One little query can’t hurt… right?
ExecuteSQL is one of those FileMaker features that developers tend to love. It can simplify complicated data retrieval, reduce relationship graph clutter, and make reporting tasks much easier. But it can also create some surprisingly painful performance problems when used in the wrong places.
The gist of it
Our DevCast conversation starts with a look at several real-world systems that have become noticeably slower over time. In many cases, the culprit isn’t FileMaker itself, but ExecuteSQL running in places developers don’t always think about. These might be areas like unstored calculations, hide conditions, and other interface elements that may be evaluating far more often than expected.
One of the biggest challenges is that these issues often don’t show up right away. A solution with a few hundred records may perform flawlessly during development and testing. But fast forward a year, add hundreds of thousands of records and a few dozen users, and suddenly those same SQL calls are bearing much more weight onto the server.
Over the years, our devs have encountered a plethora of complex SQL queries, nested SQL statements, and multi-table joins that looked harmless at first but became major bottlenecks as processes grew.
The conversation continues with the topic of AI-generated calculations. While AI code saves mountains of time in the moment, SQL calls can be introduced into solutions without developers fully considering the long-term impact.
But the possibility that things can go wrong doesn’t mean ExecuteSQL should be avoided. It remains an excellent tool for things like virtual list reporting, metadata queries, quick lookups, and simplifying complicated relationships.
The DevCast conversation continues with our team sharing strategies they’re using to improve performance in existing systems, including replacing dynamic calculations with stored values, moving logic into scripts, and shifting work to scheduled processes when real-time calculations aren’t necessary.
Key takeaways
Context matters. Understanding where a query runs, how often it executes, and what your data might look like a year from now can make all the difference. Since today’s convenience can easily become tomorrow’s performance problem, a little bit of foresight can save a lot of troubleshooting later.
How has your system aged?
What worked perfectly just a few years ago may be slowing you down today. A fresh review can uncover hidden inefficiencies, outdated approaches, and opportunities to modernize. Curious about how your system is holding up? Let’s talk at a date and time that works best for you.
This piece represents a collaboration between the human authors and AI technologies, which assisted in both drafting and refinement. The authors maintain full responsibility for the final content.