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This PR is based on #4229, thanks prabeesh. Closes #4229 Author: Prabeesh K <prabsmails@gmail.com> Author: zsxwing <zsxwing@gmail.com> Author: prabs <prabsmails@gmail.com> Author: Prabeesh K <prabeesh.k@namshi.com> Closes #7833 from zsxwing/pr4229 and squashes the following commits: 9570bec [zsxwing] Fix the variable name and check null in finally 4a9c79e [zsxwing] Fix pom.xml indentation abf5f18 [zsxwing] Merge branch 'master' into pr4229 935615c [zsxwing] Fix the flaky MQTT tests 47278c5 [zsxwing] Include the project class files 478f844 [zsxwing] Add unpack 5f8a1d4 [zsxwing] Make the maven build generate the test jar for Python MQTT tests 734db99 [zsxwing] Merge branch 'master' into pr4229 126608a [Prabeesh K] address the comments b90b709 [Prabeesh K] Merge pull request #1 from zsxwing/pr4229 d07f454 [zsxwing] Register StreamingListerner before starting StreamingContext; Revert unncessary changes; fix the python unit test a6747cb [Prabeesh K] wait for starting the receiver before publishing data 87fc677 [Prabeesh K] address the comments: 97244ec [zsxwing] Make sbt build the assembly test jar for streaming mqtt 80474d1 [Prabeesh K] fix 1f0cfe9 [Prabeesh K] python style fix e1ee016 [Prabeesh K] scala style fix a5a8f9f [Prabeesh K] added Python test 9767d82 [Prabeesh K] implemented Python-friendly class a11968b [Prabeesh K] fixed python style 795ec27 [Prabeesh K] address comments ee387ae [Prabeesh K] Fix assembly jar location of mqtt-assembly 3f4df12 [Prabeesh K] updated version b34c3c1 [prabs] adress comments 3aa7fff [prabs] Added Python streaming mqtt word count example b7d42ff [prabs] Mqtt streaming support in Python |
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assembly | ||
bagel | ||
bin | ||
build | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
launcher | ||
mllib | ||
network | ||
project | ||
python | ||
R | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
unsafe | ||
yarn | ||
.gitattributes | ||
.gitignore | ||
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CONTRIBUTING.md | ||
LICENSE | ||
make-distribution.sh | ||
NOTICE | ||
pom.xml | ||
pylintrc | ||
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 DataFrames, 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:
build/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 tests for a module, or individual 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.