Db

Dbeel – A distributed thread-per-core db

Hacker News

Dbeel – A distributed thread-per-core db

For a while now I had the urge to learn how to pick the "right" database for projects, I looked around online at different databases to try to understand their different traits, and was bombarded with marketing instead of actual info on the advantages and drawbacks. The only solution for me was to learn some database internals (I read the wonderful book written by Alex Petrov), and the problems seemed so interesting, I could not fight the urge and went ahead with trying to write my own db. Anyway, ~9 months have passed and I feel like I'm "done", I have learned a ton, and really proud of dbeel. Now I can pick a database and way more easily understand what are its most defining traits. Some short time in the future, I will release a blog post or something, where I will write about the fundamental problems you need to solve when writing a performant data system like a database, so stay tuned :)

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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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