3 Kryo related changes.
1. Call Kryo setReferences before calling user specified Kryo registrator. This is done so the user specified registrator can override the default setting.
2. Register more internal classes (MapStatus, BlockManagerId).
3. Slightly refactored the internal class registration to allocate less memory.
Add spark-tools assembly to spark-class'ss classpath
This commit adds an assembly for `spark-tools` and adds it to `spark-class`'s classpath, allowing the JavaAPICompletenessChecker to be run against Spark 0.8+ with
./spark-class org.apache.spark.tools.JavaAPICompletenessChecker
Previously, this tool was run through the `run` script. I chose to add this to `run-example` because I didn't want to duplicate code in a `run-tool` script.
Fix secure hdfs access for spark on yarn
https://github.com/apache/incubator-spark/pull/23 broke secure hdfs access. Not sure if it works with secure hdfs on standalone. Fixing it at least for spark on yarn.
The broadcasting of jobconf change also broke secure hdfs access as it didn't take into account things calling the getPartitions before sparkContext is initialized. The DAGScheduler does this as it tries to getShuffleMapStage.
This adds a metrics sink for graphite. The sink must
be configured with the host and port of a graphite node
and optionally may be configured with a prefix that will
be prepended to all metrics that are sent to graphite.
Include appId in executor cmd line args
add the appId back into the executor cmd line args.
I also made a pretty lame regression test, just to make sure it doesn't get dropped in the future. not sure it will run on the build server, though, b/c `ExecutorRunner.buildCommandSeq()` expects to be abel to run the scripts in `bin`.
Add Spark multi-user support for standalone mode and Mesos
This PR add multi-user support for Spark both standalone mode and Mesos (coarse and fine grained ) mode, user can specify the user name who submit app through environment variable `SPARK_USER` or use default one. Executor will communicate with Hadoop using specified user name.
Also I fixed one bug in JobLogger when different user wrote job log to specified folder which has no right file permission.
I separate previous [PR750](https://github.com/mesos/spark/pull/750) into two PRs, in this PR I only solve multi-user support problem. I will try to solve security auth problem in subsequent PR because security auth is a complicated problem especially for Shark Server like long-run app (both Kerberos TGT and HDFS delegation token should be renewed or re-created through app's run time).
Removed unused return value in SparkContext.runJob
Return type of this `runJob` version is `Unit`:
def runJob[T, U: ClassManifest](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
allowLocal: Boolean,
resultHandler: (Int, U) => Unit) {
...
}
It's obviously unnecessary to "return" `result`.
Attempt to fix SparkListenerSuite breakage
Could not reproduce locally, but this test could've been flaky if the build machine was too fast, due to typo. (index 0 is intentionally slowed down to ensure total time is >= 1 ms)
This should be merged into branch-0.8 as well.
Ignore a task update status if the executor doesn't exist anymore.
Otherwise if the scheduler receives a task update message when the executor's been removed, the scheduler would hang.
It is pretty hard to add unit tests for these right now because it is hard to mock the cluster scheduler. We should do that once @kayousterhout finishes merging the local scheduler and the cluster scheduler.