[SPARK-1777] Prevent OOMs from single partitions

**Problem.** When caching, we currently unroll the entire RDD partition before making sure we have enough free memory. This is a common cause for OOMs especially when (1) the BlockManager has little free space left in memory, and (2) the partition is large.

**Solution.** We maintain a global memory pool of `M` bytes shared across all threads, similar to the way we currently manage memory for shuffle aggregation. Then, while we unroll each partition, periodically check if there is enough space to continue. If not, drop enough RDD blocks to ensure we have at least `M` bytes to work with, then try again. If we still don't have enough space to unroll the partition, give up and drop the block to disk directly if applicable.

**New configurations.**
- `spark.storage.bufferFraction` - the value of `M` as a fraction of the storage memory. (default: 0.2)
- `spark.storage.safetyFraction` - a margin of safety in case size estimation is slightly off. This is the equivalent of the existing `spark.shuffle.safetyFraction`. (default 0.9)

For more detail, see the [design document](https://issues.apache.org/jira/secure/attachment/12651793/spark-1777-design-doc.pdf). Tests pending for performance and memory usage patterns.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1165 from andrewor14/them-rdd-memories and squashes the following commits:

e77f451 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
c7c8832 [Andrew Or] Simplify logic + update a few comments
269d07b [Andrew Or] Very minor changes to tests
6645a8a [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
b7e165c [Andrew Or] Add new tests for unrolling blocks
f12916d [Andrew Or] Slightly clean up tests
71672a7 [Andrew Or] Update unrollSafely tests
369ad07 [Andrew Or] Correct ensureFreeSpace and requestMemory behavior
f4d035c [Andrew Or] Allow one thread to unroll multiple blocks
a66fbd2 [Andrew Or] Rename a few things + update comments
68730b3 [Andrew Or] Fix weird scalatest behavior
e40c60d [Andrew Or] Fix MIMA excludes
ff77aa1 [Andrew Or] Fix tests
1a43c06 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
b9a6eee [Andrew Or] Simplify locking behavior on unrollMemoryMap
ed6cda4 [Andrew Or] Formatting fix (super minor)
f9ff82e [Andrew Or] putValues -> putIterator + putArray
beb368f [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
8448c9b [Andrew Or] Fix tests
a49ba4d [Andrew Or] Do not expose unroll memory check period
69bc0a5 [Andrew Or] Always synchronize on putLock before unrollMemoryMap
3f5a083 [Andrew Or] Simplify signature of ensureFreeSpace
dce55c8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
8288228 [Andrew Or] Synchronize put and unroll properly
4f18a3d [Andrew Or] bufferFraction -> unrollFraction
28edfa3 [Andrew Or] Update a few comments / log messages
728323b [Andrew Or] Do not synchronize every 1000 elements
5ab2329 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
129c441 [Andrew Or] Fix bug: Use toArray rather than array
9a65245 [Andrew Or] Update a few comments + minor control flow changes
57f8d85 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
abeae4f [Andrew Or] Add comment clarifying the MEMORY_AND_DISK case
3dd96aa [Andrew Or] AppendOnlyBuffer -> Vector (+ a few small changes)
f920531 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
0871835 [Andrew Or] Add an effective storage level interface to BlockManager
64e7d4c [Andrew Or] Add/modify a few comments (minor)
8af2f35 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
4f4834e [Andrew Or] Use original storage level for blocks dropped to disk
ecc8c2d [Andrew Or] Fix binary incompatibility
24185ea [Andrew Or] Avoid dropping a block back to disk if reading from disk
2b7ee66 [Andrew Or] Fix bug in SizeTracking*
9b9a273 [Andrew Or] Fix tests
20eb3e5 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
649bdb3 [Andrew Or] Document spark.storage.bufferFraction
a10b0e7 [Andrew Or] Add initial memory request threshold + rename a few things
e9c3cb0 [Andrew Or] cacheMemoryMap -> unrollMemoryMap
198e374 [Andrew Or] Unfold -> unroll
0d50155 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
d9d02a8 [Andrew Or] Remove unused param in unfoldSafely
ec728d8 [Andrew Or] Add tests for safe unfolding of blocks
22b2209 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
078eb83 [Andrew Or] Add check for hasNext in PrimitiveVector.iterator
0871535 [Andrew Or] Fix tests in BlockManagerSuite
d68f31e [Andrew Or] Safely unfold blocks for all memory puts
5961f50 [Andrew Or] Fix tests
195abd7 [Andrew Or] Refactor: move unfold logic to MemoryStore
1e82d00 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
3ce413e [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
d5dd3b4 [Andrew Or] Free buffer memory in finally
ea02eec [Andrew Or] Fix tests
b8e1d9c [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
a8704c1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
e1b8b25 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
87aa75c [Andrew Or] Fix mima excludes again (typo)
11eb921 [Andrew Or] Clarify comment (minor)
50cae44 [Andrew Or] Remove now duplicate mima exclude
7de5ef9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
df47265 [Andrew Or] Fix binary incompatibility
6d05a81 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
f94f5af [Andrew Or] Update a few comments (minor)
776aec9 [Andrew Or] Prevent OOM if a single RDD partition is too large
bbd3eea [Andrew Or] Fix CacheManagerSuite to use Array
97ea499 [Andrew Or] Change BlockManager interface to use Arrays
c12f093 [Andrew Or] Add SizeTrackingAppendOnlyBuffer and tests
[SPARK-2514] [mllib] Random RDD generator

Utilities for generating random RDDs.

RandomRDD and RandomVectorRDD are created instead of using `sc.parallelize(range:Range)` because `Range` objects in Scala can only have `size <= Int.MaxValue`.

The object `RandomRDDGenerators` can be transformed into a generator class to reduce the number of auxiliary methods for optional arguments.

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

Closes #1520 from dorx/randomRDD and squashes the following commits:

01121ac [Doris Xin] reviewer comments
6bf27d8 [Doris Xin] Merge branch 'master' into randomRDD
a8ea92d [Doris Xin] Reviewer comments
063ea0b [Doris Xin] Merge branch 'master' into randomRDD
aec68eb [Doris Xin] newline
bc90234 [Doris Xin] units passed.
d56cacb [Doris Xin] impl with RandomRDD
92d6f1c [Doris Xin] solution for Cloneable
df5bcff [Doris Xin] Merge branch 'generator' into randomRDD
f46d928 [Doris Xin] WIP
49ed20d [Doris Xin] alternative poisson distribution generator
7cb0e40 [Doris Xin] fix for data inconsistency
8881444 [Doris Xin] RandomRDDGenerator: initial design
Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC server"

This reverts commit f6ff2a61d00d12481bfb211ae13d6992daacdcc2.
SPARK-2651: Add maven scalastyle plugin

Can be run as: "mvn scalastyle:check"

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

Closes #1550 from rahulsinghaliitd/SPARK-2651 and squashes the following commits:

53748dd [Rahul Singhal] SPARK-2651: Add maven scalastyle plugin
[SPARK-1550] [PySpark] Allow SparkContext creation after failed attempts

This addresses a PySpark issue where a failed attempt to construct SparkContext would prevent any future SparkContext creation.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1606 from JoshRosen/SPARK-1550 and squashes the following commits:

ec7fadc [Josh Rosen] [SPARK-1550] [PySpark] Allow SparkContext creation after failed attempts
WIP
[SPARK-2523] [SQL] Hadoop table scan bug fixing

In HiveTableScan.scala, ObjectInspector was created for all of the partition based records, which probably causes ClassCastException if the object inspector is not identical among table & partitions.

This is the follow up with:
https://github.com/apache/spark/pull/1408
https://github.com/apache/spark/pull/1390

I've run a micro benchmark in my local with 15000000 records totally, and got the result as below:

With This Patch  |  Partition-Based Table  |  Non-Partition-Based Table
------------ | ------------- | -------------
No  |  1927 ms  |  1885 ms
Yes  | 1541 ms  |  1524 ms

It showed this patch will also improve the performance.

PS:  the benchmark code is also attached. (thanks liancheng )
```
package org.apache.spark.sql.hive

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._

object HiveTableScanPrepare extends App {
  case class Record(key: String, value: String)

  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"$i", s"val_$i")))

  import hiveContext._

  hql("SHOW TABLES")
  hql("DROP TABLE if exists part_scan_test")
  hql("DROP TABLE if exists scan_test")
  hql("DROP TABLE if exists records")
  rdd.registerAsTable("records")

  hql("""CREATE TABLE part_scan_test (key STRING, value STRING) PARTITIONED BY (part1 string, part2 STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)
  hql("""CREATE TABLE scan_test (key STRING, value STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)

  for (part1 <- 2000 until 2001) {
    for (part2 <- 1 to 5) {
      hql(s"""from records
                 | insert into table part_scan_test PARTITION (part1='$part1', part2='2010-01-$part2')
                 | select key, value
               """.stripMargin)
      hql(s"""from records
                 | insert into table scan_test select key, value
               """.stripMargin)
    }
  }
}

object HiveTableScanTest extends App {
  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  import hiveContext._

  hql("SHOW TABLES")
  val part_scan_test = hql("select key, value from part_scan_test")
  val scan_test = hql("select key, value from scan_test")

  val r_part_scan_test = (0 to 5).map(i => benchmark(part_scan_test))
  val r_scan_test = (0 to 5).map(i => benchmark(scan_test))
  println("Scanning Partition-Based Table")
  r_part_scan_test.foreach(printResult)
  println("Scanning Non-Partition-Based Table")
  r_scan_test.foreach(printResult)

  def printResult(result: (Long, Long)) {
    println(s"Duration: ${result._1} ms Result: ${result._2}")
  }

  def benchmark(srdd: SchemaRDD) = {
    val begin = System.currentTimeMillis()
    val result = srdd.count()
    val end = System.currentTimeMillis()
    ((end - begin), result)
  }
}
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1439 from chenghao-intel/hadoop_table_scan and squashes the following commits:

888968f [Cheng Hao] Fix issues in code style
27540ba [Cheng Hao] Fix the TableScan Bug while partition serde differs
40a24a7 [Cheng Hao] Add Unit Test
[SPARK-2479][MLlib] Comparing floating-point numbers using relative error in UnitTests

Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors.

Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result.

That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored.

Based on discussion in the community, we have implemented two different APIs for relative tolerance, and absolute tolerance. It makes sense that test writers should know which one they need depending on their circumstances.

Developers also need to explicitly specify the eps, and there is no default value which will sometimes cause confusion.

When comparing against zero using relative tolerance, a exception will be raised to warn users that it's meaningless.

For relative tolerance, users can now write

    assert(23.1 ~== 23.52 relTol 0.02)
    assert(23.1 ~== 22.74 relTol 0.02)
    assert(23.1 ~= 23.52 relTol 0.02)
    assert(23.1 ~= 22.74 relTol 0.02)
    assert(!(23.1 !~= 23.52 relTol 0.02))
    assert(!(23.1 !~= 22.74 relTol 0.02))

    // This will throw exception with the following message.
    // "Did not expect 23.1 and 23.52 to be within 0.02 using relative tolerance."
    assert(23.1 !~== 23.52 relTol 0.02)

    // "Expected 23.1 and 22.34 to be within 0.02 using relative tolerance."
    assert(23.1 ~== 22.34 relTol 0.02)

For absolute error,

    assert(17.8 ~== 17.99 absTol 0.2)
    assert(17.8 ~== 17.61 absTol 0.2)
    assert(17.8 ~= 17.99 absTol 0.2)
    assert(17.8 ~= 17.61 absTol 0.2)
    assert(!(17.8 !~= 17.99 absTol 0.2))
    assert(!(17.8 !~= 17.61 absTol 0.2))

    // This will throw exception with the following message.
    // "Did not expect 17.8 and 17.99 to be within 0.2 using absolute error."
    assert(17.8 !~== 17.99 absTol 0.2)

    // "Expected 17.8 and 17.59 to be within 0.2 using absolute error."
    assert(17.8 ~== 17.59 absTol 0.2)

Authors:
  DB Tsai <dbtsaialpinenow.com>
  Marek Kolodziej <marekalpinenow.com>

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #1425 from dbtsai/SPARK-2479_comparing_floating_point and squashes the following commits:

8c7cbcc [DB Tsai] Alpine Data Labs
[SPARK-2410][SQL] Merging Hive Thrift/JDBC server (with Maven profile fix)

JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)

Another try for #1399 & #1600. Those two PR breaks Jenkins builds because we made a separate profile `hive-thriftserver` in sub-project `assembly`, but the `hive-thriftserver` module is defined outside the `hive-thriftserver` profile. Thus every time a pull request that doesn't touch SQL code will also execute test suites defined in `hive-thriftserver`, but tests fail because related .class files are not included in the assembly jar.

In the most recent commit, module `hive-thriftserver` is moved into its own profile to fix this problem. All previous commits are squashed for clarity.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1620 from liancheng/jdbc-with-maven-fix and squashes the following commits:

629988e [Cheng Lian] Moved hive-thriftserver module definition into its own profile
ec3c7a7 [Cheng Lian] Cherry picked the Hive Thrift server
Use commons-lang3 in SignalLogger rather than commons-lang

Spark only transitively depends on the latter, based on the Hadoop version.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1621 from aarondav/lang3 and squashes the following commits:

93c93bf [Aaron Davidson] Use commons-lang3 in SignalLogger rather than commons-lang
Excess judgment

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #1629 from watermen/bug-fix2 and squashes the following commits:

59b7237 [Yadong Qi] Update HiveQl.scala
[SPARK-2580] [PySpark] keep silent in worker if JVM close the socket

During rdd.take(n), JVM will close the socket if it had got enough data, the Python worker should keep silent in this case.

In the same time, the worker should not print the trackback into stderr if it send the traceback to JVM successfully.

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

Closes #1625 from davies/error and squashes the following commits:

4fbcc6d [Davies Liu] disable log4j during testing when exception is expected.
cc14202 [Davies Liu] keep silent in worker if JVM close the socket
[SPARK-791] [PySpark] fix pickle itemgetter with cloudpickle

fix the problem with pickle operator.itemgetter with multiple index.

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

Closes #1627 from davies/itemgetter and squashes the following commits:

aabd7fa [Davies Liu] fix pickle itemgetter with cloudpickle
[SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.

The pull request includes two changes:

1. Removes SortOrder introduced by SPARK-2125. The key ordering already includes the SortOrder information since an Ordering can be reverse. This is similar to Java's Comparator interface. Rarely does an API accept both a Comparator as well as a SortOrder.

2. Replaces the sortWith call in HashShuffleReader with an in-place quick sort.

Author: Reynold Xin <rxin@apache.org>

Closes #1631 from rxin/sortOrder and squashes the following commits:

c9d37e1 [Reynold Xin] [SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.
[SPARK-2174][MLLIB] treeReduce and treeAggregate

In `reduce` and `aggregate`, the driver node spends linear time on the number of partitions. It becomes a bottleneck when there are many partitions and the data from each partition is big.

SPARK-1485 (#506) tracks the progress of implementing AllReduce on Spark. I did several implementations including butterfly, reduce + broadcast, and treeReduce + broadcast. treeReduce + BT broadcast seems to be right way to go for Spark. Using binary tree may introduce some overhead in communication, because the driver still need to coordinate on data shuffling. In my experiments, n -> sqrt(n) -> 1 gives the best performance in general, which is why I set "depth = 2" in MLlib algorithms. But it certainly needs more testing.

I left `treeReduce` and `treeAggregate` public for easy testing. Some numbers from a test on 32-node m3.2xlarge cluster.

code:

~~~
import breeze.linalg._
import org.apache.log4j._

Logger.getRootLogger.setLevel(Level.OFF)

for (n <- Seq(1, 10, 100, 1000, 10000, 100000, 1000000)) {
  val vv = sc.parallelize(0 until 1024, 1024).map(i => DenseVector.zeros[Double](n))
  var start = System.nanoTime(); vv.treeReduce(_ + _, 2); println((System.nanoTime() - start) / 1e9)
  start = System.nanoTime(); vv.reduce(_ + _); println((System.nanoTime() - start) / 1e9)
}
~~~

out:

| n | treeReduce(,2) | reduce |
|---|---------------------|-----------|
| 10 | 0.215538731 | 0.204206899 |
| 100 | 0.278405907 | 0.205732582 |
| 1000 | 0.208972182 | 0.214298272 |
| 10000 | 0.194792071 | 0.349353687 |
| 100000 | 0.347683285 | 6.086671892 |
| 1000000 | 2.589350682 | 66.572906702 |

CC: @pwendell

This is clearly more scalable than the default implementation. My question is whether we should use this implementation in `reduce` and `aggregate` or put them as separate methods. The concern is that users may use `reduce` and `aggregate` as collect, where having multiple stages doesn't reduce the data size. However, in this case, `collect` is more appropriate.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1110 from mengxr/tree and squashes the following commits:

c6cd267 [Xiangrui Meng] make depth default to 2
b04b96a [Xiangrui Meng] address comments
9bcc5d3 [Xiangrui Meng] add depth for readability
7495681 [Xiangrui Meng] fix compile error
142a857 [Xiangrui Meng] merge master
d58a087 [Xiangrui Meng] move treeReduce and treeAggregate to mllib
8a2a59c [Xiangrui Meng] Merge branch 'master' into tree
be6a88a [Xiangrui Meng] use treeAggregate in mllib
0f94490 [Xiangrui Meng] add docs
eb71c33 [Xiangrui Meng] add treeReduce
fe42a5e [Xiangrui Meng] add treeAggregate
Minor indentation and comment typo fixes.

Author: Aaron Staple <astaple@gmail.com>

Closes #1630 from staple/minor and squashes the following commits:

6f295a2 [Aaron Staple] Fix typos in comment about ExprId.
8566467 [Aaron Staple] Fix off by one column indentation in SqlParser.
[STREAMING] SPARK-1729. Make Flume pull data from source, rather than the current pu...

...sh model

Currently Spark uses Flume's internal Avro Protocol to ingest data from Flume. If the executor running the
receiver fails, it currently has to be restarted on the same node to be able to receive data.

This commit adds a new Sink which can be deployed to a Flume agent. This sink can be polled by a new
DStream that is also included in this commit. This model ensures that data can be pulled into Spark from
Flume even if the receiver is restarted on a new node. This also allows the receiver to receive data on
multiple threads for better performance.

Author: Hari Shreedharan <harishreedharan@gmail.com>
Author: Hari Shreedharan <hshreedharan@apache.org>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: harishreedharan <hshreedharan@cloudera.com>

Closes #807 from harishreedharan/master and squashes the following commits:

e7f70a3 [Hari Shreedharan] Merge remote-tracking branch 'asf-git/master'
96cfb6f [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
e48d785 [Hari Shreedharan] Documenting flume-sink being ignored for Mima checks.
5f212ce [Hari Shreedharan] Ignore Spark Sink from mima.
981bf62 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
7a1bc6e [Hari Shreedharan] Fix SparkBuild.scala
a082eb3 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
1f47364 [Hari Shreedharan] Minor fixes.
73d6f6d [Hari Shreedharan] Cleaned up tests a bit. Added some docs in multiple places.
65b76b4 [Hari Shreedharan] Fixing the unit test.
e59cc20 [Hari Shreedharan] Use SparkFlumeEvent instead of the new type. Also, Flume Polling Receiver now uses the store(ArrayBuffer) method.
f3c99d1 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
3572180 [Hari Shreedharan] Adding a license header, making Jenkins happy.
799509f [Hari Shreedharan] Fix a compile issue.
3c5194c [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
d248d22 [harishreedharan] Merge pull request #1 from tdas/flume-polling
10b6214 [Tathagata Das] Changed public API, changed sink package, and added java unit test to make sure Java API is callable from Java.
1edc806 [Hari Shreedharan] SPARK-1729. Update logging in Spark Sink.
8c00289 [Hari Shreedharan] More debug messages
393bd94 [Hari Shreedharan] SPARK-1729. Use LinkedBlockingQueue instead of ArrayBuffer to keep track of connections.
120e2a1 [Hari Shreedharan] SPARK-1729. Some test changes and changes to utils classes.
9fd0da7 [Hari Shreedharan] SPARK-1729. Use foreach instead of map for all Options.
8136aa6 [Hari Shreedharan] Adding TransactionProcessor to map on returning batch of data
86aa274 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
205034d [Hari Shreedharan] Merging master in
4b0c7fc [Hari Shreedharan] FLUME-1729. New Flume-Spark integration.
bda01fc [Hari Shreedharan] FLUME-1729. Flume-Spark integration.
0d69604 [Hari Shreedharan] FLUME-1729. Better Flume-Spark integration.
3c23c18 [Hari Shreedharan] SPARK-1729. New Spark-Flume integration.
70bcc2a [Hari Shreedharan] SPARK-1729. New Flume-Spark integration.
d6fa3aa [Hari Shreedharan] SPARK-1729. New Flume-Spark integration.
e7da512 [Hari Shreedharan] SPARK-1729. Fixing import order
9741683 [Hari Shreedharan] SPARK-1729. Fixes based on review.
c604a3c [Hari Shreedharan] SPARK-1729. Optimize imports.
0f10788 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
87775aa [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
8df37e4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
03d6c1c [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
08176ad [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
d24d9d4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
6d6776a [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
[SQL]change some test lists

1. there's no `hook_context.q` but a `hook_context_cs.q` in query folder
2. there's no `compute_stats_table.q` in query folder
3. there's no `having1.q` in query folder
4. `udf_E` and `udf_PI` appear twice in white list

Author: Daoyuan <daoyuan.wang@intel.com>

Closes #1634 from adrian-wang/testcases and squashes the following commits:

d7482ce [Daoyuan] change some test lists
[SPARK-2730][SQL] When retrieving a value from a Map, GetItem evaluates key twice

JIRA: https://issues.apache.org/jira/browse/SPARK-2730

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

Closes #1637 from yhuai/SPARK-2730 and squashes the following commits:

1a9f24e [Yin Huai] Remove unnecessary key evaluation.
[SPARK-2674] [SQL] [PySpark] support datetime type for SchemaRDD

Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema().

In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling.

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

Closes #1601 from davies/date and squashes the following commits:

f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list
c9d607a [Davies Liu] convert datetype for runtime
709d40d [Davies Liu] remove brackets
96db384 [Davies Liu] support datetime type for SchemaRDD