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Run MapReduce in client's browser.


Install using npm using the following command:

npm install


An example application can be found inside example/ directory of the source code. The example generates chunks of data constituting person names from an NLTK corpus. The map/reduce prepares a dictionary of alphabets as keys and the number of names starting with the particular alphabet as the value.

Configuring the server

var infantry = require('infantry');

// Specify where your data lies. See "Data Splitting" section for more details
// The actual input data files will go into `input/` directory inside the path mentioned in the example below.
var store = + '/data', '.txt');

// Set your configuration object
var env = {
        store: store,

        // This points to the file containing your map-reduce code. See "Program code" section for more details.
        programSource: __dirname + '/map.js',

// Initialize the app using `env` as configuration object.
var inf =;

// Start the server on port 8080

Data Splitting

As of now, Infantry uses a very simple storage interface. Because of that, splitting of data has to be taken care of manually by the user. Simply create a directory, and split your dataset into files with same extension. Name of the file will be used as the key to be used for executing map job for that set.

Program code

The user has to write their map-reduce application inside a file and point the programSource in the configuration object. Its upto the user what goes inside this file but there are two requirements which should be met for the entire thing to work, that is, the user defined map/reduce functions. A typical file would look something like this:

(function() {
    return {
        map: function(data) {
            // `data` can be be of any format (depending on the dataset) and its upto the user how to handle it.

            // `results` should be the result of your map computation and an array of key-value pairs (objects).
            // The notation of the object should be: {key: key, val: value}
            return results;
        reduce: function(key, results) {
            // `key` is a string and `results` is an array here.

            // In the returned object `key` should refer to the same key passed to the function.
            // `result` is the result of your reduce computation.
            return {key: key, val: result};

Above is just a template of the map/reduce functions that can be written. What exactly can be written and made to work is really upto the user's understanding of MapReduce and capabilities of Infantry.

Customizing storage

Only filesystem based storage is possible for now. Customizable backends to come.