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[return to "River: A fast, robust job queue for Go and Postgres"]
1. bgentr+9l[view] [source] 2023-11-20 17:26:05
>>bo0tzz+(OP)
Hi HN, I'm one of the authors of River along with Brandur. We've been working on this library for a few months and thought it was about time we get it out into the world.

Transactional job queues have been a recurring theme throughout my career as a backend and distributed systems engineer at Heroku, Opendoor, and Mux. Despite the problems with non-transactional queues being well understood I keep encountering these same problems. I wrote a bit about them here in our docs: https://riverqueue.com/docs/transactional-enqueueing

Ultimately I want to help engineers be able to focus their time on building a reliable product, not chasing down distributed systems edge cases. I think most people underestimate just how far you can get with this model—most systems will never outgrow the scaling constraints and the rest are generally better off not worrying about these problems until they truly need to.

Please check out the website and docs for more info. We have a lot more coming but first we want to iron out the API design with the community and get some feedback on what features people are most excited for. https://riverqueue.com/

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2. ThePro+yE2[view] [source] 2023-11-21 05:56:14
>>bgentr+9l
Really cool. I'm working on .Net project that I've also adopted a "single dependency" stance on; that being Postgres. I'm pretty thrilled to see I'm not the only one lol!

I plan to use Orleans to handle a lot of the heavy HA/scale lifting. It can likely stand in for Redis in a lot of cache use cases(in some non-obvious ways), and am anticipating writing a Postgres stream provider for it when the time comes.. Will likely end up writing a Postres job queue as well so will definitely check out River for inspiration.

A lot of postgres drivers, including the .Net defacto Npgsql, support logical decode these days which unlocks a ton of exciting use cases and patterns via log processing.

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