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Marcelo Vanzin f9e569452e [SPARK-5466] Add explicit guava dependencies where needed.
One side-effect of shading guava is that it disappears as a transitive
dependency. For Hadoop 2.x, this was masked by the fact that Hadoop
itself depends on guava. But certain versions of Hadoop 1.x also
shade guava, leaving either no guava or some random version pulled
by another dependency on the classpath.

So be explicit about the dependency in modules that use guava directly,
which is the right thing to do anyway.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #4272 from vanzin/SPARK-5466 and squashes the following commits:

e3f30e5 [Marcelo Vanzin] Dependency for catalyst is not needed.
d3b2c84 [Marcelo Vanzin] [SPARK-5466] Add explicit guava dependencies where needed.
2015-01-29 13:00:45 -08:00
assembly
bagel
bin
build
conf
core [SPARK-5466] Add explicit guava dependencies where needed. 2015-01-29 13:00:45 -08:00
data/mllib
dev
docker
docs
ec2
examples
external
extras
graphx [SPARK-5466] Add explicit guava dependencies where needed. 2015-01-29 13:00:45 -08:00
mllib [SPARK-5477] refactor stat.py 2015-01-29 10:11:44 -08:00
network
project
python [SPARK-5477] refactor stat.py 2015-01-29 10:11:44 -08:00
repl
sbin
sbt
sql [SQL] Various DataFrame DSL update. 2015-01-29 00:01:10 -08:00
streaming [SPARK-5466] Add explicit guava dependencies where needed. 2015-01-29 13:00:45 -08:00
tools
yarn
.gitattributes
.gitignore
.rat-excludes
CONTRIBUTING.md
LICENSE
NOTICE
README.md
make-distribution.sh
pom.xml
scalastyle-config.xml
tox.ini

README.md

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.

http://spark.apache.org/

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 with Maven".

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.