AllyDB – An in-memory database similar to Redis, built using Elixir

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Hey, everyone. I am currently working on AllyDB, which is basically my own Redis, which I am writing in Elixir. Currently, the database is nowhere close to being ready, as you can see in the roadmap, but I am doing my best to add stuff as fast as possible. Currently the implementation is very simple, with an in memory table, an append log persistence system, as well as an interval persistence system as a backup. The database could definitely be optimized further, especially when it comes to persistence, which I am planning to do in the future. I'm also planning to use Rust NIFs for specific tasks for the performance gains over Elixir and BEAM. Writes and deletes are currently asynchronous, but I will add blocking versions of them soon. I am trying to make the system as fault tolerant as possible, and currently everything except the TCP connections are fault tolerant. I'm also working on a TypeScript client for the project, so yeah, that kind of sums it up. Feel free to check the project and the roadmap out, and let me know what I could improve or give feature or optimization ideas! Thanks, and have a nice one!

Visit product View on Hacker NewsFeb 20, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, using · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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

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