Improving the speed of large MySQL inserts

Obsessive Web Stats (OWS) has been a surprisingly addictive project for me. I had started doing some (limited) work on it before Boys State in June, and I figured it would be something simple that would be fun to work on… then I started trying to optimize for performance.

The next version of OWS (v0.8) will definitely be using a Star Schema to store its data. I’ve found that by implementing this, it reduces the database size by up to 50-75%. Which, is definitely a positive thing. And its cut some query times by 75% as well, which I’m pretty excited about. Check out this console screenshot from show_info.php:

Name                          Rows    Data      Idx
virtualroadside_com           105798  7.9 MB    26.9 MB
virtualroadside_com_agent     1558    196.6 KB  114.7 KB
virtualroadside_com_bytes     6336    229.4 KB  163.8 KB
virtualroadside_com_config    4       16.4 KB   16.4 KB
virtualroadside_com_date      292     16.4 KB   16.4 KB
virtualroadside_com_host      5517    245.8 KB  180.2 KB
virtualroadside_com_method    5       16.4 KB   16.4 KB
virtualroadside_com_protocol  2       16.4 KB   16.4 KB
virtualroadside_com_referrer  2773    409.6 KB  491.5 KB
virtualroadside_com_request   3893    540.7 KB  786.4 KB
virtualroadside_com_status    1       16.4 KB   16.4 KB
virtualroadside_com_time      46409   1.6 MB    3.2 MB
virtualroadside_com_user      2       16.4 KB   16.4 KB

Total Data:     11.2 MB
Total Indexes:  31.9 MB

Checking dimensions..............OK.

Dimension  Rows   Unique  Status
host       5605   5605    OK
user       2      2       OK
date       292    292     OK
time       45710  45710   OK
method     5      5       OK
request    3768   3768    OK
protocol   2      2       OK
status     1      1       OK
bytes      6769   6769    OK
referrer   2690   2690    OK
agent      1470   1470    OK

However, inserts are currently horribly slow. As in, almost unbearably slow. Actually, its not so bad initially: 36 seconds for 10,000 logfile lines, which ends up being around 100,000 SQL queries to insert/retrieve data. However, in-memory caching measures reduce the number of actual SQL queries to around 5-10,000 or so. Not too shabby for my Pentium III.

Once the main ‘fact table’ gets to around 100,000 rows, the insert times start declining… the insert times were around 900 seconds at the 1 millionth row.

Right now, I’m inserting data like so: There are 12 tables holding the different dimensions of the data. Each insert to a new row, I check to see if the dimension key already exists, in which case I reuse it. Of course, this brings up the question of whether I’m properly denormalizing the data or not. One of the useful things I found was the following magic command:


This cut my insert time by about 1/6 or so. Pretty awesome. I also tried


But unfortunately, this makes zero difference on an InnoDB table. I did try switching to a MyISAM based table, but that didn’t seem to make much of a difference either. I’ve been busy scouring the web for performance tips, but I think one of the biggest barriers at the moment is my hardware: Dual Pentium III 500 Mhz with 1GB of ram, and 20gb/40gb IDE disks. I moved the MySQL database to my roommates computer (Pentium D, 1GB RAM, Raid IV) and performance for retrievals went way up, but the inserts are still pretty slow — though not as slow as on the PIII.

In conclusion, I’ve been able to get pretty decent data retrieval speeds from switching to the OLAP data layout, but inserts suck — and as such, OWS still doesn’t scale well. If you use OWS, let me know how its working for you! I’m always interested in hearing other opinions. 🙂

Note: I hate the tag feature of WordPress. It should ask me to tag the article AFTER I’ve written it, otherwise I just forget to do so. Theres some auto-tagging plugins but I haven’t tried them yet. Yes, I realize its open source, but I really don’t feel like hacking on WordPress right now… lol.

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