The version number of Spark in docs/_config.yaml for master branch should be 1.2.0 for now.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #2943 from sarutak/SPARK-4089 and squashes the following commits:

aba7fb4 [Kousuke Saruta] Fixed the version number of Spark in _config.yaml

Added completed Python API for MLlib.feature

Normalizer
StandardScalerModel
StandardScaler
HashTF
IDFModel
IDF

cc mengxr

Author: Davies Liu <davies@databricks.com>
Author: Davies Liu <davies.liu@gmail.com>

Closes #2819 from davies/feature and squashes the following commits:

4f48f48 [Davies Liu] add a note for HashingTF
67f6d21 [Davies Liu] address comments
b628693 [Davies Liu] rollback changes in Word2Vec
efb4f4f [Davies Liu] Merge branch 'master' into feature
806c7c2 [Davies Liu] address comments
3abb8c2 [Davies Liu] address comments
59781b9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into feature
a405ae7 [Davies Liu] fix tests
7a1891a [Davies Liu] fix tests
486795f [Davies Liu] update programming guide, HashTF -> HashingTF
8a50584 [Davies Liu] Python API for mllib.feature

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #2878 from ScrapCodes/SPARK-4032/deprecate-yarn-alpha and squashes the following commits:

17e9857 [Prashant Sharma] added deperecated comment to Client and ExecutorRunnable.
3a34b1e [Prashant Sharma] Updated docs...
4608dea [Prashant Sharma] [SPARK-4032] Deprecate YARN alpha support in Spark 1.2

In sql-programming-guide.md, there is a wrong package name "scala.math.sql".

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #2873 from sarutak/wrong-packagename-fix and squashes the following commits:

4d5ecf4 [Kousuke Saruta] Fixed wrong package name in sql-programming-guide.md

This pull request is a first step towards the implementation of a stable, pull-based progress / status API for Spark (see [SPARK-2321](https://issues.apache.org/jira/browse/SPARK-2321)).  For now, I'd like to discuss the basic implementation, API names, and overall interface design.  Once we arrive at a good design, I'll go back and add additional methods to expose more information via these API.

#### Design goals:

- Pull-based API
- Usable from Java / Scala / Python (eventually, likely with a wrapper)
- Can be extended to expose more information without introducing binary incompatibilities.
- Returns immutable objects.
- Don't leak any implementation details, preserving our freedom to change the implementation.

#### Implementation:

- Add public methods (`getJobInfo`, `getStageInfo`) to SparkContext to allow status / progress information to be retrieved.
- Add public interfaces (`SparkJobInfo`, `SparkStageInfo`) for our API return values.  These interfaces consist entirely of Java-style getter methods.  The interfaces are currently implemented in Java.  I decided to explicitly separate the interface from its implementation (`SparkJobInfoImpl`, `SparkStageInfoImpl`) in order to prevent users from constructing these responses themselves.
-Allow an existing JobProgressListener to be used when constructing a live SparkUI.  This allows us to re-use this listeners in the implementation of this status API.  There are a few reasons why this listener re-use makes sense:
   - The status API and web UI are guaranteed to show consistent information.
   - These listeners are already well-tested.
   - The same garbage-collection / information retention configurations can apply to both this API and the web UI.
- Extend JobProgressListener to maintain `jobId -> Job` and `stageId -> Stage` mappings.

The progress API methods are implemented in a separate trait that's mixed into SparkContext.  This helps to avoid SparkContext.scala from becoming larger and more difficult to read.

Author: Josh Rosen <joshrosen@databricks.com>
Author: Josh Rosen <joshrosen@apache.org>

Closes #2696 from JoshRosen/progress-reporting-api and squashes the following commits:

e6aa78d [Josh Rosen] Add tests.
b585c16 [Josh Rosen] Accept SparkListenerBus instead of more specific subclasses.
c96402d [Josh Rosen] Address review comments.
2707f98 [Josh Rosen] Expose current stage attempt id
c28ba76 [Josh Rosen] Update demo code:
646ff1d [Josh Rosen] Document spark.ui.retainedJobs.
7f47d6d [Josh Rosen] Clean up SparkUI constructors, per Andrew's feedback.
b77b3d8 [Josh Rosen] Merge remote-tracking branch 'origin/master' into progress-reporting-api
787444c [Josh Rosen] Move status API methods into trait that can be mixed into SparkContext.
f9a9a00 [Josh Rosen] More review comments:
3dc79af [Josh Rosen] Remove creation of unused listeners in SparkContext.
249ca16 [Josh Rosen] Address several review comments:
da5648e [Josh Rosen] Add example of basic progress reporting in Java.
7319ffd [Josh Rosen] Add getJobIdsForGroup() and num*Tasks() methods.
cc568e5 [Josh Rosen] Add note explaining that interfaces should not be implemented outside of Spark.
6e840d4 [Josh Rosen] Remove getter-style names and "consistent snapshot" semantics:
08cbec9 [Josh Rosen] Begin to sketch the interfaces for a stable, public status API.
ac2d13a [Josh Rosen] Add jobId->stage, stageId->stage mappings in JobProgressListener
24de263 [Josh Rosen] Create UI listeners in SparkContext instead of in Tabs:

Given that a lot of users are trying to use hive 0.13 in spark, and the incompatibility between hive-0.12 and hive-0.13 on the API level I want to propose following approach, which has no or minimum impact on existing hive-0.12 support, but be able to jumpstart the development of hive-0.13 and future version support.

Approach: Introduce “hive-version” property,  and manipulate pom.xml files to support different hive version at compiling time through shim layer, e.g., hive-0.12.0 and hive-0.13.1. More specifically,

1. For each different hive version, there is a very light layer of shim code to handle API differences, sitting in sql/hive/hive-version, e.g., sql/hive/v0.12.0 or sql/hive/v0.13.1

2. Add a new profile hive-default active by default, which picks up all existing configuration and hive-0.12.0 shim (v0.12.0)  if no hive.version is specified.

3. If user specifies different version (currently only 0.13.1 by -Dhive.version = 0.13.1), hive-versions profile will be activated, which pick up hive-version specific shim layer and configuration, mainly the hive jars and hive-version shim, e.g., v0.13.1.

4. With this approach, nothing is changed with current hive-0.12 support.

No change by default: sbt/sbt -Phive
For example: sbt/sbt -Phive -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 assembly

To enable hive-0.13: sbt/sbt -Dhive.version=0.13.1
For example: sbt/sbt -Dhive.version=0.13.1 -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 assembly

Note that in hive-0.13, hive-thriftserver is not enabled, which should be fixed by other Jira, and we don’t need -Phive with -Dhive.version in building (probably we should use -Phive -Dhive.version=xxx instead after thrift server is also supported in hive-0.13.1).

Author: Zhan Zhang <zhazhan@gmail.com>
Author: zhzhan <zhazhan@gmail.com>
Author: Patrick Wendell <pwendell@gmail.com>

Closes #2241 from zhzhan/spark-2706 and squashes the following commits:

3ece905 [Zhan Zhang] minor fix
410b668 [Zhan Zhang] solve review comments
cbb4691 [Zhan Zhang] change run-test for new options
0d4d2ed [Zhan Zhang] rebase
497b0f4 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
8fad1cf [Zhan Zhang] change the pom file and make hive-0.13.1 as the default
ab028d1 [Zhan Zhang] rebase
4a2e36d [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
4cb1b93 [zhzhan] Merge pull request #1 from pwendell/pr-2241
b0478c0 [Patrick Wendell] Changes to simplify the build of SPARK-2706
2b50502 [Zhan Zhang] rebase
a72c0d4 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
cb22863 [Zhan Zhang] correct the typo
20f6cf7 [Zhan Zhang] solve compatability issue
f7912a9 [Zhan Zhang] rebase and solve review feedback
301eb4a [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
10c3565 [Zhan Zhang] address review comments
6bc9204 [Zhan Zhang] rebase and remove temparory repo
d3aa3f2 [Zhan Zhang] Merge branch 'master' into spark-2706
cedcc6f [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
3ced0d7 [Zhan Zhang] rebase
d9b981d [Zhan Zhang] rebase and fix error due to rollback
adf4924 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
3dd50e8 [Zhan Zhang] solve conflicts and remove unnecessary implicts
d10bf00 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
dc7bdb3 [Zhan Zhang] solve conflicts
7e0cc36 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
d7c3e1e [Zhan Zhang] Merge branch 'master' into spark-2706
68deb11 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
d48bd18 [Zhan Zhang] address review comments
3ee3b2b [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
57ea52e [Zhan Zhang] Merge branch 'master' into spark-2706
2b0d513 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
9412d24 [Zhan Zhang] address review comments
f4af934 [Zhan Zhang] rebase
1ccd7cc [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
128b60b [Zhan Zhang] ignore 0.12.0 test cases for the time being
af9feb9 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
5f5619f [Zhan Zhang] restructure the directory and different hive version support
05d3683 [Zhan Zhang] solve conflicts
e4c1982 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
94b4fdc [Zhan Zhang] Spark-2706: hive-0.13.1 support on spark
87ebf3b [Zhan Zhang] Merge branch 'master' into spark-2706
921e914 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
f896b2a [Zhan Zhang] Merge branch 'master' into spark-2706
789ea21 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
cb53a2c [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
f6a8a40 [Zhan Zhang] revert
ba14f28 [Zhan Zhang] test
dbedff3 [Zhan Zhang] Merge remote-tracking branch 'upstream/master'
70964fe [Zhan Zhang] revert
fe0f379 [Zhan Zhang] Merge branch 'master' of https://github.com/zhzhan/spark
70ffd93 [Zhan Zhang] revert
42585ec [Zhan Zhang] test
7d5fce2 [Zhan Zhang] test

Thare are some inconsistent spellings 'MLlib' and 'MLLib' in some documents and source codes.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #2903 from sarutak/SPARK-4055 and squashes the following commits:

b031640 [Kousuke Saruta] Fixed inconsistent spelling "MLlib and MLLib"

Author: Sandy Ryza <sandy@cloudera.com>

Closes #789 from sryza/sandy-spark-1813 and squashes the following commits:

48b05e9 [Sandy Ryza] Simplify
b824932 [Sandy Ryza] Allow both spark.kryo.classesToRegister and spark.kryo.registrator at the same time
6a15bb7 [Sandy Ryza] Small fix
a2278c0 [Sandy Ryza] Respond to review comments
6ef592e [Sandy Ryza] SPARK-1813. Add a utility to SparkConf that makes using Kryo really easy

This patch attempts to fix SPARK-2546 in `branch-1.0` and `branch-1.1`.  The underlying problem is that thread-safety issues in Hadoop Configuration objects may cause Spark tasks to get stuck in infinite loops.  The approach taken here is to clone a new copy of the JobConf for each task rather than sharing a single copy between tasks.  Note that there are still Configuration thread-safety issues that may affect the driver, but these seem much less likely to occur in practice and will be more complex to fix (see discussion on the SPARK-2546 ticket).

This cloning is guarded by a new configuration option (`spark.hadoop.cloneConf`) and is disabled by default in order to avoid unexpected performance regressions for workloads that are unaffected by the Configuration thread-safety issues.

Author: Josh Rosen <joshrosen@apache.org>

Closes #2684 from JoshRosen/jobconf-fix-backport and squashes the following commits:

f14f259 [Josh Rosen] Add configuration option to control cloning of Hadoop JobConf.
b562451 [Josh Rosen] Remove unused jobConfCacheKey field.
dd25697 [Josh Rosen] [SPARK-2546] [1.0 / 1.1 backport] Clone JobConf for each task.

(cherry picked from commit 2cd40db2)
Signed-off-by: Josh Rosen <joshrosen@databricks.com>

Conflicts:
	core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala

Having Python examples in Streaming Programming Guide.

Also add RecoverableNetworkWordCount example.

Author: Davies Liu <davies.liu@gmail.com>
Author: Davies Liu <davies@databricks.com>

Closes #2808 from davies/pyguide and squashes the following commits:

8d4bec4 [Davies Liu] update readme
26a7e37 [Davies Liu] fix format
3821c4d [Davies Liu] address comments, add missing file
7e4bb8a [Davies Liu] add Python examples in Streaming Programming Guide

Introduced in https://github.com/pwendell/spark/commit/f7e79bc42c1635686c3af01eef147dae92de2529, I'm not sure why we need two spark.executor.memory here.

Author: WangTaoTheTonic <barneystinson@aliyun.com>
Author: WangTao <barneystinson@aliyun.com>

Closes #2745 from WangTaoTheTonic/redundantconfig and squashes the following commits:

e7564dc [WangTao] too long line
fdbdb1f [WangTaoTheTonic] trivial workaround
d06b6e5 [WangTaoTheTonic] remove redundant spark.executor.memory in doc

Something about the 2.3.4 upgrade seems to have made the issue manifest where all the services disconnect from each other after exactly 1000 seconds (which is the heartbeat interval). [This post](https://groups.google.com/forum/#!topic/akka-user/X3xzpTCbEFs) suggests that heartbeat pause should be greater than heartbeat interval, and increasing the pause from 600s to 6000s seems to have rectified the issue. My current cluster has now exceeded 1400s of uptime without failure!

I do not know why this fixed it, because the threshold we have set for the failure detector is the exponent of a timeout, and 300 is extremely large. Perhaps the default failure detector changed in 2.3.4 and now ignores threshold.

Author: Aaron Davidson <aaron@databricks.com>

Closes #2784 from aarondav/fix-timeout and squashes the following commits:

bd1151a [Aaron Davidson] Increase pause, don't decrease interval
9cb0372 [Aaron Davidson] [SPARK-3923] Decrease Akka heartbeat interval below heartbeat pause

This is another implementation about #1256
cc andrewor14 vanzin

Author: GuoQiang Li <witgo@qq.com>

Closes #2379 from witgo/SPARK-2098-new and squashes the following commits:

4ef1cbd [GuoQiang Li] review commit
49ef70e [GuoQiang Li] Refactor getDefaultPropertiesFile
c45d20c [GuoQiang Li] All Spark processes should support spark-defaults.conf, config file

HT to Diana, just proposing an implementation of her suggestion, which I rather agreed with. Is there a second/third for the motion?

Refer to "self-contained" rather than "standalone" apps to avoid confusion with standalone deployment mode. And fix placement of reference to this in MLlib docs.

Author: Sean Owen <sowen@cloudera.com>

Closes #2787 from srowen/SPARK-1307 and squashes the following commits:

b5b82e2 [Sean Owen] Refer to "self-contained" rather than "standalone" apps to avoid confusion with standalone deployment mode. And fix placement of reference to this in MLlib docs.

There are three  [Custom Receiver Guide] links in streaming doc, the first is wrong.

Author: w00228970 <wangfei1@huawei.com>
Author: wangfei <wangfei1@huawei.com>

Closes #2749 from scwf/streaming-doc and squashes the following commits:

0cd76b7 [wangfei] update link tojump to the Akka-specific section
45b0646 [w00228970] wrong link in streaming doc

This pull request addresses a few issues related to PySpark's IPython support:

- Fix the remaining uses of the '-u' flag, which IPython doesn't support (see SPARK-3772).
- Change PYSPARK_PYTHON_OPTS to PYSPARK_DRIVER_PYTHON_OPTS, so that the old name is reserved in case we ever want to allow the worker Python options to be customized (this variable was introduced in #2554 and hasn't landed in a release yet, so this doesn't break any compatibility).
- Introduce a PYSPARK_DRIVER_PYTHON option that allows the driver to use `ipython` while the workers use a different Python version.
- Attempt to use Python 2.7 by default if PYSPARK_PYTHON is not specified.
- Retain the old semantics for IPYTHON=1 and IPYTHON_OPTS (to avoid breaking existing example programs).

There are more details in a block comment in `bin/pyspark`.

Author: Josh Rosen <joshrosen@apache.org>

Closes #2651 from JoshRosen/SPARK-3772 and squashes the following commits:

7b8eb86 [Josh Rosen] More changes to PySpark python executable configuration:
c4f5778 [Josh Rosen] [SPARK-3772] Allow ipython to be used by Pyspark workers; IPython fixes:

It took me a minute to track this down, so I thought it could be useful to have it in the docs.

I'm unsure if 512mb is the default for spark.driver.memory? Also - there could be a better value for the 'description' to differentiate it from spark.executor.memory.

Author: nartz <nartzpod@gmail.com>
Author: Nathan Artz <nathanartz@Nathans-MacBook-Pro.local>

Closes #2410 from nartz/docs/add-spark-driver-memory-to-config-docs and squashes the following commits:

a2f6c62 [nartz] Update configuration.md
74521b8 [Nathan Artz] add spark.driver.memory to config docs

Retire Epydoc, use Sphinx to generate API docs.

Refine Sphinx docs, also convert some docstrings into Sphinx style.

It looks like:
![api doc](https://cloud.githubusercontent.com/assets/40902/4538272/9e2d4f10-4dec-11e4-8d96-6e45a8fe51f9.png)

Author: Davies Liu <davies.liu@gmail.com>

Closes #2689 from davies/docs and squashes the following commits:

bf4a0a5 [Davies Liu] fix links
3fb1572 [Davies Liu] fix _static in jekyll
65a287e [Davies Liu] fix scripts and logo
8524042 [Davies Liu] Merge branch 'master' of github.com:apache/spark into docs
d5b874a [Davies Liu] Merge branch 'master' of github.com:apache/spark into docs
4bc1c3c [Davies Liu] refactor
746d0b6 [Davies Liu] @param -> :param
240b393 [Davies Liu] replace epydoc with sphinx doc

Add testing with sbt to doc ```building-spark.md```

Author: scwf <wangfei1@huawei.com>

Closes #2629 from scwf/sbt-doc and squashes the following commits:

fd9cf29 [scwf] add testing with sbt to docs

In building-spark.md, there are some examples for making assembled package with maven but the example for building with sbt is only about for compiling.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #2627 from sarutak/SPARK-3763 and squashes the following commits:

fadb990 [Kousuke Saruta] Modified the example to build with sbt in building-spark.md

Author: Brenden Matthews <brenden@diddyinc.com>

Closes #2401 from brndnmtthws/master and squashes the following commits:

4abaa5d [Brenden Matthews] [SPARK-3535][Mesos] Fix resource handling.

Update of PR #997.

With this PR, setting SPARK_CONF_DIR overrides SPARK_HOME/conf (not only spark-defaults.conf and spark-env).

Author: EugenCepoi <cepoi.eugen@gmail.com>

Closes #2481 from EugenCepoi/SPARK-2058 and squashes the following commits:

0bb32c2 [EugenCepoi] use orElse orNull and fixing trailing percent in compute-classpath.cmd
77f35d7 [EugenCepoi] SPARK-2058: Overriding SPARK_HOME/conf with SPARK_CONF_DIR

Author: scwf <wangfei1@huawei.com>

Closes #2632 from scwf/compress-doc and squashes the following commits:

7983a1a [scwf] snappy is the default compression codec for broadcast

Redone against the recent master branch (https://github.com/apache/spark/pull/1391)

Author: Nishkam Ravi <nravi@cloudera.com>
Author: nravi <nravi@c1704.halxg.cloudera.com>
Author: nishkamravi2 <nishkamravi@gmail.com>

Closes #2485 from nishkamravi2/master_nravi and squashes the following commits:

636a9ff [nishkamravi2] Update YarnAllocator.scala
8f76c8b [Nishkam Ravi] Doc change for yarn memory overhead
35daa64 [Nishkam Ravi] Slight change in the doc for yarn memory overhead
5ac2ec1 [Nishkam Ravi] Remove out
dac1047 [Nishkam Ravi] Additional documentation for yarn memory overhead issue
42c2c3d [Nishkam Ravi] Additional changes for yarn memory overhead issue
362da5e [Nishkam Ravi] Additional changes for yarn memory overhead
c726bd9 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
f00fa31 [Nishkam Ravi] Improving logging for AM memoryOverhead
1cf2d1e [nishkamravi2] Update YarnAllocator.scala
ebcde10 [Nishkam Ravi] Modify default YARN memory_overhead-- from an additive constant to a multiplier (redone to resolve merge conflicts)
2e69f11 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
efd688a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark
2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark
3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark
5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark
eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark
df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456)
6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed)
5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456)
681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles

We have changed the output format of `printSchema`. This PR will update our SQL programming guide to show the updated format. Also, it fixes a typo (the value type of `StructType` in Java API).

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #2630 from yhuai/sqlDoc and squashes the following commits:

267d63e [Yin Huai] Update the output of printSchema and fix a typo.

### Problem

The section "Using the shell" in Spark Programming Guide (https://spark.apache.org/docs/latest/programming-guide.html#using-the-shell) says that we can run pyspark REPL through IPython.
But a folloing command does not run IPython but a default Python executable.

```
$ IPYTHON=1 ./bin/pyspark
Python 2.7.8 (default, Jul  2 2014, 10:14:46)
...
```

the spark/bin/pyspark script on the commit b235e013 decides which executable and options it use folloing way.

1. if PYSPARK_PYTHON unset
   * → defaulting to "python"
2. if IPYTHON_OPTS set
   * → set IPYTHON "1"
3. some python scripts passed to ./bin/pyspak → run it with ./bin/spark-submit
   * out of this issues scope
4. if IPYTHON set as "1"
   * → execute $PYSPARK_PYTHON (default: ipython) with arguments $IPYTHON_OPTS
   * otherwise execute $PYSPARK_PYTHON

Therefore, when PYSPARK_PYTHON is unset, python is executed though IPYTHON is "1".
In other word, when PYSPARK_PYTHON is unset, IPYTHON_OPS and IPYTHON has no effect on decide which command to use.

PYSPARK_PYTHON | IPYTHON_OPTS | IPYTHON | resulting command | expected command
---- | ---- | ----- | ----- | -----
(unset → defaults to python) | (unset) | (unset) | python | (same)
(unset → defaults to python) | (unset) | 1 | python | ipython
(unset → defaults to python) | an_option | (unset → set to 1) | python an_option | ipython an_option
(unset → defaults to python) | an_option | 1 | python an_option | ipython an_option
ipython | (unset) | (unset) | ipython | (same)
ipython | (unset) | 1 | ipython | (same)
ipython | an_option | (unset → set to 1) | ipython an_option | (same)
ipython | an_option | 1 | ipython an_option | (same)

### Suggestion

The pyspark script should determine firstly whether a user wants to run IPython or other executables.

1. if IPYTHON_OPTS set
   * set IPYTHON "1"
2.  if IPYTHON has a value "1"
   * PYSPARK_PYTHON defaults to "ipython" if not set
3. PYSPARK_PYTHON defaults to "python" if not set

See the pull request for more detailed modification.

Author: cocoatomo <cocoatomo77@gmail.com>

Closes #2554 from cocoatomo/issues/cannot-run-ipython-without-options and squashes the following commits:

d2a9b06 [cocoatomo] [SPARK-3706][PySpark] Use PYTHONUNBUFFERED environment variable instead of -u option
264114c [cocoatomo] [SPARK-3706][PySpark] Remove the sentence about deprecated environment variables
42e02d5 [cocoatomo] [SPARK-3706][PySpark] Replace environment variables used to customize execution of PySpark REPL
10d56fb [cocoatomo] [SPARK-3706][PySpark] Cannot run IPython REPL with IPYTHON set to "1" and PYSPARK_PYTHON unset

This patch add profiling support for PySpark, it will show the profiling results
before the driver exits, here is one example:

```
============================================================
Profile of RDD<id=3>
============================================================
         5146507 function calls (5146487 primitive calls) in 71.094 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  5144576   68.331    0.000   68.331    0.000 statcounter.py:44(merge)
       20    2.735    0.137   71.071    3.554 statcounter.py:33(__init__)
       20    0.017    0.001    0.017    0.001 {cPickle.dumps}
     1024    0.003    0.000    0.003    0.000 t.py:16(<lambda>)
       20    0.001    0.000    0.001    0.000 {reduce}
       21    0.001    0.000    0.001    0.000 {cPickle.loads}
       20    0.001    0.000    0.001    0.000 copy_reg.py:95(_slotnames)
       41    0.001    0.000    0.001    0.000 serializers.py:461(read_int)
       40    0.001    0.000    0.002    0.000 serializers.py:179(_batched)
       62    0.000    0.000    0.000    0.000 {method 'read' of 'file' objects}
       20    0.000    0.000   71.072    3.554 rdd.py:863(<lambda>)
       20    0.000    0.000    0.001    0.000 serializers.py:198(load_stream)
    40/20    0.000    0.000   71.072    3.554 rdd.py:2093(pipeline_func)
       41    0.000    0.000    0.002    0.000 serializers.py:130(load_stream)
       40    0.000    0.000   71.072    1.777 rdd.py:304(func)
       20    0.000    0.000   71.094    3.555 worker.py:82(process)
```

Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk
by `sc.dump_profiles(path)`, such as

```python
>>> sc._conf.set("spark.python.profile", "true")
>>> rdd = sc.parallelize(range(100)).map(str)
>>> rdd.count()
100
>>> sc.show_profiles()
============================================================
Profile of RDD<id=1>
============================================================
         284 function calls (276 primitive calls) in 0.001 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        4    0.000    0.000    0.000    0.000 serializers.py:198(load_stream)
        4    0.000    0.000    0.000    0.000 {reduce}
     12/4    0.000    0.000    0.001    0.000 rdd.py:2092(pipeline_func)
        4    0.000    0.000    0.000    0.000 {cPickle.loads}
        4    0.000    0.000    0.000    0.000 {cPickle.dumps}
      104    0.000    0.000    0.000    0.000 rdd.py:852(<genexpr>)
        8    0.000    0.000    0.000    0.000 serializers.py:461(read_int)
       12    0.000    0.000    0.000    0.000 rdd.py:303(func)
```
The profiling is disabled by default, can be enabled by "spark.python.profile=true".

Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump"

This is bugfix of #2351 cc JoshRosen

Author: Davies Liu <davies.liu@gmail.com>

Closes #2556 from davies/profiler and squashes the following commits:

e68df5a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
858e74c [Davies Liu] compatitable with python 2.6
7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles()
2b0daf2 [Davies Liu] fix docs
7a56c24 [Davies Liu] bugfix
cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext
fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
09d02c3 [Davies Liu] Merge branch 'master' into profiler
c23865c [Davies Liu] Merge branch 'master' into profiler
15d6f18 [Davies Liu] add docs for two configs
dadee1a [Davies Liu] add docs string and clear profiles after show or dump
4f8309d [Davies Liu] address comment, add tests
0a5b6eb [Davies Liu] fix Python UDF
4b20494 [Davies Liu] add profile for python

As suggested by mateiz , and because it came up on the mailing list again last week, this attempts to document that ordering of elements is not guaranteed across RDD evaluations in groupBy, zip, and partition-wise RDD methods. Suggestions welcome about the wording, or other methods that need a note.

Author: Sean Owen <sowen@cloudera.com>

Closes #2508 from srowen/SPARK-3356 and squashes the following commits:

b7c96fd [Sean Owen] Undo change to programming guide
ad4aeec [Sean Owen] Don't mention ordering in partition-wise methods, reword description of ordering for zip methods per review, and add similar note to programming guide, which mentions groupByKey (but not zip methods)
fce943b [Sean Owen] Note that ordering of elements is not guaranteed across RDD evaluations in groupBy, zip, and partition-wise RDD methods

https://issues.apache.org/jira/browse/SPARK-3715

Author: WangTaoTheTonic <barneystinson@aliyun.com>

Closes #2567 from WangTaoTheTonic/minortypo and squashes the following commits:

9cc3f7a [WangTaoTheTonic] minor typo

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #2540 from CrazyJvm/standalone-core and squashes the following commits:

66d9fc6 [CrazyJvm] use "--total-executor-cores" rather than "--cores" after spark-shell

i.e
./spark-ec2 --region=us-west-1 stop yourclustername

Author: Jeff Steinmetz <jeffrey.steinmetz@gmail.com>

Closes #2473 from jeffsteinmetz/master and squashes the following commits:

7491f2c [Jeff Steinmetz] fix case in EC2 cluster setup documentation
bd3d777 [Jeff Steinmetz] standardized ec2 documenation to use <lower-case> sample args
2bf4a57 [Jeff Steinmetz] standardized ec2 documenation to use <lower-case> sample args
68d8372 [Jeff Steinmetz] standardized ec2 documenation to use <lower-case> sample args
d2ab6e2 [Jeff Steinmetz] standardized ec2 documenation to use <lower-case> sample args
520e6dc [Jeff Steinmetz] standardized ec2 documenation to use <lower-case> sample args
37fc876 [Jeff Steinmetz] stop, start and destroy require the EC2_REGION

Author: Michael Armbrust <michael@databricks.com>

Closes #2527 from marmbrus/patch-1 and squashes the following commits:

a0f9f1c [Michael Armbrust] [SQL][DOCS] Clarify that the server is for JDBC and ODBC

This reverts commit 1aa549ba.

This PR for [SPARK-3614](https://issues.apache.org/jira/browse/SPARK-3614) adds functionality for filtering out terms which do not appear in at least a minimum number of documents.

This is implemented using a minimumOccurence parameter (default 0).  When terms' document frequencies are less than minimumOccurence, their IDFs are set to 0, just like when the DF is 0.  As a result, the TF-IDFs for the terms are found to be 0, as if the terms were not present in the documents.

This PR makes the following changes:
* Add a minimumOccurence parameter to the IDF and DocumentFrequencyAggregator classes.
* Create a parameter-less constructor for IDF with a default minimumOccurence value of 0 to remain backwards-compatibility with the original IDF API.
* Sets the IDFs to 0 for terms which DFs are less than minimumOccurence
* Add tests to the Spark IDFSuite and Java JavaTfIdfSuite test suites
* Updated the MLLib Feature Extraction programming guide to describe the new feature

Author: RJ Nowling <rnowling@gmail.com>

Closes #2494 from rnowling/spark-3614-idf-filter and squashes the following commits:

0aa3c63 [RJ Nowling] Fix identation
e6523a8 [RJ Nowling] Remove unnecessary toDouble's from IDFSuite
bfa82ec [RJ Nowling] Add space after if
30d20b3 [RJ Nowling] Add spaces around equals signs
9013447 [RJ Nowling] Add space before division operator
79978fc [RJ Nowling] Remove unnecessary semi-colon
40fd70c [RJ Nowling] Change minimumOccurence to minDocFreq in code and docs
47850ab [RJ Nowling] Changed minimumOccurence to Int from Long
9fb4093 [RJ Nowling] Remove unnecessary lines from IDF class docs
1fc09d8 [RJ Nowling] Add backwards-compatible constructor to DocumentFrequencyAggregator
1801fd2 [RJ Nowling] Fix style errors in IDF.scala
6897252 [RJ Nowling] Preface minimumOccurence members with val to make them final and immutable
a200bab [RJ Nowling] Remove unnecessary else statement
4b974f5 [RJ Nowling] Remove accidentally-added import from testing
c0cc643 [RJ Nowling] Add minimumOccurence filtering to IDF

This patch add profiling support for PySpark, it will show the profiling results
before the driver exits, here is one example:

```
============================================================
Profile of RDD<id=3>
============================================================
         5146507 function calls (5146487 primitive calls) in 71.094 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  5144576   68.331    0.000   68.331    0.000 statcounter.py:44(merge)
       20    2.735    0.137   71.071    3.554 statcounter.py:33(__init__)
       20    0.017    0.001    0.017    0.001 {cPickle.dumps}
     1024    0.003    0.000    0.003    0.000 t.py:16(<lambda>)
       20    0.001    0.000    0.001    0.000 {reduce}
       21    0.001    0.000    0.001    0.000 {cPickle.loads}
       20    0.001    0.000    0.001    0.000 copy_reg.py:95(_slotnames)
       41    0.001    0.000    0.001    0.000 serializers.py:461(read_int)
       40    0.001    0.000    0.002    0.000 serializers.py:179(_batched)
       62    0.000    0.000    0.000    0.000 {method 'read' of 'file' objects}
       20    0.000    0.000   71.072    3.554 rdd.py:863(<lambda>)
       20    0.000    0.000    0.001    0.000 serializers.py:198(load_stream)
    40/20    0.000    0.000   71.072    3.554 rdd.py:2093(pipeline_func)
       41    0.000    0.000    0.002    0.000 serializers.py:130(load_stream)
       40    0.000    0.000   71.072    1.777 rdd.py:304(func)
       20    0.000    0.000   71.094    3.555 worker.py:82(process)
```

Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk
by `sc.dump_profiles(path)`, such as

```python
>>> sc._conf.set("spark.python.profile", "true")
>>> rdd = sc.parallelize(range(100)).map(str)
>>> rdd.count()
100
>>> sc.show_profiles()
============================================================
Profile of RDD<id=1>
============================================================
         284 function calls (276 primitive calls) in 0.001 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        4    0.000    0.000    0.000    0.000 serializers.py:198(load_stream)
        4    0.000    0.000    0.000    0.000 {reduce}
     12/4    0.000    0.000    0.001    0.000 rdd.py:2092(pipeline_func)
        4    0.000    0.000    0.000    0.000 {cPickle.loads}
        4    0.000    0.000    0.000    0.000 {cPickle.dumps}
      104    0.000    0.000    0.000    0.000 rdd.py:852(<genexpr>)
        8    0.000    0.000    0.000    0.000 serializers.py:461(read_int)
       12    0.000    0.000    0.000    0.000 rdd.py:303(func)
```
The profiling is disabled by default, can be enabled by "spark.python.profile=true".

Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump"

Author: Davies Liu <davies.liu@gmail.com>

Closes #2351 from davies/profiler and squashes the following commits:

7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles()
2b0daf2 [Davies Liu] fix docs
7a56c24 [Davies Liu] bugfix
cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext
fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
09d02c3 [Davies Liu] Merge branch 'master' into profiler
c23865c [Davies Liu] Merge branch 'master' into profiler
15d6f18 [Davies Liu] add docs for two configs
dadee1a [Davies Liu] add docs string and clear profiles after show or dump
4f8309d [Davies Liu] address comment, add tests
0a5b6eb [Davies Liu] fix Python UDF
4b20494 [Davies Liu] add profile for python

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #2444 from sarutak/slaves-scripts-modification and squashes the following commits:

eff7394 [Kousuke Saruta] Improve the description about Cluster Launch Script in docs/spark-standalone.md
7858225 [Kousuke Saruta] Modified sbin/slaves to use the environment variable "SPARK_SSH_FOREGROUND" as a flag
53d7121 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into slaves-scripts-modification
e570431 [Kousuke Saruta] Added a description for SPARK_SSH_FOREGROUND variable
7120a0c [Kousuke Saruta] Added a description about default host for sbin/slaves
1bba8a9 [Kousuke Saruta] Added SPARK_SSH_FOREGROUND flag to sbin/slaves
88e2f17 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into slaves-scripts-modification
297e75d [Kousuke Saruta] Modified sbin/slaves not to export HOSTLIST

Add warnings to KMeans, GeneralizedLinearAlgorithm, and computeSVD when called with input data that is not cached. KMeans is implemented iteratively, and I believe that GeneralizedLinearAlgorithm’s current optimizers are iterative and its future optimizers are also likely to be iterative. RowMatrix’s computeSVD is iterative against an RDD when run in DistARPACK mode. ALS and DecisionTree are iterative as well, but they implement RDD caching internally so do not require a warning.

I added a warning to GeneralizedLinearAlgorithm rather than inside its optimizers, where the iteration actually occurs, because internally GeneralizedLinearAlgorithm maps its input data to an uncached RDD before passing it to an optimizer. (In other words, the warning would be printed for every GeneralizedLinearAlgorithm run, regardless of whether its input is cached, if the warning were in GradientDescent or other optimizer.) I assume that use of an uncached RDD by GeneralizedLinearAlgorithm is intentional, and that the mapping there (adding label, intercepts and scaling) is a lightweight operation. Arguably a user calling an optimizer such as GradientDescent will be knowledgable enough to cache their data without needing a log warning, so lack of a warning in the optimizers may be ok.

Some of the documentation examples making use of these iterative algorithms did not cache their training RDDs (while others did). I updated the examples to always cache. I also fixed some (unrelated) minor errors in the documentation examples.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #2347 from staple/SPARK-1484 and squashes the following commits:

bd49701 [Aaron Staple] Address review comments.
ab2d4a4 [Aaron Staple] Disable warnings on python code path.
a7a0f99 [Aaron Staple] Change code comments per review comments.
7cca1dc [Aaron Staple] Change warning message text.
c77e939 [Aaron Staple] [SPARK-1484][MLLIB] Warn when running an iterative algorithm on uncached data.
3b6c511 [Aaron Staple] Minor doc example fixes.

leftOuterJoin and rightOuterJoin are already implemented.  This patch adds fullOuterJoin.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #1395 from staple/SPARK-546 and squashes the following commits:

1f5595c [Aaron Staple] Fix python style
7ac0aa9 [Aaron Staple] [SPARK-546] Add full outer join to RDD and DStream.
3b5d137 [Aaron Staple] In JavaPairDStream, make class tag specification in rightOuterJoin consistent with other functions.
31f2956 [Aaron Staple] Fix left outer join documentation comments.

Assign value of yarn container log directory to java opts "spark.yarn.app.container.log.dir", So user defined log4j.properties can reference this value and write log to YARN container's log directory.
Otherwise, user defined file appender will only write to container's CWD, and log files in CWD will not be displayed on YARN UI，and either cannot be aggregated to HDFS log directory after job finished.

User defined log4j.properties reference example:
log4j.appender.rolling_file.File = ${spark.yarn.app.container.log.dir}/spark.log

Author: peng.zhang <peng.zhang@xiaomi.com>

Closes #1573 from renozhang/yarn-log-dir and squashes the following commits:

16c5cb8 [peng.zhang] Update doc
f2b5e2a [peng.zhang] Change variable's name, and update running-on-yarn.md
503ea2d [peng.zhang] Support log4j log to yarn container dir

Author: Grega Kespret <grega.kespret@gmail.com>

Closes #2479 from gregakespret/patch-1 and squashes the following commits:

dd6b90a [Grega Kespret] Update docs to use jsonRDD instead of wrong jsonRdd.