To run unit test, start SBT console and type:
compile
test-only org.apache.spark.util.XORShiftRandomSuite
To run benchmark, type:
project core
console
Once the Scala console starts, type:
org.apache.spark.util.XORShiftRandom.benchmark(100000000)
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.
spark-assembly.jar fails to authenticate with YARN ResourceManager
The META-INF/services/ sbt MergeStrategy was discarding support for Kerberos, among others. This pull request changes to a merge strategy similar to sbt-assembly's default. I've also included an update to sbt-assembly 0.9.2, a minor fix to it's zip file handling.
Allow spark on yarn to be run from HDFS.
Allows the spark.jar, app.jar, and log4j.properties to be put into hdfs. Allows you to specify the files on a different hdfs cluster and it will copy them over. It makes sure permissions are correct and makes sure to put things into public distributed cache so they can be reused amongst users if their permissions are appropriate. Also add a bit of error handling for missing arguments.
