### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/33140 to propose a simpler idea for integrating columnar execution into AQE.
Instead of making the `ColumnarToRowExec` and `RowToColumnarExec` dynamic to handle `AdaptiveSparkPlanExec`, it's simpler to let the consumer decide if it needs columnar output or not, and pass a boolean flag to `AdaptiveSparkPlanExec`.
For Spark vendors, they can set the flag differently in their custom columnar parquet writing command when the input plan is `AdaptiveSparkPlanExec`.
One argument is if we need to look at the final plan of AQE and consume the data differently (either row or columnar format). I can't think of a use case and I think we can always statically know if the AQE plan should output row or columnar data.
### Why are the changes needed?
code simplification.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
manual test
Closes#33624 from cloud-fan/aqe.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 8714eefe6f)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR upgrades Jetty version to `9.4.43.v20210629`.
### Why are the changes needed?
To address vulnerability https://nvd.nist.gov/vuln/detail/CVE-2021-34429 which affects Jetty `9.4.42.v20210604`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI
Closes#33656 from this/upgrade-jetty-9.4.43.
Lead-authored-by: Sajith Ariyarathna <sajith.janaprasad@gmail.com>
Co-authored-by: Sajith Ariyarathna <this@users.noreply.github.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 5a22f9ceaf)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
On the client side, we are currently randomizing the order of push requests before processing each request. In addition we can further randomize the order of blocks within each push request before pushing them.
In our benchmark, this has resulted in a 60%-70% reduction of blocks that fail to be merged due to bock collision (the existing block merge ratio is already pretty good in general, and this further improves it).
### Why are the changes needed?
Improve block merge ratio for push-based shuffle
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Straightforward small change, no additional test needed.
Closes#33649 from Victsm/SPARK-36423.
Lead-authored-by: Min Shen <mshen@linkedin.com>
Co-authored-by: Min Shen <victor.nju@gmail.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
(cherry picked from commit 6e729515fd)
Signed-off-by: Mridul Muralidharan <mridulatgmail.com>
### What changes were proposed in this pull request?
Add local-cluster mode option to submitting-applications.md
### Why are the changes needed?
Help users to find/use this option for unit tests.
### Does this PR introduce _any_ user-facing change?
Yes, docs changed.
### How was this patch tested?
`SKIP_API=1 bundle exec jekyll build`
<img width="460" alt="docchange" src="https://user-images.githubusercontent.com/87687356/127125380-6beb4601-7cf4-4876-b2c6-459454ce2a02.png">
Closes#33537 from yutoacts/SPARK-595.
Lead-authored-by: Yuto Akutsu <yuto.akutsu@jp.nttdata.com>
Co-authored-by: Yuto Akutsu <yuto.akutsu@nttdata.com>
Co-authored-by: Yuto Akutsu <87687356+yutoacts@users.noreply.github.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
(cherry picked from commit 41b011e416)
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This PR reverts the change in SPARK-36429 (#33654).
See [conversation](https://github.com/apache/spark/pull/33654#issuecomment-894160037).
### Why are the changes needed?
To recover CIs.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#33670 from sarutak/revert-SPARK-36429.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
(cherry picked from commit e17612d0bf)
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
Currently, references to grouping sets are reported as errors after aggregated expressions, e.g.
```
SELECT count(name) c, name
FROM VALUES ('Alice'), ('Bob') people(name)
GROUP BY name GROUPING SETS(name);
```
Error in query: expression 'people.`name`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
### Why are the changes needed?
Fix the map anonymous function in the constructAggregateExprs function does not use underscores to avoid
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests.
Closes#33574 from gaoyajun02/SPARK-36339.
Lead-authored-by: gaoyajun02 <gaoyajun02@gmail.com>
Co-authored-by: gaoyajun02 <gaoyajun02@meituan.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 888f8f03c8)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR updates a golden to comply with the change in SPARK-36429 (#33654).
### Why are the changes needed?
To recover GA failure.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GA itself.
Closes#33663 from sarutak/followup-SPARK-36429.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 63c7d1847d)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, when `set spark.sql.timestampType=TIMESTAMP_NTZ`, the behavior is different between `from_json` and `from_csv`.
```
-- !query
select from_json('{"t":"26/October/2015"}', 't Timestamp', map('timestampFormat', 'dd/MMMMM/yyyy'))
-- !query schema
struct<from_json({"t":"26/October/2015"}):struct<t:timestamp_ntz>>
-- !query output
{"t":null}
```
```
-- !query
select from_csv('26/October/2015', 't Timestamp', map('timestampFormat', 'dd/MMMMM/yyyy'))
-- !query schema
struct<>
-- !query output
java.lang.Exception
Unsupported type: timestamp_ntz
```
We should make `from_json` throws exception too.
This PR fix the discussion below
https://github.com/apache/spark/pull/33640#discussion_r682862523
### Why are the changes needed?
Make the behavior of `from_json` more reasonable.
### Does this PR introduce _any_ user-facing change?
'Yes'.
from_json throwing Exception when we set spark.sql.timestampType=TIMESTAMP_NTZ.
### How was this patch tested?
Tests updated.
Closes#33654 from beliefer/SPARK-36429.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently `datetime.sql` contains a lot of tests and will be run 3 times: default mode, ansi mode, ntz mode. It wastes the test time and also large test files are hard to read.
This PR proposes to split it into smaller ones:
1. `date.sql`, which contains date literals, functions and operations. It will be run twice with default and ansi mode.
2. `timestamp.sql`, which contains timestamp (no ltz or ntz suffix) literals, functions and operations. It will be run 4 times: default mode + ans off, defaul mode + ansi on, ntz mode + ansi off, ntz mode + ansi on.
3. `datetime_special.sql`, which create datetime values whose year is outside of [0, 9999]. This is a separated file as JDBC doesn't support them and need to ignore this test file. It will be run 4 times as well.
4. `timestamp_ltz.sql`, which contains timestamp_ltz literals and constructors. It will be run twice with default and ntz mode, to make sure its result doesn't change with the timestamp mode. Note that, operations with ltz are tested by `timestamp.sql`
5. `timestamp_ntz.sql`, which contains timestamp_ntz literals and constructors. It will be run twice with default and ntz mode, to make sure its result doesn't change with the timestamp mode. Note that, operations with ntz are tested by `timestamp.sql`
### Why are the changes needed?
reduce test run time.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#33640 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Add documentation for new functions try_cast/try_add/try_divide
### Why are the changes needed?
Better documentation. These new functions are useful when migrating to the ANSI dialect.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Build docs and preview:
Closes#33638 from gengliangwang/addDocForTry.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 8a35243fa7)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As followup to raise memory for two places forgotten.
### Why are the changes needed?
Raise memory for GHA.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
GA
Closes#33658 from viirya/increasing-mem-ga-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit cd070f1b9c)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR fixes the issue that `ConfigEntry` to be introduced to the doc field directly without calling `.key`, which causes malformed documents on the web site and in the result of `SET -v`
1. https://spark.apache.org/docs/3.1.2/configuration.html#static-sql-configuration - spark.sql.hive.metastore.jars
2. set -v
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
no, but contains doc fix
### How was this patch tested?
new tests
Closes#33647 from yaooqinn/SPARK-36421.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit c7fa3c9090)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes a GA failure which is related to downloading lintr dependencies.
```
* installing *source* package ‘devtools’ ...
** package ‘devtools’ successfully unpacked and MD5 sums checked
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
*** copying figures
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (devtools)
The downloaded source packages are in
‘/tmp/Rtmpv53Ix4/downloaded_packages’
Using bundled GitHub PAT. Please add your own PAT to the env var `GITHUB_PAT`
Error: Failed to install 'unknown package' from GitHub:
HTTP error 401.
Bad credentials
```
I re-triggered the GA job but it still fail with the same error.
https://github.com/apache/spark/runs/3257853825
The issue seems to happen when downloading lintr dependencies from GitHub. So, the solution is to change the way to download them.
### Why are the changes needed?
To recover GA.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GA itself.
Closes#33660 from sarutak/fix-r-package-issue.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit e13bd586f1)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This reverts SPARK-31475, as there are always more concurrent jobs running in AQE mode, especially when running multiple queries at the same time. Currently, the broadcast timeout does not record accurately for the BroadcastQueryStageExec only, but also including the time waiting for being scheduled. If all the resources are currently being occupied for materializing other stages, it timeouts without a chance to run actually.
The default value is 300s, and it's hard to adjust the timeout for AQE mode. Usually, you need an extremely large number for real-world cases. As you can see in the example, above, the timeout we used for it was 1800s, and obviously, it needed 3x more or something
### Why are the changes needed?
AQE is default now, we can make it more stable with this PR
### Does this PR introduce _any_ user-facing change?
yes, broadcast timeout now is not used for AQE
### How was this patch tested?
modified test
Closes#33636 from yaooqinn/SPARK-36414.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 0c94e47aec)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
RemoveNoopOperators should keep output schema
### Why are the changes needed?
Expand function
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#33587 from AngersZhuuuu/SPARK-36355.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 02810eecbf)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### 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>
