SPARK-2748 [MLLIB] [GRAPHX] Loss of precision for small arguments to Math.exp, Math.log

In a few places in MLlib, an expression of the form `log(1.0 + p)` is evaluated. When p is so small that `1.0 + p == 1.0`, the result is 0.0. However the correct answer is very near `p`. This is why `Math.log1p` exists.

Similarly for one instance of `exp(m) - 1` in GraphX; there's a special `Math.expm1` method.

While the errors occur only for very small arguments, given their use in machine learning algorithms, this is entirely possible.

Also note the related PR for Python: https://github.com/apache/spark/pull/1652

Author: Sean Owen <srowen@gmail.com>

Closes #1659 from srowen/SPARK-2748 and squashes the following commits:

c5926d4 [Sean Owen] Use log1p, expm1 for better precision for tiny arguments
[SPARK-2521] Broadcast RDD object (instead of sending it along with every task)

This is a resubmission of #1452. It was reverted because it broke the build.

Currently (as of Spark 1.0.1), Spark sends RDD object (which contains closures) using Akka along with the task itself to the executors. This is inefficient because all tasks in the same stage use the same RDD object, but we have to send RDD object multiple times to the executors. This is especially bad when a closure references some variable that is very large. The current design led to users having to explicitly broadcast large variables.

The patch uses broadcast to send RDD objects and the closures to executors, and use Akka to only send a reference to the broadcast RDD/closure along with the partition specific information for the task. For those of you who know more about the internals, Spark already relies on broadcast to send the Hadoop JobConf every time it uses the Hadoop input, because the JobConf is large.

The user-facing impact of the change include:

1. Users won't need to decide what to broadcast anymore, unless they would want to use a large object multiple times in different operations
2. Task size will get smaller, resulting in faster scheduling and higher task dispatch throughput.

In addition, the change will simplify some internals of Spark, eliminating the need to maintain task caches and the complex logic to broadcast JobConf (which also led to a deadlock recently).

A simple way to test this:
```scala
val a = new Array[Byte](1000*1000); scala.util.Random.nextBytes(a);
sc.parallelize(1 to 1000, 1000).map { x => a; x }.groupBy { x => a; x }.count
```

Numbers on 3 r3.8xlarge instances on EC2
```
master branch: 5.648436068 s, 4.715361895 s, 5.360161877 s
with this change: 3.416348793 s, 1.477846558 s, 1.553432156 s
```

Author: Reynold Xin <rxin@apache.org>

Closes #1498 from rxin/broadcast-task and squashes the following commits:

f7364db [Reynold Xin] Code review feedback.
f8535dc [Reynold Xin] Fixed the style violation.
252238d [Reynold Xin] Serialize the final task closure as well as ShuffleDependency in taskBinary.
111007d [Reynold Xin] Fix broadcast tests.
797c247 [Reynold Xin] Properly send SparkListenerStageSubmitted and SparkListenerStageCompleted.
bab1d8b [Reynold Xin] Check for NotSerializableException in submitMissingTasks.
cf38450 [Reynold Xin] Use TorrentBroadcastFactory.
991c002 [Reynold Xin] Use HttpBroadcast.
de779f8 [Reynold Xin] Fix TaskContextSuite.
cc152fc [Reynold Xin] Don't cache the RDD broadcast variable.
d256b45 [Reynold Xin] Fixed unit test failures. One more to go.
cae0af3 [Reynold Xin] [SPARK-2521] Broadcast RDD object (instead of sending it along with every task).
[SPARK-2747] git diff --dirstat can miss sql changes and not run Hive tests

dev/run-tests use "git diff --dirstat master" to check whether sql is changed. However, --dirstat won't show sql if sql's change is negligible (e.g. 1k loc change in core, and only 1 loc change in hive).

We should use "git diff --name-only master" instead.

Author: Reynold Xin <rxin@apache.org>

Closes #1656 from rxin/hiveTest and squashes the following commits:

f5eab9f [Reynold Xin] [SPARK-2747] git diff --dirstat can miss sql changes and not run Hive tests.
Avoid numerical instability

This avoids basically doing 1 - 1, for example:

```python
>>> from math import exp
>>> margin = -40
>>> 1 - 1 / (1 + exp(margin))
0.0
>>> exp(margin) / (1 + exp(margin))
4.248354255291589e-18
>>>
```

Author: Naftali Harris <naftaliharris@gmail.com>

Closes #1652 from naftaliharris/patch-2 and squashes the following commits:

0d55a9f [Naftali Harris] Avoid numerical instability
[SPARK-2544][MLLIB] Improve ALS algorithm resource usage

Author: GuoQiang Li <witgo@qq.com>
Author: witgo <witgo@qq.com>

Closes #929 from witgo/improve_als and squashes the following commits:

ea25033 [GuoQiang Li] checkpoint products 3,6,9 ...
154dccf [GuoQiang Li] checkpoint products only
c5779ff [witgo] Improve ALS algorithm resource usage
[SPARK-2746] Set SBT_MAVEN_PROFILES only when it is not set explicitly by the user.

Author: Reynold Xin <rxin@apache.org>

Closes #1655 from rxin/SBT_MAVEN_PROFILES and squashes the following commits:

b268c4b [Reynold Xin] [SPARK-2746] Set SBT_MAVEN_PROFILES only when it is not set explicitly by the user.
Wrap FWDIR in quotes.
Wrap FWDIR in quotes in dev/check-license.
More wrapping FWDIR in quotes.
[SQL] Fix compiling of catalyst docs.

Author: Michael Armbrust <michael@databricks.com>

Closes #1653 from marmbrus/fixDocs and squashes the following commits:

0aa1feb [Michael Armbrust] Fix compiling of catalyst docs.
dev/check-license wrap folders in quotes.
[SPARK-2024] Add saveAsSequenceFile to PySpark

JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024

This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats.

* Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs.

* Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types.

* No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples.

* Added HBase and Cassandra output examples to show how custom output formats and converters can be used.

cc MLnick mateiz ahirreddy pwendell

Author: Kan Zhang <kzhang@apache.org>

Closes #1338 from kanzhang/SPARK-2024 and squashes the following commits:

c01e3ef [Kan Zhang] [SPARK-2024] code formatting
6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD
d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10
57a7a5e [Kan Zhang] [SPARK-2024] correcting typo
75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD
0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests
9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests
0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases
7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark
Wrap JAR_DL in dev/check-license.
Set AMPLAB_JENKINS_BUILD_PROFILE.
Properly pass SBT_MAVEN_PROFILES into sbt.
SPARK-2749 [BUILD]. Spark SQL Java tests aren't compiling in Jenkins' Maven builds; missing junit:junit dep

The Maven-based builds in the build matrix have been failing for a few days:

https://amplab.cs.berkeley.edu/jenkins/view/Spark/

On inspection, it looks like the Spark SQL Java tests don't compile:

https://amplab.cs.berkeley.edu/jenkins/view/Spark/job/Spark-Master-Maven-pre-YARN/hadoop.version=1.0.4,label=centos/244/consoleFull

I confirmed it by repeating the command vs master:

`mvn -Dhadoop.version=1.0.4 -Dlabel=centos -DskipTests clean package`

The problem is that this module doesn't depend on JUnit. In fact, none of the modules do, but `com.novocode:junit-interface` (the SBT-JUnit bridge) pulls it in, in most places. However this module doesn't depend on `com.novocode:junit-interface`

Adding the `junit:junit` dependency fixes the compile problem. In fact, the other modules with Java tests should probably depend on it explicitly instead of happening to get it via `com.novocode:junit-interface`, since that is a bit SBT/Scala-specific (and I am not even sure it's needed).

Author: Sean Owen <srowen@gmail.com>

Closes #1660 from srowen/SPARK-2749 and squashes the following commits:

858ff7c [Sean Owen] Add explicit junit dep to other modules with Java tests for robustness
9636794 [Sean Owen] Add junit dep so that Spark SQL Java tests compile