diff options
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/configuration.md | 2 | ||||
| -rw-r--r-- | docs/python-programming-guide.md | 5 | ||||
| -rw-r--r-- | docs/spark-standalone.md | 38 |
3 files changed, 37 insertions, 8 deletions
diff --git a/docs/configuration.md b/docs/configuration.md index 6717757781..b1a0e19167 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -371,7 +371,7 @@ Apart from these, the following properties are also available, and may be useful <tr> <td>spark.shuffle.consolidateFiles</td> - <td>false</td> + <td>true</td> <td> If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations. </td> diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index dc187b3efe..c4236f8312 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -99,8 +99,9 @@ $ MASTER=local[4] ./bin/pyspark ## IPython -It is also possible to launch PySpark in [IPython](http://ipython.org), the enhanced Python interpreter. -To do this, set the `IPYTHON` variable to `1` when running `bin/pyspark`: +It is also possible to launch PySpark in [IPython](http://ipython.org), the +enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To +use IPython, set the `IPYTHON` variable to `1` when running `bin/pyspark`: {% highlight bash %} $ IPYTHON=1 ./bin/pyspark diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index f47d41f966..2a186261b7 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -10,11 +10,7 @@ In addition to running on the Mesos or YARN cluster managers, Spark also provide # Installing Spark Standalone to a Cluster -The easiest way to deploy Spark is by running the `./make-distribution.sh` script to create a binary distribution. -This distribution can be deployed to any machine with the Java runtime installed; there is no need to install Scala. - -The recommended procedure is to deploy and start the master on one node first, get the master spark URL, -then modify `conf/spark-env.sh` in the `dist/` directory before deploying to all the other nodes. +To install Spark Standlone mode, you simply place a compiled version of Spark on each node on the cluster. You can obtain pre-built versions of Spark with each release or [build it yourself](index.html#building). # Starting a Cluster Manually @@ -150,6 +146,38 @@ automatically set MASTER from the `SPARK_MASTER_IP` and `SPARK_MASTER_PORT` vari You can also pass an option `-c <numCores>` to control the number of cores that spark-shell uses on the cluster. +# Launching Applications Inside the Cluster + +You may also run your application entirely inside of the cluster by submitting your application driver using the submission client. The syntax for submitting applications is as follows: + + + ./spark-class org.apache.spark.deploy.Client launch + [client-options] \ + <cluster-url> <application-jar-url> <main-class> \ + [application-options] + + cluster-url: The URL of the master node. + application-jar-url: Path to a bundled jar including your application and all dependencies. Currently, the URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. + main-class: The entry point for your application. + + Client Options: + --memory <count> (amount of memory, in MB, allocated for your driver program) + --cores <count> (number of cores allocated for your driver program) + --supervise (whether to automatically restart your driver on application or node failure) + --verbose (prints increased logging output) + +Keep in mind that your driver program will be executed on a remote worker machine. You can control the execution environment in the following ways: + + * _Environment variables_: These will be captured from the environment in which you launch the client and applied when launching the driver program. + * _Java options_: You can add java options by setting `SPARK_JAVA_OPTS` in the environment in which you launch the submission client. + * _Dependencies_: You'll still need to call `sc.addJar` inside of your program to make your bundled application jar visible on all worker nodes. + +Once you submit a driver program, it will appear in the cluster management UI at port 8080 and +be assigned an identifier. If you'd like to prematurely terminate the program, you can do so using +the same client: + + ./spark-class org.apache.spark.deploy.client.DriverClient kill <driverId> + # Resource Scheduling The standalone cluster mode currently only supports a simple FIFO scheduler across applications. |