b92bd5a2f2
Provide a parent class for the Params case classes used in many MLlib examples, where the parent class pretty-prints the case class fields: Param1Name Param1Value Param2Name Param2Value ... Using this class will make it easier to print test settings to logs. Also, updated DecisionTreeRunner to print a little more info. CC: mengxr Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #2700 from jkbradley/dtrunner-update and squashes the following commits: cff873f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update 7a08ae4 [Joseph K. Bradley] code review comment updates b4d2043 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update d8228a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update 0fc9c64 [Joseph K. Bradley] Added abstract TestParams class for mllib example parameters 12b7798 [Joseph K. Bradley] Added abstract class TestParams for pretty-printing Params values 5f84f03 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update f7441b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update 19eb6fc [Joseph K. Bradley] Updated DecisionTreeRunner to print training time. |
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assembly | ||
bagel | ||
bin | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
mllib | ||
project | ||
python | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
yarn | ||
.gitignore | ||
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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. 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.