Metadata-Version: 1.0
Name: Ziggy
Version: 0.1.3.1
Summary: Python modules for Hadoop Streaming
Home-page: http://pypi.python.org/pypi/Ziggy/
Author: Daniel McClary
Author-email: dan.mcclary@northwestern.edu
License: LICENSE.txt
Description: ===========
        Ziggy
        ===========
        
        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 setup.py hadoop
        	python setup.py install
        
        Ziggy was authored by Dan McClary, Ph.D. and originates in the
         `Amaral Lab at Northwestern University <http://amaral.northwestern.edu>`_.
         
        =====================
        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 setup.py 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 setup.py hadoop`` 
        to create the hadoop_config.py modules.  Then run
        ``python setup.py hadoop``
        to install Ziggy
        
        Ziggy's Features
        =====================
        
        HDMC
        ----------
        
        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.
        
        HDFS
        ----------
        
        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``. 
        
        Utilities
        ----------
        
        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** ``ziggy.util.search``
        provides file names and line numbers a number of files in an HDFS directory or file.
        ``ziggy.util.grep`` provides the lines themselves.
        
        GraphReduce
        --------------
        
        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``
        
        Examples
        ----------
        *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/mapper.py'
            reducer = '/path/to/reducer.py'
            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)
            hdmc.execute_and_wait(hadoop_call)
        	
        *Building a Monte Carlo Job*::
        
            import ziggy.hdmc as hdmc
            mapper = '/path/to/job_with_needs_to_be_done_many_times.py'
            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)
        	
Platform: UNKNOWN
