In the Configuration section, the **spark.yarn.driver.memoryOverhead** and **spark.yarn.am.memoryOverhead**‘s default value should be "driverMemory * 0.10, with minimum of 384" and "AM memory * 0.10, with minimum of 384" respectively. Because from Spark 1.4.0, the **MEMORY_OVERHEAD_FACTOR** is set to 0.1.0, not 0.07.

Author: yangping.wu <wyphao.2007@163.com>

Closes #8797 from 397090770/SparkOnYarnDocError.

JIRA: https://issues.apache.org/jira/browse/SPARK-10459

As mentioned in the JIRA, `PythonUDF` actually could process `UnsafeRow`.

Specially, the rows in `childResults` in `BatchPythonEvaluation` will be projected to a `MutableRow`. So I think we can enable `canProcessUnsafeRows` for `BatchPythonEvaluation` and get rid of redundant `ConvertToSafe`.

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

Closes #8616 from viirya/pyudf-unsafe.

Should be the same as SPARK-7808 but use Java for the code example.
It would be great to add package doc for `spark.ml.feature`.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8740 from holdenk/SPARK-10077-JAVA-PACKAGE-DOC-FOR-SPARK.ML.FEATURE.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8692 from yu-iskw/SPARK-10282.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8665 from yu-iskw/SPARK-10274.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8689 from yu-iskw/SPARK-10279.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8685 from yu-iskw/SPARK-10278.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8691 from yu-iskw/SPARK-10281.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8693 from yu-iskw/SPARK-10283.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8694 from yu-iskw/SPARK-10284.

Author: Cheng Lian <lian@databricks.com>

Closes #8784 from liancheng/typo-fix.

This fixes https://issues.apache.org/jira/browse/SPARK-9794 by using a real ISO8601 parser. (courtesy of the xml component of the standard java library)

cc: angelini

Author: Kevin Cox <kevincox@kevincox.ca>

Closes #8396 from kevincox/kevincox-sql-time-parsing.

1. Support collecting data of MapType from DataFrame.
2. Support data of MapType in createDataFrame.

Author: Sun Rui <rui.sun@intel.com>

Closes #8711 from sun-rui/SPARK-10050.

Set `X-Frame-Options: SAMEORIGIN` to protect against frame-related vulnerability

Author: Sean Owen <sowen@cloudera.com>

Closes #8745 from srowen/SPARK-10589.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8677 from yu-iskw/SPARK-10276.

The calculation of Spark version is downloading
Scala and Zinc in the build directory which is
inflating the size of the source distribution.

Reseting the repo before packaging the source
distribution fix this issue.

Author: Luciano Resende <lresende@apache.org>

Closes #8774 from lresende/spark-10511.

Author: Vinod K C <vinod.kc@huawei.com>

Closes #8682 from vinodkc/fix_SPARK-10516.

Various ML guide cleanups.

* ml-guide.md: Make it easier to access the algorithm-specific guides.
* LDA user guide: EM often begins with useless topics, but running longer generally improves them dramatically.  E.g., 10 iterations on a Wikipedia dataset produces useless topics, but 50 iterations produces very meaningful topics.
* mllib-feature-extraction.html#elementwiseproduct: “w” parameter should be “scalingVec”
* Clean up Binarizer user guide a little.
* Document in Pipeline that users should not put an instance into the Pipeline in more than 1 place.
* spark.ml Word2Vec user guide: clean up grammar/writing
* Chi Sq Feature Selector docs: Improve text in doc.

CC: mengxr feynmanliang

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

Closes #8752 from jkbradley/mlguide-fixes-1.5.

Current implementation uses query with a LIMIT clause to find if table already exists. This syntax works only in some database systems. This patch changes the default query to the one that is likely to work on most databases, and adds a new method to the  JdbcDialect abstract class to allow  dialects to override the default query.

I looked at using the JDBC meta data calls, it turns out there is no common way to find the current schema, catalog..etc.  There is a new method Connection.getSchema() , but that is available only starting jdk1.7 , and existing jdbc drivers may not have implemented it.  Other option was to use jdbc escape syntax clause for LIMIT, not sure on how well this supported in all the databases also. After looking at all the jdbc metadata options my conclusion was most common way is to use the simple select query with 'where 1 =0' , and allow dialects to customize as needed

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #8676 from sureshthalamati/table_exists_spark-9078.

Instead of relying on `DataFrames` to verify our answers, we can just use simple arrays. This significantly simplifies the test logic for `LocalNode`s and reduces a lot of code duplicated from `SparkPlanTest`.

This also fixes an additional issue [SPARK-10624](https://issues.apache.org/jira/browse/SPARK-10624) where the output of `TakeOrderedAndProjectNode` is not actually ordered.

Author: Andrew Or <andrew@databricks.com>

Closes #8764 from andrewor14/sql-local-tests-cleanup.

When speculative execution is enabled, consider a scenario where the authorized committer of a particular output partition fails during the OutputCommitter.commitTask() call. In this case, the OutputCommitCoordinator is supposed to release that committer's exclusive lock on committing once that task fails. However, due to a unit mismatch (we used task attempt number in one place and task attempt id in another) the lock will not be released, causing Spark to go into an infinite retry loop.

This bug was masked by the fact that the OutputCommitCoordinator does not have enough end-to-end tests (the current tests use many mocks). Other factors contributing to this bug are the fact that we have many similarly-named identifiers that have different semantics but the same data types (e.g. attemptNumber and taskAttemptId, with inconsistent variable naming which makes them difficult to distinguish).

This patch adds a regression test and fixes this bug by always using task attempt numbers throughout this code.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8544 from JoshRosen/SPARK-10381.

Remove return statements in RDD.takeSample and wrap it withScope

Author: vinodkc <vinod.kc.in@gmail.com>
Author: vinodkc <vinodkc@users.noreply.github.com>
Author: Vinod K C <vinod.kc@huawei.com>

Closes #8730 from vinodkc/fix_takesample_return.

The idea is that we should separate the function call that does memory reservation (i.e. prepare) from the function call that consumes the input (e.g. open()), so all operators can be a chance to reserve memory before they are all consumed.

Author: Reynold Xin <rxin@databricks.com>

Closes #8761 from rxin/SPARK-10612.

*Note: this is for master branch only.* The fix for branch-1.5 is at #8721.

The query execution ID is currently passed from a thread to its children, which is not the intended behavior. This led to `IllegalArgumentException: spark.sql.execution.id is already set` when running queries in parallel, e.g.:
```
(1 to 100).par.foreach { _ =>
  sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count()
}
```
The cause is `SparkContext`'s local properties are inherited by default. This patch adds a way to exclude keys we don't want to be inherited, and makes SQL go through that code path.

Author: Andrew Or <andrew@databricks.com>

Closes #8710 from andrewor14/concurrent-sql-executions.

In fraud detection dataset, almost all the samples are negative while only couple of them are positive. This type of high imbalanced data will bias the models toward negative resulting poor performance. In python-scikit, they provide a correction allowing users to Over-/undersample the samples of each class according to the given weights. In auto mode, selects weights inversely proportional to class frequencies in the training set. This can be done in a more efficient way by multiplying the weights into loss and gradient instead of doing actual over/undersampling in the training dataset which is very expensive.
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
On the other hand, some of the training data maybe more important like the training samples from tenure users while the training samples from new users maybe less important. We should be able to provide another "weight: Double" information in the LabeledPoint to weight them differently in the learning algorithm.

Author: DB Tsai <dbt@netflix.com>
Author: DB Tsai <dbt@dbs-mac-pro.corp.netflix.com>

Closes #7884 from dbtsai/SPARK-7685.

Sometimes we can't push down the whole `Project` though `Sort`, but we still have a chance to push down part of it.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8644 from cloud-fan/column-prune.

JIRA: https://issues.apache.org/jira/browse/SPARK-10437

If an expression in `SortOrder` is a resolved one, such as `count(1)`, the corresponding rule in `Analyzer` to make it work in order by will not be applied.

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

Closes #8599 from viirya/orderby-agg.

This reverts commit 8abef21d.

* a follow-up to 16b6d186 as `--num-executors` flag is not suppported.
* links + formatting

Author: Jacek Laskowski <jacek.laskowski@deepsense.io>

Closes #8762 from jaceklaskowski/docs-spark-on-yarn.

Closes #8767
Closes #2491
Closes #6795
Closes #2096
Closes #7722

Missed this when reviewing `pyspark.mllib.random` for SPARK-10275.

Author: noelsmith <mail@noelsmith.com>

Closes #8773 from noel-smith/mllib-random-versionadded-fix.

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.

jira: https://issues.apache.org/jira/browse/SPARK-10491

We implemented dspr with sparse vector support in `RowMatrix`. This method is also used in WeightedLeastSquares and other places. It would be useful to move it to `linalg.BLAS`.

Let me know if new UT needed.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8663 from hhbyyh/movedspr.

Author: Reynold Xin <rxin@databricks.com>

Closes #8350 from rxin/1.6.

Comments preceding toMessage method state: "The edge partition is encoded in the lower
   * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.". References to bytes should be changed to bits.

This contribution is my original work and I license the work to the Spark project under it's open source license.

Author: Robin East <robin.east@xense.co.uk>

Closes #8756 from insidedctm/master.

Links work now properly + consistent use of *Spark standalone cluster* (Spark uppercase + lowercase the rest -- seems agreed in the other places in the docs).

Author: Jacek Laskowski <jacek.laskowski@deepsense.io>

Closes #8759 from jaceklaskowski/docs-submitting-apps.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8666 from yu-iskw/SPARK-10275.

Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings).

Added since to methods + "versionadded::" to classes (derived from the git file history in pyspark).

Author: noelsmith <mail@noelsmith.com>

Closes #8633 from noel-smith/SPARK-10273-since-mllib-feature.

[SPARK-9793] [MLLIB] [PYSPARK] PySpark DenseVector, SparseVector implement __eq__ and __hash__ correctly

PySpark DenseVector, SparseVector ```__eq__``` method should use semantics equality, and DenseVector can compared with SparseVector.
Implement PySpark DenseVector, SparseVector ```__hash__``` method based on the first 16 entries. That will make PySpark Vector objects can be used in collections.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8166 from yanboliang/spark-9793.