Cleanup on Connection and ConnectionManager

Simple cleanup on Connection and ConnectionManager to make IDE happy while working of issue:
1. Replace var with var
2. Add parentheses to Queue#dequeu to be consistent with side-effects.
3. Remove return on final line of a method.

Author: Henry Saputra <henry.saputra@gmail.com>

Closes #1060 from hsaputra/cleanup_connection_classes and squashes the following commits:

245fd09 [Henry Saputra] Cleanup on Connection and ConnectionManager to make IDE happy while working of issue: 1. Replace var with var 2. Add parentheses to Queue#dequeu to be consistent with side-effects. 3. Remove return on final line of a method.
fixed typo in docstring for min()

Hi, I found this typo while learning spark and thought I'd do a pull request.

Author: Jeff Thompson <jeffreykeatingthompson@gmail.com>

Closes #1065 from jkthompson/docstring-typo-minmax and squashes the following commits:

29b6a26 [Jeff Thompson] fixed typo in docstring for min()
SPARK-554.  Add aggregateByKey.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #705 from sryza/sandy-spark-554 and squashes the following commits:

2302b8f [Sandy Ryza] Add MIMA exclude
f52e0ad [Sandy Ryza] Fix Python tests for real
2f3afa3 [Sandy Ryza] Fix Python test
0b735e9 [Sandy Ryza] Fix line lengths
ae56746 [Sandy Ryza] Fix doc (replace T with V)
c2be415 [Sandy Ryza] Java and Python aggregateByKey
23bf400 [Sandy Ryza] SPARK-554.  Add aggregateByKey.
[SPARK-2088] fix NPE in toString

After deserialization, the transient field creationSiteInfo does not get backfilled with the default value, but the toString method, which is invoked by the serializer, expects the field to always be non-null. An NPE is thrown when toString is called by the serializer when creationSiteInfo is null.

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

Closes #1028 from dorx/toStringNPE and squashes the following commits:

f20021e [Doris Xin] unit test for toString after desrialization
6f0a586 [Doris Xin] Merge branch 'master' into toStringNPE
f47fecf [Doris Xin] Merge branch 'master' into toStringNPE
76199c6 [Doris Xin] [SPARK-2088] fix NPE in toString
[SPARK-2080] Yarn: report HS URL in client mode, correct user in cluster mode.

Yarn client mode was not setting the app's tracking URL to the
History Server's URL when configured by the user. Now client mode
behaves the same as cluster mode.

In SparkContext.scala, the "user.name" system property had precedence
over the SPARK_USER environment variable. This means that SPARK_USER
was never used, since "user.name" is always set by the JVM. In Yarn
cluster mode, this means the application always reported itself as
being run by user "yarn" (or whatever user was running the Yarn NM).
One could argue that the correct fix would be to use UGI.getCurrentUser()
here, but at least for Yarn that will match what SPARK_USER is set
to.

Author: Marcelo Vanzin <vanzin@cloudera.com>

This patch had conflicts when merged, resolved by
Committer: Thomas Graves <tgraves@apache.org>

Closes #1002 from vanzin/yarn-client-url and squashes the following commits:

4046e04 [Marcelo Vanzin] Set HS link in yarn-alpha also.
4c692d9 [Marcelo Vanzin] Yarn: report HS URL in client mode, correct user in cluster mode.
SPARK-1843: Replace assemble-deps with env variable.

(This change is actually small, I moved some logic into
compute-classpath that was previously in spark-class).

Assemble deps has existed for a while to allow developers to
run local code with new changes quickly. When I'm developing I
typically use a simpler approach which just prepends the Spark
classes to the classpath before the assembly jar. This is well
defined in the JVM and the Spark classes take precedence over those
in the assembly.

This approach is portable across both builds which is the main reason I'd
like to switch to it. It's also a bit easier to toggle on and off quickly.

The way you use this is the following:
```
$ ./bin/spark-shell # Use spark with the normal assembly
$ export SPARK_PREPEND_CLASSES=true
$ ./bin/spark-shell # Now it's using compiled classes
$ unset SPARK_PREPEND_CLASSES
$ ./bin/spark-shell # Back to normal
```

Author: Patrick Wendell <pwendell@gmail.com>

Closes #877 from pwendell/assemble-deps and squashes the following commits:

8a11345 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into assemble-deps
faa3168 [Patrick Wendell] Adding a warning for compatibility
3f151a7 [Patrick Wendell] Small fix
bbfb73c [Patrick Wendell] Review feedback
328e9f8 [Patrick Wendell] SPARK-1843: Replace assemble-deps with env variable.
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