### What changes were proposed in this pull request?
This PR proposes to upgrade R version to 4.0.2 in the release docker image. As of SPARK-31918, we should make a release with R 4.0.0+ which works with R 3.5+ too.
### Why are the changes needed?
To unblock releases on CRAN.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Manually tested via scripts under `dev/create-release`, manually attaching to the container and checking the R version.
Closes#28922 from HyukjinKwon/SPARK-32089.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR tries to address the comment: https://github.com/apache/spark/pull/28645#discussion_r442183888
It changes `canUpCast/canCast` to allow cast from sub UDT to base UDT, in order to achieve the goal to allow UserDefinedType to use `ExpressionEncoder` to deserialize rows in ScalaUDF as well.
One thing that needs to mention is, even we allow cast from sub UDT to base UDT, it doesn't really do the cast in `Cast`. Because, yet, sub UDT and base UDT are considered as the same type(because of #16660), see:
5264164a67/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala (L81-L86)5264164a67/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala (L92-L95)
Therefore, the optimize rule `SimplifyCast` will eliminate the cast at the end.
### Why are the changes needed?
Reduce the special case caused by `UserDefinedType` in `ResolveEncodersInUDF` and `ScalaUDF`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
It should be covered by the test of `SPARK-19311`, which is also updated a little in this PR.
Closes#28920 from Ngone51/fix-udf-udt.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This change simplifies the ArrowColumnVector ListArray accessor to use provided Arrow APIs available in v0.15.0 to calculate element indices.
### Why are the changes needed?
This simplifies the code by avoiding manual calculations on the Arrow offset buffer and makes use of more stable APIs.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#28915 from BryanCutler/arrow-simplify-ArrowColumnVector-ListArray-SPARK-32080.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
R version 4.0.2 was released, see https://cran.r-project.org/doc/manuals/r-release/NEWS.html. This PR targets to upgrade R version in AppVeyor CI environment.
### Why are the changes needed?
To test the latest R versions before the release, and see if there are any regressions.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
AppVeyor will test.
Closes#28909 from HyukjinKwon/SPARK-32074.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Reset listenerRegistered when application end.
### Why are the changes needed?
Within a jvm, stop and create `SparkContext` multi times will cause the bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add UT.
Closes#28899 from ulysses-you/SPARK-32062.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fix app id link for multi attempts application in history summary page
If attempt id is available (yarn), app id link url will contain correct attempt id, like `/history/application_1561589317410_0002/1/jobs/`.
If attempt id is not available (standalone), app id link url will not contain fake attempt id, like `/history/app-20190404053606-0000/jobs/`.
### Why are the changes needed?
This PR is for fixing [32028](https://issues.apache.org/jira/browse/SPARK-32028). App id link use application attempt count as attempt id. this would cause link url wrong for below cases:
1. there are multi attempts, all links point to last attempt
2. if there is one attempt, but attempt id is not 1 (before attempt maybe crash or fail to gerenerate event file). link url points to worng attempt (1) here.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Tested this manually.
Closes#28867 from zhli1142015/fix-appid-link-in-history-page.
Authored-by: Zhen Li <zhli@microsoft.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Spark 3.0 accidentally dropped R < 3.5. It is built by R 3.6.3 which not support R < 3.5:
```
Error in readRDS(pfile) : cannot read workspace version 3 written by R 3.6.3; need R 3.5.0 or newer version.
```
In fact, with SPARK-31918, we will have to drop R < 3.5 entirely to support R 4.0.0. This is inevitable to release on CRAN because they require to make the tests pass with the latest R.
### Why are the changes needed?
To show the supported versions correctly, and support R 4.0.0 to unblock the releases.
### Does this PR introduce _any_ user-facing change?
In fact, no because Spark 3.0.0 already does not work with R < 3.5.
Compared to Spark 2.4, yes. R < 3.5 would not work.
### How was this patch tested?
Jenkins should test it out.
Closes#28908 from HyukjinKwon/SPARK-32073.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to ignore S4 generic methods under SparkR namespace in closure cleaning to support R 4.0.0+.
Currently, when you run the codes that runs R native codes, it fails as below with R 4.0.0:
```r
df <- createDataFrame(lapply(seq(100), function (e) list(value=e)))
count(dapply(df, function(x) as.data.frame(x[x$value < 50,]), schema(df)))
```
```
org.apache.spark.SparkException: R unexpectedly exited.
R worker produced errors: Error in lapply(part, FUN) : attempt to bind a variable to R_UnboundValue
```
The root cause seems to be related to when an S4 generic method is manually included into the closure's environment via `SparkR:::cleanClosure`. For example, when an RRDD is created via `createDataFrame` with calling `lapply` to convert, `lapply` itself:
f53d8c63e8/R/pkg/R/RDD.R (L484)
is added into the environment of the cleaned closure - because this is not an exposed namespace; however, this is broken in R 4.0.0+ for an unknown reason with an error message such as "attempt to bind a variable to R_UnboundValue".
Actually, we don't need to add the `lapply` into the environment of the closure because it is not supposed to be called in worker side. In fact, there is no private generic methods supposed to be called in worker side in SparkR at all from my understanding.
Therefore, this PR takes a simpler path to work around just by explicitly excluding the S4 generic methods under SparkR namespace to support R 4.0.0. in SparkR.
### Why are the changes needed?
To support R 4.0.0+ with SparkR, and unblock the releases on CRAN. CRAN requires the tests pass with the latest R.
### Does this PR introduce _any_ user-facing change?
Yes, it will support R 4.0.0 to end-users.
### How was this patch tested?
Manually tested. Both CRAN and tests with R 4.0.1:
```
══ testthat results ═══════════════════════════════════════════════════════════
[ OK: 13 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 0 ]
✔ | OK F W S | Context
✔ | 11 | binary functions [2.5 s]
✔ | 4 | functions on binary files [2.1 s]
✔ | 2 | broadcast variables [0.5 s]
✔ | 5 | functions in client.R
✔ | 46 | test functions in sparkR.R [6.3 s]
✔ | 2 | include R packages [0.3 s]
✔ | 2 | JVM API [0.2 s]
✔ | 75 | MLlib classification algorithms, except for tree-based algorithms [86.3 s]
✔ | 70 | MLlib clustering algorithms [44.5 s]
✔ | 6 | MLlib frequent pattern mining [3.0 s]
✔ | 8 | MLlib recommendation algorithms [9.6 s]
✔ | 136 | MLlib regression algorithms, except for tree-based algorithms [76.0 s]
✔ | 8 | MLlib statistics algorithms [0.6 s]
✔ | 94 | MLlib tree-based algorithms [85.2 s]
✔ | 29 | parallelize() and collect() [0.5 s]
✔ | 428 | basic RDD functions [25.3 s]
✔ | 39 | SerDe functionality [2.2 s]
✔ | 20 | partitionBy, groupByKey, reduceByKey etc. [3.9 s]
✔ | 4 | functions in sparkR.R
✔ | 16 | SparkSQL Arrow optimization [19.2 s]
✔ | 6 | test show SparkDataFrame when eager execution is enabled. [1.1 s]
✔ | 1175 | SparkSQL functions [134.8 s]
✔ | 42 | Structured Streaming [478.2 s]
✔ | 16 | tests RDD function take() [1.1 s]
✔ | 14 | the textFile() function [2.9 s]
✔ | 46 | functions in utils.R [0.7 s]
✔ | 0 1 | Windows-specific tests
────────────────────────────────────────────────────────────────────────────────
test_Windows.R:22: skip: sparkJars tag in SparkContext
Reason: This test is only for Windows, skipped
────────────────────────────────────────────────────────────────────────────────
══ Results ═════════════════════════════════════════════════════════════════════
Duration: 987.3 s
OK: 2304
Failed: 0
Warnings: 0
Skipped: 1
...
Status: OK
+ popd
Tests passed.
```
Note that I tested to build SparkR in R 4.0.0, and run the tests with R 3.6.3. It all passed. See also [the comment in the JIRA](https://issues.apache.org/jira/browse/SPARK-31918?focusedCommentId=17142837&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-17142837).
Closes#28907 from HyukjinKwon/SPARK-31918.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Replace Decimal by Int op in the `MakeInterval` & `MakeTimestamp` expression. For instance, `(secs * Decimal(MICROS_PER_SECOND)).toLong` can be replaced by the unscaled long because the former one already contains microseconds.
### Why are the changes needed?
To improve performance.
Before:
```
make_timestamp(): Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
...
make_timestamp(2019, 1, 2, 3, 4, 50.123456) 94 99 4 10.7 93.8 38.8X
```
After:
```
make_timestamp(2019, 1, 2, 3, 4, 50.123456) 76 92 15 13.1 76.5 48.1X
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- By existing test suites `IntervalExpressionsSuite`, `DateExpressionsSuite` and etc.
- Re-generate results of `MakeDateTimeBenchmark` in the environment:
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1 (ami-06f2f779464715dc5) |
| Java | OpenJDK 64-Bit Server VM 1.8.0_252 and OpenJDK 64-Bit Server VM 11.0.7+10 |
Closes#28886 from MaxGekk/make_interval-opt-decimal.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`OracleIntegrationSuite` is not using the latest oracle JDBC driver. In this PR I've upgraded the driver to the latest which supports JDK8, JDK9, and JDK11.
### Why are the changes needed?
Old JDBC driver.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Existing integration tests (especially `OracleIntegrationSuite`)
Closes#28893 from gaborgsomogyi/SPARK-32049.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to upgrade to Apache Commons Lang 3.10.
### Why are the changes needed?
This will bring the latest bug fixes like [LANG-1453](https://issues.apache.org/jira/browse/LANG-1453).
https://commons.apache.org/proper/commons-lang/release-notes/RELEASE-NOTES-3.10.txt
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the Jenkins.
Closes#28889 from williamhyun/commons-lang-3.10.
Authored-by: William Hyun <williamhyun3@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Extract common code from the expressions that get date or time fields from input dates/timestamps to new expressions `GetDateField` and `GetTimeField`, and re-use the common traits from the affected classes.
### Why are the changes needed?
Code deduplication improves maintainability.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By `DateExpressionsSuite`
Closes#28894 from MaxGekk/get-date-time-field-expr.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Call the `replace()` method from `UTF8String` to remove the `GMT` string from the input of `DateTimeUtils.cleanLegacyTimestampStr`. It removes all `GMT` substrings.
### Why are the changes needed?
Simpler code improves maintainability
### Does this PR introduce _any_ user-facing change?
Should not
### How was this patch tested?
By existing test suites `JsonSuite` and `UnivocityParserSuite`.
Closes#28892 from MaxGekk/simplify-cleanLegacyTimestampStr.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR changes the references of the `PartialMerge`/`Final` `AggregateExpression` from `aggBufferAttributes` to `inputAggBufferAttributes`.
After this change, the tests of `SPARK-31620` can fail on the assertion of `QueryTest.assertEmptyMissingInput`. So, this PR also fixes it by overriding the `inputAggBufferAttributes` of the Aggregate operators.
### Why are the changes needed?
With my understanding of Aggregate framework, especially, according to the logic of `AggUtils.planAggXXX`, I think for the `PartialMerge`/`Final` `AggregateExpression` the right references should be `inputAggBufferAttributes`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Before this patch, for an Aggregate operator, its input attributes will always be equal to or more than(because it refers to its own attributes while it should refer to the attributes from the child) its reference attributes. Therefore, its missing inputs must always be empty and break nothing. Thus, it's impossible to add a UT for this patch.
However, after correcting the right references in this PR, the problem is then exposed by `QueryTest.assertEmptyMissingInput` in the UT of SPARK-31620, since missing inputs are no longer always empty. This PR can fix the problem.
Closes#28869 from Ngone51/fix-agg-reference.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
A minor PR that adds a couple of usage examples for ArrayFilter and ArrayExists that shows how to deal with NULL data.
### Why are the changes needed?
Enhances the examples that shows how to filter out null values from an array and also to test if null value exists in an array.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Tested manually.
Closes#28890 from dilipbiswal/array_func_description.
Authored-by: Dilip Biswal <dkbiswal@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch applies `NormalizeFloatingNumbers` to distinct aggregate to fix a regression of distinct aggregate on NaNs.
### Why are the changes needed?
We added `NormalizeFloatingNumbers` optimization rule in 3.0.0 to normalize special floating numbers (NaN and -0.0). But it is missing in distinct aggregate so causes a regression. We need to apply this rule on distinct aggregate to fix it.
### Does this PR introduce _any_ user-facing change?
Yes, fixing a regression of distinct aggregate on NaNs.
### How was this patch tested?
Added unit test.
Closes#28876 from viirya/SPARK-32038.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add compatibility tests for streaming state store format.
### Why are the changes needed?
After SPARK-31894, we have a validation checking for the streaming state store. It's better to add integrated tests in the PR builder as soon as the breaking changes introduced.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Test only.
Closes#28725 from xuanyuanking/compatibility_check.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Introduces the concept of a `MapOutputMetadata` opaque object that can be returned from map output writers.
Note that this PR only proposes the API changes on the shuffle writer side. Following patches will be proposed for actually accepting the metadata on the driver and persisting it in the driver's shuffle metadata storage plugin.
### Why are the changes needed?
For a more complete design discussion on this subject as a whole, refer to [this design document](https://docs.google.com/document/d/1Aj6IyMsbS2sdIfHxLvIbHUNjHIWHTabfknIPoxOrTjk/edit#).
### Does this PR introduce any user-facing change?
Enables additional APIs for the shuffle storage plugin tree. Usage will become more apparent as the API evolves.
### How was this patch tested?
No tests here, since this is only an API-side change that is not consumed by core Spark itself.
Closes#28616 from mccheah/return-map-output-metadata.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR port https://issues.apache.org/jira/browse/HIVE-14817 for spark thrift server.
### Why are the changes needed?
When stopping the HiveServer2, the non-daemon thread stops the server from terminating
```sql
"HiveServer2-Background-Pool: Thread-79" #79 prio=5 os_prio=31 tid=0x00007fde26138800 nid=0x13713 waiting on condition [0x0000700010c32000]
java.lang.Thread.State: TIMED_WAITING (sleeping)
at java.lang.Thread.sleep(Native Method)
at org.apache.hive.service.cli.session.SessionManager$1.sleepInterval(SessionManager.java:178)
at org.apache.hive.service.cli.session.SessionManager$1.run(SessionManager.java:156)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
```
Here is an example to reproduce:
https://github.com/yaooqinn/kyuubi/blob/master/kyuubi-spark-sql-engine/src/main/scala/org/apache/kyuubi/spark/SparkSQLEngineApp.scala
Also, it causes issues as HIVE-14817 described which
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Passing Jenkins
Closes#28870 from yaooqinn/SPARK-32034.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
To allow alternatives to serviceaccounts
### Why are the changes needed?
Although we provide some authentication configuration, such as spark.kubernetes.authenticate.driver.mounted.oauthTokenFile, spark.kubernetes.authenticate.driver.mounted.caCertFile, etc.
But there is a bug as we forced the service account so when we use one of them, driver still use the KUBERNETES_SERVICE_ACCOUNT_TOKEN_PATH file, and the error look like bellow:
the KUBERNETES_SERVICE_ACCOUNT_TOKEN_PATH serviceAccount not exists
### Does this PR introduce any user-facing change?
Yes user can now use `spark.kubernetes.authenticate.driver.mounted.caCertFile`
or token file by `spark.kubernetes.authenticate.driver.mounted.oauthTokenFile`
## How was this patch tested?
Manually passed the certificates using `spark.kubernetes.authenticate.driver.mounted.caCertFile`
or token file by `spark.kubernetes.authenticate.driver.mounted.oauthTokenFile` if there is no default service account available.
Closes#24601 from Udbhav30/serviceaccount.
Authored-by: Udbhav30 <u.agrawal30@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add a new config `spark.sql.files.minPartitionNum` to control file split partition in local session.
### Why are the changes needed?
Aims to control file split partitions in session level.
More details see discuss in [PR-28778](https://github.com/apache/spark/pull/28778).
### Does this PR introduce _any_ user-facing change?
Yes, new config.
### How was this patch tested?
Add UT.
Closes#28853 from ulysses-you/SPARK-32019.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add a generic ClassificationSummary trait
### Why are the changes needed?
Add a generic ClassificationSummary trait so all the classification models can use it to implement summary.
Currently in classification, we only have summary implemented in ```LogisticRegression```. There are requests to implement summary for ```LinearSVCModel``` in https://issues.apache.org/jira/browse/SPARK-20249 and to implement summary for ```RandomForestClassificationModel``` in https://issues.apache.org/jira/browse/SPARK-23631. If we add a generic ClassificationSummary trait and put all the common code there, we can easily add summary to ```LinearSVCModel``` and ```RandomForestClassificationModel```, and also add summary to all the other classification models.
We can use the same approach to add a generic RegressionSummary trait to regression package and implement summary for all the regression models.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
existing tests
Closes#28710 from huaxingao/summary_trait.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Comparing to the long-running ThriftServer via start-script, we are more likely to hit the issue https://issues.apache.org/jira/browse/HIVE-10415 / https://issues.apache.org/jira/browse/SPARK-31626 in the developer API `startWithContext`
This PR apply SPARK-31626 to the developer API `startWithContext`
### Why are the changes needed?
Fix the issue described in SPARK-31626
### Does this PR introduce _any_ user-facing change?
Yes, the hive scratch dir will be deleted if cleanup is enabled for calling `startWithContext`
### How was this patch tested?
new test
Closes#28784 from yaooqinn/SPARK-31957.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Change precision of seconds and its fraction from 8 to 18 to be able to construct intervals of max allowed microseconds value (long).
### Why are the changes needed?
To improve UX of Spark SQL.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
- Add tests to IntervalExpressionsSuite
- Add an example to the `MakeInterval` expression
- Add tests to `interval.sql`
Closes#28873 from MaxGekk/make_interval-sec-precision.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
1. Add judge equal as bigger condition in `org.apache.spark.sql.catalyst.expressions.Sequence.TemporalSequenceImpl#eval`
2. Unit test for interval `day`, `month`, `year`
### Why are the changes needed?
Bug exists when sequence input get same equal start and end dates, which will occur `while loop` forever
### Does this PR introduce _any_ user-facing change?
Yes,
Before this PR, people will get a `java.lang.ArrayIndexOutOfBoundsException`, when eval as below:
`sql("select sequence(cast('2011-03-01' as date), cast('2011-03-01' as date), interval 1 year)").show(false)
`
### How was this patch tested?
Unit test.
Closes#28819 from TJX2014/master-SPARK-31980.
Authored-by: TJX2014 <xiaoxingstack@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
When two bucketed tables with different number of buckets are joined, it can introduce a full shuffle:
```
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", "0")
val df1 = (0 until 20).map(i => (i % 5, i % 13, i.toString)).toDF("i", "j", "k")
val df2 = (0 until 20).map(i => (i % 7, i % 11, i.toString)).toDF("i", "j", "k")
df1.write.format("parquet").bucketBy(8, "i").saveAsTable("t1")
df2.write.format("parquet").bucketBy(4, "i").saveAsTable("t2")
val t1 = spark.table("t1")
val t2 = spark.table("t2")
val joined = t1.join(t2, t1("i") === t2("i"))
joined.explain
== Physical Plan ==
*(5) SortMergeJoin [i#44], [i#50], Inner
:- *(2) Sort [i#44 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(i#44, 200), true, [id=#105]
: +- *(1) Project [i#44, j#45, k#46]
: +- *(1) Filter isnotnull(i#44)
: +- *(1) ColumnarToRow
: +- FileScan parquet default.t1[i#44,j#45,k#46] Batched: true, DataFilters: [isnotnull(i#44)], Format: Parquet, Location: InMemoryFileIndex[...], PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int,j:int,k:string>, SelectedBucketsCount: 8 out of 8
+- *(4) Sort [i#50 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(i#50, 200), true, [id=#115]
+- *(3) Project [i#50, j#51, k#52]
+- *(3) Filter isnotnull(i#50)
+- *(3) ColumnarToRow
+- FileScan parquet default.t2[i#50,j#51,k#52] Batched: true, DataFilters: [isnotnull(i#50)], Format: Parquet, Location: InMemoryFileIndex[...], PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int,j:int,k:string>, SelectedBucketsCount: 4 out of 4
```
This PR proposes to introduce coalescing buckets when the following conditions are met to eliminate the full shuffle:
- Join is the sort merge one (which is created only for equi-join).
- Join keys match with output partition expressions on their respective sides.
- The larger bucket number is divisible by the smaller bucket number.
- `spark.sql.bucketing.coalesceBucketsInSortMergeJoin.enabled` is set to `true`.
- The ratio of the number of buckets should be less than the value set in `spark.sql.bucketing.coalesceBucketsInSortMergeJoin.maxBucketRatio`.
### Why are the changes needed?
Eliminating the full shuffle can benefit for scenarios where two large tables are joined. Especially when the tables are already bucketed but differ in the number of buckets, we could take advantage of it.
### Does this PR introduce any user-facing change?
If the bucket coalescing conditions explained above are met, a full shuffle can be eliminated (also note that you will see `SelectedBucketsCount: 8 out of 8 (Coalesced to 4)` in the physical plan):
```
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", "0")
spark.conf.set("spark.sql.bucketing.coalesceBucketsInSortMergeJoin.enabled", "true")
val df1 = (0 until 20).map(i => (i % 5, i % 13, i.toString)).toDF("i", "j", "k")
val df2 = (0 until 20).map(i => (i % 7, i % 11, i.toString)).toDF("i", "j", "k")
df1.write.format("parquet").bucketBy(8, "i").saveAsTable("t1")
df2.write.format("parquet").bucketBy(4, "i").saveAsTable("t2")
val t1 = spark.table("t1")
val t2 = spark.table("t2")
val joined = t1.join(t2, t1("i") === t2("i"))
joined.explain
== Physical Plan ==
*(3) SortMergeJoin [i#44], [i#50], Inner
:- *(1) Sort [i#44 ASC NULLS FIRST], false, 0
: +- *(1) Project [i#44, j#45, k#46]
: +- *(1) Filter isnotnull(i#44)
: +- *(1) ColumnarToRow
: +- FileScan parquet default.t1[i#44,j#45,k#46] Batched: true, DataFilters: [isnotnull(i#44)], Format: Parquet, Location: InMemoryFileIndex[...], PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int,j:int,k:string>, SelectedBucketsCount: 8 out of 8 (Coalesced to 4)
+- *(2) Sort [i#50 ASC NULLS FIRST], false, 0
+- *(2) Project [i#50, j#51, k#52]
+- *(2) Filter isnotnull(i#50)
+- *(2) ColumnarToRow
+- FileScan parquet default.t2[i#50,j#51,k#52] Batched: true, DataFilters: [isnotnull(i#50)], Format: Parquet, Location: InMemoryFileIndex[...], PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int,j:int,k:string>, SelectedBucketsCount: 4 out of 4
```
### How was this patch tested?
Added unit tests
Closes#28123 from imback82/coalescing_bucket.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Spark uses an old and deprecated API named `KafkaConsumer.poll(long)` which never returns and stays in live lock if metadata is not updated (for instance when broker disappears at consumer creation). Please see [Kafka documentation](https://kafka.apache.org/25/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll-long-) and [standalone test application](https://github.com/gaborgsomogyi/kafka-get-assignment) for further details.
In this PR I've applied the new `KafkaConsumer.poll(Duration)` API on executor side. Please note driver side still uses the old API which will be fixed in SPARK-32032.
### Why are the changes needed?
Infinite wait in `KafkaConsumer.poll(long)`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#28871 from gaborgsomogyi/SPARK-32033.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
#31029 # What changes were proposed in this pull request?
In YarnSchedulerBackend, we should avoid using the global execution context for its Future. Otherwise if user's Spark application also uses global execution context for its Future, the user is facing indeterministic behavior in terms of the thread's context class loader.
### Why are the changes needed?
When running tpc-ds test (https://github.com/databricks/spark-sql-perf), occasionally we see error related to class not found:
2020-02-04 20:00:26,673 ERROR yarn.ApplicationMaster: User class threw exception: scala.ScalaReflectionException: class com.databricks.spark.sql.perf.ExperimentRun in JavaMirror with
sun.misc.Launcher$AppClassLoader28ba21f3 of type class sun.misc.Launcher$AppClassLoader with classpath [...]
and parent being sun.misc.Launcher$ExtClassLoader3ff5d147 of type class sun.misc.Launcher$ExtClassLoader with classpath [...]
and parent being primordial classloader with boot classpath [...] not found.
This is the root cause for the problem:
Spark driver starts ApplicationMaster in the main thread, which starts a user thread and set MutableURLClassLoader to that thread's ContextClassLoader.
userClassThread = startUserApplication()
The main thread then setup YarnSchedulerBackend RPC endpoints, which handles these calls using scala Future with the default global ExecutionContext:
doRequestTotalExecutors
doKillExecutors
So for the main thread and user thread, whoever starts the future first get a chance to set ContextClassLoader to the default thread pool:
- If main thread starts a future to handle doKillExecutors() before user thread does then the default thread pool thread's ContextClassLoader would be the default (AppClassLoader).
- If user thread starts a future first then the thread pool thread will have MutableURLClassLoader.
Note that only MutableURLClassLoader can load user provided class for the Spark app, you will see errors related to class not found if the ContextClassLoader is AppClassLoader.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing unit tests and manual tests
Closes#27843 from shanyu/shanyu-31029.
Authored-by: Shanyu Zhao <shzhao@microsoft.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This PR adds support for typed Scala UDF to accept composed type of case class, e.g. Seq[T], Array[T], Map[Int, T] (assuming T is case class type), as input parameter type.
### Why are the changes needed?
After #27937, typed Scala UDF now has supported case class as its input parameter type. However, it can not accept the composed type of case class, such as Seq[T], Array[T], Map[Int, T] (assuming T is case class type), which causing confuse(e.g. https://github.com/apache/spark/pull/27937#discussion_r422699979) to the user.
### Does this PR introduce _any_ user-facing change?
Yes.
Run the query:
```
scala> case class Person(name: String, age: Int)
scala> Seq((1, Seq(Person("Jack", 5)))).toDF("id", "persons").withColumn("ages", udf{ s: Seq[Person] => s.head.age }.apply(col("persons"))).show
```
Before:
```
org.apache.spark.SparkException: Failed to execute user defined function($read$$Lambda$2861/628175152: (array<struct<name:string,age:int>>) => int)
at org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1129)
at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:156)
at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(InterpretedMutableProjection.scala:83)
at org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation$$anonfun$apply$17.$anonfun$applyOrElse$69(Optimizer.scala:1492)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
....
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast to Person
at $anonfun$res3$1(<console>:30)
at $anonfun$res3$1$adapted(<console>:30)
at org.apache.spark.sql.catalyst.expressions.ScalaUDF.$anonfun$f$2(ScalaUDF.scala:156)
at org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1126)
... 142 more
```
After:
```
+---+-----------+----+
| id| persons|ages|
+---+-----------+----+
| 1|[[Jack, 5]]| [5]|
+---+-----------+----+
```
### How was this patch tested?
Added tests.
Closes#28645 from Ngone51/impr-udf.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR brings https://github.com/apache/spark/pull/28751 back
- It once reverted by 4a25200 because of inevitable maven test failure
- See related updates in this followup a0187cd6b5
- And reverted again because of the flakiness of the added unit tests
- In this PR, The flakiness reason found is caused by the hive metastore connection that the SparkSQLCLIService trying to create which turns out is unnecessary at all. This metastore client points to a dummy metastore server only.
- Also, add some cleanups for SharedThriftServer trait in before and after to prevent its configurations being polluted or polluting others
### Why are the changes needed?
fix flaky test
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
passing sbt and maven tests
Closes#28835 from yaooqinn/SPARK-31926-F.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Introduce UnsafeRow format validation for streaming state store.
### Why are the changes needed?
Currently, Structured Streaming directly puts the UnsafeRow into StateStore without any schema validation. It's a dangerous behavior when users reusing the checkpoint file during migration. Any changes or bug fix related to the aggregate function may cause random exceptions, even the wrong answer, e.g SPARK-28067.
### Does this PR introduce _any_ user-facing change?
Yes. If the underlying changes are detected when the checkpoint is reused during migration, the InvalidUnsafeRowException will be thrown.
### How was this patch tested?
UT added. Will also add integrated tests for more scenario in another PR separately.
Closes#28707 from xuanyuanking/SPARK-31894.
Lead-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Co-authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Move TimeZoneUTC and TimeZoneGMT to DateTimeTestUtils
2. Remove TimeZoneGMT
3. Use ZoneId.systemDefault() instead of defaultTimeZone().toZoneId
4. Alias SQLDate & SQLTimestamp to internal types of DateType and TimestampType
5. Avoid one `*` `DateTimeUtils`.`in fromJulianDay()`
6. Use toTotalMonths in `DateTimeUtils`.`subtractDates()`
7. Remove `julianCommonEraStart`, `timestampToString()`, `microsToEpochDays()`, `epochDaysToMicros()`, `instantToDays()` from `DateTimeUtils`.
8. Make splitDate() private.
9. Remove `def daysToMicros(days: Int): Long` and `def microsToDays(micros: Long): Int`.
### Why are the changes needed?
This simplifies the common code related to date-time operations, and should improve maintainability. In particular:
1. TimeZoneUTC and TimeZoneGMT are moved to DateTimeTestUtils because they are used only in tests
2. TimeZoneGMT can be removed because it is equal to TimeZoneUTC
3. After the PR #27494, Spark expressions and DateTimeUtils functions switched to ZoneId instead of TimeZone completely. `defaultTimeZone()` with `TimeZone` as return type is not needed anymore.
4. SQLDate and SQLTimestamp types can be explicitly aliased to internal types of DateType and and TimestampType instead of declaring this in a comment.
5. Avoid one `*` `DateTimeUtils`.`in fromJulianDay()`.
6. Use toTotalMonths in `DateTimeUtils`.`subtractDates()`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test suites
Closes#27617 from MaxGekk/move-time-zone-consts.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Emphasize the Streaming tab is for DStream API.
### Why are the changes needed?
Some users reported that it's a little confusing of the streaming tab and structured streaming tab.
### Does this PR introduce _any_ user-facing change?
Document change.
### How was this patch tested?
N/A
Closes#28854 from xuanyuanking/minor-doc.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fix executor container name typo. `executor` should be `spark-kubernetes-executor`.
### Why are the changes needed?
The Executor pod container name the users actually get from their Kubernetes clusters is different from that described in the documentation.
For example, below is what a user get from an executor pod.
```
Containers:
spark-kubernetes-executor:
Container ID: docker://aaaabbbbccccddddeeeeffff
Image: <imagename>
Image ID: docker-pullable://0000.dkr.ecr.us-east-0.amazonaws.com/spark
Port: 7079/TCP
Host Port: 0/TCP
Args:
executor
State: Running
Started: Thu, 28 May 2020 05:54:04 -0700
Ready: True
Restart Count: 0
Limits:
memory: 16Gi
```
### Does this PR introduce _any_ user-facing change?
Document change.
### How was this patch tested?
N/A
Closes#28862 from yuj/patch-1.
Authored-by: James Yu <yuj@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
This is auto-updated by running script `translate-contributors.py`
Closes#28861 from cloud-fan/update.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Better error message when SPARK_HOME or spark,test.home is not set.
### Why are the changes needed?
Currently the error message is not easily consumable as it prints (see below) the real error after printing the current environment which is rather long.
**Old output**
`
time.name" -> "Java(TM) SE Runtime Environment", "sun.boot.library.path" -> "/Library/Java/JavaVirtualMachines/jdk1.8.0_221.jdk/Contents/Home/jre/lib",
"java.vm.version" -> "25.221-b11",
. . .
. . .
. . .
) did not contain key "SPARK_HOME" spark.test.home or SPARK_HOME is not set.
at org.scalatest.Assertions.newAssertionFailedExceptio
`
**New output**
An exception or error caused a run to abort: spark.test.home or SPARK_HOME is not set.
org.scalatest.exceptions.TestFailedException: spark.test.home or SPARK_HOME is not set
### Does this PR introduce any user-facing change?
`
No.
### How was this patch tested?
Ran the tests in intellej manually to see the new error.
Closes#28825 from dilipbiswal/minor-spark-31950-followup.
Authored-by: Dilip Biswal <dkbiswal@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
If a Spark distribution has built-in hadoop runtime, Spark will not populate the hadoop classpath from `yarn.application.classpath` and `mapreduce.application.classpath` when a job is submitted to Yarn. Users can override this behavior by setting `spark.yarn.populateHadoopClasspath` to `true`.
### Why are the changes needed?
Without this, Spark will populate the hadoop classpath from `yarn.application.classpath` and `mapreduce.application.classpath` even Spark distribution has built-in hadoop. This results jar conflict and many unexpected behaviors in runtime.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually test with two builds, with-hadoop and no-hadoop builds.
Closes#28788 from dbtsai/yarn-classpath.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This PR changes the test to get an active executorId and set it as preferred location instead of setting a fixed preferred location.
### Why are the changes needed?
The test is flaky. After checking the [log](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/124086/artifact/core/), I find the root cause is:
Two test cases from different test suites got submitted at the same time because of concurrent execution. In this particular case, the two test cases (from DistributedSuite and BarrierTaskContextSuite) both launch under local-cluster mode. The two applications are submitted at the SAME time so they have the same applications(app-20200615210132-0000). Thus, when the cluster of BarrierTaskContextSuite is launching executors, it failed to create the directory for the executor 0, because the path (/home/jenkins/workspace/work/app-app-20200615210132-0000/0) has been used by the cluster of DistributedSuite. Therefore, it has to launch executor 1 and 2 instead, that lead to non of the tasks can get preferred locality thus they got scheduled together and lead to the test failure.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The test can not be reproduced locally. We can only know it's been fixed when it's no longer flaky on Jenkins.
Closes#28849 from Ngone51/fix-spark-32000.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Add method `getTimeFormatters` to `HiveResult` which creates timestamp and date formatters.
2. Move creation of `dateFormatter` and `timestampFormatter` from the constructor of the `HiveResult` object to `HiveResult. hiveResultString()` via `getTimeFormatters`. This allows to resolve time zone ID from Spark's session time zone `spark.sql.session.timeZone` and create date/timestamp formatters only once before collecting `java.sql.Timestamp`/`java.sql.Date` values.
3. Create date/timestamp formatters once in SparkExecuteStatementOperation.
### Why are the changes needed?
To fix perf regression comparing to Spark 2.4
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- By existing test suite `HiveResultSuite` and etc.
- Re-generate benchmarks results of `DateTimeBenchmark` in the environment:
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1 (ami-06f2f779464715dc5) |
| Java | OpenJDK 64-Bit Server VM 1.8.0_252 and OpenJDK 64-Bit Server VM 11.0.7+10 |
Closes#28842 from MaxGekk/opt-toHiveString-oss-master.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This aims to replace `executorIdsToBeRemoved` with `executorIdToBeRemoved`.
### Why are the changes needed?
Since a wrong variable is used currently, `ArrayBuffer()` is always displayed.
```
20/06/16 19:33:31 WARN ExecutorAllocationManager: Not removing executor ArrayBuffer() because the ResourceProfile was UNKNOWN!
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual.
Closes#28847 from dongjoon-hyun/SPARK-29148.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to remove the warning about multi-thread in local properties, and change the guide to use `collectWithJobGroup` for multi-threads for now because:
- It is too noisy to users who don't use multiple threads - the number of this single thread case is arguably more prevailing.
- There was a critical issue found about pin-thread mode SPARK-32010, which will be fixed in Spark 3.1.
- To smoothly migrate, `RDD.collectWithJobGroup` was added, which will be deprecated in Spark 3.1 with SPARK-32010 fixed.
I will target to deprecate `RDD.collectWithJobGroup`, and make this pin-thread mode stable in Spark 3.1. In the future releases, I plan to make this mode as a default mode, and remove `RDD.collectWithJobGroup` away.
### Why are the changes needed?
To avoid guiding users a feature with a critical issue, and provide a proper workaround for now.
### Does this PR introduce _any_ user-facing change?
Yes, warning message and documentation.
### How was this patch tested?
Manually tested:
Before:
```
>>> spark.sparkContext.setLocalProperty("a", "b")
/.../spark/python/pyspark/util.py:141: UserWarning: Currently, 'setLocalProperty' (set to local
properties) with multiple threads does not properly work.
Internally threads on PVM and JVM are not synced, and JVM thread can be reused for multiple
threads on PVM, which fails to isolate local properties for each thread on PVM.
To work around this, you can set PYSPARK_PIN_THREAD to true (see SPARK-22340). However,
note that it cannot inherit the local properties from the parent thread although it isolates each
thread on PVM and JVM with its own local properties.
To work around this, you should manually copy and set the local properties from the parent thread
to the child thread when you create another thread.
```
After:
```
>>> spark.sparkContext.setLocalProperty("a", "b")
```
Closes#28845 from HyukjinKwon/SPARK-32011.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Change the max step from 1 week to 30 minutes in the tests `RebaseDateTimeSuite`.`generate 'gregorian-julian-rebase-micros.json'` and `generate 'julian-gregorian-rebase-micros.json'`.
2. Parallelise JSON files generation in the function `generateRebaseJson` by using `ThreadUtils.parmap`.
### Why are the changes needed?
1. To prevent the bugs that are fixed by https://github.com/apache/spark/pull/28787 and https://github.com/apache/spark/pull/28816.
2. The parallelisation speeds up JSON file generation.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By generating the JSON file `julian-gregorian-rebase-micros.json`.
Closes#28827 from MaxGekk/rebase-30-min.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
When loading DataFrames from JDBC datasource with Kerberos authentication, remote executors (yarn-client/cluster etc. modes) fail to establish a connection due to lack of Kerberos ticket or ability to generate it.
This is a real issue when trying to ingest data from kerberized data sources (SQL Server, Oracle) in enterprise environment where exposing simple authentication access is not an option due to IT policy issues.
In this PR I've added MS SQL support.
What this PR contains:
* Added `MSSQLConnectionProvider`
* Added `MSSQLConnectionProviderSuite`
* Changed MS SQL JDBC driver to use the latest (test scope only)
* Changed `MsSqlServerIntegrationSuite` docker image to use the latest
* Added a version comment to `MariaDBConnectionProvider` to increase trackability
### Why are the changes needed?
Missing JDBC kerberos support.
### Does this PR introduce _any_ user-facing change?
Yes, now user is able to connect to MS SQL using kerberos.
### How was this patch tested?
* Additional + existing unit tests
* Existing integration tests
* Test on cluster manually
Closes#28635 from gaborgsomogyi/SPARK-31337.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@apache.org>
### What changes were proposed in this pull request?
This patch adds user-specified fold column support to `CrossValidator`. User can assign fold numbers to dataset instead of letting Spark do random splits.
### Why are the changes needed?
This gives `CrossValidator` users more flexibility in splitting folds.
### Does this PR introduce _any_ user-facing change?
Yes, a new `foldCol` param is added to `CrossValidator`. User can use it to specify custom fold splitting.
### How was this patch tested?
Added unit tests.
Closes#28704 from viirya/SPARK-31777.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
Close LevelDBIterator when LevelDB.close() is called.
### Why are the changes needed?
This pull request would prevent JNI resources leaking from Level DB instance and its' iterators. In before implementation JNI resources from LevelDBIterator are cleaned by finalize() function. This behavior is also mentioned in comments of ["LevelDBIterator.java"](https://github.com/apache/spark/blob/master/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDBIterator.java) by squito . But if DB instance is already closed, then iterator's close method would be ignored. LevelDB's iterator would keep level db files opened (for the case table cache is filled up), till iterator.close() is called. Then these JNI resources (file handle) would be leaked.
This JNI resource leaking issue would cause the problem described in [SPARK-31929](https://issues.apache.org/jira/browse/SPARK-31929) on Windows: in spark history server, leaked file handle for level db files would trigger "IOException" when HistoryServerDiskManager try to remove them for releasing disk space.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add unit test and manually tested it.
Closes#28769 from zhli1142015/close-leveldbiterator-when-leveldb.close.
Authored-by: Zhen Li <zhli@microsoft.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
To avoid the unnecessary CNF computation for full-outer joins, this PR fixes code for filtering out full-outer joins at the entrance of the rule.
### Why are the changes needed?
To mitigate optimizer overhead.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#28810 from maropu/SPARK-31705.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Replace `CAST(... AS TIMESTAMP` by `TIMESTAMP_SECONDS` in the following benchmarks:
- ExtractBenchmark
- DateTimeBenchmark
- FilterPushdownBenchmark
- InExpressionBenchmark
### Why are the changes needed?
The benchmarks fail w/o the changes:
```
[info] Running benchmark: datetime +/- interval
[info] Running case: date + interval(m)
[error] Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot resolve 'CAST(`id` AS TIMESTAMP)' due to data type mismatch: cannot cast bigint to timestamp,you can enable the casting by setting spark.sql.legacy.allowCastNumericToTimestamp to true,but we strongly recommend using function TIMESTAMP_SECONDS/TIMESTAMP_MILLIS/TIMESTAMP_MICROS instead.; line 1 pos 5;
[error] 'Project [(cast(cast(id#0L as timestamp) as date) + 1 months) AS (CAST(CAST(id AS TIMESTAMP) AS DATE) + INTERVAL '1 months')#2]
[error] +- Range (0, 10000000, step=1, splits=Some(1))
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected benchmarks.
Closes#28843 from MaxGekk/GuoPhilipse-31710-fix-compatibility-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
HyukjinKwon
yi.wu
Bryan Cutler
ulysses
Max Gekk
sidedoorleftroad
Zhen Li
Gabor Somogyi
William Hyun
Dilip Biswal
Liang-Chi Hsieh
Yuanjian Li
mcheah
Kent Yao
Udbhav30
Huaxin Gao
TJX2014
Terry Kim
Shanyu Zhao
Jungtaek Lim (HeartSaVioR)
James Yu
Wenchen Fan
DB Tsai
Dongjoon Hyun
Takeshi Yamamuro
