[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