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In the third installment of the PostGIS Performance series, I wanted to talk about performance around bounding boxes.

Geometry data is different from most column types you find in a relational database. The objects in a geometry column can be wildly different in the amount of the data domain they cover, and the amount of physical size they take up on disk.

PostgreSQL 19 changes this week ALTER SUBSCRIPTION : REFRESH SEQUENCES syntax added error_on_null() added for checking if the input is the NULL value COPY TO now works with partitioned tables full text search : database default collation now used for parsing psql : Improve tab completion for large objects. PostgreSQL 19 articles PostgreSQL 18 articles Waiting for PostgreSQL 19 – Support COPY TO for partitioned tables. (2025-10-22) - Hubert 'depesz' Lubaczewski Say Hello to OIDC in PostgreSQL 18!

Upgrading to a new major version of PostgreSQL has become faster and safer with pg_upgrade, but for teams using logical replication, a persistent pain point remained: the state of their subscribers was lost, forcing a tedious and high-risk rebuild of their replication setups.

When communicating UUID-v7 values to third parties, the creation dates inside them are leaked. In this post, let's see how to encrypt the timestamp to solve that problem.

Due to an unfortunate recent visitation by the Virus of the Decade (so far), I have a backlog of these which I'm trying to work through, so in the remote chance anyone is waiting with bated breath for the newest editions, my apologies. Normal service will be resumed as soon as humanly possible.

Almost a month late, but I hope you enjoy it!

PostgreSQL and NUMA, part 2 of 4

This series covers the specifics of running PostgreSQL on large systems with many processors. My experience is that people often spend months learning the basics when confronted with the problem. This series tries to dispel these difficulties by providing a clear background into the topics in question. The hope is that future generations of database engineers and administrators don’t have to spend months figuring things out through trial and error.

In one of our earlier blogs, we explored how improper volatility settings in PL/pgSQL functions — namely using IMMUTABLE, STABLE, or VOLATILE — can lead to unexpected behavior and performance issues during migrations.

As a database consulting company, we are often faced with analytics and reporting related tasks which seem to be easy on the surface but are in reality not that trivial. The number of those seemingly simple things is longer than one might think, especially in the area of reporting

I pushed the most boring change imaginable, add an index. Our CI/CD pipeline is textbook ==> spin up a fresh DB, run every migration file in one single transaction, in sequential manner. If anything hiccups, the whole thing rolls back and the change never hits main. Foolproof autotests.

Enter The Drama Queen :

As part of the new ABI compliance reporting system for the PostgreSQL Buildfarm, there has long been a question: if a Buildfarm run fails due to an ABI incompatibility, how can it be marked successful again once the ABI break is fixed? This post explains the introduction of a new file, .abi-compliance-history, in the PostgreSQL source tree and how it is used to track ABI breaks.

When using tsearch one usually, often, creates a tsvector column to put data in, and then create index on it. But, do you really need the index? I wrote once already that you don't have to, but then a person talked with me on IRC, and pointed this section of docs: One advantage of the … Continue reading "Do you really need tsvector column?"

As I explained in my talk on PostgreSQL Conference Europe 2025, data corruption can be silently present in any PostgreSQL database and will remain undetected until we physically read corrupted data. There can be many reasons why some data blocks in tables or other objects can be damaged. Even modern storage hardware is far from being infallible. Binary backups done with pg_basebackup tool – which is very common backup strategy in PostgreSQL environment – leave these problems hidden. Because they do not check data but copy whole data files as they are.

Beyond Start and End columns with PostgreSQL range types

One of the most read articles at boringSQL is Time to Better Know The Time in PostgreSQL where we dived into the complexities of storing and handling time operations in PostgreSQL. While the article introduced the range data types, there's so much more to them. And not only for handling time ranges.

PostgreSQL received attention through the following contributions at Data Stack Conf 2025 on Oct 29, 2025:

Speaker

• Radoslav Stanoev
• Pavlo Golub
• Lætitia Avrot
• Valeria Bogatyreva
• Devrim Gündüz

PostgreSQL Booth Staff

• Devrim Gündüz
• Pavlo Golub

Gabriele Quaresima spoke at Cloud Native Bergen on Tuesday, October 28, 2025.

 I wrote a post in my MySQL blog on migrating from MySQL to PostgreSQL using DBeaver. You can pass it along to your acquaintances who want to get off the Dolphin and on the Elephant. 

Not only will DBeaver move your tables and data, but you can compare them afterwards. In the post, I outline the process in five steps. DBeaver will let you do it in four. 

High availability in PostgreSQL doesn't always require a globally distributed architecture. Sometimes you need reliable failover and replication within a single datacentre or region. pgEdge Enterprise Postgres handles this scenario with a production-ready PostgreSQL distribution that includes the tools you need for high availability out of the box.

From time to time I get to investigate issues with some sort of a batch process. It’s getting more and more common that such processes use very high memory limits (maintenance_work_mem and work_mem). I suppose some DBAs follow the logic that “more is better”, not realizing it can hurt the performance quite a bit.

Let me demonstrate this using an example I ran across while testing a fix for parallel builds of GIN indexes. The bug is not particularly interesting or complex, but it required a fairly high value for maintenance_work_mem (the initial report used 20GB).

PostgreSQL stores data in fixed‑size blocks (pages), normally 8 KB. When a client updates or inserts data, PostgreSQL does not immediately write those changes to disk. Instead, it loads the affected page into shared memory (shared buffers), makes the modification there, and marks the page as dirty. A “dirty page” means the version of that page in memory is newer than the on‑disk copy.

In #PostgresMarathon 2-009 and #PostgresMarathon 2-010, we explored why execution 6 causes a lock explosion when building a generic plan for partitioned tables — the planner must lock all 52 relations because it can't prune without parameter values.

Today we'll test what actually happens with different plan_cache_mode settings.