Aside from trivial formatting changes, use nulls instead of Options for
DiskMapIterator, and add documentation for spark.shuffle.externalSorting
and spark.shuffle.memoryFraction.
Also, set spark.shuffle.memoryFraction to 0.3, and spark.storage.memoryFraction = 0.6.
This is an alternative to the existing approach, which evenly distributes the
collective shuffle memory among all running tasks. In the new approach, each
thread requests a chunk of memory whenever its map is about to multiplicatively
grow. If there is sufficient memory in the global pool, the thread allocates it
and grows its map. Otherwise, it spills.
A danger with the previous approach is that a new task may quickly fill up its
map before old tasks finish spilling, potentially causing an OOM. This approach
prevents this scenario as it favors existing tasks over new tasks; any thread
that may step over the boundary of other threads defensively backs off and
starts spilling.
Testing through spark-perf reveals: (1) When no spills have occured, the
performance of external sorting using this memory management approach is
essentially the same as without external sorting. (2) When one or more spills
have occured, the performance of external sorting is a small multiple (3x) worse
Simplify and fix pyspark script.
This patch removes compatibility for IPython < 1.0 but fixes the launch
script and makes it much simpler.
I tested this using the three commands in the PySpark documentation page:
1. IPYTHON=1 ./pyspark
2. IPYTHON_OPTS="notebook" ./pyspark
3. IPYTHON_OPTS="notebook --pylab inline" ./pyspark
There are two changes:
- We rely on PYTHONSTARTUP env var to start PySpark
- Removed the quotes around $IPYTHON_OPTS... having quotes
gloms them together as a single argument passed to `exec` which
seemed to cause ipython to fail (it instead expects them as
multiple arguments).
Add some missing Java API methods
These are primarily for setting job groups, canceling jobs, and setting names on RDDs. Seemed like useful stuff to expose in Java.
Bug fixes for updating the RDD block's memory and disk usage information
Bug fixes for updating the RDD block's memory and disk usage information.
From the code context, we can find that the memSize and diskSize here are both always equal to the size of the block. Actually, they never be zero. Thus, the logic here is wrong for recording the block usage in BlockStatus, especially for the blocks which are dropped from memory to ensure space for the new input rdd blocks. I have tested it that this would cause the storage metrics shown in the Storage webpage wrong and misleading. With this patch, the metrics will be okay.
Finally, Merry Christmas, guys:)
SPARK-998: Support Launching Driver Inside of Standalone Mode
[NOTE: I need to bring the tests up to date with new changes, so for now they will fail]
This patch provides support for launching driver programs inside of a standalone cluster manager. It also supports monitoring and re-launching of driver programs which is useful for long running, recoverable applications such as Spark Streaming jobs. For those jobs, this patch allows a deployment mode which is resilient to the failure of any worker node, failure of a master node (provided a multi-master setup), and even failures of the applicaiton itself, provided they are recoverable on a restart. Driver information, such as the status and logs from a driver, is displayed in the UI
There are a few small TODO's here, but the code is generally feature-complete. They are:
- Bring tests up to date and add test coverage
- Restarting on failure should be optional and maybe off by default.
- See if we can re-use akka connections to facilitate clients behind a firewall
A sensible place to start for review would be to look at the `DriverClient` class which presents users the ability to launch their driver program. I've also added an example program (`DriverSubmissionTest`) that allows you to test this locally and play around with killing workers, etc. Most of the code is devoted to persisting driver state in the cluster manger, exposing it in the UI, and dealing correctly with various types of failures.
Instructions to test locally:
- `sbt/sbt assembly/assembly examples/assembly`
- start a local version of the standalone cluster manager
```
./spark-class org.apache.spark.deploy.client.DriverClient \
-j -Dspark.test.property=something \
-e SPARK_TEST_KEY=SOMEVALUE \
launch spark://10.99.1.14:7077 \
../path-to-examples-assembly-jar \
org.apache.spark.examples.DriverSubmissionTest 1000 some extra options --some-option-here -X 13
```
- Go in the UI and make sure it started correctly, look at the output etc
- Kill workers, the driver program, masters, etc.
Minor style cleanup. Mostly on indenting & line width changes.
Focused on the few important files since they are the files that new contributors usually read first.
Don't delegate to users `sbt`.
This changes our `sbt/sbt` script to not delegate to the user's `sbt`
even if it is present. If users already have sbt installed and they
want to use their own sbt, we'd expect them to just call sbt directly
from within Spark. We no longer set any enironment variables or anything
from this script, so they should just launch sbt directly on their own.
There are a number of hard-to-debug issues which can come from the
current appraoch. One is if the user is unaware of an existing sbt
installation and now without explanation their build breaks because
they haven't configured options correctly (such as permgen size)
within their sbt (reported by @patmcdonough). Another is if the user has a much older version
of sbt hanging around, in which case some of the older versions
don't acutally work well when newer verisons of sbt are specified
in the build file (reported by @marmbrus). A third is if the user
has done some other modification to their sbt script, such as
setting it to delegate to sbt/sbt in Spark, and this causes
that to break (also reported by @marmbrus).
So to keep things simple let's just avoid this path and
remove it. Any user who already has sbt and wants to build
spark with it should be able to understand easily how to do it.
This changes our `sbt/sbt` script to not delegate to the user's `sbt`
even if it is present. If users already have sbt installed and they
want to use their own sbt, we'd expect them to just call sbt directly
from within Spark. We no longer set any enironment variables or anything
from this script, so they should just launch sbt directly on their own.
There are a number of hard-to-debug issues which can come from the
current appraoch. One is if the user is unaware of an existing sbt
installation and now without explanation their build breaks because
they haven't configured options correctly (such as permgen size)
within their sbt. Another is if the user has a much older version
of sbt hanging around, in which case some of the older versions
don't acutally work well when newer verisons of sbt are specified
in the build file (reported by @marmbrus). A third is if the user
has done some other modification to their sbt script, such as
setting it to delegate to sbt/sbt in Spark, and this causes
that to break (also reported by @marmbrus).
So to keep things simple let's just avoid this path and
remove it. Any user who already has sbt and wants to build
spark with it should be able to understand easily how to do it.
Fixing config option "retained_stages" => "retainedStages".
This is a very esoteric option and it's out of sync with the style we use.
So it seems fitting to fix it for 0.9.0.
Set boolean param name for call to SparkHadoopMapReduceUtil.newTaskAttemptID
Set boolean param name for call to SparkHadoopMapReduceUtil.newTaskAttemptID to make it clear which param being set.
Add CDH Repository to Maven Build
At some point this was removed from the Maven build... so I'm adding it back. It's needed for the Hadoop2 tests we run on Jenkins and it's also included in the SBT build.
Remove calls to deprecated mapred's OutputCommitter.cleanupJob
Since Hadoop 1.0.4 the mapred OutputCommitter.commitJob should do cleanup job via call to OutputCommitter.cleanupJob,
Remove SparkHadoopWriter.cleanup since it is used only by PairRDDFunctions.
In fact the implementation of mapred OutputCommitter.commitJob looks like this:
public void commitJob(JobContext jobContext) throws IOException {
cleanupJob(jobContext);
}
support distributing extra files to worker for yarn client mode
So that user doesn't need to package all dependency into one assemble jar as spark app jar
the mapred OutputCommitter.commitJob should do cleanup job.
In fact the implementation of mapred OutputCommitter.commitJob looks like this:
public void commitJob(JobContext jobContext) throws IOException {
cleanupJob(jobContext);
}
(The jobContext input argument is type of org.apache.hadoop.mapred.JobContext)
SPARK-1009 Updated MLlib docs to show how to use it in Python
In addition added detailed examples for regression, clustering and recommendation algorithms in a separate Scala section. Fixed a few minor issues with existing documentation.
