I built a distributed key-value store in Rust (~2K LOC)

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Hi HN, I built nanokv, a small distributed object/kv store in Rust. This started as a personal learning project to understand distributed systems, concurrency, and Rust at scale, but it evolved into something that’s actually usable: Architecture: Coordinator (metadata, placement, 2PC) + volume servers (blob storage) Features: replication, consistency, health checks, repair, rebuild, rebalance, GC APIs: simple REST (GET/PUT/DELETE) Ops: verify/repair/rebuild tools, OpenTelemetry tracing, K6 benchmarks Performance: on my laptop, 64 MB objects sustain ~600–1000 MB/s per stream; aggregate throughput climbs into hundreds of MB/s with replication. It’s nowhere near the sophistication of MinIO or SeaweedFS, but the code is simple (a few thousand lines), and the focus is on clarity and hackability rather than features. I learned a lot about backpressure (multi-semaphores for control vs data), streaming I/O, and debugging bottlenecks with tracing. If you’re curious about building storage systems in Rust, the repo has a detailed README and benchmarks. I’d love feedback: Does the design make sense? What would you like to see if this grew beyond a learning project? Thanks for reading!

Visit product View on Hacker NewsSep 19, 2025

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

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

55%55% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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