Impove Spark on Yarn Error handling
Improve cli error handling and only allow a certain number of worker failures before failing the application. This will help prevent users from doing foolish things and their jobs running forever. For instance using 32 bit java but trying to allocate 8G containers. This loops forever without this change, now it errors out after a certain number of retries. The number of tries is configurable. Also increase the frequency we ping the RM to increase speed at which we get containers if they die. The Yarn MR app defaults to pinging the RM every 1 seconds, so the default of 5 seconds here is fine. But that is configurable as well in case people want to change it.
I do want to make sure there aren't any cases that calling stopExecutors in CoarseGrainedSchedulerBackend would cause problems? I couldn't think of any and testing on standalone cluster as well as yarn.
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.
