[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
Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service
Add security to shuffle service (INCOMPLETE)

This commit adds logic that allows the shuffle server to use
SASL to authenticate shuffle requests. This is currently not
working yet for two reasons:

(1) The ExternalShuffleClient doesn't actually have the
authentication logic yet. This will be implemented shortly
in a separate PR.

(2) All supported Yarn versions use a different version of
guava that does not have the base encoding util functions
that the SASL server uses. This will also be fixed in a
separate PR.