SQL is really good at retrieving a set of data based on a key or range of keys. Whereas NoSQL products are really good at writing things and retrieving one item from storage. When looking at redoing our architecture a few years ago to be more scalable, I had to consider these two issues. For what it is worth, the NoSQL market was not nearly as mature as it is now. So, my choices were much more limited. In the end, we decided to stick with MySQL. It turns out that a primary or unique key lookup on a MySQL/InnoDB table is really fast. It is sort of like having a key/value storage system. And, I can still do range based queries against it.
But, back to Dathan's problem: clicks. We store clicks at dealnews. Lots of clicks. We also store views. We store more views than we do clicks. So, lots of views and lots of clicks. (Sorry for the vague numbers, company secrets and all. We are a top 1,000 Compete.com site during peak shopping season.) And we do it all in MySQL. And we do it all with one server. I should disclose we are deploying a second server, but it is more for high availability than processing power. Like Dathan, we only use about the last 24 hours of data at any given time. There are three keys for us doing logging like this in MySQL.
MyISAM supports concurrent inserts. Concurrent inserts means that inserts can add rows to the end of a table while selects are being performed on other parts of the data set. This is exactly the use case for our logging. There are caveats with range queries as pointed out by the MySQL Performance Blog.
MySQL (and InnoDB in particular) really sucks at deleting rows. Like, really sucks. Deleting causes locks. Bleh. So, we never delete rows from our logging tables. Instead, nightly we rotate the tables. RENAME TABLE is an (near) atomic process in MySQL. So, we just create a new table.
create table clicks_new like clicks;
rename table clicks to clicks_2010032500001, clicks_new to clicks;
Tada! We now have an empty table for today's clicks. We now drop any table with a date stamp that is longer than x days old. Drops are fast, we like drops.
For querying these tables, we use UNION. It works really well. We just issue a SHOW TABLES LIKE 'clicks%' and union the query across all the tables. Works like a charm.
So, I get a lot of flack at work for my outright lust for Gearman. It is my new duct tape. When you have a scalability problem, there is a good chance you can solve it with Gearman. So, how does this help with logging to MySQL? Well, sometimes, MySQL can become backed up with inserts. It happens to the best of us. So, instead of letting that pile up in our web requests, we let it pile up in Gearman. Instead of having our web scripts write to MySQL directly, we have them fire Gearman background jobs with the logging data in them. The Gearman workers can then write to the MySQL server when it is available. Under normal operating procedure, that is in near real time. But, if the MySQL server does get backed up, the jobs just queue up in Gearman and are processed when the MySQL server is available.
BONUS! Insert Delayed
This is our old trick before we used Gearman. MySQL (MyISAM) has a neat feature where you can have inserts delayed until the table is available. The query is sent to the MySQL server and it answers with success immediately to the client. This means your web script can continue on and not get blocked waiting for the insert. But, MySQL will only queue up so many before it starts erroring out. So, it is not as fool proof as a job processing system like Gearman.
To log with MySQL:
- Use MyISAM with concurrent inserts
- Rotate tables daily and use UNION to query
- Use delayed inserts with MySQL or a job processing agent like Gearman
PS: You may be asking, "Brian, what about Partitioned Tables?" I asked myself that before deploying this solution. More importantly, in IRC I asked Brian Aker about MySQL partitioned tables. I am paraphrasing, but he said that if I ever think I might alter that table, I would not trust it with the partitions in MySQL. So, that kind of turned me off of them.