Package Exports

map-reduce
map-reduce/range

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Readme

Map Reduce for leveldb (via levelup)

Incremental map-reduces and real-time results.

Waat?

An "incremental map reduce" means when you update one key, only a relevant protion of the data needs to be recalculated.

"real-time results" means that you can listen to the database, and recieve change notifications on the fly! a la level-live-stream

Example

create a simple map-reduce

var LevelUp   = require('levelup')
var SubLevel  = require('level-sublevel')
var MapReduce = require('map-reduce')

var db = SubLevel(LevelUp(file))

var mapDb = 
  MapReduce(
    db, //the parent db
    'example',  //name.
    function (key, value, emit) {
      //perform some mapping.
      var obj = JSON.parse(value)
      //emit(key, value)
      //key may be an array of strings. 
      //value must be a string or buffer.
      emit(['all', obj.group], ''+obj.lines.length)
    },
    function (acc, value, key) {
      //reduce little into big
      //must return a string or buffer.
      return ''+(Number(acc) + Number(value))
    },
    //pass in the initial value for the reduce.
    //*must* be a string or buffer.
    '0'
  })
})

map-reduce uses level-trigger to make map reduces durable.

querying results.

  //get all the results in a specific group
  //start:[...] implies end:.. to be the end of that group.
  mapDb.creatReadStream({range: ['all', group]}) 

  //get all the results in under a group.
  mapDb.creatReadStream({range: ['all', true]}) 

  //get all the top level 
  mapDb.creatReadStream({range: [true]})

complex aggregations

map-reduce with multiple levels of aggregation.

suppose we are building a database of all the street-food in the world. the data looks like this:

{
  country: USA | Germany | Cambodia, etc...
  state:   CA | NY | '', etc...
  city: Oakland | New York | Berlin | Phnom Penh, etc...
  type: taco | chili-dog | doner | noodles, etc...
}

We will aggregate to counts per-region, that look like this:

//say: under the key USA
{
  'taco': 23497,
  'chili-dog': 5643,
  etc...
}

first we'll map the raw data to ([country, state, city],type) tuples. then we'll count up all the instances of a particular type in that region!

var LevelUp   = require('levelup')
var SubLevel  = require('level-sublevel')
var MapReduce = require('map-reduce')

var db = SubLevel(LevelUp(file))
var mapDb = 
  MapReduce(
    db,
    'streetfood',
    function (key, value, emit) {
      //perform some mapping.
      var obj = JSON.parse(value)
      //emit(key, value)
      //key may be an array of strings. 
      //value must be a string or buffer.
      emit(
        [obj.country, obj.state || '', obj.city],
        //notice that we are just returning a string.
        JSON.stringify(obj.type)
      )
    },
    function (acc, value) {
      acc = JSON.parse(acc)
      value = JSON.parse(value)
      //check if this is top level data, like 'taco' or 'noodle'
      if('string' === typeof value) {
        //increment by one (remember to set as a number if it was undefined)
        acc[value] = (acc[value] || 0) ++
        return JSON.stringify(acc)
      }
      //if we get to here, we are combining two aggregates.
      //say, all the cities in a state, or all the countries in the world.
      //value and acc will both be objects {taco: number, doner: number2, etc...}

      for(var type in value) {
        //add the counts for each type together...
        //remembering to check that it is set as a value...
        acc[type] = (acc[type] || 0) + value[type]
      }
      //stringify the object, so that it can be written to disk!
      return JSON.stringify(acc)
    },
    '{}')

then query it like this:

mapDb.createReadStream({range: ['USA', 'CA', true]})
  .pipe(...)

License

MIT