Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A 900GB events table where every query scanned four years to read one day. Range partitioning by month cut our dashboard queries from 8s to under 200ms.
A 40GB index on a 6GB table was the first sign. Queries were fine until they weren't. Here's how we found the bloat and cleared it with zero downtime.
State drift is silent until a deploy fails or an outage reveals it. The scheduled plan-and-diff pipeline that surfaces console hotfixes and manual edits while they're still cheap to reconcile.
The "three pillars" framing misses the point — what matters is correlating across them. The patterns that earn their place and the tooling decisions that pay back.
Sharding isn't just "split the table" — the shard key choice cascades through queries, joins, rebalancing, and operations. The decisions that pay off and the ones we redid.
pg_upgrade is fast but takes downtime; logical replication lets you cut over while the old DB still serves traffic. The runbook, the gotchas, and the post-cutover checklist.
The single most useful Postgres extension you might not be using. The queries it surfaces, the indexes it implies, and the operational discipline of reading it weekly.
Version-pinned modules across many repos. The release process, semver discipline, and the breaking-change communication that keeps a shared registry sane.
EXPLAIN ANALYZE output is dense and intimidating. Once you can read it, most slow-query investigations finish in minutes. The patterns we keep seeing.
Backups are easy. Restores are hard. The quarterly drill we run, what's failed during it, and the discipline that makes "we have backups" actually mean something.
Replication is the foundation of database HA. What we monitor, how we practice failover, and the gotchas that show up only when you actually fail over.
Why Postgres connection limits bite at unexpected times, the pooling layer we put in front, and the pool-mode tradeoffs we learned the hard way.
