Current SHS (Spark History Server) has two different ACLs:

* ACL of base URL, it is controlled by "spark.acls.enabled" or "spark.ui.acls.enabled", and with this enabled, only user configured with "spark.admin.acls" (or group) or "spark.ui.view.acls" (or group), or the user who started SHS could list all the applications, otherwise none of them can be listed. This will also affect REST APIs which listing the summary of all apps and one app.
* Per application ACL. This is controlled by "spark.history.ui.acls.enabled". With this enabled only history admin user and user/group who ran this app can access the details of this app.

With this two ACLs, we may encounter several unexpected behaviors:

1. if base URL's ACL (`spark.acls.enable`) is enabled but user A has no view permission. User "A" cannot see the app list but could still access details of it's own app.
2. if ACLs of base URL (`spark.acls.enable`) is disabled, then user "A" could download any application's event log, even it is not run by user "A".
3. The changes of Live UI's ACL will affect History UI's ACL which share the same conf file.

The unexpected behaviors is mainly because we have two different ACLs, ideally we should have only one to manage all.

So to improve SHS's ACL mechanism, here in this PR proposed to:

1. Disable "spark.acls.enable" and only use "spark.history.ui.acls.enable" for history server.
2. Check permission for event-log download REST API.

With this PR:

1. Admin user could see/download the list of all applications, as well as application details.
2. Normal user could see the list of all applications, but can only download and check the details of applications accessible to him.

New UTs are added, also verified in real cluster.

CC tgravescs vanzin please help to review, this PR changes the semantics you did previously. Thanks a lot.

Author: jerryshao <sshao@hortonworks.com>

Closes #17755 from jerryshao/SPARK-20239-2.1-backport.

Using Option(name) instead of Some(name) to prevent runtime failures when using accumulators created like the following
```
sparkContext.accumulator(0, null)
```

Author: Sergey Zhemzhitsky <szhemzhitski@gmail.com>

Closes #17740 from szhem/SPARK-20404-null-acc-names.

(cherry picked from commit 0bc7a902)
Signed-off-by: Sean Owen <sowen@cloudera.com>

## What changes were proposed in this pull request?

Just added the Maven `test`goal.

## How was this patch tested?

No test needed, just a trivial documentation fix.

Author: Armin Braun <me@obrown.io>

Closes #17756 from original-brownbear/SPARK-20455.

(cherry picked from commit c8f12195)
Signed-off-by: Sean Owen <sowen@cloudera.com>

## What changes were proposed in this pull request?

In `randomSplit`, It is possible that the underlying dataset doesn't guarantee the ordering of rows in its constituent partitions each time a split is materialized which could result in overlapping
splits.

To prevent this, as part of SPARK-12662, we explicitly sort each input partition to make the ordering deterministic. Given that `MapTypes` cannot be sorted this patch explicitly prunes them out from the sort order. Additionally, if the resulting sort order is empty, this patch then materializes the dataset to guarantee determinism.

## How was this patch tested?

Extended `randomSplit on reordered partitions` in `DataFrameStatSuite` to also test for dataframes with mapTypes nested mapTypes.

Author: Sameer Agarwal <sameerag@cs.berkeley.edu>

Closes #17751 from sameeragarwal/randomsplit2.

(cherry picked from commit 31345fde)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-19611 fixes a regression from 2.0 where Spark silently fails to read case-sensitive fields missing a case-sensitive schema in the table properties. The fix is to detect this situation, infer the schema, and write the case-sensitive schema into the metastore.

However this can incur an unexpected performance hit the first time such a problematic table is queried (and there is a high false-positive rate here since most tables don't actually have case-sensitive fields).

This PR changes the default to NEVER_INFER (same behavior as 2.1.0). In 2.2, we can consider leaving the default to INFER_AND_SAVE.

## How was this patch tested?

Unit tests.

Author: Eric Liang <ekl@databricks.com>

Closes #17749 from ericl/spark-20450.

[SPARK-20407][TESTS][BACKPORT-2.1] ParquetQuerySuite 'Enabling/disabling ignoreCorruptFiles' flaky test

## What changes were proposed in this pull request?

SharedSQLContext.afterEach now calls DebugFilesystem.assertNoOpenStreams inside eventually.
SQLTestUtils withTempDir calls waitForTasksToFinish before deleting the directory.

## How was this patch tested?
New test but marked as ignored because it takes 30s. Can be unignored for review.

Author: Bogdan Raducanu <bogdan@databricks.com>

Closes #17720 from bogdanrdc/SPARK-20407-BACKPORT2.1.

Hello
PR #10991 removed the built-in history view from Spark Standalone, so the history server is no longer useful to Yarn or Mesos only.

Author: Hervé <dud225@users.noreply.github.com>

Closes #17709 from dud225/patch-1.

(cherry picked from commit 34767997)
Signed-off-by: Sean Owen <sowen@cloudera.com>

## What changes were proposed in this pull request?

It's illegal to have aggregate function in GROUP BY, and we should fail at analysis phase, if this happens.

## How was this patch tested?

new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #17704 from cloud-fan/minor.

## What changes were proposed in this pull request?

Also went through the same file to ensure other string concatenation are correct.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #17691 from zsxwing/fix-error-message.

(cherry picked from commit 39e303a8)
Signed-off-by: Shixiong Zhu <shixiong@databricks.com>

[SPARK-20359][SQL] Avoid unnecessary execution in EliminateOuterJoin optimization that can lead to NPE

Avoid necessary execution that can lead to NPE in EliminateOuterJoin and add test in DataFrameSuite to confirm NPE is no longer thrown

## What changes were proposed in this pull request?
Change leftHasNonNullPredicate and rightHasNonNullPredicate to lazy so they are only executed when needed.

## How was this patch tested?

Added test in DataFrameSuite that failed before this fix and now succeeds. Note that a test in catalyst project would be better but i am unsure how to do this.

Please review http://spark.apache.org/contributing.html

 before opening a pull request.

Author: Koert Kuipers <koert@tresata.com>

Closes #17660 from koertkuipers/feat-catch-npe-in-eliminate-outer-join.

(cherry picked from commit 608bf30f)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

## What changes were proposed in this pull request?

fix typo

## How was this patch tested?

manual

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17663 from felixcheung/likedoctypo.

(cherry picked from commit b0a1e93e)
Signed-off-by: Felix Cheung <felixcheung@apache.org>

[SPARK-20349][SQL][REVERT-BRANCH2.1] ListFunctions returns duplicate functions after using persistent functions

Revert the changes of https://github.com/apache/spark/pull/17646 made in Branch 2.1, because it breaks the build. It needs the parser interface, but SessionCatalog in branch 2.1 does not have it.

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

The session catalog caches some persistent functions in the `FunctionRegistry`, so there can be duplicates. Our Catalog API `listFunctions` does not handle it.

It would be better if `SessionCatalog` API can de-duplciate the records, instead of doing it by each API caller. In `FunctionRegistry`, our functions are identified by the unquoted string. Thus, this PR is try to parse it using our parser interface and then de-duplicate the names.

### How was this patch tested?
Added test cases.

Author: Xiao Li <gatorsmile@gmail.com>

Closes #17661 from gatorsmile/compilationFix17646.

This patch fixes a bug in the way LIKE patterns are translated to Java regexes. The bug causes any character following an escaped backslash to be escaped, i.e. there is double-escaping.
A concrete example is the following pattern:`'%\\%'`. The expected Java regex that this pattern should correspond to (according to the behavior described below) is `'.*\\.*'`, however the current situation leads to `'.*\\%'` instead.

---

Update: in light of the discussion that ensued, we should explicitly define the expected behaviour of LIKE expressions, especially in certain edge cases. With the help of gatorsmile, we put together a list of different RDBMS and their variations wrt to certain standard features.

| RDBMS\Features | Wildcards | Default escape [1] | Case sensitivity |
| --- | --- | --- | --- |
| [MS SQL Server](https://msdn.microsoft.com/en-us/library/ms179859.aspx) | _, %, [], [^] | none | no |
| [Oracle](https://docs.oracle.com/cd/B12037_01/server.101/b10759/conditions016.htm) | _, % | none | yes |
| [DB2 z/OS](http://www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/sqlref/src/tpc/db2z_likepredicate.html) | _, % | none | yes |
| [MySQL](http://dev.mysql.com/doc/refman/5.7/en/string-comparison-functions.html) | _, % | none | no |
| [PostreSQL](https://www.postgresql.org/docs/9.0/static/functions-matching.html) | _, % | \ | yes |
| [Hive](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF) | _, % | none | yes |
| Current Spark | _, % | \ | yes |

[1] Default escape character: most systems do not have a default escape character, instead the user can specify one by calling a like expression with an escape argument [A] LIKE [B] ESCAPE [C]. This syntax is currently not supported by Spark, however I would volunteer to implement this feature in a separate ticket.

The specifications are often quite terse and certain scenarios are undocumented, so here is a list of scenarios that I am uncertain about and would appreciate any input. Specifically I am looking for feedback on whether or not Spark's current behavior should be changed.
1. [x] Ending a pattern with the escape sequence, e.g. `like 'a\'`.
   PostreSQL gives an error: 'LIKE pattern must not end with escape character', which I personally find logical. Currently, Spark allows "non-terminated" escapes and simply ignores them as part of the pattern.
   According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), ending a pattern in an escape character is invalid.
   _Proposed new behaviour in Spark: throw AnalysisException_
2. [x] Empty input, e.g. `'' like ''`
   Postgres and DB2 will match empty input only if the pattern is empty as well, any other combination of empty input will not match. Spark currently follows this rule.
3. [x] Escape before a non-special character, e.g. `'a' like '\a'`.
   Escaping a non-wildcard character is not really documented but PostgreSQL just treats it verbatim, which I also find the least surprising behavior. Spark does the same.
   According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html

), it is invalid to follow an escape character with anything other than an escape character, an underscore or a percent sign.
   _Proposed new behaviour in Spark: throw AnalysisException_

The current specification is also described in the operator's source code in this patch.

Extra case in regex unit tests.

Author: Jakob Odersky <jakob@odersky.com>

This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>

Closes #15398 from jodersky/SPARK-17647.

(cherry picked from commit e5fee3e4)
Signed-off-by: Reynold Xin <rxin@databricks.com>

### What changes were proposed in this pull request?
The session catalog caches some persistent functions in the `FunctionRegistry`, so there can be duplicates. Our Catalog API `listFunctions` does not handle it.

It would be better if `SessionCatalog` API can de-duplciate the records, instead of doing it by each API caller. In `FunctionRegistry`, our functions are identified by the unquoted string. Thus, this PR is try to parse it using our parser interface and then de-duplicate the names.

### How was this patch tested?
Added test cases.

Author: Xiao Li <gatorsmile@gmail.com>

Closes #17646 from gatorsmile/showFunctions.

(cherry picked from commit 01ff0350)
Signed-off-by: Xiao Li <gatorsmile@gmail.com>

[SPARK-20335][SQL][BACKPORT-2.1] Children expressions of Hive UDF impacts the determinism of Hive UDF

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

This PR is to backport https://github.com/apache/spark/pull/17635 to Spark 2.1

---
```JAVA
  /**
   * Certain optimizations should not be applied if UDF is not deterministic.
   * Deterministic UDF returns same result each time it is invoked with a
   * particular input. This determinism just needs to hold within the context of
   * a query.
   *
   * return true if the UDF is deterministic
   */
  boolean deterministic() default true;
```

Based on the definition of [UDFType](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/UDFType.java#L42-L50), when Hive UDF's children are non-deterministic, Hive UDF is also non-deterministic.

### How was this patch tested?
Added test cases.

Author: Xiao Li <gatorsmile@gmail.com>

Closes #17652 from gatorsmile/backport-17635.

## What changes were proposed in this pull request?

Synchronize access to openStreams map.

## How was this patch tested?

Existing tests.

Author: Bogdan Raducanu <bogdan@databricks.com>

Closes #17592 from bogdanrdc/SPARK-20243.

[SPARK-19946][TESTS][BACKPORT-2.1] DebugFilesystem.assertNoOpenStreams should report the open streams to help debugging

## What changes were proposed in this pull request?
Backport for PR #17292
DebugFilesystem.assertNoOpenStreams throws an exception with a cause exception that actually shows the code line which leaked the stream.

## How was this patch tested?
New test in SparkContextSuite to check there is a cause exception.

Author: Bogdan Raducanu <bogdan@databricks.com>

Closes #17632 from bogdanrdc/SPARK-19946-BRANCH2.1.

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

This is to backport the PR https://github.com/apache/spark/pull/17265 to Spark 2.1 branch.

---
Since we are using shim for most Hive metastore APIs, the exceptions thrown by the underlying method of Method.invoke() are wrapped by `InvocationTargetException`. Instead of doing it one by one, we should handle all of them in the `withClient`. If any of them is missing, the error message could looks unfriendly. For example, below is an example for dropping tables.

```
Expected exception org.apache.spark.sql.AnalysisException to be thrown, but java.lang.reflect.InvocationTargetException was thrown.
ScalaTestFailureLocation: org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14 at (ExternalCatalogSuite.scala:193)
org.scalatest.exceptions.TestFailedException: Expected exception org.apache.spark.sql.AnalysisException to be thrown, but java.lang.reflect.InvocationTargetException was thrown.
	at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:496)
	at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
	at org.scalatest.Assertions$class.intercept(Assertions.scala:1004)
	at org.scalatest.FunSuite.intercept(FunSuite.scala:1555)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply$mcV$sp(ExternalCatalogSuite.scala:193)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
	at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
	at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
	at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
	at org.scalatest.Transformer.apply(Transformer.scala:22)
	at org.scalatest.Transformer.apply(Transformer.scala:20)
	at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
	at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68)
	at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
	at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
	at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
	at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
	at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(ExternalCatalogSuite.scala:40)
	at org.scalatest.BeforeAndAfterEach$class.runTest(BeforeAndAfterEach.scala:255)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite.runTest(ExternalCatalogSuite.scala:40)
	at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
	at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
	at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413)
	at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401)
	at scala.collection.immutable.List.foreach(List.scala:381)
	at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
	at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396)
	at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483)
	at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208)
	at org.scalatest.FunSuite.runTests(FunSuite.scala:1555)
	at org.scalatest.Suite$class.run(Suite.scala:1424)
	at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555)
	at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
	at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
	at org.scalatest.SuperEngine.runImpl(Engine.scala:545)
	at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212)
	at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:31)
	at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257)
	at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256)
	at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:31)
	at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:55)
	at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$3.apply(Runner.scala:2563)
	at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$3.apply(Runner.scala:2557)
	at scala.collection.immutable.List.foreach(List.scala:381)
	at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:2557)
	at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1044)
	at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1043)
	at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:2722)
	at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:1043)
	at org.scalatest.tools.Runner$.run(Runner.scala:883)
	at org.scalatest.tools.Runner.run(Runner.scala)
	at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2(ScalaTestRunner.java:138)
	at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:28)
	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 com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)
Caused by: java.lang.reflect.InvocationTargetException
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at org.apache.spark.sql.hive.client.Shim_v0_14.dropTable(HiveShim.scala:736)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply$mcV$sp(HiveClientImpl.scala:451)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply(HiveClientImpl.scala:451)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply(HiveClientImpl.scala:451)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:287)
	at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:228)
	at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:227)
	at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:270)
	at org.apache.spark.sql.hive.client.HiveClientImpl.dropTable(HiveClientImpl.scala:450)
	at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply$mcV$sp(HiveExternalCatalog.scala:456)
	at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply(HiveExternalCatalog.scala:454)
	at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply(HiveExternalCatalog.scala:454)
	at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:94)
	at org.apache.spark.sql.hive.HiveExternalCatalog.dropTable(HiveExternalCatalog.scala:454)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply$mcV$sp(ExternalCatalogSuite.scala:194)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply(ExternalCatalogSuite.scala:194)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply(ExternalCatalogSuite.scala:194)
	at org.scalatest.Assertions$class.intercept(Assertions.scala:997)
	... 57 more
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found)
	at org.apache.hadoop.hive.ql.metadata.Hive.dropTable(Hive.java:1038)
	... 79 more
Caused by: NoSuchObjectException(message:db2.unknown_table table not found)
	at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.get_table_core(HiveMetaStore.java:1808)
	at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.get_table(HiveMetaStore.java:1778)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invoke(RetryingHMSHandler.java:107)
	at com.sun.proxy.$Proxy10.get_table(Unknown Source)
	at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.getTable(HiveMetaStoreClient.java:1208)
	at org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.getTable(SessionHiveMetaStoreClient.java:131)
	at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.dropTable(HiveMetaStoreClient.java:952)
	at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.dropTable(HiveMetaStoreClient.java:904)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.invoke(RetryingMetaStoreClient.java:156)
	at com.sun.proxy.$Proxy11.dropTable(Unknown Source)
	at org.apache.hadoop.hive.ql.metadata.Hive.dropTable(Hive.java:1035)
	... 79 more
```

After unwrapping the exception, the message is like
```
org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found);
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found);
	at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:100)
	at org.apache.spark.sql.hive.HiveExternalCatalog.dropTable(HiveExternalCatalog.scala:460)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply$mcV$sp(ExternalCatalogSuite.scala:193)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
	at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
	at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
...
```
### How was this patch tested?
N/A

Author: Xiao Li <gatorsmile@gmail.com>

Closes #17627 from gatorsmile/backport-17265.

## What changes were proposed in this pull request?

`o.a.s.streaming.StreamingContextSuite.SPARK-18560 Receiver data should be deserialized properly` is flaky is because there is a potential dead-lock in StandaloneSchedulerBackend which causes `await` timeout. Here is the related stack trace:
```
"Thread-31" #211 daemon prio=5 os_prio=31 tid=0x00007fedd4808000 nid=0x16403 waiting on condition [0x00007000239b7000]
   java.lang.Thread.State: TIMED_WAITING (parking)
	at sun.misc.Unsafe.park(Native Method)
	- parking to wait for  <0x000000079b49ca10> (a scala.concurrent.impl.Promise$CompletionLatch)
	at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
	at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedNanos(AbstractQueuedSynchronizer.java:1037)
	at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1328)
	at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
	at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
	at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
	at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:201)
	at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
	at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:92)
	at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:76)
	at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stop(CoarseGrainedSchedulerBackend.scala:402)
	at org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend.org$apache$spark$scheduler$cluster$StandaloneSchedulerBackend$$stop(StandaloneSchedulerBackend.scala:213)
	- locked <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
	at org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend.stop(StandaloneSchedulerBackend.scala:116)
	- locked <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
	at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:517)
	at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1657)
	at org.apache.spark.SparkContext$$anonfun$stop$8.apply$mcV$sp(SparkContext.scala:1921)
	at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1302)
	at org.apache.spark.SparkContext.stop(SparkContext.scala:1920)
	at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:708)
	at org.apache.spark.streaming.StreamingContextSuite$$anonfun$43$$anonfun$apply$mcV$sp$66$$anon$3.run(StreamingContextSuite.scala:827)

"dispatcher-event-loop-3" #18 daemon prio=5 os_prio=31 tid=0x00007fedd603a000 nid=0x6203 waiting for monitor entry [0x0000700003be4000]
   java.lang.Thread.State: BLOCKED (on object monitor)
	at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.org$apache$spark$scheduler$cluster$CoarseGrainedSchedulerBackend$DriverEndpoint$$makeOffers(CoarseGrainedSchedulerBackend.scala:253)
	- waiting to lock <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
	at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anonfun$receive$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:124)
	at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:117)
	at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:205)
	at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:101)
	at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:213)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
```

This PR removes `synchronized` and changes `stopping` to AtomicBoolean to ensure idempotent to fix the dead-lock.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #17610 from zsxwing/SPARK-20131.

(cherry picked from commit c5f1cc37)
Signed-off-by: Shixiong Zhu <shixiong@databricks.com>

## What changes were proposed in this pull request?
AssertNotNull's toString/simpleString dumps the entire walkedTypePath. walkedTypePath is used for error message reporting and shouldn't be part of the output.

## How was this patch tested?
Manually tested.

Author: Reynold Xin <rxin@databricks.com>

Closes #17616 from rxin/SPARK-20304.

(cherry picked from commit 54085538)
Signed-off-by: Xiao Li <gatorsmile@gmail.com>

## What changes were proposed in this pull request?
Update count distinct error message for streaming datasets/dataframes to match current behavior. These aggregations are not yet supported, regardless of whether the dataset/dataframe is aggregated.

Author: jtoka <jason.tokayer@gmail.com>

Closes #17609 from jtoka/master.

(cherry picked from commit 2e1fd46e)
Signed-off-by: Sean Owen <sowen@cloudera.com>

## What changes were proposed in this pull request?

1. Omitted space between the sentences: `... on static data.The Spark SQL engine will ...` -> `... on static data. The Spark SQL engine will ...`
2. Omitted colon in Output Model section.

## How was this patch tested?

None.

Author: Lee Dongjin <dongjin@apache.org>

Closes #17564 from dongjinleekr/feature/fix-programming-guide.

(cherry picked from commit b9384382)
Signed-off-by: Sean Owen <sowen@cloudera.com>

## What changes were proposed in this pull request?

`NaNvl(float value, null)` will be converted into `NaNvl(float value, Cast(null, DoubleType))` and finally `NaNvl(Cast(float value, DoubleType), Cast(null, DoubleType))`.

This will cause mismatching in the output type when the input type is float.

By adding extra rule in TypeCoercion can resolve this issue.

## How was this patch tested?

unite tests.

Please review http://spark.apache.org/contributing.html

 before opening a pull request.

Author: DB Tsai <dbt@netflix.com>

Closes #17606 from dbtsai/fixNaNvl.

(cherry picked from commit 8ad63ee1)
Signed-off-by: DB Tsai <dbtsai@dbtsai.com>

## What changes were proposed in this pull request?

Fix the since tag when backporting critical bugs (SPARK-18555) from 2.2 branch into 2.1 branch.

## How was this patch tested?

N/A

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: DB Tsai <dbtsai@dbtsai.com>

Closes #17600 from dbtsai/branch-2.1.

## What changes were proposed in this pull request?

This PR  fixs the following failure:
```
sbt.ForkMain$ForkError: java.lang.AssertionError: null
	at org.junit.Assert.fail(Assert.java:86)
	at org.junit.Assert.assertTrue(Assert.java:41)
	at org.junit.Assert.assertTrue(Assert.java:52)
	at org.apache.spark.network.RequestTimeoutIntegrationSuite.furtherRequestsDelay(RequestTimeoutIntegrationSuite.java:230)
	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:497)
	at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:50)
	at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
	at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:47)
	at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
	at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:26)
	at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:27)
	at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:325)
	at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:78)
	at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:57)
	at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
	at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
	at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
	at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
	at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
	at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
	at org.junit.runners.Suite.runChild(Suite.java:128)
	at org.junit.runners.Suite.runChild(Suite.java:27)
	at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
	at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
	at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
	at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
	at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
	at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
	at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
	at org.junit.runner.JUnitCore.run(JUnitCore.java:115)
	at com.novocode.junit.JUnitRunner$1.execute(JUnitRunner.java:132)
	at sbt.ForkMain$Run$2.call(ForkMain.java:296)
	at sbt.ForkMain$Run$2.call(ForkMain.java:286)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
```

It happens several times per month on [Jenkins](http://spark-tests.appspot.com/test-details?suite_name=org.apache.spark.network.RequestTimeoutIntegrationSuite&test_name=furtherRequestsDelay). The failure is because `callback1` may not be called before `assertTrue(callback1.failure instanceof IOException);`. It's pretty easy to reproduce this error by adding a sleep before this line: https://github.com/apache/spark/blob/379b0b0bbdbba2278ce3bcf471bd75f6ffd9cf0d/common/network-common/src/test/java/org/apache/spark/network/RequestTimeoutIntegrationSuite.java#L267



The fix is straightforward: just use the latch to wait until `callback1` is called.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #17599 from zsxwing/SPARK-17564.

(cherry picked from commit 734dfbfc)
Signed-off-by: Reynold Xin <rxin@databricks.com>

[SPARK-20270][SQL] na.fill should not change the values in long or integer when the default value is in double

## What changes were proposed in this pull request?

This bug was partially addressed in SPARK-18555 https://github.com/apache/spark/pull/15994

, but the root cause isn't completely solved. This bug is pretty critical since it changes the member id in Long in our application if the member id can not be represented by Double losslessly when the member id is very big.

Here is an example how this happens, with
```
      Seq[(java.lang.Long, java.lang.Double)]((null, 3.14), (9123146099426677101L, null),
        (9123146560113991650L, 1.6), (null, null)).toDF("a", "b").na.fill(0.2),
```
the logical plan will be
```
== Analyzed Logical Plan ==
a: bigint, b: double
Project [cast(coalesce(cast(a#232L as double), cast(0.2 as double)) as bigint) AS a#240L, cast(coalesce(nanvl(b#233, cast(null as double)), 0.2) as double) AS b#241]
+- Project [_1#229L AS a#232L, _2#230 AS b#233]
   +- LocalRelation [_1#229L, _2#230]
```

Note that even the value is not null, Spark will cast the Long into Double first. Then if it's not null, Spark will cast it back to Long which results in losing precision.

The behavior should be that the original value should not be changed if it's not null, but Spark will change the value which is wrong.

With the PR, the logical plan will be
```
== Analyzed Logical Plan ==
a: bigint, b: double
Project [coalesce(a#232L, cast(0.2 as bigint)) AS a#240L, coalesce(nanvl(b#233, cast(null as double)), cast(0.2 as double)) AS b#241]
+- Project [_1#229L AS a#232L, _2#230 AS b#233]
   +- LocalRelation [_1#229L, _2#230]
```
which behaves correctly without changing the original Long values and also avoids extra cost of unnecessary casting.

## How was this patch tested?

unit test added.

+cc srowen rxin cloud-fan gatorsmile

Thanks.

Author: DB Tsai <dbt@netflix.com>

Closes #17577 from dbtsai/fixnafill.

(cherry picked from commit 1a0bc416)
Signed-off-by: DB Tsai <dbtsai@dbtsai.com>

## What changes were proposed in this pull request?

   DataSet.na.fill(0) used on a DataSet which has a long value column, it will change the original long value.

   The reason is that the type of the function fill's param is Double, and the numeric columns are always cast to double(`fillCol[Double](f, value)`) .
```
  def fill(value: Double, cols: Seq[String]): DataFrame = {
    val columnEquals = df.sparkSession.sessionState.analyzer.resolver
    val projections = df.schema.fields.map { f =>
      // Only fill if the column is part of the cols list.
      if (f.dataType.isInstanceOf[NumericType] && cols.exists(col => columnEquals(f.name, col))) {
        fillCol[Double](f, value)
      } else {
        df.col(f.name)
      }
    }
    df.select(projections : _*)
  }
```

 For example:
```
scala> val df = Seq[(Long, Long)]((1, 2), (-1, -2), (9123146099426677101L, 9123146560113991650L)).toDF("a", "b")
df: org.apache.spark.sql.DataFrame = [a: bigint, b: bigint]

scala> df.show
+-------------------+-------------------+
|                  a|                  b|
+-------------------+-------------------+
|                  1|                  2|
|                 -1|                 -2|
|9123146099426677101|9123146560113991650|
+-------------------+-------------------+

scala> df.na.fill(0).show
+-------------------+-------------------+
|                  a|                  b|
+-------------------+-------------------+
|                  1|                  2|
|                 -1|                 -2|
|9123146099426676736|9123146560113991680|
+-------------------+-------------------+
 ```

the original values changed [which is not we expected result]:
```
 9123146099426677101 -> 9123146099426676736
 9123146560113991650 -> 9123146560113991680
```

## How was this patch tested?

unit test added.

Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)>

Closes #15994 from windpiger/nafillMissupOriginalValue.

(cherry picked from commit 508de38c)
Signed-off-by: DB Tsai <dbtsai@dbtsai.com>

## What changes were proposed in this pull request?

Saw the following failure locally:

```
Traceback (most recent call last):
  File "/home/jenkins/workspace/python/pyspark/streaming/tests.py", line 351, in test_cogroup
    self._test_func(input, func, expected, sort=True, input2=input2)
  File "/home/jenkins/workspace/python/pyspark/streaming/tests.py", line 162, in _test_func
    self.assertEqual(expected, result)
AssertionError: Lists differ: [[(1, ([1], [2])), (2, ([1], [... != []

First list contains 3 additional elements.
First extra element 0:
[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))]

+ []
- [[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))],
-  [(1, ([1, 1, 1], [])), (2, ([1], [])), (4, ([], [1]))],
-  [('', ([1, 1], [1, 2])), ('a', ([1, 1], [1, 1])), ('b', ([1], [1]))]]
```

It also happened on Jenkins: http://spark-tests.appspot.com/builds/spark-branch-2.1-test-sbt-hadoop-2.7/120



It's because when the machine is overloaded, the timeout is not enough. This PR just increases the timeout to 30 seconds.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #17597 from zsxwing/SPARK-20285.

(cherry picked from commit f9a50ba2)
Signed-off-by: Shixiong Zhu <shixiong@databricks.com>

## What changes were proposed in this pull request?

Weigher.weigh needs to return Int but it is possible for an Array[FileStatus] to have size > Int.maxValue. To avoid this, the size is scaled down by a factor of 32. The maximumWeight of the cache is also scaled down by the same factor.

## How was this patch tested?
New test in FileIndexSuite

Author: Bogdan Raducanu <bogdan@databricks.com>

Closes #17591 from bogdanrdc/SPARK-20280.

(cherry picked from commit f6dd8e0e)
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>

## What changes were proposed in this pull request?
sq/core module currently declares asm as a test scope dependency. Transitively it should actually be a normal dependency since the actual core module defines it. This occasionally confuses IntelliJ.

## How was this patch tested?
N/A - This is a build change.

Author: Reynold Xin <rxin@databricks.com>

Closes #17574 from rxin/SPARK-20264.

(cherry picked from commit 7bfa05e0)
Signed-off-by: Xiao Li <gatorsmile@gmail.com>

## What changes were proposed in this pull request?
This error message doesn't get properly formatted because of a missing `s`.  Currently the error looks like:

```
Caused by: java.lang.IllegalArgumentException: requirement failed: indices should be one-based and in ascending order; found current=$current, previous=$previous; line="$line"
```
(note the literal `$current` instead of the interpolated value)

Please review http://spark.apache.org/contributing.html

 before opening a pull request.

Author: Vijay Ramesh <vramesh@demandbase.com>

Closes #17572 from vijaykramesh/master.

(cherry picked from commit 261eaf51)
Signed-off-by: Sean Owen <sowen@cloudera.com>

AssertNotNull currently throws RuntimeException. It should throw NullPointerException, which is more specific.

N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #17573 from rxin/SPARK-20262.

(cherry picked from commit e1afc4dc)
Signed-off-by: Xiao Li <gatorsmile@gmail.com>

[SPARK-20246][SQL] should not push predicate down through aggregate with non-deterministic expressions

## What changes were proposed in this pull request?

Similar to `Project`, when `Aggregate` has non-deterministic expressions, we should not push predicate down through it, as it will change the number of input rows and thus change the evaluation result of non-deterministic expressions in `Aggregate`.

## How was this patch tested?

new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #17562 from cloud-fan/filter.

(cherry picked from commit 7577e9c3)
Signed-off-by: Xiao Li <gatorsmile@gmail.com>

## What changes were proposed in this pull request?

1. '/applications/[app-id]/stages' in rest api.status should add description '?status=[active|complete|pending|failed] list only stages in the state.'

Now the lack of this description, resulting in the use of this api do not know the use of the status through the brush stage list.

2.'/applications/[app-id]/stages/[stage-id]' in REST API,remove redundant description ‘?status=[active|complete|pending|failed] list only stages in the state.’.
Because only one stage is determined based on stage-id.

code:
  GET
  def stageList(QueryParam("status") statuses: JList[StageStatus]): Seq[StageData] = {
    val listener = ui.jobProgressListener
    val stageAndStatus = AllStagesResource.stagesAndStatus(ui)
    val adjStatuses = {
      if (statuses.isEmpty()) {
        Arrays.asList(StageStatus.values(): _*)
      } else {
        statuses
      }
    };

## How was this patch tested?

manual tests

Please review http://spark.apache.org/contributing.html

 before opening a pull request.

Author: 郭小龙 10207633 <guo.xiaolong1@zte.com.cn>

Closes #17534 from guoxiaolongzte/SPARK-20218.

(cherry picked from commit 9e0893b5)
Signed-off-by: Sean Owen <sowen@cloudera.com>

## What changes were proposed in this pull request?

`_convert_to_vector` converts a scipy sparse matrix to csc matrix for initializing `SparseVector`. However, it doesn't guarantee the converted csc matrix has sorted indices and so a failure happens when you do something like that:

    from scipy.sparse import lil_matrix
    lil = lil_matrix((4, 1))
    lil[1, 0] = 1
    lil[3, 0] = 2
    _convert_to_vector(lil.todok())

    File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 78, in _convert_to_vector
      return SparseVector(l.shape[0], csc.indices, csc.data)
    File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 556, in __init__
      % (self.indices[i], self.indices[i + 1]))
    TypeError: Indices 3 and 1 are not strictly increasing

A simple test can confirm that `dok_matrix.tocsc()` won't guarantee sorted indices:

    >>> from scipy.sparse import lil_matrix
    >>> lil = lil_matrix((4, 1))
    >>> lil[1, 0] = 1
    >>> lil[3, 0] = 2
    >>> dok = lil.todok()
    >>> csc = dok.tocsc()
    >>> csc.has_sorted_indices
    0
    >>> csc.indices
    array([3, 1], dtype=int32)

I checked the source codes of scipy. The only way to guarantee it is `csc_matrix.tocsr()` and `csr_matrix.tocsc()`.

## How was this patch tested?

Existing tests.

Please review http://spark.apache.org/contributing.html

 before opening a pull request.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #17532 from viirya/make-sure-sorted-indices.

(cherry picked from commit 12206058)
Signed-off-by: Joseph K. Bradley <joseph@databricks.com>