Better Dev Link

The internet is incredible. It’s nearly impossible to keep people away from. But it can also be unreliable: packets drop, links congest, bits mangle, and data corrupts. Oh, it’s dangerous out there! (I’m writing this in Kramer’s tone)

Sometimes you just want something that: Works with your existing database, Doesn’t require external services, Is easy to understand and debug. That’s what the OP built. A search engine that uses your existing database, respects your current architecture, and gives you full control over how it works.

Marginalia is an experimental search engine. The Marginalia Search index has been partially rewritten to perform much better, using new data structures designed to make better use of modern hardware. This post will cover the new design, and will also touch upon some of the unexpected and unintuitive performance characteristics of NVMe SSDs when it comes to read sizes.

As a computer engineer who has spent half a decade working with caches at Intel and Sun, I’ve learnt a thing or two about cache-coherency. This was one of the hardest concepts to learn back in college – but once you’ve truly understood it, it gives you a great appreciation for system design principles.

I decided to experiment with writing an object store from scratch as a fun learning exercise, and to see how much I could get out of NVMe disks I had on my bare metal machines. Specifically, I wanted to really optimize for random reads and small objects — serving user content where the lower the latency, the better. This would not focus on other features; S3 is bottomless, distributed, and managed for example.

Postgres has an awesome amount of data collected in its own internal tables. Postgres hackers know all about this - but software developers and folks working with day to day Postgres tasks often miss out the good stuff. Today I want to walk through some of the most important Postgres internal data catalog details. What they are, what is in them, and how they might help you understand more about what is happening inside your database.

This could be a cheap way to have a Kafka-like consumer out of the box by consuming your Postgres WAL log. WAL log is like a stream of change happen to your database (update/create/delete) and you can decode them to JSON to see the change and act on them

A deep dive into how JavaScript source maps work under the hood, with examples showing how all the pieces fit together.

Beside the usualy way of await fetch to load JSON API, we now can also use import directly. We had this in NodeJS with build tool already but now it’s natively available in the browser too. Lets see when we should use what.