PUG Stuttgart

PUG Stuttgart happened on June, 26th - hosted by Aleshkova Daria

Speakers

• Marco Klöckler about Semantics in the Cloud
• Ilya Kosmodemiansky about Operating PostgreSQL as a Data Source for Analytics Pipelines
• Sikandra Chaudhary about PostgreSQL 18: Exploring the Latest Innovations

Prague PostgreSQL Meetup

Prague PostgreSQL Meetup on June 23 organized by Gulcin Yildirim

Speakers

• Henrietta Dombrovskaya about Securing Your PostgreSQL Data: A Comprehensive Guide to Protecting Your Database Assets
• Boris Novikov about Querying Temporal Data in Postgres

Swiss PGDay 2025

Swiss PGDay 2025 took place on June 26th and 27th in Rapperswil (Switzerland)

Program committee

• Marion Baumgartner
• Johannes Graën
• Stefan Keller
• Andreas Geppert
• Michelle von Känel
• Stephan Wagner

Organization committee

• Marion Baumgartner
• Andreas Geppert
• Stephan Wagner
• Tobias Bussmann
• Stefan Keller
• Michelle von Känel
• Daniel Westermann

Speakers

• Bruce Momjian How Open Source and Democracy Drive Postgres
• Gulcin Yildirim Jelinek Anatomy of Table-Level Locks in PostgreSQL
• Laurenz Albe Mach das nicht!
• Patrick Stählin Using logical replication for fun and profit
• Aarno Aukia Operating PostgreSQL at Scale: Lessons from Hundreds of Instances in Regulated Private Clouds
• [Bertrand Hartwig-Peillon] pgAssistant
• Gianni Ciolli The Why and What of WAL
• Daniel Krefl Hacking pgvector for performance
• Kaarel Moppel Das 1x1 der Testdatengenerierung mit Postgres
• Franck Pachot Normalize or De-normalize? Relational SQL Columns or JSON Document Attributes?
• Andreas Geppert Tabellen vs Objekte: warum nicht einfach beides (in Postgres)?
• Devrim Gunduz Know the less known about PostgreSQL
• Dirk Krautschick Benchmarking – Eine unerwartete Reise
• J

Vibhor Kumar and Marc Linster; last updated July 10 2025

Great, big monolithic databases that assembled all the company’s data used to be considered a good thing. When I was Technical Director at Digital Equipment (a long time ago), our business goal was to bring ‘it’ all together into one enormous database instance, so that we could get a handle on the different businesses and have a clear picture of the current state of affairs. We were dreaming of one place where we could see which components were used where, what product was more profitable, and what parts of the business could be evaluated and optimized.

What changed? Why do we now consider monoliths to be dinosaurs that inhibit progress and that should be replaced with a new micro-services architecture?

This article reviews the pros and cons associated with large, monolithic databases, before diving into modular database (micro-)services. We review their advantages and challenges, describe a real-world problem from our consulting background, and outline design principles. The article ends with a discussion of Postgres building blocks for microservices.

Why did we ever want the big monolithic database?

Every business I know has been struggling with uniform definitions, such as a uniform price list with historical prices, or a single source of truth, such as the definite list of customers and their purchases. Trying to move all the data into one ginormous system with referential integrity is very tempting, and when it works, it can be very rewarding.

There are also other operational benefits, such as a single maintenance window, a single set of operating instructions, a single vendor, and a single change management process.
However, this centralized approach begins to show its limitations as the database grows to an extreme scale, leading to performance bottlenecks and inflexibility.

Challenges when dealing with monoliths

The challenges of monolithic systems are significant, and many architects believe that the

When implementing an optimization for derived clause lookup myself, Amit Langote and David Rowley argued about the initial size of hash table (which would hold the clauses). See some discussions around this email on pgsql-hackers.

The hash_create() API in PostgreSQL takes initial size as an argument. It allocates memory for those many hash entries upfront. If more entries are added, it will expand that memory later. The point of argument was what should be the initial size of the hash table, introduced by that patch, containing the derived clauses. During the discussion, David hypothesised that the size of the hash table affects the efficiency of the hash table operations depending upon whether the hash table fits cache line. While I thought it's reasonable to assume so, the practical impact wouldn't be noticeable. I thought that beyond saving a few bytes choosing the right hash table size wasn't going to have any noticeable effects. If an derived clause lookup or insert became a bit slower, nobody would even notice it. It was practically easy to address David's concern by using the number of derived clauses at the time of creating the hash table to decide initial size of the hash table. The patch was committed.

Within a few months, I faced the same problem again when working on resizing shared buffers without server restart. The buffer manager maintains a buffer look table in the form of a hash table to map a page to buffer. When the number of configured buffers changes upon a server restart the size of buffer lookup table also changes. Doing that in a running server would be significant work. To avoid that, we could create a buffer lookup table large enough to accommodate future buffer size needs. Even if the buffer pool shrinks or expands, the size of the buffer lookup table would not change. As long as the expansion is within the buffer lookup table size limit, it could be done without a restart. Buffer lookup table isn't as large as the buffer pool itself, thus wasting a bit of memory can be consi

In Part 1 of this series, we discussed what active-active databases are and identified some “good” reasons for considering them, primarily centered around extreme high availability and critical write availability during regional outages. Now, let’s turn our attention to the less compelling justifications and the substantial challenges that come with implementing such a setup.

What are “bad” reasons?

1. Scaling write throughput
 Trying to scale your write capacity by deploying active-active across regions may sound like a clean horizontal solution, but it is rarely that simple. Write coordination, conflict resolution, and replication overhead introduce latency that defeats the purpose.
If you are thinking about low latency writes between regions like Australia and the US, keep in mind that you will still be paying the round-trip cost, typically 150-200ms+, to maintain consistency. Physics don’t do any favors.
Even if you have multiple primaries, unless you accept weaker consistency or potential conflicts, your writes will not scale linearly. In many real-world cases, throughput actually suffers compared to a well tuned primary replica setup.
If your real goal is better throughput, you are usually better served by:
   • Regional read replicas
   • Sharding
   • Task queuing and eventual delegation
2. Performance
 Performance is often used as a vague justification. But active-active is not a panaceum for general slowness. If the issue is database bottlenecks, reasons may be as basic as not enough work spent on data structuring, indexing, query tuning or scaling reads before jumping into multi primary deployments.
Way too often we find that performance problems come not from scale limits, but from poor design and neglected maintenance. We are in 2025 and lessons like this classic one on fast inserts are still painfully relevant.
If what you are really facing is application level latency, that is a different challenge. And even then, active-active with strong guara

Last week I posted about how we often don’t pick the optimal plan. I got asked about difficulties when trying to reproduce my results, so I’ll address that first (I forgot to mention a couple details). I also got questions about how to best spot this issue, and ways to mitigate this. I’ll discuss that too, although I don’t have any great solutions, but I’ll briefly discuss a couple possible planner/executor improvements that might allow handling this better.

PostgreSQL 19 development is now officially under way, so from now on any new features will be committed to that version. Any significant PostgreSQL 18 changes (e.g. reversions or substantial changes to already committed features) will be noted here separately (there were none this week).

The first round of new PostgreSQL 19 features is here:

more...

We are excited to announce the schedule for PGDay UK 2025 has been published. We've got an exciting line up for talks over a range of topics. There will be something for everyone attending.

Take a look at what we have going on: https://pgday.uk/events/pgdayuk2025/schedule/

We'd like to extend our gratitude to the whole CFP team, who did an amazing job selecting the talks to make up the schedule.

Thank you to all speakers whom submitted talks, it's always a shame that we can't accept all, and as ever it's a tough choice to choose the talks for the schedule. Be it your 100th time or 1st time submitting a talk, we hope you submit again in the future and at other PostgreSQL Europe events.

PGDay UK 2025 is taking place in London on September 9th, so don't forget to register for PGDay UK 2025, before it's too late!

https://2025.pgday.uk/

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.

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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!