Adding algorithm for implicit feedback data to ALS
Initial commit for job killing.
Properly handle job failure when the job gets killed.
Added a submitJob interface that returns a Future of the result.
Allow SparkContext.submitJob to submit a job for only a subset of the partitions.
Added a hack to kill all active jobs in SparkContext.
Fixed a bug for zero partition in JobWaiter.
Made task killing work for standalone cluster schedulers.
More logging changes (task killing for local cluster doesn't work yet).
Added task killing iterator to RDDs that take inputs.
Added comment to InterruptibleIterator.
Wrap around cached data to InterruptibleIterator.
Made output of CoGroup and aggregations interruptible.
Serialize and restore spark.cleaner.ttl to savepoint
Merge remote-tracking branch 'upstream/master' into implicit-als
Support distributed cache files and archives on spark on yarn and attempt to cleanup the staging directory on exit
Standalone Scheduler fault recovery

Implements a basic form of Standalone Scheduler fault recovery. In particular,
this allows faults to be manually recovered from by means of restarting the
Master process on the same machine. This is the majority of the code necessary
for general fault tolerance, which will first elect a leader and then recover
the Master state.

In order to enable fault recovery, the Master will persist a small amount of state related
to the registration of Workers and Applications to disk. If the Master is started and
sees that this state is still around, it will enter Recovery mode, during which time it
will not schedule any new Executors on Workers (but it does accept the registration of
new Clients and Workers).

At this point, the Master attempts to reconnect to all Workers and Client applications
that were registered at the time of failure. After confirming either the existence
or nonexistence of all such nodes (within a certain timeout), the Master will exit
Recovery mode and resume normal scheduling.
Standalone Scheduler fault tolerance using ZooKeeper

This patch implements full distributed fault tolerance for standalone scheduler Masters.
There is only one master Leader at a time, which is actively serving scheduling
requests. If this Leader crashes, another master will eventually be elected, reconstruct
the state from the first Master, and continue serving scheduling requests.

Leader election is performed using the ZooKeeper leader election pattern. We try to minimize
the use of ZooKeeper and the assumptions about ZooKeeper's behavior, so there is a layer of
retries and session monitoring on top of the ZooKeeper client.

Master failover follows directly from the single-node Master recovery via the file
system (patch 194ba4b8), save that the Master state is stored in ZooKeeper instead.

Configuration:
By default, no recovery mechanism is enabled (spark.deploy.recoveryMode = NONE).
By setting spark.deploy.recoveryMode to ZOOKEEPER and setting spark.deploy.zookeeper.url
to an appropriate ZooKeeper URL, ZooKeeper recovery mode is enabled.
By setting spark.deploy.recoveryMode to FILESYSTEM and setting spark.deploy.recoveryDirectory
to an appropriate directory accessible by the Master, we will keep the behavior of from 194ba4b8.

Additionally, places where a Master could be specificied by a spark:// url can now take
comma-delimited lists to specify backup masters. Note that this is only used for registration
of NEW Workers and application Clients. Once a Worker or Client has registered with the
Master Leader, it is "in the system" and will never need to register again.

Forthcoming:
Documentation, tests (! - only ad hoc testing has been performed so far)
I do not intend for this commit to be merged until tests are added, but this patch should
still be mostly reviewable until then.
Add license notices
SPARK-900 Use coarser grained naming for metrics