48cecf673c
This change does a few things to make the hadoop-provided profile more useful: - Create new profiles for other libraries / services that might be provided by the infrastructure - Simplify and fix the poms so that the profiles are only activated while building assemblies. - Fix tests so that they're able to run when the profiles are activated - Add a new env variable to be used by distributions that use these profiles to provide the runtime classpath for Spark jobs and daemons. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #2982 from vanzin/SPARK-4048 and squashes the following commits: 82eb688 [Marcelo Vanzin] Add a comment. eb228c0 [Marcelo Vanzin] Fix borked merge. 4e38f4e [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 9ef79a3 [Marcelo Vanzin] Alternative way to propagate test classpath to child processes. 371ebee [Marcelo Vanzin] Review feedback. 52f366d [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 83099fc [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 7377e7b [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 322f882 [Marcelo Vanzin] Fix merge fail. f24e9e7 [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 8b00b6a [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 9640503 [Marcelo Vanzin] Cleanup child process log message. 115fde5 [Marcelo Vanzin] Simplify a comment (and make it consistent with another pom). e3ab2da [Marcelo Vanzin] Fix hive-thriftserver profile. 7820d58 [Marcelo Vanzin] Fix CliSuite with provided profiles. 1be73d4 [Marcelo Vanzin] Restore flume-provided profile. d1399ed [Marcelo Vanzin] Restore jetty dependency. 82a54b9 [Marcelo Vanzin] Remove unused profile. 5c54a25 [Marcelo Vanzin] Fix HiveThriftServer2Suite with *-provided profiles. 1fc4d0b [Marcelo Vanzin] Update dependencies for hive-thriftserver. f7b3bbe [Marcelo Vanzin] Add snappy to hadoop-provided list. 9e4e001 [Marcelo Vanzin] Remove duplicate hive profile. d928d62 [Marcelo Vanzin] Redirect child stderr to parent's log. 4d67469 [Marcelo Vanzin] Propagate SPARK_DIST_CLASSPATH on Yarn. 417d90e [Marcelo Vanzin] Introduce "SPARK_DIST_CLASSPATH". 2f95f0d [Marcelo Vanzin] Propagate classpath to child processes during testing. 1adf91c [Marcelo Vanzin] Re-enable maven-install-plugin for a few projects. 284dda6 [Marcelo Vanzin] Rework the "hadoop-provided" profile, add new ones. |
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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 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.