The secrets leveldb isn't being moved if you run spark shuffle services without yarn nm recovery on and then turn it on.  This fixes that.  I unfortunately missed this when I ported the patch from our internal branch 2 to master branch due to the changes for the recovery path.  Note this only applies to master since it is the only place the yarn nm recovery dir is used.

Unit tests ran and tested on 8 node cluster.  Fresh startup with NM recovery, fresh startup no nm recovery, switching between no nm recovery and recovery.  Also tested running applications to make sure wasn't affected by rolling upgrade.

Author: Thomas Graves <tgraves@prevailsail.corp.gq1.yahoo.com>
Author: Tom Graves <tgraves@apache.org>

Closes #14999 from tgravescs/SPARK-17433.

## What changes were proposed in this pull request?

(Please fill in changes proposed in this fix)
Streaming doc correction.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Author: Satendra Kumar <satendra@knoldus.com>

Closes #14996 from satendrakumar06/patch-1.

## What changes were proposed in this pull request?
SparkR ```spark.als``` arguments ```reg``` should be 0.1 by default, which need to be consistent with ML.

## How was this patch tested?
Existing tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15021 from yanboliang/spark-17464.

## What changes were proposed in this pull request?

This patch adds methods for extracting major and minor versions as Int types in Scala from a Spark version string.

Motivation: There are many hacks within Spark's codebase to identify and compare Spark versions. We should add a simple utility to standardize these code paths, especially since there have been mistakes made in the past. This will let us add unit tests as well.  Currently, I want this functionality to check Spark versions to provide backwards compatibility for ML model persistence.

## How was this patch tested?

Unit tests

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #15017 from jkbradley/version-parsing.

## What changes were proposed in this pull request?

This pull request adds the functionality to enable accessing worker and application UI through master UI itself. Thus helps in accessing SparkUI when running spark cluster in closed networks e.g. Kubernetes. Cluster admin needs to expose only spark master UI and rest of the UIs can be in the private network, master UI will reverse proxy the connection request to corresponding resource. It adds the path for workers/application UIs as

WorkerUI: <http/https>://master-publicIP:<port>/target/workerID/
ApplicationUI: <http/https>://master-publicIP:<port>/target/appID/

This makes it easy for users to easily protect the Spark master cluster access by putting some reverse proxy e.g. https://github.com/bitly/oauth2_proxy

## How was this patch tested?

The functionality has been tested manually and there is a unit test too for testing access to worker UI with reverse proxy address.

pwendell bomeng BryanCutler can you please review it, thanks.

Author: Gurvinder Singh <gurvinder.singh@uninett.no>

Closes #13950 from gurvindersingh/rproxy.

## What changes were proposed in this pull request?

Before this change, we would always allocate 64MB per aggregation task for the first-level hash map storage, even when running in low-memory situations such as local mode. This changes it to use the memory manager default page size, which is automatically reduced from 64MB in these situations.

cc ooq JoshRosen

## How was this patch tested?

Tested manually with `bin/spark-shell --master=local[32]` and verifying that `(1 to math.pow(10, 3).toInt).toDF("n").withColumn("m", 'n % 2).groupBy('m).agg(sum('n)).show` does not crash.

Author: Eric Liang <ekl@databricks.com>

Closes #15016 from ericl/sc-4483.

[SPARK-17200][PROJECT INFRA][BUILD][SPARKR] Automate building and testing on Windows (currently SparkR only)

## What changes were proposed in this pull request?

This PR adds the build automation on Windows with [AppVeyor](https://www.appveyor.com/) CI tool.

Currently, this only runs the tests for SparkR as we have been having some issues with testing Windows-specific PRs (e.g. https://github.com/apache/spark/pull/14743 and https://github.com/apache/spark/pull/13165) and hard time to verify this.

One concern is, this build is dependent on [steveloughran/winutils](https://github.com/steveloughran/winutils) for pre-built Hadoop bin package (who is a Hadoop PMC member).

## How was this patch tested?

Manually, https://ci.appveyor.com/project/HyukjinKwon/spark/build/88-SPARK-17200-build-profile
This takes roughly 40 mins.

Some tests are already being failed and this was found in https://github.com/apache/spark/pull/14743#issuecomment-241405287.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14859 from HyukjinKwon/SPARK-17200-build.

## What changes were proposed in this pull request?

additional options were not passed down in write.df.

## How was this patch tested?

unit tests
falaki shivaram

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #15010 from felixcheung/testreadoptions.

## What changes were proposed in this pull request?

In `PreprocessDDL` we will check if table columns are duplicated. However, this checking ignores case sensitivity config(it's always case-sensitive) and lead to different result between `HiveExternalCatalog` and `InMemoryCatalog`. `HiveExternalCatalog` will throw exception because hive metastore is always case-nonsensitive, and `InMemoryCatalog` is fine.

This PR fixes it.

## How was this patch tested?

a new test in DDLSuite

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14994 from cloud-fan/check-dup.

### What changes were proposed in this pull request?
The original [JIRA Hive-1642](https://issues.apache.org/jira/browse/HIVE-1642) delivered the test cases `auto_joinXYZ` for verifying the results when the joins are automatically converted to map-join. Basically, most of them are just copied from the corresponding `joinXYZ`.

After comparison between `auto_joinXYZ` and `joinXYZ`, below is a list of duplicate cases:
```
    "auto_join0",
    "auto_join1",
    "auto_join10",
    "auto_join11",
    "auto_join12",
    "auto_join13",
    "auto_join14",
    "auto_join14_hadoop20",
    "auto_join15",
    "auto_join17",
    "auto_join18",
    "auto_join2",
    "auto_join20",
    "auto_join21",
    "auto_join23",
    "auto_join24",
    "auto_join3",
    "auto_join4",
    "auto_join5",
    "auto_join6",
    "auto_join7",
    "auto_join8",
    "auto_join9"
```

We can remove all of them without affecting the test coverage.

### How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #14635 from gatorsmile/removeAuto.

## What changes were proposed in this pull request?

DAGScheduler invalidates shuffle files when an executor loss event occurs, but not when the external shuffle service is enabled. This is because when shuffle service is on, the shuffle file lifetime can exceed the executor lifetime.

However, it also doesn't invalidate shuffle files when the shuffle service itself is lost (due to whole slave loss). This can cause long hangs when slaves are lost since the file loss is not detected until a subsequent stage attempts to read the shuffle files.

The proposed fix is to also invalidate shuffle files when an executor is lost due to a `SlaveLost` event.

## How was this patch tested?

Unit tests, also verified on an actual cluster that slave loss invalidates shuffle files immediately as expected.

cc mateiz

Author: Eric Liang <ekl@databricks.com>

Closes #14931 from ericl/sc-4439.

## What changes were proposed in this pull request?

Fixing the typo in the unit test of CodeGenerationSuite.scala

## How was this patch tested?
Ran the unit test after fixing the typo and it passes

Author: Srinivasa Reddy Vundela <vsr@cloudera.com>

Closes #14989 from vundela/typo_fix.

## What changes were proposed in this pull request?

`select size(null)` returns -1 in Hive. In order to be compatible, we should return `-1`.

## How was this patch tested?

unit test in `CollectionFunctionsSuite` and `DataFrameFunctionsSuite`.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #14991 from adrian-wang/size.

[SPARK-17339][SPARKR][CORE] Fix some R tests and use Path.toUri in SparkContext for Windows paths in SparkR

## What changes were proposed in this pull request?

This PR fixes the Windows path issues in several APIs. Please refer https://issues.apache.org/jira/browse/SPARK-17339 for more details.

## How was this patch tested?

Tests via AppVeyor CI - https://ci.appveyor.com/project/HyukjinKwon/spark/build/82-SPARK-17339-fix-r

Also, manually,

![2016-09-06 3 14 38](https://cloud.githubusercontent.com/assets/6477701/18263406/b93a98be-7444-11e6-9521-b28ee65a4771.png)

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14960 from HyukjinKwon/SPARK-17339.

[SPARK-17359][SQL][MLLIB] Use ArrayBuffer.+=(A) instead of ArrayBuffer.append(A) in performance critical paths

## What changes were proposed in this pull request?

We should generally use `ArrayBuffer.+=(A)` rather than `ArrayBuffer.append(A)`, because `append(A)` would involve extra boxing / unboxing.

## How was this patch tested?

N/A

Author: Liwei Lin <lwlin7@gmail.com>

Closes #14914 from lw-lin/append_to_plus_eq_v2.

## What changes were proposed in this pull request?

Fixed bug in `dapplyCollect` by changing the `compute` function of `worker.R` to explicitly handle raw (binary) vectors.

cc shivaram

## How was this patch tested?

Unit tests

Author: Clark Fitzgerald <clarkfitzg@gmail.com>

Closes #14783 from clarkfitzg/SPARK-16785.

[SPARK-17372][SQL][STREAMING] Avoid serialization issues by using Arrays to save file names in FileStreamSource

## What changes were proposed in this pull request?

When we create a filestream on a directory that has partitioned subdirs (i.e. dir/x=y/), then ListingFileCatalog.allFiles returns the files in the dir as Seq[String] which internally is a Stream[String]. This is because of this [line](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala#L93), where a LinkedHashSet.values.toSeq returns Stream. Then when the [FileStreamSource](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala#L79) filters this Stream[String] to remove the seen files, it creates a new Stream[String], which has a filter function that has a $outer reference to the FileStreamSource (in Scala 2.10). Trying to serialize this Stream[String] causes NotSerializableException. This will happened even if there is just one file in the dir.

Its important to note that this behavior is different in Scala 2.11. There is no $outer reference to FileStreamSource, so it does not throw NotSerializableException. However, with a large sequence of files (tested with 10000 files), it throws StackOverflowError. This is because how Stream class is implemented. Its basically like a linked list, and attempting to serialize a long Stream requires *recursively* going through linked list, thus resulting in StackOverflowError.

In short, across both Scala 2.10 and 2.11, serialization fails when both the following conditions are true.
- file stream defined on a partitioned directory
- directory has 10k+ files

The right solution is to convert the seq to an array before writing to the log. This PR implements this fix in two ways.
- Changing all uses for HDFSMetadataLog to ensure Array is used instead of Seq
- Added a `require` in HDFSMetadataLog such that it is never used with type Seq

## How was this patch tested?

Added unit test that test that ensures the file stream source can handle with 10000 files. This tests fails in both Scala 2.10 and 2.11 with different failures as indicated above.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #14987 from tdas/SPARK-17372.

## What changes were proposed in this pull request?

Previously we have 2 conditions to decide whether a data source table is hive-compatible:

1. the data source is file-based and has a corresponding Hive serde
2. have a `path` entry in data source options/storage properties

However, if condition 1 is true, condition 2 must be true too, as we will put the default table path into data source options/storage properties for managed data source tables.

There is also a potential issue: we will set the `locationUri` even for managed table.

This PR removes the condition 2 and only set the `locationUri` for external data source tables.

Note: this is also a first step to unify the `path` of data source tables and `locationUri` of hive serde tables. For hive serde tables, `locationUri` is only set for external table. For data source tables, `path` is always set. We can make them consistent after this PR.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14809 from cloud-fan/minor2.

### What changes were proposed in this pull request?
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/64956/testReport/junit/org.apache.spark.sql.hive/StatisticsSuite/test_statistics_of_LogicalRelation_converted_from_MetastoreRelation/
```
org.apache.spark.sql.hive.StatisticsSuite.test statistics of LogicalRelation converted from MetastoreRelation

Failing for the past 1 build (Since Failed#64956 )
Took 1.4 sec.
Error Message

org.scalatest.exceptions.TestFailedException: 6871 did not equal 4236
Stacktrace

sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException: 6871 did not equal 4236
	at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
```

This fix does not check the exact value of `sizeInBytes`. Instead, we compare whether it is larger than zero and compare the values between different values.

In addition, we also combine `checkMetastoreRelationStats` and `checkLogicalRelationStats` into the same checking function.

### How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #14978 from gatorsmile/spark17408.

## What changes were proposed in this pull request?

It seems that old shuffle map tasks hanging around after a stage resubmit will delete intended shuffle output files on stop(), causing downstream stages to fail even after successful resubmit completion. This can happen easily if the prior map task is waiting for a network timeout when its stage is resubmitted.

This can cause unnecessary stage resubmits, sometimes multiple times as fetch fails cause a cascade of shuffle file invalidations, and confusing FetchFailure messages that report shuffle index files missing from the local disk.

Given that IndexShuffleBlockResolver commits data atomically, it seems unnecessary to ever delete committed task output: even in the rare case that a task is failed after it finishes committing shuffle output, it should be safe to retain that output.

## How was this patch tested?

Prior to the fix proposed in https://github.com/apache/spark/pull/14931, I was able to reproduce this behavior by killing slaves in the middle of a large shuffle. After this patch, stages were no longer resubmitted multiple times due to shuffle index loss.

cc JoshRosen vanzin

Author: Eric Liang <ekl@databricks.com>

Closes #14932 from ericl/dont-remove-committed-files.

## What changes were proposed in this pull request?

Fix the 'ask' type parameter in 'removeExecutor' to eliminate a lot of error logs `Cannot cast java.lang.Boolean to scala.runtime.Nothing$`

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #14983 from zsxwing/SPARK-17316-3.

To make the log file more readable, rework some of the logging done
by the AM:

- log executor command / env just once, since they're all almost the same;
  the information that changes, such as executor ID, is already available
  in other log messages.
- avoid printing logs when nothing happens, especially when updating the
  container requests in the allocator.
- print fewer log messages when requesting many unlocalized executors,
  instead of repeating the same message multiple times.
- removed some logs that seemed unnecessary.

In the process, I slightly fixed up the wording in a few log messages, and
did some minor clean up of method arguments that were redundant.

Tested by running existing unit tests, and analyzing the logs of an
application that exercises dynamic allocation by forcing executors
to be allocated and be killed in waves.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #14943 from vanzin/SPARK-15891.

## What changes were proposed in this pull request?
Join processing in the parser relies on the fact that the grammar produces a right nested trees, for instance the parse tree for `select * from a join b join c` is expected to produce a tree similar to `JOIN(a, JOIN(b, c))`. However there are cases in which this (invariant) is violated, like:
```sql
SELECT COUNT(1)
FROM test T1
     CROSS JOIN test T2
     JOIN test T3
      ON T3.col = T1.col
     JOIN test T4
      ON T4.col = T1.col
```
In this case the parser returns a tree in which Joins are located on both the left and the right sides of the parent join node.

This PR introduces a different grammar rule which does not make this assumption. The new rule takes a relation and searches for zero or more joined relations. As a bonus processing is much easier.

## How was this patch tested?
Existing tests and I have added a regression test to the plan parser suite.

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #14867 from hvanhovell/SPARK-17296.

## What changes were proposed in this pull request?

This patch fixes a `java.io.StreamCorruptedException` error affecting remote reads of cached values when certain data types are used. The problem stems from #11801 / SPARK-13990, a patch to have Spark automatically pick the "best" serializer when caching RDDs. If PySpark cached a PythonRDD, then this would be cached as an `RDD[Array[Byte]]` and the automatic serializer selection would pick KryoSerializer for replication and block transfer. However, the `getRemoteValues()` / `getRemoteBytes()` code path did not pass proper class tags in order to enable the same serializer to be used during deserialization, causing Java to be inappropriately used instead of Kryo, leading to the StreamCorruptedException.

We already fixed a similar bug in #14311, which dealt with similar issues in block replication. Prior to that patch, it seems that we had no tests to ensure that block replication actually succeeded. Similarly, prior to this bug fix patch it looks like we had no tests to perform remote reads of cached data, which is why this bug was able to remain latent for so long.

This patch addresses the bug by modifying `BlockManager`'s `get()` and  `getRemoteValues()` methods to accept ClassTags, allowing the proper class tag to be threaded in the `getOrElseUpdate` code path (which is used by `rdd.iterator`)

## How was this patch tested?

Extended the caching tests in `DistributedSuite` to exercise the `getRemoteValues` path, plus manual testing to verify that the PySpark bug reproduction in SPARK-17110 is fixed.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #14952 from JoshRosen/SPARK-17110.

## What changes were proposed in this pull request?
1, remove unnecessary `require()`, because it will make following check useless.
2, update the error msg.

## How was this patch tested?
no test

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #14972 from zhengruifeng/del_unnecessary_check.

## What changes were proposed in this pull request?
TRIM/LTRIM/RTRIM should not strips characters other than spaces, we were trimming all chars small than ASCII 0x20(space)

## How was this patch tested?
fixed existing tests.

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #14924 from techaddict/SPARK-17299.

## What changes were proposed in this pull request?

Upgrades the Snappy version to 1.1.2.6 from 1.1.2.4, release notes: https://github.com/xerial/snappy-java/blob/master/Milestone.md mention "Fix a bug in SnappyInputStream when reading compressed data that happened to have the same first byte with the stream magic header (#142)"

## How was this patch tested?
Existing unit tests using the latest IBM Java 8 on Intel, Power and Z architectures (little and big-endian)

Author: Adam Roberts <aroberts@uk.ibm.com>

Closes #14958 from a-roberts/master.

## What changes were proposed in this pull request?

In LongToUnsafeRowMap, we use offset of a value as pointer, stored in a array also in the page for chained values. The offset is not portable, because Platform.LONG_ARRAY_OFFSET will be different with different JVM Heap size, then the deserialized LongToUnsafeRowMap will be corrupt.

This PR will change to use portable address (without Platform.LONG_ARRAY_OFFSET).

## How was this patch tested?

Added a test case with random generated keys, to improve the coverage. But this test is not a regression test, that could require a Spark cluster that have at least 32G heap in driver or executor.

Author: Davies Liu <davies@databricks.com>

Closes #14927 from davies/longmap.

## What changes were proposed in this pull request?

This PR adds better error messages for malformed record when reading a JSON file using DataFrameReader.

For example, for query:
```
import org.apache.spark.sql.types._
val corruptRecords = spark.sparkContext.parallelize("""{"a":{, b:3}""" :: Nil)
val schema = StructType(StructField("a", StringType, true) :: Nil)
val jsonDF = spark.read.schema(schema).json(corruptRecords)
```

**Before change:**
We silently replace corrupted line with null
```
scala> jsonDF.show
+----+
|   a|
+----+
|null|
+----+
```

**After change:**
Add an explicit warning message:
```
scala> jsonDF.show
16/09/02 14:43:16 WARN JacksonParser: Found at least one malformed records (sample: {"a":{, b:3}). The JSON reader will replace
all malformed records with placeholder null in current PERMISSIVE parser mode.
To find out which corrupted records have been replaced with null, please use the
default inferred schema instead of providing a custom schema.

Code example to print all malformed records (scala):
===================================================
// The corrupted record exists in column _corrupt_record.
val parsedJson = spark.read.json("/path/to/json/file/test.json")

+----+
|   a|
+----+
|null|
+----+
```

###

## How was this patch tested?

Unit test.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #14929 from clockfly/logwarning_if_schema_not_contain_corrupted_record.

## What changes were proposed in this pull request?
```weights``` of ```MultilayerPerceptronClassificationModel``` should be the output weights of layers rather than initial weights, this PR correct it.

## How was this patch tested?
Doc change.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14967 from yanboliang/mlp-weights.

## What changes were proposed in this pull request?

class `org.apache.spark.sql.types.Metadata` is widely used in mllib to store some ml attributes. `Metadata` is commonly stored in `Alias` expression.

```
case class Alias(child: Expression, name: String)(
    val exprId: ExprId = NamedExpression.newExprId,
    val qualifier: Option[String] = None,
    val explicitMetadata: Option[Metadata] = None,
    override val isGenerated: java.lang.Boolean = false)
```

The `Metadata` can take a big memory footprint since the number of attributes is big ( in scale of million). When `toJSON` is called on `Alias` expression, the `Metadata` will also be converted to a big JSON string.
If a plan contains many such kind of `Alias` expressions, it may trigger out of memory error when `toJSON` is called, since converting all `Metadata` references to JSON will take huge memory.

With this PR, we will skip scanning Metadata when doing JSON conversion. For a reproducer of the OOM, and analysis, please look at jira https://issues.apache.org/jira/browse/SPARK-17356.

## How was this patch tested?

Existing tests.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #14915 from clockfly/json_oom.

## What changes were proposed in this pull request?

Using the public `Catalog` API, users can create a file-based data source table, without giving the path options. For this case, currently we can create the table successfully, but fail when we read it. Ideally we should fail during creation.

This is because when we create data source table, we resolve the data source relation without validating path: `resolveRelation(checkPathExist = false)`.

Looking back to why we add this trick(`checkPathExist`), it's because when we call `resolveRelation` for managed table, we add the path to data source options but the path is not created yet. So why we add this not-yet-created path to data source options? This PR fix the problem by adding path to options after we call `resolveRelation`. Then we can remove the `checkPathExist` parameter in `DataSource.resolveRelation` and do some related cleanups.

## How was this patch tested?

existing tests and new test in `CatalogSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14921 from cloud-fan/check-path.

## What changes were proposed in this pull request?
Cached table(parquet/orc) couldn't be shard between beelines, because the `sameResult` method used by `CacheManager` always return false(`sparkSession` are different) when compare two `HadoopFsRelation` in different beelines. So we make `sparkSession` a curry parameter.

## How was this patch tested?
Beeline1
```
1: jdbc:hive2://localhost:10000> CACHE TABLE src_pqt;
+---------+--+
| Result  |
+---------+--+
+---------+--+
No rows selected (5.143 seconds)
1: jdbc:hive2://localhost:10000> EXPLAIN SELECT * FROM src_pqt;
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
|                                                                                                                                                                                                            plan                                                                                                                                                                                                            |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
| == Physical Plan ==
InMemoryTableScan [key#49, value#50]
   +- InMemoryRelation [key#49, value#50], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), `src_pqt`
         +- *FileScan parquet default.src_pqt[key#0,value#1] Batched: true, Format: ParquetFormat, InputPaths: hdfs://199.0.0.1:9000/qiyadong/src_pqt, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<key:int,value:string>  |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
```

Beeline2
```
0: jdbc:hive2://localhost:10000> EXPLAIN SELECT * FROM src_pqt;
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
|                                                                                                                                                                                                            plan                                                                                                                                                                                                            |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
| == Physical Plan ==
InMemoryTableScan [key#68, value#69]
   +- InMemoryRelation [key#68, value#69], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), `src_pqt`
         +- *FileScan parquet default.src_pqt[key#0,value#1] Batched: true, Format: ParquetFormat, InputPaths: hdfs://199.0.0.1:9000/qiyadong/src_pqt, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<key:int,value:string>  |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
```

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #14913 from watermen/SPARK-17358.

## What changes were proposed in this pull request?

`TreeNode.toJSON` requires a subclass to explicitly override otherCopyArgs to include currying construction arguments, otherwise it reports AssertException telling that the construction argument values' count doesn't match the construction argument names' count.

For class `MetastoreRelation`, it has a currying construction parameter `client: HiveClient`, but Spark forgets to add it to the list of otherCopyArgs.

## How was this patch tested?

Unit tests.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #14928 from clockfly/metastore_relation_toJSON.

## What changes were proposed in this pull request?

If `ScalaUDF` throws exceptions during executing user code, sometimes it's hard for users to figure out what's wrong, especially when they use Spark shell. An example
```
org.apache.spark.SparkException: Job aborted due to stage failure: Task 12 in stage 325.0 failed 4 times, most recent failure: Lost task 12.3 in stage 325.0 (TID 35622, 10.0.207.202): java.lang.NullPointerException
	at line8414e872fb8b42aba390efc153d1611a12.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:40)
	at line8414e872fb8b42aba390efc153d1611a12.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:40)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
...
```
We should catch these exceptions and rethrow them with better error message, to say that the exception is happened in scala udf.

This PR also does some clean up for `ScalaUDF` and add a unit test suite for it.

## How was this patch tested?

the new test suite

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14850 from cloud-fan/npe.

[SPARK-17072][SQL] support table-level statistics generation and storing into/loading from metastore

## What changes were proposed in this pull request?

1. Support generation table-level statistics for
    - hive tables in HiveExternalCatalog
    - data source tables in HiveExternalCatalog
    - data source tables in InMemoryCatalog.
2. Add a property "catalogStats" in CatalogTable to hold statistics in Spark side.
3. Put logics of statistics transformation between Spark and Hive in HiveClientImpl.
4. Extend Statistics class by adding rowCount (will add estimatedSize when we have column stats).

## How was this patch tested?

add unit tests

Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wangzhenhua@huawei.com>

Closes #14712 from wzhfy/tableStats.

## What changes were proposed in this pull request?

It's really weird that we allow users to specify database in both from table name and to table name
 in `ALTER TABLE RENAME TO`, while logically we can't support rename a table to a different database.

Both postgres and MySQL disallow this syntax, it's reasonable to follow them and simply our code.

## How was this patch tested?

new test in `DDLCommandSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14955 from cloud-fan/rename.

### What changes were proposed in this pull request?
When we trying to read a table and then write to the same table using the `Overwrite` save mode, we got a very confusing error message:
For example,
```Scala
      Seq((1, 2)).toDF("i", "j").write.saveAsTable("tab1")
      table("tab1").write.mode(SaveMode.Overwrite).saveAsTable("tab1")
```

```
Job aborted.
org.apache.spark.SparkException: Job aborted.
	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1.apply$mcV$sp
...
Caused by: org.apache.spark.SparkException: Task failed while writing rows
	at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:266)
	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
	at org.apache.spark.sql.execution.datasources
```

After the PR, we will issue an `AnalysisException`:
```
Cannot overwrite table `tab1` that is also being read from
```
### How was this patch tested?
Added test cases.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #14954 from gatorsmile/ctasQueryAnalyze.

## What changes were proposed in this pull request?
Since we have updated breeze version to 0.12, we should remove work around for bug of breeze sparse matrix in v0.11.
I checked all mllib code and found this is the only work around for breeze 0.11.

## How was this patch tested?
Existing tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14953 from yanboliang/matrices.

## What changes were proposed in this pull request?

Related to https://github.com/apache/spark/pull/14524 -- just the 'fix' rather than a behavior change.

- PythonMLlibAPI methods that take a seed now always take a `java.lang.Long` consistently, allowing the Python API to specify "no seed"
- .mllib's Word2VecModel seemed to be an odd man out in .mllib in that it picked its own random seed. Instead it defaults to None, meaning, letting the Scala implementation pick a seed
- BisectingKMeansModel arguably should not hard-code a seed for consistency with .mllib, I think. However I left it.

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #14826 from srowen/SPARK-16832.2.