Disco is a lightweight, open-source framework for distributed computing based on the MapReduce paradigm.
Disco is powerful and easy to use, thanks to Python. Disco distributes and replicates your data, and schedules your jobs efficiently. Disco even includes the tools you need to index billions of data points and query them in real-time.
Disco was born in Nokia Research Center in 2008 to solve real challenges in handling massive amounts of data. Disco has been actively developed since then by Nokia and many other companies who use it for a variety of purposes, such as log analysis, probabilistic modelling, data mining, and full-text indexing.
from disco.core import Job, result_iterator def map(line, params): for word in line.split(): yield word, 1 def reduce(iter, params): from disco.util import kvgroup for word, counts in kvgroup(sorted(iter)): yield word, sum(counts) if __name__ == '__main__': input = ["http://discoproject.org/media/text/chekhov.txt"] job = Job().run(input=input, map=map, reduce=reduce) for word, count in result_iterator(job.wait()): print word, count
This is a fully working Disco script that computes word frequencies in a text corpus. Disco distributes the script automatically to a cluster, so it can utilize all available CPUs in parallel. For details, see Disco tutorial.
Need help with Disco? We can be reached on our IRC channel #discoproject at Freenode or on the Disco discussion group, or by opening an issue at Disco repository at GitHub.