mrjob 0.4.3

Python MapReduce framework

mrjob is a Python 2.6+ package that helps you write and run Hadoop Streaming jobs.

Stable version (v0.4.3) documentation

Development version documentation

mrjob fully supports Amazon’s Elastic MapReduce (EMR) service, which allows you to buy time on a Hadoop cluster on an hourly basis. It also works with your own Hadoop cluster.

Some important features:

• Run jobs on EMR, your own Hadoop cluster, or locally (for testing).

• Write multi-step jobs (one map-reduce step feeds into the next)

• Duplicate your production environment inside Hadoop

  • Upload your source tree and put it in your job’s $PYTHONPATH

  • Run make and other setup scripts

  • Set environment variables (e.g. $TZ)

  • Easily install python packages from tarballs (EMR only)

  • Setup handled transparently by mrjob.conf config file

• Automatically interpret error logs from EMR

• SSH tunnel to hadoop job tracker on EMR

• Minimal setup

  • To run on EMR, set $AWS_ACCESS_KEY_ID and $AWS_SECRET_ACCESS_KEY

  • To run on your Hadoop cluster, install simplejson and make sure $HADOOP_HOME is set.

Installation

From PyPI:

pip install mrjob

From source:

python setup.py install

A Simple Map Reduce Job

Code for this example and more live in mrjob/examples.

"""The classic MapReduce job: count the frequency of words.
"""
from mrjob.job import MRJob
import re

WORD_RE = re.compile(r"[\w']+")


class MRWordFreqCount(MRJob):

    def mapper(self, _, line):
        for word in WORD_RE.findall(line):
            yield (word.lower(), 1)

    def combiner(self, word, counts):
        yield (word, sum(counts))

    def reducer(self, word, counts):
        yield (word, sum(counts))


if __name__ == '__main__':
     MRWordFreqCount.run()

Try It Out!

# locally
python mrjob/examples/mr_word_freq_count.py README.rst > counts
# on EMR
python mrjob/examples/mr_word_freq_count.py README.rst -r emr > counts
# on your Hadoop cluster
python mrjob/examples/mr_word_freq_count.py README.rst -r hadoop > counts

Setting up EMR on Amazon

• create an Amazon Web Services account

• sign up for Elastic MapReduce

• Get your access and secret keys (click “Security Credentials” on your account page)

• Set the environment variables $AWS_ACCESS_KEY_ID and $AWS_SECRET_ACCESS_KEY accordingly

Advanced Configuration

To run in other AWS regions, upload your source tree, run make, and use other advanced mrjob features, you’ll need to set up mrjob.conf. mrjob looks for its conf file in:

• The contents of $MRJOB_CONF

• ~/.mrjob.conf

• /etc/mrjob.conf

See the mrjob.conf documentation for more information.

Project Links

• Source code

• Documentation

• Discussion group

Reference

• Hadoop Streaming

• Elastic MapReduce

More Information

• PyCon 2011 mrjob overview

• Introduction to Recommendations and MapReduce with mrjob (source code)

• Social Graph Analysis Using Elastic MapReduce and PyPy

Thanks to Greg Killion (blind-works.net) for the logo.