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Having attended PGConf.DE'2025 and discussed the practice of using Postgres on large databases there, I was surprised to regularly hear the opinion that query planning time is a significant issue. As a developer, it was surprising to learn that this factor can, for example, slow down the decision to move to a partitioned schema, which seems like a logical step once the number of records in a table exceeds 100 million. Well, let's figure it out.

The obvious way out of this situation is to use prepared statements, initially intended for reusing labour-intensive parts such as parse trees and query plans. For more specifics, let's look at a simple table scan with a large number of partitions (see initialisation script):

EXPLAIN (ANALYZE, COSTS OFF, MEMORY, TIMING OFF)
SELECT * FROM test WHERE y = 127;

/*
...
   ->  Seq Scan on l256 test_256
         Filter: (y = 127)
 Planning:
   Buffers: shared hit=1536
   Memory: used=3787kB  allocated=4104kB
 Planning Time: 61.272 ms
 Execution Time: 4.929 ms
*/

In this scenario involving a selection from a table with 256 partitions, my laptop's PostgreSQL took approximately 60 milliseconds for the planning phase and only 5 milliseconds for execution. During the planning process, it allocated 4 MB of RAM and accessed 1,500 data pages. Quite substantial overhead for a production environment! In this case, PostgreSQL has generated a custom plan that is compiled anew each time the query is executed, choosing an execution strategy based on the query parameter values during optimisation. To improve efficiency, let's parameterise this query and store it in the 'Plan Cache' of the backend by executing PREPARE:

PREPARE tst (integer) AS SELECT * FROM test WHERE y = $1;
EXPLAIN (ANALYZE, COSTS OFF, MEMORY, TIMING OFF) EXECUTE tst(127);

/*
...
   ->  Seq Scan on l256 test_256
         Filter: (y = $1)
 Planning:
   Buffers: shared hit=1536
   Memory: used=3772kB  allocated=4120kB
 Planning Time: 59.525 ms
 Execution Time: 5.184 ms
*/

The planning workload remains the same s

Introduction

Over the last decade, when working on databases where UUID Version 4¹ was picked as the primary key data type, these databases usually have bad performance and excessive IO.

UUID is a native data type that can be stored as binary data, with various versions outlined in the RFC. Version 4 is mostly random bits, obfuscating information like when the value was created, or where it was generated.

Version 4 UUIDs are easy to work with in Postgres as the gen_random_uuid()² function generates values natively since version 13 (2020).

I’ve learned there are misconceptions about UUID Version 4, and sometimes the reasons users pick this data type is based on them.

Because of the poor performance, misconceptions, and available alternatives, I’ve come around to a simple position: Avoid UUID Version 4 for primary keys.

My more controversial take is to avoid UUIDs in general, but I understand there are some legitimate scenarios where there aren’t practical alternatives.

As a database enthusiast, I wanted to have an articulated position on this classic “Integer v. UUID” debate.

Among databases folks, debating these alternatives may be tired and clichéd. However, from my consulting work, I can say that I’m working on databases with UUID v4 as the primary key in 2024 and 2025, and seeing the issues discussed in this post.

Let’s dig in.

The scope of UUIDs in this post

• UUIDs (or GUID in Microsoft speak)³) are long strings of 36 characters, 32 digits, 4 hyphens, stored as 128 bits (16 byte) values, stored using the binary uuid data type in Postgres
• The RFC documents how the 128 bits are set
• The bits for UUID Version 4 are mostly random values
• UUID Version 7 includes a timestamp in the first 48 bits, which works much better with database indexes compared with random values

Although unreleased as of this writing, and pulled from Postgres 17 previously, UUID V7 is part of Postgres 18⁴ scheduled for release in the Fall of 2025.

What kind of app database

PostgreSQL user groups are a fantastic way to build new connections and engage with the local community. Last week, I had the pleasure of speaking at the Stuttgart meetup, where I gave a talk on “Operating PostgreSQL as a Data Source for Analytics Pipelines.”

Below are my slides and a brief overview of the talk. If you missed the meetup but would be interested in an online repeat, let me know in the comments below!

PostgreSQL in Evolving Analytics Pipelines

As modern analytics pipelines evolve beyond simple dashboards into real-time and ML-driven environments, PostgreSQL continues to prove itself as a powerful, flexible, and community-driven database.
In my talk, I explored how PostgreSQL fits into modern data workflows and how to operate it effectively as a source for analytics.

From OLTP to Analytics

PostgreSQL is widely used for OLTP workloads – but can it serve OLAP needs as well? With physical or logical replication, PostgreSQL can act as a robust data source for analytics, enabling teams to offload read-intensive queries without compromising production.

Physical replication provides an easy-to-operate, read-only copy of your production PostgreSQL database. It lets you use the full power of SQL and relational features for reporting – without the risk of data scientists or analysts impacting production. It offers strong performance, though with some limitations: no materialized views, no temporary tables, and limited schema flexibility. Honestly, there are more ways analysts could harm production even from the replica side.

Logical replication offers a better solution:

• It allows schema adaptation (e.g., different partitioning strategies)
• Data retention beyond what’s on the primary
• Support for multiple data sources feeding into one destination

However, it also brings complexity – especially around DDL handling, failover, and more awareness from participating teams.

The Shift Toward Modern Data Analytics

Data analytics in 2025 is more than jus

Housekeeping announcements:

This week there have been a couple of renamings:

Both of these items were added during the PostgreSQL 18 development cycle so do not have any implications for backwards compatibility.

There have also been some fixes related to:

See below for other commits of note.

more...

The basic promise of a query optimizer is that it picks the “optimal” query plan. But there’s a catch - the plan selection relies on cost estimates, calculated from selectivity estimates and cost of basic resources (I/O, CPU, …). So the question is, how often do we actually pick the “fastest” plan? And the truth is we actually make mistakes quite often.

Consider the following chart, with durations of a simple SELECT query with a range condition. The condition is varied to match different fractions of the table, shown on the x-axis (fraction of pages with matching rows). The plan is forced to use different scan methods using enable_ options, and the dark points mark runs when the scan method “won” even without using the enable_ parameters.

It shows that for selectivities ~1-5% (the x-axis is logarithmic), the planner picks an index scan, but this happens to be a poor choice. It takes up to ~10 seconds, and a simple “dumb” sequential scan would complete the query in ~2 seconds.

PgPedia Week has been delayed this week due to malaise and other personal circumstances.

more...

When running a PostgreSQL database in a High Availability (HA) cluster, it’s easy to assume that having multiple nodes means your data is safe. But HA is not a replacement for backups. If someone accidentally deletes important data or runs a wrong update query, that change will quickly spread to all nodes in the cluster. Without proper safeguards, that data is gone everywhere. In these cases, only a backup can help you restore what was lost.

The case mentioned above isn’t the only reason backups are important. In fact, many industries have strict compliance requirements that make regular backups mandatory. This makes backups essential not just for recovering lost data, but also for meeting regulatory standards.

Barman is a popular tool in the PostgreSQL ecosystem for managing backups, especially in High Availability (HA) environments. It’s known for being easy to set up and for offering multiple types and modes of backups. However, this flexibility can also be a bit overwhelming at first. That’s why I’m writing this blog to break down each backup option in a simple and clear way, so you can choose the one that best fits your business needs.

1. Backup Type in Barman

Barman primarily supports three backup types through various combinations of backup methods. The table below highlights the/z key differences between them

    Even the most experienced database professionals are known to feel a little anxious when peering into an unfamiliar database. Hopefully, they inspect to see how the data is normalized and how the various tables are combined to answer complex queries.  Entity Relationship Maps (ERM) provide a visual overview of how tables are related and can document the structure of the data.

    The Community Edition of DBeaver can provide an ERM easily.  First, connect to the database. Right-click on the 'Tables' and then select 'View Diagram'.

The ERM is then displayed.

And it gets better! If you are not sure how two tables are joined? Click on the link between the tables, and the names of the columns you will want to use in your JOIN statement are highlighted.

Conclusion

    Exploring an unfamiliar database can be daunting. But Entity Relationship Maps provide a way to navigate the territory. And Dbeaver is a fantastic tool for working with databases.

Explore the new readiness probe introduced in CloudNativePG 1.26, which advances Kubernetes-native lifecycle management for PostgreSQL. Building on the improved probing infrastructure discussed in my previous article, this piece focuses on how readiness probes ensure that only fully synchronised and healthy instances—particularly replicas—are eligible to serve traffic or be promoted to primary. Special emphasis is placed on the streaming probe type and its integration with synchronous replication, giving administrators fine-grained control over failover behaviour and data consistency.

The registration for PGConf.EU 2025, which will take place on 21-24 October in Riga, is now open.

We have a limited number of tickets available for purchase with the discount code EARLYBIRD.

This year, the first day of training sessions has been replaced with a Community Events Day. This day has a more limited space, and can be booked as part of the conference registration process or added later, as long as seats last.

We hope you will be able to join us in Riga in October!

With First Steps with Logical Replication we set up a basic working replication between a publisher and a subscriber and were introduced to the fundamental concepts. In this article, we're going to expand on the practical aspects of logical replication operational management, monitoring, and dive deep into the foundations of logical decoding.

Initial Data Copy

As we demonstrated in the first part, when setting up the subscriber, you can choose (or not to) to rely on initial data copy using the option WITH (copy_data = false). While the default copy is incredibly useful behavior, this default has characteristics you should understand before using it in a production environment.

The mechanism effectively asks the publisher to copy the table data by taking a snapshot (courtesy of MVCC), sending it to the subscriber, and thanks to the replication slot "bookmark," seamlessly continues streaming the changes from the point the snapshot was taken.

Simplicity is the key feature here, as a single command handles the snapshot, transfer, and transition to ongoing streaming.

The trade-off you're making is when it comes to performance, solely due to the fact that it's using a single process per table. While it works almost instantly for test tables, you will encounter notable delay and overhead when dealing with tables with gigabytes of data.

Although parallelism can be controlled by the max_sync_workers_per_subscription configuration parameter, it still might leave you waiting for hours (and days) for any real-life database to get replicated. You can monitor whether the tables have already been synchronized or are still waiting/in progress using the pg_subscription_rel catalog.

SELECT srrelid::regclass AS table_name, srsubstate
FROM pg_subscription_rel;

Where each table will have one of the following states:

• i not yet started
• d copy is in progress
• s syncing (or waiting for confirmation)
• r done & replicating

Luckily, the state r indicates that the s

On June, 9th, Andrzej Nowicki held a talk about “From Queries to Pints: Building a Beer Recommendation System with pgvector” at the Malmö PostgreSQL User Group.

On June, 3rd the 5. PostgreSQL User Group NRW MeetUp took place in Germany.

Speakers:
* Josef Machytka about “Boldly Migrate to PostgreSQL - Introducing credativ-pg-migrator" * Mathis Rudolf about "PostgreSQL Locking – Das I in ACID" * Christoph Berg about "Modern VACUUM"

POSETTE: An Event for Postgres 2025 took place June 10-12 online. Organized by:

• Aaron Wislang
• Abe Omorogbe
• Ama Kusi
• Bilge Erdim
• Jeremy Asomaning
• Linda Leste
• Pooja Yarabothu
• Teresa Giacomini

Talk Selection Team: * Claire Giordano * Daniel Gustafsson * Krishnakumar “KK” Ravi * Melanie Plageman

Hosts: * Adam Wolk * Boriss Mejías * Claire Giordano * Derk van Veen * Floor Drees * Melanie Plageman * Thomas Munro

Speakers: * Abe Omorogbe * Adam Wolk * Alexander Kukushkin * Amit Langote * Ashutosh Bapat * Bohan Zhang * Boriss Mejías * Bruce Momjian * Charles Feddersen * Chris Ellis * Cédric Villemain * David Rowley * Derk van Veen * Ellyne Phneah * Gayathri Paderla * Heikki Linnakangas * Jan Karremans * Jelte Fennema-Nio * Jimmy Zelinskie * Johannes Schuetzner * Karen Jex * Krishnakumar “KK” Ravi * Lukas Fittl * Marco Slot * Matt McFarland * Michael John Pena * Nacho Alonso Portillo * Neeta Goel * Nitin Jadhav * Palak Chaturvedi * Pamela Fox * Peter Farkas * Philippe Noël * Polina Bungina * Rahila Syed * Robert Haas * Sandeep Rajeev * Sarah Conway * Sarat Balijapelli * Shinya Kato * Silvano Coriani * Taiob Ali * Tomas Vondra * Varun Dhawan * Vinod Sridharan

It’s been a while since SQL:2023 was published, and work on the SQL standard continues. Nowadays, everyone in the database field wants vectors, and SQL now has them, too.

(I’m using the term “SQL:202y” for “the next SQL standard after SQL:2023”. I took this naming convention from the C standard, but it’s not an official term for the SQL standard. The current schedule suggests a new release in 2028, but we’ll see.)

Vectors are a popular topic for databases now, related to LLM and “AI” use cases. There is lots of information about this out there; I’m going to keep it simple here and just aim to describe the new SQL features. The basic idea is that you have some relational data in tables, let’s say textual product descriptions, or perhaps images. And then you run this data through … something?, an LLM? — this part is not covered by SQL at the moment — and you get a back vectors. And the idea is that vectors that are mathematically close to each other represent semantically similar data. So where before an application might have searched for “matching” things by just string equality or pattern matching or full-text search, it could now match semantically. Many database management systems have added support for this now, so it makes sense to standardize some of the common parts.

So there is now a new data type in SQL called vector:

CREATE TABLE items (
    id int PRIMARY KEY,
    somedata varchar,
    embedding vector(100, integer)
);

The vector type takes two arguments: A dimension count and a coordinate type. The coordinate type is either an existing numeric type or possibly one of additional implementation-defined keywords. For example, an implementation might choose to support vectors with float16 internal values.

Here is how you could insert data into this type:

INSERT INTO items VALUES (1, 'foo', vector('[1,2,3,4,5,...]', 100, integer));

The vector() constructor takes a serialization of the actual vector data and again a dimension count and a coordinate type.

Again, how you actuall

PGDay UK 2025 will be held in London, England, on Tuesday, September 9, 2025 at the Cavendish Conference Centre.

It features a full day with a track of PostgreSQL presentations from global PostgreSQL experts. It will cover a wide range of topics of interest.

Registration has now opened. Seats are limited so we recommend that you register early if you are interested! There are 20 Early bird discounted tickets available until the 31th of July 2025; grab yours before they run out or the campaign ends.

We will publish the schedule soon.

PGDay.UK is proud to be a PostgreSQL Community Recognised Conference.

We look forward to seeing you in London!

When designing a highly available PostgreSQL cluster, two popular tools often come into the conversation: Pgpool-II and Patroni. Both are widely used in production environments, offer solid performance, and aim to improve resilience and reduce downtime; however, they take different approaches to achieving this goal.

We often get questions during webinars/talks and customer calls about which tool is better suited for production deployments. So, we decided to put together this blog to help you understand the differences and guide you in choosing the right solution based on your specific use case.

Before we dive into comparing these two great tools for achieving high availability, let’s first take a quick look at some of the key components involved in building a highly available and resilient setup.

Load Balancing

Load balancing helps distribute incoming SELECT queries evenly across read replicas. By offloading read traffic from the primary node, it can focus on handling write operations more efficiently. Heavy read workloads like reporting queries or dashboards can be directed to standby nodes, reducing the burden on the primary. This can also increase the overall transactions per second (TPS)

Connection Pooling

Connection pooling helps manage database connections efficiently, especially in high-concurrency environments. PostgreSQL has a connection limit per server, and opening/closing connections is expensive. Connection poolers can maintain a pool of persistent connections and reuse them for incoming client requests, which reduces overhead and boosts performance. This is equally important when many clients or applications interact with the database simultaneously.

Auto Failover

Auto failover ensures that when the primary database goes down, another healthy standby is automatically promoted to take its place, minimizing manual intervention. Auto failover is a central component to achieving true high availability and reducing downtime during node failures.

Consensus-Based Voti

In a recent Hacker News discussion, there was some confusion about the differences between OrioleDB and Neon. Both look alike at first glance. Both promise a "next‑gen Postgres". Both have support for cloud‑native storage.

This post explains how the two projects differ in practice. And importantly, OrioleDB is more than an undo log for PostgreSQL.

The Core Differences​

OrioleDB​

OrioleDB is a Postgres extension. It implements a Table Access Method to replace the default storage method (Heap), providing the following key features:

1. MVCC based on UNDO, which prevents bloat as much as possible;
2. IO-friendly copy‑on‑write checkpoints with very compact row‑level WAL;
3. An effective shared memory caching layer based on squizzled pointers.

Neon​

Neon uses the default Table Access Method (Heap) and replaces the storage layer. The WAL is written to the safekeepers, and blocks are read from page servers backed by object storage, providing instant branching and scale-to-zero capabilities.

Architecture comparison​

CPU Scalability​

OrioleDB eliminates scalability bottlenecks in the PostgreSQL buffer manager and WAL writer by introducing a new shared memory caching layer based on squizzled pointers and row-level write-ahead logging (WAL). Therefore, OrioleDB can handle a high-intensity read-write workload on large virtual machines.

Neon compute nodes have roughly the same scalability as stock PostgreSQL. Still, Neon allows for the instant addition of more read-only compute nodes connected to the same multi-tenant storage.

IO Scalability​

OrioleDB implements copy-on-write checkpoints, which improve locality of writes. Also, OrioleDB’s row-level WAL saves write IOPS due to its smaller volume.

Neon implements a distributed network storage layer that can potentially scale to infinity. The drawback is network latency, which could be very significant in contrast with fast local NVMe.

VACUUM & Data bloat​

OrioleDB implements block-level and row-level UNDO log

PostgreSQL active-active replication, do you really need it?

Before we start, what is active-active?

Active-active, also referred to as multi-primary, is a setup where multiple database nodes can accept writes at the same time and propagate those changes to the others. In comparison, regular streaming replication in PostgreSQL allows only one node (the primary) to accept writes. All other nodes (replicas) are read-only and follow changes.

In an active-active setup:

CloudNativePG 1.26 introduces enhanced support for Kubernetes startup probes, giving users finer control over how and when PostgreSQL instances are marked as “started.” This article explores the new capabilities, including both basic and advanced configuration modes, and explains the different probe strategies—such as pg_isready, SQL query, and streaming for replicas. It provides practical guidance for improving the reliability of high-availability Postgres clusters by aligning startup conditions with actual database readiness.

© Laurenz Albe 2025

Everybody wants good performance. When it comes to the execution of SQL statements, accurate optimizer statistics are key. With the upcoming v18 release, PostgreSQL will preserve the optimizer statistics during an upgrade with dump/restore or pg_upgrade (see commit 1fd1bd8710 and following). With the beta testing season for PostgreSQL v18 opened, it is time to get acquainted with the new feature.

A word about statistics

First, let's clear up a frequent source of confusion. We use the word “statistics” for two quite different things in PostgreSQL:

• Information about the activities of the database: PostgreSQL gathers these monitoring data in shared memory and persists them across restarts in the pg_stat directory. You can access these data through the pg_stat_* views.
• Information about the content and distribution of the data in the user tables: ANALYZE gathers these data and stores them in the catalog tables pg_class, pg_statistic and pg_statistic_ext_data. These data allow the query planner to estimate the cost and result row count of query execution plans and allow PostgreSQL to find the best execution plan.

I'll talk about the latter kind of statistics here, and I call them “optimizer statistics” (as opposed to “monitoring statistics”).

Upgrade and optimizer statistics before PostgreSQL v18

Before PostgreSQL v18, there was no way to preserve optimizer statistics across a (major) upgrade. That was no big problem if you upgraded using pg_dumpall — loading the dump will automatically trigger autoanalyze. Waiting for the statistics collection to finish constitutes a additional delay until the database is ready for use. However, dump/restore is slow enough that that additional delay doesn't really matter much. At least you need no manual intervention to gather the statistics!

The situation with pg_upgrade was considerably worse. pg_upgrade dumps and restores only the metadata from the catalog tables and leaves the data files unchanged. pg_upgrade doesn