[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.
[SPARK-2588][SQL] Add some more DSLs.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1491 from ueshin/issues/SPARK-2588 and squashes the following commits:

43d0a46 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-2588
1023ea0 [Takuya UESHIN] Modify tests to use DSLs.
2310bf1 [Takuya UESHIN] Add some more DSLs.
SPARK-2277: clear host->rack info properly

Hi mridulm, I just think of this issue of [#1212](https://github.com/apache/spark/pull/1212): I added FakeRackUtil to hold the host -> rack mapping. It should be cleaned up after use so that it won't mess up with test cases others may add later.
Really sorry about this.

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

Closes #1454 from lirui-intel/SPARK-2277-fix-UT and squashes the following commits:

f8ea25c [Rui Li] SPARK-2277: clear host->rack info properly
SPARK-2226:  [SQL] transform HAVING clauses with aggregate expressions that aren't in the aggregation list

This change adds an analyzer rule to
  1. find expressions in `HAVING` clause filters that depend on unresolved attributes,
  2. push these expressions down to the underlying aggregates, and then
  3. project them away above the filter.

It also enables the `HAVING` queries in the Hive compatibility suite.

Author: William Benton <willb@redhat.com>

Closes #1497 from willb/spark-2226 and squashes the following commits:

92c9a93 [William Benton] Removed unnecessary import
f1d4f34 [William Benton] Cleanups missed in prior commit
0e1624f [William Benton] Incorporated suggestions from @marmbrus; thanks!
541d4ee [William Benton] Cleanups from review
5a12647 [William Benton] Explanatory comments and stylistic cleanups.
c7f2b2c [William Benton] Whitelist HAVING queries.
29a26e3 [William Benton] Added rule to handle unresolved attributes in HAVING clauses (SPARK-2226)
[SPARK-2569][SQL] Fix shipping of TEMPORARY hive UDFs.

Instead of shipping just the name and then looking up the info on the workers, we now ship the whole classname.  Also, I refactored the file as it was getting pretty large to move out the type conversion code to its own file.

Author: Michael Armbrust <michael@databricks.com>

Closes #1552 from marmbrus/fixTempUdfs and squashes the following commits:

b695904 [Michael Armbrust] Make add jar execute with Hive.  Ship the whole function class name since sometimes we cannot lookup temporary functions on the workers.
[SPARK-2102][SQL][CORE] Add option for kryo registration required and use a resource pool in Spark SQL for Kryo instances.

Author: Ian O Connell <ioconnell@twitter.com>

Closes #1377 from ianoc/feature/SPARK-2102 and squashes the following commits:

5498566 [Ian O Connell] Docs update suggested by Patrick
20e8555 [Ian O Connell] Slight style change
f92c294 [Ian O Connell] Add docs for new KryoSerializer option
f3735c8 [Ian O Connell] Add using a kryo resource pool for the SqlSerializer
4e5c342 [Ian O Connell] Register the SparkConf for kryo, it gets swept into serialization
665805a [Ian O Connell] Add a spark.kryo.registrationRequired option for configuring the Kryo Serializer
[SPARK-2549] Functions defined inside of other functions trigger failures

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

Closes #1510 from ScrapCodes/SPARK-2549/fun-in-fun and squashes the following commits:

9458bc5 [Prashant Sharma] Tested by removing an inner function from excludes.
bc03b1c [Prashant Sharma] SPARK-2549 Functions defined inside of other functions trigger failures
[SPARK-2484][SQL] Build should not run hivecompatibility tests by default.

Author: witgo <witgo@qq.com>

Closes #1403 from witgo/hive_compatibility and squashes the following commits:

4e5ecdb [witgo] The default does not run hive compatibility tests
Do not synchronize every 1000 elements

The new mechanism is as follows: Each thread maintains a local
threshold, and checks every N elements whether its vector's size
has exceeded the threshold. If so, request more memory, equal to
the current vector size * F, then set the new threshold to this value.
In my implementation, N = 16 and F = 1.5. We also start off with an
initial threshold of 1MB (arbitrary).

Note that estimating the size is cheap because of our underlying
implementation of SizeTrackingVector. This avoids synchronizing
across all threads every 1000 elements as before, which could be
expensive.
Update a few comments / log messages
bufferFraction -> unrollFraction