1896c6e7c9
External spilling - generalize batching logic The existing implementation consists of a hack for Kryo specifically and only works for LZF compression. Introducing an intermediate batch-level stream takes care of pre-fetching and other arbitrary behavior of higher level streams in a more general way. Author: Andrew Or <andrewor14@gmail.com> == Merge branch commits == commit 3ddeb7ef89a0af2b685fb5d071aa0f71c975cc82 Author: Andrew Or <andrewor14@gmail.com> Date: Wed Feb 5 12:09:32 2014 -0800 Also privatize fields commit 090544a87a0767effd0c835a53952f72fc8d24f0 Author: Andrew Or <andrewor14@gmail.com> Date: Wed Feb 5 10:58:23 2014 -0800 Privatize methods commit 13920c918efe22e66a1760b14beceb17a61fd8cc Author: Andrew Or <andrewor14@gmail.com> Date: Tue Feb 4 16:34:15 2014 -0800 Update docs commit bd5a1d7350467ed3dc19c2de9b2c9f531f0e6aa3 Author: Andrew Or <andrewor14@gmail.com> Date: Tue Feb 4 13:44:24 2014 -0800 Typo: phyiscal -> physical commit 287ef44e593ad72f7434b759be3170d9ee2723d2 Author: Andrew Or <andrewor14@gmail.com> Date: Tue Feb 4 13:38:32 2014 -0800 Avoid reading the entire batch into memory; also simplify streaming logic Additionally, address formatting comments. commit 3df700509955f7074821e9aab1e74cb53c58b5a5 Merge: a531d2e 164489d Author: Andrew Or <andrewor14@gmail.com> Date: Mon Feb 3 18:27:49 2014 -0800 Merge branch 'master' of github.com:andrewor14/incubator-spark commit a531d2e347acdcecf2d0ab72cd4f965ab5e145d8 Author: Andrew Or <andrewor14@gmail.com> Date: Mon Feb 3 18:18:04 2014 -0800 Relax assumptions on compressors and serializers when batching This commit introduces an intermediate layer of an input stream on the batch level. This guards against interference from higher level streams (i.e. compression and deserialization streams), especially pre-fetching, without specifically targeting particular libraries (Kryo) and forcing shuffle spill compression to use LZF. commit 164489d6f176bdecfa9dabec2dfce5504d1ee8af Author: Andrew Or <andrewor14@gmail.com> Date: Mon Feb 3 18:18:04 2014 -0800 Relax assumptions on compressors and serializers when batching This commit introduces an intermediate layer of an input stream on the batch level. This guards against interference from higher level streams (i.e. compression and deserialization streams), especially pre-fetching, without specifically targeting particular libraries (Kryo) and forcing shuffle spill compression to use LZF. |
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examples | ||
external | ||
graphx | ||
mllib | ||
project | ||
python | ||
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sbt | ||
streaming | ||
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make-distribution.sh | ||
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pom.xml | ||
README.md |
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 2.10. The project is built using Simple Build Tool (SBT), which can be obtained here. If SBT is installed we will use the system version of sbt otherwise we will attempt to download it automatically. 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:
./bin/spark-shell
Or, for the Python API, the Python shell (./bin/pyspark
).
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 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.
Running tests
Testing first requires Building Spark. Once Spark is built, tests can be run using:
./sbt/sbt test
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 2.2.X, 2.1.X, 2.0.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
# Apache Hadoop 2.2.X and newer
$ SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt assembly
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 1.2.1 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.