Expert PostgreSQL tutorials, tips, and performance optimization guides. Learn database administration, query optimization, and best practices for PostgreSQL 18 and beyond.
PostgreSQL on this site is the main hub for everything related to designing, running, and improving modern Postgres databases. It brings together practical guidance on performance tuning, real‑world architecture patterns, learning resources, and hands‑on examples so developers and DBAs can get more out of PostgreSQL in production. From book recommendations and beginner tutorials to deep dives into scaling, extensions, and advanced features, this category is where readers go to level up every aspect of their Postgres stack.
Need a running total, a ranking, or a month-over-month comparison in SQL? The usual instinct is a self-join, a correlated subquery, or a stack of CTEs. PostgreSQL window functions make those patterns shorter, faster, and far easier to read — without collapsing your rows the way GROUP BY does. This step-by-step tutorial walks through window […]
Dead tuples are invisible—until they aren’t. On write-heavy workloads—order and status tables, event streams, telemetry from Houston’s energy corridor—every UPDATE and DELETE leaves an old row version behind. PostgreSQL’s MVCC design is why readers never block writers, but the tradeoff is that stale row versions accumulate until autovacuum sweeps them away. When autovacuum falls behind, […]
Embeddings search is no longer a novelty feature; it is table stakes for Postgres teams building RAG pipelines, semantic search, and recommendation engines. pgvector, the open-source extension that adds vector columns and nearest-neighbor search to PostgreSQL, is where most of those teams start. The current v0.8.x line supports Postgres 13 and later, with single-precision, half-precision, […]
On August 13, 2026 the PostgreSQL Global Development Group released 18.6, 17.11, 16.15, 15.19, 14.24 and 19 Beta 3. If you run logical replication, manage pg_upgrade plans, or operate in FIPS-enforced environments, this is not a routine minor release — two of the security fixes change runtime behavior in ways that can break production after […]
You added the index. You waited for the CREATE INDEX to finish. You re-ran the query. And EXPLAIN still shows a Seq Scan. The query is still slow. If that sounds familiar, you are not alone — it is one of the most common performance questions Houston DBAs bring up in tuning sessions. Before you […]
August closed with a run of PostgreSQL ecosystem news that matters well beyond the release calendar: a billion-scale vector search proof point on Aurora, a new managed Postgres service built specifically for AI agents, EXPLAIN support in Aurora DSQL, and fresh licensing math for the Postgres-vs-SQL Server decision. For Houston teams running Postgres across energy, […]
Vector search used to mean bolting a separate database onto your stack. Two open-source PostgreSQL extensions have changed that: pgvector and pgvectorscale. Both are free, both run inside Postgres, and both have shipped meaningful upgrades in the past year — pgvector is at v0.8.6 with iterative index scans and half-precision vectors, while pgvectorscale 0.9.0 added […]
OpenAI’s engineering team recently published “Scaling PostgreSQL to power 800 million ChatGPT users” — a detailed account of how ChatGPT’s primary data store survives traffic that would flatten most databases. The headline: it’s one PostgreSQL primary handling every write, roughly 50 read replicas, and a stack of operational habits that keep the write path alive. […]
Full-text search is one of those features teams assume requires Elasticsearch or a separate search service. PostgreSQL has had it built in for years: tsvector, tsquery, GIN indexes, and relevance ranking are all part of the core database. For product catalogs, document libraries, log search, and site search up to millions of rows, it is […]
OpenAI’s engineers recently shared how PostgreSQL sits at the core of ChatGPT’s infrastructure, serving 800 million users. You do not need ChatGPT-scale traffic for the lesson to apply. In Houston, the same failure pattern shows up in energy trading dashboards, medical claims pipelines, and logistics telematics fleets: the database slows down under load long before […]
