Ziggy provides a collection of python methods for Hadoop Streaming. Ziggy is useful for building complex MapReduce programs, using Hadoop for batch processing of many files, Monte Carlo processes, graph algorithms, and common utility tasks (e.g. sort, search). Typical usage often looks like this:

#!/usr/bin/env python

import ziggy.hdmc as hdmc
    from glob import glob

    files_to_process = glob("/some/path/*")
    results = hdmc.submit_checkpoint_inline(script_to_run, output_filename, files_to_process, argument_string)

To install run:

python hadoop
python install

Ziggy was authored by Dan McClary, Ph.D. and originates in the Amaral Lab at Northwestern University.

Click here for the Python Package Index page.

Installation Details

Unsurprisingly, Ziggy requires a Hadoop cluster. To make Ziggy work with your cluster, you need to edit the setup.cfg file before running python hadoop. This ensures that Ziggy creates the correct configuration files for its modules.

setup.cfg currently contains 3 definitions that must be specified. These are:

  • hadoop-home: The HADOOP_HOME for your system. For example, the default on our clusters at Northwestern is /usr/local/hadoop.

  • num-map-tasks: The total number of map tasks your cluster for which your cluster is configured. The default is 20.

  • shared-tmp-space: This is the path to a shared space (usually via NFS) available to all nodes on your Hadoop cluster. While this space is not necessary for building and executing custom Hadoop-streaming calls, the "checkpointing" calls in HDMC require a shared directory from which to coordinate task and data distribution.

Once these are set to your liking, run python hadoop to create the modules. Then run python hadoop to install Ziggy

Ziggy's Features


HDMC provides 3 principle means of interacting with a Hadoop server. Import it using import ziggy.hdmc. The interaction types are:

  • Call Assembly: Building custom and executing Hadoop streaming calls. This is done using the build_generic_hadoop_call, execute and execute_and_wait methods.

  • Monte Carlo Mapping: Running Monte Carlo-type operations by providing only a mapping script and a number of iterations. This is done using the submit_inline and submit methods.

  • Data/Argument Distribution: Processing several datafiles or a list of arguments in parallel across mappers. This is done using the submit_checkpoint_inline and the submit_checkpoint methods.

It should be noted that Monte Carlo Mapping and Data Distribution very much violate the spirit of Hadoop. However, they do provide a very simple way to mimic traditional compute-cluster tasks without the need for cluster management along the lines of SGE or Torque. Similarly, they don't require a real cluster, just a Hadoop installation.


Ziggy provides methods for interacting with the HDFS distributed filesystem from within Python. These methods can be accessed by importing, for example, import ziggy.hdmc.hdfs Method calls mimic those found under hadoop dfs.


Ziggy provides a number of simple utilities for manipulating very large datasets with Hadoop. Utilities provided include: search, grep, numeric sort, and ascii sort. Each of these is accessed under ziggy.util. Note provides file names and line numbers a number of files in an HDFS directory or file. ziggy.util.grep provides the lines themselves.


While Hadoop's Map/Reduce paradigm is poorly suited to graph algorithms, the GraphReduce modules allow for certain graph analyses on a Hadoop Cluster. Currently analyes are limited to: degree-based measures, shortest-path based measures, page-rank measures, and connected-component measures. Except for page-rank, all path-derived measures rely on parallel breadth-first search. See the Epydoc documentation for more information. GraphReduce can be accessed by importing ziggy.GraphReduce


Building a custom Hadoop streaming call:

import ziggy.hdmc as hdmc
import ziggy.hdmc.hdfs as hdfs
#load data to hdfs
hdfs.copyToHDFS(localfilename, hfds_input_filename)
mapper = '/path/to/'
reducer = '/path/to/'
output_filename ='hdfs_relative/output_filename'
supporting_files = [list,of,files,mappers,require]
maps = 20
hadoop_call = hdmc.build_generic_hadoop_call(mapper, reducer, hdfs_input_filename, output_filename, supporting_files, maps)

Building a Monte Carlo Job:

import ziggy.hdmc as hdmc
mapper = '/path/to/'
iterations = 1000
output_file = 'output_filename'
hdmc.submit_inline(mapper, output_file, iterations)

Building a Task Distribution Job:

import ziggy.hdmc as hdmc
url_list = [a, list, of, url, strings]
mapper = '/path/to/script/which/takes/a/url/as/sys.argv[1].py'
output_filename = 'output_file_name'
supporting_files = []
hdmc.submit_checkpoint_inline(mapper, output_filename, url_list, supporting_files, files=False)

Building a Data Distribution Job:

import ziggy.hdmc as hdmc
file_list = [a, list, of, filenames, usually, provided, by, glob]
mapper = '/path/to/script/which/takes/a/filename/as/sys.argv[1].py'
output_filename = 'output_file_name'
supporting_files = [filenames, my, mapper,needs]
hdmc.submit_checkpoint_inline(mapper, output_filename, file_list, supporting_files, files=True)