Frigatebird – high performance OLAP database built on io_uring

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I’ve spent the last few months building Frigatebird, a high performance columnar SQL database written in Rust. The goal wasn't just "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 execution pipeline. I explicitly avoided async runtimes in favor of manual thread scheduling and atomic work stealing to maximize cache locality. Code is structured to match the architecture diagrams exactly. currently it only supports single table operations (no JOINS yet), would love to hear your thoughts on it.

Visit product View on Hacker NewsJan 27, 2026

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

87%87% 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, pipe, io · Missing: https docs, excited, just released

82%82% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

79%79% 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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · 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

24%24% 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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