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A library to assist in writing Hadoop MapReduce jobs in Clojure.
Originally written by Stuart Sierra (
Extended by Roman Scherer, Christopher Miles, Ian Eslick,
Dave Lambert, Alex Ott, and other.
Stable releases are available via
For more information
on Clojure,
on Hadoop,
Also see Stuart's presentation about this library at
Introduction to work with library is available at
Copyright (c) Stuart Sierra, 2009. All rights reserved. The use and
distribution terms for this software are covered by the Eclipse Public
License 1.0 ( which can
be found in the file LICENSE.html at the root of this distribution.
By using this software in any fashion, you are agreeing to be bound by
the terms of this license. You must not remove this notice, or any
other, from this software.
This library requires Java 6 JDK,
Building from source requires Leiningen,
If you downloaded the library distribution as a .zip or .tar file,
everything is pre-built and there is nothing you need to do.
If you downloaded the sources from Git, then you need to run the build
with Leiningen. In the top-level directory of this project, run:
lein jar
This compiles and builds the JAR file.
After building, copy the file from
to something short, like "examples.jar". Each of the *.clj files in
the test/clojure_hadoop/examples directory contains instructions for
running that example.
The wordcount examples can also be run via the "lein test" command.
After building, include the "clojure-hadoop-${VERSION}.jar" file
in the lib/ directory of the JAR you submit as your Hadoop job.
You can depend on clojure-hadoop in your Maven 2 projects by adding
the following lines to your pom.xml:
<url> </url>
This library provides different layers of abstraction away from the
raw Hadoop API.
Layer 1: clojure-hadoop.imports
Provides convenience functions for importing the many classes and
interfaces in the Hadoop API.
Layer 2: clojure-hadoop.gen
Provides gen-class macros to generate the multiple classes needed
for a MapReduce job. See the example file "wordcount1.clj" for a
demonstration of these macros.
Layer 3: clojure-hadoop.wrap
clojure-hadoop.wrap: provides wrapper functions that automatically
convert between Hadoop Text objects and Clojure data structures.
See the example file "wordcount2.clj" for a demonstration of these
Layer 4: clojure-hadoop.job
Provides a complete implementation of a Hadoop MapReduce job that
can be dynamically configured to use any Clojure functions in the
map and reduce phases. See the example file "wordcount3.clj" for
a demonstration of this usage.
Layer 5: clojure-hadoop.defjob
A convenient macro to configure MapReduce jobs with Clojure code.
See the example files "wordcount4.clj" and "wordcount5.clj" for
demonstrations of this macro.
Layer 6: clojure-hadoop.defjob - Specifying JobConf parameters
Often its necessary to specify parameters in the job's
configuration to in order to enable dynamic map/reduce jobs.
Hadoop natively enables this through the -D<key>=<value>
commandline specification.
Using the convenient defjob macro, "wordcount6.clj" demonstrates
how to set job configuration (JobConf) parameters either via
the commandline, or as part of the defjob defintion within the file.
Layer 7: clojure-hadoop.config - Adding files and archives
to the DistributedCache.
Example file "wordcount7.clj" demonstrates how to specify files
and archives for distribution to across nodes via the
DistributedCache, as well as how to access the files
during the mapper-setup or reducer-setup phases.
* README.txt changed to reflect the Leiningen build process (Roman Scherer).
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