d85fe41b2b
This commit does not change any code -- only file organization. There are two components of this change: (1) Moving files out of the cluster package, and down a level to the scheduling package. These files are all used by the local scheduler in addition to the cluster scheduler(s), so should not be in the cluster package. As a result of this change, none of the files in the local package reference files in the cluster package. (2) Moving the mesos package to within the cluster package. The mesos scheduling code is for a cluster, and represents a specific case of cluster scheduling (the Mesos-related classes often subclass cluster scheduling classes). Thus, the most logical place for it is within the cluster package. |
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assembly | ||
bagel | ||
bin | ||
conf | ||
core | ||
docs | ||
ec2 | ||
examples | ||
mllib | ||
project | ||
python | ||
repl | ||
repl-bin | ||
sbt | ||
streaming | ||
tools | ||
yarn | ||
.gitignore | ||
kmeans_data.txt | ||
LICENSE | ||
lr_data.txt | ||
make-distribution.sh | ||
NOTICE | ||
pagerank_data.txt | ||
pom.xml | ||
pyspark | ||
pyspark.cmd | ||
pyspark2.cmd | ||
README.md | ||
run-example | ||
run-example.cmd | ||
run-example2.cmd | ||
spark-class | ||
spark-class.cmd | ||
spark-class2.cmd | ||
spark-executor | ||
spark-shell | ||
spark-shell.cmd |
Apache Spark
Lightning-Fast Cluster Computing - http://spark.incubator.apache.org/
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.incubator.apache.org/documentation.html. This README file only contains basic setup instructions.
Building
Spark requires Scala 2.9.3 (Scala 2.10 is not yet supported). The project is built using Simple Build Tool (SBT), which is packaged with it. To build Spark and its example programs, run:
sbt/sbt assembly
Once you've built Spark, the easiest way to start using it is the shell:
./spark-shell
Or, for the Python API, the Python shell (./pyspark
).
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./run-example <class> <params>
. For example:
./run-example org.apache.spark.examples.SparkLR local[2]
will run the Logistic Regression example locally on 2 CPUs.
Each of the example programs prints usage help if no params are given.
All of the Spark samples take a <master>
parameter that is the cluster URL
to connect to. This can be a mesos:// or spark:// URL, or "local" to run
locally with one thread, or "local[N]" to run locally with N threads.
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.
You can change the version by setting the SPARK_HADOOP_VERSION
environment
when building Spark.
For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:
# Apache Hadoop 1.2.1
$ SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
# Cloudera CDH 4.2.0 with MapReduce v1
$ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly
For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
with YARN, also set SPARK_YARN=true
:
# Apache Hadoop 2.0.5-alpha
$ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
# Cloudera CDH 4.2.0 with MapReduce v2
$ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly
For convenience, these variables may also be set through the conf/spark-env.sh
file
described below.
When developing a Spark application, specify the Hadoop version by adding the
"hadoop-client" artifact to your project's dependencies. For example, if you're
using Hadoop 1.0.1 and build your application using SBT, add this entry to
libraryDependencies
:
"org.apache.hadoop" % "hadoop-client" % "1.2.1"
If your project is built with Maven, add this to your POM file's <dependencies>
section:
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>1.2.1</version>
</dependency>
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.
Apache Incubator Notice
Apache Spark is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
Contributing to Spark
Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so.