[SPARK-3011][SQL] _temporary directory should be filtered out by sqlContext.parquetFile

Author: Chia-Yung Su <chiayung@appier.com>

Closes #1924 from joesu/bugfix-spark3011 and squashes the following commits:

c7e44f2 [Chia-Yung Su] match syntax
f8fc32a [Chia-Yung Su] filter out tmp dir

(cherry picked from commit 078f3fbda860e2f5de34153c55dfc3fecb4256e9)
Signed-off-by: Michael Armbrust <michael@databricks.com>
[SPARK-2927][SQL] Add a conf to configure if we always read Binary columns stored in Parquet as String columns

This PR adds a new conf flag `spark.sql.parquet.binaryAsString`. When it is `true`, if there is no parquet metadata file available to provide the schema of the data, we will always treat binary fields stored in parquet as string fields. This conf is used to provide a way to read string fields generated without UTF8 decoration.

JIRA: https://issues.apache.org/jira/browse/SPARK-2927

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

Closes #1855 from yhuai/parquetBinaryAsString and squashes the following commits:

689ffa9 [Yin Huai] Add missing "=".
80827de [Yin Huai] Unit test.
1765ca4 [Yin Huai] Use .toBoolean.
9d3f199 [Yin Huai] Merge remote-tracking branch 'upstream/master' into parquetBinaryAsString
5d436a1 [Yin Huai] The initial support of adding a conf to treat binary columns stored in Parquet as string columns.

(cherry picked from commit add75d4831fdc35712bf8b737574ea0bc677c37c)
Signed-off-by: Michael Armbrust <michael@databricks.com>
[SQL] Python JsonRDD UTF8 Encoding Fix

Only encode unicode objects to UTF-8, and not strings

Author: Ahir Reddy <ahirreddy@gmail.com>

Closes #1914 from ahirreddy/json-rdd-unicode-fix1 and squashes the following commits:

ca4e9ba [Ahir Reddy] Encoding Fix

(cherry picked from commit fde692b361773110c262abe219e7c8128bd76419)
Signed-off-by: Michael Armbrust <michael@databricks.com>
[SPARK-2925] [sql]fix spark-sql and start-thriftserver shell bugs when set --driver-java-options

https://issues.apache.org/jira/browse/SPARK-2925

Run cmd like this will get the error
bin/spark-sql --driver-java-options '-Xdebug -Xnoagent -Xrunjdwp:transport=dt_socket,address=8788,server=y,suspend=y'

Error: Unrecognized option '-Xnoagent'.
Run with --help for usage help or --verbose for debug output

Author: wangfei <wangfei_hello@126.com>
Author: wangfei <wangfei1@huawei.com>

Closes #1851 from scwf/patch-2 and squashes the following commits:

516554d [wangfei] quote variables to fix this issue
8bd40f2 [wangfei] quote variables to fix this problem
e6d79e3 [wangfei] fix start-thriftserver bug when set driver-java-options
948395d [wangfei] fix spark-sql error when set --driver-java-options

(cherry picked from commit 267fdffe2743bc2dc706c8ac8af0ae33a358a5d3)
Signed-off-by: Michael Armbrust <michael@databricks.com>
Minor cleanup of metrics.Source

- Added override.
- Marked some variables as private.

Author: Reynold Xin <rxin@apache.org>

Closes #1943 from rxin/metricsSource and squashes the following commits:

fbfa943 [Reynold Xin] Minor cleanup of metrics.Source. - Added override. - Marked some variables as private.

(cherry picked from commit eaeb0f76fa0f103c7db0f3975cb8562715410973)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-2979][MLlib] Improve the convergence rate by minimizing the condition number

In theory, the scale of your inputs are irrelevant to logistic regression.
You can "theoretically" multiply X1 by 1E6 and the estimate for β1 will
adjust accordingly. It will be 1E-6 times smaller than the original β1, due
to the invariance property of MLEs.

However, during the optimization process, the convergence (rate)
depends on the condition number of the training dataset. Scaling
the variables often reduces this condition number, thus improving
the convergence rate.

Without reducing the condition number, some training datasets
mixing the columns with different scales may not be able to converge.

GLMNET and LIBSVM packages perform the scaling to reduce
the condition number, and return the weights in the original scale.
See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf

Here, if useFeatureScaling is enabled, we will standardize the training
features by dividing the variance of each column (without subtracting
the mean to densify the sparse vector), and train the model in the
scaled space. Then we transform the coefficients from the scaled space
to the original scale as GLMNET and LIBSVM do.

Currently, it's only enabled in LogisticRegressionWithLBFGS.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #1897 from dbtsai/dbtsai-feature-scaling and squashes the following commits:

f19fc02 [DB Tsai] Added more comments
1d85289 [DB Tsai] Improve the convergence rate by minimize the condition number in LOR with LBFGS

(cherry picked from commit 96221067572e5955af1a7710b0cca33a73db4bd5)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Revert  [SPARK-3011][SQL] _temporary directory should be filtered out by sqlContext.parquetFile

Reverts #1924 due to build failures with hadoop 0.23.

Author: Michael Armbrust <michael@databricks.com>

Closes #1949 from marmbrus/revert1924 and squashes the following commits:

6bff940 [Michael Armbrust] Revert "[SPARK-3011][SQL] _temporary directory should be filtered out by sqlContext.parquetFile"

(cherry picked from commit a7f8a4f5ee757450ce8d4028021441435081cf53)
Signed-off-by: Michael Armbrust <michael@databricks.com>
SPARK-3009: Reverted readObject method in ApplicationInfo so that Applic...

...ationInfo is initialized properly after deserialization

Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>

Closes #1947 from jacek-lewandowski/master and squashes the following commits:

713b2f1 [Jacek Lewandowski] SPARK-3009: Reverted readObject method in ApplicationInfo so that ApplicationInfo is initialized properly after deserialization

(cherry picked from commit a75bc7a21db07258913d038bf604c0a3c1e55b46)
Signed-off-by: Andrew Or <andrewor14@gmail.com>