/usr/share/doc/python-mrjob/README.rst is in python-mrjob 0.3.3.2-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | mrjob
=====
.. image:: http://github.com/yelp/mrjob/raw/master/docs/logos/logo_medium.png
mrjob is a Python 2.5+ package that helps you write and run Hadoop Streaming
jobs.
`Main documentation <http://packages.python.org/mrjob/>`_
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
``$HADOOP_HOME`` is set.
Installation
------------
From PyPI:
``pip install mrjob``
From source:
``python setup.py install``
A Simple Map Reduce Job
-----------------------
Code for this example and more live in ``mrjob/examples``.
::
"""The classic MapReduce job: count the frequency of words.
"""
from mrjob.job import MRJob
import re
WORD_RE = re.compile(r"[\w']+")
class MRWordFreqCount(MRJob):
def mapper(self, _, line):
for word in WORD_RE.findall(line):
yield (word.lower(), 1)
def combiner(self, word, counts):
yield (word, sum(counts))
def reducer(self, word, counts):
yield (word, sum(counts))
if __name__ == '__main__':
MRWordFreqCount.run()
Try It Out!
-----------
::
# locally
python mrjob/examples/mr_word_freq_count.py README.rst > counts
# on EMR
python mrjob/examples/mr_word_freq_count.py README.rst -r emr > counts
# on your Hadoop cluster
python mrjob/examples/mr_word_freq_count.py README.rst -r hadoop > counts
Setting up EMR on Amazon
------------------------
* create an `Amazon Web Services account <http://aws.amazon.com/>`_
* sign up for `Elastic MapReduce <http://aws.amazon.com/elasticmapreduce/>`_
* Get your access and secret keys (click "Security Credentials" on
`your account page <http://aws.amazon.com/account/>`_)
* Set the environment variables ``$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
<http://packages.python.org/mrjob/configs-conf.html>`_ 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 <mailto:greg@blind-works.net>`_
(`blind-works.net <http://www.blind-works.net/>`_) for the logo.
|