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.
SPARK-2766:  ScalaReflectionSuite  throw an llegalArgumentException in JDK 6

Author: GuoQiang Li <witgo@qq.com>

Closes #1683 from witgo/SPARK-2766 and squashes the following commits:

d0db00c [GuoQiang Li] ScalaReflectionSuite  throw an llegalArgumentException in JDK 6
[SPARK-2777][MLLIB] change ALS factors storage level to MEMORY_AND_DISK

Now the factors are persisted in memory only. If they get kicked off by later jobs, we might have to start the computation from very beginning. A better solution is changing the storage level to `MEMORY_AND_DISK`.

srowen

Author: Xiangrui Meng <meng@databricks.com>

Closes #1700 from mengxr/als-level and squashes the following commits:

c103d76 [Xiangrui Meng] change ALS factors storage level to MEMORY_AND_DISK
[SPARK-2782][mllib] Bug fix for getRanks in SpearmanCorrelation

getRanks computes the wrong rank when numPartition >= size in the input RDDs before this patch. added units to address this bug.

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

Closes #1710 from dorx/correlationBug and squashes the following commits:

733def4 [Doris Xin] bugs and reviewer comments.
31db920 [Doris Xin] revert unnecessary change
043ff83 [Doris Xin] bug fix for spearman corner case
[SPARK-2702][Core] Upgrade Tachyon dependency to 0.5.0

Author: Haoyuan Li <haoyuan@cs.berkeley.edu>

Closes #1651 from haoyuan/upgrade-tachyon and squashes the following commits:

6f3f98f [Haoyuan Li] upgrade tachyon to 0.5.0
SPARK-2632, SPARK-2576. Fixed by only importing what is necessary during class definition.

Without this patch, it imports everything available in the scope.

```scala

scala> val a = 10l
val a = 10l
a: Long = 10

scala> import a._
import a._
import a._

scala> case class A(a: Int) // show
case class A(a: Int) // show
class $read extends Serializable {
  def <init>() = {
    super.<init>;
    ()
  };
  class $iwC extends Serializable {
    def <init>() = {
      super.<init>;
      ()
    };
    class $iwC extends Serializable {
      def <init>() = {
        super.<init>;
        ()
      };
      import org.apache.spark.SparkContext._;
      class $iwC extends Serializable {
        def <init>() = {
          super.<init>;
          ()
        };
        val $VAL5 = $line5.$read.INSTANCE;
        import $VAL5.$iw.$iw.$iw.$iw.a;
        class $iwC extends Serializable {
          def <init>() = {
            super.<init>;
            ()
          };
          import a._;
          class $iwC extends Serializable {
            def <init>() = {
              super.<init>;
              ()
            };
            class $iwC extends Serializable {
              def <init>() = {
                super.<init>;
                ()
              };
              case class A extends scala.Product with scala.Serializable {
                <caseaccessor> <paramaccessor> val a: Int = _;
                def <init>(a: Int) = {
                  super.<init>;
                  ()
                }
              }
            };
            val $iw = new $iwC.<init>
          };
          val $iw = new $iwC.<init>
        };
        val $iw = new $iwC.<init>
      };
      val $iw = new $iwC.<init>
    };
    val $iw = new $iwC.<init>
  };
  val $iw = new $iwC.<init>
}
object $read extends scala.AnyRef {
  def <init>() = {
    super.<init>;
    ()
  };
  val INSTANCE = new $read.<init>
}
defined class A
```

With this patch, it just imports  only the necessary.

```scala

scala> val a = 10l
val a = 10l
a: Long = 10

scala> import a._
import a._
import a._

scala> case class A(a: Int) // show
case class A(a: Int) // show
class $read extends Serializable {
  def <init>() = {
    super.<init>;
    ()
  };
  class $iwC extends Serializable {
    def <init>() = {
      super.<init>;
      ()
    };
    class $iwC extends Serializable {
      def <init>() = {
        super.<init>;
        ()
      };
      case class A extends scala.Product with scala.Serializable {
        <caseaccessor> <paramaccessor> val a: Int = _;
        def <init>(a: Int) = {
          super.<init>;
          ()
        }
      }
    };
    val $iw = new $iwC.<init>
  };
  val $iw = new $iwC.<init>
}
object $read extends scala.AnyRef {
  def <init>() = {
    super.<init>;
    ()
  };
  val INSTANCE = new $read.<init>
}
defined class A

scala>

```

This patch also adds a `:fallback` mode on being enabled it will restore the spark-shell's 1.0.0 behaviour.

Author: Prashant Sharma <scrapcodes@gmail.com>
Author: Yin Huai <huai@cse.ohio-state.edu>
Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1635 from ScrapCodes/repl-fix-necessary-imports and squashes the following commits:

b1968d2 [Prashant Sharma] Added toschemaRDD to test case.
0b712bb [Yin Huai] Add a REPL test to test importing a method.
02ad8ff [Yin Huai] Add a REPL test for importing SQLContext.createSchemaRDD.
ed6d0c7 [Prashant Sharma] Added a fallback mode, incase users run into issues while using repl.
b63d3b2 [Prashant Sharma] SPARK-2632, SPARK-2576. Fixed by only importing what is necessary during class definition.
SPARK-2738. Remove redundant imports in BlockManagerSuite

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1642 from sryza/sandy-spark-2738 and squashes the following commits:

a923e4e [Sandy Ryza] SPARK-2738. Remove redundant imports in BlockManagerSuite
[SPARK-2670] FetchFailedException should be thrown when local fetch has failed

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #1578 from sarutak/SPARK-2670 and squashes the following commits:

85c8938 [Kousuke Saruta] Removed useless results.put for fail fast
e8713cc [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
d353984 [Kousuke Saruta] Refined assertion messages in BlockFetcherIteratorSuite.scala
03bcb02 [Kousuke Saruta] Merge branch 'SPARK-2670' of github.com:sarutak/spark into SPARK-2670
5d05855 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
4fca130 [Kousuke Saruta] Added test cases for BasicBlockFetcherIterator
b7b8250 [Kousuke Saruta] Modified BasicBlockFetchIterator to fail fast when local fetch error has been occurred
a3a9be1 [Kousuke Saruta] Modified BlockFetcherIterator for SPARK-2670
460dc01 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
e310c0b [Kousuke Saruta] Modified BlockFetcherIterator to handle local fetch failure as fatch fail
SPARK-983. Support external sorting in sortByKey()

This patch simply uses the ExternalSorter class from sort-based shuffle.

Closes #931 and Closes #1090

Author: Matei Zaharia <matei@databricks.com>

Closes #1677 from mateiz/spark-983 and squashes the following commits:

96b3fda [Matei Zaharia] SPARK-983. Support external sorting in sortByKey()
SPARK-2134: Report metrics before application finishes

Author: Rahul Singhal <rahul.singhal@guavus.com>

Closes #1076 from rahulsinghaliitd/SPARK-2134 and squashes the following commits:

15f18b6 [Rahul Singhal] SPARK-2134: Report metrics before application finishes
[Spark 2557] fix LOCAL_N_REGEX in createTaskScheduler and make local-n and local-n-failures consistent

[SPARK-2557](https://issues.apache.org/jira/browse/SPARK-2557)

Author: Ye Xianjin <advancedxy@gmail.com>

Closes #1464 from advancedxy/SPARK-2557 and squashes the following commits:

d844d67 [Ye Xianjin] add local-*-n-failures, bad-local-n, bad-local-n-failures test case
3bbc668 [Ye Xianjin] fix LOCAL_N_REGEX regular expression and make local_n_failures accept * as all cores on the computer
[SPARK-2103][Streaming] Change to ClassTag for KafkaInputDStream and fix reflection issue

This PR updates previous Manifest for KafkaInputDStream's Decoder to ClassTag, also fix the problem addressed in [SPARK-2103](https://issues.apache.org/jira/browse/SPARK-2103).

Previous Java interface cannot actually get the type of Decoder, so when using this Manifest to reconstruct the decode object will meet reflection exception.

Also for other two Java interfaces, ClassTag[String] is useless because calling Scala API will get the right implicit ClassTag.

Current Kafka unit test cannot actually verify the interface. I've tested these interfaces in my local and distribute settings.

Author: jerryshao <saisai.shao@intel.com>

Closes #1508 from jerryshao/SPARK-2103 and squashes the following commits:

e90c37b [jerryshao] Add Mima excludes
7529810 [jerryshao] Change Manifest to ClassTag for KafkaInputDStream's Decoder and fix Decoder construct issue when using Java API
SPARK-2768 [MLLIB] Add product, user recommend method to MatrixFactorizationModel

Right now, `MatrixFactorizationModel` can only predict a score for one or more `(user,product)` tuples. As a comment in the file notes, it would be more useful to expose a recommend method, that computes top N scoring products for a user (or vice versa – users for a product).

(This also corrects some long lines in the Java ALS test suite.)

As you can see, it's a little messy to access the class from Java. Should there be a Java-friendly wrapper for it? with a pointer about where that should go, I could add that.

Author: Sean Owen <srowen@gmail.com>

Closes #1687 from srowen/SPARK-2768 and squashes the following commits:

b349675 [Sean Owen] Additional review changes
c9edb04 [Sean Owen] Updates from code review
7bc35f9 [Sean Owen] Add recommend methods to MatrixFactorizationModel