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Explore how pgvector's indexes work, choose the right one for your needs, and find the best option for your critical data.

The post Understanding Indexes in pgvector appeared first on Stormatics.

Introduction

Since the PostgreSQL 9.6 version was released, the feature related to query parallelism has appeared and has become a good option to improve query performance. Since then, the evolution of parallelism has been growing, resulting in better performance in the database. To manage the behavior of parallelism, there are some parameters that you can tune, for example:

Recently I have been working with some queries in Postgres where I noticed either it has decided not to use an index and perform a sequential scan, or it decided to use an alternative index over a composite partial index. This was quite puzzling, especially when you know there are indexes in the system that can perform these queries faster. So what gives? After some research, I stumbled upon random_page_cost (ref).

pgagroal, the fast connection pooler for PostgreSQL, has reached a new stable release!

pgagroal 1.6.0 has been released

A couple of days ago, pgagroal version 1.6.0 has been released.

Introduction

I recently came across this email thread discussion from several years ago, which discussed ways to enable a client to choose from a list of client certificates to send to the server. The benefit is obvious; when a client has to communicate with different PostgreSQL servers with different TLS settings and trust structure, the feature can help reduce the application’s effort to figure out what certificate to send to what server.

It’s been great to see the interest and participation of the monthly PGSQL Phriday blogging event grow over the last 15 months. And like any new venture, it takes time to see and understand the best way to organize it and what the best parameters are to encourage more participation from month to month. With ... Read more

PostgreSQL uses the concept of a timeline to identify a series of WAL records in space and time. Each timeline is identified by a number, a decimal in some places, hexadecimal in others. Each time a database is recovered using point in time recovery and sometimes during standby/replica promotion, a new timeline is generated.

In this article, I’ll show Neon autoscaling in action by running a load test using one of Postgres’ most popular benchmarking tool, pgbench. The test simulates 30 clients running a heavy query.

One of the perks of my new gig at Tembo is that I have more time to work on PGXN. In the last ten years I’ve had very little time to give, so things have stagnated. The API, for example, hasn’t seen a meaningful update since 2016!

But that’s all changed now, and every bit of the PGXN architecture has experienced a fair bit of TLC in the last few weeks. A quick review.

Every software distribution system deals with versioning. Early in the design of PGXN, I decided to require semantic versions (SemVer), a clearly-defined and widely-adopted version standard, even in its pre-1.0 specification.

Barman stands as a widely used open-source tool dedicated to managing backup and disaster recovery operations for PostgreSQL databases.

The post PostgreSQL Backup and Recovery Management using Barman appeared first on Stormatics.

The PostgreSQL 2024Q1 back-branch releases 16.2, 15.6, 14.11, 13.14 and 12.18 on February 8th 2024. Besides fixing a security issue (CVE-2024-0985) and the usual bugs, they are somewhat unique in that they address two performance problems by backporting fixes already introduced into the master branch before. In this blog post, we describe two quick benchmarks that show how the new point releases have improved.

The past year has seen a surge in interest in Postgres extension distribution. A number of people have noted in particular the challenges in finding and installing extensions.

Migrating data can be an involved process, often requiring specialized knowledge and expertise. This is especially true when concerned with cross-database transitions, such as moving data from a document-oriented system like MongoDB to a relational one like PostgreSQL (henceforth Mongo and Postgres).

If you’ve been following along with our Builders podcasts, you know that in every episode I interview one distinguished guest. This episode #3 is particularly interesting, because our guest is Gianni Ciolli, EDB VP and Field CTO, and we touch on everything from EDB's Trusted Postgres Architect solution to Galileo’s scientific method.

Lately I’ve been thinking a lot about metadata for Postgres extensions.

So here's a very delayed blog post! Back in late October 2023, I visited the FOSSCOMM conference in Heraklion, Crete, Greece, organised for the 15th year running (!) by the Greek open source community. As a community conference, there is a very heavy student presence, a really broad range of topics, and geeky stuff, and that is just awesome. The DIY spirit, volunteering and camaraderie brings out the best in people and makes it all enjoyable for attendees.

Ok you got a database, how do you optimize SQL performances? To answer this question you need a lot of time and effort in order to understand workloads and performance patterns, evaluate degradation and apply corrective measures. However there are standard practices that you can implement to improve performances. This SQL optimization guide will showcase some best practices that apply across almost every database and can be a good starting point to optimize your database workloads.

There are multiple PostgreSQL configuration parameters that determine what exactly to log. In Postgres 16, there are 30 of them if you exclude the ones related to the file naming and location. We rarely change them when we create a new instance. In a best-case scenario, your organization has a default set of cnfiguration parameters which are applied when a new instance is created.