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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