Min, Max, Sum, Count, Avg, and Std deviation using MongoDB MapReduce

functions.js

// derived from http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm

function map() {
    emit(1, // Or put a GROUP BY key here
         {sum: this.value, // the field you want stats for
          min: this.value,
          max: this.value,
          count:1,
          diff: 0, // M2,n:  sum((val-mean)^2)
    });
}

function reduce(key, values) {
    var a = values[0]; // will reduce into here
    for (var i=1/*!*/; i < values.length; i++){
        var b = values[i]; // will merge 'b' into 'a'


        // temp helpers
        var delta = a.sum/a.count - b.sum/b.count; // a.mean - b.mean
        var weight = (a.count * b.count)/(a.count + b.count);
        
        // do the reducing
        a.diff += b.diff + delta*delta*weight;
        a.sum += b.sum;
        a.count += b.count;
        a.min = Math.min(a.min, b.min);
        a.max = Math.max(a.max, b.max);
    }

    return a;
}

function finalize(key, value){ 
    value.avg = value.sum / value.count;
    value.variance = value.diff / value.count;
    value.stddev = Math.sqrt(value.variance);
    return value;
}

gistfile1.js

> load('functions.js')
> db.stuff.drop()
false
> db.stuff.insert({value:1})
> db.stuff.insert({value:2})
> db.stuff.insert({value:2})
> db.stuff.insert({value:2})
> db.stuff.insert({value:3})
> db.stuff.mapReduce(map, reduce, {finalize:finalize, out:{inline:1}}).results[0]
{
	"_id" : 1,
	"value" : {
		"sum" : 10,
		"min" : 1,
		"max" : 3,
		"count" : 5,
		"diff" : 2,
		"avg" : 2,
		"variance" : 0.4,
		"stddev" : 0.6324555320336759
	}
}

You sir, rock!
Thanks so much :)

Thank you for posting this!

Thank you, It's very useful for me.

Can we merge two groups if there are duplicate entries in them

example I have two groups group1 and group2 having userName and usage

group1
user1 10
user2 12
user3 15

group2
user2 14
user3 13
user4 16

merged
user1 10
user2 26
user3 28
user4 16

stdev - 7.3485

i want to find st dev of merged group, but problem is because of high data volume I can't maintain userName , so i am maintaining sum and user count, and by probablistic counting algorithm I can find unique users also

group1
count-3, sum-37, diff-9.935

group2
count-3, sum-43, diff-7.919

merged group
unique- 4
group1:: count-3, sum-37, diff-9.935
group2:: count-3, sum-43, diff-7.919

now with this information available, can i find standard deviation of merged group??
Thanks!!

What is the license on this code?

@jalava

What is the license on this code?

Public Domain

Word of warning, this is doing population standard dev and variance, NOT SAMPLE.

I cleaned up the code and included sample variance and standard dev.

https://gist.github.com/Pyrolistical/8139958

Very useful code

Thank you!