e733d655df
SPARK-1076: zipWithIndex and zipWithUniqueId to RDD Assign ranks to an ordered or unordered data set is a common operation. This could be done by first counting records in each partition and then assign ranks in parallel. The purpose of assigning ranks to an unordered set is usually to get a unique id for each item, e.g., to map feature names to feature indices. In such cases, the assignment could be done without counting records, saving one spark job. https://spark-project.atlassian.net/browse/SPARK-1076 == update == Because assigning ranks is very similar to Scala's zipWithIndex, I changed the method name to zipWithIndex and put the index in the value field. Author: Xiangrui Meng <meng@databricks.com> Closes #578 and squashes the following commits: 52a05e1 [Xiangrui Meng] changed assignRanks to zipWithIndex changed assignUniqueIds to zipWithUniqueId minor updates 756881c [Xiangrui Meng] simplified RankedRDD by implementing assignUniqueIds separately moved couting iterator size to Utils do not count items in the last partition and skip counting if there is only one partition 630868c [Xiangrui Meng] newline 21b434b [Xiangrui Meng] add assignRanks and assignUniqueIds to RDD |
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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.