Part of [SPARK-2456] Removed some HashMaps from DAGScheduler by storing information in Stage.

This is part of the scheduler cleanup/refactoring effort to make the scheduler code easier to maintain.

@kayousterhout @markhamstra please take a look ...

Author: Reynold Xin <rxin@apache.org>

Closes #1561 from rxin/dagSchedulerHashMaps and squashes the following commits:

1c44e15 [Reynold Xin] Clear pending tasks in submitMissingTasks.
620a0d1 [Reynold Xin] Use filterKeys.
5b54404 [Reynold Xin] Code review feedback.
c1e9a1c [Reynold Xin] Removed some HashMaps from DAGScheduler by storing information in Stage.
[SPARK-2659][SQL] Fix division semantics for hive

Author: Michael Armbrust <michael@databricks.com>

Closes #1557 from marmbrus/fixDivision and squashes the following commits:

b85077f [Michael Armbrust] Fix unit tests.
af98f29 [Michael Armbrust] Change DIV to long type
0c29ae8 [Michael Armbrust] Fix division semantics for hive
[SPARK-1458] [PySpark] Expose sc.version in Java and PySpark

Author: Josh Rosen <joshrosen@apache.org>

Closes #1596 from JoshRosen/spark-1458 and squashes the following commits:

fdbb0bf [Josh Rosen] Add SparkContext.version to Python & Java [SPARK-1458]
[SPARK-2696] Reduce default value of spark.serializer.objectStreamReset

The current default value of spark.serializer.objectStreamReset is 10,000.
When trying to re-partition (e.g., to 64 partitions) a large file (e.g., 500MB), containing 1MB records, the serializer will cache 10000 x 1MB x 64 ~= 640 GB which will cause out of memory errors.

This patch sets the default value to a more reasonable default value (100).

Author: Hossein <hossein@databricks.com>

Closes #1595 from falaki/objectStreamReset and squashes the following commits:

650a935 [Hossein] Updated documentation
1aa0df8 [Hossein] Reduce default value of spark.serializer.objectStreamReset
[SPARK-2652] [PySpark] Turning some default configs for PySpark

Add several default configs for PySpark, related to serialization in JVM.

spark.serializer = org.apache.spark.serializer.KryoSerializer
spark.serializer.objectStreamReset = 100
spark.rdd.compress = True

This will help to reduce the memory usage during RDD.partitionBy()

Author: Davies Liu <davies.liu@gmail.com>

Closes #1568 from davies/conf and squashes the following commits:

cd316f1 [Davies Liu] remove duplicated line
f71a355 [Davies Liu] rebase to master, add spark.rdd.compress = True
8f63f45 [Davies Liu] Merge branch 'master' into conf
8bc9f08 [Davies Liu] fix unittest
c04a83d [Davies Liu] some default configs for PySpark
[SPARK-2279] Added emptyRDD method to Java API

Added emptyRDD method to Java API with tests.

Author: bpaulin <bob@bobpaulin.com>

Closes #1597 from bobpaulin/SPARK-2279 and squashes the following commits:

5ad57c2 [bpaulin] [SPARK-2279] Added emptyRDD method to Java API
[SPARK-2704] Name threads in ConnectionManager and mark them as daemon.

handleMessageExecutor, handleReadWriteExecutor, and handleConnectExecutor are not marked as daemon and not named. I think there exists some condition in which Spark programs won't terminate because of this.

Stack dump attached in https://issues.apache.org/jira/browse/SPARK-2704

Author: Reynold Xin <rxin@apache.org>

Closes #1604 from rxin/daemon and squashes the following commits:

98d6a6c [Reynold Xin] [SPARK-2704] Name threads in ConnectionManager and mark them as daemon.
[SPARK-2601] [PySpark] Fix Py4J error when transforming pickleFiles

Similar to SPARK-1034, the problem was that Py4J didn’t cope well with the fake ClassTags used in the Java API.  It doesn’t look like there’s any reason why PythonRDD needs to take a ClassTag, since it just ignores the type of the previous RDD, so I removed the type parameter and we no longer pass ClassTags from Python.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1605 from JoshRosen/spark-2601 and squashes the following commits:

b68e118 [Josh Rosen] Fix Py4J error when transforming pickleFiles [SPARK-2601]
SPARK-2680: Lower spark.shuffle.memoryFraction to 0.2 by default

Author: Matei Zaharia <matei@databricks.com>

Closes #1593 from mateiz/spark-2680 and squashes the following commits:

3c949c4 [Matei Zaharia] Lower spark.shuffle.memoryFraction to 0.2 by default
[SPARK-2361][MLLIB] Use broadcast instead of serializing data directly into task closure

We saw task serialization problems with large feature dimension, which could be avoid if we don't serialize data directly into task but use broadcast variables. This PR uses broadcast in both training and prediction and adds tests to make sure the task size is small.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1427 from mengxr/broadcast-new and squashes the following commits:

b9a1228 [Xiangrui Meng] style update
b97c184 [Xiangrui Meng] minimal change to LBFGS
9ebadcc [Xiangrui Meng] add task size test to RowMatrix
9427bf0 [Xiangrui Meng] add task size tests to linear methods
e0a5cf2 [Xiangrui Meng] add task size test to GD
28a8411 [Xiangrui Meng] add test for NaiveBayes
380778c [Xiangrui Meng] update KMeans test
bccab92 [Xiangrui Meng] add task size test to LBFGS
02103ba [Xiangrui Meng] remove print
e73d68e [Xiangrui Meng] update tests for k-means
174cb15 [Xiangrui Meng] use local-cluster for test with a small akka.frameSize
1928a5a [Xiangrui Meng] add test for KMeans task size
e00c2da [Xiangrui Meng] use broadcast in GD, KMeans
010d076 [Xiangrui Meng] modify NaiveBayesModel and GLM to use broadcast