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Frigatebird – embedded OLAP database built on io_uring

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Frigatebird – embedded OLAP database built on io_uring

I’ve spent a better part of the last year building Frigatebird, a high performance columnar SQL database written in Rust. The goal wasn't just to "make it work" but to use every systems programming trick available to maximize throughput on Linux. Frigatebird is an OLAP engine built from first principles. It features a custom storage engine (Walrus) that uses io_uring for batched writes, a custom spin lock allocator, and a push based volcano execution pipeline. I explicitly avoided async runtimes in favor of manual thread scheduling and atomic work stealing to maximize cache locality. currently it only supports single table operations (no JOINS yet), would love to hear your thoughts on it code: < https://github.com/Frigatebird-db/frigatebird >

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, maximize · Missing: reddit linkedin, podcasting, created
85%85% 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, code · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% 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
31%31% 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
27%27% 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.

Incorrect prediction on native model

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