SPARK-2085: [MLlib] Apply user-specific regularization instead of uniform regularization in ALS

The current implementation of ALS takes a single regularization parameter and apply it on both of the user factors and the product factors. This kind of regularization can be less effective while user number is significantly larger than the number of products (and vice versa). For example, if we have 10M users and 1K product, regularization on user factors will dominate. Following the discussion in [this thread](http://apache-spark-user-list.1001560.n3.nabble.com/possible-bug-in-Spark-s-ALS-implementation-tt2567.html#a2704), the implementation in this PR will regularize each factor vector by #ratings * lambda.

Author: Shuo Xiang <sxiang@twitter.com>

Closes #1026 from coderxiang/als-reg and squashes the following commits:

93dfdb4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into als-reg
b98f19c [Shuo Xiang] merge latest master
52c7b58 [Shuo Xiang] Apply user-specific regularization instead of uniform regularization in Alternating Least Squares (ALS)
document laziness of parallelize

Took me several hours to figure out this behavior. It would be good to highlight it in the documentation.

Author: Ariel Rabkin <asrabkin@cs.princeton.edu>

Closes #1070 from asrabkin/master and squashes the following commits:

29a076e [Ariel Rabkin] doc fix
SPARK-1939 Refactor takeSample method in RDD to use ScaSRS

Modified the takeSample method in RDD to use the ScaSRS sampling technique to improve performance. Added a private method that computes sampling rate > sample_size/total to ensure sufficient sample size with success rate >= 0.9999. Added a unit test for the private method to validate choice of sampling rate.

Author: Doris Xin <doris.s.xin@gmail.com>
Author: dorx <doris.s.xin@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #916 from dorx/takeSample and squashes the following commits:

5b061ae [Doris Xin] merge master
444e750 [Doris Xin] edge cases
3de882b [dorx] Merge pull request #2 from mengxr/SPARK-1939
82dde31 [Xiangrui Meng] update pyspark's takeSample
48d954d [Doris Xin] remove unused imports from RDDSuite
fb1452f [Doris Xin] allowing num to be greater than count in all cases
1481b01 [Doris Xin] washing test tubes and making coffee
dc699f3 [Doris Xin] give back imports removed by accident in rdd.py
64e445b [Doris Xin] logwarnning as soon as it enters the while loop
55518ed [Doris Xin] added TODO for logging in rdd.py
eff89e2 [Doris Xin] addressed reviewer comments.
ecab508 [Doris Xin] "fixed checkstyle violation
0a9b3e3 [Doris Xin] "reviewer comment addressed"
f80f270 [Doris Xin] Merge branch 'master' into takeSample
ae3ad04 [Doris Xin] fixed edge cases to prevent overflow
065ebcd [Doris Xin] Merge branch 'master' into takeSample
9bdd36e [Doris Xin] Check sample size and move computeFraction
e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample
7cab53a [Doris Xin] fixed import bug in rdd.py
ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD
1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
[Minor] Fix style, formatting and naming in BlockManager etc.

This is a precursor to a bigger change. I wanted to separate out the relatively insignificant changes so the ultimate PR is not inflated.

(Warning: this PR is full of unimportant nitpicks)

Author: Andrew Or <andrewor14@gmail.com>

Closes #1058 from andrewor14/bm-minor and squashes the following commits:

8e12eaf [Andrew Or] SparkException -> BlockException
c36fd53 [Andrew Or] Make parts of BlockManager more readable
0a5f378 [Andrew Or] Entry -> MemoryEntry
e9762a5 [Andrew Or] Tone down string interpolation (minor reverts)
c4de9ac [Andrew Or] Merge branch 'master' of github.com:apache/spark into bm-minor
b3470f1 [Andrew Or] More string interpolation (minor)
7f9dcab [Andrew Or] Use string interpolation (minor)
94a425b [Andrew Or] Refactor against duplicate code + minor changes
8a6a7dc [Andrew Or] Exception -> SparkException
97c410f [Andrew Or] Deal with MIMA excludes
2480f1d [Andrew Or] Fixes in StorgeLevel.scala
abb0163 [Andrew Or] Style, formatting and naming fixes
[SPARK-1516]Throw exception in yarn client instead of run system.exit directly.

All the changes is in  the package of "org.apache.spark.deploy.yarn":
    1) Throw exception in ClinetArguments and ClientBase instead of exit directly.
    2) in Client's main method, if exception is caught, it will exit with code 1, otherwise exit with code 0.

After the fix, if user integrate the spark yarn client into their applications, when the argument is wrong or the running is finished, the application won't be terminated.

Author: John Zhao <jzhao@alpinenow.com>

Closes #490 from codeboyyong/jira_1516_systemexit_inyarnclient and squashes the following commits:

138cb48 [John Zhao] [SPARK-1516]Throw exception in yarn clinet instead of run system.exit directly. All the changes is in  the package of "org.apache.spark.deploy.yarn": 1) Add a ClientException with an exitCode 2) Throws exception in ClinetArguments and ClientBase instead of exit directly 3) in Client's main method, catch exception and exit with the exitCode.
[SPARK-2135][SQL] Use planner for in-memory scans

Author: Michael Armbrust <michael@databricks.com>

Closes #1072 from marmbrus/cachedStars and squashes the following commits:

8757c8e [Michael Armbrust] Use planner for in-memory scans.
[HOTFIX] add math3 version to pom

Passed `mvn package`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1075 from mengxr/takeSample-fix and squashes the following commits:

45b4590 [Xiangrui Meng] add math3 version to pom
Workaround in Spark for ConcurrentModification issue (JIRA Hadoop-10456, Spark-1097)

This fix has gone into Hadoop 2.4.1. For developers using <  2.4.1, it would be good to have a workaround in Spark as well.

Fix has been tested for performance as well, no regressions found.

Author: nravi <nravi@c1704.halxg.cloudera.com>

Closes #1000 from nishkamravi2/master and squashes the following commits:

eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark
df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456)
6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed)
5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456)
681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles
[SPARK-1964][SQL] Add timestamp to HiveMetastoreTypes.toMetastoreType

Author: Michael Armbrust <michael@databricks.com>

Closes #1061 from marmbrus/timestamp and squashes the following commits:

79c3903 [Michael Armbrust] Add timestamp to HiveMetastoreTypes.toMetastoreType()
[SPARK-2094][SQL] "Exactly once" semantics for DDL and command statements

## Related JIRA issues

- Main issue:

  - [SPARK-2094](https://issues.apache.org/jira/browse/SPARK-2094): Ensure exactly once semantics for DDL/Commands

- Issues resolved as dependencies:

  - [SPARK-2081](https://issues.apache.org/jira/browse/SPARK-2081): Undefine output() from the abstract class Command and implement it in concrete subclasses
  - [SPARK-2128](https://issues.apache.org/jira/browse/SPARK-2128): No plan for DESCRIBE
  - [SPARK-1852](https://issues.apache.org/jira/browse/SPARK-1852): SparkSQL Queries with Sorts run before the user asks them to

- Other related issue:

  - [SPARK-2129](https://issues.apache.org/jira/browse/SPARK-2129): NPE thrown while lookup a view

    Two test cases, `join_view` and `mergejoin_mixed`, within the `HiveCompatibilitySuite` are removed from the whitelist to workaround this issue.

## PR Overview

This PR defines physical plans for DDL statements and commands and wraps their side effects in a lazy field `PhysicalCommand.sideEffectResult`, so that they are executed eagerly and exactly once.  Also, as a positive side effect, now DDL statements and commands can be turned into proper `SchemaRDD`s and let user query the execution results.

This PR defines schemas for the following DDL/commands:

- EXPLAIN command

  - `plan`: String, the plan explanation

- SET command

  - `key`: String, the key(s) of the propert(y/ies) being set or queried
  - `value`: String, the value(s) of the propert(y/ies) being queried

- Other Hive native command

  - `result`: String, execution result returned by Hive

  **NOTE**: We should refine schemas for different native commands by defining physical plans for them in the future.

## Examples

### EXPLAIN command

Take the "EXPLAIN" command as an example, we first execute the command and obtain a `SchemaRDD` at the same time, then query the `plan` field with the schema DSL:

```
scala> loadTestTable("src")
...

scala> val q0 = hql("EXPLAIN SELECT key, COUNT(*) FROM src GROUP BY key")
...
q0: org.apache.spark.sql.SchemaRDD =
SchemaRDD[0] at RDD at SchemaRDD.scala:98
== Query Plan ==
ExplainCommandPhysical [plan#11:0]
 Aggregate false, [key#4], [key#4,SUM(PartialCount#6L) AS c_1#2L]
  Exchange (HashPartitioning [key#4:0], 200)
   Exchange (HashPartitioning [key#4:0], 200)
    Aggregate true, [key#4], [key#4,COUNT(1) AS PartialCount#6L]
     HiveTableScan [key#4], (MetastoreRelation default, src, None), None

scala> q0.select('plan).collect()
...
[ExplainCommandPhysical [plan#24:0]
 Aggregate false, [key#17], [key#17,SUM(PartialCount#19L) AS c_1#2L]
  Exchange (HashPartitioning [key#17:0], 200)
   Exchange (HashPartitioning [key#17:0], 200)
    Aggregate true, [key#17], [key#17,COUNT(1) AS PartialCount#19L]
     HiveTableScan [key#17], (MetastoreRelation default, src, None), None]

scala>
```

### SET command

In this example we query all the properties set in `SQLConf`, register the result as a table, and then query the table with HiveQL:

```
scala> val q1 = hql("SET")
...
q1: org.apache.spark.sql.SchemaRDD =
SchemaRDD[7] at RDD at SchemaRDD.scala:98
== Query Plan ==
<SET command: executed by Hive, and noted by SQLContext>

scala> q1.registerAsTable("properties")

scala> hql("SELECT key, value FROM properties ORDER BY key LIMIT 10").foreach(println)
...
== Query Plan ==
TakeOrdered 10, [key#51:0 ASC]
 Project [key#51:0,value#52:1]
  SetCommandPhysical None, None, [key#55:0,value#56:1]), which has no missing parents
14/06/12 12:19:27 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from Stage 5 (SchemaRDD[21] at RDD at SchemaRDD.scala:98
== Query Plan ==
TakeOrdered 10, [key#51:0 ASC]
 Project [key#51:0,value#52:1]
  SetCommandPhysical None, None, [key#55:0,value#56:1])
...
[datanucleus.autoCreateSchema,true]
[datanucleus.autoStartMechanismMode,checked]
[datanucleus.cache.level2,false]
[datanucleus.cache.level2.type,none]
[datanucleus.connectionPoolingType,BONECP]
[datanucleus.fixedDatastore,false]
[datanucleus.identifierFactory,datanucleus1]
[datanucleus.plugin.pluginRegistryBundleCheck,LOG]
[datanucleus.rdbms.useLegacyNativeValueStrategy,true]
[datanucleus.storeManagerType,rdbms]

scala>
```

### "Exactly once" semantics

At last, an example of the "exactly once" semantics:

```
scala> val q2 = hql("CREATE TABLE t1(key INT, value STRING)")
...
q2: org.apache.spark.sql.SchemaRDD =
SchemaRDD[28] at RDD at SchemaRDD.scala:98
== Query Plan ==
<Native command: executed by Hive>

scala> table("t1")
...
res9: org.apache.spark.sql.SchemaRDD =
SchemaRDD[32] at RDD at SchemaRDD.scala:98
== Query Plan ==
HiveTableScan [key#58,value#59], (MetastoreRelation default, t1, None), None

scala> q2.collect()
...
res10: Array[org.apache.spark.sql.Row] = Array([])

scala>
```

As we can see, the "CREATE TABLE" command is executed eagerly right after the `SchemaRDD` is created, and referencing the `SchemaRDD` again won't trigger a duplicated execution.

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

Closes #1071 from liancheng/exactlyOnceCommand and squashes the following commits:

d005b03 [Cheng Lian] Made "SET key=value" returns the newly set key value pair
f6c7715 [Cheng Lian] Added test cases for DDL/command statement RDDs
1d00937 [Cheng Lian] Makes SchemaRDD DSLs work for DDL/command statement RDDs
5c7e680 [Cheng Lian] Bug fix: wrong type used in pattern matching
48aa2e5 [Cheng Lian] Refined SQLContext.emptyResult as an empty RDD[Row]
cc64f32 [Cheng Lian] Renamed physical plan classes for DDL/commands
74789c1 [Cheng Lian] Fixed failing test cases
0ad343a [Cheng Lian] Added physical plan for DDL and commands to ensure the "exactly once" semantics
Small correction in Streaming Programming Guide doc

Corrected description of `repartition` function under 'Level of Parallelism in Data Receiving'.

Author: akkomar <ak.komar@gmail.com>

Closes #1079 from akkomar/streaming-guide-doc and squashes the following commits:

32dfc62 [akkomar] Corrected description of `repartition` function under 'Level of Parallelism in Data Receiving'.
[Spark-2137][SQL] Timestamp UDFs broken

https://issues.apache.org/jira/browse/SPARK-2137

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

Closes #1081 from yhuai/SPARK-2137 and squashes the following commits:

c04f910 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2137
205f17b [Yin Huai] Make Hive UDF wrapper support Timestamp.
[SPARK-2079] Support batching when serializing SchemaRDD to Python

Added batching with default batch size 10 in SchemaRDD.javaToPython

Author: Kan Zhang <kzhang@apache.org>

Closes #1023 from kanzhang/SPARK-2079 and squashes the following commits:

2d1915e [Kan Zhang] [SPARK-2079] Add batching in SchemaRDD.javaToPython
19b0c09 [Kan Zhang] [SPARK-2079] Removing unnecessary wrapping in SchemaRDD.javaToPython
[SPARK-2013] Documentation for saveAsPickleFile and pickleFile in Python

Author: Kan Zhang <kzhang@apache.org>

Closes #983 from kanzhang/SPARK-2013 and squashes the following commits:

0e128bb [Kan Zhang] [SPARK-2013] minor update
e728516 [Kan Zhang] [SPARK-2013] Documentation for saveAsPickleFile and pickleFile in Python
[SPARK-1837] NumericRange should be partitioned in the same way as other...

... sequences

Author: Kan Zhang <kzhang@apache.org>

Closes #776 from kanzhang/SPARK-1837 and squashes the following commits:

e48f018 [Kan Zhang] [SPARK-1837] code refactoring
67c33b5 [Kan Zhang] minor change
403f9b1 [Kan Zhang] [SPARK-1837] NumericRange should be partitioned in the same way as other sequences
[SQL] Support transforming TreeNodes with Option children.

Thanks goes to @marmbrus for his implementation.

Author: Michael Armbrust <michael@databricks.com>
Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1074 from concretevitamin/option-treenode and squashes the following commits:

ef27b85 [Zongheng Yang] Merge pull request #1 from marmbrus/pr/1074
73133c2 [Michael Armbrust] TreeNodes can't be inner classes.
ab78420 [Zongheng Yang] Add a test.
2ccb721 [Michael Armbrust] Add support for transformation of optional children.
[SPARK-937] adding EXITED executor state and not relaunching cleanly exited executors

There seems to be 2 issues.

1. When job is done, driver asks executor to shutdown. However, this clean exit was assigned FAILED executor state by Worker. I introduced EXITED executor state for executors who voluntarily exit (both normal and abnormal exit depending on the exit code).

2. When Master gets notified an executor has exited, it launches another one to replace it, regardless of reason why the executor had exited. When the reason was job has finished, the unnecessary replacement got subsequently killed when App disassociates. This launching and killing of unnecessary executors shows up in the log and is confusing to users. I added check for executor exit status and avoid launching (and subsequent killing) of unnecessary replacements when executors exit cleanly.

One could ask the scheduler to tell Master job is done so that Master wouldn't launch the replacement executor. However, there is a race condition between App telling Master job is done and Worker telling Master an executor had exited. There is no guarantee the former will happen before the later. Instead, I chose to check the exit code when executor exits. If the exit code is 0, I assume executor has been asked to shutdown by driver and Master will not launch replacements.

Due to race condition, it could also happen that (although didn't happen on my local cluster), Master detects App disassociation event before the executor exits by itself. In such cases, the executor will be rightfully killed and labeled as KILLED, while the App state will show FINISHED.

Author: Kan Zhang <kzhang@apache.org>

Closes #306 from kanzhang/SPARK-1118 and squashes the following commits:

cb0cc86 [Kan Zhang] [SPARK-937] adding EXITED executor state and not relaunching cleanly exited executors
SPARK-1999: StorageLevel in storage tab and RDD Storage Info never changes

StorageLevel in 'storage tab' and 'RDD Storage Info' never changes even if you call rdd.unpersist() and then you give the rdd another different storage level.

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #968 from CrazyJvm/ui-storagelevel and squashes the following commits:

62555fa [CrazyJvm] change RDDInfo constructor param 'storageLevel' to var, so there's need to add another variable _storageLevel。
9f1571e [CrazyJvm] JIRA https://issues.apache.org/jira/browse/SPARK-1999 UI : StorageLevel in storage tab and RDD Storage Info never changes
SPARK-2148 Add link to requirements for custom equals() and hashcode() methods

https://issues.apache.org/jira/browse/SPARK-2148

Author: Andrew Ash <andrew@andrewash.com>

Closes #1092 from ash211/SPARK-2148 and squashes the following commits:

93513df [Andrew Ash] SPARK-2148 Add link to requirements for custom equals() and hashcode() methods
Updating docs to include missing information about reducers and clarify ...

...how the OFFHEAP storage level works (there has been confusion around this).

Author: Ali Ghodsi <alig@cs.berkeley.edu>

Closes #1089 from alig/master and squashes the following commits:

ca8114d [Ali Ghodsi] Updating docs to include missing information about reducers and clarify how the OFFHEAP storage level works (there has been confusion around this).