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…GraphGenerators This PR removes a stale TODO comment in `GraphGenerators.scala` Just comment removed. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12839 from dongjoon-hyun/SPARK-15057. (cherry picked from commit 46965cd) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ntegrationSuite Remove redundant set master in OutputCommitCoordinatorIntegrationSuite, as we are already setting it in SparkContext below on line 43. existing tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #13168 from techaddict/minor-1. (cherry picked from commit 3facca5) Signed-off-by: Reynold Xin <rxin@databricks.com>
This reverts commit 1fc0f95.
…f output of surrounded SerializeFromObject.
## What changes were proposed in this pull request?
The following code:
```
val ds = Seq(("a", 1), ("b", 2), ("c", 3)).toDS()
ds.filter(_._1 == "b").select(expr("_1").as[String]).foreach(println(_))
```
throws an Exception:
```
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: _1#420
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
...
Cause: java.lang.RuntimeException: Couldn't find _1#420 in [_1#416,_2#417]
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
...
```
This is because `EmbedSerializerInFilter` rule drops the `exprId`s of output of surrounded `SerializeFromObject`.
The analyzed and optimized plans of the above example are as follows:
```
== Analyzed Logical Plan ==
_1: string
Project [_1#420]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, scala.Tuple2]._1, true) AS _1#420,input[0, scala.Tuple2]._2 AS _2#421]
+- Filter <function1>.apply
+- DeserializeToObject newInstance(class scala.Tuple2), obj#419: scala.Tuple2
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
== Optimized Logical Plan ==
!Project [_1#420]
+- Filter <function1>.apply
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
```
This PR fixes `EmbedSerializerInFilter` rule to keep `exprId`s of output of surrounded `SerializeFromObject`.
The plans after this patch are as follows:
```
== Analyzed Logical Plan ==
_1: string
Project [_1#420]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, scala.Tuple2]._1, true) AS _1#420,input[0, scala.Tuple2]._2 AS _2#421]
+- Filter <function1>.apply
+- DeserializeToObject newInstance(class scala.Tuple2), obj#419: scala.Tuple2
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
== Optimized Logical Plan ==
Project [_1#416]
+- Filter <function1>.apply
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
```
## How was this patch tested?
Existing tests and I added a test to check if `filter and then select` works.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes #13096 from ueshin/issues/SPARK-15313.
(cherry picked from commit d5e1c5a)
Signed-off-by: Reynold Xin <rxin@databricks.com>
This was accidentally reverted in f8d0177.
## What changes were proposed in this pull request? Like TRUNCATE TABLE Command in Hive, TRUNCATE TABLE is also supported by Hive. See the link: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL Below is the related Hive JIRA: https://issues.apache.org/jira/browse/HIVE-446 This PR is to implement such a command for truncate table excluded column truncation(HIVE-4005). ## How was this patch tested? Added a test case. Author: Lianhui Wang <lianhuiwang09@gmail.com> Closes #13170 from lianhuiwang/truncate. (cherry picked from commit 09a0051) Signed-off-by: Reynold Xin <rxin@databricks.com>
…d when size mismatch happened in LogisticRegression ## What changes were proposed in this pull request? Explicitly tell user initial coefficients is ignored if its size doesn't match expected size in LogisticRegression ## How was this patch tested? local build Author: dding3 <dingding@dingding-ubuntu.sh.intel.com> Closes #12948 from dding3/master. (cherry picked from commit a78fbfa) Signed-off-by: Xiangrui Meng <meng@databricks.com>
…._, asML/fromML ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) In this DataFrame example, we use VectorImplicits._, which is private API. Since Vectors object has public API, we use Vectors.fromML instead of implicts. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Manually run the example. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #13213 from wangmiao1981/ml. (cherry picked from commit 4c7a6b3) Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
MLlib are not recommended to use, and some methods are even deprecated.
Update the warning message to recommend ML usage.
```
def showWarning() {
System.err.println(
"""WARN: This is a naive implementation of Logistic Regression and is given as an example!
|Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
|org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
|for more conventional use.
""".stripMargin)
}
```
To
```
def showWarning() {
System.err.println(
"""WARN: This is a naive implementation of Logistic Regression and is given as an example!
|Please use org.apache.spark.ml.classification.LogisticRegression
|for more conventional use.
""".stripMargin)
}
```
## How was this patch tested?
local build
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #13190 from zhengruifeng/update_recd.
(cherry picked from commit 47a2940)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request? Correct some typos and incorrectly worded sentences. ## How was this patch tested? Doc changes only. Note that many of these changes were identified by whomfire01 Author: sethah <seth.hendrickson16@gmail.com> Closes #13180 from sethah/ml_guide_audit. (cherry picked from commit 5e20350) Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request? * ```GeneralizedLinearRegression``` API docs enhancement. * The default value of ```GeneralizedLinearRegression``` ```linkPredictionCol``` is not set rather than empty. This will consistent with other similar params such as ```weightCol``` * Make some methods more private. * Fix a minor bug of LinearRegression. * Fix some other issues. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13129 from yanboliang/spark-15339. (cherry picked from commit c94b34e) Signed-off-by: Xiangrui Meng <meng@databricks.com>
#### What changes were proposed in this pull request? `refreshTable` was a method in `HiveContext`. It was deleted accidentally while we were migrating the APIs. This PR is to add it back to `HiveContext`. In addition, in `SparkSession`, we put it under the catalog namespace (`SparkSession.catalog.refreshTable`). #### How was this patch tested? Changed the existing test cases to use the function `refreshTable`. Also added a test case for refreshTable in `hivecontext-compatibility` Author: gatorsmile <gatorsmile@gmail.com> Closes #13156 from gatorsmile/refreshTable. (cherry picked from commit 39fd469) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When we parse DDLs involving table or database properties, we need to validate the values.
E.g. if we alter a database's property without providing a value:
```
ALTER DATABASE my_db SET DBPROPERTIES('some_key')
```
Then we'll ignore it with Hive, but override the property with the in-memory catalog. Inconsistencies like these arise because we don't validate the property values.
In such cases, we should throw exceptions instead.
## How was this patch tested?
`DDLCommandSuite`
Author: Andrew Or <andrew@databricks.com>
Closes #13205 from andrewor14/ddl-prop-values.
(cherry picked from commit 2573750)
Signed-off-by: Andrew Or <andrew@databricks.com>
## What changes were proposed in this pull request? There is no way to use the Hive catalog in `pyspark-shell`. This is because we used to create a `SparkContext` before calling `SparkSession.enableHiveSupport().getOrCreate()`, which just gets the existing `SparkContext` instead of creating a new one. As a result, `spark.sql.catalogImplementation` was never propagated. ## How was this patch tested? Manual. Author: Andrew Or <andrew@databricks.com> Closes #13203 from andrewor14/fix-pyspark-shell. (cherry picked from commit c32b1b1) Signed-off-by: Andrew Or <andrew@databricks.com>
…nkPredictionCol for GeneralizedLinearRegression ## What changes were proposed in this pull request? Default value mismatch of param linkPredictionCol for GeneralizedLinearRegression between PySpark and Scala. That is because default value conflict between #13106 and #13129. This causes ml.tests failed. ## How was this patch tested? Existing tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #13220 from viirya/hotfix-regresstion. (cherry picked from commit 4e73933) Signed-off-by: Nick Pentreath <nickp@za.ibm.com>
…cess start successfully but script output fail message ## What changes were proposed in this pull request? fix the bug: The spark daemon shell script error, daemon process start successfully but script output fail message ## How was this patch tested? existing test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #13172 from WeichenXu123/fix-spark-15203. (cherry picked from commit a3ceb87) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? Update example code in examples/src/main/r/ml.R to reflect the new algorithms. * spark.glm and glm * spark.survreg * spark.naiveBayes * spark.kmeans ## How was this patch tested? Offline test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13000 from yanboliang/spark-15222. (cherry picked from commit 9a9c6f5) Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
The following code generates wrong schema:
```
val schema = new StructType().add(
"struct",
new StructType()
.add("i", IntegerType, nullable = false)
.add(
"s",
new StructType().add("int", IntegerType, nullable = false),
nullable = false),
nullable = false)
val ds = sqlContext.range(10).map(l => Row(l, Row(l)))(RowEncoder(schema))
ds.printSchema()
```
This should print as follows:
```
root
|-- struct: struct (nullable = false)
| |-- i: integer (nullable = false)
| |-- s: struct (nullable = false)
| | |-- int: integer (nullable = false)
```
but the result is:
```
root
|-- struct: struct (nullable = false)
| |-- col1: integer (nullable = false)
| |-- col2: struct (nullable = false)
| | |-- col1: integer (nullable = false)
```
This PR fixes `RowEncoder` to preserve nested column name.
## How was this patch tested?
Existing tests and I added a test to check if `RowEncoder` preserves nested column name.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes #13090 from ueshin/issues/SPARK-15308.
(cherry picked from commit d2e1aa9)
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? We started this convention to append Command suffix to all SQL commands. However, not all commands follow that convention. This patch adds Command suffix to all RunnableCommands. ## How was this patch tested? Updated test cases to reflect the renames. Author: Reynold Xin <rxin@databricks.com> Closes #13215 from rxin/SPARK-15435. (cherry picked from commit e8adc55) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ld preserve metadata of value expressions if it is NamedExpression. ## What changes were proposed in this pull request? `CreateNamedStruct` and `CreateNamedStructUnsafe` should preserve metadata of value expressions if it is `NamedExpression` like `CreateStruct` or `CreateStructUnsafe` are doing. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #13193 from ueshin/issues/SPARK-15400. (cherry picked from commit 2cbe96e) Signed-off-by: Reynold Xin <rxin@databricks.com>
…rguments is specified
(Please fill in changes proposed in this fix)
In 2.0, ./bin/spark-submit doesn't print out usage, but it raises an exception.
In this PR, an exception handling is added in the Main.java when the exception is thrown. In the handling code, if there is no additional argument, it prints out usage.
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Manually tested.
./bin/spark-submit
Usage: spark-submit [options] <app jar | python file> [app arguments]
Usage: spark-submit --kill [submission ID] --master [spark://...]
Usage: spark-submit --status [submission ID] --master [spark://...]
Usage: spark-submit run-example [options] example-class [example args]
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
on one of the worker machines inside the cluster ("cluster")
(Default: client).
--class CLASS_NAME Your application's main class (for Java / Scala apps).
--name NAME A name of your application.
--jars JARS Comma-separated list of local jars to include on the driver
and executor classpaths.
--packages Comma-separated list of maven coordinates of jars to include
on the driver and executor classpaths. Will search the local
maven repo, then maven central and any additional remote
repositories given by --repositories. The format for the
coordinates should be groupId:artifactId:version.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes #13163 from wangmiao1981/submit.
(cherry picked from commit fe2fcb4)
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
… in generated code ## What changes were proposed in this pull request? This PR introduce place holder for comment in generated code and the purpose is same for #12939 but much safer. Generated code to be compiled doesn't include actual comments but includes place holder instead. Place holders in generated code will be replaced with actual comments only at the time of logging. Also, this PR can resolve SPARK-15205. ## How was this patch tested? Existing tests. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #12979 from sarutak/SPARK-15205. (cherry picked from commit 22947cd) Signed-off-by: Davies Liu <davies.liu@gmail.com>
## What changes were proposed in this pull request?
Right now inferring the schema for case classes happens before searching the SQLUserDefinedType annotation, so the SQLUserDefinedType annotation for case classes doesn't work.
This PR simply changes the inferring order to resolve it. I also reenabled the java.math.BigDecimal test and added two tests for `List`.
## How was this patch tested?
`encodeDecodeTest(UDTCaseClass(new java.net.URI("http://spark.apache.org/")), "udt with case class")`
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #12965 from zsxwing/SPARK-15190.
(cherry picked from commit dfa61f7)
Signed-off-by: Michael Armbrust <michael@databricks.com>
…rit… This reverts commit 8d05a7a from #12855, which seems to have caused regressions when working with empty DataFrames. Author: Michael Armbrust <michael@databricks.com> Closes #13181 from marmbrus/revert12855. (cherry picked from commit 2ba3ff0) Signed-off-by: Michael Armbrust <michael@databricks.com>
## What changes were proposed in this pull request?
Currently, the explain of a query with whole-stage codegen looks like this
```
>>> df = sqlCtx.range(1000);df2 = sqlCtx.range(1000);df.join(pyspark.sql.functions.broadcast(df2), 'id').explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [id#1L]
: +- BroadcastHashJoin [id#1L], [id#4L], Inner, BuildRight, None
: :- Range 0, 1, 4, 1000, [id#1L]
: +- INPUT
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint]))
+- WholeStageCodegen
: +- Range 0, 1, 4, 1000, [id#4L]
```
The problem is that the plan looks much different than logical plan, make us hard to understand the plan (especially when the logical plan is not showed together).
This PR will change it to:
```
>>> df = sqlCtx.range(1000);df2 = sqlCtx.range(1000);df.join(pyspark.sql.functions.broadcast(df2), 'id').explain()
== Physical Plan ==
*Project [id#0L]
+- *BroadcastHashJoin [id#0L], [id#3L], Inner, BuildRight, None
:- *Range 0, 1, 4, 1000, [id#0L]
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
+- *Range 0, 1, 4, 1000, [id#3L]
```
The `*`before the plan means that it's part of whole-stage codegen, it's easy to understand.
## How was this patch tested?
Manually ran some queries and check the explain.
Author: Davies Liu <davies@databricks.com>
Closes #13204 from davies/explain_codegen.
(cherry picked from commit 0e70fd6)
Signed-off-by: Reynold Xin <rxin@databricks.com>
…en calling sc.setLogLevel ## What changes were proposed in this pull request? Currently `SparkContext API setLogLevel(level: String) `can not handle lower case or mixed case input string. But `org.apache.log4j.Level.toLevel` can take lowercase or mixed case. This PR is to allow case-insensitive user input for the log level. ## How was this patch tested? A unit testcase is added. Author: Xin Wu <xinwu@us.ibm.com> Closes #13422 from xwu0226/reset_loglevel. (cherry picked from commit 28ad0f7) Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
…r clear ## What changes were proposed in this pull request? For input object of non-flat type, we can't encode it to row if it's null, as Spark SQL doesn't allow row to be null, only its columns can be null. This PR explicitly add this constraint and throw exception if users break it. ## How was this patch tested? several new tests Author: Wenchen Fan <wenchen@databricks.com> Closes #13469 from cloud-fan/null-object. (cherry picked from commit 11c83f8) Signed-off-by: Cheng Lian <lian@databricks.com>
## What changes were proposed in this pull request? As of this patch, the following throws an exception because the schemas may not match: ``` CREATE TABLE students (age INT, name STRING) AS SELECT * FROM boxes ``` but this is OK: ``` CREATE TABLE students AS SELECT * FROM boxes ``` ## How was this patch tested? SQLQuerySuite, HiveDDLCommandSuite Author: Andrew Or <andrew@databricks.com> Closes #13490 from andrewor14/ctas-no-column. (cherry picked from commit b1cc7da) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…Classifier ## What changes were proposed in this pull request? MultilayerPerceptronClassifier is missing step size, solver, and weights. Add these params. Also clarify the scaladoc a bit while we are updating these params. Eventually we should follow up and unify the HasSolver params (filed https://issues.apache.org/jira/browse/SPARK-15169 ) ## How was this patch tested? Doc tests Author: Holden Karau <holden@us.ibm.com> Closes #12943 from holdenk/SPARK-15168-add-missing-params-to-MultilayerPerceptronClassifier. (cherry picked from commit 67cc89f) Signed-off-by: Nick Pentreath <nickp@za.ibm.com>
## What changes were proposed in this pull request? Currently, the memory for temporary buffer used by TimSort is always allocated as on-heap without bookkeeping, it could cause OOM both in on-heap and off-heap mode. This PR will try to manage that by preallocate it together with the pointer array, same with RadixSort. It both works for on-heap and off-heap mode. This PR also change the loadFactor of BytesToBytesMap to 0.5 (it was 0.70), it enables use to radix sort also makes sure that we have enough memory for timsort. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #13318 from davies/fix_timsort. (cherry picked from commit 3074f57) Signed-off-by: Davies Liu <davies.liu@gmail.com>
…ation tokens to be added in current user credential. ## What changes were proposed in this pull request? The credentials are not added to the credentials of UserGroupInformation.getCurrentUser(). Further if the client has possibility to login using keytab then the updateDelegationToken thread is not started on client. ## How was this patch tested? ran dev/run-tests Author: Subroto Sanyal <ssanyal@datameer.com> Closes #13499 from subrotosanyal/SPARK-15754-save-ugi-from-changing. (cherry picked from commit 61d729a) Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
…rquetfile/avrofile' ## What changes were proposed in this pull request? Now Spark SQL can support 'create table src stored as orc/parquet/avro' for orc/parquet/avro table. But Hive can support both commands: ' stored as orc/parquet/avro' and 'stored as orcfile/parquetfile/avrofile'. So this PR supports these keywords 'orcfile/parquetfile/avrofile' in Spark SQL. ## How was this patch tested? add unit tests Author: Lianhui Wang <lianhuiwang09@gmail.com> Closes #13500 from lianhuiwang/SPARK-15756. (cherry picked from commit 2ca563c) Signed-off-by: Reynold Xin <rxin@databricks.com>
…"micro" f1_score ## What changes were proposed in this pull request? 1, del precision,recall in `ml.MulticlassClassificationEvaluator` 2, update user guide for `mlllib.weightedFMeasure` ## How was this patch tested? local build Author: Ruifeng Zheng <ruifengz@foxmail.com> Closes #13390 from zhengruifeng/clarify_f1. (cherry picked from commit 2099e05) Signed-off-by: Sean Owen <sowen@cloudera.com>
…in license copyright ## What changes were proposed in this pull request? Per conversation on dev list, add missing modernizr license. Specify "2014 and onwards" in copyright statement. ## How was this patch tested? (none required) Author: Sean Owen <sowen@cloudera.com> Closes #13510 from srowen/ModernizrLicense. (cherry picked from commit 681387b) Signed-off-by: Sean Owen <sowen@cloudera.com>
…on Metadata.empty We should cache `Metadata.hashCode` and use a singleton for `Metadata.empty` because calculating metadata hashCodes appears to be a bottleneck for certain workloads. We should also cache `StructType.hashCode`. In an optimizer stress-test benchmark run by ericl, these `hashCode` calls accounted for roughly 40% of the total CPU time and this bottleneck was completely eliminated by the caching added by this patch. Author: Josh Rosen <joshrosen@databricks.com> Closes #13504 from JoshRosen/metadata-fix. (cherry picked from commit 091f81e) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
## What changes were proposed in this pull request?
In forType function of object RandomDataGenerator, the code following:
if (maybeSqlTypeGenerator.isDefined){
....
Some(generator)
} else{
None
}
will be changed. Instead, maybeSqlTypeGenerator.map will be used.
## How was this patch tested?
All of the current unit tests passed.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes #13448 from Sherry302/master.
(cherry picked from commit 0f307db)
Signed-off-by: Sean Owen <sowen@cloudera.com>
…form "EST" is … ## What changes were proposed in this pull request? Stop using the abbreviated and ambiguous timezone "EST" in a test, since it is machine-local default timezone dependent, and fails in different timezones. Fixed [SPARK-15723](https://issues.apache.org/jira/browse/SPARK-15723). ## How was this patch tested? Note that to reproduce this problem in any locale/timezone, you can modify the scalatest-maven-plugin argLine to add a timezone: <argLine>-ea -Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=${CodeCacheSize} -Duser.timezone="Australia/Sydney"</argLine> and run $ mvn test -DwildcardSuites=org.apache.spark.status.api.v1.SimpleDateParamSuite -Dtest=none. Equally this will fix it in an effected timezone: <argLine>-ea -Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=${CodeCacheSize} -Duser.timezone="America/New_York"</argLine> To test the fix, apply the above change to `pom.xml` to set test TZ to `Australia/Sydney`, and confirm the test now passes. Author: Brett Randall <javabrett@gmail.com> Closes #13462 from javabrett/SPARK-15723-SimpleDateParamSuite. (cherry picked from commit 4e767d0) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? 1, remove comments `:: Experimental ::` for non-experimental API 2, add comments `:: Experimental ::` for experimental API 3, add comments `:: DeveloperApi ::` for developerApi API ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13514 from zhengruifeng/del_experimental. (cherry picked from commit 372fa61) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? changes in R/README.md - Make step of generating SparkR document more clear. - link R/DOCUMENTATION.md from R/README.md - turn on some code syntax highlight in R/README.md ## How was this patch tested? local test Author: Kai Jiang <jiangkai@gmail.com> Closes #13488 from vectorijk/R-Readme. (cherry picked from commit 8a91105) Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
…object ## What changes were proposed in this pull request? This PR improves the error handling of `RowEncoder`. When we create a `RowEncoder` with a given schema, we should validate the data type of input object. e.g. we should throw an exception when a field is boolean but is declared as a string column. This PR also removes the support to use `Product` as a valid external type of struct type. This support is added at #9712, but is incomplete, e.g. nested product, product in array are both not working. However, we never officially support this feature and I think it's ok to ban it. ## How was this patch tested? new tests in `RowEncoderSuite`. Author: Wenchen Fan <wenchen@databricks.com> Closes #13401 from cloud-fan/bug. (cherry picked from commit 30c4774) Signed-off-by: Cheng Lian <lian@databricks.com>
… in PartitionStatistics `PartitionStatistics` uses `foldLeft` and list concatenation (`++`) to flatten an iterator of lists, but this is extremely inefficient compared to simply doing `flatMap`/`flatten` because it performs many unnecessary object allocations. Simply replacing this `foldLeft` by a `flatMap` results in decent performance gains when constructing PartitionStatistics instances for tables with many columns. This patch fixes this and also makes two similar changes in MLlib and streaming to try to fix all known occurrences of this pattern. Author: Josh Rosen <joshrosen@databricks.com> Closes #13491 from JoshRosen/foldleft-to-flatmap. (cherry picked from commit 26c1089) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ession testing ## What changes were proposed in this pull request? This change fixes a crash in TungstenAggregate while executing "Dataset complex Aggregator" test case due to IndexOutOfBoundsException. jira entry for detail: https://issues.apache.org/jira/browse/SPARK-15704 ## How was this patch tested? Using existing unit tests (including DatasetBenchmark) Author: Hiroshi Inoue <inouehrs@jp.ibm.com> Closes #13446 from inouehrs/fix_aggregate. (cherry picked from commit 79268aa) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr fixes the behaviour of `format("csv").option("quote", null)` along with one of spark-csv.
Also, it explicitly sets default values for CSV options in python.
## How was this patch tested?
Added tests in CSVSuite.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes #13372 from maropu/SPARK-15585.
(cherry picked from commit b7e8d1c)
Signed-off-by: Reynold Xin <rxin@databricks.com>
…ehaivour" This reverts commit 9e7e2f9.
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #13515 from zhengruifeng/an_a.
(cherry picked from commit fd8af39)
Signed-off-by: Sean Owen <sowen@cloudera.com>
… many ML examples ## What changes were proposed in this pull request? Since [SPARK-15617](https://issues.apache.org/jira/browse/SPARK-15617) deprecated ```precision``` in ```MulticlassClassificationEvaluator```, many ML examples broken. ```python pyspark.sql.utils.IllegalArgumentException: u'MulticlassClassificationEvaluator_4c3bb1d73d8cc0cedae6 parameter metricName given invalid value precision.' ``` We should use ```accuracy``` to replace ```precision``` in these examples. ## How was this patch tested? Offline tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13519 from yanboliang/spark-15771. (cherry picked from commit a952528) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? 1, add accuracy for MulticlassMetrics 2, deprecate overall precision,recall,f1 and recommend accuracy usage ## How was this patch tested? manual tests in pyspark shell Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13511 from zhengruifeng/deprecate_py_precisonrecall. (cherry picked from commit 00ad4f0) Signed-off-by: Sean Owen <sowen@cloudera.com>
…ublic ## What changes were proposed in this pull request? Made DefaultParamsReadable, DefaultParamsWritable public. Also added relevant doc and annotations. Added UnaryTransformerExample to demonstrate use of UnaryTransformer and DefaultParamsReadable,Writable. ## How was this patch tested? Wrote example making use of the now-public APIs. Compiled and ran locally Author: Joseph K. Bradley <joseph@databricks.com> Closes #13461 from jkbradley/defaultparamswritable. (cherry picked from commit 4c74ee8) Signed-off-by: Joseph K. Bradley <joseph@databricks.com>
BindReferences contains a n^2 loop which causes performance issues when operating over large schemas: to determine the ordinal of an attribute reference, we perform a linear scan over the `input` array. Because input can sometimes be a `List`, the call to `input(ordinal).nullable` can also be O(n). Instead of performing a linear scan, we can convert the input into an array and build a hash map to map from expression ids to ordinals. The greater up-front cost of the map construction is offset by the fact that an expression can contain multiple attribute references, so the cost of the map construction is amortized across a number of lookups. Perf. benchmarks to follow. /cc ericl Author: Josh Rosen <joshrosen@databricks.com> Closes #13505 from JoshRosen/bind-references-improvement. (cherry picked from commit 0b8d694) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
… SparkLauncher ## What changes were proposed in this pull request? This situation can happen when the LauncherConnection gets an exception while reading through the socket and terminating silently without notifying making the client/listener think that the job is still in previous state. The fix force sends a notification to client that the job finished with unknown status and let client handle it accordingly. ## How was this patch tested? Added a unit test. Author: Subroto Sanyal <ssanyal@datameer.com> Closes #13497 from subrotosanyal/SPARK-15652-handle-spark-submit-jvm-crash. (cherry picked from commit c409e23) Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request? This PR makes sure the typed Filter doesn't change the Dataset schema. **Before the change:** ``` scala> val df = spark.range(0,9) scala> df.schema res12: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false)) scala> val afterFilter = df.filter(_=>true) scala> afterFilter.schema // !!! schema is CHANGED!!! Column name is changed from id to value, nullable is changed from false to true. res13: org.apache.spark.sql.types.StructType = StructType(StructField(value,LongType,true)) ``` SerializeFromObject and DeserializeToObject are inserted to wrap the Filter, and these two can possibly change the schema of Dataset. **After the change:** ``` scala> afterFilter.schema // schema is NOT changed. res47: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false)) ``` ## How was this patch tested? Unit test. Author: Sean Zhong <seanzhong@databricks.com> Closes #13529 from clockfly/spark-15632. (cherry picked from commit 0e0904a) Signed-off-by: Cheng Lian <lian@databricks.com>
… output
## What changes were proposed in this pull request?
This PR allows customization of verbosity in explain output. After change, `dataframe.explain()` and `dataframe.explain(true)` has different verbosity output for physical plan.
Currently, this PR only enables verbosity string for operator `HashAggregateExec` and `SortAggregateExec`. We will gradually enable verbosity string for more operators in future.
**Less verbose mode:** dataframe.explain(extended = false)
`output=[count(a)#85L]` is **NOT** displayed for HashAggregate.
```
scala> Seq((1,2,3)).toDF("a", "b", "c").createTempView("df2")
scala> spark.sql("select count(a) from df2").explain()
== Physical Plan ==
*HashAggregate(key=[], functions=[count(1)])
+- Exchange SinglePartition
+- *HashAggregate(key=[], functions=[partial_count(1)])
+- LocalTableScan
```
**Verbose mode:** dataframe.explain(extended = true)
`output=[count(a)#85L]` is displayed for HashAggregate.
```
scala> spark.sql("select count(a) from df2").explain(true) // "output=[count(a)#85L]" is added
...
== Physical Plan ==
*HashAggregate(key=[], functions=[count(1)], output=[count(a)#85L])
+- Exchange SinglePartition
+- *HashAggregate(key=[], functions=[partial_count(1)], output=[count#87L])
+- LocalTableScan
```
## How was this patch tested?
Manual test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes #13535 from clockfly/verbose_breakdown_2.
(cherry picked from commit 5f731d6)
Signed-off-by: Cheng Lian <lian@databricks.com>
…ark.serializer=org.apache.spark.serializer.KryoSerializer
…ark.serializer=org.apache.spark.serializer.KryoSerializer
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