Simple cleanup on Spark's Scala code
Simple cleanup on Spark's Scala code while testing some modules:
-) Remove some of unused imports as I found them
-) Remove ";" in the imports statements
-) Remove () at the end of method calls like size that does not have size effect.
-) Remove some of unused imports as I found them
-) Remove ";" in the imports statements
-) Remove () at the end of method call like size that does not have size effect.
Fix bug where scheduler could hang after task failure.
When a task fails, we need to call reviveOffers() so that the
task can be rescheduled on a different machine. In the current code,
the state in ClusterTaskSetManager indicating which tasks are
pending may be updated after revive offers is called (there's a
race condition here), so when revive offers is called, the task set
manager does not yet realize that there are failed tasks that need
to be relaunched.
This isn't currently unit tested but will be once my pull request for
merging the cluster and local schedulers goes in -- at which point
many more of the unit tests will exercise the code paths through
the cluster scheduler (currently the failure test suite uses the local
scheduler, which is why we didn't see this bug before).
Don't retry tasks when they fail due to a NotSerializableException
As with my previous pull request, this will be unit tested once the Cluster and Local schedulers get merged.
When a task fails, we need to call reviveOffers() so that the
task can be rescheduled on a different machine. In the current code,
the state in ClusterTaskSetManager indicating which tasks are
pending may be updated after revive offers is called (there's a
race condition here), so when revive offers is called, the task set
manager does not yet realize that there are failed tasks that need
to be relaunched.
Don't ignore spark.cores.max when using Mesos Coarse mode
totalCoresAcquired is decremented but never incremented, causing Spark to effectively ignore spark.cores.max in coarse grained Mesos mode.
Migrate the daemon thread started by DAGScheduler to Akka actor
`DAGScheduler` adopts an event queue and a daemon thread polling the it to process events sent to a `DAGScheduler`. This is a classical actor use case. By migrating this thread to Akka actor, we may benefit from both cleaner code and better performance (context switching cost of Akka actor is much less than that of a native thread).
But things become a little complicated when taking existing test code into consideration.
Code in `DAGSchedulerSuite` is somewhat tightly coupled with `DAGScheduler`, and directly calls `DAGScheduler.processEvent` instead of posting event messages to `DAGScheduler`. To minimize code change, I chose to let the actor to delegate messages to `processEvent`. Maybe this doesn't follow conventional actor usage, but I tried to make it apparently correct.
Another tricky part is that, since `DAGScheduler` depends on the `ActorSystem` provided by its field `env`, `env` cannot be null. But the `dagScheduler` field created in `DAGSchedulerSuite.before` was given a null `env`. What's more, `BlockManager.blockIdsToBlockManagers` checks whether `env` is null to determine whether to run the production code or the test code (bad smell here, huh?). I went through all callers of `BlockManager.blockIdsToBlockManagers`, and made sure that if `env != null` holds, then `blockManagerMaster == null` must also hold. That's the logic behind `BlockManager.scala` [line 896](https://github.com/liancheng/incubator-spark/compare/dagscheduler-actor-refine?expand=1#diff-2b643ea78c1add0381754b1f47eec132L896).
At last, since `DAGScheduler` instances are always `start()`ed after creation, I removed the `start()` method, and starts the `eventProcessActor` within the constructor.
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.
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`.
