Author: scwf <wangfei1@huawei.com>
Author: Marcelo Vanzin <vanzin@cloudera.com>
Author: WangTaoTheTonic <wangtao111@huawei.com>
Author: w00228970 <wangfei1@huawei.com>

Closes #10238 from vanzin/SPARK-2750.

This is a convenience method added to the SBT build for developers, though if people think its useful we could consider adding a official script that runs using the assembly instead of compiling on demand.  It simply compiles spark (without requiring an assembly), and invokes Spark Submit to download / run the package.

Example Usage:
```
$ build/sbt
> sparkPackage com.databricks:spark-sql-perf_2.10:0.2.4 com.databricks.spark.sql.perf.RunBenchmark --help
```

Author: Michael Armbrust <michael@databricks.com>

Closes #10834 from marmbrus/sparkPackageRunner.

SPARK-11295 Add packages to JUnit output for Python tests

This improves grouping/display of test case results.

Author: Gábor Lipták <gliptak@gmail.com>

Closes #9263 from gliptak/SPARK-11295.

Call `dealias` on local types to fix schema generation for abstract type members, such as

```scala
type KeyValue = (Int, String)
```

Add simple test

Author: Jakob Odersky <jodersky@gmail.com>

Closes #10749 from jodersky/aliased-schema.

See https://issues.apache.org/jira/browse/SPARK-12560

This isn't causing any problems currently because the tests for string predicate pushdown are currently disabled.  I ran into this while trying to turn them back on with a different version of parquet.  Figure it was good to fix now in any case.

Author: Imran Rashid <irashid@cloudera.com>

Closes #10510 from squito/SPARK-12560.

JIRA: https://issues.apache.org/jira/browse/SPARK-12867

When intersecting one nullable column with one non-nullable column, the result will not contain any null. Thus, we can make nullability of `intersect` stricter.

liancheng Could you please check if the code changes are appropriate? Also added test cases to verify the results. Thanks!

Author: gatorsmile <gatorsmile@gmail.com>

Closes #10812 from gatorsmile/nullabilityIntersect.

CC jkbradley mengxr dbtsai

Author: Feynman Liang <feynman.liang@gmail.com>

Closes #10743 from feynmanliang/SPARK-12804.

This is a step in implementing SPARK-10620, which migrates TaskMetrics to accumulators.

TaskMetrics has a bunch of var's, some are fully public, some are `private[spark]`. This is bad coding style that makes it easy to accidentally overwrite previously set metrics. This has happened a few times in the past and caused bugs that were difficult to debug.

Instead, we should have get-or-create semantics, which are more readily understandable. This makes sense in the case of TaskMetrics because these are just aggregated metrics that we want to collect throughout the task, so it doesn't matter who's incrementing them.

Parent PR: #10717

Author: Andrew Or <andrew@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>
Author: andrewor14 <andrew@databricks.com>

Closes #10815 from andrewor14/get-or-create-metrics.

for normal parquet file without bucket, it's file name ends with a jobUUID which maybe all numbers and mistakeny regarded as bucket id. This PR improves the format of bucket id in file name by using a different seperator, `_`, so that the regex is more robust.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10799 from cloud-fan/fix-bucket.

From the coverage issues for 1.6 : Add Python API for mllib.clustering.BisectingKMeans.

Author: Holden Karau <holden@us.ibm.com>

Closes #10150 from holdenk/SPARK-11937-python-api-coverage-SPARK-11944-python-mllib.clustering.BisectingKMeans.

Change assertion's message so it's consistent with the code. The old message says that the invoked method was lapack.dports, where in fact it was lapack.dppsv method.

Author: Wojciech Jurczyk <wojtek.jurczyk@gmail.com>

Closes #10818 from wjur/wjur/rename_error_message.

Fix order of arguments that Pyspark RDD.fold passes to its op -  should be (acc, obj) like other implementations.

Obviously, this is a potentially breaking change, so can only happen for 2.x

CC davies

Author: Sean Owen <sowen@cloudera.com>

Closes #10771 from srowen/SPARK-7683.

Author: proflin <proflin.me@gmail.com>

Closes #10824 from proflin/master.

https://issues.apache.org/jira/browse/SPARK-12668

Spark CSV datasource has been being merged (filed in [SPARK-12420](https://issues.apache.org/jira/browse/SPARK-12420)). This is a quicky PR that simply renames several CSV options to  similar Pandas and R.

- Alias for delimiter ­-> sep
- charset -­> encoding

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #10800 from HyukjinKwon/SPARK-12668.

This PR is to fix the master's build break.

The following tests failed due to the import order issues in the master.
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49651/consoleFull
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49652/consoleFull
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49653/consoleFull

Author: gatorsmile <gatorsmile@gmail.com>

Closes #10823 from gatorsmile/importOrder.

This is a small step in implementing SPARK-10620, which migrates TaskMetrics to accumulators. This patch is strictly a cleanup patch and introduces no change in functionality. It literally just renames 3 fields for consistency. Today we have:

```
inputMetrics.recordsRead
outputMetrics.bytesWritten
shuffleReadMetrics.localBlocksFetched
...
shuffleWriteMetrics.shuffleRecordsWritten
shuffleWriteMetrics.shuffleBytesWritten
shuffleWriteMetrics.shuffleWriteTime
```

The shuffle write ones are kind of redundant. We can drop the `shuffle` part in the method names. I added backward compatible (but deprecated) methods with the old names.

Parent PR: #10717

Author: Andrew Or <andrew@databricks.com>

Closes #10811 from andrewor14/rename-things.

Currently SortMergeJoin and BroadcastHashJoin do not support condition, the need a followed Filter for that, the result projection to generate UnsafeRow could be very expensive if they generate lots of rows and could be filtered mostly by condition.

This PR brings the support of condition for SortMergeJoin and BroadcastHashJoin, just like other outer joins do.

This could improve the performance of Q72 by 7x (from 120s to 16.5s).

Author: Davies Liu <davies@databricks.com>

Closes #10653 from davies/filter_join.

Based on discussions in #10801, I'm submitting a pull request to rename ParserDialect to ParserInterface.

Author: Reynold Xin <rxin@databricks.com>

Closes #10817 from rxin/SPARK-12889.

This PR added instructions to get Kinesis assembly jar for Python users in the Kinesis integration page like Kafka doc.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10822 from zsxwing/kinesis-doc.

This reverts commit 591c88c9. `lint-java` doesn't work on a machine with a clean Maven cache.

This PR added instructions to get flume assembly jar for Python users in the flume integration page like Kafka doc.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10746 from zsxwing/flume-doc.

Right now, the bucket tests are kind of hard to understand, this PR simplifies them and add more commetns.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10813 from cloud-fan/bucket-comment.

In SPARK-10743 we wrap cast with `UnresolvedAlias` to give `Cast` a better alias if possible. However, for cases like `filter`, the `UnresolvedAlias` can't be resolved and actually we don't need a better alias for this case.  This PR move the cast wrapping logic to `Column.named` so that we will only do it when we need a alias name.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10781 from cloud-fan/bug.

This pull request removes the public developer parser API for external parsers. Given everything a parser depends on (e.g. logical plans and expressions) are internal and not stable, external parsers will break with every release of Spark. It is a bad idea to create the illusion that Spark actually supports pluggable parsers. In addition, this also reduces incentives for 3rd party projects to contribute parse improvements back to Spark.

Author: Reynold Xin <rxin@databricks.com>

Closes #10801 from rxin/SPARK-12855.

This patch refactors portions of the BlockManager and CacheManager in order to avoid having to pass `evictedBlocks` lists throughout the code. It appears that these lists were only consumed by `TaskContext.taskMetrics`, so the new code now directly updates the metrics from the lower-level BlockManager methods.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #10776 from JoshRosen/SPARK-10985.

This is a small step in implementing SPARK-10620, which migrates `TaskMetrics` to accumulators. This patch is strictly a cleanup patch and introduces no change in functionality. It literally just moves classes to their own files to avoid having single monolithic ones that contain 10 different classes.

Parent PR: #10717

Author: Andrew Or <andrew@databricks.com>

Closes #10810 from andrewor14/move-things.

Currently `summary()` fails on a GLM model fitted over a vector feature missing ML attrs, since the output feature attrs will also have no name. We can avoid this situation by forcing `VectorAssembler` to make up suitable names when inputs are missing names.

cc mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #10323 from ericl/spark-12346.

I was reading this part of the analyzer code again and got confused by the difference between findWiderTypeForTwo and findTightestCommonTypeOfTwo.

I also simplified WidenSetOperationTypes to make it a lot simpler. The easiest way to review this one is to just read the original code, and the new code. The logic is super simple.

Author: Reynold Xin <rxin@databricks.com>

Closes #10802 from rxin/SPARK-12873.

Addresses the comments from Yin.
https://github.com/apache/spark/pull/10520

Author: Dilip Biswal <dbiswal@us.ibm.com>

Closes #10758 from dilipbiswal/spark-12558-followup.

I create new pr since original pr long time no update.
Please help to review.

srowen

Author: Tommy YU <tummyyu@163.com>

Closes #10756 from Wenpei/add_since_to_recomm.

similar to https://github.com/apache/spark/pull/10784, use `Murmur3_x86_32.hashUnsafeBytes` instead.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10791 from cloud-fan/string-hashcode.

Slight correction: I'm leaving sparkR as-is (ie. R file not supported) and fixed only run-tests.sh as shivaram described.

I also assume we are going to cover all doc changes in https://issues.apache.org/jira/browse/SPARK-12846 instead of here.

rxin shivaram zjffdu

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #10792 from felixcheung/sparkRcmd.

The idea is simple, use `SpecificMutableRow` instead of `GenericMutableRow` as result row for safe projection.

A simple benchmark shows about 1.5x speed up for primitive types, code: https://gist.github.com/cloud-fan/fa77713ccebf0823b2ab#file-safeprojectionbenchmark-scala

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10790 from cloud-fan/safe-projection.

This is the initial work for whole stage codegen, it support Projection/Filter/Range, we will continue work on this to support more physical operators.

A micro benchmark show that a query with range, filter and projection could be 3X faster then before.

It's turned on by default. For a tree that have at least two chained plans, a WholeStageCodegen will be inserted into it, for example, the following plan
```
Limit 10
+- Project [(id#5L + 1) AS (id + 1)#6L]
   +- Filter ((id#5L & 1) = 1)
      +- Range 0, 1, 4, 10, [id#5L]
```
will be translated into
```
Limit 10
+- WholeStageCodegen
      +- Project [(id#1L + 1) AS (id + 1)#2L]
         +- Filter ((id#1L & 1) = 1)
            +- Range 0, 1, 4, 10, [id#1L]
```

Here is the call graph to generate Java source for A and B (A  support codegen, but B does not):

```
  *   WholeStageCodegen       Plan A               FakeInput        Plan B
  * =========================================================================
  *
  * -> execute()
  *     |
  *  doExecute() -------->   produce()
  *                             |
  *                          doProduce()  -------> produce()
  *                                                   |
  *                                                doProduce() ---> execute()
  *                                                   |
  *                                                consume()
  *                          doConsume()  ------------|
  *                             |
  *  doConsume()  <-----    consume()
```

A SparkPlan that support codegen need to implement doProduce() and doConsume():

```
def doProduce(ctx: CodegenContext): (RDD[InternalRow], String)
def doConsume(ctx: CodegenContext, child: SparkPlan, input: Seq[ExprCode]): String
```

Author: Davies Liu <davies@databricks.com>

Closes #10735 from davies/whole2.

http://spark.apache.org/docs/latest/ml-guide.html#example-pipeline
```
val sameModel = Pipeline.load("/tmp/spark-logistic-regression-model")
```
should be
```
val sameModel = PipelineModel.load("/tmp/spark-logistic-regression-model")
```
cc: jkbradley

Author: Jeff Lam <sha0lin@alumni.carnegiemellon.edu>

Closes #10769 from Agent007/SPARK-12722.

We iterate the bytes to calculate hashCode before, but now we have `Murmur3_x86_32.hashUnsafeBytes` that don't require the bytes to be word algned, we should use that instead.

A simple benchmark shows it's about 3 X faster, benchmark code: https://gist.github.com/cloud-fan/fa77713ccebf0823b2ab#file-arrayhashbenchmark-scala

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10784 from cloud-fan/array-hashcode.

[SPARK-12840] [SQL] Support passing arbitrary objects (not just expressions) into code generated classes

This is a refactor to support codegen for aggregation and broadcast join.

Author: Davies Liu <davies@databricks.com>

Closes #10777 from davies/rename2.

This inlines a few of the Parquet decoders and adds vectorized APIs to support decoding in batch.
There are a few particulars in the Parquet encodings that make this much more efficient. In
particular, RLE encodings are very well suited for batch decoding. The Parquet 2.0 encodings are
also very suited for this.

This is a work in progress and does not affect the current execution. In subsequent patches, we will
support more encodings and types before enabling this.

Simple benchmarks indicate this can decode single ints about > 3x faster.

Author: Nong Li <nong@databricks.com>
Author: Nong <nongli@gmail.com>

Closes #10593 from nongli/spark-12644.

This PR adds the support to read bucketed tables, and correctly populate `outputPartitioning`, so that we can avoid shuffle for some cases.

TODO(follow-up PRs):

* bucket pruning
* avoid shuffle for bucketed table join when use any super-set of the bucketing key.
 (we should re-visit it after https://issues.apache.org/jira/browse/SPARK-12704 is fixed)
* recognize hive bucketed table

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10604 from cloud-fan/bucket-read.

This patch adds a Hadoop 2.7 build profile in order to let us automate tests against that version.

/cc rxin srowen

Author: Josh Rosen <joshrosen@databricks.com>

Closes #10775 from JoshRosen/add-hadoop-2.7-profile.