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.
Add missing space after "Serialized" in StorageLevel
Current code creates outputs like:
scala> res0.getStorageLevel.description
res2: String = Serialized1x Replicated
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.
Fix UnicodeEncodeError in PySpark saveAsTextFile() (SPARK-970)
This fixes [SPARK-970](https://spark-project.atlassian.net/browse/SPARK-970), an issue where PySpark's saveAsTextFile() could throw UnicodeEncodeError when called on an RDD of Unicode strings.
Please merge this into master and branch-0.8.
...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
This also includes:
-Change `isNewYarn` to `isNewHadoop`, since the protobuf-2.5 dependency is from Hadoop-2.2 itself.
-Regexp bugix
Credits to @alig for this patch.
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.
Bugfix: SPARK-965 & SPARK-966
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](https://spark-project.atlassian.net/browse/SPARK-966) for details.
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.
