Docker files drawn mostly from Matt Masse. Some updates from Andre Schumacher.

One major change was the use of messages instead of raw functions as the
parameter of Akka scheduled timers. Since messages are serialized, unlike
raw functions, the behavior is easier to think about and doesn't cause
race conditions when exceptions are thrown.

Another change is to avoid using global pointers that might change without
a lock.

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.

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.

Bug fix in master build

Improved organization of scheduling packages.

This commit does not change any code -- only file organization.
Please let me know if there was some masterminded strategy behind
the existing organization that I failed to understand!

There are two components of this change:
(1) Moving files out of the cluster package, and down
a level to the scheduling package. These files are all used by
the local scheduler in addition to the cluster scheduler(s), so
should not be in the cluster package. As a result of this change,
none of the files in the local package reference files in the
cluster package.

(2) Moving the mesos package to within the cluster package.
The mesos scheduling code is for a cluster, and represents a
specific case of cluster scheduling (the Mesos-related classes
often subclass cluster scheduling classes). Thus, the most logical
place for it seems to be within the cluster package.

The one thing about the scheduling code that seems a little funny to me
is the naming of the SchedulerBackends.  The StandaloneSchedulerBackend
is not just for Standalone mode, but instead is used by Mesos coarse grained
mode and Yarn, and the backend that *is* just for Standalone mode is instead called SparkDeploySchedulerBackend. I didn't change this because I wasn't sure if there
was a reason for this naming that I'm just not aware of.

EC2 SSH improvements

Add mapPartitionsWithIndex

some minor fixes to MemoryStore

This is a repeat of #5, moved to its own branch in my repo.

This makes all updates to   on ; it skips on synchronizing the reads where it can get away with it.

Smarter take/limit implementation.

This commit does not change any code -- only file organization.

There are two components of this change:
(1) Moving files out of the cluster package, and down
a level to the scheduling package. These files are all used by
the local scheduler in addition to the cluster scheduler(s), so
should not be in the cluster package. As a result of this change,
none of the files in the local package reference files in the
cluster package.

(2) Moving the mesos package to within the cluster package.
The mesos scheduling code is for a cluster, and represents a
specific case of cluster scheduling (the Mesos-related classes
often subclass cluster scheduling classes). Thus, the most logical
place for it is within the cluster package.

Fix spacing so java.io.tmpdir doesn't run on with SPARK_JAVA_OPTS

Refactor FairSchedulableBuilder

1. Configuration can be read from classpath if not set explicitly.
2. Add missing close handler.

Fix PR926 local properties issues in Spark Streaming like scenarios

Add "org.apache." prefix to packages in spark-class

After unit tests, clear port properties unconditionally

Lacking this, the if/case statements never trigger on Spark 0.8.0+.

Make "currentMemory" @volatile, so that it's reads in ensureFreeSpace() are atomic and up-to-date--i.e., currentMemory can't increase while putLock is held (though it could decrease, which would only help ensureFreeSpace()).

In MapOutputTrackerSuite, the "remote fetch" test sets spark.driver.port
and spark.hostPort, assuming that they will be cleared by
LocalSparkContext. However, the test never sets sc, so it remains null,
causing LocalSparkContext to skip clearing these properties. Subsequent
tests therefore fail with java.net.BindException: "Address already in
use".

This commit makes LocalSparkContext clear the properties even if sc is
null.

Fix issue with spark_ec2 seeing empty security groups

Under unknown, but occasional, circumstances, reservation.groups is empty
despite reservation.instances each having groups. This means that the
spark_ec2 get_existing_clusters() method would fail to find any instances.
To fix it, we simply use the instances' groups as the source of truth.

Note that this is actually just a revival of PR #827, now that the issue
has been reproduced.