Deduplicate Local and Cluster schedulers.
The code in LocalScheduler/LocalTaskSetManager was nearly identical
to the code in ClusterScheduler/ClusterTaskSetManager. The redundancy
made making updating the schedulers unnecessarily painful and error-
prone. This commit combines the two into a single TaskScheduler/
TaskSetManager.
Unfortunately the diff makes this change look much more invasive than it is -- TaskScheduler.scala is only superficially changed (names updated, overrides removed) from the old ClusterScheduler.scala, and the same with
TaskSetManager.scala.
Thanks @rxin for suggesting this change!
Clean up shuffle files once their metadata is gone
Previously, we would only clean the in-memory metadata for consolidated shuffle files.
Additionally, fixes a bug where the Metadata Cleaner was ignoring type-specific TTLs.
Refactored the streaming scheduler and added StreamingListener interface
- Refactored the streaming scheduler for cleaner code. Specifically, the JobManager was renamed to JobScheduler, as it does the actual scheduling of Spark jobs to the SparkContext. The earlier Scheduler was renamed to JobGenerator, as it actually generates the jobs from the DStreams. The JobScheduler starts the JobGenerator. Also, moved all the scheduler related code from spark.streaming to spark.streaming.scheduler package.
- Implemented the StreamingListener interface, similar to SparkListener. The streaming version of StatusReportListener prints the batch processing time statistics (for now). Added StreamingListernerSuite to test it.
- Refactored streaming TestSuiteBase for deduping code in the other streaming testsuites.
Track and report task result serialisation time.
- DirectTaskResult now has a ByteBuffer valueBytes instead of a T value.
- DirectTaskResult now has a member function T value() that deserialises valueBytes.
- Executor serialises value into a ByteBuffer and passes it to DTR's ctor.
- Executor tracks the time taken to do so and puts it in a new field in TaskMetrics.
- StagePage now reports serialisation time from TaskMetrics along with the other things it reported.
Previously, we would only clean the in-memory metadata for consolidated
shuffle files.
Additionally, fixes a bug where the Metadata Cleaner was ignoring type-
specific TTLs.
Add collectPartition to JavaRDD interface.
This interface is useful for implementing `take` from other language frontends where the data is serialized. Also remove `takePartition` from PythonRDD and use `collectPartition` in rdd.py.
Thanks @concretevitamin for the original change and tests.
Change the implementation to use runJob instead of PartitionPruningRDD.
Also update the unit tests and the python take implementation
to use the new interface.
despite having a low number of nodes and relatively small workload (16 nodes, <1.5 TB data).
This would cause an entire job to fail at the beginning of the reduce phase.
There is no particular reason for this value to be small as a timeout should only occur
in an exceptional situation.
Also centralized the reading of spark.akka.askTimeout to AkkaUtils (surely this can later
be cleaned up to use Typesafe).
Finally, deleted some lurking implicits. If anyone can think of a reason they should still
be there, please let me know.
Fix for spark.task.maxFailures not enforced correctly.
Docs at http://spark.incubator.apache.org/docs/latest/configuration.html say:
```
spark.task.maxFailures
Number of individual task failures before giving up on the job. Should be greater than or equal to 1. Number of allowed retries = this value - 1.
```
Previous implementation worked incorrectly. When for example `spark.task.maxFailures` was set to 1, the job was aborted only after the second task failure, not after the first one.
- Refactored Scheduler + JobManager to JobGenerator + JobScheduler and
added JobSet for cleaner code. Moved scheduler related code to
streaming.scheduler package.
- Added StreamingListener trait (similar to SparkListener) to enable
gathering to streaming stats like processing times and delays.
StreamingContext.addListener() to added listeners.
- Deduped some code in streaming tests by modifying TestSuiteBase, and
added StreamingListenerSuite.
- Made file stream more robust to transient failures.
- Changed Spark.setCheckpointDir API to not have the second
'useExisting' parameter. Spark will always create a unique directory
for checkpointing underneath the directory provide to the funtion.
- Fixed bug wrt local relative paths as checkpoint directory.
- Made DStream and RDD checkpointing use
SparkContext.hadoopConfiguration, so that more HDFS compatible
filesystems are supported for checkpointing.
stageId <--> jobId mapping in DAGScheduler
Okay, I think this one is ready to go -- or at least it's ready for review and discussion. It's a carry-over of https://github.com/mesos/spark/pull/842 with updates for the newer job cancellation functionality. The prior discussion still applies. I've actually changed the job cancellation flow a bit: Instead of ``cancelTasks`` going to the TaskScheduler and then ``taskSetFailed`` coming back to the DAGScheduler (resulting in ``abortStage`` there), the DAGScheduler now takes care of figuring out which stages should be cancelled, tells the TaskScheduler to cancel tasks for those stages, then does the cleanup within the DAGScheduler directly without the need for any further prompting by the TaskScheduler.
I know of three outstanding issues, each of which can and should, I believe, be handled in follow-up pull requests:
1) https://spark-project.atlassian.net/browse/SPARK-960
2) JobLogger should be re-factored to eliminate duplication
3) Related to 2), the WebUI should also become a consumer of the DAGScheduler's new understanding of the relationship between jobs and stages so that it can display progress indication and the like grouped by job. Right now, some of this information is just being sent out as part of ``SparkListenerJobStart`` messages, but more or different job <--> stage information may need to be exported from the DAGScheduler to meet listeners needs.
Except for the eventQueue -> Actor commit, the rest can be cherry-picked almost cleanly into branch-0.8. A little merging is needed in MapOutputTracker and the DAGScheduler. Merged versions of those files are in aba2b40ce0
Note that between the recent Actor change in the DAGScheduler and the cleaning up of DAGScheduler data structures on job completion in this PR, some races have been introduced into the DAGSchedulerSuite. Those tests usually pass, and I don't think that better-behaved code that doesn't directly inspect DAGScheduler data structures should be seeing any problems, but I'll work on fixing DAGSchedulerSuite as either an addition to this PR or as a separate request.
UPDATE: Fixed the race that I introduced. Created a JIRA issue (SPARK-965) for the one that was introduced with the switch to eventProcessorActor in the DAGScheduler.
Change the name of input argument in ClusterScheduler#initialize from context to backend.
The SchedulerBackend used to be called ClusterSchedulerContext so just want to make small
change of the input param in the ClusterScheduler#initialize to reflect this.
Added logging of scheduler delays to UI
This commit adds two metrics to the UI:
1) The time to get task results, if they're fetched remotely
2) The scheduler delay. When the scheduler starts getting overwhelmed (because it can't keep up with the rate at which tasks are being submitted), the result is that tasks get delayed on the tail-end: the message from the worker saying that the task has completed ends up in a long queue and takes a while to be processed by the scheduler. This commit records that delay in the UI so that users can tell when the scheduler is becoming the bottleneck.
Memoize preferred locations in ZippedPartitionsBaseRDD
so preferred location computation doesn't lead to exponential explosion.
This was a problem in GraphX where we have a whole chain of RDDs that are ZippedPartitionsRDD's, and the preferred locations were taking eternity to compute.
(cherry picked from commit e36fe55a03)
Signed-off-by: Reynold Xin <rxin@apache.org>
The SchedulerBackend used to be called ClusterSchedulerContext so just want to make small
change of the input param in the ClusterScheduler#initialize to reflect this.
Hadoop 2.2 migration
Includes support for the YARN API stabilized in the Hadoop 2.2 release, and a few style patches.
Short description for each set of commits:
a98f5a0 - "Misc style changes in the 'yarn' package"
a67ebf4 - "A few more style fixes in the 'yarn' package"
Both of these are some minor style changes, such as fixing lines over 100 chars, to the existing YARN code.
ab8652f - "Add a 'new-yarn' directory ... "
Copies everything from `SPARK_HOME/yarn` to `SPARK_HOME/new-yarn`. No actual code changes here.
4f1c3fa - "Hadoop 2.2 YARN API migration ..."
API patches to code in the `SPARK_HOME/new-yarn` directory. There are a few more small style changes mixed in, too.
Based on @colorant's Hadoop 2.2 support for the scala-2.10 branch in #141.
a1a1c62 - "Add optional Hadoop 2.2 settings in sbt build ... "
If Spark should be built against Hadoop 2.2, then:
a) the `org.apache.spark.deploy.yarn` package will be compiled from the `new-yarn` directory.
b) Protobuf v2.5 will be used as a Spark dependency, since Hadoop 2.2 depends on it. Also, Spark will be built against a version of Akka v2.0.5 that's built against Protobuf 2.5, named `akka-2.0.5-protobuf-2.5`. The patched Akka is here: https://github.com/harveyfeng/akka/tree/2.0.5-protobuf-2.5, and was published to local Ivy during testing.
There's also a new boolean environment variable, `SPARK_IS_NEW_HADOOP`, that users can manually set if their `SPARK_HADOOP_VERSION` specification does not start with `2.2`, which is how the build file tries to detect a 2.2 version. Not sure if this is necessary or done in the best way, though...
Fix small bug in web UI and minor clean-up.
There was a bug where sorting order didn't work correctly for write time metrics.
I also cleaned up some earlier code that fixed the same issue for read and
write bytes.
There was a bug where sorting order didn't work correctly for write time metrics.
I also cleaned up some earlier code that fixed the same issue for read and
write bytes.
...and make sure that DAGScheduler data structures are cleaned up on job completion.
Initial effort and discussion at https://github.com/mesos/spark/pull/842
Re-enable zk:// urls for Mesos SparkContexts
This was broken in PR #71 when we explicitly disallow anything that didn't fit a mesos:// url.
Although it is not really clear that a zk:// url should match Mesos, it is what the docs say and it is necessary for backwards compatibility.
Additionally added a unit test for the creation of all types of TaskSchedulers. Since YARN and Mesos are not necessarily available in the system, they are allowed to pass as long as the YARN/Mesos code paths are exercised.
Scheduler quits when newStage fails
The current scheduler thread does not handle exceptions from newStage stage while launching new jobs. The thread fails on any exception that gets triggered at that level, leaving the cluster hanging with no schduler.
The current scheduler thread does not handle exceptions from createStage stage while launching new jobs. The thread fails on any exception that gets triggered at that level, leaving the cluster hanging with no schduler.
This was broken in PR #71 when we explicitly disallow anything that
didn't fit a mesos:// url.
Although it is not really clear that a zk:// url should match Mesos,
it is what the docs say and it is necessary for backwards compatibility.
SPARK-965: https://spark-project.atlassian.net/browse/SPARK-965
SPARK-966: https://spark-project.atlassian.net/browse/SPARK-966
* Add back DAGScheduler.start(), eventProcessActor is created and started here.
Notice that function is only called by SparkContext.
* Cancel the scheduled stage resubmission task when stopping eventProcessActor
* Add a new DAGSchedulerEvent ResubmitFailedStages
This event message is sent by the scheduled stage resubmission task to eventProcessActor. In this way, DAGScheduler.resubmitFailedStages is guaranteed to be executed from the same thread that runs DAGScheduler.processEvent.
Please refer to discussion in SPARK-966 for details.
add http timeout for httpbroadcast
While pulling task bytecode from HttpBroadcast server, there's no timeout value set. This may cause spark executor code hang and other task in the same executor process wait for the lock. I have encountered the issue in my cluster. Here's the stacktrace I captured : https://gist.github.com/haitaoyao/7655830
So add a time out value to ensure the task fail fast.
Custom Serializers for PySpark
This pull request adds support for custom serializers to PySpark. For now, all Python-transformed (or parallelize()d RDDs) are serialized with the same serializer that's specified when creating SparkContext.
For now, PySpark includes `PickleSerDe` and `MarshalSerDe` classes for using Python's `pickle` and `marshal` serializers. It's pretty easy to add support for other serializers, although I still need to add instructions on this.
A few notable changes:
- The Scala `PythonRDD` class no longer manipulates Pickled objects; data from `textFile` is written to Python as MUTF-8 strings. The Python code performs the appropriate bookkeeping to track which deserializer should be used when reading an underlying JavaRDD. This mechanism could also be used to support other data exchange formats, such as MsgPack.
- Several magic numbers were refactored into constants.
- Batching is implemented by wrapping / decorating an unbatched SerDe.
Log a warning if a task's serialized size is very big
As per Reynold's instructions, we now create a warning level log entry if a task's serialized size is too big. "Too big" is currently defined as 100kb. This warning message is generated at most once for each stage.
OpenHashSet fixes
Incorporated ideas from pull request #200.
- Use Murmur Hash 3 finalization step to scramble the bits of HashCode
instead of the simpler version in java.util.HashMap; the latter one
had trouble with ranges of consecutive integers. Murmur Hash 3 is used
by fastutil.
- Don't check keys for equality when re-inserting due to growing the
table; the keys will already be unique.
- Remember the grow threshold instead of recomputing it on each insert
Also added unit tests for size estimation for specialized hash sets and maps.
Use the proper partition index in mapPartitionsWIthIndex
mapPartitionsWithIndex uses TaskContext.partitionId as the partition index. TaskContext.partitionId used to be identical to the partition index in a RDD. However, pull request #186 introduced a scenario (with partition pruning) that the two can be different. This pull request uses the right partition index in all mapPartitionsWithIndex related calls.
Also removed the extra MapPartitionsWIthContextRDD and put all the mapPartitions related functionality in MapPartitionsRDD.
For SPARK-527, Support spark-shell when running on YARN
sync to trunk and resubmit here
In current YARN mode approaching, the application is run in the Application Master as a user program thus the whole spark context is on remote.
This approaching won't support application that involve local interaction and need to be run on where it is launched.
So In this pull request I have a YarnClientClusterScheduler and backend added.
With this scheduler, the user application is launched locally,While the executor will be launched by YARN on remote nodes with a thin AM which only launch the executor and monitor the Driver Actor status, so that when client app is done, it can finish the YARN Application as well.
This enables spark-shell to run upon YARN.
This also enable other Spark applications to have the spark context to run locally with a master-url "yarn-client". Thus e.g. SparkPi could have the result output locally on console instead of output in the log of the remote machine where AM is running on.
Docs also updated to show how to use this yarn-client mode.
- Use Murmur Hash 3 finalization step to scramble the bits of HashCode
instead of the simpler version in java.util.HashMap; the latter one
had trouble with ranges of consecutive integers. Murmur Hash 3 is used
by fastutil.
- Don't check keys for equality when re-inserting due to growing the
table; the keys will already be unique
- Remember the grow threshold instead of recomputing it on each insert
