### What changes were proposed in this pull request?
According to the feedback from GitHub, the change causing memory issue has been rolled back. We can try to raise memory again for GA.
### Why are the changes needed?
Trying higher memory settings for GA. It could speed up the testing time.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
GA
Closes#33623 from viirya/increasing-mem-ga.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 7d13ac177b)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
When channel terminated will call `connectionTerminated` and remove corresponding StreamState,
then all coming request on this StreamState will throw NPE like
```
2021-07-31 22:00:24,810 ERROR server.ChunkFetchRequestHandler (ChunkFetchRequestHandler.java:lambda$respond$1(146)) - Error sending result ChunkFetchFailure[streamChunkId=StreamChunkId[streamId=1119950114515,chunkIndex=0],errorString=java.lang.NullPointerException
at org.apache.spark.network.server.OneForOneStreamManager.getChunk(OneForOneStreamManager.java:80)
at org.apache.spark.network.server.ChunkFetchRequestHandler.processFetchRequest(ChunkFetchRequestHandler.java:101)
at org.apache.spark.network.server.ChunkFetchRequestHandler.channelRead0(ChunkFetchRequestHandler.java:82)
at org.apache.spark.network.server.ChunkFetchRequestHandler.channelRead0(ChunkFetchRequestHandler.java:51)
at org.sparkproject.io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:99)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.access$600(AbstractChannelHandlerContext.java:61)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext$7.run(AbstractChannelHandlerContext.java:370)
at org.sparkproject.io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:164)
at org.sparkproject.io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:472)
at org.sparkproject.io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:500)
at org.sparkproject.io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:989)
at org.sparkproject.io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
at org.sparkproject.io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
at java.lang.Thread.run(Thread.java:748)
] to /ip:53818; closing connection
java.nio.channels.ClosedChannelException
at org.sparkproject.io.netty.channel.AbstractChannel$AbstractUnsafe.newClosedChannelException(AbstractChannel.java:957)
at org.sparkproject.io.netty.channel.AbstractChannel$AbstractUnsafe.write(AbstractChannel.java:865)
at org.sparkproject.io.netty.channel.DefaultChannelPipeline$HeadContext.write(DefaultChannelPipeline.java:1367)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.invokeWrite0(AbstractChannelHandlerContext.java:717)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.invokeWrite(AbstractChannelHandlerContext.java:709)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.write(AbstractChannelHandlerContext.java:792)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.write(AbstractChannelHandlerContext.java:702)
at org.sparkproject.io.netty.handler.codec.MessageToMessageEncoder.write(MessageToMessageEncoder.java:112)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.invokeWrite0(AbstractChannelHandlerContext.java:717)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.invokeWrite(AbstractChannelHandlerContext.java:709)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.write(AbstractChannelHandlerContext.java:792)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.write(AbstractChannelHandlerContext.java:702)
at org.sparkproject.io.netty.handler.timeout.IdleStateHandler.write(IdleStateHandler.java:302)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.invokeWrite0(AbstractChannelHandlerContext.java:717)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext.invokeWriteAndFlush(AbstractChannelHandlerContext.java:764)
at org.sparkproject.io.netty.channel.AbstractChannelHandlerContext$WriteTask.run(AbstractChannelHandlerContext.java:1104)
at org.sparkproject.io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:164)
at org.sparkproject.io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:472)
at org.sparkproject.io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:497)
at org.sparkproject.io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:989)
at org.sparkproject.io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
at org.sparkproject.io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
at java.lang.Thread.run(Thread.java:748)
```
Since JVM will not show stack of NPE exception if it happen many times.
```
021-07-28 08:25:44,720 ERROR server.ChunkFetchRequestHandler (ChunkFetchRequestHandler.java:lambda$respond$1(146)) - Error sending result ChunkFetchFailure[streamChunkId=StreamChunkId[streamId=1187623335353,chunkIndex=11],errorString=java.lang.NullPoint
erException
] to /10.130.10.5:42148; closing connection
java.nio.channels.ClosedChannelException
```
Makes user confused.
We should improved this error message?
### Why are the changes needed?
Improve error message
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Closes#33622 from AngersZhuuuu/SPARK-36391.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmaihu@gmail.com>
Co-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: yi.wu <yi.wu@databricks.com>
(cherry picked from commit b377ea26e2)
Signed-off-by: yi.wu <yi.wu@databricks.com>
### What changes were proposed in this pull request?
Add doc for the shuffle checksum configs in `configuration.md`.
### Why are the changes needed?
doc
### Does this PR introduce _any_ user-facing change?
No, since Spark 3.2 hasn't been released.
### How was this patch tested?
Pass existed tests.
Closes#33637 from Ngone51/SPARK-36384.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 3b92c721b5)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes an issue that no tests in `hadoop-cloud` are compiled and run unless `hadoop-3.2` profile is activated explicitly.
The root cause seems similar to SPARK-36067 (#33276) so the solution is to activate `hadoop-3.2` profile in `hadoop-cloud/pom.xml` by default.
This PR introduced an empty profile for `hadoop-2.7`. Without this, building with `hadoop-2.7` fails.
### Why are the changes needed?
`hadoop-3.2` profile should be activated by default so tests in `hadoop-cloud` also should be compiled and run without activating `hadoop-3.2` profile explicitly.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Confirmed tests in `hadoop-cloud` ran with both SBT and Maven.
```
build/sbt -Phadoop-cloud "hadoop-cloud/test"
...
[info] CommitterBindingSuite:
[info] - BindingParquetOutputCommitter binds to the inner committer (258 milliseconds)
[info] - committer protocol can be serialized and deserialized (11 milliseconds)
[info] - local filesystem instantiation (3 milliseconds)
[info] - reject dynamic partitioning (1 millisecond)
[info] Run completed in 1 second, 234 milliseconds.
[info] Total number of tests run: 4
[info] Suites: completed 1, aborted 0
[info] Tests: succeeded 4, failed 0, canceled 0, ignored 0, pending 0
[info] All tests passed.
build/mvn -Phadoop-cloud -pl hadoop-cloud test
...
CommitterBindingSuite:
- BindingParquetOutputCommitter binds to the inner committer
- committer protocol can be serialized and deserialized
- local filesystem instantiation
- reject dynamic partitioning
Run completed in 560 milliseconds.
Total number of tests run: 4
Suites: completed 2, aborted 0
Tests: succeeded 4, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
I also confirmed building with `-Phadoop-2.7` successfully finishes with both SBT and Maven.
```
build/sbt -Phadoop-cloud -Phadoop-2.7 "hadoop-cloud/Test/compile"
build/mvn -Phadoop-cloud -Phadoop-2.7 -pl hadoop-cloud testCompile
```
Closes#33277 from sarutak/fix-hadoop-3.2-cloud.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 0f5c3a4fd6)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to skip rolling event log directories which has only `appstatus` file.
### Why are the changes needed?
Currently, Spark History server shows `IllegalArgumentException` warning, but the event log might arrive later. The situation also can happen when the job is killed before uploading its first log to the remote storages like S3.
```
21/07/30 07:38:26 WARN FsHistoryProvider:
Error while reading new log s3a://.../eventlog_v2_spark-95b5c736c8e44037afcf152534d08771
java.lang.IllegalArgumentException: requirement failed:
Log directory must contain at least one event log file!
...
at org.apache.spark.deploy.history.RollingEventLogFilesFileReader.files$lzycompute(EventLogFileReaders.scala:216)
```
### Does this PR introduce _any_ user-facing change?
Yes. Users will not see `IllegalArgumentException` warnings.
### How was this patch tested?
Pass the CIs with the newly added test case.
Closes#33586 from dongjoon-hyun/SPARK-36354.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
(cherry picked from commit 28a2a2238f)
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Clean up older shuffleMergeId shuffle files when finalize request for higher shuffleMergeId is received when no blocks pushed for the corresponding shuffleMergeId. This is identified as part of https://github.com/apache/spark/pull/33034#discussion_r680610872.
### Why are the changes needed?
Without this change, older shuffleMergeId files won't be cleaned up properly.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added changes to existing unit test to address this case.
Closes#33605 from venkata91/SPARK-32923-follow-on.
Authored-by: Venkata krishnan Sowrirajan <vsowrirajan@linkedin.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
(cherry picked from commit d8169493b6)
Signed-off-by: Mridul Muralidharan <mridulatgmail.com>
### What changes were proposed in this pull request?
This PR is followup for https://github.com/apache/spark/pull/32964, to improve the warning message.
### Why are the changes needed?
To improve the warning message.
### Does this PR introduce _any_ user-facing change?
The warning is changed from "Deprecated in 3.2, Use `spark.to_spark_io` instead." to "Deprecated in 3.2, Use `DataFrame.spark.to_spark_io` instead."
### How was this patch tested?
Manually run `dev/lint-python`
Closes#33631 from itholic/SPARK-35811-followup.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 3d72c20e64)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR removes obsolete `contributing-to-spark.md` which is not referenced from anywhere.
### Why are the changes needed?
Just clean up.
### Does this PR introduce _any_ user-facing change?
No. Users can't have access to contributing-to-spark.html unless they directly point to the URL.
### How was this patch tested?
Built the document and confirmed that this change doesn't affect the result.
Closes#33619 from sarutak/remove-obsolete-contribution-doc.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit c31b653806)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add the Resolver to `checkColumnNotExists` to check name exist in case sensitive.
### Why are the changes needed?
At now the resolver is `_ == _` of `findNestedField` called by `checkColumnNotExists`
Add `alter.conf.resolver` to it.
[SPARK-36381](https://issues.apache.org/jira/browse/SPARK-36381)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add ut tests
Closes#33618 from Peng-Lei/sensitive-cloumn-name.
Authored-by: PengLei <peng.8lei@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 87d49cbcb1)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to ban `YearMonthIntervalType` and `DayTimeIntervalType` at the analysis phase while creating a table using a built-in filed-based datasource or writing a dataset to such datasource. In particular, add the following case:
```scala
case _: DayTimeIntervalType | _: YearMonthIntervalType => false
```
to all methods that override either:
- V2 `FileTable.supportsDataType()`
- V1 `FileFormat.supportDataType()`
### Why are the changes needed?
To improve user experience with Spark SQL, and output a proper error message at the analysis phase.
### Does this PR introduce _any_ user-facing change?
Yes but ANSI interval types haven't released yet. So, for users this is new behavior.
### How was this patch tested?
By running the affected test suites:
```
$ build/sbt -Phive-2.3 "test:testOnly *HiveOrcSourceSuite"
```
Closes#33580 from MaxGekk/interval-ban-in-ds.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 67cbc93263)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Fix `NullPointerException` in `Executor.stop()`.
### Why are the changes needed?
Some initialization steps could fail before the initialization of `metricsPoller`, `heartbeater`, `threadPool`, which results in the null of `metricsPoller`, `heartbeater`, `threadPool`. For example, I encountered a failure of:
c20af53580/core/src/main/scala/org/apache/spark/executor/Executor.scala (L137)
where the executor itself failed to register at the driver.
This PR helps to eliminate the error messages when the issue happens to not confuse users:
<details>
<summary><mark><font color=darkred>[click to see the detailed error message]</font></mark></summary>
<pre>
21/07/23 16:04:10 WARN Executor: Unable to stop executor metrics poller
java.lang.NullPointerException
at org.apache.spark.executor.Executor.stop(Executor.scala:318)
at org.apache.spark.executor.Executor.$anonfun$stopHookReference$1(Executor.scala:76)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$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:2025)
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$$anon$2.run(ShutdownHookManager.scala:178)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
21/07/23 16:04:10 WARN Executor: Unable to stop heartbeater
java.lang.NullPointerException
at org.apache.spark.executor.Executor.stop(Executor.scala:324)
at org.apache.spark.executor.Executor.$anonfun$stopHookReference$1(Executor.scala:76)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2025)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$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$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
21/07/23 16:04:10 ERROR Utils: Uncaught exception in thread shutdown-hook-0
java.lang.NullPointerException
at org.apache.spark.executor.Executor.$anonfun$stop$3(Executor.scala:334)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:231)
at org.apache.spark.executor.Executor.stop(Executor.scala:334)
at org.apache.spark.executor.Executor.$anonfun$stopHookReference$1(Executor.scala:76)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$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:2025)
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)
</pre>
</details>
### Does this PR introduce _any_ user-facing change?
Yes, users won't see error messages of `NullPointerException` after this fix.
### How was this patch tested?
Pass existing tests.
Closes#33620 from Ngone51/spark-36383-3.2.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR adds test considering the case `spark.sql.legacy.interval.enabled` is `true` for SPARK-35815.
### Why are the changes needed?
SPARK-35815 (#33456) changes `Dataset.withWatermark` to accept ANSI interval literals as `delayThreshold` but I noticed the change didn't work with `spark.sql.legacy.interval.enabled=true`.
We can't detect this issue because there is no test which considers the legacy interval type at that time.
In SPARK-36323 (#33551), this issue was resolved but it's better to add test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#33606 from sarutak/test-watermark-with-legacy-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 92cdb17d1a)
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/30494
This PR proposes a new way to optimize the final query stage in AQE. We first collect the effective user-specified repartition (semantic-wise, user-specified repartition is only effective if it's the root node or under a few simple nodes), and get the required distribution for the final plan. When we optimize the final query stage, we skip certain `AQEShuffleReadRule` if it breaks the required distribution.
### Why are the changes needed?
The current solution for optimizing the final query stage is pretty hacky and overkill. As an example, the newly added rule `OptimizeSkewInRebalancePartitions` can hardly apply as it's very common that the query plan has shuffles with origin `ENSURE_REQUIREMENTS`, which is not supported by `OptimizeSkewInRebalancePartitions`.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
updated tests
Closes#33541 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit dd80457ffb)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This a followup of the recent work such as https://github.com/apache/spark/pull/33200
For `ALTER TABLE` commands, the logical plans do not have the common `AlterTable` prefix in the name and just use names like `SetTableLocation`. This PR proposes to follow the same naming rule in `ALTER TABE ... COLUMN` commands.
This PR also moves these AlterTable commands to a individual file and give them a base trait.
### Why are the changes needed?
name simplification
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing test
Closes#33609 from cloud-fan/dsv2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 7cb9c1c241)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Better error messages for DataTypeOps against lists.
### Why are the changes needed?
Currently, DataTypeOps against lists throw a Py4JJavaError, we shall throw a TypeError with proper messages instead.
### Does this PR introduce _any_ user-facing change?
Yes. A TypeError message will be showed rather than a Py4JJavaError.
From:
```py
>>> import pyspark.pandas as ps
>>> ps.Series([1, 2, 3]) > [3, 2, 1]
Traceback (most recent call last):
...
py4j.protocol.Py4JJavaError: An error occurred while calling o107.gt.
: java.lang.RuntimeException: Unsupported literal type class java.util.ArrayList [3, 2, 1]
...
```
To:
```py
>>> import pyspark.pandas as ps
>>> ps.Series([1, 2, 3]) > [3, 2, 1]
Traceback (most recent call last):
...
TypeError: The operation can not be applied to list.
```
### How was this patch tested?
Unit tests.
Closes#33581 from xinrong-databricks/data_type_ops_list.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 8ca11fe39f)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
With SPARK-32922, we added a change that ShuffleBlockId can have a negative mapId. This was to support push-based shuffle where -1 as mapId indicated a push-merged block. However with SPARK-32923, a different type of BlockId was introduced - ShuffleMergedId, but reverting the change to ShuffleBlockId was missed.
### Why are the changes needed?
This reverts the changes to `ShuffleBlockId` which will never have a negative mapId.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Modified the unit test to verify the newly added ShuffleMergedBlockId.
Closes#33616 from otterc/SPARK-36389.
Authored-by: Chandni Singh <singh.chandni@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit 2712343a27)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Partially backport from #33598 to avoid unexpected error caused by pandas 1.3.
### Why are the changes needed?
If uses tries to use pandas 1.3 as the underlying pandas, it will raise unexpected errors caused by removed APIs or behavior change.
Note that pandas API on Spark 3.2 will still follow the pandas 1.2 behavior.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33614 from ueshin/issues/SPARK-36367/3.2/partially_backport.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds ANSI/ISO SQLSTATE standards to the error guidelines.
### Why are the changes needed?
Provides visibility and consistency to the SQLSTATEs assigned to error classes.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not needed; docs only
Closes#33560 from karenfeng/sqlstate-manual.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 63517eb430)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
This PR proposes to fail properly so JSON parser can proceed and parse the input with the permissive mode.
Previously, we passed `null`s as are, the root `InternalRow`s became `null`s, and it causes the query fails even with permissive mode on.
Now, we fail explicitly if `null` is passed when the input array contains `null`.
Note that this is consistent with non-array JSON input:
**Permissive mode:**
```scala
spark.read.json(Seq("""{"a": "str"}""", """null""").toDS).collect()
```
```
res0: Array[org.apache.spark.sql.Row] = Array([str], [null])
```
**Failfast mode**:
```scala
spark.read.option("mode", "failfast").json(Seq("""{"a": "str"}""", """null""").toDS).collect()
```
```
org.apache.spark.SparkException: Malformed records are detected in record parsing. Parse Mode: FAILFAST. To process malformed records as null result, try setting the option 'mode' as 'PERMISSIVE'.
at org.apache.spark.sql.catalyst.util.FailureSafeParser.parse(FailureSafeParser.scala:70)
at org.apache.spark.sql.DataFrameReader.$anonfun$json$7(DataFrameReader.scala:540)
at scala.collection.Iterator$$anon$11.nextCur(Iterator.scala:484)
```
To make the permissive mode to proceed and parse without throwing an exception.
**Permissive mode:**
```scala
spark.read.json(Seq("""[{"a": "str"}, null]""").toDS).collect()
```
Before:
```
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
```
After:
```
res0: Array[org.apache.spark.sql.Row] = Array([null])
```
NOTE that this behaviour is consistent when JSON object is malformed:
```scala
spark.read.schema("a int").json(Seq("""[{"a": 123}, {123123}, {"a": 123}]""").toDS).collect()
```
```
res0: Array[org.apache.spark.sql.Row] = Array([null])
```
Since we're parsing _one_ JSON array, related records all fail together.
**Failfast mode:**
```scala
spark.read.option("mode", "failfast").json(Seq("""[{"a": "str"}, null]""").toDS).collect()
```
Before:
```
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
```
After:
```
org.apache.spark.SparkException: Malformed records are detected in record parsing. Parse Mode: FAILFAST. To process malformed records as null result, try setting the option 'mode' as 'PERMISSIVE'.
at org.apache.spark.sql.catalyst.util.FailureSafeParser.parse(FailureSafeParser.scala:70)
at org.apache.spark.sql.DataFrameReader.$anonfun$json$7(DataFrameReader.scala:540)
at scala.collection.Iterator$$anon$11.nextCur(Iterator.scala:484)
```
Manually tested, and unit test was added.
Closes#33608 from HyukjinKwon/SPARK-36379.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit 0bbcbc6508)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
For added UT, without this patch will failed as below
```
[info] - SHOW TABLES V2: SPARK-36086: CollapseProject project replace alias should use origin column name *** FAILED *** (4 seconds, 935 milliseconds)
[info] java.lang.RuntimeException: After applying rule org.apache.spark.sql.catalyst.optimizer.CollapseProject in batch Operator Optimization before Inferring Filters, the structural integrity of the plan is broken.
[info] at org.apache.spark.sql.errors.QueryExecutionErrors$.structuralIntegrityIsBrokenAfterApplyingRuleError(QueryExecutionErrors.scala:1217)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:229)
[info] at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
[info] at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
[info] at scala.collection.immutable.List.foldLeft(List.scala:91)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:208)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:200)
[info] at scala.collection.immutable.List.foreach(List.scala:431)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:200)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179)
[info] at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
```
CollapseProject project replace alias should use origin column name
### Why are the changes needed?
Fix bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#33576 from AngersZhuuuu/SPARK-36086.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit f3173956cb)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change the `NullType.simpleString` to "void" to set "void" as the formal type name of `NullType`
### Why are the changes needed?
This PR is intended to address the type name discussion in PR #28833. Here are the reasons:
1. The type name of NullType is displayed everywhere, e.g. schema string, error message, document. Hence it's not possible to hide it from users, we have to choose a proper name
2. The "void" is widely used as the type name of "NULL", e.g. Hive, pgSQL
3. Changing to "void" can enable the round trip of `toDDL`/`fromDDL` for NullType. (i.e. make `from_json(col, schema.toDDL)`) work
### Does this PR introduce _any_ user-facing change?
Yes, the type name of "NULL" is changed from "null" to "void". for example:
```
scala> sql("select null as a, 1 as b").schema.catalogString
res5: String = struct<a:void,b:int>
```
### How was this patch tested?
existing test cases
Closes#33437 from linhongliu-db/SPARK-36224-void-type-name.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 2f700773c2)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds support to diagnose shuffle data corruption. Basically, the diagnosis mechanism works like this:
The shuffler reader would calculate the checksum (c1) for the corrupted shuffle block and send it to the server where the block is stored. At the server, it would read back the checksum (c2) that is stored in the checksum file and recalculate the checksum (c3) for the corresponding shuffle block. Then, if c2 != c3, we suspect the corruption is caused by the disk issue. Otherwise, if c1 != c3, we suspect the corruption is caused by the network issue. Otherwise, the checksum verifies pass. In any case of the error, the cause remains unknown.
After the shuffle reader receives the diagnosis response, it'd take the action bases on the type of cause. Only in case of the network issue, we'd give a retry. Otherwise, we'd throw the fetch failure directly. Also note that, if the corruption happens inside BufferReleasingInputStream, the reducer will throw the fetch failure immediately no matter what the cause is since the data has been partially consumed by downstream RDDs. If corruption happens again after retry, the reducer will throw the fetch failure directly this time without the diagnosis.
Please check out https://github.com/apache/spark/pull/32385 to see the completed proposal of the shuffle checksum project.
### Why are the changes needed?
Shuffle data corruption is a long-standing issue in Spark. For example, in SPARK-18105, people continually reports corruption issue. However, data corruption is difficult to reproduce in most cases and even harder to tell the root cause. We don't know if it's a Spark issue or not. With the diagnosis support for the shuffle corruption, Spark itself can at least distinguish the cause between disk and network, which is very important for users.
### Does this PR introduce _any_ user-facing change?
Yes, users may know the cause of the shuffle corruption after this change.
### How was this patch tested?
Added tests.
Closes#33451 from Ngone51/SPARK-36206.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
(cherry picked from commit a98d919da4)
Signed-off-by: Mridul Muralidharan <mridulatgmail.com>
### What changes were proposed in this pull request?
Currently, v2 ALTER TABLE ADD COLUMNS does not check duplicates for the user specified columns. For example,
```
spark.sql(s"CREATE TABLE $t (id int) USING $v2Format")
spark.sql("ALTER TABLE $t ADD COLUMNS (data string, data string)")
```
doesn't fail the analysis, and it's up to the catalog implementation to handle it. For v1 command, the duplication is checked before invoking the catalog.
### Why are the changes needed?
To check the duplicate columns during analysis and be consistent with v1 command.
### Does this PR introduce _any_ user-facing change?
Yes, now the above will command will print out the fllowing:
```
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the user specified columns: `data`
```
### How was this patch tested?
Added new unit tests
Closes#33600 from imback82/alter_add_duplicate_columns.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 3b713e7f61)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
[[SPARK-23243](https://issues.apache.org/jira/browse/SPARK-23243)] and [[SPARK-25341](https://issues.apache.org/jira/browse/SPARK-25341)] addressed cases of stage retries for indeterminate stage involving operations like repartition. This PR addresses the same issues in the context of push-based shuffle. Currently there is no way to distinguish the current execution of a stage for a shuffle ID. Therefore the changes explained below are necessary.
Core changes are summarized as follows:
1. Introduce a new variable `shuffleMergeId` in `ShuffleDependency` which is monotonically increasing value tracking the temporal ordering of execution of <stage-id, stage-attempt-id> for a shuffle ID.
2. Correspondingly make changes in the push-based shuffle protocol layer in `MergedShuffleFileManager`, `BlockStoreClient` passing the `shuffleMergeId` in order to keep track of the shuffle output in separate files on the shuffle service side.
3. `DAGScheduler` increments the `shuffleMergeId` tracked in `ShuffleDependency` in the cases of a indeterministic stage execution
4. Deterministic stage will have `shuffleMergeId` set to 0 as no special handling is needed in this case and indeterminate stage will have `shuffleMergeId` starting from 1.
New protocol changes are needed due to the reasons explained above.
No
Added new unit tests in `RemoteBlockPushResolverSuite, DAGSchedulerSuite, BlockIdSuite, ErrorHandlerSuite`
Closes#33034 from venkata91/SPARK-32923.
Authored-by: Venkata krishnan Sowrirajan <vsowrirajan@linkedin.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
(cherry picked from commit c039d99812)
Signed-off-by: Mridul Muralidharan <mridulatgmail.com>
### What changes were proposed in this pull request?
SPARK-32919 added support for fetching shuffle push merger locations with push-based shuffle. Filter out driver host in the shuffle push merger locations as driver won't participate in the shuffle merge also fix ClassCastException in the RemoveShufflePushMergerLocations.
### Why are the changes needed?
No
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added unit tests.
Closes#33425 from venkata91/SPARK-32919-follow-up.
Authored-by: Venkata krishnan Sowrirajan <vsowrirajan@linkedin.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
(cherry picked from commit 2a18f82940)
Signed-off-by: Mridul Muralidharan <mridulatgmail.com>
### What changes were proposed in this pull request?
This patch set value to `<NA>` (pd.NA) in BooleanExtensionOps and StringExtensionOps.
### Why are the changes needed?
The pandas behavior:
```python
>>> pd.Series([True, False, None], dtype="boolean").astype(str).tolist()
['True', 'False', '<NA>']
>>> pd.Series(['s1', 's2', None], dtype="string").astype(str).tolist()
['1', '2', '<NA>']
```
pandas on spark
```python
>>> import pandas as pd
>>> from pyspark import pandas as ps
# Before
>>> ps.from_pandas(pd.Series([True, False, None], dtype="boolean")).astype(str).tolist()
['True', 'False', 'None']
>>> ps.from_pandas(pd.Series(['s1', 's2', None], dtype="string")).astype(str).tolist()
['True', 'False', 'None']
# After
>>> ps.from_pandas(pd.Series([True, False, None], dtype="boolean")).astype(str).tolist()
['True', 'False', '<NA>']
>>> ps.from_pandas(pd.Series(['s1', 's2', None], dtype="string")).astype(str).tolist()
['s1', 's2', '<NA>']
```
See more in [SPARK-35976](https://issues.apache.org/jira/browse/SPARK-35976)
### Does this PR introduce _any_ user-facing change?
Yes, return `<NA>` when None to follow the pandas behavior
### How was this patch tested?
Change the ut to cover this scenario.
Closes#33585 from Yikun/SPARK-35976.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit f04e991e6a)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove old workarounds related to null ordering.
### Why are the changes needed?
In pandas-on-Spark, there are still some remaining places to call `Column._jc.(asc|desc)_nulls_(first|last)` as a workaround from Koalas to support Spark 2.3.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Modified a couple of tests and existing tests.
Closes#33597 from ueshin/issues/SPARK-36365/nulls_first_last.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 90d31dfcb7)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a followup of https://github.com/apache/spark/pull/33570, which mistakenly changed the default value of the default index
### Why are the changes needed?
It was mistakenly changed. It was changed to check if the tests actually pass but I forgot to change it back.
### Does this PR introduce _any_ user-facing change?
No, it's not related yet. It fixes up the mistake of the default value mistakenly changed.
(Changed default value makes the test flaky because of the order affected by extra shuffle)
### How was this patch tested?
Manually tested.
Closes#33596 from HyukjinKwon/SPARK-36338-followup.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 74a6b9d23b)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to upgrade PySpark GitHub Action job to use the latest docker image `20210730` having `sklearn` and `mlflow` additionally.
- 5ca94453d1
```
$ docker run -it --rm dongjoon/apache-spark-github-action-image:20210730 python3.9 -m pip list | grep mlflow
mlflow 1.19.0
$ docker run -it --rm dongjoon/apache-spark-github-action-image:20210730 python3.9 -m pip list | grep sklearn
sklearn 0.0
```
### Why are the changes needed?
This will save the installation time.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the GitHub Action PySpark jobs.
Closes#33595 from dongjoon-hyun/SPARK-36345.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 0e65ed5fb9)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Changes in this PR:
- `AdaptiveSparkPlanExec` has new methods `finalPlanSupportsColumnar` and `doExecuteColumnar` to support adaptive queries where the final query stage produces columnar data.
- `SessionState` now has a new set of injectable rules named `finalQueryStagePrepRules` that can be applied to the final query stage.
- `AdaptiveSparkPlanExec` can now safely be wrapped by either `RowToColumnarExec` or `ColumnarToRowExec`.
A Spark plugin can use the new rules to remove the root `ColumnarToRowExec` transition that is inserted by previous rules and at execution time can call `finalPlanSupportsColumnar` to see if the final query stage is columnar. If the plan is columnar then the plugin can safely call `doExecuteColumnar`. The adaptive plan can be wrapped in either `RowToColumnarExec` or `ColumnarToRowExec` to force a particular output format. There are fast paths in both of these operators to avoid any redundant transitions.
### Why are the changes needed?
Without this change it is necessary to use reflection to get the final physical plan to determine whether it is columnar and to execute it is a columnar plan. `AdaptiveSparkPlanExec` only provides public methods for row-based execution.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I have manually tested this patch with the RAPIDS Accelerator for Apache Spark.
Closes#33140 from andygrove/support-columnar-adaptive.
Authored-by: Andy Grove <andygrove73@gmail.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
(cherry picked from commit 0f538402fb)
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Move some logic related to `F.nanvl` to `DataTypeOps`.
### Why are the changes needed?
There are several places to branch by `FloatType` or `DoubleType` to use `F.nanvl` but `DataTypeOps` should handle it.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33582 from ueshin/issues/SPARK-36350/nan_to_null.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 895e3f5e2a)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to implement `distributed-sequence` index in Scala side.
### Why are the changes needed?
- Avoid unnecessary (de)serialization
- Keep the nullability in the input DataFrame when `distributed-sequence` is enabled. During the serialization, all fields are being nullable for now (see https://github.com/apache/spark/pull/32775#discussion_r645882104)
### Does this PR introduce _any_ user-facing change?
No to end users since pandas API on Spark is not released yet.
```python
import pyspark.pandas as ps
ps.set_option('compute.default_index_type', 'distributed-sequence')
ps.range(1).spark.print_schema()
```
Before:
```
root
|-- id: long (nullable = true)
```
After:
```
root
|-- id: long (nullable = false)
```
### How was this patch tested?
Manually tested, and existing tests should cover them.
Closes#33570 from HyukjinKwon/SPARK-36338.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit c6140d4d0a)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a partial revert of https://github.com/apache/spark/pull/33567 that keeps the logic to skip mlflow related tests if that's not installed.
### Why are the changes needed?
It's consistent with other libraries, e.g) PyArrow.
It also fixes up the potential dev breakage (see also https://github.com/apache/spark/pull/33567#issuecomment-889841829)
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
This is a partial revert. CI should test it out too.
Closes#33589 from HyukjinKwon/SPARK-36254.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit dd2ca0aee2)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/33352 , to simplify the JDBC aggregate pushdown:
1. We should get the schema of the aggregate query by asking the JDBC server, instead of calculating it by ourselves. This can simplify the code a lot, and is also more robust: the data type of SUM may vary in different databases, it's fragile to assume they are always the same as Spark.
2. because of 1, now we can remove the `dataType` property from the public `Sum` expression.
This PR also contains some small improvements:
1. Spark should deduplicate the aggregate expressions before pushing them down.
2. Improve the `toString` of public aggregate expressions to make them more SQL.
### Why are the changes needed?
code and API simplification
### Does this PR introduce _any_ user-facing change?
this API is not released yet.
### How was this patch tested?
existing tests
Closes#33579 from cloud-fan/dsv2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 387a251a68)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes adding a Python package, `mlflow` and `sklearn` to enable the MLflow test in pandas API on Spark.
### Why are the changes needed?
To enable the MLflow test in pandas API on Spark.
### Does this PR introduce _any_ user-facing change?
No, it's test-only
### How was this patch tested?
Manually test on local, with `python/run-tests --testnames pyspark.pandas.mlflow`.
Closes#33567 from itholic/SPARK-36254.
Lead-authored-by: itholic <haejoon.lee@databricks.com>
Co-authored-by: Haejoon Lee <44108233+itholic@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit abce61f3fd)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Since we have add log about commit time, I think this useful and we can make user know it directly in SQL tab's UI.
### Why are the changes needed?
Make user can directly know commit duration.
### Does this PR introduce _any_ user-facing change?
User can see file commit duration in SQL tab's SQL plan graph
### How was this patch tested?
Mannul tested
Closes#33553 from AngersZhuuuu/SPARK-34399-FOLLOWUP.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is follow-up for https://github.com/apache/spark/pull/33414 to support the more options for `mode` argument for all APIs that has `mode` argument, not only `DataFrame.to_csv`.
### Why are the changes needed?
To keep the usage consistency for the arguments that have same name.
### Does this PR introduce _any_ user-facing change?
More options is available for all APIs that has `mode` argument, same as `DataFrame.to_csv`
### How was this patch tested?
Manually test on local
Closes#33569 from itholic/SPARK-35085-followup.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 94cb2bbbc2)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Move both `PruneFileSourcePartitionsSuite` and `PrunePartitionSuiteBase` to the package `org.apache.spark.sql.execution.datasources`. Did a few refactoring to enable this.
### Why are the changes needed?
Currently both `PruneFileSourcePartitionsSuite` and `PrunePartitionSuiteBase` are in package `org.apache.spark.sql.hive.execution` which doesn't look correct as these tests are not specific to Hive. Therefore, it's better to move them into `org.apache.spark.sql.execution.datasources`, the same place where the rule `PruneFileSourcePartitions` is at.
### Does this PR introduce _any_ user-facing change?
No, it's just test refactoring.
### How was this patch tested?
Using existing tests:
```
build/sbt "sql/testOnly *PruneFileSourcePartitionsSuite"
```
and
```
build/sbt "hive/testOnly *PruneHiveTablePartitionsSuite"
```
Closes#33564 from sunchao/SPARK-36136-partitions-suite.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 0ece865ea4)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Just to fix some typos in ShuffleBlockPusher class.
### Why are the changes needed?
Fix the typos, make code clear.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No need test.
Closes#33575 from zhuqi-lucas/master.
Authored-by: zhuqi-lucas <821684824@qq.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 900b38d5fa)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Reuse `IndexOpsMixin.isnull()` where the null check is needed.
### Why are the changes needed?
There are some places where we can reuse `IndexOpsMixin.isnull()` instead of directly using Spark `Column`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33562 from ueshin/issues/SPARK-36333/reuse_isnull.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 07ed82be0b)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
As the discussion in https://github.com/apache/spark/pull/32928/files#r654049392, after confirming the compatibility, we can use a newer RocksDB version for the state store implementation.
### Why are the changes needed?
For further ARM support and leverage the bug fix for the newer version.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33578 from xuanyuanking/SPARK-36347.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
(cherry picked from commit 4cd5fa96d8)
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Improve the error message for wrong type when calling dropDuplicates in pyspark.
### Why are the changes needed?
The current error message is cryptic and can be unclear to less experienced users.
### Does this PR introduce _any_ user-facing change?
Yes, it adds a type error for when a user gives the wrong type to dropDuplicates
### How was this patch tested?
There is currently no testing for error messages in pyspark dataframe functions
Closes#33364 from sammyjmoseley/sm/add-type-checking-for-drop-duplicates.
Lead-authored-by: Samuel Moseley <smoseley@palantir.com>
Co-authored-by: Sammy Moseley <moseley.sammy@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit a07df1acc6)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to support ANSI interval literals for `TimeWindow`.
### Why are the changes needed?
Watermark also supports ANSI interval literals so it's great to support for `TimeWindow`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#33551 from sarutak/window-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit db18866742)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This is a follow up of https://github.com/apache/spark/pull/33447, where the unit test is disabled, due to failure after memory setting changed. I found the root cause is after https://github.com/apache/spark/pull/33447, in unit test, Spark memory page byte size is changed from `67108864` to `33554432` [1]. So the shuffled hash join build size is also changed accordingly due to [memory page byte size change](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala#L457). Previously the unit test is checking the exact value of build size, so it no longer works. Here we change the unit test to verify the relative value of build size, and it should work.
[1]: I printed out the memory page byte size explicitly in unit test - `org.apache.spark.SparkException: chengsu pageSizeBytes: 33554432!` in https://github.com/c21/spark/runs/3186680616?check_suite_focus=true .
### Why are the changes needed?
Make previously disabled unit test work.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Changed unit test itself.
Closes#33494 from c21/test.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 6a8dd3229a)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In PR #32959, we found some weird datetime strings that can be parsed. ([details](https://github.com/apache/spark/pull/32959#discussion_r665015489))
This PR blocks the invalid datetime string.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
Yes, below strings will have different results when cast to datetime.
```sql
select cast('12::' as timestamp); -- Before: 2021-07-07 12:00:00, After: NULL
select cast('T' as timestamp); -- Before: 2021-07-07 00:00:00, After: NULL
```
### How was this patch tested?
some new test cases
Closes#33490 from linhongliu-db/SPARK-35780-block-invalid-format.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit ed0e351f05)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Improve the rest of DataTypeOps tests by avoiding joins.
### Why are the changes needed?
bool, string, numeric DataTypeOps tests have been improved by avoiding joins.
We should improve the rest of the DataTypeOps tests in the same way.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#33546 from xinrong-databricks/test_no_join.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 9c5cb99d6e)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Proposing adding new metrics to `customMetrics` under the `stateOperators` in `StreamingQueryProgress` event These metrics help have better visibility into the RocksDB based state store in streaming jobs. For full details of metrics, refer to https://issues.apache.org/jira/browse/SPARK-36236.
### Why are the changes needed?
Current metrics available for the RockDB state store, do not provide observability into many operations such as how much time is spent by the RocksDB in compaction and what is the cache hit ratio. These metrics help compare performance differences in state store operations between slow and fast microbatches .
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unittests
Closes#33455 from vkorukanti/rocksdb-metrics.
Authored-by: Venki Korukanti <venki.korukanti@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit eb4d1c0332)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Adjust `astype` of fractional Series with missing values to follow pandas.
Non-goal: Adjust the issue of `astype` of Decimal Series with missing values to follow pandas.
### Why are the changes needed?
`astype` of fractional Series with missing values doesn't behave the same as pandas, for example, float Series returns itself when `astype` integer, while a ValueError is raised in pandas.
We ought to follow pandas.
### Does this PR introduce _any_ user-facing change?
Yes.
From:
```py
>>> import numpy as np
>>> import pyspark.pandas as ps
>>> psser = ps.Series([1, 2, np.nan])
>>> psser.astype(int)
0 1.0
1 2.0
2 NaN
dtype: float64
```
To:
```py
>>> import numpy as np
>>> import pyspark.pandas as ps
>>> psser = ps.Series([1, 2, np.nan])
>>> psser.astype(int)
Traceback (most recent call last):
...
ValueError: Cannot convert fractions with missing values to integer
```
### How was this patch tested?
Unit tests.
Closes#33466 from xinrong-databricks/extension_astype.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 01213095e2)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
