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Can AI tune a Postgres query? Give it only the SQL & you'll probably get some plausible-sounding advice. Give it the same context an experienced Postgres engineer would use, & things get much more interesting.
In this talk, we'll first look at the Postgres performance, logging, & configuration data that's most useful for query tuning. We'll discuss why collecting the right information matters whether the consumer is a human or an AI, along with safeguards that help us use that information effectively & safely.
Then we'll put it to the test. We'll work through a query performance problem by hand, examining the questions & evidence that guide the tuning process, before giving an AI access to that same environment through the pganalyze MCP server.
Along the way, we'll explore how MCP can change the query tuning lifecycle, from finding the right problem to validating the fix, & which parts still require the same careful thinking they always have.
Speaker:
Ryan Booz
Ryan is a Solutions Engineer at pganalyze focusing on PostgreSQL. Ryan has worked as a PostgreSQL advocate, developer, DBA & product manager for more than 25 years, primarily working with time-series data on PostgreSQL & the Microsoft Data Platform.
Ryan is a long-time DBA & developer, starting with MySQL & Postgres in the late 90s. He spent more than 15 years working with SQL Server before returning to PostgreSQL full-time in 2018. He's at the top of his game when he's learning something new about the data platform or teaching others about the technology he loves.
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