Dr

Drop-in SQS replacement based on SQLite

Hacker News

Drop-in SQS replacement based on SQLite

Hi! I wanted to share an open source API-compatible replacement for SQS. It's written in Go, distributes as a single binary, and uses SQLite for underlying storage. I wrote this because I wanted a queue with all the bells and whistles - searching, scheduling into the future, observability, and rate limiting - all the things that many modern task queue systems have. But I didn't want to rewrite my app, which was already using SQS. And I was frustrated that many of the best solutions out there (BullMQ, Oban, Sidekiq) were language-specific. So I made an SQS-compatible replacement. All you have to do is replace the endpoint using AWS' native library in your language of choice. For example, the queue works with Celery - you just change the connection string. From there, you can see all of your messages and their status, which is hard today in the SQS console (and flower doesn't support SQS.) It is written to be pluggable. The queue implementation uses SQLite, but I've been experimenting with RocksDB as a backend and you could even write one that uses Postgres. Similarly, you could implement multiple protocols (AMQP, PubSub, etc) on top of the underlying queue. I started with SQS because it is simple and I use it a lot. It is written to be as easy to deploy as possible - a single go binary. I'm working on adding distributed and autoscale functionality as the next layer. Today I have search, observability (via prometheus), unlimited message sizes, and the ability to schedule messages arbitrarily in the future. In terms of monetization, the goal is to just have a hosted queue system. I believe this can be cheaper than SQS without sacrificing performance. Just as Backblaze and Minio have had success competing in the S3 space, I wanted to take a crack at queues. I'd love your feedback!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, compatible · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, open · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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