[SPARK-2737] Add retag() method for changing RDDs' ClassTags.

The Java API's use of fake ClassTags doesn't seem to cause any problems for Java users, but it can lead to issues when passing JavaRDDs' underlying RDDs to Scala code (e.g. in the MLlib Java API wrapper code). If we call collect() on a Scala RDD with an incorrect ClassTag, this causes ClassCastExceptions when we try to allocate an array of the wrong type (for example, see SPARK-2197).

There are a few possible fixes here. An API-breaking fix would be to completely remove the fake ClassTags and require Java API users to pass java.lang.Class instances to all parallelize() calls and add returnClass fields to all Function implementations. This would be extremely verbose.

Instead, this patch adds internal APIs to "repair" a Scala RDD with an incorrect ClassTag by wrapping it and overriding its ClassTag. This should be okay for cases where the Scala code that calls collect() knows what type of array should be allocated, which is the case in the MLlib wrappers.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1639 from JoshRosen/SPARK-2737 and squashes the following commits:

572b4c8 [Josh Rosen] Replace newRDD[T] with mapPartitions().
469d941 [Josh Rosen] Preserve partitioner in retag().
af78816 [Josh Rosen] Allow retag() to get classTag implicitly.
d1d54e6 [Josh Rosen] [SPARK-2737] Add retag() method for changing RDDs' ClassTags.
[SPARK-2497] Included checks for module symbols too.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1463 from ScrapCodes/SPARK-2497/mima-exclude-all and squashes the following commits:

72077b1 [Prashant Sharma] Check separately for module symbols.
cd96192 [Prashant Sharma] SPARK-2497 Produce "member excludes" irrespective of the fact that class itself is excluded or not.
automatically set master according to `spark.master` in `spark-defaults....

automatically set master according to `spark.master` in `spark-defaults.conf`

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #1644 from CrazyJvm/standalone-guide and squashes the following commits:

bb12b95 [CrazyJvm] automatically set master according to `spark.master` in `spark-defaults.conf`
[SPARK-2762] SparkILoop leaks memory in multi-repl configurations

This pull request is a small refactor so that a partial function (hence a closure) is not created. Instead, a regular function is used. The behavior of the code is not changed.

Author: Timothy Hunter <timhunter@databricks.com>

Closes #1674 from thunterdb/closure_issue and squashes the following commits:

e1e664d [Timothy Hunter] simplify closure
[SPARK-2743][SQL] Resolve original attributes in ParquetTableScan

Author: Michael Armbrust <michael@databricks.com>

Closes #1647 from marmbrus/parquetCase and squashes the following commits:

a1799b7 [Michael Armbrust] move comment
2a2a68b [Michael Armbrust] Merge remote-tracking branch 'apache/master' into parquetCase
bb35d5b [Michael Armbrust] Fix test case that produced an invalid plan.
e6870bf [Michael Armbrust] Better error message.
539a2e1 [Michael Armbrust] Resolve original attributes in ParquetTableScan
[SPARK-2397][SQL] Deprecate LocalHiveContext

LocalHiveContext is redundant with HiveContext.  The only difference is it creates `./metastore` instead of `./metastore_db`.

Author: Michael Armbrust <michael@databricks.com>

Closes #1641 from marmbrus/localHiveContext and squashes the following commits:

e5ec497 [Michael Armbrust] Add deprecation version
626e056 [Michael Armbrust] Don't remove from imports yet
905cc5f [Michael Armbrust] Merge remote-tracking branch 'apache/master' into localHiveContext
1c2727e [Michael Armbrust] Deprecate LocalHiveContext
SPARK-2028: Expose mapPartitionsWithInputSplit in HadoopRDD

This allows users to gain access to the InputSplit which backs each partition.

An alternative solution would have been to have a .withInputSplit() method which returns a new RDD[(InputSplit, (K, V))], but this is confusing because you could not cache this RDD or shuffle it, as InputSplit is not inherently serializable.

Author: Aaron Davidson <aaron@databricks.com>

Closes #973 from aarondav/hadoop and squashes the following commits:

9c9112b [Aaron Davidson] Add JavaAPISuite test
9942cd7 [Aaron Davidson] Add Java API
1284a3a [Aaron Davidson] SPARK-2028: Expose mapPartitionsWithInputSplit in HadoopRDD
SPARK-2664. Deal with `--conf` options in spark-submit that relate to fl...

...ags

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1665 from sryza/sandy-spark-2664 and squashes the following commits:

0518c63 [Sandy Ryza] SPARK-2664. Deal with `--conf` options in spark-submit that relate to flags
SPARK-2749 [BUILD] Part 2. Fix a follow-on scalastyle error

The test compile error is fixed, but the build still fails because of one scalastyle error.

https://amplab.cs.berkeley.edu/jenkins/view/Spark/job/Spark-Master-Maven-pre-YARN/lastFailedBuild/hadoop.version=1.0.4,label=centos/console

Author: Sean Owen <srowen@gmail.com>

Closes #1690 from srowen/SPARK-2749 and squashes the following commits:

1c9e7a6 [Sean Owen] Also: fix scalastyle error by wrapping a long line
SPARK-2646. log4j initialization not quite compatible with log4j 2.x

The logging code that handles log4j initialization leads to an stack overflow error when used with log4j 2.x, which has just been released. This occurs even a downstream project has correctly adjusted SLF4J bindings, and that is the right thing to do for log4j 2.x, since it is effectively a separate project from 1.x.

Here is the relevant bit of Logging.scala:

```
  private def initializeLogging() {
    // If Log4j is being used, but is not initialized, load a default properties file
    val binder = StaticLoggerBinder.getSingleton
    val usingLog4j = binder.getLoggerFactoryClassStr.endsWith("Log4jLoggerFactory")
    val log4jInitialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements
    if (!log4jInitialized && usingLog4j) {
      val defaultLogProps = "org/apache/spark/log4j-defaults.properties"
      Option(Utils.getSparkClassLoader.getResource(defaultLogProps)) match {
        case Some(url) =>
          PropertyConfigurator.configure(url)
          log.info(s"Using Spark's default log4j profile: $defaultLogProps")
        case None =>
          System.err.println(s"Spark was unable to load $defaultLogProps")
      }
    }
    Logging.initialized = true

    // Force a call into slf4j to initialize it. Avoids this happening from mutliple threads
    // and triggering this: http://mailman.qos.ch/pipermail/slf4j-dev/2010-April/002956.html
    log
  }
```

The first minor issue is that there is a call to a logger inside this method, which is initializing logging. In this situation, it ends up causing the initialization to be called recursively until the stack overflow. It would be slightly tidier to log this only after Logging.initialized = true. Or not at all. But it's not the root problem, or else, it would not work at all now.

The calls to log4j classes here always reference log4j 1.2 no matter what. For example, there is not getAllAppenders in log4j 2.x. That's fine. Really, "usingLog4j" means "using log4j 1.2" and "log4jInitialized" means "log4j 1.2 is initialized".

usingLog4j should be false for log4j 2.x, because the initialization only matters for log4j 1.2. But, it's true, and that's the real issue. And log4jInitialized is always false, since calls to the log4j 1.2 API are stubs and no-ops in this setup, where the caller has swapped in log4j 2.x. Hence the loop.

This is fixed, I believe, if "usingLog4j" can be false for log4j 2.x. The SLF4J static binding class has the same name for both versions, unfortunately, which causes the issue. However they're in different packages. For example, if the test included "... and begins with org.slf4j", it should work, as the SLF4J binding for log4j 2.x is provided by log4j 2.x at the moment, and is in package org.apache.logging.slf4j.

Of course, I assume that SLF4J will eventually offer its own binding. I hope to goodness they at least name the binding class differently, or else this will again not work. But then some other check can probably be made.

Author: Sean Owen <srowen@gmail.com>

Closes #1547 from srowen/SPARK-2646 and squashes the following commits:

92a9898 [Sean Owen] System.out -> System.err
94be4c7 [Sean Owen] Add back log message as System.out, with informational comment
a7f8876 [Sean Owen] Updates from review
6f3c1d3 [Sean Owen] Remove log statement in logging initialization, and distinguish log4j 1.2 from 2.0, to avoid stack overflow in initialization
[SPARK-2511][MLLIB] add HashingTF and IDF

This is roughly the TF-IDF implementation used in the Databricks Cloud Demo: http://databricks.com/cloud/ .

Both `HashingTF` and `IDF` are implemented as transformers, similar to scikit-learn.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1671 from mengxr/tfidf and squashes the following commits:

7d65888 [Xiangrui Meng] use JavaConverters._
5fe9ec4 [Xiangrui Meng] fix unit test
6e214ec [Xiangrui Meng] add apache header
cfd9aed [Xiangrui Meng] add Java-friendly methods move classes to mllib.feature
3814440 [Xiangrui Meng] add HashingTF and IDF
[SPARK-2523] [SQL] Hadoop table scan bug fixing (fix failing Jenkins maven test)

This PR tries to resolve the broken Jenkins maven test issue introduced by #1439. Now, we create a single query test to run both the setup work and the test query.

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

Closes #1669 from yhuai/SPARK-2523-fixTest and squashes the following commits:

358af1a [Yin Huai] Make partition_based_table_scan_with_different_serde run atomically.
Improvements to merge_spark_pr.py

This commit fixes a couple of issues in the merge_spark_pr.py developer script:

- Allow recovery from failed cherry-picks.
- Fix detection of pull requests that have already been merged.

Both of these fixes are useful when backporting changes.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1668 from JoshRosen/pr-script-improvements and squashes the following commits:

ff4f33a [Josh Rosen] Default SPARK_HOME to cwd(); detect missing JIRA credentials.
ed5bc57 [Josh Rosen] Improvements for backporting using merge_spark_pr:
Docs: monitoring, streaming programming guide

Fix several awkward wordings and grammatical issues in the following
documents:

*   docs/monitoring.md

*   docs/streaming-programming-guide.md

Author: kballou <kballou@devnulllabs.io>

Closes #1662 from kennyballou/grammar_fixes and squashes the following commits:

e1b8ad6 [kballou] Docs: monitoring, streaming programming guide
SPARK-2740: allow user to specify ascending and numPartitions for sortBy...

It should be more convenient if user can specify ascending and numPartitions when calling sortByKey.

Author: Rui Li <rui.li@intel.com>

Closes #1645 from lirui-intel/spark-2740 and squashes the following commits:

fb5d52e [Rui Li] SPARK-2740: allow user to specify ascending and numPartitions for sortByKey
SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark

Prior to this change, every PySpark task completion opened a new socket to the accumulator server, passed its updates through, and then quit. I'm not entirely sure why PySpark always sends accumulator updates, but regardless this causes a very rapid buildup of ephemeral TCP connections that remain in the TCP_WAIT state for around a minute before being cleaned up.

Rather than trying to allow these sockets to be cleaned up faster, this patch simply reuses the connection between tasks completions (since they're fed updates in a single-threaded manner by the DAGScheduler anyway).

The only tricky part here was making sure that the AccumulatorServer was able to shutdown in a timely manner (i.e., stop polling for new data), and this was accomplished via minor feats of magic.

I have confirmed that this patch eliminates the buildup of ephemeral sockets due to the accumulator updates. However, I did note that there were still significant sockets being created against the PySpark daemon port, but my machine was not able to create enough sockets fast enough to fail. This may not be the last time we've seen this issue, though.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1503 from aarondav/accum and squashes the following commits:

b3e12f7 [Aaron Davidson] SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark
[SPARK-2531 & SPARK-2436] [SQL] Optimize the BuildSide when planning BroadcastNestedLoopJoin.

This PR resolves the following two tickets:

- [SPARK-2531](https://issues.apache.org/jira/browse/SPARK-2531): BNLJ currently assumes the build side is the right relation. This patch refactors some of its logic to take into account a BuildSide properly.
- [SPARK-2436](https://issues.apache.org/jira/browse/SPARK-2436): building on top of the above, we simply use the physical size statistics (if available) of both relations, and make the smaller relation the build side in the planner.

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1448 from concretevitamin/bnlj-buildSide and squashes the following commits:

1780351 [Zongheng Yang] Use size estimation to decide optimal build side of BNLJ.
68e6c5b [Zongheng Yang] Consolidate two adjacent pattern matchings.
96d312a [Zongheng Yang] Use a while loop instead of collection methods chaining.
4bc525e [Zongheng Yang] Make BroadcastNestedLoopJoin take a BuildSide.
[SPARK-2724] Python version of RandomRDDGenerators

RandomRDDGenerators but without support for randomRDD and randomVectorRDD, which take in arbitrary DistributionGenerator.

`randomRDD.py` is named to avoid collision with the built-in Python `random` package.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1628 from dorx/pythonRDD and squashes the following commits:

55c6de8 [Doris Xin] review comments. all python units passed.
f831d9b [Doris Xin] moved default args logic into PythonMLLibAPI
2d73917 [Doris Xin] fix for linalg.py
8663e6a [Doris Xin] reverting back to a single python file for random
f47c481 [Doris Xin] docs update
687aac0 [Doris Xin] add RandomRDDGenerators.py to run-tests
4338f40 [Doris Xin] renamed randomRDD to rand and import as random
29d205e [Doris Xin] created mllib.random package
bd2df13 [Doris Xin] typos
07ddff2 [Doris Xin] units passed.
23b2ecd [Doris Xin] WIP
[SPARK-2756] [mllib] Decision tree bug fixes

(1) Inconsistent aggregate (agg) indexing for unordered features.
(2) Fixed gain calculations for edge cases.
(3) One-off error in choosing thresholds for continuous features for small datasets.
(4) (not a bug) Changed meaning of tree depth by 1 to fit scikit-learn and rpart. (Depth 1 used to mean 1 leaf node; depth 0 now means 1 leaf node.)

Other updates, to help with tests:
* Updated DecisionTreeRunner to print more info.
* Added utility functions to DecisionTreeModel: toString, depth, numNodes
* Improved internal DecisionTree documentation

Bug fix details:

(1) Indexing was inconsistent for aggregate calculations for unordered features (in multiclass classification with categorical features, where the features had few enough values such that they could be considered unordered, i.e., isSpaceSufficientForAllCategoricalSplits=true).

* updateBinForUnorderedFeature indexed agg as (node, feature, featureValue, binIndex), where
** featureValue was from arr (so it was a feature value)
** binIndex was in [0,…, 2^(maxFeatureValue-1)-1)
* The rest of the code indexed agg as (node, feature, binIndex, label).
* Corrected this bug by changing updateBinForUnorderedFeature to use the second indexing pattern.

Unit tests in DecisionTreeSuite
* Updated a few tests to train a model and test its training accuracy, which catches the indexing bug from updateBinForUnorderedFeature() discussed above.
* Added new test (“stump with categorical variables for multiclass classification, with just enough bins”) to test bin extremes.

(2) Bug fix: calculateGainForSplit (for classification):
* It used to return dummy prediction values when either the right or left children had 0 weight.  These were incorrect for multiclass classification.  It has been corrected.

Updated impurities to allow for count = 0.  This was related to the above bug fix for calculateGainForSplit (for classification).

Small updates to documentation and coding style.

(3) Bug fix: Off-by-1 when finding thresholds for splits for continuous features.

* Exhibited bug in new test in DecisionTreeSuite: “stump with 1 continuous variable for binary classification, to check off-by-1 error”
* Description: When finding thresholds for possible splits for continuous features in DecisionTree.findSplitsBins, the thresholds were set according to individual training examples’ feature values.
* Fix: The threshold is set to be the average of 2 consecutive (sorted) examples’ feature values.  E.g.: If the old code set the threshold using example i, the new code sets the threshold using exam
* Note: In 4 DecisionTreeSuite tests with all labels identical, removed check of threshold since it is somewhat arbitrary.

CC: mengxr manishamde  Please let me know if I missed something!

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #1673 from jkbradley/decisiontree-bugfix and squashes the following commits:

2b20c61 [Joseph K. Bradley] Small doc and style updates
dab0b67 [Joseph K. Bradley] Added documentation for DecisionTree internals
8bb8aa0 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
978cfcf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
6eed482 [Joseph K. Bradley] In DecisionTree: Changed from using procedural syntax for functions returning Unit to explicitly writing Unit return type.
376dca2 [Joseph K. Bradley] Updated meaning of maxDepth by 1 to fit scikit-learn and rpart. * In code, replaced usages of maxDepth <-- maxDepth + 1 * In params, replace settings of maxDepth <-- maxDepth - 1
59750f8 [Joseph K. Bradley] * Updated Strategy to check numClassesForClassification only if algo=Classification. * Updates based on comments: ** DecisionTreeRunner *** Made dataFormat arg default to libsvm ** Small cleanups ** tree.Node: Made recursive helper methods private, and renamed them.
52e17c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
da50db7 [Joseph K. Bradley] Added one more test to DecisionTreeSuite: stump with 2 continuous variables for binary classification.  Caused problems in past, but fixed now.
8ea8750 [Joseph K. Bradley] Bug fix: Off-by-1 when finding thresholds for splits for continuous features.
2283df8 [Joseph K. Bradley] 2 bug fixes.
73fbea2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
5f920a1 [Joseph K. Bradley] Demonstration of bug before submitting fix: Updated DecisionTreeSuite so that 3 tests fail.  Will describe bug in next commit.
[SPARK-2779] [SQL] asInstanceOf[Map[...]] should use scala.collection.Map instead of scala.collection.immutable.Map

Since we let users create Rows. It makes sense to accept mutable Maps as values of MapType columns.

JIRA: https://issues.apache.org/jira/browse/SPARK-2779

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

Closes #1705 from yhuai/SPARK-2779 and squashes the following commits:

00d72fd [Yin Huai] Use scala.collection.Map.