MapReduce with MongoDB really, really slow (30 hours vs 20 minutes in MySQL for a equivalent database)

Question

I am doing now some data analyse tests and in the first, really simple I have got very strange results.

The idea is the following: from an internet access log (a collection with a document for each access, for the tests 90 millions of documents). I want to get the number of access by domain (what will be a GROUP BY in MySQL), and get the 10 most accessed domains

The script I have made in JavaScript is really simple :

/* Counts each domain url */
m = function () {
    emit(this.domain, 1 );
}

r = function (key, values)    {
    total = 0;
    for (var i in values)    {
        total += Number(i);
    }

    return total;
}

/* Store of visits per domain statistics on NonFTP_Access_log_domain_visits collection */
res = db.NonFTP_Access_log.mapReduce(m, r, { out: { replace : "NonFTP_Access_log_domain_visits" } } );
db.NonFTP_Access_log_domain_visits.ensureIndex({ "value": 1});
db.NonFTP_Access_log_domain_visits.find({}).sort({ "value":-1 }).limit(10).forEach(printjson);

The equivalent in MySQL is :

drop table if exists NonFTP_Access_log_domain_visits;
create table NonFTP_Access_log_domain_visits (
    `domain` varchar(255) NOT NULL,
    `value` int unsigned not null,
    PRIMARY KEY  (`domain`),
    KEY `value_index` (`value`)
    ) ENGINE=MyISAM DEFAULT CHARSET=utf8
    select domain, count(*) as value from NonFTP_Access_log group by domain;
select * from NonFTP_Access_log_domain_visits order by value desc limit 10;

Well, MongoDB takes 30 hours to get the results and MySQL 20 minutes! After reading a little I have arrived to the conclusion that for data analyse we will have to use Hadoop as MongoDB is really slow. The answers to questions like this say that:

• MongoDB uses only thread
• Javascript is just too slow

What am I doing wrong? Are this results normal? Should I use Hadoop?

We are making this test on the following environment:

• Operating System: Suse Linux Enterprise Server 10 (Virtual Server on Xen)
• RAM: 10 Gb
• Cores: 32 (AMD Opteron Processor 6128)

Answer 1

I've actually answered this very similar question before. The limitations of Map Reduce in MongoDB have been outlined previously - as you mentioned, it is single threaded, it has to be converted to Java Script (spidermonkey) and back etc.

That is why there are other options:

1. The MongoDB Hadoop Connector (officially supported)
2. The Aggregation Framework (Requires 2.1+)

As of this writing the 2.2.0 stable release was not yet out, but it was up to RC2, so the release should be imminent. I would recommend giving it a shot as a more meaningful comparison for this type of testing.