[SPARK-3886] [PySpark] simplify serializer, use AutoBatchedSerializer by default.

This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1.

Author: Davies Liu <davies@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Josh Rosen <joshrosen@databricks.com>

Closes #2920 from davies/fix_autobatch and squashes the following commits:

e544ef9 [Davies Liu] revert unrelated change
6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
1d557fc [Davies Liu] fix tests
8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
76abdce [Davies Liu] clean up
53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
b4292ce [Davies Liu] fix bug in master
d79744c [Davies Liu] recover hive tests
be37ece [Davies Liu] refactor
eb3938d [Davies Liu] refactor serializer in scala
8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
fixed MLlib Naive-Bayes java example bug

the filter tests Double objects by references whereas it should test their values

Author: Dariusz Kobylarz <darek.kobylarz@gmail.com>

Closes #3081 from dkobylarz/master and squashes the following commits:

5d43a39 [Dariusz Kobylarz] naive bayes example update
a304b93 [Dariusz Kobylarz] fixed MLlib Naive-Bayes java example bug
[Spark-4060] [MLlib] exposing special rdd functions to the public

Author: Niklas Wilcke <1wilcke@informatik.uni-hamburg.de>

Closes #2907 from numbnut/master and squashes the following commits:

7f7c767 [Niklas Wilcke] [Spark-4060] [MLlib] exposing special rdd functions to the public, #2907
[SPARK-2938] Support SASL authentication in NettyBlockTransferService

Also lays the groundwork for supporting it inside the external shuffle service.

Author: Aaron Davidson <aaron@databricks.com>

Closes #3087 from aarondav/sasl and squashes the following commits:

3481718 [Aaron Davidson] Delete rogue println
44f8410 [Aaron Davidson] Delete documentation - muahaha!
eb9f065 [Aaron Davidson] Improve documentation and add end-to-end test at Spark-level
a6b95f1 [Aaron Davidson] Address comments
785bbde [Aaron Davidson] Cleanup
79973cb [Aaron Davidson] Remove unused file
151b3c5 [Aaron Davidson] Add docs, timeout config, better failure handling
f6177d7 [Aaron Davidson] Cleanup SASL state upon connection termination
7b42adb [Aaron Davidson] Add unit tests
8191bcb [Aaron Davidson] [SPARK-2938] Support SASL authentication in NettyBlockTransferService
[SQL] Add String option for DSL AS

Author: Michael Armbrust <michael@databricks.com>

Closes #3097 from marmbrus/asString and squashes the following commits:

6430520 [Michael Armbrust] Add String option for DSL AS
[SPARK-3964] [MLlib] [PySpark] add Hypothesis test Python API

```
pyspark.mllib.stat.StatisticschiSqTest(observed, expected=None)
    :: Experimental ::

    If `observed` is Vector, conduct Pearson's chi-squared goodness
    of fit test of the observed data against the expected distribution,
    or againt the uniform distribution (by default), with each category
    having an expected frequency of `1 / len(observed)`.
    (Note: `observed` cannot contain negative values)

    If `observed` is matrix, conduct Pearson's independence test on the
    input contingency matrix, which cannot contain negative entries or
    columns or rows that sum up to 0.

    If `observed` is an RDD of LabeledPoint, conduct Pearson's independence
    test for every feature against the label across the input RDD.
    For each feature, the (feature, label) pairs are converted into a
    contingency matrix for which the chi-squared statistic is computed.
    All label and feature values must be categorical.

    :param observed: it could be a vector containing the observed categorical
                     counts/relative frequencies, or the contingency matrix
                     (containing either counts or relative frequencies),
                     or an RDD of LabeledPoint containing the labeled dataset
                     with categorical features. Real-valued features will be
                     treated as categorical for each distinct value.
    :param expected: Vector containing the expected categorical counts/relative
                     frequencies. `expected` is rescaled if the `expected` sum
                     differs from the `observed` sum.
    :return: ChiSquaredTest object containing the test statistic, degrees
             of freedom, p-value, the method used, and the null hypothesis.
```

Author: Davies Liu <davies@databricks.com>

Closes #3091 from davies/his and squashes the following commits:

145d16c [Davies Liu] address comments
0ab0764 [Davies Liu] fix float
5097d54 [Davies Liu] add Hypothesis test Python API
[SPARK-4029][Streaming] Update streaming driver to reliably save and recover received block metadata on driver failures

As part of the initiative of preventing data loss on driver failure, this JIRA tracks the sub task of modifying the streaming driver to reliably save received block metadata, and recover them on driver restart.

This was solved by introducing a `ReceivedBlockTracker` that takes all the responsibility of managing the metadata of received blocks (i.e. `ReceivedBlockInfo`, and any actions on them (e.g, allocating blocks to batches, etc.). All actions to block info get written out to a write ahead log (using `WriteAheadLogManager`). On recovery, all the actions are replaying to recreate the pre-failure state of the `ReceivedBlockTracker`, which include the batch-to-block allocations and the unallocated blocks.

Furthermore, the `ReceiverInputDStream` was modified to create `WriteAheadLogBackedBlockRDD`s when file segment info is present in the `ReceivedBlockInfo`. After recovery of all the block info (through recovery `ReceivedBlockTracker`), the `WriteAheadLogBackedBlockRDD`s gets recreated with the recovered info, and jobs submitted. The data of the blocks gets pulled from the write ahead logs, thanks to the segment info present in the `ReceivedBlockInfo`.

This is still a WIP. Things that are missing here are.

- *End-to-end integration tests:* Unit tests that tests the driver recovery, by killing and restarting the streaming context, and verifying all the input data gets processed. This has been implemented but not included in this PR yet. A sneak peek of that DriverFailureSuite can be found in this PR (on my personal repo): https://github.com/tdas/spark/pull/25 I can either include it in this PR, or submit that as a separate PR after this gets in.

- *WAL cleanup:* Cleaning up the received data write ahead log, by calling `ReceivedBlockHandler.cleanupOldBlocks`. This is being worked on.

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

Closes #3026 from tdas/driver-ha-rbt and squashes the following commits:

a8009ed [Tathagata Das] Added comment
1d704bb [Tathagata Das] Enabled storing recovered WAL-backed blocks to BM
2ee2484 [Tathagata Das] More minor changes based on PR
47fc1e3 [Tathagata Das] Addressed PR comments.
9a7e3e4 [Tathagata Das] Refactored ReceivedBlockTracker API a bit to make things a little cleaner for users of the tracker.
af63655 [Tathagata Das] Minor changes.
fce2b21 [Tathagata Das] Removed commented lines
59496d3 [Tathagata Das] Changed class names, made allocation more explicit and added cleanup
19aec7d [Tathagata Das] Fixed casting bug.
f66d277 [Tathagata Das] Fix line lengths.
cda62ee [Tathagata Das] Added license
25611d6 [Tathagata Das] Minor changes before submitting PR
7ae0a7fb [Tathagata Das] Transferred changes from driver-ha-working branch
[SPARK-4197] [mllib] GradientBoosting API cleanup and examples in Scala, Java

### Summary

* Made it easier to construct default Strategy and BoostingStrategy and to set parameters using simple types.
* Added Scala and Java examples for GradientBoostedTrees
* small cleanups and fixes

### Details

GradientBoosting bug fixes (“bug” = bad default options)
* Force boostingStrategy.weakLearnerParams.algo = Regression
* Force boostingStrategy.weakLearnerParams.impurity = impurity.Variance
* Only persist data if not yet persisted (since it causes an error if persisted twice)

BoostingStrategy
* numEstimators: renamed to numIterations
* removed subsamplingRate (duplicated by Strategy)
* removed categoricalFeaturesInfo since it belongs with the weak learner params (since boosting can be oblivious to feature type)
* Changed algo to var (not val) and added BeanProperty, with overload taking String argument
* Added assertValid() method
* Updated defaultParams() method and eliminated defaultWeakLearnerParams() since that belongs in Strategy

Strategy (for DecisionTree)
* Changed algo to var (not val) and added BeanProperty, with overload taking String argument
* Added setCategoricalFeaturesInfo method taking Java Map.
* Cleaned up assertValid
* Changed val’s to def’s since parameters can now be changed.

CC: manishamde mengxr codedeft

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

Closes #3094 from jkbradley/gbt-api and squashes the following commits:

7a27e22 [Joseph K. Bradley] scalastyle fix
52013d5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into gbt-api
e9b8410 [Joseph K. Bradley] Summary of changes
[SPARK-4242] [Core] Add SASL to external shuffle service

Does three things: (1) Adds SASL to ExternalShuffleClient, (2) puts SecurityManager in BlockManager's constructor, and (3) adds unit test.

Author: Aaron Davidson <aaron@databricks.com>

Closes #3108 from aarondav/sasl-client and squashes the following commits:

48b622d [Aaron Davidson] Screw it, let's just get LimitedInputStream
3543b70 [Aaron Davidson] Back out of pom change due to unknown test issue?
b58518a [Aaron Davidson] ByteStreams.limit() not available :(
cbe451a [Aaron Davidson] Address comments
2bf2908 [Aaron Davidson] [SPARK-4242] [Core] Add SASL to external shuffle service
[SPARK-3984] [SPARK-3983] Fix incorrect scheduler delay and display task deserialization time in UI

This commit fixes the scheduler delay in the UI (which previously
included things that are not scheduler delay, like time to
deserialize the task and serialize the result), and also
adds information about time to deserialize tasks to the optional
additional metrics.  Time to deserialize the task can be large relative
to task time for short jobs, and understanding when it is high can help
developers realize that they should try to reduce closure size (e.g, by including
less data in the task description).

cc shivaram etrain

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #2832 from kayousterhout/SPARK-3983 and squashes the following commits:

0c1398e [Kay Ousterhout] Fixed ordering
531575d [Kay Ousterhout] Removed executor launch time
1f13afe [Kay Ousterhout] Minor spacing fixes
335be4b [Kay Ousterhout] Made metrics hideable
5bc3cba [Kay Ousterhout] [SPARK-3984] [SPARK-3983] Improve UI task metrics.
SPARK-4222 [CORE] use readFully in FixedLengthBinaryRecordReader

replaces the existing read() call with readFully().

Author: industrial-sloth <industrial-sloth@users.noreply.github.com>

Closes #3093 from industrial-sloth/branch-1.2-fixedLenRecRdr and squashes the following commits:

a245c8a [industrial-sloth] use readFully in FixedLengthBinaryRecordReader

(cherry picked from commit 6844e7a8219ac78790a422ffd5054924e7d2bea1)
Signed-off-by: Matei Zaharia <matei@databricks.com>
[SPARK-3797] Run external shuffle service in Yarn NM

This creates a new module `network/yarn` that depends on `network/shuffle` recently created in #3001. This PR introduces a custom Yarn auxiliary service that runs the external shuffle service. As of the changes here this shuffle service is required for using dynamic allocation with Spark.

This is still WIP mainly because it doesn't handle security yet. I have tested this on a stable Yarn cluster.

Author: Andrew Or <andrew@databricks.com>

Closes #3082 from andrewor14/yarn-shuffle-service and squashes the following commits:

ef3ddae [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service
0ee67a2 [Andrew Or] Minor wording suggestions
1c66046 [Andrew Or] Remove unused provided dependencies
0eb6233 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service
6489db5 [Andrew Or] Try catch at the right places
7b71d8f [Andrew Or] Add detailed java docs + reword a few comments
d1124e4 [Andrew Or] Add security to shuffle service (INCOMPLETE)
5f8a96f [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service
9b6e058 [Andrew Or] Address various feedback
f48b20c [Andrew Or] Fix tests again
f39daa6 [Andrew Or] Do not make network-yarn an assembly module
761f58a [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service
15a5b37 [Andrew Or] Fix build for Hadoop 1.x
baff916 [Andrew Or] Fix tests
5bf9b7e [Andrew Or] Address a few minor comments
5b419b8 [Andrew Or] Add missing license header
804e7ff [Andrew Or] Include the Yarn shuffle service jar in the distribution
cd076a4 [Andrew Or] Require external shuffle service for dynamic allocation
ea764e0 [Andrew Or] Connect to Yarn shuffle service only if it's enabled
1bf5109 [Andrew Or] Use the shuffle service port specified through hadoop config
b4b1f0c [Andrew Or] 4 tabs -> 2 tabs
43dcb96 [Andrew Or] First cut integration of shuffle service with Yarn aux service
b54a0c4 [Andrew Or] Initial skeleton for Yarn shuffle service
SPARK-4040. Update documentation to exemplify use of local (n) value, fo...

This is a minor docs update which helps to clarify the way local[n] is used for streaming apps.

Author: jay@apache.org <jayunit100>

Closes #2964 from jayunit100/SPARK-4040 and squashes the following commits:

35b5a5e [jay@apache.org] SPARK-4040: Update documentation to exemplify use of local (n) value.
Merge branch 'master' of github.com:apache/spark into binary