### What changes were proposed in this pull request?
`ScanOperation` collects adjacent Projects and Filters. The caller side always assume that the collected Filters should run before collected Projects, which means `ScanOperation` effectively pushes Filter through Project.
Following `PushPredicateThroughNonJoin`, we should not push Filter through nondeterministic Project. This PR fixes `ScanOperation` to follow this rule.
### Why are the changes needed?
Fix a bug that violates the semantic of nondeterministic expressions.
### Does this PR introduce _any_ user-facing change?
Most likely no change, but in some cases, this is a correctness bug fix which changes the query result.
### How was this patch tested?
existing tests
Closes#34023 from cloud-fan/scan.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit dfd5237c0c)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR refactors test code in order to improve the debugability of `SparkSubmitSuite`.
The `sql/hive` module contains a `SparkSubmitTestUtils` helper class which launches `spark-submit` and captures its output in order to display better error messages when tests fail. This helper is currently used by `HiveSparkSubmitSuite` and `HiveExternalCatalogVersionsSuite`, but isn't used by `SparkSubmitSuite`.
In this PR, I moved `SparkSubmitTestUtils` and `ProcessTestUtils` into the `core` module and updated `SparkSubmitSuite`, `BufferHolderSparkSubmitSuite`, and `WholestageCodegenSparkSubmitSuite` to use the relocated helper classes. This required me to change `SparkSubmitTestUtils` to make its timeouts configurable and to generalize its method for locating the `spark-submit` binary.
### Why are the changes needed?
Previously, `SparkSubmitSuite` tests would fail with messages like:
```
[info] - launch simple application with spark-submit *** FAILED *** (1 second, 832 milliseconds)
[info] Process returned with exit code 101. See the log4j logs for more detail. (SparkSubmitSuite.scala:1551)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:472)
```
which require the Spark developer to hunt in log4j logs in order to view the logs from the failed `spark-submit` command.
After this change, those tests will fail with detailed error messages that include the text of failed command plus timestamped logs captured from the failed proces:
```
[info] - launch simple application with spark-submit *** FAILED *** (2 seconds, 800 milliseconds)
[info] spark-submit returned with exit code 101.
[info] Command line: '/Users/joshrosen/oss-spark/bin/spark-submit' '--class' 'invalidClassName' '--name' 'testApp' '--master' 'local' '--conf' 'spark.ui.enabled=false' '--conf' 'spark.master.rest.enabled=false' 'file:/Users/joshrosen/oss-spark/target/tmp/spark-0a8a0c93-3aaf-435d-9cf3-b97abd318d91/testJar-1631768004882.jar'
[info]
[info] 2021-09-15 21:53:26.041 - stderr> SLF4J: Class path contains multiple SLF4J bindings.
[info] 2021-09-15 21:53:26.042 - stderr> SLF4J: Found binding in [jar:file:/Users/joshrosen/oss-spark/assembly/target/scala-2.12/jars/slf4j-log4j12-1.7.30.jar!/org/slf4j/impl/StaticLoggerBinder.class]
[info] 2021-09-15 21:53:26.042 - stderr> SLF4J: Found binding in [jar:file:/Users/joshrosen/.m2/repository/org/slf4j/slf4j-log4j12/1.7.30/slf4j-log4j12-1.7.30.jar!/org/slf4j/impl/StaticLoggerBinder.class]
[info] 2021-09-15 21:53:26.042 - stderr> SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
[info] 2021-09-15 21:53:26.042 - stderr> SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
[info] 2021-09-15 21:53:26.619 - stderr> Error: Failed to load class invalidClassName. (SparkSubmitTestUtils.scala:97)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:472)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I manually ran the affected test suites.
Closes#34013 from JoshRosen/SPARK-36774-move-SparkSubmitTestUtils-to-core.
Authored-by: Josh Rosen <joshrosen@databricks.com>
Signed-off-by: Josh Rosen <joshrosen@databricks.com>
(cherry picked from commit 3ae6e6775b)
Signed-off-by: Josh Rosen <joshrosen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to upgrade Apache ORC to 1.6.11 to bring the latest bug fixes.
### Why are the changes needed?
Apache ORC 1.6.11 has the following fixes.
- https://issues.apache.org/jira/projects/ORC/versions/12350499
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs.
Closes#33971 from dongjoon-hyun/SPARK-36732.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit c217797297)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Upgrade Apache Parquet to 1.12.1
### Why are the changes needed?
Parquet 1.12.1 contains the following bug fixes:
- PARQUET-2064: Make Range public accessible in RowRanges
- PARQUET-2022: ZstdDecompressorStream should close `zstdInputStream`
- PARQUET-2052: Integer overflow when writing huge binary using dictionary encoding
- PARQUET-1633: Fix integer overflow
- PARQUET-2054: fix TCP leaking when calling ParquetFileWriter.appendFile
- PARQUET-2072: Do Not Determine Both Min/Max for Binary Stats
- PARQUET-2073: Fix estimate remaining row count in ColumnWriteStoreBase
- PARQUET-2078: Failed to read parquet file after writing with the same
In particular PARQUET-2078 is a blocker for the upcoming Apache Spark 3.2.0 release.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests + a new test for the issue in SPARK-36696
Closes#33969 from sunchao/upgrade-parquet-12.1.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
(cherry picked from commit a927b0836b)
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
For query
```
select arrays_overlap(array(cast('nan' as double), 1d), array(cast('nan' as double)))
```
This returns [false], but it should return [true].
This issue is caused by `scala.mutable.HashSet` can't handle `Double.NaN` and `Float.NaN`.
### Why are the changes needed?
Fix bug
### Does this PR introduce _any_ user-facing change?
arrays_overlap won't handle equal `NaN` value
### How was this patch tested?
Added UT
Closes#34006 from AngersZhuuuu/SPARK-36755.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit b665782f0d)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
According to https://github.com/apache/spark/pull/33955#discussion_r708570515 use normalized NaN
### Why are the changes needed?
Use normalized NaN for duplicated NaN value
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Exiting UT
Closes#34003 from AngersZhuuuu/SPARK-36702-FOLLOWUP.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 638085953f)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
For query
```
select array_union(array(cast('nan' as double), cast('nan' as double)), array())
```
This returns [NaN, NaN], but it should return [NaN].
This issue is caused by `OpenHashSet` can't handle `Double.NaN` and `Float.NaN` too.
In this pr we add a wrap for OpenHashSet that can handle `null`, `Double.NaN`, `Float.NaN` together
### Why are the changes needed?
Fix bug
### Does this PR introduce _any_ user-facing change?
ArrayUnion won't show duplicated `NaN` value
### How was this patch tested?
Added UT
Closes#33955 from AngersZhuuuu/SPARK-36702-WrapOpenHashSet.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit f71f37755d)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fix InferFiltersFromGenerate bug, InferFiltersFromGenerate should not infer filter for generate when the children contain an expression which is instance of `org.apache.spark.sql.catalyst.expressions.UserDefinedExpression`.
Before this pr, the following case will throw an exception.
```scala
spark.udf.register("vec", (i: Int) => (0 until i).toArray)
sql("select explode(vec(8)) as c1").show
```
```
Once strategy's idempotence is broken for batch Infer Filters
GlobalLimit 21 GlobalLimit 21
+- LocalLimit 21 +- LocalLimit 21
+- Project [cast(c1#3 as string) AS c1#12] +- Project [cast(c1#3 as string) AS c1#12]
+- Generate explode(vec(8)), false, [c1#3] +- Generate explode(vec(8)), false, [c1#3]
+- Filter ((size(vec(8), true) > 0) AND isnotnull(vec(8))) +- Filter ((size(vec(8), true) > 0) AND isnotnull(vec(8)))
! +- OneRowRelation +- Filter ((size(vec(8), true) > 0) AND isnotnull(vec(8)))
! +- OneRowRelation
java.lang.RuntimeException:
Once strategy's idempotence is broken for batch Infer Filters
GlobalLimit 21 GlobalLimit 21
+- LocalLimit 21 +- LocalLimit 21
+- Project [cast(c1#3 as string) AS c1#12] +- Project [cast(c1#3 as string) AS c1#12]
+- Generate explode(vec(8)), false, [c1#3] +- Generate explode(vec(8)), false, [c1#3]
+- Filter ((size(vec(8), true) > 0) AND isnotnull(vec(8))) +- Filter ((size(vec(8), true) > 0) AND isnotnull(vec(8)))
! +- OneRowRelation +- Filter ((size(vec(8), true) > 0) AND isnotnull(vec(8)))
! +- OneRowRelation
at org.apache.spark.sql.errors.QueryExecutionErrors$.onceStrategyIdempotenceIsBrokenForBatchError(QueryExecutionErrors.scala:1200)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.checkBatchIdempotence(RuleExecutor.scala:168)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:254)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:200)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:200)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:179)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$optimizedPlan$1(QueryExecution.scala:138)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:196)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:196)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:134)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:130)
at org.apache.spark.sql.execution.QueryExecution.assertOptimized(QueryExecution.scala:148)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executedPlan$1(QueryExecution.scala:166)
at org.apache.spark.sql.execution.QueryExecution.withCteMap(QueryExecution.scala:73)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:163)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:163)
at org.apache.spark.sql.execution.QueryExecution.simpleString(QueryExecution.scala:214)
at org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$explainString(QueryExecution.scala:259)
at org.apache.spark.sql.execution.QueryExecution.explainString(QueryExecution.scala:228)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:98)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3731)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2755)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2962)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:288)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:327)
at org.apache.spark.sql.Dataset.show(Dataset.scala:807)
```
### Does this PR introduce _any_ user-facing change?
No, only bug fix.
### How was this patch tested?
Unit test.
Closes#33956 from cfmcgrady/SPARK-36715.
Authored-by: Fu Chen <cfmcgrady@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 52c5ff20ca)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
As [reported on `devspark.apache.org`](https://lists.apache.org/thread.html/r84cff66217de438f1389899e6d6891b573780159cd45463acf3657aa%40%3Cdev.spark.apache.org%3E), the published POMs when building with Scala 2.13 have the `scala-parallel-collections` dependency only in the `scala-2.13` profile of the pom.
### What changes were proposed in this pull request?
This PR suggests to work around this by un-commenting the `scala-parallel-collections` dependency when switching to 2.13 using the the `change-scala-version.sh` script.
I included an upgrade to scala-parallel-collections version 1.0.3, the changes compared to 0.2.0 are minor.
- removed OSGi metadata
- renamed some internal inner classes
- added `Automatic-Module-Name`
### Why are the changes needed?
According to the posts, this solves issues for developers that write unit tests for their applications.
Stephen Coy suggested to use the https://www.mojohaus.org/flatten-maven-plugin. While this sounds like a more principled solution, it is possibly too risky to do at this specific point in time?
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Locally
Closes#33948 from lrytz/parCollDep.
Authored-by: Lukas Rytz <lukas.rytz@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
(cherry picked from commit 1a62e6a2c1)
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fixed wrong documentation on Cot API
### Why are the changes needed?
[Doc](https://spark.apache.org/docs/latest/api/sql/index.html#cot) says `1/java.lang.Math.cot` but it should be `1/java.lang.Math.tan`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual check.
Closes#33978 from yutoacts/SPARK-36738.
Authored-by: Yuto Akutsu <yuto.akutsu@nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 3747cfdb40)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to ensure that HiveThriftServer2Suites (e.g. `thriftserver.UISeleniumSuite`) stop Thrift JDBC server on exit using shutdown hook.
### Why are the changes needed?
Normally, HiveThriftServer2Suites stops Thrift JDBC server via `afterAll` method.
But, if they are killed by signal (e.g. Ctrl-C), Thrift JDBC server will be remain.
```
$ jps
2792969 SparkSubmit
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Killed `thriftserver.UISeleniumSuite` by Ctrl-C and confirmed no Thrift JDBC server is remain by jps.
Closes#33967 from sarutak/stop-thrift-on-exit.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit c36d70836d)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add a few LZ4 wrapper classes for Parquet Lz4 compression output that uses Hadoop Lz4 codec.
### Why are the changes needed?
Currently we use Hadop 3.3.1's shaded client libraries. Lz4 is a provided dependency in Hadoop Common 3.3.1 for Lz4Codec. But it isn't excluded from relocation in these libraries. So to use lz4 as Parquet codec, we will hit the exception even we include lz4 as dependency.
```
[info] Cause: java.lang.NoClassDefFoundError: org/apache/hadoop/shaded/net/jpountz/lz4/LZ4Factory
[info] at org.apache.hadoop.io.compress.lz4.Lz4Compressor.<init>(Lz4Compressor.java:66)
[info] at org.apache.hadoop.io.compress.Lz4Codec.createCompressor(Lz4Codec.java:119)
[info] at org.apache.hadoop.io.compress.CodecPool.getCompressor(CodecPool.java:152)
[info] at org.apache.hadoop.io.compress.CodecPool.getCompressor(CodecPool.java:168)
```
Before the issue is fixed at Hadoop new release, we can add a few wrapper classes for Lz4 codec.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Modified test.
Closes#33940 from viirya/lz4-wrappers.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 6bcf330191)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
fix SimplifyConditionalsInPredicate to be null-safe
Reproducible:
```
import org.apache.spark.sql.types.{StructField, BooleanType, StructType}
import org.apache.spark.sql.Row
val schema = List(
StructField("b", BooleanType, true)
)
val data = Seq(
Row(true),
Row(false),
Row(null)
)
val df = spark.createDataFrame(
spark.sparkContext.parallelize(data),
StructType(schema)
)
// cartesian product of true / false / null
val df2 = df.select(col("b") as "cond").crossJoin(df.select(col("b") as "falseVal"))
df2.createOrReplaceTempView("df2")
spark.sql("SELECT * FROM df2 WHERE IF(cond, FALSE, falseVal)").show()
// actual:
// +-----+--------+
// | cond|falseVal|
// +-----+--------+
// |false| true|
// +-----+--------+
spark.sql("SET spark.sql.optimizer.excludedRules=org.apache.spark.sql.catalyst.optimizer.SimplifyConditionalsInPredicate")
spark.sql("SELECT * FROM df2 WHERE IF(cond, FALSE, falseVal)").show()
// expected:
// +-----+--------+
// | cond|falseVal|
// +-----+--------+
// |false| true|
// | null| true|
// +-----+--------+
```
### Why are the changes needed?
is a regression that leads to incorrect results
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#33928 from hypercubestart/fix-SimplifyConditionalsInPredicate.
Authored-by: Andrew Liu <andrewlliu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 9b633f2075)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, we have `FieldReference` for aggregate column type, should be `NamedReference` instead
### Why are the changes needed?
`FieldReference` is a private class, should use `NamedReference` instead
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#33927 from huaxingao/agg_followup.
Authored-by: Huaxin Gao <huaxin_gao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 23794fb303)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
SPARK-26346 upgrade Parquet related modules from 1.10.1 to 1.11.1 and `parquet-jackson 1.11.1` use `com.fasterxml.jackson` instead of `org.codehaus.jackson`.
So, there are warning logs related to
```
17:12:17.605 WARN org.apache.hadoop.fs.FileSystem: Cannot load filesystem
java.util.ServiceConfigurationError: org.apache.hadoop.fs.FileSystem: Provider org.apache.hadoop.hdfs.web.WebHdfsFileSystem could not be instantiated
...
Caused by: java.lang.ClassNotFoundException: org.codehaus.jackson.map.ObjectMapper
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
...
```
when test `sql/core` modules with `hadoop-2.7` profile.
This pr adds test dependencies related to `org.codehaus.jackson` in `sql/core` module when `hadoop-2.7` profile is activated.
### Why are the changes needed?
Clean up test warning logs that shouldn't exist.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- Pass GA or Jenkins Tests.
- Manual test `mvn clean test -pl sql/core -am -DwildcardSuites=none -Phadoop-2.7`
**Before**
No test failed, but warning logs as follows:
```
[INFO] Running test.org.apache.spark.sql.JavaBeanDeserializationSuite
22:42:45.211 WARN org.apache.hadoop.util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
22:42:46.827 WARN org.apache.hadoop.fs.FileSystem: Cannot load filesystem
java.util.ServiceConfigurationError: org.apache.hadoop.fs.FileSystem: Provider org.apache.hadoop.hdfs.web.WebHdfsFileSystem could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at org.apache.hadoop.fs.FileSystem.loadFileSystems(FileSystem.java:2631)
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2650)
at org.apache.hadoop.fs.FsUrlStreamHandlerFactory.<init>(FsUrlStreamHandlerFactory.java:62)
at org.apache.spark.sql.internal.SharedState$.liftedTree1$1(SharedState.scala:181)
at org.apache.spark.sql.internal.SharedState$.org$apache$spark$sql$internal$SharedState$$setFsUrlStreamHandlerFactory(SharedState.scala:180)
at org.apache.spark.sql.internal.SharedState.<init>(SharedState.scala:54)
at org.apache.spark.sql.SparkSession.$anonfun$sharedState$1(SparkSession.scala:135)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.SparkSession.sharedState$lzycompute(SparkSession.scala:135)
at org.apache.spark.sql.SparkSession.sharedState(SparkSession.scala:134)
at org.apache.spark.sql.internal.BaseSessionStateBuilder.build(BaseSessionStateBuilder.scala:335)
at org.apache.spark.sql.test.TestSparkSession.sessionState$lzycompute(TestSQLContext.scala:42)
at org.apache.spark.sql.test.TestSparkSession.sessionState(TestSQLContext.scala:41)
at org.apache.spark.sql.SparkSession.$anonfun$new$3(SparkSession.scala:109)
at scala.Option.map(Option.scala:230)
at org.apache.spark.sql.SparkSession.$anonfun$new$1(SparkSession.scala:109)
at org.apache.spark.sql.internal.SQLConf$.get(SQLConf.scala:194)
at org.apache.spark.sql.types.DataType.sameType(DataType.scala:97)
at org.apache.spark.sql.catalyst.analysis.TypeCoercion$.$anonfun$haveSameType$1(TypeCoercion.scala:291)
at org.apache.spark.sql.catalyst.analysis.TypeCoercion$.$anonfun$haveSameType$1$adapted(TypeCoercion.scala:291)
at scala.collection.LinearSeqOptimized.forall(LinearSeqOptimized.scala:85)
at scala.collection.LinearSeqOptimized.forall$(LinearSeqOptimized.scala:82)
at scala.collection.immutable.List.forall(List.scala:89)
at org.apache.spark.sql.catalyst.analysis.TypeCoercion$.haveSameType(TypeCoercion.scala:291)
at org.apache.spark.sql.catalyst.expressions.ComplexTypeMergingExpression.dataTypeCheck(Expression.scala:1074)
at org.apache.spark.sql.catalyst.expressions.ComplexTypeMergingExpression.dataTypeCheck$(Expression.scala:1069)
at org.apache.spark.sql.catalyst.expressions.If.dataTypeCheck(conditionalExpressions.scala:37)
at org.apache.spark.sql.catalyst.expressions.ComplexTypeMergingExpression.org$apache$spark$sql$catalyst$expressions$ComplexTypeMergingExpression$$internalDataType(Expression.scala:1080)
at org.apache.spark.sql.catalyst.expressions.ComplexTypeMergingExpression.org$apache$spark$sql$catalyst$expressions$ComplexTypeMergingExpression$$internalDataType$(Expression.scala:1079)
at org.apache.spark.sql.catalyst.expressions.If.org$apache$spark$sql$catalyst$expressions$ComplexTypeMergingExpression$$internalDataType$lzycompute(conditionalExpressions.scala:37)
at org.apache.spark.sql.catalyst.expressions.If.org$apache$spark$sql$catalyst$expressions$ComplexTypeMergingExpression$$internalDataType(conditionalExpressions.scala:37)
at org.apache.spark.sql.catalyst.expressions.ComplexTypeMergingExpression.dataType(Expression.scala:1084)
at org.apache.spark.sql.catalyst.expressions.ComplexTypeMergingExpression.dataType$(Expression.scala:1084)
at org.apache.spark.sql.catalyst.expressions.If.dataType(conditionalExpressions.scala:37)
at org.apache.spark.sql.catalyst.expressions.objects.MapObjects.$anonfun$dataType$4(objects.scala:815)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.catalyst.expressions.objects.MapObjects.dataType(objects.scala:815)
at org.apache.spark.sql.catalyst.expressions.CreateNamedStruct.$anonfun$dataType$9(complexTypeCreator.scala:416)
at scala.collection.immutable.List.map(List.scala:290)
at org.apache.spark.sql.catalyst.expressions.CreateNamedStruct.dataType$lzycompute(complexTypeCreator.scala:410)
at org.apache.spark.sql.catalyst.expressions.CreateNamedStruct.dataType(complexTypeCreator.scala:409)
at org.apache.spark.sql.catalyst.expressions.CreateNamedStruct.dataType(complexTypeCreator.scala:398)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.isSerializedAsStruct(ExpressionEncoder.scala:309)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.isSerializedAsStructForTopLevel(ExpressionEncoder.scala:319)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.<init>(ExpressionEncoder.scala:248)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.javaBean(ExpressionEncoder.scala:75)
at org.apache.spark.sql.Encoders$.bean(Encoders.scala:154)
at org.apache.spark.sql.Encoders.bean(Encoders.scala)
at test.org.apache.spark.sql.JavaBeanDeserializationSuite.testBeanWithArrayFieldDeserialization(JavaBeanDeserializationSuite.java:75)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:59)
at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:56)
at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:26)
at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:27)
at org.junit.runners.ParentRunner$3.evaluate(ParentRunner.java:306)
at org.junit.runners.BlockJUnit4ClassRunner$1.evaluate(BlockJUnit4ClassRunner.java:100)
at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:366)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:103)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:63)
at org.junit.runners.ParentRunner$4.run(ParentRunner.java:331)
at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:79)
at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:329)
at org.junit.runners.ParentRunner.access$100(ParentRunner.java:66)
at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:293)
at org.junit.runners.ParentRunner$3.evaluate(ParentRunner.java:306)
at org.junit.runners.ParentRunner.run(ParentRunner.java:413)
at org.apache.maven.surefire.junit4.JUnit4Provider.execute(JUnit4Provider.java:364)
at org.apache.maven.surefire.junit4.JUnit4Provider.executeWithRerun(JUnit4Provider.java:272)
at org.apache.maven.surefire.junit4.JUnit4Provider.executeTestSet(JUnit4Provider.java:237)
at org.apache.maven.surefire.junit4.JUnit4Provider.invoke(JUnit4Provider.java:158)
at org.apache.maven.surefire.booter.ForkedBooter.runSuitesInProcess(ForkedBooter.java:428)
at org.apache.maven.surefire.booter.ForkedBooter.execute(ForkedBooter.java:162)
at org.apache.maven.surefire.booter.ForkedBooter.run(ForkedBooter.java:562)
at org.apache.maven.surefire.booter.ForkedBooter.main(ForkedBooter.java:548)
Caused by: java.lang.NoClassDefFoundError: org/codehaus/jackson/map/ObjectMapper
at org.apache.hadoop.hdfs.web.WebHdfsFileSystem.<clinit>(WebHdfsFileSystem.java:129)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at java.lang.Class.newInstance(Class.java:442)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:380)
... 81 more
Caused by: java.lang.ClassNotFoundException: org.codehaus.jackson.map.ObjectMapper
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:419)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:352)
... 88 more
```
**After**
There are no more warning logs like above
Closes#33926 from LuciferYang/SPARK-36684.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
(cherry picked from commit acd9c92fa8)
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR filters out `ExtractValues`s that contains any aggregation function in the `NestedColumnAliasing` rule to prevent cases where aggregations are pushed down into projections.
### Why are the changes needed?
To handle a corner/missed case in `NestedColumnAliasing` that can cause users to encounter a runtime exception.
Consider the following schema:
```
root
|-- a: struct (nullable = true)
| |-- c: struct (nullable = true)
| | |-- e: string (nullable = true)
| |-- d: integer (nullable = true)
|-- b: string (nullable = true)
```
and the query:
`SELECT MAX(a).c.e FROM (SELECT a, b FROM test_aggregates) GROUP BY b`
Executing the query before this PR will result in the error:
```
java.lang.UnsupportedOperationException: Cannot generate code for expression: max(input[0, struct<c:struct<e:string>,d:int>, true])
at org.apache.spark.sql.errors.QueryExecutionErrors$.cannotGenerateCodeForExpressionError(QueryExecutionErrors.scala:83)
at org.apache.spark.sql.catalyst.expressions.Unevaluable.doGenCode(Expression.scala:312)
at org.apache.spark.sql.catalyst.expressions.Unevaluable.doGenCode$(Expression.scala:311)
at org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression.doGenCode(interfaces.scala:99)
...
```
The optimised plan before this PR is:
```
'Aggregate [b#1], [_extract_e#5 AS max(a).c.e#3]
+- 'Project [max(a#0).c.e AS _extract_e#5, b#1]
+- Relation default.test_aggregates[a#0,b#1] parquet
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
A new unit test in `NestedColumnAliasingSuite`. The test consists of the repro mentioned earlier.
The produced optimized plan is checked for equivalency with a plan of the form:
```
Aggregate [b#452], [max(a#451).c.e AS max('a)[c][e]#456]
+- LocalRelation <empty>, [a#451, b#452]
```
Closes#33921 from vicennial/spark-36677.
Authored-by: Venkata Sai Akhil Gudesa <venkata.gudesa@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 2ed6e7bc5d)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This patch mainly proposes to add some e2e test cases in Spark for codec used by main datasources.
### Why are the changes needed?
We found there is no e2e test cases available for main datasources like Parquet, Orc. It makes developers harder to identify possible bugs early. We should add such tests in Spark.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added tests.
Closes#33912 from viirya/SPARK-36670.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 5a0ae694d0)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
Fix regression in AQEShuffleReadExec when used in conjunction with Spark plugins with custom partitioning.
Signed-off-by: Andy Grove <andygrove73gmail.com>
### What changes were proposed in this pull request?
Return `UnknownPartitioning` rather than throw an exception in `AQEShuffleReadExec`.
### Why are the changes needed?
The [RAPIDS Accelerator for Apache Spark](https://github.com/NVIDIA/spark-rapids) replaces `AQEShuffleReadExec` with a custom operator that runs on the GPU. Due to changes in [SPARK-36315](dd80457ffb), Spark now throws an exception if the shuffle exchange does not have recognized partitioning, and this happens before the postStageOptimizer rules so there is no opportunity to replace this operator now.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I am still in the process of testing this change. I will update the PR in the next few days with status.
Closes#33910 from andygrove/SPARK-36666.
Authored-by: Andy Grove <andygrove73@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
(cherry picked from commit f78d8394dc)
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to ensure StateStoreProvider instances are properly closed for each test in StateStoreSuite/RocksDBStateStoreSuite.
### Why are the changes needed?
While this doesn't break the test, this is a bad practice and may possibly make nasty problems in the future.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UTs
Closes#33916 from HeartSaVioR/SPARK-36667.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 093c2080fe)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Promote spark.sql.execution.topKSortFallbackThreshold to a user-facing config
### Why are the changes needed?
spark.sql.execution.topKSortFallbackThreshold now is an internal config hidden from users Integer.MAX_VALUE - 15 as its default. In many real-world cases, if the K is very big, there would be performance issues.
It's better to leave this choice to users
### Does this PR introduce _any_ user-facing change?
spark.sql.execution.topKSortFallbackThreshold is now user-facing
### How was this patch tested?
passing GA
Closes#33904 from yaooqinn/SPARK-36659.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>
(cherry picked from commit 7f1ad7be18)
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `sequence` builtin function causes `ArrayIndexOutOfBoundsException` if the arguments are under the condition of `start == stop && step < 0`.
This is an example.
```
SELECT sequence(timestamp'2021-08-31', timestamp'2021-08-31', -INTERVAL 1 month);
21/09/02 04:14:42 ERROR SparkSQLDriver: Failed in [SELECT sequence(timestamp'2021-08-31', timestamp'2021-08-31', -INTERVAL 1 month)]
java.lang.ArrayIndexOutOfBoundsException: 1
```
Actually, this example succeeded before SPARK-31980 (#28819) was merged.
### Why are the changes needed?
Bug fix.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New tests.
Closes#33895 from sarutak/fix-sequence-issue.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
(cherry picked from commit cf3bc65e69)
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
This PR aims to update comments in `gen-sql-config-docs.py`.
### Why are the changes needed?
To make it up to date according to Spark version 3.2.0 release.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A.
Closes#33902 from williamhyun/fixtool.
Authored-by: William Hyun <william@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit b72fa5ef1c)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Two case of using undefined window frame as below should provide proper error message
1. For case using undefined window frame with window function
```
SELECT nth_value(employee_name, 2) OVER w second_highest_salary
FROM basic_pays;
```
origin error message is
```
Window function nth_value(employee_name#x, 2, false) requires an OVER clause.
```
It's confused that in use use a window frame `w` but it's not defined.
Now the error message is
```
Window specification w is not defined in the WINDOW clause.
```
2. For case using undefined window frame with aggregation function
```
SELECT SUM(salary) OVER w sum_salary
FROM basic_pays;
```
origin error message is
```
Error in query: unresolved operator 'Aggregate [unresolvedwindowexpression(sum(salary#2), WindowSpecReference(w)) AS sum_salary#34]
+- SubqueryAlias spark_catalog.default.basic_pays
+- HiveTableRelation [`default`.`employees`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [name#0, dept#1, salary#2, age#3], Partition Cols: []]
```
In this case, when convert GlobalAggregate, should skip UnresolvedWindowExpression
Now the error message is
```
Window specification w is not defined in the WINDOW clause.
```
### Why are the changes needed?
Provide proper error message
### Does this PR introduce _any_ user-facing change?
Yes, error messages are improved as described in desc
### How was this patch tested?
Added UT
Closes#33892 from AngersZhuuuu/SPARK-36637.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 568ad6aa44)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to fix bugs around prefix-scan for both HDFS backed state store and RocksDB state store.
> HDFS backed state store
We did "shallow-copy" on copying prefix map, which leads the values of prefix map (mutable Set) to be "same instances" across multiple versions. This PR fixes it via creating a new mutable Set and copying elements.
> RocksDB state store
Prefix-scan iterators are only closed on RocksDB.rollback(), which is only called in RocksDBStateStore.abort().
While `RocksDBStateStore.abort()` method will be called for streaming session window (since it has two physical plans for read and write), other stateful operators which only have read-write physical plan will call either commit or abort, and don't close the iterators on committing. These unclosed iterators can be "reused" and produce incorrect outputs.
This PR ensures that resetting prefix-scan iterators is done on loading RocksDB, which was only done in rollback.
### Why are the changes needed?
Please refer the above section on explanation of bugs and treatments.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Modified UT which failed without this PR and passes with this PR.
Closes#33870 from HeartSaVioR/SPARK-36619.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 60a72c938a)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Spark SQL includes a data source that can read data from other databases using JDBC.
Spark also supports the case-insensitive option `pushDownPredicate`.
According to http://spark.apache.org/docs/latest/sql-data-sources-jdbc.html, If set `pushDownPredicate` to false, no filter will be pushed down to the JDBC data source and thus all filters will be handled by Spark.
But I find it still be pushed down to JDBC data source.
### Why are the changes needed?
Fix bug `pushDownPredicate`=false failed to prevent push down filters to JDBC data source.
### Does this PR introduce _any_ user-facing change?
'No'.
The output of query will not change.
### How was this patch tested?
Jenkins test.
Closes#33822 from beliefer/SPARK-36574.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit fcc91cfec4)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The `try_add` function allows the following inputs:
- number, number
- date, number
- date, interval
- timestamp, interval
- interval, interval
And, the `try_divide` function allows the following inputs:
- number, number
- interval, number
However, in the current code, there are only examples and tests about the (number, number) inputs. We should enhance the docs to let users know that the functions can be used for datetime and interval operations too.
### Why are the changes needed?
Improve documentation and tests.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
New UT
Also build docs for preview:
Closes#33861 from gengliangwang/enhanceTryDoc.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 8a52ad9f82)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to document `window` & `session_window` function in SQL API doc page.
Screenshot of functions:
> window
> session_window
### Why are the changes needed?
Description is missing in both `window` / `session_window` functions for SQL API page.
### Does this PR introduce _any_ user-facing change?
Yes, the description of `window` / `session_window` functions will be available in SQL API page.
### How was this patch tested?
Only doc changes.
Closes#33846 from HeartSaVioR/SPARK-36595.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
(cherry picked from commit bc32144a91)
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Compare the 3.2.0 API doc with the latest release version 3.1.2. Fix the following issues:
- Add missing `Since` annotation for new APIs
- Remove the leaking class/object in API doc
### Why are the changes needed?
Improve API docs
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT
Closes#33845 from gengliangwang/SPARK-36457-3.2.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/33837
It is to fix compilation error: https://github.com/apache/spark/runs/3431646840
### Why are the changes needed?
Fix a compilation error
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass existing UTs
Closes#33851 from gengliangwang/fixCompile.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This is the patch on branch-3.2 for https://github.com/apache/spark/pull/33842. See the description in the other PR.
### Why are the changes needed?
Avoid OOM/performance regression when reading ORC table with nested column types.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `OrcSourceSuite.scala`.
Closes#33843 from c21/branch-3.2.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
In the PR, I propose to use the session time zone ( see the SQL config `spark.sql.session.timeZone`) instead of JVM default time zone while converting of special timestamp_ntz strings such as "today", "tomorrow" and so on.
Current implementation is based on the system time zone, and it controverses to other functions/classes that use the session time zone. For example, Spark doesn't respects user's settings:
```sql
$ export TZ="Europe/Amsterdam"
$ ./bin/spark-sql -S
spark-sql> select timestamp_ntz'now';
2021-08-25 18:12:36.233
spark-sql> set spark.sql.session.timeZone=America/Los_Angeles;
spark.sql.session.timeZone America/Los_Angeles
spark-sql> select timestamp_ntz'now';
2021-08-25 18:14:40.547
```
Yes. For the example above, after the changes:
```sql
spark-sql> select timestamp_ntz'now';
2021-08-25 18:47:46.832
spark-sql> set spark.sql.session.timeZone=America/Los_Angeles;
spark.sql.session.timeZone America/Los_Angeles
spark-sql> select timestamp_ntz'now';
2021-08-25 09:48:05.211
```
By running the affected test suites:
```
$ build/sbt "test:testOnly *DateTimeUtilsSuite"
```
Closes#33838 from MaxGekk/fix-ts_ntz-special-values.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 159ff9fd14)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Spark 3.2.0 includes two new functions `regexp` and `regexp_like`, which are identical to `rlike`. However, in the generated documentation. the since versions of both functions are `1.0.0` since they are based on the expression `RLike`:
- https://dist.apache.org/repos/dist/dev/spark/v3.2.0-rc1-docs/_site/api/sql/index.html#regexp
- https://dist.apache.org/repos/dist/dev/spark/v3.2.0-rc1-docs/_site/api/sql/index.html#regexp_like
This PR is to:
* Support setting `since` version in FunctionRegistry
* Correct the `since` version of `regexp` and `regexp_like`
### Why are the changes needed?
Correct the SQL doc
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Run
```
sh sql/create-docs.sh
```
and check the SQL doc manually
Closes#33834 from gengliangwang/allowSQLFunVersion.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 18143fb426)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR fixes an issue that there is no way to redact sensitive information in Spark Thrift Server log.
For example, JDBC password can be exposed in the log.
```
21/08/25 18:52:37 INFO SparkExecuteStatementOperation: Submitting query 'CREATE TABLE mytbl2(a int) OPTIONS(url="jdbc:mysql//example.com:3306", driver="com.mysql.jdbc.Driver", dbtable="test_tbl", user="test_usr", password="abcde")' with ca14ae38-1aaf-4bf4-a099-06b8e5337613
```
### Why are the changes needed?
Bug fix.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Ran ThriftServer, connect to it and execute `CREATE TABLE mytbl2(a int) OPTIONS(url="jdbc:mysql//example.com:3306", driver="com.mysql.jdbc.Driver", dbtable="test_tbl", user="test_usr", password="abcde");` with `spark.sql.redaction.string.regex=((?i)(?<=password=))(".*")|('.*')`
Then, confirmed the log.
```
21/08/25 18:54:11 INFO SparkExecuteStatementOperation: Submitting query 'CREATE TABLE mytbl2(a int) OPTIONS(url="jdbc:mysql//example.com:3306", driver="com.mysql.jdbc.Driver", dbtable="test_tbl", user="test_usr", password=*********(redacted))' with ffc627e2-b1a8-4d83-ab6d-d819b3ccd909
```
Closes#33832 from sarutak/fix-SPARK-36398.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
(cherry picked from commit b2ff01608f)
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
In the PR, I propose to add new correctness rule `SpecialDatetimeValues` to the final analysis phase. It replaces casts of strings to date/timestamp_ltz/timestamp_ntz by literals of such types if the strings contain special datetime values like `today`, `yesterday` and `tomorrow`, and the input strings are foldable.
### Why are the changes needed?
1. To avoid a breaking change.
2. To improve user experience with Spark SQL. After the PR https://github.com/apache/spark/pull/32714, users have to use typed literals instead of implicit casts. For instance,
at Spark 3.1:
```sql
select ts_col > 'now';
```
but the query fails at the moment, and users have to use typed timestamp literal:
```sql
select ts_col > timestamp'now';
```
### Does this PR introduce _any_ user-facing change?
No. Previous release 3.1 has supported the feature already till it was removed by https://github.com/apache/spark/pull/32714.
### How was this patch tested?
1. Manually tested via the sql command line:
```sql
spark-sql> select cast('today' as date);
2021-08-24
spark-sql> select timestamp('today');
2021-08-24 00:00:00
spark-sql> select timestamp'tomorrow' > 'today';
true
```
2. By running new test suite:
```
$ build/sbt "sql/testOnly org.apache.spark.sql.catalyst.optimizer.SpecialDatetimeValuesSuite"
```
Closes#33816 from MaxGekk/foldable-datetime-special-values.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit df0ec56723)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to move distributed-sequence index implementation to SQL plan to leverage optimizations such as column pruning.
```python
import pyspark.pandas as ps
ps.set_option('compute.default_index_type', 'distributed-sequence')
ps.range(10).id.value_counts().to_frame().spark.explain()
```
**Before:**
```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#51L DESC NULLS LAST], true, 0
+- Exchange rangepartitioning(count#51L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=#70]
+- HashAggregate(keys=[id#37L], functions=[count(1)], output=[__index_level_0__#48L, count#51L])
+- Exchange hashpartitioning(id#37L, 200), ENSURE_REQUIREMENTS, [id=#67]
+- HashAggregate(keys=[id#37L], functions=[partial_count(1)], output=[id#37L, count#63L])
+- Project [id#37L]
+- Filter atleastnnonnulls(1, id#37L)
+- Scan ExistingRDD[__index_level_0__#36L,id#37L]
# ^^^ Base DataFrame created by the output RDD from zipWithIndex (and checkpointed)
```
**After:**
```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#275L DESC NULLS LAST], true, 0
+- Exchange rangepartitioning(count#275L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=#174]
+- HashAggregate(keys=[id#258L], functions=[count(1)])
+- HashAggregate(keys=[id#258L], functions=[partial_count(1)])
+- Filter atleastnnonnulls(1, id#258L)
+- Range (0, 10, step=1, splits=16)
# ^^^ Removed the Spark job execution for `zipWithIndex`
```
### Why are the changes needed?
To leverage optimization of SQL engine and avoid unnecessary shuffle to create default index.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unittests were added. Also, this PR will test all unittests in pandas API on Spark after switching the default index implementation to `distributed-sequence`.
Closes#33807 from HyukjinKwon/SPARK-36559.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 93cec49212)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add `aggregate` package under `sql/catalyst/src/main/java/org/apache/spark/sql/connector/expressions` and move all the aggregates (e.g. `Count`, `Max`, `Min`, etc.) there.
### Why are the changes needed?
Right now these aggregates are under `sql/catalyst/src/main/java/org/apache/spark/sql/connector/expressions`. It looks OK now, but we plan to add a new `filter` package under `expressions` for all the DSV2 filters. It will look strange that filters have their own package, but aggregates don't.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#33815 from huaxingao/agg_package.
Authored-by: Huaxin Gao <huaxin_gao@apple.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit cd2342691d)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
For the hive table, the actual write path and the schema handling are inconsistent when `spark.sql.legacy.charVarcharAsString` is true.
This causes problems like SPARK-36552 described.
In this PR we respect `spark.sql.legacy.charVarcharAsString` when generates hive table schema from spark data types.
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
yes, when `spark.sql.legacy.charVarcharAsString` is true, hive table with char/varchar will respect string behavior.
### How was this patch tested?
newly added test
Closes#33798 from yaooqinn/SPARK-36552.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit f918c123a0)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/33775 commits the debug code mistakely.
This PR revert the test path.
### Why are the changes needed?
Revoke debug code.
### Does this PR introduce _any_ user-facing change?
'No'.
Just adjust test.
### How was this patch tested?
Revert non-ansi test path.
Closes#33787 from beliefer/SPARK-36428-followup2.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 462aa7cd3c)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
