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.
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.
