River: a Fast, Robust Job Queue for Go + Postgres
Years ago I wrote about my trouble with a job queue in Postgres, in which table bloat caused by long-running queries slowed down the workers’ capacity to lock jobs as they hunted across millions of dead tuples trying to find a live one. A job queue in a database can have sharp edges, but I’d understated in that writeup the benefits that came with it. When used well, transactions and background job
How to Manage Connections Efficiently in Postgres, or Any Database — brandur.org
You start building your new project. You’ve heard good things about Postgres, so you choose it as your database. As advertised, it proves to be a satisfying tool and progress is good. You put your project into production for the first time and like you’d hoped, things go smoothly as Postgres turns out to be well-suited for production use as well. The first few months go well and traffic continues
Reflections on Rust, and the sand castle metaphor — brandur.org
A month ago, I wrote about how I was frustrated with my progress in Rust. These days, I’m still no expert, but I’ve made progress. Programming languages get easier to learn the more of them you know. A lot of this is just pattern matching: syntax changes, but there’s a lot of overlap in features, and the similarities get more apparent. Some of my recent acquisitions have been very fast to learn –
Touring a Fast, Safe, and Complete(ish) Web Service in Rust — brandur.org
For years now, I’ve been having a crisis of faith in interpreted languages. They’re fast and fun to work in at small scale, but when you have a project that gets big, their attractive veneer quickly washes away. A big Ruby or JavaScript (just to name a few) program in production is a never ending game of whack-a-mock – you fix one problem only to find a new one somewhere else. No matter how many t
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