jrutil VS PostgreSQL

I've also tried to rewrite some of my challenging queries [0] in my hypothetical syntax and while I think your observation about pipeline length is correct, the result still came out much better than SQL. Frankly, even in F#, most of my pipelines are around 5 functions too. In my view, pipelines are just a convenient mental model. I'd love to see your sketches, here are my (very WIP) concepts: [1]

[0]: for example this monstrosity: https://gitlab.com/dvdkon/jrutil/-/blob/dbb971c18526e68dcc97...

PostgreSQL

In this tutorial, we will look at how to use Docker-compose to run PostgreSQL and MongoDB services. Find out what PostgreSQL means. Visit their website to learn more about MongoDB. A NestJs server application will also be created, along with a server image and instructions on ways to add it as a service to our container. Visit NestJs here to learn more. We will also use API calls to communicate with the database containers.

Hi everyone, in this post we're going to build a boilerplate to start any kind of Symfony project, such like a monolith or an API. We'll use the top tier app server FrankenPHP written in Go. The boilerplate will also use PostgreSQL SGDB for relational database.

Supabase Auth is a simple, secure, and open-source authentication service that provides authentication and user management for your applications. It's built on top of PostgreSQL and works seamlessly with Supabase's database and storage services.

One of the most exciting features of Sveltos is its flexibility in managing multiple clusters. You can easily scale this process to handle fleets of clusters across different regions or environments. For instance, if you wanted to deploy additional applications like https://coder.com/ or https://www.postgresql.org/, you can do so seamlessly across your development clusters.

While both Postgres and ClickHouse serve different purposes, the key distinction lies in how they handle replication and sharding. Postgres is primarily designed for transactional workloads (OLTP), where data consistency and durability are prioritized. On the other hand, ClickHouse is tailored for analytical workloads (OLAP), and optimized for high-speed querying and large-scale data analysis.

For your information, the tuplesort.c module flags when the Sort algorithm will use disk by setting the state to SORTEDONTAPE at this line. Disk interactions is handled by the logtape.c module.

This error happens when using a relational database, like PostgreSQL (one of the most popular, and advanced open-source SQL databases on the market).

This tutorial is going to use Postgres for its database because it is well battle-tested, but you're going to use the Prisma ORM to abstract away the database layer. This gives you the flexibility to use a variety of databases, and simplifies the API that you use to interact with it.

Yes, it's a balancing act. I think it's better to have safer defaults here, not prioritizing extra performance. People who are concerned with performance to the degree where checksums would matter need to consult configuration anyway (in many aspects, not only this one) and can disable this specific thing easily.

For the reference, change is in https://github.com/postgres/postgres/commit/04bec894a04c (so should land in PostgreSQL 18 in a year, unless it will get reverted for some reason).