Spark (branch-0.5)

Lightning-Fast Cluster Computing - http://www.spark-project.org/

Earliest non-alpha version

# fxrc @ popos in ~/SourceCode/spark-0.5 on git:main o [14:12:42] 
$ scc .
───────────────────────────────────────────────────────────────────────────────
Language                 Files     Lines   Blanks  Comments     Code Complexity
───────────────────────────────────────────────────────────────────────────────
Scala                       81     10683     1605      1222     7856       1094
XML                         66       868        9         0      859          0
Shell                        2         7        0         2        5          0
gitignore                    2        94       17        34       43          0
BASH                         1        74       12        11       51         11
Markdown                     1        72       28         0       44          0
───────────────────────────────────────────────────────────────────────────────
Total                      153     11798     1671      1269     8858       1105
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop (organic) $266,868
Estimated Schedule Effort (organic) 8.325440 months
Estimated People Required (organic) 2.847780
───────────────────────────────────────────────────────────────────────────────
Processed 393946 bytes, 0.394 megabytes (SI)
───────────────────────────────────────────────────────────────────────────────

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project wiki at http://github.com/mesos/spark/wiki. This file only contains basic setup instructions.

Building

Spark requires Scala 2.9.2. The project is built using Simple Build Tool (SBT), which is packaged with it. To build Spark and its example programs, run:

sbt/sbt package

To run Spark, you will need to have Scala's bin in your PATH, or you will need to set the SCALA_HOME environment variable to point to where you've installed Scala. Scala must be accessible through one of these methods on Mesos slave nodes as well as on the master.

To run one of the examples, first run sbt/sbt package to build them. Then use ./run <class> <params>. For example:

./run spark.examples.SparkLR local[2]

will run the Logistic Regression example locally on 2 CPUs.

Each of the example programs prints usage help if no params are given.

All of the Spark samples take a <host> parameter that is the Mesos master to connect to. This can be a Mesos URL, or "local" to run locally with one thread, or "local[N]" to run locally with N threads.

A Note About Hadoop

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the HDFS API has changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs. You can change the version by setting the HADOOP_VERSION variable at the top of project/SparkBuild.scala, then rebuilding Spark.

Configuration

Spark can be configured through two files: conf/java-opts and conf/spark-env.sh.

In java-opts, you can add flags to be passed to the JVM when running Spark.

In spark-env.sh, you can set any environment variables you wish to be available when running Spark programs, such as PATH, SCALA_HOME, etc. There are also several Spark-specific variables you can set:

SPARK_CLASSPATH: Extra entries to be added to the classpath, separated by ":".
SPARK_MEM: Memory for Spark to use, in the format used by java's -Xmx option (for example, -Xmx200m means 200 MB, -Xmx1g means 1 GB, etc).
SPARK_LIBRARY_PATH: Extra entries to add to java.library.path for locating shared libraries.
SPARK_JAVA_OPTS: Extra options to pass to JVM.
MESOS_NATIVE_LIBRARY: Your Mesos library, if you want to run on a Mesos cluster. For example, this might be /usr/local/lib/libmesos.so on Linux.

Note that spark-env.sh must be a shell script (it must be executable and start with a #! header to specify the shell to use).

fxrcode / spark-0.5