One thing I’ve learned over the years is that no matter how polished your application logic is, database performance will eventually make or break the user experience. Working with InterSystems IRIS, I recently ran into this firsthand. A client of ours was building a reporting dashboard that worked flawlessly during testing—but once their production dataset grew into the millions, response times slowed to a crawl.
At first glance, it looked like a hardware issue. The servers were under pressure, memory usage spiked, and everyone was convinced they needed to scale up infrastructure. But digging deeper into IRIS told a different story. The real problem wasn’t the hardware at all—it was the SQL execution plans.
Here’s the practical approach I took, step by step:
Step 1: Check the SQL Performance Tools
IRIS comes with a very handy SQL Performance Monitor. Running the problematic queries through it showed me exactly what was happening: full table scans on every request. Once I saw the plan, it was clear the optimizer didn’t have the right options available.
Step 2: Revisit Indexing Strategy
We had indices in place, but not for the way the queries were actually written. For example, the dashboard filtered heavily on a date field, yet there was no index on that column. Once I created an index there, execution time dropped from seconds to under 200 milliseconds. That single change made a dramatic difference.
Step 3: Rewrite a Few Queries
Not every fix was about indices. Some queries were structured in ways that prevented the optimizer from doing its best work. Replacing a couple of subqueries with joins and simplifying redundant conditions gave IRIS more flexibility to pick efficient paths. These were small changes in code, but they unlocked big improvements.
Step 4: Test at Scale, Not Just in Dev
One of the traps I see often—and fell into myself here—is testing only with small datasets. A query that runs lightning fast on 100 rows may behave very differently on 10 million. After tuning, I made sure to stress-test queries against production-like volumes. This gave confidence that the improvements would hold up over time.
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By the end of this process, the dashboard went from sluggish to snappy, without touching the hardware. For me, the big lesson was that InterSystems IRIS already has the tools you need to diagnose and resolve these bottlenecks—you just have to lean on them.
If you’re building with IRIS, my advice is simple: make performance monitoring part of your workflow early. Don’t wait until users complain. The SQL Performance Monitor, combined with a thoughtful indexing strategy and query design, can save you hours (and headaches) later.
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