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.confconfig file
- Upload your source tree and put it in your job's
- Automatically interpret error logs from EMR
- SSH tunnel to hadoop job tracker on EMR
- Minimal setup
- To run on EMR, set
$AWS_ACCESS_KEY_IDand$AWS_SECRET_ACCESS_KEY - To run on your Hadoop cluster, install
simplejsonand make sure$HADOOP_HOMEis set.
- To run on EMR, set
From PyPI:
pip install mrjob
From source:
python setup.py install
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()# 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
- 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
$AWS_ACCESS_KEY_IDand$AWS_SECRET_ACCESS_KEYaccordingly
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.
- 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.