### What changes were proposed in this pull request?
Similar to SPARK-35532, the main change of this pr is add `scala-2.13` profile to external/kafka-0-10-sql/pom.xml, external/avro/pom.xml and sql/hive-thriftserver/pom.xml, the `scala-2.13` profile include dependency on `scala-parallel-collections_2.13`, then all(34) spark modules can maven test independently.
### Why are the changes needed?
Ensure alll(34) spark modules can be maven test independently in Scala 2.13
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the GitHub Action Scala 2.13 job
- Manual test：
1. Execute
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13
```
2. maven test `external/kafka-0-10-sql` module
```
mvn test -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl external/kafka-0-10-sql
```
**before**
```
Discovery starting.
Discovery completed in 857 milliseconds.
Run starting. Expected test count is: 464
...
KafkaRelationSuiteV2:
- explicit earliest to latest offsets
- default starting and ending offsets
- explicit offsets
- default starting and ending offsets with headers
- timestamp provided for starting and ending
- timestamp provided for starting, offset provided for ending
- timestamp provided for ending, offset provided for starting
- timestamp provided for starting, ending not provided
- timestamp provided for ending, starting not provided
- global timestamp provided for starting and ending
- no matched offset for timestamp - startingOffsets
- preferences on offset related options
- no matched offset for timestamp - endingOffsets
*** RUN ABORTED ***
java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:182)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:220)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:217)
...
Cause: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
...
```
**After**
```
Run completed in 33 minutes, 51 seconds.
Total number of tests run: 464
Suites: completed 31, aborted 0
Tests: succeeded 464, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
3. maven test `external/avro` module
```
mvn test -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl external/avro
```
**before**
```
Discovery starting.
Discovery completed in 2 seconds, 765 milliseconds.
Run starting. Expected test count is: 255
AvroReadSchemaSuite:
- append column at the end
- hide column at the end
- append column into middle
- hide column in the middle
- add a nested column at the end of the leaf struct column
- add a nested column in the middle of the leaf struct column
- add a nested column at the end of the middle struct column
- add a nested column in the middle of the middle struct column
- hide a nested column at the end of the leaf struct column
- hide a nested column in the middle of the leaf struct column
- hide a nested column at the end of the middle struct column
- hide a nested column in the middle of the middle struct column
*** RUN ABORTED ***
java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:182)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:220)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:217)
...
Cause: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
...
```
**After**
```
Run completed in 1 minute, 42 seconds.
Total number of tests run: 255
Suites: completed 12, aborted 0
Tests: succeeded 255, failed 0, canceled 0, ignored 2, pending 0
All tests passed.
```
4. maven test `sql/hive-thriftserver` module
```
mvn test -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl sql/hive-thriftserver
```
**before**
```
- union.sql *** FAILED ***
"1 a
1 a
2 b
2 b" did not contain "Exception" Exception did not match for query #2
SELECT *
FROM (SELECT * FROM t1
UNION ALL
SELECT * FROM t1), expected: 1 a
1 a
2 b
2 b, but got: java.sql.SQLException
org.apache.hive.service.cli.HiveSQLException: Error running query: java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.sql.hive.thriftserver.HiveThriftServerErrors$.runningQueryError(HiveThriftServerErrors.scala:38)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecuteStatementOperation$$execute(SparkExecuteStatementOperation.scala:324)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.$anonfun$run$2(SparkExecuteStatementOperation.scala:229)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
at org.apache.spark.sql.hive.thriftserver.SparkOperation.withLocalProperties(SparkOperation.scala:79)
at org.apache.spark.sql.hive.thriftserver.SparkOperation.withLocalProperties$(SparkOperation.scala:63)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.withLocalProperties(SparkExecuteStatementOperation.scala:43)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.run(SparkExecuteStatementOperation.scala:229)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.run(SparkExecuteStatementOperation.scala:224)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1878)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2.run(SparkExecuteStatementOperation.scala:238)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:182)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:220)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:217)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:178)
at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:323)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:389)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3719)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2987)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3710)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:774)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3708)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2987)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecuteStatementOperation$$execute(SparkExecuteStatementOperation.scala:299)
... 16 more
Caused by: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
... 40 more (ThriftServerQueryTestSuite.scala:209)
```
**After**
```
Run completed in 29 minutes, 17 seconds.
Total number of tests run: 535
Suites: completed 20, aborted 0
Tests: succeeded 535, failed 0, canceled 0, ignored 17, pending 0
All tests passed.
```
Closes#32994 from LuciferYang/SPARK-35838.
Authored-by: YangJie <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
When we call periodToMonths and durationToMicros with certain type field, we should pass endField parameter.
### Why are the changes needed?
When we call periodToMonths and durationToMicros with certain type field, we should pass endField parameter.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existed UT
Closes#32984 from AngersZhuuuu/SPARK-35726-35769.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
#32513 added the case class `CommandResult` so as we can eagerly execute command locally. But we forgot to update
`isLocal` of `Dataset`.
### Why are the changes needed?
`Dataset.isLocal` should consider `CommandResult`.
### Does this PR introduce _any_ user-facing change?
Yes. If the SQL plan is `CommandResult`, `Dataset.isLocal` must return true.
### How was this patch tested?
No test.
Closes#32963 from beliefer/SPARK-35378-followup2.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fix how we measure the metric `allUpdatesTimeMs` in `FlatMapGroupsWithStateExec` similar to other streaming stateful operators.
### Why are the changes needed?
Metric `allUpdatesTimeMs` meant to capture the start to end walltime of the operator `FlatMapGroupsWithStateExec`, but currently it just [captures](79362c4efc/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FlatMapGroupsWithStateExec.scala (L121)) the iterator creation time.
Fix it to measure similar to how other stateful operators measure. Example one [here](79362c4efc/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala (L406)). This measurement is not perfect due to the nature of the lazy iterator and also includes the time the consumer operator spent in processing the current operator output, but it should give a good signal when comparing the metric in one microbatch to the metric in another microbatch.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UTs for regression. Due to the nature of metric type (time), it is hard to write a UT, but have manually verified.
Closes#32952 from vkorukanti/SPARK-35799.
Authored-by: Venki Korukanti <venki.korukanti@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
The char/varchar type should be mapped to orc's string type too, see https://orc.apache.org/docs/types.html
### Why are the changes needed?
fix a regression
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#33001 from yaooqinn/SPARK-35700.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Implement new SQL function: `to_timestamp_ntz`.
The syntax is similar to the built-in function `to_timestamp`:
```
to_timestamp_ntz ( <date_expr> )
to_timestamp_ntz ( <timestamp_expr> )
to_timestamp_ntz ( <string_expr> [ , <format> ] )
```
The naming is from snowflake: https://docs.snowflake.com/en/sql-reference/functions/to_timestamp.html
### Why are the changes needed?
Adds a new SQL function to create a literal/column of timestamp without time zone.
It's convenient for both end-users and developers.
### Does this PR introduce _any_ user-facing change?
Yes, a new SQL function `to_timestamp_ntz`.
### How was this patch tested?
Unit tests
Closes#32995 from gengliangwang/toTimestampNtz.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR adds test to check `sum` and `avg` works with all the `YearMonthInterval` types.
### Why are the changes needed?
To ensure the results of aggregations are what is expected.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32988 from sarutak/check-interval-agg-ym.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Extend the `CollapseWindow` rule to collapse `Window` nodes, that have `Project` between them.
### Why are the changes needed?
The analyzer will turn a `dataset.withColumn("colName", expressionWithWindowFunction)` method call to a `Project - Window - Project` chain in the logical plan. When this method is called multiple times in a row, then the projects can block the `Window` nodes from being collapsed by the current `CollapseWindow` rule.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#31677 from tanelk/SPARK-34565_collapse_windows.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to add 2 new methods that accept one field and produce either `YearMonthIntervalType` or `DayTimeIntervalType`.
### Why are the changes needed?
To improve code maintenance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By existing test suites.
Closes#32997 from MaxGekk/ansi-interval-types-single-field.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Support Cast between different field DayTimeIntervalType
### Why are the changes needed?
Make user convenient to get different field DayTimeIntervalType
### Does this PR introduce _any_ user-facing change?
User can call cast DayTimeIntervalType(DAY, SECOND) to DayTimeIntervalType(DAY, MINUTE) etc
### How was this patch tested?
Added UT
Closes#32975 from AngersZhuuuu/SPARK-35820.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Add a test.
### Why are the changes needed?
The SubqueryExpression refactor PR https://github.com/apache/spark/pull/32687 actually fixes the bug of `SubqueryExpression.references`. So this follow-up PR adds a regression unit test for it.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added a new test.
Closes#32990 from Ngone51/spark-35545-followup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes error message shown when changing a column type to year-month/day-time interval type is attempted.
### Why are the changes needed?
It's for consistent behavior.
Updating column types to interval types are prohibited for V2 source tables.
So, if we attempt to update the type of a column to the conventional interval type, an error message like `Error in query: Cannot update <table> field <column> to interval type;`.
But, for year-month/day-time interval types, another error message like `Error in query: Cannot update <table> field <column>:<type> cannot be cast to interval year;`.
You can reproduce with the following procedure.
```
$ bin/spark-sql
spark-sql> SET spark.sql.catalog.mycatalog=<a catalog implementation class>;
spark-sql> CREATE TABLE mycatalog.t1(c1 int) USING <V2 datasource implementation class>;
spark-sql> ALTER TABLE mycatalog.t1 ALTER COLUMN c1 TYPE interval year to month;
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Modified an existing test.
Closes#32978 from sarutak/err-msg-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `IntervalUtils.toYearMonthIntervalString` doesn't consider the case that year-month interval type is casted as month interval type.
If a year-month interval data is casted as month interval, the value of the year is multiplied by `12` and added to the value of month. For example, `INTERVAL '1-2' YEAR TO MONTH` will be `INTERVAL '14' MONTH` if it's casted.
If this behavior is intended, it's stringified to be `'INTERVAL 14' MONTH` but currently, it will be `INTERVAL '2' MONTH`
### Why are the changes needed?
It's a bug if the behavior of cast is intended.
### Does this PR introduce _any_ user-facing change?
No, because this feature is not released yet.
### How was this patch tested?
Modified the tests added in SPARK-35771 (#32924).
Closes#32982 from sarutak/fix-toYearMonthIntervalString.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Support Cast between different field YearMonthIntervalType
### Why are the changes needed?
Make user convenient to get different field YearMonthIntervalType
### Does this PR introduce _any_ user-facing change?
User can call cast YearMonthIntervalType(YEAR, MONTH) to YearMonthIntervalType(YEAR, YEAR) etc
### How was this patch tested?
Added UT
Closes#32974 from AngersZhuuuu/SPARK-35819.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Support truncate java.time.Duration by fields of day-time interval type.
### Why are the changes needed?
To respect fields of the target day-time interval types.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32950 from AngersZhuuuu/SPARK-35726.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This patch proposes to add an internal config for ignoring metadata of `FileStreamSink` when reading the output path.
### Why are the changes needed?
`FileStreamSink` produces a metadata directory which logs output files per micro-batch. When we read from the output path, Spark will look at the metadata and ignore other files not in the log.
Normally it works well. But for some use-cases, we may need to ignore the metadata when reading the output path. For example, when we change the streaming query and must to run it with new checkpoint directory, we cannot use previous metadata. If we create a new metadata too, when we read the output path later in Spark, Spark only reads the files listed in the new metadata. The files written before we use new checkpoint and metadata are ignored by Spark.
Although seems we can output to different output directory every time, but it is bad idea as we will produce many directories unnecessarily.
We need a config for ignoring the metadata of `FileStreamSink` when reading the output path.
### Does this PR introduce _any_ user-facing change?
Added a config for ignoring metadata of FileStreamSink when reading the output.
### How was this patch tested?
Unit tests.
Closes#32702 from viirya/ignore-metadata.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR improves `Distinct` statistics estimation by rewrite it to `Aggregate`.
### Why are the changes needed?
1. The current implementation will lack column statistics.
2. Some rules before the `ReplaceDistinctWithAggregate` may use it. For example: https://github.com/apache/spark/pull/31113/files#diff-11264d807efa58054cca2d220aae8fba644ee0f0f2a4722c46d52828394846efR1808
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32291 from wangyum/SPARK-35185.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
Change `AQEPropagateEmptyRelation` from `transformUp` to `transformUpWithPruning
### Why are the changes needed?
To avoid unnecessary iteration during AQE optimizer.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass CI.
Closes#32742 from ulysses-you/aqe-transformUpWithPruning.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Support truncate java.time.Period by fields of year-month interval type
### Why are the changes needed?
To follow the SQL standard and respect the field restriction of the target year-month type.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32945 from AngersZhuuuu/SPARK-35769.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR extends the following tests to use day-time intervals.
* StreamingOuterJoinSuite.right outer with watermark range condition
* StreamingOuterJoinSuite.left outer with watermark range condition
### Why are the changes needed?
Currently, there are no tests to use day-time intervals.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New assertions.
Closes#32953 from sarutak/stream-join-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Extend the Cast expression and support StringType in casting to TimestampWithoutTZType.
Closes#32898
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32936 from gengliangwang/castStringToTswtz.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR is a follow-up for SPARK-34382. It refines the lateral join syntax to only allow the LATERAL keyword to be in front of subqueries, instead of all `relationPriamry`. For example, `SELECT * FROM t1, LATERAL t2` should not be allowed.
### Why are the changes needed?
To be consistent with Postgres.
### Does this PR introduce _any_ user-facing change?
Yes. After this PR, the LATERAL keyword can only be in front of subqueries.
```scala
sql("SELECT * FROM t1, LATERAL t2")
org.apache.spark.sql.catalyst.parser.ParseException:
LATERAL can only be used with subquery(line 1, pos 26)
== SQL ==
select * from t1, lateral t2
--------------------------^^^
```
### How was this patch tested?
New unit tests.
Closes#32937 from allisonwang-db/spark-35789-lateral-join-parser.
Authored-by: allisonwang-db <allison.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/32513 added the case class `CommandResult` in package `org.apache.spark.sql.expression`. It is not suitable, so this PR move `CommandResult` from `org.apache.spark.sql.expression` to `org.apache.spark.sql.catalyst.plans.logical`.
### Why are the changes needed?
Make `CommandResult` in suitable package.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
No need.
Closes#32942 from beliefer/SPARK-35378-followup.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
There might be `SparkPlan` nodes where canonicalization on executor side can cause issues. This is a follow-up fix to conversation https://github.com/apache/spark/pull/32885/files#r651019687.
### Why are the changes needed?
To avoid potential NPEs when canonicalization happens on executors.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#32947 from peter-toth/SPARK-35798-fix-sparkplan.sqlcontext-usage.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`RelationConversions` is actually an optimization rule while it's executed in the analysis phase.
For view, it's designed to only capture semantic configs, so we should ignore the optimization
configs that will be used in the analysis phase.
This PR also fixes the issue that view resolution will always use the default value for uncaptured config
### Why are the changes needed?
Bugfix
### Does this PR introduce _any_ user-facing change?
Yes, after this PR view resolution will respect the values set in the current session for the below configs
```
"spark.sql.hive.convertMetastoreParquet"
"spark.sql.hive.convertMetastoreOrc"
"spark.sql.hive.convertInsertingPartitionedTable"
"spark.sql.hive.convertMetastoreCtas"
```
### How was this patch tested?
By running new UT:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *HiveSQLViewSuite"
```
Closes#32941 from linhongliu-db/SPARK-35792-ignore-convert-configs.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support Parse DayTimeIntervalType from JSON
### Why are the changes needed?
this will allow to store day-second intervals as table columns into Hive external catalog.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32930 from AngersZhuuuu/SPARK-35732.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/32513
It's hard to keep the command execution name for `DataFrameWriter`, as the command logical plan is a bit messy (DS v1, file source and hive and different command logical plans) and sometimes it's hard to distinguish "insert" and "save".
However, `DataFrameWriterV2` only produce v2 commands which are pretty clean. It's easy to keep the command execution name for them.
### Why are the changes needed?
less breaking changes.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#32919 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Providing a new function make_dt_interval to construct DayTimeIntervalType value
### Why are the changes needed?
As the JIRA described, we should provide a function to construct DayTimeIntervalType value
### Does this PR introduce _any_ user-facing change?
Yes, a new make_dt_interval function provided
### How was this patch tested?
Updated UTs, manual testing
Closes#32601 from copperybean/work.
Authored-by: copperybean <copperybean.zhang@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Parse YearMonthIntervalType from JSON.
### Why are the changes needed?
This will allow to store year-month intervals as table columns into Hive external catalog.
### Does this PR introduce _any_ user-facing change?
People can store year-month interval types as json string.
### How was this patch tested?
Added UT.
Closes#32929 from AngersZhuuuu/SPARK-35770.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
NULL-aware ANTI join (https://issues.apache.org/jira/browse/SPARK-32290) detects NULL join keys during building the map for `HashedRelation`, and will immediately return `HashedRelationWithAllNullKeys` without taking care of the map built already. Before returning `HashedRelationWithAllNullKeys`, the map needs to be freed properly to save memory and keep memory accounting correctly.
### Why are the changes needed?
Save memory and keep memory accounting correctly for the join query.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests introduced in https://github.com/apache/spark/pull/29104 .
Closes#32939 from c21/free-null-aware.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set the list of read columns in the task configuration to reduce reading of ORC data.
### Why are the changes needed?
Now, the ORC reader will read all columns of the ORC table when the task configuration does not set the list of read columns . Therefore, we should set the list of read columns in the task configuration to reduce reading of ORC data.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
exist unittests
Closes#32923 from weixiuli/SPARK-35783.
Authored-by: weixiuli <weixiuli@jd.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Remove the usage of the enumerating subclasses of `StateStoreCustomMetric` dependency.
To achieve it, add couple of utility methods to `StateStoreCustomMetric`
* `withNewDesc(desc : String)` to `StateStoreCustomMetric` for cloning the instance with a new `desc` (currently used in `SymmetricHashJoinStateManager`)
* `createSQLMetric(sparkContext: sparkContext): SQLMetric` for creating a corresponding `SQLMetric` to show the metric in UI and accumulate at the query level (currently used in `statefulOperator. stateStoreCustomMetrics`)
### Why are the changes needed?
Code in [SymmetricHashJoinStateManager](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/SymmetricHashJoinStateManager.scala#L321) and [StateStoreWriter](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala#L129) rely on the subclass implementations of [StateStoreCustomMetric](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L187).
If a new subclass of `StateStoreCustomMetric` is added, it requires code changes to `SymmetricHashJoinStateManager` and `StateStoreWriter`, and we may miss the update if there is no existing test coverage.
To prevent these issues add a couple of utility methods to `StateStoreCustomMetric` as mentioned above.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT and a new UT
Closes#32914 from vkorukanti/SPARK-35763.
Authored-by: Venki Korukanti <venki.korukanti@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This upgrade default Hadoop version from 3.2.1 to 3.3.1. The changes here are simply update the version number and dependency file.
### Why are the changes needed?
Hadoop 3.3.1 just came out, which comes with many client-side improvements such as for S3A/ABFS (20% faster when accessing S3). These are important for users who want to use Spark in a cloud environment.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Existing unit tests in Spark
- Manually tested using my S3 bucket for event log dir:
```
bin/spark-shell \
-c spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID \
-c spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY \
-c spark.eventLog.enabled=true
-c spark.eventLog.dir=s3a://<my-bucket>
```
- Manually tested against docker-based YARN dev cluster, by running `SparkPi`.
Closes#30135 from sunchao/SPARK-29250.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
It will not generate `tmpOutputFile`, `tmpErrOutputFile` and `sessionDirs` since [SPARK-35286](https://issues.apache.org/jira/browse/SPARK-35286). So we can remove `HiveClientImpl.closeState` to avoid these exceptions:
```
java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File
```
### Why are the changes needed?
1. Avoid incompatible exceptions.
2. Remove useless code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the GitHub Action
- Manual test：
Execute
```
mvn clean install -DskipTests -pl sql/hive -am
mvn test -pl sql/hive -DwildcardSuites=org.apache.spark.sql.hive.client.VersionsSuite -Dtest=none
```
**Before**
```
Run completed in 17 minutes, 18 seconds.
Total number of tests run: 867
Suites: completed 2, aborted 0
Tests: succeeded 867, failed 0, canceled 0, ignored 1, pending 0
All tests passed.
15:04:02.407 WARN org.apache.hadoop.hive.metastore.ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 2.3.0
15:04:02.408 WARN org.apache.hadoop.hive.metastore.ObjectStore: setMetaStoreSchemaVersion called but recording version is disabled: version = 2.3.0, comment = Set by MetaStore yangjie010.2.30.21
15:04:02.441 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database default, returning NoSuchObjectException
15:04:03.140 ERROR org.apache.spark.util.Utils: Uncaught exception in thread shutdown-hook-0
java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$closeState$1(HiveClientImpl.scala:168)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:312)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:243)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:242)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:292)
at org.apache.spark.sql.hive.client.HiveClientImpl.closeState(HiveClientImpl.scala:158)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$new$1(HiveClientImpl.scala:175)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1994)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
15:04:03.141 WARN org.apache.hadoop.util.ShutdownHookManager: ShutdownHook '$anon$2' failed, java.util.concurrent.ExecutionException: java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
java.util.concurrent.ExecutionException: java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
at java.util.concurrent.FutureTask.report(FutureTask.java:122)
at java.util.concurrent.FutureTask.get(FutureTask.java:206)
at org.apache.hadoop.util.ShutdownHookManager.executeShutdown(ShutdownHookManager.java:124)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:95)
Caused by: java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$closeState$1(HiveClientImpl.scala:168)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:312)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:243)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:242)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:292)
at org.apache.spark.sql.hive.client.HiveClientImpl.closeState(HiveClientImpl.scala:158)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$new$1(HiveClientImpl.scala:175)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1994)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
```
**After**
```
Run completed in 11 minutes, 41 seconds.
Total number of tests run: 867
Suites: completed 2, aborted 0
Tests: succeeded 867, failed 0, canceled 0, ignored 1, pending 0
All tests passed.
```
Closes#32693 from LuciferYang/SPARK-35556.
Lead-authored-by: YangJie <yangjie01@baidu.com>
Co-authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
Currently, `ResolveAggregateFunctions` is a complicated rule that recursively calls the entire analyzer to resolve aggregate functions in parent nodes of aggregate. It's kind of necessary as we need to do many things to identify the aggregate function and push it down to the aggregate node: resolve columns as if they are in the aggregate node, resolve functions, apply type coercion, etc. However, this is overly complicated and it's hard to fully understand how the resolution is done there. It also leads to hacks such as the [char/varchar hack](https://github.com/apache/spark/blob/v3.1.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2396-L2401), [subquery hack](https://github.com/apache/spark/blob/v3.1.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2274-L2277), [grouping function hack](https://github.com/apache/spark/blob/v3.1.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2465-L2467), etc.
This PR simplifies the `ResolveAggregateFunctions` rule and clarifies the resolution logic. To resolve aggregate functions/grouping columns in HAVING, ORDER BY and `df.where`, we expand the aggregate node below to output these required aggregate functions/grouping columns. In details, when resolving an expression from the parent of an aggregate node:
1. try to resolve columns with `agg.child` and wrap the result with `TempResolvedColumn`.
2. try to resolve subqueries with `agg.child`
3. if the expression is not resolved, return it and wait for other rules to resolve it, such as resolve functions, type coercions, etc.
4. if the expression is resolved, we transform it and push aggregate functions/grouping columns into the aggregate node below.
4.1 the expression may already present in `agg.aggregateExpressions`, we can simply replace the expression with attr ref.
4.2 if a `TempResolvedColumn` is neither inside an aggregate function, or wrap a grouping column, turn it back to an `UnresolvedAttribute`
5. after the main resolution batch, remove all `TempResolvedColumn` and turn them back to `UnresolvedAttribute`.
### Why are the changes needed?
Code cleanup
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing test
Closes#32470 from cloud-fan/agg2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to format year-month interval to strings using the start and end fields of `YearMonthIntervalType`.
### Why are the changes needed?
Currently, they are ignored, and any `YearMonthIntervalType` is formatted as `INTERVAL YEAR TO MONTH`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32924 from sarutak/year-month-interval-format.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR extends the parser rules to be able to parse the following types:
* INTERVAL YEAR
* INTERVAL YEAR TO MONTH
* INTERVAL MONTH
### Why are the changes needed?
For ANSI compliance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New assertion.
Closes#32922 from sarutak/parse-any-year-month.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Cache commonly occurring duplicate Some objects in SQLMetrics by using a Guava cache and reusing the existing Guava String Interner to avoid duplicate strings in JSONProtocol. Also with AccumulatorV2 we have seen lot of Some(-1L) and Some(0L) occurrences in a heap dump that is naively interned by having reusing a already constructed Some(-1L) and Some(0L)
To give some context on the impact and the garbage got accumulated, below are the details of the complex spark job which we troubleshooted and figured out the bottlenecks. **tl;dr - In short, major issues were the accumulation of duplicate objects mainly from SQLMetrics.**
Greater than 25% of the 40G driver heap filled with (a very large number of) **duplicate**, immutable objects.
1. Very large number of **duplicate** immutable objects.
- Type of metric is represented by `'scala.Some("sql")'` - which is created for each metric.
- Fixing this reduced memory usage from 4GB to a few bytes.
2. `scala.Some(0)` and `scala.Some(-1)` are very common metric values (typically to indicate absence of metric)
- Individually the values are all immutable, but spark sql was creating a new instance each time.
- Intern'ing these resulted in saving ~4.5GB for a 40G heap.
3. Using string interpolation for metric names.
- Interpolation results in creation of a new string object.
- We end up with a very large number of metric names - though the number of unique strings is miniscule.
- ~7.5 GB in the 40 GB heap : which went down to a few KB's when fixed.
### Why are the changes needed?
To reduce overall driver memory footprint which eventually reduces the Full GC pauses.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Since these are memory related optimizations, unit tests are not added. These changes are added in our internal platform which made it possible for one of the complex spark job continuously failing to succeed along with other set of optimizations.
Closes#32754 from venkata91/SPARK-35613.
Authored-by: Venkata krishnan Sowrirajan <vsowrirajan@linkedin.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
### What changes were proposed in this pull request?
This PR improves `Repartition` and `RepartitionByExpr` statistics estimation using child statistics.
### Why are the changes needed?
The current implementation will missing column stat. For example:
```sql
CREATE TABLE t1 USING parquet AS SELECT id % 10 AS key FROM range(100);
ANALYZE TABLE t1 COMPUTE STATISTICS FOR ALL COLUMNS;
set spark.sql.cbo.enabled=true;
EXPLAIN COST SELECT key FROM (SELECT key FROM t1 DISTRIBUTE BY key) t GROUP BY key;
```
Before this PR:
```
== Optimized Logical Plan ==
Aggregate [key#2950L], [key#2950L], Statistics(sizeInBytes=1600.0 B)
+- RepartitionByExpression [key#2950L], Statistics(sizeInBytes=1600.0 B, rowCount=100)
+- Relation default.t1[key#2950L] parquet, Statistics(sizeInBytes=1600.0 B, rowCount=100)
```
After this PR:
```
== Optimized Logical Plan ==
Aggregate [key#2950L], [key#2950L], Statistics(sizeInBytes=160.0 B, rowCount=10)
+- RepartitionByExpression [key#2950L], Statistics(sizeInBytes=1600.0 B, rowCount=100)
+- Relation default.t1[key#2950L] parquet, Statistics(sizeInBytes=1600.0 B, rowCount=100)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32309 from wangyum/SPARK-35203.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/31964
We should only quote the column name when nested column predicate pushdown is enabled, otherwise the data source side may not have the logic to parse the quoted column name and fail. This is not a problem before #31964 , as we don't quote the column name if there is no dot in the name. But #31964 changed it.
### Why are the changes needed?
fix a query failure
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new test
Closes#32807 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is to fix the maximal allowed number of rows check in `BroadcastExchangeExec`. After https://github.com/apache/spark/pull/27828, the max number of rows is calculated based on max capacity of `BytesToBytesMap` (previous value before the PR is 512000000). This calculation is not accurate as only `UnsafeHashedRelation` is using `BytesToBytesMap`. `LongHashedRelation` (used for broadcast join on key with long data type) has limit of [512000000](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala#L584), and `BroadcastNestedLoopJoinExec` is not depending on `HashedRelation` at all.
The change is to only specialize the max rows limit when needed. Keep other broadcast case with the previous limit - 512000000.
### Why are the changes needed?
Fix code logic and avoid unexpected behavior.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#32911 from c21/broadcast.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Remove commons-httpclient as a direct dependency for Hadoop-3.2 profile.
Hadoop-2.7 profile distribution still has it, hadoop-client has a compile dependency on commons-httpclient, thus we cannot remove it for Hadoop-2.7 profile.
```
[INFO] +- org.apache.hadoop:hadoop-client:jar:2.7.4:compile
[INFO] | +- org.apache.hadoop:hadoop-common:jar:2.7.4:compile
[INFO] | | +- commons-cli:commons-cli:jar:1.2:compile
[INFO] | | +- xmlenc:xmlenc:jar:0.52:compile
[INFO] | | +- commons-httpclient:commons-httpclient:jar:3.1:compile
```
### Why are the changes needed?
Spark is pulling in commons-httpclient as a dependency directly. commons-httpclient went EOL years ago and there are most likely CVEs not being reported against it, thus we should remove it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Existing unittests
- Checked the dependency tree before and after introducing the changes
Before:
```
./build/mvn dependency:tree -Phadoop-3.2 | grep -i "commons-httpclient"
Using `mvn` from path: /usr/bin/mvn
[INFO] +- commons-httpclient:commons-httpclient:jar:3.1:compile
[INFO] | +- commons-httpclient:commons-httpclient:jar:3.1:provided
```
After
```
./build/mvn dependency:tree | grep -i "commons-httpclient"
Using `mvn` from path: /Users/sumeet.gajjar/cloudera/upstream-spark/build/apache-maven-3.6.3/bin/mvn
```
P.S. Reopening this since [spark upgraded](463daabd5a) its `hive.version` to `2.3.9` which does not have a dependency on `commons-httpclient`.
Closes#32912 from sumeetgajjar/SPARK-35429.
Authored-by: Sumeet Gajjar <sumeetgajjar93@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Extend `YearMonthIntervalType` to support interval fields. Valid interval field values:
- 0 (YEAR)
- 1 (MONTH)
After the changes, the following year-month interval types are supported:
1. `YearMonthIntervalType(0, 0)` or `YearMonthIntervalType(YEAR, YEAR)`
2. `YearMonthIntervalType(0, 1)` or `YearMonthIntervalType(YEAR, MONTH)`. **It is the default one**.
3. `YearMonthIntervalType(1, 1)` or `YearMonthIntervalType(MONTH, MONTH)`
Closes#32825
### Why are the changes needed?
In the current implementation, Spark supports only `interval year to month` but the SQL standard allows to specify the start and end fields. The changes will allow to follow ANSI SQL standard more precisely.
### Does this PR introduce _any_ user-facing change?
Yes but `YearMonthIntervalType` has not been released yet.
### How was this patch tested?
By existing test suites.
Closes#32909 from MaxGekk/add-fields-to-YearMonthIntervalType.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
`CoalesceExec` needlessly calls `child.execute` twice when it could just call it once and re-use the results. This only happens when `numPartitions == 1`.
### Why are the changes needed?
It is more efficient to execute the child plan once rather than twice.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
There are no functional changes. This is just a performance optimization, so the existing tests should be sufficient to catch any regressions.
Closes#32920 from andygrove/coalesce-exec-executes-twice.
Authored-by: Andy Grove <andygrove73@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add a new function to support construct YearMonthIntervalType from integral fields
### Why are the changes needed?
Add a new function to support construct YearMonthIntervalType from integral fields
### Does this PR introduce _any_ user-facing change?
Yea user can use `make_ym_interval` to construct TearMonthIntervalType from years/months integral fields
### How was this patch tested?
Added UT
Closes#32645 from AngersZhuuuu/SPARK-35129.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Currently, the file CastSuite.scala becomes big: 2000 lines, 2 base classes, 4 test suites.
In my previous work of Timestamp without time zone, I planned to put new test cases in CastSuiteBase, but they were accidentally added in AnsiCastSuiteBase.
This PR is to break the file down into 3 files. It also moves the test cases about timestamp without time zone to the right base class.
### Why are the changes needed?
Make development and review easier.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#32918 from gengliangwang/refactorCastSuite.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Extended `RemoveRedundantAggregates` to remove deduplicating aggregations before aggregations that ignore duplicates.
### Why are the changes needed?
Performance imporovement.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Extending existing UT
Closes#32904 from tanelk/SPARK-33122_followup2_distinct_agg.
Authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/core/src/main/scala/org/apache/spark/sql/execution/streaming`.
### Why are the changes needed?
It will largely help with standardization of error messages and its maintenance.
### Does this PR introduce _any_ user-facing change?
No. Error messages remain unchanged.
### How was this patch tested?
No new tests - pass all original tests to make sure it doesn't break any existing behavior.
Closes#32880 from beliefer/SPARK-35056.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to override the typeName() method in TimestampWithoutTZType, and assign it a name according to the ANSI SQL standard
### Why are the changes needed?
To improve Spark SQL user experience, and have readable types in error messages.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32915 from gengliangwang/typename.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Currently, there are some expressions that overwrite `semanticEquals`, which makes it not symmetrical. Ideally, expressions should overwrite `canonicalized` instead of `semanticEquals`.
This PR marks `semanticEquals` as final, and implement `canonicalized` for the few expressions that overwrote `semanticEquals` before.
### Why are the changes needed?
To avoid subtle bugs (I haven't found a real bug yet).
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
a new test
Closes#32885 from cloud-fan/attr.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup PR for SPARK-35736(#32893) and SPARK-35737(#32892).
This PR moves a common logic to `object DayTimeIntervalType`.
That logic is like `val strToFieldIndex = DayTimeIntervalType.dayTimeFields.map(i => DayTimeIntervalType.fieldToString(i) -> (i).toMap`, a `Map` which maps each time unit to the corresponding day-time field index.
### Why are the changes needed?
That logic appeared in the change in SPARK-35736 and SPARK-35737 so it can be a common logic and it's better to avoid the similar logic scattered.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32905 from sarutak/followup-SPARK-35736-35737.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes `StreamingJoinHelper` to be able to handle day-time interval.
### Why are the changes needed?
In the current master, `StreamingJoinHelper.getStateValueWatermark` can't handle conditions which contain day-time interval literals.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New assertions added to `StreamingJoinHlelperSuite`.
Closes#32896 from sarutak/streamingjoinhelper-daytime.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR add a feature which parse day-time interval literals to tightest type.
### Why are the changes needed?
To comply with the ANSI behavior.
For example, `INTERVAL '10 20:30' DAY TO MINUTE` should be parsed as `DayTimeIntervalType(DAY, MINUTE)` but not as `DayTimeIntervalType(DAY, SECOND)`.
### Does this PR introduce _any_ user-facing change?
No because `DayTimeIntervalType` will be introduced in `3.2.0`.
### How was this patch tested?
New tests.
Closes#32892 from sarutak/tight-daytime-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR adda a feature which allow the parser parse any day-time interval types in SQL.
### Why are the changes needed?
To comply with ANSI standard, we additionally need to support the following types.
* INTERVAL DAY
* INTERVAL DAY TO HOUR
* INTERVAL DAY TO MINUTE
* INTERVAL HOUR
* INTERVAL HOUR TO MINUTE
* INTERVAL HOUR TO SECOND
* INTERVAL MINUTE
* INTERVAL MINUTE TO SECOND
* INTERVAL SECOND
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New tests.
Closes#32893 from sarutak/parse-any-day-time.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
1. Extend the Cast expression and support TimestampType in casting to TimestampWithoutTZType.
2. There was a mistake in casting TimestampWithoutTZType as TimestampType in https://github.com/apache/spark/pull/32864. The target value should be `sourceValue - timeZoneOffset` instead of being the same value.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32878 from gengliangwang/timestampToTimestampWithoutTZ.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Using copy-on-write for `SQLConf.sqlConfEntries` and `SQLConf.staticConfKeys` to reduce contention in concurrent workloads.
### Why are the changes needed?
The global locks used to protect `SQLConf.sqlConfEntries` map and the `SQLConf.staticConfKeys` set can cause significant contention on the `SQLConf` instance in a concurrent setting.
Using copy-on-write versions should reduce the contention given that modifications to the configs are relatively rare.
Closes#32865 from haiyangsun-db/SPARK-35701.
Authored-by: Haiyang Sun <haiyang.sun@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR add a feature which formats day-time interval to strings using the start and end fields of `DayTimeIntervalType`.
### Why are the changes needed?
Currently, they are ignored, and any `DayTimeIntervalType` is formatted as `INTERVAL DAY TO SECOND.`
### Does this PR introduce _any_ user-facing change?
Yes. The format of day-time intervals is determined the start and end fields.
### How was this patch tested?
New test.
Closes#32891 from sarutak/interval-format.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Instantiate a new Hive client through `Hive.getWithoutRegisterFns(conf, false)` instead of `Hive.get(conf)`, if `Hive` version is >= '2.3.9' (the built-in version).
### Why are the changes needed?
[HIVE-10319](https://issues.apache.org/jira/browse/HIVE-10319) introduced a new API `get_all_functions` which is only supported in Hive 1.3.0/2.0.0 and up. As result, when Spark 3.x talks to a HMS service of version 1.2 or lower, the following error will occur:
```
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.thrift.TApplicationException: Invalid method name: 'get_all_functions'
at org.apache.hadoop.hive.ql.metadata.Hive.getAllFunctions(Hive.java:3897)
at org.apache.hadoop.hive.ql.metadata.Hive.reloadFunctions(Hive.java:248)
at org.apache.hadoop.hive.ql.metadata.Hive.registerAllFunctionsOnce(Hive.java:231)
... 96 more
Caused by: org.apache.thrift.TApplicationException: Invalid method name: 'get_all_functions'
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.recv_get_all_functions(ThriftHiveMetastore.java:3845)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.get_all_functions(ThriftHiveMetastore.java:3833)
```
The `get_all_functions` is called only when `doRegisterAllFns` is set to true:
```java
private Hive(HiveConf c, boolean doRegisterAllFns) throws HiveException {
conf = c;
if (doRegisterAllFns) {
registerAllFunctionsOnce();
}
}
```
what this does is to register all Hive permanent functions defined in HMS in Hive's `FunctionRegistry` class, via iterating through results from `get_all_functions`. To Spark, this seems unnecessary as it loads Hive permanent (not built-in) UDF via directly calling the HMS API, i.e., `get_function`. The `FunctionRegistry` is only used in loading Hive's built-in function that is not supported by Spark. At this time, it only applies to `histogram_numeric`.
[HIVE-21563](https://issues.apache.org/jira/browse/HIVE-21563) introduced a new API `getWithoutRegisterFns` which skips the above registration and is available in Hive 2.3.9. Therefore, Spark should adopt it to avoid the cost.
### Does this PR introduce _any_ user-facing change?
Yes with this fix Spark now should be able to talk to HMS server with Hive 1.2.x and lower.
### How was this patch tested?
Manually started a HMS server of Hive version 1.2.2. Without the PR it failed with the above exception. With the PR the error disappeared and I can successfully perform common operations such as create table, create database, list tables, etc.
Closes#32887 from sunchao/SPARK-35321-new.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
This patch adds log warn when `keyWithIndexToValue` returns null value in `SymmetricHashJoinStateManager`.
### Why are the changes needed?
Once we get null from state store in SymmetricHashJoinStateManager, it is better to add meaningful logging for the case. It is better for debugging.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32828 from viirya/fix-ss-joinstatemanager-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR is used to do add a UT to check if user-defined cached batch are completely released when clearCache called.
### Why are the changes needed?
Add a new UT file RefCountedTestCachedBatchSerializerSuite.scala
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT is added, org.apache.spark.sql.execution.columnar.RefCountedTestCachedBatchSerializerSuite
Closes#32717 from xuechendi/support_manual_close_in_InMemoryRelation.
Authored-by: Chendi Xue <chendi.xue@intel.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Extend DayTimeIntervalType to support interval fields. Valid interval field values:
- 0 (DAY)
- 1 (HOUR)
- 2 (MINUTE)
- 3 (SECOND)
After the changes, the following day-time interval types are supported:
1. `DayTimeIntervalType(0, 0)` or `DayTimeIntervalType(DAY, DAY)`
2. `DayTimeIntervalType(0, 1)` or `DayTimeIntervalType(DAY, HOUR)`
3. `DayTimeIntervalType(0, 2)` or `DayTimeIntervalType(DAY, MINUTE)`
4. `DayTimeIntervalType(0, 3)` or `DayTimeIntervalType(DAY, SECOND)`. **It is the default one**. The second fraction precision is microseconds.
5. `DayTimeIntervalType(1, 1)` or `DayTimeIntervalType(HOUR, HOUR)`
6. `DayTimeIntervalType(1, 2)` or `DayTimeIntervalType(HOUR, MINUTE)`
7. `DayTimeIntervalType(1, 3)` or `DayTimeIntervalType(HOUR, SECOND)`
8. `DayTimeIntervalType(2, 2)` or `DayTimeIntervalType(MINUTE, MINUTE)`
9. `DayTimeIntervalType(2, 3)` or `DayTimeIntervalType(MINUTE, SECOND)`
10. `DayTimeIntervalType(3, 3)` or `DayTimeIntervalType(SECOND, SECOND)`
### Why are the changes needed?
In the current implementation, Spark supports only `interval day to second` but the SQL standard allows to specify the start and end fields. The changes will allow to follow ANSI SQL standard more precisely.
### Does this PR introduce _any_ user-facing change?
Yes but `DayTimeIntervalType` has not been released yet.
### How was this patch tested?
By existing test suites.
Closes#32849 from MaxGekk/day-time-interval-type-units.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Collect observed metrics from cached and adaptive execution sub-trees.
### Why are the changes needed?
Currently persisting/caching will hide all observed metrics in that sub-tree from reaching the `QueryExecutionListeners`. Adaptive query execution can also hide the metrics from reaching `QueryExecutionListeners`.
### Does this PR introduce _any_ user-facing change?
Bugfix
### How was this patch tested?
New UTs
Closes#32862 from tanelk/SPARK-35695_collect_metrics_persist.
Lead-authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Co-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The STRUCT type syntax is defined like this:
STRUCT(fieldNmae: fileType [NOT NULL][COMMENT stringLiteral][,.....])
So the field list is nearly the same as a column list
if we could make ':' optional it would be so much cleaner an less proprietary
### Why are the changes needed?
ease of use
### Does this PR introduce _any_ user-facing change?
Yes, you can use Struct type list is nearly the same as a column list
### How was this patch tested?
unit tests
Closes#32858 from jerqi/master.
Authored-by: RoryQi <1242949407@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
It seems like spark inner join is performing a cartesian join in self joining using `joinWith`
To produce this issues:
```
val df = spark.range(0,3)
df.joinWith(df, df("id") === df("id")).show()
```
Before this pull request, the result is
+---+---+
| _1 | _2 |
+---+---+
| 0 | 0 |
| 0 | 1 |
| 0 | 2 |
| 1 | 0 |
| 1 | 1 |
| 1 | 2 |
| 2 | 0 |
| 2 | 1 |
| 2 | 2 |
+---+---+
The expected result is
+---+---+
| _1 | _2 |
+---+---+
| 0 | 0 |
| 1 | 1 |
| 2 | 2 |
+---+---+
### Why are the changes needed?
correctness
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
add test
Closes#32863 from dgd-contributor/SPARK-35652_join_and_joinWith_in_seft_joining.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of #32586. We introduced `ExpressionContainmentOrdering` to sort common expressions according to their parent-child relations. For unrelated expressions, previously the ordering returns -1 which is not correct and can possibly lead to transitivity issue.
### Why are the changes needed?
To fix the possible transitivity issue of `ExpressionContainmentOrdering`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#32870 from viirya/SPARK-35439-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Extend the Cast expression and support DateType in casting to TimestampWithoutTZType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32873 from gengliangwang/dateToTswtz.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Remove duplicated code in the form of `readXXX` in `VectorizedRleValuesReader`. For instance:
```java
public void readIntegers(
int total,
WritableColumnVector c,
int rowId,
int level,
VectorizedValuesReader data) throws IOException {
int left = total;
while (left > 0) {
if (this.currentCount == 0) this.readNextGroup();
int n = Math.min(left, this.currentCount);
switch (mode) {
case RLE:
if (currentValue == level) {
data.readIntegers(n, c, rowId);
} else {
c.putNulls(rowId, n);
}
break;
case PACKED:
for (int i = 0; i < n; ++i) {
if (currentBuffer[currentBufferIdx++] == level) {
c.putInt(rowId + i, data.readInteger());
} else {
c.putNull(rowId + i);
}
}
break;
}
rowId += n;
left -= n;
currentCount -= n;
}
}
```
and replace with:
```java
public void readBatch(
int total,
int offset,
WritableColumnVector values,
int maxDefinitionLevel,
VectorizedValuesReader valueReader,
ParquetVectorUpdater updater) throws IOException {
int left = total;
while (left > 0) {
if (this.currentCount == 0) this.readNextGroup();
int n = Math.min(left, this.currentCount);
switch (mode) {
case RLE:
if (currentValue == maxDefinitionLevel) {
updater.updateBatch(n, offset, values, valueReader);
} else {
values.putNulls(offset, n);
}
break;
case PACKED:
for (int i = 0; i < n; ++i) {
if (currentBuffer[currentBufferIdx++] == maxDefinitionLevel) {
updater.update(offset + i, values, valueReader);
} else {
values.putNull(offset + i);
}
}
break;
}
offset += n;
left -= n;
currentCount -= n;
}
}
```
where the `ParquetVectorUpdater` is type specific, and has different implementations under `updateBatch` and `update`. Together, this also changes code paths handling timestamp types to use the batch read API for decoding definition levels.
2. Similar to the above, this removes code duplication in `VectorizedColumnReader.decodeDictionaryIds`. Now different implementations are under `ParquetVectorUpdater.decodeSingleDictionaryId`.
### Why are the changes needed?
`VectorizedRleValuesReader` and `VectorizedColumnReader` are becoming increasingly harder to maintain, as any change touches the above logic **will need to be replicated in 20+ places**. The issue becomes even more serious when we are going to implement column index (for instance, see how the change [here](https://github.com/apache/spark/pull/32753/files#diff-a01e174e178366aadf07f64ee690d47d343b2ca416a4a2b2ea735887c22d5934R191) has to be replicated multiple times) and complex type support (in progress) for the vectorized path.
In addition, currently dictionary decoding (see `VectorizedColumnReader.decodeDictionaryIds`) and non-dictionary decoding are handled separately, and therefore the same (very complicated) branching logic based on input Spark & Parquet types have to be replicated in two places, which is another burden for code maintenance.
The original intention is for performance. However these days JIT compilers tend to be very effective on this and will inline virtual calls aggressively to eliminate the method invocation costs (see [this](https://shipilev.net/blog/2015/black-magic-method-dispatch/) and [this](http://insightfullogic.com/blog/2014/may/12/fast-and-megamorphic-what-influences-method-invoca/)). I've also done benchmarks using a modified `DataSourceReadBenchmark` and `DateTimeRebaseBenchmark` and the result is almost exact the same before and after the change. The results can be found [here](https://gist.github.com/sunchao/674afbf942ccc2370bdcfa33efb4471c), and [here's](https://github.com/sunchao/spark/tree/parquet-refactor) the source code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32777 from sunchao/SPARK-35640.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This pr upgrades built-in Hive to 2.3.9. Hive 2.3.9 changes:
- [HIVE-17155] - findConfFile() in HiveConf.java has some issues with the conf path
- [HIVE-24797] - Disable validate default values when parsing Avro schemas
- [HIVE-24608] - Switch back to get_table in HMS client for Hive 2.3.x
- [HIVE-21200] - Vectorization: date column throwing java.lang.UnsupportedOperationException for parquet
- [HIVE-21563] - Improve Table#getEmptyTable performance by disabling registerAllFunctionsOnce
- [HIVE-19228] - Remove commons-httpclient 3.x usage
### Why are the changes needed?
Fix regression caused by AVRO-2035.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32750 from wangyum/SPARK-34512.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Extend the Cast expression and support TimestampWithoutTZType in casting to DateType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32869 from gengliangwang/castToDate.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `Dataset.observe` doesn't work if `CollectMetricsExec` in a task handles multiple partitions.
If `coalesce` follows `observe` and the number of partitions shrinks after `coalesce`, `CollectMetricsExec` can handle multiple partitions in a task.
### Why are the changes needed?
The current implementation of `CollectMetricsExec` doesn't consider the case it can handle multiple partitions.
Because new `updater` is created for each partition even though those partitions belong to the same task, `collector.setState(updater)` raise an assertion error.
This is a simple reproducible example.
```
$ bin/spark-shell --master "local[1]"
scala> spark.range(1, 4, 1, 3).observe("my_event", count($"id").as("count_val")).coalesce(2).collect
```
```
java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:208)
at org.apache.spark.sql.execution.AggregatingAccumulator.setState(AggregatingAccumulator.scala:204)
at org.apache.spark.sql.execution.CollectMetricsExec.$anonfun$doExecute$2(CollectMetricsExec.scala:72)
at org.apache.spark.sql.execution.CollectMetricsExec.$anonfun$doExecute$2$adapted(CollectMetricsExec.scala:71)
at org.apache.spark.TaskContext$$anon$1.onTaskCompletion(TaskContext.scala:125)
at org.apache.spark.TaskContextImpl.$anonfun$markTaskCompleted$1(TaskContextImpl.scala:124)
at org.apache.spark.TaskContextImpl.$anonfun$markTaskCompleted$1$adapted(TaskContextImpl.scala:124)
at org.apache.spark.TaskContextImpl.$anonfun$invokeListeners$1(TaskContextImpl.scala:137)
at org.apache.spark.TaskContextImpl.$anonfun$invokeListeners$1$adapted(TaskContextImpl.scala:135)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32786 from sarutak/fix-collectmetricsexec.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Extend the Cast expression and support TimestampWithoutTZType in casting to TimestampType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32864 from gengliangwang/castToTimestamp.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
#31637 removed the usage of `CheckAnalysis.checkAlterTablePartition` but didn't remove the function.
### Why are the changes needed?
To removed an unused function.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32855 from imback82/SPARK-34524-followup.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use `UnresolvedHint.resolved = child.resolved` instead `UnresolvedHint.resolved = false`, then the plan contains `UnresolvedHint` child can be optimized by rule in batch `Resolution`.
For instance, before this pr, the following plan can't be optimized by `ResolveReferences`.
```
!'Project [*]
+- SubqueryAlias __auto_generated_subquery_name
+- UnresolvedHint use_hash
+- Project [42 AS 42#10]
+- OneRowRelation
```
### Why are the changes needed?
fix hint in subquery bug
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32841 from cfmcgrady/SPARK-35673.
Authored-by: Fu Chen <cfmcgrady@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### Why are the changes needed?
With Long.minValue cast to an instant, secs will be floored in function microsToInstant and cause overflow when multiply with Micros_per_second
```
def microsToInstant(micros: Long): Instant = {
val secs = Math.floorDiv(micros, MICROS_PER_SECOND)
// Unfolded Math.floorMod(us, MICROS_PER_SECOND) to reuse the result of
// the above calculation of `secs` via `floorDiv`.
val mos = micros - secs * MICROS_PER_SECOND <- it will overflow here
Instant.ofEpochSecond(secs, mos * NANOS_PER_MICROS)
}
```
But the overflow is acceptable because it won't produce any change to the result
However, when convert the instant back to micro value, it will raise Overflow Error
```
def instantToMicros(instant: Instant): Long = {
val us = Math.multiplyExact(instant.getEpochSecond, MICROS_PER_SECOND) <- It overflow here
val result = Math.addExact(us, NANOSECONDS.toMicros(instant.getNano))
result
}
```
Code to reproduce this error
```
instantToMicros(microToInstant(Long.MinValue))
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Test added
Closes#32839 from dgd-contributor/SPARK-35679_instantToMicro.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Add `PartitioningCollection` in EnsureRequirements during remove shuffle.
### Why are the changes needed?
Currently `EnsureRequirements` only check if child has semantic equal `HashPartitioning` and remove
redundant shuffle. We can enhance this case using `PartitioningCollection`.
### Does this PR introduce _any_ user-facing change?
Yes, plan might be changed.
### How was this patch tested?
Add test.
Closes#32815 from ulysses-you/shuffle-node.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
Add an `internalRegisterFunction` for the built-in function registry. So that
we can skip the unnecessary function normalization.
### Why are the changes needed?
small refactor
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing ut
Closes#32842 from linhongliu-db/function-refactor.
Lead-authored-by: Linhong Liu <linhong.liu@databricks.com>
Co-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR changes an occurrence of `Seq` to `collections.Seq` in `NestedColumnAliasing`.
### Why are the changes needed?
In the current master, `NestedColumnAliasing` doesn't work with Scala 2.13 and the relevant tests fail.
The following are examples.
* `NestedColumnAliasingSuite`
* Subclasses of `SchemaPruningSuite`
* `ColumnPruningSuite`
```
NestedColumnAliasingSuite:
[info] - Pushing a single nested field projection *** FAILED *** (14 milliseconds)
[info] scala.MatchError: (none#211451,ArrayBuffer(name#211451.middle)) (of class scala.Tuple2)
[info] at org.apache.spark.sql.catalyst.optimizer.NestedColumnAliasing$.$anonfun$getAttributeToExtractValues$5(NestedColumnAliasing.scala:258)
[info] at scala.collection.StrictOptimizedMapOps.flatMap(StrictOptimizedMapOps.scala:31)
[info] at scala.collection.StrictOptimizedMapOps.flatMap$(StrictOptimizedMapOps.scala:30)
[info] at scala.collection.immutable.HashMap.flatMap(HashMap.scala:39)
[info] at org.apache.spark.sql.catalyst.optimizer.NestedColumnAliasing$.getAttributeToExtractValues(NestedColumnAliasing.scala:258)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Ran tests mentioned above and all passed with Scala 2.13.
Closes#32848 from sarutak/followup-SPARK-35194-2.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the examples of `rand` and `randn`.
### Why are the changes needed?
SPARK-23643 (#20793) fixes an issue which is related to the seed and it causes the result of `rand` and `randn`.
Now the results of `SELECT rand(0)` and `SELECT randn((null)` are `0.7604953758285915` and `1.6034991609278433` respectively, and they should be deterministic because the number of partitions are always 1 (the leaf node is `OneRowRelation`).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Built the doc and confirmed it.
Closes#32844 from sarutak/rand-example.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Extend the Cast expression and support TimestampWithoutTZType in casting to StringType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32846 from gengliangwang/tswtzToString.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR adds support for lateral subqueries. A lateral subquery is a subquery preceded by the `LATERAL` keyword in the FROM clause of a query that can reference columns in the preceding FROM items. For example:
```sql
SELECT * FROM t1, LATERAL (SELECT * FROM t2 WHERE t1.a = t2.c)
```
A new subquery expression`LateralSubquery` is used to represent a lateral subquery. It is similar to `ScalarSubquery` but can return multiple rows and columns. A new logical unary node `LateralJoin` is used to represent a lateral join.
Here is the analyzed plan for the above query:
```scala
Project [a, b, c, d]
+- LateralJoin lateral-subquery [a], Inner
: +- Project [c, d]
: +- Filter (outer(a) = c)
: +- Relation [c, d]
+- Relation [a, b]
```
Similar to a correlated subquery, a lateral subquery can be viewed as a dependent (nested loop) join where the evaluation of the right subtree depends on the current value of the left subtree. The same technique to decorrelate a subquery is used to decorrelate a lateral join:
```scala
Project [a, b, c, d]
+- LateralJoin lateral-subquery [a && a = c], Inner // pull up correlated predicates as join conditions
: +- Project [c, d]
: +- Relation [c, d]
+- Relation [a, b]
```
Then the lateral join can be rewritten into a normal join:
```scala
Join Inner (a = c)
:- Relation [a, b]
+- Relation [c, d]
```
#### Follow-ups:
1. Similar to rewriting correlated scalar subqueries, rewriting lateral joins is also subject to the COUNT bug (See SPARK-15370 for more details). This is **not** handled in the current PR as it requires a sizeable amount of refactoring. It will be addressed in a subsequent PR (SPARK-35551).
2. Currently Spark does use outer query references to resolve star expressions in subqueries. This is not lateral subquery specific and can be handled in a separate PR (SPARK-35618)
### Why are the changes needed?
To support an ANSI SQL feature.
### Does this PR introduce _any_ user-facing change?
Yes. It allows users to use lateral subqueries in the FROM clause of a query.
### How was this patch tested?
- Parser test: `PlanParserSuite.scala`
- Analyzer test: `ResolveSubquerySuite.scala`
- Optimizer test: `PullupCorrelatedPredicatesSuite.scala`
- SQL test: `join-lateral.sql`, `postgreSQL/join.sql`
Closes#32303 from allisonwang-db/spark-34382-lateral.
Lead-authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `TimestampWithoutTZType` to `DataTypeTestUtils.ordered`/`atomicTypes`, and implement values generation of those types in `LiteralGenerator`/`RandomDataGenerator`. In this way, the types will be tested automatically in:
1. ArithmeticExpressionSuite:
- "function least"
- "function greatest"
2. PredicateSuite
- "BinaryComparison consistency check"
- "AND, OR, EqualTo, EqualNullSafe consistency check"
3. ConditionalExpressionSuite
- "if"
4. RandomDataGeneratorSuite
- "Basic types"
5. CastSuite
- "null cast"
- "up-cast"
- "SPARK-27671: cast from nested null type in struct"
6. OrderingSuite
- "GenerateOrdering with TimestampWithoutTZType"
7. PredicateSuite
- "IN with different types"
8. UnsafeRowSuite
- "calling get(ordinal, datatype) on null columns"
9. SortSuite
- "sorting on TimestampWithoutTZType ..."
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suites.
Closes#32843 from gengliangwang/atomicTest.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The changed [unit test](https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingJoinSuite.scala#L566) was introduce in https://github.com/apache/spark/pull/21587, to fix the planner side of thing for stream-stream join. Ideally check the query result should catch the bug, but it would be better to add plan check to make the purpose of unit test more clearly and catch future bug from planner change.
### Why are the changes needed?
Improve unit test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Changed test itself.
Closes#32836 from c21/ss-test.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Handle type coercion when resolving V2 function. In particular:
- prior to evaluating function arguments, insert cast whenever the argument type doesn't match the expected input type.
- use `BoundFunction.inputTypes()` to lookup magic method for scalar function
### Why are the changes needed?
For V2 functions, the actual argument types should not necessarily match those of the input types, and Spark should handle type coercion whenever it is needed.
### Does this PR introduce _any_ user-facing change?
Yes. Now V2 function resolution should be able to handle type coercion properly.
### How was this patch tested?
Added a few new tests.
Closes#32764 from sunchao/SPARK-35390.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR enhances `RepartitionByExpression` to make it coalesce partitions efficiently by AQE. Usually used to merge small files.
The basic logic is: Spark first tries to coalesce partitions, if it cannot be coalesced, then use the local shuffle reader to read data to avoid exchange the data over the network.
Usage:
```sql
SELECT /*+ REPARTITION */ * FROM t
```
```scala
df.repartition()
```
For example:
coalesce small output files | local shuffle reader
--- | ---
| 
### Why are the changes needed?
Coalesce partitions efficiently.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32781 from wangyum/SPARK-35650.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Write tests for timestamp without time zone in UDF as input parameters and results.
### Why are the changes needed?
It follows https://github.com/apache/spark/pull/31779 to improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#32840 from gengliangwang/tswtzUDF.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/hive/src/main/scala/org/apache/spark/sql/hive/client`.
### Why are the changes needed?
It will largely help with standardization of error messages and its maintenance.
### Does this PR introduce _any_ user-facing change?
No. Error messages remain unchanged.
### How was this patch tested?
No new tests - pass all original tests to make sure it doesn't break any existing behavior.
Closes#32763 from beliefer/SPARK-35058.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend Spark SQL API to accept `java.time.LocalDateTime` as an external type of recently added new Catalyst type - `TimestampWithoutTZ`. The Java class `java.time.LocalDateTime` has a similar semantic to ANSI SQL timestamp without timezone type, and it is the most suitable to be an external type for `TimestampWithoutTZType`. In more details:
* Added `TimestampWithoutTZConverter` which converts java.time.LocalDateTime instances to/from internal representation of the Catalyst type `TimestampWithoutTZType` (to Long type). The `TimestampWithoutTZConverter` object uses new methods of DateTimeUtils:
* localDateTimeToMicros() converts the input date time to the total length in microseconds.
* microsToLocalDateTime() obtains a java.time.LocalDateTime
* Support new type `TimestampWithoutTZType` in RowEncoder via the methods createDeserializerForLocalDateTime() and createSerializerForLocalDateTime().
* Extended the Literal API to construct literals from `java.time.LocalDateTime` instances.
### Why are the changes needed?
To allow users parallelization of `java.time.LocalDateTime` collections, and construct timestamp without time zone columns. Also to collect such columns back to the driver side.
### Does this PR introduce _any_ user-facing change?
The PR extends existing functionality. So, users can parallelize instances of the java.time.LocalDateTime class and collect them back.
```
scala> val ds = Seq(java.time.LocalDateTime.parse("1970-01-01T00:00:00")).toDS
ds: org.apache.spark.sql.Dataset[java.time.LocalDateTime] = [value: timestampwithouttz]
scala> ds.collect()
res0: Array[java.time.LocalDateTime] = Array(1970-01-01T00:00)
```
### How was this patch tested?
New unit tests
Closes#32814 from gengliangwang/LocalDateTime.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Move `assume` into methods at `PythonUDFSuite`.
### Why are the changes needed?
When we run Spark test with such command:
`./build/mvn -Phadoop-2.7 -Phive -Phive-thriftserver -Pyarn -Pkubernetes clean test`
get this exception:
```
PythonUDFSuite:
org.apache.spark.sql.execution.python.PythonUDFSuite *** ABORTED ***
java.lang.RuntimeException: Unable to load a Suite class that was discovered in the runpath: org.apache.spark.sql.execution.python.PythonUDFSuite
at org.scalatest.tools.DiscoverySuite$.getSuiteInstance(DiscoverySuite.scala:81)
at org.scalatest.tools.DiscoverySuite.$anonfun$nestedSuites$1(DiscoverySuite.scala:38)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
```
The test env has no PYSpark module so it failed.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
manual
Closes#32833 from ulysses-you/SPARK-35687.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The implementation for the save operation of RocksDBFileManager.
### Why are the changes needed?
Save all the files in the given local checkpoint directory as a committed version in DFS.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT added.
Closes#32582 from xuanyuanking/SPARK-35436.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Currently, Spark eagerly executes commands on the caller side of `QueryExecution`, which is a bit hacky as `QueryExecution` is not aware of it and leads to confusion.
For example, if you run `sql("show tables").collect()`, you will see two queries with identical query plans in the web UI.
The first query is triggered at `Dataset.logicalPlan`, which eagerly executes the command.
The second query is triggered at `Dataset.collect`, which is the normal query execution.
From the web UI, it's hard to tell that these two queries are caused by eager command execution.
This PR proposes to move the eager command execution to `QueryExecution`, and turn the command plan to `CommandResult` to indicate that command has been executed already. Now `sql("show tables").collect()` still triggers two queries, but the quey plans are not identical. The second query becomes:
In addition to the UI improvements, this PR also has other benefits:
1. Simplifies code as caller side no need to worry about eager command execution. `QueryExecution` takes care of it.
2. It helps https://github.com/apache/spark/pull/32442 , where there can be more plan nodes above commands, and we need to replace commands with something like local relation that produces unsafe rows.
### Why are the changes needed?
Explained above.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#32513 from beliefer/SPARK-35378.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Refactor LocalDateTimeUDT as YearUDT in UserDefinedTypeSuite
### Why are the changes needed?
As we are going to support java.time.LocalDateTime as an external type of TimestampWithoutTZ type https://github.com/apache/spark/pull/32814, registering java.time.LocalDateTime as UDT will cause test failures: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/139469/testReport/
This PR is to unblock https://github.com/apache/spark/pull/32814.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32824 from gengliangwang/UDTFollowUp.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR fixes an issue that both key and value of state schema cannot accept long length (>65535 bytes) JSON.
To solve the problem explained below, JSON represented schema is divided into chunks whose maximum length is 65535 bytes, and each chunk is written by `DataOutputStream.writeUTF`.
As the solution changes the format of the schema, the version is also changes from `1` to `2` but old version schema is still acceptable to ensures backward compatibility.
### Why are the changes needed?
In the current implementation, writing state schema fails if the length of schema exceeds 65535 bytes and `UTFDataFormatException` is thrown.
It's due to the limitation of `DataOutputStream.writeUTF`.
`writeUTF` writes a length field first and it's 2 bytes width, meaning the maximum content length is limited to `2^16-1`=`65535` bytes.
https://docs.oracle.com/javase/8/docs/api/java/io/DataOutputStream.html#writeUTF-java.lang.String-
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New tests.
Closes#32788 from sarutak/fix-UTFDataFormatException.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Fix Scala doc for removed parameters for `InvokeLike.invoke`.
### Why are the changes needed?
#32532 forgot to update the Scala doc after removing 2 parameters for `InvokeLike.invoke`. This fixes it.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#32827 from sunchao/SPARK-35384-followup.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch removes the usage of putting null into StateStore.
### Why are the changes needed?
According to `get` method doc in `StateStore` API, it returns non-null row if the key exists. So basically we should avoid write null to `StateStore`. You cannot distinguish if the returned null row is because the key doesn't exist, or the value is actually null. And due to the defined behavior of `get`, it is quite easy to cause NPE error if the caller doesn't expect to get a null if the caller believes the key exists.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added test.
Closes#32796 from viirya/fix-ss-joinstatemanager.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This patch introduces a new option to specify the minimum number of offsets to read per trigger i.e. minOffsetsPerTrigger and maxTriggerDelay to avoid the infinite wait for the trigger.
This new option will allow skipping trigger/batch when the number of records available in Kafka is low. This is a very useful feature in cases where we have a sudden burst of data at certain intervals in a day and data volume is low for the rest of the day.
'maxTriggerDelay' option will help to avoid cases of infinite delay in scheduling trigger and the trigger will happen irrespective of records available if the maxTriggerDelay time exceeds the last trigger. It would be an optional parameter with a default value of 15 mins. This option will be only applicable if minOffsetsPerTrigger is set.
minOffsetsPerTrigger option would be optional of course, but once specified it would take precedence over maxOffestsPerTrigger which will be honored only after minOffsetsPerTrigger is satisfied.
### Why are the changes needed?
There are many scenarios where there is a sudden burst of data at certain intervals in a day and data volume is low for the rest of the day. Tunning such jobs is difficult as decreasing trigger processing time increasing the number of batches and hence cluster resource usage and adds to small file issues. Increasing trigger processing time adds consumer lag. This patch tries to address this issue.
### How was this patch tested?
This patch was tested by adding test cases as well as manually on a cluster where the job was running for a full one day with a data burst happening once a day.
Here is the picture of databurst and hence consumer lag:
<img width="1198" alt="Screenshot 2021-04-29 at 11 39 35 PM" src="https://user-images.githubusercontent.com/1044003/116997587-9b2ab180-acfa-11eb-91fd-524802ce3316.png">
This is how the job behaved at burst time running every 4.5 mins (which is the specified trigger time):
<img width="1154" alt="Burst Time" src="https://user-images.githubusercontent.com/1044003/116997919-12f8dc00-acfb-11eb-9b0a-98387fc67560.png">
This is job behavior during the non-burst time where it is skipping 2 to 3 triggers and running once every 9 to 13.5 mins
<img width="1154" alt="Non Burst Time" src="https://user-images.githubusercontent.com/1044003/116998244-8b5f9d00-acfb-11eb-8340-33d47149ef81.png">
Here are some more stats from the two-run i.e. one normal run and the other with minOffsetsperTrigger set:
| Run | Data Size | Number of Batch Runs | Number of Files |
| ------------- | ------------- |------------- |------------- |
| Normal Run | 54.2 GB | 320 | 21968 |
| Run with minOffsetsperTrigger | 54.2 GB | 120 | 12104 |
Closes#32653 from satishgopalani/SPARK-35312.
Authored-by: Satish Gopalani <satish.gopalani@pubmatic.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Cleanup unreachable code.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existed test.
Closes#32791 from pan3793/cleanup.
Authored-by: Cheng Pan <379377944@qq.com>
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
Extend Catalyst's type system by a new type that conforms to the SQL standard (see SQL:2016, section 4.6.2): TimestampWithoutTZType represents the timestamp without time zone type
### Why are the changes needed?
Spark SQL today supports the TIMESTAMP data type. However the semantics provided actually match TIMESTAMP WITH LOCAL TIMEZONE as defined by Oracle. Timestamps embedded in a SQL query or passed through JDBC are presumed to be in session local timezone and cast to UTC before being processed.
These are desirable semantics in many cases, such as when dealing with calendars.
In many (more) other cases, such as when dealing with log files it is desirable that the provided timestamps not be altered.
SQL users expect that they can model either behavior and do so by using TIMESTAMP WITHOUT TIME ZONE for time zone insensitive data and TIMESTAMP WITH LOCAL TIME ZONE for time zone sensitive data.
Most traditional RDBMS map TIMESTAMP to TIMESTAMP WITHOUT TIME ZONE and will be surprised to see TIMESTAMP WITH LOCAL TIME ZONE, a feature that does not exist in the standard.
In this new feature, we will introduce TIMESTAMP WITH LOCAL TIMEZONE to describe the existing timestamp type and add TIMESTAMP WITHOUT TIME ZONE for standard semantic.
Using these two types will provide clarity.
This is a starting PR. See more details in https://issues.apache.org/jira/browse/SPARK-35662
### Does this PR introduce _any_ user-facing change?
Yes, a new data type for Timestamp without time zone type. It is still in development.
### How was this patch tested?
Unit test
Closes#32802 from gengliangwang/TimestampNTZType.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
It's a long-standing bug that we forgot to resolve `UnresolvedAlias` in `CollectMetrics`. It's a bit hard to trigger this bug before 3.2 as most likely people won't create `UnresolvedAlias` when calling `Dataset.observe`. However things have been changed after https://github.com/apache/spark/pull/30974
This PR proposes to handle `CollectMetrics` in the rule `ResolveAliases`.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
updated test
Closes#32803 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
