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…se checksum files ## What changes were proposed in this pull request? When the metadata logs for various parts of Structured Streaming are stored on non-HDFS filesystems such as NFS or ext4, the HDFSMetadataLog class leaves hidden HDFS-style checksum (CRC) files in the log directory, one file per batch. This PR modifies HDFSMetadataLog so that it detects the use of a filesystem that doesn't use CRC files and removes the CRC files. ## How was this patch tested? Modified an existing test case in HDFSMetadataLogSuite to check whether HDFSMetadataLog correctly removes CRC files on the local POSIX filesystem. Ran the entire regression suite. Author: frreiss <frreiss@us.ibm.com> Closes #15027 from frreiss/fred-17475. (cherry picked from commit 620da3b) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ITION for Datasource tables There are a couple issues with the current 2.1 behavior when inserting into Datasource tables with partitions managed by Hive. (1) OVERWRITE TABLE ... PARTITION will actually overwrite the entire table instead of just the specified partition. (2) INSERT|OVERWRITE does not work with partitions that have custom locations. This PR fixes both of these issues for Datasource tables managed by Hive. The behavior for legacy tables or when `manageFilesourcePartitions = false` is unchanged. There is one other issue in that INSERT OVERWRITE with dynamic partitions will overwrite the entire table instead of just the updated partitions, but this behavior is pretty complicated to implement for Datasource tables. We should address that in a future release. Unit tests. Author: Eric Liang <ekl@databricks.com> Closes #15705 from ericl/sc-4942. (cherry picked from commit abefe2e) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? This patch adds support for all file formats in structured streaming sinks. This is actually a very small change thanks to all the previous refactoring done using the new internal commit protocol API. ## How was this patch tested? Updated FileStreamSinkSuite to add test cases for json, text, and parquet. Author: Reynold Xin <rxin@databricks.com> Closes #15711 from rxin/SPARK-18192. (cherry picked from commit a36653c) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? The PR fixes the bug that the QueryStartedEvent is not logged the postToAll() in the original code is actually calling StreamingQueryListenerBus.postToAll() which has no listener at all....we shall post by sparkListenerBus.postToAll(s) and this.postToAll() to trigger local listeners as well as the listeners registered in LiveListenerBus zsxwing ## How was this patch tested? The following snapshot shows that QueryStartedEvent has been logged correctly  Author: CodingCat <zhunansjtu@gmail.com> Closes #15675 from CodingCat/SPARK-18144. (cherry picked from commit 85c5424) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
## What changes were proposed in this pull request? This adds information to the web UI thread dump page about the JVM locks held by threads and the locks that threads are blocked waiting to acquire. This should help find cases where lock contention is causing Spark applications to run slowly. ## How was this patch tested? Tested by applying this patch and viewing the change in the web UI.  Additions: - A "Thread Locking" column with the locks held by the thread or that are blocking the thread - Links from the a blocked thread to the thread holding the lock - Stack frames show where threads are inside `synchronized` blocks, "holding Monitor(...)" Author: Ryan Blue <blue@apache.org> Closes #15088 from rdblue/SPARK-17532-add-thread-lock-info. (cherry picked from commit 2dc0480) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request?
This patch uses `{% highlight lang %}...{% endhighlight %}` to highlight code snippets in the `Structured Streaming Kafka010 integration doc` and the `Spark Streaming Kafka010 integration doc`.
This patch consists of two commits:
- the first commit fixes only the leading spaces -- this is large
- the second commit adds the highlight instructions -- this is much simpler and easier to review
## How was this patch tested?
SKIP_API=1 jekyll build
## Screenshots
**Before**
**After**
Author: Liwei Lin <lwlin7@gmail.com>
Closes #15715 from lw-lin/doc-highlight-code-snippet.
(cherry picked from commit 98ede49)
Signed-off-by: Sean Owen <sowen@cloudera.com>
…rFormat to Locale.US ## What changes were proposed in this pull request? Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat` ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15610 from srowen/SPARK-18076. (cherry picked from commit 9c8deef) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`. This PR includes: 1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`). 2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees. 3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`. 4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved. 5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns. ## How was this patch tested? Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully. Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`. Author: eyal farago <eyal farago> Author: Herman van Hovell <hvanhovell@databricks.com> Author: eyal farago <eyal.farago@gmail.com> Author: Eyal Farago <eyal.farago@actimize.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Author: eyalfa <eyal.farago@gmail.com> Closes #15718 from hvanhovell/SPARK-16839-2. (cherry picked from commit f151bd1) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request? This pr is to add pattern-matching entries for array data in `Literal.apply`. ## How was this patch tested? Added tests in `LiteralExpressionSuite`. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #15257 from maropu/SPARK-17683. (cherry picked from commit 4af0ce2) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? Copied description for row and range based frame boundary from https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala#L56 Added examples to show different behavior of rangeBetween and rowsBetween when involving duplicate values. Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. Author: buzhihuojie <ren.weiluo@gmail.com> Closes #15727 from david-weiluo-ren/improveDocForRangeAndRowsBetween. (cherry picked from commit 742e0fe) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ouldn't change after coalesce or union ## What changes were proposed in this pull request? When a user appended a column using a "nondeterministic" function to a DataFrame, e.g., `rand`, `randn`, and `monotonically_increasing_id`, the expected semantic is the following: - The value in each row should remain unchanged, as if we materialize the column immediately, regardless of later DataFrame operations. However, since we use `TaskContext.getPartitionId` to get the partition index from the current thread, the values from nondeterministic columns might change if we call `union` or `coalesce` after. `TaskContext.getPartitionId` returns the partition index of the current Spark task, which might not be the corresponding partition index of the DataFrame where we defined the column. See the unit tests below or JIRA for examples. This PR uses the partition index from `RDD.mapPartitionWithIndex` instead of `TaskContext` and fixes the partition initialization logic in whole-stage codegen, normal codegen, and codegen fallback. `initializeStatesForPartition(partitionIndex: Int)` was added to `Projection`, `Nondeterministic`, and `Predicate` (codegen) and initialized right after object creation in `mapPartitionWithIndex`. `newPredicate` now returns a `Predicate` instance rather than a function for proper initialization. ## How was this patch tested? Unit tests. (Actually I'm not very confident that this PR fixed all issues without introducing new ones ...) cc: rxin davies Author: Xiangrui Meng <meng@databricks.com> Closes #15567 from mengxr/SPARK-14393. (cherry picked from commit 02f2031) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ed to driver in yarn mode ## What changes were proposed in this pull request? spark.files is still passed to driver in yarn mode, so SparkContext will still handle it which cause the error in the jira desc. ## How was this patch tested? Tested manually in a 5 node cluster. As this issue only happens in multiple node cluster, so I didn't write test for it. Author: Jeff Zhang <zjffdu@apache.org> Closes #15669 from zjffdu/SPARK-18160. (cherry picked from commit 3c24299) Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
…/test against staging artifacts ## What changes were proposed in this pull request? Adds a `snapshots-and-staging profile` so that RCs of projects like Hadoop and HBase can be used in developer-only build and test runs. There's a comment above the profile telling people not to use this in production. There's no attempt to do the same for SBT, as Ivy is different. ## How was this patch tested? Tested by building against the Hadoop 2.7.3 RC 1 JARs without the profile (and without any local copy of the 2.7.3 artifacts), the build failed ``` mvn install -DskipTests -Pyarn,hadoop-2.7,hive -Dhadoop.version=2.7.3 ... [INFO] ------------------------------------------------------------------------ [INFO] Building Spark Project Launcher 2.1.0-SNAPSHOT [INFO] ------------------------------------------------------------------------ Downloading: https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-client/2.7.3/hadoop-client-2.7.3.pom [WARNING] The POM for org.apache.hadoop:hadoop-client:jar:2.7.3 is missing, no dependency information available Downloading: https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-client/2.7.3/hadoop-client-2.7.3.jar [INFO] ------------------------------------------------------------------------ [INFO] Reactor Summary: [INFO] [INFO] Spark Project Parent POM ........................... SUCCESS [ 4.482 s] [INFO] Spark Project Tags ................................. SUCCESS [ 17.402 s] [INFO] Spark Project Sketch ............................... SUCCESS [ 11.252 s] [INFO] Spark Project Networking ........................... SUCCESS [ 13.458 s] [INFO] Spark Project Shuffle Streaming Service ............ SUCCESS [ 9.043 s] [INFO] Spark Project Unsafe ............................... SUCCESS [ 16.027 s] [INFO] Spark Project Launcher ............................. FAILURE [ 1.653 s] [INFO] Spark Project Core ................................. SKIPPED ... ``` With the profile, the build completed ``` mvn install -DskipTests -Pyarn,hadoop-2.7,hive,snapshots-and-staging -Dhadoop.version=2.7.3 ``` Author: Steve Loughran <stevel@apache.org> Closes #14646 from steveloughran/stevel/SPARK-17058-support-asf-snapshots. (cherry picked from commit 37d9522) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? RuntimeReplaceable is used to create aliases for expressions, but the way it deals with type coercion is pretty weird (each expression is responsible for how to handle type coercion, which does not obey the normal implicit type cast rules). This patch simplifies its handling by allowing the analyzer to traverse into the actual expression of a RuntimeReplaceable. ## How was this patch tested? - Correctness should be guaranteed by existing unit tests already - Removed SQLCompatibilityFunctionSuite and moved it sql-compatibility-functions.sql - Added a new test case in sql-compatibility-functions.sql for verifying explain behavior. Author: Reynold Xin <rxin@databricks.com> Closes #15723 from rxin/SPARK-18214. (cherry picked from commit fd90541) Signed-off-by: Reynold Xin <rxin@databricks.com>
…or hive serde table ## What changes were proposed in this pull request? Due to a limitation of hive metastore(table location must be directory path, not file path), we always store `path` for data source table in storage properties, instead of the `locationUri` field. However, we should not expose this difference to `CatalogTable` level, but just treat it as a hack in `HiveExternalCatalog`, like we store table schema of data source table in table properties. This PR unifies `path` and `locationUri` outside of `HiveExternalCatalog`, both data source table and hive serde table should use the `locationUri` field. This PR also unifies the way we handle default table location for managed table. Previously, the default table location of hive serde managed table is set by external catalog, but the one of data source table is set by command. After this PR, we follow the hive way and the default table location is always set by external catalog. For managed non-file-based tables, we will assign a default table location and create an empty directory for it, the table location will be removed when the table is dropped. This is reasonable as metastore doesn't care about whether a table is file-based or not, and an empty table directory has no harm. For external non-file-based tables, ideally we can omit the table location, but due to a hive metastore issue, we will assign a random location to it, and remove it right after the table is created. See SPARK-15269 for more details. This is fine as it's well isolated in `HiveExternalCatalog`. To keep the existing behaviour of the `path` option, in this PR we always add the `locationUri` to storage properties using key `path`, before passing storage properties to `DataSource` as data source options. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15024 from cloud-fan/path. (cherry picked from commit 3a1bc6f) Signed-off-by: Yin Huai <yhuai@databricks.com>
…ssion and improve documentation ## What changes were proposed in this pull request? This PR proposes to change the documentation for functions. Please refer the discussion from #15513 The changes include - Re-indent the documentation - Add examples/arguments in `extended` where the arguments are multiple or specific format (e.g. xml/ json). For examples, the documentation was updated as below: ### Functions with single line usage **Before** - `pow` ``` sql Usage: pow(x1, x2) - Raise x1 to the power of x2. Extended Usage: > SELECT pow(2, 3); 8.0 ``` - `current_timestamp` ``` sql Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation. Extended Usage: No example for current_timestamp. ``` **After** - `pow` ``` sql Usage: pow(expr1, expr2) - Raises `expr1` to the power of `expr2`. Extended Usage: Examples: > SELECT pow(2, 3); 8.0 ``` - `current_timestamp` ``` sql Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation. Extended Usage: No example/argument for current_timestamp. ``` ### Functions with (already) multiple line usage **Before** - `approx_count_distinct` ``` sql Usage: approx_count_distinct(expr) - Returns the estimated cardinality by HyperLogLog++. approx_count_distinct(expr, relativeSD=0.05) - Returns the estimated cardinality by HyperLogLog++ with relativeSD, the maximum estimation error allowed. Extended Usage: No example for approx_count_distinct. ``` - `percentile_approx` ``` sql Usage: percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric column `col` at the given percentage. The value of percentage must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) - Returns the approximate percentile array of column `col` at the given percentage array. Each value of the percentage array must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. Extended Usage: No example for percentile_approx. ``` **After** - `approx_count_distinct` ``` sql Usage: approx_count_distinct(expr[, relativeSD]) - Returns the estimated cardinality by HyperLogLog++. `relativeSD` defines the maximum estimation error allowed. Extended Usage: No example/argument for approx_count_distinct. ``` - `percentile_approx` ``` sql Usage: percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric column `col` at the given percentage. The value of percentage must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0. In this case, returns the approximate percentile array of column `col` at the given percentage array. Extended Usage: Examples: > SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100); [10.0,10.0,10.0] > SELECT percentile_approx(10.0, 0.5, 100); 10.0 ``` ## How was this patch tested? Manually tested **When examples are multiple** ``` sql spark-sql> describe function extended reflect; Function: reflect Class: org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection Usage: reflect(class, method[, arg1[, arg2 ..]]) - Calls a method with reflection. Extended Usage: Examples: > SELECT reflect('java.util.UUID', 'randomUUID'); c33fb387-8500-4bfa-81d2-6e0e3e930df2 > SELECT reflect('java.util.UUID', 'fromString', 'a5cf6c42-0c85-418f-af6c-3e4e5b1328f2'); a5cf6c42-0c85-418f-af6c-3e4e5b1328f2 ``` **When `Usage` is in single line** ``` sql spark-sql> describe function extended min; Function: min Class: org.apache.spark.sql.catalyst.expressions.aggregate.Min Usage: min(expr) - Returns the minimum value of `expr`. Extended Usage: No example/argument for min. ``` **When `Usage` is already in multiple lines** ``` sql spark-sql> describe function extended percentile_approx; Function: percentile_approx Class: org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile Usage: percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric column `col` at the given percentage. The value of percentage must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0. In this case, returns the approximate percentile array of column `col` at the given percentage array. Extended Usage: Examples: > SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100); [10.0,10.0,10.0] > SELECT percentile_approx(10.0, 0.5, 100); 10.0 ``` **When example/argument is missing** ``` sql spark-sql> describe function extended rank; Function: rank Class: org.apache.spark.sql.catalyst.expressions.Rank Usage: rank() - Computes the rank of a value in a group of values. The result is one plus the number of rows preceding or equal to the current row in the ordering of the partition. The values will produce gaps in the sequence. Extended Usage: No example/argument for rank. ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #15677 from HyukjinKwon/SPARK-17963-1. (cherry picked from commit 7eb2ca8) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
…sting ### What changes were proposed in this pull request? So far, we have limited test case coverage about implicit type casting. We need to draw a matrix to find all the possible casting pairs. - Reorged the existing test cases - Added all the possible type casting pairs - Drawed a matrix to show the implicit type casting. The table is very wide. Maybe hard to review. Thus, you also can access the same table via the link to [a google sheet](https://docs.google.com/spreadsheets/d/19PS4ikrs-Yye_mfu-rmIKYGnNe-NmOTt5DDT1fOD3pI/edit?usp=sharing). SourceType\CastToType | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | BooleanType | StringType | DateType | TimestampType | ArrayType | MapType | StructType | NullType | CalendarIntervalType | DecimalType | NumericType | IntegralType ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ----------- **ByteType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(3, 0) | ByteType | ByteType **ShortType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(5, 0) | ShortType | ShortType **IntegerType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(10, 0) | IntegerType | IntegerType **LongType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(20, 0) | LongType | LongType **DoubleType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(30, 15) | DoubleType | IntegerType **FloatType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(14, 7) | FloatType | IntegerType **Dec(10, 2)** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(10, 2) | Dec(10, 2) | IntegerType **BinaryType** | X | X | X | X | X | X | X | BinaryType | X | StringType | X | X | X | X | X | X | X | X | X | X **BooleanType** | X | X | X | X | X | X | X | X | BooleanType | StringType | X | X | X | X | X | X | X | X | X | X **StringType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | X | StringType | DateType | TimestampType | X | X | X | X | X | DecimalType(38, 18) | DoubleType | X **DateType** | X | X | X | X | X | X | X | X | X | StringType | DateType | TimestampType | X | X | X | X | X | X | X | X **TimestampType** | X | X | X | X | X | X | X | X | X | StringType | DateType | TimestampType | X | X | X | X | X | X | X | X **ArrayType** | X | X | X | X | X | X | X | X | X | X | X | X | ArrayType* | X | X | X | X | X | X | X **MapType** | X | X | X | X | X | X | X | X | X | X | X | X | X | MapType* | X | X | X | X | X | X **StructType** | X | X | X | X | X | X | X | X | X | X | X | X | X | X | StructType* | X | X | X | X | X **NullType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | BooleanType | StringType | DateType | TimestampType | ArrayType | MapType | StructType | NullType | CalendarIntervalType | DecimalType(38, 18) | DoubleType | IntegerType **CalendarIntervalType** | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | CalendarIntervalType | X | X | X Note: ArrayType\*, MapType\*, StructType\* are castable only when the internal child types also match; otherwise, not castable ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #15691 from gatorsmile/implicitTypeCasting. (cherry picked from commit 9ddec86) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request? [SPARK-18200](https://issues.apache.org/jira/browse/SPARK-18200) reports Apache Spark 2.x raises `java.lang.IllegalArgumentException: requirement failed: Invalid initial capacity` while running `triangleCount`. The root cause is that `VertexSet`, a type alias of `OpenHashSet`, does not allow zero as a initial size. This PR loosens the restriction to allow zero. ## How was this patch tested? Pass the Jenkins test with a new test case in `OpenHashSetSuite`. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15741 from dongjoon-hyun/SPARK-18200. (cherry picked from commit d24e736) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? In Spark 1.6 and earlier, we can drop the database we are using. In Spark 2.0, native implementation prevent us from dropping current database, which may break some old queries. This PR would re-enable the feature. ## How was this patch tested? one new unit test in `SessionCatalogSuite`. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #15011 from adrian-wang/dropcurrent. (cherry picked from commit 96cc1b5) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
…re module ## What changes were proposed in this pull request? This patch moves the new commit protocol API from sql/core to core module, so we can use it in the future in the RDD API. As part of this patch, I also moved the speficiation of the random uuid for the write path out of the commit protocol, and instead pass in a job id. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #15731 from rxin/SPARK-18219. (cherry picked from commit 937af59) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? I was reading this part of the code and was really confused by the "partition" parameter. This patch adds some documentation for it to reduce confusion in the future. I also looked around other logical plans but most of them are either already documented, or pretty self-evident to people that know Spark SQL. ## How was this patch tested? N/A - doc change only. Author: Reynold Xin <rxin@databricks.com> Closes #15749 from rxin/doc-improvement. (cherry picked from commit 0ea5d5b) Signed-off-by: Reynold Xin <rxin@databricks.com>
…BTClassifier ## What changes were proposed in this pull request? Add missing 'subsamplingRate' of pyspark GBTClassifier ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15692 from zhengruifeng/gbt_subsamplingRate. (cherry picked from commit 9dc9f9a) Signed-off-by: Yanbo Liang <ybliang8@gmail.com>
…False in FilterExec
### What changes were proposed in this pull request?
When `FilterExec` contains `isNotNull`, which could be inferred and pushed down or users specified, we convert the nullability of the involved columns if the top-layer expression is null-intolerant. However, this is not correct, if the top-layer expression is not a leaf expression, it could still tolerate the null when it has null-tolerant child expressions.
For example, `cast(coalesce(a#5, a#15) as double)`. Although `cast` is a null-intolerant expression, but obviously`coalesce` is null-tolerant. Thus, it could eat null.
When the nullability is wrong, we could generate incorrect results in different cases. For example,
``` Scala
val df1 = Seq((1, 2), (2, 3)).toDF("a", "b")
val df2 = Seq((2, 5), (3, 4)).toDF("a", "c")
val joinedDf = df1.join(df2, Seq("a"), "outer").na.fill(0)
val df3 = Seq((3, 1)).toDF("a", "d")
joinedDf.join(df3, "a").show
```
The optimized plan is like
```
Project [a#29, b#30, c#31, d#42]
+- Join Inner, (a#29 = a#41)
:- Project [cast(coalesce(cast(coalesce(a#5, a#15) as double), 0.0) as int) AS a#29, cast(coalesce(cast(b#6 as double), 0.0) as int) AS b#30, cast(coalesce(cast(c#16 as double), 0.0) as int) AS c#31]
: +- Filter isnotnull(cast(coalesce(cast(coalesce(a#5, a#15) as double), 0.0) as int))
: +- Join FullOuter, (a#5 = a#15)
: :- LocalRelation [a#5, b#6]
: +- LocalRelation [a#15, c#16]
+- LocalRelation [a#41, d#42]
```
Without the fix, it returns an empty result. With the fix, it can return a correct answer:
```
+---+---+---+---+
| a| b| c| d|
+---+---+---+---+
| 3| 0| 4| 1|
+---+---+---+---+
```
### How was this patch tested?
Added test cases to verify the nullability changes in FilterExec. Also added a test case for verifying the reported incorrect result.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #15523 from gatorsmile/nullabilityFilterExec.
(cherry picked from commit 66a99f4)
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
…InCatalog ## What changes were proposed in this pull request? This patch renames partitionProviderIsHive to tracksPartitionsInCatalog, as the old name was too Hive specific. ## How was this patch tested? Should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #15750 from rxin/SPARK-18244. (cherry picked from commit b17057c) Signed-off-by: Reynold Xin <rxin@databricks.com>
hive.exec.stagingdir have no effect in spark2.0.1， Hive confs in hive-site.xml will be loaded in `hadoopConf`, so we should use `hadoopConf` in `InsertIntoHiveTable` instead of `SessionState.conf` Author: 福星 <fuxing@wacai.com> Closes #15744 from ClassNotFoundExp/master. (cherry picked from commit 1629331) Signed-off-by: Reynold Xin <rxin@databricks.com>
… --files and --archives ## What changes were proposed in this pull request? During spark-submit, if yarn dist cache is instructed to add same file under --files and --archives, This code change ensures the spark yarn distributed cache behaviour is retained i.e. to warn and fail if same files is mentioned in both --files and --archives. ## How was this patch tested? Manually tested: 1. if same jar is mentioned in --jars and --files it will continue to submit the job. - basically functionality [SPARK-14423] #12203 is unchanged 1. if same file is mentioned in --files and --archives it will fail to submit the job. Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. … under archives and files Author: Kishor Patil <kpatil@yahoo-inc.com> Closes #15627 from kishorvpatil/spark18099. (cherry picked from commit 098e4ca) Signed-off-by: Tom Graves <tgraves@yahoo-inc.com>
## What changes were proposed in this pull request? Increase poll timeout to try and address flaky test ## How was this patch tested? Ran existing unit tests Author: cody koeninger <cody@koeninger.org> Closes #15737 from koeninger/SPARK-18212. (cherry picked from commit 67659c9) Signed-off-by: Michael Armbrust <michael@databricks.com>
## What changes were proposed in this pull request? This patch improves error reporting for FileStressSuite, when there is an error in Spark itself (not user code). This works by simply tightening the exception verification, and gets rid of the unnecessary thread for starting the stream. Also renamed the class FileStreamStressSuite to make it more obvious it is a streaming suite. ## How was this patch tested? This is a test only change and I manually verified error reporting by injecting some bug in the addBatch code for FileStreamSink. Author: Reynold Xin <rxin@databricks.com> Closes #15757 from rxin/SPARK-18257. (cherry picked from commit f22954a) Signed-off-by: Reynold Xin <rxin@databricks.com>
…oop < 2.6 are deprecated in Spark 2.1.0 ## What changes were proposed in this pull request? Document that Java 7, Python 2.6, Scala 2.10, Hadoop < 2.6 are deprecated in Spark 2.1.0. This does not actually implement any of the change in SPARK-18138, just peppers the documentation with notices about it. ## How was this patch tested? Doc build Author: Sean Owen <sowen@cloudera.com> Closes #15733 from srowen/SPARK-18138. (cherry picked from commit dc4c600) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? `QueryExecution.toString` currently captures `java.lang.Throwable`s; this is far from a best practice and can lead to confusing situation or invalid application states. This PR fixes this by only capturing `AnalysisException`s. ## How was this patch tested? Added a `QueryExecutionSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15760 from hvanhovell/SPARK-18259. (cherry picked from commit aa412c5) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ing incorrect result
## What changes were proposed in this pull request?
This PR blocks an incorrect result scenario in scalar subquery where there are GROUP BY column(s)
that are not part of the correlated predicate(s).
Example:
// Incorrect result
Seq(1).toDF("c1").createOrReplaceTempView("t1")
Seq((1,1),(1,2)).toDF("c1","c2").createOrReplaceTempView("t2")
sql("select (select sum(-1) from t2 where t1.c1=t2.c1 group by t2.c2) from t1").show
// How can selecting a scalar subquery from a 1-row table return 2 rows?
## How was this patch tested?
sql/test, catalyst/test
new test case covering the reported problem is added to SubquerySuite.scala
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes #15936 from nsyca/scalarSubqueryIncorrect-1.
(cherry picked from commit 45ea46b)
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
…l getTable once ## What changes were proposed in this pull request? HiveExternalCatalog.listPartitions should only call `getTable` once, instead of calling it for every partitions. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #15978 from cloud-fan/perf. (cherry picked from commit 702cd40) Signed-off-by: Andrew Or <andrewor14@gmail.com>
…ions when table doesn't exist ## What changes were proposed in this pull request? While this behavior is debatable, consider the following use case: ```sql UNCACHE TABLE foo; CACHE TABLE foo AS SELECT * FROM bar ``` The command above fails the first time you run it. But I want to run the command above over and over again, and I don't want to change my code just for the first run of it. The issue is that subsequent `CACHE TABLE` commands do not overwrite the existing table. Now we can do: ```sql UNCACHE TABLE IF EXISTS foo; CACHE TABLE foo AS SELECT * FROM bar ``` ## How was this patch tested? Unit tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #15896 from brkyvz/uncache. (cherry picked from commit bdc8153) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
…with Spark jobs ## What changes were proposed in this pull request? This PR adds `CachedKafkaConsumer.getAndIgnoreLostData` to handle corner cases of `failOnDataLoss=false`. It also resolves [SPARK-18529](https://issues.apache.org/jira/browse/SPARK-18529) after refactoring codes: Timeout will throw a TimeoutException. ## How was this patch tested? Because I cannot find any way to manually control the Kafka server to clean up logs, it's impossible to write unit tests for each corner case. Therefore, I just created `test("stress test for failOnDataLoss=false")` which should cover most of corner cases. I also modified some existing tests to test for both `failOnDataLoss=false` and `failOnDataLoss=true` to make sure it doesn't break existing logic. Author: Shixiong Zhu <shixiong@databricks.com> Closes #15820 from zsxwing/failOnDataLoss. (cherry picked from commit 2fd101b) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
…ive serde table
### What changes were proposed in this pull request?
In Spark 2.0, `SaveAsTable` does not work when the target table is a Hive serde table, but Spark 1.6 works.
**Spark 1.6**
``` Scala
scala> sql("create table sample.sample stored as SEQUENCEFILE as select 1 as key, 'abc' as value")
res2: org.apache.spark.sql.DataFrame = []
scala> val df = sql("select key, value as value from sample.sample")
df: org.apache.spark.sql.DataFrame = [key: int, value: string]
scala> df.write.mode("append").saveAsTable("sample.sample")
scala> sql("select * from sample.sample").show()
+---+-----+
|key|value|
+---+-----+
| 1| abc|
| 1| abc|
+---+-----+
```
**Spark 2.0**
``` Scala
scala> df.write.mode("append").saveAsTable("sample.sample")
org.apache.spark.sql.AnalysisException: Saving data in MetastoreRelation sample, sample
is not supported.;
```
So far, we do not plan to support it in Spark 2.1 due to the risk. Spark 1.6 works because it internally uses insertInto. But, if we change it back it will break the semantic of saveAsTable (this method uses by-name resolution instead of using by-position resolution used by insertInto). More extra changes are needed to support `hive` as a `format` in DataFrameWriter.
Instead, users should use insertInto API. This PR corrects the error messages. Users can understand how to bypass it before we support it in a separate PR.
### How was this patch tested?
Test cases are added
Author: gatorsmile <gatorsmile@gmail.com>
Closes #15926 from gatorsmile/saveAsTableFix5.
(cherry picked from commit 9c42d4a)
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
…tasource tables created using CTAS
## What changes were proposed in this pull request?
Fixes the inconsistency of error raised between data source and hive serde
tables when schema is specified in CTAS scenario. In the process the grammar for
create table (datasource) is simplified.
**before:**
``` SQL
spark-sql> create table t2 (c1 int, c2 int) using parquet as select * from t1;
Error in query:
mismatched input 'as' expecting {<EOF>, '.', 'OPTIONS', 'CLUSTERED', 'PARTITIONED'}(line 1, pos 64)
== SQL ==
create table t2 (c1 int, c2 int) using parquet as select * from t1
----------------------------------------------------------------^^^
```
**After:**
```SQL
spark-sql> create table t2 (c1 int, c2 int) using parquet as select * from t1
> ;
Error in query:
Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 1, pos 0)
== SQL ==
create table t2 (c1 int, c2 int) using parquet as select * from t1
^^^
```
## How was this patch tested?
Added a new test in CreateTableAsSelectSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #15968 from dilipbiswal/ctas.
(cherry picked from commit 39a1d30)
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
…ampType
## What changes were proposed in this pull request?
Changed Kafka timestamp column type to TimestampType.
## How was this patch tested?
`test("Kafka column types")`.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #15969 from zsxwing/SPARK-18530.
(cherry picked from commit d0212eb)
Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…ear data ## What changes were proposed in this pull request? * Fix SparkR ```spark.glm``` errors when fitting on collinear data, since ```standard error of coefficients, t value and p value``` are not available in this condition. * Scala/Python GLM summary should throw exception if users get ```standard error of coefficients, t value and p value``` but the underlying WLS was solved by local "l-bfgs". ## How was this patch tested? Add unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15930 from yanboliang/spark-18501. (cherry picked from commit 982b82e) Signed-off-by: Yanbo Liang <ybliang8@gmail.com>
…r unsupported argument types in reflect/java_method function
## What changes were proposed in this pull request?
This PR proposes throwing an `AnalysisException` with a proper message rather than `NoSuchElementException` with the message ` key not found: TimestampType` when unsupported types are given to `reflect` and `java_method` functions.
```scala
spark.range(1).selectExpr("reflect('java.lang.String', 'valueOf', cast('1990-01-01' as timestamp))")
```
produces
**Before**
```
java.util.NoSuchElementException: key not found: TimestampType
at scala.collection.MapLike$class.default(MapLike.scala:228)
at scala.collection.AbstractMap.default(Map.scala:59)
at scala.collection.MapLike$class.apply(MapLike.scala:141)
at scala.collection.AbstractMap.apply(Map.scala:59)
at org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection$$anonfun$findMethod$1$$anonfun$apply$1.apply(CallMethodViaReflection.scala:159)
...
```
**After**
```
cannot resolve 'reflect('java.lang.String', 'valueOf', CAST('1990-01-01' AS TIMESTAMP))' due to data type mismatch: arguments from the third require boolean, byte, short, integer, long, float, double or string expressions; line 1 pos 0;
'Project [unresolvedalias(reflect(java.lang.String, valueOf, cast(1990-01-01 as timestamp)), Some(<function1>))]
+- Range (0, 1, step=1, splits=Some(2))
...
```
Added message is,
```
arguments from the third require boolean, byte, short, integer, long, float, double or string expressions
```
## How was this patch tested?
Tests added in `CallMethodViaReflection`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #15694 from HyukjinKwon/SPARK-18179.
(cherry picked from commit 2559fb4)
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? Updates links to the wiki to links to the new location of content on spark.apache.org. ## How was this patch tested? Doc builds Author: Sean Owen <sowen@cloudera.com> Closes #15967 from srowen/SPARK-18073.1. (cherry picked from commit 7e0cd1d) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? In Spark SQL, some expression may output safe format values, e.g. `CreateArray`, `CreateStruct`, `Cast`, etc. When we compare 2 values, we should be able to compare safe and unsafe formats. The `GreaterThan`, `LessThan`, etc. in Spark SQL already handles it, but the `EqualTo` doesn't. This PR fixes it. ## How was this patch tested? new unit test and regression test Author: Wenchen Fan <wenchen@databricks.com> Closes #15929 from cloud-fan/type-aware. (cherry picked from commit 84284e8) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
…blePerfStatsSuite ## What changes were proposed in this pull request? This would help catch accidental O(n) calls to the hive client as in https://issues.apache.org/jira/browse/SPARK-18507 ## How was this patch tested? Checked that the test fails before https://issues.apache.org/jira/browse/SPARK-18507 was patched. cc cloud-fan Author: Eric Liang <ekl@databricks.com> Closes #15985 from ericl/spark-18545. (cherry picked from commit 85235ed) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request? TaskMemoryManager has a memory leak detector that gets called at task completion callback and checks whether any memory has not been released. If they are not released by the time the callback is invoked, TaskMemoryManager releases them. The current error message says something like the following: ``` WARN [Executor task launch worker-0] org.apache.spark.memory.TaskMemoryManager - leak 16.3 MB memory from org.apache.spark.unsafe.map.BytesToBytesMap33fb6a15 In practice, there are multiple reasons why these can be triggered in the normal code path (e.g. limit, or task failures), and the fact that these messages are log means the "leak" is fixed by TaskMemoryManager. ``` To not confuse users, this patch downgrade the message from warning to debug level, and avoids using the word "leak" since it is not actually a leak. ## How was this patch tested? N/A - this is a simple logging improvement. Author: Reynold Xin <rxin@databricks.com> Closes #15989 from rxin/SPARK-18557. (cherry picked from commit 9785ed4) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request? The current implementation of column stats uses the base64 encoding of the internal UnsafeRow format to persist statistics (in table properties in Hive metastore). This is an internal format that is not stable across different versions of Spark and should NOT be used for persistence. In addition, it would be better if statistics stored in the catalog is human readable. This pull request introduces the following changes: 1. Created a single ColumnStat class to for all data types. All data types track the same set of statistics. 2. Updated the implementation for stats collection to get rid of the dependency on internal data structures (e.g. InternalRow, or storing DateType as an int32). For example, previously dates were stored as a single integer, but are now stored as java.sql.Date. When we implement the next steps of CBO, we can add code to convert those back into internal types again. 3. Documented clearly what JVM data types are being used to store what data. 4. Defined a simple Map[String, String] interface for serializing and deserializing column stats into/from the catalog. 5. Rearranged the method/function structure so it is more clear what the supported data types are, and also moved how stats are generated into ColumnStat class so they are easy to find. ## How was this patch tested? Removed most of the original test cases created for column statistics, and added three very simple ones to cover all the cases. The three test cases validate: 1. Roundtrip serialization works. 2. Behavior when analyzing non-existent column or unsupported data type column. 3. Result for stats collection for all valid data types. Also moved parser related tests into a parser test suite and added an explicit serialization test for the Hive external catalog. Author: Reynold Xin <rxin@databricks.com> Closes #15959 from rxin/SPARK-18522. (cherry picked from commit 70ad07a) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request? When we try to create the default database, we ask hive to do nothing if it already exists. However, Hive will log an error message instead of doing nothing, and the error message is quite annoying and confusing. In this PR, we only create default database if it doesn't exist. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #15993 from cloud-fan/default-db. (cherry picked from commit f129ebc) Signed-off-by: Andrew Or <andrewor14@gmail.com>
…Types ## What changes were proposed in this pull request? ### The Issue If I specify my schema when doing ```scala spark.read .schema(someSchemaWherePartitionColumnsAreStrings) ``` but if the partition inference can infer it as IntegerType or I assume LongType or DoubleType (basically fixed size types), then once UnsafeRows are generated, your data will be corrupted. ### Proposed solution The partition handling code path is kind of a mess. In my fix I'm probably adding to the mess, but at least trying to standardize the code path. The real issue is that a user that uses the `spark.read` code path can never clearly specify what the partition columns are. If you try to specify the fields in `schema`, we practically ignore what the user provides, and fall back to our inferred data types. What happens in the end is data corruption. My solution tries to fix this by always trying to infer partition columns the first time you specify the table. Once we find what the partition columns are, we try to find them in the user specified schema and use the dataType provided there, or fall back to the smallest common data type. We will ALWAYS append partition columns to the user's schema, even if they didn't ask for it. We will only use the data type they provided if they specified it. While this is confusing, this has been the behavior since Spark 1.6, and I didn't want to change this behavior in the QA period of Spark 2.1. We may revisit this decision later. A side effect of this PR is that we won't need #15942 if this PR goes in. ## How was this patch tested? Regression tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #15951 from brkyvz/partition-corruption. (cherry picked from commit 0d1bf2b) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request? This PR addressed the rest comments in #15951. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #15997 from zsxwing/SPARK-18510-follow-up. (cherry picked from commit 223fa21) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
…del and GaussianMixtureModel ## What changes were proposed in this pull request? add `setFeaturesCol` and `setPredictionCol` for BiKModel and GMModel add `setProbabilityCol` for GMModel ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15957 from zhengruifeng/bikm_set. (cherry picked from commit 2dfabec) Signed-off-by: Yanbo Liang <ybliang8@gmail.com>
…orrect results ## What changes were proposed in this pull request? - Raise Analysis exception when correlated predicates exist in the descendant operators of either operand of a Full outer join in a subquery as well as in a FOJ operator itself - Raise Analysis exception when correlated predicates exists in a Window operator (a side effect inadvertently introduced by SPARK-17348) ## How was this patch tested? Run sql/test catalyst/test and new test cases, added to SubquerySuite, showing the reported incorrect results. Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #16005 from nsyca/FOJ-incorrect.1. (cherry picked from commit a367d5f) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
… links ## What changes were proposed in this pull request? NOT BUG, just adjust the position of driver log link to keep the same style with other executors log link.  ## How was this patch tested? no Author: uncleGen <hustyugm@gmail.com> Closes #16001 from uncleGen/SPARK-18575. (cherry picked from commit f58a8aa) Signed-off-by: Sean Owen <sowen@cloudera.com>
…n one namenode which can stuck the startup of SparkHistory server ## What changes were proposed in this pull request? Instead of using the setSafeMode method that check the first namenode used the one which permitts to check only for active NNs ## How was this patch tested? manual tests Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. This commit is contributed by Criteo SA under the Apache v2 licence. Author: n.fraison <n.fraison@criteo.com> Closes #15648 from ashangit/SPARK-18119. (cherry picked from commit f42db0c) Signed-off-by: Sean Owen <sowen@cloudera.com>
…at will help unidoc/genjavadoc compatibility
## What changes were proposed in this pull request?
This PR only tries to fix things that looks pretty straightforward and were fixed in other previous PRs before.
This PR roughly fixes several things as below:
- Fix unrecognisable class and method links in javadoc by changing it from `[[..]]` to `` `...` ``
```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/DataStreamReader.java:226: error: reference not found
[error] * Loads text files and returns a {link DataFrame} whose schema starts with a string column named
```
- Fix an exception annotation and remove code backticks in `throws` annotation
Currently, sbt unidoc with Java 8 complains as below:
```
[error] .../java/org/apache/spark/sql/streaming/StreamingQuery.java:72: error: unexpected text
[error] * throws StreamingQueryException, if <code>this</code> query has terminated with an exception.
```
`throws` should specify the correct class name from `StreamingQueryException,` to `StreamingQueryException` without backticks. (see [JDK-8007644](https://bugs.openjdk.java.net/browse/JDK-8007644)).
- Fix `[[http..]]` to `<a href="http..."></a>`.
```diff
- * [[https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https Oracle
- * blog page]].
+ * <a href="https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https">
+ * Oracle blog page</a>.
```
`[[http...]]` link markdown in scaladoc is unrecognisable in javadoc.
- It seems class can't have `return` annotation. So, two cases of this were removed.
```
[error] .../java/org/apache/spark/mllib/regression/IsotonicRegression.java:27: error: invalid use of return
[error] * return New instance of IsotonicRegression.
```
- Fix < to `<` and > to `>` according to HTML rules.
- Fix `</p>` complaint
- Exclude unrecognisable in javadoc, `constructor`, `todo` and `groupname`.
## How was this patch tested?
Manually tested by `jekyll build` with Java 7 and 8
```
java version "1.7.0_80"
Java(TM) SE Runtime Environment (build 1.7.0_80-b15)
Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)
```
```
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)
```
Note: this does not yet make sbt unidoc suceed with Java 8 yet but it reduces the number of errors with Java 8.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #15999 from HyukjinKwon/SPARK-3359-errors.
(cherry picked from commit 51b1c15)
Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? In `HyperLogLogPlusPlus`, if the relative error is so small that p >= 19, it will cause ArrayIndexOutOfBoundsException in `THRESHOLDS(p-4)` . We should check `p` and when p >= 19, regress to the original HLL result and use the small range correction they use. The pr also fixes the upper bound in the log info in `require()`. The upper bound is computed by: ``` val relativeSD = 1.106d / Math.pow(Math.E, p * Math.log(2.0d) / 2.0d) ``` which is derived from the equation for computing `p`: ``` val p = 2.0d * Math.log(1.106d / relativeSD) / Math.log(2.0d) ``` ## How was this patch tested? add test cases for: 1. checking validity of parameter relatvieSD 2. estimation with smaller relative error so that p >= 19 Author: Zhenhua Wang <wzh_zju@163.com> Author: wangzhenhua <wangzhenhua@huawei.com> Closes #15990 from wzhfy/hllppRsd. (cherry picked from commit 5ecdc7c) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request? The expression `in(empty seq)` is invalid in some data source. Since `in(empty seq)` is always false, we should generate `in(empty seq)` to false literal in optimizer. The sql `SELECT * FROM t WHERE a IN ()` throws a `ParseException` which is consistent with Hive, don't need to change that behavior. ## How was this patch tested? Add new test case in `OptimizeInSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15977 from jiangxb1987/isin-empty. (cherry picked from commit e2fb9fd) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request? The nullability of `InputFileName` should be `false`. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #16007 from ueshin/issues/SPARK-18583. (cherry picked from commit a88329d) Signed-off-by: Reynold Xin <rxin@databricks.com>
|
Can one of the admins verify this patch? |
|
Cloud you please close this PR? |
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Please review http://spark.apache.org/contributing.html before opening a pull request.