Neues vom PostgreSQL Planet

Build Vercel Cron Jobs in Next.js with CRON_SECRET, PostgreSQL locks, bounded backfill, safe retries, and a practical production debugging guide.

Your daily report runs at 02:00 UTC. One morning it never reaches the app. The following day it arrives twice, and both invocations try to produce the same report. A route that processes “today” on each request skips the missing day and may repeat yesterday’s work. The fix is to record which days are due and which were completed in PostgreSQL.

Build PostgreSQL vector search with pgvector and Next.js. Compare keyword and hybrid ranking, enforce tenant access, and reproduce filtered HNSW recall.

A support engineer searches for AUTH-4017. The most useful result is the ticket with that exact error code. Another engineer types “sign-in works until I close the browser” and wants the ticket about a session cookie disappearing on restart. These queries need different kinds of evidence. A useful search feature can combine both without sending the entire support archive to a model.

Lately, the PostgreSQL’s bug-report mailing list has been receiving a large number of AI-generated bug reports. Often, these come in flurries of 10-15 bugs, each from the same email address. These bug reports are of, shall we say, highly variable quality. Just looking through a few of the recent … (I consult on PostgreSQL through PGX Inc..)

PostgreSQL 14 receives its final minor release tomorrow, November 12, 2026. No new security or bug fixes ship for it after that. Your database keeps running, but every vulnerability found after that date stays open on your servers.

PostgreSQL 15 follows on November 11, 2027. If you run either version in production, this post covers the three decisions that matter: which version to move to, which method to use, and how to make the switchover a non-event.

A PostgreSQL database holds logic as well as data, in functions, triggers and Row-Level Security (RLS) policies. On Supabase and Neon much of that logic can be reached straight from the browser. UnitAutogen for PostgreSQL generates three kinds of test for it, together with the mock data each test needs, so nothing is written by hand.

So far, the storage internals series has focused on heap pages. We began by opening the 8KB page, then traced MVCC visibility through xmin, xmax, and snapshots. Next we observed HOT updates evade index maintenance, and finally tracked VACUUM as it went through its phases.

On 4th of November, 2026, we had the PostgreSQL November Meetup in Berlin. AWS hosted it again, this time we had two speakers from Germany and UK.

The Meetup took place in the Amazontower (EDGE East Side Tower Berlin) in Berlin, across the Uber Arena and right next to the East Side Mall.

A backend keeps its mapping of the arena in its cache. As a result, if another backend drops or truncates the table, the memory of the old arena is not returned until that backend disconnects, so the cache has to be invalidated when the relation changes. This post uses the relcache invalidation messages of Postgres to find out when a table has changed, shows why the callback can only remember what has to be done, and detaches the mappings when the transaction ends.

AI-First, AI-Ready, AI-Accountable, AI-Value-Driven. Four phrases now crowd enterprise strategy decks. They aren’t stages, and they aren’t interchangeable. They’re four questions every leader needs to answer about the same piece of work.

No, this is not in commemoration of the late Tim Curry. After repeatedly writing about the Swiss PGDay, I want to share a personal impression from the European PostgreSQL conference 2026.

Recently there has been some discussion about why so many features got reverted from PostgreSQL 19. I've read blogposts, talked with people, spent some time thinking. And it got me curious. I noticed lately that there is more mails to pgsql-bugs mailing list – basically reports of bugs, or conversations about them. I also noticed … Continue reading "What LLMs do to PostgreSQL and DBAs"

On 4 November, the PostgreSQL Meetup Group Berlin met.

Organized by:

• Andreas Scherbaum
• Daria Aleshkova
• Oleksii Kliukin
• Sergey Dudoladov

Speakers:

Also posted in Microsoft Blog for PostgreSQL

Same Query, Same Index, Different Performance

I recently worked with a customer planning a migration from on-premises PostgreSQL to Azure Database for PostgreSQL. During performance testing, they noticed something troubling: a query that completed in tens of milliseconds would now take 1-2 seconds in the Azure environment being evaluated.

In the previous post I argued that migrations don’t earn Git’s protections by being made of files: two incompatible schema changes live in two separate migration files, so there is nothing to textually conflict, and both branches merge clean. The repository accepts the pair without comment.

json vs jsonb in PostgreSQL 17 and 18, measured on six document shapes: when jsonb is 23% smaller or 41% larger, read speed, GIN, and what it changes.

Once a proxy decides which replica should serve a read, it still has to find or create a connection to that replica. This article is about how Polar Proxy organizes its reader connection pool and trims the hot path so that connection handling stops competing with the actual work of answering queries.

These are thirteen things I measured in one sitting. Each one is small. A few of them surprised me anyway.

Setup

PostgreSQL 18.6, built from source, default settings (work_mem 4MB, shared_buffers 128MB, 2 parallel workers per gather). MacBook Air M5, 10 cores, 24 GB RAM, internal SSD. This is a different machine from my earlier posts, so don't compare times across posts.

 My last post leaned hard on the DB-Engines ranking, and I admitted that ranking measures popularity, not installed base or revenue. Several of you called me on that, politely and otherwise. Fair enough.

`scram_iterations` embeds itself in each password when set and stays there forever, making it a per-password property masquerading as a server setting. (I consult on PostgreSQL through PGX Inc..)

PostgreSQL and NUMA, Part 4 of 4 This series covers the specifics of running PostgreSQL on large systems with many processors. My experience is that people spend months often 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.