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).
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.
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
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.
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).
Conf improvements
There are two new features.
1. Allow users to set arbitrary akka configurations via spark conf.
2. Allow configuration to be printed in logs for diagnosis.
Add a script to download sbt if not present on the system
As per the discussion on the dev mailing list this script will use the system sbt if present or otherwise attempt to install the sbt launcher. The fall back error message in the event it fails instructs the user to install sbt. While the URLs it fetches from aren't controlled by the spark project directly, they are stable and the current authoritative sources.
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.
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.
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.
I've diff'd this patch against my own -- since they were both created
independently, this means that two sets of eyes have gone over all the
merge conflicts that were created, so I'm feeling significantly more
confident in the resulting PR.
@rxin has looked at the changes to the repl and is resoundingly
confident that they are correct.
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.
This patch adds an operator called repartition with more straightforward
semantics than the current `coalesce` operator. There are a few use cases
where this operator is useful:
1. If a user wants to increase the number of partitions in the RDD. This
is more common now with streaming. E.g. a user is ingesting data on one
node but they want to add more partitions to ensure parallelism of
subsequent operations across threads or the cluster.
Right now they have to call rdd.coalesce(numSplits, shuffle=true) - that's
super confusing.
2. If a user has input data where the number of partitions is not known. E.g.
> sc.textFile("some file").coalesce(50)....
This is both vague semantically (am I growing or shrinking this RDD) but also,
may not work correctly if the base RDD has fewer than 50 partitions.
The new operator forces shuffles every time, so it will always produce exactly
the number of new partitions. It also throws an exception rather than silently
not-working if a bad input is passed.
I am currently adding streaming tests (requires refactoring some of the test
suite to allow testing at partition granularity), so this is not ready for
merge yet. But feedback is welcome.
Add classmethod to SparkContext to set system properties.
Add a new classmethod to SparkContext to set system properties like is
possible in Scala/Java. Unlike the Java/Scala implementations, there's
no access to System until the JVM bridge is created. Since
SparkContext handles that, move the initialization of the JVM
connection to a separate classmethod that can safely be called
repeatedly as long as the same instance (or no instance) is provided.
Standalone Scheduler fault tolerance using ZooKeeper
This patch implements full distributed fault tolerance for standalone scheduler Masters.
There is only one master Leader at a time, which is actively serving scheduling
requests. If this Leader crashes, another master will eventually be elected, reconstruct
the state from the first Master, and continue serving scheduling requests.
Leader election is performed using the ZooKeeper leader election pattern. We try to minimize
the use of ZooKeeper and the assumptions about ZooKeeper's behavior, so there is a layer of
retries and session monitoring on top of the ZooKeeper client.
Master failover follows directly from the single-node Master recovery via the file
system (patch d5a96fe), save that the Master state is stored in ZooKeeper instead.
Configuration:
By default, no recovery mechanism is enabled (spark.deploy.recoveryMode = NONE).
By setting spark.deploy.recoveryMode to ZOOKEEPER and setting spark.deploy.zookeeper.url
to an appropriate ZooKeeper URL, ZooKeeper recovery mode is enabled.
By setting spark.deploy.recoveryMode to FILESYSTEM and setting spark.deploy.recoveryDirectory
to an appropriate directory accessible by the Master, we will keep the behavior of from d5a96fe.
Additionally, places where a Master could be specificied by a spark:// url can now take
comma-delimited lists to specify backup masters. Note that this is only used for registration
of NEW Workers and application Clients. Once a Worker or Client has registered with the
Master Leader, it is "in the system" and will never need to register again.
Conflicts:
bagel/pom.xml
core/pom.xml
core/src/test/scala/org/apache/spark/ui/UISuite.scala
examples/pom.xml
mllib/pom.xml
pom.xml
project/SparkBuild.scala
repl/pom.xml
streaming/pom.xml
tools/pom.xml
In scala 2.10, a shorter representation is used for naming artifacts
so changed to shorter scala version for artifacts and made it a property in pom.
