Add SBT target to assemble dependencies
This pull request is an attempt to address the long assembly build times during development. Instead of rebuilding the assembly jar for every Spark change, this pull request adds a new SBT target `spark` that packages all the Spark modules and builds an assembly of the dependencies.
So the work flow that should work now would be something like
```
./sbt/sbt spark # Doing this once should suffice
## Make changes
./sbt/sbt compile
./sbt/sbt test or ./spark-shell
```
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.
This commit makes Spark invocation saner by using an assembly JAR to
find all of Spark's dependencies instead of adding all the JARs in
lib_managed. It also packages the examples into an assembly and uses
that as SPARK_EXAMPLES_JAR. Finally, it replaces the old "run" script
with two better-named scripts: "run-examples" for examples, and
"spark-class" for Spark internal classes (e.g. REPL, master, etc). This
is also designed to minimize the confusion people have in trying to use
"run" to run their own classes; it's not meant to do that, but now at
least if they look at it, they can modify run-examples to do a decent
job for them.
As part of this, Bagel's examples are also now properly moved to the
examples package instead of bagel.
For debugging and analysis purposes, it's nice to have the exact command
used to launch Spark contained within the logs. This adds the necessary
hooks to make that possible.
This is used to find methods in the Scala API that
need to be ported to the Java API. To use it:
./run spark.tools.JavaAPICompletenessChecker
Conflicts:
project/SparkBuild.scala
run
run2.cmd
