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The Landscape Has Changed

When I was at Turnitin, we were still kind of riding the tail end of the dot-com boom. People were rushing to ship things, and brief outages were not exactly good, but they were considered a normal part of running software on the internet. If the site was down for a few minutes, you’d shrug, dig in, and fix it.

PostgreSQL forbids indexing with the default text search configuration because it's mutable—change the setting, and your index silently disagrees with reality.

A NOT IN query can return the wrong answer without telling you. It is valid SQL, it runs without an error, and it hands back a perfectly well-formed result set that happens to be empty when it should not be. No warning, no hint, nothing in the logs: just zero rows where you expected hundreds, and a database that considers it correct.

Default_tablespace quietly routes tables and indexes to alternate storage — but three quirks can surprise you, and tablespaces themselves are mostly…

Force the use of the partition key and protect your PostgreSQL server from full partition scans

PostgreSQL's table access method interface lets you plug in alternative storage engines.

If you have ever been in the middle of a database migration and suddenly found your logical replication slots missing, you know how unsettling that feeling is. No obvious error. No warning. Just gone. That is exactly what happened to us while migrating data from a Patroni-managed cluster to a standalone PostgreSQL cluster. What started as a smooth operation turned into a debugging session that taught us something important about how Patroni behaves after a restart.

Recently, a new type of question has entered the database arena: what did this data look like last Tuesday? Maybe it's the price of a product before the holiday sale kicked in, or which department an employee belonged to before that reorg nobody asked for. Short of adding an entire audit trigger system, how can we know what data looked like before and after a change at that exact date?The SQL:2011 standard formalized a proper solution over a decade ago with temporal tables. Other database engines adopted pieces of it relatively quickly. Characteristically, Postgres took its time.

Raise `default_statistics_target` from 100 to 500 and ANALYZE slows down forever — but sometimes that's the right call.

See how we reduced branching times from 20+ seconds to around a second and what use-cases these speed improvements enable.

A colleague asked me recently if there was an API for querying the PostgreSQL Buildfarm database. I told him there was not. I'm aware that a number of people have been scraping the web pages for data, so it seemed like there was a good case for something better. And with a little help from claude code, I create one. It's live now. There's a full description at https://github.com/PGBuildFarm/server-code/blob/main/API.md

Query planning is a two-job problem: rewrite your SQL into a better shape, then search billions of possible join orders for the cheapest one.

PostgreSQL's dozen `debug_` parameters are its own testing machinery, exposed as runtime settings.

TL;DR: The pgEdge AI DBA Workbench is four services on a shared Postgres datastore: a collector, a server, an alerter, and a React client that renders the dashboards and the chat panel where Ellie lives. Ellie is an agentic loop that drives any LLM you choose (Claude, ChatGPT, Ollama, or anything OpenAI-compatible) through a fixed set of database-aware tool calls. The model never queries Postgres directly, which is rather the point.