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Conflicts:
core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
core/src/main/scala/org/apache/spark/deploy/master/Master.scala
core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
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Fix handling of empty SPARK_EXAMPLES_JAR
Currently if SPARK_EXAMPLES_JAR is left unset you get a null pointer exception when running the examples (atleast on spark on yarn). The null now gets turned into a string of "null" when its put into the SparkConf so addJar no longer properly ignores it. This fixes that so that it can be left unset.
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Quiet ERROR-level Akka Logs
This fixes an issue I've seen where akka logs a bunch of things at ERROR level when connecting to a standalone cluster, even in the normal case. I noticed that even when lifecycle logging was disabled, the netty code inside of akka still logged away via akka's EndpointWriter class. There are also some other log streams that I think are new in akka 2.2.1 that I've disabled.
Finally, I added some better logging to the standalone client. This makes it more clear when a connection failure occurs what is going on. Previously it never explicitly said if a connection attempt had failed.
The commit messages here have some more detail.
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Without these it's a bit less clear what's going on for the user.
One thing I realize when doing this is that akka itself actually retries
the initial association. So the retry we currently have is redundant with
akka's.
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I noticed when connecting to a standalone cluster Spark gives a bunch
of Akka ERROR logs that make it seem like something is failing.
This patch does two things:
1. Akka dead letter logging is turned on/off according to the existing
lifecycle spark property.
2. We explicitly silence akka's EndpointWriter log in log4j. This is necessary
because for some reason that log doesn't pick up on the lifecycle
logging settings. After a few hours of debugging this was the only solution
I found that worked.
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Removing SPARK_EXAMPLES_JAR in the code
This re-writes all of the examples to use the `SparkContext.jarOfClass` mechanism for loading the examples jar. This necessary for environments like YARN and the Standalone mode where example programs will be submit from inside the cluster rather than at the client using `./spark-example`.
This still leaves SPARK_EXAMPLES_JAR in place in the shell scripts for setting up the classpath if `./spark-example` is run.
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no constructor that accepts SparkConf.
This maintains backward compatibility with older serializers implemented by users.
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Conflicts:
core/src/test/scala/org/apache/spark/DriverSuite.scala
docs/python-programming-guide.md
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Spark-915 segregate scripts
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spark-915-segregate-scripts
Conflicts:
bin/spark-shell
core/pom.xml
core/src/main/scala/org/apache/spark/SparkContext.scala
core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala
core/src/test/scala/org/apache/spark/DriverSuite.scala
python/run-tests
sbin/compute-classpath.sh
sbin/spark-class
sbin/stop-slaves.sh
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instead of SPARK_MEM, user should add application jars to SPARK_CLASSPATH
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
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Signed-off-by: shane-huang <shengsheng.huang@intel.com>
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Signed-off-by: shane-huang <shengsheng.huang@intel.com>
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Closes #316
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Remove erroneous FAILED state for killed tasks.
Currently, when tasks are killed, the Executor first sends a
status update for the task with a "KILLED" state, and then
sends a second status update with a "FAILED" state saying that
the task failed due to an exception. The second FAILED state is
misleading/unncessary, and occurs due to a NonLocalReturnControl
Exception that gets thrown due to the way we kill tasks. This
commit eliminates that problem.
I'm not at all sure that this is the best way to fix this problem,
so alternate suggestions welcome. @rxin guessing you're the right
person to look at this.
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Currently, when tasks are killed, the Executor first sends a
status update for the task with a "KILLED" state, and then
sends a second status update with a "FAILED" state saying that
the task failed due to an exception. The second FAILED state is
misleading/unncessary, and occurs due to a NonLocalReturnControl
Exception that gets thrown due to the way we kill tasks. This
commit eliminates that problem.
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Improvements to DStream window ops and refactoring of Spark's CheckpointSuite
- Added a new RDD - PartitionerAwareUnionRDD. Using this RDD, one can take multiple RDDs partitioned by the same partitioner and unify them into a single RDD while preserving the partitioner. So m RDDs with p partitions each will be unified to a single RDD with p partitions and the same partitioner. The preferred location for each partition of the unified RDD will be the most common preferred location of the corresponding partitions of the parent RDDs. For example, location of partition 0 of the unified RDD will be where most of partition 0 of the parent RDDs are located.
- Improved the performance of DStream's reduceByKeyAndWindow and groupByKeyAndWindow. Both these operations work by doing per-batch reduceByKey/groupByKey and then using PartitionerAwareUnionRDD to union the RDDs across the window. This eliminates a shuffle related to the window operation, which can reduce batch processing time by 30-40% for simple workloads.
- Fixed bugs and simplified Spark's CheckpointSuite. Some of the tests were incorrect and unreliable. Added missing tests for ZippedRDD. I can go into greater detail if necessary.
- Added mapSideCombine option to combineByKeyAndWindow.
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CheckpointSuite to make the tests simpler and more reliable. Added missing test for ZippedRDD.
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Conflicts:
streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala
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This function was leftover from a while ago, and now just
passes all calls through to the abort() function, so this
commit deletes it.
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SPARK-991: Report information gleaned from a Python stacktrace in the UI
Scala:
- Added setCallSite/clearCallSite to SparkContext and JavaSparkContext.
These functions mutate a LocalProperty called "externalCallSite."
- Add a wrapper, getCallSite, that checks for an externalCallSite and, if
none is found, calls the usual Utils.formatSparkCallSite.
- Change everything that calls Utils.formatSparkCallSite to call
getCallSite instead. Except getCallSite.
- Add wrappers to setCallSite/clearCallSite wrappers to JavaSparkContext.
Python:
- Add a gruesome hack to rdd.py that inspects the traceback and guesses
what you want to see in the UI.
- Add a RAII wrapper around said gruesome hack that calls
setCallSite/clearCallSite as appropriate.
- Wire said RAII wrapper up around three calls into the Scala code.
I'm not sure that I hit all the spots with the RAII wrapper. I'm also
not sure that my gruesome hack does exactly what we want.
One could also approach this change by refactoring
runJob/submitJob/runApproximateJob to take a call site, then threading
that parameter through everything that needs to know it.
One might object to the pointless-looking wrappers in JavaSparkContext.
Unfortunately, I can't directly access the SparkContext from
Python---or, if I can, I don't know how---so I need to wrap everything
that matters in JavaSparkContext.
Conflicts:
core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
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Also replaced SparkConf.getOrElse with just a "get" that takes a default
value, and added getInt, getLong, etc to make code that uses this
simpler later on.
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Conflicts:
core/src/main/scala/org/apache/spark/SparkContext.scala
core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
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Conflicts:
project/SparkBuild.scala
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Conflicts:
core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
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Typesafe Config caches system properties the first time it's invoked
by default, ignoring later changes unless you do something special
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