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Sorts all configuration options present on the `configuration.md` page to ease readability. Author: Brennon York <brennon.york@capitalone.com> Closes #3863 from brennonyork/SPARK-1182 and squashes the following commits: 5696f21 [Brennon York] fixed merge conflict with port comments 81a7b10 [Brennon York] capitalized A in Allocation e240486 [Brennon York] moved all spark.mesos properties into the running-on-mesos doc 7de5f75 [Brennon York] moved serialization from application to compression and serialization section a16fec0 [Brennon York] moved shuffle settings from network to shuffle f8fa286 [Brennon York] sorted encryption category 1023f15 [Brennon York] moved initialExecutors e9d62aa [Brennon York] fixed akka.heartbeat.interval 25e6f6f [Brennon York] moved spark.executer.user* 4625ade [Brennon York] added spark.executor.extra* items 4ee5648 [Brennon York] fixed merge conflicts 1b49234 [Brennon York] sorting mishap 2b5758b [Brennon York] sorting mishap 6fbdf42 [Brennon York] sorting mishap 55dc6f8 [Brennon York] sorted security ec34294 [Brennon York] sorted dynamic allocation 2a7c4a3 [Brennon York] sorted scheduling aa9acdc [Brennon York] sorted networking a4380b8 [Brennon York] sorted execution behavior 27f3919 [Brennon York] sorted compression and serialization 80a5bbb [Brennon York] sorted spark ui 3f32e5b [Brennon York] sorted shuffle behavior 6c51b38 [Brennon York] sorted runtime environment efe9d6f [Brennon York] sorted application properties |
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assembly | ||
bagel | ||
bin | ||
build | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
mllib | ||
network | ||
project | ||
python | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
yarn | ||
.gitattributes | ||
.gitignore | ||
.rat-excludes | ||
CONTRIBUTING.md | ||
LICENSE | ||
make-distribution.sh | ||
NOTICE | ||
pom.xml | ||
README.md | ||
scalastyle-config.xml | ||
tox.ini |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run all automated tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.