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
Add graphite sink for metrics
This adds a metrics sink for graphite. The sink must
be configured with the host and port of a graphite node
and optionally may be configured with a prefix that will
be prepended to all metrics that are sent to graphite.
XORShift RNG with unit tests and benchmark
This patch was introduced to address SPARK-950 - the discussion below the ticket explains not only the rationale, but also the design and testing decisions: https://spark-project.atlassian.net/browse/SPARK-950
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)
XORShiftRandom is also an object with a main method taking the
number of iterations as an argument, so you can also run it
from the command line.
Fix 'timeWriting' stat for shuffle files
Due to concurrent git branches, changes from shuffle file consolidation patch
caused the shuffle write timing patch to no longer actually measure the time,
since it requires time be measured after the stream has been closed.
Also changed the semantics of the index parameter in mapPartitionsWithIndex from the partition index of the output partition to the partition index in the current RDD.
AppendOnlyMap fixes
- Chose a more random reshuffling step for values returned by Object.hashCode to avoid some long chaining that was happening for consecutive integers (e.g. `sc.makeRDD(1 to 100000000, 100).map(t => (t, t)).reduceByKey(_ + _).count`)
- Some other small optimizations throughout (see commit comments)
- 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
- 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.
- Use Object.equals() instead of Scala's == to compare keys, because the
latter does extra casts for numeric types (see the equals method in
https://github.com/scala/scala/blob/master/src/library/scala/runtime/BoxesRunTime.java)
Support preservesPartitioning in RDD.zipPartitions
In `RDD.zipPartitions`, add support for a `preservesPartitioning` option (similar to `RDD.mapPartitions`) that reuses the first RDD's partitioner.
Due to concurrent git branches, changes from shuffle file consolidation patch
caused the shuffle write timing patch to no longer actually measure the time,
since it requires time be measured after the stream has been closed.
With this scheduler, the user application is launched locally,
While the executor will be launched by YARN on remote nodes.
This enables spark-shell to run upon YARN.
PartitionPruningRDD is using index from parent
I was getting a ArrayIndexOutOfBoundsException exception after doing union on pruned RDD. The index it was using on the partition was the index in the original RDD not the new pruned RDD.
Cleanup to remove semicolons (;) from Scala code
-) The main reason for this PR is to remove semicolons from single statements of Scala code.
-) Remove unused imports as I see them
-) Fix ASF comment header from some of files (bad copy paste I suppose)
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.
