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sync #9

Merged
merged 27 commits into from
Jun 2, 2020
Merged

sync #9

merged 27 commits into from
Jun 2, 2020

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yaooqinn and others added 27 commits May 29, 2020 04:13
@yaooqinn @cloud-fan
[SPARK-31833][SQL][TEST-HIVE1.2] Set HiveThriftServer2 with actual po…
fe1da29 
…rt while configured 0

### What changes were proposed in this pull request?

When I was developing some stuff based on the `DeveloperAPI ` `org.apache.spark.sql.hive.thriftserver.HiveThriftServer2#startWithContext`, I need to use thrift port randomly to avoid race on ports. But the `org.apache.hive.service.cli.thrift.ThriftCLIService#getPortNumber` do not respond to me with the actual bound port but always 0.

And the server log is not right too, after starting the server, it's hard to form to the right JDBC connection string.

```
INFO ThriftCLIService: Starting ThriftBinaryCLIService on port 0 with 5...500 worker threads
```

Indeed, the `53742` is the right port
```shell
lsof -nP -p `cat ./pid/spark-kentyao-org.apache.spark.sql.hive.thriftserver.HiveThriftServer2-1.pid` | grep LISTEN
java    18990 kentyao  288u    IPv6 0x22858e3e60d6a0a7      0t0                 TCP 10.242.189.214:53723 (LISTEN)
java    18990 kentyao  290u    IPv6 0x22858e3e60d68827      0t0                 TCP *:4040 (LISTEN)
java    18990 kentyao  366u    IPv6 0x22858e3e60d66987      0t0                 TCP 10.242.189.214:53724 (LISTEN)
java    18990 kentyao  438u    IPv6 0x22858e3e60d65d47      0t0                 TCP *:53742 (LISTEN)
```

In the PR, when the port is configured 0, the `portNum` will be set to the real used port during the start process.

Also use 0 in thrift related tests to avoid potential flakiness.

### Why are the changes needed?

1 fix API bug
2 reduce test flakiness

### Does this PR introduce _any_ user-facing change?

yes, `org.apache.hive.service.cli.thrift.ThriftCLIService#getPortNumber` will always give you the actual port when it is configured 0.

### How was this patch tested?

modified unit tests

Closes #28651 from yaooqinn/SPARK-31833.

Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@maryannxue @cloud-fan
[SPARK-31865][SQL] Fix complex AQE query stage not reused
9030230 
### What changes were proposed in this pull request?

This PR fixes the issue of complex query stages that contain sub stages not being reused at times due to dynamic plan changes. This PR synchronizes the "finished" flag between all reused stages so that the runtime replanning would always produce the same sub plan for their potentially reusable parent stages.

### Why are the changes needed?

Without this change, complex query stages that contain sub stages will sometimes not be reused due to dynamic plan changes and the status of their child query stages not being synced.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Manually tested TPC-DS q47 and q57. Before this PR, the reuse of the biggest stage would happen with a 50/50 chance; and after this PR, it will happen 100% of the time.

Closes #28670 from maryannxue/fix-aqe-reuse.

Authored-by: Maryann Xue <maryann.xue@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@maryannxue @cloud-fan
[SPARK-31862][SQL] Remove exception wrapping in AQE
45864fa 
### What changes were proposed in this pull request?

This PR removes the excessive exception wrapping in AQE so that error messages are less verbose and mostly consistent with non-aqe execution. Exceptions from stage materialization are now only wrapped with `SparkException` if there are multiple stage failures. Also, stage cancelling errors will not be included as part the exception thrown, but rather just be error logged.

### Why are the changes needed?

This will make the AQE error reporting more readable and debuggable.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Updated existing tests.

Closes #28668 from maryannxue/spark-31862.

Authored-by: Maryann Xue <maryann.xue@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@MaxGekk @cloud-fan
[SPARK-31855][SQL][TESTS] Check reading date/timestamp from Avro file…
37a1fb8 
…s w/ and w/o Spark version

### What changes were proposed in this pull request?
1. Add the following parquet files to the resource folder `external/avro/src/test/resources`:
   - Files saved by Spark 2.4.5 (cee4ecb) without meta info `org.apache.spark.version`
      - `before_1582_date_v2_4_5.avro` with a date column: `avro.schema	{"type":"record","name":"topLevelRecord","fields":[{"name":"dt","type":[{"type":"int","logicalType":"date"},"null"]}]}`
      - `before_1582_timestamp_millis_v2_4_5.avro` with a timestamp column: `avro.schema	{"type":"record","name":"test","namespace":"logical","fields":[{"name":"dt","type":["null",{"type":"long","logicalType":"timestamp-millis"}],"default":null}]}`
      - `before_1582_timestamp_micros_v2_4_5.avro` with a timestamp column: `avro.schema	{"type":"record","name":"topLevelRecord","fields":[{"name":"dt","type":[{"type":"long","logicalType":"timestamp-micros"},"null"]}]}`
    - Files saved by Spark 2.4.6-rc3 (570848d) with the meta info `org.apache.spark.version	2.4.6`:
      - `before_1582_date_v2_4_6.avro` is similar to `before_1582_date_v2_4_5.avro` except Spark version in parquet meta info.
      - `before_1582_timestamp_micros_v2_4_6.avro` is similar to `before_1582_timestamp_micros_v2_4_5.avro` except meta info.
      - `before_1582_timestamp_millis_v2_4_6.avro` is similar to `before_1582_timestamp_millis_v2_4_5.avro` except meta info.
2. Removed a few avro files becaused they are replaced by Avro files generated by Spark 2.4.5 above.
3. Add new test "generate test files for checking compatibility with Spark 2.4" to `AvroSuite` (marked as ignored). The parquet files above were generated by this test.
4. Modified "SPARK-31159: compatibility with Spark 2.4 in reading dates/timestamps" in `AvroSuite` to use new parquet files.

### Why are the changes needed?
To improve test coverage.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
By `AvroV1Suite` and `AvroV2Suite`.

Closes #28664 from MaxGekk/avro-update-resource-files.

Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@wangyum @cloud-fan
[SPARK-28481][SQL] More expressions should extend NullIntolerant
91148f4 
### What changes were proposed in this pull request?

1. Make more expressions extend `NullIntolerant`.
2. Add a checker(in `ExpressionInfoSuite`) to identify whether the expression is `NullIntolerant`.

### Why are the changes needed?

Avoid skew join if the join column has many null values and can improve query performance. For examples:
```sql
CREATE TABLE t1(c1 string, c2 string) USING parquet;
CREATE TABLE t2(c1 string, c2 string) USING parquet;
EXPLAIN SELECT t1.* FROM t1 JOIN t2 ON upper(t1.c1) = upper(t2.c1);
```

Before and after this PR:
```sql
== Physical Plan ==
*(2) Project [c1#5, c2#6]
+- *(2) BroadcastHashJoin [upper(c1#5)], [upper(c1#7)], Inner, BuildLeft
   :- BroadcastExchange HashedRelationBroadcastMode(List(upper(input[0, string, true]))), [id=#41]
   :  +- *(1) ColumnarToRow
   :     +- FileScan parquet default.t1[c1#5,c2#6]
   +- *(2) ColumnarToRow
      +- FileScan parquet default.t2[c1#7]

== Physical Plan ==
*(2) Project [c1#5, c2#6]
+- *(2) BroadcastHashJoin [upper(c1#5)], [upper(c1#7)], Inner, BuildRight
   :- *(2) Project [c1#5, c2#6]
   :  +- *(2) Filter isnotnull(c1#5)
   :     +- *(2) ColumnarToRow
   :        +- FileScan parquet default.t1[c1#5,c2#6]
   +- BroadcastExchange HashedRelationBroadcastMode(List(upper(input[0, string, true]))), [id=#59]
      +- *(1) Project [c1#7]
         +- *(1) Filter isnotnull(c1#7)
            +- *(1) ColumnarToRow
               +- FileScan parquet default.t2[c1#7]

```

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Unit test.

Closes #28626 from wangyum/SPARK-28481.

Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@HeartSaVioR @dongjoon-hyun
[SPARK-31214][BUILD] Upgrade Janino to 3.1.2
fe1d1e2 
### What changes were proposed in this pull request?

This PR proposes to upgrade Janino to 3.1.2 which is released recently.

Major changes were done for refactoring, as well as there're lots of "no commit message". Belows are the pairs of (commit title, commit) which seem to deal with some bugs or specific improvements (not purposed to refactor) after 3.0.15.

* Issue #119: Guarantee executing popOperand() in popUninitializedVariableOperand() via moving popOperand() out of "assert"
* Issue #116: replace operand to final target type if boxing conversion and widening reference conversion happen together
* Merged pull request `#114` "Grow the code for relocatables, and do fixup, and relocate".
  * janino-compiler/janino@367c58e
* issue `#107`: Janino requires "org.codehaus.commons.compiler.io", but commons-compiler does not export this package
  * janino-compiler/janino@f7d9959
* Throw an NYI CompileException when a static interface method is invoked.
  * janino-compiler/janino@efd3884
* Fixed the promotion of the array access index expression (see JLS7 15.13 Array Access Expressions)
  * janino-compiler/janino@32fdb5f
* Issue `#104`: ClassLoaderIClassLoader 's ClassNotFoundException handle mechanism enhancement
  * janino-compiler/janino@6e8a97d

You can see the changelog from the link: http://janino-compiler.github.io/janino/changelog.html

### Why are the changes needed?

We got some report on failure on user's query which Janino throws error on compiling generated code. The issue is here: janino-compiler/janino#113 It contains the information of generated code, symptom (error), and analysis of the bug, so please refer the link for more details.
Janino 3.1.1 contains the PR janino-compiler/janino#114 which would enable Janino to succeed to compile user's query properly. I've also fixed a couple of more bugs as 3.1.1 made Spark UTs fail - hence we need to upgrade to 3.1.2.

Furthermore, from my testing, janino-compiler/janino#90 (which Josh Rosen filed before) seems to be also resolved in 3.1.2 as well.

Looks like Janino is maintained by one person and there's no even version branches and releases/tags so we can't expect Janino maintainer to release a new bugfix version - hence have to try out new minor version.

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

Existing UTs.

Closes #27860 from HeartSaVioR/SPARK-31101.

Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
@dongjoon-hyun
[SPARK-31858][BUILD] Upgrade commons-io to 2.5 in Hadoop 3.2 profile
625abca 
### What changes were proposed in this pull request?

This PR aims to upgrade `commons-io` from 2.4 to 2.5 for Apache Spark 3.1.

### Why are the changes needed?

Since Hadoop 3.1, `commons-io` 2.5 is used.
- https://issues.apache.org/jira/browse/HADOOP-15261

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Pass the Jenkins with Hadoop-3.2 profile.

Maven dependency is verified via `test-dependencies.sh` automatically. SBT dependency can be verified like the following manually.
```
build/sbt -Phadoop-3.2 "core/dependencyTree" | grep commons-io:commons-io | head -n1
[info]   | | +-commons-io:commons-io:2.5
```

Closes #28665 from dongjoon-hyun/SPARK-31858.

Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
@sarutak @dongjoon-hyun
[SPARK-31756][WEBUI] Add real headless browser support for UI test
d3eba5b 
### What changes were proposed in this pull request?

This PR mainly adds two things.

1. Real headless browser support for UI test
2. A test suite using headless Chrome as one instance of  those browsers.

Also, for environment where Chrome and Chrome driver is not installed, `ChromeUITest` tag is added to filter out the test suite.
By default, test suites with `ChromeUITest` is disabled.

### Why are the changes needed?

In the current master, there are two problems for UI test.
1. Lots of tests especially JavaScript related ones are done manually.
Appearance is better to be confirmed by our eyes but logic should be tested by test cases ideally.

2. Compared to the real web browsers, HtmlUnit doesn't seem to support JavaScript enough.
I added a JavaScript related test before for SPARK-31534 using HtmlUnit which is simple library based headless browser for test.
The test I added works somehow but some JavaScript related error is shown in unit-tests.log.

```
======= EXCEPTION START ========
Exception class=[net.sourceforge.htmlunit.corejs.javascript.JavaScriptException]
com.gargoylesoftware.htmlunit.ScriptException: Error: TOOLTIP: Option "sanitizeFn" provided type "window" but expected type "(null|function)". (http://192.168.1.209:60724/static/jquery-3.4.1.min.js#2)
        at com.gargoylesoftware.htmlunit.javascript.JavaScriptEngine$HtmlUnitContextAction.run(JavaScriptEngine.java:904)
        at net.sourceforge.htmlunit.corejs.javascript.Context.call(Context.java:628)
        at net.sourceforge.htmlunit.corejs.javascript.ContextFactory.call(ContextFactory.java:515)
        at com.gargoylesoftware.htmlunit.javascript.JavaScriptEngine.callFunction(JavaScriptEngine.java:835)
        at com.gargoylesoftware.htmlunit.javascript.JavaScriptEngine.callFunction(JavaScriptEngine.java:807)
        at com.gargoylesoftware.htmlunit.InteractivePage.executeJavaScriptFunctionIfPossible(InteractivePage.java:216)
        at com.gargoylesoftware.htmlunit.javascript.background.JavaScriptFunctionJob.runJavaScript(JavaScriptFunctionJob.java:52)
        at com.gargoylesoftware.htmlunit.javascript.background.JavaScriptExecutionJob.run(JavaScriptExecutionJob.java:102)
        at com.gargoylesoftware.htmlunit.javascript.background.JavaScriptJobManagerImpl.runSingleJob(JavaScriptJobManagerImpl.java:426)
        at com.gargoylesoftware.htmlunit.javascript.background.DefaultJavaScriptExecutor.run(DefaultJavaScriptExecutor.java:157)
        at java.lang.Thread.run(Thread.java:748)
Caused by: net.sourceforge.htmlunit.corejs.javascript.JavaScriptException: Error: TOOLTIP: Option "sanitizeFn" provided type "window" but expected type "(null|function)". (http://192.168.1.209:60724/static/jquery-3.4.1.min.js#2)
        at net.sourceforge.htmlunit.corejs.javascript.Interpreter.interpretLoop(Interpreter.java:1009)
        at net.sourceforge.htmlunit.corejs.javascript.Interpreter.interpret(Interpreter.java:800)
        at net.sourceforge.htmlunit.corejs.javascript.InterpretedFunction.call(InterpretedFunction.java:105)
        at net.sourceforge.htmlunit.corejs.javascript.ContextFactory.doTopCall(ContextFactory.java:413)
        at com.gargoylesoftware.htmlunit.javascript.HtmlUnitContextFactory.doTopCall(HtmlUnitContextFactory.java:252)
        at net.sourceforge.htmlunit.corejs.javascript.ScriptRuntime.doTopCall(ScriptRuntime.java:3264)
        at com.gargoylesoftware.htmlunit.javascript.JavaScriptEngine$4.doRun(JavaScriptEngine.java:828)
        at com.gargoylesoftware.htmlunit.javascript.JavaScriptEngine$HtmlUnitContextAction.run(JavaScriptEngine.java:889)
        ... 10 more
JavaScriptException value = Error: TOOLTIP: Option "sanitizeFn" provided type "window" but expected type "(null|function)".
== CALLING JAVASCRIPT ==
  function () {
      throw e;
  }
======= EXCEPTION END ========
```
I tried to upgrade HtmlUnit to 2.40.0 but what is worse, the test become not working even though it works on real browsers like Chrome, Safari and Firefox without error.
```
[info] UISeleniumSuite:
[info] - SPARK-31534: text for tooltip should be escaped *** FAILED *** (17 seconds, 745 milliseconds)
[info]   The code passed to eventually never returned normally. Attempted 2 times over 12.910785232 seconds. Last failure message: com.gargoylesoftware.htmlunit.ScriptException: ReferenceError: Assignment to undefined "regeneratorRuntime" in strict mode (http://192.168.1.209:62132/static/vis-timeline-graph2d.min.js#52(Function)#1)
```
To resolve those problems, it's better to support headless browser for UI test.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

I tested with following patterns. Both Chrome and Chrome driver should be installed to test.

1. sbt / with default excluded tags (ChromeUISeleniumSuite is expected to be skipped and SQLQueryTestSuite is expected to succeed)
`build/sbt -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver "testOnly org.apache.spark.ui.ChromeUISeleniumSuite org.apache.spark.sql.SQLQueryTestSuite"

2. sbt / overwrite default excluded tags as empty string (Both suites are expected to succeed)
`build/sbt -Dtest.default.exclude.tags= -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver "testOnly org.apache.spark.ui.ChromeUISeleniumSuite org.apache.spark.sql.SQLQueryTestSuite"

3. sbt / set `test.exclude.tags` to `org.apache.spark.tags.ExtendedSQLTest` (Both suites are expected to be skipped)
`build/sbt -Dtest.exclude.tags=org.apache.spark.tags.ExtendedSQLTest -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver "testOnly org.apache.spark.ui.ChromeUISeleniumSuite org.apache.spark.sql.SQLQueryTestSuite"

4. Maven / with default excluded tags (ChromeUISeleniumSuite is expected to be skipped and SQLQueryTestSuite is expected to succeed)
`build/mvn -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver -Dtest=none -DwildcardSuites=org.apache.spark.ui.ChromeUISeleniumSuite,org.apache.spark.sql.SQLQueryTestSuite test`

5. Maven / overwrite default excluded tags as empty string (Both suites are expected to succeed)
`build/mvn -Dtest.default.exclude.tags= -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver -Dtest=none -DwildcardSuites=org.apache.spark.ui.ChromeUISeleniumSuite,org.apache.spark.sql.SQLQueryTestSuite test`

6. Maven / set `test.exclude.tags` to `org.apache.spark.tags.ExtendedSQLTest` (Both suites are expected to be skipped)
`build/mvn -Dtest.exclude.tags=org.apache.spark.tags.ExtendedSQLTest  -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver -Dtest=none -DwildcardSuites=org.apache.spark.ui.ChromeUISeleniumSuite,org.apache.spark.sql.SQLQueryTestSuite test`

Closes #28627 from sarutak/real-headless-browser-support-take2.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
@juliuszsompolski @cloud-fan
[SPARK-31859][SPARK-31861][SPARK-31863] Fix Thriftserver session time…
af35691 
…zone issues

### What changes were proposed in this pull request?

Timestamp literals in Spark are interpreted as timestamps in local timezone spark.sql.session.timeZone.

If JDBC client is e.g. in TimeZone UTC-7, and sets spark.sql.session.timeZone to PST, and sends a query "SELECT timestamp '2020-05-20 12:00:00'", and the JVM timezone of the Spark cluster is e.g. UTC+2, then what currently happens is:
* The timestamp literal in the query is interpreted as 12:00:00 UTC-7, i.e. 19:00:00 UTC.
* When it's returned from the query, it is collected as a java.sql.Timestamp object with Dataset.collect(), and put into a Thriftserver RowSet.
* Before sending it over the wire, the Timestamp is converted to String. This happens in explicitly in ColumnValue for RowBasedSet, and implicitly in ColumnBuffer for ColumnBasedSet (all non-primitive types are converted toString() there). The conversion toString uses JVM timezone, which results in a "21:00:00" (UTC+2) string representation.
* The client JDBC application parses gets a "21:00:00" Timestamp back (in it's JVM timezone; if the JDBC application cares about the correct UTC internal value, it should set spark.sql.session.timeZone to be consistent with its JVM timezone)

The problem is caused by the conversion happening in Thriftserver RowSet with the generic toString() function, instead of using HiveResults.toHiveString() that takes care of correct, timezone respecting conversions. This PR fixes it by converting the Timestamp values to String earlier, in SparkExecuteStatementOperation, using that function. This fixes SPARK-31861.

Thriftserver also did not work spark.sql.datetime.java8API.enabled, because the conversions in RowSet expected an Timestamp object instead of Instant object. Using HiveResults.toHiveString() also fixes that. For this reason, we also convert Date values in SparkExecuteStatementOperation as well - so that HiveResults.toHiveString() handles LocalDate as well. This fixes SPARK-31859.

Thriftserver also did not correctly set the active SparkSession. Because of that, configuration obtained using SQLConf.get was not the correct session configuration. This affected getting the correct spark.sql.session.timeZone. It is fixed by extending the use of SparkExecuteStatementOperation.withSchedulerPool to also set the correct active SparkSession. When the correct session is set, we also no longer need to maintain the pool mapping in a sessionToActivePool map. The scheduler pool can be just correctly retrieved from the session config. "withSchedulerPool" is renamed to "withLocalProperties" and moved into a mixin helper trait, because it should be applied with every operation. This fixes SPARK-31863.

I used the opportunity to move some repetitive code from the operations to the mixin helper trait.

Closes #28671 from juliuszsompolski/SPARK-31861.

Authored-by: Juliusz Sompolski <julek@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@maryannxue @cloud-fan
[SPARK-31864][SQL] Adjust AQE skew join trigger condition
b9737c3 
### What changes were proposed in this pull request?

This PR makes a minor change in deciding whether a partition is skewed by comparing the partition size to the median size of coalesced partitions instead of median size of raw partitions before coalescing.

### Why are the changes needed?

This change is line with target size criteria of splitting skew join partitions and can also cope with situations of extra empty partitions caused by over-partitioning. This PR has also improved skew join tests in AQE tests.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Updated UTs.

Closes #28669 from maryannxue/spark-31864.

Authored-by: Maryann Xue <maryann.xue@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@huaxingao @srowen
[SPARK-31866][SQL][DOCS] Add COALESCE/REPARTITION/REPARTITION_BY_RANG…
1b780f3 
…E Hints to SQL Reference

### What changes were proposed in this pull request?
Add Coalesce/Repartition/Repartition_By_Range Hints to SQL Reference

### Why are the changes needed?
To make SQL reference complete

### Does this PR introduce _any_ user-facing change?
<img width="1100" alt="Screen Shot 2020-05-29 at 6 46 38 PM" src="https://user-images.githubusercontent.com/13592258/83316782-d6fcf300-a1dc-11ea-87f6-e357b9c739fd.png">

<img width="1099" alt="Screen Shot 2020-05-29 at 6 43 30 PM" src="https://user-images.githubusercontent.com/13592258/83316784-d8c6b680-a1dc-11ea-95ea-10a1f75dcef9.png">

Only the the above pages are changed. The following two pages are the same as before.

<img width="1100" alt="Screen Shot 2020-05-28 at 10 05 27 PM" src="https://user-images.githubusercontent.com/13592258/83223474-bfb3fc00-a12f-11ea-807a-824a618afa0b.png">

<img width="1099" alt="Screen Shot 2020-05-28 at 10 05 08 PM" src="https://user-images.githubusercontent.com/13592258/83223478-c2165600-a12f-11ea-806e-a1e57dc35ef4.png">

### How was this patch tested?
Manually build and check

Closes #28672 from huaxingao/coalesce_hint.

Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
@yaooqinn @cloud-fan
[SPARK-31867][SQL] Disable year type datetime patterns which are long…
547c5bf 
…er than 10

### What changes were proposed in this pull request?

As mentioned in #28673 and suggested via cloud-fan at #28673 (comment)

In this PR, we disable datetime pattern in the form of `y..y` and `Y..Y` whose lengths are greater than 10 to avoid sort of JDK bug as described below

he new datetime formatter introduces silent data change like,

```sql
spark-sql> select from_unixtime(1, 'yyyyyyyyyyy-MM-dd');
NULL
spark-sql> set spark.sql.legacy.timeParserPolicy=legacy;
spark.sql.legacy.timeParserPolicy	legacy
spark-sql> select from_unixtime(1, 'yyyyyyyyyyy-MM-dd');
00000001970-01-01
spark-sql>
```

For patterns that support `SignStyle.EXCEEDS_PAD`, e.g. `y..y`(len >=4), when using the `NumberPrinterParser` to format it

```java
switch (signStyle) {
  case EXCEEDS_PAD:
    if (minWidth < 19 && value >= EXCEED_POINTS[minWidth]) {
      buf.append(decimalStyle.getPositiveSign());
    }
    break;

           ....
```
the `minWidth` == `len(y..y)`
the `EXCEED_POINTS` is

```java
/**
         * Array of 10 to the power of n.
         */
        static final long[] EXCEED_POINTS = new long[] {
            0L,
            10L,
            100L,
            1000L,
            10000L,
            100000L,
            1000000L,
            10000000L,
            100000000L,
            1000000000L,
            10000000000L,
        };
```

So when the `len(y..y)` is greater than 10, ` ArrayIndexOutOfBoundsException` will be raised.

 And at the caller side, for `from_unixtime`, the exception will be suppressed and silent data change occurs. for `date_format`, the `ArrayIndexOutOfBoundsException` will continue.

### Why are the changes needed?
fix silent data change

### Does this PR introduce _any_ user-facing change?

Yes, SparkUpgradeException will take place of `null` result when the pattern contains 10 or more continuous  'y' or 'Y'

### How was this patch tested?

new tests

Closes #28684 from yaooqinn/SPARK-31867-2.

Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@MaxGekk @cloud-fan
[SPARK-31874][SQL] Use FastDateFormat as the legacy fractional form…
47dc332 
…atter

### What changes were proposed in this pull request?
1. Replace `SimpleDateFormat` by `FastDateFormat` as the legacy formatter of `FractionTimestampFormatter`.
2. Optimise `LegacyFastTimestampFormatter` for `java.sql.Timestamp` w/o fractional part.

### Why are the changes needed?
1. By default `HiveResult`.`hiveResultString` retrieves timestamps values as instances of `java.sql.Timestamp`, and uses the legacy parser `SimpleDateFormat` to convert the timestamps to strings. After the fix #28024, the fractional formatter and its companion - legacy formatter `SimpleDateFormat` are created per every value. By switching from `LegacySimpleTimestampFormatter` to `LegacyFastTimestampFormatter`, we can utilize the internal cache of `FastDateFormat`, and avoid parsing the default pattern `yyyy-MM-dd HH:mm:ss`.
2. The second change in the method `def format(ts: Timestamp): String` of `LegacyFastTimestampFormatter` is needed to optimize the formatter for patterns without the fractional part and avoid conversions to microseconds.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
By existing tests in `TimestampFormatter`.

Closes #28678 from MaxGekk/fastdateformat-as-legacy-frac-formatter.

Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@huaxingao @srowen
[SPARK-31840][ML] Add instance weight support in LogisticRegressionSu…
45cf5e9 
…mmary

### What changes were proposed in this pull request?
Add instance weight support in LogisticRegressionSummary

### Why are the changes needed?
LogisticRegression, MulticlassClassificationEvaluator and BinaryClassificationEvaluator support instance weight. We should support instance weight in LogisticRegressionSummary too.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Add new tests

Closes #28657 from huaxingao/weighted_summary.

Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
@HyukjinKwon
[SPARK-31788][CORE][DSTREAM][PYTHON] Recover the support of union for…
29c51d6 
… different types of RDD and DStreams

### What changes were proposed in this pull request?

This PR manually specifies the class for the input array being used in `(SparkContext|StreamingContext).union`. It fixes a regression introduced from SPARK-25737.

```python
rdd1 = sc.parallelize([1,2,3,4,5])
rdd2 = sc.parallelize([6,7,8,9,10])
pairRDD1 = rdd1.zip(rdd2)
sc.union([pairRDD1, pairRDD1]).collect()
```

in the current master and `branch-3.0`:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/context.py", line 870, in union
    jrdds[i] = rdds[i]._jrdd
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_collections.py", line 238, in __setitem__
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_collections.py", line 221, in __set_item
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 332, in get_return_value
py4j.protocol.Py4JError: An error occurred while calling None.None. Trace:
py4j.Py4JException: Cannot convert org.apache.spark.api.java.JavaPairRDD to org.apache.spark.api.java.JavaRDD
	at py4j.commands.ArrayCommand.convertArgument(ArrayCommand.java:166)
	at py4j.commands.ArrayCommand.setArray(ArrayCommand.java:144)
	at py4j.commands.ArrayCommand.execute(ArrayCommand.java:97)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
```

which works in Spark 2.4.5:

```
[(1, 6), (2, 7), (3, 8), (4, 9), (5, 10), (1, 6), (2, 7), (3, 8), (4, 9), (5, 10)]
```

It assumed the class of the input array is the same `JavaRDD` or `JavaDStream`; however, that can be different such as `JavaPairRDD`.

This fix is based on redsanket's initial approach, and will be co-authored.

### Why are the changes needed?

To fix a regression from Spark 2.4.5.

### Does this PR introduce _any_ user-facing change?

No, it's only in unreleased branches. This is to fix a regression.

### How was this patch tested?

Manually tested, and a unittest was added.

Closes #28648 from HyukjinKwon/SPARK-31788.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
@HyukjinKwon
[SPARK-31849][PYTHON][SQL] Make PySpark SQL exceptions more Pythonic
e694660 
### What changes were proposed in this pull request?

This PR proposes to make PySpark exception more Pythonic by hiding JVM stacktrace by default. It can be enabled by turning on `spark.sql.pyspark.jvmStacktrace.enabled` configuration.

```
Traceback (most recent call last):
  ...
pyspark.sql.utils.PythonException:
  An exception was thrown from Python worker in the executor. The below is the Python worker stacktrace.
Traceback (most recent call last):
  ...
```

If this `spark.sql.pyspark.jvmStacktrace.enabled` is enabled, it appends:

```
JVM stacktrace:
org.apache.spark.Exception: ...
  ...
```

For example, the codes below:

```python
from pyspark.sql.functions import udf
udf
def divide_by_zero(v):
    raise v / 0

spark.range(1).select(divide_by_zero("id")).show()
```

will show an error messages that looks like Python exception thrown from the local.

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is off (default)</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/dataframe.py", line 427, in show
    print(self._jdf.showString(n, 20, vertical))
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 131, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
  An exception was thrown from Python worker in the executor. The below is the Python worker stacktrace.
Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero
```

</details>

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is on</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/dataframe.py", line 427, in show
    print(self._jdf.showString(n, 20, vertical))
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 137, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
  An exception was thrown from Python worker in the executor. The below is the Python worker stacktrace.
Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

JVM stacktrace:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 4, 192.168.35.193, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2117)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2066)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2065)
	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2065)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1021)
	at scala.Option.foreach(Option.scala:407)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2297)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2246)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2235)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:823)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2108)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2129)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2148)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420)
	at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
	at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3653)
	at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3644)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
	at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3642)
	at org.apache.spark.sql.Dataset.head(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.take(Dataset.scala:2902)
	at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
	at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
	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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more
```

</details>

<details>
<summary>Python exception message without this change</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/dataframe.py", line 427, in show
    print(self._jdf.showString(n, 20, vertical))
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 98, in deco
    return f(*a, **kw)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o160.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 10 in stage 5.0 failed 4 times, most recent failure: Lost task 10.3 in stage 5.0 (TID 37, 192.168.35.193, executor 3): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2117)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2066)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2065)
	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2065)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1021)
	at scala.Option.foreach(Option.scala:407)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2297)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2246)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2235)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:823)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2108)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2129)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2148)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420)
	at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
	at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3653)
	at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3644)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
	at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3642)
	at org.apache.spark.sql.Dataset.head(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.take(Dataset.scala:2902)
	at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
	at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
	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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more
```

</details>

<br/>

Another example with Python 3.7:

```python
sql("a")
```

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is off (default)</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/session.py", line 646, in sql
    return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 131, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^
```

</details>

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is on</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/session.py", line 646, in sql
    return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 131, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^

JVM stacktrace:
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^

	at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:266)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:133)
	at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:49)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:604)
	at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:604)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:601)
	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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
```

</details>

<details>
<summary>Python exception message without this change</summary>

```
Traceback (most recent call last):
  File "/.../spark/python/pyspark/sql/utils.py", line 98, in deco
    return f(*a, **kw)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o26.sql.
: org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^

	at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:266)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:133)
	at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:49)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:604)
	at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:604)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:601)
	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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/session.py", line 646, in sql
    return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 102, in deco
    raise converted
pyspark.sql.utils.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^
```

</details>

### Why are the changes needed?

Currently, PySpark exceptions are very unfriendly to Python users with causing a bunch of JVM stacktrace. See "Python exception message without this change" above.

### Does this PR introduce _any_ user-facing change?

Yes, it will change the exception message. See the examples above.

### How was this patch tested?

Manually tested by

```bash
./bin/pyspark --conf spark.sql.pyspark.jvmStacktrace.enabled=true
```

and running the examples above.

Closes #28661 from HyukjinKwon/python-debug.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
@maropu @cloud-fan
[SPARK-31854][SQL] Invoke in MapElementsExec should not propagate null
b806fc4 
### What changes were proposed in this pull request?

This PR intends to fix a bug of `Dataset.map` below when the whole-stage codegen enabled;
```
scala> val ds = Seq(1.asInstanceOf[Integer], null.asInstanceOf[Integer]).toDS()

scala> sql("SET spark.sql.codegen.wholeStage=true")

scala> ds.map(v=>(v,v)).explain
== Physical Plan ==
*(1) SerializeFromObject [assertnotnull(input[0, scala.Tuple2, true])._1.intValue AS _1#69, assertnotnull(input[0, scala.Tuple2, true])._2.intValue AS _2#70]
+- *(1) MapElements <function1>, obj#68: scala.Tuple2
   +- *(1) DeserializeToObject staticinvoke(class java.lang.Integer, ObjectType(class java.lang.Integer), valueOf, value#1, true, false), obj#67: java.lang.Integer
      +- LocalTableScan [value#1]

// `AssertNotNull` in `SerializeFromObject` will fail;
scala> ds.map(v => (v, v)).show()
java.lang.NullPointerException: Null value appeared in non-nullable fails:
top level Product input object
If the schema is inferred from a Scala tuple/case class, or a Java bean, please try to use scala.Option[_] or other nullable types (e.g. java.lang.Integer instead of int/scala.Int).

// When the whole-stage codegen disabled, the query works well;
scala> sql("SET spark.sql.codegen.wholeStage=false")
scala> ds.map(v=>(v,v)).show()
+----+----+
|  _1|  _2|
+----+----+
|   1|   1|
|null|null|
+----+----+
```
A root cause is that `Invoke` used in `MapElementsExec` propagates input null, and then [AssertNotNull](https://github.com/apache/spark/blob/1b780f364bfbb46944fe805a024bb6c32f5d2dde/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala#L253-L255) in `SerializeFromObject` fails because a top-level row becomes null. So, `MapElementsExec` should not return `null` but `(null, null)`.

NOTE: the generated code of the query above in the current master;
```
/* 033 */   private void mapelements_doConsume_0(java.lang.Integer mapelements_expr_0_0, boolean mapelements_exprIsNull_0_0) throws java.io.IOException {
/* 034 */     boolean mapelements_isNull_1 = true;
/* 035 */     scala.Tuple2 mapelements_value_1 = null;
/* 036 */     if (!false) {
/* 037 */       mapelements_resultIsNull_0 = false;
/* 038 */
/* 039 */       if (!mapelements_resultIsNull_0) {
/* 040 */         mapelements_resultIsNull_0 = mapelements_exprIsNull_0_0;
/* 041 */         mapelements_mutableStateArray_0[0] = mapelements_expr_0_0;
/* 042 */       }
/* 043 */
/* 044 */       mapelements_isNull_1 = mapelements_resultIsNull_0;
/* 045 */       if (!mapelements_isNull_1) {
/* 046 */         Object mapelements_funcResult_0 = null;
/* 047 */         mapelements_funcResult_0 = ((scala.Function1) references[1] /* literal */).apply(mapelements_mutableStateArray_0[0]);
/* 048 */
/* 049 */         if (mapelements_funcResult_0 != null) {
/* 050 */           mapelements_value_1 = (scala.Tuple2) mapelements_funcResult_0;
/* 051 */         } else {
/* 052 */           mapelements_isNull_1 = true;
/* 053 */         }
/* 054 */
/* 055 */       }
/* 056 */     }
/* 057 */
/* 058 */     serializefromobject_doConsume_0(mapelements_value_1, mapelements_isNull_1);
/* 059 */
/* 060 */   }
```

The generated code w/ this fix;
```
/* 032 */   private void mapelements_doConsume_0(java.lang.Integer mapelements_expr_0_0, boolean mapelements_exprIsNull_0_0) throws java.io.IOException {
/* 033 */     boolean mapelements_isNull_1 = true;
/* 034 */     scala.Tuple2 mapelements_value_1 = null;
/* 035 */     if (!false) {
/* 036 */       mapelements_mutableStateArray_0[0] = mapelements_expr_0_0;
/* 037 */
/* 038 */       mapelements_isNull_1 = false;
/* 039 */       if (!mapelements_isNull_1) {
/* 040 */         Object mapelements_funcResult_0 = null;
/* 041 */         mapelements_funcResult_0 = ((scala.Function1) references[1] /* literal */).apply(mapelements_mutableStateArray_0[0]);
/* 042 */
/* 043 */         if (mapelements_funcResult_0 != null) {
/* 044 */           mapelements_value_1 = (scala.Tuple2) mapelements_funcResult_0;
/* 045 */           mapelements_isNull_1 = false;
/* 046 */         } else {
/* 047 */           mapelements_isNull_1 = true;
/* 048 */         }
/* 049 */
/* 050 */       }
/* 051 */     }
/* 052 */
/* 053 */     serializefromobject_doConsume_0(mapelements_value_1, mapelements_isNull_1);
/* 054 */
/* 055 */   }
```

### Why are the changes needed?

Bugfix.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Added tests.

Closes #28681 from maropu/SPARK-31854.

Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@Ngone51 @cloud-fan
[SPARK-31837][CORE] Shift to the new highest locality level if there …
bc24c99 
…is when recomputeLocality

### What changes were proposed in this pull request?

This PR proposes to shift to the new most local locality level if there're any new more local locality levels are added during `TaskSetManager.recomputeLocality`.

### Why are the changes needed?

There's a race condition between `resourceOffers` and `submitTasks`. If `submitTasks` happens before `resourceOffers`, especially when there are no executors added to `TaskSchedulerImpl` at all, the `TaskSetManager` 's `myLocalityLevels`  will only have ANY locality level, see:

https://github.com/apache/spark/blob/8b4862953a879a9b3ba6f57e669efc383df68b7c/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala#L218

And then, `resourceOffers` is called with new executors added to `TaskSchedulerImpl`. Then, `recomputeLocality` will be called because of `executorAdded`. During `recomputeLocality`, the TaskSetManager's `myLocalityLevels` might have PROCESS_LOCAL, NODE_LOCAL, ANY(because at this time we could find alive executors from TaskSchedulerImpl). But the `TaskSetManager` will stick to the previous locality level, which is ANY, see:

https://github.com/apache/spark/blob/8b4862953a879a9b3ba6f57e669efc383df68b7c/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala#L1107-L1114

As a result, in the first round of `resourceOffers`, the new version delay scheduling won't take effect and  `TaskSetManager` can only schedule tasks at ANY level.

Please note that the problem also exists in old version delay scheduling but the impact is minor because we always reset the locality level after successfully launching a task, which is broken in the new version of dealy scheduling.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Updated and added new test.

Closes #28656 from Ngone51/impr_recompute.

Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@cnZach @srowen
[SPARK-29683][YARN] False report isAllNodeBlacklisted when RM is havi…
e70df2c 
…ng issue

### What changes were proposed in this pull request?

Improve the check logic on if all node managers are really being backlisted.

### Why are the changes needed?

I observed when the AM is out of sync with ResourceManager, or RM is having issue report back with current number of available NMs, something like below happens:
...
20/05/13 09:01:21 INFO RetryInvocationHandler: java.io.EOFException: End of File Exception between local host is: "client.zyx.com/x.x.x.124"; destination host is: "rm.zyx.com":8030; : java.io.EOFException; For more details see:  http://wiki.apache.org/hadoop/EOFException, while invoking ApplicationMasterProtocolPBClientImpl.allocate over rm543. Trying to failover immediately.
...
20/05/13 09:01:28 WARN AMRMClientImpl: ApplicationMaster is out of sync with ResourceManager, hence resyncing.
...

then the spark job would suddenly run into AllNodeBlacklisted state:
...
20/05/13 09:01:31 INFO ApplicationMaster: Final app status: FAILED, exitCode: 11, (reason: Due to executor failures all available nodes are blacklisted)
...

but actually there's no black listed nodes in currentBlacklistedYarnNodes, and I do not see any blacklisting message from:
https://github.com/apache/spark/blob/master/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocatorBlacklistTracker.scala#L119

We should only return isAllNodeBlacklisted =true when we see there are >0  numClusterNodes AND 'currentBlacklistedYarnNodes.size >= numClusterNodes'.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

A minor change. No changes on tests.

Closes #28606 from cnZach/false_AllNodeBlacklisted_when_RM_is_having_issue.

Authored-by: Yuexin Zhang <zach.yx.zhang@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
@sarutak @srowen
[SPARK-31804][WEBUI] Add real headless browser support for HistorySer…
6a895d0 
…ver tests

### What changes were proposed in this pull request?

This PR adds two things.

Real headless browser support for HistoryServer tests.
A test suite using headless Chrome as one instance of those browsers.

### Why are the changes needed?

The motivation is same as #28578 .
In the current master, there is a testcase for HistoryServer which uses Ajax so we need the support for HistoryServer tests.

Also this change is necessary to upgrade HtmlUnit (See #28585)

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

I tested with following patterns. Both Chrome and Chrome driver should be installed to test.
1. sbt / with default excluded tags (ChromeUIHistoryServerSuite is expected to be skipped and SQLQueryTestSuite is expected to succeed)
`build/sbt -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver "testOnly org.apache.spark.deploy.history.ChromeUIHistoryServerSuite org.apache.spark.sql.SQLQueryTestSuite"

2. sbt / overwrite default excluded tags as empty string (Both suites are expected to succeed)
`build/sbt -Dtest.default.exclude.tags= -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver "testOnly org.apache.spark.deploy.history.ChromeUIHistoryServerSuite org.apache.spark.sql.SQLQueryTestSuite"

3. sbt / set `test.exclude.tags` to `org.apache.spark.tags.ExtendedSQLTest` (Both suites are expected to be skipped)
`build/sbt -Dtest.exclude.tags=org.apache.spark.tags.ExtendedSQLTest -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver "testOnly org.apache.spark.deploy.history.ChromeUIHistoryServerSuite org.apache.spark.sql.SQLQueryTestSuite"

4. Maven / with default excluded tags (ChromeUIHistoryServerSuite is expected to be skipped and SQLQueryTestSuite is expected to succeed)
`build/mvn -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver -Dtest=none -DwildcardSuites=org.apache.spark.deploy.history.ChromeUIHistoryServerSuite,org.apache.spark.sql.SQLQueryTestSuite test`

5. Maven / overwrite default excluded tags as empty string (Both suites are expected to succeed)
`build/mvn -Dtest.default.exclude.tags= -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver -Dtest=none -DwildcardSuites=org.apache.spark.deploy.history.ChromeUIHistoryServerSuite,org.apache.spark.sql.SQLQueryTestSuite test`

6. Maven / set `test.exclude.tags` to `org.apache.spark.tags.ExtendedSQLTest` (Both suites are expected to be skipped)
`build/mvn -Dtest.exclude.tags=org.apache.spark.tags.ExtendedSQLTest  -Dspark.test.webdriver.chrome.driver=/path/to/chromedriver -Dtest=none -DwildcardSuites=org.apache.spark.deploy.history.ChromeUIHistoryServerSuite,org.apache.spark.sql.SQLQueryTestSuite test`

Closes #28622 from sarutak/headless-browser-support-for-historyserver.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
@MaxGekk @cloud-fan
[SPARK-31885][SQL] Fix filter push down for old millis timestamps to …
9c0dc28 
…Parquet

### What changes were proposed in this pull request?
Fixed conversions of `java.sql.Timestamp` to milliseconds in `ParquetFilter` by using existing functions from `DateTimeUtils` `fromJavaTimestamp()` and `microsToMillis()`.

### Why are the changes needed?
The changes fix the bug:
```scala
scala> spark.conf.set("spark.sql.parquet.outputTimestampType", "TIMESTAMP_MILLIS")
scala> spark.conf.set("spark.sql.legacy.parquet.datetimeRebaseModeInWrite", "CORRECTED")
scala> Seq(java.sql.Timestamp.valueOf("1000-06-14 08:28:53.123")).toDF("ts").write.mode("overwrite").parquet("/Users/maximgekk/tmp/ts_millis_old_filter")
scala> spark.read.parquet("/Users/maximgekk/tmp/ts_millis_old_filter").filter($"ts" === "1000-06-14 08:28:53.123").show(false)
+---+
|ts |
+---+
+---+
```

### Does this PR introduce _any_ user-facing change?
Yes, after the changes (for the example above):
```scala
scala> spark.read.parquet("/Users/maximgekk/tmp/ts_millis_old_filter").filter($"ts" === "1000-06-14 08:28:53.123").show(false)
+-----------------------+
|ts                     |
+-----------------------+
|1000-06-14 08:28:53.123|
+-----------------------+
```

### How was this patch tested?
Modified tests in `ParquetFilterSuite` to check old timestamps.

Closes #28693 from MaxGekk/parquet-ts-millis-filter.

Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@dongjoon-hyun
[SPARK-31881][K8S][TESTS] Support Hadoop 3.2 K8s integration tests
17586f9 
### What changes were proposed in this pull request?

This PR aims to support Hadoop 3.2 K8s integration tests.

### Why are the changes needed?

Currently, K8s integration suite assumes Hadoop 2.7 and has hard-coded parts.

### Does this PR introduce _any_ user-facing change?

No. This is a dev-only change.

### How was this patch tested?

Pass the Jenkins K8s IT (with Hadoop 2.7) and do the manual testing for Hadoop 3.2 as described in `README.md`.

```
./dev/dev-run-integration-tests.sh --hadoop-profile hadoop-3.2
```

I verified this manually like the following.
```
$ resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh \
--spark-tgz .../spark-3.1.0-SNAPSHOT-bin-3.2.0.tgz \
--exclude-tags r \
--hadoop-profile hadoop-3.2
...
KubernetesSuite:
- Run SparkPi with no resources
- Run SparkPi with a very long application name.
- Use SparkLauncher.NO_RESOURCE
- Run SparkPi with a master URL without a scheme.
- Run SparkPi with an argument.
- Run SparkPi with custom labels, annotations, and environment variables.
- All pods have the same service account by default
- Run extraJVMOptions check on driver
- Run SparkRemoteFileTest using a remote data file
- Run SparkPi with env and mount secrets.
- Run PySpark on simple pi.py example
- Run PySpark with Python2 to test a pyfiles example
- Run PySpark with Python3 to test a pyfiles example
- Run PySpark with memory customization
- Run in client mode.
- Start pod creation from template
- PVs with local storage
- Launcher client dependencies
- Test basic decommissioning
Run completed in 8 minutes, 49 seconds.
Total number of tests run: 19
Suites: completed 2, aborted 0
Tests: succeeded 19, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```

Closes #28689 from dongjoon-hyun/SPARK-31881.

Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
@holdenk
[SPARK-31889][BUILD] Docker release script does not allocate enough m…
ab9e5a2 
…emory to reliably publish

### What changes were proposed in this pull request?
Allow overriding the zinc options in the docker release and set a higher so the publish step can succeed consistently.

### Why are the changes needed?

The publish step experiences memory pressure.

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?
Running test locally with fake user to see if publish step (besides svn part) succeeds

Closes #28698 from holdenk/SPARK-31889-docker-release-script-does-not-allocate-enough-memory-to-reliably-publish.

Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
@HyukjinKwon @dongjoon-hyun
[SPARK-28344][SQL][FOLLOW-UP] Check the ambiguous self-join only if t…
ea45fc5 
…here is a join in the plan

### What changes were proposed in this pull request?

This PR proposes to check `DetectAmbiguousSelfJoin` only if there is `Join` in the plan. Currently, the checking is too strict even to non-join queries.

For example, the codes below don't have join at all but it fails as the ambiguous self-join:

```scala
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions.sum
val df = Seq(1, 1, 2, 2).toDF("A")
val w = Window.partitionBy(df("A"))
df.select(df("A").alias("X"), sum(df("A")).over(w)).explain(true)
```

It is because `ExtractWindowExpressions` can create a `AttributeReference` with the same metadata but a different expression ID, see:

https://github.com/apache/spark/blob/0fd98abd859049dc3b200492487041eeeaa8f737/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2679
https://github.com/apache/spark/blob/71c73d58f6e88d2558ed2e696897767d93bac60f/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala#L63
https://github.com/apache/spark/blob/5945d46c11a86fd85f9e65f24c2e88f368eee01f/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala#L180

Before:

```
'Project [A#19 AS X#21, sum(A#19) windowspecdefinition(A#19, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS sum(A) OVER (PARTITION BY A unspecifiedframe$())#23L]
+- Relation[A#19] parquet
```

After:

```
Project [X#21, sum(A) OVER (PARTITION BY A unspecifiedframe$())#23L]
+- Project [X#21, A#19, sum(A) OVER (PARTITION BY A unspecifiedframe$())#23L, sum(A) OVER (PARTITION BY A unspecifiedframe$())#23L]
   +- Window [sum(A#19) windowspecdefinition(A#19, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS sum(A) OVER (PARTITION BY A unspecifiedframe$())#23L], [A#19]
      +- Project [A#19 AS X#21, A#19]
         +- Relation[A#19] parquet
```

`X#21` holds the same metadata of DataFrame ID and column position with `A#19` but it has a different expression ID which ends up with the checking fails.

### Why are the changes needed?

To loose the checking and make users not surprised.

### Does this PR introduce _any_ user-facing change?

It's the changes in unreleased branches only.

### How was this patch tested?

Manually tested and unittest was added.

Closes #28695 from HyukjinKwon/SPARK-28344-followup.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
@manuzhang @cloud-fan
[SPARK-31870][SQL][TESTS] Fix "Do not optimize skew join if additiona…
283814a 
…l shuffle" test having no skew join

### What changes were proposed in this pull request?
Fix configurations and ensure there is skew join in the test "Do not optimize skew join if additional shuffle".

### Why are the changes needed?
The existing "Do not optimize skew join if additional shuffle" test has no skew join at all.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Fixed existing test.

Closes #28679 from manuzhang/spark-31870.

Authored-by: manuzhang <owenzhang1990@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
@tgravescs @HyukjinKwon
[SPARK-29150][CORE] Update RDD API for Stage level scheduling to be p…
ff4a97d 
…ublic

### What changes were proposed in this pull request?

This PR is to make the RDD api for stage level scheduling public.  All the other jiras for functionality are in so now we can make it public for people to use and tag it properly.

### Why are the changes needed?

to make it usable.

### Does this PR introduce _any_ user-facing change?

Yes the api is now public

### How was this patch tested?

Unit tests and manually tested.

Closes #28697 from tgravescs/SPARK-29150.

Lead-authored-by: Thomas Graves <tgraves@apache.org>
Co-authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
@HyukjinKwon @dongjoon-hyun
[SPARK-29137][ML][PYTHON][TESTS] Increase the timeout for StreamingLi…
64cb6f7 
…nearRegressionWithTests.test_train_prediction

### What changes were proposed in this pull request?

It increases the timeout for `StreamingLinearRegressionWithTests.test_train_prediction`

```
Traceback (most recent call last):
  File "/home/jenkins/workspace/SparkPullRequestBuilder3/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 503, in test_train_prediction
    self._eventually(condition)
  File "/home/jenkins/workspace/SparkPullRequestBuilder3/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 69, in _eventually
    lastValue = condition()
  File "/home/jenkins/workspace/SparkPullRequestBuilder3/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 498, in condition
    self.assertGreater(errors[1] - errors[-1], 2)
AssertionError: 1.672640157855923 not greater than 2
```

This could likely happen when the PySpark tests run in parallel and it become slow.

### Why are the changes needed?

To make the tests less flaky. Seems it's being reported multiple times:

https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/123144/consoleFull
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/123146/testReport/
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/123141/testReport/
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/123142/testReport/

### Does this PR introduce _any_ user-facing change?

No, test-only.

### How was this patch tested?

Jenkins will test it out.

Closes #28701 from HyukjinKwon/SPARK-29137.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
@GuoPhilipse GuoPhilipse merged commit c3546eb into GuoPhilipse:master Jun 2, 2020
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