### What changes were proposed in this pull request?
This adds two new additional metrics to `ExternalBlockHandler`:
- `blockTransferRate` -- for indicating the rate of transferring blocks, vs. the data within them
- `blockTransferAvgSize_1min` -- a 1-minute trailing average of block sizes transferred by the ESS
Additionally, this enhances `YarnShuffleServiceMetrics` to expose the histogram/`Snapshot` information from `Timer` metrics within `ExternalBlockHandler`.
### Why are the changes needed?
Currently `ExternalBlockHandler` exposes some useful metrics, but is lacking around metrics for the rate of block transfers. We have `blockTransferRateBytes` to tell us the rate of _bytes_, but no metric to tell us the rate of _blocks_, which is especially relevant when running the ESS on HDDs that are sensitive to random reads. Many small block transfers can have a negative impact on performance, but won't show up as a spike in `blockTransferRateBytes` since the sizes are small. Thus the new metrics to show information around average block size and block transfer rate are very useful to monitor the health/performance of the ESS, especially when running on HDDs.
For the `YarnShuffleServiceMetrics`, currently the three `Timer` metrics exposed by `ExternalBlockHandler` are being underutilized in a YARN-based environment -- they are basically treated as a `Meter`, only exposing rate-based information, when the metrics themselves are collected detailed histograms of timing information. We should expose this information for better observability.
### Does this PR introduce _any_ user-facing change?
Yes, there are two entirely new metrics for the ESS, as documented in `monitoring.md`. Additionally in a YARN environment, `Timer` metrics exposed by the ESS will include more rich timing information.
### How was this patch tested?
New unit tests are added to verify that new metrics are showing up as expected.
We have been running this patch internally for approx. 1 year and have found it to be useful for monitoring the health of ESS and diagnosing performance issues.
Closes#32388 from xkrogen/xkrogen-SPARK-35258-ess-new-metrics.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
### What changes were proposed in this pull request?
This PR group exception messages in sql/catalyst/src/main/scala/org/apache/spark/sql (except catalyst)
### Why are the changes needed?
It will largely help with standardization of error messages and its maintenance.
### Does this PR introduce any user-facing change?
No. Error messages remain unchanged.
### How was this patch tested?
No new tests - pass all original tests to make sure it doesn't break any existing behavior.
Closes#32916 from dgd-contributor/SPARK-35064_catalyst_group_error.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
PR #32441 hid the internal view properties for describe table command, But the `show tblproperties view` case is not covered.
### Why are the changes needed?
Avoid internal properties confusing the users.
### Does this PR introduce _any_ user-facing change?
Yes
Before this change, the user will see below output for `show tblproperties test_view`
```
....
p1 v1
p2 v2
view.catalogAndNamespace.numParts 2
view.catalogAndNamespace.part.0 spark_catalog
view.catalogAndNamespace.part.1 default
view.query.out.col.0 c1
view.query.out.numCols 1
view.referredTempFunctionsNames []
view.referredTempViewNames []
...
```
After this change, the internal properties will be hidden.
```
....
p1 v1
p2 v2
...
```
### How was this patch tested?
existing UT
Closes#33016 from jerqi/hide_show_tblproperties.
Authored-by: RoryQi <1242949407@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add a metric to track the number of duplicates dropped in input in streaming deduplication operator. Also introduce a `StatefulOperatorCustomMetric` to allow stateful operators to output their own unique metrics in `StateOperatorProgress.customMetrics` in `StreamingQueryProgress`.
### Why are the changes needed?
1. Having the duplicates dropped count help monitor and debug any incorrect results issue or find reasons for state size increases in dedupe operator.
2. New API `StatefulOperatorCustomMetric` allows stateful operators to expose their own unique metrics in `StateOperatorProgress.customMetrics` in `StreamingQueryProgress`
### Does this PR introduce _any_ user-facing change?
Yes. For deduplication stateful operator a new metric `numDuplicatesDropped` is shown in `StateOperatorProgress` within `StreamingQueryProgress`. Example `StreamingQueryProgress` output in JSON form.
```
{
"id" : "510be3cd-a955-4faf-8456-d97c78d39af5",
"runId" : "c170c4cd-04cb-4a28-b054-74020e3998e1",
...
,
"stateOperators" : [ {
"numRowsTotal" : 1,
"numRowsUpdated" : 1,
"numRowsDroppedByWatermark" : 0,
"customMetrics" : {
"loadedMapCacheHitCount" : 0,
"loadedMapCacheMissCount" : 0,
"numDuplicatesDropped" : 0,
"stateOnCurrentVersionSizeBytes" : 392
}
}],
...
}
```
### How was this patch tested?
Existing UTs for regression and added a UT.
Closes#33065 from vkorukanti/SPARK-35880.
Authored-by: Venki Korukanti <venki.korukanti@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Refines type hints in `pyspark.pandas.window`.
Also, some refactoring is included to clean up the type hierarchy of `Rolling` and `Expanding`.
### Why are the changes needed?
We can use more strict type hints for functions in pyspark.pandas.window using the generic way.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33097 from ueshin/issues/SPARK-35901/window.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes move `to_pandas_on_spark` function from `pyspark.pandas.frame` to `pyspark.sql.dataframe`, and added the related tests to the PySpark DataFrame tests.
### Why are the changes needed?
Because now the Koalas is ported into PySpark, so we don't need to Spark auto-patch anymore.
And also `to_pandas_on_spark` is belongs to the pandas-on-Spark DataFrame doesn't look make sense.
### Does this PR introduce _any_ user-facing change?
No, it's kinda internal refactoring stuff.
### How was this patch tested?
Added the related tests and manually check they're passed.
Closes#33054 from itholic/SPARK-35605.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The hyperlinks in Python code blocks in [Spark SQL Guide - Getting Started](https://spark.apache.org/docs/latest/sql-getting-started.html) currently point to invalid addresses and return 404. This pull request fixes that issue by pointing them to correct links in Python API docs.
### Why are the changes needed?
Error in documentation classifies as a bug and hence needs to be fixed.
### Does this PR introduce _any_ user-facing change?
Yes. This PR fixes documentation error in https://spark.apache.org/docs/latest/sql-getting-started.html
### How was this patch tested?
This patch was locally built after cloning the repo from scratch and then doing a clean build after fixing the required problems.
Closes#33107 from dhruvildave/sql-doc.
Authored-by: Dhruvil Dave <dhruvil.dave@outlook.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This patch fixes `PromotePrecision` where it overwrites `genCode` where subexpression elimination should happen.
### Why are the changes needed?
`PromotePrecision` overwrites `genCode` where subexpression elimination should happen. So if it is most top expression of a subexpression, it is never replaced.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added test.
Closes#33103 from viirya/fix-precision.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Add unit test case for MySQLDialect.getCatalystType
### Why are the changes needed?
add unit test case
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit Test
Closes#33087 from zengruios/SPARK-35893.
Authored-by: zengruios <578395184@qq.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
1. Make `RebalancePartitions` extend `RepartitionOperation`.
2. Make `CollapseRepartition` support `RebalancePartitions`.
### Why are the changes needed?
`CollapseRepartition` can optimize `RebalancePartitions` if possible.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#33099 from wangyum/SPARK-35904.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Fix UT mistake in SQLQuerySuite
### Why are the changes needed?
Fix UT mistake in SQLQuerySuite
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existed UT
Closes#33092 from AngersZhuuuu/SPARK-33338-FOLLOWUP.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Like SPARK-8397, this PR aims to parameterize TPCDSQueryBenchmark's Spark master by reusing `spark.sql.test.master`.
### Why are the changes needed?
This is helpful for testers.
### Does this PR introduce _any_ user-facing change?
No. This is a test environment.
### How was this patch tested?
Manually, I checked the performance difference with TPCDS 10g data.
Closes#33098 from dongjoon-hyun/SPARK-35903.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Support the following operation:
- TimestampWithoutTZ - Year-Month interval
The following operation is actually supported in https://github.com/apache/spark/pull/33076/. This PR is to add end-to-end tests for them:
- TimestampWithoutTZ - Calendar interval
- TimestampWithoutTZ - Daytime interval
### Why are the changes needed?
Support subtracting all 3 interval types from a timestamp without time zone
### Does this PR introduce _any_ user-facing change?
No, the timestamp without time zone type is not release yet.
### How was this patch tested?
Unit tests
Closes#33086 from gengliangwang/subtract.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes perf regression at the executor side when creating fetch requests with large initial partitions
In NetEase, we had an online job that took `45min` to "fetch" about 100MB of shuffle data, which actually turned out that it was just collecting fetch requests slowly. Normally, such a task should finish in seconds.
See the `DEBUG` log
```
21/06/22 11:52:26 DEBUG BlockManagerStorageEndpoint: Sent response: 0 to kyuubi.163.org:
21/06/22 11:53:05 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 3941440 at BlockManagerId(12, .., 43559, None) with 19 blocks
21/06/22 11:53:44 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 3716400 at BlockManagerId(20, .., 38287, None) with 18 blocks
21/06/22 11:54:41 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 4559280 at BlockManagerId(6, .., 39689, None) with 22 blocks
21/06/22 11:55:08 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 3120160 at BlockManagerId(33, .., 39449, None) with 15 blocks
```
I also create a test case locally with my local laptop docker env to give some reproducible cases.
```
bin/spark-sql --conf spark.kubernetes.file.upload.path=./ --master k8s://https://kubernetes.docker.internal:6443 --conf spark.kubernetes.container.image=yaooqinn/spark:v20210624-5 -c spark.kubernetes.context=docker-for-desktop_1 --num-executors 5 --driver-memory 5g --conf spark.kubernetes.executor.podNamePrefix=sparksql
```
```sql
SET spark.sql.adaptive.enabled=true;
SET spark.sql.shuffle.partitions=3000;
SELECT /*+ REPARTITION */ 1 as pid, id from range(1, 1000000, 1, 500);
SELECT /*+ REPARTITION(pid, id) */ 1 as pid, id from range(1, 1000000, 1, 500);
```
### Why are the changes needed?
fix perf regression which was introduced by SPARK-29292 (3ad4863673) in v3.1.0.
3ad4863673 is for support compilation with scala 2.13 but the performance losses is huge. We need to consider backporting this PR to branch 3.1.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Mannully,
#### before
```log
21/06/23 13:54:22 DEBUG ShuffleBlockFetcherIterator: maxBytesInFlight: 50331648, targetRemoteRequestSize: 10066329, maxBlocksInFlightPerAddress: 2147483647
21/06/23 13:54:38 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 2314708 at BlockManagerId(2, 10.1.3.114, 36423, None) with 86 blocks
21/06/23 13:54:59 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 2636612 at BlockManagerId(3, 10.1.3.115, 34293, None) with 87 blocks
21/06/23 13:55:18 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 2508706 at BlockManagerId(4, 10.1.3.116, 41869, None) with 90 blocks
21/06/23 13:55:34 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 2350854 at BlockManagerId(5, 10.1.3.117, 45787, None) with 85 blocks
21/06/23 13:55:34 INFO ShuffleBlockFetcherIterator: Getting 438 (11.8 MiB) non-empty blocks including 90 (2.5 MiB) local and 0 (0.0 B) host-local and 348 (9.4 MiB) remote blocks
21/06/23 13:55:34 DEBUG ShuffleBlockFetcherIterator: Sending request for 87 blocks (2.5 MiB) from 10.1.3.115:34293
21/06/23 13:55:34 INFO TransportClientFactory: Successfully created connection to /10.1.3.115:34293 after 1 ms (0 ms spent in bootstraps)
21/06/23 13:55:34 DEBUG ShuffleBlockFetcherIterator: Sending request for 90 blocks (2.4 MiB) from 10.1.3.116:41869
21/06/23 13:55:34 INFO TransportClientFactory: Successfully created connection to /10.1.3.116:41869 after 2 ms (0 ms spent in bootstraps)
21/06/23 13:55:34 DEBUG ShuffleBlockFetcherIterator: Sending request for 85 blocks (2.2 MiB) from 10.1.3.117:45787
```
```log
21/06/23 14:00:45 INFO MapOutputTracker: Broadcast outputstatuses size = 411, actual size = 828997
21/06/23 14:00:45 INFO MapOutputTrackerWorker: Got the map output locations
21/06/23 14:00:45 DEBUG ShuffleBlockFetcherIterator: maxBytesInFlight: 50331648, targetRemoteRequestSize: 10066329, maxBlocksInFlightPerAddress: 2147483647
21/06/23 14:00:55 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 1894389 at BlockManagerId(2, 10.1.3.114, 36423, None) with 99 blocks
21/06/23 14:01:04 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 1919993 at BlockManagerId(3, 10.1.3.115, 34293, None) with 100 blocks
21/06/23 14:01:14 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 1977186 at BlockManagerId(5, 10.1.3.117, 45787, None) with 103 blocks
21/06/23 14:01:23 DEBUG ShuffleBlockFetcherIterator: Creating fetch request of 1938336 at BlockManagerId(4, 10.1.3.116, 41869, None) with 101 blocks
21/06/23 14:01:23 INFO ShuffleBlockFetcherIterator: Getting 500 (9.1 MiB) non-empty blocks including 97 (1820.3 KiB) local and 0 (0.0 B) host-local and 403 (7.4 MiB) remote blocks
21/06/23 14:01:23 DEBUG ShuffleBlockFetcherIterator: Sending request for 101 blocks (1892.9 KiB) from 10.1.3.116:41869
21/06/23 14:01:23 DEBUG ShuffleBlockFetcherIterator: Sending request for 103 blocks (1930.8 KiB) from 10.1.3.117:45787
21/06/23 14:01:23 DEBUG ShuffleBlockFetcherIterator: Sending request for 99 blocks (1850.0 KiB) from 10.1.3.114:36423
21/06/23 14:01:23 DEBUG ShuffleBlockFetcherIterator: Sending request for 100 blocks (1875.0 KiB) from 10.1.3.115:34293
21/06/23 14:01:23 INFO ShuffleBlockFetcherIterator: Started 4 remote fetches in 37889 ms
```
#### After
```log
21/06/24 13:01:16 DEBUG ShuffleBlockFetcherIterator: maxBytesInFlight: 50331648, targetRemoteRequestSize: 10066329, maxBlocksInFlightPerAddress: 2147483647
21/06/24 13:01:16 INFO ShuffleBlockFetcherIterator: ==> Call blockInfos.map(_._2).sum: 40 ms
21/06/24 13:01:16 INFO ShuffleBlockFetcherIterator: ==> Call mergeFetchBlockInfo for shuffle_0_9_2990_2997/9: 0 ms
21/06/24 13:01:16 INFO ShuffleBlockFetcherIterator: ==> Call mergeFetchBlockInfo for shuffle_0_15_2395_2997/15: 0 ms
```
Closes#33063 from yaooqinn/SPARK-35879.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
Adds more type annotations in the files `python/pyspark/pandas/data_type_ops/*.py` and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#33094 from ueshin/issues/SPARK-35466/disallow_untyped_defs_data_ops.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR adds a utility to convert public connector expressions to Catalyst expressions.
Notable differences:
- Switched to `QueryCompilationErrors` from an explicit `AnalysisException`.
- Decoupled the resolving logic for v2 references into separate methods to use in other places.
### Why are the changes needed?
These changes are needed as more and more places require this logic and it is better to implement it in a single place.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33096 from aokolnychyi/spark-35899.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to add a new scalastyle rule to enforce not importing `scala.collection.Seq` and `scala.collection.IndexedSeq` which conflicts with `scala.Seq` and `scala.IndexedSeq`.
The problem occurs as Scala 2.13 changed the alias of `scala.Seq` and `scala.IndexedSeq`. Before Scala 2.13, they were `scala.collection.Seq` and `scala.collection.IndexedSeq`. After Scala 2.13, they become `scala.collection.immutable.Seq` and `scala.collection.immutable.IndexedSeq`.
Please refer below doc for more details.
https://docs.scala-lang.org/overviews/core/collections-migration-213.html
### Why are the changes needed?
We have seen Seq/IndexedSeq issues on cross-compilation of Scala 2.12 / 2.13. While I'm not sure this can prevent all cases, this will prevent the simple case of breaking cross compilation.
### Does this PR introduce _any_ user-facing change?
No change on end user. Contributors will be restricted but shouldn't block them doing the right thing.
### How was this patch tested?
Ran scalastyle against current master (before #33084)
```
> dev/scalastyle
Scalastyle checks failed at following occurrences:
[error] /Users/Jungtaek.Lim/WorkArea/ScalaProjects/spark-apache/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/RocksDBFileManager.scala:28:0:
[error] Don't import scala.collection.Seq and scala.collection.IndexedSeq as it may bring some problems with cross-build between Scala 2.12 and 2.13.
[error]
[error] Please refer below page to see the details of changes around Seq.
[error] https://docs.scala-lang.org/overviews/core/collections-migration-213.html
[error]
[error] If you really need to use scala.collection.Seq or scala.collection.IndexedSeq, please use the fully-qualified name instead.
[error]
[error] /Users/Jungtaek.Lim/WorkArea/ScalaProjects/spark-apache/core/src/main/scala/org/apache/spark/util/Utils.scala:37:0:
[error] Don't import scala.collection.Seq and scala.collection.IndexedSeq as it may bring some problems with cross-build between Scala 2.12 and 2.13.
[error]
[error] Please refer below page to see the details of changes around Seq.
[error] https://docs.scala-lang.org/overviews/core/collections-migration-213.html
[error]
[error] If you really need to use scala.collection.Seq or scala.collection.IndexedSeq, please use the fully-qualified name instead.
[error]
[error] Total time: 15 s, completed Jun 25, 2021 9:01:32 PM
```
Closes#33085 from HeartSaVioR/SPARK-35894.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Update Ivy from 2.4.0 to 2.5.0.
- https://ant.apache.org/ivy/history/2.5.0/release-notes.html
### Why are the changes needed?
This brings various improvements and bug fixes. Most notably, the adding of `ivy.maven.lookup.sources` and `ivy.maven.lookup.javadoc` configs can significantly speed up module resolution time if these are turned off, especially behind a proxy. These could arguably be turned off by default, because when submitting jobs you probably don't care about the sources or javadoc jars. I didn't include that here but happy to look into if it's desired.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT and build passes
Closes#33088 from Kimahriman/feature/ivy-update.
Authored-by: Adam Binford <adamq43@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Fix the consistent break on Scala 2.13 build caused by the PR https://github.com/apache/spark/pull/32767
### Why are the changes needed?
Fix the consistent break.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33084 from xuanyuanking/SPARK-35628-follow.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Refactor the logic for constructing the user classpath from `yarn.ApplicationMaster` into `yarn.Client` so that it can be leveraged on the executor side as well, instead of having the driver construct it and pass it to the executor via command-line arguments. A new method, `getUserClassPath`, is added to `CoarseGrainedExecutorBackend` which defaults to `Nil` (consistent with the existing behavior where non-YARN resource managers do not configure the user classpath). `YarnCoarseGrainedExecutorBackend` overrides this to construct the user classpath from the existing `APP_JAR` and `SECONDARY_JARS` configs.
### Why are the changes needed?
User-provided JARs are made available to executors using a custom classloader, so they do not appear on the standard Java classpath. Instead, they are passed as a list to the executor which then creates a classloader out of the URLs. Currently in the case of YARN, this list of JARs is crafted by the Driver (in `ExecutorRunnable`), which then passes the information to the executors (`CoarseGrainedExecutorBackend`) by specifying each JAR on the executor command line as `--user-class-path /path/to/myjar.jar`. This can cause extremely long argument lists when there are many JARs, which can cause the OS argument length to be exceeded, typically manifesting as the error message:
> /bin/bash: Argument list too long
A [Google search](https://www.google.com/search?q=spark%20%22%2Fbin%2Fbash%3A%20argument%20list%20too%20long%22&oq=spark%20%22%2Fbin%2Fbash%3A%20argument%20list%20too%20long%22) indicates that this is not a theoretical problem and afflicts real users, including ours. Passing this list using the configurations instead resolves this issue.
### Does this PR introduce _any_ user-facing change?
No, except for fixing the bug, allowing for larger JAR lists to be passed successfully. Configuration of JARs is identical to before.
### How was this patch tested?
New unit tests were added in `YarnClusterSuite`. Also, we have been running a similar fix internally for 4 months with great success.
Closes#32810 from xkrogen/xkrogen-SPARK-35672-classpath-scalable.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This patches the hadoop configuration so that fs.s3a.endpoint is set to
s3.amazonaws.com if neither it nor fs.s3a.endpoint.region is set.
This stops S3A Filesystem creation failing with the error
"Unable to find a region via the region provider chain."
in some non-EC2 deployments.
See: HADOOP-17771.
when spark options are propagated to the hadoop configuration
in SparkHadoopUtils. the fs.s3a.endpoint value is set to
"s3.amazonaws.com" if unset and no explicit region
is set in fs.s3a.endpoint.region.
### Why are the changes needed?
A regression in Hadoop 3.3.1 has surfaced which causes S3A filesystem
instantiation to fail outside EC2 deployments if the host lacks
a CLI configuration in ~/.aws/config declaring the region, or
the `AWS_REGION` environment variable
HADOOP-17771 fixes this in Hadoop-3.3.2+, but
this spark patch will correct the behavior when running
Spark with the 3.3.1 artifacts.
It is harmless for older versions and compatible
with hadoop releases containing the HADOOP-17771
fix.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
New tests to verify propagation logic from spark conf to hadoop conf.
Closes#33064 from steveloughran/SPARK-35878-regions.
Authored-by: Steve Loughran <stevel@cloudera.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Supprot the following operations:
- TimestampWithoutTZ + Calendar interval
- TimestampWithoutTZ + Year-Month interval
- TimestampWithoutTZ + Daytime interval
### Why are the changes needed?
Support basic '+' operator for timestamp without time zone type.
### Does this PR introduce _any_ user-facing change?
No, the timestamp without time zone type is not release yet.
### How was this patch tested?
Unit tests
Closes#33076 from gengliangwang/addForNewTS.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
The implementation for the load operation of RocksDBFileManager.
### Why are the changes needed?
Provide the functionality of loading all necessary files for specific checkpoint versions from DFS to the given local directory.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT added.
Closes#32767 from xuanyuanking/SPARK-35628.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/29137 , which has some issues when running EXPLAIN FORMATTED
```
AdaptiveSparkPlan (13)
+- == Final Plan ==
* HashAggregate (12)
+- CustomShuffleReader (11)
+- ShuffleQueryStage (10)
+- Exchange (9)
+- * HashAggregate (8)
+- * Project (7)
+- * BroadcastHashJoin Inner BuildRight (6)
:- * LocalTableScan (1)
+- BroadcastQueryStage (5)
+- BroadcastExchange (4)
+- * Project (3)
+- * LocalTableScan (2)
+- == Initial Plan ==
HashAggregate (unknown)
+- Exchange (unknown)
+- HashAggregate (unknown)
+- Project (unknown)
+- BroadcastHashJoin Inner BuildRight (unknown)
:- Project (unknown)
: +- LocalTableScan (unknown)
+- BroadcastExchange (unknown)
+- Project (3)
+- LocalTableScan (2)
```
Some nodes do not have an ID and show `unknown`. This PR fixes the issue.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
EXPLAIN FORMATTED with AQE displays correctly.
### How was this patch tested?
new tests
Closes#33067 from cloud-fan/explain.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to migrate the following `ALTER TABLE ... RENAME COLUMN` command to use `UnresolvedTable` as a `child` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-29900](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce _any_ user-facing change?
After this PR, the above `ALTER TABLE ... RENAME COLUMN` commands will have a consistent resolution behavior.
### How was this patch tested?
Updated existing tests.
Closes#33066 from imback82/alter_rename.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/frame.py` and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#33073 from ueshin/issues/SPARK-35471/disallow_untyped_defs_frame.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to find and set JAVA_HOME from the javac location.
### Why are the changes needed?
Since SPARK-35850, maven compile fails with Java8 when there is no JAVA_HOME and `java` path is JRE java.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually run mvn in vanilla ubuntu.
Closes#33075 from williamhyun/util.
Authored-by: William Hyun <william@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is a followup PR for SPARK-34320 (#32854).
That PR changed the error message of `ALTER TABLE` but `V2JDBCTest` didn't comply with the change.
### Why are the changes needed?
To fix`v2.*JDBCSuite` failure.
```
[info] - SPARK-33034: ALTER TABLE ... add new columns (173 milliseconds)
[info] - SPARK-33034: ALTER TABLE ... drop column *** FAILED *** (126 milliseconds)
[info] "Cannot delete missing field bad_column in postgresql.alt_table schema: root
[info] |-- C2: string (nullable = true)
[info] ; line 1 pos 0;
[info] 'AlterTableDropColumns [unresolvedfieldname(bad_column)]
[info] +- ResolvedTable org.apache.spark.sql.execution.datasources.v2.jdbc.JDBCTableCatalog7f4b7516, alt_table, JDBCTable(alt_table,StructType(StructField(C2,StringType,true)),org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions5842301d), [C2#1879]
[info] " did not contain "Cannot delete missing field bad_column in alt_table schema" (V2JDBCTest.scala:106)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:472)
[info] at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:471)
[info] at org.scalatest.Assertions$.newAssertionFailedException(Assertions.scala:1231)
[info] at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:1295)
[info] at org.apache.spark.sql.jdbc.v2.V2JDBCTest.$anonfun$$init$$6(V2JDBCTest.scala:106)
[info] at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
[info] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1461)
[info] at org.apache.spark.sql.test.SQLTestUtilsBase.withTable(SQLTestUtils.scala:305)
[info] at org.apache.spark.sql.test.SQLTestUtilsBase.withTable$(SQLTestUtils.scala:303)
[info] at org.apache.spark.sql.jdbc.DockerJDBCIntegrationSuite.withTable(DockerJDBCIntegrationSuite.scala:95)
[info] at org.apache.spark.sql.jdbc.v2.V2JDBCTest.$anonfun$$init$$5(V2JDBCTest.scala:95)
[info] at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
[info] at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
[info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info] at org.scalatest.Transformer.apply(Transformer.scala:22)
[info] at org.scalatest.Transformer.apply(Transformer.scala:20)
[info] at org.scalatest.funsuite.AnyFunSuiteLike$$anon$1.apply(AnyFunSuiteLike.scala:190)
[info] at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:190)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.invokeWithFixture$1(AnyFunSuiteLike.scala:188)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTest$1(AnyFunSuiteLike.scala:200)
[info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTest(AnyFunSuiteLike.scala:200)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTest$(AnyFunSuiteLike.scala:182)
[info] at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:62)
[info] at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:234)
[info] at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:227)
[info] at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:62)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTests$1(AnyFunSuiteLike.scala:233)
[info] at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:413)
[info] at scala.collection.immutable.List.foreach(List.scala:431)
[info] at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
[info] at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:396)
[info] at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:475)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTests(AnyFunSuiteLike.scala:233)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTests$(AnyFunSuiteLike.scala:232)
[info] at org.scalatest.funsuite.AnyFunSuite.runTests(AnyFunSuite.scala:1563)
[info] at org.scalatest.Suite.run(Suite.scala:1112)
[info] at org.scalatest.Suite.run$(Suite.scala:1094)
[info] at org.scalatest.funsuite.AnyFunSuite.org$scalatest$funsuite$AnyFunSuiteLike$$super$run(AnyFunSuite.scala:1563)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$run$1(AnyFunSuiteLike.scala:237)
[info] at org.scalatest.SuperEngine.runImpl(Engine.scala:535)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.run(AnyFunSuiteLike.scala:237)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.run$(AnyFunSuiteLike.scala:236)
[info] at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:62)
[info] at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
[info] at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
[info] at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
[info] at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:62)
[info] at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:318)
[info] at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:513)
[info] at sbt.ForkMain$Run.lambda$runTest$1(ForkMain.java:413)
[info] at java.util.concurrent.FutureTask.run(FutureTask.java:266)
[info] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
[info] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
[info] at java.lang.Thread.run(Thread.java:748)
[info] - SPARK-33034: ALTER TABLE ... update column type (122 milliseconds)
[info] - SPARK-33034: ALTER TABLE ... rename column (93 milliseconds)
[info] - SPARK-33034: ALTER TABLE ... update column nullability (92 milliseconds)
[info] - CREATE TABLE with table comment (38 milliseconds)
[info] - CREATE TABLE with table property (52 milliseconds)
[info] MySQLIntegrationSuite:
[info] - Basic test (61 milliseconds)
[info] - Numeric types (67 milliseconds)
[info] - Date types (59 milliseconds)
[info] - String types (50 milliseconds)
[info] - Basic write test (216 milliseconds)
[info] - query JDBC option (64 milliseconds)
[info] Run completed in 19 minutes, 43 seconds.
[info] Total number of tests run: 89
[info] Suites: completed 14, aborted 0
[info] Tests: succeeded 84, failed 5, canceled 0, ignored 0, pending 0
[info] *** 5 TESTS FAILED ***
[error] Failed tests:
[error] org.apache.spark.sql.jdbc.v2.OracleIntegrationSuite
[error] org.apache.spark.sql.jdbc.v2.MsSqlServerIntegrationSuite
[error] org.apache.spark.sql.jdbc.v2.DB2IntegrationSuite
[error] org.apache.spark.sql.jdbc.v2.MySQLIntegrationSuite
[error] org.apache.spark.sql.jdbc.v2.PostgresIntegrationSuite
[error] (docker-integration-tests / Test / test) sbt.TestsFailedException: Tests unsuccessful
[error] Total time: 1223 s (20:23), completed Jun 25, 2021 1:31:04 AM
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
docker-integration-tests on GA.
Closes#33074 from sarutak/followup-SPARK-34320.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
This PR aims to use `keyserver.ubuntu.com` as a keyserver for CRAN.
### Why are the changes needed?
Currently, both servers fail and K8s IT fails at SparkR image building phase.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20K8s%20Builds/job/spark-master-test-k8s/801/console
```
$ docker run -it --rm openjdk:11 /bin/bash
root3e89a8d05378:/# echo "deb http://cloud.r-project.org/bin/linux/debian buster-cran35/" >> /etc/apt/sources.list
root3e89a8d05378:/# (apt-key adv --keyserver keys.gnupg.net --recv-key 'E19F5F87128899B192B1A2C2AD5F960A256A04AF' || apt-key adv --keyserver keys.openpgp.org --recv-key 'E19F5F87128899B192B1A2C2AD5F960A256A04AF')
Executing: /tmp/apt-key-gpghome.8lNIiUuhoE/gpg.1.sh --keyserver keys.gnupg.net --recv-key E19F5F87128899B192B1A2C2AD5F960A256A04AF
gpg: keyserver receive failed: No name
Executing: /tmp/apt-key-gpghome.stxb8XUlx8/gpg.1.sh --keyserver keys.openpgp.org --recv-key E19F5F87128899B192B1A2C2AD5F960A256A04AF
gpg: key AD5F960A256A04AF: new key but contains no user ID - skipped
gpg: Total number processed: 1
gpg: w/o user IDs: 1
root3e89a8d05378:/# apt-get update
...
Err:3 http://cloud.r-project.org/bin/linux/debian buster-cran35/ InRelease
The following signatures couldn't be verified because the public key is not available: NO_PUBKEY FCAE2A0E115C3D8A
...
W: GPG error: http://cloud.r-project.org/bin/linux/debian buster-cran35/ InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY FCAE2A0E115C3D8A
E: The repository 'http://cloud.r-project.org/bin/linux/debian buster-cran35/ InRelease' is not signed.
N: Updating from such a repository can't be done securely, and is therefore disabled by default.
N: See apt-secure(8) manpage for repository creation and user configuration details.
```
`keyserver.ubuntu.com` is a recommended backup server in CRAN document.
- http://cloud.r-project.org/bin/linux/debian/
```
$ docker run -it --rm openjdk:11 /bin/bash
rootc9b183e45ffe:/# echo "deb http://cloud.r-project.org/bin/linux/debian buster-cran35/" >> /etc/apt/sources.list
rootc9b183e45ffe:/# apt-key adv --keyserver keyserver.ubuntu.com --recv-key 'E19F5F87128899B192B1A2C2AD5F960A256A04AF'
Executing: /tmp/apt-key-gpghome.P6cxYkOge7/gpg.1.sh --keyserver keyserver.ubuntu.com --recv-key E19F5F87128899B192B1A2C2AD5F960A256A04AF
gpg: key AD5F960A256A04AF: public key "Johannes Ranke (Wissenschaftlicher Berater) <johannes.rankejrwb.de>" imported
gpg: Total number processed: 1
gpg: imported: 1
rootc9b183e45ffe:/# apt-get update
Get:1 http://deb.debian.org/debian buster InRelease [122 kB]
Get:2 http://security.debian.org/debian-security buster/updates InRelease [65.4 kB]
Get:3 http://cloud.r-project.org/bin/linux/debian buster-cran35/ InRelease [4375 B]
Get:4 http://deb.debian.org/debian buster-updates InRelease [51.9 kB]
Get:5 http://cloud.r-project.org/bin/linux/debian buster-cran35/ Packages [53.3 kB]
Get:6 http://security.debian.org/debian-security buster/updates/main arm64 Packages [287 kB]
Get:7 http://deb.debian.org/debian buster/main arm64 Packages [7735 kB]
Get:8 http://deb.debian.org/debian buster-updates/main arm64 Packages [14.5 kB]
Fetched 8334 kB in 2s (4537 kB/s)
Reading package lists... Done
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass K8s IT Jenkins SparkR image building. Or, manually do the following.
```
$ bin/docker-image-tool.sh -R kubernetes/dockerfiles/spark/bindings/R/Dockerfile build
```
Closes#33071 from dongjoon-hyun/SPARK-35885.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Update "Caching Data in Memory" section, add suggestion to call DataFrame `unpersist` method to make it consistent with previous suggestion of using `persist` method.
### Why are the changes needed?
Keep documentation consistent.
### Does this PR introduce _any_ user-facing change?
Yes, fixes the user-facing docs.
### How was this patch tested?
Manually.
Closes#33069 from Silverlight42/caching-data-doc.
Authored-by: Carlos Peña <Cdpm42@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is to introduce a new sets of APIs `newTaskTempFile` and `newTaskTempFileAbsPath` inside `FileCommitProtocol`, to allow more flexible file naming of Spark output. The major change is to pass `FileNameSpec` into `FileCommitProtocol`, instead of original `ext` (currently having `prefix` and `ext`), to allow individual `FileCommitProtocol` implementation comes up with more flexible file names (e.g. has a custom `prefix`) for Hive/Presto bucketing - https://github.com/apache/spark/pull/30003. Provide a default implementations of the added APIs, so all existing implementation of `FileCommitProtocol` is NOT being broken.
### Why are the changes needed?
To make commit protocol more flexible in terms of Spark output file name.
Pre-requisite of https://github.com/apache/spark/pull/30003.
### Does this PR introduce _any_ user-facing change?
Yes for developers who implement/run custom implementation of `FileCommitProtocol`. They can choose to implement for the newly added API.
### How was this patch tested?
Existing unit tests as this is just adding an API.
Closes#33012 from c21/commit-protocol-api.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
After the RC vote, the release manager still need to do many work to finalize the release. This PR updates the script the automatize some steps:
1. create the final git tag
2. publish to pypi
3. publish docs to spark-website
4. move the release binaries from dev directory to release directory.
5. update the KEYS file
### Why are the changes needed?
easy the work of release manager.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
tested with the recent 3.0.3.
Closes#33055 from cloud-fan/release.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR change the behavior of unit-to-unit interval syntax to prohibit the case that the same units are specified for FROM and TO.
### Why are the changes needed?
For ANSI compliance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#33057 from sarutak/prohibit-unit-pattern.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR changes the unionByName with null filling logic to append new nested struct fields from the right side of the union to the schema versus sorting fields alphabetically. It removes the need to use UpdateField expressions, and just directly projects new nested structs from each side of the union with the correct schema. This changes the union'd schema from being alphabetically sorted previously to now "left dominant", where the fields from the left side of the union are included and then the missing ones from the right are added in the same order found originally.
### Why are the changes needed?
Certain nested structs would cause unionByName with null filling to error out due to part of the logic for rewriting the expression tree to sort the structs.
### Does this PR introduce _any_ user-facing change?
Yes, nested struct fields will be in a different order after unionByName with null filling than before, though shouldn't cause much effective difference.
### How was this patch tested?
Updated existing tests based on the new StructField ordering and added a new test for the case that was broken originally.
Closes#33040 from Kimahriman/union-by-name-struct-order.
Authored-by: Adam Binford <adamq43@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to migrate the following `ALTER TABLE ... DROP COLUMNS` command to use `UnresolvedTable` as a `child` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-29900](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce _any_ user-facing change?
After this PR, the above `ALTER TABLE ... DROP COLUMNS` commands will have a consistent resolution behavior.
### How was this patch tested?
Updated existing tests.
Closes#32854 from imback82/alter_alternative.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Current Literal.create(data, dataType) for Period to YearMonthIntervalType and Duration to DayTimeIntervalType is not correct.
if data type is Period/Duration, it will create converter of default YearMonthIntervalType/DayTimeIntervalType, then the result is not correct, this pr fix this bug.
### Why are the changes needed?
Fix bug when use Literal.create()
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#33056 from AngersZhuuuu/SPARK-35871.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Revert 8a1995f936
### Why are the changes needed?
The merged test doesn't check different interval fields, actually. Need to apply this https://github.com/apache/spark/pull/33056 first of all.
### Does this PR introduce _any_ user-facing change?
No. This is tests.
### How was this patch tested?
By existing GAs.
Closes#33060 from MaxGekk/revert-Peng-Lei-SPARK-35728.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Revert 3904c0edba
### Why are the changes needed?
The merged test doesn't check different interval fields, actually. Need to apply this https://github.com/apache/spark/pull/33056 first of all.
### Does this PR introduce _any_ user-facing change?
No. This is tests.
### How was this patch tested?
By existing GAs.
Closes#33059 from MaxGekk/revert-Peng-Lei-SPARK-35778.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/series.py` and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#33045 from ueshin/issues/SPARK-35476/disallow_untyped_defs_series.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Check multiply/divide of year-month intervals of any fields by numeric.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Expanded existed test cases.
Closes#33051 from Peng-Lei/SPARK-35778.
Authored-by: PengLei <18066542445@189.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
1. The testcase is just cover the DayTimeIntervalType() */ numeric
2. Add testcase for following intervals */ numeric:
INTERVAL DAY
INTERVAL DAY TO HOUR
INTERVAL DAY TO MINUTE
INTERVAL HOUR
INTERVAL HOUR TO MINUTE
INTERVAL HOUR TO SECOND
INTERVAL MINUTE
INTERVAL MINUTE TO SECOND
INTERVAL SECOND
### Why are the changes needed?
Add testcase coverage.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existed testcase
Closes#33014 from Peng-Lei/SPARK-35728.
Authored-by: PengLei <18066542445@189.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
* Add a new repartition operator `RebalanceRepartition`.
* Support a new hint `REBALANCE`
After this patch, user can run this query:
```sql
SELECT /*+ REBALANCE(c) */ * FROM t
```
### Why are the changes needed?
Add a new hint to distingush if we can optimize it safely.
This new hint can let AQE optimize with `CustomShuffleReaderExec` safely. Currently, AQE can only coalesce shuffle partitions but can not expand shuffle partitions due to the semantics of output partitioning.
Let's say we have a query:
```sql
SELECT /*+ REPARTITION(col) */ * FROM t
```
AQE can not expand the shuffle partitions even if `col` is skewed because expanding shuffle partitions will break the hashed output paritioning of `RepartitionByExpression`. But if the query is use`REPARTITION_BY_AQE`, AQE can optimize it without considering the semantics of output partitioning.
### Does this PR introduce _any_ user-facing change?
Yes, a new hint.
### How was this patch tested?
Add test.
Closes#32932 from ulysses-you/SPARK-35786.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to add a migration guide for legacy Koalas users in pandas API on Spark.
### Why are the changes needed?
For easier migration.
### Does this PR introduce _any_ user-facing change?
Yes, this adds a new page for migration from Koalas.
### How was this patch tested?
Manually built the docs and checked manually.
Closes#33050 from HyukjinKwon/SPARK-35301.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
… sum of the digits is greater than 38
### What changes were proposed in this pull request?
Since Spark 3.1.1, NULL is returned when casting a string with many decimal places to a decimal type. If the sum of the digits before and after the decimal point is less than 39, a value is returned. From 39 digits, however, NULL is returned.
This worked until Spark 3.0.X.
Code to reproduce:
A string with 2 decimal places in front of the decimal point and 37 decimal places after the decimal point returns null
```
val data = Seq(
"28.9259999999999983799625624669715762138",
"28.925999999999998379962562466971576213",
"2.9259999999999983799625624669715762138"
)
val df = data.toDF("num")
df.withColumn("numConverted", col("num").cast("decimal(38, 5)")).show()
```
before this pull request, the result is
+----------------------+---------------+
| num |numConverted|
+----------------------+---------------+
|28.92599999999999...| null|
|28.92599999999999...| 28.92600|
|2.925999999999998...| 2.92600|
+----------------------+---------------+
the correct result should be
+----------------------+---------------+
| num |numConverted|
+----------------------+---------------+
|28.92599999999999...| 28.92600|
|28.92599999999999...| 28.92600|
|2.925999999999998...| 2.92600|
+----------------------+---------------+
The problem occur since https://issues.apache.org/jira/browse/SPARK-32706, it because the fast fail is checking precision length, which should only check the whole number part length of the input value, not the precision length
### Why are the changes needed?
correctness
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
test added
Closes#33011 from dgd-contributor/SPARK-35841_castStringToDecimalTypeError.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Simplify the condition code which is introduced by [SPARK-35282](https://issues.apache.org/jira/browse/SPARK-35282).
### Why are the changes needed?
Reduce the code size and make code more readable.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass CI
Closes#33046 from ulysses-you/simplify-shj.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support take into account year-month interval field in cast
##### Rule cast to target YearMonthIntervalType
| string | demo | strict target type | months |
|---|---|---|---|
| [+\|-]y-m | 1-1 | YearMonthIntervalType(YEAR. MONTH) | 13 |
| [+\|-]y| 1 | YearMonthIntervalType(YEAR. YEAR) | 12 |
| [+\|-]m | 1 | YearMonthIntervalType(MONTH. MONTH) | 1 |
| INTERVAL [+\|-]'[+\|-]y-m' YEAR TO MONTH | interval '1-1' year to month | YearMonthIntervalType(YEAR. MONTH) | 13 |
| INTERVAL [+\|-]'[+\|-]m' MONTH | interval '1' month | YearMonthIntervalType(MONTH. MONTH) | 1 |
| INTERVAL [+\|-]'[+\|-]y' YEAR | interval '1' year | YearMonthIntervalType(YEAR.YEAR) | 12 |
### Why are the changes needed?
Support take into account year-month interval field in cast
### Does this PR introduce _any_ user-facing change?
user can use `cast(str, YearMonthInterval(YEAR, YEAR))` etc
### How was this patch tested?
Added UT
Closes#32940 from AngersZhuuuu/SPARK-35768.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Check all day-time interval types in UDF.
### Why are the changes needed?
New checks should improve test coverage.
### Does this PR introduce _any_ user-facing change?
Yes but `DayTimeIntervalType` has not been released yet.
### How was this patch tested?
Existed UT.
Closes#33047 from AngersZhuuuu/SPARK-35730.
Lead-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Co-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR follow-up for SPARK-35696 to fix incorrect underline in the documents to remove warnings.
### Why are the changes needed?
We should build the docs without any incorrect documentation style
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually build docs and see the warning is removed
Closes#33052 from itholic/SPARK-35696-followup.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In a secure Yarn cluster, even though HBase or Kafka, or Hive services are not used in the user application, yarn client unnecessarily trying to generate Delegations token from these services. This will add additional delays while submitting spark application in a yarn cluster
Also during HBase delegation token generation step in the application submit stage, HBaseDelegationTokenProvider prints a full Exception Stack trace and it causes a noisy warning.
Apart from printing exception stack trace, Application submission taking more time as it retries connection to HBase master multiple times before it gives up. So, if HBase is not used in the user Applications, it is better to suggest User disable HBase Delegation Token generation.
This PR aims to avoid printing full Exception Stack by just printing just Exception name and also add a suggestion message to disable `Delegation Token generation` if service is not used in the Spark Application.
eg: `If HBase is not used, set spark.security.credentials.hbase.enabled to false`
### Why are the changes needed?
To avoid printing full Exception stack trace in WARN log
#### Before the fix
----------------
```
spark-shell --master yarn
.......
.......
21/06/12 14:29:41 WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase
java.lang.reflect.InvocationTargetException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.security.HBaseDelegationTokenProvider.obtainDelegationTokensWithHBaseConn(HBaseDelegationT
okenProvider.scala:93)
at org.apache.spark.deploy.security.HBaseDelegationTokenProvider.obtainDelegationTokens(HBaseDelegationTokenProvider.
scala:60)
at org.apache.spark.deploy.security.HadoopDelegationTokenManager$$anonfun$6.apply(HadoopDelegationTokenManager.scala:
166)
at org.apache.spark.deploy.security.HadoopDelegationTokenManager$$anonfun$6.apply(HadoopDelegationTokenManager.scala:
164)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
at scala.collection.MapLike$DefaultValuesIterable.foreach(MapLike.scala:206)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.deploy.security.HadoopDelegationTokenManager.obtainDelegationTokens(HadoopDelegationTokenManager.
scala:164)
```
#### After the fix
------------
```
spark-shell --master yarn
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
21/06/13 02:10:02 WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase due to java.lang.reflect.InvocationTargetException Retrying to fetch HBase security token with hbase connection parameter.
21/06/13 02:10:40 WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase java.lang.reflect.InvocationTargetException. If HBase is not used, set spark.security.credentials.hbase.enabled to false
21/06/13 02:10:47 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
```
### Does this PR introduce _any_ user-facing change?
Yes, in the log, it avoids printing full Exception stack trace.
Instread prints this.
**WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase java.lang.reflect.InvocationTargetException. If HBase is not used, set spark.security.credentials.hbase.enabled to false**
### How was this patch tested?
Tested manually as it can be verified only in a secure cluster
Closes#32894 from vinodkc/br_fix_Hbase_DT_Exception_stack_printing.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
