[SPARK-2470] PEP8 fixes to PySpark

This pull request aims to resolve all outstanding PEP8 violations in PySpark.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Author: nchammas <nicholas.chammas@gmail.com>

Closes #1505 from nchammas/master and squashes the following commits:

98171af [Nicholas Chammas] [SPARK-2470] revert PEP 8 fixes to cloudpickle
cba7768 [Nicholas Chammas] [SPARK-2470] wrap expression list in parentheses
e178dbe [Nicholas Chammas] [SPARK-2470] style - change position of line break
9127d2b [Nicholas Chammas] [SPARK-2470] wrap expression lists in parentheses
22132a4 [Nicholas Chammas] [SPARK-2470] wrap conditionals in parentheses
24639bc [Nicholas Chammas] [SPARK-2470] fix whitespace for doctest
7d557b7 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to tests.py
8f8e4c0 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to storagelevel.py
b3b96cf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to statcounter.py
d644477 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to worker.py
aa3a7b6 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to sql.py
1916859 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to shell.py
95d1d95 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to serializers.py
a0fec2e [Nicholas Chammas] [SPARK-2470] PEP8 fixes to mllib
c85e1e5 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to join.py
d14f2f1 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to __init__.py
81fcb20 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to resultiterable.py
1bde265 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to java_gateway.py
7fc849c [Nicholas Chammas] [SPARK-2470] PEP8 fixes to daemon.py
ca2d28b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to context.py
f4e0039 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to conf.py
a6d5e4b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to cloudpickle.py
f0a7ebf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to rddsampler.py
4dd148f [nchammas] Merge pull request #5 from apache/master
f7e4581 [Nicholas Chammas] unrelated pep8 fix
a36eed0 [Nicholas Chammas] name ec2 instances and security groups consistently
de7292a [nchammas] Merge pull request #4 from apache/master
2e4fe00 [nchammas] Merge pull request #3 from apache/master
89fde08 [nchammas] Merge pull request #2 from apache/master
69f6e22 [Nicholas Chammas] PEP8 fixes
2627247 [Nicholas Chammas] broke up lines before they hit 100 chars
6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names
69da6cf [nchammas] Merge pull request #1 from apache/master
[SPARK-2452] Create a new valid for each  instead of using lineId.

Author: Prashant Sharma <prashant@apache.org>

Closes #1441 from ScrapCodes/SPARK-2452/multi-statement and squashes the following commits:

26c5c72 [Prashant Sharma] Added a test case.
7e8d28d [Prashant Sharma] SPARK-2452, create a new valid for each  instead of using lineId, because Line ids can be same sometimes.
[SPARK-2612] [mllib] Fix data skew in ALS

Author: peng.zhang <peng.zhang@xiaomi.com>

Closes #1521 from renozhang/fix-als and squashes the following commits:

b5727a4 [peng.zhang] Remove no need argument
1a4f7a0 [peng.zhang] Fix data skew in ALS
[MLLIB] make Mima ignore updateFeatures (private) in ALS

Fix Mima issues in #1521.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1533 from mengxr/mima-als and squashes the following commits:

78386e1 [Xiangrui Meng] make Mima ignore updateFeatures (private) in ALS
SPARK-2047: Introduce an in-mem Sorter, and use it to reduce mem usage

### Why and what?
Currently, the AppendOnlyMap performs an "in-place" sort by converting its array of [key, value, key, value] pairs into a an array of [(key, value), (key, value)] pairs. However, this causes us to allocate many Tuple2 objects, which come at a nontrivial overhead.

This patch adds a Sorter API, intended for in memory sorts, which simply ports the Android Timsort implementation (available under Apache v2) and abstracts the interface in a way which introduces no more than 1 virtual function invocation of overhead at each abstraction point.

Please compare our port of the Android Timsort sort with the original implementation: http://www.diffchecker.com/wiwrykcl

### Memory implications
An AppendOnlyMap contains N kv pairs, which results in roughly 2N elements within its underlying array. Each of these elements is 4 bytes wide in a [compressed OOPS](https://wikis.oracle.com/display/HotSpotInternals/CompressedOops) system, which is the default.

Today's approach immediately allocates N Tuple2 objects, which take up 24N bytes in total (exposed via YourKit), and undergoes a Java sort. The Java 6 version immediately copies the entire array (4N bytes here), while the Java 7 version has a worst-case allocation of half the array (2N bytes).
This results in a worst-case sorting overhead of 24N + 2N = 26N bytes (for Java 7).

The Sorter does not require allocating any tuples, but since it uses Timsort, it may copy up to half the entire array in the worst case.
This results in a worst-case sorting overhead of 4N bytes.

Thus, we have reduced the worst-case overhead of the sort by roughly 22 bytes times the number of elements.

### Performance implications
As the destructiveSortedIterator is used for spilling in an ExternalAppendOnlyMap, the purpose of this patch is to provide stability by reducing memory usage rather than improve performance. However, because it implements Timsort, it also brings a substantial performance boost over our prior implementation.

Here are the results of a microbenchmark that sorted 25 million, randomly distributed (Float, Int) pairs. The Java Arrays.sort() tests were run **only on the keys**, and thus moved less data. Our current implementation is called "Tuple-sort using Arrays.sort()" while the new implementation is "KV-array using Sorter".

<table>
<tr><th>Test</th><th>First run (JDK6)</th><th>Average of 10 (JDK6)</th><th>First run (JDK7)</th><th>Average of 10 (JDK7)</th></tr>
<tr><td>primitive Arrays.sort()</td><td>3216 ms</td><td>1190 ms</td><td>2724 ms</td><td>131 ms (!!)</td></tr>
<tr><td>Arrays.sort()</td><td>18564 ms</td><td>2006 ms</td><td>13201 ms</td><td>878 ms</td></tr>
<tr><td>Tuple-sort using Arrays.sort()</td><td>31813 ms</td><td>3550 ms</td><td>20990 ms</td><td>1919 ms</td></tr>
<tr><td><b>KV-array using Sorter</b></td><td></td><td></td><td><b>15020 ms</b></td><td><b>834 ms</b></td></tr>
</table>

The results show that this Sorter performs exactly as expected (after the first run) -- it is as fast as the Java 7 Arrays.sort() (which shares the same algorithm), but is significantly faster than the Tuple-sort on Java 6 or 7.

In short, this patch should significantly improve performance for users running either Java 6 or 7.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1502 from aarondav/sort and squashes the following commits:

652d936 [Aaron Davidson] Update license, move Sorter to java src
a7b5b1c [Aaron Davidson] fix licenses
5c0efaf [Aaron Davidson] Update tmpLength
ec395c8 [Aaron Davidson] Ignore benchmark (again) and fix docs
034bf10 [Aaron Davidson] Change to Apache v2 Timsort
b97296c [Aaron Davidson] Don't try to run benchmark on Jenkins + private[spark]
6307338 [Aaron Davidson] SPARK-2047: Introduce an in-mem Sorter, and use it to reduce mem usage
[SPARK-2615] [SQL] Add Equal Sign "==" Support for HiveQl

Currently, the "==" in HiveQL expression will cause exception thrown, this patch will fix it.

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

Closes #1522 from chenghao-intel/equal and squashes the following commits:

f62a0ff [Cheng Hao] Add == Support for HiveQl
Graphx example

fix examples

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #1523 from CrazyJvm/graphx-example and squashes the following commits:

663457a [CrazyJvm] outDegrees does not take parameters
7cfff1d [CrazyJvm] fix example for joinVertices
[YARN][SPARK-2606]:In some cases,the spark UI pages display incorrect

The issue is caused by #1112 .

Author: GuoQiang Li <witgo@qq.com>

Closes #1501 from witgo/webui_style and squashes the following commits:

4b34998 [GuoQiang Li] In some cases, pages display incorrect in WebUI
[YARN] SPARK-2577: File upload to viewfs is broken due to mount point re...

Opting to the option 2 defined in SPARK-2577, i.e., retrieve and pass the correct file system object to addResource.

Author: Gera Shegalov <gera@twitter.com>

Closes #1483 from gerashegalov/master and squashes the following commits:

90c9087 [Gera Shegalov] [YARN] SPARK-2577: File upload to viewfs is broken due to mount point resolution
Remove GraphX MessageToPartition for compatibility with sort-based shuffle

MessageToPartition was used in `Graph#partitionBy`. Unlike a Tuple2, it marked the key as transient to avoid sending it over the network. However, it was incompatible with sort-based shuffle (SPARK-2045) and represented only a minor optimization: for partitionBy, it improved performance by 6.3% (30.4 s to 28.5 s) and reduced communication by 5.6% (114.2 MB to 107.8 MB).

Author: Ankur Dave <ankurdave@gmail.com>

Closes #1537 from ankurdave/remove-MessageToPartition and squashes the following commits:

f9d0054 [Ankur Dave] Remove MessageToPartition
ab71364 [Ankur Dave] Remove unused VertexBroadcastMsg
[SPARK-2617] Correct doc and usages of preservesPartitioning

The name `preservesPartitioning` is ambiguous: 1) preserves the indices of partitions, 2) preserves the partitioner. The latter is correct and `preservesPartitioning` should really be called `preservesPartitioner` to avoid confusion. Unfortunately, this is already part of the API and we cannot change. We should be clear in the doc and fix wrong usages.

This PR

1. adds notes in `maPartitions*`,
2. makes `RDD.sample` preserve partitioner,
3. changes `preservesPartitioning` to false in  `RDD.zip` because the keys of the first RDD are no longer the keys of the zipped RDD,
4. fixes some wrong usages in MLlib.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1526 from mengxr/preserve-partitioner and squashes the following commits:

b361e65 [Xiangrui Meng] update doc based on pwendell's comments
3b1ba19 [Xiangrui Meng] update doc
357575c [Xiangrui Meng] fix unit test
20b4816 [Xiangrui Meng] Merge branch 'master' into preserve-partitioner
d1caa65 [Xiangrui Meng] add doc to explain preservesPartitioning fix wrong usage of preservesPartitioning make sample preserse partitioning
[SPARK-2609] Log thread ID when spilling ExternalAppendOnlyMap

It's useful to know whether one thread is constantly spilling or multiple threads are spilling relatively infrequently. Right now everything looks a little jumbled and we can't tell which lines belong to the same thread. For instance:

```
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (194 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 10 MB to disk (197 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 9 MB to disk (45 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 23 MB to disk (198 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 38 MB to disk (25 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 161 MB to disk (25 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 0 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (166 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (200 times so far)
```

Author: Andrew Or <andrewor14@gmail.com>

Closes #1517 from andrewor14/external-log and squashes the following commits:

90e48bb [Andrew Or] Log thread ID when spilling
[CORE] SPARK-2640: In "local[N]", free cores of the only executor should be touched by "spark.task.cpus" for every finish/start-up of tasks.

Make spark's "local[N]" better.
In our company, we use "local[N]" in production. It works exellentlly. It's our best choice.

Author: woshilaiceshide <woshilaiceshide@qq.com>

Closes #1544 from woshilaiceshide/localX and squashes the following commits:

6c85154 [woshilaiceshide] [CORE] SPARK-2640: In "local[N]", free cores of the only executor should be touched by "spark.task.cpus" for every finish/start-up of tasks.