April 11, 2005. It’s been a while since I’ve written much about my day-to-day, but this morning I found myself deep in a debugging session that taught me more than just how to fix a slow query.

We’re running a LAMP stack here at the company, and we’ve been using MySQL for our database back end. Our application has grown pretty organically over the years as new features were added one by one. We had an issue today where one of our key pages was suddenly taking much longer to load than usual. After some initial checks, I suspected a database bottleneck, but it wasn’t immediately obvious which query was causing the problem.

The first thing I did was run top on our MySQL server and noticed that there were a few queries that were running for an unusually long time. One of them stood out: a query related to one of our most popular pages. It was something like this:

SELECT * FROM articles WHERE category = 'news' ORDER BY date DESC LIMIT 10;

Now, the articles table has tens of thousands of rows and is growing every day. So it’s no wonder that a query could take longer to execute.

I knew I needed to get more information about this slow query before I could start making changes. I decided to add some logging around our application to capture the exact SQL statements being executed by each request. This involved adding log_queries and show_time settings in our MySQL configuration file, which required a bit of fiddling.

Once that was set up, I deployed a new version of our application with these changes and waited for traffic to pick up again. After about an hour, the log began to fill up with queries from users hitting the page. Filtering through all this data took some time, but eventually, I found what I needed:

SELECT * FROM articles WHERE category = 'news' ORDER BY date DESC LIMIT 10;

This query was indeed taking a long time to execute. Digging deeper into the logs, I noticed that it was executing multiple times with different values in category, even though those categories were cached or pre-populated in our application.

I realized that we had a caching issue in the application code where we weren’t properly reusing database connections or results. This meant every time a request came in for the “news” category, it was hitting the database again instead of using an already fetched and cached result.

To fix this, I went through our application codebase and added caching layers around these queries. Specifically, I used memcached to store the results of frequent queries. For each query execution, we checked if the result was already in cache before executing it against the database. This drastically reduced the load on MySQL.

After implementing the caching changes, I ran a few tests again and saw significant improvements. The query that had been taking minutes was now returning in seconds. Our page load times for this key page dropped from 10-20 seconds to just under 5 seconds. That’s a pretty big win for our users!

This debugging experience taught me the importance of proper caching strategies and the value of logging and monitoring tools when troubleshooting performance issues. It also highlighted how even small changes can have significant impacts on user experience.

In the broader context, I feel lucky to be working with such dynamic tools as MySQL and memcached at a time where open-source projects like these were really taking off. As we continue to evolve our stack, I’m looking forward to exploring more advanced database optimization techniques and staying up-to-date with new tools that can help us scale even further.

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That’s what I’ve been doing today—digging into the heart of our app’s performance issues. Hopefully, this little tale gives a glimpse into the real work we do here and the challenges we face as developers in 2005.