooleanType => "boolean" case DateType => "date" - case d: DecimalType => HiveShim.decimalMetastoreString(d) + case d: DecimalType => decimalMetastoreString(d) case TimestampType => "timestamp" case NullType => "void" case udt: UserDefinedType[_] => toMetastoreType(udt.sqlType) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 04d40bbb2bced..ca4b80b51b23f 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -19,17 +19,16 @@ package org.apache.spark.sql.hive import java.sql.Date -import scala.collection.mutable.ArrayBuffer - import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.ql.Context +import org.apache.hadoop.hive.serde.serdeConstants +import org.apache.hadoop.hive.ql.{ErrorMsg, Context} import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo} import org.apache.hadoop.hive.ql.lib.Node -import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.hive.ql.parse._ import org.apache.hadoop.hive.ql.plan.PlanUtils -import org.apache.spark.sql.AnalysisException +import org.apache.hadoop.hive.ql.session.SessionState +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ @@ -38,6 +37,7 @@ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.trees.CurrentOrigin import org.apache.spark.sql.execution.ExplainCommand import org.apache.spark.sql.sources.DescribeCommand +import org.apache.spark.sql.hive.HiveShim._ import org.apache.spark.sql.hive.client._ import org.apache.spark.sql.hive.execution.{HiveNativeCommand, DropTable, AnalyzeTable, HiveScriptIOSchema} import org.apache.spark.sql.types._ @@ -45,6 +45,7 @@ import org.apache.spark.util.random.RandomSampler /* Implicit conversions */ import scala.collection.JavaConversions._ +import scala.collection.mutable.ArrayBuffer /** * Used when we need to start parsing the AST before deciding that we are going to pass the command @@ -56,13 +57,19 @@ private[hive] case object NativePlaceholder extends LogicalPlan { override def output: Seq[Attribute] = Seq.empty } -case class CreateTableAsSelect( +private[hive] case class CreateTableAsSelect( tableDesc: HiveTable, child: LogicalPlan, allowExisting: Boolean) extends UnaryNode with Command { override def output: Seq[Attribute] = Seq.empty[Attribute] - override lazy val resolved: Boolean = tableDesc.specifiedDatabase.isDefined && childrenResolved + override lazy val resolved: Boolean = + tableDesc.specifiedDatabase.isDefined && + tableDesc.schema.size > 0 && + tableDesc.serde.isDefined && + tableDesc.inputFormat.isDefined && + tableDesc.outputFormat.isDefined && + childrenResolved } /** Provides a mapping from HiveQL statements to catalyst logical plans and expression trees. */ @@ -240,12 +247,23 @@ private[hive] object HiveQl { * Otherwise, there will be Null pointer exception, * when retrieving properties form HiveConf. */ - val hContext = new Context(new HiveConf()) + val hContext = new Context(hiveConf) val node = ParseUtils.findRootNonNullToken((new ParseDriver).parse(sql, hContext)) hContext.clear() node } + /** + * Returns the HiveConf + */ + private[this] def hiveConf(): HiveConf = { + val ss = SessionState.get() // SessionState is lazy initializaion, it can be null here + if (ss == null) { + new HiveConf() + } else { + ss.getConf + } + } /** Returns a LogicalPlan for a given HiveQL string. */ def parseSql(sql: String): LogicalPlan = hqlParser.parse(sql) @@ -476,8 +494,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C DropTable(tableName, ifExists.nonEmpty) // Support "ANALYZE TABLE tableNmae COMPUTE STATISTICS noscan" case Token("TOK_ANALYZE", - Token("TOK_TAB", Token("TOK_TABNAME", tableNameParts) :: partitionSpec) :: - isNoscan) => + Token("TOK_TAB", Token("TOK_TABNAME", tableNameParts) :: partitionSpec) :: + isNoscan) => // Reference: // https://cwiki.apache.org/confluence/display/Hive/StatsDev#StatsDev-ExistingTables if (partitionSpec.nonEmpty) { @@ -547,6 +565,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val ( Some(tableNameParts) :: _ /* likeTable */ :: + externalTable :: Some(query) :: allowExisting +: ignores) = @@ -554,6 +573,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C Seq( "TOK_TABNAME", "TOK_LIKETABLE", + "EXTERNAL", "TOK_QUERY", "TOK_IFNOTEXISTS", "TOK_TABLECOMMENT", @@ -576,43 +596,153 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C children) val (db, tableName) = extractDbNameTableName(tableNameParts) - var tableDesc = - HiveTable( - specifiedDatabase = db, - name = tableName, - schema = Seq.empty, - partitionColumns = Seq.empty, - properties = Map.empty, - serdeProperties = Map.empty, - tableType = ManagedTable, - location = None, - inputFormat = None, - outputFormat = None, - serde = None) - - // TODO: Handle all the cases here... - children.foreach { - case Token("TOK_TBLRCFILE", Nil) => - import org.apache.hadoop.hive.ql.io.{RCFileInputFormat, RCFileOutputFormat} + // TODO add bucket support + var tableDesc: HiveTable = HiveTable( + specifiedDatabase = db, + name = tableName, + schema = Seq.empty[HiveColumn], + partitionColumns = Seq.empty[HiveColumn], + properties = Map[String, String](), + serdeProperties = Map[String, String](), + tableType = if (externalTable.isDefined) ExternalTable else ManagedTable, + location = None, + inputFormat = None, + outputFormat = None, + serde = None, + viewText = None) + + // default storage type abbriviation (e.g. RCFile, ORC, PARQUET etc.) + val defaultStorageType = hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTFILEFORMAT) + // handle the default format for the storage type abbriviation + tableDesc = if ("SequenceFile".equalsIgnoreCase(defaultStorageType)) { + tableDesc.copy( + inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"), + outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat")) + } else if ("RCFile".equalsIgnoreCase(defaultStorageType)) { + tableDesc.copy( + inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"), + outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"), + serde = Option(hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTRCFILESERDE))) + } else if ("ORC".equalsIgnoreCase(defaultStorageType)) { + tableDesc.copy( + inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"), + outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"), + serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde")) + } else if ("PARQUET".equalsIgnoreCase(defaultStorageType)) { + tableDesc.copy( + inputFormat = + Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"), + outputFormat = + Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"), + serde = + Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")) + } else { + tableDesc.copy( + inputFormat = + Option("org.apache.hadoop.mapred.TextInputFormat"), + outputFormat = + Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat")) + } + + children.collect { + case list @ Token("TOK_TABCOLLIST", _) => + val cols = BaseSemanticAnalyzer.getColumns(list, true) + if (cols != null) { + tableDesc = tableDesc.copy( + schema = cols.map { field => + HiveColumn(field.getName, field.getType, field.getComment) + }) + } + case Token("TOK_TABLECOMMENT", child :: Nil) => + val comment = BaseSemanticAnalyzer.unescapeSQLString(child.getText) + // TODO support the sql text + tableDesc = tableDesc.copy(viewText = Option(comment)) + case Token("TOK_TABLEPARTCOLS", list @ Token("TOK_TABCOLLIST", _) :: Nil) => + val cols = BaseSemanticAnalyzer.getColumns(list(0), false) + if (cols != null) { + tableDesc = tableDesc.copy( + partitionColumns = cols.map { field => + HiveColumn(field.getName, field.getType, field.getComment) + }) + } + case Token("TOK_TABLEROWFORMAT", Token("TOK_SERDEPROPS", child :: Nil) :: Nil) => + val serdeParams = new java.util.HashMap[String, String]() + child match { + case Token("TOK_TABLEROWFORMATFIELD", rowChild1 :: rowChild2) => + val fieldDelim = BaseSemanticAnalyzer.unescapeSQLString (rowChild1.getText()) + serdeParams.put(serdeConstants.FIELD_DELIM, fieldDelim) + serdeParams.put(serdeConstants.SERIALIZATION_FORMAT, fieldDelim) + if (rowChild2.length > 1) { + val fieldEscape = BaseSemanticAnalyzer.unescapeSQLString (rowChild2(0).getText) + serdeParams.put(serdeConstants.ESCAPE_CHAR, fieldEscape) + } + case Token("TOK_TABLEROWFORMATCOLLITEMS", rowChild :: Nil) => + val collItemDelim = BaseSemanticAnalyzer.unescapeSQLString(rowChild.getText) + serdeParams.put(serdeConstants.COLLECTION_DELIM, collItemDelim) + case Token("TOK_TABLEROWFORMATMAPKEYS", rowChild :: Nil) => + val mapKeyDelim = BaseSemanticAnalyzer.unescapeSQLString(rowChild.getText) + serdeParams.put(serdeConstants.MAPKEY_DELIM, mapKeyDelim) + case Token("TOK_TABLEROWFORMATLINES", rowChild :: Nil) => + val lineDelim = BaseSemanticAnalyzer.unescapeSQLString(rowChild.getText) + if (!(lineDelim == "\n") && !(lineDelim == "10")) { + throw new AnalysisException( + SemanticAnalyzer.generateErrorMessage( + rowChild, + ErrorMsg.LINES_TERMINATED_BY_NON_NEWLINE.getMsg)) + } + serdeParams.put(serdeConstants.LINE_DELIM, lineDelim) + case Token("TOK_TABLEROWFORMATNULL", rowChild :: Nil) => + val nullFormat = BaseSemanticAnalyzer.unescapeSQLString(rowChild.getText) + // TODO support the nullFormat + case _ => assert(false) + } + tableDesc = tableDesc.copy( + serdeProperties = tableDesc.serdeProperties ++ serdeParams) + case Token("TOK_TABLELOCATION", child :: Nil) => + var location = BaseSemanticAnalyzer.unescapeSQLString(child.getText) + location = EximUtil.relativeToAbsolutePath(hiveConf, location) + tableDesc = tableDesc.copy(location = Option(location)) + case Token("TOK_TABLESERIALIZER", child :: Nil) => tableDesc = tableDesc.copy( - outputFormat = Option(classOf[RCFileOutputFormat].getName), - inputFormat = Option(classOf[RCFileInputFormat[_, _]].getName)) + serde = Option(BaseSemanticAnalyzer.unescapeSQLString(child.getChild(0).getText))) + if (child.getChildCount == 2) { + val serdeParams = new java.util.HashMap[String, String]() + BaseSemanticAnalyzer.readProps( + (child.getChild(1).getChild(0)).asInstanceOf[ASTNode], serdeParams) + tableDesc = tableDesc.copy(serdeProperties = tableDesc.serdeProperties ++ serdeParams) + } + case Token("TOK_FILEFORMAT_GENERIC", child :: Nil) => + throw new SemanticException( + "Unrecognized file format in STORED AS clause:${child.getText}") + case Token("TOK_TBLRCFILE", Nil) => + tableDesc = tableDesc.copy( + inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"), + outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat")) if (tableDesc.serde.isEmpty) { tableDesc = tableDesc.copy( serde = Option("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe")) } + case Token("TOK_TBLORCFILE", Nil) => tableDesc = tableDesc.copy( inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"), - outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"), - serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde")) + outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat")) + if (tableDesc.serde.isEmpty) { + tableDesc = tableDesc.copy( + serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde")) + } case Token("TOK_TBLPARQUETFILE", Nil) => tableDesc = tableDesc.copy( - inputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"), - outputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"), - serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")) + inputFormat = + Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"), + outputFormat = + Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat")) + if (tableDesc.serde.isEmpty) { + tableDesc = tableDesc.copy( + serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")) + } case Token("TOK_TABLESERIALIZER", Token("TOK_SERDENAME", Token(serdeName, Nil) :: otherProps) :: Nil) => @@ -627,18 +757,25 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token("TOK_TABLEPROPERTIES", list :: Nil) => tableDesc = tableDesc.copy(properties = tableDesc.properties ++ getProperties(list)) - - case _ => + case list @ Token("TOK_TABLEFILEFORMAT", _) => + tableDesc = tableDesc.copy( + inputFormat = + Option(BaseSemanticAnalyzer.unescapeSQLString(list.getChild(0).getText)), + outputFormat = + Option(BaseSemanticAnalyzer.unescapeSQLString(list.getChild(1).getText))) + case Token("TOK_STORAGEHANDLER", _) => + throw new AnalysisException(ErrorMsg.CREATE_NON_NATIVE_AS.getMsg()) + case _ => // Unsupport features } CreateTableAsSelect(tableDesc, nodeToPlan(query), allowExisting != None) - // If its not a "CREATE TABLE AS" like above then just pass it back to hive as a native command. + // If its not a "CTAS" like above then take it as a native command case Token("TOK_CREATETABLE", _) => NativePlaceholder // Support "TRUNCATE TABLE table_name [PARTITION partition_spec]" case Token("TOK_TRUNCATETABLE", - Token("TOK_TABLE_PARTITION",table)::Nil) => NativePlaceholder + Token("TOK_TABLE_PARTITION", table) :: Nil) => NativePlaceholder case Token("TOK_QUERY", queryArgs) if Seq("TOK_FROM", "TOK_INSERT").contains(queryArgs.head.getText) => @@ -1014,7 +1151,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Seq(false, false) => Inner }.toBuffer - val joinedTables = tables.reduceLeft(Join(_,_, Inner, None)) + val joinedTables = tables.reduceLeft(Join(_, _, Inner, None)) // Must be transform down. val joinedResult = joinedTables transform { @@ -1034,7 +1171,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C // worth the number of hacks that will be required to implement it. Namely, we need to add // some sort of mapped star expansion that would expand all child output row to be similarly // named output expressions where some aggregate expression has been applied (i.e. First). - ??? // Aggregate(groups, Star(None, First(_)) :: Nil, joinedResult) + // Aggregate(groups, Star(None, First(_)) :: Nil, joinedResult) + throw new UnsupportedOperationException case Token(allJoinTokens(joinToken), relation1 :: @@ -1169,16 +1307,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C HiveParser.DecimalLiteral) /* Case insensitive matches */ - val ARRAY = "(?i)ARRAY".r - val COALESCE = "(?i)COALESCE".r val COUNT = "(?i)COUNT".r - val AVG = "(?i)AVG".r val SUM = "(?i)SUM".r - val MAX = "(?i)MAX".r - val MIN = "(?i)MIN".r - val UPPER = "(?i)UPPER".r - val LOWER = "(?i)LOWER".r - val RAND = "(?i)RAND".r val AND = "(?i)AND".r val OR = "(?i)OR".r val NOT = "(?i)NOT".r @@ -1192,8 +1322,6 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val BETWEEN = "(?i)BETWEEN".r val WHEN = "(?i)WHEN".r val CASE = "(?i)CASE".r - val SUBSTR = "(?i)SUBSTR(?:ING)?".r - val SQRT = "(?i)SQRT".r protected def nodeToExpr(node: Node): Expression = node match { /* Attribute References */ @@ -1215,18 +1343,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C UnresolvedStar(Some(name)) /* Aggregate Functions */ - case Token("TOK_FUNCTION", Token(AVG(), Nil) :: arg :: Nil) => Average(nodeToExpr(arg)) - case Token("TOK_FUNCTION", Token(COUNT(), Nil) :: arg :: Nil) => Count(nodeToExpr(arg)) case Token("TOK_FUNCTIONSTAR", Token(COUNT(), Nil) :: Nil) => Count(Literal(1)) case Token("TOK_FUNCTIONDI", Token(COUNT(), Nil) :: args) => CountDistinct(args.map(nodeToExpr)) - case Token("TOK_FUNCTION", Token(SUM(), Nil) :: arg :: Nil) => Sum(nodeToExpr(arg)) case Token("TOK_FUNCTIONDI", Token(SUM(), Nil) :: arg :: Nil) => SumDistinct(nodeToExpr(arg)) - case Token("TOK_FUNCTION", Token(MAX(), Nil) :: arg :: Nil) => Max(nodeToExpr(arg)) - case Token("TOK_FUNCTION", Token(MIN(), Nil) :: arg :: Nil) => Min(nodeToExpr(arg)) - - /* System functions about string operations */ - case Token("TOK_FUNCTION", Token(UPPER(), Nil) :: arg :: Nil) => Upper(nodeToExpr(arg)) - case Token("TOK_FUNCTION", Token(LOWER(), Nil) :: arg :: Nil) => Lower(nodeToExpr(arg)) /* Casts */ case Token("TOK_FUNCTION", Token("TOK_STRING", Nil) :: arg :: Nil) => @@ -1276,7 +1395,6 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token("&", left :: right:: Nil) => BitwiseAnd(nodeToExpr(left), nodeToExpr(right)) case Token("|", left :: right:: Nil) => BitwiseOr(nodeToExpr(left), nodeToExpr(right)) case Token("^", left :: right:: Nil) => BitwiseXor(nodeToExpr(left), nodeToExpr(right)) - case Token("TOK_FUNCTION", Token(SQRT(), Nil) :: arg :: Nil) => Sqrt(nodeToExpr(arg)) /* Comparisons */ case Token("=", left :: right:: Nil) => EqualTo(nodeToExpr(left), nodeToExpr(right)) @@ -1331,17 +1449,6 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token("[", child :: ordinal :: Nil) => UnresolvedExtractValue(nodeToExpr(child), nodeToExpr(ordinal)) - /* Other functions */ - case Token("TOK_FUNCTION", Token(ARRAY(), Nil) :: children) => - CreateArray(children.map(nodeToExpr)) - case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand() - case Token("TOK_FUNCTION", Token(RAND(), Nil) :: seed :: Nil) => Rand(seed.toString.toLong) - case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) => - Substring(nodeToExpr(string), nodeToExpr(pos), Literal.create(Integer.MAX_VALUE, IntegerType)) - case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) => - Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length)) - case Token("TOK_FUNCTION", Token(COALESCE(), Nil) :: list) => Coalesce(list.map(nodeToExpr)) - /* Window Functions */ case Token("TOK_FUNCTION", Token(name, Nil) +: args :+ Token("TOK_WINDOWSPEC", spec)) => val function = UnresolvedWindowFunction(name, args.map(nodeToExpr)) @@ -1423,6 +1530,10 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C """.stripMargin) } + /* Case insensitive matches for Window Specification */ + val PRECEDING = "(?i)preceding".r + val FOLLOWING = "(?i)following".r + val CURRENT = "(?i)current".r def nodesToWindowSpecification(nodes: Seq[ASTNode]): WindowSpec = nodes match { case Token(windowName, Nil) :: Nil => // Refer to a window spec defined in the window clause. @@ -1476,11 +1587,19 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C } else { val frameType = rowFrame.map(_ => RowFrame).getOrElse(RangeFrame) def nodeToBoundary(node: Node): FrameBoundary = node match { - case Token("preceding", Token(count, Nil) :: Nil) => - if (count == "unbounded") UnboundedPreceding else ValuePreceding(count.toInt) - case Token("following", Token(count, Nil) :: Nil) => - if (count == "unbounded") UnboundedFollowing else ValueFollowing(count.toInt) - case Token("current", Nil) => CurrentRow + case Token(PRECEDING(), Token(count, Nil) :: Nil) => + if (count.toLowerCase() == "unbounded") { + UnboundedPreceding + } else { + ValuePreceding(count.toInt) + } + case Token(FOLLOWING(), Token(count, Nil) :: Nil) => + if (count.toLowerCase() == "unbounded") { + UnboundedFollowing + } else { + ValueFollowing(count.toInt) + } + case Token(CURRENT(), Nil) => CurrentRow case _ => throw new NotImplementedError( s"""No parse rules for the Window Frame Boundary based on Node ${node.getName} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala new file mode 100644 index 0000000000000..d08c594151654 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala @@ -0,0 +1,248 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import java.io.{InputStream, OutputStream} +import java.rmi.server.UID + +/* Implicit conversions */ +import scala.collection.JavaConversions._ +import scala.language.implicitConversions +import scala.reflect.ClassTag + +import com.esotericsoftware.kryo.Kryo +import com.esotericsoftware.kryo.io.{Input, Output} +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.ql.exec.{UDF, Utilities} +import org.apache.hadoop.hive.ql.plan.{FileSinkDesc, TableDesc} +import org.apache.hadoop.hive.serde2.ColumnProjectionUtils +import org.apache.hadoop.hive.serde2.avro.AvroGenericRecordWritable +import org.apache.hadoop.hive.serde2.objectinspector.primitive.HiveDecimalObjectInspector +import org.apache.hadoop.io.Writable + +import org.apache.spark.Logging +import org.apache.spark.sql.types.Decimal +import org.apache.spark.util.Utils + +private[hive] object HiveShim { + // Precision and scale to pass for unlimited decimals; these are the same as the precision and + // scale Hive 0.13 infers for BigDecimals from sources that don't specify them (e.g. UDFs) + val UNLIMITED_DECIMAL_PRECISION = 38 + val UNLIMITED_DECIMAL_SCALE = 18 + + /* + * This function in hive-0.13 become private, but we have to do this to walkaround hive bug + */ + private def appendReadColumnNames(conf: Configuration, cols: Seq[String]) { + val old: String = conf.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, "") + val result: StringBuilder = new StringBuilder(old) + var first: Boolean = old.isEmpty + + for (col <- cols) { + if (first) { + first = false + } else { + result.append(',') + } + result.append(col) + } + conf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, result.toString) + } + + /* + * Cannot use ColumnProjectionUtils.appendReadColumns directly, if ids is null or empty + */ + def appendReadColumns(conf: Configuration, ids: Seq[Integer], names: Seq[String]) { + if (ids != null && ids.nonEmpty) { + ColumnProjectionUtils.appendReadColumns(conf, ids) + } + if (names != null && names.nonEmpty) { + appendReadColumnNames(conf, names) + } + } + + /* + * Bug introduced in hive-0.13. AvroGenericRecordWritable has a member recordReaderID that + * is needed to initialize before serialization. + */ + def prepareWritable(w: Writable): Writable = { + w match { + case w: AvroGenericRecordWritable => + w.setRecordReaderID(new UID()) + case _ => + } + w + } + + def toCatalystDecimal(hdoi: HiveDecimalObjectInspector, data: Any): Decimal = { + if (hdoi.preferWritable()) { + Decimal(hdoi.getPrimitiveWritableObject(data).getHiveDecimal().bigDecimalValue, + hdoi.precision(), hdoi.scale()) + } else { + Decimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue(), hdoi.precision(), hdoi.scale()) + } + } + + /** + * This class provides the UDF creation and also the UDF instance serialization and + * de-serialization cross process boundary. + * + * Detail discussion can be found at https://github.com/apache/spark/pull/3640 + * + * @param functionClassName UDF class name + */ + private[hive] case class HiveFunctionWrapper(var functionClassName: String) + extends java.io.Externalizable { + + // for Serialization + def this() = this(null) + + @transient + def deserializeObjectByKryo[T: ClassTag]( + kryo: Kryo, + in: InputStream, + clazz: Class[_]): T = { + val inp = new Input(in) + val t: T = kryo.readObject(inp, clazz).asInstanceOf[T] + inp.close() + t + } + + @transient + def serializeObjectByKryo( + kryo: Kryo, + plan: Object, + out: OutputStream) { + val output: Output = new Output(out) + kryo.writeObject(output, plan) + output.close() + } + + def deserializePlan[UDFType](is: java.io.InputStream, clazz: Class[_]): UDFType = { + deserializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), is, clazz) + .asInstanceOf[UDFType] + } + + def serializePlan(function: AnyRef, out: java.io.OutputStream): Unit = { + serializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), function, out) + } + + private var instance: AnyRef = null + + def writeExternal(out: java.io.ObjectOutput) { + // output the function name + out.writeUTF(functionClassName) + + // Write a flag if instance is null or not + out.writeBoolean(instance != null) + if (instance != null) { + // Some of the UDF are serializable, but some others are not + // Hive Utilities can handle both cases + val baos = new java.io.ByteArrayOutputStream() + serializePlan(instance, baos) + val functionInBytes = baos.toByteArray + + // output the function bytes + out.writeInt(functionInBytes.length) + out.write(functionInBytes, 0, functionInBytes.length) + } + } + + def readExternal(in: java.io.ObjectInput) { + // read the function name + functionClassName = in.readUTF() + + if (in.readBoolean()) { + // if the instance is not null + // read the function in bytes + val functionInBytesLength = in.readInt() + val functionInBytes = new Array[Byte](functionInBytesLength) + in.read(functionInBytes, 0, functionInBytesLength) + + // deserialize the function object via Hive Utilities + instance = deserializePlan[AnyRef](new java.io.ByteArrayInputStream(functionInBytes), + Utils.getContextOrSparkClassLoader.loadClass(functionClassName)) + } + } + + def createFunction[UDFType <: AnyRef](): UDFType = { + if (instance != null) { + instance.asInstanceOf[UDFType] + } else { + val func = Utils.getContextOrSparkClassLoader + .loadClass(functionClassName).newInstance.asInstanceOf[UDFType] + if (!func.isInstanceOf[UDF]) { + // We cache the function if it's no the Simple UDF, + // as we always have to create new instance for Simple UDF + instance = func + } + func + } + } + } + + /* + * Bug introduced in hive-0.13. FileSinkDesc is serializable, but its member path is not. + * Fix it through wrapper. + */ + implicit def wrapperToFileSinkDesc(w: ShimFileSinkDesc): FileSinkDesc = { + val f = new FileSinkDesc(new Path(w.dir), w.tableInfo, w.compressed) + f.setCompressCodec(w.compressCodec) + f.setCompressType(w.compressType) + f.setTableInfo(w.tableInfo) + f.setDestTableId(w.destTableId) + f + } + + /* + * Bug introduced in hive-0.13. FileSinkDesc is serializable, but its member path is not. + * Fix it through wrapper. + */ + private[hive] class ShimFileSinkDesc( + var dir: String, + var tableInfo: TableDesc, + var compressed: Boolean) + extends Serializable with Logging { + var compressCodec: String = _ + var compressType: String = _ + var destTableId: Int = _ + + def setCompressed(compressed: Boolean) { + this.compressed = compressed + } + + def getDirName(): String = dir + + def setDestTableId(destTableId: Int) { + this.destTableId = destTableId + } + + def setTableInfo(tableInfo: TableDesc) { + this.tableInfo = tableInfo + } + + def setCompressCodec(intermediateCompressorCodec: String) { + compressCodec = intermediateCompressorCodec + } + + def setCompressType(intermediateCompressType: String) { + compressType = intermediateCompressType + } + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala index be9249a8b1f44..452b7f0bcc749 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala @@ -24,7 +24,7 @@ import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate -import org.apache.spark.sql.catalyst.expressions.{Row, _} +import org.apache.spark.sql.catalyst.expressions.{InternalRow, _} import org.apache.spark.sql.catalyst.planning._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan @@ -137,10 +137,10 @@ private[hive] trait HiveStrategies { val partitionLocations = partitions.map(_.getLocation) if (partitionLocations.isEmpty) { - PhysicalRDD(plan.output, sparkContext.emptyRDD[Row]) :: Nil + PhysicalRDD(plan.output, sparkContext.emptyRDD[InternalRow]) :: Nil } else { hiveContext - .parquetFile(partitionLocations: _*) + .read.parquet(partitionLocations: _*) .addPartitioningAttributes(relation.partitionKeys) .lowerCase .where(unresolvedOtherPredicates) @@ -152,7 +152,7 @@ private[hive] trait HiveStrategies { } else { hiveContext - .parquetFile(relation.hiveQlTable.getDataLocation.toString) + .read.parquet(relation.hiveQlTable.getDataLocation.toString) .lowerCase .where(unresolvedOtherPredicates) .select(unresolvedProjection: _*) @@ -165,7 +165,7 @@ private[hive] trait HiveStrategies { // TODO: Remove this hack for Spark 1.3. case iae: java.lang.IllegalArgumentException if iae.getMessage.contains("Can not create a Path from an empty string") => - PhysicalRDD(plan.output, sparkContext.emptyRDD[Row]) :: Nil + PhysicalRDD(plan.output, sparkContext.emptyRDD[InternalRow]) :: Nil } case _ => Nil } @@ -221,14 +221,14 @@ private[hive] trait HiveStrategies { object HiveDDLStrategy extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { case CreateTableUsing( - tableName, userSpecifiedSchema, provider, false, opts, allowExisting, managedIfNoPath) => + tableName, userSpecifiedSchema, provider, false, opts, allowExisting, managedIfNoPath) => ExecutedCommand( CreateMetastoreDataSource( tableName, userSpecifiedSchema, provider, opts, allowExisting, managedIfNoPath)) :: Nil - case CreateTableUsingAsSelect(tableName, provider, false, mode, opts, query) => + case CreateTableUsingAsSelect(tableName, provider, false, partitionCols, mode, opts, query) => val cmd = - CreateMetastoreDataSourceAsSelect(tableName, provider, mode, opts, query) + CreateMetastoreDataSourceAsSelect(tableName, provider, partitionCols, mode, opts, query) ExecutedCommand(cmd) :: Nil case _ => Nil diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala index b69312f0f8717..439f39bafc926 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala @@ -17,7 +17,6 @@ package org.apache.spark.sql.hive -import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{Path, PathFilter} import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants._ @@ -25,26 +24,25 @@ import org.apache.hadoop.hive.ql.exec.Utilities import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Table => HiveTable} import org.apache.hadoop.hive.ql.plan.{PlanUtils, TableDesc} import org.apache.hadoop.hive.serde2.Deserializer -import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, StructObjectInspector} import org.apache.hadoop.hive.serde2.objectinspector.primitive._ +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, StructObjectInspector} import org.apache.hadoop.io.Writable import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf} -import org.apache.spark.SerializableWritable +import org.apache.spark.{Logging} import org.apache.spark.broadcast.Broadcast -import org.apache.spark.Logging import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD} import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.types.DateUtils -import org.apache.spark.util.Utils +import org.apache.spark.sql.catalyst.util.DateUtils +import org.apache.spark.util.{SerializableConfiguration, Utils} /** * A trait for subclasses that handle table scans. */ private[hive] sealed trait TableReader { - def makeRDDForTable(hiveTable: HiveTable): RDD[Row] + def makeRDDForTable(hiveTable: HiveTable): RDD[InternalRow] - def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[Row] + def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[InternalRow] } @@ -73,13 +71,13 @@ class HadoopTableReader( // TODO: set aws s3 credentials. private val _broadcastedHiveConf = - sc.sparkContext.broadcast(new SerializableWritable(hiveExtraConf)) + sc.sparkContext.broadcast(new SerializableConfiguration(hiveExtraConf)) - override def makeRDDForTable(hiveTable: HiveTable): RDD[Row] = + override def makeRDDForTable(hiveTable: HiveTable): RDD[InternalRow] = makeRDDForTable( hiveTable, Class.forName( - relation.tableDesc.getSerdeClassName, true, Utils.getSparkClassLoader) + relation.tableDesc.getSerdeClassName, true, Utils.getContextOrSparkClassLoader) .asInstanceOf[Class[Deserializer]], filterOpt = None) @@ -95,7 +93,7 @@ class HadoopTableReader( def makeRDDForTable( hiveTable: HiveTable, deserializerClass: Class[_ <: Deserializer], - filterOpt: Option[PathFilter]): RDD[Row] = { + filterOpt: Option[PathFilter]): RDD[InternalRow] = { assert(!hiveTable.isPartitioned, """makeRDDForTable() cannot be called on a partitioned table, since input formats may differ across partitions. Use makeRDDForTablePartitions() instead.""") @@ -126,7 +124,7 @@ class HadoopTableReader( deserializedHadoopRDD } - override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[Row] = { + override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[InternalRow] = { val partitionToDeserializer = partitions.map(part => (part, part.getDeserializer.getClass.asInstanceOf[Class[Deserializer]])).toMap makeRDDForPartitionedTable(partitionToDeserializer, filterOpt = None) @@ -145,7 +143,7 @@ class HadoopTableReader( def makeRDDForPartitionedTable( partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]], - filterOpt: Option[PathFilter]): RDD[Row] = { + filterOpt: Option[PathFilter]): RDD[InternalRow] = { // SPARK-5068:get FileStatus and do the filtering locally when the path is not exists def verifyPartitionPath( @@ -172,7 +170,7 @@ class HadoopTableReader( path.toString + tails } - val partPath = HiveShim.getDataLocationPath(partition) + val partPath = partition.getDataLocation val partNum = Utilities.getPartitionDesc(partition).getPartSpec.size(); var pathPatternStr = getPathPatternByPath(partNum, partPath) if (!pathPatternSet.contains(pathPatternStr)) { @@ -187,7 +185,7 @@ class HadoopTableReader( val hivePartitionRDDs = verifyPartitionPath(partitionToDeserializer) .map { case (partition, partDeserializer) => val partDesc = Utilities.getPartitionDesc(partition) - val partPath = HiveShim.getDataLocationPath(partition) + val partPath = partition.getDataLocation val inputPathStr = applyFilterIfNeeded(partPath, filterOpt) val ifc = partDesc.getInputFileFormatClass .asInstanceOf[java.lang.Class[InputFormat[Writable, Writable]]] @@ -244,7 +242,7 @@ class HadoopTableReader( // Even if we don't use any partitions, we still need an empty RDD if (hivePartitionRDDs.size == 0) { - new EmptyRDD[Row](sc.sparkContext) + new EmptyRDD[InternalRow](sc.sparkContext) } else { new UnionRDD(hivePartitionRDDs(0).context, hivePartitionRDDs) } @@ -277,7 +275,7 @@ class HadoopTableReader( val rdd = new HadoopRDD( sc.sparkContext, - _broadcastedHiveConf.asInstanceOf[Broadcast[SerializableWritable[Configuration]]], + _broadcastedHiveConf.asInstanceOf[Broadcast[SerializableConfiguration]], Some(initializeJobConfFunc), inputFormatClass, classOf[Writable], @@ -320,12 +318,12 @@ private[hive] object HadoopTableReader extends HiveInspectors with Logging { rawDeser: Deserializer, nonPartitionKeyAttrs: Seq[(Attribute, Int)], mutableRow: MutableRow, - tableDeser: Deserializer): Iterator[Row] = { + tableDeser: Deserializer): Iterator[InternalRow] = { val soi = if (rawDeser.getObjectInspector.equals(tableDeser.getObjectInspector)) { rawDeser.getObjectInspector.asInstanceOf[StructObjectInspector] } else { - HiveShim.getConvertedOI( + ObjectInspectorConverters.getConvertedOI( rawDeser.getObjectInspector, tableDeser.getObjectInspector).asInstanceOf[StructObjectInspector] } @@ -364,10 +362,10 @@ private[hive] object HadoopTableReader extends HiveInspectors with Logging { row.update(ordinal, HiveShim.toCatalystDecimal(oi, value)) case oi: TimestampObjectInspector => (value: Any, row: MutableRow, ordinal: Int) => - row.update(ordinal, oi.getPrimitiveJavaObject(value).clone()) + row.setLong(ordinal, DateUtils.fromJavaTimestamp(oi.getPrimitiveJavaObject(value))) case oi: DateObjectInspector => (value: Any, row: MutableRow, ordinal: Int) => - row.update(ordinal, DateUtils.fromJavaDate(oi.getPrimitiveJavaObject(value))) + row.setInt(ordinal, DateUtils.fromJavaDate(oi.getPrimitiveJavaObject(value))) case oi: BinaryObjectInspector => (value: Any, row: MutableRow, ordinal: Int) => row.update(ordinal, oi.getPrimitiveJavaObject(value)) @@ -392,7 +390,7 @@ private[hive] object HadoopTableReader extends HiveInspectors with Logging { i += 1 } - mutableRow: Row + mutableRow: InternalRow } } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala index 6bca9d0179fe3..42c2d4c98ffb2 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala @@ -27,7 +27,7 @@ import scala.language.reflectiveCalls import org.apache.hadoop.fs.Path import org.apache.hadoop.hive.metastore.api.Database import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.metastore.TableType +import org.apache.hadoop.hive.metastore.{TableType => HTableType} import org.apache.hadoop.hive.metastore.api import org.apache.hadoop.hive.metastore.api.FieldSchema import org.apache.hadoop.hive.ql.metadata @@ -54,13 +54,15 @@ import org.apache.spark.sql.execution.QueryExecutionException * @param version the version of hive used when pick function calls that are not compatible. * @param config a collection of configuration options that will be added to the hive conf before * opening the hive client. + * @param initClassLoader the classloader used when creating the `state` field of + * this ClientWrapper. */ private[hive] class ClientWrapper( version: HiveVersion, - config: Map[String, String]) + config: Map[String, String], + initClassLoader: ClassLoader) extends ClientInterface - with Logging - with ReflectionMagic { + with Logging { // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. private val outputBuffer = new java.io.OutputStream { @@ -90,13 +92,27 @@ private[hive] class ClientWrapper( } } + private val shim = version match { + case hive.v12 => new Shim_v0_12() + case hive.v13 => new Shim_v0_13() + case hive.v14 => new Shim_v0_14() + } + + // Create an internal session state for this ClientWrapper. val state = { val original = Thread.currentThread().getContextClassLoader - Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + // Switch to the initClassLoader. + Thread.currentThread().setContextClassLoader(initClassLoader) val ret = try { val oldState = SessionState.get() if (oldState == null) { val initialConf = new HiveConf(classOf[SessionState]) + // HiveConf is a Hadoop Configuration, which has a field of classLoader and + // the initial value will be the current thread's context class loader + // (i.e. initClassLoader at here). + // We call initialConf.setClassLoader(initClassLoader) at here to make + // this action explicit. + initialConf.setClassLoader(initClassLoader) config.foreach { case (k, v) => logDebug(s"Hive Config: $k=$v") initialConf.set(k, v) @@ -119,6 +135,7 @@ private[hive] class ClientWrapper( def conf: HiveConf = SessionState.get().getConf // TODO: should be a def?s + // When we create this val client, the HiveConf of it (conf) is the one associated with state. private val client = Hive.get(conf) /** @@ -126,16 +143,12 @@ private[hive] class ClientWrapper( */ private def withHiveState[A](f: => A): A = synchronized { val original = Thread.currentThread().getContextClassLoader - Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + // Set the thread local metastore client to the client associated with this ClientWrapper. Hive.set(client) - version match { - case hive.v12 => - classOf[SessionState] - .callStatic[SessionState, SessionState]("start", state) - case hive.v13 => - classOf[SessionState] - .callStatic[SessionState, SessionState]("setCurrentSessionState", state) - } + // setCurrentSessionState will use the classLoader associated + // with the HiveConf in `state` to override the context class loader of the current + // thread. + shim.setCurrentSessionState(state) val ret = try f finally { Thread.currentThread().setContextClassLoader(original) } @@ -193,15 +206,12 @@ private[hive] class ClientWrapper( properties = h.getParameters.toMap, serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap, tableType = h.getTableType match { - case TableType.MANAGED_TABLE => ManagedTable - case TableType.EXTERNAL_TABLE => ExternalTable - case TableType.VIRTUAL_VIEW => VirtualView - case TableType.INDEX_TABLE => IndexTable - }, - location = version match { - case hive.v12 => Option(h.call[URI]("getDataLocation")).map(_.toString) - case hive.v13 => Option(h.call[Path]("getDataLocation")).map(_.toString) + case HTableType.MANAGED_TABLE => ManagedTable + case HTableType.EXTERNAL_TABLE => ExternalTable + case HTableType.VIRTUAL_VIEW => VirtualView + case HTableType.INDEX_TABLE => IndexTable }, + location = shim.getDataLocation(h), inputFormat = Option(h.getInputFormatClass).map(_.getName), outputFormat = Option(h.getOutputFormatClass).map(_.getName), serde = Option(h.getSerializationLib), @@ -225,14 +235,13 @@ private[hive] class ClientWrapper( table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment))) table.properties.foreach { case (k, v) => qlTable.setProperty(k, v) } table.serdeProperties.foreach { case (k, v) => qlTable.setSerdeParam(k, v) } - version match { - case hive.v12 => - table.location.map(new URI(_)).foreach(u => qlTable.call[URI, Unit]("setDataLocation", u)) - case hive.v13 => - table.location - .map(new org.apache.hadoop.fs.Path(_)) - .foreach(qlTable.call[Path, Unit]("setDataLocation", _)) - } + + // set owner + qlTable.setOwner(conf.getUser) + // set create time + qlTable.setCreateTime((System.currentTimeMillis() / 1000).asInstanceOf[Int]) + + table.location.foreach { loc => shim.setDataLocation(qlTable, loc) } table.inputFormat.map(toInputFormat).foreach(qlTable.setInputFormatClass) table.outputFormat.map(toOutputFormat).foreach(qlTable.setOutputFormatClass) table.serde.foreach(qlTable.setSerializationLib) @@ -273,13 +282,7 @@ private[hive] class ClientWrapper( override def getAllPartitions(hTable: HiveTable): Seq[HivePartition] = withHiveState { val qlTable = toQlTable(hTable) - val qlPartitions = version match { - case hive.v12 => - client.call[metadata.Table, JSet[metadata.Partition]]("getAllPartitionsForPruner", qlTable) - case hive.v13 => - client.call[metadata.Table, JSet[metadata.Partition]]("getAllPartitionsOf", qlTable) - } - qlPartitions.toSeq.map(toHivePartition) + shim.getAllPartitions(client, qlTable).map(toHivePartition) } override def listTables(dbName: String): Seq[String] = withHiveState { @@ -309,15 +312,7 @@ private[hive] class ClientWrapper( val tokens: Array[String] = cmd_trimmed.split("\\s+") // The remainder of the command. val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim() - val proc: CommandProcessor = version match { - case hive.v12 => - classOf[CommandProcessorFactory] - .callStatic[String, HiveConf, CommandProcessor]("get", tokens(0), conf) - case hive.v13 => - classOf[CommandProcessorFactory] - .callStatic[Array[String], HiveConf, CommandProcessor]("get", Array(tokens(0)), conf) - } - + val proc = shim.getCommandProcessor(tokens(0), conf) proc match { case driver: Driver => val response: CommandProcessorResponse = driver.run(cmd) @@ -328,21 +323,7 @@ private[hive] class ClientWrapper( } driver.setMaxRows(maxRows) - val results = version match { - case hive.v12 => - val res = new JArrayList[String] - driver.call[JArrayList[String], Boolean]("getResults", res) - res.toSeq - case hive.v13 => - val res = new JArrayList[Object] - driver.call[JList[Object], Boolean]("getResults", res) - res.map { r => - r match { - case s: String => s - case a: Array[Object] => a(0).asInstanceOf[String] - } - } - } + val results = shim.getDriverResults(driver) driver.close() results @@ -376,8 +357,8 @@ private[hive] class ClientWrapper( holdDDLTime: Boolean, inheritTableSpecs: Boolean, isSkewedStoreAsSubdir: Boolean): Unit = withHiveState { - - client.loadPartition( + shim.loadPartition( + client, new Path(loadPath), // TODO: Use URI tableName, partSpec, @@ -392,7 +373,8 @@ private[hive] class ClientWrapper( tableName: String, replace: Boolean, holdDDLTime: Boolean): Unit = withHiveState { - client.loadTable( + shim.loadTable( + client, new Path(loadPath), tableName, replace, @@ -407,7 +389,8 @@ private[hive] class ClientWrapper( numDP: Int, holdDDLTime: Boolean, listBucketingEnabled: Boolean): Unit = withHiveState { - client.loadDynamicPartitions( + shim.loadDynamicPartitions( + client, new Path(loadPath), tableName, partSpec, diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala new file mode 100644 index 0000000000000..5ae2dbb50d86b --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala @@ -0,0 +1,362 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import java.lang.{Boolean => JBoolean, Integer => JInteger} +import java.lang.reflect.{Method, Modifier} +import java.net.URI +import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, Set => JSet} + +import scala.collection.JavaConversions._ + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.ql.Driver +import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table} +import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory} +import org.apache.hadoop.hive.ql.session.SessionState + +/** + * A shim that defines the interface between ClientWrapper and the underlying Hive library used to + * talk to the metastore. Each Hive version has its own implementation of this class, defining + * version-specific version of needed functions. + * + * The guideline for writing shims is: + * - always extend from the previous version unless really not possible + * - initialize methods in lazy vals, both for quicker access for multiple invocations, and to + * avoid runtime errors due to the above guideline. + */ +private[client] sealed abstract class Shim { + + /** + * Set the current SessionState to the given SessionState. Also, set the context classloader of + * the current thread to the one set in the HiveConf of this given `state`. + * @param state + */ + def setCurrentSessionState(state: SessionState): Unit + + /** + * This shim is necessary because the return type is different on different versions of Hive. + * All parameters are the same, though. + */ + def getDataLocation(table: Table): Option[String] + + def setDataLocation(table: Table, loc: String): Unit + + def getAllPartitions(hive: Hive, table: Table): Seq[Partition] + + def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor + + def getDriverResults(driver: Driver): Seq[String] + + def loadPartition( + hive: Hive, + loadPath: Path, + tableName: String, + partSpec: JMap[String, String], + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit + + def loadTable( + hive: Hive, + loadPath: Path, + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit + + def loadDynamicPartitions( + hive: Hive, + loadPath: Path, + tableName: String, + partSpec: JMap[String, String], + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit + + protected def findStaticMethod(klass: Class[_], name: String, args: Class[_]*): Method = { + val method = findMethod(klass, name, args: _*) + require(Modifier.isStatic(method.getModifiers()), + s"Method $name of class $klass is not static.") + method + } + + protected def findMethod(klass: Class[_], name: String, args: Class[_]*): Method = { + klass.getMethod(name, args: _*) + } + +} + +private[client] class Shim_v0_12 extends Shim { + + private lazy val startMethod = + findStaticMethod( + classOf[SessionState], + "start", + classOf[SessionState]) + private lazy val getDataLocationMethod = findMethod(classOf[Table], "getDataLocation") + private lazy val setDataLocationMethod = + findMethod( + classOf[Table], + "setDataLocation", + classOf[URI]) + private lazy val getAllPartitionsMethod = + findMethod( + classOf[Hive], + "getAllPartitionsForPruner", + classOf[Table]) + private lazy val getCommandProcessorMethod = + findStaticMethod( + classOf[CommandProcessorFactory], + "get", + classOf[String], + classOf[HiveConf]) + private lazy val getDriverResultsMethod = + findMethod( + classOf[Driver], + "getResults", + classOf[JArrayList[String]]) + private lazy val loadPartitionMethod = + findMethod( + classOf[Hive], + "loadPartition", + classOf[Path], + classOf[String], + classOf[JMap[String, String]], + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE) + private lazy val loadTableMethod = + findMethod( + classOf[Hive], + "loadTable", + classOf[Path], + classOf[String], + JBoolean.TYPE, + JBoolean.TYPE) + private lazy val loadDynamicPartitionsMethod = + findMethod( + classOf[Hive], + "loadDynamicPartitions", + classOf[Path], + classOf[String], + classOf[JMap[String, String]], + JBoolean.TYPE, + JInteger.TYPE, + JBoolean.TYPE, + JBoolean.TYPE) + + override def setCurrentSessionState(state: SessionState): Unit = { + // Starting from Hive 0.13, setCurrentSessionState will internally override + // the context class loader of the current thread by the class loader set in + // the conf of the SessionState. So, for this Hive 0.12 shim, we add the same + // behavior and make shim.setCurrentSessionState of all Hive versions have the + // consistent behavior. + Thread.currentThread().setContextClassLoader(state.getConf.getClassLoader) + startMethod.invoke(null, state) + } + + override def getDataLocation(table: Table): Option[String] = + Option(getDataLocationMethod.invoke(table)).map(_.toString()) + + override def setDataLocation(table: Table, loc: String): Unit = + setDataLocationMethod.invoke(table, new URI(loc)) + + override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] = + getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq + + override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor = + getCommandProcessorMethod.invoke(null, token, conf).asInstanceOf[CommandProcessor] + + override def getDriverResults(driver: Driver): Seq[String] = { + val res = new JArrayList[String]() + getDriverResultsMethod.invoke(driver, res) + res.toSeq + } + + override def loadPartition( + hive: Hive, + loadPath: Path, + tableName: String, + partSpec: JMap[String, String], + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit = { + loadPartitionMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean, + holdDDLTime: JBoolean, inheritTableSpecs: JBoolean, isSkewedStoreAsSubdir: JBoolean) + } + + override def loadTable( + hive: Hive, + loadPath: Path, + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit = { + loadTableMethod.invoke(hive, loadPath, tableName, replace: JBoolean, holdDDLTime: JBoolean) + } + + override def loadDynamicPartitions( + hive: Hive, + loadPath: Path, + tableName: String, + partSpec: JMap[String, String], + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit = { + loadDynamicPartitionsMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean, + numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean) + } + +} + +private[client] class Shim_v0_13 extends Shim_v0_12 { + + private lazy val setCurrentSessionStateMethod = + findStaticMethod( + classOf[SessionState], + "setCurrentSessionState", + classOf[SessionState]) + private lazy val setDataLocationMethod = + findMethod( + classOf[Table], + "setDataLocation", + classOf[Path]) + private lazy val getAllPartitionsMethod = + findMethod( + classOf[Hive], + "getAllPartitionsOf", + classOf[Table]) + private lazy val getCommandProcessorMethod = + findStaticMethod( + classOf[CommandProcessorFactory], + "get", + classOf[Array[String]], + classOf[HiveConf]) + private lazy val getDriverResultsMethod = + findMethod( + classOf[Driver], + "getResults", + classOf[JList[Object]]) + + override def setCurrentSessionState(state: SessionState): Unit = + setCurrentSessionStateMethod.invoke(null, state) + + override def setDataLocation(table: Table, loc: String): Unit = + setDataLocationMethod.invoke(table, new Path(loc)) + + override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] = + getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq + + override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor = + getCommandProcessorMethod.invoke(null, Array(token), conf).asInstanceOf[CommandProcessor] + + override def getDriverResults(driver: Driver): Seq[String] = { + val res = new JArrayList[Object]() + getDriverResultsMethod.invoke(driver, res) + res.map { r => + r match { + case s: String => s + case a: Array[Object] => a(0).asInstanceOf[String] + } + } + } + +} + +private[client] class Shim_v0_14 extends Shim_v0_13 { + + private lazy val loadPartitionMethod = + findMethod( + classOf[Hive], + "loadPartition", + classOf[Path], + classOf[String], + classOf[JMap[String, String]], + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE) + private lazy val loadTableMethod = + findMethod( + classOf[Hive], + "loadTable", + classOf[Path], + classOf[String], + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE) + private lazy val loadDynamicPartitionsMethod = + findMethod( + classOf[Hive], + "loadDynamicPartitions", + classOf[Path], + classOf[String], + classOf[JMap[String, String]], + JBoolean.TYPE, + JInteger.TYPE, + JBoolean.TYPE, + JBoolean.TYPE, + JBoolean.TYPE) + + override def loadPartition( + hive: Hive, + loadPath: Path, + tableName: String, + partSpec: JMap[String, String], + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit = { + loadPartitionMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean, + holdDDLTime: JBoolean, inheritTableSpecs: JBoolean, isSkewedStoreAsSubdir: JBoolean, + JBoolean.TRUE, JBoolean.FALSE) + } + + override def loadTable( + hive: Hive, + loadPath: Path, + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit = { + loadTableMethod.invoke(hive, loadPath, tableName, replace: JBoolean, holdDDLTime: JBoolean, + JBoolean.TRUE, JBoolean.FALSE, JBoolean.FALSE) + } + + override def loadDynamicPartitions( + hive: Hive, + loadPath: Path, + tableName: String, + partSpec: JMap[String, String], + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit = { + loadDynamicPartitionsMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean, + numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean, JBoolean.FALSE) + } + +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala index 7f94c93ba49c1..0934ad5034671 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.hive.client import java.io.File +import java.lang.reflect.InvocationTargetException import java.net.{URL, URLClassLoader} import java.util @@ -28,6 +29,7 @@ import org.apache.commons.io.{FileUtils, IOUtils} import org.apache.spark.Logging import org.apache.spark.deploy.SparkSubmitUtils +import org.apache.spark.util.Utils import org.apache.spark.sql.catalyst.util.quietly import org.apache.spark.sql.hive.HiveContext @@ -48,30 +50,27 @@ private[hive] object IsolatedClientLoader { def hiveVersion(version: String): HiveVersion = version match { case "12" | "0.12" | "0.12.0" => hive.v12 case "13" | "0.13" | "0.13.0" | "0.13.1" => hive.v13 + case "14" | "0.14" | "0.14.0" => hive.v14 } private def downloadVersion(version: HiveVersion): Seq[URL] = { - val hiveArtifacts = - (Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde") ++ - (if (version.hasBuiltinsJar) "hive-builtins" :: Nil else Nil)) - .map(a => s"org.apache.hive:$a:${version.fullVersion}") :+ - "com.google.guava:guava:14.0.1" :+ - "org.apache.hadoop:hadoop-client:2.4.0" :+ - "mysql:mysql-connector-java:5.1.12" + val hiveArtifacts = version.extraDeps ++ + Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde") + .map(a => s"org.apache.hive:$a:${version.fullVersion}") ++ + Seq("com.google.guava:guava:14.0.1", + "org.apache.hadoop:hadoop-client:2.4.0") val classpath = quietly { SparkSubmitUtils.resolveMavenCoordinates( hiveArtifacts.mkString(","), Some("http://www.datanucleus.org/downloads/maven2"), - None) + None, + exclusions = version.exclusions) } val allFiles = classpath.split(",").map(new File(_)).toSet // TODO: Remove copy logic. - val tempDir = File.createTempFile("hive", "v" + version.toString) - tempDir.delete() - tempDir.mkdir() - + val tempDir = Utils.createTempDir(namePrefix = s"hive-${version}") allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir)) tempDir.listFiles().map(_.toURL) } @@ -91,14 +90,13 @@ private[hive] object IsolatedClientLoader { * `ClientInterface`, unless `isolationOn` is set to `false`. * * @param version The version of hive on the classpath. used to pick specific function signatures - * that are not compatibile accross versions. + * that are not compatible across versions. * @param execJars A collection of jar files that must include hive and hadoop. * @param config A set of options that will be added to the HiveConf of the constructed client. * @param isolationOn When true, custom versions of barrier classes will be constructed. Must be * true unless loading the version of hive that is on Sparks classloader. - * @param rootClassLoader The system root classloader. Must not know about hive classes. + * @param rootClassLoader The system root classloader. Must not know about Hive classes. * @param baseClassLoader The spark classloader that is used to load shared classes. - * */ private[hive] class IsolatedClientLoader( val version: HiveVersion, @@ -106,11 +104,13 @@ private[hive] class IsolatedClientLoader( val config: Map[String, String] = Map.empty, val isolationOn: Boolean = true, val rootClassLoader: ClassLoader = ClassLoader.getSystemClassLoader.getParent.getParent, - val baseClassLoader: ClassLoader = Thread.currentThread().getContextClassLoader) + val baseClassLoader: ClassLoader = Thread.currentThread().getContextClassLoader, + val sharedPrefixes: Seq[String] = Seq.empty, + val barrierPrefixes: Seq[String] = Seq.empty) extends Logging { // Check to make sure that the root classloader does not know about Hive. - assert(Try(baseClassLoader.loadClass("org.apache.hive.HiveConf")).isFailure) + assert(Try(rootClassLoader.loadClass("org.apache.hadoop.hive.conf.HiveConf")).isFailure) /** All jars used by the hive specific classloader. */ protected def allJars = execJars.toArray @@ -122,13 +122,14 @@ private[hive] class IsolatedClientLoader( name.startsWith("scala.") || name.startsWith("com.google") || name.startsWith("java.lang.") || - name.startsWith("java.net") + name.startsWith("java.net") || + sharedPrefixes.exists(name.startsWith) /** True if `name` refers to a spark class that must see specific version of Hive. */ protected def isBarrierClass(name: String): Boolean = - name.startsWith("org.apache.spark.sql.hive.execution.PairSerDe") || name.startsWith(classOf[ClientWrapper].getName) || - name.startsWith(classOf[ReflectionMagic].getName) + name.startsWith(classOf[Shim].getName) || + barrierPrefixes.exists(name.startsWith) protected def classToPath(name: String): String = name.replaceAll("\\.", "/") + ".class" @@ -143,6 +144,7 @@ private[hive] class IsolatedClientLoader( def doLoadClass(name: String, resolve: Boolean): Class[_] = { val classFileName = name.replaceAll("\\.", "/") + ".class" if (isBarrierClass(name) && isolationOn) { + // For barrier classes, we construct a new copy of the class. val bytes = IOUtils.toByteArray(baseClassLoader.getResourceAsStream(classFileName)) logDebug(s"custom defining: $name - ${util.Arrays.hashCode(bytes)}") defineClass(name, bytes, 0, bytes.length) @@ -150,6 +152,7 @@ private[hive] class IsolatedClientLoader( logDebug(s"hive class: $name - ${getResource(classToPath(name))}") super.loadClass(name, resolve) } else { + // For shared classes, we delegate to baseClassLoader. logDebug(s"shared class: $name") baseClassLoader.loadClass(name) } @@ -165,14 +168,19 @@ private[hive] class IsolatedClientLoader( classLoader .loadClass(classOf[ClientWrapper].getName) .getConstructors.head - .newInstance(version, config) + .newInstance(version, config, classLoader) .asInstanceOf[ClientInterface] } catch { - case ReflectionException(cnf: NoClassDefFoundError) => - throw new ClassNotFoundException( - s"$cnf when creating Hive client using classpath: ${execJars.mkString(", ")}\n" + - "Please make sure that jars for your version of hive and hadoop are included in the " + - s"paths passed to ${HiveContext.HIVE_METASTORE_JARS}.") + case e: InvocationTargetException => + if (e.getCause().isInstanceOf[NoClassDefFoundError]) { + val cnf = e.getCause().asInstanceOf[NoClassDefFoundError] + throw new ClassNotFoundException( + s"$cnf when creating Hive client using classpath: ${execJars.mkString(", ")}\n" + + "Please make sure that jars for your version of hive and hadoop are included in the " + + s"paths passed to ${HiveContext.HIVE_METASTORE_JARS}.") + } else { + throw e + } } finally { Thread.currentThread.setContextClassLoader(baseClassLoader) } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala deleted file mode 100644 index c600b158c5460..0000000000000 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala +++ /dev/null @@ -1,208 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive.client - -import scala.reflect._ - -/** Unwraps reflection exceptions. */ -private[client] object ReflectionException { - def unapply(a: Throwable): Option[Throwable] = a match { - case ite: java.lang.reflect.InvocationTargetException => Option(ite.getCause) - case _ => None - } -} - -/** - * Provides implicit functions on any object for calling methods reflectively. - */ -protected trait ReflectionMagic { - /** code for InstanceMagic - println( - (1 to 22).map { n => - def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") - val types = repeat(n => s"A$n <: AnyRef : ClassTag") - val inArgs = repeat(n => s"a$n: A$n") - val erasure = repeat(n => s"classTag[A$n].erasure") - val outArgs = repeat(n => s"a$n") - s"""|def call[$types, R](name: String, $inArgs): R = { - | clazz.getMethod(name, $erasure).invoke(a, $outArgs).asInstanceOf[R] - |}""".stripMargin - }.mkString("\n") - ) - */ - - // scalastyle:off - protected implicit class InstanceMagic(a: Any) { - private val clazz = a.getClass - - def call[R](name: String): R = { - clazz.getMethod(name).invoke(a).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { - clazz.getMethod(name, classTag[A1].erasure).invoke(a, a1).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(a, a1, a2).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(a, a1, a2, a3).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(a, a1, a2, a3, a4).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(a, a1, a2, a3, a4, a5).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(a, a1, a2, a3, a4, a5, a6).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] - } - def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { - clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] - } - } - - /** code for StaticMagic - println( - (1 to 22).map { n => - def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") - val types = repeat(n => s"A$n <: AnyRef : ClassTag") - val inArgs = repeat(n => s"a$n: A$n") - val erasure = repeat(n => s"classTag[A$n].erasure") - val outArgs = repeat(n => s"a$n") - s"""|def callStatic[$types, R](name: String, $inArgs): R = { - | c.getDeclaredMethod(name, $erasure).invoke(c, $outArgs).asInstanceOf[R] - |}""".stripMargin - }.mkString("\n") - ) - */ - - protected implicit class StaticMagic(c: Class[_]) { - def callStatic[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { - c.getDeclaredMethod(name, classTag[A1].erasure).invoke(c, a1).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(c, a1, a2).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(c, a1, a2, a3).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(c, a1, a2, a3, a4).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(c, a1, a2, a3, a4, a5).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(c, a1, a2, a3, a4, a5, a6).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] - } - def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { - c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] - } - } - // scalastyle:on -} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala index 7db9200d47440..27a3d8f5896cc 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala @@ -19,15 +19,27 @@ package org.apache.spark.sql.hive /** Support for interacting with different versions of the HiveMetastoreClient */ package object client { - private[client] abstract class HiveVersion(val fullVersion: String, val hasBuiltinsJar: Boolean) + private[client] abstract class HiveVersion( + val fullVersion: String, + val extraDeps: Seq[String] = Nil, + val exclusions: Seq[String] = Nil) // scalastyle:off private[client] object hive { - case object v10 extends HiveVersion("0.10.0", true) - case object v11 extends HiveVersion("0.11.0", false) - case object v12 extends HiveVersion("0.12.0", false) - case object v13 extends HiveVersion("0.13.1", false) + case object v12 extends HiveVersion("0.12.0") + case object v13 extends HiveVersion("0.13.1") + + // Hive 0.14 depends on calcite 0.9.2-incubating-SNAPSHOT which does not exist in + // maven central anymore, so override those with a version that exists. + // + // org.pentaho:pentaho-aggdesigner-algorithm is also nowhere to be found, so exclude + // it explicitly. If it's needed by the metastore client, users will have to dig it + // out of somewhere and use configuration to point Spark at the correct jars. + case object v14 extends HiveVersion("0.14.0", + Seq("org.apache.calcite:calcite-core:1.3.0-incubating", + "org.apache.calcite:calcite-avatica:1.3.0-incubating"), + Seq("org.pentaho:pentaho-aggdesigner-algorithm")) } // scalastyle:on - -} \ No newline at end of file + +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala index 91e6ac4032204..0e4a2427a9c15 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala @@ -17,11 +17,9 @@ package org.apache.spark.sql.hive.execution -import org.apache.hadoop.hive.ql.plan.CreateTableDesc - import org.apache.spark.annotation.Experimental -import org.apache.spark.sql.SQLContext -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.{AnalysisException, SQLContext} +import org.apache.spark.sql.catalyst.expressions.InternalRow import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, LogicalPlan} import org.apache.spark.sql.execution.RunnableCommand import org.apache.spark.sql.hive.client.{HiveTable, HiveColumn} @@ -29,13 +27,10 @@ import org.apache.spark.sql.hive.{HiveContext, MetastoreRelation, HiveMetastoreT /** * Create table and insert the query result into it. - * @param database the database name of the new relation - * @param tableName the table name of the new relation + * @param tableDesc the Table Describe, which may contains serde, storage handler etc. * @param query the query whose result will be insert into the new relation * @param allowExisting allow continue working if it's already exists, otherwise * raise exception - * @param desc the CreateTableDesc, which may contains serde, storage handler etc. - */ private[hive] case class CreateTableAsSelect( @@ -47,7 +42,7 @@ case class CreateTableAsSelect( def database: String = tableDesc.database def tableName: String = tableDesc.name - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] lazy val metastoreRelation: MetastoreRelation = { import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe @@ -55,17 +50,25 @@ case class CreateTableAsSelect( import org.apache.hadoop.io.Text import org.apache.hadoop.mapred.TextInputFormat - val withSchema = + val withFormat = tableDesc.copy( - schema = - query.output.map(c => - HiveColumn(c.name, HiveMetastoreTypes.toMetastoreType(c.dataType), null)), inputFormat = tableDesc.inputFormat.orElse(Some(classOf[TextInputFormat].getName)), outputFormat = tableDesc.outputFormat .orElse(Some(classOf[HiveIgnoreKeyTextOutputFormat[Text, Text]].getName)), serde = tableDesc.serde.orElse(Some(classOf[LazySimpleSerDe].getName()))) + + val withSchema = if (withFormat.schema.isEmpty) { + // Hive doesn't support specifying the column list for target table in CTAS + // However we don't think SparkSQL should follow that. + tableDesc.copy(schema = + query.output.map(c => + HiveColumn(c.name, HiveMetastoreTypes.toMetastoreType(c.dataType), null))) + } else { + withFormat + } + hiveContext.catalog.client.createTable(withSchema) // Get the Metastore Relation @@ -80,14 +83,13 @@ case class CreateTableAsSelect( if (allowExisting) { // table already exists, will do nothing, to keep consistent with Hive } else { - throw - new org.apache.hadoop.hive.metastore.api.AlreadyExistsException(s"$database.$tableName") + throw new AnalysisException(s"$database.$tableName already exists.") } } else { hiveContext.executePlan(InsertIntoTable(metastoreRelation, Map(), query, true, false)).toRdd } - Seq.empty[Row] + Seq.empty[InternalRow] } override def argString: String = { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala index 6fce69b58b85e..a89381000ad5f 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala @@ -21,12 +21,10 @@ import scala.collection.JavaConversions._ import org.apache.hadoop.hive.metastore.api.FieldSchema -import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} -import org.apache.spark.sql.execution.{SparkPlan, RunnableCommand} -import org.apache.spark.sql.hive.{HiveContext, MetastoreRelation} -import org.apache.spark.sql.hive.HiveShim import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow} +import org.apache.spark.sql.execution.RunnableCommand +import org.apache.spark.sql.hive.MetastoreRelation /** * Implementation for "describe [extended] table". @@ -37,7 +35,7 @@ case class DescribeHiveTableCommand( override val output: Seq[Attribute], isExtended: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { // Trying to mimic the format of Hive's output. But not exactly the same. var results: Seq[(String, String, String)] = Nil @@ -59,7 +57,7 @@ case class DescribeHiveTableCommand( } results.map { case (name, dataType, comment) => - Row(name, dataType, comment) + InternalRow(name, dataType, comment) } } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala index 60a9bb630d0d9..87f8e3f7fcfcc 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala @@ -1,34 +1,34 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive.execution - -import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Row} -import org.apache.spark.sql.execution.RunnableCommand -import org.apache.spark.sql.hive.HiveContext -import org.apache.spark.sql.SQLContext -import org.apache.spark.sql.types.StringType - -private[hive] -case class HiveNativeCommand(sql: String) extends RunnableCommand { - - override def output: Seq[AttributeReference] = - Seq(AttributeReference("result", StringType, nullable = false)()) - - override def run(sqlContext: SQLContext): Seq[Row] = - sqlContext.asInstanceOf[HiveContext].runSqlHive(sql).map(Row(_)) -} +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.execution + +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, InternalRow} +import org.apache.spark.sql.execution.RunnableCommand +import org.apache.spark.sql.hive.HiveContext +import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.types.StringType + +private[hive] +case class HiveNativeCommand(sql: String) extends RunnableCommand { + + override def output: Seq[AttributeReference] = + Seq(AttributeReference("result", StringType, nullable = false)()) + + override def run(sqlContext: SQLContext): Seq[InternalRow] = + sqlContext.asInstanceOf[HiveContext].runSqlHive(sql).map(InternalRow(_)) +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala index 0a5f19eee7105..1f5e4af2e4746 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala @@ -63,7 +63,7 @@ case class HiveTableScan( BindReferences.bindReference(pred, relation.partitionKeys) } - // Create a local copy of hiveconf,so that scan specific modifications should not impact + // Create a local copy of hiveconf,so that scan specific modifications should not impact // other queries @transient private[this] val hiveExtraConf = new HiveConf(context.hiveconf) @@ -72,7 +72,7 @@ case class HiveTableScan( addColumnMetadataToConf(hiveExtraConf) @transient - private[this] val hadoopReader = + private[this] val hadoopReader = new HadoopTableReader(attributes, relation, context, hiveExtraConf) private[this] def castFromString(value: String, dataType: DataType) = { @@ -129,7 +129,7 @@ case class HiveTableScan( } } - override def execute(): RDD[Row] = if (!relation.hiveQlTable.isPartitioned) { + protected override def doExecute(): RDD[InternalRow] = if (!relation.hiveQlTable.isPartitioned) { hadoopReader.makeRDDForTable(relation.hiveQlTable) } else { hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions)) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala index de8954d5dec99..05f425f2b65f3 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala @@ -19,26 +19,26 @@ package org.apache.spark.sql.hive.execution import java.util -import scala.collection.JavaConversions._ - import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.hadoop.hive.metastore.MetaStoreUtils -import org.apache.hadoop.hive.ql.metadata.Hive import org.apache.hadoop.hive.ql.plan.TableDesc import org.apache.hadoop.hive.ql.{Context, ErrorMsg} import org.apache.hadoop.hive.serde2.Serializer import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption import org.apache.hadoop.hive.serde2.objectinspector._ -import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf} +import org.apache.hadoop.mapred.{FileOutputFormat, JobConf} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow} import org.apache.spark.sql.execution.{UnaryNode, SparkPlan} +import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc} import org.apache.spark.sql.hive._ -import org.apache.spark.sql.hive.{ ShimFileSinkDesc => FileSinkDesc} -import org.apache.spark.sql.hive.HiveShim._ -import org.apache.spark.{SerializableWritable, SparkException, TaskContext} +import org.apache.spark.{SparkException, TaskContext} + +import scala.collection.JavaConversions._ +import org.apache.spark.util.SerializableJobConf private[hive] case class InsertIntoHiveTable( @@ -62,10 +62,10 @@ case class InsertIntoHiveTable( def output: Seq[Attribute] = child.output def saveAsHiveFile( - rdd: RDD[Row], + rdd: RDD[InternalRow], valueClass: Class[_], fileSinkConf: FileSinkDesc, - conf: SerializableWritable[JobConf], + conf: SerializableJobConf, writerContainer: SparkHiveWriterContainer): Unit = { assert(valueClass != null, "Output value class not set") conf.value.setOutputValueClass(valueClass) @@ -84,7 +84,7 @@ case class InsertIntoHiveTable( writerContainer.commitJob() // Note that this function is executed on executor side - def writeToFile(context: TaskContext, iterator: Iterator[Row]): Unit = { + def writeToFile(context: TaskContext, iterator: Iterator[InternalRow]): Unit = { val serializer = newSerializer(fileSinkConf.getTableInfo) val standardOI = ObjectInspectorUtils .getStandardObjectInspector( @@ -106,7 +106,7 @@ case class InsertIntoHiveTable( } writerContainer - .getLocalFileWriter(row) + .getLocalFileWriter(row, table.schema) .write(serializer.serialize(outputData, standardOI)) } @@ -121,12 +121,12 @@ case class InsertIntoHiveTable( * * Note: this is run once and then kept to avoid double insertions. */ - protected[sql] lazy val sideEffectResult: Seq[Row] = { + protected[sql] lazy val sideEffectResult: Seq[InternalRow] = { // Have to pass the TableDesc object to RDD.mapPartitions and then instantiate new serializer // instances within the closure, since Serializer is not serializable while TableDesc is. val tableDesc = table.tableDesc val tableLocation = table.hiveQlTable.getDataLocation - val tmpLocation = HiveShim.getExternalTmpPath(hiveContext, tableLocation) + val tmpLocation = hiveContext.getExternalTmpPath(tableLocation.toUri) val fileSinkConf = new FileSinkDesc(tmpLocation.toString, tableDesc, false) val isCompressed = sc.hiveconf.getBoolean( ConfVars.COMPRESSRESULT.varname, ConfVars.COMPRESSRESULT.defaultBoolVal) @@ -173,7 +173,7 @@ case class InsertIntoHiveTable( } val jobConf = new JobConf(sc.hiveconf) - val jobConfSer = new SerializableWritable(jobConf) + val jobConfSer = new SerializableJobConf(jobConf) val writerContainer = if (numDynamicPartitions > 0) { val dynamicPartColNames = partitionColumnNames.takeRight(numDynamicPartitions) @@ -194,12 +194,10 @@ case class InsertIntoHiveTable( if (partition.nonEmpty) { // loadPartition call orders directories created on the iteration order of the this map - val orderedPartitionSpec = new util.LinkedHashMap[String,String]() - table.hiveQlTable.getPartCols().foreach{ - entry=> - orderedPartitionSpec.put(entry.getName,partitionSpec.get(entry.getName).getOrElse("")) + val orderedPartitionSpec = new util.LinkedHashMap[String, String]() + table.hiveQlTable.getPartCols().foreach { entry => + orderedPartitionSpec.put(entry.getName, partitionSpec.get(entry.getName).getOrElse("")) } - val partVals = MetaStoreUtils.getPvals(table.hiveQlTable.getPartCols, partitionSpec) // inheritTableSpecs is set to true. It should be set to false for a IMPORT query // which is currently considered as a Hive native command. @@ -253,10 +251,13 @@ case class InsertIntoHiveTable( // however for now we return an empty list to simplify compatibility checks with hive, which // does not return anything for insert operations. // TODO: implement hive compatibility as rules. - Seq.empty[Row] + Seq.empty[InternalRow] } - override def executeCollect(): Array[Row] = sideEffectResult.toArray + override def executeCollect(): Array[Row] = + sideEffectResult.toArray - override def execute(): RDD[Row] = sqlContext.sparkContext.parallelize(sideEffectResult, 1) + protected override def doExecute(): RDD[InternalRow] = { + sqlContext.sparkContext.parallelize(sideEffectResult, 1) + } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala index 3eddda3b28c66..9d8872aa47d1f 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.hive.execution import java.io.{BufferedReader, DataInputStream, DataOutputStream, EOFException, InputStreamReader} +import java.lang.ProcessBuilder.Redirect import java.util.Properties import scala.collection.JavaConversions._ @@ -54,19 +55,25 @@ case class ScriptTransformation( override def otherCopyArgs: Seq[HiveContext] = sc :: Nil - def execute(): RDD[Row] = { + protected override def doExecute(): RDD[InternalRow] = { child.execute().mapPartitions { iter => val cmd = List("/bin/bash", "-c", script) val builder = new ProcessBuilder(cmd) + // redirectError(Redirect.INHERIT) would consume the error output from buffer and + // then print it to stderr (inherit the target from the current Scala process). + // If without this there would be 2 issues: + // 1) The error msg generated by the script process would be hidden. + // 2) If the error msg is too big to chock up the buffer, the input logic would be hung + builder.redirectError(Redirect.INHERIT) val proc = builder.start() val inputStream = proc.getInputStream val outputStream = proc.getOutputStream val reader = new BufferedReader(new InputStreamReader(inputStream)) - + val (outputSerde, outputSoi) = ioschema.initOutputSerDe(output) - val iterator: Iterator[Row] = new Iterator[Row] with HiveInspectors { - var cacheRow: Row = null + val iterator: Iterator[InternalRow] = new Iterator[InternalRow] with HiveInspectors { + var cacheRow: InternalRow = null var curLine: String = null var eof: Boolean = false @@ -83,7 +90,7 @@ case class ScriptTransformation( } } - def deserialize(): Row = { + def deserialize(): InternalRow = { if (cacheRow != null) return cacheRow val mutableRow = new SpecificMutableRow(output.map(_.dataType)) @@ -95,7 +102,7 @@ case class ScriptTransformation( val raw = outputSerde.deserialize(writable) val dataList = outputSoi.getStructFieldsDataAsList(raw) val fieldList = outputSoi.getAllStructFieldRefs() - + var i = 0 dataList.foreach( element => { if (element == null) { @@ -113,11 +120,11 @@ case class ScriptTransformation( } } - override def next(): Row = { + override def next(): InternalRow = { if (!hasNext) { throw new NoSuchElementException } - + if (outputSerde == null) { val prevLine = curLine curLine = reader.readLine() @@ -192,7 +199,7 @@ case class HiveScriptIOSchema ( val inputRowFormatMap = inputRowFormat.toMap.withDefault((k) => defaultFormat(k)) val outputRowFormatMap = outputRowFormat.toMap.withDefault((k) => defaultFormat(k)) - + def initInputSerDe(input: Seq[Expression]): (AbstractSerDe, ObjectInspector) = { val (columns, columnTypes) = parseAttrs(input) val serde = initSerDe(inputSerdeClass, columns, columnTypes, inputSerdeProps) @@ -206,22 +213,22 @@ case class HiveScriptIOSchema ( } def parseAttrs(attrs: Seq[Expression]): (Seq[String], Seq[DataType]) = { - + val columns = attrs.map { case aref: AttributeReference => aref.name case e: NamedExpression => e.name case _ => null } - + val columnTypes = attrs.map { case aref: AttributeReference => aref.dataType case e: NamedExpression => e.dataType - case _ => null + case _ => null } (columns, columnTypes) } - + def initSerDe(serdeClassName: String, columns: Seq[String], columnTypes: Seq[DataType], serdeProps: Seq[(String, String)]): AbstractSerDe = { @@ -240,7 +247,7 @@ case class HiveScriptIOSchema ( (kv._1.split("'")(1), kv._2.split("'")(1)) }).toMap + (serdeConstants.LIST_COLUMNS -> columns.mkString(",")) propsMap = propsMap + (serdeConstants.LIST_COLUMN_TYPES -> columnTypesNames) - + val properties = new Properties() properties.putAll(propsMap) serde.initialize(null, properties) @@ -261,7 +268,7 @@ case class HiveScriptIOSchema ( null } } - + def initOutputputSoi(outputSerde: AbstractSerDe): StructObjectInspector = { if (outputSerde != null) { outputSerde.getObjectInspector().asInstanceOf[StructObjectInspector] diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala index abab1a223a43a..aad58bfa2e6e0 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala @@ -22,7 +22,7 @@ import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.util._ import org.apache.spark.sql.sources._ import org.apache.spark.sql.{SaveMode, DataFrame, SQLContext} -import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} +import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.RunnableCommand import org.apache.spark.sql.hive.HiveContext @@ -39,9 +39,9 @@ import org.apache.spark.util.Utils private[hive] case class AnalyzeTable(tableName: String) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { sqlContext.asInstanceOf[HiveContext].analyze(tableName) - Seq.empty[Row] + Seq.empty[InternalRow] } } @@ -53,7 +53,7 @@ case class DropTable( tableName: String, ifExists: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] val ifExistsClause = if (ifExists) "IF EXISTS " else "" try { @@ -70,7 +70,7 @@ case class DropTable( hiveContext.invalidateTable(tableName) hiveContext.runSqlHive(s"DROP TABLE $ifExistsClause$tableName") hiveContext.catalog.unregisterTable(Seq(tableName)) - Seq.empty[Row] + Seq.empty[InternalRow] } } @@ -83,7 +83,7 @@ case class AddJar(path: String) extends RunnableCommand { schema.toAttributes } - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] val currentClassLoader = Utils.getContextOrSparkClassLoader @@ -91,26 +91,32 @@ case class AddJar(path: String) extends RunnableCommand { val jarURL = new java.io.File(path).toURL val newClassLoader = new java.net.URLClassLoader(Array(jarURL), currentClassLoader) Thread.currentThread.setContextClassLoader(newClassLoader) - org.apache.hadoop.hive.ql.metadata.Hive.get().getConf().setClassLoader(newClassLoader) - - // Add jar to isolated hive classloader + // We need to explicitly set the class loader associated with the conf in executionHive's + // state because this class loader will be used as the context class loader of the current + // thread to execute any Hive command. + // We cannot use `org.apache.hadoop.hive.ql.metadata.Hive.get().getConf()` because Hive.get() + // returns the value of a thread local variable and its HiveConf may not be the HiveConf + // associated with `executionHive.state` (for example, HiveContext is created in one thread + // and then add jar is called from another thread). + hiveContext.executionHive.state.getConf.setClassLoader(newClassLoader) + // Add jar to isolated hive (metadataHive) class loader. hiveContext.runSqlHive(s"ADD JAR $path") // Add jar to executors hiveContext.sparkContext.addJar(path) - Seq(Row(0)) + Seq(InternalRow(0)) } } private[hive] case class AddFile(path: String) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] hiveContext.runSqlHive(s"ADD FILE $path") hiveContext.sparkContext.addFile(path) - Seq.empty[Row] + Seq.empty[InternalRow] } } @@ -123,12 +129,12 @@ case class CreateMetastoreDataSource( allowExisting: Boolean, managedIfNoPath: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] if (hiveContext.catalog.tableExists(tableName :: Nil)) { if (allowExisting) { - return Seq.empty[Row] + return Seq.empty[InternalRow] } else { throw new AnalysisException(s"Table $tableName already exists.") } @@ -146,11 +152,12 @@ case class CreateMetastoreDataSource( hiveContext.catalog.createDataSourceTable( tableName, userSpecifiedSchema, + Array.empty[String], provider, optionsWithPath, isExternal) - Seq.empty[Row] + Seq.empty[InternalRow] } } @@ -158,11 +165,12 @@ private[hive] case class CreateMetastoreDataSourceAsSelect( tableName: String, provider: String, + partitionColumns: Array[String], mode: SaveMode, options: Map[String, String], query: LogicalPlan) extends RunnableCommand { - override def run(sqlContext: SQLContext): Seq[Row] = { + override def run(sqlContext: SQLContext): Seq[InternalRow] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] var createMetastoreTable = false var isExternal = true @@ -186,15 +194,15 @@ case class CreateMetastoreDataSourceAsSelect( s"Or, if you are using SQL CREATE TABLE, you need to drop $tableName first.") case SaveMode.Ignore => // Since the table already exists and the save mode is Ignore, we will just return. - return Seq.empty[Row] + return Seq.empty[InternalRow] case SaveMode.Append => // Check if the specified data source match the data source of the existing table. - val resolved = - ResolvedDataSource(sqlContext, Some(query.schema), provider, optionsWithPath) + val resolved = ResolvedDataSource( + sqlContext, Some(query.schema.asNullable), partitionColumns, provider, optionsWithPath) val createdRelation = LogicalRelation(resolved.relation) EliminateSubQueries(sqlContext.table(tableName).logicalPlan) match { - case l @ LogicalRelation(i: InsertableRelation) => - if (i != createdRelation.relation) { + case l @ LogicalRelation(_: InsertableRelation | _: HadoopFsRelation) => + if (l.relation != createdRelation.relation) { val errorDescription = s"Cannot append to table $tableName because the resolved relation does not " + s"match the existing relation of $tableName. " + @@ -202,14 +210,13 @@ case class CreateMetastoreDataSourceAsSelect( s"table $tableName and using its data source and options." val errorMessage = s""" - |$errorDescription - |== Relations == - |${sideBySide( - s"== Expected Relation ==" :: - l.toString :: Nil, - s"== Actual Relation ==" :: - createdRelation.toString :: Nil).mkString("\n")} - """.stripMargin + |$errorDescription + |== Relations == + |${sideBySide( + s"== Expected Relation ==" :: l.toString :: Nil, + s"== Actual Relation ==" :: createdRelation.toString :: Nil + ).mkString("\n")} + """.stripMargin throw new AnalysisException(errorMessage) } existingSchema = Some(l.schema) @@ -229,12 +236,13 @@ case class CreateMetastoreDataSourceAsSelect( val data = DataFrame(hiveContext, query) val df = existingSchema match { // If we are inserting into an existing table, just use the existing schema. - case Some(schema) => sqlContext.createDataFrame(data.queryExecution.toRdd, schema) + case Some(schema) => sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, schema) case None => data } // Create the relation based on the data of df. - val resolved = ResolvedDataSource(sqlContext, provider, mode, optionsWithPath, df) + val resolved = + ResolvedDataSource(sqlContext, provider, partitionColumns, mode, optionsWithPath, df) if (createMetastoreTable) { // We will use the schema of resolved.relation as the schema of the table (instead of @@ -243,6 +251,7 @@ case class CreateMetastoreDataSourceAsSelect( hiveContext.catalog.createDataSourceTable( tableName, Some(resolved.relation.schema), + partitionColumns, provider, optionsWithPath, isExternal) @@ -250,6 +259,6 @@ case class CreateMetastoreDataSourceAsSelect( // Refresh the cache of the table in the catalog. hiveContext.refreshTable(tableName) - Seq.empty[Row] + Seq.empty[InternalRow] } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala index fd0b6f058595d..4986b1ea9d906 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala @@ -17,11 +17,9 @@ package org.apache.spark.sql.hive -import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator.AggregationBuffer -import org.apache.hadoop.hive.ql.udf.generic.GenericUDFUtils.ConversionHelper -import org.apache.spark.sql.AnalysisException - import scala.collection.mutable.ArrayBuffer +import scala.collection.JavaConversions._ +import scala.util.Try import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ConstantObjectInspector} import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory.ObjectInspectorOptions @@ -30,54 +28,64 @@ import org.apache.hadoop.hive.ql.exec._ import org.apache.hadoop.hive.ql.udf.{UDFType => HiveUDFType} import org.apache.hadoop.hive.ql.udf.generic._ import org.apache.hadoop.hive.ql.udf.generic.GenericUDF._ +import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator.AggregationBuffer +import org.apache.hadoop.hive.ql.udf.generic.GenericUDFUtils.ConversionHelper import org.apache.spark.Logging +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.analysis +import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.FunctionBuilder import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.hive.HiveShim._ import org.apache.spark.sql.types._ -/* Implicit conversions */ -import scala.collection.JavaConversions._ -private[hive] abstract class HiveFunctionRegistry +private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry) extends analysis.FunctionRegistry with HiveInspectors { def getFunctionInfo(name: String): FunctionInfo = FunctionRegistry.getFunctionInfo(name) - def lookupFunction(name: String, children: Seq[Expression]): Expression = { - // We only look it up to see if it exists, but do not include it in the HiveUDF since it is - // not always serializable. - val functionInfo: FunctionInfo = - Option(FunctionRegistry.getFunctionInfo(name.toLowerCase)).getOrElse( - sys.error(s"Couldn't find function $name")) - - val functionClassName = functionInfo.getFunctionClass.getName - - if (classOf[UDF].isAssignableFrom(functionInfo.getFunctionClass)) { - HiveSimpleUdf(new HiveFunctionWrapper(functionClassName), children) - } else if (classOf[GenericUDF].isAssignableFrom(functionInfo.getFunctionClass)) { - HiveGenericUdf(new HiveFunctionWrapper(functionClassName), children) - } else if ( - classOf[AbstractGenericUDAFResolver].isAssignableFrom(functionInfo.getFunctionClass)) { - HiveGenericUdaf(new HiveFunctionWrapper(functionClassName), children) - } else if (classOf[UDAF].isAssignableFrom(functionInfo.getFunctionClass)) { - HiveUdaf(new HiveFunctionWrapper(functionClassName), children) - } else if (classOf[GenericUDTF].isAssignableFrom(functionInfo.getFunctionClass)) { - HiveGenericUdtf(new HiveFunctionWrapper(functionClassName), children) - } else { - sys.error(s"No handler for udf ${functionInfo.getFunctionClass}") + override def lookupFunction(name: String, children: Seq[Expression]): Expression = { + Try(underlying.lookupFunction(name, children)).getOrElse { + // We only look it up to see if it exists, but do not include it in the HiveUDF since it is + // not always serializable. + val functionInfo: FunctionInfo = + Option(FunctionRegistry.getFunctionInfo(name.toLowerCase)).getOrElse( + throw new AnalysisException(s"undefined function $name")) + + val functionClassName = functionInfo.getFunctionClass.getName + + if (classOf[UDF].isAssignableFrom(functionInfo.getFunctionClass)) { + HiveSimpleUdf(new HiveFunctionWrapper(functionClassName), children) + } else if (classOf[GenericUDF].isAssignableFrom(functionInfo.getFunctionClass)) { + HiveGenericUdf(new HiveFunctionWrapper(functionClassName), children) + } else if ( + classOf[AbstractGenericUDAFResolver].isAssignableFrom(functionInfo.getFunctionClass)) { + HiveGenericUdaf(new HiveFunctionWrapper(functionClassName), children) + } else if (classOf[UDAF].isAssignableFrom(functionInfo.getFunctionClass)) { + HiveUdaf(new HiveFunctionWrapper(functionClassName), children) + } else if (classOf[GenericUDTF].isAssignableFrom(functionInfo.getFunctionClass)) { + HiveGenericUdtf(new HiveFunctionWrapper(functionClassName), children) + } else { + sys.error(s"No handler for udf ${functionInfo.getFunctionClass}") + } } } + + override def registerFunction(name: String, builder: FunctionBuilder): Unit = + throw new UnsupportedOperationException } private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, children: Seq[Expression]) extends Expression with HiveInspectors with Logging { - type EvaluatedType = Any + type UDFType = UDF + override def deterministic: Boolean = isUDFDeterministic + override def nullable: Boolean = true @transient @@ -112,8 +120,10 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre @transient protected lazy val cached: Array[AnyRef] = new Array[AnyRef](children.length) + override def isThreadSafe: Boolean = false + // TODO: Finish input output types. - override def eval(input: Row): Any = { + override def eval(input: InternalRow): Any = { unwrap( FunctionRegistry.invoke(method, function, conversionHelper .convertIfNecessary(wrap(children.map(c => c.eval(input)), arguments, cached): _*): _*), @@ -139,7 +149,8 @@ private[hive] class DeferredObjectAdapter(oi: ObjectInspector) private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, children: Seq[Expression]) extends Expression with HiveInspectors with Logging { type UDFType = GenericUDF - type EvaluatedType = Any + + override def deterministic: Boolean = isUDFDeterministic override def nullable: Boolean = true @@ -169,7 +180,9 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr lazy val dataType: DataType = inspectorToDataType(returnInspector) - override def eval(input: Row): Any = { + override def isThreadSafe: Boolean = false + + override def eval(input: InternalRow): Any = { returnInspector // Make sure initialized. var i = 0 @@ -316,7 +329,7 @@ private[hive] case class HiveWindowFunction( // The object inspector of values returned from the Hive window function. @transient - protected lazy val returnInspector = { + protected lazy val returnInspector = { evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors) } @@ -336,9 +349,7 @@ private[hive] case class HiveWindowFunction( def nullable: Boolean = true - override type EvaluatedType = Any - - override def eval(input: Row): Any = + override def eval(input: InternalRow): Any = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") @transient @@ -362,7 +373,7 @@ private[hive] case class HiveWindowFunction( evaluator.reset(hiveEvaluatorBuffer) } - override def prepareInputParameters(input: Row): AnyRef = { + override def prepareInputParameters(input: InternalRow): AnyRef = { wrap(inputProjection(input), inputInspectors, new Array[AnyRef](children.length)) } // Add input parameters for a single row. @@ -413,7 +424,7 @@ private[hive] case class HiveGenericUdaf( protected lazy val resolver: AbstractGenericUDAFResolver = funcWrapper.createFunction() @transient - protected lazy val objectInspector = { + protected lazy val objectInspector = { val parameterInfo = new SimpleGenericUDAFParameterInfo(inspectors.toArray, false, false) resolver.getEvaluator(parameterInfo) .init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors.toArray) @@ -446,7 +457,7 @@ private[hive] case class HiveUdaf( new GenericUDAFBridge(funcWrapper.createFunction()) @transient - protected lazy val objectInspector = { + protected lazy val objectInspector = { val parameterInfo = new SimpleGenericUDAFParameterInfo(inspectors.toArray, false, false) resolver.getEvaluator(parameterInfo) .init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors.toArray) @@ -483,7 +494,11 @@ private[hive] case class HiveGenericUdtf( extends Generator with HiveInspectors { @transient - protected lazy val function: GenericUDTF = funcWrapper.createFunction() + protected lazy val function: GenericUDTF = { + val fun: GenericUDTF = funcWrapper.createFunction() + fun.setCollector(collector) + fun + } @transient protected lazy val inputInspectors = children.map(toInspector) @@ -494,37 +509,45 @@ private[hive] case class HiveGenericUdtf( @transient protected lazy val udtInput = new Array[AnyRef](children.length) + @transient + protected lazy val collector = new UDTFCollector + lazy val elementTypes = outputInspector.getAllStructFieldRefs.map { field => (inspectorToDataType(field.getFieldObjectInspector), true) } - override def eval(input: Row): TraversableOnce[Row] = { + override def eval(input: InternalRow): TraversableOnce[InternalRow] = { outputInspector // Make sure initialized. val inputProjection = new InterpretedProjection(children) - val collector = new UDTFCollector - function.setCollector(collector) + function.process(wrap(inputProjection(input), inputInspectors, udtInput)) collector.collectRows() } protected class UDTFCollector extends Collector { - var collected = new ArrayBuffer[Row] + var collected = new ArrayBuffer[InternalRow] override def collect(input: java.lang.Object) { // We need to clone the input here because implementations of // GenericUDTF reuse the same object. Luckily they are always an array, so // it is easy to clone. - collected += unwrap(input, outputInspector).asInstanceOf[Row] + collected += unwrap(input, outputInspector).asInstanceOf[InternalRow] } - def collectRows(): Seq[Row] = { + def collectRows(): Seq[InternalRow] = { val toCollect = collected - collected = new ArrayBuffer[Row] + collected = new ArrayBuffer[InternalRow] toCollect } } + override def terminate(): TraversableOnce[InternalRow] = { + outputInspector // Make sure initialized. + function.close() + collector.collectRows() + } + override def toString: String = { s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" } @@ -546,12 +569,12 @@ private[hive] case class HiveUdafFunction( } else { funcWrapper.createFunction[AbstractGenericUDAFResolver]() } - + private val inspectors = exprs.map(toInspector).toArray - - private val function = { + + private val function = { val parameterInfo = new SimpleGenericUDAFParameterInfo(inspectors, false, false) - resolver.getEvaluator(parameterInfo) + resolver.getEvaluator(parameterInfo) } private val returnInspector = function.init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors) @@ -559,15 +582,15 @@ private[hive] case class HiveUdafFunction( private val buffer = function.getNewAggregationBuffer - override def eval(input: Row): Any = unwrap(function.evaluate(buffer), returnInspector) + override def eval(input: InternalRow): Any = unwrap(function.evaluate(buffer), returnInspector) @transient val inputProjection = new InterpretedProjection(exprs) @transient protected lazy val cached = new Array[AnyRef](exprs.length) - - def update(input: Row): Unit = { + + def update(input: InternalRow): Unit = { val inputs = inputProjection(input) function.iterate(buffer, wrap(inputs, inspectors, cached)) } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala index 8398da268174d..8b928861fcc70 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala @@ -34,8 +34,10 @@ import org.apache.hadoop.hive.common.FileUtils import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.sql.Row import org.apache.spark.{Logging, SerializableWritable, SparkHadoopWriter} -import org.apache.spark.sql.hive.{ShimFileSinkDesc => FileSinkDesc} -import org.apache.spark.sql.hive.HiveShim._ +import org.apache.spark.sql.catalyst.util.DateUtils +import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc} +import org.apache.spark.sql.types._ +import org.apache.spark.util.SerializableJobConf /** * Internal helper class that saves an RDD using a Hive OutputFormat. @@ -56,7 +58,7 @@ private[hive] class SparkHiveWriterContainer( PlanUtils.configureOutputJobPropertiesForStorageHandler(tableDesc) Utilities.copyTableJobPropertiesToConf(tableDesc, jobConf) } - protected val conf = new SerializableWritable(jobConf) + protected val conf = new SerializableJobConf(jobConf) private var jobID = 0 private var splitID = 0 @@ -69,7 +71,7 @@ private[hive] class SparkHiveWriterContainer( @transient protected lazy val jobContext = newJobContext(conf.value, jID.value) @transient private lazy val taskContext = newTaskAttemptContext(conf.value, taID.value) @transient private lazy val outputFormat = - conf.value.getOutputFormat.asInstanceOf[HiveOutputFormat[AnyRef,Writable]] + conf.value.getOutputFormat.asInstanceOf[HiveOutputFormat[AnyRef, Writable]] def driverSideSetup() { setIDs(0, 0, 0) @@ -92,7 +94,7 @@ private[hive] class SparkHiveWriterContainer( "part-" + numberFormat.format(splitID) + extension } - def getLocalFileWriter(row: Row): FileSinkOperator.RecordWriter = writer + def getLocalFileWriter(row: Row, schema: StructType): FileSinkOperator.RecordWriter = writer def close() { // Seems the boolean value passed into close does not matter. @@ -195,16 +197,25 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer( jobConf.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, oldMarker) } - override def getLocalFileWriter(row: Row): FileSinkOperator.RecordWriter = { + override def getLocalFileWriter(row: Row, schema: StructType): FileSinkOperator.RecordWriter = { + def convertToHiveRawString(col: String, value: Any): String = { + val raw = String.valueOf(value) + schema(col).dataType match { + case DateType => DateUtils.toString(raw.toInt) + case _: DecimalType => BigDecimal(raw).toString() + case _ => raw + } + } + val dynamicPartPath = dynamicPartColNames .zip(row.toSeq.takeRight(dynamicPartColNames.length)) .map { case (col, rawVal) => - val string = if (rawVal == null) null else String.valueOf(rawVal) + val string = if (rawVal == null) null else convertToHiveRawString(col, rawVal) val colString = if (string == null || string.isEmpty) { defaultPartName } else { - FileUtils.escapePathName(string) + FileUtils.escapePathName(string, defaultPartName) } s"/$col=$colString" }.mkString @@ -217,12 +228,11 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer( newFileSinkDesc.setCompressCodec(fileSinkConf.getCompressCodec) newFileSinkDesc.setCompressType(fileSinkConf.getCompressType) - val path = { - val outputPath = FileOutputFormat.getOutputPath(conf.value) - assert(outputPath != null, "Undefined job output-path") - val workPath = new Path(outputPath, dynamicPartPath.stripPrefix("/")) - new Path(workPath, getOutputName) - } + // use the path like ${hive_tmp}/_temporary/${attemptId}/ + // to avoid write to the same file when `spark.speculation=true` + val path = FileOutputFormat.getTaskOutputPath( + conf.value, + dynamicPartPath.stripPrefix("/") + "/" + getOutputName) HiveFileFormatUtils.getHiveRecordWriter( conf.value, diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala new file mode 100644 index 0000000000000..1e51173a19882 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala @@ -0,0 +1,69 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.ql.io.orc.{OrcFile, Reader} +import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector + +import org.apache.spark.Logging +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.sql.hive.HiveMetastoreTypes +import org.apache.spark.sql.types.StructType + +private[orc] object OrcFileOperator extends Logging{ + def getFileReader(pathStr: String, config: Option[Configuration] = None ): Reader = { + val conf = config.getOrElse(new Configuration) + val fspath = new Path(pathStr) + val fs = fspath.getFileSystem(conf) + val orcFiles = listOrcFiles(pathStr, conf) + + // TODO Need to consider all files when schema evolution is taken into account. + OrcFile.createReader(fs, orcFiles.head) + } + + def readSchema(path: String, conf: Option[Configuration]): StructType = { + val reader = getFileReader(path, conf) + val readerInspector = reader.getObjectInspector.asInstanceOf[StructObjectInspector] + val schema = readerInspector.getTypeName + HiveMetastoreTypes.toDataType(schema).asInstanceOf[StructType] + } + + def getObjectInspector(path: String, conf: Option[Configuration]): StructObjectInspector = { + getFileReader(path, conf).getObjectInspector.asInstanceOf[StructObjectInspector] + } + + def listOrcFiles(pathStr: String, conf: Configuration): Seq[Path] = { + val origPath = new Path(pathStr) + val fs = origPath.getFileSystem(conf) + val path = origPath.makeQualified(fs) + val paths = SparkHadoopUtil.get.listLeafStatuses(fs, origPath) + .filterNot(_.isDir) + .map(_.getPath) + .filterNot(_.getName.startsWith("_")) + .filterNot(_.getName.startsWith(".")) + + if (paths == null || paths.size == 0) { + throw new IllegalArgumentException( + s"orcFileOperator: path $path does not have valid orc files matching the pattern") + } + + paths + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala new file mode 100644 index 0000000000000..250e73a4dba92 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala @@ -0,0 +1,144 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import org.apache.hadoop.hive.common.`type`.{HiveChar, HiveDecimal, HiveVarchar} +import org.apache.hadoop.hive.ql.io.sarg.SearchArgument +import org.apache.hadoop.hive.ql.io.sarg.SearchArgument.Builder +import org.apache.hadoop.hive.serde2.io.DateWritable + +import org.apache.spark.Logging +import org.apache.spark.sql.sources._ + +/** + * It may be optimized by push down partial filters. But we are conservative here. + * Because if some filters fail to be parsed, the tree may be corrupted, + * and cannot be used anymore. + */ +private[orc] object OrcFilters extends Logging { + def createFilter(expr: Array[Filter]): Option[SearchArgument] = { + expr.reduceOption(And).flatMap { conjunction => + val builder = SearchArgument.FACTORY.newBuilder() + buildSearchArgument(conjunction, builder).map(_.build()) + } + } + + private def buildSearchArgument(expression: Filter, builder: Builder): Option[Builder] = { + def newBuilder = SearchArgument.FACTORY.newBuilder() + + def isSearchableLiteral(value: Any) = value match { + // These are types recognized by the `SearchArgumentImpl.BuilderImpl.boxLiteral()` method. + case _: String | _: Long | _: Double | _: DateWritable | _: HiveDecimal | _: HiveChar | + _: HiveVarchar | _: Byte | _: Short | _: Integer | _: Float => true + case _ => false + } + + // lian: I probably missed something here, and had to end up with a pretty weird double-checking + // pattern when converting `And`/`Or`/`Not` filters. + // + // The annoying part is that, `SearchArgument` builder methods like `startAnd()` `startOr()`, + // and `startNot()` mutate internal state of the builder instance. This forces us to translate + // all convertible filters with a single builder instance. However, before actually converting a + // filter, we've no idea whether it can be recognized by ORC or not. Thus, when an inconvertible + // filter is found, we may already end up with a builder whose internal state is inconsistent. + // + // For example, to convert an `And` filter with builder `b`, we call `b.startAnd()` first, and + // then try to convert its children. Say we convert `left` child successfully, but find that + // `right` child is inconvertible. Alas, `b.startAnd()` call can't be rolled back, and `b` is + // inconsistent now. + // + // The workaround employed here is that, for `And`/`Or`/`Not`, we first try to convert their + // children with brand new builders, and only do the actual conversion with the right builder + // instance when the children are proven to be convertible. + // + // P.S.: Hive seems to use `SearchArgument` together with `ExprNodeGenericFuncDesc` only. + // Usage of builder methods mentioned above can only be found in test code, where all tested + // filters are known to be convertible. + + expression match { + case And(left, right) => + val tryLeft = buildSearchArgument(left, newBuilder) + val tryRight = buildSearchArgument(right, newBuilder) + + val conjunction = for { + _ <- tryLeft + _ <- tryRight + lhs <- buildSearchArgument(left, builder.startAnd()) + rhs <- buildSearchArgument(right, lhs) + } yield rhs.end() + + // For filter `left AND right`, we can still push down `left` even if `right` is not + // convertible, and vice versa. + conjunction + .orElse(tryLeft.flatMap(_ => buildSearchArgument(left, builder))) + .orElse(tryRight.flatMap(_ => buildSearchArgument(right, builder))) + + case Or(left, right) => + for { + _ <- buildSearchArgument(left, newBuilder) + _ <- buildSearchArgument(right, newBuilder) + lhs <- buildSearchArgument(left, builder.startOr()) + rhs <- buildSearchArgument(right, lhs) + } yield rhs.end() + + case Not(child) => + for { + _ <- buildSearchArgument(child, newBuilder) + negate <- buildSearchArgument(child, builder.startNot()) + } yield negate.end() + + case EqualTo(attribute, value) => + Option(value) + .filter(isSearchableLiteral) + .map(builder.equals(attribute, _)) + + case LessThan(attribute, value) => + Option(value) + .filter(isSearchableLiteral) + .map(builder.lessThan(attribute, _)) + + case LessThanOrEqual(attribute, value) => + Option(value) + .filter(isSearchableLiteral) + .map(builder.lessThanEquals(attribute, _)) + + case GreaterThan(attribute, value) => + Option(value) + .filter(isSearchableLiteral) + .map(builder.startNot().lessThanEquals(attribute, _).end()) + + case GreaterThanOrEqual(attribute, value) => + Option(value) + .filter(isSearchableLiteral) + .map(builder.startNot().lessThan(attribute, _).end()) + + case IsNull(attribute) => + Some(builder.isNull(attribute)) + + case IsNotNull(attribute) => + Some(builder.startNot().isNull(attribute).end()) + + case In(attribute, values) => + Option(values) + .filter(_.forall(isSearchableLiteral)) + .map(builder.in(attribute, _)) + + case _ => None + } + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala new file mode 100644 index 0000000000000..dbce39f21d271 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala @@ -0,0 +1,304 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import java.util.Properties + +import com.google.common.base.Objects +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, Path} +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.hadoop.hive.ql.io.orc.{OrcInputFormat, OrcOutputFormat, OrcSerde, OrcSplit} +import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils +import org.apache.hadoop.io.{NullWritable, Writable} +import org.apache.hadoop.mapred.{InputFormat => MapRedInputFormat, JobConf, RecordWriter, Reporter} +import org.apache.hadoop.mapreduce.lib.input.FileInputFormat +import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext} + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.mapred.SparkHadoopMapRedUtil +import org.apache.spark.rdd.{HadoopRDD, RDD} +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.hive.{HiveContext, HiveInspectors, HiveMetastoreTypes, HiveShim} +import org.apache.spark.sql.sources.{Filter, _} +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{Row, SQLContext} +import org.apache.spark.{Logging} +import org.apache.spark.util.SerializableConfiguration + +/* Implicit conversions */ +import scala.collection.JavaConversions._ + +private[sql] class DefaultSource extends HadoopFsRelationProvider { + def createRelation( + sqlContext: SQLContext, + paths: Array[String], + dataSchema: Option[StructType], + partitionColumns: Option[StructType], + parameters: Map[String, String]): HadoopFsRelation = { + assert( + sqlContext.isInstanceOf[HiveContext], + "The ORC data source can only be used with HiveContext.") + + new OrcRelation(paths, dataSchema, None, partitionColumns, parameters)(sqlContext) + } +} + +private[orc] class OrcOutputWriter( + path: String, + dataSchema: StructType, + context: TaskAttemptContext) + extends OutputWriter with SparkHadoopMapRedUtil with HiveInspectors { + + private val serializer = { + val table = new Properties() + table.setProperty("columns", dataSchema.fieldNames.mkString(",")) + table.setProperty("columns.types", dataSchema.map { f => + HiveMetastoreTypes.toMetastoreType(f.dataType) + }.mkString(":")) + + val serde = new OrcSerde + serde.initialize(context.getConfiguration, table) + serde + } + + // Object inspector converted from the schema of the relation to be written. + private val structOI = { + val typeInfo = + TypeInfoUtils.getTypeInfoFromTypeString( + HiveMetastoreTypes.toMetastoreType(dataSchema)) + + TypeInfoUtils + .getStandardJavaObjectInspectorFromTypeInfo(typeInfo) + .asInstanceOf[StructObjectInspector] + } + + // Used to hold temporary `Writable` fields of the next row to be written. + private val reusableOutputBuffer = new Array[Any](dataSchema.length) + + // Used to convert Catalyst values into Hadoop `Writable`s. + private val wrappers = structOI.getAllStructFieldRefs.map { ref => + wrapperFor(ref.getFieldObjectInspector) + }.toArray + + // `OrcRecordWriter.close()` creates an empty file if no rows are written at all. We use this + // flag to decide whether `OrcRecordWriter.close()` needs to be called. + private var recordWriterInstantiated = false + + private lazy val recordWriter: RecordWriter[NullWritable, Writable] = { + recordWriterInstantiated = true + + val conf = context.getConfiguration + val partition = context.getTaskAttemptID.getTaskID.getId + val filename = f"part-r-$partition%05d-${System.currentTimeMillis}%015d.orc" + + new OrcOutputFormat().getRecordWriter( + new Path(path, filename).getFileSystem(conf), + conf.asInstanceOf[JobConf], + new Path(path, filename).toString, + Reporter.NULL + ).asInstanceOf[RecordWriter[NullWritable, Writable]] + } + + override def write(row: Row): Unit = { + var i = 0 + while (i < row.length) { + reusableOutputBuffer(i) = wrappers(i)(row(i)) + i += 1 + } + + recordWriter.write( + NullWritable.get(), + serializer.serialize(reusableOutputBuffer, structOI)) + } + + override def close(): Unit = { + if (recordWriterInstantiated) { + recordWriter.close(Reporter.NULL) + } + } +} + +@DeveloperApi +private[sql] class OrcRelation( + override val paths: Array[String], + maybeDataSchema: Option[StructType], + maybePartitionSpec: Option[PartitionSpec], + override val userDefinedPartitionColumns: Option[StructType], + parameters: Map[String, String])( + @transient val sqlContext: SQLContext) + extends HadoopFsRelation(maybePartitionSpec) + with Logging { + + private[sql] def this( + paths: Array[String], + maybeDataSchema: Option[StructType], + maybePartitionSpec: Option[PartitionSpec], + parameters: Map[String, String])( + sqlContext: SQLContext) = { + this( + paths, + maybeDataSchema, + maybePartitionSpec, + maybePartitionSpec.map(_.partitionColumns), + parameters)(sqlContext) + } + + override val dataSchema: StructType = maybeDataSchema.getOrElse { + OrcFileOperator.readSchema( + paths.head, Some(sqlContext.sparkContext.hadoopConfiguration)) + } + + override def needConversion: Boolean = false + + override def equals(other: Any): Boolean = other match { + case that: OrcRelation => + paths.toSet == that.paths.toSet && + dataSchema == that.dataSchema && + schema == that.schema && + partitionColumns == that.partitionColumns + case _ => false + } + + override def hashCode(): Int = { + Objects.hashCode( + paths.toSet, + dataSchema, + schema, + partitionColumns) + } + + override def buildScan( + requiredColumns: Array[String], + filters: Array[Filter], + inputPaths: Array[FileStatus]): RDD[Row] = { + val output = StructType(requiredColumns.map(dataSchema(_))).toAttributes + OrcTableScan(output, this, filters, inputPaths).execute() + } + + override def prepareJobForWrite(job: Job): OutputWriterFactory = { + new OutputWriterFactory { + override def newInstance( + path: String, + dataSchema: StructType, + context: TaskAttemptContext): OutputWriter = { + new OrcOutputWriter(path, dataSchema, context) + } + } + } +} + +private[orc] case class OrcTableScan( + attributes: Seq[Attribute], + @transient relation: OrcRelation, + filters: Array[Filter], + @transient inputPaths: Array[FileStatus]) + extends Logging + with HiveInspectors { + + @transient private val sqlContext = relation.sqlContext + + private def addColumnIds( + output: Seq[Attribute], + relation: OrcRelation, + conf: Configuration): Unit = { + val ids = output.map(a => relation.dataSchema.fieldIndex(a.name): Integer) + val (sortedIds, sortedNames) = ids.zip(attributes.map(_.name)).sorted.unzip + HiveShim.appendReadColumns(conf, sortedIds, sortedNames) + } + + // Transform all given raw `Writable`s into `Row`s. + private def fillObject( + path: String, + conf: Configuration, + iterator: Iterator[Writable], + nonPartitionKeyAttrs: Seq[(Attribute, Int)], + mutableRow: MutableRow): Iterator[Row] = { + val deserializer = new OrcSerde + val soi = OrcFileOperator.getObjectInspector(path, Some(conf)) + val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map { + case (attr, ordinal) => + soi.getStructFieldRef(attr.name.toLowerCase) -> ordinal + }.unzip + val unwrappers = fieldRefs.map(unwrapperFor) + // Map each tuple to a row object + iterator.map { value => + val raw = deserializer.deserialize(value) + var i = 0 + while (i < fieldRefs.length) { + val fieldValue = soi.getStructFieldData(raw, fieldRefs(i)) + if (fieldValue == null) { + mutableRow.setNullAt(fieldOrdinals(i)) + } else { + unwrappers(i)(fieldValue, mutableRow, fieldOrdinals(i)) + } + i += 1 + } + mutableRow: Row + } + } + + def execute(): RDD[Row] = { + val job = new Job(sqlContext.sparkContext.hadoopConfiguration) + val conf = job.getConfiguration + + // Tries to push down filters if ORC filter push-down is enabled + if (sqlContext.conf.orcFilterPushDown) { + OrcFilters.createFilter(filters).foreach { f => + conf.set(OrcTableScan.SARG_PUSHDOWN, f.toKryo) + conf.setBoolean(ConfVars.HIVEOPTINDEXFILTER.varname, true) + } + } + + // Sets requested columns + addColumnIds(attributes, relation, conf) + + if (inputPaths.nonEmpty) { + FileInputFormat.setInputPaths(job, inputPaths.map(_.getPath): _*) + } + + val inputFormatClass = + classOf[OrcInputFormat] + .asInstanceOf[Class[_ <: MapRedInputFormat[NullWritable, Writable]]] + + val rdd = sqlContext.sparkContext.hadoopRDD( + conf.asInstanceOf[JobConf], + inputFormatClass, + classOf[NullWritable], + classOf[Writable] + ).asInstanceOf[HadoopRDD[NullWritable, Writable]] + + val wrappedConf = new SerializableConfiguration(conf) + + rdd.mapPartitionsWithInputSplit { case (split: OrcSplit, iterator) => + val mutableRow = new SpecificMutableRow(attributes.map(_.dataType)) + fillObject( + split.getPath.toString, + wrappedConf.value, + iterator.map(_._2), + attributes.zipWithIndex, + mutableRow) + } + } +} + +private[orc] object OrcTableScan { + // This constant duplicates `OrcInputFormat.SARG_PUSHDOWN`, which is unfortunately not public. + private[orc] val SARG_PUSHDOWN = "sarg.pushdown" +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index 1f40a5340c2ce..f901bd8171508 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -26,15 +26,16 @@ import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContaine import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.hive.ql.parse.VariableSubstitution import org.apache.hadoop.hive.ql.processors._ -import org.apache.hadoop.hive.serde2.RegexSerDe import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe import org.apache.hadoop.hive.serde2.avro.AvroSerDe -import org.apache.spark.sql.SQLConf + +import org.apache.spark.sql.catalyst.CatalystConf import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.CacheTableCommand import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.execution.HiveNativeCommand +import org.apache.spark.sql.SQLConf import org.apache.spark.util.Utils import org.apache.spark.{SparkConf, SparkContext} @@ -47,7 +48,14 @@ import scala.collection.JavaConversions._ // SPARK-3729: Test key required to check for initialization errors with config. object TestHive extends TestHiveContext( - new SparkContext("local[2]", "TestSQLContext", new SparkConf().set("spark.sql.test", ""))) + new SparkContext( + System.getProperty("spark.sql.test.master", "local[2]"), + "TestSQLContext", + new SparkConf() + .set("spark.sql.test", "") + .set( + "spark.sql.hive.metastore.barrierPrefixes", + "org.apache.spark.sql.hive.execution.PairSerDe"))) /** * A locally running test instance of Spark's Hive execution engine. @@ -74,9 +82,11 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { lazy val warehousePath = Utils.createTempDir() + private lazy val temporaryConfig = newTemporaryConfiguration() + /** Sets up the system initially or after a RESET command */ protected override def configure(): Map[String, String] = - newTemporaryConfiguration() ++ Map("hive.metastore.warehouse.dir" -> warehousePath.toString) + temporaryConfig ++ Map("hive.metastore.warehouse.dir" -> warehousePath.toString) val testTempDir = Utils.createTempDir() @@ -102,11 +112,11 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { protected[hive] class SQLSession extends super.SQLSession { /** Fewer partitions to speed up testing. */ protected[sql] override lazy val conf: SQLConf = new SQLConf { - override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt - + override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, 5) // TODO as in unit test, conf.clear() probably be called, all of the value will be cleared. // The super.getConf(SQLConf.DIALECT) is "sql" by default, we need to set it as "hiveql" override def dialect: String = super.getConf(SQLConf.DIALECT, "hiveql") + override def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE, false) } } @@ -178,7 +188,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { } } - case class TestTable(name: String, commands: (()=>Unit)*) + case class TestTable(name: String, commands: (() => Unit)*) protected[hive] implicit class SqlCmd(sql: String) { def cmd: () => Unit = { @@ -242,8 +252,8 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { | 'serialization.format'='${classOf[TBinaryProtocol].getName}' |) |STORED AS - |INPUTFORMAT '${classOf[SequenceFileInputFormat[_,_]].getName}' - |OUTPUTFORMAT '${classOf[SequenceFileOutputFormat[_,_]].getName}' + |INPUTFORMAT '${classOf[SequenceFileInputFormat[_, _]].getName}' + |OUTPUTFORMAT '${classOf[SequenceFileOutputFormat[_, _]].getName}' """.stripMargin) runSqlHive( diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java new file mode 100644 index 0000000000000..c4828c4717643 --- /dev/null +++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java @@ -0,0 +1,78 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package test.org.apache.spark.sql.hive; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.*; +import org.apache.spark.sql.expressions.Window; +import org.apache.spark.sql.hive.HiveContext; +import org.apache.spark.sql.hive.test.TestHive$; + +public class JavaDataFrameSuite { + private transient JavaSparkContext sc; + private transient HiveContext hc; + + DataFrame df; + + private void checkAnswer(DataFrame actual, List expected) { + String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected); + if (errorMessage != null) { + Assert.fail(errorMessage); + } + } + + @Before + public void setUp() throws IOException { + hc = TestHive$.MODULE$; + sc = new JavaSparkContext(hc.sparkContext()); + + List jsonObjects = new ArrayList(10); + for (int i = 0; i < 10; i++) { + jsonObjects.add("{\"key\":" + i + ", \"value\":\"str" + i + "\"}"); + } + df = hc.jsonRDD(sc.parallelize(jsonObjects)); + df.registerTempTable("window_table"); + } + + @After + public void tearDown() throws IOException { + // Clean up tables. + hc.sql("DROP TABLE IF EXISTS window_table"); + } + + @Test + public void saveTableAndQueryIt() { + checkAnswer( + df.select(functions.avg("key").over( + Window.partitionBy("value").orderBy("key").rowsBetween(-1, 1))), + hc.sql("SELECT avg(key) " + + "OVER (PARTITION BY value " + + " ORDER BY key " + + " ROWS BETWEEN 1 preceding and 1 following) " + + "FROM window_table").collectAsList()); + } +} diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java similarity index 89% rename from sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java rename to sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java index 53ddecf57958b..64d1ce92931eb 100644 --- a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java +++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java @@ -14,7 +14,8 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.sql.hive; + +package test.org.apache.spark.sql.hive; import java.io.File; import java.io.IOException; @@ -36,6 +37,7 @@ import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.QueryTest$; import org.apache.spark.sql.Row; +import org.apache.spark.sql.hive.HiveContext; import org.apache.spark.sql.hive.test.TestHive$; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.StructField; @@ -81,7 +83,7 @@ public void setUp() throws IOException { jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}"); } JavaRDD rdd = sc.parallelize(jsonObjects); - df = sqlContext.jsonRDD(rdd); + df = sqlContext.read().json(rdd); df.registerTempTable("jsonTable"); } @@ -96,7 +98,11 @@ public void tearDown() throws IOException { public void saveExternalTableAndQueryIt() { Map options = new HashMap(); options.put("path", path.toString()); - df.saveAsTable("javaSavedTable", "org.apache.spark.sql.json", SaveMode.Append, options); + df.write() + .format("org.apache.spark.sql.json") + .mode(SaveMode.Append) + .options(options) + .saveAsTable("javaSavedTable"); checkAnswer( sqlContext.sql("SELECT * FROM javaSavedTable"), @@ -115,7 +121,11 @@ public void saveExternalTableAndQueryIt() { public void saveExternalTableWithSchemaAndQueryIt() { Map options = new HashMap(); options.put("path", path.toString()); - df.saveAsTable("javaSavedTable", "org.apache.spark.sql.json", SaveMode.Append, options); + df.write() + .format("org.apache.spark.sql.json") + .mode(SaveMode.Append) + .options(options) + .saveAsTable("javaSavedTable"); checkAnswer( sqlContext.sql("SELECT * FROM javaSavedTable"), @@ -138,7 +148,11 @@ public void saveExternalTableWithSchemaAndQueryIt() { @Test public void saveTableAndQueryIt() { Map options = new HashMap(); - df.saveAsTable("javaSavedTable", "org.apache.spark.sql.json", SaveMode.Append, options); + df.write() + .format("org.apache.spark.sql.json") + .mode(SaveMode.Append) + .options(options) + .saveAsTable("javaSavedTable"); checkAnswer( sqlContext.sql("SELECT * FROM javaSavedTable"), diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFIntegerToString.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFIntegerToString.java similarity index 100% rename from sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFIntegerToString.java rename to sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFIntegerToString.java diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListListInt.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListListInt.java similarity index 100% rename from sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListListInt.java rename to sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListListInt.java diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListString.java similarity index 100% rename from sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java rename to sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListString.java diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFStringString.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFStringString.java similarity index 100% rename from sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFStringString.java rename to sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFStringString.java diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFTwoListList.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFTwoListList.java similarity index 100% rename from sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFTwoListList.java rename to sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFTwoListList.java diff --git a/sql/hive/src/test/resources/TestUDTF.jar b/sql/hive/src/test/resources/TestUDTF.jar new file mode 100644 index 0000000000000..514f2d5d26fd3 Binary files /dev/null and b/sql/hive/src/test/resources/TestUDTF.jar differ diff --git a/sql/hive/src/test/resources/golden/Test UDTF.close in Lateral Views-0-ac5c96224a534f07b49462ad76620678 b/sql/hive/src/test/resources/golden/Test UDTF.close in Lateral Views-0-ac5c96224a534f07b49462ad76620678 new file mode 100644 index 0000000000000..946e72fc87c2e --- /dev/null +++ b/sql/hive/src/test/resources/golden/Test UDTF.close in Lateral Views-0-ac5c96224a534f07b49462ad76620678 @@ -0,0 +1,2 @@ +97 500 +97 500 diff --git a/sql/hive/src/test/resources/golden/Test UDTF.close in SELECT-0-517f834fef35b896ec64399f42b2a151 b/sql/hive/src/test/resources/golden/Test UDTF.close in SELECT-0-517f834fef35b896ec64399f42b2a151 new file mode 100644 index 0000000000000..a5c8806279fa7 --- /dev/null +++ b/sql/hive/src/test/resources/golden/Test UDTF.close in SELECT-0-517f834fef35b896ec64399f42b2a151 @@ -0,0 +1,2 @@ +3 +3 diff --git a/sql/hive/src/test/resources/golden/timestamp cast #5-0-dbd7bcd167d322d6617b884c02c7f247 b/sql/hive/src/test/resources/golden/timestamp cast #5-0-dbd7bcd167d322d6617b884c02c7f247 index 27de46fdf22ac..84a31a5a6970b 100644 --- a/sql/hive/src/test/resources/golden/timestamp cast #5-0-dbd7bcd167d322d6617b884c02c7f247 +++ b/sql/hive/src/test/resources/golden/timestamp cast #5-0-dbd7bcd167d322d6617b884c02c7f247 @@ -1 +1 @@ --0.0010000000000000009 +-0.001 diff --git a/sql/hive/src/test/resources/hive-contrib-0.13.1.jar b/sql/hive/src/test/resources/hive-contrib-0.13.1.jar new file mode 100644 index 0000000000000..ce0740d9245a7 Binary files /dev/null and b/sql/hive/src/test/resources/hive-contrib-0.13.1.jar differ diff --git a/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala b/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala new file mode 100644 index 0000000000000..e1715177e3f1b --- /dev/null +++ b/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala @@ -0,0 +1,43 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.sql.hive.HiveContext + +/** + * Entry point in test application for SPARK-8489. + * + * This file is not meant to be compiled during tests. It is already included + * in a pre-built "test.jar" located in the same directory as this file. + * This is included here for reference only and should NOT be modified without + * rebuilding the test jar itself. + * + * This is used in org.apache.spark.sql.hive.HiveSparkSubmitSuite. + */ +object Main { + def main(args: Array[String]) { + println("Running regression test for SPARK-8489.") + val sc = new SparkContext("local", "testing") + val hc = new HiveContext(sc) + // This line should not throw scala.reflect.internal.MissingRequirementError. + // See SPARK-8470 for more detail. + val df = hc.createDataFrame(Seq(MyCoolClass("1", "2", "3"))) + df.collect() + println("Regression test for SPARK-8489 success!") + } +} + diff --git a/core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala b/sql/hive/src/test/resources/regression-test-SPARK-8489/MyCoolClass.scala similarity index 76% rename from core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala rename to sql/hive/src/test/resources/regression-test-SPARK-8489/MyCoolClass.scala index d75959f480756..b1681745c2ef7 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala +++ b/sql/hive/src/test/resources/regression-test-SPARK-8489/MyCoolClass.scala @@ -15,10 +15,6 @@ * limitations under the License. */ -package org.apache.spark.util.collection +/** Dummy class used in regression test SPARK-8489. */ +case class MyCoolClass(past: String, present: String, future: String) -private[spark] class PairIterator[K, V](iter: Iterator[Any]) extends Iterator[(K, V)] { - def hasNext: Boolean = iter.hasNext - - def next(): (K, V) = (iter.next().asInstanceOf[K], iter.next().asInstanceOf[V]) -} diff --git a/sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar b/sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar new file mode 100644 index 0000000000000..4f59fba9eab55 Binary files /dev/null and b/sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar differ diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala index fc6c3c35037b0..39d315aaeab57 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala @@ -57,7 +57,7 @@ class CachedTableSuite extends QueryTest { checkAnswer( sql("SELECT * FROM src s"), preCacheResults) - + uncacheTable("src") assertCached(sql("SELECT * FROM src"), 0) } @@ -162,7 +162,7 @@ class CachedTableSuite extends QueryTest { test("REFRESH TABLE also needs to recache the data (data source tables)") { val tempPath: File = Utils.createTempDir() tempPath.delete() - table("src").save(tempPath.toString, "parquet", SaveMode.Overwrite) + table("src").write.mode(SaveMode.Overwrite).parquet(tempPath.toString) sql("DROP TABLE IF EXISTS refreshTable") createExternalTable("refreshTable", tempPath.toString, "parquet") checkAnswer( @@ -172,7 +172,7 @@ class CachedTableSuite extends QueryTest { sql("CACHE TABLE refreshTable") assertCached(table("refreshTable")) // Append new data. - table("src").save(tempPath.toString, "parquet", SaveMode.Append) + table("src").write.mode(SaveMode.Append).parquet(tempPath.toString) // We are still using the old data. assertCached(table("refreshTable")) checkAnswer( diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala new file mode 100644 index 0000000000000..fb10f8583da99 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala @@ -0,0 +1,65 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import org.apache.spark.sql.{DataFrame, QueryTest} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.scalatest.BeforeAndAfterAll + +// TODO ideally we should put the test suite into the package `sql`, as +// `hive` package is optional in compiling, however, `SQLContext.sql` doesn't +// support the `cube` or `rollup` yet. +class HiveDataFrameAnalyticsSuite extends QueryTest with BeforeAndAfterAll { + private var testData: DataFrame = _ + + override def beforeAll() { + testData = Seq((1, 2), (2, 4)).toDF("a", "b") + TestHive.registerDataFrameAsTable(testData, "mytable") + } + + override def afterAll(): Unit = { + TestHive.dropTempTable("mytable") + } + + test("rollup") { + checkAnswer( + testData.rollup($"a" + $"b", $"b").agg(sum($"a" - $"b")), + sql("select a + b, b, sum(a - b) from mytable group by a + b, b with rollup").collect() + ) + + checkAnswer( + testData.rollup("a", "b").agg(sum("b")), + sql("select a, b, sum(b) from mytable group by a, b with rollup").collect() + ) + } + + test("cube") { + checkAnswer( + testData.cube($"a" + $"b", $"b").agg(sum($"a" - $"b")), + sql("select a + b, b, sum(a - b) from mytable group by a + b, b with cube").collect() + ) + + checkAnswer( + testData.cube("a", "b").agg(sum("b")), + sql("select a, b, sum(b) from mytable group by a, b with cube").collect() + ) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala new file mode 100644 index 0000000000000..efb3f2545db84 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala @@ -0,0 +1,215 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import org.apache.spark.sql.{Row, QueryTest} +import org.apache.spark.sql.expressions.Window +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ + +class HiveDataFrameWindowSuite extends QueryTest { + + test("reuse window partitionBy") { + val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + val w = Window.partitionBy("key").orderBy("value") + + checkAnswer( + df.select( + lead("key", 1).over(w), + lead("value", 1).over(w)), + Row(1, "1") :: Row(2, "2") :: Row(null, null) :: Row(null, null) :: Nil) + } + + test("reuse window orderBy") { + val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + val w = Window.orderBy("value").partitionBy("key") + + checkAnswer( + df.select( + lead("key", 1).over(w), + lead("value", 1).over(w)), + Row(1, "1") :: Row(2, "2") :: Row(null, null) :: Row(null, null) :: Nil) + } + + test("lead") { + val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + + checkAnswer( + df.select( + lead("value", 1).over(Window.partitionBy($"key").orderBy($"value"))), + sql( + """SELECT + | lead(value) OVER (PARTITION BY key ORDER BY value) + | FROM window_table""".stripMargin).collect()) + } + + test("lag") { + val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + + checkAnswer( + df.select( + lag("value", 1).over(Window.partitionBy($"key").orderBy($"value"))), + sql( + """SELECT + | lag(value) OVER (PARTITION BY key ORDER BY value) + | FROM window_table""".stripMargin).collect()) + } + + test("lead with default value") { + val df = Seq((1, "1"), (1, "1"), (2, "2"), (1, "1"), + (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + checkAnswer( + df.select( + lead("value", 2, "n/a").over(Window.partitionBy("key").orderBy("value"))), + sql( + """SELECT + | lead(value, 2, "n/a") OVER (PARTITION BY key ORDER BY value) + | FROM window_table""".stripMargin).collect()) + } + + test("lag with default value") { + val df = Seq((1, "1"), (1, "1"), (2, "2"), (1, "1"), + (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + checkAnswer( + df.select( + lag("value", 2, "n/a").over(Window.partitionBy($"key").orderBy($"value"))), + sql( + """SELECT + | lag(value, 2, "n/a") OVER (PARTITION BY key ORDER BY value) + | FROM window_table""".stripMargin).collect()) + } + + test("rank functions in unspecific window") { + val df = Seq((1, "1"), (2, "2"), (1, "2"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + checkAnswer( + df.select( + $"key", + max("key").over(Window.partitionBy("value").orderBy("key")), + min("key").over(Window.partitionBy("value").orderBy("key")), + mean("key").over(Window.partitionBy("value").orderBy("key")), + count("key").over(Window.partitionBy("value").orderBy("key")), + sum("key").over(Window.partitionBy("value").orderBy("key")), + ntile(2).over(Window.partitionBy("value").orderBy("key")), + rowNumber().over(Window.partitionBy("value").orderBy("key")), + denseRank().over(Window.partitionBy("value").orderBy("key")), + rank().over(Window.partitionBy("value").orderBy("key")), + cumeDist().over(Window.partitionBy("value").orderBy("key")), + percentRank().over(Window.partitionBy("value").orderBy("key"))), + sql( + s"""SELECT + |key, + |max(key) over (partition by value order by key), + |min(key) over (partition by value order by key), + |avg(key) over (partition by value order by key), + |count(key) over (partition by value order by key), + |sum(key) over (partition by value order by key), + |ntile(2) over (partition by value order by key), + |row_number() over (partition by value order by key), + |dense_rank() over (partition by value order by key), + |rank() over (partition by value order by key), + |cume_dist() over (partition by value order by key), + |percent_rank() over (partition by value order by key) + |FROM window_table""".stripMargin).collect()) + } + + test("aggregation and rows between") { + val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + checkAnswer( + df.select( + avg("key").over(Window.partitionBy($"value").orderBy($"key").rowsBetween(-1, 2))), + sql( + """SELECT + | avg(key) OVER + | (PARTITION BY value ORDER BY key ROWS BETWEEN 1 preceding and 2 following) + | FROM window_table""".stripMargin).collect()) + } + + test("aggregation and range betweens") { + val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + checkAnswer( + df.select( + avg("key").over(Window.partitionBy($"value").orderBy($"key").rangeBetween(-1, 1))), + sql( + """SELECT + | avg(key) OVER + | (PARTITION BY value ORDER BY key RANGE BETWEEN 1 preceding and 1 following) + | FROM window_table""".stripMargin).collect()) + } + + test("aggregation and rows betweens with unbounded") { + val df = Seq((1, "1"), (2, "2"), (2, "3"), (1, "3"), (3, "2"), (4, "3")).toDF("key", "value") + df.registerTempTable("window_table") + checkAnswer( + df.select( + $"key", + last("value").over( + Window.partitionBy($"value").orderBy($"key").rowsBetween(0, Long.MaxValue)), + last("value").over( + Window.partitionBy($"value").orderBy($"key").rowsBetween(Long.MinValue, 0)), + last("value").over(Window.partitionBy($"value").orderBy($"key").rowsBetween(-1, 3))), + sql( + """SELECT + | key, + | last_value(value) OVER + | (PARTITION BY value ORDER BY key ROWS between current row and unbounded following), + | last_value(value) OVER + | (PARTITION BY value ORDER BY key ROWS between unbounded preceding and current row), + | last_value(value) OVER + | (PARTITION BY value ORDER BY key ROWS between 1 preceding and 3 following) + | FROM window_table""".stripMargin).collect()) + } + + test("aggregation and range betweens with unbounded") { + val df = Seq((1, "1"), (2, "2"), (2, "2"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + df.registerTempTable("window_table") + checkAnswer( + df.select( + $"key", + last("value").over( + Window.partitionBy($"value").orderBy($"key").rangeBetween(1, Long.MaxValue)) + .equalTo("2") + .as("last_v"), + avg("key").over(Window.partitionBy("value").orderBy("key").rangeBetween(Long.MinValue, 1)) + .as("avg_key1"), + avg("key").over(Window.partitionBy("value").orderBy("key").rangeBetween(0, Long.MaxValue)) + .as("avg_key2"), + avg("key").over(Window.partitionBy("value").orderBy("key").rangeBetween(-1, 0)) + .as("avg_key3") + ), + sql( + """SELECT + | key, + | last_value(value) OVER + | (PARTITION BY value ORDER BY key RANGE 1 preceding) == "2", + | avg(key) OVER + | (PARTITION BY value ORDER BY key RANGE BETWEEN unbounded preceding and 1 following), + | avg(key) OVER + | (PARTITION BY value ORDER BY key RANGE BETWEEN current row and unbounded following), + | avg(key) OVER + | (PARTITION BY value ORDER BY key RANGE BETWEEN 1 preceding and current row) + | FROM window_table""".stripMargin).collect()) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala index 2a7374cc172b7..aff0456b37ed5 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala @@ -26,12 +26,13 @@ import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectIns import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory.ObjectInspectorOptions import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory import org.apache.hadoop.io.LongWritable -import org.scalatest.FunSuite -import org.apache.spark.sql.catalyst.expressions.{Literal, Row} +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.expressions.{Literal, InternalRow} import org.apache.spark.sql.types._ +import org.apache.spark.sql.Row -class HiveInspectorSuite extends FunSuite with HiveInspectors { +class HiveInspectorSuite extends SparkFunSuite with HiveInspectors { test("Test wrap SettableStructObjectInspector") { val udaf = new UDAFPercentile.PercentileLongEvaluator() udaf.init() @@ -45,7 +46,7 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { classOf[UDAFPercentile.State], ObjectInspectorOptions.JAVA).asInstanceOf[StructObjectInspector] - val a = unwrap(state, soi).asInstanceOf[Row] + val a = unwrap(state, soi).asInstanceOf[InternalRow] val b = wrap(a, soi).asInstanceOf[UDAFPercentile.State] val sfCounts = soi.getStructFieldRef("counts") @@ -78,10 +79,10 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { Literal(java.sql.Date.valueOf("2014-09-23")) :: Literal(Decimal(BigDecimal(123.123))) :: Literal(new java.sql.Timestamp(123123)) :: - Literal(Array[Byte](1,2,3)) :: - Literal.create(Seq[Int](1,2,3), ArrayType(IntegerType)) :: - Literal.create(Map[Int, Int](1->2, 2->1), MapType(IntegerType, IntegerType)) :: - Literal.create(Row(1,2.0d,3.0f), + Literal(Array[Byte](1, 2, 3)) :: + Literal.create(Seq[Int](1, 2, 3), ArrayType(IntegerType)) :: + Literal.create(Map[Int, Int](1 -> 2, 2 -> 1), MapType(IntegerType, IntegerType)) :: + Literal.create(Row(1, 2.0d, 3.0f), StructType(StructField("c1", IntegerType) :: StructField("c2", DoubleType) :: StructField("c3", FloatType) :: Nil)) :: @@ -111,8 +112,8 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { case DecimalType() => PrimitiveObjectInspectorFactory.writableHiveDecimalObjectInspector case StructType(fields) => ObjectInspectorFactory.getStandardStructObjectInspector( - java.util.Arrays.asList(fields.map(f => f.name) :_*), - java.util.Arrays.asList(fields.map(f => toWritableInspector(f.dataType)) :_*)) + java.util.Arrays.asList(fields.map(f => f.name) : _*), + java.util.Arrays.asList(fields.map(f => toWritableInspector(f.dataType)) : _*)) } def checkDataType(dt1: Seq[DataType], dt2: Seq[DataType]): Unit = { @@ -127,7 +128,7 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { } } - def checkValues(row1: Seq[Any], row2: Row): Unit = { + def checkValues(row1: Seq[Any], row2: InternalRow): Unit = { row1.zip(row2.toSeq).foreach { case (r1, r2) => checkValue(r1, r2) } @@ -203,7 +204,7 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { }) checkValues(row, - unwrap(wrap(Row.fromSeq(row), toInspector(dt)), toInspector(dt)).asInstanceOf[Row]) + unwrap(wrap(Row.fromSeq(row), toInspector(dt)), toInspector(dt)).asInstanceOf[InternalRow]) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala index fa8e11ffec2b4..e9bb32667936c 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala @@ -17,13 +17,13 @@ package org.apache.spark.sql.hive +import org.apache.spark.SparkFunSuite import org.apache.spark.sql.hive.test.TestHive -import org.scalatest.FunSuite import org.apache.spark.sql.test.ExamplePointUDT import org.apache.spark.sql.types.StructType -class HiveMetastoreCatalogSuite extends FunSuite { +class HiveMetastoreCatalogSuite extends SparkFunSuite { test("struct field should accept underscore in sub-column name") { val metastr = "struct" diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala index 7ff5719adb3ab..af68615e8e9d6 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala @@ -17,10 +17,9 @@ package org.apache.spark.sql.hive -import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.parquet.ParquetTest -import org.apache.spark.sql.{QueryTest, SQLConf} +import org.apache.spark.sql.{QueryTest, Row, SQLConf} case class Cases(lower: String, UPPER: String) @@ -55,8 +54,8 @@ class HiveParquetSuite extends QueryTest with ParquetTest { test(s"$prefix: Converting Hive to Parquet Table via saveAsParquetFile") { withTempPath { dir => - sql("SELECT * FROM src").saveAsParquetFile(dir.getCanonicalPath) - parquetFile(dir.getCanonicalPath).registerTempTable("p") + sql("SELECT * FROM src").write.parquet(dir.getCanonicalPath) + read.parquet(dir.getCanonicalPath).registerTempTable("p") withTempTable("p") { checkAnswer( sql("SELECT * FROM src ORDER BY key"), @@ -68,8 +67,8 @@ class HiveParquetSuite extends QueryTest with ParquetTest { test(s"$prefix: INSERT OVERWRITE TABLE Parquet table") { withParquetTable((1 to 10).map(i => (i, s"val_$i")), "t") { withTempPath { file => - sql("SELECT * FROM t LIMIT 1").saveAsParquetFile(file.getCanonicalPath) - parquetFile(file.getCanonicalPath).registerTempTable("p") + sql("SELECT * FROM t LIMIT 1").write.parquet(file.getCanonicalPath) + read.parquet(file.getCanonicalPath).registerTempTable("p") withTempTable("p") { // let's do three overwrites for good measure sql("INSERT OVERWRITE TABLE p SELECT * FROM t") @@ -82,11 +81,11 @@ class HiveParquetSuite extends QueryTest with ParquetTest { } } - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "true") { + withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API.key -> "true") { run("Parquet data source enabled") } - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "false") { + withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API.key -> "false") { run("Parquet data source disabled") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala new file mode 100644 index 0000000000000..f765395e148af --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala @@ -0,0 +1,178 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.ql.session.SessionState +import org.apache.hadoop.hive.serde.serdeConstants +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.hive.client.{ManagedTable, HiveColumn, ExternalTable, HiveTable} +import org.scalatest.BeforeAndAfterAll + + +class HiveQlSuite extends SparkFunSuite with BeforeAndAfterAll { + override def beforeAll() { + if (SessionState.get() == null) { + SessionState.start(new HiveConf()) + } + } + + private def extractTableDesc(sql: String): (HiveTable, Boolean) = { + HiveQl.createPlan(sql).collect { + case CreateTableAsSelect(desc, child, allowExisting) => (desc, allowExisting) + }.head + } + + test("Test CTAS #1") { + val s1 = + """CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view + |(viewTime INT, + |userid BIGINT, + |page_url STRING, + |referrer_url STRING, + |ip STRING COMMENT 'IP Address of the User', + |country STRING COMMENT 'country of origination') + |COMMENT 'This is the staging page view table' + |PARTITIONED BY (dt STRING COMMENT 'date type', hour STRING COMMENT 'hour of the day') + |ROW FORMAT DELIMITED FIELDS TERMINATED BY '\054' STORED AS RCFILE + |LOCATION '/user/external/page_view' + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |AS SELECT * FROM src""".stripMargin + + val (desc, exists) = extractTableDesc(s1) + assert(exists == true) + assert(desc.specifiedDatabase == Some("mydb")) + assert(desc.name == "page_view") + assert(desc.tableType == ExternalTable) + assert(desc.location == Some("/user/external/page_view")) + assert(desc.schema == + HiveColumn("viewtime", "int", null) :: + HiveColumn("userid", "bigint", null) :: + HiveColumn("page_url", "string", null) :: + HiveColumn("referrer_url", "string", null) :: + HiveColumn("ip", "string", "IP Address of the User") :: + HiveColumn("country", "string", "country of origination") :: Nil) + // TODO will be SQLText + assert(desc.viewText == Option("This is the staging page view table")) + assert(desc.partitionColumns == + HiveColumn("dt", "string", "date type") :: + HiveColumn("hour", "string", "hour of the day") :: Nil) + assert(desc.serdeProperties == + Map((serdeConstants.SERIALIZATION_FORMAT, "\054"), (serdeConstants.FIELD_DELIM, "\054"))) + assert(desc.inputFormat == Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat")) + assert(desc.outputFormat == Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat")) + assert(desc.serde == Option("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe")) + assert(desc.properties == Map(("p1", "v1"), ("p2", "v2"))) + } + + test("Test CTAS #2") { + val s2 = + """CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view + |(viewTime INT, + |userid BIGINT, + |page_url STRING, + |referrer_url STRING, + |ip STRING COMMENT 'IP Address of the User', + |country STRING COMMENT 'country of origination') + |COMMENT 'This is the staging page view table' + |PARTITIONED BY (dt STRING COMMENT 'date type', hour STRING COMMENT 'hour of the day') + |ROW FORMAT SERDE 'parquet.hive.serde.ParquetHiveSerDe' + | STORED AS + | INPUTFORMAT 'parquet.hive.DeprecatedParquetInputFormat' + | OUTPUTFORMAT 'parquet.hive.DeprecatedParquetOutputFormat' + |LOCATION '/user/external/page_view' + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |AS SELECT * FROM src""".stripMargin + + val (desc, exists) = extractTableDesc(s2) + assert(exists == true) + assert(desc.specifiedDatabase == Some("mydb")) + assert(desc.name == "page_view") + assert(desc.tableType == ExternalTable) + assert(desc.location == Some("/user/external/page_view")) + assert(desc.schema == + HiveColumn("viewtime", "int", null) :: + HiveColumn("userid", "bigint", null) :: + HiveColumn("page_url", "string", null) :: + HiveColumn("referrer_url", "string", null) :: + HiveColumn("ip", "string", "IP Address of the User") :: + HiveColumn("country", "string", "country of origination") :: Nil) + // TODO will be SQLText + assert(desc.viewText == Option("This is the staging page view table")) + assert(desc.partitionColumns == + HiveColumn("dt", "string", "date type") :: + HiveColumn("hour", "string", "hour of the day") :: Nil) + assert(desc.serdeProperties == Map()) + assert(desc.inputFormat == Option("parquet.hive.DeprecatedParquetInputFormat")) + assert(desc.outputFormat == Option("parquet.hive.DeprecatedParquetOutputFormat")) + assert(desc.serde == Option("parquet.hive.serde.ParquetHiveSerDe")) + assert(desc.properties == Map(("p1", "v1"), ("p2", "v2"))) + } + + test("Test CTAS #3") { + val s3 = """CREATE TABLE page_view AS SELECT * FROM src""" + val (desc, exists) = extractTableDesc(s3) + assert(exists == false) + assert(desc.specifiedDatabase == None) + assert(desc.name == "page_view") + assert(desc.tableType == ManagedTable) + assert(desc.location == None) + assert(desc.schema == Seq.empty[HiveColumn]) + assert(desc.viewText == None) // TODO will be SQLText + assert(desc.serdeProperties == Map()) + assert(desc.inputFormat == Option("org.apache.hadoop.mapred.TextInputFormat")) + assert(desc.outputFormat == Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat")) + assert(desc.serde.isEmpty) + assert(desc.properties == Map()) + } + + test("Test CTAS #4") { + val s4 = + """CREATE TABLE page_view + |STORED BY 'storage.handler.class.name' AS SELECT * FROM src""".stripMargin + intercept[AnalysisException] { + extractTableDesc(s4) + } + } + + test("Test CTAS #5") { + val s5 = """CREATE TABLE ctas2 + | ROW FORMAT SERDE "org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe" + | WITH SERDEPROPERTIES("serde_p1"="p1","serde_p2"="p2") + | STORED AS RCFile + | TBLPROPERTIES("tbl_p1"="p11", "tbl_p2"="p22") + | AS + | SELECT key, value + | FROM src + | ORDER BY key, value""".stripMargin + val (desc, exists) = extractTableDesc(s5) + assert(exists == false) + assert(desc.specifiedDatabase == None) + assert(desc.name == "ctas2") + assert(desc.tableType == ManagedTable) + assert(desc.location == None) + assert(desc.schema == Seq.empty[HiveColumn]) + assert(desc.viewText == None) // TODO will be SQLText + assert(desc.serdeProperties == Map(("serde_p1" -> "p1"), ("serde_p2" -> "p2"))) + assert(desc.inputFormat == Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat")) + assert(desc.outputFormat == Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat")) + assert(desc.serde == Option("org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe")) + assert(desc.properties == Map(("tbl_p1" -> "p11"), ("tbl_p2" -> "p22"))) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala new file mode 100644 index 0000000000000..ab443032be20d --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala @@ -0,0 +1,195 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import java.io.File + +import org.apache.spark._ +import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext} +import org.apache.spark.util.{ResetSystemProperties, Utils} +import org.scalatest.Matchers +import org.scalatest.concurrent.Timeouts +import org.scalatest.time.SpanSugar._ + +/** + * This suite tests spark-submit with applications using HiveContext. + */ +class HiveSparkSubmitSuite + extends SparkFunSuite + with Matchers + with ResetSystemProperties + with Timeouts { + + // TODO: rewrite these or mark them as slow tests to be run sparingly + + def beforeAll() { + System.setProperty("spark.testing", "true") + } + + test("SPARK-8368: includes jars passed in through --jars") { + val unusedJar = TestUtils.createJarWithClasses(Seq.empty) + val jar1 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassA")) + val jar2 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassB")) + val jar3 = TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath() + val jar4 = TestHive.getHiveFile("hive-hcatalog-core-0.13.1.jar").getCanonicalPath() + val jarsString = Seq(jar1, jar2, jar3, jar4).map(j => j.toString).mkString(",") + val args = Seq( + "--class", SparkSubmitClassLoaderTest.getClass.getName.stripSuffix("$"), + "--name", "SparkSubmitClassLoaderTest", + "--master", "local-cluster[2,1,512]", + "--jars", jarsString, + unusedJar.toString, "SparkSubmitClassA", "SparkSubmitClassB") + runSparkSubmit(args) + } + + test("SPARK-8020: set sql conf in spark conf") { + val unusedJar = TestUtils.createJarWithClasses(Seq.empty) + val args = Seq( + "--class", SparkSQLConfTest.getClass.getName.stripSuffix("$"), + "--name", "SparkSQLConfTest", + "--master", "local-cluster[2,1,512]", + unusedJar.toString) + runSparkSubmit(args) + } + + test("SPARK-8489: MissingRequirementError during reflection") { + // This test uses a pre-built jar to test SPARK-8489. In a nutshell, this test creates + // a HiveContext and uses it to create a data frame from an RDD using reflection. + // Before the fix in SPARK-8470, this results in a MissingRequirementError because + // the HiveContext code mistakenly overrides the class loader that contains user classes. + // For more detail, see sql/hive/src/test/resources/regression-test-SPARK-8489/*scala. + val testJar = "sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar" + val args = Seq("--class", "Main", testJar) + runSparkSubmit(args) + } + + // NOTE: This is an expensive operation in terms of time (10 seconds+). Use sparingly. + // This is copied from org.apache.spark.deploy.SparkSubmitSuite + private def runSparkSubmit(args: Seq[String]): Unit = { + val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!")) + val process = Utils.executeCommand( + Seq("./bin/spark-submit") ++ args, + new File(sparkHome), + Map("SPARK_TESTING" -> "1", "SPARK_HOME" -> sparkHome)) + try { + val exitCode = failAfter(120 seconds) { process.waitFor() } + if (exitCode != 0) { + fail(s"Process returned with exit code $exitCode. See the log4j logs for more detail.") + } + } finally { + // Ensure we still kill the process in case it timed out + process.destroy() + } + } +} + +// This object is used for testing SPARK-8368: https://issues.apache.org/jira/browse/SPARK-8368. +// We test if we can load user jars in both driver and executors when HiveContext is used. +object SparkSubmitClassLoaderTest extends Logging { + def main(args: Array[String]) { + Utils.configTestLog4j("INFO") + val conf = new SparkConf() + val sc = new SparkContext(conf) + val hiveContext = new TestHiveContext(sc) + val df = hiveContext.createDataFrame((1 to 100).map(i => (i, i))).toDF("i", "j") + // First, we load classes at driver side. + try { + Class.forName(args(0), true, Thread.currentThread().getContextClassLoader) + Class.forName(args(1), true, Thread.currentThread().getContextClassLoader) + } catch { + case t: Throwable => + throw new Exception("Could not load user class from jar:\n", t) + } + // Second, we load classes at the executor side. + val result = df.mapPartitions { x => + var exception: String = null + try { + Class.forName(args(0), true, Thread.currentThread().getContextClassLoader) + Class.forName(args(1), true, Thread.currentThread().getContextClassLoader) + } catch { + case t: Throwable => + exception = t + "\n" + t.getStackTraceString + exception = exception.replaceAll("\n", "\n\t") + } + Option(exception).toSeq.iterator + }.collect() + if (result.nonEmpty) { + throw new Exception("Could not load user class from jar:\n" + result(0)) + } + + // Load a Hive UDF from the jar. + hiveContext.sql( + """ + |CREATE TEMPORARY FUNCTION example_max + |AS 'org.apache.hadoop.hive.contrib.udaf.example.UDAFExampleMax' + """.stripMargin) + val source = + hiveContext.createDataFrame((1 to 10).map(i => (i, s"str$i"))).toDF("key", "val") + source.registerTempTable("sourceTable") + // Load a Hive SerDe from the jar. + hiveContext.sql( + """ + |CREATE TABLE t1(key int, val string) + |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe' + """.stripMargin) + // Actually use the loaded UDF and SerDe. + hiveContext.sql( + "INSERT INTO TABLE t1 SELECT example_max(key) as key, val FROM sourceTable GROUP BY val") + val count = hiveContext.table("t1").orderBy("key", "val").count() + if (count != 10) { + throw new Exception(s"table t1 should have 10 rows instead of $count rows") + } + } +} + +// This object is used for testing SPARK-8020: https://issues.apache.org/jira/browse/SPARK-8020. +// We test if we can correctly set spark sql configurations when HiveContext is used. +object SparkSQLConfTest extends Logging { + def main(args: Array[String]) { + Utils.configTestLog4j("INFO") + // We override the SparkConf to add spark.sql.hive.metastore.version and + // spark.sql.hive.metastore.jars to the beginning of the conf entry array. + // So, if metadataHive get initialized after we set spark.sql.hive.metastore.version but + // before spark.sql.hive.metastore.jars get set, we will see the following exception: + // Exception in thread "main" java.lang.IllegalArgumentException: Builtin jars can only + // be used when hive execution version == hive metastore version. + // Execution: 0.13.1 != Metastore: 0.12. Specify a vaild path to the correct hive jars + // using $HIVE_METASTORE_JARS or change spark.sql.hive.metastore.version to 0.13.1. + val conf = new SparkConf() { + override def getAll: Array[(String, String)] = { + def isMetastoreSetting(conf: String): Boolean = { + conf == "spark.sql.hive.metastore.version" || conf == "spark.sql.hive.metastore.jars" + } + // If there is any metastore settings, remove them. + val filteredSettings = super.getAll.filterNot(e => isMetastoreSetting(e._1)) + + // Always add these two metastore settings at the beginning. + ("spark.sql.hive.metastore.version" -> "0.12") +: + ("spark.sql.hive.metastore.jars" -> "maven") +: + filteredSettings + } + + // For this simple test, we do not really clone this object. + override def clone: SparkConf = this + } + val sc = new SparkContext(conf) + val hiveContext = new TestHiveContext(sc) + // Run a simple command to make sure all lazy vals in hiveContext get instantiated. + hiveContext.tables().collect() + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala index ecb990e8aac91..aa5dbe2db6903 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala @@ -53,7 +53,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { sql("CREATE TABLE createAndInsertTest (key int, value string)") // Add some data. - testData.insertInto("createAndInsertTest") + testData.write.mode(SaveMode.Append).insertInto("createAndInsertTest") // Make sure the table has also been updated. checkAnswer( @@ -62,7 +62,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { ) // Add more data. - testData.insertInto("createAndInsertTest") + testData.write.mode(SaveMode.Append).insertInto("createAndInsertTest") // Make sure the table has been updated. checkAnswer( @@ -71,7 +71,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { ) // Now overwrite. - testData.insertInto("createAndInsertTest", overwrite = true) + testData.write.mode(SaveMode.Overwrite).insertInto("createAndInsertTest") // Make sure the registered table has also been updated. checkAnswer( @@ -160,7 +160,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { "p1=a"::"p2=b"::"p3=c"::"p4=c"::"p5=1"::Nil , "p1=a"::"p2=b"::"p3=c"::"p4=c"::"p5=4"::Nil ) - assert(listFolders(tmpDir,List()).sortBy(_.toString()) == expected.sortBy(_.toString)) + assert(listFolders(tmpDir, List()).sortBy(_.toString()) == expected.sortBy(_.toString)) sql("DROP TABLE table_with_partition") sql("DROP TABLE tmp_table") } @@ -240,7 +240,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { checkAnswer(sql("select key,value from table_with_partition where ds='1' "), testData.collect().toSeq ) - + // test difference type of field sql("ALTER TABLE table_with_partition CHANGE COLUMN key key BIGINT") checkAnswer(sql("select key,value from table_with_partition where ds='1' "), diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala index e12a6c21ccac4..1c15997ea8e6d 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala @@ -29,7 +29,7 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll { import org.apache.spark.sql.hive.test.TestHive.implicits._ val df = - sparkContext.parallelize((1 to 10).map(i => (i,s"str$i"))).toDF("key", "value") + sparkContext.parallelize((1 to 10).map(i => (i, s"str$i"))).toDF("key", "value") override def beforeAll(): Unit = { // The catalog in HiveContext is a case insensitive one. diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala index 47c60f651d14c..cc294bc3e8bc3 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala @@ -21,744 +21,833 @@ import java.io.File import scala.collection.mutable.ArrayBuffer -import org.scalatest.BeforeAndAfterEach +import org.scalatest.BeforeAndAfterAll import org.apache.hadoop.fs.Path -import org.apache.hadoop.hive.metastore.TableType -import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.mapred.InvalidInputException import org.apache.spark.sql._ -import org.apache.spark.util.Utils -import org.apache.spark.sql.types._ import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable} +import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ import org.apache.spark.sql.parquet.ParquetRelation2 import org.apache.spark.sql.sources.LogicalRelation +import org.apache.spark.sql.test.SQLTestUtils +import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils /** * Tests for persisting tables created though the data sources API into the metastore. */ -class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { - - override def afterEach(): Unit = { - reset() - Utils.deleteRecursively(tempPath) - } +class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll { + override val sqlContext = TestHive - val filePath = Utils.getSparkClassLoader.getResource("sample.json").getFile - var tempPath: File = Utils.createTempDir() - tempPath.delete() + var jsonFilePath: String = _ - test ("persistent JSON table") { - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) - - checkAnswer( - sql("SELECT * FROM jsonTable"), - jsonFile(filePath).collect().toSeq) + override def beforeAll(): Unit = { + jsonFilePath = Utils.getSparkClassLoader.getResource("sample.json").getFile } - test ("persistent JSON table with a user specified schema") { - sql( - s""" - |CREATE TABLE jsonTable ( - |a string, - |b String, - |`c_!@(3)` int, - |`` Struct<`d!`:array, `=`:array>>) - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) - - jsonFile(filePath).registerTempTable("expectedJsonTable") + test("persistent JSON table") { + withTable("jsonTable") { + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) - checkAnswer( - sql("SELECT a, b, `c_!@(3)`, ``.`d!`, ``.`=` FROM jsonTable"), - sql("SELECT a, b, `c_!@(3)`, ``.`d!`, ``.`=` FROM expectedJsonTable").collect().toSeq) + checkAnswer( + sql("SELECT * FROM jsonTable"), + read.json(jsonFilePath).collect().toSeq) + } } - test ("persistent JSON table with a user specified schema with a subset of fields") { - // This works because JSON objects are self-describing and JSONRelation can get needed - // field values based on field names. - sql( - s""" - |CREATE TABLE jsonTable (`` Struct<`=`:array>>, b String) - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) - - val innerStruct = StructType( - StructField("=", ArrayType(StructType(StructField("Dd2", BooleanType, true) :: Nil))) :: Nil) - val expectedSchema = StructType( - StructField("", innerStruct, true) :: - StructField("b", StringType, true) :: Nil) - - assert(expectedSchema === table("jsonTable").schema) - - jsonFile(filePath).registerTempTable("expectedJsonTable") + test("persistent JSON table with a user specified schema") { + withTable("jsonTable") { + sql( + s"""CREATE TABLE jsonTable ( + |a string, + |b String, + |`c_!@(3)` int, + |`` Struct<`d!`:array, `=`:array>>) + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) + + withTempTable("expectedJsonTable") { + read.json(jsonFilePath).registerTempTable("expectedJsonTable") + checkAnswer( + sql("SELECT a, b, `c_!@(3)`, ``.`d!`, ``.`=` FROM jsonTable"), + sql("SELECT a, b, `c_!@(3)`, ``.`d!`, ``.`=` FROM expectedJsonTable")) + } + } + } - checkAnswer( - sql("SELECT b, ``.`=` FROM jsonTable"), - sql("SELECT b, ``.`=` FROM expectedJsonTable").collect().toSeq) + test("persistent JSON table with a user specified schema with a subset of fields") { + withTable("jsonTable") { + // This works because JSON objects are self-describing and JSONRelation can get needed + // field values based on field names. + sql( + s"""CREATE TABLE jsonTable (`` Struct<`=`:array>>, b String) + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) + + val innerStruct = StructType(Seq( + StructField("=", ArrayType(StructType(StructField("Dd2", BooleanType, true) :: Nil))))) + + val expectedSchema = StructType(Seq( + StructField("", innerStruct, true), + StructField("b", StringType, true))) + + assert(expectedSchema === table("jsonTable").schema) + + withTempTable("expectedJsonTable") { + read.json(jsonFilePath).registerTempTable("expectedJsonTable") + checkAnswer( + sql("SELECT b, ``.`=` FROM jsonTable"), + sql("SELECT b, ``.`=` FROM expectedJsonTable")) + } + } } test("resolve shortened provider names") { - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) + withTable("jsonTable") { + sql( + s""" + |CREATE TABLE jsonTable + |USING org.apache.spark.sql.json + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) - checkAnswer( - sql("SELECT * FROM jsonTable"), - jsonFile(filePath).collect().toSeq) + checkAnswer( + sql("SELECT * FROM jsonTable"), + read.json(jsonFilePath).collect().toSeq) + } } test("drop table") { - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) + withTable("jsonTable") { + sql( + s""" + |CREATE TABLE jsonTable + |USING org.apache.spark.sql.json + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) - checkAnswer( - sql("SELECT * FROM jsonTable"), - jsonFile(filePath).collect().toSeq) + checkAnswer( + sql("SELECT * FROM jsonTable"), + read.json(jsonFilePath)) - sql("DROP TABLE jsonTable") + sql("DROP TABLE jsonTable") - intercept[Exception] { - sql("SELECT * FROM jsonTable").collect() - } + intercept[Exception] { + sql("SELECT * FROM jsonTable").collect() + } - assert( - (new File(filePath)).exists(), - "The table with specified path is considered as an external table, " + - "its data should not deleted after DROP TABLE.") + assert( + new File(jsonFilePath).exists(), + "The table with specified path is considered as an external table, " + + "its data should not deleted after DROP TABLE.") + } } test("check change without refresh") { - val tempDir = File.createTempFile("sparksql", "json", Utils.createTempDir()) - tempDir.delete() - sparkContext.parallelize(("a", "b") :: Nil).toDF() - .toJSON.saveAsTextFile(tempDir.getCanonicalPath) - - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json - |OPTIONS ( - | path '${tempDir.getCanonicalPath}' - |) - """.stripMargin) - - checkAnswer( - sql("SELECT * FROM jsonTable"), - Row("a", "b")) - - Utils.deleteRecursively(tempDir) - sparkContext.parallelize(("a1", "b1", "c1") :: Nil).toDF() - .toJSON.saveAsTextFile(tempDir.getCanonicalPath) - - // Schema is cached so the new column does not show. The updated values in existing columns - // will show. - checkAnswer( - sql("SELECT * FROM jsonTable"), - Row("a1", "b1")) - - sql("REFRESH TABLE jsonTable") - - // Check that the refresh worked - checkAnswer( - sql("SELECT * FROM jsonTable"), - Row("a1", "b1", "c1")) - Utils.deleteRecursively(tempDir) + withTempPath { tempDir => + withTable("jsonTable") { + (("a", "b") :: Nil).toDF().toJSON.saveAsTextFile(tempDir.getCanonicalPath) + + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json + |OPTIONS ( + | path '${tempDir.getCanonicalPath}' + |) + """.stripMargin) + + checkAnswer( + sql("SELECT * FROM jsonTable"), + Row("a", "b")) + + Utils.deleteRecursively(tempDir) + (("a1", "b1", "c1") :: Nil).toDF().toJSON.saveAsTextFile(tempDir.getCanonicalPath) + + // Schema is cached so the new column does not show. The updated values in existing columns + // will show. + checkAnswer( + sql("SELECT * FROM jsonTable"), + Row("a1", "b1")) + + sql("REFRESH TABLE jsonTable") + + // Check that the refresh worked + checkAnswer( + sql("SELECT * FROM jsonTable"), + Row("a1", "b1", "c1")) + } + } } test("drop, change, recreate") { - val tempDir = File.createTempFile("sparksql", "json", Utils.createTempDir()) - tempDir.delete() - sparkContext.parallelize(("a", "b") :: Nil).toDF() - .toJSON.saveAsTextFile(tempDir.getCanonicalPath) - - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json - |OPTIONS ( - | path '${tempDir.getCanonicalPath}' - |) - """.stripMargin) - - checkAnswer( - sql("SELECT * FROM jsonTable"), - Row("a", "b")) - - Utils.deleteRecursively(tempDir) - sparkContext.parallelize(("a", "b", "c") :: Nil).toDF() - .toJSON.saveAsTextFile(tempDir.getCanonicalPath) - - sql("DROP TABLE jsonTable") - - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json - |OPTIONS ( - | path '${tempDir.getCanonicalPath}' - |) - """.stripMargin) - - // New table should reflect new schema. - checkAnswer( - sql("SELECT * FROM jsonTable"), - Row("a", "b", "c")) - Utils.deleteRecursively(tempDir) + withTempPath { tempDir => + (("a", "b") :: Nil).toDF().toJSON.saveAsTextFile(tempDir.getCanonicalPath) + + withTable("jsonTable") { + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json + |OPTIONS ( + | path '${tempDir.getCanonicalPath}' + |) + """.stripMargin) + + checkAnswer( + sql("SELECT * FROM jsonTable"), + Row("a", "b")) + + Utils.deleteRecursively(tempDir) + (("a", "b", "c") :: Nil).toDF().toJSON.saveAsTextFile(tempDir.getCanonicalPath) + + sql("DROP TABLE jsonTable") + + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json + |OPTIONS ( + | path '${tempDir.getCanonicalPath}' + |) + """.stripMargin) + + // New table should reflect new schema. + checkAnswer( + sql("SELECT * FROM jsonTable"), + Row("a", "b", "c")) + } + } } test("invalidate cache and reload") { - sql( - s""" - |CREATE TABLE jsonTable (`c_!@(3)` int) - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) + withTable("jsonTable") { + sql( + s"""CREATE TABLE jsonTable (`c_!@(3)` int) + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) - jsonFile(filePath).registerTempTable("expectedJsonTable") + withTempTable("expectedJsonTable") { + read.json(jsonFilePath).registerTempTable("expectedJsonTable") - checkAnswer( - sql("SELECT * FROM jsonTable"), - sql("SELECT `c_!@(3)` FROM expectedJsonTable").collect().toSeq) + checkAnswer( + sql("SELECT * FROM jsonTable"), + sql("SELECT `c_!@(3)` FROM expectedJsonTable").collect().toSeq) - // Discard the cached relation. - invalidateTable("jsonTable") + // Discard the cached relation. + invalidateTable("jsonTable") - checkAnswer( - sql("SELECT * FROM jsonTable"), - sql("SELECT `c_!@(3)` FROM expectedJsonTable").collect().toSeq) + checkAnswer( + sql("SELECT * FROM jsonTable"), + sql("SELECT `c_!@(3)` FROM expectedJsonTable").collect().toSeq) - invalidateTable("jsonTable") - val expectedSchema = StructType(StructField("c_!@(3)", IntegerType, true) :: Nil) + invalidateTable("jsonTable") + val expectedSchema = StructType(StructField("c_!@(3)", IntegerType, true) :: Nil) - assert(expectedSchema === table("jsonTable").schema) + assert(expectedSchema === table("jsonTable").schema) + } + } } test("CTAS") { - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) - - sql( - s""" - |CREATE TABLE ctasJsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${tempPath}' - |) AS - |SELECT * FROM jsonTable - """.stripMargin) - - assert(table("ctasJsonTable").schema === table("jsonTable").schema) - - checkAnswer( - sql("SELECT * FROM ctasJsonTable"), - sql("SELECT * FROM jsonTable").collect()) + withTempPath { tempPath => + withTable("jsonTable", "ctasJsonTable") { + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) + + sql( + s"""CREATE TABLE ctasJsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$tempPath' + |) AS + |SELECT * FROM jsonTable + """.stripMargin) + + assert(table("ctasJsonTable").schema === table("jsonTable").schema) + + checkAnswer( + sql("SELECT * FROM ctasJsonTable"), + sql("SELECT * FROM jsonTable").collect()) + } + } } test("CTAS with IF NOT EXISTS") { - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) - - sql( - s""" - |CREATE TABLE ctasJsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${tempPath}' - |) AS - |SELECT * FROM jsonTable - """.stripMargin) - - // Create the table again should trigger a AnalysisException. - val message = intercept[AnalysisException] { - sql( - s""" - |CREATE TABLE ctasJsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${tempPath}' - |) AS - |SELECT * FROM jsonTable - """.stripMargin) - }.getMessage - assert(message.contains("Table ctasJsonTable already exists."), - "We should complain that ctasJsonTable already exists") - - // The following statement should be fine if it has IF NOT EXISTS. - // It tries to create a table ctasJsonTable with a new schema. - // The actual table's schema and data should not be changed. - sql( - s""" - |CREATE TABLE IF NOT EXISTS ctasJsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${tempPath}' - |) AS - |SELECT a FROM jsonTable - """.stripMargin) - - // Discard the cached relation. - invalidateTable("ctasJsonTable") - - // Schema should not be changed. - assert(table("ctasJsonTable").schema === table("jsonTable").schema) - // Table data should not be changed. - checkAnswer( - sql("SELECT * FROM ctasJsonTable"), - sql("SELECT * FROM jsonTable").collect()) + withTempPath { path => + val tempPath = path.getCanonicalPath + + withTable("jsonTable", "ctasJsonTable") { + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) + + sql( + s"""CREATE TABLE ctasJsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$tempPath' + |) AS + |SELECT * FROM jsonTable + """.stripMargin) + + // Create the table again should trigger a AnalysisException. + val message = intercept[AnalysisException] { + sql( + s"""CREATE TABLE ctasJsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$tempPath' + |) AS + |SELECT * FROM jsonTable + """.stripMargin) + }.getMessage + + assert( + message.contains("Table ctasJsonTable already exists."), + "We should complain that ctasJsonTable already exists") + + // The following statement should be fine if it has IF NOT EXISTS. + // It tries to create a table ctasJsonTable with a new schema. + // The actual table's schema and data should not be changed. + sql( + s"""CREATE TABLE IF NOT EXISTS ctasJsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$tempPath' + |) AS + |SELECT a FROM jsonTable + """.stripMargin) + + // Discard the cached relation. + invalidateTable("ctasJsonTable") + + // Schema should not be changed. + assert(table("ctasJsonTable").schema === table("jsonTable").schema) + // Table data should not be changed. + checkAnswer( + sql("SELECT * FROM ctasJsonTable"), + sql("SELECT * FROM jsonTable").collect()) + } + } } test("CTAS a managed table") { - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${filePath}' - |) - """.stripMargin) - - val expectedPath = catalog.hiveDefaultTableFilePath("ctasJsonTable") - val filesystemPath = new Path(expectedPath) - val fs = filesystemPath.getFileSystem(sparkContext.hadoopConfiguration) - if (fs.exists(filesystemPath)) fs.delete(filesystemPath, true) - - // It is a managed table when we do not specify the location. - sql( - s""" - |CREATE TABLE ctasJsonTable - |USING org.apache.spark.sql.json.DefaultSource - |AS - |SELECT * FROM jsonTable - """.stripMargin) - - assert(fs.exists(filesystemPath), s"$expectedPath should exist after we create the table.") - - sql( - s""" - |CREATE TABLE loadedTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path '${expectedPath}' - |) - """.stripMargin) - - assert(table("ctasJsonTable").schema === table("loadedTable").schema) + withTable("jsonTable", "ctasJsonTable", "loadedTable") { + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$jsonFilePath' + |) + """.stripMargin) + + val expectedPath = catalog.hiveDefaultTableFilePath("ctasJsonTable") + val filesystemPath = new Path(expectedPath) + val fs = filesystemPath.getFileSystem(sparkContext.hadoopConfiguration) + if (fs.exists(filesystemPath)) fs.delete(filesystemPath, true) + + // It is a managed table when we do not specify the location. + sql( + s"""CREATE TABLE ctasJsonTable + |USING org.apache.spark.sql.json.DefaultSource + |AS + |SELECT * FROM jsonTable + """.stripMargin) - checkAnswer( - sql("SELECT * FROM ctasJsonTable"), - sql("SELECT * FROM loadedTable").collect() - ) + assert(fs.exists(filesystemPath), s"$expectedPath should exist after we create the table.") + + sql( + s"""CREATE TABLE loadedTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '$expectedPath' + |) + """.stripMargin) + + assert(table("ctasJsonTable").schema === table("loadedTable").schema) - sql("DROP TABLE ctasJsonTable") - assert(!fs.exists(filesystemPath), s"$expectedPath should not exist after we drop the table.") + checkAnswer( + sql("SELECT * FROM ctasJsonTable"), + sql("SELECT * FROM loadedTable")) + + sql("DROP TABLE ctasJsonTable") + assert(!fs.exists(filesystemPath), s"$expectedPath should not exist after we drop the table.") + } } test("SPARK-5286 Fail to drop an invalid table when using the data source API") { - sql( - s""" - |CREATE TABLE jsonTable - |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | path 'it is not a path at all!' - |) - """.stripMargin) - - sql("DROP TABLE jsonTable").collect().foreach(println) + withTable("jsonTable") { + sql( + s"""CREATE TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path 'it is not a path at all!' + |) + """.stripMargin) + + sql("DROP TABLE jsonTable").collect().foreach(println) + } } test("SPARK-5839 HiveMetastoreCatalog does not recognize table aliases of data source tables.") { - val originalDefaultSource = conf.defaultDataSourceName + withTable("savedJsonTable") { + // Save the df as a managed table (by not specifying the path). + (1 to 10) + .map(i => i -> s"str$i") + .toDF("a", "b") + .write + .format("json") + .saveAsTable("savedJsonTable") - val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) - val df = jsonRDD(rdd) - - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") - // Save the df as a managed table (by not specifiying the path). - df.saveAsTable("savedJsonTable") - - checkAnswer( - sql("SELECT * FROM savedJsonTable where savedJsonTable.a < 5"), - (1 to 4).map(i => Row(i, s"str${i}"))) - - checkAnswer( - sql("SELECT * FROM savedJsonTable tmp where tmp.a > 5"), - (6 to 10).map(i => Row(i, s"str${i}"))) - - invalidateTable("savedJsonTable") + checkAnswer( + sql("SELECT * FROM savedJsonTable where savedJsonTable.a < 5"), + (1 to 4).map(i => Row(i, s"str$i"))) - checkAnswer( - sql("SELECT * FROM savedJsonTable where savedJsonTable.a < 5"), - (1 to 4).map(i => Row(i, s"str${i}"))) + checkAnswer( + sql("SELECT * FROM savedJsonTable tmp where tmp.a > 5"), + (6 to 10).map(i => Row(i, s"str$i"))) - checkAnswer( - sql("SELECT * FROM savedJsonTable tmp where tmp.a > 5"), - (6 to 10).map(i => Row(i, s"str${i}"))) + invalidateTable("savedJsonTable") - // Drop table will also delete the data. - sql("DROP TABLE savedJsonTable") + checkAnswer( + sql("SELECT * FROM savedJsonTable where savedJsonTable.a < 5"), + (1 to 4).map(i => Row(i, s"str$i"))) - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) + checkAnswer( + sql("SELECT * FROM savedJsonTable tmp where tmp.a > 5"), + (6 to 10).map(i => Row(i, s"str$i"))) + } } test("save table") { - val originalDefaultSource = conf.defaultDataSourceName - - val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) - val df = jsonRDD(rdd) - - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") - // Save the df as a managed table (by not specifiying the path). - df.saveAsTable("savedJsonTable") - - checkAnswer( - sql("SELECT * FROM savedJsonTable"), - df.collect()) - - // Right now, we cannot append to an existing JSON table. - intercept[RuntimeException] { - df.saveAsTable("savedJsonTable", SaveMode.Append) - } - - // We can overwrite it. - df.saveAsTable("savedJsonTable", SaveMode.Overwrite) - checkAnswer( - sql("SELECT * FROM savedJsonTable"), - df.collect()) - - // When the save mode is Ignore, we will do nothing when the table already exists. - df.select("b").saveAsTable("savedJsonTable", SaveMode.Ignore) - assert(df.schema === table("savedJsonTable").schema) - checkAnswer( - sql("SELECT * FROM savedJsonTable"), - df.collect()) - - // Drop table will also delete the data. - sql("DROP TABLE savedJsonTable") - intercept[InvalidInputException] { - jsonFile(catalog.hiveDefaultTableFilePath("savedJsonTable")) + withTempPath { path => + val tempPath = path.getCanonicalPath + + withTable("savedJsonTable") { + val df = (1 to 10).map(i => i -> s"str$i").toDF("a", "b") + + withSQLConf(SQLConf.DEFAULT_DATA_SOURCE_NAME.key -> "json") { + // Save the df as a managed table (by not specifying the path). + df.write.saveAsTable("savedJsonTable") + + checkAnswer(sql("SELECT * FROM savedJsonTable"), df) + + // Right now, we cannot append to an existing JSON table. + intercept[RuntimeException] { + df.write.mode(SaveMode.Append).saveAsTable("savedJsonTable") + } + + // We can overwrite it. + df.write.mode(SaveMode.Overwrite).saveAsTable("savedJsonTable") + checkAnswer(sql("SELECT * FROM savedJsonTable"), df) + + // When the save mode is Ignore, we will do nothing when the table already exists. + df.select("b").write.mode(SaveMode.Ignore).saveAsTable("savedJsonTable") + assert(df.schema === table("savedJsonTable").schema) + checkAnswer(sql("SELECT * FROM savedJsonTable"), df) + + // Drop table will also delete the data. + sql("DROP TABLE savedJsonTable") + intercept[InvalidInputException] { + read.json(catalog.hiveDefaultTableFilePath("savedJsonTable")) + } + } + + // Create an external table by specifying the path. + withSQLConf(SQLConf.DEFAULT_DATA_SOURCE_NAME.key -> "not a source name") { + df.write + .format("org.apache.spark.sql.json") + .mode(SaveMode.Append) + .option("path", tempPath.toString) + .saveAsTable("savedJsonTable") + + checkAnswer(sql("SELECT * FROM savedJsonTable"), df) + } + + // Data should not be deleted after we drop the table. + sql("DROP TABLE savedJsonTable") + checkAnswer(read.json(tempPath.toString), df) + } } - - // Create an external table by specifying the path. - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") - df.saveAsTable( - "savedJsonTable", - "org.apache.spark.sql.json", - SaveMode.Append, - Map("path" -> tempPath.toString)) - checkAnswer( - sql("SELECT * FROM savedJsonTable"), - df.collect()) - - // Data should not be deleted after we drop the table. - sql("DROP TABLE savedJsonTable") - checkAnswer( - jsonFile(tempPath.toString), - df.collect()) - - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) } test("create external table") { - val originalDefaultSource = conf.defaultDataSourceName - - val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) - val df = jsonRDD(rdd) + withTempPath { tempPath => + withTable("savedJsonTable", "createdJsonTable") { + val df = read.json(sparkContext.parallelize((1 to 10).map { i => + s"""{ "a": $i, "b": "str$i" }""" + })) + + withSQLConf(SQLConf.DEFAULT_DATA_SOURCE_NAME.key -> "not a source name") { + df.write + .format("json") + .mode(SaveMode.Append) + .option("path", tempPath.toString) + .saveAsTable("savedJsonTable") + } + + withSQLConf(SQLConf.DEFAULT_DATA_SOURCE_NAME.key -> "json") { + createExternalTable("createdJsonTable", tempPath.toString) + assert(table("createdJsonTable").schema === df.schema) + checkAnswer(sql("SELECT * FROM createdJsonTable"), df) + + assert( + intercept[AnalysisException] { + createExternalTable("createdJsonTable", jsonFilePath.toString) + }.getMessage.contains("Table createdJsonTable already exists."), + "We should complain that createdJsonTable already exists") + } + + // Data should not be deleted. + sql("DROP TABLE createdJsonTable") + checkAnswer(read.json(tempPath.toString), df) + + // Try to specify the schema. + withSQLConf(SQLConf.DEFAULT_DATA_SOURCE_NAME.key -> "not a source name") { + val schema = StructType(StructField("b", StringType, true) :: Nil) + createExternalTable( + "createdJsonTable", + "org.apache.spark.sql.json", + schema, + Map("path" -> tempPath.toString)) + + checkAnswer( + sql("SELECT * FROM createdJsonTable"), + sql("SELECT b FROM savedJsonTable")) + + sql("DROP TABLE createdJsonTable") + + assert( + intercept[RuntimeException] { + createExternalTable( + "createdJsonTable", + "org.apache.spark.sql.json", + schema, + Map.empty[String, String]) + }.getMessage.contains("'path' must be specified for json data."), + "We should complain that path is not specified.") + } + } + } + } - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") - df.saveAsTable( - "savedJsonTable", - "org.apache.spark.sql.json", - SaveMode.Append, - Map("path" -> tempPath.toString)) + test("scan a parquet table created through a CTAS statement") { + withSQLConf( + HiveContext.CONVERT_METASTORE_PARQUET.key -> "true", + SQLConf.PARQUET_USE_DATA_SOURCE_API.key -> "true") { + + withTempTable("jt") { + (1 to 10).map(i => i -> s"str$i").toDF("a", "b").registerTempTable("jt") + + withTable("test_parquet_ctas") { + sql( + """CREATE TABLE test_parquet_ctas STORED AS PARQUET + |AS SELECT tmp.a FROM jt tmp WHERE tmp.a < 5 + """.stripMargin) + + checkAnswer( + sql(s"SELECT a FROM test_parquet_ctas WHERE a > 2 "), + Row(3) :: Row(4) :: Nil) + + table("test_parquet_ctas").queryExecution.optimizedPlan match { + case LogicalRelation(p: ParquetRelation2) => // OK + case _ => + fail(s"test_parquet_ctas should have be converted to ${classOf[ParquetRelation2]}") + } + } + } + } + } - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") - createExternalTable("createdJsonTable", tempPath.toString) - assert(table("createdJsonTable").schema === df.schema) - checkAnswer( - sql("SELECT * FROM createdJsonTable"), - df.collect()) + test("Pre insert nullability check (ArrayType)") { + withTable("arrayInParquet") { + { + val df = (Tuple1(Seq(Int.box(1), null: Integer)) :: Nil).toDF("a") + val expectedSchema = + StructType( + StructField( + "a", + ArrayType(IntegerType, containsNull = true), + nullable = true) :: Nil) + + assert(df.schema === expectedSchema) + + df.write + .format("parquet") + .mode(SaveMode.Overwrite) + .saveAsTable("arrayInParquet") + } - var message = intercept[AnalysisException] { - createExternalTable("createdJsonTable", filePath.toString) - }.getMessage - assert(message.contains("Table createdJsonTable already exists."), - "We should complain that ctasJsonTable already exists") + { + val df = (Tuple1(Seq(2, 3)) :: Nil).toDF("a") + val expectedSchema = + StructType( + StructField( + "a", + ArrayType(IntegerType, containsNull = false), + nullable = true) :: Nil) + + assert(df.schema === expectedSchema) + + df.write + .format("parquet") + .mode(SaveMode.Append) + .insertInto("arrayInParquet") + } - // Data should not be deleted. - sql("DROP TABLE createdJsonTable") - checkAnswer( - jsonFile(tempPath.toString), - df.collect()) - - // Try to specify the schema. - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") - val schema = StructType(StructField("b", StringType, true) :: Nil) - createExternalTable( - "createdJsonTable", - "org.apache.spark.sql.json", - schema, - Map("path" -> tempPath.toString)) - checkAnswer( - sql("SELECT * FROM createdJsonTable"), - sql("SELECT b FROM savedJsonTable").collect()) - - sql("DROP TABLE createdJsonTable") - - message = intercept[RuntimeException] { - createExternalTable( - "createdJsonTable", - "org.apache.spark.sql.json", - schema, - Map.empty[String, String]) - }.getMessage - assert( - message.contains("'path' must be specified for json data."), - "We should complain that path is not specified.") - - sql("DROP TABLE savedJsonTable") - conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) - } + (Tuple1(Seq(4, 5)) :: Nil).toDF("a") + .write + .mode(SaveMode.Append) + .saveAsTable("arrayInParquet") // This one internally calls df2.insertInto. - if (HiveShim.version == "0.13.1") { - test("scan a parquet table created through a CTAS statement") { - val originalConvertMetastore = getConf("spark.sql.hive.convertMetastoreParquet", "true") - val originalUseDataSource = getConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") - setConf("spark.sql.hive.convertMetastoreParquet", "true") - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + (Tuple1(Seq(Int.box(6), null: Integer)) :: Nil).toDF("a") + .write + .mode(SaveMode.Append) + .saveAsTable("arrayInParquet") - val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) - jsonRDD(rdd).registerTempTable("jt") - sql( - """ - |create table test_parquet_ctas STORED AS parquET - |AS select tmp.a from jt tmp where tmp.a < 5 - """.stripMargin) + refreshTable("arrayInParquet") checkAnswer( - sql(s"SELECT a FROM test_parquet_ctas WHERE a > 2 "), - Row(3) :: Row(4) :: Nil - ) - - table("test_parquet_ctas").queryExecution.optimizedPlan match { - case LogicalRelation(p: ParquetRelation2) => // OK - case _ => - fail( - "test_parquet_ctas should be converted to " + - s"${classOf[ParquetRelation2].getCanonicalName}") - } - - // Clenup and reset confs. - sql("DROP TABLE IF EXISTS jt") - sql("DROP TABLE IF EXISTS test_parquet_ctas") - setConf("spark.sql.hive.convertMetastoreParquet", originalConvertMetastore) - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalUseDataSource) + sql("SELECT a FROM arrayInParquet"), + Row(ArrayBuffer(1, null)) :: + Row(ArrayBuffer(2, 3)) :: + Row(ArrayBuffer(4, 5)) :: + Row(ArrayBuffer(6, null)) :: Nil) } } - test("Pre insert nullability check (ArrayType)") { - val df1 = - createDataFrame(Tuple1(Seq(Int.box(1), null.asInstanceOf[Integer])) :: Nil).toDF("a") - val expectedSchema1 = - StructType( - StructField("a", ArrayType(IntegerType, containsNull = true), nullable = true) :: Nil) - assert(df1.schema === expectedSchema1) - df1.saveAsTable("arrayInParquet", "parquet", SaveMode.Overwrite) - - val df2 = - createDataFrame(Tuple1(Seq(2, 3)) :: Nil).toDF("a") - val expectedSchema2 = - StructType( - StructField("a", ArrayType(IntegerType, containsNull = false), nullable = true) :: Nil) - assert(df2.schema === expectedSchema2) - df2.insertInto("arrayInParquet", overwrite = false) - createDataFrame(Tuple1(Seq(4, 5)) :: Nil).toDF("a") - .saveAsTable("arrayInParquet", SaveMode.Append) // This one internally calls df2.insertInto. - createDataFrame(Tuple1(Seq(Int.box(6), null.asInstanceOf[Integer])) :: Nil).toDF("a") - .saveAsTable("arrayInParquet", "parquet", SaveMode.Append) - refreshTable("arrayInParquet") + test("Pre insert nullability check (MapType)") { + withTable("mapInParquet") { + { + val df = (Tuple1(Map(1 -> (null: Integer))) :: Nil).toDF("a") + val expectedSchema = + StructType( + StructField( + "a", + MapType(IntegerType, IntegerType, valueContainsNull = true), + nullable = true) :: Nil) + + assert(df.schema === expectedSchema) + + df.write + .format("parquet") + .mode(SaveMode.Overwrite) + .saveAsTable("mapInParquet") + } - checkAnswer( - sql("SELECT a FROM arrayInParquet"), - Row(ArrayBuffer(1, null)) :: - Row(ArrayBuffer(2, 3)) :: - Row(ArrayBuffer(4, 5)) :: - Row(ArrayBuffer(6, null)) :: Nil) + { + val df = (Tuple1(Map(2 -> 3)) :: Nil).toDF("a") + val expectedSchema = + StructType( + StructField( + "a", + MapType(IntegerType, IntegerType, valueContainsNull = false), + nullable = true) :: Nil) + + assert(df.schema === expectedSchema) + + df.write + .format("parquet") + .mode(SaveMode.Append) + .insertInto("mapInParquet") + } - sql("DROP TABLE arrayInParquet") - } + (Tuple1(Map(4 -> 5)) :: Nil).toDF("a") + .write + .format("parquet") + .mode(SaveMode.Append) + .saveAsTable("mapInParquet") // This one internally calls df2.insertInto. - test("Pre insert nullability check (MapType)") { - val df1 = - createDataFrame(Tuple1(Map(1 -> null.asInstanceOf[Integer])) :: Nil).toDF("a") - val mapType1 = MapType(IntegerType, IntegerType, valueContainsNull = true) - val expectedSchema1 = - StructType( - StructField("a", mapType1, nullable = true) :: Nil) - assert(df1.schema === expectedSchema1) - df1.saveAsTable("mapInParquet", "parquet", SaveMode.Overwrite) - - val df2 = - createDataFrame(Tuple1(Map(2 -> 3)) :: Nil).toDF("a") - val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = false) - val expectedSchema2 = - StructType( - StructField("a", mapType2, nullable = true) :: Nil) - assert(df2.schema === expectedSchema2) - df2.insertInto("mapInParquet", overwrite = false) - createDataFrame(Tuple1(Map(4 -> 5)) :: Nil).toDF("a") - .saveAsTable("mapInParquet", SaveMode.Append) // This one internally calls df2.insertInto. - createDataFrame(Tuple1(Map(6 -> null.asInstanceOf[Integer])) :: Nil).toDF("a") - .saveAsTable("mapInParquet", "parquet", SaveMode.Append) - refreshTable("mapInParquet") + (Tuple1(Map(6 -> null.asInstanceOf[Integer])) :: Nil).toDF("a") + .write + .format("parquet") + .mode(SaveMode.Append) + .saveAsTable("mapInParquet") - checkAnswer( - sql("SELECT a FROM mapInParquet"), - Row(Map(1 -> null)) :: - Row(Map(2 -> 3)) :: - Row(Map(4 -> 5)) :: - Row(Map(6 -> null)) :: Nil) + refreshTable("mapInParquet") - sql("DROP TABLE mapInParquet") + checkAnswer( + sql("SELECT a FROM mapInParquet"), + Row(Map(1 -> null)) :: + Row(Map(2 -> 3)) :: + Row(Map(4 -> 5)) :: + Row(Map(6 -> null)) :: Nil) + } } test("SPARK-6024 wide schema support") { - // We will need 80 splits for this schema if the threshold is 4000. - val schema = StructType((1 to 5000).map(i => StructField(s"c_${i}", StringType, true))) - assert( - schema.json.size > conf.schemaStringLengthThreshold, - "To correctly test the fix of SPARK-6024, the value of " + - s"spark.sql.sources.schemaStringLengthThreshold needs to be less than ${schema.json.size}") - // Manually create a metastore data source table. - catalog.createDataSourceTable( - tableName = "wide_schema", - userSpecifiedSchema = Some(schema), - provider = "json", - options = Map("path" -> "just a dummy path"), - isExternal = false) - - invalidateTable("wide_schema") - - val actualSchema = table("wide_schema").schema - assert(schema === actualSchema) + withSQLConf(SQLConf.SCHEMA_STRING_LENGTH_THRESHOLD.key -> "4000") { + withTable("wide_schema") { + // We will need 80 splits for this schema if the threshold is 4000. + val schema = StructType((1 to 5000).map(i => StructField(s"c_$i", StringType, true))) + + // Manually create a metastore data source table. + catalog.createDataSourceTable( + tableName = "wide_schema", + userSpecifiedSchema = Some(schema), + partitionColumns = Array.empty[String], + provider = "json", + options = Map("path" -> "just a dummy path"), + isExternal = false) + + invalidateTable("wide_schema") + + val actualSchema = table("wide_schema").schema + assert(schema === actualSchema) + } + } } test("SPARK-6655 still support a schema stored in spark.sql.sources.schema") { val tableName = "spark6655" - val schema = StructType(StructField("int", IntegerType, true) :: Nil) - - val hiveTable = HiveTable( - specifiedDatabase = Some("default"), - name = tableName, - schema = Seq.empty, - partitionColumns = Seq.empty, - properties = Map( - "spark.sql.sources.provider" -> "json", - "spark.sql.sources.schema" -> schema.json, - "EXTERNAL" -> "FALSE"), - tableType = ManagedTable, - serdeProperties = Map( - "path" -> catalog.hiveDefaultTableFilePath(tableName))) - - catalog.client.createTable(hiveTable) - - invalidateTable(tableName) - val actualSchema = table(tableName).schema - assert(schema === actualSchema) - sql(s"drop table $tableName") + withTable(tableName) { + val schema = StructType(StructField("int", IntegerType, true) :: Nil) + val hiveTable = HiveTable( + specifiedDatabase = Some("default"), + name = tableName, + schema = Seq.empty, + partitionColumns = Seq.empty, + properties = Map( + "spark.sql.sources.provider" -> "json", + "spark.sql.sources.schema" -> schema.json, + "EXTERNAL" -> "FALSE"), + tableType = ManagedTable, + serdeProperties = Map( + "path" -> catalog.hiveDefaultTableFilePath(tableName))) + + catalog.client.createTable(hiveTable) + + invalidateTable(tableName) + val actualSchema = table(tableName).schema + assert(schema === actualSchema) + } } + test("Saving partition columns information") { + val df = (1 to 10).map(i => (i, i + 1, s"str$i", s"str${i + 1}")).toDF("a", "b", "c", "d") + val tableName = s"partitionInfo_${System.currentTimeMillis()}" + + withTable(tableName) { + df.write.format("parquet").partitionBy("d", "b").saveAsTable(tableName) + invalidateTable(tableName) + val metastoreTable = catalog.client.getTable("default", tableName) + val expectedPartitionColumns = StructType(df.schema("d") :: df.schema("b") :: Nil) + val actualPartitionColumns = + StructType( + metastoreTable.partitionColumns.map(c => + StructField(c.name, HiveMetastoreTypes.toDataType(c.hiveType)))) + // Make sure partition columns are correctly stored in metastore. + assert( + expectedPartitionColumns.sameType(actualPartitionColumns), + s"Partitions columns stored in metastore $actualPartitionColumns is not the " + + s"partition columns defined by the saveAsTable operation $expectedPartitionColumns.") + + // Check the content of the saved table. + checkAnswer( + table(tableName).select("c", "b", "d", "a"), + df.select("c", "b", "d", "a")) + } + } test("insert into a table") { - def createDF(from: Int, to: Int): DataFrame = - createDataFrame((from to to).map(i => Tuple2(i, s"str$i"))).toDF("c1", "c2") + def createDF(from: Int, to: Int): DataFrame = { + (from to to).map(i => i -> s"str$i").toDF("c1", "c2") + } - createDF(0, 9).saveAsTable("insertParquet", "parquet") - checkAnswer( - sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), - (6 to 9).map(i => Row(i, s"str$i"))) + withTable("insertParquet") { + createDF(0, 9).write.format("parquet").saveAsTable("insertParquet") + checkAnswer( + sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), + (6 to 9).map(i => Row(i, s"str$i"))) - intercept[AnalysisException] { - createDF(10, 19).saveAsTable("insertParquet", "parquet") - } + intercept[AnalysisException] { + createDF(10, 19).write.format("parquet").saveAsTable("insertParquet") + } - createDF(10, 19).saveAsTable("insertParquet", "parquet", SaveMode.Append) - checkAnswer( - sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), - (6 to 19).map(i => Row(i, s"str$i"))) + createDF(10, 19).write.mode(SaveMode.Append).format("parquet").saveAsTable("insertParquet") + checkAnswer( + sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), + (6 to 19).map(i => Row(i, s"str$i"))) - createDF(20, 29).saveAsTable("insertParquet", "parquet", SaveMode.Append) - checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 25"), - (6 to 24).map(i => Row(i, s"str$i"))) + createDF(20, 29).write.mode(SaveMode.Append).format("parquet").saveAsTable("insertParquet") + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 25"), + (6 to 24).map(i => Row(i, s"str$i"))) - intercept[AnalysisException] { - createDF(30, 39).saveAsTable("insertParquet") - } + intercept[AnalysisException] { + createDF(30, 39).write.saveAsTable("insertParquet") + } - createDF(30, 39).saveAsTable("insertParquet", SaveMode.Append) - checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 35"), - (6 to 34).map(i => Row(i, s"str$i"))) + createDF(30, 39).write.mode(SaveMode.Append).saveAsTable("insertParquet") + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 35"), + (6 to 34).map(i => Row(i, s"str$i"))) - createDF(40, 49).insertInto("insertParquet") - checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 45"), - (6 to 44).map(i => Row(i, s"str$i"))) + createDF(40, 49).write.mode(SaveMode.Append).insertInto("insertParquet") + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 45"), + (6 to 44).map(i => Row(i, s"str$i"))) - createDF(50, 59).saveAsTable("insertParquet", SaveMode.Overwrite) - checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 51 AND p.c1 < 55"), - (52 to 54).map(i => Row(i, s"str$i"))) - createDF(60, 69).saveAsTable("insertParquet", SaveMode.Ignore) - checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p"), - (50 to 59).map(i => Row(i, s"str$i"))) + createDF(50, 59).write.mode(SaveMode.Overwrite).saveAsTable("insertParquet") + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 51 AND p.c1 < 55"), + (52 to 54).map(i => Row(i, s"str$i"))) + createDF(60, 69).write.mode(SaveMode.Ignore).saveAsTable("insertParquet") + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p"), + (50 to 59).map(i => Row(i, s"str$i"))) + + createDF(70, 79).write.mode(SaveMode.Overwrite).insertInto("insertParquet") + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p"), + (70 to 79).map(i => Row(i, s"str$i"))) + } + } + + test("SPARK-8156:create table to specific database by 'use dbname' ") { + + val df = (1 to 3).map(i => (i, s"val_$i", i * 2)).toDF("a", "b", "c") + sqlContext.sql("""create database if not exists testdb8156""") + sqlContext.sql("""use testdb8156""") + df.write + .format("parquet") + .mode(SaveMode.Overwrite) + .saveAsTable("ttt3") - createDF(70, 79).insertInto("insertParquet", overwrite = true) checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p"), - (70 to 79).map(i => Row(i, s"str$i"))) + sqlContext.sql("show TABLES in testdb8156").filter("tableName = 'ttt3'"), + Row("ttt3", false)) + sqlContext.sql("""use default""") + sqlContext.sql("""drop database if exists testdb8156 CASCADE""") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala index 4990092df6a99..017bc2adc103b 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala @@ -20,16 +20,17 @@ package org.apache.spark.sql.hive import com.google.common.io.Files import org.apache.spark.sql.{QueryTest, _} -import org.apache.spark.sql.hive.test.TestHive -import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.util.Utils class QueryPartitionSuite extends QueryTest { - import org.apache.spark.sql.hive.test.TestHive.implicits._ + + private lazy val ctx = org.apache.spark.sql.hive.test.TestHive + import ctx.implicits._ + import ctx.sql test("SPARK-5068: query data when path doesn't exist"){ - val testData = TestHive.sparkContext.parallelize( + val testData = ctx.sparkContext.parallelize( (1 to 10).map(i => TestData(i, i.toString))).toDF() testData.registerTempTable("testData") @@ -48,8 +49,8 @@ class QueryPartitionSuite extends QueryTest { // test for the exist path checkAnswer(sql("select key,value from table_with_partition"), - testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect - ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect) + testData.toDF.collect ++ testData.toDF.collect + ++ testData.toDF.collect ++ testData.toDF.collect) // delete the path of one partition tmpDir.listFiles @@ -58,8 +59,7 @@ class QueryPartitionSuite extends QueryTest { // test for after delete the path checkAnswer(sql("select key,value from table_with_partition"), - testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect - ++ testData.toSchemaRDD.collect) + testData.toDF.collect ++ testData.toDF.collect ++ testData.toDF.collect) sql("DROP TABLE table_with_partition") sql("DROP TABLE createAndInsertTest") diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala index 8afe5459d4f1b..93dcb10f7a296 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala @@ -17,16 +17,13 @@ package org.apache.spark.sql.hive -import org.scalatest.FunSuite - -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.serializer.JavaSerializer -import org.apache.spark.sql.hive.test.TestHive -class SerializationSuite extends FunSuite { +class SerializationSuite extends SparkFunSuite { test("[SPARK-5840] HiveContext should be serializable") { - val hiveContext = TestHive + val hiveContext = org.apache.spark.sql.hive.test.TestHive hiveContext.hiveconf val serializer = new JavaSerializer(new SparkConf()).newInstance() val bytes = serializer.serialize(hiveContext) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala index 00a69de9e4262..f067ea0d4fc75 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala @@ -23,13 +23,18 @@ import scala.reflect.ClassTag import org.apache.spark.sql.{Row, SQLConf, QueryTest} import org.apache.spark.sql.execution.joins._ -import org.apache.spark.sql.hive.test.TestHive -import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.execution._ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { - TestHive.reset() - TestHive.cacheTables = false + + private lazy val ctx: HiveContext = { + val ctx = org.apache.spark.sql.hive.test.TestHive + ctx.reset() + ctx.cacheTables = false + ctx + } + + import ctx.sql test("parse analyze commands") { def assertAnalyzeCommand(analyzeCommand: String, c: Class[_]) { @@ -72,17 +77,13 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { test("analyze MetastoreRelations") { def queryTotalSize(tableName: String): BigInt = - catalog.lookupRelation(Seq(tableName)).statistics.sizeInBytes + ctx.catalog.lookupRelation(Seq(tableName)).statistics.sizeInBytes // Non-partitioned table sql("CREATE TABLE analyzeTable (key STRING, value STRING)").collect() sql("INSERT INTO TABLE analyzeTable SELECT * FROM src").collect() sql("INSERT INTO TABLE analyzeTable SELECT * FROM src").collect() - // TODO: How does it works? needs to add it back for other hive version. - if (HiveShim.version =="0.12.0") { - assert(queryTotalSize("analyzeTable") === conf.defaultSizeInBytes) - } sql("ANALYZE TABLE analyzeTable COMPUTE STATISTICS noscan") assert(queryTotalSize("analyzeTable") === BigInt(11624)) @@ -110,7 +111,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { |SELECT * FROM src """.stripMargin).collect() - assert(queryTotalSize("analyzeTable_part") === conf.defaultSizeInBytes) + assert(queryTotalSize("analyzeTable_part") === ctx.conf.defaultSizeInBytes) sql("ANALYZE TABLE analyzeTable_part COMPUTE STATISTICS noscan") @@ -121,9 +122,9 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { // Try to analyze a temp table sql("""SELECT * FROM src""").registerTempTable("tempTable") intercept[UnsupportedOperationException] { - analyze("tempTable") + ctx.analyze("tempTable") } - catalog.unregisterTable(Seq("tempTable")) + ctx.catalog.unregisterTable(Seq("tempTable")) } test("estimates the size of a test MetastoreRelation") { @@ -151,8 +152,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { val sizes = df.queryExecution.analyzed.collect { case r if ct.runtimeClass.isAssignableFrom(r.getClass) => r.statistics.sizeInBytes } - assert(sizes.size === 2 && sizes(0) <= conf.autoBroadcastJoinThreshold - && sizes(1) <= conf.autoBroadcastJoinThreshold, + assert(sizes.size === 2 && sizes(0) <= ctx.conf.autoBroadcastJoinThreshold + && sizes(1) <= ctx.conf.autoBroadcastJoinThreshold, s"query should contain two relations, each of which has size smaller than autoConvertSize") // Using `sparkPlan` because for relevant patterns in HashJoin to be @@ -163,10 +164,10 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { checkAnswer(df, expectedAnswer) // check correctness of output - TestHive.conf.settings.synchronized { - val tmp = conf.autoBroadcastJoinThreshold + ctx.conf.settings.synchronized { + val tmp = ctx.conf.autoBroadcastJoinThreshold - sql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=-1""") + sql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1""") df = sql(query) bhj = df.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j } assert(bhj.isEmpty, "BroadcastHashJoin still planned even though it is switched off") @@ -175,7 +176,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { assert(shj.size === 1, "ShuffledHashJoin should be planned when BroadcastHashJoin is turned off") - sql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=$tmp""") + sql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=$tmp""") } after() @@ -207,8 +208,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { .isAssignableFrom(r.getClass) => r.statistics.sizeInBytes } - assert(sizes.size === 2 && sizes(1) <= conf.autoBroadcastJoinThreshold - && sizes(0) <= conf.autoBroadcastJoinThreshold, + assert(sizes.size === 2 && sizes(1) <= ctx.conf.autoBroadcastJoinThreshold + && sizes(0) <= ctx.conf.autoBroadcastJoinThreshold, s"query should contain two relations, each of which has size smaller than autoConvertSize") // Using `sparkPlan` because for relevant patterns in HashJoin to be @@ -221,10 +222,10 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { checkAnswer(df, answer) // check correctness of output - TestHive.conf.settings.synchronized { - val tmp = conf.autoBroadcastJoinThreshold + ctx.conf.settings.synchronized { + val tmp = ctx.conf.autoBroadcastJoinThreshold - sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=-1") + sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1") df = sql(leftSemiJoinQuery) bhj = df.queryExecution.sparkPlan.collect { case j: BroadcastLeftSemiJoinHash => j @@ -237,7 +238,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { assert(shj.size === 1, "LeftSemiJoinHash should be planned when BroadcastHashJoin is turned off") - sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=$tmp") + sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=$tmp") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala index 85b6bc93d7122..4056dee777574 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala @@ -17,20 +17,20 @@ package org.apache.spark.sql.hive -/* Implicits */ - import org.apache.spark.sql.QueryTest -import org.apache.spark.sql.hive.test.TestHive._ case class FunctionResult(f1: String, f2: String) class UDFSuite extends QueryTest { + + private lazy val ctx = org.apache.spark.sql.hive.test.TestHive + test("UDF case insensitive") { - udf.register("random0", () => { Math.random()}) - udf.register("RANDOM1", () => { Math.random()}) - udf.register("strlenScala", (_: String).length + (_:Int)) - assert(sql("SELECT RANDOM0() FROM src LIMIT 1").head().getDouble(0) >= 0.0) - assert(sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0) - assert(sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5) + ctx.udf.register("random0", () => { Math.random() }) + ctx.udf.register("RANDOM1", () => { Math.random() }) + ctx.udf.register("strlenScala", (_: String).length + (_: Int)) + assert(ctx.sql("SELECT RANDOM0() FROM src LIMIT 1").head().getDouble(0) >= 0.0) + assert(ctx.sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0) + assert(ctx.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala index 321dc8d7322b8..9a571650b6e25 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala @@ -17,18 +17,17 @@ package org.apache.spark.sql.hive.client -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkFunSuite} import org.apache.spark.sql.catalyst.util.quietly import org.apache.spark.util.Utils -import org.scalatest.FunSuite /** - * A simple set of tests that call the methods of a hive ClientInterface, loading different version - * of hive from maven central. These tests are simple in that they are mostly just testing to make - * sure that reflective calls are not throwing NoSuchMethod error, but the actually functionallity + * A simple set of tests that call the methods of a hive ClientInterface, loading different version + * of hive from maven central. These tests are simple in that they are mostly just testing to make + * sure that reflective calls are not throwing NoSuchMethod error, but the actually functionality * is not fully tested. */ -class VersionsSuite extends FunSuite with Logging { +class VersionsSuite extends SparkFunSuite with Logging { private def buildConf() = { lazy val warehousePath = Utils.createTempDir() lazy val metastorePath = Utils.createTempDir() @@ -73,7 +72,7 @@ class VersionsSuite extends FunSuite with Logging { assert(getNestedMessages(e) contains "Unknown column 'A0.OWNER_NAME' in 'field list'") } - private val versions = Seq("12", "13") + private val versions = Seq("12", "13", "14") private var client: ClientInterface = null diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala index 23ece7e7cf6e9..b0d3dd44daedc 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala @@ -17,11 +17,11 @@ package org.apache.spark.sql.hive.execution -import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite} import org.apache.spark.sql.hive.test.TestHiveContext -import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.BeforeAndAfterAll -class ConcurrentHiveSuite extends FunSuite with BeforeAndAfterAll { +class ConcurrentHiveSuite extends SparkFunSuite with BeforeAndAfterAll { ignore("multiple instances not supported") { test("Multiple Hive Instances") { (1 to 10).map { i => diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala index 9c056e493bfde..c9dd4c0935a72 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala @@ -19,9 +19,9 @@ package org.apache.spark.sql.hive.execution import java.io._ -import org.scalatest.{BeforeAndAfterAll, FunSuite, GivenWhenThen} +import org.scalatest.{BeforeAndAfterAll, GivenWhenThen} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkFunSuite} import org.apache.spark.sql.sources.DescribeCommand import org.apache.spark.sql.execution.{SetCommand, ExplainCommand} import org.apache.spark.sql.catalyst.planning.PhysicalOperation @@ -40,7 +40,7 @@ import org.apache.spark.sql.hive.test.TestHive * configured using system properties. */ abstract class HiveComparisonTest - extends FunSuite with BeforeAndAfterAll with GivenWhenThen with Logging { + extends SparkFunSuite with BeforeAndAfterAll with GivenWhenThen with Logging { /** * When set, any cache files that result in test failures will be deleted. Used when the test @@ -273,7 +273,7 @@ abstract class HiveComparisonTest } val hiveCacheFiles = queryList.zipWithIndex.map { - case (queryString, i) => + case (queryString, i) => val cachedAnswerName = s"$testCaseName-$i-${getMd5(queryString)}" new File(answerCache, cachedAnswerName) } @@ -304,7 +304,7 @@ abstract class HiveComparisonTest // other DDL has not been executed yet. hiveQueries.foreach(_.logical) val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map { - case ((queryString, i), hiveQuery, cachedAnswerFile)=> + case ((queryString, i), hiveQuery, cachedAnswerFile) => try { // Hooks often break the harness and don't really affect our test anyway, don't // even try running them. diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index 7d728fe87bda7..51dabc67fa7c1 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -20,16 +20,16 @@ package org.apache.spark.sql.hive.execution import java.io.File import java.util.{Locale, TimeZone} -import org.scalatest.BeforeAndAfter - import scala.util.Try +import org.scalatest.BeforeAndAfter + import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.spark.{SparkFiles, SparkException} import org.apache.spark.sql.{AnalysisException, DataFrame, Row} +import org.apache.spark.sql.catalyst.expressions.Cast import org.apache.spark.sql.catalyst.plans.logical.Project -import org.apache.spark.sql.functions._ import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ @@ -52,14 +52,32 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles")) // Add Locale setting Locale.setDefault(Locale.US) + sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}") + // The function source code can be found at: + // https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF + sql( + """ + |CREATE TEMPORARY FUNCTION udtf_count2 + |AS 'org.apache.spark.sql.hive.execution.GenericUDTFCount2' + """.stripMargin) } override def afterAll() { TestHive.cacheTables = false TimeZone.setDefault(originalTimeZone) Locale.setDefault(originalLocale) + sql("DROP TEMPORARY FUNCTION udtf_count2") } + createQueryTest("Test UDTF.close in Lateral Views", + """ + |SELECT key, cc + |FROM src LATERAL VIEW udtf_count2(value) dd AS cc + """.stripMargin, false) // false mean we have to keep the temp function in registry + + createQueryTest("Test UDTF.close in SELECT", + "SELECT udtf_count2(a) FROM (SELECT 1 AS a FROM src LIMIT 3) table", false) + test("SPARK-4908: concurrent hive native commands") { (1 to 100).par.map { _ => sql("USE default") @@ -91,13 +109,13 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { | SELECT key FROM gen_tmp ORDER BY key ASC; """.stripMargin) - test("multiple generator in projection") { + test("multiple generators in projection") { intercept[AnalysisException] { - sql("SELECT explode(map(key, value)), key FROM src").collect() + sql("SELECT explode(array(key, key)), explode(array(key, key)) FROM src").collect() } intercept[AnalysisException] { - sql("SELECT explode(map(key, value)) as k1, k2, key FROM src").collect() + sql("SELECT explode(array(key, key)) as k1, explode(array(key, key)) FROM src").collect() } } @@ -398,6 +416,25 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { |SELECT * FROM createdtable; """.stripMargin) + test("SPARK-7270: consider dynamic partition when comparing table output") { + sql(s"CREATE TABLE test_partition (a STRING) PARTITIONED BY (b BIGINT, c STRING)") + sql(s"CREATE TABLE ptest (a STRING, b BIGINT, c STRING)") + + val analyzedPlan = sql( + """ + |INSERT OVERWRITE table test_partition PARTITION (b=1, c) + |SELECT 'a', 'c' from ptest + """.stripMargin).queryExecution.analyzed + + assertResult(false, "Incorrect cast detected\n" + analyzedPlan) { + var hasCast = false + analyzedPlan.collect { + case p: Project => p.transformExpressionsUp { case c: Cast => hasCast = true; c } + } + hasCast + } + } + createQueryTest("transform", "SELECT TRANSFORM (key) USING 'cat' AS (tKey) FROM src") @@ -837,15 +874,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { |WITH serdeproperties('s1'='9') """.stripMargin) } - // Now only verify 0.12.0, and ignore other versions due to binary compatibility - // current TestSerDe.jar is from 0.12.0 - if (HiveShim.version == "0.12.0") { - sql(s"ADD JAR $testJar") - sql( - """ALTER TABLE alter1 SET SERDE 'org.apache.hadoop.hive.serde2.TestSerDe' - |WITH serdeproperties('s1'='9') - """.stripMargin) - } sql("DROP TABLE alter1") } @@ -853,15 +881,13 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { // this is a test case from mapjoin_addjar.q val testJar = TestHive.getHiveFile("hive-hcatalog-core-0.13.1.jar").getCanonicalPath val testData = TestHive.getHiveFile("data/files/sample.json").getCanonicalPath - if (HiveShim.version == "0.13.1") { - sql(s"ADD JAR $testJar") - sql( - """CREATE TABLE t1(a string, b string) - |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'""".stripMargin) - sql(s"""LOAD DATA LOCAL INPATH "$testData" INTO TABLE t1""") - sql("select * from src join t1 on src.key = t1.a") - sql("DROP TABLE t1") - } + sql(s"ADD JAR $testJar") + sql( + """CREATE TABLE t1(a string, b string) + |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'""".stripMargin) + sql(s"""LOAD DATA LOCAL INPATH "$testData" INTO TABLE t1""") + sql("select * from src join t1 on src.key = t1.a") + sql("DROP TABLE t1") } test("ADD FILE command") { @@ -1058,14 +1084,16 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { val testKey = "spark.sql.key.usedfortestonly" val testVal = "test.val.0" val nonexistentKey = "nonexistent" - val KV = "([^=]+)=([^=]*)".r - def collectResults(df: DataFrame): Set[(String, String)] = + def collectResults(df: DataFrame): Set[Any] = df.collect().map { case Row(key: String, value: String) => key -> value - case Row(KV(key, value)) => key -> value + case Row(key: String, defaultValue: String, doc: String) => (key, defaultValue, doc) }.toSet conf.clear() + val expectedConfs = conf.getAllDefinedConfs.toSet + assertResult(expectedConfs)(collectResults(sql("SET -v"))) + // "SET" itself returns all config variables currently specified in SQLConf. // TODO: Should we be listing the default here always? probably... assert(sql("SET").collect().size == 0) @@ -1076,16 +1104,12 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { assert(hiveconf.get(testKey, "") == testVal) assertResult(Set(testKey -> testVal))(collectResults(sql("SET"))) - assertResult(Set(testKey -> testVal))(collectResults(sql("SET -v"))) sql(s"SET ${testKey + testKey}=${testVal + testVal}") assert(hiveconf.get(testKey + testKey, "") == testVal + testVal) assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) { collectResults(sql("SET")) } - assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) { - collectResults(sql("SET -v")) - } // "SET key" assertResult(Set(testKey -> testVal)) { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala index 8ad3627504229..b08db6de2d2f6 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.hive.execution import org.apache.spark.sql.AnalysisException -import org.apache.spark.sql.hive.test.TestHive.{sparkContext, jsonRDD, sql} +import org.apache.spark.sql.hive.test.TestHive.{read, sparkContext, jsonRDD, sql} import org.apache.spark.sql.hive.test.TestHive.implicits._ case class Nested(a: Int, B: Int) @@ -31,14 +31,14 @@ case class Data(a: Int, B: Int, n: Nested, nestedArray: Seq[Nested]) class HiveResolutionSuite extends HiveComparisonTest { test("SPARK-3698: case insensitive test for nested data") { - jsonRDD(sparkContext.makeRDD( + read.json(sparkContext.makeRDD( """{"a": [{"a": {"a": 1}}]}""" :: Nil)).registerTempTable("nested") // This should be successfully analyzed sql("SELECT a[0].A.A from nested").queryExecution.analyzed } test("SPARK-5278: check ambiguous reference to fields") { - jsonRDD(sparkContext.makeRDD( + read.json(sparkContext.makeRDD( """{"a": [{"b": 1, "B": 2}]}""" :: Nil)).registerTempTable("nested") // there are 2 filed matching field name "b", we should report Ambiguous reference error @@ -77,7 +77,7 @@ class HiveResolutionSuite extends HiveComparisonTest { test("case insensitivity with scala reflection") { // Test resolution with Scala Reflection - sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil) + sparkContext.parallelize(Data(1, 2, Nested(1, 2), Seq(Nested(1, 2))) :: Nil) .toDF().registerTempTable("caseSensitivityTest") val query = sql("SELECT a, b, A, B, n.a, n.b, n.A, n.B FROM caseSensitivityTest") @@ -88,14 +88,14 @@ class HiveResolutionSuite extends HiveComparisonTest { ignore("case insensitivity with scala reflection joins") { // Test resolution with Scala Reflection - sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil) + sparkContext.parallelize(Data(1, 2, Nested(1, 2), Seq(Nested(1, 2))) :: Nil) .toDF().registerTempTable("caseSensitivityTest") sql("SELECT * FROM casesensitivitytest a JOIN casesensitivitytest b ON a.a = b.a").collect() } test("nested repeated resolution") { - sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil) + sparkContext.parallelize(Data(1, 2, Nested(1, 2), Seq(Nested(1, 2))) :: Nil) .toDF().registerTempTable("nestedRepeatedTest") assert(sql("SELECT nestedArray[0].a FROM nestedRepeatedTest").collect().head(0) === 1) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala index ab53c6309e089..2209fc2f30a3c 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala @@ -61,7 +61,7 @@ class HiveTableScanSuite extends HiveComparisonTest { TestHive.sql("select KEY from tb where VALUE='just_for_test' limit 5").collect() TestHive.sql("drop table tb") } - + test("Spark-4077: timestamp query for null value") { TestHive.sql("DROP TABLE IF EXISTS timestamp_query_null") TestHive.sql( @@ -71,12 +71,12 @@ class HiveTableScanSuite extends HiveComparisonTest { FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n' """.stripMargin) - val location = + val location = Utils.getSparkClassLoader.getResource("data/files/issue-4077-data.txt").getFile() - + TestHive.sql(s"LOAD DATA LOCAL INPATH '$location' INTO TABLE timestamp_query_null") - assert(TestHive.sql("SELECT time from timestamp_query_null limit 2").collect() - === Array(Row(java.sql.Timestamp.valueOf("2014-12-11 00:00:00")),Row(null))) + assert(TestHive.sql("SELECT time from timestamp_query_null limit 2").collect() + === Array(Row(java.sql.Timestamp.valueOf("2014-12-11 00:00:00")), Row(null))) TestHive.sql("DROP TABLE timestamp_query_null") } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala index 7f49eac490572..ce5985888f540 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala @@ -101,7 +101,7 @@ class HiveUdfSuite extends QueryTest { sql("DROP TEMPORARY FUNCTION IF EXISTS test_avg") TestHive.reset() } - + test("SPARK-2693 udaf aggregates test") { checkAnswer(sql("SELECT percentile(key, 1) FROM src LIMIT 1"), sql("SELECT max(key) FROM src").collect().toSeq) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index c605f1017547a..a2e666586c186 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -17,15 +17,14 @@ package org.apache.spark.sql.hive.execution +import org.apache.spark.sql.catalyst.DefaultParserDialect import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.errors.DialectException -import org.apache.spark.sql.DefaultDialect -import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} -import org.apache.spark.sql.hive.MetastoreRelation +import org.apache.spark.sql._ import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ -import org.apache.spark.sql.hive.{HiveQLDialect, HiveShim} +import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation} import org.apache.spark.sql.parquet.ParquetRelation2 import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.types._ @@ -53,7 +52,7 @@ case class WindowData( area: String, product: Int) /** A SQL Dialect for testing purpose, and it can not be nested type */ -class MyDialect extends DefaultDialect +class MyDialect extends DefaultParserDialect /** * A collection of hive query tests where we generate the answers ourselves instead of depending on @@ -192,16 +191,16 @@ class SQLQuerySuite extends QueryTest { } } - val originalConf = getConf("spark.sql.hive.convertCTAS", "false") + val originalConf = convertCTAS - setConf("spark.sql.hive.convertCTAS", "true") + setConf(HiveContext.CONVERT_CTAS, true) sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value") sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value") var message = intercept[AnalysisException] { sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value") }.getMessage - assert(message.contains("Table ctas1 already exists")) + assert(message.contains("ctas1 already exists")) checkRelation("ctas1", true) sql("DROP TABLE ctas1") @@ -236,7 +235,7 @@ class SQLQuerySuite extends QueryTest { checkRelation("ctas1", false) sql("DROP TABLE ctas1") - setConf("spark.sql.hive.convertCTAS", originalConf) + setConf(HiveContext.CONVERT_CTAS, originalConf) } test("SQL Dialect Switching") { @@ -247,7 +246,7 @@ class SQLQuerySuite extends QueryTest { // set the dialect back to the DefaultSQLDialect sql("SET spark.sql.dialect=sql") - assert(getSQLDialect().getClass === classOf[DefaultDialect]) + assert(getSQLDialect().getClass === classOf[DefaultParserDialect]) sql("SET spark.sql.dialect=hiveql") assert(getSQLDialect().getClass === classOf[HiveQLDialect]) @@ -314,7 +313,7 @@ class SQLQuerySuite extends QueryTest { SELECT key, value FROM src ORDER BY key, value""").collect().toSeq) - intercept[org.apache.hadoop.hive.metastore.api.AlreadyExistsException] { + intercept[AnalysisException] { sql( """CREATE TABLE ctas4 AS | SELECT key, value FROM src ORDER BY key, value""".stripMargin).collect() @@ -328,41 +327,57 @@ class SQLQuerySuite extends QueryTest { "org.apache.hadoop.hive.ql.io.RCFileInputFormat", "org.apache.hadoop.hive.ql.io.RCFileOutputFormat", "org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe", - "serde_p1=p1", "serde_p2=p2", "tbl_p1=p11", "tbl_p2=p22","MANAGED_TABLE" + "serde_p1=p1", "serde_p2=p2", "tbl_p1=p11", "tbl_p2=p22", "MANAGED_TABLE" ) - if (HiveShim.version =="0.13.1") { - val origUseParquetDataSource = conf.parquetUseDataSourceApi - try { - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") - sql( - """CREATE TABLE ctas5 - | STORED AS parquet AS - | SELECT key, value - | FROM src - | ORDER BY key, value""".stripMargin).collect() - - checkExistence(sql("DESC EXTENDED ctas5"), true, - "name:key", "type:string", "name:value", "ctas5", - "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat", - "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat", - "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe", - "MANAGED_TABLE" - ) - - val default = getConf("spark.sql.hive.convertMetastoreParquet", "true") - // use the Hive SerDe for parquet tables - sql("set spark.sql.hive.convertMetastoreParquet = false") - checkAnswer( - sql("SELECT key, value FROM ctas5 ORDER BY key, value"), - sql("SELECT key, value FROM src ORDER BY key, value").collect().toSeq) - sql(s"set spark.sql.hive.convertMetastoreParquet = $default") - } finally { - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, origUseParquetDataSource.toString) - } + val origUseParquetDataSource = conf.parquetUseDataSourceApi + try { + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false) + sql( + """CREATE TABLE ctas5 + | STORED AS parquet AS + | SELECT key, value + | FROM src + | ORDER BY key, value""".stripMargin).collect() + + checkExistence(sql("DESC EXTENDED ctas5"), true, + "name:key", "type:string", "name:value", "ctas5", + "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat", + "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat", + "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe", + "MANAGED_TABLE" + ) + + val default = convertMetastoreParquet + // use the Hive SerDe for parquet tables + sql("set spark.sql.hive.convertMetastoreParquet = false") + checkAnswer( + sql("SELECT key, value FROM ctas5 ORDER BY key, value"), + sql("SELECT key, value FROM src ORDER BY key, value").collect().toSeq) + sql(s"set spark.sql.hive.convertMetastoreParquet = $default") + } finally { + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, origUseParquetDataSource) } } + test("specifying the column list for CTAS") { + Seq((1, "111111"), (2, "222222")).toDF("key", "value").registerTempTable("mytable1") + + sql("create table gen__tmp(a int, b string) as select key, value from mytable1") + checkAnswer( + sql("SELECT a, b from gen__tmp"), + sql("select key, value from mytable1").collect()) + sql("DROP TABLE gen__tmp") + + sql("create table gen__tmp(a double, b double) as select key, value from mytable1") + checkAnswer( + sql("SELECT a, b from gen__tmp"), + sql("select cast(key as double), cast(value as double) from mytable1").collect()) + sql("DROP TABLE gen__tmp") + + sql("drop table mytable1") + } + test("command substitution") { sql("set tbl=src") checkAnswer( @@ -426,10 +441,10 @@ class SQLQuerySuite extends QueryTest { test("SPARK-4825 save join to table") { val testData = sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)).toDF() sql("CREATE TABLE test1 (key INT, value STRING)") - testData.insertInto("test1") + testData.write.mode(SaveMode.Append).insertInto("test1") sql("CREATE TABLE test2 (key INT, value STRING)") - testData.insertInto("test2") - testData.insertInto("test2") + testData.write.mode(SaveMode.Append).insertInto("test2") + testData.write.mode(SaveMode.Append).insertInto("test2") sql("CREATE TABLE test AS SELECT COUNT(a.value) FROM test1 a JOIN test2 b ON a.key = b.key") checkAnswer( table("test"), @@ -493,6 +508,12 @@ class SQLQuerySuite extends QueryTest { } } + test("SPARK-4699 HiveContext should be case insensitive by default") { + checkAnswer( + sql("SELECT KEY FROM Src ORDER BY value"), + sql("SELECT key FROM src ORDER BY value").collect().toSeq) + } + test("SPARK-5284 Insert into Hive throws NPE when a inner complex type field has a null value") { val schema = StructType( StructField("s", @@ -530,26 +551,49 @@ class SQLQuerySuite extends QueryTest { test("SPARK-4296 Grouping field with Hive UDF as sub expression") { val rdd = sparkContext.makeRDD( """{"a": "str", "b":"1", "c":"1970-01-01 00:00:00"}""" :: Nil) - jsonRDD(rdd).registerTempTable("data") + read.json(rdd).registerTempTable("data") checkAnswer( sql("SELECT concat(a, '-', b), year(c) FROM data GROUP BY concat(a, '-', b), year(c)"), Row("str-1", 1970)) dropTempTable("data") - jsonRDD(rdd).registerTempTable("data") + read.json(rdd).registerTempTable("data") checkAnswer(sql("SELECT year(c) + 1 FROM data GROUP BY year(c) + 1"), Row(1971)) dropTempTable("data") } - test("resolve udtf with single alias") { + test("resolve udtf in projection #1") { val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i + 1}]}""")) - jsonRDD(rdd).registerTempTable("data") + read.json(rdd).registerTempTable("data") val df = sql("SELECT explode(a) AS val FROM data") val col = df("val") } + test("resolve udtf in projection #2") { + val rdd = sparkContext.makeRDD((1 to 2).map(i => s"""{"a":[$i, ${i + 1}]}""")) + jsonRDD(rdd).registerTempTable("data") + checkAnswer(sql("SELECT explode(map(1, 1)) FROM data LIMIT 1"), Row(1, 1) :: Nil) + checkAnswer(sql("SELECT explode(map(1, 1)) as (k1, k2) FROM data LIMIT 1"), Row(1, 1) :: Nil) + intercept[AnalysisException] { + sql("SELECT explode(map(1, 1)) as k1 FROM data LIMIT 1") + } + + intercept[AnalysisException] { + sql("SELECT explode(map(1, 1)) as (k1, k2, k3) FROM data LIMIT 1") + } + } + + // TGF with non-TGF in project is allowed in Spark SQL, but not in Hive + test("TGF with non-TGF in projection") { + val rdd = sparkContext.makeRDD( """{"a": "1", "b":"1"}""" :: Nil) + jsonRDD(rdd).registerTempTable("data") + checkAnswer( + sql("SELECT explode(map(a, b)) as (k1, k2), a, b FROM data"), + Row("1", "1", "1", "1") :: Nil) + } + test("logical.Project should not be resolved if it contains aggregates or generators") { // This test is used to test the fix of SPARK-5875. // The original issue was that Project's resolved will be true when it contains @@ -558,9 +602,9 @@ class SQLQuerySuite extends QueryTest { // PreInsertionCasts will actually start to work before ImplicitGenerate and then // generates an invalid query plan. val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i + 1}]}""")) - jsonRDD(rdd).registerTempTable("data") - val originalConf = getConf("spark.sql.hive.convertCTAS", "false") - setConf("spark.sql.hive.convertCTAS", "false") + read.json(rdd).registerTempTable("data") + val originalConf = convertCTAS + setConf(HiveContext.CONVERT_CTAS, false) sql("CREATE TABLE explodeTest (key bigInt)") table("explodeTest").queryExecution.analyzed match { @@ -577,7 +621,7 @@ class SQLQuerySuite extends QueryTest { sql("DROP TABLE explodeTest") dropTempTable("data") - setConf("spark.sql.hive.convertCTAS", originalConf) + setConf(HiveContext.CONVERT_CTAS, originalConf) } test("sanity test for SPARK-6618") { @@ -590,7 +634,7 @@ class SQLQuerySuite extends QueryTest { sql(s"DROP TABLE $tableName") } } - + test("SPARK-5203 union with different decimal precision") { Seq.empty[(Decimal, Decimal)] .toDF("d1", "d2") @@ -601,12 +645,20 @@ class SQLQuerySuite extends QueryTest { .queryExecution.analyzed } - test("test script transform") { + test("test script transform for stdout") { val data = (1 to 100000).map { i => (i, i, i) } data.toDF("d1", "d2", "d3").registerTempTable("script_trans") assert(100000 === sql("SELECT TRANSFORM (d1, d2, d3) USING 'cat' AS (a,b,c) FROM script_trans") - .queryExecution.toRdd.count()) + .queryExecution.toRdd.count()) + } + + ignore("test script transform for stderr") { + val data = (1 to 100000).map { i => (i, i, i) } + data.toDF("d1", "d2", "d3").registerTempTable("script_trans") + assert(0 === + sql("SELECT TRANSFORM (d1, d2, d3) USING 'cat 1>&2' AS (a,b,c) FROM script_trans") + .queryExecution.toRdd.count()) } test("window function: udaf with aggregate expressin") { @@ -752,10 +804,162 @@ class SQLQuerySuite extends QueryTest { ).map(i => Row(i._1, i._2, i._3, i._4))) } + test("window function: multiple window expressions in a single expression") { + val nums = sparkContext.parallelize(1 to 10).map(x => (x, x % 2)).toDF("x", "y") + nums.registerTempTable("nums") + + val expected = + Row(1, 1, 1, 55, 1, 57) :: + Row(0, 2, 3, 55, 2, 60) :: + Row(1, 3, 6, 55, 4, 65) :: + Row(0, 4, 10, 55, 6, 71) :: + Row(1, 5, 15, 55, 9, 79) :: + Row(0, 6, 21, 55, 12, 88) :: + Row(1, 7, 28, 55, 16, 99) :: + Row(0, 8, 36, 55, 20, 111) :: + Row(1, 9, 45, 55, 25, 125) :: + Row(0, 10, 55, 55, 30, 140) :: Nil + + val actual = sql( + """ + |SELECT + | y, + | x, + | sum(x) OVER w1 AS running_sum, + | sum(x) OVER w2 AS total_sum, + | sum(x) OVER w3 AS running_sum_per_y, + | ((sum(x) OVER w1) + (sum(x) OVER w2) + (sum(x) OVER w3)) as combined2 + |FROM nums + |WINDOW w1 AS (ORDER BY x ROWS BETWEEN UnBOUNDED PRECEDiNG AND CuRRENT RoW), + | w2 AS (ORDER BY x ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOuNDED FoLLOWING), + | w3 AS (PARTITION BY y ORDER BY x ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) + """.stripMargin) + + checkAnswer(actual, expected) + + dropTempTable("nums") + } + test("test case key when") { (1 to 5).map(i => (i, i.toString)).toDF("k", "v").registerTempTable("t") checkAnswer( sql("SELECT CASE k WHEN 2 THEN 22 WHEN 4 THEN 44 ELSE 0 END, v FROM t"), Row(0, "1") :: Row(22, "2") :: Row(0, "3") :: Row(44, "4") :: Row(0, "5") :: Nil) } + + test("SPARK-7595: Window will cause resolve failed with self join") { + checkAnswer(sql( + """ + |with + | v1 as (select key, count(value) over (partition by key) cnt_val from src), + | v2 as (select v1.key, v1_lag.cnt_val from v1, v1 v1_lag where v1.key = v1_lag.key) + | select * from v2 order by key limit 1 + """.stripMargin), Row(0, 3)) + } + + test("SPARK-7269 Check analysis failed in case in-sensitive") { + Seq(1, 2, 3).map { i => + (i.toString, i.toString) + }.toDF("key", "value").registerTempTable("df_analysis") + sql("SELECT kEy from df_analysis group by key").collect() + sql("SELECT kEy+3 from df_analysis group by key+3").collect() + sql("SELECT kEy+3, a.kEy, A.kEy from df_analysis A group by key").collect() + sql("SELECT cast(kEy+1 as Int) from df_analysis A group by cast(key+1 as int)").collect() + sql("SELECT cast(kEy+1 as Int) from df_analysis A group by key+1").collect() + sql("SELECT 2 from df_analysis A group by key+1").collect() + intercept[AnalysisException] { + sql("SELECT kEy+1 from df_analysis group by key+3") + } + intercept[AnalysisException] { + sql("SELECT cast(key+2 as Int) from df_analysis A group by cast(key+1 as int)") + } + } + + test("Cast STRING to BIGINT") { + checkAnswer(sql("SELECT CAST('775983671874188101' as BIGINT)"), Row(775983671874188101L)) + } + + // `Math.exp(1.0)` has different result for different jdk version, so not use createQueryTest + test("udf_java_method") { + checkAnswer(sql( + """ + |SELECT java_method("java.lang.String", "valueOf", 1), + | java_method("java.lang.String", "isEmpty"), + | java_method("java.lang.Math", "max", 2, 3), + | java_method("java.lang.Math", "min", 2, 3), + | java_method("java.lang.Math", "round", 2.5), + | java_method("java.lang.Math", "exp", 1.0), + | java_method("java.lang.Math", "floor", 1.9) + |FROM src tablesample (1 rows) + """.stripMargin), + Row( + "1", + "true", + java.lang.Math.max(2, 3).toString, + java.lang.Math.min(2, 3).toString, + java.lang.Math.round(2.5).toString, + java.lang.Math.exp(1.0).toString, + java.lang.Math.floor(1.9).toString)) + } + + test("dynamic partition value test") { + try { + sql("set hive.exec.dynamic.partition.mode=nonstrict") + // date + sql("drop table if exists dynparttest1") + sql("create table dynparttest1 (value int) partitioned by (pdate date)") + sql( + """ + |insert into table dynparttest1 partition(pdate) + | select count(*), cast('2015-05-21' as date) as pdate from src + """.stripMargin) + checkAnswer( + sql("select * from dynparttest1"), + Seq(Row(500, java.sql.Date.valueOf("2015-05-21")))) + + // decimal + sql("drop table if exists dynparttest2") + sql("create table dynparttest2 (value int) partitioned by (pdec decimal(5, 1))") + sql( + """ + |insert into table dynparttest2 partition(pdec) + | select count(*), cast('100.12' as decimal(5, 1)) as pdec from src + """.stripMargin) + checkAnswer( + sql("select * from dynparttest2"), + Seq(Row(500, new java.math.BigDecimal("100.1")))) + } finally { + sql("drop table if exists dynparttest1") + sql("drop table if exists dynparttest2") + sql("set hive.exec.dynamic.partition.mode=strict") + } + } + + test("Call add jar in a different thread (SPARK-8306)") { + @volatile var error: Option[Throwable] = None + val thread = new Thread { + override def run() { + // To make sure this test works, this jar should not be loaded in another place. + TestHive.sql( + s"ADD JAR ${TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath()}") + try { + TestHive.sql( + """ + |CREATE TEMPORARY FUNCTION example_max + |AS 'org.apache.hadoop.hive.contrib.udaf.example.UDAFExampleMax' + """.stripMargin) + } catch { + case throwable: Throwable => + error = Some(throwable) + } + } + } + thread.start() + thread.join() + error match { + case Some(throwable) => + fail("CREATE TEMPORARY FUNCTION should not fail.", throwable) + case None => // OK + } + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala new file mode 100644 index 0000000000000..080af5bb23c16 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala @@ -0,0 +1,59 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import org.apache.hadoop.fs.Path + +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.sql.sources.HadoopFsRelationTest +import org.apache.spark.sql.types._ + +class OrcHadoopFsRelationSuite extends HadoopFsRelationTest { + override val dataSourceName: String = classOf[DefaultSource].getCanonicalName + + import sqlContext._ + import sqlContext.implicits._ + + test("save()/load() - partitioned table - simple queries - partition columns in data") { + withTempDir { file => + val basePath = new Path(file.getCanonicalPath) + val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf) + val qualifiedBasePath = fs.makeQualified(basePath) + + for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) { + val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2") + sparkContext + .parallelize(for (i <- 1 to 3) yield (i, s"val_$i", p1)) + .toDF("a", "b", "p1") + .write + .format("orc") + .save(partitionDir.toString) + } + + val dataSchemaWithPartition = + StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true)) + + checkQueries( + load( + source = dataSourceName, + options = Map( + "path" -> file.getCanonicalPath, + "dataSchema" -> dataSchemaWithPartition.json))) + } + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala new file mode 100644 index 0000000000000..8707f9f936be6 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala @@ -0,0 +1,257 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import java.io.File +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.expressions.InternalRow +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.util.Utils +import org.scalatest.BeforeAndAfterAll + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe.TypeTag + + +// The data where the partitioning key exists only in the directory structure. +case class OrcParData(intField: Int, stringField: String) + +// The data that also includes the partitioning key +case class OrcParDataWithKey(intField: Int, pi: Int, stringField: String, ps: String) + +// TODO This test suite duplicates ParquetPartitionDiscoverySuite a lot +class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll { + val defaultPartitionName = ConfVars.DEFAULTPARTITIONNAME.defaultVal + + def withTempDir(f: File => Unit): Unit = { + val dir = Utils.createTempDir().getCanonicalFile + try f(dir) finally Utils.deleteRecursively(dir) + } + + def makeOrcFile[T <: Product: ClassTag: TypeTag]( + data: Seq[T], path: File): Unit = { + data.toDF().write.format("orc").mode("overwrite").save(path.getCanonicalPath) + } + + + def makeOrcFile[T <: Product: ClassTag: TypeTag]( + df: DataFrame, path: File): Unit = { + df.write.format("orc").mode("overwrite").save(path.getCanonicalPath) + } + + protected def withTempTable(tableName: String)(f: => Unit): Unit = { + try f finally TestHive.dropTempTable(tableName) + } + + protected def makePartitionDir( + basePath: File, + defaultPartitionName: String, + partitionCols: (String, Any)*): File = { + val partNames = partitionCols.map { case (k, v) => + val valueString = if (v == null || v == "") defaultPartitionName else v.toString + s"$k=$valueString" + } + + val partDir = partNames.foldLeft(basePath) { (parent, child) => + new File(parent, child) + } + + assert(partDir.mkdirs(), s"Couldn't create directory $partDir") + partDir + } + + test("read partitioned table - normal case") { + withTempDir { base => + for { + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } { + makeOrcFile( + (1 to 10).map(i => OrcParData(i, i.toString)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + read.format("orc").load(base.getCanonicalPath).registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } yield Row(i, i.toString, pi, ps)) + + checkAnswer( + sql("SELECT intField, pi FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + _ <- Seq("foo", "bar") + } yield Row(i, pi)) + + checkAnswer( + sql("SELECT * FROM t WHERE pi = 1"), + for { + i <- 1 to 10 + ps <- Seq("foo", "bar") + } yield Row(i, i.toString, 1, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps = 'foo'"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + } yield Row(i, i.toString, pi, "foo")) + } + } + } + + test("read partitioned table - partition key included in orc file") { + withTempDir { base => + for { + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } { + makeOrcFile( + (1 to 10).map(i => OrcParDataWithKey(i, pi, i.toString, ps)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + read.format("orc").load(base.getCanonicalPath).registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } yield Row(i, pi, i.toString, ps)) + + checkAnswer( + sql("SELECT intField, pi FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + _ <- Seq("foo", "bar") + } yield Row(i, pi)) + + checkAnswer( + sql("SELECT * FROM t WHERE pi = 1"), + for { + i <- 1 to 10 + ps <- Seq("foo", "bar") + } yield Row(i, 1, i.toString, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps = 'foo'"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + } yield Row(i, pi, i.toString, "foo")) + } + } + } + + + test("read partitioned table - with nulls") { + withTempDir { base => + for { + // Must be `Integer` rather than `Int` here. `null.asInstanceOf[Int]` results in a zero... + pi <- Seq(1, null.asInstanceOf[Integer]) + ps <- Seq("foo", null.asInstanceOf[String]) + } { + makeOrcFile( + (1 to 10).map(i => OrcParData(i, i.toString)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + read + .format("orc") + .option(ConfVars.DEFAULTPARTITIONNAME.varname, defaultPartitionName) + .load(base.getCanonicalPath) + .registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, null.asInstanceOf[Integer]) + ps <- Seq("foo", null.asInstanceOf[String]) + } yield Row(i, i.toString, pi, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE pi IS NULL"), + for { + i <- 1 to 10 + ps <- Seq("foo", null.asInstanceOf[String]) + } yield Row(i, i.toString, null, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps IS NULL"), + for { + i <- 1 to 10 + pi <- Seq(1, null.asInstanceOf[Integer]) + } yield Row(i, i.toString, pi, null)) + } + } + } + + test("read partitioned table - with nulls and partition keys are included in Orc file") { + withTempDir { base => + for { + pi <- Seq(1, 2) + ps <- Seq("foo", null.asInstanceOf[String]) + } { + makeOrcFile( + (1 to 10).map(i => OrcParDataWithKey(i, pi, i.toString, ps)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + read + .format("orc") + .option(ConfVars.DEFAULTPARTITIONNAME.varname, defaultPartitionName) + .load(base.getCanonicalPath) + .registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + ps <- Seq("foo", null.asInstanceOf[String]) + } yield Row(i, pi, i.toString, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps IS NULL"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + } yield Row(i, pi, i.toString, null)) + } + } + } +} + diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala new file mode 100644 index 0000000000000..267d22c6b5f1e --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala @@ -0,0 +1,292 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import java.io.File + +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.hadoop.hive.ql.io.orc.CompressionKind +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.expressions.InternalRow +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ + +case class AllDataTypesWithNonPrimitiveType( + stringField: String, + intField: Int, + longField: Long, + floatField: Float, + doubleField: Double, + shortField: Short, + byteField: Byte, + booleanField: Boolean, + array: Seq[Int], + arrayContainsNull: Seq[Option[Int]], + map: Map[Int, Long], + mapValueContainsNull: Map[Int, Option[Long]], + data: (Seq[Int], (Int, String))) + +case class BinaryData(binaryData: Array[Byte]) + +case class Contact(name: String, phone: String) + +case class Person(name: String, age: Int, contacts: Seq[Contact]) + +class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest { + + def getTempFilePath(prefix: String, suffix: String = ""): File = { + val tempFile = File.createTempFile(prefix, suffix) + tempFile.delete() + tempFile + } + + test("Read/write All Types") { + val data = (0 to 255).map { i => + (s"$i", i, i.toLong, i.toFloat, i.toDouble, i.toShort, i.toByte, i % 2 == 0) + } + + withOrcFile(data) { file => + checkAnswer( + sqlContext.read.format("orc").load(file), + data.toDF().collect()) + } + } + + test("Read/write binary data") { + withOrcFile(BinaryData("test".getBytes("utf8")) :: Nil) { file => + val bytes = read.format("orc").load(file).head().getAs[Array[Byte]](0) + assert(new String(bytes, "utf8") === "test") + } + } + + test("Read/write all types with non-primitive type") { + val data = (0 to 255).map { i => + AllDataTypesWithNonPrimitiveType( + s"$i", i, i.toLong, i.toFloat, i.toDouble, i.toShort, i.toByte, i % 2 == 0, + 0 until i, + (0 until i).map(Option(_).filter(_ % 3 == 0)), + (0 until i).map(i => i -> i.toLong).toMap, + (0 until i).map(i => i -> Option(i.toLong)).toMap + (i -> None), + (0 until i, (i, s"$i"))) + } + + withOrcFile(data) { file => + checkAnswer( + read.format("orc").load(file), + data.toDF().collect()) + } + } + + test("Creating case class RDD table") { + val data = (1 to 100).map(i => (i, s"val_$i")) + sparkContext.parallelize(data).toDF().registerTempTable("t") + withTempTable("t") { + checkAnswer(sql("SELECT * FROM t"), data.toDF().collect()) + } + } + + test("Simple selection form ORC table") { + val data = (1 to 10).map { i => + Person(s"name_$i", i, (0 to 1).map { m => Contact(s"contact_$m", s"phone_$m") }) + } + + withOrcTable(data, "t") { + // ppd: + // leaf-0 = (LESS_THAN_EQUALS age 5) + // expr = leaf-0 + assert(sql("SELECT name FROM t WHERE age <= 5").count() === 5) + + // ppd: + // leaf-0 = (LESS_THAN_EQUALS age 5) + // expr = (not leaf-0) + assertResult(10) { + sql("SELECT name, contacts FROM t where age > 5") + .flatMap(_.getAs[Seq[_]]("contacts")) + .count() + } + + // ppd: + // leaf-0 = (LESS_THAN_EQUALS age 5) + // leaf-1 = (LESS_THAN age 8) + // expr = (and (not leaf-0) leaf-1) + { + val df = sql("SELECT name, contacts FROM t WHERE age > 5 AND age < 8") + assert(df.count() === 2) + assertResult(4) { + df.flatMap(_.getAs[Seq[_]]("contacts")).count() + } + } + + // ppd: + // leaf-0 = (LESS_THAN age 2) + // leaf-1 = (LESS_THAN_EQUALS age 8) + // expr = (or leaf-0 (not leaf-1)) + { + val df = sql("SELECT name, contacts FROM t WHERE age < 2 OR age > 8") + assert(df.count() === 3) + assertResult(6) { + df.flatMap(_.getAs[Seq[_]]("contacts")).count() + } + } + } + } + + test("save and load case class RDD with `None`s as orc") { + val data = ( + None: Option[Int], + None: Option[Long], + None: Option[Float], + None: Option[Double], + None: Option[Boolean] + ) :: Nil + + withOrcFile(data) { file => + checkAnswer( + read.format("orc").load(file), + Row(Seq.fill(5)(null): _*)) + } + } + + // We only support zlib in Hive 0.12.0 now + test("Default compression options for writing to an ORC file") { + withOrcFile((1 to 100).map(i => (i, s"val_$i"))) { file => + assertResult(CompressionKind.ZLIB) { + OrcFileOperator.getFileReader(file).getCompression + } + } + } + + // Following codec is supported in hive-0.13.1, ignore it now + ignore("Other compression options for writing to an ORC file - 0.13.1 and above") { + val data = (1 to 100).map(i => (i, s"val_$i")) + val conf = sparkContext.hadoopConfiguration + + conf.set(ConfVars.HIVE_ORC_DEFAULT_COMPRESS.varname, "SNAPPY") + withOrcFile(data) { file => + assertResult(CompressionKind.SNAPPY) { + OrcFileOperator.getFileReader(file).getCompression + } + } + + conf.set(ConfVars.HIVE_ORC_DEFAULT_COMPRESS.varname, "NONE") + withOrcFile(data) { file => + assertResult(CompressionKind.NONE) { + OrcFileOperator.getFileReader(file).getCompression + } + } + + conf.set(ConfVars.HIVE_ORC_DEFAULT_COMPRESS.varname, "LZO") + withOrcFile(data) { file => + assertResult(CompressionKind.LZO) { + OrcFileOperator.getFileReader(file).getCompression + } + } + } + + test("simple select queries") { + withOrcTable((0 until 10).map(i => (i, i.toString)), "t") { + checkAnswer( + sql("SELECT `_1` FROM t where t.`_1` > 5"), + (6 until 10).map(Row.apply(_))) + + checkAnswer( + sql("SELECT `_1` FROM t as tmp where tmp.`_1` < 5"), + (0 until 5).map(Row.apply(_))) + } + } + + test("appending") { + val data = (0 until 10).map(i => (i, i.toString)) + createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") + withOrcTable(data, "t") { + sql("INSERT INTO TABLE t SELECT * FROM tmp") + checkAnswer(table("t"), (data ++ data).map(Row.fromTuple)) + } + catalog.unregisterTable(Seq("tmp")) + } + + test("overwriting") { + val data = (0 until 10).map(i => (i, i.toString)) + createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") + withOrcTable(data, "t") { + sql("INSERT OVERWRITE TABLE t SELECT * FROM tmp") + checkAnswer(table("t"), data.map(Row.fromTuple)) + } + catalog.unregisterTable(Seq("tmp")) + } + + test("self-join") { + // 4 rows, cells of column 1 of row 2 and row 4 are null + val data = (1 to 4).map { i => + val maybeInt = if (i % 2 == 0) None else Some(i) + (maybeInt, i.toString) + } + + withOrcTable(data, "t") { + val selfJoin = sql("SELECT * FROM t x JOIN t y WHERE x.`_1` = y.`_1`") + val queryOutput = selfJoin.queryExecution.analyzed.output + + assertResult(4, "Field count mismatches")(queryOutput.size) + assertResult(2, "Duplicated expression ID in query plan:\n $selfJoin") { + queryOutput.filter(_.name == "_1").map(_.exprId).size + } + + checkAnswer(selfJoin, List(Row(1, "1", 1, "1"), Row(3, "3", 3, "3"))) + } + } + + test("nested data - struct with array field") { + val data = (1 to 10).map(i => Tuple1((i, Seq("val_$i")))) + withOrcTable(data, "t") { + checkAnswer(sql("SELECT `_1`.`_2`[0] FROM t"), data.map { + case Tuple1((_, Seq(string))) => Row(string) + }) + } + } + + test("nested data - array of struct") { + val data = (1 to 10).map(i => Tuple1(Seq(i -> "val_$i"))) + withOrcTable(data, "t") { + checkAnswer(sql("SELECT `_1`[0].`_2` FROM t"), data.map { + case Tuple1(Seq((_, string))) => Row(string) + }) + } + } + + test("columns only referenced by pushed down filters should remain") { + withOrcTable((1 to 10).map(Tuple1.apply), "t") { + checkAnswer(sql("SELECT `_1` FROM t WHERE `_1` < 10"), (1 to 9).map(Row.apply(_))) + } + } + + test("SPARK-5309 strings stored using dictionary compression in orc") { + withOrcTable((0 until 1000).map(i => ("same", "run_" + i / 100, 1)), "t") { + checkAnswer( + sql("SELECT `_1`, `_2`, SUM(`_3`) FROM t GROUP BY `_1`, `_2`"), + (0 until 10).map(i => Row("same", "run_" + i, 100))) + + checkAnswer( + sql("SELECT `_1`, `_2`, SUM(`_3`) FROM t WHERE `_2` = 'run_5' GROUP BY `_1`, `_2`"), + List(Row("same", "run_5", 100))) + } + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala new file mode 100644 index 0000000000000..82e08caf46457 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala @@ -0,0 +1,146 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import java.io.File + +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.{QueryTest, Row} + +case class OrcData(intField: Int, stringField: String) + +abstract class OrcSuite extends QueryTest with BeforeAndAfterAll { + var orcTableDir: File = null + var orcTableAsDir: File = null + + override def beforeAll(): Unit = { + super.beforeAll() + + orcTableAsDir = File.createTempFile("orctests", "sparksql") + orcTableAsDir.delete() + orcTableAsDir.mkdir() + + // Hack: to prepare orc data files using hive external tables + orcTableDir = File.createTempFile("orctests", "sparksql") + orcTableDir.delete() + orcTableDir.mkdir() + import org.apache.spark.sql.hive.test.TestHive.implicits._ + + sparkContext + .makeRDD(1 to 10) + .map(i => OrcData(i, s"part-$i")) + .toDF() + .registerTempTable(s"orc_temp_table") + + sql( + s"""CREATE EXTERNAL TABLE normal_orc( + | intField INT, + | stringField STRING + |) + |STORED AS ORC + |LOCATION '${orcTableAsDir.getCanonicalPath}' + """.stripMargin) + + sql( + s"""INSERT INTO TABLE normal_orc + |SELECT intField, stringField FROM orc_temp_table + """.stripMargin) + } + + override def afterAll(): Unit = { + orcTableDir.delete() + orcTableAsDir.delete() + } + + test("create temporary orc table") { + checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_source"), Row(10)) + + checkAnswer( + sql("SELECT * FROM normal_orc_source"), + (1 to 10).map(i => Row(i, s"part-$i"))) + + checkAnswer( + sql("SELECT * FROM normal_orc_source where intField > 5"), + (6 to 10).map(i => Row(i, s"part-$i"))) + + checkAnswer( + sql("SELECT COUNT(intField), stringField FROM normal_orc_source GROUP BY stringField"), + (1 to 10).map(i => Row(1, s"part-$i"))) + } + + test("create temporary orc table as") { + checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_as_source"), Row(10)) + + checkAnswer( + sql("SELECT * FROM normal_orc_source"), + (1 to 10).map(i => Row(i, s"part-$i"))) + + checkAnswer( + sql("SELECT * FROM normal_orc_source WHERE intField > 5"), + (6 to 10).map(i => Row(i, s"part-$i"))) + + checkAnswer( + sql("SELECT COUNT(intField), stringField FROM normal_orc_source GROUP BY stringField"), + (1 to 10).map(i => Row(1, s"part-$i"))) + } + + test("appending insert") { + sql("INSERT INTO TABLE normal_orc_source SELECT * FROM orc_temp_table WHERE intField > 5") + + checkAnswer( + sql("SELECT * FROM normal_orc_source"), + (1 to 5).map(i => Row(i, s"part-$i")) ++ (6 to 10).flatMap { i => + Seq.fill(2)(Row(i, s"part-$i")) + }) + } + + test("overwrite insert") { + sql( + """INSERT OVERWRITE TABLE normal_orc_as_source + |SELECT * FROM orc_temp_table WHERE intField > 5 + """.stripMargin) + + checkAnswer( + sql("SELECT * FROM normal_orc_as_source"), + (6 to 10).map(i => Row(i, s"part-$i"))) + } +} + +class OrcSourceSuite extends OrcSuite { + override def beforeAll(): Unit = { + super.beforeAll() + + sql( + s"""CREATE TEMPORARY TABLE normal_orc_source + |USING org.apache.spark.sql.hive.orc + |OPTIONS ( + | PATH '${new File(orcTableAsDir.getAbsolutePath).getCanonicalPath}' + |) + """.stripMargin) + + sql( + s"""CREATE TEMPORARY TABLE normal_orc_as_source + |USING org.apache.spark.sql.hive.orc + |OPTIONS ( + | PATH '${new File(orcTableAsDir.getAbsolutePath).getCanonicalPath}' + |) + """.stripMargin) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala new file mode 100644 index 0000000000000..5daf691aa8c53 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala @@ -0,0 +1,80 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import java.io.File + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe.TypeTag + +import org.apache.spark.sql.test.SQLTestUtils +import org.apache.spark.sql._ + +private[sql] trait OrcTest extends SQLTestUtils { + lazy val sqlContext = org.apache.spark.sql.hive.test.TestHive + + import sqlContext.sparkContext + import sqlContext.implicits._ + + /** + * Writes `data` to a Orc file, which is then passed to `f` and will be deleted after `f` + * returns. + */ + protected def withOrcFile[T <: Product: ClassTag: TypeTag] + (data: Seq[T]) + (f: String => Unit): Unit = { + withTempPath { file => + sparkContext.parallelize(data).toDF().write.format("orc").save(file.getCanonicalPath) + f(file.getCanonicalPath) + } + } + + /** + * Writes `data` to a Orc file and reads it back as a [[DataFrame]], + * which is then passed to `f`. The Orc file will be deleted after `f` returns. + */ + protected def withOrcDataFrame[T <: Product: ClassTag: TypeTag] + (data: Seq[T]) + (f: DataFrame => Unit): Unit = { + withOrcFile(data)(path => f(sqlContext.read.format("orc").load(path))) + } + + /** + * Writes `data` to a Orc file, reads it back as a [[DataFrame]] and registers it as a + * temporary table named `tableName`, then call `f`. The temporary table together with the + * Orc file will be dropped/deleted after `f` returns. + */ + protected def withOrcTable[T <: Product: ClassTag: TypeTag] + (data: Seq[T], tableName: String) + (f: => Unit): Unit = { + withOrcDataFrame(data) { df => + sqlContext.registerDataFrameAsTable(df, tableName) + withTempTable(tableName)(f) + } + } + + protected def makeOrcFile[T <: Product: ClassTag: TypeTag]( + data: Seq[T], path: File): Unit = { + data.toDF().write.format("orc").mode(SaveMode.Overwrite).save(path.getCanonicalPath) + } + + protected def makeOrcFile[T <: Product: ClassTag: TypeTag]( + df: DataFrame, path: File): Unit = { + df.write.format("orc").mode(SaveMode.Overwrite).save(path.getCanonicalPath) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index d5dd0bf58e702..c2e09800933b5 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -21,18 +21,14 @@ import java.io.File import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.{QueryTest, SQLConf, SaveMode} -import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD} import org.apache.spark.sql.hive.execution.HiveTableScan -import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ -import org.apache.spark.sql.json.JSONRelation -import org.apache.spark.sql.sources.{InsertIntoDataSource, LogicalRelation} import org.apache.spark.sql.parquet.{ParquetRelation2, ParquetTableScan} -import org.apache.spark.sql.SaveMode +import org.apache.spark.sql.sources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation} import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, QueryTest, Row, SQLConf, SaveMode} import org.apache.spark.util.Utils // The data where the partitioning key exists only in the directory structure. @@ -40,7 +36,7 @@ case class ParquetData(intField: Int, stringField: String) // The data that also includes the partitioning key case class ParquetDataWithKey(p: Int, intField: Int, stringField: String) -case class StructContainer(intStructField :Int, stringStructField: String) +case class StructContainer(intStructField: Int, stringStructField: String) case class ParquetDataWithComplexTypes( intField: Int, @@ -153,11 +149,11 @@ class ParquetMetastoreSuiteBase extends ParquetPartitioningTest { } val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}""")) - jsonRDD(rdd1).registerTempTable("jt") + read.json(rdd1).registerTempTable("jt") val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":[$i, null]}""")) - jsonRDD(rdd2).registerTempTable("jt_array") + read.json(rdd2).registerTempTable("jt_array") - setConf("spark.sql.hive.convertMetastoreParquet", "true") + setConf(HiveContext.CONVERT_METASTORE_PARQUET, true) } override def afterAll(): Unit = { @@ -168,7 +164,7 @@ class ParquetMetastoreSuiteBase extends ParquetPartitioningTest { sql("DROP TABLE normal_parquet") sql("DROP TABLE IF EXISTS jt") sql("DROP TABLE IF EXISTS jt_array") - setConf("spark.sql.hive.convertMetastoreParquet", "false") + setConf(HiveContext.CONVERT_METASTORE_PARQUET, false) } test(s"conversion is working") { @@ -203,14 +199,14 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' """.stripMargin) - conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true) } override def afterAll(): Unit = { super.afterAll() sql("DROP TABLE IF EXISTS test_parquet") - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } test("scan an empty parquet table") { @@ -294,10 +290,10 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { ) table("test_parquet_ctas").queryExecution.optimizedPlan match { - case LogicalRelation(p: ParquetRelation2) => // OK - case _ => - fail( - s"test_parquet_ctas should be converted to ${classOf[ParquetRelation2].getCanonicalName}") + case LogicalRelation(_: ParquetRelation2) => // OK + case _ => fail( + "test_parquet_ctas should be converted to " + + s"${classOf[ParquetRelation2].getCanonicalName}") } sql("DROP TABLE IF EXISTS test_parquet_ctas") @@ -318,12 +314,10 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt") df.queryExecution.executedPlan match { - case ExecutedCommand( - InsertIntoDataSource( - LogicalRelation(r: ParquetRelation2), query, overwrite)) => // OK + case ExecutedCommand(InsertIntoHadoopFsRelation(_: ParquetRelation2, _, _)) => // OK case o => fail("test_insert_parquet should be converted to a " + s"${classOf[ParquetRelation2].getCanonicalName} and " + - s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." + + s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan. " + s"However, found a ${o.toString} ") } @@ -350,9 +344,7 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array") df.queryExecution.executedPlan match { - case ExecutedCommand( - InsertIntoDataSource( - LogicalRelation(r: ParquetRelation2), query, overwrite)) => // OK + case ExecutedCommand(InsertIntoHadoopFsRelation(r: ParquetRelation2, _, _)) => // OK case o => fail("test_insert_parquet should be converted to a " + s"${classOf[ParquetRelation2].getCanonicalName} and " + s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." + @@ -392,10 +384,58 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { sql("DROP TABLE ms_convert") } + def collectParquetRelation(df: DataFrame): ParquetRelation2 = { + val plan = df.queryExecution.analyzed + plan.collectFirst { + case LogicalRelation(r: ParquetRelation2) => r + }.getOrElse { + fail(s"Expecting a ParquetRelation2, but got:\n$plan") + } + } + + test("SPARK-7749: non-partitioned metastore Parquet table lookup should use cached relation") { + sql( + s"""CREATE TABLE nonPartitioned ( + | key INT, + | value STRING + |) + |STORED AS PARQUET + """.stripMargin) + + // First lookup fills the cache + val r1 = collectParquetRelation(table("nonPartitioned")) + // Second lookup should reuse the cache + val r2 = collectParquetRelation(table("nonPartitioned")) + // They should be the same instance + assert(r1 eq r2) + + sql("DROP TABLE nonPartitioned") + } + + test("SPARK-7749: partitioned metastore Parquet table lookup should use cached relation") { + sql( + s"""CREATE TABLE partitioned ( + | key INT, + | value STRING + |) + |PARTITIONED BY (part INT) + |STORED AS PARQUET + """.stripMargin) + + // First lookup fills the cache + val r1 = collectParquetRelation(table("partitioned")) + // Second lookup should reuse the cache + val r2 = collectParquetRelation(table("partitioned")) + // They should be the same instance + assert(r1 eq r2) + + sql("DROP TABLE partitioned") + } + test("Caching converted data source Parquet Relations") { - def checkCached(tableIdentifer: catalog.QualifiedTableName): Unit = { + def checkCached(tableIdentifier: catalog.QualifiedTableName): Unit = { // Converted test_parquet should be cached. - catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) match { + catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) match { case null => fail("Converted test_parquet should be cached in the cache.") case logical @ LogicalRelation(parquetRelation: ParquetRelation2) => // OK case other => @@ -421,30 +461,30 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' """.stripMargin) - var tableIdentifer = catalog.QualifiedTableName("default", "test_insert_parquet") + var tableIdentifier = catalog.QualifiedTableName("default", "test_insert_parquet") // First, make sure the converted test_parquet is not cached. - assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null) // Table lookup will make the table cached. table("test_insert_parquet") - checkCached(tableIdentifer) + checkCached(tableIdentifier) // For insert into non-partitioned table, we will do the conversion, // so the converted test_insert_parquet should be cached. invalidateTable("test_insert_parquet") - assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null) sql( """ |INSERT INTO TABLE test_insert_parquet |select a, b from jt """.stripMargin) - checkCached(tableIdentifer) + checkCached(tableIdentifier) // Make sure we can read the data. checkAnswer( sql("select * from test_insert_parquet"), sql("select a, b from jt").collect()) // Invalidate the cache. invalidateTable("test_insert_parquet") - assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null) // Create a partitioned table. sql( @@ -461,8 +501,8 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' """.stripMargin) - tableIdentifer = catalog.QualifiedTableName("default", "test_parquet_partitioned_cache_test") - assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + tableIdentifier = catalog.QualifiedTableName("default", "test_parquet_partitioned_cache_test") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null) sql( """ |INSERT INTO TABLE test_parquet_partitioned_cache_test @@ -471,18 +511,18 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { """.stripMargin) // Right now, insert into a partitioned Parquet is not supported in data source Parquet. // So, we expect it is not cached. - assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null) sql( """ |INSERT INTO TABLE test_parquet_partitioned_cache_test |PARTITION (date='2015-04-02') |select a, b from jt """.stripMargin) - assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null) // Make sure we can cache the partitioned table. table("test_parquet_partitioned_cache_test") - checkCached(tableIdentifer) + checkCached(tableIdentifier) // Make sure we can read the data. checkAnswer( sql("select STRINGField, date, intField from test_parquet_partitioned_cache_test"), @@ -494,7 +534,7 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { """.stripMargin).collect()) invalidateTable("test_parquet_partitioned_cache_test") - assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null) sql("DROP TABLE test_insert_parquet") sql("DROP TABLE test_parquet_partitioned_cache_test") @@ -506,12 +546,12 @@ class ParquetDataSourceOffMetastoreSuite extends ParquetMetastoreSuiteBase { override def beforeAll(): Unit = { super.beforeAll() - conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false) } override def afterAll(): Unit = { super.afterAll() - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } test("MetastoreRelation in InsertIntoTable will not be converted") { @@ -624,16 +664,16 @@ class ParquetSourceSuiteBase extends ParquetPartitioningTest { sql("drop table if exists spark_6016_fix") // Create a DataFrame with two partitions. So, the created table will have two parquet files. - val df1 = jsonRDD(sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i}"""), 2)) - df1.saveAsTable("spark_6016_fix", "parquet", SaveMode.Overwrite) + val df1 = read.json(sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i}"""), 2)) + df1.write.mode(SaveMode.Overwrite).format("parquet").saveAsTable("spark_6016_fix") checkAnswer( sql("select * from spark_6016_fix"), (1 to 10).map(i => Row(i)) ) // Create a DataFrame with four partitions. So, the created table will have four parquet files. - val df2 = jsonRDD(sparkContext.parallelize((1 to 10).map(i => s"""{"b":$i}"""), 4)) - df2.saveAsTable("spark_6016_fix", "parquet", SaveMode.Overwrite) + val df2 = read.json(sparkContext.parallelize((1 to 10).map(i => s"""{"b":$i}"""), 4)) + df2.write.mode(SaveMode.Overwrite).format("parquet").saveAsTable("spark_6016_fix") // For the bug of SPARK-6016, we are caching two outdated footers for df1. Then, // since the new table has four parquet files, we are trying to read new footers from two files // and then merge metadata in footers of these four (two outdated ones and two latest one), @@ -652,12 +692,12 @@ class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase { override def beforeAll(): Unit = { super.beforeAll() - conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true) } override def afterAll(): Unit = { super.afterAll() - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } test("values in arrays and maps stored in parquet are always nullable") { @@ -670,7 +710,7 @@ class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase { StructField("a", arrayType1, nullable = true) :: Nil) assert(df.schema === expectedSchema1) - df.saveAsTable("alwaysNullable", "parquet") + df.write.format("parquet").saveAsTable("alwaysNullable") val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = true) val arrayType2 = ArrayType(IntegerType, containsNull = true) @@ -693,13 +733,13 @@ class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase { val filePath = new File(tempDir, "testParquet").getCanonicalPath val filePath2 = new File(tempDir, "testParquet2").getCanonicalPath - val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str") + val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") val df2 = df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").max("y.int") - intercept[RuntimeException](df2.saveAsParquetFile(filePath)) + intercept[Throwable](df2.write.parquet(filePath)) val df3 = df2.toDF("str", "max_int") - df3.saveAsParquetFile(filePath2) - val df4 = parquetFile(filePath2) + df3.write.parquet(filePath2) + val df4 = read.parquet(filePath2) checkAnswer(df4, Row("1", 1) :: Row("2", 2) :: Row("3", 3) :: Nil) assert(df4.columns === Array("str", "max_int")) } @@ -710,12 +750,12 @@ class ParquetDataSourceOffSourceSuite extends ParquetSourceSuiteBase { override def beforeAll(): Unit = { super.beforeAll() - conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false) } override def afterAll(): Unit = { super.afterAll() - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf) } } @@ -738,14 +778,14 @@ abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll sparkContext.makeRDD(1 to 10) .map(i => ParquetData(i, s"part-$p")) .toDF() - .saveAsParquetFile(partDir.getCanonicalPath) + .write.parquet(partDir.getCanonicalPath) } sparkContext .makeRDD(1 to 10) .map(i => ParquetData(i, s"part-1")) .toDF() - .saveAsParquetFile(new File(normalTableDir, "normal").getCanonicalPath) + .write.parquet(new File(normalTableDir, "normal").getCanonicalPath) partitionedTableDirWithKey = Utils.createTempDir() @@ -754,7 +794,7 @@ abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll sparkContext.makeRDD(1 to 10) .map(i => ParquetDataWithKey(p, i, s"part-$p")) .toDF() - .saveAsParquetFile(partDir.getCanonicalPath) + .write.parquet(partDir.getCanonicalPath) } partitionedTableDirWithKeyAndComplexTypes = Utils.createTempDir() @@ -764,7 +804,7 @@ abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll sparkContext.makeRDD(1 to 10).map { i => ParquetDataWithKeyAndComplexTypes( p, i, s"part-$p", StructContainer(i, f"${i}_string"), 1 to i) - }.toDF().saveAsParquetFile(partDir.getCanonicalPath) + }.toDF().write.parquet(partDir.getCanonicalPath) } partitionedTableDirWithComplexTypes = Utils.createTempDir() @@ -773,7 +813,7 @@ abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll val partDir = new File(partitionedTableDirWithComplexTypes, s"p=$p") sparkContext.makeRDD(1 to 10).map { i => ParquetDataWithComplexTypes(i, s"part-$p", StructContainer(i, f"${i}_string"), 1 to i) - }.toDF().saveAsParquetFile(partDir.getCanonicalPath) + }.toDF().write.parquet(partDir.getCanonicalPath) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala new file mode 100644 index 0000000000000..0f959b3d0b86d --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala @@ -0,0 +1,164 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources + +import java.text.NumberFormat +import java.util.UUID + +import com.google.common.base.Objects +import org.apache.hadoop.fs.{FileStatus, Path} +import org.apache.hadoop.io.{NullWritable, Text} +import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, TextOutputFormat} +import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext} + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.expressions.{Cast, Literal} +import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.sql.{Row, SQLContext} + +/** + * A simple example [[HadoopFsRelationProvider]]. + */ +class SimpleTextSource extends HadoopFsRelationProvider { + override def createRelation( + sqlContext: SQLContext, + paths: Array[String], + schema: Option[StructType], + partitionColumns: Option[StructType], + parameters: Map[String, String]): HadoopFsRelation = { + new SimpleTextRelation(paths, schema, partitionColumns, parameters)(sqlContext) + } +} + +class AppendingTextOutputFormat(outputFile: Path) extends TextOutputFormat[NullWritable, Text] { + val numberFormat = NumberFormat.getInstance() + + numberFormat.setMinimumIntegerDigits(5) + numberFormat.setGroupingUsed(false) + + override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = { + val split = context.getTaskAttemptID.getTaskID.getId + val name = FileOutputFormat.getOutputName(context) + new Path(outputFile, s"$name-${numberFormat.format(split)}-${UUID.randomUUID()}") + } +} + +class SimpleTextOutputWriter(path: String, context: TaskAttemptContext) extends OutputWriter { + private val recordWriter: RecordWriter[NullWritable, Text] = + new AppendingTextOutputFormat(new Path(path)).getRecordWriter(context) + + override def write(row: Row): Unit = { + val serialized = row.toSeq.map(_.toString).mkString(",") + recordWriter.write(null, new Text(serialized)) + } + + override def close(): Unit = { + recordWriter.close(context) + } +} + +/** + * A simple example [[HadoopFsRelation]], used for testing purposes. Data are stored as comma + * separated string lines. When scanning data, schema must be explicitly provided via data source + * option `"dataSchema"`. + */ +class SimpleTextRelation( + override val paths: Array[String], + val maybeDataSchema: Option[StructType], + override val userDefinedPartitionColumns: Option[StructType], + parameters: Map[String, String])( + @transient val sqlContext: SQLContext) + extends HadoopFsRelation { + + import sqlContext.sparkContext + + override val dataSchema: StructType = + maybeDataSchema.getOrElse(DataType.fromJson(parameters("dataSchema")).asInstanceOf[StructType]) + + override def equals(other: Any): Boolean = other match { + case that: SimpleTextRelation => + this.paths.sameElements(that.paths) && + this.maybeDataSchema == that.maybeDataSchema && + this.dataSchema == that.dataSchema && + this.partitionColumns == that.partitionColumns + + case _ => false + } + + override def hashCode(): Int = + Objects.hashCode(paths, maybeDataSchema, dataSchema, partitionColumns) + + override def buildScan(inputStatuses: Array[FileStatus]): RDD[Row] = { + val fields = dataSchema.map(_.dataType) + + sparkContext.textFile(inputStatuses.map(_.getPath).mkString(",")).map { record => + Row(record.split(",").zip(fields).map { case (value, dataType) => + // `Cast`ed values are always of Catalyst types (i.e. UTF8String instead of String, etc.) + val catalystValue = Cast(Literal(value), dataType).eval() + // Here we're converting Catalyst values to Scala values to test `needsConversion` + CatalystTypeConverters.convertToScala(catalystValue, dataType) + }: _*) + } + } + + override def prepareJobForWrite(job: Job): OutputWriterFactory = new OutputWriterFactory { + override def newInstance( + path: String, + dataSchema: StructType, + context: TaskAttemptContext): OutputWriter = { + new SimpleTextOutputWriter(path, context) + } + } +} + +/** + * A simple example [[HadoopFsRelationProvider]]. + */ +class CommitFailureTestSource extends HadoopFsRelationProvider { + override def createRelation( + sqlContext: SQLContext, + paths: Array[String], + schema: Option[StructType], + partitionColumns: Option[StructType], + parameters: Map[String, String]): HadoopFsRelation = { + new CommitFailureTestRelation(paths, schema, partitionColumns, parameters)(sqlContext) + } +} + +class CommitFailureTestRelation( + override val paths: Array[String], + maybeDataSchema: Option[StructType], + override val userDefinedPartitionColumns: Option[StructType], + parameters: Map[String, String])( + @transient sqlContext: SQLContext) + extends SimpleTextRelation( + paths, maybeDataSchema, userDefinedPartitionColumns, parameters)(sqlContext) { + override def prepareJobForWrite(job: Job): OutputWriterFactory = new OutputWriterFactory { + override def newInstance( + path: String, + dataSchema: StructType, + context: TaskAttemptContext): OutputWriter = { + new SimpleTextOutputWriter(path, context) { + override def close(): Unit = { + sys.error("Intentional task commitment failure for testing purpose.") + } + } + } + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala new file mode 100644 index 0000000000000..76469d7a3d6a5 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala @@ -0,0 +1,612 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources + +import java.io.File + +import com.google.common.io.Files +import org.apache.hadoop.fs.Path + +import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.sql._ +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.test.SQLTestUtils +import org.apache.spark.sql.types._ + +abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils { + override lazy val sqlContext: SQLContext = TestHive + + import sqlContext.sql + import sqlContext.implicits._ + + val dataSourceName = classOf[SimpleTextSource].getCanonicalName + + val dataSchema = + StructType( + Seq( + StructField("a", IntegerType, nullable = false), + StructField("b", StringType, nullable = false))) + + lazy val testDF = (1 to 3).map(i => (i, s"val_$i")).toDF("a", "b") + + lazy val partitionedTestDF1 = (for { + i <- 1 to 3 + p2 <- Seq("foo", "bar") + } yield (i, s"val_$i", 1, p2)).toDF("a", "b", "p1", "p2") + + lazy val partitionedTestDF2 = (for { + i <- 1 to 3 + p2 <- Seq("foo", "bar") + } yield (i, s"val_$i", 2, p2)).toDF("a", "b", "p1", "p2") + + lazy val partitionedTestDF = partitionedTestDF1.unionAll(partitionedTestDF2) + + def checkQueries(df: DataFrame): Unit = { + // Selects everything + checkAnswer( + df, + for (i <- 1 to 3; p1 <- 1 to 2; p2 <- Seq("foo", "bar")) yield Row(i, s"val_$i", p1, p2)) + + // Simple filtering and partition pruning + checkAnswer( + df.filter('a > 1 && 'p1 === 2), + for (i <- 2 to 3; p2 <- Seq("foo", "bar")) yield Row(i, s"val_$i", 2, p2)) + + // Simple projection and filtering + checkAnswer( + df.filter('a > 1).select('b, 'a + 1), + for (i <- 2 to 3; _ <- 1 to 2; _ <- Seq("foo", "bar")) yield Row(s"val_$i", i + 1)) + + // Simple projection and partition pruning + checkAnswer( + df.filter('a > 1 && 'p1 < 2).select('b, 'p1), + for (i <- 2 to 3; _ <- Seq("foo", "bar")) yield Row(s"val_$i", 1)) + + // Project many copies of columns with different types (reproduction for SPARK-7858) + checkAnswer( + df.filter('a > 1 && 'p1 < 2).select('b, 'b, 'b, 'b, 'p1, 'p1, 'p1, 'p1), + for (i <- 2 to 3; _ <- Seq("foo", "bar")) + yield Row(s"val_$i", s"val_$i", s"val_$i", s"val_$i", 1, 1, 1, 1)) + + // Self-join + df.registerTempTable("t") + withTempTable("t") { + checkAnswer( + sql( + """SELECT l.a, r.b, l.p1, r.p2 + |FROM t l JOIN t r + |ON l.a = r.a AND l.p1 = r.p1 AND l.p2 = r.p2 + """.stripMargin), + for (i <- 1 to 3; p1 <- 1 to 2; p2 <- Seq("foo", "bar")) yield Row(i, s"val_$i", p1, p2)) + } + } + + test("save()/load() - non-partitioned table - Overwrite") { + withTempPath { file => + testDF.write.mode(SaveMode.Overwrite).format(dataSourceName).save(file.getCanonicalPath) + testDF.write.mode(SaveMode.Overwrite).format(dataSourceName).save(file.getCanonicalPath) + + checkAnswer( + sqlContext.read.format(dataSourceName) + .option("path", file.getCanonicalPath) + .option("dataSchema", dataSchema.json) + .load(), + testDF.collect()) + } + } + + test("save()/load() - non-partitioned table - Append") { + withTempPath { file => + testDF.write.mode(SaveMode.Overwrite).format(dataSourceName).save(file.getCanonicalPath) + testDF.write.mode(SaveMode.Append).format(dataSourceName).save(file.getCanonicalPath) + + checkAnswer( + sqlContext.read.format(dataSourceName) + .option("dataSchema", dataSchema.json) + .load(file.getCanonicalPath).orderBy("a"), + testDF.unionAll(testDF).orderBy("a").collect()) + } + } + + test("save()/load() - non-partitioned table - ErrorIfExists") { + withTempDir { file => + intercept[RuntimeException] { + testDF.write.format(dataSourceName).mode(SaveMode.ErrorIfExists).save(file.getCanonicalPath) + } + } + } + + test("save()/load() - non-partitioned table - Ignore") { + withTempDir { file => + testDF.write.mode(SaveMode.Ignore).format(dataSourceName).save(file.getCanonicalPath) + + val path = new Path(file.getCanonicalPath) + val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) + assert(fs.listStatus(path).isEmpty) + } + } + + test("save()/load() - partitioned table - simple queries") { + withTempPath { file => + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.ErrorIfExists) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + checkQueries( + sqlContext.read.format(dataSourceName) + .option("dataSchema", dataSchema.json) + .load(file.getCanonicalPath)) + } + } + + test("save()/load() - partitioned table - Overwrite") { + withTempPath { file => + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + checkAnswer( + sqlContext.read.format(dataSourceName) + .option("dataSchema", dataSchema.json) + .load(file.getCanonicalPath), + partitionedTestDF.collect()) + } + } + + test("save()/load() - partitioned table - Append") { + withTempPath { file => + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Append) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + checkAnswer( + sqlContext.read.format(dataSourceName) + .option("dataSchema", dataSchema.json) + .load(file.getCanonicalPath), + partitionedTestDF.unionAll(partitionedTestDF).collect()) + } + } + + test("save()/load() - partitioned table - Append - new partition values") { + withTempPath { file => + partitionedTestDF1.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + partitionedTestDF2.write + .format(dataSourceName) + .mode(SaveMode.Append) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + checkAnswer( + sqlContext.read.format(dataSourceName) + .option("dataSchema", dataSchema.json) + .load(file.getCanonicalPath), + partitionedTestDF.collect()) + } + } + + test("save()/load() - partitioned table - ErrorIfExists") { + withTempDir { file => + intercept[RuntimeException] { + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.ErrorIfExists) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + } + } + } + + test("save()/load() - partitioned table - Ignore") { + withTempDir { file => + partitionedTestDF.write + .format(dataSourceName).mode(SaveMode.Ignore).save(file.getCanonicalPath) + + val path = new Path(file.getCanonicalPath) + val fs = path.getFileSystem(SparkHadoopUtil.get.conf) + assert(fs.listStatus(path).isEmpty) + } + } + + test("saveAsTable()/load() - non-partitioned table - Overwrite") { + testDF.write.format(dataSourceName).mode(SaveMode.Overwrite) + .option("dataSchema", dataSchema.json) + .saveAsTable("t") + + withTable("t") { + checkAnswer(sqlContext.table("t"), testDF.collect()) + } + } + + test("saveAsTable()/load() - non-partitioned table - Append") { + testDF.write.format(dataSourceName).mode(SaveMode.Overwrite).saveAsTable("t") + testDF.write.format(dataSourceName).mode(SaveMode.Append).saveAsTable("t") + + withTable("t") { + checkAnswer(sqlContext.table("t"), testDF.unionAll(testDF).orderBy("a").collect()) + } + } + + test("saveAsTable()/load() - non-partitioned table - ErrorIfExists") { + Seq.empty[(Int, String)].toDF().registerTempTable("t") + + withTempTable("t") { + intercept[AnalysisException] { + testDF.write.format(dataSourceName).mode(SaveMode.ErrorIfExists).saveAsTable("t") + } + } + } + + test("saveAsTable()/load() - non-partitioned table - Ignore") { + Seq.empty[(Int, String)].toDF().registerTempTable("t") + + withTempTable("t") { + testDF.write.format(dataSourceName).mode(SaveMode.Ignore).saveAsTable("t") + assert(sqlContext.table("t").collect().isEmpty) + } + } + + test("saveAsTable()/load() - partitioned table - simple queries") { + partitionedTestDF.write.format(dataSourceName) + .mode(SaveMode.Overwrite) + .option("dataSchema", dataSchema.json) + .saveAsTable("t") + + withTable("t") { + checkQueries(sqlContext.table("t")) + } + } + + test("saveAsTable()/load() - partitioned table - Overwrite") { + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + withTable("t") { + checkAnswer(sqlContext.table("t"), partitionedTestDF.collect()) + } + } + + test("saveAsTable()/load() - partitioned table - Append") { + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Append) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + withTable("t") { + checkAnswer(sqlContext.table("t"), partitionedTestDF.unionAll(partitionedTestDF).collect()) + } + } + + test("saveAsTable()/load() - partitioned table - Append - new partition values") { + partitionedTestDF1.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + partitionedTestDF2.write + .format(dataSourceName) + .mode(SaveMode.Append) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + withTable("t") { + checkAnswer(sqlContext.table("t"), partitionedTestDF.collect()) + } + } + + test("saveAsTable()/load() - partitioned table - Append - mismatched partition columns") { + partitionedTestDF1.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + // Using only a subset of all partition columns + intercept[Throwable] { + partitionedTestDF2.write + .format(dataSourceName) + .mode(SaveMode.Append) + .option("dataSchema", dataSchema.json) + .partitionBy("p1") + .saveAsTable("t") + } + } + + test("saveAsTable()/load() - partitioned table - ErrorIfExists") { + Seq.empty[(Int, String)].toDF().registerTempTable("t") + + withTempTable("t") { + intercept[AnalysisException] { + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.ErrorIfExists) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + } + } + } + + test("saveAsTable()/load() - partitioned table - Ignore") { + Seq.empty[(Int, String)].toDF().registerTempTable("t") + + withTempTable("t") { + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Ignore) + .option("dataSchema", dataSchema.json) + .partitionBy("p1", "p2") + .saveAsTable("t") + + assert(sqlContext.table("t").collect().isEmpty) + } + } + + test("Hadoop style globbing") { + withTempPath { file => + partitionedTestDF.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .partitionBy("p1", "p2") + .save(file.getCanonicalPath) + + val df = sqlContext.read + .format(dataSourceName) + .option("dataSchema", dataSchema.json) + .load(s"${file.getCanonicalPath}/p1=*/p2=???") + + val expectedPaths = Set( + s"${file.getCanonicalFile}/p1=1/p2=foo", + s"${file.getCanonicalFile}/p1=2/p2=foo", + s"${file.getCanonicalFile}/p1=1/p2=bar", + s"${file.getCanonicalFile}/p1=2/p2=bar" + ).map { p => + val path = new Path(p) + val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) + path.makeQualified(fs.getUri, fs.getWorkingDirectory).toString + } + + val actualPaths = df.queryExecution.analyzed.collectFirst { + case LogicalRelation(relation: HadoopFsRelation) => + relation.paths.toSet + }.getOrElse { + fail("Expect an FSBasedRelation, but none could be found") + } + + assert(actualPaths === expectedPaths) + checkAnswer(df, partitionedTestDF.collect()) + } + } + + test("Partition column type casting") { + withTempPath { file => + val input = partitionedTestDF.select('a, 'b, 'p1.cast(StringType).as('ps), 'p2) + + input + .write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .partitionBy("ps", "p2") + .saveAsTable("t") + + withTempTable("t") { + checkAnswer(sqlContext.table("t"), input.collect()) + } + } + } + + test("SPARK-7616: adjust column name order accordingly when saving partitioned table") { + val df = (1 to 3).map(i => (i, s"val_$i", i * 2)).toDF("a", "b", "c") + + df.write + .format(dataSourceName) + .mode(SaveMode.Overwrite) + .partitionBy("c", "a") + .saveAsTable("t") + + withTable("t") { + checkAnswer(sqlContext.table("t"), df.select('b, 'c, 'a).collect()) + } + } +} + +class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest { + override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName + + import sqlContext._ + + test("save()/load() - partitioned table - simple queries - partition columns in data") { + withTempDir { file => + val basePath = new Path(file.getCanonicalPath) + val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf) + val qualifiedBasePath = fs.makeQualified(basePath) + + for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) { + val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2") + sparkContext + .parallelize(for (i <- 1 to 3) yield s"$i,val_$i,$p1") + .saveAsTextFile(partitionDir.toString) + } + + val dataSchemaWithPartition = + StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true)) + + checkQueries( + read.format(dataSourceName) + .option("dataSchema", dataSchemaWithPartition.json) + .load(file.getCanonicalPath)) + } + } +} + +class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils { + import TestHive.implicits._ + + override val sqlContext = TestHive + + val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName + + test("SPARK-7684: commitTask() failure should fallback to abortTask()") { + withTempPath { file => + val df = (1 to 3).map(i => i -> s"val_$i").toDF("a", "b") + intercept[SparkException] { + df.write.format(dataSourceName).save(file.getCanonicalPath) + } + + val fs = new Path(file.getCanonicalPath).getFileSystem(SparkHadoopUtil.get.conf) + assert(!fs.exists(new Path(file.getCanonicalPath, "_temporary"))) + } + } +} + +class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest { + override val dataSourceName: String = classOf[parquet.DefaultSource].getCanonicalName + + import sqlContext._ + import sqlContext.implicits._ + + test("save()/load() - partitioned table - simple queries - partition columns in data") { + withTempDir { file => + val basePath = new Path(file.getCanonicalPath) + val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf) + val qualifiedBasePath = fs.makeQualified(basePath) + + for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) { + val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2") + sparkContext + .parallelize(for (i <- 1 to 3) yield (i, s"val_$i", p1)) + .toDF("a", "b", "p1") + .write.parquet(partitionDir.toString) + } + + val dataSchemaWithPartition = + StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true)) + + checkQueries( + read.format(dataSourceName) + .option("dataSchema", dataSchemaWithPartition.json) + .load(file.getCanonicalPath)) + } + } + + test("SPARK-7868: _temporary directories should be ignored") { + withTempPath { dir => + val df = Seq("a", "b", "c").zipWithIndex.toDF() + + df.write + .format("parquet") + .save(dir.getCanonicalPath) + + df.write + .format("parquet") + .save(s"${dir.getCanonicalPath}/_temporary") + + checkAnswer(read.format("parquet").load(dir.getCanonicalPath), df.collect()) + } + } + + test("SPARK-8014: Avoid scanning output directory when SaveMode isn't SaveMode.Append") { + withTempDir { dir => + val path = dir.getCanonicalPath + val df = Seq(1 -> "a").toDF() + + // Creates an arbitrary file. If this directory gets scanned, ParquetRelation2 will throw + // since it's not a valid Parquet file. + val emptyFile = new File(path, "empty") + Files.createParentDirs(emptyFile) + Files.touch(emptyFile) + + // This shouldn't throw anything. + df.write.format("parquet").mode(SaveMode.Ignore).save(path) + + // This should only complain that the destination directory already exists, rather than file + // "empty" is not a Parquet file. + assert { + intercept[RuntimeException] { + df.write.format("parquet").mode(SaveMode.ErrorIfExists).save(path) + }.getMessage.contains("already exists") + } + + // This shouldn't throw anything. + df.write.format("parquet").mode(SaveMode.Overwrite).save(path) + checkAnswer(read.format("parquet").load(path), df) + } + } + + test("SPARK-8079: Avoid NPE thrown from BaseWriterContainer.abortJob") { + withTempPath { dir => + intercept[AnalysisException] { + // Parquet doesn't allow field names with spaces. Here we are intentionally making an + // exception thrown from the `ParquetRelation2.prepareForWriteJob()` method to trigger + // the bug. Please refer to spark-8079 for more details. + range(1, 10) + .withColumnRenamed("id", "a b") + .write + .format("parquet") + .save(dir.getCanonicalPath) + } + } + } +} diff --git a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala b/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala deleted file mode 100644 index 33e96eaabfbf6..0000000000000 --- a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala +++ /dev/null @@ -1,265 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive - -import java.net.URI -import java.util.{ArrayList => JArrayList, Properties} - -import scala.collection.JavaConversions._ -import scala.language.implicitConversions - -import org.apache.hadoop.{io => hadoopIo} -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.Path -import org.apache.hadoop.hive.common.`type`.HiveDecimal -import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.ql.Context -import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table} -import org.apache.hadoop.hive.ql.plan.{CreateTableDesc, FileSinkDesc, TableDesc} -import org.apache.hadoop.hive.ql.processors._ -import org.apache.hadoop.hive.ql.stats.StatsSetupConst -import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Deserializer, io => hiveIo} -import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, ObjectInspector, PrimitiveObjectInspector} -import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory -import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} -import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfo, TypeInfoFactory} -import org.apache.hadoop.io.{NullWritable, Writable} -import org.apache.hadoop.mapred.InputFormat - -import org.apache.spark.sql.types.{UTF8String, Decimal, DecimalType} - -private[hive] case class HiveFunctionWrapper(functionClassName: String) - extends java.io.Serializable { - - // for Serialization - def this() = this(null) - - import org.apache.spark.util.Utils._ - def createFunction[UDFType <: AnyRef](): UDFType = { - getContextOrSparkClassLoader - .loadClass(functionClassName).newInstance.asInstanceOf[UDFType] - } -} - -/** - * A compatibility layer for interacting with Hive version 0.12.0. - */ -private[hive] object HiveShim { - val version = "0.12.0" - - def getTableDesc( - serdeClass: Class[_ <: Deserializer], - inputFormatClass: Class[_ <: InputFormat[_, _]], - outputFormatClass: Class[_], - properties: Properties) = { - new TableDesc(serdeClass, inputFormatClass, outputFormatClass, properties) - } - - def getStringWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.STRING, - getStringWritable(value)) - - def getIntWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.INT, - getIntWritable(value)) - - def getDoubleWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.DOUBLE, - getDoubleWritable(value)) - - def getBooleanWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.BOOLEAN, - getBooleanWritable(value)) - - def getLongWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.LONG, - getLongWritable(value)) - - def getFloatWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.FLOAT, - getFloatWritable(value)) - - def getShortWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.SHORT, - getShortWritable(value)) - - def getByteWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.BYTE, - getByteWritable(value)) - - def getBinaryWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.BINARY, - getBinaryWritable(value)) - - def getDateWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.DATE, - getDateWritable(value)) - - def getTimestampWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.TIMESTAMP, - getTimestampWritable(value)) - - def getDecimalWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.DECIMAL, - getDecimalWritable(value)) - - def getPrimitiveNullWritableConstantObjectInspector: ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - PrimitiveCategory.VOID, null) - - def getStringWritable(value: Any): hadoopIo.Text = - if (value == null) null else new hadoopIo.Text(value.asInstanceOf[UTF8String].toString) - - def getIntWritable(value: Any): hadoopIo.IntWritable = - if (value == null) null else new hadoopIo.IntWritable(value.asInstanceOf[Int]) - - def getDoubleWritable(value: Any): hiveIo.DoubleWritable = - if (value == null) null else new hiveIo.DoubleWritable(value.asInstanceOf[Double]) - - def getBooleanWritable(value: Any): hadoopIo.BooleanWritable = - if (value == null) null else new hadoopIo.BooleanWritable(value.asInstanceOf[Boolean]) - - def getLongWritable(value: Any): hadoopIo.LongWritable = - if (value == null) null else new hadoopIo.LongWritable(value.asInstanceOf[Long]) - - def getFloatWritable(value: Any): hadoopIo.FloatWritable = - if (value == null) null else new hadoopIo.FloatWritable(value.asInstanceOf[Float]) - - def getShortWritable(value: Any): hiveIo.ShortWritable = - if (value == null) null else new hiveIo.ShortWritable(value.asInstanceOf[Short]) - - def getByteWritable(value: Any): hiveIo.ByteWritable = - if (value == null) null else new hiveIo.ByteWritable(value.asInstanceOf[Byte]) - - def getBinaryWritable(value: Any): hadoopIo.BytesWritable = - if (value == null) null else new hadoopIo.BytesWritable(value.asInstanceOf[Array[Byte]]) - - def getDateWritable(value: Any): hiveIo.DateWritable = - if (value == null) null else new hiveIo.DateWritable(value.asInstanceOf[Int]) - - def getTimestampWritable(value: Any): hiveIo.TimestampWritable = - if (value == null) { - null - } else { - new hiveIo.TimestampWritable(value.asInstanceOf[java.sql.Timestamp]) - } - - def getDecimalWritable(value: Any): hiveIo.HiveDecimalWritable = - if (value == null) { - null - } else { - new hiveIo.HiveDecimalWritable( - HiveShim.createDecimal(value.asInstanceOf[Decimal].toJavaBigDecimal)) - } - - def getPrimitiveNullWritable: NullWritable = NullWritable.get() - - def createDriverResultsArray = new JArrayList[String] - - def processResults(results: JArrayList[String]) = results - - def getStatsSetupConstTotalSize = StatsSetupConst.TOTAL_SIZE - - def getStatsSetupConstRawDataSize = StatsSetupConst.RAW_DATA_SIZE - - def createDefaultDBIfNeeded(context: HiveContext) = { } - - def getCommandProcessor(cmd: Array[String], conf: HiveConf) = { - CommandProcessorFactory.get(cmd(0), conf) - } - - def createDecimal(bd: java.math.BigDecimal): HiveDecimal = { - new HiveDecimal(bd) - } - - def appendReadColumns(conf: Configuration, ids: Seq[Integer], names: Seq[String]) { - ColumnProjectionUtils.appendReadColumnIDs(conf, ids) - ColumnProjectionUtils.appendReadColumnNames(conf, names) - } - - def getExternalTmpPath(context: Context, uri: URI) = { - context.getExternalTmpFileURI(uri) - } - - def getDataLocationPath(p: Partition) = p.getPartitionPath - - def getAllPartitionsOf(client: Hive, tbl: Table) = client.getAllPartitionsForPruner(tbl) - - def compatibilityBlackList = Seq( - "decimal_.*", - "udf7", - "drop_partitions_filter2", - "show_.*", - "serde_regex", - "udf_to_date", - "udaf_collect_set", - "udf_concat" - ) - - def setLocation(tbl: Table, crtTbl: CreateTableDesc): Unit = { - tbl.setDataLocation(new Path(crtTbl.getLocation()).toUri()) - } - - def decimalMetastoreString(decimalType: DecimalType): String = "decimal" - - def decimalTypeInfo(decimalType: DecimalType): TypeInfo = - TypeInfoFactory.decimalTypeInfo - - def decimalTypeInfoToCatalyst(inspector: PrimitiveObjectInspector): DecimalType = { - DecimalType.Unlimited - } - - def toCatalystDecimal(hdoi: HiveDecimalObjectInspector, data: Any): Decimal = { - if (hdoi.preferWritable()) { - Decimal(hdoi.getPrimitiveWritableObject(data).getHiveDecimal().bigDecimalValue) - } else { - Decimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue()) - } - } - - def getConvertedOI( - inputOI: ObjectInspector, - outputOI: ObjectInspector): ObjectInspector = { - ObjectInspectorConverters.getConvertedOI(inputOI, outputOI, true) - } - - def prepareWritable(w: Writable): Writable = { - w - } - - def setTblNullFormat(crtTbl: CreateTableDesc, tbl: Table) = {} -} - -private[hive] class ShimFileSinkDesc( - var dir: String, - var tableInfo: TableDesc, - var compressed: Boolean) - extends FileSinkDesc(dir, tableInfo, compressed) { -} diff --git a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala deleted file mode 100644 index dbc5e029e2047..0000000000000 --- a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala +++ /dev/null @@ -1,457 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive - -import java.rmi.server.UID -import java.util.{Properties, ArrayList => JArrayList} -import java.io.{OutputStream, InputStream} - -import scala.collection.JavaConversions._ -import scala.language.implicitConversions -import scala.reflect.ClassTag - -import com.esotericsoftware.kryo.Kryo -import com.esotericsoftware.kryo.io.Input -import com.esotericsoftware.kryo.io.Output -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.Path -import org.apache.hadoop.hive.common.StatsSetupConst -import org.apache.hadoop.hive.common.`type`.HiveDecimal -import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.ql.Context -import org.apache.hadoop.hive.ql.exec.{UDF, Utilities} -import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table} -import org.apache.hadoop.hive.ql.plan.{CreateTableDesc, FileSinkDesc, TableDesc} -import org.apache.hadoop.hive.ql.processors.CommandProcessorFactory -import org.apache.hadoop.hive.serde.serdeConstants -import org.apache.hadoop.hive.serde2.avro.AvroGenericRecordWritable -import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} -import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectInspectorConverters, PrimitiveObjectInspector} -import org.apache.hadoop.hive.serde2.typeinfo.{DecimalTypeInfo, TypeInfo, TypeInfoFactory} -import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Deserializer, io => hiveIo} -import org.apache.hadoop.io.{NullWritable, Writable} -import org.apache.hadoop.mapred.InputFormat -import org.apache.hadoop.{io => hadoopIo} - -import org.apache.spark.Logging -import org.apache.spark.sql.types.{Decimal, DecimalType, UTF8String} -import org.apache.spark.util.Utils._ - -/** - * This class provides the UDF creation and also the UDF instance serialization and - * de-serialization cross process boundary. - * - * Detail discussion can be found at https://github.com/apache/spark/pull/3640 - * - * @param functionClassName UDF class name - */ -private[hive] case class HiveFunctionWrapper(var functionClassName: String) - extends java.io.Externalizable { - - // for Serialization - def this() = this(null) - - @transient - def deserializeObjectByKryo[T: ClassTag]( - kryo: Kryo, - in: InputStream, - clazz: Class[_]): T = { - val inp = new Input(in) - val t: T = kryo.readObject(inp,clazz).asInstanceOf[T] - inp.close() - t - } - - @transient - def serializeObjectByKryo( - kryo: Kryo, - plan: Object, - out: OutputStream ) { - val output: Output = new Output(out) - kryo.writeObject(output, plan) - output.close() - } - - def deserializePlan[UDFType](is: java.io.InputStream, clazz: Class[_]): UDFType = { - deserializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), is, clazz) - .asInstanceOf[UDFType] - } - - def serializePlan(function: AnyRef, out: java.io.OutputStream): Unit = { - serializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), function, out) - } - - private var instance: AnyRef = null - - def writeExternal(out: java.io.ObjectOutput) { - // output the function name - out.writeUTF(functionClassName) - - // Write a flag if instance is null or not - out.writeBoolean(instance != null) - if (instance != null) { - // Some of the UDF are serializable, but some others are not - // Hive Utilities can handle both cases - val baos = new java.io.ByteArrayOutputStream() - serializePlan(instance, baos) - val functionInBytes = baos.toByteArray - - // output the function bytes - out.writeInt(functionInBytes.length) - out.write(functionInBytes, 0, functionInBytes.length) - } - } - - def readExternal(in: java.io.ObjectInput) { - // read the function name - functionClassName = in.readUTF() - - if (in.readBoolean()) { - // if the instance is not null - // read the function in bytes - val functionInBytesLength = in.readInt() - val functionInBytes = new Array[Byte](functionInBytesLength) - in.read(functionInBytes, 0, functionInBytesLength) - - // deserialize the function object via Hive Utilities - instance = deserializePlan[AnyRef](new java.io.ByteArrayInputStream(functionInBytes), - getContextOrSparkClassLoader.loadClass(functionClassName)) - } - } - - def createFunction[UDFType <: AnyRef](): UDFType = { - if (instance != null) { - instance.asInstanceOf[UDFType] - } else { - val func = getContextOrSparkClassLoader - .loadClass(functionClassName).newInstance.asInstanceOf[UDFType] - if (!func.isInstanceOf[UDF]) { - // We cache the function if it's no the Simple UDF, - // as we always have to create new instance for Simple UDF - instance = func - } - func - } - } -} - -/** - * A compatibility layer for interacting with Hive version 0.13.1. - */ -private[hive] object HiveShim { - val version = "0.13.1" - - def getTableDesc( - serdeClass: Class[_ <: Deserializer], - inputFormatClass: Class[_ <: InputFormat[_, _]], - outputFormatClass: Class[_], - properties: Properties) = { - new TableDesc(inputFormatClass, outputFormatClass, properties) - } - - - def getStringWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.stringTypeInfo, getStringWritable(value)) - - def getIntWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.intTypeInfo, getIntWritable(value)) - - def getDoubleWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.doubleTypeInfo, getDoubleWritable(value)) - - def getBooleanWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.booleanTypeInfo, getBooleanWritable(value)) - - def getLongWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.longTypeInfo, getLongWritable(value)) - - def getFloatWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.floatTypeInfo, getFloatWritable(value)) - - def getShortWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.shortTypeInfo, getShortWritable(value)) - - def getByteWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.byteTypeInfo, getByteWritable(value)) - - def getBinaryWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.binaryTypeInfo, getBinaryWritable(value)) - - def getDateWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.dateTypeInfo, getDateWritable(value)) - - def getTimestampWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.timestampTypeInfo, getTimestampWritable(value)) - - def getDecimalWritableConstantObjectInspector(value: Any): ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.decimalTypeInfo, getDecimalWritable(value)) - - def getPrimitiveNullWritableConstantObjectInspector: ObjectInspector = - PrimitiveObjectInspectorFactory.getPrimitiveWritableConstantObjectInspector( - TypeInfoFactory.voidTypeInfo, null) - - def getStringWritable(value: Any): hadoopIo.Text = - if (value == null) null else new hadoopIo.Text(value.asInstanceOf[UTF8String].toString) - - def getIntWritable(value: Any): hadoopIo.IntWritable = - if (value == null) null else new hadoopIo.IntWritable(value.asInstanceOf[Int]) - - def getDoubleWritable(value: Any): hiveIo.DoubleWritable = - if (value == null) { - null - } else { - new hiveIo.DoubleWritable(value.asInstanceOf[Double]) - } - - def getBooleanWritable(value: Any): hadoopIo.BooleanWritable = - if (value == null) { - null - } else { - new hadoopIo.BooleanWritable(value.asInstanceOf[Boolean]) - } - - def getLongWritable(value: Any): hadoopIo.LongWritable = - if (value == null) null else new hadoopIo.LongWritable(value.asInstanceOf[Long]) - - def getFloatWritable(value: Any): hadoopIo.FloatWritable = - if (value == null) { - null - } else { - new hadoopIo.FloatWritable(value.asInstanceOf[Float]) - } - - def getShortWritable(value: Any): hiveIo.ShortWritable = - if (value == null) null else new hiveIo.ShortWritable(value.asInstanceOf[Short]) - - def getByteWritable(value: Any): hiveIo.ByteWritable = - if (value == null) null else new hiveIo.ByteWritable(value.asInstanceOf[Byte]) - - def getBinaryWritable(value: Any): hadoopIo.BytesWritable = - if (value == null) { - null - } else { - new hadoopIo.BytesWritable(value.asInstanceOf[Array[Byte]]) - } - - def getDateWritable(value: Any): hiveIo.DateWritable = - if (value == null) null else new hiveIo.DateWritable(value.asInstanceOf[Int]) - - def getTimestampWritable(value: Any): hiveIo.TimestampWritable = - if (value == null) { - null - } else { - new hiveIo.TimestampWritable(value.asInstanceOf[java.sql.Timestamp]) - } - - def getDecimalWritable(value: Any): hiveIo.HiveDecimalWritable = - if (value == null) { - null - } else { - // TODO precise, scale? - new hiveIo.HiveDecimalWritable( - HiveShim.createDecimal(value.asInstanceOf[Decimal].toJavaBigDecimal)) - } - - def getPrimitiveNullWritable: NullWritable = NullWritable.get() - - def createDriverResultsArray = new JArrayList[Object] - - def processResults(results: JArrayList[Object]) = { - results.map { r => - r match { - case s: String => s - case a: Array[Object] => a(0).asInstanceOf[String] - } - } - } - - def getStatsSetupConstTotalSize = StatsSetupConst.TOTAL_SIZE - - def getStatsSetupConstRawDataSize = StatsSetupConst.RAW_DATA_SIZE - - def createDefaultDBIfNeeded(context: HiveContext) = { - context.runSqlHive("CREATE DATABASE default") - context.runSqlHive("USE default") - } - - def getCommandProcessor(cmd: Array[String], conf: HiveConf) = { - CommandProcessorFactory.get(cmd, conf) - } - - def createDecimal(bd: java.math.BigDecimal): HiveDecimal = { - HiveDecimal.create(bd) - } - - /* - * This function in hive-0.13 become private, but we have to do this to walkaround hive bug - */ - private def appendReadColumnNames(conf: Configuration, cols: Seq[String]) { - val old: String = conf.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, "") - val result: StringBuilder = new StringBuilder(old) - var first: Boolean = old.isEmpty - - for (col <- cols) { - if (first) { - first = false - } else { - result.append(',') - } - result.append(col) - } - conf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, result.toString) - } - - /* - * Cannot use ColumnProjectionUtils.appendReadColumns directly, if ids is null or empty - */ - def appendReadColumns(conf: Configuration, ids: Seq[Integer], names: Seq[String]) { - if (ids != null && ids.size > 0) { - ColumnProjectionUtils.appendReadColumns(conf, ids) - } - if (names != null && names.size > 0) { - appendReadColumnNames(conf, names) - } - } - - def getExternalTmpPath(context: Context, path: Path) = { - context.getExternalTmpPath(path.toUri) - } - - def getDataLocationPath(p: Partition) = p.getDataLocation - - def getAllPartitionsOf(client: Hive, tbl: Table) = client.getAllPartitionsOf(tbl) - - def compatibilityBlackList = Seq() - - def setLocation(tbl: Table, crtTbl: CreateTableDesc): Unit = { - tbl.setDataLocation(new Path(crtTbl.getLocation())) - } - - /* - * Bug introdiced in hive-0.13. FileSinkDesc is serializable, but its member path is not. - * Fix it through wrapper. - * */ - implicit def wrapperToFileSinkDesc(w: ShimFileSinkDesc): FileSinkDesc = { - var f = new FileSinkDesc(new Path(w.dir), w.tableInfo, w.compressed) - f.setCompressCodec(w.compressCodec) - f.setCompressType(w.compressType) - f.setTableInfo(w.tableInfo) - f.setDestTableId(w.destTableId) - f - } - - // Precision and scale to pass for unlimited decimals; these are the same as the precision and - // scale Hive 0.13 infers for BigDecimals from sources that don't specify them (e.g. UDFs) - private val UNLIMITED_DECIMAL_PRECISION = 38 - private val UNLIMITED_DECIMAL_SCALE = 18 - - def decimalMetastoreString(decimalType: DecimalType): String = decimalType match { - case DecimalType.Fixed(precision, scale) => s"decimal($precision,$scale)" - case _ => s"decimal($UNLIMITED_DECIMAL_PRECISION,$UNLIMITED_DECIMAL_SCALE)" - } - - def decimalTypeInfo(decimalType: DecimalType): TypeInfo = decimalType match { - case DecimalType.Fixed(precision, scale) => new DecimalTypeInfo(precision, scale) - case _ => new DecimalTypeInfo(UNLIMITED_DECIMAL_PRECISION, UNLIMITED_DECIMAL_SCALE) - } - - def decimalTypeInfoToCatalyst(inspector: PrimitiveObjectInspector): DecimalType = { - val info = inspector.getTypeInfo.asInstanceOf[DecimalTypeInfo] - DecimalType(info.precision(), info.scale()) - } - - def toCatalystDecimal(hdoi: HiveDecimalObjectInspector, data: Any): Decimal = { - if (hdoi.preferWritable()) { - Decimal(hdoi.getPrimitiveWritableObject(data).getHiveDecimal().bigDecimalValue, - hdoi.precision(), hdoi.scale()) - } else { - Decimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue(), hdoi.precision(), hdoi.scale()) - } - } - - def getConvertedOI(inputOI: ObjectInspector, outputOI: ObjectInspector): ObjectInspector = { - ObjectInspectorConverters.getConvertedOI(inputOI, outputOI) - } - - /* - * Bug introduced in hive-0.13. AvroGenericRecordWritable has a member recordReaderID that - * is needed to initialize before serialization. - */ - def prepareWritable(w: Writable): Writable = { - w match { - case w: AvroGenericRecordWritable => - w.setRecordReaderID(new UID()) - case _ => - } - w - } - - def setTblNullFormat(crtTbl: CreateTableDesc, tbl: Table) = { - if (crtTbl != null && crtTbl.getNullFormat() != null) { - tbl.setSerdeParam(serdeConstants.SERIALIZATION_NULL_FORMAT, crtTbl.getNullFormat()) - } - } -} - -/* - * Bug introduced in hive-0.13. FileSinkDesc is serilizable, but its member path is not. - * Fix it through wrapper. - */ -private[hive] class ShimFileSinkDesc( - var dir: String, - var tableInfo: TableDesc, - var compressed: Boolean) - extends Serializable with Logging { - var compressCodec: String = _ - var compressType: String = _ - var destTableId: Int = _ - - def setCompressed(compressed: Boolean) { - this.compressed = compressed - } - - def getDirName = dir - - def setDestTableId(destTableId: Int) { - this.destTableId = destTableId - } - - def setTableInfo(tableInfo: TableDesc) { - this.tableInfo = tableInfo - } - - def setCompressCodec(intermediateCompressorCodec: String) { - compressCodec = intermediateCompressorCodec - } - - def setCompressType(intermediateCompressType: String) { - compressType = intermediateCompressType - } -} diff --git a/streaming/pom.xml b/streaming/pom.xml index 5ca55a4f680bb..697895e72fe5b 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -40,6 +40,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + @@ -105,13 +112,5 @@ - - - ../python - - pyspark/streaming/*.py - - - diff --git a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css b/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.css similarity index 85% rename from core/src/main/resources/org/apache/spark/ui/static/streaming-page.css rename to streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.css index 5da9d631ad124..ec12616b58d87 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css +++ b/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.css @@ -31,7 +31,7 @@ } .tooltip-inner { - max-width: 500px !important; // Make sure we only have one line tooltip + max-width: 500px !important; /* Make sure we only have one line tooltip */ } .line { @@ -56,3 +56,11 @@ .histogram { width: auto; } + +span.expand-input-rate { + cursor: pointer; +} + +tr.batch-table-cell-highlight > td { + background-color: #D6FFE4 !important; +} diff --git a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js b/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.js similarity index 79% rename from core/src/main/resources/org/apache/spark/ui/static/streaming-page.js rename to streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.js index a4e03b156f13e..75251f493ad22 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js +++ b/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.js @@ -98,7 +98,16 @@ function drawTimeline(id, data, minX, maxX, minY, maxY, unitY, batchInterval) { var x = d3.scale.linear().domain([minX, maxX]).range([0, width]); var y = d3.scale.linear().domain([minY, maxY]).range([height, 0]); - var xAxis = d3.svg.axis().scale(x).orient("bottom").tickFormat(function(d) { return timeFormat[d]; }); + var xAxis = d3.svg.axis().scale(x).orient("bottom").tickFormat(function(d) { + var formattedDate = timeFormat[d]; + var dotIndex = formattedDate.indexOf('.'); + if (dotIndex >= 0) { + // Remove milliseconds + return formattedDate.substring(0, dotIndex); + } else { + return formattedDate; + } + }); var formatYValue = d3.format(",.2f"); var yAxis = d3.svg.axis().scale(y).orient("left").ticks(5).tickFormat(formatYValue); @@ -137,6 +146,12 @@ function drawTimeline(id, data, minX, maxX, minY, maxY, unitY, batchInterval) { .attr("class", "line") .attr("d", line); + // If the user click one point in the graphs, jump to the batch row and highlight it. And + // recovery the batch row after 3 seconds if necessary. + // We need to remember the last clicked batch so that we can recovery it. + var lastClickedBatch = null; + var lastTimeout = null; + // Add points to the line. However, we make it invisible at first. But when the user moves mouse // over a point, it will be displayed with its detail. svg.selectAll(".point") @@ -145,6 +160,7 @@ function drawTimeline(id, data, minX, maxX, minY, maxY, unitY, batchInterval) { .attr("stroke", "white") // white and opacity = 0 make it invisible .attr("fill", "white") .attr("opacity", "0") + .style("cursor", "pointer") .attr("cx", function(d) { return x(d.x); }) .attr("cy", function(d) { return y(d.y); }) .attr("r", function(d) { return 3; }) @@ -166,7 +182,29 @@ function drawTimeline(id, data, minX, maxX, minY, maxY, unitY, batchInterval) { .attr("opacity", "0"); }) .on("click", function(d) { - window.location.href = "batch/?id=" + d.x; + if (lastTimeout != null) { + window.clearTimeout(lastTimeout); + } + if (lastClickedBatch != null) { + clearBatchRow(lastClickedBatch); + lastClickedBatch = null; + } + lastClickedBatch = d.x; + highlightBatchRow(lastClickedBatch) + lastTimeout = window.setTimeout(function () { + lastTimeout = null; + if (lastClickedBatch != null) { + clearBatchRow(lastClickedBatch); + lastClickedBatch = null; + } + }, 3000); // Clean up after 3 seconds + + var batchSelector = $("#batch-" + d.x); + var topOffset = batchSelector.offset().top - 15; + if (topOffset < 0) { + topOffset = 0; + } + $('html,body').animate({scrollTop: topOffset}, 200); }); } @@ -209,6 +247,9 @@ function drawHistogram(id, values, minY, maxY, unitY, batchInterval) { svg.append("g") .attr("class", "x axis") .call(xAxis) + .append("text") + .attr("transform", "translate(" + (margin.left + width - 40) + ", 15)") + .text("#batches"); svg.append("g") .attr("class", "y axis") @@ -252,23 +293,29 @@ function drawHistogram(id, values, minY, maxY, unitY, batchInterval) { } $(function() { - function getParameterFromURL(param) - { - var parameters = window.location.search.substring(1); // Remove "?" - var keyValues = parameters.split('&'); - for (var i = 0; i < keyValues.length; i++) - { - var paramKeyValue = keyValues[i].split('='); - if (paramKeyValue[0] == param) - { - return paramKeyValue[1]; - } + var status = window.localStorage && window.localStorage.getItem("show-streams-detail") == "true"; + + $("span.expand-input-rate").click(function() { + status = !status; + $("#inputs-table").toggle('collapsed'); + // Toggle the class of the arrow between open and closed + $(this).find('.expand-input-rate-arrow').toggleClass('arrow-open').toggleClass('arrow-closed'); + if (window.localStorage) { + window.localStorage.setItem("show-streams-detail", "" + status); } - } + }); - if (getParameterFromURL("show-streams-detail") == "true") { - // Show the details for all InputDStream - $('#inputs-table').toggle('collapsed'); - $('#triangle').html('▼'); + if (status) { + $("#inputs-table").toggle('collapsed'); + // Toggle the class of the arrow between open and closed + $(this).find('.expand-input-rate-arrow').toggleClass('arrow-open').toggleClass('arrow-closed'); } }); + +function highlightBatchRow(batch) { + $("#batch-" + batch).parent().addClass("batch-table-cell-highlight"); +} + +function clearBatchRow(batch) { + $("#batch-" + batch).parent().removeClass("batch-table-cell-highlight"); +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 7bfae253c3a0c..d8dc4e4101664 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -102,6 +102,44 @@ object Checkpoint extends Logging { Seq.empty } } + + /** Serialize the checkpoint, or throw any exception that occurs */ + def serialize(checkpoint: Checkpoint, conf: SparkConf): Array[Byte] = { + val compressionCodec = CompressionCodec.createCodec(conf) + val bos = new ByteArrayOutputStream() + val zos = compressionCodec.compressedOutputStream(bos) + val oos = new ObjectOutputStream(zos) + Utils.tryWithSafeFinally { + oos.writeObject(checkpoint) + } { + oos.close() + } + bos.toByteArray + } + + /** Deserialize a checkpoint from the input stream, or throw any exception that occurs */ + def deserialize(inputStream: InputStream, conf: SparkConf): Checkpoint = { + val compressionCodec = CompressionCodec.createCodec(conf) + var ois: ObjectInputStreamWithLoader = null + Utils.tryWithSafeFinally { + + // ObjectInputStream uses the last defined user-defined class loader in the stack + // to find classes, which maybe the wrong class loader. Hence, a inherited version + // of ObjectInputStream is used to explicitly use the current thread's default class + // loader to find and load classes. This is a well know Java issue and has popped up + // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627) + val zis = compressionCodec.compressedInputStream(inputStream) + ois = new ObjectInputStreamWithLoader(zis, + Thread.currentThread().getContextClassLoader) + val cp = ois.readObject.asInstanceOf[Checkpoint] + cp.validate() + cp + } { + if (ois != null) { + ois.close() + } + } + } } @@ -189,17 +227,10 @@ class CheckpointWriter( } def write(checkpoint: Checkpoint, clearCheckpointDataLater: Boolean) { - val bos = new ByteArrayOutputStream() - val zos = compressionCodec.compressedOutputStream(bos) - val oos = new ObjectOutputStream(zos) - Utils.tryWithSafeFinally { - oos.writeObject(checkpoint) - } { - oos.close() - } try { + val bytes = Checkpoint.serialize(checkpoint, conf) executor.execute(new CheckpointWriteHandler( - checkpoint.checkpointTime, bos.toByteArray, clearCheckpointDataLater)) + checkpoint.checkpointTime, bytes, clearCheckpointDataLater)) logDebug("Submitted checkpoint of time " + checkpoint.checkpointTime + " writer queue") } catch { case rej: RejectedExecutionException => @@ -264,25 +295,8 @@ object CheckpointReader extends Logging { checkpointFiles.foreach(file => { logInfo("Attempting to load checkpoint from file " + file) try { - var ois: ObjectInputStreamWithLoader = null - var cp: Checkpoint = null - Utils.tryWithSafeFinally { - val fis = fs.open(file) - // ObjectInputStream uses the last defined user-defined class loader in the stack - // to find classes, which maybe the wrong class loader. Hence, a inherited version - // of ObjectInputStream is used to explicitly use the current thread's default class - // loader to find and load classes. This is a well know Java issue and has popped up - // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627) - val zis = compressionCodec.compressedInputStream(fis) - ois = new ObjectInputStreamWithLoader(zis, - Thread.currentThread().getContextClassLoader) - cp = ois.readObject.asInstanceOf[Checkpoint] - } { - if (ois != null) { - ois.close() - } - } - cp.validate() + val fis = fs.open(file) + val cp = Checkpoint.deserialize(fis, conf) logInfo("Checkpoint successfully loaded from file " + file) logInfo("Checkpoint was generated at time " + cp.checkpointTime) return Some(cp) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala index 9c7f698840778..40789c66f3991 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala @@ -45,7 +45,7 @@ final private[streaming] class DStreamGraph extends Serializable with Logging { startTime = time outputStreams.foreach(_.initialize(zeroTime)) outputStreams.foreach(_.remember(rememberDuration)) - outputStreams.foreach(_.validate) + outputStreams.foreach(_.validateAtStart) inputStreams.par.foreach(_.start()) } } @@ -157,10 +157,10 @@ final private[streaming] class DStreamGraph extends Serializable with Logging { def validate() { this.synchronized { - assert(batchDuration != null, "Batch duration has not been set") + require(batchDuration != null, "Batch duration has not been set") // assert(batchDuration >= Milliseconds(100), "Batch duration of " + batchDuration + // " is very low") - assert(getOutputStreams().size > 0, "No output streams registered, so nothing to execute") + require(getOutputStreams().size > 0, "No output operations registered, so nothing to execute") } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index bbdb4e8af036c..1708f309fc002 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -17,28 +17,33 @@ package org.apache.spark.streaming -import java.io.InputStream -import java.util.concurrent.atomic.AtomicInteger +import java.io.{InputStream, NotSerializableException} +import java.util.concurrent.atomic.{AtomicInteger, AtomicReference} import scala.collection.Map import scala.collection.mutable.Queue import scala.reflect.ClassTag +import scala.util.control.NonFatal import akka.actor.{Props, SupervisorStrategy} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.{BytesWritable, LongWritable, Text} -import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import org.apache.hadoop.mapreduce.lib.input.TextInputFormat +import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} + import org.apache.spark._ -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.input.FixedLengthBinaryInputFormat -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDD, RDDOperationScope} +import org.apache.spark.serializer.SerializationDebugger import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.StreamingContextState._ import org.apache.spark.streaming.dstream._ import org.apache.spark.streaming.receiver.{ActorReceiver, ActorSupervisorStrategy, Receiver} -import org.apache.spark.streaming.scheduler._ +import org.apache.spark.streaming.scheduler.{JobScheduler, StreamingListener} import org.apache.spark.streaming.ui.{StreamingJobProgressListener, StreamingTab} +import org.apache.spark.util.{CallSite, Utils} /** * Main entry point for Spark Streaming functionality. It provides methods used to create @@ -131,7 +136,7 @@ class StreamingContext private[streaming] ( if (sc_ != null) { sc_ } else if (isCheckpointPresent) { - new SparkContext(cp_.createSparkConf()) + SparkContext.getOrCreate(cp_.createSparkConf()) } else { throw new SparkException("Cannot create StreamingContext without a SparkContext") } @@ -152,7 +157,7 @@ class StreamingContext private[streaming] ( cp_.graph.restoreCheckpointData() cp_.graph } else { - assert(batchDur_ != null, "Batch duration for streaming context cannot be null") + require(batchDur_ != null, "Batch duration for StreamingContext cannot be null") val newGraph = new DStreamGraph() newGraph.setBatchDuration(batchDur_) newGraph @@ -193,14 +198,11 @@ class StreamingContext private[streaming] ( assert(env.metricsSystem != null) env.metricsSystem.registerSource(streamingSource) - /** Enumeration to identify current state of the StreamingContext */ - private[streaming] object StreamingContextState extends Enumeration { - type CheckpointState = Value - val Initialized, Started, Stopped = Value - } + private var state: StreamingContextState = INITIALIZED - import StreamingContextState._ - private[streaming] var state = Initialized + private val startSite = new AtomicReference[CallSite](null) + + private var shutdownHookRef: AnyRef = _ /** * Return the associated Spark context @@ -237,21 +239,46 @@ class StreamingContext private[streaming] ( } } + private[streaming] def isCheckpointingEnabled: Boolean = { + checkpointDir != null + } + private[streaming] def initialCheckpoint: Checkpoint = { if (isCheckpointPresent) cp_ else null } private[streaming] def getNewInputStreamId() = nextInputStreamId.getAndIncrement() + /** + * Execute a block of code in a scope such that all new DStreams created in this body will + * be part of the same scope. For more detail, see the comments in `doCompute`. + * + * Note: Return statements are NOT allowed in the given body. + */ + private[streaming] def withScope[U](body: => U): U = sparkContext.withScope(body) + + /** + * Execute a block of code in a scope such that all new DStreams created in this body will + * be part of the same scope. For more detail, see the comments in `doCompute`. + * + * Note: Return statements are NOT allowed in the given body. + */ + private[streaming] def withNamedScope[U](name: String)(body: => U): U = { + RDDOperationScope.withScope(sc, name, allowNesting = false, ignoreParent = false)(body) + } + /** * Create an input stream with any arbitrary user implemented receiver. * Find more details at: http://spark.apache.org/docs/latest/streaming-custom-receivers.html * @param receiver Custom implementation of Receiver + * + * @deprecated As of 1.0.0", replaced by `receiverStream`. */ @deprecated("Use receiverStream", "1.0.0") - def networkStream[T: ClassTag]( - receiver: Receiver[T]): ReceiverInputDStream[T] = { - receiverStream(receiver) + def networkStream[T: ClassTag](receiver: Receiver[T]): ReceiverInputDStream[T] = { + withNamedScope("network stream") { + receiverStream(receiver) + } } /** @@ -259,9 +286,10 @@ class StreamingContext private[streaming] ( * Find more details at: http://spark.apache.org/docs/latest/streaming-custom-receivers.html * @param receiver Custom implementation of Receiver */ - def receiverStream[T: ClassTag]( - receiver: Receiver[T]): ReceiverInputDStream[T] = { - new PluggableInputDStream[T](this, receiver) + def receiverStream[T: ClassTag](receiver: Receiver[T]): ReceiverInputDStream[T] = { + withNamedScope("receiver stream") { + new PluggableInputDStream[T](this, receiver) + } } /** @@ -281,7 +309,7 @@ class StreamingContext private[streaming] ( name: String, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2, supervisorStrategy: SupervisorStrategy = ActorSupervisorStrategy.defaultStrategy - ): ReceiverInputDStream[T] = { + ): ReceiverInputDStream[T] = withNamedScope("actor stream") { receiverStream(new ActorReceiver[T](props, name, storageLevel, supervisorStrategy)) } @@ -298,7 +326,7 @@ class StreamingContext private[streaming] ( hostname: String, port: Int, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 - ): ReceiverInputDStream[String] = { + ): ReceiverInputDStream[String] = withNamedScope("socket text stream") { socketStream[String](hostname, port, SocketReceiver.bytesToLines, storageLevel) } @@ -336,7 +364,7 @@ class StreamingContext private[streaming] ( hostname: String, port: Int, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 - ): ReceiverInputDStream[T] = { + ): ReceiverInputDStream[T] = withNamedScope("raw socket stream") { new RawInputDStream[T](this, hostname, port, storageLevel) } @@ -410,7 +438,7 @@ class StreamingContext private[streaming] ( * file system. File names starting with . are ignored. * @param directory HDFS directory to monitor for new file */ - def textFileStream(directory: String): DStream[String] = { + def textFileStream(directory: String): DStream[String] = withNamedScope("text file stream") { fileStream[LongWritable, Text, TextInputFormat](directory).map(_._2.toString) } @@ -432,14 +460,15 @@ class StreamingContext private[streaming] ( @Experimental def binaryRecordsStream( directory: String, - recordLength: Int): DStream[Array[Byte]] = { + recordLength: Int): DStream[Array[Byte]] = withNamedScope("binary records stream") { val conf = sc_.hadoopConfiguration conf.setInt(FixedLengthBinaryInputFormat.RECORD_LENGTH_PROPERTY, recordLength) val br = fileStream[LongWritable, BytesWritable, FixedLengthBinaryInputFormat]( - directory, FileInputDStream.defaultFilter : Path => Boolean, newFilesOnly=true, conf) + directory, FileInputDStream.defaultFilter: Path => Boolean, newFilesOnly = true, conf) val data = br.map { case (k, v) => val bytes = v.getBytes - assert(bytes.length == recordLength, "Byte array does not have correct length") + require(bytes.length == recordLength, "Byte array does not have correct length. " + + s"${bytes.length} did not equal recordLength: $recordLength") bytes } data @@ -479,7 +508,7 @@ class StreamingContext private[streaming] ( /** * Create a unified DStream from multiple DStreams of the same type and same slide duration. */ - def union[T: ClassTag](streams: Seq[DStream[T]]): DStream[T] = { + def union[T: ClassTag](streams: Seq[DStream[T]]): DStream[T] = withScope { new UnionDStream[T](streams.toArray) } @@ -490,7 +519,7 @@ class StreamingContext private[streaming] ( def transform[T: ClassTag]( dstreams: Seq[DStream[_]], transformFunc: (Seq[RDD[_]], Time) => RDD[T] - ): DStream[T] = { + ): DStream[T] = withScope { new TransformedDStream[T](dstreams, sparkContext.clean(transformFunc)) } @@ -505,30 +534,77 @@ class StreamingContext private[streaming] ( assert(graph != null, "Graph is null") graph.validate() - assert( - checkpointDir == null || checkpointDuration != null, + require( + !isCheckpointingEnabled || checkpointDuration != null, "Checkpoint directory has been set, but the graph checkpointing interval has " + "not been set. Please use StreamingContext.checkpoint() to set the interval." ) + + // Verify whether the DStream checkpoint is serializable + if (isCheckpointingEnabled) { + val checkpoint = new Checkpoint(this, Time.apply(0)) + try { + Checkpoint.serialize(checkpoint, conf) + } catch { + case e: NotSerializableException => + throw new NotSerializableException( + "DStream checkpointing has been enabled but the DStreams with their functions " + + "are not serializable\n" + + SerializationDebugger.improveException(checkpoint, e).getMessage() + ) + } + } + } + + /** + * :: DeveloperApi :: + * + * Return the current state of the context. The context can be in three possible states - + * - StreamingContextState.INTIALIZED - The context has been created, but not been started yet. + * Input DStreams, transformations and output operations can be created on the context. + * - StreamingContextState.ACTIVE - The context has been started, and been not stopped. + * Input DStreams, transformations and output operations cannot be created on the context. + * - StreamingContextState.STOPPED - The context has been stopped and cannot be used any more. + */ + @DeveloperApi + def getState(): StreamingContextState = synchronized { + state } /** * Start the execution of the streams. * - * @throws SparkException if the context has already been started or stopped. + * @throws IllegalStateException if the StreamingContext is already stopped. */ def start(): Unit = synchronized { - if (state == Started) { - throw new SparkException("StreamingContext has already been started") - } - if (state == Stopped) { - throw new SparkException("StreamingContext has already been stopped") + state match { + case INITIALIZED => + startSite.set(DStream.getCreationSite()) + sparkContext.setCallSite(startSite.get) + StreamingContext.ACTIVATION_LOCK.synchronized { + StreamingContext.assertNoOtherContextIsActive() + try { + validate() + scheduler.start() + state = StreamingContextState.ACTIVE + } catch { + case NonFatal(e) => + logError("Error starting the context, marking it as stopped", e) + scheduler.stop(false) + state = StreamingContextState.STOPPED + throw e + } + StreamingContext.setActiveContext(this) + } + shutdownHookRef = Utils.addShutdownHook( + StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown) + uiTab.foreach(_.attach()) + logInfo("StreamingContext started") + case ACTIVE => + logWarning("StreamingContext has already been started") + case STOPPED => + throw new IllegalStateException("StreamingContext has already been stopped") } - validate() - sparkContext.setCallSite(DStream.getCreationSite()) - scheduler.start() - uiTab.foreach(_.attach()) - state = Started } /** @@ -543,6 +619,8 @@ class StreamingContext private[streaming] ( * Wait for the execution to stop. Any exceptions that occurs during the execution * will be thrown in this thread. * @param timeout time to wait in milliseconds + * + * @deprecated As of 1.3.0, replaced by `awaitTerminationOrTimeout(Long)`. */ @deprecated("Use awaitTerminationOrTimeout(Long) instead", "1.3.0") def awaitTermination(timeout: Long) { @@ -588,21 +666,36 @@ class StreamingContext private[streaming] ( * received data to be completed */ def stop(stopSparkContext: Boolean, stopGracefully: Boolean): Unit = synchronized { - state match { - case Initialized => logWarning("StreamingContext has not been started yet") - case Stopped => logWarning("StreamingContext has already been stopped") - case Started => - scheduler.stop(stopGracefully) - logInfo("StreamingContext stopped successfully") - waiter.notifyStop() + try { + state match { + case INITIALIZED => + logWarning("StreamingContext has not been started yet") + case STOPPED => + logWarning("StreamingContext has already been stopped") + case ACTIVE => + scheduler.stop(stopGracefully) + uiTab.foreach(_.detach()) + StreamingContext.setActiveContext(null) + waiter.notifyStop() + if (shutdownHookRef != null) { + Utils.removeShutdownHook(shutdownHookRef) + } + logInfo("StreamingContext stopped successfully") + } + // Even if we have already stopped, we still need to attempt to stop the SparkContext because + // a user might stop(stopSparkContext = false) and then call stop(stopSparkContext = true). + if (stopSparkContext) sc.stop() + } finally { + // The state should always be Stopped after calling `stop()`, even if we haven't started yet + state = STOPPED } - // Even if the streaming context has not been started, we still need to stop the SparkContext. - // Even if we have already stopped, we still need to attempt to stop the SparkContext because - // a user might stop(stopSparkContext = false) and then call stop(stopSparkContext = true). - if (stopSparkContext) sc.stop() - uiTab.foreach(_.detach()) - // The state should always be Stopped after calling `stop()`, even if we haven't started yet: - state = Stopped + } + + private def stopOnShutdown(): Unit = { + val stopGracefully = conf.getBoolean("spark.streaming.stopGracefullyOnShutdown", false) + logInfo(s"Invoking stop(stopGracefully=$stopGracefully) from shutdown hook") + // Do not stop SparkContext, let its own shutdown hook stop it + stop(stopSparkContext = false, stopGracefully = stopGracefully) } } @@ -613,8 +706,49 @@ class StreamingContext private[streaming] ( object StreamingContext extends Logging { - private[streaming] val DEFAULT_CLEANER_TTL = 3600 + /** + * Lock that guards activation of a StreamingContext as well as access to the singleton active + * StreamingContext in getActiveOrCreate(). + */ + private val ACTIVATION_LOCK = new Object() + + private val SHUTDOWN_HOOK_PRIORITY = Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY + 1 + + private val activeContext = new AtomicReference[StreamingContext](null) + private def assertNoOtherContextIsActive(): Unit = { + ACTIVATION_LOCK.synchronized { + if (activeContext.get() != null) { + throw new IllegalStateException( + "Only one StreamingContext may be started in this JVM. " + + "Currently running StreamingContext was started at" + + activeContext.get.startSite.get.longForm) + } + } + } + + private def setActiveContext(ssc: StreamingContext): Unit = { + ACTIVATION_LOCK.synchronized { + activeContext.set(ssc) + } + } + + /** + * :: Experimental :: + * + * Get the currently active context, if there is one. Active means started but not stopped. + */ + @Experimental + def getActive(): Option[StreamingContext] = { + ACTIVATION_LOCK.synchronized { + Option(activeContext.get()) + } + } + + /** + * @deprecated As of 1.3.0, replaced by implicit functions in the DStream companion object. + * This is kept here only for backward compatibility. + */ @deprecated("Replaced by implicit functions in the DStream companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def toPairDStreamFunctions[K, V](stream: DStream[(K, V)]) @@ -624,10 +758,26 @@ object StreamingContext extends Logging { } /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the StreamingContext - * will be created by called the provided `creatingFunc`. + * :: Experimental :: + * + * Either return the "active" StreamingContext (that is, started but not stopped), or create a + * new StreamingContext that is + * @param creatingFunc Function to create a new StreamingContext + */ + @Experimental + def getActiveOrCreate(creatingFunc: () => StreamingContext): StreamingContext = { + ACTIVATION_LOCK.synchronized { + getActive().getOrElse { creatingFunc() } + } + } + + /** + * :: Experimental :: + * + * Either get the currently active StreamingContext (that is, started but not stopped), + * OR recreate a StreamingContext from checkpoint data in the given path. If checkpoint data + * does not exist in the provided, then create a new StreamingContext by calling the provided + * `creatingFunc`. * * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program * @param creatingFunc Function to create a new StreamingContext @@ -637,63 +787,41 @@ object StreamingContext extends Logging { * error in reading checkpoint data. By default, an exception will be * thrown on error. */ - def getOrCreate( + @Experimental + def getActiveOrCreate( checkpointPath: String, creatingFunc: () => StreamingContext, hadoopConf: Configuration = new Configuration(), createOnError: Boolean = false ): StreamingContext = { - val checkpointOption = CheckpointReader.read( - checkpointPath, new SparkConf(), hadoopConf, createOnError) - checkpointOption.map(new StreamingContext(null, _, null)).getOrElse(creatingFunc()) - } - - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the StreamingContext - * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note - * that the SparkConf configuration in the checkpoint data will not be restored as the - * SparkContext has already been created. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new StreamingContext using the given SparkContext - * @param sparkContext SparkContext using which the StreamingContext will be created - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: SparkContext => StreamingContext, - sparkContext: SparkContext - ): StreamingContext = { - getOrCreate(checkpointPath, creatingFunc, sparkContext, createOnError = false) + ACTIVATION_LOCK.synchronized { + getActive().getOrElse { getOrCreate(checkpointPath, creatingFunc, hadoopConf, createOnError) } + } } /** * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be * recreated from the checkpoint data. If the data does not exist, then the StreamingContext - * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note - * that the SparkConf configuration in the checkpoint data will not be restored as the - * SparkContext has already been created. + * will be created by called the provided `creatingFunc`. * * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new StreamingContext using the given SparkContext - * @param sparkContext SparkContext using which the StreamingContext will be created - * @param createOnError Whether to create a new StreamingContext if there is an + * @param creatingFunc Function to create a new StreamingContext + * @param hadoopConf Optional Hadoop configuration if necessary for reading from the + * file system + * @param createOnError Optional, whether to create a new StreamingContext if there is an * error in reading checkpoint data. By default, an exception will be * thrown on error. */ def getOrCreate( checkpointPath: String, - creatingFunc: SparkContext => StreamingContext, - sparkContext: SparkContext, - createOnError: Boolean + creatingFunc: () => StreamingContext, + hadoopConf: Configuration = new Configuration(), + createOnError: Boolean = false ): StreamingContext = { val checkpointOption = CheckpointReader.read( - checkpointPath, sparkContext.conf, sparkContext.hadoopConfiguration, createOnError) - checkpointOption.map(new StreamingContext(sparkContext, _, null)) - .getOrElse(creatingFunc(sparkContext)) + checkpointPath, new SparkConf(), hadoopConf, createOnError) + checkpointOption.map(new StreamingContext(null, _, null)).getOrElse(creatingFunc()) } /** @@ -715,7 +843,7 @@ object StreamingContext extends Logging { ): SparkContext = { val conf = SparkContext.updatedConf( new SparkConf(), master, appName, sparkHome, jars, environment) - createNewSparkContext(conf) + new SparkContext(conf) } private[streaming] def rddToFileName[T](prefix: String, suffix: String, time: Time): String = { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContextState.java b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContextState.java new file mode 100644 index 0000000000000..d7b639383ee34 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContextState.java @@ -0,0 +1,45 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming; + +import org.apache.spark.annotation.DeveloperApi; + +/** + * :: DeveloperApi :: + * + * Represents the state of a StreamingContext. + */ +@DeveloperApi +public enum StreamingContextState { + /** + * The context has been created, but not been started yet. + * Input DStreams, transformations and output operations can be created on the context. + */ + INITIALIZED, + + /** + * The context has been started, and been not stopped. + * Input DStreams, transformations and output operations cannot be created on the context. + */ + ACTIVE, + + /** + * The context has been stopped and cannot be used any more. + */ + STOPPED +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Time.scala b/streaming/src/main/scala/org/apache/spark/streaming/Time.scala index 42c49678d24f0..92cfd7d40338c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Time.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Time.scala @@ -63,6 +63,11 @@ case class Time(private val millis: Long) { new Time((this.millis / t) * t) } + def floor(that: Duration, zeroTime: Time): Time = { + val t = that.milliseconds + new Time(((this.millis - zeroTime.milliseconds) / t) * t + zeroTime.milliseconds) + } + def isMultipleOf(that: Duration): Boolean = (this.millis % that.milliseconds == 0) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala index 93baad19e3ee1..959ac9c177f81 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala @@ -227,7 +227,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * @param numPartitions Number of partitions of each RDD in the new DStream. */ def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int) - :JavaPairDStream[K, JIterable[V]] = { + : JavaPairDStream[K, JIterable[V]] = { dstream.groupByKeyAndWindow(windowDuration, slideDuration, numPartitions) .mapValues(asJavaIterable _) } @@ -247,7 +247,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner - ):JavaPairDStream[K, JIterable[V]] = { + ): JavaPairDStream[K, JIterable[V]] = { dstream.groupByKeyAndWindow(windowDuration, slideDuration, partitioner) .mapValues(asJavaIterable _) } @@ -262,7 +262,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * batching interval */ def reduceByKeyAndWindow(reduceFunc: JFunction2[V, V, V], windowDuration: Duration) - :JavaPairDStream[K, V] = { + : JavaPairDStream[K, V] = { dstream.reduceByKeyAndWindow(reduceFunc, windowDuration) } @@ -281,7 +281,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( reduceFunc: JFunction2[V, V, V], windowDuration: Duration, slideDuration: Duration - ):JavaPairDStream[K, V] = { + ): JavaPairDStream[K, V] = { dstream.reduceByKeyAndWindow(reduceFunc, windowDuration, slideDuration) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 572d7d8e8753d..989e3a729ebc2 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -148,6 +148,9 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { /** The underlying SparkContext */ val sparkContext = new JavaSparkContext(ssc.sc) + /** + * @deprecated As of 0.9.0, replaced by `sparkContext` + */ @deprecated("use sparkContext", "0.9.0") val sc: JavaSparkContext = sparkContext @@ -578,6 +581,28 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { ssc.addStreamingListener(streamingListener) } + /** + * :: DeveloperApi :: + * + * Return the current state of the context. The context can be in three possible states - + *
        + *
      • + * StreamingContextState.INTIALIZED - The context has been created, but not been started yet. + * Input DStreams, transformations and output operations can be created on the context. + *
        • + *
      • + * StreamingContextState.ACTIVE - The context has been started, and been not stopped. + * Input DStreams, transformations and output operations cannot be created on the context. + *
        • + *
      • + * StreamingContextState.STOPPED - The context has been stopped and cannot be used any more. + *
        • + *
      + */ + def getState(): StreamingContextState = { + ssc.getState() + } + /** * Start the execution of the streams. */ @@ -597,6 +622,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { * Wait for the execution to stop. Any exceptions that occurs during the execution * will be thrown in this thread. * @param timeout time to wait in milliseconds + * @deprecated As of 1.3.0, replaced by `awaitTerminationOrTimeout(Long)`. */ @deprecated("Use awaitTerminationOrTimeout(Long) instead", "1.3.0") def awaitTermination(timeout: Long): Unit = { @@ -655,6 +681,7 @@ object JavaStreamingContext { * * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program * @param factory JavaStreamingContextFactory object to create a new JavaStreamingContext + * @deprecated As of 1.4.0, replaced by `getOrCreate` without JavaStreamingContextFactor. */ @deprecated("use getOrCreate without JavaStreamingContextFactor", "1.4.0") def getOrCreate( @@ -677,6 +704,7 @@ object JavaStreamingContext { * @param factory JavaStreamingContextFactory object to create a new JavaStreamingContext * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible * file system + * @deprecated As of 1.4.0, replaced by `getOrCreate` without JavaStreamingContextFactor. */ @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( @@ -702,6 +730,7 @@ object JavaStreamingContext { * file system * @param createOnError Whether to create a new JavaStreamingContext if there is an * error in reading checkpoint data. + * @deprecated As of 1.4.0, replaced by `getOrCreate` without JavaStreamingContextFactor. */ @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( @@ -782,51 +811,6 @@ object JavaStreamingContext { new JavaStreamingContext(ssc) } - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the provided factory - * will be used to create a JavaStreamingContext. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new JavaStreamingContext - * @param sparkContext SparkContext using which the StreamingContext will be created - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], - sparkContext: JavaSparkContext - ): JavaStreamingContext = { - val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { - creatingFunc.call(new JavaSparkContext(sparkContext)).ssc - }, sparkContext.sc) - new JavaStreamingContext(ssc) - } - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the provided factory - * will be used to create a JavaStreamingContext. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new JavaStreamingContext - * @param sparkContext SparkContext using which the StreamingContext will be created - * @param createOnError Whether to create a new JavaStreamingContext if there is an - * error in reading checkpoint data. - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], - sparkContext: JavaSparkContext, - createOnError: Boolean - ): JavaStreamingContext = { - val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { - creatingFunc.call(new JavaSparkContext(sparkContext)).ssc - }, sparkContext.sc, createOnError) - new JavaStreamingContext(ssc) - } - /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala index 4c28654ef6413..d06401245ff17 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala @@ -109,7 +109,7 @@ private[python] object PythonTransformFunctionSerializer { } def serialize(func: PythonTransformFunction): Array[Byte] = { - assert(serializer != null, "Serializer has not been registered!") + require(serializer != null, "Serializer has not been registered!") // get the id of PythonTransformFunction in py4j val h = Proxy.getInvocationHandler(func.asInstanceOf[Proxy]) val f = h.getClass().getDeclaredField("id") @@ -119,7 +119,7 @@ private[python] object PythonTransformFunctionSerializer { } def deserialize(bytes: Array[Byte]): PythonTransformFunction = { - assert(serializer != null, "Serializer has not been registered!") + require(serializer != null, "Serializer has not been registered!") serializer.loads(bytes) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala index f1f8a70655996..192aa6a139bcb 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala @@ -25,12 +25,13 @@ import scala.language.implicitConversions import scala.reflect.ClassTag import scala.util.matching.Regex -import org.apache.spark.{Logging, SparkException} -import org.apache.spark.rdd.{BlockRDD, PairRDDFunctions, RDD} +import org.apache.spark.{Logging, SparkContext, SparkException} +import org.apache.spark.rdd.{BlockRDD, PairRDDFunctions, RDD, RDDOperationScope} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext.rddToFileName import org.apache.spark.streaming.scheduler.Job +import org.apache.spark.streaming.ui.UIUtils import org.apache.spark.util.{CallSite, MetadataCleaner, Utils} /** @@ -60,6 +61,8 @@ abstract class DStream[T: ClassTag] ( @transient private[streaming] var ssc: StreamingContext ) extends Serializable with Logging { + validateAtInit() + // ======================================================================= // Methods that should be implemented by subclasses of DStream // ======================================================================= @@ -71,7 +74,7 @@ abstract class DStream[T: ClassTag] ( def dependencies: List[DStream[_]] /** Method that generates a RDD for the given time */ - def compute (validTime: Time): Option[RDD[T]] + def compute(validTime: Time): Option[RDD[T]] // ======================================================================= // Methods and fields available on all DStreams @@ -109,6 +112,44 @@ abstract class DStream[T: ClassTag] ( /* Set the creation call site */ private[streaming] val creationSite = DStream.getCreationSite() + /** + * The base scope associated with the operation that created this DStream. + * + * This is the medium through which we pass the DStream operation name (e.g. updatedStateByKey) + * to the RDDs created by this DStream. Note that we never use this scope directly in RDDs. + * Instead, we instantiate a new scope during each call to `compute` based on this one. + * + * This is not defined if the DStream is created outside of one of the public DStream operations. + */ + protected[streaming] val baseScope: Option[String] = { + Option(ssc.sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY)) + } + + /** + * Make a scope that groups RDDs created in the same DStream operation in the same batch. + * + * Each DStream produces many scopes and each scope may be shared by other DStreams created + * in the same operation. Separate calls to the same DStream operation create separate scopes. + * For instance, `dstream.map(...).map(...)` creates two separate scopes per batch. + */ + private def makeScope(time: Time): Option[RDDOperationScope] = { + baseScope.map { bsJson => + val formattedBatchTime = UIUtils.formatBatchTime( + time.milliseconds, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false) + val bs = RDDOperationScope.fromJson(bsJson) + val baseName = bs.name // e.g. countByWindow, "kafka stream [0]" + val scopeName = + if (baseName.length > 10) { + // If the operation name is too long, wrap the line + s"$baseName\n@ $formattedBatchTime" + } else { + s"$baseName @ $formattedBatchTime" + } + val scopeId = s"${bs.id}_${time.milliseconds}" + new RDDOperationScope(scopeName, id = scopeId) + } + } + /** Persist the RDDs of this DStream with the given storage level */ def persist(level: StorageLevel): DStream[T] = { if (this.isInitialized) { @@ -171,43 +212,57 @@ abstract class DStream[T: ClassTag] ( dependencies.foreach(_.initialize(zeroTime)) } - private[streaming] def validate() { - assert(rememberDuration != null, "Remember duration is set to null") + private def validateAtInit(): Unit = { + ssc.getState() match { + case StreamingContextState.INITIALIZED => + // good to go + case StreamingContextState.ACTIVE => + throw new IllegalStateException( + "Adding new inputs, transformations, and output operations after " + + "starting a context is not supported") + case StreamingContextState.STOPPED => + throw new IllegalStateException( + "Adding new inputs, transformations, and output operations after " + + "stopping a context is not supported") + } + } + + private[streaming] def validateAtStart() { + require(rememberDuration != null, "Remember duration is set to null") - assert( + require( !mustCheckpoint || checkpointDuration != null, "The checkpoint interval for " + this.getClass.getSimpleName + " has not been set." + " Please use DStream.checkpoint() to set the interval." ) - assert( + require( checkpointDuration == null || context.sparkContext.checkpointDir.isDefined, - "The checkpoint directory has not been set. Please use StreamingContext.checkpoint()" + - " or SparkContext.checkpoint() to set the checkpoint directory." + "The checkpoint directory has not been set. Please set it by StreamingContext.checkpoint()." ) - assert( + require( checkpointDuration == null || checkpointDuration >= slideDuration, "The checkpoint interval for " + this.getClass.getSimpleName + " has been set to " + checkpointDuration + " which is lower than its slide time (" + slideDuration + "). " + "Please set it to at least " + slideDuration + "." ) - assert( + require( checkpointDuration == null || checkpointDuration.isMultipleOf(slideDuration), "The checkpoint interval for " + this.getClass.getSimpleName + " has been set to " + checkpointDuration + " which not a multiple of its slide time (" + slideDuration + "). " + - "Please set it to a multiple " + slideDuration + "." + "Please set it to a multiple of " + slideDuration + "." ) - assert( + require( checkpointDuration == null || storageLevel != StorageLevel.NONE, "" + this.getClass.getSimpleName + " has been marked for checkpointing but the storage " + "level has not been set to enable persisting. Please use DStream.persist() to set the " + "storage level to use memory for better checkpointing performance." ) - assert( + require( checkpointDuration == null || rememberDuration > checkpointDuration, "The remember duration for " + this.getClass.getSimpleName + " has been set to " + rememberDuration + " which is not more than the checkpoint interval (" + @@ -216,7 +271,7 @@ abstract class DStream[T: ClassTag] ( val metadataCleanerDelay = MetadataCleaner.getDelaySeconds(ssc.conf) logInfo("metadataCleanupDelay = " + metadataCleanerDelay) - assert( + require( metadataCleanerDelay < 0 || rememberDuration.milliseconds < metadataCleanerDelay * 1000, "It seems you are doing some DStream window operation or setting a checkpoint interval " + "which requires " + this.getClass.getSimpleName + " to remember generated RDDs for more " + @@ -226,7 +281,7 @@ abstract class DStream[T: ClassTag] ( math.ceil(rememberDuration.milliseconds / 1000.0).toInt + " seconds." ) - dependencies.foreach(_.validate()) + dependencies.foreach(_.validateAtStart()) logInfo("Slide time = " + slideDuration) logInfo("Storage level = " + storageLevel) @@ -278,28 +333,23 @@ abstract class DStream[T: ClassTag] ( * Get the RDD corresponding to the given time; either retrieve it from cache * or compute-and-cache it. */ - private[streaming] def getOrCompute(time: Time): Option[RDD[T]] = { + private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = { // If RDD was already generated, then retrieve it from HashMap, // or else compute the RDD generatedRDDs.get(time).orElse { // Compute the RDD if time is valid (e.g. correct time in a sliding window) // of RDD generation, else generate nothing. if (isTimeValid(time)) { - // Set the thread-local property for call sites to this DStream's creation site - // such that RDDs generated by compute gets that as their creation site. - // Note that this `getOrCompute` may get called from another DStream which may have - // set its own call site. So we store its call site in a temporary variable, - // set this DStream's creation site, generate RDDs and then restore the previous call site. - val prevCallSite = ssc.sparkContext.getCallSite() - ssc.sparkContext.setCallSite(creationSite) - // Disable checks for existing output directories in jobs launched by the streaming - // scheduler, since we may need to write output to an existing directory during checkpoint - // recovery; see SPARK-4835 for more details. We need to have this call here because - // compute() might cause Spark jobs to be launched. - val rddOption = PairRDDFunctions.disableOutputSpecValidation.withValue(true) { - compute(time) + + val rddOption = createRDDWithLocalProperties(time) { + // Disable checks for existing output directories in jobs launched by the streaming + // scheduler, since we may need to write output to an existing directory during checkpoint + // recovery; see SPARK-4835 for more details. We need to have this call here because + // compute() might cause Spark jobs to be launched. + PairRDDFunctions.disableOutputSpecValidation.withValue(true) { + compute(time) + } } - ssc.sparkContext.setCallSite(prevCallSite) rddOption.foreach { case newRDD => // Register the generated RDD for caching and checkpointing @@ -320,6 +370,41 @@ abstract class DStream[T: ClassTag] ( } } + /** + * Wrap a body of code such that the call site and operation scope + * information are passed to the RDDs created in this body properly. + */ + protected def createRDDWithLocalProperties[U](time: Time)(body: => U): U = { + val scopeKey = SparkContext.RDD_SCOPE_KEY + val scopeNoOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY + // Pass this DStream's operation scope and creation site information to RDDs through + // thread-local properties in our SparkContext. Since this method may be called from another + // DStream, we need to temporarily store any old scope and creation site information to + // restore them later after setting our own. + val prevCallSite = ssc.sparkContext.getCallSite() + val prevScope = ssc.sparkContext.getLocalProperty(scopeKey) + val prevScopeNoOverride = ssc.sparkContext.getLocalProperty(scopeNoOverrideKey) + + try { + ssc.sparkContext.setCallSite(creationSite) + // Use the DStream's base scope for this RDD so we can (1) preserve the higher level + // DStream operation name, and (2) share this scope with other DStreams created in the + // same operation. Disallow nesting so that low-level Spark primitives do not show up. + // TODO: merge callsites with scopes so we can just reuse the code there + makeScope(time).foreach { s => + ssc.sparkContext.setLocalProperty(scopeKey, s.toJson) + ssc.sparkContext.setLocalProperty(scopeNoOverrideKey, "true") + } + + body + } finally { + // Restore any state that was modified before returning + ssc.sparkContext.setCallSite(prevCallSite) + ssc.sparkContext.setLocalProperty(scopeKey, prevScope) + ssc.sparkContext.setLocalProperty(scopeNoOverrideKey, prevScopeNoOverride) + } + } + /** * Generate a SparkStreaming job for the given time. This is an internal method that * should not be called directly. This default implementation creates a job @@ -439,7 +524,7 @@ abstract class DStream[T: ClassTag] ( // ======================================================================= /** Return a new DStream by applying a function to all elements of this DStream. */ - def map[U: ClassTag](mapFunc: T => U): DStream[U] = { + def map[U: ClassTag](mapFunc: T => U): DStream[U] = ssc.withScope { new MappedDStream(this, context.sparkContext.clean(mapFunc)) } @@ -447,26 +532,31 @@ abstract class DStream[T: ClassTag] ( * Return a new DStream by applying a function to all elements of this DStream, * and then flattening the results */ - def flatMap[U: ClassTag](flatMapFunc: T => Traversable[U]): DStream[U] = { + def flatMap[U: ClassTag](flatMapFunc: T => Traversable[U]): DStream[U] = ssc.withScope { new FlatMappedDStream(this, context.sparkContext.clean(flatMapFunc)) } /** Return a new DStream containing only the elements that satisfy a predicate. */ - def filter(filterFunc: T => Boolean): DStream[T] = new FilteredDStream(this, filterFunc) + def filter(filterFunc: T => Boolean): DStream[T] = ssc.withScope { + new FilteredDStream(this, context.sparkContext.clean(filterFunc)) + } /** * Return a new DStream in which each RDD is generated by applying glom() to each RDD of * this DStream. Applying glom() to an RDD coalesces all elements within each partition into * an array. */ - def glom(): DStream[Array[T]] = new GlommedDStream(this) - + def glom(): DStream[Array[T]] = ssc.withScope { + new GlommedDStream(this) + } /** * Return a new DStream with an increased or decreased level of parallelism. Each RDD in the * returned DStream has exactly numPartitions partitions. */ - def repartition(numPartitions: Int): DStream[T] = this.transform(_.repartition(numPartitions)) + def repartition(numPartitions: Int): DStream[T] = ssc.withScope { + this.transform(_.repartition(numPartitions)) + } /** * Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs @@ -476,7 +566,7 @@ abstract class DStream[T: ClassTag] ( def mapPartitions[U: ClassTag]( mapPartFunc: Iterator[T] => Iterator[U], preservePartitioning: Boolean = false - ): DStream[U] = { + ): DStream[U] = ssc.withScope { new MapPartitionedDStream(this, context.sparkContext.clean(mapPartFunc), preservePartitioning) } @@ -484,14 +574,15 @@ abstract class DStream[T: ClassTag] ( * Return a new DStream in which each RDD has a single element generated by reducing each RDD * of this DStream. */ - def reduce(reduceFunc: (T, T) => T): DStream[T] = + def reduce(reduceFunc: (T, T) => T): DStream[T] = ssc.withScope { this.map(x => (null, x)).reduceByKey(reduceFunc, 1).map(_._2) + } /** * Return a new DStream in which each RDD has a single element generated by counting each RDD * of this DStream. */ - def count(): DStream[Long] = { + def count(): DStream[Long] = ssc.withScope { this.map(_ => (null, 1L)) .transform(_.union(context.sparkContext.makeRDD(Seq((null, 0L)), 1))) .reduceByKey(_ + _) @@ -505,24 +596,29 @@ abstract class DStream[T: ClassTag] ( * `numPartitions` not specified). */ def countByValue(numPartitions: Int = ssc.sc.defaultParallelism)(implicit ord: Ordering[T] = null) - : DStream[(T, Long)] = + : DStream[(T, Long)] = ssc.withScope { this.map(x => (x, 1L)).reduceByKey((x: Long, y: Long) => x + y, numPartitions) + } /** * Apply a function to each RDD in this DStream. This is an output operator, so * 'this' DStream will be registered as an output stream and therefore materialized. + * + * @deprecated As of 0.9.0, replaced by `foreachRDD`. */ @deprecated("use foreachRDD", "0.9.0") - def foreach(foreachFunc: RDD[T] => Unit): Unit = { + def foreach(foreachFunc: RDD[T] => Unit): Unit = ssc.withScope { this.foreachRDD(foreachFunc) } /** * Apply a function to each RDD in this DStream. This is an output operator, so * 'this' DStream will be registered as an output stream and therefore materialized. + * + * @deprecated As of 0.9.0, replaced by `foreachRDD`. */ @deprecated("use foreachRDD", "0.9.0") - def foreach(foreachFunc: (RDD[T], Time) => Unit): Unit = { + def foreach(foreachFunc: (RDD[T], Time) => Unit): Unit = ssc.withScope { this.foreachRDD(foreachFunc) } @@ -530,17 +626,18 @@ abstract class DStream[T: ClassTag] ( * Apply a function to each RDD in this DStream. This is an output operator, so * 'this' DStream will be registered as an output stream and therefore materialized. */ - def foreachRDD(foreachFunc: RDD[T] => Unit) { - this.foreachRDD((r: RDD[T], t: Time) => foreachFunc(r)) + def foreachRDD(foreachFunc: RDD[T] => Unit): Unit = ssc.withScope { + val cleanedF = context.sparkContext.clean(foreachFunc, false) + this.foreachRDD((r: RDD[T], t: Time) => cleanedF(r)) } /** * Apply a function to each RDD in this DStream. This is an output operator, so * 'this' DStream will be registered as an output stream and therefore materialized. */ - def foreachRDD(foreachFunc: (RDD[T], Time) => Unit) { - // because the DStream is reachable from the outer object here, and because - // DStreams can't be serialized with closures, we can't proactively check + def foreachRDD(foreachFunc: (RDD[T], Time) => Unit): Unit = ssc.withScope { + // because the DStream is reachable from the outer object here, and because + // DStreams can't be serialized with closures, we can't proactively check // it for serializability and so we pass the optional false to SparkContext.clean new ForEachDStream(this, context.sparkContext.clean(foreachFunc, false)).register() } @@ -549,9 +646,9 @@ abstract class DStream[T: ClassTag] ( * Return a new DStream in which each RDD is generated by applying a function * on each RDD of 'this' DStream. */ - def transform[U: ClassTag](transformFunc: RDD[T] => RDD[U]): DStream[U] = { - // because the DStream is reachable from the outer object here, and because - // DStreams can't be serialized with closures, we can't proactively check + def transform[U: ClassTag](transformFunc: RDD[T] => RDD[U]): DStream[U] = ssc.withScope { + // because the DStream is reachable from the outer object here, and because + // DStreams can't be serialized with closures, we can't proactively check // it for serializability and so we pass the optional false to SparkContext.clean val cleanedF = context.sparkContext.clean(transformFunc, false) transform((r: RDD[T], t: Time) => cleanedF(r)) @@ -561,12 +658,12 @@ abstract class DStream[T: ClassTag] ( * Return a new DStream in which each RDD is generated by applying a function * on each RDD of 'this' DStream. */ - def transform[U: ClassTag](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U] = { - // because the DStream is reachable from the outer object here, and because - // DStreams can't be serialized with closures, we can't proactively check + def transform[U: ClassTag](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U] = ssc.withScope { + // because the DStream is reachable from the outer object here, and because + // DStreams can't be serialized with closures, we can't proactively check // it for serializability and so we pass the optional false to SparkContext.clean val cleanedF = context.sparkContext.clean(transformFunc, false) - val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { assert(rdds.length == 1) cleanedF(rdds.head.asInstanceOf[RDD[T]], time) } @@ -579,9 +676,9 @@ abstract class DStream[T: ClassTag] ( */ def transformWith[U: ClassTag, V: ClassTag]( other: DStream[U], transformFunc: (RDD[T], RDD[U]) => RDD[V] - ): DStream[V] = { - // because the DStream is reachable from the outer object here, and because - // DStreams can't be serialized with closures, we can't proactively check + ): DStream[V] = ssc.withScope { + // because the DStream is reachable from the outer object here, and because + // DStreams can't be serialized with closures, we can't proactively check // it for serializability and so we pass the optional false to SparkContext.clean val cleanedF = ssc.sparkContext.clean(transformFunc, false) transformWith(other, (rdd1: RDD[T], rdd2: RDD[U], time: Time) => cleanedF(rdd1, rdd2)) @@ -593,9 +690,9 @@ abstract class DStream[T: ClassTag] ( */ def transformWith[U: ClassTag, V: ClassTag]( other: DStream[U], transformFunc: (RDD[T], RDD[U], Time) => RDD[V] - ): DStream[V] = { - // because the DStream is reachable from the outer object here, and because - // DStreams can't be serialized with closures, we can't proactively check + ): DStream[V] = ssc.withScope { + // because the DStream is reachable from the outer object here, and because + // DStreams can't be serialized with closures, we can't proactively check // it for serializability and so we pass the optional false to SparkContext.clean val cleanedF = ssc.sparkContext.clean(transformFunc, false) val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { @@ -611,7 +708,7 @@ abstract class DStream[T: ClassTag] ( * Print the first ten elements of each RDD generated in this DStream. This is an output * operator, so this DStream will be registered as an output stream and there materialized. */ - def print() { + def print(): Unit = ssc.withScope { print(10) } @@ -619,7 +716,7 @@ abstract class DStream[T: ClassTag] ( * Print the first num elements of each RDD generated in this DStream. This is an output * operator, so this DStream will be registered as an output stream and there materialized. */ - def print(num: Int) { + def print(num: Int): Unit = ssc.withScope { def foreachFunc: (RDD[T], Time) => Unit = { (rdd: RDD[T], time: Time) => { val firstNum = rdd.take(num + 1) @@ -651,7 +748,7 @@ abstract class DStream[T: ClassTag] ( * the new DStream will generate RDDs); must be a multiple of this * DStream's batching interval */ - def window(windowDuration: Duration, slideDuration: Duration): DStream[T] = { + def window(windowDuration: Duration, slideDuration: Duration): DStream[T] = ssc.withScope { new WindowedDStream(this, windowDuration, slideDuration) } @@ -669,7 +766,7 @@ abstract class DStream[T: ClassTag] ( reduceFunc: (T, T) => T, windowDuration: Duration, slideDuration: Duration - ): DStream[T] = { + ): DStream[T] = ssc.withScope { this.reduce(reduceFunc).window(windowDuration, slideDuration).reduce(reduceFunc) } @@ -694,7 +791,7 @@ abstract class DStream[T: ClassTag] ( invReduceFunc: (T, T) => T, windowDuration: Duration, slideDuration: Duration - ): DStream[T] = { + ): DStream[T] = ssc.withScope { this.map(x => (1, x)) .reduceByKeyAndWindow(reduceFunc, invReduceFunc, windowDuration, slideDuration, 1) .map(_._2) @@ -710,7 +807,9 @@ abstract class DStream[T: ClassTag] ( * the new DStream will generate RDDs); must be a multiple of this * DStream's batching interval */ - def countByWindow(windowDuration: Duration, slideDuration: Duration): DStream[Long] = { + def countByWindow( + windowDuration: Duration, + slideDuration: Duration): DStream[Long] = ssc.withScope { this.map(_ => 1L).reduceByWindow(_ + _, _ - _, windowDuration, slideDuration) } @@ -731,8 +830,7 @@ abstract class DStream[T: ClassTag] ( slideDuration: Duration, numPartitions: Int = ssc.sc.defaultParallelism) (implicit ord: Ordering[T] = null) - : DStream[(T, Long)] = - { + : DStream[(T, Long)] = ssc.withScope { this.map(x => (x, 1L)).reduceByKeyAndWindow( (x: Long, y: Long) => x + y, (x: Long, y: Long) => x - y, @@ -747,32 +845,40 @@ abstract class DStream[T: ClassTag] ( * Return a new DStream by unifying data of another DStream with this DStream. * @param that Another DStream having the same slideDuration as this DStream. */ - def union(that: DStream[T]): DStream[T] = new UnionDStream[T](Array(this, that)) + def union(that: DStream[T]): DStream[T] = ssc.withScope { + new UnionDStream[T](Array(this, that)) + } /** * Return all the RDDs defined by the Interval object (both end times included) */ - def slice(interval: Interval): Seq[RDD[T]] = { + def slice(interval: Interval): Seq[RDD[T]] = ssc.withScope { slice(interval.beginTime, interval.endTime) } /** * Return all the RDDs between 'fromTime' to 'toTime' (both included) */ - def slice(fromTime: Time, toTime: Time): Seq[RDD[T]] = { + def slice(fromTime: Time, toTime: Time): Seq[RDD[T]] = ssc.withScope { if (!isInitialized) { throw new SparkException(this + " has not been initialized") } - if (!(fromTime - zeroTime).isMultipleOf(slideDuration)) { - logWarning("fromTime (" + fromTime + ") is not a multiple of slideDuration (" - + slideDuration + ")") + + val alignedToTime = if ((toTime - zeroTime).isMultipleOf(slideDuration)) { + toTime + } else { + logWarning("toTime (" + toTime + ") is not a multiple of slideDuration (" + + slideDuration + ")") + toTime.floor(slideDuration, zeroTime) } - if (!(toTime - zeroTime).isMultipleOf(slideDuration)) { - logWarning("toTime (" + fromTime + ") is not a multiple of slideDuration (" - + slideDuration + ")") + + val alignedFromTime = if ((fromTime - zeroTime).isMultipleOf(slideDuration)) { + fromTime + } else { + logWarning("fromTime (" + fromTime + ") is not a multiple of slideDuration (" + + slideDuration + ")") + fromTime.floor(slideDuration, zeroTime) } - val alignedToTime = toTime.floor(slideDuration) - val alignedFromTime = fromTime.floor(slideDuration) logInfo("Slicing from " + fromTime + " to " + toTime + " (aligned to " + alignedFromTime + " and " + alignedToTime + ")") @@ -787,7 +893,7 @@ abstract class DStream[T: ClassTag] ( * The file name at each batch interval is generated based on `prefix` and * `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsObjectFiles(prefix: String, suffix: String = "") { + def saveAsObjectFiles(prefix: String, suffix: String = ""): Unit = ssc.withScope { val saveFunc = (rdd: RDD[T], time: Time) => { val file = rddToFileName(prefix, suffix, time) rdd.saveAsObjectFile(file) @@ -800,7 +906,7 @@ abstract class DStream[T: ClassTag] ( * of elements. The file name at each batch interval is generated based on * `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsTextFiles(prefix: String, suffix: String = "") { + def saveAsTextFiles(prefix: String, suffix: String = ""): Unit = ssc.withScope { val saveFunc = (rdd: RDD[T], time: Time) => { val file = rddToFileName(prefix, suffix, time) rdd.saveAsTextFile(file) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala index eca69f00188e4..86a8e2beff57c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala @@ -26,10 +26,9 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path, PathFilter} import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} -import org.apache.spark.{SparkConf, SerializableWritable} import org.apache.spark.rdd.{RDD, UnionRDD} import org.apache.spark.streaming._ -import org.apache.spark.util.{TimeStampedHashMap, Utils} +import org.apache.spark.util.{SerializableConfiguration, TimeStampedHashMap, Utils} /** * This class represents an input stream that monitors a Hadoop-compatible filesystem for new @@ -69,7 +68,7 @@ import org.apache.spark.util.{TimeStampedHashMap, Utils} * processing semantics are undefined. */ private[streaming] -class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( +class FileInputDStream[K, V, F <: NewInputFormat[K, V]]( @transient ssc_ : StreamingContext, directory: String, filter: Path => Boolean = FileInputDStream.defaultFilter, @@ -78,7 +77,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]) extends InputDStream[(K, V)](ssc_) { - private val serializableConfOpt = conf.map(new SerializableWritable(_)) + private val serializableConfOpt = conf.map(new SerializableConfiguration(_)) /** * Minimum duration of remembering the information of selected files. Defaults to 60 seconds. @@ -251,7 +250,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( /** Generate one RDD from an array of files */ private def filesToRDD(files: Seq[String]): RDD[(K, V)] = { - val fileRDDs = files.map(file =>{ + val fileRDDs = files.map { file => val rdd = serializableConfOpt.map(_.value) match { case Some(config) => context.sparkContext.newAPIHadoopFile( file, @@ -267,7 +266,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( "Refer to the streaming programming guide for more details.") } rdd - }) + } new UnionRDD(context.sparkContext, fileRDDs) } @@ -294,7 +293,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( private def readObject(ois: ObjectInputStream): Unit = Utils.tryOrIOException { logDebug(this.getClass().getSimpleName + ".readObject used") ois.defaultReadObject() - generatedRDDs = new mutable.HashMap[Time, RDD[(K,V)]] () + generatedRDDs = new mutable.HashMap[Time, RDD[(K, V)]]() batchTimeToSelectedFiles = new mutable.HashMap[Time, Array[String]] with mutable.SynchronizedMap[Time, Array[String]] recentlySelectedFiles = new mutable.HashSet[String]() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala index 685a32e1d280d..c109ceccc6989 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala @@ -37,7 +37,7 @@ class ForEachDStream[T: ClassTag] ( override def generateJob(time: Time): Option[Job] = { parent.getOrCompute(time) match { case Some(rdd) => - val jobFunc = () => { + val jobFunc = () => createRDDWithLocalProperties(time) { ssc.sparkContext.setCallSite(creationSite) foreachFunc(rdd, time) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala index 9716adb62817c..d58c99a8ff321 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala @@ -17,10 +17,13 @@ package org.apache.spark.streaming.dstream -import org.apache.spark.streaming.{Time, Duration, StreamingContext} - import scala.reflect.ClassTag +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDDOperationScope +import org.apache.spark.streaming.{Time, Duration, StreamingContext} +import org.apache.spark.util.Utils + /** * This is the abstract base class for all input streams. This class provides methods * start() and stop() which is called by Spark Streaming system to start and stop receiving data. @@ -44,10 +47,31 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext) /** This is an unique identifier for the input stream. */ val id = ssc.getNewInputStreamId() + /** A human-readable name of this InputDStream */ + private[streaming] def name: String = { + // e.g. FlumePollingDStream -> "Flume polling stream" + val newName = Utils.getFormattedClassName(this) + .replaceAll("InputDStream", "Stream") + .split("(?=[A-Z])") + .filter(_.nonEmpty) + .mkString(" ") + .toLowerCase + .capitalize + s"$newName [$id]" + } + /** - * The name of this InputDStream. By default, it's the class name with its id. + * The base scope associated with the operation that created this DStream. + * + * For InputDStreams, we use the name of this DStream as the scope name. + * If an outer scope is given, we assume that it includes an alternative name for this stream. */ - private[streaming] def name: String = s"${getClass.getSimpleName}-$id" + protected[streaming] override val baseScope: Option[String] = { + val scopeName = Option(ssc.sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY)) + .map { json => RDDOperationScope.fromJson(json).name + s" [$id]" } + .getOrElse(name.toLowerCase) + Some(new RDDOperationScope(scopeName).toJson) + } /** * Checks whether the 'time' is valid wrt slideDuration for generating RDD. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala index 8a58571632447..71bec96d46c8d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala @@ -24,20 +24,23 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} -import org.apache.spark.{HashPartitioner, Partitioner, SerializableWritable} +import org.apache.spark.{HashPartitioner, Partitioner} import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Duration, Time} import org.apache.spark.streaming.StreamingContext.rddToFileName +import org.apache.spark.util.{SerializableConfiguration, SerializableJobConf} /** * Extra functions available on DStream of (key, value) pairs through an implicit conversion. */ -class PairDStreamFunctions[K, V](self: DStream[(K,V)]) +class PairDStreamFunctions[K, V](self: DStream[(K, V)]) (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K]) extends Serializable { private[streaming] def ssc = self.ssc + private[streaming] def sparkContext = self.context.sparkContext + private[streaming] def defaultPartitioner(numPartitions: Int = self.ssc.sc.defaultParallelism) = { new HashPartitioner(numPartitions) } @@ -46,7 +49,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to * generate the RDDs with Spark's default number of partitions. */ - def groupByKey(): DStream[(K, Iterable[V])] = { + def groupByKey(): DStream[(K, Iterable[V])] = ssc.withScope { groupByKey(defaultPartitioner()) } @@ -54,7 +57,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to * generate the RDDs with `numPartitions` partitions. */ - def groupByKey(numPartitions: Int): DStream[(K, Iterable[V])] = { + def groupByKey(numPartitions: Int): DStream[(K, Iterable[V])] = ssc.withScope { groupByKey(defaultPartitioner(numPartitions)) } @@ -62,7 +65,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Return a new DStream by applying `groupByKey` on each RDD. The supplied * org.apache.spark.Partitioner is used to control the partitioning of each RDD. */ - def groupByKey(partitioner: Partitioner): DStream[(K, Iterable[V])] = { + def groupByKey(partitioner: Partitioner): DStream[(K, Iterable[V])] = ssc.withScope { val createCombiner = (v: V) => ArrayBuffer[V](v) val mergeValue = (c: ArrayBuffer[V], v: V) => (c += v) val mergeCombiner = (c1: ArrayBuffer[V], c2: ArrayBuffer[V]) => (c1 ++ c2) @@ -75,7 +78,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * merged using the associative reduce function. Hash partitioning is used to generate the RDDs * with Spark's default number of partitions. */ - def reduceByKey(reduceFunc: (V, V) => V): DStream[(K, V)] = { + def reduceByKey(reduceFunc: (V, V) => V): DStream[(K, V)] = ssc.withScope { reduceByKey(reduceFunc, defaultPartitioner()) } @@ -84,7 +87,9 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * merged using the supplied reduce function. Hash partitioning is used to generate the RDDs * with `numPartitions` partitions. */ - def reduceByKey(reduceFunc: (V, V) => V, numPartitions: Int): DStream[(K, V)] = { + def reduceByKey( + reduceFunc: (V, V) => V, + numPartitions: Int): DStream[(K, V)] = ssc.withScope { reduceByKey(reduceFunc, defaultPartitioner(numPartitions)) } @@ -93,9 +98,10 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * merged using the supplied reduce function. org.apache.spark.Partitioner is used to control * the partitioning of each RDD. */ - def reduceByKey(reduceFunc: (V, V) => V, partitioner: Partitioner): DStream[(K, V)] = { - val cleanedReduceFunc = ssc.sc.clean(reduceFunc) - combineByKey((v: V) => v, cleanedReduceFunc, cleanedReduceFunc, partitioner) + def reduceByKey( + reduceFunc: (V, V) => V, + partitioner: Partitioner): DStream[(K, V)] = ssc.withScope { + combineByKey((v: V) => v, reduceFunc, reduceFunc, partitioner) } /** @@ -104,12 +110,20 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * org.apache.spark.rdd.PairRDDFunctions in the Spark core documentation for more information. */ def combineByKey[C: ClassTag]( - createCombiner: V => C, - mergeValue: (C, V) => C, - mergeCombiner: (C, C) => C, - partitioner: Partitioner, - mapSideCombine: Boolean = true): DStream[(K, C)] = { - new ShuffledDStream[K, V, C](self, createCombiner, mergeValue, mergeCombiner, partitioner, + createCombiner: V => C, + mergeValue: (C, V) => C, + mergeCombiner: (C, C) => C, + partitioner: Partitioner, + mapSideCombine: Boolean = true): DStream[(K, C)] = ssc.withScope { + val cleanedCreateCombiner = sparkContext.clean(createCombiner) + val cleanedMergeValue = sparkContext.clean(mergeValue) + val cleanedMergeCombiner = sparkContext.clean(mergeCombiner) + new ShuffledDStream[K, V, C]( + self, + cleanedCreateCombiner, + cleanedMergeValue, + cleanedMergeCombiner, + partitioner, mapSideCombine) } @@ -121,7 +135,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * @param windowDuration width of the window; must be a multiple of this DStream's * batching interval */ - def groupByKeyAndWindow(windowDuration: Duration): DStream[(K, Iterable[V])] = { + def groupByKeyAndWindow(windowDuration: Duration): DStream[(K, Iterable[V])] = ssc.withScope { groupByKeyAndWindow(windowDuration, self.slideDuration, defaultPartitioner()) } @@ -136,8 +150,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * DStream's batching interval */ def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration) - : DStream[(K, Iterable[V])] = - { + : DStream[(K, Iterable[V])] = ssc.withScope { groupByKeyAndWindow(windowDuration, slideDuration, defaultPartitioner()) } @@ -157,7 +170,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) windowDuration: Duration, slideDuration: Duration, numPartitions: Int - ): DStream[(K, Iterable[V])] = { + ): DStream[(K, Iterable[V])] = ssc.withScope { groupByKeyAndWindow(windowDuration, slideDuration, defaultPartitioner(numPartitions)) } @@ -176,7 +189,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner - ): DStream[(K, Iterable[V])] = { + ): DStream[(K, Iterable[V])] = ssc.withScope { val createCombiner = (v: Iterable[V]) => new ArrayBuffer[V] ++= v val mergeValue = (buf: ArrayBuffer[V], v: Iterable[V]) => buf ++= v val mergeCombiner = (buf1: ArrayBuffer[V], buf2: ArrayBuffer[V]) => buf1 ++= buf2 @@ -198,7 +211,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def reduceByKeyAndWindow( reduceFunc: (V, V) => V, windowDuration: Duration - ): DStream[(K, V)] = { + ): DStream[(K, V)] = ssc.withScope { reduceByKeyAndWindow(reduceFunc, windowDuration, self.slideDuration, defaultPartitioner()) } @@ -217,7 +230,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) reduceFunc: (V, V) => V, windowDuration: Duration, slideDuration: Duration - ): DStream[(K, V)] = { + ): DStream[(K, V)] = ssc.withScope { reduceByKeyAndWindow(reduceFunc, windowDuration, slideDuration, defaultPartitioner()) } @@ -238,7 +251,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) windowDuration: Duration, slideDuration: Duration, numPartitions: Int - ): DStream[(K, V)] = { + ): DStream[(K, V)] = ssc.withScope { reduceByKeyAndWindow(reduceFunc, windowDuration, slideDuration, defaultPartitioner(numPartitions)) } @@ -260,11 +273,10 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner - ): DStream[(K, V)] = { - val cleanedReduceFunc = ssc.sc.clean(reduceFunc) - self.reduceByKey(cleanedReduceFunc, partitioner) + ): DStream[(K, V)] = ssc.withScope { + self.reduceByKey(reduceFunc, partitioner) .window(windowDuration, slideDuration) - .reduceByKey(cleanedReduceFunc, partitioner) + .reduceByKey(reduceFunc, partitioner) } /** @@ -294,8 +306,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) slideDuration: Duration = self.slideDuration, numPartitions: Int = ssc.sc.defaultParallelism, filterFunc: ((K, V)) => Boolean = null - ): DStream[(K, V)] = { - + ): DStream[(K, V)] = ssc.withScope { reduceByKeyAndWindow( reduceFunc, invReduceFunc, windowDuration, slideDuration, defaultPartitioner(numPartitions), filterFunc @@ -328,7 +339,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) slideDuration: Duration, partitioner: Partitioner, filterFunc: ((K, V)) => Boolean - ): DStream[(K, V)] = { + ): DStream[(K, V)] = ssc.withScope { val cleanedReduceFunc = ssc.sc.clean(reduceFunc) val cleanedInvReduceFunc = ssc.sc.clean(invReduceFunc) @@ -349,7 +360,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) */ def updateStateByKey[S: ClassTag]( updateFunc: (Seq[V], Option[S]) => Option[S] - ): DStream[(K, S)] = { + ): DStream[(K, S)] = ssc.withScope { updateStateByKey(updateFunc, defaultPartitioner()) } @@ -365,7 +376,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def updateStateByKey[S: ClassTag]( updateFunc: (Seq[V], Option[S]) => Option[S], numPartitions: Int - ): DStream[(K, S)] = { + ): DStream[(K, S)] = ssc.withScope { updateStateByKey(updateFunc, defaultPartitioner(numPartitions)) } @@ -382,9 +393,10 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def updateStateByKey[S: ClassTag]( updateFunc: (Seq[V], Option[S]) => Option[S], partitioner: Partitioner - ): DStream[(K, S)] = { + ): DStream[(K, S)] = ssc.withScope { + val cleanedUpdateF = sparkContext.clean(updateFunc) val newUpdateFunc = (iterator: Iterator[(K, Seq[V], Option[S])]) => { - iterator.flatMap(t => updateFunc(t._2, t._3).map(s => (t._1, s))) + iterator.flatMap(t => cleanedUpdateF(t._2, t._3).map(s => (t._1, s))) } updateStateByKey(newUpdateFunc, partitioner, true) } @@ -406,7 +418,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)], partitioner: Partitioner, rememberPartitioner: Boolean - ): DStream[(K, S)] = { + ): DStream[(K, S)] = ssc.withScope { new StateDStream(self, ssc.sc.clean(updateFunc), partitioner, rememberPartitioner, None) } @@ -425,9 +437,10 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) updateFunc: (Seq[V], Option[S]) => Option[S], partitioner: Partitioner, initialRDD: RDD[(K, S)] - ): DStream[(K, S)] = { + ): DStream[(K, S)] = ssc.withScope { + val cleanedUpdateF = sparkContext.clean(updateFunc) val newUpdateFunc = (iterator: Iterator[(K, Seq[V], Option[S])]) => { - iterator.flatMap(t => updateFunc(t._2, t._3).map(s => (t._1, s))) + iterator.flatMap(t => cleanedUpdateF(t._2, t._3).map(s => (t._1, s))) } updateStateByKey(newUpdateFunc, partitioner, true, initialRDD) } @@ -451,7 +464,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) partitioner: Partitioner, rememberPartitioner: Boolean, initialRDD: RDD[(K, S)] - ): DStream[(K, S)] = { + ): DStream[(K, S)] = ssc.withScope { new StateDStream(self, ssc.sc.clean(updateFunc), partitioner, rememberPartitioner, Some(initialRDD)) } @@ -460,8 +473,8 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Return a new DStream by applying a map function to the value of each key-value pairs in * 'this' DStream without changing the key. */ - def mapValues[U: ClassTag](mapValuesFunc: V => U): DStream[(K, U)] = { - new MapValuedDStream[K, V, U](self, mapValuesFunc) + def mapValues[U: ClassTag](mapValuesFunc: V => U): DStream[(K, U)] = ssc.withScope { + new MapValuedDStream[K, V, U](self, sparkContext.clean(mapValuesFunc)) } /** @@ -470,8 +483,8 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) */ def flatMapValues[U: ClassTag]( flatMapValuesFunc: V => TraversableOnce[U] - ): DStream[(K, U)] = { - new FlatMapValuedDStream[K, V, U](self, flatMapValuesFunc) + ): DStream[(K, U)] = ssc.withScope { + new FlatMapValuedDStream[K, V, U](self, sparkContext.clean(flatMapValuesFunc)) } /** @@ -479,7 +492,8 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Hash partitioning is used to generate the RDDs with Spark's default number * of partitions. */ - def cogroup[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W: ClassTag]( + other: DStream[(K, W)]): DStream[(K, (Iterable[V], Iterable[W]))] = ssc.withScope { cogroup(other, defaultPartitioner()) } @@ -487,8 +501,9 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. */ - def cogroup[W: ClassTag](other: DStream[(K, W)], numPartitions: Int) - : DStream[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W: ClassTag]( + other: DStream[(K, W)], + numPartitions: Int): DStream[(K, (Iterable[V], Iterable[W]))] = ssc.withScope { cogroup(other, defaultPartitioner(numPartitions)) } @@ -499,7 +514,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def cogroup[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner - ): DStream[(K, (Iterable[V], Iterable[W]))] = { + ): DStream[(K, (Iterable[V], Iterable[W]))] = ssc.withScope { self.transformWith( other, (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.cogroup(rdd2, partitioner) @@ -510,7 +525,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. * Hash partitioning is used to generate the RDDs with Spark's default number of partitions. */ - def join[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (V, W))] = { + def join[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (V, W))] = ssc.withScope { join[W](other, defaultPartitioner()) } @@ -518,7 +533,9 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. */ - def join[W: ClassTag](other: DStream[(K, W)], numPartitions: Int): DStream[(K, (V, W))] = { + def join[W: ClassTag]( + other: DStream[(K, W)], + numPartitions: Int): DStream[(K, (V, W))] = ssc.withScope { join[W](other, defaultPartitioner(numPartitions)) } @@ -529,7 +546,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def join[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner - ): DStream[(K, (V, W))] = { + ): DStream[(K, (V, W))] = ssc.withScope { self.transformWith( other, (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.join(rdd2, partitioner) @@ -541,7 +558,8 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default * number of partitions. */ - def leftOuterJoin[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (V, Option[W]))] = { + def leftOuterJoin[W: ClassTag]( + other: DStream[(K, W)]): DStream[(K, (V, Option[W]))] = ssc.withScope { leftOuterJoin[W](other, defaultPartitioner()) } @@ -553,7 +571,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def leftOuterJoin[W: ClassTag]( other: DStream[(K, W)], numPartitions: Int - ): DStream[(K, (V, Option[W]))] = { + ): DStream[(K, (V, Option[W]))] = ssc.withScope { leftOuterJoin[W](other, defaultPartitioner(numPartitions)) } @@ -565,7 +583,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def leftOuterJoin[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner - ): DStream[(K, (V, Option[W]))] = { + ): DStream[(K, (V, Option[W]))] = ssc.withScope { self.transformWith( other, (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.leftOuterJoin(rdd2, partitioner) @@ -577,7 +595,8 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default * number of partitions. */ - def rightOuterJoin[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (Option[V], W))] = { + def rightOuterJoin[W: ClassTag]( + other: DStream[(K, W)]): DStream[(K, (Option[V], W))] = ssc.withScope { rightOuterJoin[W](other, defaultPartitioner()) } @@ -589,7 +608,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def rightOuterJoin[W: ClassTag]( other: DStream[(K, W)], numPartitions: Int - ): DStream[(K, (Option[V], W))] = { + ): DStream[(K, (Option[V], W))] = ssc.withScope { rightOuterJoin[W](other, defaultPartitioner(numPartitions)) } @@ -601,7 +620,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def rightOuterJoin[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner - ): DStream[(K, (Option[V], W))] = { + ): DStream[(K, (Option[V], W))] = ssc.withScope { self.transformWith( other, (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.rightOuterJoin(rdd2, partitioner) @@ -613,7 +632,8 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default * number of partitions. */ - def fullOuterJoin[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (Option[V], Option[W]))] = { + def fullOuterJoin[W: ClassTag]( + other: DStream[(K, W)]): DStream[(K, (Option[V], Option[W]))] = ssc.withScope { fullOuterJoin[W](other, defaultPartitioner()) } @@ -625,7 +645,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def fullOuterJoin[W: ClassTag]( other: DStream[(K, W)], numPartitions: Int - ): DStream[(K, (Option[V], Option[W]))] = { + ): DStream[(K, (Option[V], Option[W]))] = ssc.withScope { fullOuterJoin[W](other, defaultPartitioner(numPartitions)) } @@ -637,7 +657,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def fullOuterJoin[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner - ): DStream[(K, (Option[V], Option[W]))] = { + ): DStream[(K, (Option[V], Option[W]))] = ssc.withScope { self.transformWith( other, (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.fullOuterJoin(rdd2, partitioner) @@ -651,7 +671,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def saveAsHadoopFiles[F <: OutputFormat[K, V]]( prefix: String, suffix: String - )(implicit fm: ClassTag[F]) { + )(implicit fm: ClassTag[F]): Unit = ssc.withScope { saveAsHadoopFiles(prefix, suffix, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -667,9 +687,9 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf = new JobConf(ssc.sparkContext.hadoopConfiguration) - ) { + ): Unit = ssc.withScope { // Wrap conf in SerializableWritable so that ForeachDStream can be serialized for checkpoints - val serializableConf = new SerializableWritable(conf) + val serializableConf = new SerializableJobConf(conf) val saveFunc = (rdd: RDD[(K, V)], time: Time) => { val file = rddToFileName(prefix, suffix, time) rdd.saveAsHadoopFile(file, keyClass, valueClass, outputFormatClass, serializableConf.value) @@ -684,7 +704,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) def saveAsNewAPIHadoopFiles[F <: NewOutputFormat[K, V]]( prefix: String, suffix: String - )(implicit fm: ClassTag[F]) { + )(implicit fm: ClassTag[F]): Unit = ssc.withScope { saveAsNewAPIHadoopFiles(prefix, suffix, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -700,9 +720,9 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)]) valueClass: Class[_], outputFormatClass: Class[_ <: NewOutputFormat[_, _]], conf: Configuration = ssc.sparkContext.hadoopConfiguration - ) { + ): Unit = ssc.withScope { // Wrap conf in SerializableWritable so that ForeachDStream can be serialized for checkpoints - val serializableConf = new SerializableWritable(conf) + val serializableConf = new SerializableConfiguration(conf) val saveFunc = (rdd: RDD[(K, V)], time: Time) => { val file = rddToFileName(prefix, suffix, time) rdd.saveAsNewAPIHadoopFile( diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala index 5cfe43a1ce726..e76e7eb0dea19 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala @@ -70,30 +70,41 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray // Register the input blocks information into InputInfoTracker - val inputInfo = InputInfo(id, blockInfos.map(_.numRecords).sum) + val inputInfo = InputInfo(id, blockInfos.flatMap(_.numRecords).sum) ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) - // Are WAL record handles present with all the blocks - val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty } + if (blockInfos.nonEmpty) { + // Are WAL record handles present with all the blocks + val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty } - if (areWALRecordHandlesPresent) { - // If all the blocks have WAL record handle, then create a WALBackedBlockRDD - val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray - val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray - new WriteAheadLogBackedBlockRDD[T]( - ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid) - } else { - // Else, create a BlockRDD. However, if there are some blocks with WAL info but not others - // then that is unexpected and log a warning accordingly. - if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) { - if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { - logError("Some blocks do not have Write Ahead Log information; " + - "this is unexpected and data may not be recoverable after driver failures") - } else { - logWarning("Some blocks have Write Ahead Log information; this is unexpected") + if (areWALRecordHandlesPresent) { + // If all the blocks have WAL record handle, then create a WALBackedBlockRDD + val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray + val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray + new WriteAheadLogBackedBlockRDD[T]( + ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid) + } else { + // Else, create a BlockRDD. However, if there are some blocks with WAL info but not + // others then that is unexpected and log a warning accordingly. + if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) { + if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { + logError("Some blocks do not have Write Ahead Log information; " + + "this is unexpected and data may not be recoverable after driver failures") + } else { + logWarning("Some blocks have Write Ahead Log information; this is unexpected") + } } + new BlockRDD[T](ssc.sc, blockIds) + } + } else { + // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD + // according to the configuration + if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { + new WriteAheadLogBackedBlockRDD[T]( + ssc.sparkContext, Array.empty, Array.empty, Array.empty) + } else { + new BlockRDD[T](ssc.sc, Array.empty) } - new BlockRDD[T](ssc.sc, blockIds) } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala index 1385ccbf56ee5..6a583bf2a3626 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala @@ -38,14 +38,14 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( _windowDuration: Duration, _slideDuration: Duration, partitioner: Partitioner - ) extends DStream[(K,V)](parent.ssc) { + ) extends DStream[(K, V)](parent.ssc) { - assert(_windowDuration.isMultipleOf(parent.slideDuration), + require(_windowDuration.isMultipleOf(parent.slideDuration), "The window duration of ReducedWindowedDStream (" + _windowDuration + ") " + "must be multiple of the slide duration of parent DStream (" + parent.slideDuration + ")" ) - assert(_slideDuration.isMultipleOf(parent.slideDuration), + require(_slideDuration.isMultipleOf(parent.slideDuration), "The slide duration of ReducedWindowedDStream (" + _slideDuration + ") " + "must be multiple of the slide duration of parent DStream (" + parent.slideDuration + ")" ) @@ -58,7 +58,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( super.persist(StorageLevel.MEMORY_ONLY_SER) reducedStream.persist(StorageLevel.MEMORY_ONLY_SER) - def windowDuration: Duration = _windowDuration + def windowDuration: Duration = _windowDuration override def dependencies: List[DStream[_]] = List(reducedStream) @@ -68,7 +68,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( override def parentRememberDuration: Duration = rememberDuration + windowDuration - override def persist(storageLevel: StorageLevel): DStream[(K,V)] = { + override def persist(storageLevel: StorageLevel): DStream[(K, V)] = { super.persist(storageLevel) reducedStream.persist(storageLevel) this @@ -118,7 +118,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( // Get the RDD of the reduced value of the previous window val previousWindowRDD = - getOrCompute(previousWindow.endTime).getOrElse(ssc.sc.makeRDD(Seq[(K,V)]())) + getOrCompute(previousWindow.endTime).getOrElse(ssc.sc.makeRDD(Seq[(K, V)]())) // Make the list of RDDs that needs to cogrouped together for reducing their reduced values val allRDDs = new ArrayBuffer[RDD[(K, V)]]() += previousWindowRDD ++= oldRDDs ++= newRDDs diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala index 7757ccac09a58..e0ffd5d86b435 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala @@ -25,19 +25,19 @@ import scala.reflect.ClassTag private[streaming] class ShuffledDStream[K: ClassTag, V: ClassTag, C: ClassTag]( - parent: DStream[(K,V)], + parent: DStream[(K, V)], createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiner: (C, C) => C, partitioner: Partitioner, mapSideCombine: Boolean = true - ) extends DStream[(K,C)] (parent.ssc) { + ) extends DStream[(K, C)] (parent.ssc) { override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration - override def compute(validTime: Time): Option[RDD[(K,C)]] = { + override def compute(validTime: Time): Option[RDD[(K, C)]] = { parent.getOrCompute(validTime) match { case Some(rdd) => Some(rdd.combineByKey[C]( createCombiner, mergeValue, mergeCombiner, partitioner, mapSideCombine)) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala index 8b72bcf20653d..5ce5b7aae6e69 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala @@ -17,6 +17,8 @@ package org.apache.spark.streaming.dstream +import scala.util.control.NonFatal + import org.apache.spark.streaming.StreamingContext import org.apache.spark.storage.StorageLevel import org.apache.spark.util.NextIterator @@ -74,13 +76,17 @@ class SocketReceiver[T: ClassTag]( while(!isStopped && iterator.hasNext) { store(iterator.next) } - logInfo("Stopped receiving") - restart("Retrying connecting to " + host + ":" + port) + if (!isStopped()) { + restart("Socket data stream had no more data") + } else { + logInfo("Stopped receiving") + } } catch { case e: java.net.ConnectException => restart("Error connecting to " + host + ":" + port, e) - case t: Throwable => - restart("Error receiving data", t) + case NonFatal(e) => + logWarning("Error receiving data", e) + restart("Error receiving data", e) } finally { if (socket != null) { socket.close() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala index de8718d0a80fe..621d6dff788f4 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala @@ -51,7 +51,7 @@ class StateDStream[K: ClassTag, V: ClassTag, S: ClassTag]( val finalFunc = (iterator: Iterator[(K, (Iterable[V], Iterable[S]))]) => { val i = iterator.map(t => { val itr = t._2._2.iterator - val headOption = if(itr.hasNext) Some(itr.next) else None + val headOption = if (itr.hasNext) Some(itr.next()) else None (t._1, t._2._1.toSeq, headOption) }) updateFuncLocal(i) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala index 899865a906c27..4efba039f8959 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala @@ -44,7 +44,7 @@ class WindowedDStream[T: ClassTag]( // Persist parent level by default, as those RDDs are going to be obviously reused. parent.persist(StorageLevel.MEMORY_ONLY_SER) - def windowDuration: Duration = _windowDuration + def windowDuration: Duration = _windowDuration override def dependencies: List[DStream[_]] = List(parent) @@ -68,7 +68,7 @@ class WindowedDStream[T: ClassTag]( new PartitionerAwareUnionRDD(ssc.sc, rddsInWindow) } else { logDebug("Using normal union for windowing at " + validTime) - new UnionRDD(ssc.sc,rddsInWindow) + new UnionRDD(ssc.sc, rddsInWindow) } Some(windowRDD) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala index ffce6a4c3c74c..31ce8e1ec14d7 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala @@ -23,12 +23,11 @@ import java.util.UUID import scala.reflect.ClassTag import scala.util.control.NonFatal -import org.apache.commons.io.FileUtils - import org.apache.spark._ import org.apache.spark.rdd.BlockRDD import org.apache.spark.storage.{BlockId, StorageLevel} import org.apache.spark.streaming.util._ +import org.apache.spark.util.SerializableConfiguration /** * Partition class for [[org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD]]. @@ -94,7 +93,7 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( // Hadoop configuration is not serializable, so broadcast it as a serializable. @transient private val hadoopConfig = sc.hadoopConfiguration - private val broadcastedHadoopConf = new SerializableWritable(hadoopConfig) + private val broadcastedHadoopConf = new SerializableConfiguration(hadoopConfig) override def isValid(): Boolean = true diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala index 4bebcc5aa7ca0..92b51ce39234c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala @@ -24,7 +24,7 @@ import scala.collection.mutable.ArrayBuffer import org.apache.spark.{Logging, SparkConf} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.util.RecurringTimer -import org.apache.spark.util.{SystemClock, Utils} +import org.apache.spark.util.SystemClock /** Listener object for BlockGenerator events */ private[streaming] trait BlockGeneratorListener { @@ -80,6 +80,8 @@ private[streaming] class BlockGenerator( private val clock = new SystemClock() private val blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms") + require(blockIntervalMs > 0, s"'spark.streaming.blockInterval' should be a positive value") + private val blockIntervalTimer = new RecurringTimer(clock, blockIntervalMs, updateCurrentBuffer, "BlockGenerator") private val blockQueueSize = conf.getInt("spark.streaming.blockQueueSize", 10) @@ -164,7 +166,7 @@ private[streaming] class BlockGenerator( private def keepPushingBlocks() { logInfo("Started block pushing thread") try { - while(!stopped) { + while (!stopped) { Option(blocksForPushing.poll(100, TimeUnit.MILLISECONDS)) match { case Some(block) => pushBlock(block) case None => @@ -191,7 +193,7 @@ private[streaming] class BlockGenerator( logError(message, t) listener.onError(message, t) } - + private def pushBlock(block: Block) { listener.onPushBlock(block.id, block.buffer) logInfo("Pushed block " + block.id) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala index 97db9ded83367..8df542b367d27 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala @@ -17,8 +17,9 @@ package org.apache.spark.streaming.receiver +import com.google.common.util.concurrent.{RateLimiter => GuavaRateLimiter} + import org.apache.spark.{Logging, SparkConf} -import com.google.common.util.concurrent.{RateLimiter=>GuavaRateLimiter} /** Provides waitToPush() method to limit the rate at which receivers consume data. * diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala index 651b534ac1900..c8dd6e06812dc 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala @@ -32,7 +32,10 @@ import org.apache.spark.{Logging, SparkConf, SparkException} /** Trait that represents the metadata related to storage of blocks */ private[streaming] trait ReceivedBlockStoreResult { - def blockId: StreamBlockId // Any implementation of this trait will store a block id + // Any implementation of this trait will store a block id + def blockId: StreamBlockId + // Any implementation of this trait will have to return the number of records + def numRecords: Option[Long] } /** Trait that represents a class that handles the storage of blocks received by receiver */ @@ -51,7 +54,8 @@ private[streaming] trait ReceivedBlockHandler { * that stores the metadata related to storage of blocks using * [[org.apache.spark.streaming.receiver.BlockManagerBasedBlockHandler]] */ -private[streaming] case class BlockManagerBasedStoreResult(blockId: StreamBlockId) +private[streaming] case class BlockManagerBasedStoreResult( + blockId: StreamBlockId, numRecords: Option[Long]) extends ReceivedBlockStoreResult @@ -62,13 +66,22 @@ private[streaming] case class BlockManagerBasedStoreResult(blockId: StreamBlockI private[streaming] class BlockManagerBasedBlockHandler( blockManager: BlockManager, storageLevel: StorageLevel) extends ReceivedBlockHandler with Logging { - + def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = { + + var numRecords = None: Option[Long] + val putResult: Seq[(BlockId, BlockStatus)] = block match { case ArrayBufferBlock(arrayBuffer) => - blockManager.putIterator(blockId, arrayBuffer.iterator, storageLevel, tellMaster = true) + numRecords = Some(arrayBuffer.size.toLong) + blockManager.putIterator(blockId, arrayBuffer.iterator, storageLevel, + tellMaster = true) case IteratorBlock(iterator) => - blockManager.putIterator(blockId, iterator, storageLevel, tellMaster = true) + val countIterator = new CountingIterator(iterator) + val putResult = blockManager.putIterator(blockId, countIterator, storageLevel, + tellMaster = true) + numRecords = countIterator.count + putResult case ByteBufferBlock(byteBuffer) => blockManager.putBytes(blockId, byteBuffer, storageLevel, tellMaster = true) case o => @@ -79,7 +92,7 @@ private[streaming] class BlockManagerBasedBlockHandler( throw new SparkException( s"Could not store $blockId to block manager with storage level $storageLevel") } - BlockManagerBasedStoreResult(blockId) + BlockManagerBasedStoreResult(blockId, numRecords) } def cleanupOldBlocks(threshTime: Long) { @@ -96,6 +109,7 @@ private[streaming] class BlockManagerBasedBlockHandler( */ private[streaming] case class WriteAheadLogBasedStoreResult( blockId: StreamBlockId, + numRecords: Option[Long], walRecordHandle: WriteAheadLogRecordHandle ) extends ReceivedBlockStoreResult @@ -151,12 +165,17 @@ private[streaming] class WriteAheadLogBasedBlockHandler( */ def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = { + var numRecords = None: Option[Long] // Serialize the block so that it can be inserted into both val serializedBlock = block match { case ArrayBufferBlock(arrayBuffer) => + numRecords = Some(arrayBuffer.size.toLong) blockManager.dataSerialize(blockId, arrayBuffer.iterator) case IteratorBlock(iterator) => - blockManager.dataSerialize(blockId, iterator) + val countIterator = new CountingIterator(iterator) + val serializedBlock = blockManager.dataSerialize(blockId, countIterator) + numRecords = countIterator.count + serializedBlock case ByteBufferBlock(byteBuffer) => byteBuffer case _ => @@ -181,7 +200,7 @@ private[streaming] class WriteAheadLogBasedBlockHandler( // Combine the futures, wait for both to complete, and return the write ahead log record handle val combinedFuture = storeInBlockManagerFuture.zip(storeInWriteAheadLogFuture).map(_._2) val walRecordHandle = Await.result(combinedFuture, blockStoreTimeout) - WriteAheadLogBasedStoreResult(blockId, walRecordHandle) + WriteAheadLogBasedStoreResult(blockId, numRecords, walRecordHandle) } def cleanupOldBlocks(threshTime: Long) { @@ -199,3 +218,23 @@ private[streaming] object WriteAheadLogBasedBlockHandler { new Path(checkpointDir, new Path("receivedData", streamId.toString)).toString } } + +/** + * A utility that will wrap the Iterator to get the count + */ +private class CountingIterator[T](iterator: Iterator[T]) extends Iterator[T] { + private var _count = 0 + + private def isFullyConsumed: Boolean = !iterator.hasNext + + def hasNext(): Boolean = iterator.hasNext + + def count(): Option[Long] = { + if (isFullyConsumed) Some(_count) else None + } + + def next(): T = { + _count += 1 + iterator.next() + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala index 4943f29395d12..33be067ebdaf2 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala @@ -18,14 +18,14 @@ package org.apache.spark.streaming.receiver import java.nio.ByteBuffer +import java.util.concurrent.CountDownLatch import scala.collection.mutable.ArrayBuffer +import scala.concurrent._ import org.apache.spark.{Logging, SparkConf} import org.apache.spark.storage.StreamBlockId -import java.util.concurrent.CountDownLatch -import scala.concurrent._ -import ExecutionContext.Implicits.global +import org.apache.spark.util.ThreadUtils /** * Abstract class that is responsible for supervising a Receiver in the worker. @@ -46,6 +46,9 @@ private[streaming] abstract class ReceiverSupervisor( // Attach the executor to the receiver receiver.attachExecutor(this) + private val futureExecutionContext = ExecutionContext.fromExecutorService( + ThreadUtils.newDaemonCachedThreadPool("receiver-supervisor-future", 128)) + /** Receiver id */ protected val streamId = receiver.streamId @@ -111,6 +114,7 @@ private[streaming] abstract class ReceiverSupervisor( stoppingError = error.orNull stopReceiver(message, error) onStop(message, error) + futureExecutionContext.shutdownNow() stopLatch.countDown() } @@ -150,6 +154,8 @@ private[streaming] abstract class ReceiverSupervisor( /** Restart receiver with delay */ def restartReceiver(message: String, error: Option[Throwable], delay: Int) { Future { + // This is a blocking action so we should use "futureExecutionContext" which is a cached + // thread pool. logWarning("Restarting receiver with delay " + delay + " ms: " + message, error.getOrElse(null)) stopReceiver("Restarting receiver with delay " + delay + "ms: " + message, error) @@ -158,7 +164,7 @@ private[streaming] abstract class ReceiverSupervisor( logInfo("Starting receiver again") startReceiver() logInfo("Receiver started again") - } + }(futureExecutionContext) } /** Check if receiver has been marked for stopping */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala index 92938379b9c17..6078cdf8f8790 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala @@ -137,15 +137,10 @@ private[streaming] class ReceiverSupervisorImpl( blockIdOption: Option[StreamBlockId] ) { val blockId = blockIdOption.getOrElse(nextBlockId) - val numRecords = receivedBlock match { - case ArrayBufferBlock(arrayBuffer) => arrayBuffer.size - case _ => -1 - } - val time = System.currentTimeMillis val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock) logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms") - + val numRecords = blockStoreResult.numRecords val blockInfo = ReceivedBlockInfo(streamId, numRecords, metadataOption, blockStoreResult) trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo)) logDebug(s"Reported block $blockId") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala index a72efccf2f994..7c0db8a863c67 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala @@ -23,7 +23,9 @@ import org.apache.spark.Logging import org.apache.spark.streaming.{Time, StreamingContext} /** To track the information of input stream at specified batch time. */ -private[streaming] case class InputInfo(inputStreamId: Int, numRecords: Long) +private[streaming] case class InputInfo(inputStreamId: Int, numRecords: Long) { + require(numRecords >= 0, "numRecords must not be negative") +} /** * This class manages all the input streams as well as their input data statistics. The information diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index 1d1ddaaccf217..4af9b6d3b56ab 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -126,6 +126,10 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { eventLoop.post(ErrorReported(msg, e)) } + def isStarted(): Boolean = synchronized { + eventLoop != null + } + private def processEvent(event: JobSchedulerEvent) { try { event match { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala index dc11e84f29965..656ac80df8979 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala @@ -24,11 +24,13 @@ import org.apache.spark.streaming.util.WriteAheadLogRecordHandle /** Information about blocks received by the receiver */ private[streaming] case class ReceivedBlockInfo( streamId: Int, - numRecords: Long, + numRecords: Option[Long], metadataOption: Option[Any], blockStoreResult: ReceivedBlockStoreResult ) { + require(numRecords.isEmpty || numRecords.get >= 0, "numRecords must not be negative") + @volatile private var _isBlockIdValid = true def blockId: StreamBlockId = blockStoreResult.blockId diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala index a9f4147a5f020..7720259a5d794 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala @@ -153,7 +153,7 @@ private[streaming] class ReceivedBlockTracker( * returns only after the files are cleaned up. */ def cleanupOldBatches(cleanupThreshTime: Time, waitForCompletion: Boolean): Unit = synchronized { - assert(cleanupThreshTime.milliseconds < clock.getTimeMillis()) + require(cleanupThreshTime.milliseconds < clock.getTimeMillis()) val timesToCleanup = timeToAllocatedBlocks.keys.filter { _ < cleanupThreshTime }.toSeq logInfo("Deleting batches " + timesToCleanup) writeToLog(BatchCleanupEvent(timesToCleanup)) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala index f73f7e705ee0d..e6cdbec11e94c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala @@ -21,10 +21,12 @@ import scala.collection.mutable.{HashMap, SynchronizedMap} import scala.language.existentials import org.apache.spark.streaming.util.WriteAheadLogUtils -import org.apache.spark.{Logging, SerializableWritable, SparkEnv, SparkException} +import org.apache.spark.{Logging, SparkEnv, SparkException} import org.apache.spark.rpc._ import org.apache.spark.streaming.{StreamingContext, Time} -import org.apache.spark.streaming.receiver.{CleanupOldBlocks, Receiver, ReceiverSupervisorImpl, StopReceiver} +import org.apache.spark.streaming.receiver.{CleanupOldBlocks, Receiver, ReceiverSupervisorImpl, + StopReceiver} +import org.apache.spark.util.SerializableConfiguration /** * Messages used by the NetworkReceiver and the ReceiverTracker to communicate @@ -230,7 +232,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false class ReceiverLauncher { @transient val env = ssc.env @volatile @transient private var running = false - @transient val thread = new Thread() { + @transient val thread = new Thread() { override def run() { try { SparkEnv.set(env) @@ -294,7 +296,8 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false } val checkpointDirOption = Option(ssc.checkpointDir) - val serializableHadoopConf = new SerializableWritable(ssc.sparkContext.hadoopConfiguration) + val serializableHadoopConf = + new SerializableConfiguration(ssc.sparkContext.hadoopConfiguration) // Function to start the receiver on the worker node val startReceiver = (iterator: Iterator[Receiver[_]]) => { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala index 2960b528d4c5e..f702bd5bc9466 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala @@ -17,29 +17,36 @@ package org.apache.spark.streaming.ui +import java.text.SimpleDateFormat +import java.util.Date + import scala.xml.Node import org.apache.spark.ui.{UIUtils => SparkUIUtils} -private[ui] abstract class BatchTableBase(tableId: String) { +private[ui] abstract class BatchTableBase(tableId: String, batchInterval: Long) { protected def columns: Seq[Node] = { Batch Time Input Size - Scheduling Delay - Processing Time + Scheduling Delay + {SparkUIUtils.tooltip("Time taken by Streaming scheduler to submit jobs of a batch", "top")} + + Processing Time + {SparkUIUtils.tooltip("Time taken to process all jobs of a batch", "top")} } protected def baseRow(batch: BatchUIData): Seq[Node] = { val batchTime = batch.batchTime.milliseconds - val formattedBatchTime = SparkUIUtils.formatDate(batch.batchTime.milliseconds) + val formattedBatchTime = UIUtils.formatBatchTime(batchTime, batchInterval) val eventCount = batch.numRecords val schedulingDelay = batch.schedulingDelay val formattedSchedulingDelay = schedulingDelay.map(SparkUIUtils.formatDuration).getOrElse("-") val processingTime = batch.processingDelay val formattedProcessingTime = processingTime.map(SparkUIUtils.formatDuration).getOrElse("-") + val batchTimeId = s"batch-$batchTime" - + {formattedBatchTime} @@ -76,7 +83,8 @@ private[ui] abstract class BatchTableBase(tableId: String) { private[ui] class ActiveBatchTable( runningBatches: Seq[BatchUIData], - waitingBatches: Seq[BatchUIData]) extends BatchTableBase("active-batches-table") { + waitingBatches: Seq[BatchUIData], + batchInterval: Long) extends BatchTableBase("active-batches-table", batchInterval) { override protected def columns: Seq[Node] = super.columns ++ Status @@ -96,10 +104,12 @@ private[ui] class ActiveBatchTable( } } -private[ui] class CompletedBatchTable(batches: Seq[BatchUIData]) - extends BatchTableBase("completed-batches-table") { +private[ui] class CompletedBatchTable(batches: Seq[BatchUIData], batchInterval: Long) + extends BatchTableBase("completed-batches-table", batchInterval) { - override protected def columns: Seq[Node] = super.columns ++ Total Delay + override protected def columns: Seq[Node] = super.columns ++ + Total Delay + {SparkUIUtils.tooltip("Total time taken to handle a batch", "top")} override protected def renderRows: Seq[Node] = { batches.flatMap(batch => {completedBatchRow(batch)}) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala index 831f60e870f74..f75067669abe5 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala @@ -17,6 +17,8 @@ package org.apache.spark.streaming.ui +import java.text.SimpleDateFormat +import java.util.Date import javax.servlet.http.HttpServletRequest import scala.xml.{NodeSeq, Node, Text} @@ -288,7 +290,8 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") { val batchTime = Option(request.getParameter("id")).map(id => Time(id.toLong)).getOrElse { throw new IllegalArgumentException(s"Missing id parameter") } - val formattedBatchTime = SparkUIUtils.formatDate(batchTime.milliseconds) + val formattedBatchTime = + UIUtils.formatBatchTime(batchTime.milliseconds, streamingListener.batchDuration) val batchUIData = streamingListener.getBatchUIData(batchTime).getOrElse { throw new IllegalArgumentException(s"Batch $formattedBatchTime does not exist") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala index ecbebe5c6c97b..4ee7a486e370b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala @@ -166,8 +166,8 @@ private[ui] class StreamingPage(parent: StreamingTab) private def generateLoadResources(): Seq[Node] = { // scalastyle:off - - + + // scalastyle:on } @@ -186,6 +186,8 @@ private[ui] class StreamingPage(parent: StreamingTab) {SparkUIUtils.formatDate(startTime)} + ({listener.numTotalCompletedBatches} + completed batches, {listener.numTotalReceivedRecords} records)
      } @@ -199,9 +201,9 @@ private[ui] class StreamingPage(parent: StreamingTab) * @param times all time values that will be used in the graphs. */ private def generateTimeMap(times: Seq[Long]): Seq[Node] = { - val dateFormat = new SimpleDateFormat("HH:mm:ss") val js = "var timeFormat = {};\n" + times.map { time => - val formattedTime = dateFormat.format(new Date(time)) + val formattedTime = + UIUtils.formatBatchTime(time, listener.batchDuration, showYYYYMMSS = false) s"timeFormat[$time] = '$formattedTime';" }.mkString("\n") @@ -244,17 +246,6 @@ private[ui] class StreamingPage(parent: StreamingTab) val maxEventRate = eventRateForAllStreams.max.map(_.ceil.toLong).getOrElse(0L) val minEventRate = 0L - // JavaScript to show/hide the InputDStreams sub table. - val triangleJs = - s"""$$('#inputs-table').toggle('collapsed'); - |var status = false; - |if ($$(this).html() == '$BLACK_RIGHT_TRIANGLE_HTML') { - |$$(this).html('$BLACK_DOWN_TRIANGLE_HTML');status = true;} - |else {$$(this).html('$BLACK_RIGHT_TRIANGLE_HTML');status = false;} - |window.history.pushState('', - | document.title, window.location.pathname + '?show-streams-detail=' + status);""" - .stripMargin.replaceAll("\\n", "") // it must be only one single line - val batchInterval = UIUtils.convertToTimeUnit(listener.batchDuration, normalizedUnit) val jsCollector = new JsCollector @@ -326,10 +317,18 @@ private[ui] class StreamingPage(parent: StreamingTab)
      - {if (hasStream) { - {Unparsed(BLACK_RIGHT_TRIANGLE_HTML)} - }} - Input Rate + { + if (hasStream) { + + + + Input Rate + + + } else { + Input Rate + } + }
      Avg: {eventRateForAllStreams.formattedAvg} events/sec
      @@ -347,7 +346,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
      -
      Scheduling Delay
      +
      Scheduling Delay {SparkUIUtils.tooltip("Time taken by Streaming scheduler to submit jobs of a batch", "right")}
      Avg: {schedulingDelay.formattedAvg}
      @@ -357,7 +356,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
      -
      Processing Time
      +
      Processing Time {SparkUIUtils.tooltip("Time taken to process all jobs of a batch", "right")}
      Avg: {processingTime.formattedAvg}
      @@ -367,7 +366,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
      -
      Total Delay
      +
      Total Delay {SparkUIUtils.tooltip("Total time taken to handle a batch", "right")}
      Avg: {totalDelay.formattedAvg}
      @@ -475,14 +474,14 @@ private[ui] class StreamingPage(parent: StreamingTab) val activeBatchesContent = {

      Active Batches ({runningBatches.size + waitingBatches.size})

      ++ - new ActiveBatchTable(runningBatches, waitingBatches).toNodeSeq + new ActiveBatchTable(runningBatches, waitingBatches, listener.batchDuration).toNodeSeq } val completedBatchesContent = {

      Completed Batches (last {completedBatches.size} out of {listener.numTotalCompletedBatches})

      ++ - new CompletedBatchTable(completedBatches).toNodeSeq + new CompletedBatchTable(completedBatches, listener.batchDuration).toNodeSeq } activeBatchesContent ++ completedBatchesContent diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala index f307b54bb9630..e0c0f57212f55 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala @@ -17,9 +17,11 @@ package org.apache.spark.streaming.ui +import org.eclipse.jetty.servlet.ServletContextHandler + import org.apache.spark.{Logging, SparkException} import org.apache.spark.streaming.StreamingContext -import org.apache.spark.ui.{SparkUI, SparkUITab} +import org.apache.spark.ui.{JettyUtils, SparkUI, SparkUITab} import StreamingTab._ @@ -30,6 +32,8 @@ import StreamingTab._ private[spark] class StreamingTab(val ssc: StreamingContext) extends SparkUITab(getSparkUI(ssc), "streaming") with Logging { + private val STATIC_RESOURCE_DIR = "org/apache/spark/streaming/ui/static" + val parent = getSparkUI(ssc) val listener = ssc.progressListener @@ -38,12 +42,18 @@ private[spark] class StreamingTab(val ssc: StreamingContext) attachPage(new StreamingPage(this)) attachPage(new BatchPage(this)) + var staticHandler: ServletContextHandler = null + def attach() { getSparkUI(ssc).attachTab(this) + staticHandler = JettyUtils.createStaticHandler(STATIC_RESOURCE_DIR, "/static/streaming") + getSparkUI(ssc).attachHandler(staticHandler) } def detach() { getSparkUI(ssc).detachTab(this) + getSparkUI(ssc).detachHandler(staticHandler) + staticHandler = null } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala index c206f973b2c66..86cfb1fa47370 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala @@ -17,9 +17,11 @@ package org.apache.spark.streaming.ui +import java.text.SimpleDateFormat +import java.util.TimeZone import java.util.concurrent.TimeUnit -object UIUtils { +private[streaming] object UIUtils { /** * Return the short string for a `TimeUnit`. @@ -62,7 +64,7 @@ object UIUtils { * Convert `milliseconds` to the specified `unit`. We cannot use `TimeUnit.convert` because it * will discard the fractional part. */ - def convertToTimeUnit(milliseconds: Long, unit: TimeUnit): Double = unit match { + def convertToTimeUnit(milliseconds: Long, unit: TimeUnit): Double = unit match { case TimeUnit.NANOSECONDS => milliseconds * 1000 * 1000 case TimeUnit.MICROSECONDS => milliseconds * 1000 case TimeUnit.MILLISECONDS => milliseconds @@ -71,4 +73,55 @@ object UIUtils { case TimeUnit.HOURS => milliseconds / 1000.0 / 60.0 / 60.0 case TimeUnit.DAYS => milliseconds / 1000.0 / 60.0 / 60.0 / 24.0 } + + // SimpleDateFormat is not thread-safe. Don't expose it to avoid improper use. + private val batchTimeFormat = new ThreadLocal[SimpleDateFormat]() { + override def initialValue(): SimpleDateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") + } + + private val batchTimeFormatWithMilliseconds = new ThreadLocal[SimpleDateFormat]() { + override def initialValue(): SimpleDateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss.SSS") + } + + /** + * If `batchInterval` is less than 1 second, format `batchTime` with milliseconds. Otherwise, + * format `batchTime` without milliseconds. + * + * @param batchTime the batch time to be formatted + * @param batchInterval the batch interval + * @param showYYYYMMSS if showing the `yyyy/MM/dd` part. If it's false, the return value wll be + * only `HH:mm:ss` or `HH:mm:ss.SSS` depending on `batchInterval` + * @param timezone only for test + */ + def formatBatchTime( + batchTime: Long, + batchInterval: Long, + showYYYYMMSS: Boolean = true, + timezone: TimeZone = null): String = { + val oldTimezones = + (batchTimeFormat.get.getTimeZone, batchTimeFormatWithMilliseconds.get.getTimeZone) + if (timezone != null) { + batchTimeFormat.get.setTimeZone(timezone) + batchTimeFormatWithMilliseconds.get.setTimeZone(timezone) + } + try { + val formattedBatchTime = + if (batchInterval < 1000) { + batchTimeFormatWithMilliseconds.get.format(batchTime) + } else { + // If batchInterval >= 1 second, don't show milliseconds + batchTimeFormat.get.format(batchTime) + } + if (showYYYYMMSS) { + formattedBatchTime + } else { + formattedBatchTime.substring(formattedBatchTime.indexOf(' ') + 1) + } + } finally { + if (timezone != null) { + batchTimeFormat.get.setTimeZone(oldTimezones._1) + batchTimeFormatWithMilliseconds.get.setTimeZone(oldTimezones._2) + } + } + } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala index 9985fedc35141..fe6328b1ce727 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala @@ -26,7 +26,7 @@ import scala.language.postfixOps import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.util.ThreadUtils +import org.apache.spark.util.{CompletionIterator, ThreadUtils} import org.apache.spark.{Logging, SparkConf} /** @@ -124,7 +124,8 @@ private[streaming] class FileBasedWriteAheadLog( logFilesToRead.iterator.map { file => logDebug(s"Creating log reader with $file") - new FileBasedWriteAheadLogReader(file, hadoopConf) + val reader = new FileBasedWriteAheadLogReader(file, hadoopConf) + CompletionIterator[ByteBuffer, Iterator[ByteBuffer]](reader, reader.close _) } flatMap { x => x } } @@ -199,7 +200,7 @@ private[streaming] class FileBasedWriteAheadLog( /** Initialize the log directory or recover existing logs inside the directory */ private def initializeOrRecover(): Unit = synchronized { val logDirectoryPath = new Path(logDirectory) - val fileSystem = HdfsUtils.getFileSystemForPath(logDirectoryPath, hadoopConf) + val fileSystem = HdfsUtils.getFileSystemForPath(logDirectoryPath, hadoopConf) if (fileSystem.exists(logDirectoryPath) && fileSystem.getFileStatus(logDirectoryPath).isDir) { val logFileInfo = logFilesTologInfo(fileSystem.listStatus(logDirectoryPath).map { _.getPath }) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala index 4d968f8bfa7a8..408936653c790 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala @@ -27,7 +27,7 @@ object RawTextHelper { * Splits lines and counts the words. */ def splitAndCountPartitions(iter: Iterator[String]): Iterator[(String, Long)] = { - val map = new OpenHashMap[String,Long] + val map = new OpenHashMap[String, Long] var i = 0 var j = 0 while (iter.hasNext) { @@ -98,7 +98,7 @@ object RawTextHelper { * before real workload starts. */ def warmUp(sc: SparkContext) { - for(i <- 0 to 1) { + for (i <- 0 to 1) { sc.parallelize(1 to 200000, 1000) .map(_ % 1331).map(_.toString) .mapPartitions(splitAndCountPartitions).reduceByKey(_ + _, 10) diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index b1adf881dd0f5..1077b1b2cb7e3 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -70,6 +70,20 @@ public void testInitialization() { Assert.assertNotNull(ssc.sparkContext()); } + @SuppressWarnings("unchecked") + @Test + public void testContextState() { + List> inputData = Arrays.asList(Arrays.asList(1, 2, 3, 4)); + Assert.assertTrue(ssc.getState() == StreamingContextState.INITIALIZED); + JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaTestUtils.attachTestOutputStream(stream); + Assert.assertTrue(ssc.getState() == StreamingContextState.INITIALIZED); + ssc.start(); + Assert.assertTrue(ssc.getState() == StreamingContextState.ACTIVE); + ssc.stop(); + Assert.assertTrue(ssc.getState() == StreamingContextState.STOPPED); + } + @SuppressWarnings("unchecked") @Test public void testCount() { @@ -1752,29 +1766,10 @@ public JavaStreamingContext call() { Assert.assertTrue("old context not recovered", !newContextCreated.get()); ssc.stop(); - // Function to create JavaStreamingContext using existing JavaSparkContext - // without any output operations (used to detect the new context) - Function creatingFunc2 = - new Function() { - public JavaStreamingContext call(JavaSparkContext context) { - newContextCreated.set(true); - return new JavaStreamingContext(context, Seconds.apply(1)); - } - }; - - JavaSparkContext sc = new JavaSparkContext(conf); newContextCreated.set(false); - ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc2, sc); - Assert.assertTrue("new context not created", newContextCreated.get()); - ssc.stop(false); - - newContextCreated.set(false); - ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc2, sc, true); - Assert.assertTrue("new context not created", newContextCreated.get()); - ssc.stop(false); - - newContextCreated.set(false); - ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc2, sc); + JavaSparkContext sc = new JavaSparkContext(conf); + ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc, + new org.apache.hadoop.conf.Configuration()); Assert.assertTrue("old context not recovered", !newContextCreated.get()); ssc.stop(); } diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala index c0ea0491c313d..bb80bff6dc2e6 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala @@ -70,6 +70,7 @@ trait JavaTestBase extends TestSuiteBase { ssc: JavaStreamingContext, numBatches: Int, numExpectedOutput: Int): JList[JList[V]] = { implicit val cm: ClassTag[V] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] + ssc.getState() val res = runStreams[V](ssc.ssc, numBatches, numExpectedOutput) val out = new ArrayList[JList[V]]() res.map(entry => out.append(new ArrayList[V](entry))) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index 87bc20f79c3cd..08faeaa58f419 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -255,7 +255,7 @@ class BasicOperationsSuite extends TestSuiteBase { Seq( ) ) val operation = (s1: DStream[String], s2: DStream[String]) => { - s1.map(x => (x,1)).cogroup(s2.map(x => (x, "x"))).mapValues(x => (x._1.toSeq, x._2.toSeq)) + s1.map(x => (x, 1)).cogroup(s2.map(x => (x, "x"))).mapValues(x => (x._1.toSeq, x._2.toSeq)) } testOperation(inputData1, inputData2, operation, outputData, true) } @@ -427,9 +427,9 @@ class BasicOperationsSuite extends TestSuiteBase { test("updateStateByKey - object lifecycle") { val inputData = Seq( - Seq("a","b"), + Seq("a", "b"), null, - Seq("a","c","a"), + Seq("a", "c", "a"), Seq("c"), null, null @@ -557,6 +557,9 @@ class BasicOperationsSuite extends TestSuiteBase { withTestServer(new TestServer()) { testServer => withStreamingContext(new StreamingContext(conf, batchDuration)) { ssc => testServer.start() + + val batchCounter = new BatchCounter(ssc) + // Set up the streaming context and input streams val networkStream = ssc.socketTextStream("localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) @@ -587,7 +590,11 @@ class BasicOperationsSuite extends TestSuiteBase { for (i <- 0 until input.size) { testServer.send(input(i).toString + "\n") Thread.sleep(200) + val numCompletedBatches = batchCounter.getNumCompletedBatches clock.advance(batchDuration.milliseconds) + if (!batchCounter.waitUntilBatchesCompleted(numCompletedBatches + 1, 5000)) { + fail("Batch took more than 5 seconds to complete") + } collectRddInfo() } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/DStreamClosureSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/DStreamClosureSuite.scala new file mode 100644 index 0000000000000..9b5e4dc819a2b --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/DStreamClosureSuite.scala @@ -0,0 +1,196 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming + +import java.io.NotSerializableException + +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.{HashPartitioner, SparkContext, SparkException, SparkFunSuite} +import org.apache.spark.rdd.RDD +import org.apache.spark.streaming.dstream.DStream +import org.apache.spark.util.ReturnStatementInClosureException + +/** + * Test that closures passed to DStream operations are actually cleaned. + */ +class DStreamClosureSuite extends SparkFunSuite with BeforeAndAfterAll { + private var ssc: StreamingContext = null + + override def beforeAll(): Unit = { + val sc = new SparkContext("local", "test") + ssc = new StreamingContext(sc, Seconds(1)) + } + + override def afterAll(): Unit = { + ssc.stop(stopSparkContext = true) + ssc = null + } + + test("user provided closures are actually cleaned") { + val dstream = new DummyInputDStream(ssc) + val pairDstream = dstream.map { i => (i, i) } + // DStream + testMap(dstream) + testFlatMap(dstream) + testFilter(dstream) + testMapPartitions(dstream) + testReduce(dstream) + testForeach(dstream) + testForeachRDD(dstream) + testTransform(dstream) + testTransformWith(dstream) + testReduceByWindow(dstream) + // PairDStreamFunctions + testReduceByKey(pairDstream) + testCombineByKey(pairDstream) + testReduceByKeyAndWindow(pairDstream) + testUpdateStateByKey(pairDstream) + testMapValues(pairDstream) + testFlatMapValues(pairDstream) + // StreamingContext + testTransform2(ssc, dstream) + } + + /** + * Verify that the expected exception is thrown. + * + * We use return statements as an indication that a closure is actually being cleaned. + * We expect closure cleaner to find the return statements in the user provided closures. + */ + private def expectCorrectException(body: => Unit): Unit = { + try { + body + } catch { + case rse: ReturnStatementInClosureException => // Success! + case e @ (_: NotSerializableException | _: SparkException) => + throw new TestException( + s"Expected ReturnStatementInClosureException, but got $e.\n" + + "This means the closure provided by user is not actually cleaned.") + } + } + + // DStream operations + private def testMap(ds: DStream[Int]): Unit = expectCorrectException { + ds.map { _ => return; 1 } + } + private def testFlatMap(ds: DStream[Int]): Unit = expectCorrectException { + ds.flatMap { _ => return; Seq.empty } + } + private def testFilter(ds: DStream[Int]): Unit = expectCorrectException { + ds.filter { _ => return; true } + } + private def testMapPartitions(ds: DStream[Int]): Unit = expectCorrectException { + ds.mapPartitions { _ => return; Seq.empty.toIterator } + } + private def testReduce(ds: DStream[Int]): Unit = expectCorrectException { + ds.reduce { case (_, _) => return; 1 } + } + private def testForeach(ds: DStream[Int]): Unit = { + val foreachF1 = (rdd: RDD[Int], t: Time) => return + val foreachF2 = (rdd: RDD[Int]) => return + expectCorrectException { ds.foreach(foreachF1) } + expectCorrectException { ds.foreach(foreachF2) } + } + private def testForeachRDD(ds: DStream[Int]): Unit = { + val foreachRDDF1 = (rdd: RDD[Int], t: Time) => return + val foreachRDDF2 = (rdd: RDD[Int]) => return + expectCorrectException { ds.foreachRDD(foreachRDDF1) } + expectCorrectException { ds.foreachRDD(foreachRDDF2) } + } + private def testTransform(ds: DStream[Int]): Unit = { + val transformF1 = (rdd: RDD[Int]) => { return; rdd } + val transformF2 = (rdd: RDD[Int], time: Time) => { return; rdd } + expectCorrectException { ds.transform(transformF1) } + expectCorrectException { ds.transform(transformF2) } + } + private def testTransformWith(ds: DStream[Int]): Unit = { + val transformF1 = (rdd1: RDD[Int], rdd2: RDD[Int]) => { return; rdd1 } + val transformF2 = (rdd1: RDD[Int], rdd2: RDD[Int], time: Time) => { return; rdd2 } + expectCorrectException { ds.transformWith(ds, transformF1) } + expectCorrectException { ds.transformWith(ds, transformF2) } + } + private def testReduceByWindow(ds: DStream[Int]): Unit = { + val reduceF = (_: Int, _: Int) => { return; 1 } + expectCorrectException { ds.reduceByWindow(reduceF, Seconds(1), Seconds(2)) } + expectCorrectException { ds.reduceByWindow(reduceF, reduceF, Seconds(1), Seconds(2)) } + } + + // PairDStreamFunctions operations + private def testReduceByKey(ds: DStream[(Int, Int)]): Unit = { + val reduceF = (_: Int, _: Int) => { return; 1 } + expectCorrectException { ds.reduceByKey(reduceF) } + expectCorrectException { ds.reduceByKey(reduceF, 5) } + expectCorrectException { ds.reduceByKey(reduceF, new HashPartitioner(5)) } + } + private def testCombineByKey(ds: DStream[(Int, Int)]): Unit = { + expectCorrectException { + ds.combineByKey[Int]( + { _: Int => return; 1 }, + { case (_: Int, _: Int) => return; 1 }, + { case (_: Int, _: Int) => return; 1 }, + new HashPartitioner(5) + ) + } + } + private def testReduceByKeyAndWindow(ds: DStream[(Int, Int)]): Unit = { + val reduceF = (_: Int, _: Int) => { return; 1 } + val filterF = (_: (Int, Int)) => { return; false } + expectCorrectException { ds.reduceByKeyAndWindow(reduceF, Seconds(1)) } + expectCorrectException { ds.reduceByKeyAndWindow(reduceF, Seconds(1), Seconds(2)) } + expectCorrectException { ds.reduceByKeyAndWindow(reduceF, Seconds(1), Seconds(2), 5) } + expectCorrectException { + ds.reduceByKeyAndWindow(reduceF, Seconds(1), Seconds(2), new HashPartitioner(5)) + } + expectCorrectException { ds.reduceByKeyAndWindow(reduceF, reduceF, Seconds(2)) } + expectCorrectException { + ds.reduceByKeyAndWindow( + reduceF, reduceF, Seconds(2), Seconds(3), new HashPartitioner(5), filterF) + } + } + private def testUpdateStateByKey(ds: DStream[(Int, Int)]): Unit = { + val updateF1 = (_: Seq[Int], _: Option[Int]) => { return; Some(1) } + val updateF2 = (_: Iterator[(Int, Seq[Int], Option[Int])]) => { return; Seq((1, 1)).toIterator } + val initialRDD = ds.ssc.sparkContext.emptyRDD[Int].map { i => (i, i) } + expectCorrectException { ds.updateStateByKey(updateF1) } + expectCorrectException { ds.updateStateByKey(updateF1, 5) } + expectCorrectException { ds.updateStateByKey(updateF1, new HashPartitioner(5)) } + expectCorrectException { + ds.updateStateByKey(updateF1, new HashPartitioner(5), initialRDD) + } + expectCorrectException { + ds.updateStateByKey(updateF2, new HashPartitioner(5), true) + } + expectCorrectException { + ds.updateStateByKey(updateF2, new HashPartitioner(5), true, initialRDD) + } + } + private def testMapValues(ds: DStream[(Int, Int)]): Unit = expectCorrectException { + ds.mapValues { _ => return; 1 } + } + private def testFlatMapValues(ds: DStream[(Int, Int)]): Unit = expectCorrectException { + ds.flatMapValues { _ => return; Seq.empty } + } + + // StreamingContext operations + private def testTransform2(ssc: StreamingContext, ds: DStream[Int]): Unit = { + val transformF = (rdds: Seq[RDD[_]], time: Time) => { return; ssc.sparkContext.emptyRDD[Int] } + expectCorrectException { ssc.transform(Seq(ds), transformF) } + } + +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/DStreamScopeSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/DStreamScopeSuite.scala new file mode 100644 index 0000000000000..8844c9d74b933 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/DStreamScopeSuite.scala @@ -0,0 +1,172 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming + +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll} + +import org.apache.spark.{SparkContext, SparkFunSuite} +import org.apache.spark.rdd.RDDOperationScope +import org.apache.spark.streaming.dstream.DStream +import org.apache.spark.streaming.ui.UIUtils + +/** + * Tests whether scope information is passed from DStream operations to RDDs correctly. + */ +class DStreamScopeSuite extends SparkFunSuite with BeforeAndAfter with BeforeAndAfterAll { + private var ssc: StreamingContext = null + private val batchDuration: Duration = Seconds(1) + + override def beforeAll(): Unit = { + ssc = new StreamingContext(new SparkContext("local", "test"), batchDuration) + } + + override def afterAll(): Unit = { + ssc.stop(stopSparkContext = true) + } + + before { assertPropertiesNotSet() } + after { assertPropertiesNotSet() } + + test("dstream without scope") { + val dummyStream = new DummyDStream(ssc) + dummyStream.initialize(Time(0)) + + // This DStream is not instantiated in any scope, so all RDDs + // created by this stream should similarly not have a scope + assert(dummyStream.baseScope === None) + assert(dummyStream.getOrCompute(Time(1000)).get.scope === None) + assert(dummyStream.getOrCompute(Time(2000)).get.scope === None) + assert(dummyStream.getOrCompute(Time(3000)).get.scope === None) + } + + test("input dstream without scope") { + val inputStream = new DummyInputDStream(ssc) + inputStream.initialize(Time(0)) + + val baseScope = inputStream.baseScope.map(RDDOperationScope.fromJson) + val scope1 = inputStream.getOrCompute(Time(1000)).get.scope + val scope2 = inputStream.getOrCompute(Time(2000)).get.scope + val scope3 = inputStream.getOrCompute(Time(3000)).get.scope + + // This DStream is not instantiated in any scope, so all RDDs + assertDefined(baseScope, scope1, scope2, scope3) + assert(baseScope.get.name.startsWith("dummy stream")) + assertScopeCorrect(baseScope.get, scope1.get, 1000) + assertScopeCorrect(baseScope.get, scope2.get, 2000) + assertScopeCorrect(baseScope.get, scope3.get, 3000) + } + + test("scoping simple operations") { + val inputStream = new DummyInputDStream(ssc) + val mappedStream = inputStream.map { i => i + 1 } + val filteredStream = mappedStream.filter { i => i % 2 == 0 } + filteredStream.initialize(Time(0)) + + val mappedScopeBase = mappedStream.baseScope.map(RDDOperationScope.fromJson) + val mappedScope1 = mappedStream.getOrCompute(Time(1000)).get.scope + val mappedScope2 = mappedStream.getOrCompute(Time(2000)).get.scope + val mappedScope3 = mappedStream.getOrCompute(Time(3000)).get.scope + val filteredScopeBase = filteredStream.baseScope.map(RDDOperationScope.fromJson) + val filteredScope1 = filteredStream.getOrCompute(Time(1000)).get.scope + val filteredScope2 = filteredStream.getOrCompute(Time(2000)).get.scope + val filteredScope3 = filteredStream.getOrCompute(Time(3000)).get.scope + + // These streams are defined in their respective scopes "map" and "filter", so all + // RDDs created by these streams should inherit the IDs and names of their parent + // DStream's base scopes + assertDefined(mappedScopeBase, mappedScope1, mappedScope2, mappedScope3) + assertDefined(filteredScopeBase, filteredScope1, filteredScope2, filteredScope3) + assert(mappedScopeBase.get.name === "map") + assert(filteredScopeBase.get.name === "filter") + assertScopeCorrect(mappedScopeBase.get, mappedScope1.get, 1000) + assertScopeCorrect(mappedScopeBase.get, mappedScope2.get, 2000) + assertScopeCorrect(mappedScopeBase.get, mappedScope3.get, 3000) + assertScopeCorrect(filteredScopeBase.get, filteredScope1.get, 1000) + assertScopeCorrect(filteredScopeBase.get, filteredScope2.get, 2000) + assertScopeCorrect(filteredScopeBase.get, filteredScope3.get, 3000) + } + + test("scoping nested operations") { + val inputStream = new DummyInputDStream(ssc) + val countStream = inputStream.countByWindow(Seconds(10), Seconds(1)) + countStream.initialize(Time(0)) + + val countScopeBase = countStream.baseScope.map(RDDOperationScope.fromJson) + val countScope1 = countStream.getOrCompute(Time(1000)).get.scope + val countScope2 = countStream.getOrCompute(Time(2000)).get.scope + val countScope3 = countStream.getOrCompute(Time(3000)).get.scope + + // Assert that all children RDDs inherit the DStream operation name correctly + assertDefined(countScopeBase, countScope1, countScope2, countScope3) + assert(countScopeBase.get.name === "countByWindow") + assertScopeCorrect(countScopeBase.get, countScope1.get, 1000) + assertScopeCorrect(countScopeBase.get, countScope2.get, 2000) + assertScopeCorrect(countScopeBase.get, countScope3.get, 3000) + + // All streams except the input stream should share the same scopes as `countStream` + def testStream(stream: DStream[_]): Unit = { + if (stream != inputStream) { + val myScopeBase = stream.baseScope.map(RDDOperationScope.fromJson) + val myScope1 = stream.getOrCompute(Time(1000)).get.scope + val myScope2 = stream.getOrCompute(Time(2000)).get.scope + val myScope3 = stream.getOrCompute(Time(3000)).get.scope + assertDefined(myScopeBase, myScope1, myScope2, myScope3) + assert(myScopeBase === countScopeBase) + assert(myScope1 === countScope1) + assert(myScope2 === countScope2) + assert(myScope3 === countScope3) + // Climb upwards to test the parent streams + stream.dependencies.foreach(testStream) + } + } + testStream(countStream) + } + + /** Assert that the RDD operation scope properties are not set in our SparkContext. */ + private def assertPropertiesNotSet(): Unit = { + assert(ssc != null) + assert(ssc.sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY) == null) + assert(ssc.sc.getLocalProperty(SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY) == null) + } + + /** Assert that the given RDD scope inherits the name and ID of the base scope correctly. */ + private def assertScopeCorrect( + baseScope: RDDOperationScope, + rddScope: RDDOperationScope, + batchTime: Long): Unit = { + assertScopeCorrect(baseScope.id, baseScope.name, rddScope, batchTime) + } + + /** Assert that the given RDD scope inherits the base name and ID correctly. */ + private def assertScopeCorrect( + baseScopeId: String, + baseScopeName: String, + rddScope: RDDOperationScope, + batchTime: Long): Unit = { + val formattedBatchTime = UIUtils.formatBatchTime( + batchTime, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false) + assert(rddScope.id === s"${baseScopeId}_$batchTime") + assert(rddScope.name.replaceAll("\\n", " ") === s"$baseScopeName @ $formattedBatchTime") + } + + /** Assert that all the specified options are defined. */ + private def assertDefined[T](options: Option[T]*): Unit = { + options.zipWithIndex.foreach { case (o, i) => assert(o.isDefined, s"Option $i was empty!") } + } + +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala index 93e6b0cd7c661..b74d67c63a788 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -39,6 +39,7 @@ import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.scheduler.{StreamingListenerBatchCompleted, StreamingListener} import org.apache.spark.util.{ManualClock, Utils} import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream} +import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD import org.apache.spark.streaming.receiver.Receiver class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { @@ -105,6 +106,36 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } } + test("socket input stream - no block in a batch") { + withTestServer(new TestServer()) { testServer => + testServer.start() + + withStreamingContext(new StreamingContext(conf, batchDuration)) { ssc => + ssc.addStreamingListener(ssc.progressListener) + + val batchCounter = new BatchCounter(ssc) + val networkStream = ssc.socketTextStream( + "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) + val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] + val outputStream = new TestOutputStream(networkStream, outputBuffer) + outputStream.register() + ssc.start() + + val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] + clock.advance(batchDuration.milliseconds) + + // Make sure the first batch is finished + if (!batchCounter.waitUntilBatchesCompleted(1, 30000)) { + fail("Timeout: cannot finish all batches in 30 seconds") + } + + networkStream.generatedRDDs.foreach { case (_, rdd) => + assert(!rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]]) + } + } + } + } + test("binary records stream") { val testDir: File = null try { @@ -387,7 +418,7 @@ class TestServer(portToBind: Int = 0) extends Logging { val servingThread = new Thread() { override def run() { try { - while(true) { + while (true) { logInfo("Accepting connections on port " + port) val clientSocket = serverSocket.accept() if (startLatch.getCount == 1) { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala index 23804237bda80..6c0c926755c20 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala @@ -25,7 +25,7 @@ import scala.concurrent.duration._ import scala.language.postfixOps import org.apache.hadoop.conf.Configuration -import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} +import org.scalatest.{BeforeAndAfter, Matchers} import org.scalatest.concurrent.Eventually._ import org.apache.spark._ @@ -41,11 +41,14 @@ import org.apache.spark.util.{ManualClock, Utils} import WriteAheadLogBasedBlockHandler._ import WriteAheadLogSuite._ -class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { +class ReceivedBlockHandlerSuite + extends SparkFunSuite + with BeforeAndAfter + with Matchers + with Logging { val conf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1") val hadoopConf = new Configuration() - val storageLevel = StorageLevel.MEMORY_ONLY_SER val streamId = 1 val securityMgr = new SecurityManager(conf) val mapOutputTracker = new MapOutputTrackerMaster(conf) @@ -53,10 +56,12 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche val serializer = new KryoSerializer(conf) val manualClock = new ManualClock val blockManagerSize = 10000000 + val blockManagerBuffer = new ArrayBuffer[BlockManager]() var rpcEnv: RpcEnv = null var blockManagerMaster: BlockManagerMaster = null var blockManager: BlockManager = null + var storageLevel: StorageLevel = null var tempDirectory: File = null before { @@ -66,20 +71,21 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche blockManagerMaster = new BlockManagerMaster(rpcEnv.setupEndpoint("blockmanager", new BlockManagerMasterEndpoint(rpcEnv, true, conf, new LiveListenerBus)), conf, true) - blockManager = new BlockManager("bm", rpcEnv, blockManagerMaster, serializer, - blockManagerSize, conf, mapOutputTracker, shuffleManager, - new NioBlockTransferService(conf, securityMgr), securityMgr, 0) - blockManager.initialize("app-id") + storageLevel = StorageLevel.MEMORY_ONLY_SER + blockManager = createBlockManager(blockManagerSize, conf) tempDirectory = Utils.createTempDir() manualClock.setTime(0) } after { - if (blockManager != null) { - blockManager.stop() - blockManager = null + for ( blockManager <- blockManagerBuffer ) { + if (blockManager != null) { + blockManager.stop() + } } + blockManager = null + blockManagerBuffer.clear() if (blockManagerMaster != null) { blockManagerMaster.stop() blockManagerMaster = null @@ -170,6 +176,130 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche } } + test("Test Block - count messages") { + // Test count with BlockManagedBasedBlockHandler + testCountWithBlockManagerBasedBlockHandler(true) + // Test count with WriteAheadLogBasedBlockHandler + testCountWithBlockManagerBasedBlockHandler(false) + } + + test("Test Block - isFullyConsumed") { + val sparkConf = new SparkConf() + sparkConf.set("spark.storage.unrollMemoryThreshold", "512") + // spark.storage.unrollFraction set to 0.4 for BlockManager + sparkConf.set("spark.storage.unrollFraction", "0.4") + // Block Manager with 12000 * 0.4 = 4800 bytes of free space for unroll + blockManager = createBlockManager(12000, sparkConf) + + // there is not enough space to store this block in MEMORY, + // But BlockManager will be able to sereliaze this block to WAL + // and hence count returns correct value. + testRecordcount(false, StorageLevel.MEMORY_ONLY, + IteratorBlock((List.fill(70)(new Array[Byte](100))).iterator), blockManager, Some(70)) + + // there is not enough space to store this block in MEMORY, + // But BlockManager will be able to sereliaze this block to DISK + // and hence count returns correct value. + testRecordcount(true, StorageLevel.MEMORY_AND_DISK, + IteratorBlock((List.fill(70)(new Array[Byte](100))).iterator), blockManager, Some(70)) + + // there is not enough space to store this block With MEMORY_ONLY StorageLevel. + // BlockManager will not be able to unroll this block + // and hence it will not tryToPut this block, resulting the SparkException + storageLevel = StorageLevel.MEMORY_ONLY + withBlockManagerBasedBlockHandler { handler => + val thrown = intercept[SparkException] { + storeSingleBlock(handler, IteratorBlock((List.fill(70)(new Array[Byte](100))).iterator)) + } + } + } + + private def testCountWithBlockManagerBasedBlockHandler(isBlockManagerBasedBlockHandler: Boolean) { + // ByteBufferBlock-MEMORY_ONLY + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_ONLY, + ByteBufferBlock(ByteBuffer.wrap(Array.tabulate(100)(i => i.toByte))), blockManager, None) + // ByteBufferBlock-MEMORY_ONLY_SER + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_ONLY_SER, + ByteBufferBlock(ByteBuffer.wrap(Array.tabulate(100)(i => i.toByte))), blockManager, None) + // ArrayBufferBlock-MEMORY_ONLY + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_ONLY, + ArrayBufferBlock(ArrayBuffer.fill(25)(0)), blockManager, Some(25)) + // ArrayBufferBlock-MEMORY_ONLY_SER + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_ONLY_SER, + ArrayBufferBlock(ArrayBuffer.fill(25)(0)), blockManager, Some(25)) + // ArrayBufferBlock-DISK_ONLY + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.DISK_ONLY, + ArrayBufferBlock(ArrayBuffer.fill(50)(0)), blockManager, Some(50)) + // ArrayBufferBlock-MEMORY_AND_DISK + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_AND_DISK, + ArrayBufferBlock(ArrayBuffer.fill(75)(0)), blockManager, Some(75)) + // IteratorBlock-MEMORY_ONLY + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_ONLY, + IteratorBlock((ArrayBuffer.fill(100)(0)).iterator), blockManager, Some(100)) + // IteratorBlock-MEMORY_ONLY_SER + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_ONLY_SER, + IteratorBlock((ArrayBuffer.fill(100)(0)).iterator), blockManager, Some(100)) + // IteratorBlock-DISK_ONLY + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.DISK_ONLY, + IteratorBlock((ArrayBuffer.fill(125)(0)).iterator), blockManager, Some(125)) + // IteratorBlock-MEMORY_AND_DISK + testRecordcount(isBlockManagerBasedBlockHandler, StorageLevel.MEMORY_AND_DISK, + IteratorBlock((ArrayBuffer.fill(150)(0)).iterator), blockManager, Some(150)) + } + + private def createBlockManager( + maxMem: Long, + conf: SparkConf, + name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = { + val transfer = new NioBlockTransferService(conf, securityMgr) + val manager = new BlockManager(name, rpcEnv, blockManagerMaster, serializer, maxMem, conf, + mapOutputTracker, shuffleManager, transfer, securityMgr, 0) + manager.initialize("app-id") + blockManagerBuffer += manager + manager + } + + /** + * Test storing of data using different types of Handler, StorageLevle and ReceivedBlocks + * and verify the correct record count + */ + private def testRecordcount(isBlockManagedBasedBlockHandler: Boolean, + sLevel: StorageLevel, + receivedBlock: ReceivedBlock, + bManager: BlockManager, + expectedNumRecords: Option[Long] + ) { + blockManager = bManager + storageLevel = sLevel + var bId: StreamBlockId = null + try { + if (isBlockManagedBasedBlockHandler) { + // test received block with BlockManager based handler + withBlockManagerBasedBlockHandler { handler => + val (blockId, blockStoreResult) = storeSingleBlock(handler, receivedBlock) + bId = blockId + assert(blockStoreResult.numRecords === expectedNumRecords, + "Message count not matches for a " + + receivedBlock.getClass.getName + + " being inserted using BlockManagerBasedBlockHandler with " + sLevel) + } + } else { + // test received block with WAL based handler + withWriteAheadLogBasedBlockHandler { handler => + val (blockId, blockStoreResult) = storeSingleBlock(handler, receivedBlock) + bId = blockId + assert(blockStoreResult.numRecords === expectedNumRecords, + "Message count not matches for a " + + receivedBlock.getClass.getName + + " being inserted using WriteAheadLogBasedBlockHandler with " + sLevel) + } + } + } finally { + // Removing the Block Id to use same blockManager for next test + blockManager.removeBlock(bId, true) + } + } + /** * Test storing of data using different forms of ReceivedBlocks and verify that they succeeded * using the given verification function @@ -247,9 +377,21 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche (blockIds, storeResults) } + /** Store single block using a handler */ + private def storeSingleBlock( + handler: ReceivedBlockHandler, + block: ReceivedBlock + ): (StreamBlockId, ReceivedBlockStoreResult) = { + val blockId = generateBlockId + val blockStoreResult = handler.storeBlock(blockId, block) + logDebug("Done inserting") + (blockId, blockStoreResult) + } + private def getWriteAheadLogFiles(): Seq[String] = { getLogFilesInDirectory(checkpointDirToLogDir(tempDirectory.toString, streamId)) } private def generateBlockId(): StreamBlockId = StreamBlockId(streamId, scala.util.Random.nextLong) } + diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala index b1af8d5eaacfb..f793a12843b2f 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala @@ -25,10 +25,10 @@ import scala.language.{implicitConversions, postfixOps} import scala.util.Random import org.apache.hadoop.conf.Configuration -import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} +import org.scalatest.{BeforeAndAfter, Matchers} import org.scalatest.concurrent.Eventually._ -import org.apache.spark.{Logging, SparkConf, SparkException} +import org.apache.spark.{Logging, SparkConf, SparkException, SparkFunSuite} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.receiver.BlockManagerBasedStoreResult import org.apache.spark.streaming.scheduler._ @@ -37,7 +37,7 @@ import org.apache.spark.streaming.util.WriteAheadLogSuite._ import org.apache.spark.util.{Clock, ManualClock, SystemClock, Utils} class ReceivedBlockTrackerSuite - extends FunSuite with BeforeAndAfter with Matchers with Logging { + extends SparkFunSuite with BeforeAndAfter with Matchers with Logging { val hadoopConf = new Configuration() val akkaTimeout = 10 seconds @@ -224,8 +224,8 @@ class ReceivedBlockTrackerSuite /** Generate blocks infos using random ids */ def generateBlockInfos(): Seq[ReceivedBlockInfo] = { - List.fill(5)(ReceivedBlockInfo(streamId, 0, None, - BlockManagerBasedStoreResult(StreamBlockId(streamId, math.abs(Random.nextInt))))) + List.fill(5)(ReceivedBlockInfo(streamId, Some(0L), None, + BlockManagerBasedStoreResult(StreamBlockId(streamId, math.abs(Random.nextInt)), Some(0L)))) } /** Get all the data written in the given write ahead log file. */ diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index a589deb1fa579..819dd2ccfe915 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -17,30 +17,31 @@ package org.apache.spark.streaming -import java.io.File +import java.io.{File, NotSerializableException} import java.util.concurrent.atomic.AtomicInteger import org.apache.commons.io.FileUtils -import org.scalatest.{Assertions, BeforeAndAfter, FunSuite} -import org.scalatest.concurrent.Timeouts import org.scalatest.concurrent.Eventually._ +import org.scalatest.concurrent.Timeouts import org.scalatest.exceptions.TestFailedDueToTimeoutException import org.scalatest.time.SpanSugar._ +import org.scalatest.{Assertions, BeforeAndAfter} -import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.receiver.Receiver import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException, SparkFunSuite} -class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts with Logging { +class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeouts with Logging { val master = "local[2]" val appName = this.getClass.getSimpleName val batchDuration = Milliseconds(500) val sparkHome = "someDir" val envPair = "key" -> "value" + val conf = new SparkConf().setMaster(master).setAppName(appName) var sc: SparkContext = null var ssc: StreamingContext = null @@ -109,15 +110,21 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) } + test("state matching") { + import StreamingContextState._ + assert(INITIALIZED === INITIALIZED) + assert(INITIALIZED != ACTIVE) + } + test("start and stop state check") { ssc = new StreamingContext(master, appName, batchDuration) addInputStream(ssc).register() - assert(ssc.state === ssc.StreamingContextState.Initialized) + assert(ssc.getState() === StreamingContextState.INITIALIZED) ssc.start() - assert(ssc.state === ssc.StreamingContextState.Started) + assert(ssc.getState() === StreamingContextState.ACTIVE) ssc.stop() - assert(ssc.state === ssc.StreamingContextState.Stopped) + assert(ssc.getState() === StreamingContextState.STOPPED) // Make sure that the SparkContext is also stopped by default intercept[Exception] { @@ -125,13 +132,48 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w } } + test("start with non-seriazable DStream checkpoints") { + val checkpointDir = Utils.createTempDir() + ssc = new StreamingContext(conf, batchDuration) + ssc.checkpoint(checkpointDir.getAbsolutePath) + addInputStream(ssc).foreachRDD { rdd => + // Refer to this.appName from inside closure so that this closure refers to + // the instance of StreamingContextSuite, and is therefore not serializable + rdd.count() + appName + } + + // Test whether start() fails early when checkpointing is enabled + val exception = intercept[NotSerializableException] { + ssc.start() + } + assert(exception.getMessage().contains("DStreams with their functions are not serializable")) + assert(ssc.getState() !== StreamingContextState.ACTIVE) + assert(StreamingContext.getActive().isEmpty) + } + + test("start failure should stop internal components") { + ssc = new StreamingContext(conf, batchDuration) + val inputStream = addInputStream(ssc) + val updateFunc = (values: Seq[Int], state: Option[Int]) => { + Some(values.sum + state.getOrElse(0)) + } + inputStream.map(x => (x, 1)).updateStateByKey[Int](updateFunc) + // Require that the start fails because checkpoint directory was not set + intercept[Exception] { + ssc.start() + } + assert(ssc.getState() === StreamingContextState.STOPPED) + assert(ssc.scheduler.isStarted === false) + } + + test("start multiple times") { ssc = new StreamingContext(master, appName, batchDuration) addInputStream(ssc).register() ssc.start() - intercept[SparkException] { - ssc.start() - } + assert(ssc.getState() === StreamingContextState.ACTIVE) + ssc.start() + assert(ssc.getState() === StreamingContextState.ACTIVE) } test("stop multiple times") { @@ -139,13 +181,16 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w addInputStream(ssc).register() ssc.start() ssc.stop() + assert(ssc.getState() === StreamingContextState.STOPPED) ssc.stop() + assert(ssc.getState() === StreamingContextState.STOPPED) } test("stop before start") { ssc = new StreamingContext(master, appName, batchDuration) addInputStream(ssc).register() ssc.stop() // stop before start should not throw exception + assert(ssc.getState() === StreamingContextState.STOPPED) } test("start after stop") { @@ -153,9 +198,10 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w ssc = new StreamingContext(master, appName, batchDuration) addInputStream(ssc).register() ssc.stop() - intercept[SparkException] { + intercept[IllegalStateException] { ssc.start() // start after stop should throw exception } + assert(ssc.getState() === StreamingContextState.STOPPED) } test("stop only streaming context") { @@ -167,6 +213,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w addInputStream(ssc).register() ssc.start() ssc.stop(stopSparkContext = false) + assert(ssc.getState() === StreamingContextState.STOPPED) assert(sc.makeRDD(1 to 100).collect().size === 100) sc.stop() @@ -379,23 +426,23 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w assert(newContextCreated, "new context not created") } - val corrutedCheckpointPath = createCorruptedCheckpoint() + val corruptedCheckpointPath = createCorruptedCheckpoint() // getOrCreate should throw exception with fake checkpoint file and createOnError = false intercept[Exception] { - ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _) + ssc = StreamingContext.getOrCreate(corruptedCheckpointPath, creatingFunction _) } // getOrCreate should throw exception with fake checkpoint file intercept[Exception] { ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, createOnError = false) + corruptedCheckpointPath, creatingFunction _, createOnError = false) } // getOrCreate should create new context with fake checkpoint file and createOnError = true testGetOrCreate { ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, createOnError = true) + corruptedCheckpointPath, creatingFunction _, createOnError = true) assert(ssc != null, "no context created") assert(newContextCreated, "new context not created") } @@ -407,27 +454,39 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _) assert(ssc != null, "no context created") assert(!newContextCreated, "old context not recovered") - assert(ssc.conf.get("someKey") === "someValue") + assert(ssc.conf.get("someKey") === "someValue", "checkpointed config not recovered") + } + + // getOrCreate should recover StreamingContext with existing SparkContext + testGetOrCreate { + sc = new SparkContext(conf) + ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _) + assert(ssc != null, "no context created") + assert(!newContextCreated, "old context not recovered") + assert(!ssc.conf.contains("someKey"), "checkpointed config unexpectedly recovered") } } - test("getOrCreate with existing SparkContext") { - val conf = new SparkConf().setMaster(master).setAppName(appName) + test("getActive and getActiveOrCreate") { + require(StreamingContext.getActive().isEmpty, "context exists from before") sc = new SparkContext(conf) - // Function to create StreamingContext that has a config to identify it to be new context var newContextCreated = false - def creatingFunction(sparkContext: SparkContext): StreamingContext = { + + def creatingFunc(): StreamingContext = { newContextCreated = true - new StreamingContext(sparkContext, batchDuration) + val newSsc = new StreamingContext(sc, batchDuration) + val input = addInputStream(newSsc) + input.foreachRDD { rdd => rdd.count } + newSsc } - // Call ssc.stop(stopSparkContext = false) after a body of cody - def testGetOrCreate(body: => Unit): Unit = { + def testGetActiveOrCreate(body: => Unit): Unit = { newContextCreated = false try { body } finally { + if (ssc != null) { ssc.stop(stopSparkContext = false) } @@ -435,55 +494,177 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w } } + // getActiveOrCreate should create new context and getActive should return it only + // after starting the context + testGetActiveOrCreate { + ssc = StreamingContext.getActiveOrCreate(creatingFunc _) + assert(ssc != null, "no context created") + assert(newContextCreated === true, "new context not created") + assert(StreamingContext.getActive().isEmpty, + "new initialized context returned before starting") + ssc.start() + assert(StreamingContext.getActive() === Some(ssc), + "active context not returned") + assert(StreamingContext.getActiveOrCreate(creatingFunc _) === ssc, + "active context not returned") + ssc.stop() + assert(StreamingContext.getActive().isEmpty, + "inactive context returned") + assert(StreamingContext.getActiveOrCreate(creatingFunc _) !== ssc, + "inactive context returned") + } + + // getActiveOrCreate and getActive should return independently created context after activating + testGetActiveOrCreate { + ssc = creatingFunc() // Create + assert(StreamingContext.getActive().isEmpty, + "new initialized context returned before starting") + ssc.start() + assert(StreamingContext.getActive() === Some(ssc), + "active context not returned") + assert(StreamingContext.getActiveOrCreate(creatingFunc _) === ssc, + "active context not returned") + ssc.stop() + assert(StreamingContext.getActive().isEmpty, + "inactive context returned") + } + } + + test("getActiveOrCreate with checkpoint") { + // Function to create StreamingContext that has a config to identify it to be new context + var newContextCreated = false + def creatingFunction(): StreamingContext = { + newContextCreated = true + new StreamingContext(conf, batchDuration) + } + + // Call ssc.stop after a body of code + def testGetActiveOrCreate(body: => Unit): Unit = { + require(StreamingContext.getActive().isEmpty) // no active context + newContextCreated = false + try { + body + } finally { + if (ssc != null) { + ssc.stop() + } + ssc = null + } + } + val emptyPath = Utils.createTempDir().getAbsolutePath() + val corruptedCheckpointPath = createCorruptedCheckpoint() + val checkpointPath = createValidCheckpoint() - // getOrCreate should create new context with empty path - testGetOrCreate { - ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _, sc, createOnError = true) + // getActiveOrCreate should return the current active context if there is one + testGetActiveOrCreate { + ssc = new StreamingContext( + conf.clone.set("spark.streaming.clock", "org.apache.spark.util.ManualClock"), batchDuration) + addInputStream(ssc).register() + ssc.start() + val returnedSsc = StreamingContext.getActiveOrCreate(checkpointPath, creatingFunction _) + assert(!newContextCreated, "new context created instead of returning") + assert(returnedSsc.eq(ssc), "returned context is not the activated context") + } + + // getActiveOrCreate should create new context with empty path + testGetActiveOrCreate { + ssc = StreamingContext.getActiveOrCreate(emptyPath, creatingFunction _) assert(ssc != null, "no context created") assert(newContextCreated, "new context not created") - assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") } - val corrutedCheckpointPath = createCorruptedCheckpoint() - - // getOrCreate should throw exception with fake checkpoint file and createOnError = false + // getActiveOrCreate should throw exception with fake checkpoint file and createOnError = false intercept[Exception] { - ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _, sc) + ssc = StreamingContext.getOrCreate(corruptedCheckpointPath, creatingFunction _) } - // getOrCreate should throw exception with fake checkpoint file + // getActiveOrCreate should throw exception with fake checkpoint file intercept[Exception] { - ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, sc, createOnError = false) + ssc = StreamingContext.getActiveOrCreate( + corruptedCheckpointPath, creatingFunction _, createOnError = false) } - // getOrCreate should create new context with fake checkpoint file and createOnError = true - testGetOrCreate { - ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, sc, createOnError = true) + // getActiveOrCreate should create new context with fake + // checkpoint file and createOnError = true + testGetActiveOrCreate { + ssc = StreamingContext.getActiveOrCreate( + corruptedCheckpointPath, creatingFunction _, createOnError = true) assert(ssc != null, "no context created") assert(newContextCreated, "new context not created") - assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") } - val checkpointPath = createValidCheckpoint() - - // StreamingContext.getOrCreate should recover context with checkpoint path - testGetOrCreate { - ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _, sc) + // getActiveOrCreate should recover context with checkpoint path, and recover old configuration + testGetActiveOrCreate { + ssc = StreamingContext.getActiveOrCreate(checkpointPath, creatingFunction _) assert(ssc != null, "no context created") assert(!newContextCreated, "old context not recovered") - assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") - assert(!ssc.conf.contains("someKey"), - "recovered StreamingContext unexpectedly has old config") + assert(ssc.conf.get("someKey") === "someValue") } } + test("multiple streaming contexts") { + sc = new SparkContext( + conf.clone.set("spark.streaming.clock", "org.apache.spark.util.ManualClock")) + ssc = new StreamingContext(sc, Seconds(1)) + val input = addInputStream(ssc) + input.foreachRDD { rdd => rdd.count } + ssc.start() + + // Creating another streaming context should not create errors + val anotherSsc = new StreamingContext(sc, Seconds(10)) + val anotherInput = addInputStream(anotherSsc) + anotherInput.foreachRDD { rdd => rdd.count } + + val exception = intercept[IllegalStateException] { + anotherSsc.start() + } + assert(exception.getMessage.contains("StreamingContext"), "Did not get the right exception") + } + test("DStream and generated RDD creation sites") { testPackage.test() } + test("throw exception on using active or stopped context") { + val conf = new SparkConf() + .setMaster(master) + .setAppName(appName) + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock") + ssc = new StreamingContext(conf, batchDuration) + require(ssc.getState() === StreamingContextState.INITIALIZED) + val input = addInputStream(ssc) + val transformed = input.map { x => x} + transformed.foreachRDD { rdd => rdd.count } + + def testForException(clue: String, expectedErrorMsg: String)(body: => Unit): Unit = { + withClue(clue) { + val ex = intercept[IllegalStateException] { + body + } + assert(ex.getMessage.toLowerCase().contains(expectedErrorMsg)) + } + } + + ssc.start() + require(ssc.getState() === StreamingContextState.ACTIVE) + testForException("no error on adding input after start", "start") { + addInputStream(ssc) } + testForException("no error on adding transformation after start", "start") { + input.map { x => x * 2 } } + testForException("no error on adding output operation after start", "start") { + transformed.foreachRDD { rdd => rdd.collect() } } + + ssc.stop() + require(ssc.getState() === StreamingContextState.STOPPED) + testForException("no error on adding input after stop", "stop") { + addInputStream(ssc) } + testForException("no error on adding transformation after stop", "stop") { + input.map { x => x * 2 } } + testForException("no error on adding output operation after stop", "stop") { + transformed.foreachRDD { rdd => rdd.collect() } } + } + def addInputStream(s: StreamingContext): DStream[Int] = { val input = (1 to 100).map(i => 1 to i) val inputStream = new TestInputStream(s, input, 1) @@ -493,9 +674,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w def createValidCheckpoint(): String = { val testDirectory = Utils.createTempDir().getAbsolutePath() val checkpointDirectory = Utils.createTempDir().getAbsolutePath() - val conf = new SparkConf().setMaster(master).setAppName(appName) - conf.set("someKey", "someValue") - ssc = new StreamingContext(conf, batchDuration) + ssc = new StreamingContext(conf.clone.set("someKey", "someValue"), batchDuration) ssc.checkpoint(checkpointDirectory) ssc.textFileStream(testDirectory).foreachRDD { rdd => rdd.count() } ssc.start() @@ -569,7 +748,9 @@ class SlowTestReceiver(totalRecords: Int, recordsPerSecond: Int) def onStop() { // Simulate slow receiver by waiting for all records to be produced - while(!SlowTestReceiver.receivedAllRecords) Thread.sleep(100) + while (!SlowTestReceiver.receivedAllRecords) { + Thread.sleep(100) + } // no clean to be done, the receiving thread should stop on it own } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index 312cce408cfe7..1dc8960d60528 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -133,8 +133,10 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { /** Check if a sequence of numbers is in increasing order */ def isInIncreasingOrder(seq: Seq[Long]): Boolean = { - for(i <- 1 until seq.size) { - if (seq(i - 1) > seq(i)) return false + for (i <- 1 until seq.size) { + if (seq(i - 1) > seq(i)) { + return false + } } true } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala index 4f70ae7f1f187..31b1aebf6a8ec 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -24,17 +24,35 @@ import scala.collection.mutable.SynchronizedBuffer import scala.language.implicitConversions import scala.reflect.ClassTag -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter import org.scalatest.time.{Span, Seconds => ScalaTestSeconds} import org.scalatest.concurrent.Eventually.timeout import org.scalatest.concurrent.PatienceConfiguration -import org.apache.spark.{SparkConf, Logging} +import org.apache.spark.{Logging, SparkConf, SparkFunSuite} import org.apache.spark.rdd.RDD import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream} import org.apache.spark.streaming.scheduler._ import org.apache.spark.util.{ManualClock, Utils} +/** + * A dummy stream that does absolutely nothing. + */ +private[streaming] class DummyDStream(ssc: StreamingContext) extends DStream[Int](ssc) { + override def dependencies: List[DStream[Int]] = List.empty + override def slideDuration: Duration = Seconds(1) + override def compute(time: Time): Option[RDD[Int]] = Some(ssc.sc.emptyRDD[Int]) +} + +/** + * A dummy input stream that does absolutely nothing. + */ +private[streaming] class DummyInputDStream(ssc: StreamingContext) extends InputDStream[Int](ssc) { + override def start(): Unit = { } + override def stop(): Unit = { } + override def compute(time: Time): Option[RDD[Int]] = Some(ssc.sc.emptyRDD[Int]) +} + /** * This is a input stream just for the testsuites. This is equivalent to a checkpointable, * replayable, reliable message queue like Kafka. It requires a sequence as input, and @@ -186,7 +204,7 @@ class BatchCounter(ssc: StreamingContext) { * This is the base trait for Spark Streaming testsuites. This provides basic functionality * to run user-defined set of input on user-defined stream operations, and verify the output. */ -trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { +trait TestSuiteBase extends SparkFunSuite with BeforeAndAfter with Logging { // Name of the framework for Spark context def framework: String = this.getClass.getSimpleName diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TimeSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/TimeSuite.scala index 5579ac364346c..e6a01656f479d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TimeSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TimeSuite.scala @@ -69,6 +69,9 @@ class TimeSuite extends TestSuiteBase { assert(new Time(1200).floor(new Duration(200)) == new Time(1200)) assert(new Time(199).floor(new Duration(200)) == new Time(0)) assert(new Time(1).floor(new Duration(1)) == new Time(1)) + assert(new Time(1350).floor(new Duration(200), new Time(50)) == new Time(1250)) + assert(new Time(1350).floor(new Duration(200), new Time(150)) == new Time(1350)) + assert(new Time(1350).floor(new Duration(200), new Time(200)) == new Time(1200)) } test("isMultipleOf") { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala index 2211f62383ce8..a08578680cff9 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala @@ -27,20 +27,20 @@ import org.scalatest.selenium.WebBrowser import org.scalatest.time.SpanSugar._ import org.apache.spark._ - - - +import org.apache.spark.ui.SparkUICssErrorHandler /** - * Selenium tests for the Spark Web UI. + * Selenium tests for the Spark Streaming Web UI. */ class UISeleniumSuite - extends FunSuite with WebBrowser with Matchers with BeforeAndAfterAll with TestSuiteBase { + extends SparkFunSuite with WebBrowser with Matchers with BeforeAndAfterAll with TestSuiteBase { implicit var webDriver: WebDriver = _ override def beforeAll(): Unit = { - webDriver = new HtmlUnitDriver + webDriver = new HtmlUnitDriver { + getWebClient.setCssErrorHandler(new SparkUICssErrorHandler) + } } override def afterAll(): Unit = { @@ -116,12 +116,12 @@ class UISeleniumSuite h4Text.exists(_.matches("Completed Batches \\(last \\d+ out of \\d+\\)")) should be (true) findAll(cssSelector("""#active-batches-table th""")).map(_.text).toSeq should be { - List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", + List("Batch Time", "Input Size", "Scheduling Delay (?)", "Processing Time (?)", "Status") } findAll(cssSelector("""#completed-batches-table th""")).map(_.text).toSeq should be { - List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", - "Total Delay") + List("Batch Time", "Input Size", "Scheduling Delay (?)", "Processing Time (?)", + "Total Delay (?)") } val batchLinks = @@ -197,4 +197,3 @@ class UISeleniumSuite } } } - diff --git a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala index 6859b65c7165f..cb017b798b2a4 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala @@ -21,15 +21,15 @@ import java.io.File import scala.util.Random import org.apache.hadoop.conf.Configuration -import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} +import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach} import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBlockId} import org.apache.spark.streaming.util.{FileBasedWriteAheadLogSegment, FileBasedWriteAheadLogWriter} import org.apache.spark.util.Utils -import org.apache.spark.{SparkConf, SparkContext, SparkException} +import org.apache.spark.{SparkConf, SparkContext, SparkException, SparkFunSuite} class WriteAheadLogBackedBlockRDDSuite - extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { + extends SparkFunSuite with BeforeAndAfterAll with BeforeAndAfterEach { val conf = new SparkConf() .setMaster("local[2]") diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala index 5478b41845943..2e210397fe7c7 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala @@ -17,12 +17,12 @@ package org.apache.spark.streaming.scheduler -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.streaming.{Time, Duration, StreamingContext} -class InputInfoTrackerSuite extends FunSuite with BeforeAndAfter { +class InputInfoTrackerSuite extends SparkFunSuite with BeforeAndAfter { private var ssc: StreamingContext = _ diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala index 2a0f45830e03c..c9175d61b1f49 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -64,7 +64,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { listener.numTotalReceivedRecords should be (0) // onBatchStarted - val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) + val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted)) listener.waitingBatches should be (Nil) listener.runningBatches should be (List(BatchUIData(batchInfoStarted))) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala index 6df1a63ab2e37..d3ca2b58f36c2 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala @@ -17,12 +17,14 @@ package org.apache.spark.streaming.ui +import java.util.TimeZone import java.util.concurrent.TimeUnit -import org.scalatest.FunSuite import org.scalatest.Matchers -class UIUtilsSuite extends FunSuite with Matchers{ +import org.apache.spark.SparkFunSuite + +class UIUtilsSuite extends SparkFunSuite with Matchers{ test("shortTimeUnitString") { assert("ns" === UIUtils.shortTimeUnitString(TimeUnit.NANOSECONDS)) @@ -64,4 +66,14 @@ class UIUtilsSuite extends FunSuite with Matchers{ val convertedTime = UIUtils.convertToTimeUnit(milliseconds, unit) convertedTime should be (expectedTime +- 1E-6) } + + test("formatBatchTime") { + val tzForTest = TimeZone.getTimeZone("America/Los_Angeles") + val batchTime = 1431637480452L // Thu May 14 14:04:40 PDT 2015 + assert("2015/05/14 14:04:40" === UIUtils.formatBatchTime(batchTime, 1000, timezone = tzForTest)) + assert("2015/05/14 14:04:40.452" === + UIUtils.formatBatchTime(batchTime, 999, timezone = tzForTest)) + assert("14:04:40" === UIUtils.formatBatchTime(batchTime, 1000, false, timezone = tzForTest)) + assert("14:04:40.452" === UIUtils.formatBatchTime(batchTime, 999, false, timezone = tzForTest)) + } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala index 9ebf7b484f421..78fc344b00177 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala @@ -20,9 +20,9 @@ package org.apache.spark.streaming.util import java.io.ByteArrayOutputStream import java.util.concurrent.TimeUnit._ -import org.scalatest.FunSuite +import org.apache.spark.SparkFunSuite -class RateLimitedOutputStreamSuite extends FunSuite { +class RateLimitedOutputStreamSuite extends SparkFunSuite { private def benchmark[U](f: => U): Long = { val start = System.nanoTime diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala index 79098bcf4861c..325ff7c74c39d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala @@ -28,15 +28,15 @@ import scala.reflect.ClassTag import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.scalatest.concurrent.Eventually._ -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.BeforeAndAfter import org.apache.spark.util.{ManualClock, Utils} -import org.apache.spark.{SparkConf, SparkException} +import org.apache.spark.{SparkConf, SparkException, SparkFunSuite} -class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { +class WriteAheadLogSuite extends SparkFunSuite with BeforeAndAfter { import WriteAheadLogSuite._ - + val hadoopConf = new Configuration() var tempDir: File = null var testDir: String = null @@ -359,7 +359,7 @@ object WriteAheadLogSuite { ): FileBasedWriteAheadLog = { if (manualClock.getTimeMillis() < 100000) manualClock.setTime(10000) val wal = new FileBasedWriteAheadLog(new SparkConf(), logDirectory, hadoopConf, 1, 1) - + // Ensure that 500 does not get sorted after 2000, so put a high base value. data.foreach { item => manualClock.advance(500) diff --git a/tools/pom.xml b/tools/pom.xml index 1c6f3e83a1819..feffde4c857eb 100644 --- a/tools/pom.xml +++ b/tools/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml diff --git a/tox.ini b/tox.ini index b568029a204cc..76e3f42cde62d 100644 --- a/tox.ini +++ b/tox.ini @@ -15,4 +15,4 @@ [pep8] max-line-length=100 -exclude=cloudpickle.py,heapq3.py +exclude=cloudpickle.py,heapq3.py,shared.py diff --git a/unsafe/pom.xml b/unsafe/pom.xml index 5b0733206b2bc..62c6354f1e203 100644 --- a/unsafe/pom.xml +++ b/unsafe/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -42,6 +42,10 @@ com.google.code.findbugs jsr305       + + com.google.guava + guava + @@ -61,6 +65,11 @@ junit-interface test + + org.mockito + mockito-all + test + target/scala-${scala.binary.version}/classes diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java index 24b2892098059..192c6714b2406 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java @@ -25,8 +25,7 @@ public final class PlatformDependent { /** * Facade in front of {@link sun.misc.Unsafe}, used to avoid directly exposing Unsafe outside of - * this package. This also lets us aovid accidental use of deprecated methods or methods that - * aren't present in Java 6. + * this package. This also lets us avoid accidental use of deprecated methods. */ public static final class UNSAFE { diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java index 19d6a169fd2ad..0b4d8d286f5f9 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java @@ -23,6 +23,8 @@ import java.util.LinkedList; import java.util.List; +import com.google.common.annotations.VisibleForTesting; + import org.apache.spark.unsafe.*; import org.apache.spark.unsafe.array.ByteArrayMethods; import org.apache.spark.unsafe.array.LongArray; @@ -36,9 +38,8 @@ * This is backed by a power-of-2-sized hash table, using quadratic probing with triangular numbers, * which is guaranteed to exhaust the space. *

      - * The map can support up to 2^31 keys because we use 32 bit MurmurHash. If the key cardinality is - * higher than this, you should probably be using sorting instead of hashing for better cache - * locality. + * The map can support up to 2^29 keys. If the key cardinality is higher than this, you should + * probably be using sorting instead of hashing for better cache locality. *

      * This class is not thread safe. */ @@ -48,6 +49,11 @@ public final class BytesToBytesMap { private static final HashMapGrowthStrategy growthStrategy = HashMapGrowthStrategy.DOUBLING; + /** + * Special record length that is placed after the last record in a data page. + */ + private static final int END_OF_PAGE_MARKER = -1; + private final TaskMemoryManager memoryManager; /** @@ -64,7 +70,7 @@ public final class BytesToBytesMap { /** * Offset into `currentDataPage` that points to the location where new data can be inserted into - * the page. + * the page. This does not incorporate the page's base offset. */ private long pageCursor = 0; @@ -74,6 +80,15 @@ public final class BytesToBytesMap { */ private static final long PAGE_SIZE_BYTES = 1L << 26; // 64 megabytes + /** + * The maximum number of keys that BytesToBytesMap supports. The hash table has to be + * power-of-2-sized and its backing Java array can contain at most (1 << 30) elements, since + * that's the largest power-of-2 that's less than Integer.MAX_VALUE. We need two long array + * entries per key, giving us a maximum capacity of (1 << 29). + */ + @VisibleForTesting + static final int MAX_CAPACITY = (1 << 29); + // This choice of page table size and page size means that we can address up to 500 gigabytes // of memory. @@ -143,6 +158,13 @@ public BytesToBytesMap( this.loadFactor = loadFactor; this.loc = new Location(); this.enablePerfMetrics = enablePerfMetrics; + if (initialCapacity <= 0) { + throw new IllegalArgumentException("Initial capacity must be greater than 0"); + } + if (initialCapacity > MAX_CAPACITY) { + throw new IllegalArgumentException( + "Initial capacity " + initialCapacity + " exceeds maximum capacity of " + MAX_CAPACITY); + } allocate(initialCapacity); } @@ -162,6 +184,55 @@ public BytesToBytesMap( */ public int size() { return size; } + private static final class BytesToBytesMapIterator implements Iterator { + + private final int numRecords; + private final Iterator dataPagesIterator; + private final Location loc; + + private int currentRecordNumber = 0; + private Object pageBaseObject; + private long offsetInPage; + + BytesToBytesMapIterator(int numRecords, Iterator dataPagesIterator, Location loc) { + this.numRecords = numRecords; + this.dataPagesIterator = dataPagesIterator; + this.loc = loc; + if (dataPagesIterator.hasNext()) { + advanceToNextPage(); + } + } + + private void advanceToNextPage() { + final MemoryBlock currentPage = dataPagesIterator.next(); + pageBaseObject = currentPage.getBaseObject(); + offsetInPage = currentPage.getBaseOffset(); + } + + @Override + public boolean hasNext() { + return currentRecordNumber != numRecords; + } + + @Override + public Location next() { + int keyLength = (int) PlatformDependent.UNSAFE.getLong(pageBaseObject, offsetInPage); + if (keyLength == END_OF_PAGE_MARKER) { + advanceToNextPage(); + keyLength = (int) PlatformDependent.UNSAFE.getLong(pageBaseObject, offsetInPage); + } + loc.with(pageBaseObject, offsetInPage); + offsetInPage += 8 + 8 + keyLength + loc.getValueLength(); + currentRecordNumber++; + return loc; + } + + @Override + public void remove() { + throw new UnsupportedOperationException(); + } + } + /** * Returns an iterator for iterating over the entries of this map. * @@ -171,27 +242,7 @@ public BytesToBytesMap( * `lookup()`, the behavior of the returned iterator is undefined. */ public Iterator iterator() { - return new Iterator() { - - private int nextPos = bitset.nextSetBit(0); - - @Override - public boolean hasNext() { - return nextPos != -1; - } - - @Override - public Location next() { - final int pos = nextPos; - nextPos = bitset.nextSetBit(nextPos + 1); - return loc.with(pos, 0, true); - } - - @Override - public void remove() { - throw new UnsupportedOperationException(); - } - }; + return new BytesToBytesMapIterator(size, dataPages.iterator(), loc); } /** @@ -268,8 +319,11 @@ public final class Location { private int valueLength; private void updateAddressesAndSizes(long fullKeyAddress) { - final Object page = memoryManager.getPage(fullKeyAddress); - final long keyOffsetInPage = memoryManager.getOffsetInPage(fullKeyAddress); + updateAddressesAndSizes( + memoryManager.getPage(fullKeyAddress), memoryManager.getOffsetInPage(fullKeyAddress)); + } + + private void updateAddressesAndSizes(Object page, long keyOffsetInPage) { long position = keyOffsetInPage; keyLength = (int) PlatformDependent.UNSAFE.getLong(page, position); position += 8; // word used to store the key size @@ -291,6 +345,12 @@ Location with(int pos, int keyHashcode, boolean isDefined) { return this; } + Location with(Object page, long keyOffsetInPage) { + this.isDefined = true; + updateAddressesAndSizes(page, keyOffsetInPage); + return this; + } + /** * Returns true if the key is defined at this position, and false otherwise. */ @@ -345,6 +405,8 @@ public int getValueLength() { *

      * It is only valid to call this method immediately after calling `lookup()` using the same key. *

      + * The key and value must be word-aligned (that is, their sizes must multiples of 8). + *

      * After calling this method, calls to `get[Key|Value]Address()` and `get[Key|Value]Length` * will return information on the data stored by this `putNewKey` call. *

      @@ -367,20 +429,29 @@ public void putNewKey( long valueBaseOffset, int valueLengthBytes) { assert (!isDefined) : "Can only set value once for a key"; - isDefined = true; assert (keyLengthBytes % 8 == 0); assert (valueLengthBytes % 8 == 0); + if (size == MAX_CAPACITY) { + throw new IllegalStateException("BytesToBytesMap has reached maximum capacity"); + } // Here, we'll copy the data into our data pages. Because we only store a relative offset from // the key address instead of storing the absolute address of the value, the key and value // must be stored in the same memory page. // (8 byte key length) (key) (8 byte value length) (value) final long requiredSize = 8 + keyLengthBytes + 8 + valueLengthBytes; - assert(requiredSize <= PAGE_SIZE_BYTES); + assert (requiredSize <= PAGE_SIZE_BYTES - 8); // Reserve 8 bytes for the end-of-page marker. size++; bitset.set(pos); - // If there's not enough space in the current page, allocate a new page: - if (currentDataPage == null || PAGE_SIZE_BYTES - pageCursor < requiredSize) { + // If there's not enough space in the current page, allocate a new page (8 bytes are reserved + // for the end-of-page marker). + if (currentDataPage == null || PAGE_SIZE_BYTES - 8 - pageCursor < requiredSize) { + if (currentDataPage != null) { + // There wasn't enough space in the current page, so write an end-of-page marker: + final Object pageBaseObject = currentDataPage.getBaseObject(); + final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor; + PlatformDependent.UNSAFE.putLong(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER); + } MemoryBlock newPage = memoryManager.allocatePage(PAGE_SIZE_BYTES); dataPages.add(newPage); pageCursor = 0; @@ -414,7 +485,7 @@ public void putNewKey( longArray.set(pos * 2 + 1, keyHashcode); updateAddressesAndSizes(storedKeyAddress); isDefined = true; - if (size > growthThreshold) { + if (size > growthThreshold && longArray.size() < MAX_CAPACITY) { growAndRehash(); } } @@ -427,8 +498,11 @@ public void putNewKey( * @param capacity the new map capacity */ private void allocate(int capacity) { - capacity = Math.max((int) Math.min(Integer.MAX_VALUE, nextPowerOf2(capacity)), 64); - longArray = new LongArray(memoryManager.allocate(capacity * 8 * 2)); + assert (capacity >= 0); + // The capacity needs to be divisible by 64 so that our bit set can be sized properly + capacity = Math.max((int) Math.min(MAX_CAPACITY, nextPowerOf2(capacity)), 64); + assert (capacity <= MAX_CAPACITY); + longArray = new LongArray(memoryManager.allocate(capacity * 8L * 2)); bitset = new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64])); this.growthThreshold = (int) (capacity * loadFactor); @@ -494,10 +568,16 @@ public long getNumHashCollisions() { return numHashCollisions; } + @VisibleForTesting + int getNumDataPages() { + return dataPages.size(); + } + /** * Grows the size of the hash table and re-hash everything. */ - private void growAndRehash() { + @VisibleForTesting + void growAndRehash() { long resizeStartTime = -1; if (enablePerfMetrics) { resizeStartTime = System.nanoTime(); @@ -508,7 +588,7 @@ private void growAndRehash() { final int oldCapacity = (int) oldBitSet.capacity(); // Allocate the new data structures - allocate(Math.min(Integer.MAX_VALUE, growthStrategy.nextCapacity(oldCapacity))); + allocate(Math.min(growthStrategy.nextCapacity(oldCapacity), MAX_CAPACITY)); // Re-mask (we don't recompute the hashcode because we stored all 32 bits of it) for (int pos = oldBitSet.nextSetBit(0); pos >= 0; pos = oldBitSet.nextSetBit(pos + 1)) { diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java index 7c321baffe82d..20654e4eeaa02 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java @@ -32,7 +32,9 @@ public interface HashMapGrowthStrategy { class Doubling implements HashMapGrowthStrategy { @Override public int nextCapacity(int currentCapacity) { - return currentCapacity * 2; + assert (currentCapacity > 0); + // Guard against overflow + return (currentCapacity * 2 > 0) ? (currentCapacity * 2) : Integer.MAX_VALUE; } } diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java index 62c29c8cc1e4d..cbbe8594627a5 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java @@ -17,6 +17,12 @@ package org.apache.spark.unsafe.memory; +import java.lang.ref.WeakReference; +import java.util.HashMap; +import java.util.LinkedList; +import java.util.Map; +import javax.annotation.concurrent.GuardedBy; + /** * Manages memory for an executor. Individual operators / tasks allocate memory through * {@link TaskMemoryManager} objects, which obtain their memory from ExecutorMemoryManager. @@ -33,6 +39,12 @@ public class ExecutorMemoryManager { */ final boolean inHeap; + @GuardedBy("this") + private final Map>> bufferPoolsBySize = + new HashMap>>(); + + private static final int POOLING_THRESHOLD_BYTES = 1024 * 1024; + /** * Construct a new ExecutorMemoryManager. * @@ -43,16 +55,57 @@ public ExecutorMemoryManager(MemoryAllocator allocator) { this.allocator = allocator; } + /** + * Returns true if allocations of the given size should go through the pooling mechanism and + * false otherwise. + */ + private boolean shouldPool(long size) { + // Very small allocations are less likely to benefit from pooling. + // At some point, we should explore supporting pooling for off-heap memory, but for now we'll + // ignore that case in the interest of simplicity. + return size >= POOLING_THRESHOLD_BYTES && allocator instanceof HeapMemoryAllocator; + } + /** * Allocates a contiguous block of memory. Note that the allocated memory is not guaranteed * to be zeroed out (call `zero()` on the result if this is necessary). */ MemoryBlock allocate(long size) throws OutOfMemoryError { - return allocator.allocate(size); + if (shouldPool(size)) { + synchronized (this) { + final LinkedList> pool = bufferPoolsBySize.get(size); + if (pool != null) { + while (!pool.isEmpty()) { + final WeakReference blockReference = pool.pop(); + final MemoryBlock memory = blockReference.get(); + if (memory != null) { + assert (memory.size() == size); + return memory; + } + } + bufferPoolsBySize.remove(size); + } + } + return allocator.allocate(size); + } else { + return allocator.allocate(size); + } } void free(MemoryBlock memory) { - allocator.free(memory); + final long size = memory.size(); + if (shouldPool(size)) { + synchronized (this) { + LinkedList> pool = bufferPoolsBySize.get(size); + if (pool == null) { + pool = new LinkedList>(); + bufferPoolsBySize.put(size, pool); + } + pool.add(new WeakReference(memory)); + } + } else { + allocator.free(memory); + } } } diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java index 9224988e6ad69..10881969dbc78 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java @@ -19,6 +19,7 @@ import java.util.*; +import com.google.common.annotations.VisibleForTesting; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -43,14 +44,22 @@ * maximum size of a long[] array, allowing us to address 8192 * 2^32 * 8 bytes, which is * approximately 35 terabytes of memory. */ -public final class TaskMemoryManager { +public class TaskMemoryManager { private final Logger logger = LoggerFactory.getLogger(TaskMemoryManager.class); - /** - * The number of entries in the page table. - */ - private static final int PAGE_TABLE_SIZE = 1 << 13; + /** The number of bits used to address the page table. */ + private static final int PAGE_NUMBER_BITS = 13; + + /** The number of bits used to encode offsets in data pages. */ + @VisibleForTesting + static final int OFFSET_BITS = 64 - PAGE_NUMBER_BITS; // 51 + + /** The number of entries in the page table. */ + private static final int PAGE_TABLE_SIZE = 1 << PAGE_NUMBER_BITS; + + /** Maximum supported data page size */ + private static final long MAXIMUM_PAGE_SIZE = (1L << OFFSET_BITS); /** Bit mask for the lower 51 bits of a long. */ private static final long MASK_LONG_LOWER_51_BITS = 0x7FFFFFFFFFFFFL; @@ -101,11 +110,9 @@ public TaskMemoryManager(ExecutorMemoryManager executorMemoryManager) { * intended for allocating large blocks of memory that will be shared between operators. */ public MemoryBlock allocatePage(long size) { - if (logger.isTraceEnabled()) { - logger.trace("Allocating {} byte page", size); - } - if (size >= (1L << 51)) { - throw new IllegalArgumentException("Cannot allocate a page with more than 2^51 bytes"); + if (size > MAXIMUM_PAGE_SIZE) { + throw new IllegalArgumentException( + "Cannot allocate a page with more than " + MAXIMUM_PAGE_SIZE + " bytes"); } final int pageNumber; @@ -120,8 +127,8 @@ public MemoryBlock allocatePage(long size) { final MemoryBlock page = executorMemoryManager.allocate(size); page.pageNumber = pageNumber; pageTable[pageNumber] = page; - if (logger.isDebugEnabled()) { - logger.debug("Allocate page number {} ({} bytes)", pageNumber, size); + if (logger.isTraceEnabled()) { + logger.trace("Allocate page number {} ({} bytes)", pageNumber, size); } return page; } @@ -130,9 +137,6 @@ public MemoryBlock allocatePage(long size) { * Free a block of memory allocated via {@link TaskMemoryManager#allocatePage(long)}. */ public void freePage(MemoryBlock page) { - if (logger.isTraceEnabled()) { - logger.trace("Freeing page number {} ({} bytes)", page.pageNumber, page.size()); - } assert (page.pageNumber != -1) : "Called freePage() on memory that wasn't allocated with allocatePage()"; executorMemoryManager.free(page); @@ -140,8 +144,8 @@ public void freePage(MemoryBlock page) { allocatedPages.clear(page.pageNumber); } pageTable[page.pageNumber] = null; - if (logger.isDebugEnabled()) { - logger.debug("Freed page number {} ({} bytes)", page.pageNumber, page.size()); + if (logger.isTraceEnabled()) { + logger.trace("Freed page number {} ({} bytes)", page.pageNumber, page.size()); } } @@ -173,14 +177,36 @@ public void free(MemoryBlock memory) { /** * Given a memory page and offset within that page, encode this address into a 64-bit long. * This address will remain valid as long as the corresponding page has not been freed. + * + * @param page a data page allocated by {@link TaskMemoryManager#allocate(long)}. + * @param offsetInPage an offset in this page which incorporates the base offset. In other words, + * this should be the value that you would pass as the base offset into an + * UNSAFE call (e.g. page.baseOffset() + something). + * @return an encoded page address. */ public long encodePageNumberAndOffset(MemoryBlock page, long offsetInPage) { - if (inHeap) { - assert (page.pageNumber != -1) : "encodePageNumberAndOffset called with invalid page"; - return (((long) page.pageNumber) << 51) | (offsetInPage & MASK_LONG_LOWER_51_BITS); - } else { - return offsetInPage; + if (!inHeap) { + // In off-heap mode, an offset is an absolute address that may require a full 64 bits to + // encode. Due to our page size limitation, though, we can convert this into an offset that's + // relative to the page's base offset; this relative offset will fit in 51 bits. + offsetInPage -= page.getBaseOffset(); } + return encodePageNumberAndOffset(page.pageNumber, offsetInPage); + } + + @VisibleForTesting + public static long encodePageNumberAndOffset(int pageNumber, long offsetInPage) { + assert (pageNumber != -1) : "encodePageNumberAndOffset called with invalid page"; + return (((long) pageNumber) << OFFSET_BITS) | (offsetInPage & MASK_LONG_LOWER_51_BITS); + } + + @VisibleForTesting + public static int decodePageNumber(long pagePlusOffsetAddress) { + return (int) ((pagePlusOffsetAddress & MASK_LONG_UPPER_13_BITS) >>> OFFSET_BITS); + } + + private static long decodeOffset(long pagePlusOffsetAddress) { + return (pagePlusOffsetAddress & MASK_LONG_LOWER_51_BITS); } /** @@ -189,7 +215,7 @@ public long encodePageNumberAndOffset(MemoryBlock page, long offsetInPage) { */ public Object getPage(long pagePlusOffsetAddress) { if (inHeap) { - final int pageNumber = (int) ((pagePlusOffsetAddress & MASK_LONG_UPPER_13_BITS) >>> 51); + final int pageNumber = decodePageNumber(pagePlusOffsetAddress); assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE); final Object page = pageTable[pageNumber].getBaseObject(); assert (page != null); @@ -204,10 +230,15 @@ public Object getPage(long pagePlusOffsetAddress) { * {@link TaskMemoryManager#encodePageNumberAndOffset(MemoryBlock, long)} */ public long getOffsetInPage(long pagePlusOffsetAddress) { + final long offsetInPage = decodeOffset(pagePlusOffsetAddress); if (inHeap) { - return (pagePlusOffsetAddress & MASK_LONG_LOWER_51_BITS); + return offsetInPage; } else { - return pagePlusOffsetAddress; + // In off-heap mode, an offset is an absolute address. In encodePageNumberAndOffset, we + // converted the absolute address into a relative address. Here, we invert that operation: + final int pageNumber = decodePageNumber(pagePlusOffsetAddress); + assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE); + return pageTable[pageNumber].getBaseOffset() + offsetInPage; } } diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java new file mode 100644 index 0000000000000..9871a70a40e69 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java @@ -0,0 +1,218 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.types; + +import java.io.Serializable; +import java.io.UnsupportedEncodingException; +import java.util.Arrays; +import javax.annotation.Nonnull; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * A UTF-8 String for internal Spark use. + *

      + * A String encoded in UTF-8 as an Array[Byte], which can be used for comparison, + * search, see http://en.wikipedia.org/wiki/UTF-8 for details. + *

      + * Note: This is not designed for general use cases, should not be used outside SQL. + */ +public final class UTF8String implements Comparable, Serializable { + + @Nonnull + private byte[] bytes; + + private static int[] bytesOfCodePointInUTF8 = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, + 5, 5, 5, 5, + 6, 6, 6, 6}; + + public static UTF8String fromBytes(byte[] bytes) { + return (bytes != null) ? new UTF8String().set(bytes) : null; + } + + public static UTF8String fromString(String str) { + return (str != null) ? new UTF8String().set(str) : null; + } + + /** + * Updates the UTF8String with String. + */ + protected UTF8String set(final String str) { + try { + bytes = str.getBytes("utf-8"); + } catch (UnsupportedEncodingException e) { + // Turn the exception into unchecked so we can find out about it at runtime, but + // don't need to add lots of boilerplate code everywhere. + PlatformDependent.throwException(e); + } + return this; + } + + /** + * Updates the UTF8String with byte[], which should be encoded in UTF-8. + */ + protected UTF8String set(final byte[] bytes) { + this.bytes = bytes; + return this; + } + + /** + * Returns the number of bytes for a code point with the first byte as `b` + * @param b The first byte of a code point + */ + public int numBytes(final byte b) { + final int offset = (b & 0xFF) - 192; + return (offset >= 0) ? bytesOfCodePointInUTF8[offset] : 1; + } + + /** + * Returns the number of code points in it. + * + * This is only used by Substring() when `start` is negative. + */ + public int length() { + int len = 0; + for (int i = 0; i < bytes.length; i+= numBytes(bytes[i])) { + len += 1; + } + return len; + } + + public byte[] getBytes() { + return bytes; + } + + /** + * Returns a substring of this. + * @param start the position of first code point + * @param until the position after last code point, exclusive. + */ + public UTF8String substring(final int start, final int until) { + if (until <= start || start >= bytes.length) { + return UTF8String.fromBytes(new byte[0]); + } + + int i = 0; + int c = 0; + for (; i < bytes.length && c < start; i += numBytes(bytes[i])) { + c += 1; + } + + int j = i; + for (; j < bytes.length && c < until; j += numBytes(bytes[i])) { + c += 1; + } + + return UTF8String.fromBytes(Arrays.copyOfRange(bytes, i, j)); + } + + public boolean contains(final UTF8String substring) { + final byte[] b = substring.getBytes(); + if (b.length == 0) { + return true; + } + + for (int i = 0; i <= bytes.length - b.length; i++) { + if (bytes[i] == b[0] && startsWith(b, i)) { + return true; + } + } + return false; + } + + private boolean startsWith(final byte[] prefix, int offsetInBytes) { + if (prefix.length + offsetInBytes > bytes.length || offsetInBytes < 0) { + return false; + } + int i = 0; + while (i < prefix.length && prefix[i] == bytes[i + offsetInBytes]) { + i++; + } + return i == prefix.length; + } + + public boolean startsWith(final UTF8String prefix) { + return startsWith(prefix.getBytes(), 0); + } + + public boolean endsWith(final UTF8String suffix) { + return startsWith(suffix.getBytes(), bytes.length - suffix.getBytes().length); + } + + public UTF8String toUpperCase() { + return UTF8String.fromString(toString().toUpperCase()); + } + + public UTF8String toLowerCase() { + return UTF8String.fromString(toString().toLowerCase()); + } + + @Override + public String toString() { + try { + return new String(bytes, "utf-8"); + } catch (UnsupportedEncodingException e) { + // Turn the exception into unchecked so we can find out about it at runtime, but + // don't need to add lots of boilerplate code everywhere. + PlatformDependent.throwException(e); + return "unknown"; // we will never reach here. + } + } + + @Override + public UTF8String clone() { + return new UTF8String().set(bytes); + } + + @Override + public int compareTo(final UTF8String other) { + final byte[] b = other.getBytes(); + for (int i = 0; i < bytes.length && i < b.length; i++) { + int res = bytes[i] - b[i]; + if (res != 0) { + return res; + } + } + return bytes.length - b.length; + } + + public int compare(final UTF8String other) { + return compareTo(other); + } + + @Override + public boolean equals(final Object other) { + if (other instanceof UTF8String) { + return Arrays.equals(bytes, ((UTF8String) other).getBytes()); + } else if (other instanceof String) { + // Used only in unit tests. + String s = (String) other; + return bytes.length >= s.length() && length() == s.length() && toString().equals(s); + } else { + return false; + } + } + + @Override + public int hashCode() { + return Arrays.hashCode(bytes); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java index 18393db9f382f..a93fc0ee297c4 100644 --- a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java +++ b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java @@ -18,7 +18,6 @@ package org.apache.spark.unsafe.bitset; import junit.framework.Assert; -import org.apache.spark.unsafe.bitset.BitSet; import org.junit.Test; import org.apache.spark.unsafe.memory.MemoryBlock; diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java index 7a5c0622d1ffb..81315f7c94645 100644 --- a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java @@ -25,24 +25,40 @@ import org.junit.Assert; import org.junit.Before; import org.junit.Test; +import org.mockito.invocation.InvocationOnMock; +import org.mockito.stubbing.Answer; +import static org.mockito.AdditionalMatchers.geq; +import static org.mockito.Mockito.*; import org.apache.spark.unsafe.array.ByteArrayMethods; +import org.apache.spark.unsafe.memory.*; import org.apache.spark.unsafe.PlatformDependent; import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET; -import org.apache.spark.unsafe.memory.ExecutorMemoryManager; -import org.apache.spark.unsafe.memory.MemoryAllocator; -import org.apache.spark.unsafe.memory.MemoryLocation; -import org.apache.spark.unsafe.memory.TaskMemoryManager; +import static org.apache.spark.unsafe.PlatformDependent.LONG_ARRAY_OFFSET; + public abstract class AbstractBytesToBytesMapSuite { private final Random rand = new Random(42); private TaskMemoryManager memoryManager; + private TaskMemoryManager sizeLimitedMemoryManager; @Before public void setup() { memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(getMemoryAllocator())); + // Mocked memory manager for tests that check the maximum array size, since actually allocating + // such large arrays will cause us to run out of memory in our tests. + sizeLimitedMemoryManager = spy(memoryManager); + when(sizeLimitedMemoryManager.allocate(geq(1L << 20))).thenAnswer(new Answer() { + @Override + public MemoryBlock answer(InvocationOnMock invocation) throws Throwable { + if (((Long) invocation.getArguments()[0] / 8) > Integer.MAX_VALUE) { + throw new OutOfMemoryError("Requested array size exceeds VM limit"); + } + return memoryManager.allocate(1L << 20); + } + }); } @After @@ -101,6 +117,7 @@ public void emptyMap() { final int keyLengthInBytes = keyLengthInWords * 8; final byte[] key = getRandomByteArray(keyLengthInWords); Assert.assertFalse(map.lookup(key, BYTE_ARRAY_OFFSET, keyLengthInBytes).isDefined()); + Assert.assertFalse(map.iterator().hasNext()); } finally { map.free(); } @@ -159,7 +176,7 @@ public void setAndRetrieveAKey() { @Test public void iteratorTest() throws Exception { - final int size = 128; + final int size = 4096; BytesToBytesMap map = new BytesToBytesMap(memoryManager, size / 2); try { for (long i = 0; i < size; i++) { @@ -167,14 +184,26 @@ public void iteratorTest() throws Exception { final BytesToBytesMap.Location loc = map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8); Assert.assertFalse(loc.isDefined()); - loc.putNewKey( - value, - PlatformDependent.LONG_ARRAY_OFFSET, - 8, - value, - PlatformDependent.LONG_ARRAY_OFFSET, - 8 - ); + // Ensure that we store some zero-length keys + if (i % 5 == 0) { + loc.putNewKey( + null, + PlatformDependent.LONG_ARRAY_OFFSET, + 0, + value, + PlatformDependent.LONG_ARRAY_OFFSET, + 8 + ); + } else { + loc.putNewKey( + value, + PlatformDependent.LONG_ARRAY_OFFSET, + 8, + value, + PlatformDependent.LONG_ARRAY_OFFSET, + 8 + ); + } } final java.util.BitSet valuesSeen = new java.util.BitSet(size); final Iterator iter = map.iterator(); @@ -183,11 +212,16 @@ public void iteratorTest() throws Exception { Assert.assertTrue(loc.isDefined()); final MemoryLocation keyAddress = loc.getKeyAddress(); final MemoryLocation valueAddress = loc.getValueAddress(); - final long key = PlatformDependent.UNSAFE.getLong( - keyAddress.getBaseObject(), keyAddress.getBaseOffset()); final long value = PlatformDependent.UNSAFE.getLong( valueAddress.getBaseObject(), valueAddress.getBaseOffset()); - Assert.assertEquals(key, value); + final long keyLength = loc.getKeyLength(); + if (keyLength == 0) { + Assert.assertTrue("value " + value + " was not divisible by 5", value % 5 == 0); + } else { + final long key = PlatformDependent.UNSAFE.getLong( + keyAddress.getBaseObject(), keyAddress.getBaseOffset()); + Assert.assertEquals(value, key); + } valuesSeen.set((int) value); } Assert.assertEquals(size, valuesSeen.cardinality()); @@ -196,6 +230,74 @@ public void iteratorTest() throws Exception { } } + @Test + public void iteratingOverDataPagesWithWastedSpace() throws Exception { + final int NUM_ENTRIES = 1000 * 1000; + final int KEY_LENGTH = 16; + final int VALUE_LENGTH = 40; + final BytesToBytesMap map = new BytesToBytesMap(memoryManager, NUM_ENTRIES); + // Each record will take 8 + 8 + 16 + 40 = 72 bytes of space in the data page. Our 64-megabyte + // pages won't be evenly-divisible by records of this size, which will cause us to waste some + // space at the end of the page. This is necessary in order for us to take the end-of-record + // handling branch in iterator(). + try { + for (int i = 0; i < NUM_ENTRIES; i++) { + final long[] key = new long[] { i, i }; // 2 * 8 = 16 bytes + final long[] value = new long[] { i, i, i, i, i }; // 5 * 8 = 40 bytes + final BytesToBytesMap.Location loc = map.lookup( + key, + LONG_ARRAY_OFFSET, + KEY_LENGTH + ); + Assert.assertFalse(loc.isDefined()); + loc.putNewKey( + key, + LONG_ARRAY_OFFSET, + KEY_LENGTH, + value, + LONG_ARRAY_OFFSET, + VALUE_LENGTH + ); + } + Assert.assertEquals(2, map.getNumDataPages()); + + final java.util.BitSet valuesSeen = new java.util.BitSet(NUM_ENTRIES); + final Iterator iter = map.iterator(); + final long key[] = new long[KEY_LENGTH / 8]; + final long value[] = new long[VALUE_LENGTH / 8]; + while (iter.hasNext()) { + final BytesToBytesMap.Location loc = iter.next(); + Assert.assertTrue(loc.isDefined()); + Assert.assertEquals(KEY_LENGTH, loc.getKeyLength()); + Assert.assertEquals(VALUE_LENGTH, loc.getValueLength()); + PlatformDependent.copyMemory( + loc.getKeyAddress().getBaseObject(), + loc.getKeyAddress().getBaseOffset(), + key, + LONG_ARRAY_OFFSET, + KEY_LENGTH + ); + PlatformDependent.copyMemory( + loc.getValueAddress().getBaseObject(), + loc.getValueAddress().getBaseOffset(), + value, + LONG_ARRAY_OFFSET, + VALUE_LENGTH + ); + for (long j : key) { + Assert.assertEquals(key[0], j); + } + for (long j : value) { + Assert.assertEquals(key[0], j); + } + valuesSeen.set((int) key[0]); + } + Assert.assertEquals(NUM_ENTRIES, valuesSeen.cardinality()); + } finally { + map.free(); + } + } + @Test public void randomizedStressTest() { final int size = 65536; @@ -247,4 +349,35 @@ public void randomizedStressTest() { map.free(); } } + + @Test + public void initialCapacityBoundsChecking() { + try { + new BytesToBytesMap(sizeLimitedMemoryManager, 0); + Assert.fail("Expected IllegalArgumentException to be thrown"); + } catch (IllegalArgumentException e) { + // expected exception + } + + try { + new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY + 1); + Assert.fail("Expected IllegalArgumentException to be thrown"); + } catch (IllegalArgumentException e) { + // expected exception + } + + // Can allocate _at_ the max capacity + BytesToBytesMap map = + new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY); + map.free(); + } + + @Test + public void resizingLargeMap() { + // As long as a map's capacity is below the max, we should be able to resize up to the max + BytesToBytesMap map = + new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY - 64); + map.growAndRehash(); + map.free(); + } } diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java index 932882f1ca248..06fb081183659 100644 --- a/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java +++ b/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java @@ -38,4 +38,27 @@ public void leakedPageMemoryIsDetected() { Assert.assertEquals(4096, manager.cleanUpAllAllocatedMemory()); } + @Test + public void encodePageNumberAndOffsetOffHeap() { + final TaskMemoryManager manager = + new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.UNSAFE)); + final MemoryBlock dataPage = manager.allocatePage(256); + // In off-heap mode, an offset is an absolute address that may require more than 51 bits to + // encode. This test exercises that corner-case: + final long offset = ((1L << TaskMemoryManager.OFFSET_BITS) + 10); + final long encodedAddress = manager.encodePageNumberAndOffset(dataPage, offset); + Assert.assertEquals(null, manager.getPage(encodedAddress)); + Assert.assertEquals(offset, manager.getOffsetInPage(encodedAddress)); + } + + @Test + public void encodePageNumberAndOffsetOnHeap() { + final TaskMemoryManager manager = + new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)); + final MemoryBlock dataPage = manager.allocatePage(256); + final long encodedAddress = manager.encodePageNumberAndOffset(dataPage, 64); + Assert.assertEquals(dataPage.getBaseObject(), manager.getPage(encodedAddress)); + Assert.assertEquals(64, manager.getOffsetInPage(encodedAddress)); + } + } diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java new file mode 100644 index 0000000000000..80c179a1b5e75 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java @@ -0,0 +1,93 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.unsafe.types; + +import java.io.UnsupportedEncodingException; + +import junit.framework.Assert; +import org.junit.Test; + +public class UTF8StringSuite { + + private void checkBasic(String str, int len) throws UnsupportedEncodingException { + Assert.assertEquals(UTF8String.fromString(str).length(), len); + Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).length(), len); + + Assert.assertEquals(UTF8String.fromString(str), str); + Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")), str); + Assert.assertEquals(UTF8String.fromString(str).toString(), str); + Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).toString(), str); + Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")), UTF8String.fromString(str)); + + Assert.assertEquals(UTF8String.fromString(str).hashCode(), + UTF8String.fromBytes(str.getBytes("utf8")).hashCode()); + } + + @Test + public void basicTest() throws UnsupportedEncodingException { + checkBasic("hello", 5); + checkBasic("世 界", 3); + } + + @Test + public void contains() { + Assert.assertTrue(UTF8String.fromString("hello").contains(UTF8String.fromString("ello"))); + Assert.assertFalse(UTF8String.fromString("hello").contains(UTF8String.fromString("vello"))); + Assert.assertFalse(UTF8String.fromString("hello").contains(UTF8String.fromString("hellooo"))); + Assert.assertTrue(UTF8String.fromString("大千世界").contains(UTF8String.fromString("千世"))); + Assert.assertFalse(UTF8String.fromString("大千世界").contains(UTF8String.fromString("世千"))); + Assert.assertFalse( + UTF8String.fromString("大千世界").contains(UTF8String.fromString("大千世界好"))); + } + + @Test + public void startsWith() { + Assert.assertTrue(UTF8String.fromString("hello").startsWith(UTF8String.fromString("hell"))); + Assert.assertFalse(UTF8String.fromString("hello").startsWith(UTF8String.fromString("ell"))); + Assert.assertFalse(UTF8String.fromString("hello").startsWith(UTF8String.fromString("hellooo"))); + Assert.assertTrue(UTF8String.fromString("数据砖头").startsWith(UTF8String.fromString("数据"))); + Assert.assertFalse(UTF8String.fromString("大千世界").startsWith(UTF8String.fromString("千"))); + Assert.assertFalse( + UTF8String.fromString("大千世界").startsWith(UTF8String.fromString("大千世界好"))); + } + + @Test + public void endsWith() { + Assert.assertTrue(UTF8String.fromString("hello").endsWith(UTF8String.fromString("ello"))); + Assert.assertFalse(UTF8String.fromString("hello").endsWith(UTF8String.fromString("ellov"))); + Assert.assertFalse(UTF8String.fromString("hello").endsWith(UTF8String.fromString("hhhello"))); + Assert.assertTrue(UTF8String.fromString("大千世界").endsWith(UTF8String.fromString("世界"))); + Assert.assertFalse(UTF8String.fromString("大千世界").endsWith(UTF8String.fromString("世"))); + Assert.assertFalse( + UTF8String.fromString("数据砖头").endsWith(UTF8String.fromString("我的数据砖头"))); + } + + @Test + public void substring() { + Assert.assertEquals( + UTF8String.fromString("hello").substring(0, 0), UTF8String.fromString("")); + Assert.assertEquals( + UTF8String.fromString("hello").substring(1, 3), UTF8String.fromString("el")); + Assert.assertEquals( + UTF8String.fromString("数据砖头").substring(0, 1), UTF8String.fromString("数")); + Assert.assertEquals( + UTF8String.fromString("数据砖头").substring(1, 3), UTF8String.fromString("据砖")); + Assert.assertEquals( + UTF8String.fromString("数据砖头").substring(3, 5), UTF8String.fromString("头")); + } +} diff --git a/yarn/pom.xml b/yarn/pom.xml index 7c8c3613e7a05..644def7501dc8 100644 --- a/yarn/pom.xml +++ b/yarn/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.10 - 1.4.0-SNAPSHOT + 1.5.0-SNAPSHOT ../pom.xml @@ -30,6 +30,7 @@ Spark Project YARN yarn + 1.9 @@ -38,6 +39,13 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-core_${scala.binary.version} + ${project.version} + test-jar + test + org.apache.hadoop hadoop-yarn-api @@ -85,7 +93,12 @@ jetty-servlet - + + + org.apache.hadoop hadoop-yarn-server-tests @@ -97,59 +110,44 @@ mockito-all test + + org.mortbay.jetty + jetty + 6.1.26 + + + org.mortbay.jetty + servlet-api + + + test + + + com.sun.jersey + jersey-core + ${jersey.version} + test + + + com.sun.jersey + jersey-json + ${jersey.version} + test + + + stax + stax-api + + + + + com.sun.jersey + jersey-server + ${jersey.version} + test + - - - - - hadoop-2.2 - - 1.9 - - - - org.mortbay.jetty - jetty - 6.1.26 - - - org.mortbay.jetty - servlet-api - - - test - - - com.sun.jersey - jersey-core - ${jersey.version} - test - - - com.sun.jersey - jersey-json - ${jersey.version} - test - - - stax - stax-api - - - - - com.sun.jersey - jersey-server - ${jersey.version} - test - - - - - + target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala index aaae6f9734a85..77af46c192cc2 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala @@ -60,8 +60,11 @@ private[yarn] class AMDelegationTokenRenewer( private val hadoopUtil = YarnSparkHadoopUtil.get - private val daysToKeepFiles = sparkConf.getInt("spark.yarn.credentials.file.retention.days", 5) - private val numFilesToKeep = sparkConf.getInt("spark.yarn.credentials.file.retention.count", 5) + private val credentialsFile = sparkConf.get("spark.yarn.credentials.file") + private val daysToKeepFiles = + sparkConf.getInt("spark.yarn.credentials.file.retention.days", 5) + private val numFilesToKeep = + sparkConf.getInt("spark.yarn.credentials.file.retention.count", 5) /** * Schedule a login from the keytab and principal set using the --principal and --keytab @@ -121,7 +124,7 @@ private[yarn] class AMDelegationTokenRenewer( import scala.concurrent.duration._ try { val remoteFs = FileSystem.get(hadoopConf) - val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val credentialsPath = new Path(credentialsFile) val thresholdTime = System.currentTimeMillis() - (daysToKeepFiles days).toMillis hadoopUtil.listFilesSorted( remoteFs, credentialsPath.getParent, @@ -160,7 +163,7 @@ private[yarn] class AMDelegationTokenRenewer( val keytabLoggedInUGI = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keytab) logInfo("Successfully logged into KDC.") val tempCreds = keytabLoggedInUGI.getCredentials - val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val credentialsPath = new Path(credentialsFile) val dst = credentialsPath.getParent keytabLoggedInUGI.doAs(new PrivilegedExceptionAction[Void] { // Get a copy of the credentials @@ -186,8 +189,7 @@ private[yarn] class AMDelegationTokenRenewer( } val nextSuffix = lastCredentialsFileSuffix + 1 val tokenPathStr = - sparkConf.get("spark.yarn.credentials.file") + - SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + nextSuffix + credentialsFile + SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + nextSuffix val tokenPath = new Path(tokenPathStr) val tempTokenPath = new Path(tokenPathStr + SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) logInfo("Writing out delegation tokens to " + tempTokenPath.toString) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 29752969e6152..83dafa4a125d2 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -32,9 +32,9 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.spark.rpc._ import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv} import org.apache.spark.SparkException -import org.apache.spark.deploy.{PythonRunner, SparkHadoopUtil} +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.deploy.history.HistoryServer -import org.apache.spark.scheduler.cluster.YarnSchedulerBackend +import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, YarnSchedulerBackend} import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.util._ @@ -46,6 +46,14 @@ private[spark] class ApplicationMaster( client: YarnRMClient) extends Logging { + // Load the properties file with the Spark configuration and set entries as system properties, + // so that user code run inside the AM also has access to them. + if (args.propertiesFile != null) { + Utils.getPropertiesFromFile(args.propertiesFile).foreach { case (k, v) => + sys.props(k) = v + } + } + // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be // optimal as more containers are available. Might need to handle this better. @@ -67,6 +75,7 @@ private[spark] class ApplicationMaster( @volatile private var reporterThread: Thread = _ @volatile private var allocator: YarnAllocator = _ + private val allocatorLock = new Object() // Fields used in client mode. private var rpcEnv: RpcEnv = null @@ -220,7 +229,7 @@ private[spark] class ApplicationMaster( sparkContextRef.compareAndSet(sc, null) } - private def registerAM(uiAddress: String, securityMgr: SecurityManager) = { + private def registerAM(_rpcEnv: RpcEnv, uiAddress: String, securityMgr: SecurityManager) = { val sc = sparkContextRef.get() val appId = client.getAttemptId().getApplicationId().toString() @@ -231,8 +240,14 @@ private[spark] class ApplicationMaster( .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}/${attemptId}" } .getOrElse("") - allocator = client.register(yarnConf, - if (sc != null) sc.getConf else sparkConf, + val _sparkConf = if (sc != null) sc.getConf else sparkConf + val driverUrl = _rpcEnv.uriOf( + SparkEnv.driverActorSystemName, + RpcAddress(_sparkConf.get("spark.driver.host"), _sparkConf.get("spark.driver.port").toInt), + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) + allocator = client.register(driverUrl, + yarnConf, + _sparkConf, if (sc != null) sc.preferredNodeLocationData else Map(), uiAddress, historyAddress, @@ -279,7 +294,7 @@ private[spark] class ApplicationMaster( sc.getConf.get("spark.driver.host"), sc.getConf.get("spark.driver.port"), isClusterMode = true) - registerAM(sc.ui.map(_.appUIAddress).getOrElse(""), securityMgr) + registerAM(rpcEnv, sc.ui.map(_.appUIAddress).getOrElse(""), securityMgr) userClassThread.join() } } @@ -289,7 +304,7 @@ private[spark] class ApplicationMaster( rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, port, sparkConf, securityMgr) waitForSparkDriver() addAmIpFilter() - registerAM(sparkConf.get("spark.driver.appUIAddress", ""), securityMgr) + registerAM(rpcEnv, sparkConf.get("spark.driver.appUIAddress", ""), securityMgr) // In client mode the actor will stop the reporter thread. reporterThread.join() @@ -300,11 +315,14 @@ private[spark] class ApplicationMaster( val expiryInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000) // we want to be reasonably responsive without causing too many requests to RM. - val schedulerInterval = - sparkConf.getTimeAsMs("spark.yarn.scheduler.heartbeat.interval-ms", "5s") + val heartbeatInterval = math.max(0, math.min(expiryInterval / 2, + sparkConf.getTimeAsMs("spark.yarn.scheduler.heartbeat.interval-ms", "3s"))) - // must be <= expiryInterval / 2. - val interval = math.max(0, math.min(expiryInterval / 2, schedulerInterval)) + // we want to check more frequently for pending containers + val initialAllocationInterval = math.min(heartbeatInterval, + sparkConf.getTimeAsMs("spark.yarn.scheduler.initial-allocation.interval", "200ms")) + + var nextAllocationInterval = initialAllocationInterval // The number of failures in a row until Reporter thread give up val reporterMaxFailures = sparkConf.getInt("spark.yarn.scheduler.reporterThread.maxFailures", 5) @@ -330,15 +348,29 @@ private[spark] class ApplicationMaster( if (!NonFatal(e) || failureCount >= reporterMaxFailures) { finish(FinalApplicationStatus.FAILED, ApplicationMaster.EXIT_REPORTER_FAILURE, "Exception was thrown " + - s"${failureCount} time(s) from Reporter thread.") - + s"$failureCount time(s) from Reporter thread.") } else { - logWarning(s"Reporter thread fails ${failureCount} time(s) in a row.", e) + logWarning(s"Reporter thread fails $failureCount time(s) in a row.", e) } } } try { - Thread.sleep(interval) + val numPendingAllocate = allocator.getNumPendingAllocate + val sleepInterval = + if (numPendingAllocate > 0) { + val currentAllocationInterval = + math.min(heartbeatInterval, nextAllocationInterval) + nextAllocationInterval = currentAllocationInterval * 2 // avoid overflow + currentAllocationInterval + } else { + nextAllocationInterval = initialAllocationInterval + heartbeatInterval + } + logDebug(s"Number of pending allocations is $numPendingAllocate. " + + s"Sleeping for $sleepInterval.") + allocatorLock.synchronized { + allocatorLock.wait(sleepInterval) + } } catch { case e: InterruptedException => } @@ -349,7 +381,8 @@ private[spark] class ApplicationMaster( t.setDaemon(true) t.setName("Reporter") t.start() - logInfo("Started progress reporter thread - sleep time : " + interval) + logInfo(s"Started progress reporter thread with (heartbeat : $heartbeatInterval, " + + s"initial allocation : $initialAllocationInterval) intervals") t } @@ -465,9 +498,11 @@ private[spark] class ApplicationMaster( new MutableURLClassLoader(urls, Utils.getContextOrSparkClassLoader) } + var userArgs = args.userArgs if (args.primaryPyFile != null && args.primaryPyFile.endsWith(".py")) { - System.setProperty("spark.submit.pyFiles", - PythonRunner.formatPaths(args.pyFiles).mkString(",")) + // When running pyspark, the app is run using PythonRunner. The second argument is the list + // of files to add to PYTHONPATH, which Client.scala already handles, so it's empty. + userArgs = Seq(args.primaryPyFile, "") ++ userArgs } if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { // TODO(davies): add R dependencies here @@ -478,9 +513,7 @@ private[spark] class ApplicationMaster( val userThread = new Thread { override def run() { try { - val mainArgs = new Array[String](args.userArgs.size) - args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size) - mainMethod.invoke(null, mainArgs) + mainMethod.invoke(null, userArgs.toArray) finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS) logDebug("Done running users class") } catch { @@ -524,8 +557,15 @@ private[spark] class ApplicationMaster( override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case RequestExecutors(requestedTotal) => Option(allocator) match { - case Some(a) => a.requestTotalExecutors(requestedTotal) - case None => logWarning("Container allocator is not ready to request executors yet.") + case Some(a) => + allocatorLock.synchronized { + if (a.requestTotalExecutors(requestedTotal)) { + allocatorLock.notifyAll() + } + } + + case None => + logWarning("Container allocator is not ready to request executors yet.") } context.reply(true) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala index ae6dc1094d724..68e9f6b4db7f4 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala @@ -26,11 +26,11 @@ class ApplicationMasterArguments(val args: Array[String]) { var userClass: String = null var primaryPyFile: String = null var primaryRFile: String = null - var pyFiles: String = null - var userArgs: Seq[String] = Seq[String]() + var userArgs: Seq[String] = Nil var executorMemory = 1024 var executorCores = 1 var numExecutors = DEFAULT_NUMBER_EXECUTORS + var propertiesFile: String = null parseArgs(args.toList) @@ -59,10 +59,6 @@ class ApplicationMasterArguments(val args: Array[String]) { primaryRFile = value args = tail - case ("--py-files") :: value :: tail => - pyFiles = value - args = tail - case ("--args" | "--arg") :: value :: tail => userArgsBuffer += value args = tail @@ -79,6 +75,10 @@ class ApplicationMasterArguments(val args: Array[String]) { executorCores = value args = tail + case ("--properties-file") :: value :: tail => + propertiesFile = value + args = tail + case _ => printUsageAndExit(1, args) } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index d21a7393478ce..da1ec2a0fe2e9 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,18 +17,21 @@ package org.apache.spark.deploy.yarn -import java.io.{ByteArrayInputStream, DataInputStream, File, FileOutputStream} +import java.io.{ByteArrayInputStream, DataInputStream, File, FileOutputStream, IOException, + OutputStreamWriter} import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException} import java.nio.ByteBuffer import java.security.PrivilegedExceptionAction -import java.util.UUID +import java.util.{Properties, UUID} import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, ListBuffer, Map} import scala.reflect.runtime.universe import scala.util.{Try, Success, Failure} +import scala.util.control.NonFatal +import com.google.common.base.Charsets.UTF_8 import com.google.common.base.Objects import com.google.common.io.Files @@ -91,30 +94,59 @@ private[spark] class Client( * available in the alpha API. */ def submitApplication(): ApplicationId = { - // Setup the credentials before doing anything else, so we have don't have issues at any point. - setupCredentials() - yarnClient.init(yarnConf) - yarnClient.start() - - logInfo("Requesting a new application from cluster with %d NodeManagers" - .format(yarnClient.getYarnClusterMetrics.getNumNodeManagers)) - - // Get a new application from our RM - val newApp = yarnClient.createApplication() - val newAppResponse = newApp.getNewApplicationResponse() - val appId = newAppResponse.getApplicationId() - - // Verify whether the cluster has enough resources for our AM - verifyClusterResources(newAppResponse) - - // Set up the appropriate contexts to launch our AM - val containerContext = createContainerLaunchContext(newAppResponse) - val appContext = createApplicationSubmissionContext(newApp, containerContext) - - // Finally, submit and monitor the application - logInfo(s"Submitting application ${appId.getId} to ResourceManager") - yarnClient.submitApplication(appContext) - appId + var appId: ApplicationId = null + try { + // Setup the credentials before doing anything else, + // so we have don't have issues at any point. + setupCredentials() + yarnClient.init(yarnConf) + yarnClient.start() + + logInfo("Requesting a new application from cluster with %d NodeManagers" + .format(yarnClient.getYarnClusterMetrics.getNumNodeManagers)) + + // Get a new application from our RM + val newApp = yarnClient.createApplication() + val newAppResponse = newApp.getNewApplicationResponse() + appId = newAppResponse.getApplicationId() + + // Verify whether the cluster has enough resources for our AM + verifyClusterResources(newAppResponse) + + // Set up the appropriate contexts to launch our AM + val containerContext = createContainerLaunchContext(newAppResponse) + val appContext = createApplicationSubmissionContext(newApp, containerContext) + + // Finally, submit and monitor the application + logInfo(s"Submitting application ${appId.getId} to ResourceManager") + yarnClient.submitApplication(appContext) + appId + } catch { + case e: Throwable => + if (appId != null) { + cleanupStagingDir(appId) + } + throw e + } + } + + /** + * Cleanup application staging directory. + */ + private def cleanupStagingDir(appId: ApplicationId): Unit = { + val appStagingDir = getAppStagingDir(appId) + try { + val preserveFiles = sparkConf.getBoolean("spark.yarn.preserve.staging.files", false) + val stagingDirPath = new Path(appStagingDir) + val fs = FileSystem.get(hadoopConf) + if (!preserveFiles && fs.exists(stagingDirPath)) { + logInfo("Deleting staging directory " + stagingDirPath) + fs.delete(stagingDirPath, true) + } + } catch { + case ioe: IOException => + logWarning("Failed to cleanup staging dir " + appStagingDir, ioe) + } } /** @@ -218,7 +250,9 @@ private[spark] class Client( * This is used for setting up a container launch context for our ApplicationMaster. * Exposed for testing. */ - def prepareLocalResources(appStagingDir: String): HashMap[String, LocalResource] = { + def prepareLocalResources( + appStagingDir: String, + pySparkArchives: Seq[String]): HashMap[String, LocalResource] = { logInfo("Preparing resources for our AM container") // Upload Spark and the application JAR to the remote file system if necessary, // and add them as local resources to the application master. @@ -248,20 +282,6 @@ private[spark] class Client( "for alternatives.") } - // If we passed in a keytab, make sure we copy the keytab to the staging directory on - // HDFS, and setup the relevant environment vars, so the AM can login again. - if (loginFromKeytab) { - logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" + - " via the YARN Secure Distributed Cache.") - val localUri = new URI(args.keytab) - val localPath = getQualifiedLocalPath(localUri, hadoopConf) - val destinationPath = copyFileToRemote(dst, localPath, replication) - val destFs = FileSystem.get(destinationPath.toUri(), hadoopConf) - distCacheMgr.addResource( - destFs, hadoopConf, destinationPath, localResources, LocalResourceType.FILE, - sparkConf.get("spark.yarn.keytab"), statCache, appMasterOnly = true) - } - def addDistributedUri(uri: URI): Boolean = { val uriStr = uri.toString() if (distributedUris.contains(uriStr)) { @@ -273,6 +293,57 @@ private[spark] class Client( } } + /** + * Distribute a file to the cluster. + * + * If the file's path is a "local:" URI, it's actually not distributed. Other files are copied + * to HDFS (if not already there) and added to the application's distributed cache. + * + * @param path URI of the file to distribute. + * @param resType Type of resource being distributed. + * @param destName Name of the file in the distributed cache. + * @param targetDir Subdirectory where to place the file. + * @param appMasterOnly Whether to distribute only to the AM. + * @return A 2-tuple. First item is whether the file is a "local:" URI. Second item is the + * localized path for non-local paths, or the input `path` for local paths. + * The localized path will be null if the URI has already been added to the cache. + */ + def distribute( + path: String, + resType: LocalResourceType = LocalResourceType.FILE, + destName: Option[String] = None, + targetDir: Option[String] = None, + appMasterOnly: Boolean = false): (Boolean, String) = { + val localURI = new URI(path.trim()) + if (localURI.getScheme != LOCAL_SCHEME) { + if (addDistributedUri(localURI)) { + val localPath = getQualifiedLocalPath(localURI, hadoopConf) + val linkname = targetDir.map(_ + "/").getOrElse("") + + destName.orElse(Option(localURI.getFragment())).getOrElse(localPath.getName()) + val destPath = copyFileToRemote(dst, localPath, replication) + distCacheMgr.addResource( + fs, hadoopConf, destPath, localResources, resType, linkname, statCache, + appMasterOnly = appMasterOnly) + (false, linkname) + } else { + (false, null) + } + } else { + (true, path.trim()) + } + } + + // If we passed in a keytab, make sure we copy the keytab to the staging directory on + // HDFS, and setup the relevant environment vars, so the AM can login again. + if (loginFromKeytab) { + logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" + + " via the YARN Secure Distributed Cache.") + val (_, localizedPath) = distribute(args.keytab, + destName = Some(sparkConf.get("spark.yarn.keytab")), + appMasterOnly = true) + require(localizedPath != null, "Keytab file already distributed.") + } + /** * Copy the given main resource to the distributed cache if the scheme is not "local". * Otherwise, set the corresponding key in our SparkConf to handle it downstream. @@ -285,33 +356,18 @@ private[spark] class Client( (SPARK_JAR, sparkJar(sparkConf), CONF_SPARK_JAR), (APP_JAR, args.userJar, CONF_SPARK_USER_JAR), ("log4j.properties", oldLog4jConf.orNull, null) - ).foreach { case (destName, _localPath, confKey) => - val localPath: String = if (_localPath != null) _localPath.trim() else "" - if (!localPath.isEmpty()) { - val localURI = new URI(localPath) - if (localURI.getScheme != LOCAL_SCHEME) { - if (addDistributedUri(localURI)) { - val src = getQualifiedLocalPath(localURI, hadoopConf) - val destPath = copyFileToRemote(dst, src, replication) - val destFs = FileSystem.get(destPath.toUri(), hadoopConf) - distCacheMgr.addResource(destFs, hadoopConf, destPath, - localResources, LocalResourceType.FILE, destName, statCache) - } - } else if (confKey != null) { + ).foreach { case (destName, path, confKey) => + if (path != null && !path.trim().isEmpty()) { + val (isLocal, localizedPath) = distribute(path, destName = Some(destName)) + if (isLocal && confKey != null) { + require(localizedPath != null, s"Path $path already distributed.") // If the resource is intended for local use only, handle this downstream // by setting the appropriate property - sparkConf.set(confKey, localPath) + sparkConf.set(confKey, localizedPath) } } } - createConfArchive().foreach { file => - require(addDistributedUri(file.toURI())) - val destPath = copyFileToRemote(dst, new Path(file.toURI()), replication) - distCacheMgr.addResource(fs, hadoopConf, destPath, localResources, LocalResourceType.ARCHIVE, - LOCALIZED_HADOOP_CONF_DIR, statCache, appMasterOnly = true) - } - /** * Do the same for any additional resources passed in through ClientArguments. * Each resource category is represented by a 3-tuple of: @@ -327,21 +383,10 @@ private[spark] class Client( ).foreach { case (flist, resType, addToClasspath) => if (flist != null && !flist.isEmpty()) { flist.split(',').foreach { file => - val localURI = new URI(file.trim()) - if (localURI.getScheme != LOCAL_SCHEME) { - if (addDistributedUri(localURI)) { - val localPath = new Path(localURI) - val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) - val destPath = copyFileToRemote(dst, localPath, replication) - distCacheMgr.addResource( - fs, hadoopConf, destPath, localResources, resType, linkname, statCache) - if (addToClasspath) { - cachedSecondaryJarLinks += linkname - } - } - } else if (addToClasspath) { - // Resource is intended for local use only and should be added to the class path - cachedSecondaryJarLinks += file.trim() + val (_, localizedPath) = distribute(file, resType = resType) + require(localizedPath != null) + if (addToClasspath) { + cachedSecondaryJarLinks += localizedPath } } } @@ -350,11 +395,31 @@ private[spark] class Client( sparkConf.set(CONF_SPARK_YARN_SECONDARY_JARS, cachedSecondaryJarLinks.mkString(",")) } + if (isClusterMode && args.primaryPyFile != null) { + distribute(args.primaryPyFile, appMasterOnly = true) + } + + pySparkArchives.foreach { f => distribute(f) } + + // The python files list needs to be treated especially. All files that are not an + // archive need to be placed in a subdirectory that will be added to PYTHONPATH. + args.pyFiles.foreach { f => + val targetDir = if (f.endsWith(".py")) Some(LOCALIZED_PYTHON_DIR) else None + distribute(f, targetDir = targetDir) + } + + // Distribute an archive with Hadoop and Spark configuration for the AM. + val (_, confLocalizedPath) = distribute(createConfArchive().getAbsolutePath(), + resType = LocalResourceType.ARCHIVE, + destName = Some(LOCALIZED_CONF_DIR), + appMasterOnly = true) + require(confLocalizedPath != null) + localResources } /** - * Create an archive with the Hadoop config files for distribution. + * Create an archive with the config files for distribution. * * These are only used by the AM, since executors will use the configuration object broadcast by * the driver. The files are zipped and added to the job as an archive, so that YARN will explode @@ -366,8 +431,11 @@ private[spark] class Client( * * Currently this makes a shallow copy of the conf directory. If there are cases where a * Hadoop config directory contains subdirectories, this code will have to be fixed. + * + * The archive also contains some Spark configuration. Namely, it saves the contents of + * SparkConf in a file to be loaded by the AM process. */ - private def createConfArchive(): Option[File] = { + private def createConfArchive(): File = { val hadoopConfFiles = new HashMap[String, File]() Seq("HADOOP_CONF_DIR", "YARN_CONF_DIR").foreach { envKey => sys.env.get(envKey).foreach { path => @@ -382,28 +450,32 @@ private[spark] class Client( } } - if (!hadoopConfFiles.isEmpty) { - val hadoopConfArchive = File.createTempFile(LOCALIZED_HADOOP_CONF_DIR, ".zip", - new File(Utils.getLocalDir(sparkConf))) - - val hadoopConfStream = new ZipOutputStream(new FileOutputStream(hadoopConfArchive)) - try { - hadoopConfStream.setLevel(0) - hadoopConfFiles.foreach { case (name, file) => - if (file.canRead()) { - hadoopConfStream.putNextEntry(new ZipEntry(name)) - Files.copy(file, hadoopConfStream) - hadoopConfStream.closeEntry() - } + val confArchive = File.createTempFile(LOCALIZED_CONF_DIR, ".zip", + new File(Utils.getLocalDir(sparkConf))) + val confStream = new ZipOutputStream(new FileOutputStream(confArchive)) + + try { + confStream.setLevel(0) + hadoopConfFiles.foreach { case (name, file) => + if (file.canRead()) { + confStream.putNextEntry(new ZipEntry(name)) + Files.copy(file, confStream) + confStream.closeEntry() } - } finally { - hadoopConfStream.close() } - Some(hadoopConfArchive) - } else { - None + // Save Spark configuration to a file in the archive. + val props = new Properties() + sparkConf.getAll.foreach { case (k, v) => props.setProperty(k, v) } + confStream.putNextEntry(new ZipEntry(SPARK_CONF_FILE)) + val writer = new OutputStreamWriter(confStream, UTF_8) + props.store(writer, "Spark configuration.") + writer.flush() + confStream.closeEntry() + } finally { + confStream.close() } + confArchive } /** @@ -431,7 +503,9 @@ private[spark] class Client( /** * Set up the environment for launching our ApplicationMaster container. */ - private def setupLaunchEnv(stagingDir: String): HashMap[String, String] = { + private def setupLaunchEnv( + stagingDir: String, + pySparkArchives: Seq[String]): HashMap[String, String] = { logInfo("Setting up the launch environment for our AM container") val env = new HashMap[String, String]() val extraCp = sparkConf.getOption("spark.driver.extraClassPath") @@ -449,9 +523,6 @@ private[spark] class Client( val renewalInterval = getTokenRenewalInterval(stagingDirPath) sparkConf.set("spark.yarn.token.renewal.interval", renewalInterval.toString) } - // Set the environment variables to be passed on to the executors. - distCacheMgr.setDistFilesEnv(env) - distCacheMgr.setDistArchivesEnv(env) // Pick up any environment variables for the AM provided through spark.yarn.appMasterEnv.* val amEnvPrefix = "spark.yarn.appMasterEnv." @@ -468,15 +539,32 @@ private[spark] class Client( env("SPARK_YARN_USER_ENV") = userEnvs } - // if spark.submit.pyArchives is in sparkConf, append pyArchives to PYTHONPATH - // that can be passed on to the ApplicationMaster and the executors. - if (sparkConf.contains("spark.submit.pyArchives")) { - var pythonPath = sparkConf.get("spark.submit.pyArchives") - if (env.contains("PYTHONPATH")) { - pythonPath = Seq(env.get("PYTHONPATH"), pythonPath).mkString(File.pathSeparator) + // If pyFiles contains any .py files, we need to add LOCALIZED_PYTHON_DIR to the PYTHONPATH + // of the container processes too. Add all non-.py files directly to PYTHONPATH. + // + // NOTE: the code currently does not handle .py files defined with a "local:" scheme. + val pythonPath = new ListBuffer[String]() + val (pyFiles, pyArchives) = args.pyFiles.partition(_.endsWith(".py")) + if (pyFiles.nonEmpty) { + pythonPath += buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + LOCALIZED_PYTHON_DIR) + } + (pySparkArchives ++ pyArchives).foreach { path => + val uri = new URI(path) + if (uri.getScheme != LOCAL_SCHEME) { + pythonPath += buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + new Path(path).getName()) + } else { + pythonPath += uri.getPath() } - env("PYTHONPATH") = pythonPath - sparkConf.setExecutorEnv("PYTHONPATH", pythonPath) + } + + // Finally, update the Spark config to propagate PYTHONPATH to the AM and executors. + if (pythonPath.nonEmpty) { + val pythonPathStr = (sys.env.get("PYTHONPATH") ++ pythonPath) + .mkString(YarnSparkHadoopUtil.getClassPathSeparator) + env("PYTHONPATH") = pythonPathStr + sparkConf.setExecutorEnv("PYTHONPATH", pythonPathStr) } // In cluster mode, if the deprecated SPARK_JAVA_OPTS is set, we need to propagate it to @@ -526,8 +614,19 @@ private[spark] class Client( logInfo("Setting up container launch context for our AM") val appId = newAppResponse.getApplicationId val appStagingDir = getAppStagingDir(appId) - val localResources = prepareLocalResources(appStagingDir) - val launchEnv = setupLaunchEnv(appStagingDir) + val pySparkArchives = + if (sys.props.getOrElse("spark.yarn.isPython", "false").toBoolean) { + findPySparkArchives() + } else { + Nil + } + val launchEnv = setupLaunchEnv(appStagingDir, pySparkArchives) + val localResources = prepareLocalResources(appStagingDir, pySparkArchives) + + // Set the environment variables to be passed on to the executors. + distCacheMgr.setDistFilesEnv(launchEnv) + distCacheMgr.setDistArchivesEnv(launchEnv) + val amContainer = Records.newRecord(classOf[ContainerLaunchContext]) amContainer.setLocalResources(localResources) amContainer.setEnvironment(launchEnv) @@ -567,13 +666,6 @@ private[spark] class Client( javaOpts += "-XX:CMSIncrementalDutyCycle=10" } - // Forward the Spark configuration to the application master / executors. - // TODO: it might be nicer to pass these as an internal environment variable rather than - // as Java options, due to complications with string parsing of nested quotes. - for ((k, v) <- sparkConf.getAll) { - javaOpts += YarnSparkHadoopUtil.escapeForShell(s"-D$k=$v") - } - // Include driver-specific java options if we are launching a driver if (isClusterMode) { val driverOpts = sparkConf.getOption("spark.driver.extraJavaOptions") @@ -626,14 +718,8 @@ private[spark] class Client( Nil } val primaryPyFile = - if (args.primaryPyFile != null) { - Seq("--primary-py-file", args.primaryPyFile) - } else { - Nil - } - val pyFiles = - if (args.pyFiles != null) { - Seq("--py-files", args.pyFiles) + if (isClusterMode && args.primaryPyFile != null) { + Seq("--primary-py-file", new Path(args.primaryPyFile).getName()) } else { Nil } @@ -649,9 +735,6 @@ private[spark] class Client( } else { Class.forName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName } - if (args.primaryPyFile != null && args.primaryPyFile.endsWith(".py")) { - args.userArgs = ArrayBuffer(args.primaryPyFile, args.pyFiles) ++ args.userArgs - } if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { args.userArgs = ArrayBuffer(args.primaryRFile) ++ args.userArgs } @@ -659,11 +742,13 @@ private[spark] class Client( Seq("--arg", YarnSparkHadoopUtil.escapeForShell(arg)) } val amArgs = - Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ pyFiles ++ primaryRFile ++ + Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ primaryRFile ++ userArgs ++ Seq( "--executor-memory", args.executorMemory.toString + "m", "--executor-cores", args.executorCores.toString, - "--num-executors ", args.numExecutors.toString) + "--num-executors ", args.numExecutors.toString, + "--properties-file", buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + LOCALIZED_CONF_DIR, SPARK_CONF_FILE)) // Command for the ApplicationMaster val commands = prefixEnv ++ Seq( @@ -742,6 +827,9 @@ private[spark] class Client( case e: ApplicationNotFoundException => logError(s"Application $appId not found.") return (YarnApplicationState.KILLED, FinalApplicationStatus.KILLED) + case NonFatal(e) => + logError(s"Failed to contact YARN for application $appId.", e) + return (YarnApplicationState.FAILED, FinalApplicationStatus.FAILED) } val state = report.getYarnApplicationState @@ -760,6 +848,7 @@ private[spark] class Client( if (state == YarnApplicationState.FINISHED || state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) { + cleanupStagingDir(appId) return (state, report.getFinalApplicationStatus) } @@ -827,6 +916,22 @@ private[spark] class Client( } } } + + private def findPySparkArchives(): Seq[String] = { + sys.env.get("PYSPARK_ARCHIVES_PATH") + .map(_.split(",").toSeq) + .getOrElse { + val pyLibPath = Seq(sys.env("SPARK_HOME"), "python", "lib").mkString(File.separator) + val pyArchivesFile = new File(pyLibPath, "pyspark.zip") + require(pyArchivesFile.exists(), + "pyspark.zip not found; cannot run pyspark application in YARN mode.") + val py4jFile = new File(pyLibPath, "py4j-0.8.2.1-src.zip") + require(py4jFile.exists(), + "py4j-0.8.2.1-src.zip not found; cannot run pyspark application in YARN mode.") + Seq(pyArchivesFile.getAbsolutePath(), py4jFile.getAbsolutePath()) + } + } + } object Client extends Logging { @@ -877,8 +982,14 @@ object Client extends Logging { // Distribution-defined classpath to add to processes val ENV_DIST_CLASSPATH = "SPARK_DIST_CLASSPATH" - // Subdirectory where the user's hadoop config files will be placed. - val LOCALIZED_HADOOP_CONF_DIR = "__hadoop_conf__" + // Subdirectory where the user's Spark and Hadoop config files will be placed. + val LOCALIZED_CONF_DIR = "__spark_conf__" + + // Name of the file in the conf archive containing Spark configuration. + val SPARK_CONF_FILE = "__spark_conf__.properties" + + // Subdirectory where the user's python files (not archives) will be placed. + val LOCALIZED_PYTHON_DIR = "__pyfiles__" /** * Find the user-defined Spark jar if configured, or return the jar containing this @@ -1003,7 +1114,7 @@ object Client extends Logging { if (isAM) { addClasspathEntry( YarnSparkHadoopUtil.expandEnvironment(Environment.PWD) + Path.SEPARATOR + - LOCALIZED_HADOOP_CONF_DIR, env) + LOCALIZED_CONF_DIR, env) } if (sparkConf.getBoolean("spark.yarn.user.classpath.first", false)) { @@ -1120,9 +1231,9 @@ object Client extends Logging { logDebug("HiveMetaStore configured in localmode") } } catch { - case e:java.lang.NoSuchMethodException => { logInfo("Hive Method not found " + e); return } - case e:java.lang.ClassNotFoundException => { logInfo("Hive Class not found " + e); return } - case e:Exception => { logError("Unexpected Exception " + e) + case e: java.lang.NoSuchMethodException => { logInfo("Hive Method not found " + e); return } + case e: java.lang.ClassNotFoundException => { logInfo("Hive Class not found " + e); return } + case e: Exception => { logError("Unexpected Exception " + e) throw new RuntimeException("Unexpected exception", e) } } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 5653c9f14dc6d..35e990602a6cf 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -30,7 +30,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) var archives: String = null var userJar: String = null var userClass: String = null - var pyFiles: String = null + var pyFiles: Seq[String] = Nil var primaryPyFile: String = null var primaryRFile: String = null var userArgs: ArrayBuffer[String] = new ArrayBuffer[String]() @@ -98,6 +98,12 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) numExecutors = initialNumExecutors } + principal = Option(principal) + .orElse(sparkConf.getOption("spark.yarn.principal")) + .orNull + keytab = Option(keytab) + .orElse(sparkConf.getOption("spark.yarn.keytab")) + .orNull } /** @@ -222,7 +228,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) args = tail case ("--py-files") :: value :: tail => - pyFiles = value + pyFiles = value.split(",") args = tail case ("--files") :: value :: tail => diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala index c592ecfdfce06..3d3a966960e9f 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala @@ -43,22 +43,22 @@ private[spark] class ClientDistributedCacheManager() extends Logging { * Add a resource to the list of distributed cache resources. This list can * be sent to the ApplicationMaster and possibly the executors so that it can * be downloaded into the Hadoop distributed cache for use by this application. - * Adds the LocalResource to the localResources HashMap passed in and saves + * Adds the LocalResource to the localResources HashMap passed in and saves * the stats of the resources to they can be sent to the executors and verified. * * @param fs FileSystem * @param conf Configuration * @param destPath path to the resource * @param localResources localResource hashMap to insert the resource into - * @param resourceType LocalResourceType + * @param resourceType LocalResourceType * @param link link presented in the distributed cache to the destination - * @param statCache cache to store the file/directory stats + * @param statCache cache to store the file/directory stats * @param appMasterOnly Whether to only add the resource to the app master */ def addResource( fs: FileSystem, conf: Configuration, - destPath: Path, + destPath: Path, localResources: HashMap[String, LocalResource], resourceType: LocalResourceType, link: String, @@ -74,15 +74,15 @@ private[spark] class ClientDistributedCacheManager() extends Logging { amJarRsrc.setSize(destStatus.getLen()) if (link == null || link.isEmpty()) throw new Exception("You must specify a valid link name") localResources(link) = amJarRsrc - + if (!appMasterOnly) { val uri = destPath.toUri() val pathURI = new URI(uri.getScheme(), uri.getAuthority(), uri.getPath(), null, link) if (resourceType == LocalResourceType.FILE) { - distCacheFiles(pathURI.toString()) = (destStatus.getLen().toString(), + distCacheFiles(pathURI.toString()) = (destStatus.getLen().toString(), destStatus.getModificationTime().toString(), visibility.name()) } else { - distCacheArchives(pathURI.toString()) = (destStatus.getLen().toString(), + distCacheArchives(pathURI.toString()) = (destStatus.getLen().toString(), destStatus.getModificationTime().toString(), visibility.name()) } } @@ -95,13 +95,13 @@ private[spark] class ClientDistributedCacheManager() extends Logging { val (keys, tupleValues) = distCacheFiles.unzip val (sizes, timeStamps, visibilities) = tupleValues.unzip3 if (keys.size > 0) { - env("SPARK_YARN_CACHE_FILES") = keys.reduceLeft[String] { (acc,n) => acc + "," + n } - env("SPARK_YARN_CACHE_FILES_TIME_STAMPS") = - timeStamps.reduceLeft[String] { (acc,n) => acc + "," + n } - env("SPARK_YARN_CACHE_FILES_FILE_SIZES") = - sizes.reduceLeft[String] { (acc,n) => acc + "," + n } - env("SPARK_YARN_CACHE_FILES_VISIBILITIES") = - visibilities.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES") = keys.reduceLeft[String] { (acc, n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_TIME_STAMPS") = + timeStamps.reduceLeft[String] { (acc, n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_FILE_SIZES") = + sizes.reduceLeft[String] { (acc, n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_VISIBILITIES") = + visibilities.reduceLeft[String] { (acc, n) => acc + "," + n } } } @@ -112,13 +112,13 @@ private[spark] class ClientDistributedCacheManager() extends Logging { val (keys, tupleValues) = distCacheArchives.unzip val (sizes, timeStamps, visibilities) = tupleValues.unzip3 if (keys.size > 0) { - env("SPARK_YARN_CACHE_ARCHIVES") = keys.reduceLeft[String] { (acc,n) => acc + "," + n } - env("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") = - timeStamps.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES") = keys.reduceLeft[String] { (acc, n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") = + timeStamps.reduceLeft[String] { (acc, n) => acc + "," + n } env("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") = - sizes.reduceLeft[String] { (acc,n) => acc + "," + n } - env("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") = - visibilities.reduceLeft[String] { (acc,n) => acc + "," + n } + sizes.reduceLeft[String] { (acc, n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") = + visibilities.reduceLeft[String] { (acc, n) => acc + "," + n } } } @@ -160,7 +160,7 @@ private[spark] class ClientDistributedCacheManager() extends Logging { def ancestorsHaveExecutePermissions( fs: FileSystem, path: Path, - statCache: Map[URI, FileStatus]): Boolean = { + statCache: Map[URI, FileStatus]): Boolean = { var current = path while (current != null) { // the subdirs in the path should have execute permissions for others @@ -197,7 +197,7 @@ private[spark] class ClientDistributedCacheManager() extends Logging { def getFileStatus(fs: FileSystem, uri: URI, statCache: Map[URI, FileStatus]): FileStatus = { val stat = statCache.get(uri) match { case Some(existstat) => existstat - case None => + case None => val newStat = fs.getFileStatus(new Path(uri)) statCache.put(uri, newStat) newStat diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala index 9d04d241dae9e..b0937083bc536 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -303,8 +303,8 @@ class ExecutorRunnable( val address = container.getNodeHttpAddress val baseUrl = s"$httpScheme$address/node/containerlogs/$containerId/$user" - env("SPARK_LOG_URL_STDERR") = s"$baseUrl/stderr?start=0" - env("SPARK_LOG_URL_STDOUT") = s"$baseUrl/stdout?start=0" + env("SPARK_LOG_URL_STDERR") = s"$baseUrl/stderr?start=-4096" + env("SPARK_LOG_URL_STDOUT") = s"$baseUrl/stdout?start=-4096" } System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k, v) => env(k) = v } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala index b8f42dadcb464..940873fbd046c 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala @@ -34,10 +34,8 @@ import org.apache.hadoop.yarn.util.RackResolver import org.apache.log4j.{Level, Logger} -import org.apache.spark.{SparkEnv, Logging, SecurityManager, SparkConf} +import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._ -import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend -import org.apache.spark.util.AkkaUtils /** * YarnAllocator is charged with requesting containers from the YARN ResourceManager and deciding @@ -53,6 +51,7 @@ import org.apache.spark.util.AkkaUtils * synchronized. */ private[yarn] class YarnAllocator( + driverUrl: String, conf: Configuration, sparkConf: SparkConf, amClient: AMRMClient[ContainerRequest], @@ -107,16 +106,27 @@ private[yarn] class YarnAllocator( new ThreadFactoryBuilder().setNameFormat("ContainerLauncher #%d").setDaemon(true).build()) launcherPool.allowCoreThreadTimeOut(true) - private val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(securityMgr.akkaSSLOptions.enabled), - SparkEnv.driverActorSystemName, - sparkConf.get("spark.driver.host"), - sparkConf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ENDPOINT_NAME) - // For testing private val launchContainers = sparkConf.getBoolean("spark.yarn.launchContainers", true) + private val labelExpression = sparkConf.getOption("spark.yarn.executor.nodeLabelExpression") + + // ContainerRequest constructor that can take a node label expression. We grab it through + // reflection because it's only available in later versions of YARN. + private val nodeLabelConstructor = labelExpression.flatMap { expr => + try { + Some(classOf[ContainerRequest].getConstructor(classOf[Resource], + classOf[Array[String]], classOf[Array[String]], classOf[Priority], classOf[Boolean], + classOf[String])) + } catch { + case e: NoSuchMethodException => { + logWarning(s"Node label expression $expr will be ignored because YARN version on" + + " classpath does not support it.") + None + } + } + } + def getNumExecutorsRunning: Int = numExecutorsRunning def getNumExecutorsFailed: Int = numExecutorsFailed @@ -136,11 +146,16 @@ private[yarn] class YarnAllocator( * Request as many executors from the ResourceManager as needed to reach the desired total. If * the requested total is smaller than the current number of running executors, no executors will * be killed. + * + * @return Whether the new requested total is different than the old value. */ - def requestTotalExecutors(requestedTotal: Int): Unit = synchronized { + def requestTotalExecutors(requestedTotal: Int): Boolean = synchronized { if (requestedTotal != targetNumExecutors) { logInfo(s"Driver requested a total number of $requestedTotal executor(s).") targetNumExecutors = requestedTotal + true + } else { + false } } @@ -211,7 +226,7 @@ private[yarn] class YarnAllocator( s"cores and ${resource.getMemory} MB memory including $memoryOverhead MB overhead") for (i <- 0 until missing) { - val request = new ContainerRequest(resource, null, null, RM_REQUEST_PRIORITY) + val request = createContainerRequest(resource) amClient.addContainerRequest(request) val nodes = request.getNodes val hostStr = if (nodes == null || nodes.isEmpty) "Any" else nodes.last @@ -230,6 +245,17 @@ private[yarn] class YarnAllocator( } } + /** + * Creates a container request, handling the reflection required to use YARN features that were + * added in recent versions. + */ + private def createContainerRequest(resource: Resource): ContainerRequest = { + nodeLabelConstructor.map { constructor => + constructor.newInstance(resource, null, null, RM_REQUEST_PRIORITY, true: java.lang.Boolean, + labelExpression.orNull) + }.getOrElse(new ContainerRequest(resource, null, null, RM_REQUEST_PRIORITY)) + } + /** * Handle containers granted by the RM by launching executors on them. * @@ -373,7 +399,9 @@ private[yarn] class YarnAllocator( // Hadoop 2.2.X added a ContainerExitStatus we should switch to use // there are some exit status' we shouldn't necessarily count against us, but for // now I think its ok as none of the containers are expected to exit - if (completedContainer.getExitStatus == -103) { // vmem limit exceeded + if (completedContainer.getExitStatus == ContainerExitStatus.PREEMPTED) { + logInfo("Container preempted: " + containerId) + } else if (completedContainer.getExitStatus == -103) { // vmem limit exceeded logWarning(memLimitExceededLogMessage( completedContainer.getDiagnostics, VMEM_EXCEEDED_PATTERN)) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala index b134751366522..7f533ee55e8bb 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala @@ -55,6 +55,7 @@ private[spark] class YarnRMClient(args: ApplicationMasterArguments) extends Logg * @param uiHistoryAddress Address of the application on the History Server. */ def register( + driverUrl: String, conf: YarnConfiguration, sparkConf: SparkConf, preferredNodeLocations: Map[String, Set[SplitInfo]], @@ -72,7 +73,7 @@ private[spark] class YarnRMClient(args: ApplicationMasterArguments) extends Logg amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress) registered = true } - new YarnAllocator(conf, sparkConf, amClient, getAttemptId(), args, securityMgr) + new YarnAllocator(driverUrl, conf, sparkConf, amClient, getAttemptId(), args, securityMgr) } /** @@ -89,9 +90,7 @@ private[spark] class YarnRMClient(args: ApplicationMasterArguments) extends Logg /** Returns the attempt ID. */ def getAttemptId(): ApplicationAttemptId = { - val containerIdString = System.getenv(ApplicationConstants.Environment.CONTAINER_ID.name()) - val containerId = ConverterUtils.toContainerId(containerIdString) - containerId.getApplicationAttemptId() + YarnSparkHadoopUtil.get.getContainerId.getApplicationAttemptId() } /** Returns the configuration for the AmIpFilter to add to the Spark UI. */ diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index ba91872107d0c..68d01c17ef720 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -33,7 +33,8 @@ import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment -import org.apache.hadoop.yarn.api.records.{Priority, ApplicationAccessType} +import org.apache.hadoop.yarn.api.records.{ApplicationAccessType, ContainerId, Priority} +import org.apache.hadoop.yarn.util.ConverterUtils import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.{SecurityManager, SparkConf, SparkException} @@ -136,12 +137,16 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { tokenRenewer.foreach(_.stop()) } + private[spark] def getContainerId: ContainerId = { + val containerIdString = System.getenv(ApplicationConstants.Environment.CONTAINER_ID.name()) + ConverterUtils.toContainerId(containerIdString) + } } object YarnSparkHadoopUtil { - // Additional memory overhead + // Additional memory overhead // 10% was arrived at experimentally. In the interest of minimizing memory waste while covering - // the common cases. Memory overhead tends to grow with container size. + // the common cases. Memory overhead tends to grow with container size. val MEMORY_OVERHEAD_FACTOR = 0.10 val MEMORY_OVERHEAD_MIN = 384 diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 99c05329b4d73..1c8d7ec57635f 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -76,7 +76,8 @@ private[spark] class YarnClientSchedulerBackend( ("--executor-memory", "SPARK_EXECUTOR_MEMORY", "spark.executor.memory"), ("--executor-cores", "SPARK_WORKER_CORES", "spark.executor.cores"), ("--executor-cores", "SPARK_EXECUTOR_CORES", "spark.executor.cores"), - ("--queue", "SPARK_YARN_QUEUE", "spark.yarn.queue") + ("--queue", "SPARK_YARN_QUEUE", "spark.yarn.queue"), + ("--py-files", null, "spark.submit.pyFiles") ) // Warn against the following deprecated environment variables: env var -> suggestion val deprecatedEnvVars = Map( @@ -86,7 +87,7 @@ private[spark] class YarnClientSchedulerBackend( optionTuples.foreach { case (optionName, envVar, sparkProp) => if (sc.getConf.contains(sparkProp)) { extraArgs += (optionName, sc.getConf.get(sparkProp)) - } else if (System.getenv(envVar) != null) { + } else if (envVar != null && System.getenv(envVar) != null) { extraArgs += (optionName, System.getenv(envVar)) if (deprecatedEnvVars.contains(envVar)) { logWarning(s"NOTE: $envVar is deprecated. Use ${deprecatedEnvVars(envVar)} instead.") diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala index aeb218a575455..33f580aaebdc0 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala @@ -17,10 +17,19 @@ package org.apache.spark.scheduler.cluster +import java.net.NetworkInterface + +import scala.collection.JavaConverters._ + +import org.apache.hadoop.yarn.api.records.NodeState +import org.apache.hadoop.yarn.client.api.YarnClient +import org.apache.hadoop.yarn.conf.YarnConfiguration + import org.apache.spark.SparkContext +import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._ import org.apache.spark.scheduler.TaskSchedulerImpl -import org.apache.spark.util.IntParam +import org.apache.spark.util.{IntParam, Utils} private[spark] class YarnClusterSchedulerBackend( scheduler: TaskSchedulerImpl, @@ -53,4 +62,71 @@ private[spark] class YarnClusterSchedulerBackend( logError("Application attempt ID is not set.") super.applicationAttemptId } + + override def getDriverLogUrls: Option[Map[String, String]] = { + var yarnClientOpt: Option[YarnClient] = None + var driverLogs: Option[Map[String, String]] = None + try { + val yarnConf = new YarnConfiguration(sc.hadoopConfiguration) + val containerId = YarnSparkHadoopUtil.get.getContainerId + yarnClientOpt = Some(YarnClient.createYarnClient()) + yarnClientOpt.foreach { yarnClient => + yarnClient.init(yarnConf) + yarnClient.start() + + // For newer versions of YARN, we can find the HTTP address for a given node by getting a + // container report for a given container. But container reports came only in Hadoop 2.4, + // so we basically have to get the node reports for all nodes and find the one which runs + // this container. For that we have to compare the node's host against the current host. + // Since the host can have multiple addresses, we need to compare against all of them to + // find out if one matches. + + // Get all the addresses of this node. + val addresses = + NetworkInterface.getNetworkInterfaces.asScala + .flatMap(_.getInetAddresses.asScala) + .toSeq + + // Find a node report that matches one of the addresses + val nodeReport = + yarnClient.getNodeReports(NodeState.RUNNING).asScala.find { x => + val host = x.getNodeId.getHost + addresses.exists { address => + address.getHostAddress == host || + address.getHostName == host || + address.getCanonicalHostName == host + } + } + + // Now that we have found the report for the Node Manager that the AM is running on, we + // can get the base HTTP address for the Node manager from the report. + // The format used for the logs for each container is well-known and can be constructed + // using the NM's HTTP address and the container ID. + // The NM may be running several containers, but we can build the URL for the AM using + // the AM's container ID, which we already know. + nodeReport.foreach { report => + val httpAddress = report.getHttpAddress + // lookup appropriate http scheme for container log urls + val yarnHttpPolicy = yarnConf.get( + YarnConfiguration.YARN_HTTP_POLICY_KEY, + YarnConfiguration.YARN_HTTP_POLICY_DEFAULT + ) + val user = Utils.getCurrentUserName() + val httpScheme = if (yarnHttpPolicy == "HTTPS_ONLY") "https://" else "http://" + val baseUrl = s"$httpScheme$httpAddress/node/containerlogs/$containerId/$user" + logDebug(s"Base URL for logs: $baseUrl") + driverLogs = Some(Map( + "stderr" -> s"$baseUrl/stderr?start=-4096", + "stdout" -> s"$baseUrl/stdout?start=-4096")) + } + } + } catch { + case e: Exception => + logInfo("Node Report API is not available in the version of YARN being used, so AM" + + " logs link will not appear in application UI", e) + } finally { + yarnClientOpt.foreach(_.close()) + } + driverLogs + } } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala index 80b57d1355a3a..804dfecde7867 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala @@ -19,7 +19,6 @@ package org.apache.spark.deploy.yarn import java.net.URI -import org.scalatest.FunSuite import org.scalatest.mock.MockitoSugar import org.mockito.Mockito.when @@ -36,16 +35,18 @@ import org.apache.hadoop.yarn.util.{Records, ConverterUtils} import scala.collection.mutable.HashMap import scala.collection.mutable.Map +import org.apache.spark.SparkFunSuite -class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { + +class ClientDistributedCacheManagerSuite extends SparkFunSuite with MockitoSugar { class MockClientDistributedCacheManager extends ClientDistributedCacheManager { - override def getVisibility(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): + override def getVisibility(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): LocalResourceVisibility = { LocalResourceVisibility.PRIVATE } } - + test("test getFileStatus empty") { val distMgr = new ClientDistributedCacheManager() val fs = mock[FileSystem] @@ -60,7 +61,7 @@ class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { val distMgr = new ClientDistributedCacheManager() val fs = mock[FileSystem] val uri = new URI("/tmp/testing") - val realFileStatus = new FileStatus(10, false, 1, 1024, 10, 10, null, "testOwner", + val realFileStatus = new FileStatus(10, false, 1, 1024, 10, 10, null, "testOwner", null, new Path("/tmp/testing")) when(fs.getFileStatus(new Path(uri))).thenReturn(new FileStatus()) val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus](uri -> realFileStatus) @@ -77,7 +78,7 @@ class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() when(fs.getFileStatus(destPath)).thenReturn(new FileStatus()) - distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, "link", + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, "link", statCache, false) val resource = localResources("link") assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) @@ -100,11 +101,11 @@ class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { assert(env.get("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") === None) // add another one and verify both there and order correct - val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", null, new Path("/tmp/testing2")) val destPath2 = new Path("file:///foo.invalid.com:8080/tmp/testing2") when(fs.getFileStatus(destPath2)).thenReturn(realFileStatus) - distMgr.addResource(fs, conf, destPath2, localResources, LocalResourceType.FILE, "link2", + distMgr.addResource(fs, conf, destPath2, localResources, LocalResourceType.FILE, "link2", statCache, false) val resource2 = localResources("link2") assert(resource2.getVisibility() === LocalResourceVisibility.PRIVATE) @@ -116,7 +117,7 @@ class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { val env2 = new HashMap[String, String]() distMgr.setDistFilesEnv(env2) val timestamps = env2("SPARK_YARN_CACHE_FILES_TIME_STAMPS").split(',') - val files = env2("SPARK_YARN_CACHE_FILES").split(',') + val files = env2("SPARK_YARN_CACHE_FILES").split(',') val sizes = env2("SPARK_YARN_CACHE_FILES_FILE_SIZES").split(',') val visibilities = env2("SPARK_YARN_CACHE_FILES_VISIBILITIES") .split(',') assert(files(0) === "file:/foo.invalid.com:8080/tmp/testing#link") @@ -140,7 +141,7 @@ class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { when(fs.getFileStatus(destPath)).thenReturn(new FileStatus()) intercept[Exception] { - distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, null, + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, null, statCache, false) } assert(localResources.get("link") === None) @@ -154,11 +155,11 @@ class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") val localResources = HashMap[String, LocalResource]() val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() - val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", null, new Path("/tmp/testing")) when(fs.getFileStatus(destPath)).thenReturn(realFileStatus) - distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", statCache, true) val resource = localResources("link") assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) @@ -188,11 +189,11 @@ class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") val localResources = HashMap[String, LocalResource]() val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() - val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", null, new Path("/tmp/testing")) when(fs.getFileStatus(destPath)).thenReturn(realFileStatus) - distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", statCache, false) val resource = localResources("link") assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index 508819e242a26..4ec976aa31387 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -33,12 +33,12 @@ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.mockito.Matchers._ import org.mockito.Mockito._ -import org.scalatest.{BeforeAndAfterAll, FunSuite, Matchers} +import org.scalatest.{BeforeAndAfterAll, Matchers} -import org.apache.spark.{SparkException, SparkConf} +import org.apache.spark.{SparkConf, SparkException, SparkFunSuite} import org.apache.spark.util.Utils -class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { +class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll { override def beforeAll(): Unit = { System.setProperty("SPARK_YARN_MODE", "true") @@ -113,7 +113,7 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { Environment.PWD.$() } cp should contain(pwdVar) - cp should contain (s"$pwdVar${Path.SEPARATOR}${Client.LOCALIZED_HADOOP_CONF_DIR}") + cp should contain (s"$pwdVar${Path.SEPARATOR}${Client.LOCALIZED_CONF_DIR}") cp should not contain (Client.SPARK_JAR) cp should not contain (Client.APP_JAR) } @@ -129,7 +129,7 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { val tempDir = Utils.createTempDir() try { - client.prepareLocalResources(tempDir.getAbsolutePath()) + client.prepareLocalResources(tempDir.getAbsolutePath(), Nil) sparkConf.getOption(Client.CONF_SPARK_USER_JAR) should be (Some(USER)) // The non-local path should be propagated by name only, since it will end up in the app's @@ -203,7 +203,7 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { def getFieldValue2[A: ClassTag, A1: ClassTag, B]( clazz: Class[_], field: String, - defaults: => B)(mapTo: A => B)(mapTo1: A1 => B): B = { + defaults: => B)(mapTo: A => B)(mapTo1: A1 => B): B = { Try(clazz.getField(field)).map(_.get(null)).map { case v: A => mapTo(v) case v1: A1 => mapTo1(v1) diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala index 455f1019d86dd..7509000771d94 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala @@ -26,13 +26,13 @@ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.client.api.AMRMClient import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest -import org.apache.spark.SecurityManager +import org.apache.spark.{SecurityManager, SparkFunSuite} import org.apache.spark.SparkConf import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._ import org.apache.spark.deploy.yarn.YarnAllocator._ import org.apache.spark.scheduler.SplitInfo -import org.scalatest.{BeforeAndAfterEach, FunSuite, Matchers} +import org.scalatest.{BeforeAndAfterEach, Matchers} class MockResolver extends DNSToSwitchMapping { @@ -46,7 +46,7 @@ class MockResolver extends DNSToSwitchMapping { def reloadCachedMappings(names: JList[String]) {} } -class YarnAllocatorSuite extends FunSuite with Matchers with BeforeAndAfterEach { +class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfterEach { val conf = new Configuration() conf.setClass( CommonConfigurationKeysPublic.NET_TOPOLOGY_NODE_SWITCH_MAPPING_IMPL_KEY, @@ -90,6 +90,7 @@ class YarnAllocatorSuite extends FunSuite with Matchers with BeforeAndAfterEach "--jar", "somejar.jar", "--class", "SomeClass") new YarnAllocator( + "not used", conf, sparkConf, rmClient, diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index d3c606e0ed998..335e966519c7c 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark.deploy.yarn import java.io.{File, FileOutputStream, OutputStreamWriter} +import java.net.URL import java.util.Properties import java.util.concurrent.TimeUnit @@ -29,11 +30,12 @@ import com.google.common.io.ByteStreams import com.google.common.io.Files import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.server.MiniYARNCluster -import org.scalatest.{BeforeAndAfterAll, FunSuite, Matchers} +import org.scalatest.{BeforeAndAfterAll, Matchers} -import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException, TestUtils} +import org.apache.spark._ import org.apache.spark.scheduler.cluster.ExecutorInfo -import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListener, SparkListenerExecutorAdded} +import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart, + SparkListenerExecutorAdded} import org.apache.spark.util.Utils /** @@ -41,7 +43,7 @@ import org.apache.spark.util.Utils * applications, and require the Spark assembly to be built before they can be successfully * run. */ -class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers with Logging { +class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matchers with Logging { // log4j configuration for the YARN containers, so that their output is collected // by YARN instead of trying to overwrite unit-tests.log. @@ -54,6 +56,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit """.stripMargin private val TEST_PYFILE = """ + |import mod1, mod2 |import sys |from operator import add | @@ -65,7 +68,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit | sc = SparkContext(conf=SparkConf()) | status = open(sys.argv[1],'w') | result = "failure" - | rdd = sc.parallelize(range(10)) + | rdd = sc.parallelize(range(10)).map(lambda x: x * mod1.func() * mod2.func()) | cnt = rdd.count() | if cnt == 10: | result = "success" @@ -74,6 +77,11 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit | sc.stop() """.stripMargin + private val TEST_PYMODULE = """ + |def func(): + | return 42 + """.stripMargin + private var yarnCluster: MiniYARNCluster = _ private var tempDir: File = _ private var fakeSparkJar: File = _ @@ -122,7 +130,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit logInfo(s"RM address in configuration is ${config.get(YarnConfiguration.RM_ADDRESS)}") fakeSparkJar = File.createTempFile("sparkJar", null, tempDir) - hadoopConfDir = new File(tempDir, Client.LOCALIZED_HADOOP_CONF_DIR) + hadoopConfDir = new File(tempDir, Client.LOCALIZED_CONF_DIR) assert(hadoopConfDir.mkdir()) File.createTempFile("token", ".txt", hadoopConfDir) } @@ -149,26 +157,12 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit } } - // Enable this once fix SPARK-6700 - test("run Python application in yarn-cluster mode") { - val primaryPyFile = new File(tempDir, "test.py") - Files.write(TEST_PYFILE, primaryPyFile, UTF_8) - val pyFile = new File(tempDir, "test2.py") - Files.write(TEST_PYFILE, pyFile, UTF_8) - var result = File.createTempFile("result", null, tempDir) + test("run Python application in yarn-client mode") { + testPySpark(true) + } - // The sbt assembly does not include pyspark / py4j python dependencies, so we need to - // propagate SPARK_HOME so that those are added to PYTHONPATH. See PythonUtils.scala. - val sparkHome = sys.props("spark.test.home") - val extraConf = Map( - "spark.executorEnv.SPARK_HOME" -> sparkHome, - "spark.yarn.appMasterEnv.SPARK_HOME" -> sparkHome) - - runSpark(false, primaryPyFile.getAbsolutePath(), - sparkArgs = Seq("--py-files", pyFile.getAbsolutePath()), - appArgs = Seq(result.getAbsolutePath()), - extraConf = extraConf) - checkResult(result) + test("run Python application in yarn-cluster mode") { + testPySpark(false) } test("user class path first in client mode") { @@ -186,6 +180,33 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit checkResult(result) } + private def testPySpark(clientMode: Boolean): Unit = { + val primaryPyFile = new File(tempDir, "test.py") + Files.write(TEST_PYFILE, primaryPyFile, UTF_8) + + val moduleDir = + if (clientMode) { + // In client-mode, .py files added with --py-files are not visible in the driver. + // This is something that the launcher library would have to handle. + tempDir + } else { + val subdir = new File(tempDir, "pyModules") + subdir.mkdir() + subdir + } + val pyModule = new File(moduleDir, "mod1.py") + Files.write(TEST_PYMODULE, pyModule, UTF_8) + + val mod2Archive = TestUtils.createJarWithFiles(Map("mod2.py" -> TEST_PYMODULE), moduleDir) + val pyFiles = Seq(pyModule.getAbsolutePath(), mod2Archive.getPath()).mkString(",") + val result = File.createTempFile("result", null, tempDir) + + runSpark(clientMode, primaryPyFile.getAbsolutePath(), + sparkArgs = Seq("--py-files", pyFiles), + appArgs = Seq(result.getAbsolutePath())) + checkResult(result) + } + private def testUseClassPathFirst(clientMode: Boolean): Unit = { // Create a jar file that contains a different version of "test.resource". val originalJar = TestUtils.createJarWithFiles(Map("test.resource" -> "ORIGINAL"), tempDir) @@ -290,10 +311,15 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit private[spark] class SaveExecutorInfo extends SparkListener { val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() + var driverLogs: Option[collection.Map[String, String]] = None override def onExecutorAdded(executor: SparkListenerExecutorAdded) { addedExecutorInfos(executor.executorId) = executor.executorInfo } + + override def onApplicationStart(appStart: SparkListenerApplicationStart): Unit = { + driverLogs = appStart.driverLogs + } } private object YarnClusterDriver extends Logging with Matchers { @@ -314,11 +340,12 @@ private object YarnClusterDriver extends Logging with Matchers { val sc = new SparkContext(new SparkConf() .set("spark.extraListeners", classOf[SaveExecutorInfo].getName) .setAppName("yarn \"test app\" 'with quotes' and \\back\\slashes and $dollarSigns")) + val conf = sc.getConf val status = new File(args(0)) var result = "failure" try { val data = sc.parallelize(1 to 4, 4).collect().toSet - assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS) data should be (Set(1, 2, 3, 4)) result = "success" } finally { @@ -335,6 +362,22 @@ private object YarnClusterDriver extends Logging with Matchers { executorInfos.foreach { info => assert(info.logUrlMap.nonEmpty) } + + // If we are running in yarn-cluster mode, verify that driver logs links and present and are + // in the expected format. + if (conf.get("spark.master") == "yarn-cluster") { + assert(listener.driverLogs.nonEmpty) + val driverLogs = listener.driverLogs.get + assert(driverLogs.size === 2) + assert(driverLogs.containsKey("stderr")) + assert(driverLogs.containsKey("stdout")) + val urlStr = driverLogs("stderr") + // Ensure that this is a valid URL, else this will throw an exception + new URL(urlStr) + val containerId = YarnSparkHadoopUtil.get.getContainerId + val user = Utils.getCurrentUserName() + assert(urlStr.endsWith(s"/node/containerlogs/$containerId/$user/stderr?start=-4096")) + } } } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala index e10b985c3c236..49bee0866dd43 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala @@ -25,15 +25,15 @@ import org.apache.hadoop.fs.Path import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.conf.YarnConfiguration -import org.scalatest.{FunSuite, Matchers} +import org.scalatest.Matchers import org.apache.hadoop.yarn.api.records.ApplicationAccessType -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException, SparkFunSuite} import org.apache.spark.util.Utils -class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging { +class YarnSparkHadoopUtilSuite extends SparkFunSuite with Matchers with Logging { val hasBash = try {