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In this series, we talk about the many different ways you can speed up PostGIS. Today let’s talk about looking across the queries with pg_stat_statements and some basic tuning.

Showing Postgres query times with pg_stat_statements

A reasonable question to ask, if you are managing a system with variable performance is: “what queries on my system are running slowly?”

I finally shared the set of functions that I use to process pgBadger raw output.

There will be more documentation, I promise, but at least the code is there, along with two of many presentations.

Enjoy! https://github.com/hettie-d/logs-processing

PostgreSQL 19 changes this week support for Eager Aggregation added column stats_reset added to pg_stat_all_tables /  indexes and related views pg_get_sequence_data() : output column page_lsn added ALL SEQUENCES support added to publications planner hooks planner_setup_hook and planner_shutdown_hook added mem_exceeded_count column added to pg_stat_replication_slots

more...

Postgres has earned its reputation as one of the world's most robust and feature-rich open-source databases. But what happens when your application grows beyond what a single database instance can handle? When your user base explodes from thousands to millions, and your data grows from gigabytes to terabytes?This is where Postgres scaling becomes critical. The good news is that Postgres offers multiple pathways to scale, each with its own advantages and use cases.

In August of this year, I published a blog entitled PostgreSQL’s incredible trip to the top with developers which shows how Postgres has become the most used, most loved and most desired database according to the Stack Overflow annual developer survey. In that blog I said, I want to do the series in two parts. After some thought, I have decided to make it a 3 part series. It will break down as follows:

In August of this year, I published a blog entitled PostgreSQL’s incredible trip to the top with developers which shows how Postgres has become the most used, most loved and most desired database according to the Stack Overflow annual developer survey. In that blog I said, I want to do the series in two parts. After some thought, I have decided to make it a 3 part series. It will break down as follows:

In this tutorial we will create PostgreSQL extension ban_sus_query. It will check that DML queries contain predicates, otherwise will just throw an error.

Next, in order not to mislead up, I will use term contrib for PostgreSQL extension, and for extension for PostgreSQL Hacker Helper VS Code extension.

In this tutorial we will create PostgreSQL extension ban_sus_query. It will check that DML queries contain predicates, otherwise will just throw an error.

Next, in order not to mislead up, I will use term contrib for PostgreSQL extension, and for extension for PostgreSQL Hacker Helper VS Code extension.

One of the ongoing challenges in database management systems (DBMS) is maintaining consistent data across multiple instances (nodes) that can independently accept client connections. If one node fails in such a system, the others must continue to operate without interruption - accepting connections and committing transactions without sacrificing consistency. An analogy for a single DBMS instance might be staying operational despite a RAM failure or intermittent access to multiple processor cores.

One of the ongoing challenges in database management systems (DBMS) is maintaining consistent data across multiple instances (nodes) that can independently accept client connections. If one node fails in such a system, the others must continue to operate without interruption - accepting connections and committing transactions without sacrificing consistency. An analogy for a single DBMS instance might be staying operational despite a RAM failure or intermittent access to multiple processor cores.

PGConf.EU schedule can feel like a parallel query gone wild, so many great talks but not enough CPU.
I built this guide to help my fellow database nerds skip the overwhelm and enjoy the best prod-DBA focussed sessions without a single deadlock.
Follow this path, and you’ll cruise through the conference like a perfectly tuned autovacuum.

When someone asks about Postgres tuning, I always say “it depends”. What “it” is can vary widely but one major factor is the read and write traffic of a Postgres database. Today let’s dig into knowing if your Postgres database is read heavy or write heavy.

Huge pages are a Linux kernel feature that allocates larger memory pages (typically 2 MB or 1 GB instead of the normal 4 KB). PostgreSQL’s shared buffer pool and dynamic shared memory segments are often tens of gigabytes, and using huge pages reduces the number of pages the processor must manage. Fewer page‑table entries mean fewer translation‑lookaside‑buffer (TLB) misses and fewer page table walks, which reduces CPU overhead and improves query throughput and parallel query performance.

For next month, I'm scheduling 2 or 3 discussions of Matthias van de Meent's talk, Improving scalability; Reducing overhead in shared memory, given at 2025.pgconf.dev (talk description here).

A couple times within the past month, I’ve had people send me a message asking if I have any suggestions about where to learn postgres. I like to share the collection of links that I’ve accumulated (and please send me more, if you have good ones!) but another thing I always say is that the public postgres slack is a nice place to see people asking questions (Discord, Telegram and IRC also have thriving Postgres user communities).

Huge thanks to everyone who came to the Prairie Postgres meetup and celebrated our first birthday with us! Thank you for helping me to rehearse my talk, and for your insightful questions!

Here are my presentation slides:

If you’ve been following the buzz around PostgreSQL, you’ve probably already heard that database level open source data-at-rest encryption is now available thanks to the Transparent Data Encryption (TDE) extension available in the Percona Distribution for PostgreSQL. So naturally, the next question is:

Where’s Percona Distribution for PostgreSQL 18?

The short answer:

It’s coming.

The slightly longer one:

It’s taking a bit of time, for all the right reasons.

System outages, hardware failures, or accidental data loss can strike without warning. What determines whether operations resume smoothly or grind to a halt is the strength of the disaster recovery setup. PostgreSQL is built with powerful features that make reliable recovery possible.

This post takes a closer look at how these components work together behind the scenes to protect data integrity, enable consistent restores, and ensure your database can recover from any failure scenario.

Something I’ve presented about recently (a couple times, now!) is how we can make AI actually useful with Postgres. Not the mystical robot overlord kind of AI that people worry about, but the practical, math-heavy kind that can actually help solve real problems with the data you already have.Let me be clear up front: AI isn't magic. It's math. Lots and lots of math. And if you can write SQL queries (which, let's be honest, is why you're here), you can build AI applications with Postgres. Let me show you how.

As was discussed in #PostgresMarathon 2-002, for a simple SELECT from a table, at planning time, Postgres locks the table and all of its indexes with AccessShareLock. A simple demo to remind it (let me be a bit weird here and save some bytes when typing SQL):