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Easy Hadoop Scripting with Pydoop Script

Pydoop Script is the easiest way to write simple MapReduce programs for Hadoop. With Pydoop Script, your code focuses on the core of the MapReduce model: the mapper and reducer functions.

Writing and Running Scripts

Write a Python module that contains the mapper and reducer functions:

def mapper(input_key, input_value, writer):
    # your computation here
    writer.emit(intermediate_key, intermediate_value)

def reducer(intermediate_key, value_iterator, writer):
    # your computation here
    writer.emit(output_key, output_value)

The program can be run as follows:

pydoop script hdfs_input hdfs_output


The following examples show how to use Pydoop Script for common problems. More examples can be found in the examples/pydoop_script subdirectory of Pydoop’s source distribution root. The Pydoop Script Guide contains more detailed information on writing and running programs.

Word Count

The word count example can be considered as the “hello world” of MapReduce. A simple application that counts the occurrence of each word in a set of text files, it is included in both the original MapReduce paper [1] and in the Hadoop documentation as a MapReduce programming tutorial. The Pydoop Script implementation requires only five lines of code:

def mapper(_, text, writer):
    for word in text.split():
        writer.emit(word, "1")

def reducer(word, icounts, writer):
    writer.emit(word, sum(map(int, icounts)))

A few more lines allow to set a combiner for local aggregation:

def combiner(word, icounts, writer):
    writer.count('combiner calls', 1)
    reducer(word, icounts, writer)

Run the example with:

pydoop script -c combiner hdfs_input hdfs_output

Note that we need to explicitly set the -c flag to activate the combiner. By default, no combiner is called.

One thing to remember is that the current Hadoop Pipes architecture runs the combiner under the hood of the executable run by pipes, so it does not update the “combiner” counters of the general Hadoop framework. Thus, if you run the above script, you’ll get a value of 0 for “Combine input/output records” in the “Map-Reduce Framework” group, but the “combiner calls” counter should be updated correctly.

Word Count with Total Number of Words

Suppose that we want to count the occurrence of specific words, like in the example above, but we also want the total number of words. For this last “global” count we can use Hadoop counters:

def mapper(_, text, writer):
    wordlist = text.split()
    for word in wordlist:
        writer.emit(word, "1")
    writer.count("num words", len(wordlist))

def reducer(word, count, writer):
    writer.emit(word, sum(map(int, count)))

The counter value will show on the JobTracker’s job page and will be present in the job logs.

Lower Case

To convert some text to lower case, create a module

def mapper(_, text, writer):
    writer.emit("", text.lower())

This is a map-only job, so we set the number of reducers to 0. To avoid leading tabs in our results, we also want an empty separator for output key-value pairs: this is done via the -t option:

pydoop script --num-reducers 0 -t '' hdfs_input hdfs_output

Job Parameters

Suppose you want to select all lines containing a substring to be given at run time. Create a module

def mapper(_, text, writer, conf):  # notice the fourth 'conf' argument
    if text.find(conf['grep-expression']) >= 0:
        writer.emit("", text)

Job parameters, like in hadoop pipes, are passed via the -D option:

pydoop script --num-reducers 0 -t '' -D grep-expression=my_substring \ hdfs_input hdfs_output


Pydoop Script makes it easy to solve simple problems. It makes it feasible to write simple (even throw-away) scripts to perform simple manipulations or analyses on your data, especially if it’s text-based.

If you can specify your algorithm in two simple functions that have no state or have a simple state that can be stored in module variables, then you can consider using Pydoop Script.

If you need something more sophisticated, then consider using the full Pydoop API.


[1]J. Dean and S. Ghemawat, MapReduce: simplified data processing on large clusters, in OSDI ‘04: 6th Symposium on Operating Systems Design and Implementation, 2004