Python MapReduce framework
Project description
mrjob is a Python 2.5+ package that helps you write and run Hadoop Streaming jobs.
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.
Links
source: <http://github.com/Yelp/mrjob>
documentation: <http://packages.python.org/mrjob/>
discussion group: <http://groups.google.com/group/mrjob>
Hadoop MapReduce: <http://hadoop.apache.org/mapreduce/>
Elastic MapReduce: <http://aws.amazon.com/documentation/elasticmapreduce/>
PyCon 2011 mrjob overview: <http://blip.tv/pycon-us-videos-2009-2010-2011/pycon-2011-mrjob-distributed-computing-for-everyone-4898987/>
Thanks to Greg Killion (blind-works.net) for the logo.
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