I see plenty of questions on SO about aggregation in MongoDB, however, I have not found a complete solution to mine yet.
Here's an example of my data:
{
"fruits" : {
"apple" : "red",
"orange" : "orange",
"plum" : "purple"
}
}
{
"fruits" : {
"apple" : "green",
"plum" : "purple"
}
}
{
"fruits" : {
"apple" : "red",
"orange" : "yellow",
"plum" : "purple"
}
}
Now, my goal is to determine the popularity of each color for each fruit, so something like this would be the output collection:
{
"_id" : "apple"
"values" : {
"red" : 2,
"green" : 1
}
}
{
"_id" : "orange"
"values" : {
"orange" : 1,
"yellow" : 1
}
}
{
"_id" : "plum"
"values" : {
"purple" : 3
}
}
I have tried various M/R functions, and in the end they either don't work, or they take exponentially long. In the context of the example (fruit), I have about 1,000 different fruits and 100,000 colors over about 10,000,000 total documents. My current working M/R is this:
map = function() {
if (!this.fruits) return;
for (var fruit in this.fruits) {
emit(fruit, {
val_array: [
{value: this.fruits[fruit], count: 1}
]
});
}
};
reduce = function(key, values) {
var collection = {
val_array: []
};
var found = false;
values.forEach(function(map_obj) {
map_obj.val_array.forEach(function(value_obj) {
found = false;
// if exists in collection, inc, else add
collection.val_array.forEach(function(coll_obj) {
if (coll_obj.value == value_obj.value) {
// the collection already has this object, increment it
coll_obj.count += value_obj.count;
found = true;
return;
}
});
if (!found) {
// the collection doesn't have this obj yet, push it
collection.val_array.push(value_obj);
}
});
});
return collection;
};
Now, this does work, and for 100 records, it takes just a second or so, but the time increases non-linearly, so 100M records would take a very long time. The problem is that I'm doing a poor-mans sub-aggregation in the reduce function with the collection array, thus requiring me to iterate over both collection and the values from my map function. Now I just need to figure out how to do this efficiently (even if it requires multiple reductions). Any suggestions are welcome!
EDIT For lack of a better place to post it, here's my solution.
First, I created a file called
mr.js:
map = function() {
if (!this.fruits) return;
var skip_fruits = {
'Watermelon':1,
'Grapefruit':1,
'Tomato':1 // yes, a tomato is a fruit
}
for (var fruit in this.fruits) {
if (skip_fruits[fruit]) continue;
var obj = {};
obj[this.fruits[fruit]] = 1;
emit(fruit, obj);
}
};
reduce = function(key, values) {
var out_values = {};
values.forEach(function(v) {
for(var k in v) { // iterate values
if (!out_values[k]) {
out_values[k] = v[k]; // init missing counter
} else {
out_values[k] += v[k];
}
}
});
return out_values;
};
var in_coll = "fruit_repo";
var out_coll = "fruit_agg_so";
var total_docs = db[in_coll].count();
var page_size = 100000;
var pages = Math.floor(total_docs / page_size);
print('Starting incremental MR job with '+pages+' pages');
db[out_coll].drop();
for (var i=0; i<pages; i++) {
var skip = page_size * i;
print("Calculating page limits for "+skip+" - "+(skip+page_size-1)+"...");
var start_date = db[in_coll].find({},{date:1}).sort({date:1}).skip(skip).limit(1)[0].date;
var end_date = db[in_coll].find({},{date:1}).sort({date:1}).skip(skip+page_size-1).limit(1)[0].date;
var mr_command = {
mapreduce: in_coll,
map: map,
reduce: reduce,
out: {reduce: out_coll},
sort: {date: 1},
query: {
date: {
$gte: start_date,
$lt: end_date
}
},
limit: (page_size - 1)
};
print("Running mapreduce for "+skip+" - "+(skip+page_size-1));
db[in_coll].runCommand(mr_command);
}
That file iterates over my entire collection, incrementally map/reducing 100k docs (sorted by date which MUST have an index!) at a time, and reducing them into a single output collection. It's used like this: mongo db_name mr.js.
Then, after a couple hours, I've got a collection with all the info. To figure out which fruits have the most colors, I use this from the mongo shell to print out the top 25:
// Show number of number of possible values per key
var keys = [];
for (var c = db.fruit_agg_so.find(); c.hasNext();) {
var obj = c.next();
if (!obj.value) break;
var len=0;for(var l in obj.value){len++;}
keys.push({key: obj['_id'], value: len});
}
keys.sort(function(a, b){
if (a.value == b.value) return 0;
return (a.value > b.value)? -1: 1;
});
for (var i=0; i<20; i++) {
print(keys[i].key+':'+keys[i].value);
}
The really cool thing about this approach is that since it's incremental, I can work with the output data while the mapreduce is running.
