Napkin math for MongoDB performance

文章来源：http://rickosborne.org/blog/2010/02/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.)

Query	Total Time	Shards Time	Final Function
1	10:44	03:46	06:58
	This was the starting query above, as written.
2	90:48	36:26	54:22
	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.
3	21:33	13:53	07:40
	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?
4	02:08	02:08	-
	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.
Query	Total Time	Map Time	Emit Loop
5	00:13	00:06	00:13
	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.
6	05:24	00:15	01:14
	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?

 

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