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PostgreSQL's JIT compiler fires based on estimated query cost, but that estimate measures data volume, not expression complexity.

...because your vendor's Cost Model Is from 2002. Since random_page_cost = 4.0 is for spinning disks and hasn't changed in 25 years, setting it to 1.1 cuts this query from 125.98ms to 68.69ms.

Postgres 19 adds SQL/PGQ graph queries. Measured against a PostgreSQL 19beta1 build, the fixed-depth graph query compiles to the exact same plan as a hand-written join, and the variable-depth traversal that graph databases were built for still falls to a recursive CTE. Apache AGE runs the same indexed traversal under a Cypher wrapper. Numbers from ExoBench in local mode.

Columns beat JSONB+GIN at everything except multi-key containment on PostgreSQL 17

'Covering indexes hurt your writes' is an architectural maxim with no number attached. Here is the number: +28% on INSERTs, +25% on UPDATEs, 1.26x faster reads, and covering wins below ~4,300 writes per analytical read. Measured on PostgreSQL 17 at 2M rows.

Tool capabilities and edition boundaries described here are accurate as of publication. Both vendors move these lines; check current documentation before making decisions based on this post.

There’s a specific moment this post is written for: your pipeline runs flyway validate, everything is green, and you conclude that your database matches your migrations.

That conclusion doesn’t follow. Not because Flyway is broken, but because validate answers a different question than the one you’re asking.

PostgreSQL's JIT compiler has two jobs: compiling expressions and deforming tuples. Here's how to isolate JIT bugs with two simple boolean toggles.

pgsonify is an experimental MIT-licensed tool that sonifies PostgreSQL health metrics as real elephant recordings. How it maps stats views to sound.

The numbers here come from my own audit and design records as of 2026-08-15. Everything described as a redesign is a plan I have started building and have not proven yet.

Google launched AlloyDB in 2022. They claimed it is fully compatible with PostgreSQL. Can be up to 100 times faster for analytical queries than vanilla Postgres. Four years later, I haven't personally seen it gain significant traction. But it comes in discussions. When people ask me what AlloyDB actually is, I was able to pin point the features, but wasn't really sure what it delivers.

PostgreSQL's JIT compiler trades upfront compilation time for faster query execution—but it silently does nothing if the LLVM library isn't installed.

Do you like building Postgres extensions? Of course you do! My "Let's Build a Postgres Extension" presentation garnered rave reviews at Postgres Conference 2026 in San Jose and PG Data 2026 in Chicago. But what if you didn't go? Sure the slides are available on both sites, but that's not quite the same, is it?Now that the dust has settled and my long series on Postgres 19 has finally reached its natural conclusion, let's get back to our regularly scheduled shenanigans.

Do you like building Postgres extensions? Of course you do! My "Let's Build a Postgres Extension" presentation garnered rave reviews at Postgres Conference 2026 in San Jose and PG Data 2026 in Chicago. But what if you didn't go? Sure the slides are available on both sites, but that's not quite the same, is it?Now that the dust has settled and my long series on Postgres 19 has finally reached its natural conclusion, let's get back to our regularly scheduled shenanigans.

A pile of containers is not a shipment. A shipment is containers plus a manifest.Apache Iceberg is the manifest. It is not a file format, it is a table format: a metadata layer that records exactly which Parquet files make up a table at every point in time. That one idea buys you things we used to think required a warehouse. Transactions on object storage, so writers never …

Choose your I/O execution engine with `io_method` and size the worker pool with `io_workers`—and yes, you can resize workers without restarting.

Please note different time and location this month at UpStart Collective at the U.S. Bancorp Tower (a.k.a. Big Pink). Please RSVP on MeetUp. Tuesday September 8, 2026 from 6:30pm to 8:30pm.

Coinciding with devopsdays Portland, OR, Sept 8-10, 2026.

In Part 6 of this special Postgres in Production deep dive series, Ryan Booz asks a question that determines how useful pg_stat_statements can be for you at all: do you have a high cardinality workload?

In Part 6 of this special Postgres in Production deep dive series, Ryan Booz asks a question that determines how useful pg_stat_statements can be for you at all: do you have a high cardinality workload?

Introduction

The purpose of this blog post is to introduce pg_walviz, a new tool to visualize PostgreSQL WAL segment files.

pg_waldump is very useful to display a human-readable rendering of the WAL. However, sometimes we also want to see how records are physically stored in a segment: the WAL pages, record fragments, continuation records, alignment padding, block references, full-page images and raw bytes.

PostgreSQL 18's new io_max_concurrency caps per-process I/O operations in flight.