Assorted clean-up for Spark-on-YARN.

In particular when the HADOOP_CONF_DIR is not not specified.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #488 from pwendell/hadoop-cleanup and squashes the following commits:

fe95f13 [Patrick Wendell] Changes based on Andrew's feeback
18d09c1 [Patrick Wendell] Review comments from Andrew
17929cc [Patrick Wendell] Assorted clean-up for Spark-on-YARN.
[Spark-1538] Fix SparkUI incorrectly hiding persisted RDDs

**Bug**: After the following command `sc.parallelize(1 to 1000).persist.map(_ + 1).count()` is run, the the persisted RDD is missing from the storage tab of the SparkUI.

**Cause**: The command creates two RDDs in one stage, a `ParallelCollectionRDD` and a `MappedRDD`. However, the existing StageInfo only keeps the RDDInfo of the last RDD associated with the stage (`MappedRDD`), and so all RDD information regarding the first RDD (`ParallelCollectionRDD`) is discarded. In this case, we persist the first RDD,  but the StorageTab doesn't know about this RDD because it is not encoded in the StageInfo.

**Fix**: Record information of all RDDs in StageInfo, instead of just the last RDD (i.e. `stage.rdd`). Since stage boundaries are marked by shuffle dependencies, the solution is to traverse the last RDD's dependency tree, visiting only ancestor RDDs related through a sequence of narrow dependencies.

---

This PR also moves RDDInfo to its own file, includes a few style fixes, and adds a unit test for constructing StageInfos.

Author: Andrew Or <andrewor14@gmail.com>

Closes #469 from andrewor14/storage-ui-fix and squashes the following commits:

07fc7f0 [Andrew Or] Add back comment that was accidentally removed (minor)
5d799fe [Andrew Or] Add comment to justify testing of getNarrowAncestors with cycles
9d0e2b8 [Andrew Or] Hide details of getNarrowAncestors from outsiders
d2bac8a [Andrew Or] Deal with cycles in RDD dependency graph + add extensive tests
2acb177 [Andrew Or] Move getNarrowAncestors to RDD.scala
bfe83f0 [Andrew Or] Backtrace RDD dependency tree to find all RDDs that belong to a Stage
[streaming][SPARK-1578] Removed requirement for TTL in StreamingContext.

Since shuffles and RDDs that are out of context are automatically cleaned by Spark core (using ContextCleaner) there is no need for setting the cleaner TTL while creating a StreamingContext.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #491 from tdas/ttl-fix and squashes the following commits:

cf01dc7 [Tathagata Das] Removed requirement for TTL in StreamingContext.
[FIX: SPARK-1376] use --arg instead of --args in SparkSubmit to avoid warning messages

Even if users use `--arg`, `SparkSubmit` still uses `--args` for child args internally, which triggers a warning message that may confuse users:

~~~
--args is deprecated. Use --arg instead.
~~~

@sryza Does it look good to you?

Author: Xiangrui Meng <meng@databricks.com>

Closes #485 from mengxr/submit-arg and squashes the following commits:

5e1b9fe [Xiangrui Meng] update test
cebbeb7 [Xiangrui Meng] use --arg instead of --args in SparkSubmit to avoid warning messages
SPARK-1562 Fix visibility / annotation of Spark SQL APIs

Author: Michael Armbrust <michael@databricks.com>

Closes #489 from marmbrus/sqlDocFixes and squashes the following commits:

acee4f3 [Michael Armbrust] Fix visibility / annotation of Spark SQL APIs
SPARK-1494 Don't initialize classes loaded by MIMA excludes.

[WIP]  Just seeing how Jenkins likes this...

Author: Michael Armbrust <michael@databricks.com>

Closes #494 from marmbrus/mima and squashes the following commits:

6eec616 [Michael Armbrust] Force hive tests to run.
acaf682 [Michael Armbrust] Don't initialize loaded classes.
[SQL] SPARK-1571 Mistake in java example code

Author: Michael Armbrust <michael@databricks.com>

Closes #496 from marmbrus/javaBeanBug and squashes the following commits:

644fedd [Michael Armbrust] Bean methods must be public.
Merge branch 'master' of https://github.com/apache/spark into zipWithIndex
SPARK-1119 and other build improvements

1. Makes assembly and examples jar naming consistent in maven/sbt.
2. Updates make-distribution.sh to use Maven and fixes some bugs.
3. Updates the create-release script to call make-distribution script.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #502 from pwendell/make-distribution and squashes the following commits:

1a97f0d [Patrick Wendell] SPARK-1119 and other build improvements
SPARK-1583: Fix a bug that using java.util.HashMap by mistake

JIRA: https://issues.apache.org/jira/browse/SPARK-1583

Does anyone know why using `java.util.HashMap` rather than `mutable.HashMap`? Some methods of `java.util.HashMap` are not generics and compiler can not help us find similar problems.

Author: zsxwing <zsxwing@gmail.com>

Closes #500 from zsxwing/SPARK-1583 and squashes the following commits:

7bfd74d [zsxwing] SPARK-1583: Fix a bug that using java.util.HashMap by mistake
SPARK-1572 Don't kill Executor if PythonRDD fails while computing parent

Previously, the behavior was that if the parent RDD threw any exception other than IOException or FileNotFoundException (which is quite possible for Hadoop input sources), the entire Executor would crash, because the default thread a uncaught exception handler calls System.exit().

This patch avoids two related issues:

  1. Always catch exceptions in this reader thread.
  2. Don't mask readerException when Python throws an EOFError
     after worker.shutdownOutput() is called.

Author: Aaron Davidson <aaron@databricks.com>

Closes #486 from aarondav/pyspark and squashes the following commits:

fbb11e9 [Aaron Davidson] Make sure FileNotFoundExceptions are handled same as before
b9acb3e [Aaron Davidson] SPARK-1572 Don't kill Executor if PythonRDD fails while computing parent
Honor default fs name when initializing event logger.

This is related to SPARK-1459 / PR #375. Without this fix,
FileLogger.createLogDir() may try to create the log dir on
HDFS, while createWriter() will try to open the log file on
the local file system, leading to interesting errors and
confusion.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #450 from vanzin/event-file-2 and squashes the following commits:

592cdb3 [Marcelo Vanzin] Honor default fs name when initializing event logger.
SPARK-1582 Invoke Thread.interrupt() when cancelling jobs

Sometimes executor threads are blocked waiting for IO or monitors, and the current implementation of job cancellation may never recover these threads. By simply invoking Thread.interrupt() during cancellation, we can often safely unblock the threads and use them for subsequent work.

Note that this feature must remain optional for now because of a bug in HDFS where Thread.interrupt() may cause nodes to be marked as permanently dead (as the InterruptedException is reinterpreted as an IOException during communication with some node).

Author: Aaron Davidson <aaron@databricks.com>

Closes #498 from aarondav/cancel and squashes the following commits:

e52b829 [Aaron Davidson] Don't use job.properties when null
82f78bb [Aaron Davidson] Update DAGSchedulerSuite
b67f472 [Aaron Davidson] Add comment on why interruptOnCancel is in setJobGroup
4cb9fd6 [Aaron Davidson] SPARK-1582 Invoke Thread.interrupt() when cancelling jobs
[SPARK-1540] Add an optional Ordering parameter to PairRDDFunctions.

In https://issues.apache.org/jira/browse/SPARK-1540 we'd like to look at Spark's API to see if we can take advantage of Comparable keys in more places, which will make external spilling more efficient. This PR is a first step towards that that shows how to pass an Ordering when available and still continue functioning otherwise. It does this using a new implicit parameter with a default value of null.

The API is currently only in Scala -- in Java we'd have to add new versions of mapToPair and such that take a Comparator, or a new method to add a "type hint" to an RDD. We can address those later though.

Unfortunately requiring all keys to be Comparable would not work without requiring RDDs in general to contain only Comparable types. The reason is that methods such as distinct() and intersection() do a shuffle, but should be usable on RDDs of any type. So ordering will have to remain an optimization for the types that can be ordered. I think this isn't a horrible outcome though because one of the nice things about Spark's API is that it works on objects of *any* type, without requiring you to specify a schema or implement Writable or stuff like that.

Author: Matei Zaharia <matei@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@apache.org>

Closes #487 from mateiz/ordered-keys and squashes the following commits:

bd565f6 [Matei Zaharia] Pass an Ordering to only one version of groupBy because the Scala language spec doesn't allow having an optional parameter on all of them (this was only compiling in Scala 2.10 due to a bug).
4629965 [Matei Zaharia] Add tests for other versions of groupBy
3beae85 [Matei Zaharia] Added a test for implicit orderings
80b7a3b [Matei Zaharia] Add an optional Ordering parameter to PairRDDFunctions.
review commit
Merge branch 'master' of https://github.com/apache/spark into zipWithIndex