jmapreduce v0.1.1

jmapreduce

a map reduce for nodejs

install

$ npm install --save jmapreduce

test

$ npm test

usage

var JMapReduce = require('jmapreduce'); 

example

general usage

var JMapReduce = require('jmapreduce');
var input = "A MapReduce program is composed of a Map() procedure (method) that\n" +
        " performs filtering and sorting\n" +
        "(such as sorting students by first name into queues,\n" +
        " one queue for each name) and a Reduce() method that \n" +
        "performs a summary operation (such as counting the number\n" +
        " of students in each queue, yielding name frequencies).\n\n" +
        " The 'MapReduce System' (also called 'infrastructure' or 'framework')\n" +
        " orchestrates the processing by marshalling\n\n\n" +
        " the distributed servers, running the various tasks in parallel,\n" +
        " managing all communications and data transfers\n" +
        " between the various parts of the system, and providing for redundancy\n" +
        " and fault tolerance.\n" +
        " The model is inspired by the map and reduce functions commonly\n" +
        " used in functional programming, although their \n" +
        "purpose in the MapReduce framework is not the same as in their\n" +
        " original forms. The key contributions of \n" +
        "the MapReduce framework are not the actual map and reduce functions,\n" +
        " but the scalability and fault-tolerance\n" +
        " achieved for a variety of applications by optimizing\n" +
        " the execution engine once. As such, a single-threaded\n" +
        " implementation of MapReduce will usually not be faster than\n" +
        " a traditional (non-MapReduce) implementation,\n" +
        " any gains are usually only seen with multi-threaded implementations.\n" +
        " The use of this model is beneficial\n" +
        " only when the optimized distributed shuffle operation (which reduces\n" +
        " network communication cost) and fault tolerance\n" +
        " features of the MapReduce framework come into play. Optimizing the communication\n" +
        " cost is essential to a good MapReduce algorithm.";

    var jmapReduce = new JMapReduce();
      jmapReduce.textData(input)
          .flatMap(function(data){
              return data.match(/[^\s]+|\s+[^\s+]$/g);
          })
          .map(function(x){
              return {key: x, value: 1};
          })
          .groupByKey()
          .reduce(0, function(a,b){
              return a + b;
          })
          .sort(function(a, b){
              return b.value - a.value;
          });
   
   // print ten first elements   
   console.log("%s", JSON.stringify(jmapReduce.toArray().slice(0, 10), null, 2));
   
   // from console 
    [
      {
        "key": "the",
        "value": 16
      },
      {
        "key": "and",
        "value": 9
      },
      {
        "key": "of",
        "value": 8
      },
      {
        "key": "a",
        "value": 7
      },
      {
        "key": "MapReduce",
        "value": 6
      },
      {
        "key": "is",
        "value": 5
      },
      {
        "key": "in",
        "value": 5
      },
      {
        "key": "by",
        "value": 4
      },
      {
        "key": "The",
        "value": 4
      },
      {
        "key": "for",
        "value": 3
      }
    ]

#textData(data) data: a string or an array of strings

  var jmapReduce = new JMapReduce();
  jmapReduce.textData('text data sample');
  jmapReduce.textData(['this is ', 'an example', ' for reading data']);