January 13, 2003 was a Wednesday. I remember it like any other day back then—no big events or world-changing news that month, just the usual routine in ops and infrastructure land.

It was around 4 PM when our monitoring system started to scream red alerts. The logs were suddenly flooded with “database connection timeout” errors, and our user-facing application was giving customers a less-than-ideal experience: 500 Internal Server Errors. For us in the operations team, it felt like waking up during an earthquake.

The first thing I did was check my favorite tool at that time—top—and saw that our web server processes were indeed maxed out. The next step was to dive into the logs and see what was happening just before the flood of errors started. It didn’t take long for me to realize we had a major issue with MySQL.

I quickly grabbed my trusty command-line tools, mysqladmin and mysqldump, but nothing seemed out of the ordinary in those diagnostics. The only thing that stood out was an unusually high number of queries being executed per second—about 10x higher than normal.

After a few minutes of scratching my head and running through some quick mental checks, I remembered a recent change we made to our application: adding a feature that involved fetching data from several tables in rapid succession. This change had been in production for just a couple of days.

I decided to take a look at the database schema and noticed something odd. The joins between these tables were more complex than necessary and, without indexes properly set up, they were causing performance issues. I had been too eager to ship this feature without fully testing its impact on the production environment.

With that in mind, I quickly ran some SQL queries from the command line to see if there was any obvious locking or other bottlenecks. Sure enough, I found a query that was taking an unusually long time and was causing the others to queue up behind it. It looked like this:

SELECT * FROM users JOIN orders ON users.id = orders.user_id WHERE orders.status = 'completed' AND DATE_SUB(NOW(), INTERVAL 7 DAY) < orders.created_at;

The query was doing more than just what it needed to do. I knew we had a problem and quickly realized that this change, while well-intentioned, had introduced a significant performance bottleneck.

I reached out to the development team, who were still in the middle of their workday back at the office (not everyone worked remotely then). We hashed out a quick fix—adding appropriate indexes and simplifying the query. I also suggested we add some logging around this specific query to make sure it didn’t cause any more issues.

Once they committed these changes, I scheduled an immediate redeploy using our internal CI/CD pipeline. Within 10 minutes, the application was back online, and the database errors stopped flooding in.

Looking back, that day taught me a valuable lesson about the importance of thorough testing and proper planning when making changes to complex systems. Debugging this issue was both frustrating and rewarding; it forced us to take a step back and reassess our approach to feature development. It’s a reminder that even with the best intentions, things can go wrong, but addressing them quickly is crucial.

That night, as I lay in bed thinking about what we learned from this experience, I felt a sense of relief and a renewed commitment to the importance of proper planning and testing before deployments. The ops world was changing rapidly back then, and it’s amazing how many things we took for granted have since become standard practice in modern DevOps.

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