## What changes were proposed in this pull request?

When KafkaSource fails on Kafka errors, we should create a new consumer to retry rather than using the existing broken one because it's possible that the broken one will fail again.

This PR also assigns a new group id to the new created consumer for a possible race condition:  the broken consumer cannot talk with the Kafka cluster in `close` but the new consumer can talk to Kafka cluster. I'm not sure if this will happen or not. Just for safety to avoid that the Kafka cluster thinks there are two consumers with the same group id in a short time window. (Note: CachedKafkaConsumer doesn't need this fix since `assign` never uses the group id.)

## How was this patch tested?

In https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/70370/console

 , it ran this flaky test 120 times and all passed.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #16282 from zsxwing/kafka-fix.

(cherry picked from commit 95efc895)
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>

## What changes were proposed in this pull request?

This PR aims to provide a pip installable PySpark package. This does a bunch of work to copy the jars over and package them with the Python code (to prevent challenges from trying to use different versions of the Python code with different versions of the JAR). It does not currently publish to PyPI but that is the natural follow up (SPARK-18129).

Done:
- pip installable on conda [manual tested]
- setup.py installed on a non-pip managed system (RHEL) with YARN [manual tested]
- Automated testing of this (virtualenv)
- packaging and signing with release-build*

Possible follow up work:
- release-build update to publish to PyPI (SPARK-18128)
- figure out who owns the pyspark package name on prod PyPI (is it someone with in the project or should we ask PyPI or should we choose a different name to publish with like ApachePySpark?)
- Windows support and or testing ( SPARK-18136 )
- investigate details of wheel caching and see if we can avoid cleaning the wheel cache during our test
- consider how we want to number our dev/snapshot versions

Explicitly out of scope:
- Using pip installed PySpark to start a standalone cluster
- Using pip installed PySpark for non-Python Spark programs

*I've done some work to test release-build locally but as a non-committer I've just done local testing.
## How was this patch tested?

Automated testing with virtualenv, manual testing with conda, a system wide install, and YARN integration.

release-build changes tested locally as a non-committer (no testing of upload artifacts to Apache staging websites)

Author: Holden Karau <holden@us.ibm.com>
Author: Juliet Hougland <juliet@cloudera.com>
Author: Juliet Hougland <not@myemail.com>

Closes #15659 from holdenk/SPARK-1267-pip-install-pyspark.

## What changes were proposed in this pull request?
This PR adds the Kafka 0.10 subproject to the build infrastructure. This makes sure Kafka 0.10 tests are only triggers when it or of its dependencies change.

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

Closes #15355 from hvanhovell/SPARK-17782.

## What changes were proposed in this pull request?

This PR adds a new project ` external/kafka-0-10-sql` for Structured Streaming Kafka source.

It's based on the design doc: https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit?usp=sharing

tdas did most of work and part of them was inspired by koeninger's work.

### Introduction

The Kafka source is a structured streaming data source to poll data from Kafka. The schema of reading data is as follows:

Column | Type
---- | ----
key | binary
value | binary
topic | string
partition | int
offset | long
timestamp | long
timestampType | int

The source can deal with deleting topics. However, the user should make sure there is no Spark job processing the data when deleting a topic.

### Configuration

The user can use `DataStreamReader.option` to set the following configurations.

Kafka Source's options | value | default | meaning
------ | ------- | ------ | -----
startingOffset | ["earliest", "latest"] | "latest" | The start point when a query is started, either "earliest" which is from the earliest offset, or "latest" which is just from the latest offset. Note: This only applies when a new Streaming query is started, and that resuming will always pick up from where the query left off.
failOnDataLost | [true, false] | true | Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or offsets are out of range). This may be a false alarm. You can disable it when it doesn't work as you expected.
subscribe | A comma-separated list of topics | (none) | The topic list to subscribe. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
subscribePattern | Java regex string | (none) | The pattern used to subscribe the topic. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
kafka.consumer.poll.timeoutMs | long | 512 | The timeout in milliseconds to poll data from Kafka in executors
fetchOffset.numRetries | int | 3 | Number of times to retry before giving up fatch Kafka latest offsets.
fetchOffset.retryIntervalMs | long | 10 | milliseconds to wait before retrying to fetch Kafka offsets

Kafka's own configurations can be set via `DataStreamReader.option` with `kafka.` prefix, e.g, `stream.option("kafka.bootstrap.servers", "host:port")`

### Usage

* Subscribe to 1 topic
```Scala
spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host:port")
  .option("subscribe", "topic1")
  .load()
```

* Subscribe to multiple topics
```Scala
spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host:port")
  .option("subscribe", "topic1,topic2")
  .load()
```

* Subscribe to a pattern
```Scala
spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host:port")
  .option("subscribePattern", "topic.*")
  .load()
```

## How was this patch tested?

The new unit tests.

Author: Shixiong Zhu <shixiong@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Author: cody koeninger <cody@koeninger.org>

Closes #15102 from zsxwing/kafka-source.

## What changes were proposed in this pull request?

Only build PRs with -Pyarn if YARN code was modified.

## How was this patch tested?

Jenkins tests (will look to verify whether -Pyarn was included in the PR builder for this one.)

Author: Sean Owen <sowen@cloudera.com>

Closes #14892 from srowen/SPARK-17329.

## What changes were proposed in this pull request?

add build_profile_flags entry to mesos build module

## How was this patch tested?

unit tests

Author: Michael Gummelt <mgummelt@mesosphere.io>

Closes #14885 from mgummelt/mesos-profile.

## What changes were proposed in this pull request?

Move Mesos code into a mvn module

## How was this patch tested?

unit tests
manually submitting a client mode and cluster mode job
spark/mesos integration test suite

Author: Michael Gummelt <mgummelt@mesosphere.io>

Closes #14637 from mgummelt/mesos-module.

In the `dev/run-tests.py` script we check a `Popen.retcode` for success using `retcode > 0`, but this is subtlety wrong because Popen's return code will be negative if the child process was terminated by a signal: https://docs.python.org/2/library/subprocess.html#subprocess.Popen.returncode

In order to properly handle signals, we should change this to check `retcode != 0` instead.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #13692 from JoshRosen/dev-run-tests-return-code-handling.

## What changes were proposed in this pull request?

This PR just enables tests for sql/streaming.py and also fixes the failures.

## How was this patch tested?

Existing unit tests.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #13655 from zsxwing/python-streaming-test.

## What changes were proposed in this pull request?
I initially asked to create a hivecontext-compatibility module to put the HiveContext there. But we are so close to Spark 2.0 release and there is only a single class in it. It seems overkill to have an entire package, which makes it more inconvenient, for a single class.

## How was this patch tested?
Tests were moved.

Author: Reynold Xin <rxin@databricks.com>

Closes #13207 from rxin/SPARK-15424.

## What changes were proposed in this pull request?

Once SPARK-14487 and SPARK-14549 are merged, we will migrate to use the new vector and matrix type in the new ml pipeline based apis.

## How was this patch tested?

Unit tests

Author: DB Tsai <dbt@netflix.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #12627 from dbtsai/SPARK-14615-NewML.

## What changes were proposed in this pull request?

(See https://github.com/apache/spark/pull/12416 where most of this was already reviewed and committed; this is just the module structure and move part. This change does not move the annotations into test scope, which was the apparently problem last time.)

Rename `spark-test-tags` -> `spark-tags`; move common annotations like `Since` to `spark-tags`

## How was this patch tested?

Jenkins tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #13074 from srowen/SPARK-15290.

## What changes were proposed in this pull request?
Renaming the streaming-kafka artifact to include kafka version, in anticipation of needing a different artifact for later kafka versions

## How was this patch tested?
Unit tests

Author: cody koeninger <cody@koeninger.org>

Closes #12946 from koeninger/SPARK-15085.

## What changes were proposed in this pull request?

The `catalog` and `conf` APIs were exposed in `SparkSession` in #12713 and #12669. This patch adds those to the python API.

## How was this patch tested?

Python tests.

Author: Andrew Or <andrew@databricks.com>

Closes #12765 from andrewor14/python-spark-session-more.

This reverts commit dae538a4.

## What changes were proposed in this pull request?

This PR adds `since` tag into the matrix and vector classes in spark-mllib-local.

## How was this patch tested?

Scala-style checks passed.

Author: Pravin Gadakh <prgadakh@in.ibm.com>

Closes #12416 from pravingadakh/SPARK-14613.

## What changes were proposed in this pull request?

This PR creates a compatibility module in sql (called `hive-1-x-compatibility`), which will host HiveContext in Spark 2.0 (moving HiveContext to here will be done separately). This module is not included in assembly because only users who still want to access HiveContext need it.

## How was this patch tested?
I manually tested `sbt/sbt -Phive package` and `mvn -Phive package -DskipTests`.

Author: Yin Huai <yhuai@databricks.com>

Closes #12580 from yhuai/compatibility.

## What changes were proposed in this pull request?

In order to separate the linear algebra, and vector matrix classes into a standalone jar, we need to setup the build first. This PR will create a new jar called mllib-local with minimal dependencies.

The previous PR was failing the build because of `spark-core:test` dependency, and that was reverted. In this PR, `FunSuite` with `// scalastyle:ignore funsuite` in mllib-local test was used, similar to sketch.

Thanks.

## How was this patch tested?

Unit tests

mengxr tedyu holdenk

Author: DB Tsai <dbt@netflix.com>

Closes #12298 from dbtsai/dbtsai-mllib-local-build-fix.

This reverts commit 1598d11b.

## What changes were proposed in this pull request?

In order to separate the linear algebra, and vector matrix classes into a standalone jar, we need to setup the build first. This PR will create a new jar called mllib-local with minimal dependencies. The test scope will still depend on spark-core and spark-core-test in order to use the common utilities, but the runtime will avoid any platform dependency. Couple platform independent classes will be moved to this package to demonstrate how this work.

## How was this patch tested?

Unit tests

Author: DB Tsai <dbt@netflix.com>

Closes #12241 from dbtsai/dbtsai-mllib-local-build.

## What changes were proposed in this pull request?

This PR moves flume back to Spark as per the discussion in the dev mail-list.

## How was this patch tested?

Existing Jenkins tests.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #11895 from zsxwing/move-flume-back.

[SPARK-13843][STREAMING] Remove streaming-flume, streaming-mqtt, streaming-zeromq, streaming-akka, streaming-twitter to Spark packages

## What changes were proposed in this pull request?

Currently there are a few sub-projects, each for integrating with different external sources for Streaming.  Now that we have better ability to include external libraries (spark packages) and with Spark 2.0 coming up, we can move the following projects out of Spark to https://github.com/spark-packages

- streaming-flume
- streaming-akka
- streaming-mqtt
- streaming-zeromq
- streaming-twitter

They are just some ancillary packages and considering the overhead of maintenance, running tests and PR failures, it's better to maintain them out of Spark. In addition, these projects can have their different release cycles and we can release them faster.

I have already copied these projects to https://github.com/spark-packages

## How was this patch tested?

Jenkins tests

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #11672 from zsxwing/remove-external-pkg.

## What changes were proposed in this pull request?

Move `docker` dirs out of top level into `external/`; move `extras/*` into `external/`

## How was this patch tested?

This is tested with Jenkins tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #11523 from srowen/SPARK-13595.

## What changes were proposed in this pull request?

```
error] Expected ID character
[error] Not a valid command: common (similar: completions)
[error] Expected project ID
[error] Expected configuration
[error] Expected ':' (if selecting a configuration)
[error] Expected key
[error] Not a valid key: common (similar: commands)
[error] common/network-yarn/test
```

`common/network-yarn` is not a valid sbt project, we should change to `network-yarn`.

## How was this patch tested?

Locally run the the unit-test.

CC rxin , we should either change here, or change the sbt project name.

Author: jerryshao <sshao@hortonworks.com>

Closes #11456 from jerryshao/build-fix.

## What changes were proposed in this pull request?
As the title says, this moves the three modules currently in network/ into common/network-*. This removes one top level, non-user-facing folder.

## How was this patch tested?
Compilation and existing tests. We should run both SBT and Maven.

Author: Reynold Xin <rxin@databricks.com>

Closes #11409 from rxin/SPARK-13529.

This patch improves our `dev/run-tests` script to test modules in a topologically-sorted order based on modules' dependencies.  This will help to ensure that bugs in upstream projects are not misattributed to downstream projects because those projects' tests were the first ones to exhibit the failure

Topological sorting is also useful for shortening the feedback loop when testing pull requests: if I make a change in SQL then the SQL tests should run before MLlib, not after.

In addition, this patch also updates our test module definitions to split `sql` into `catalyst`, `sql`, and `hive` in order to allow more tests to be skipped when changing only `hive/` files.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #10885 from JoshRosen/SPARK-8725.

This PR adds an initial implementation of count min sketch, contained in a new module spark-sketch under `common/sketch`. The implementation is based on the [`CountMinSketch` class in stream-lib][1].

As required by the [design doc][2], spark-sketch should have no external dependency.
Two classes, `Murmur3_x86_32` and `Platform` are copied to spark-sketch from spark-unsafe for hashing facilities. They'll also be used in the upcoming bloom filter implementation.

The following features will be added in future follow-up PRs:

- Serialization support
- DataFrame API integration

[1]: https://github.com/addthis/stream-lib/blob/aac6b4d23a8686b000f80baa447e0922ecac3bcb/src/main/java/com/clearspring/analytics/stream/frequency/CountMinSketch.java
[2]: https://issues.apache.org/jira/secure/attachment/12782378/BloomFilterandCount-MinSketchinSpark2.0.pdf

Author: Cheng Lian <lian@databricks.com>

Closes #10851 from liancheng/count-min-sketch.

Include the following changes:

1. Add "streaming-akka" project and org.apache.spark.streaming.akka.AkkaUtils for creating an actorStream
2. Remove "StreamingContext.actorStream" and "JavaStreamingContext.actorStream"
3. Update the ActorWordCount example and add the JavaActorWordCount example
4. Make "streaming-zeromq" depend on "streaming-akka" and update the codes accordingly

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10744 from zsxwing/streaming-akka-2.

This patch fixes a build/test issue caused by the combination of #10672 and a latent issue in the original `dev/test-dependencies` script.

First, changes which _only_ touched build files were not triggering full Jenkins runs, making it possible for a build change to be merged even though it could cause failures in other tests. The `root` build module now depends on `build`, so all tests will now be run whenever a build-related file is changed.

I also added a `clean` step to the Maven install step in `dev/test-dependencies` in order to address an issue where the dummy JARs stuck around and caused "multiple assembly JARs found" errors in tests.

/cc zsxwing

Author: Josh Rosen <joshrosen@databricks.com>

Closes #10704 from JoshRosen/fix-build-test-problems.

Author: Reynold Xin <rxin@databricks.com>

Closes #10673 from rxin/SPARK-12735.

This patch includes multiple fixes for the `dev/test-dependencies.sh` script (which was introduced in #10461):

- Use `build/mvn --force` instead of `mvn` in one additional place.
- Explicitly set a zero exit code on success.
- Set `LC_ALL=C` to make `sort` results agree across machines (see https://stackoverflow.com/questions/28881/).
- Set `should_run_build_tests=True` for `build` module (this somehow got lost).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #10543 from JoshRosen/dep-script-fixes.

This patch adds a new build check which enumerates Spark's resolved runtime classpath and saves it to a file, then diffs against that file to detect whether pull requests have introduced dependency changes. The aim of this check is to make it simpler to reason about whether pull request which modify the build have introduced new dependencies or changed transitive dependencies in a way that affects the final classpath.

This supplants the checks added in SPARK-4123 / #5093, which are currently disabled due to bugs.

This patch is based on pwendell's work in #8531.

Closes #8531.

Author: Josh Rosen <joshrosen@databricks.com>
Author: Patrick Wendell <patrick@databricks.com>

Closes #10461 from JoshRosen/SPARK-10359.

This replaces https://github.com/apache/spark/pull/9696

Invoke Checkstyle and print any errors to the console, failing the step.
Use Google's style rules modified according to
https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide
Some important checks are disabled (see TODOs in `checkstyle.xml`) due to
multiple violations being present in the codebase.

Suggest fixing those TODOs in a separate PR(s).

More on Checkstyle can be found on the [official website](http://checkstyle.sourceforge.net/).

Sample output (from [build 46345](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/46345/consoleFull)) (duplicated because I run the build twice with different profiles):

> Checkstyle checks failed at following occurrences:
[ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [error] running /home/jenkins/workspace/SparkPullRequestBuilder2/dev/lint-java ; received return code 1

Also fix some of the minor violations that didn't require sweeping changes.

Apologies for the previous botched PRs - I finally figured out the issue.

cr: JoshRosen, pwendell

> I state that the contribution is my original work, and I license the work to the project under the project's open source license.

Author: Dmitry Erastov <derastov@gmail.com>

Closes #9867 from dskrvk/master.

This commit refactors the `run-tests-jenkins` script into Python. This refactoring was done by brennonyork in #7401; this PR contains a few minor edits from joshrosen in order to bring it up to date with other recent changes.

From the original PR description (by brennonyork):

Currently a few things are left out that, could and I think should, be smaller JIRA's after this.

1. There are still a few areas where we use environment variables where we don't need to (like `CURRENT_BLOCK`). I might get around to fixing this one in lieu of everything else, but wanted to point that out.
2. The PR tests are still written in bash. I opted to not change those and just rewrite the runner into Python. This is a great follow-on JIRA IMO.
3. All of the linting scripts are still in bash as well and would likely do to just add those in as follow-on JIRA's as well.

Closes #7401.

Author: Brennon York <brennon.york@capitalone.com>

Closes #9161 from JoshRosen/run-tests-jenkins-refactoring.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8775 from vanzin/SPARK-10300.

This reverts commit 8abef21d.

This change does two things:

- tag a few tests and adds the mechanism in the build to be able to disable those tags,
  both in maven and sbt, for both junit and scalatest suites.
- add some logic to run-tests.py to disable some tags depending on what files have
  changed; that's used to disable expensive tests when a module hasn't explicitly
  been changed, to speed up testing for changes that don't directly affect those
  modules.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8437 from vanzin/test-tags.

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

Closes #8092 from tdas/SPARK-9727 and squashes the following commits:

b1b01fd [Tathagata Das] Updated streaming kinesis project name

This PR is based on #4229, thanks prabeesh.

Closes #4229

Author: Prabeesh K <prabsmails@gmail.com>
Author: zsxwing <zsxwing@gmail.com>
Author: prabs <prabsmails@gmail.com>
Author: Prabeesh K <prabeesh.k@namshi.com>

Closes #7833 from zsxwing/pr4229 and squashes the following commits:

9570bec [zsxwing] Fix the variable name and check null in finally
4a9c79e [zsxwing] Fix pom.xml indentation
abf5f18 [zsxwing] Merge branch 'master' into pr4229
935615c [zsxwing] Fix the flaky MQTT tests
47278c5 [zsxwing] Include the project class files
478f844 [zsxwing] Add unpack
5f8a1d4 [zsxwing] Make the maven build generate the test jar for Python MQTT tests
734db99 [zsxwing] Merge branch 'master' into pr4229
126608a [Prabeesh K] address the comments
b90b709 [Prabeesh K] Merge pull request #1 from zsxwing/pr4229
d07f454 [zsxwing] Register StreamingListerner before starting StreamingContext; Revert unncessary changes; fix the python unit test
a6747cb [Prabeesh K] wait for starting the receiver before publishing data
87fc677 [Prabeesh K] address the comments:
97244ec [zsxwing] Make sbt build the assembly test jar for streaming mqtt
80474d1 [Prabeesh K] fix
1f0cfe9 [Prabeesh K] python style fix
e1ee016 [Prabeesh K] scala style fix
a5a8f9f [Prabeesh K] added Python test
9767d82 [Prabeesh K] implemented Python-friendly class
a11968b [Prabeesh K] fixed python style
795ec27 [Prabeesh K] address comments
ee387ae [Prabeesh K] Fix assembly jar location of mqtt-assembly
3f4df12 [Prabeesh K] updated version
b34c3c1 [prabs] adress comments
3aa7fff [prabs] Added Python streaming mqtt word count example
b7d42ff [prabs] Mqtt streaming support in Python

This PR adds the RowMatrix, IndexedRowMatrix, and CoordinateMatrix distributed matrices to PySpark.  Each distributed matrix class acts as a wrapper around the Scala/Java counterpart by maintaining a reference to the Java object.  New distributed matrices can be created using factory methods added to DistributedMatrices, which creates the Java distributed matrix and then wraps it with the corresponding PySpark class.  This design allows for simple conversion between the various distributed matrices, and lets us re-use the Scala code.  Serialization between Python and Java is implemented using DataFrames as needed for IndexedRowMatrix and CoordinateMatrix for simplicity.  Associated documentation and unit-tests have also been added.  To facilitate code review, this PR implements access to the rows/entries as RDDs, the number of rows & columns, and conversions between the various distributed matrices (not including BlockMatrix), and does not implement the other linear algebra functions of the matrices, although this will be very simple to add now.

Author: Mike Dusenberry <mwdusenb@us.ibm.com>

Closes #7554 from dusenberrymw/SPARK-6485_Add_CoordinateMatrix_RowMatrix_IndexedMatrix_to_PySpark and squashes the following commits:

bb039cb [Mike Dusenberry] Minor documentation update.
b887c18 [Mike Dusenberry] Updating the matrix conversion logic again to make it even cleaner.  Now, we allow the 'rows' parameter in the constructors to be either an RDD or the Java matrix object. If 'rows' is an RDD, we create a Java matrix object, wrap it, and then store that.  If 'rows' is a Java matrix object of the correct type, we just wrap and store that directly.  This is only for internal usage, and publicly, we still require 'rows' to be an RDD.  We no longer store the 'rows' RDD, and instead just compute it from the Java object when needed.  The point of this is that when we do matrix conversions, we do the conversion on the Scala/Java side, which returns a Java object, so we should use that directly, but exposing 'java_matrix' parameter in the public API is not ideal. This non-public feature of allowing 'rows' to be a Java matrix object is documented in the '__init__' constructor docstrings, which are not part of the generated public API, and doctests are also included.
7f0dcb6 [Mike Dusenberry] Updating module docstring.
cfc1be5 [Mike Dusenberry] Use 'new SQLContext(matrix.rows.sparkContext)' rather than 'SQLContext.getOrCreate', as the later doesn't guarantee that the SparkContext will be the same as for the matrix.rows data.
687e345 [Mike Dusenberry] Improving conversion performance.  This adds an optional 'java_matrix' parameter to the constructors, and pulls the conversion logic out into a '_create_from_java' function. Now, if the constructors are given a valid Java distributed matrix object as 'java_matrix', they will store those internally, rather than create a new one on the Scala/Java side.
3e50b6e [Mike Dusenberry] Moving the distributed matrices to pyspark.mllib.linalg.distributed.
308f197 [Mike Dusenberry] Using properties for better documentation.
1633f86 [Mike Dusenberry] Minor documentation cleanup.
f0c13a7 [Mike Dusenberry] CoordinateMatrix should inherit from DistributedMatrix.
ffdd724 [Mike Dusenberry] Updating doctests to make documentation cleaner.
3fd4016 [Mike Dusenberry] Updating docstrings.
27cd5f6 [Mike Dusenberry] Simplifying input conversions in the constructors for each distributed matrix.
a409cf5 [Mike Dusenberry] Updating doctests to be less verbose by using lists instead of DenseVectors explicitly.
d19b0ba [Mike Dusenberry] Updating code and documentation to note that a vector-like object (numpy array, list, etc.) can be used in place of explicit Vector object, and adding conversions when necessary to RowMatrix construction.
4bd756d [Mike Dusenberry] Adding param documentation to IndexedRow and MatrixEntry.
c6bded5 [Mike Dusenberry] Move conversion logic from tuples to IndexedRow or MatrixEntry types from within the IndexedRowMatrix and CoordinateMatrix constructors to separate _convert_to_indexed_row and _convert_to_matrix_entry functions.
329638b [Mike Dusenberry] Moving the Experimental tag to the top of each docstring.
0be6826 [Mike Dusenberry] Simplifying doctests by removing duplicated rows/entries RDDs within the various tests.
c0900df [Mike Dusenberry] Adding the colons that were accidentally not inserted.
4ad6819 [Mike Dusenberry] Documenting the  and  parameters.
3b854b9 [Mike Dusenberry] Minor updates to documentation.
10046e8 [Mike Dusenberry] Updating documentation to use class constructors instead of the removed DistributedMatrices factory methods.
119018d [Mike Dusenberry] Adding static  methods to each of the distributed matrix classes to consolidate conversion logic.
4d7af86 [Mike Dusenberry] Adding type checks to the constructors.  Although it is slightly verbose, it is better for the user to have a good error message than a cryptic stacktrace.
93b6a3d [Mike Dusenberry] Pulling the DistributedMatrices Python class out of this pull request.
f6f3c68 [Mike Dusenberry] Pulling the DistributedMatrices Scala class out of this pull request.
6a3ecb7 [Mike Dusenberry] Updating pattern matching.
08f287b [Mike Dusenberry] Slight reformatting of the documentation.
a245dc0 [Mike Dusenberry] Updating Python doctests for compatability between Python 2 & 3. Since Python 3 removed the idea of a separate 'long' type, all values that would have been outputted as a 'long' (ex: '4L') will now be treated as an 'int' and outputed as one (ex: '4').  The doctests now explicitly convert to ints so that both Python 2 and 3 will have the same output.  This is fine since the values are all small, and thus can be easily represented as ints.
4d3a37e [Mike Dusenberry] Reformatting a few long Python doctest lines.
7e3ca16 [Mike Dusenberry] Fixing long lines.
f721ead [Mike Dusenberry] Updating documentation for each of the distributed matrices.
ab0e8b6 [Mike Dusenberry] Updating unit test to be more useful.
dda2f89 [Mike Dusenberry] Added wrappers for the conversions between the various distributed matrices.  Added logic to be able to access the rows/entries of the distributed matrices, which requires serialization through DataFrames for IndexedRowMatrix and CoordinateMatrix types. Added unit tests.
0cd7166 [Mike Dusenberry] Implemented the CoordinateMatrix API in PySpark, following the idea of the IndexedRowMatrix API, including using DataFrames for serialization.
3c369cb [Mike Dusenberry] Updating the architecture a bit to make conversions between the various distributed matrix types easier.  The different distributed matrix classes are now only wrappers around the Java objects, and take the Java object as an argument during construction.  This way, we can call  for example on an , which returns a reference to a Java RowMatrix object, and then construct a PySpark RowMatrix object wrapped around the Java object.  This is analogous to the behavior of PySpark RDDs and DataFrames.  We now delegate creation of the various distributed matrices from scratch in PySpark to the factory methods on .
4bdd09b [Mike Dusenberry] Implemented the IndexedRowMatrix API in PySpark, following the idea of the RowMatrix API.  Note that for the IndexedRowMatrix, we use DataFrames to serialize the data between Python and Scala/Java, so we accept PySpark RDDs, then convert to a DataFrame, then convert back to RDDs on the Scala/Java side before constructing the IndexedRowMatrix.
23bf1ec [Mike Dusenberry] Updating documentation to add PySpark RowMatrix. Inserting newline above doctest so that it renders properly in API docs.
b194623 [Mike Dusenberry] Updating design to have a PySpark RowMatrix simply create and keep a reference to a wrapper over a Java RowMatrix.  Updating DistributedMatrices factory methods to accept numRows and numCols with default values.  Updating PySpark DistributedMatrices factory method to simply create a PySpark RowMatrix. Adding additional doctests for numRows and numCols parameters.
bc2d220 [Mike Dusenberry] Adding unit tests for RowMatrix methods.
d7e316f [Mike Dusenberry] Implemented the RowMatrix API in PySpark by doing the following: Added a DistributedMatrices class to contain factory methods for creating the various distributed matrices.  Added a factory method for creating a RowMatrix from an RDD of Vectors.  Added a createRowMatrix function to the PythonMLlibAPI to interface with the factory method.  Added DistributedMatrix, DistributedMatrices, and RowMatrix classes to the pyspark.mllib.linalg api.