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Fixing P1 Queries When we look at the performance of software, we use Pnn notation to indicate the latency of an issue. A P95 problem is one that exceeds the time that 95% of the other queries take. In other words, this is the 5% slowest things happening, which can include database slowdowns that impact your application. For many years in software development, we have tended to work on the P95 or P90 issues, the slowest items. This is primarily because those can make a big difference to the system's performance. If I fix the slowest things, then the system feels faster. Certainly, I know lots of DBAs and developers will apply this logic to database queries. They focus on the slowest queries and tune them to improve the system. If most things are quicker, especially the things most users notice, then the system feels faster. We've often limited our work on latent items because the time involved in working on other issues is hard. If you choose to work on items in the P70 range (30% slowest) or the P50 items, does that make sense? There are slower items, so doesn't it make sense to focus on those P95 or P99 items when trying to increase the speed of the user experience? In software, I think this does make sense. However, in the database world, we aren't often thinking of one user's experience. We're thinking about the volume of requests from all clients. We might find there are queries that seem fast, say 400ms queries, but if these are running thousands of times a minute, we might see more benefit from clients if we reduce this to 200ms than if we speed up a 30s query to 2s. For the database world, we often think about the rate of items, not just their latency (or speed). For database queries, how do we choose what to focus on if it's not the slowest queries? we might not also want to tackle the most frequent queries either. My question might be: do we need to choose? In the age of AI, especially if tokens aren't too expensive, do we need to spend engineering time deciding? Would it make sense to let our AI coding agent evaluate and propose changes to all the queries? With a team of agents, we might have time to investigate the entire workload and try to speed everything up. This does mean we need a good system for guiding agents, separating out work, and ensuring that each agent isn't trying to add an index for every query. That's something humans have done, and agents likely will try if they don't have to coordinate their work. The code of this work isn't zero, and I'd bet there will be a significant amount of time to get this set up in some organizations, though I'd hope as people discover ways to organize agents for database work, with controls to scope their efforts and validation techniques, this becomes easier and easier. I'd like to think that at some point, DBAs will work in the same way software developers are hoping they work: thinking about problems, evaluating potential solutions, and managing AI helpers who type the actual code. Steve Jones - SSC Editor Join the debate, and respond to today's editorial on the forums |
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