Napkin math for MongoDB performance

As we all know, there are lies, damned lies, and statistics. What I’m about to present shouldn’t even qualify as statistics—it’s just a bunch of damned lies. I’m not set up to do any sort of rigorous performance testing, so these should not be construed as anything but what they are: one guy’s half-assed and probably flawed measurements.

I was playing around with MapReduce on MongoDB, trying to figure out how to code the equivalent of SQL’s COUNT(DISTINCT column) functionality. The short answer is: don’t do it. Or, if you do it, figure out a better way than I did. Along the way, I gathered some metrics on what types of operations cause what kinds of performance hits.

The Setup

My set up is a database of 3,397,115 records, all of which look something like this:

{
    "_id"   : 3002827,
    "mm"    : 7,
    "stars" : 5,
    "date"  : "2005-07-18",
    "dd"    : 18,
    "cust"  : 2213,
    "movie" : 14889,
    "yy"    : 2005,
    "title" : "Species",
    "year"  : 1995
}

Yeah, I just took the Netflix prize data and inserted ~3M records. I did the inserts across 3 shard services, all running on the same machine, which led to 9 chunks of roughly equal size. I let MongoDB handle the sharding—I didn’t manually split the shards. I ensured one index on the collection, over movie and cust, which isn’t really used for the query in question, but I thought it was worth mentioning.

Yeah, I know performance is going to suffer because I’m running 3 shards from the same hard drive. That’s kindof the point.

I ran all of this on my MacBook Pro, which is a 2.66 GHz Core 2 Duo with 4GB of 1067 MHz DDR3. I continued to do other light-duty tasks while running the tests, but nothing that should have interfered greatly.

The Queries

Here’s the starting query’s SQL equivalent:

SELECT releaseYear,
    COUNT(*) AS nRecords,
    COUNT(DISTINCT movie) AS mMovies,
    COUNT(DISTINCT cust) AS cCustomers,
    SUM(stars) AS totalStars,
    AVG(stars) AS avgStars
FROM training
WHERE (releaseYear = 1990)
GROUP BY releaseYear

And the MapReduce query itself, as I wrote it:

db.runCommand({
  mapreduce: "training",
  query: {
    year: 1990
  },
  map: function() {
    var m = {}, c = {};
    m[this.movie] = true;
    c[this.cust] = true;
    emit(
    this.year,
    { "stars": this.stars, "n": 1, "m": m, "c": c }
  )},
  reduce: function(key, vals) {
    var stars = 0, n = 0, m = {}, c = {};
    for(var i = 0; i < vals.length; i++) {
	var v = vals[i];
	stars += v.stars;
	n += v.n;
	for (var im in v.m) m[im] = true;
	for (var ic in v.c) c[ic] = true;
    }
    return { "stars": stars, "n": n, "m": m, "c": c };
  },
  finalize: function(key, val) {
    val.avg = val.stars / val.n;
    var m = 0, c = 0;
    for (var im in val.m) m++;
    for (var ic in val.c) c++;
    val.m = m;
    val.c = c;
    return val;
  },
  out: "result1",
  verbose: true
});

Those nasty bits with the for-in loops are for the COUNT(DISTINCT column) logic. This query produces the following result set:

{
    "_id"   : 1990,
    "value" : {
        "stars" : 593179,
        "n"     : 154617,
        "m"     : 7,
        "c"     : 120259,
        "avg"   : 3.8364410123078314
    }
}

The Results

All times below are in mm:ss format. (Minutes, not hours.)

QueryTotal TimeShards TimeFinal Function
110:4403:4606:58
This was the starting query above, as written.
290:4836:2654:22
I widened the release year restriction from just 1990 to 1990-1999, via { year: { $gte: 1990, $lte: 1999 } }. That's close to a linear relationship between emitted records and time elapsed.
321:3313:5307:40
I used movechunk to consolidate all of the chunks on one shard server, then shut down the other two. I reduced the release year restriction back to just 1990. It takes 2x longer than the first query, presumably due to disk bottlenecks? One shard trying to reduce 9 chunks at once?
402:0802:08-
I removed the for-in loops and COUNT(DISTINCT) logic, leaving only the plain record count and average, but was still on the one shard server, implying a 10x slowdown for that type of logic.
QueryTotal TimeMap TimeEmit Loop
500:1300:0600:13
I connected to the one remaining shard directly, instead of through mongos, and ran the previous query (no for-in). Again, this implies a 10x slowdown due to trying to process chunks simultaneously.
605:2400:1501:14
Still connected directly to the one shard (no mongos) with all of the records, I ran the original query (with for-in logic). A slowdown of 25x seems a little high, but I ran the query twice to verify it.

Lessons Learned

  • Queries scream when a single shard is left to its own devices—but when parallelism is attempted on the same shard you get a massive performance hit. Don't run different shards off the same hard drive—no matter how many cores you have.
  • Don't try to emulate COUNT(DISTINCT). Really.

I have to wonder if mongos can be tweaked to serialize queries against chunks on the same shard, to prevent disk contention issues?

Published by

Rick Osborne

I am a web geek who has been doing this sort of thing entirely too long. I rant, I muse, I whine. That is, I am not at all atypical for my breed.