2b7bd29eb6
TL;DR is there is a bit of JAR hell trouble with Netty, that can be mostly resolved and will resolve a test failure. I hit the error described at http://apache-spark-user-list.1001560.n3.nabble.com/SparkContext-startup-time-out-td1753.html while running FlumeStreamingSuite, and have for a short while (is it just me?) velvia notes: "I have found a workaround. If you add akka 2.2.4 to your dependencies, then everything works, probably because akka 2.2.4 brings in newer version of Jetty." There are at least 3 versions of Netty in play in the build: - the new Flume 1.4.0 dependency brings in io.netty:netty:3.4.0.Final, and that is the immediate problem - the custom version of akka 2.2.3 depends on io.netty:netty:3.6.6. - but, Spark Core directly uses io.netty:netty-all:4.0.17.Final The POMs try to exclude other versions of netty, but are excluding org.jboss.netty:netty, when in fact older versions of io.netty:netty (not netty-all) are also an issue. The org.jboss.netty:netty excludes are largely unnecessary. I replaced many of them with io.netty:netty exclusions until everything agreed on io.netty:netty-all:4.0.17.Final. But this didn't work, since Akka 2.2.3 doesn't work with Netty 4.x. Down-grading to 3.6.6.Final across the board made some Spark code not compile. If the build *keeps* io.netty:netty:3.6.6.Final as well, everything seems to work. Part of the reason seems to be that Netty 3.x used the old `org.jboss.netty` packages. This is less than ideal, but is no worse than the current situation. So this PR resolves the issue and improves the JAR hell, even if it leaves the existing theoretical Netty 3-vs-4 conflict: - Remove org.jboss.netty excludes where possible, for clarity; they're not needed except with Hadoop artifacts - Add io.netty:netty excludes where needed -- except, let akka keep its io.netty:netty - Change a bit of test code that actually depended on Netty 3.x, to use 4.x equivalent - Update SBT build accordingly A better change would be to update Akka far enough such that it agrees on Netty 4.x, but I don't know if that's feasible. Author: Sean Owen <sowen@cloudera.com> Closes #723 from srowen/SPARK-1789 and squashes the following commits: 43661b7 [Sean Owen] Update and add Netty excludes to prevent some JAR conflicts that cause test issues |
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ec2 | ||
examples | ||
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README.md | ||
scalastyle-config.xml |
Apache Spark
Lightning-Fast Cluster Computing - http://spark.apache.org/
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.apache.org/documentation.html. This README file only contains basic setup instructions.
Building Spark
Spark is built on Scala 2.10. To build Spark and its example programs, run:
./sbt/sbt assembly
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 org.apache.spark.examples.SparkLR
will run the Logistic Regression 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:
./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.
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.