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UnisonDB

Log-native KV database that replicates like a message bus

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Hi HN,For the past few months, I’ve been building UnisonDB — a log-native database where the Write-Ahead Log (WAL) is the database, not just a recovery mechanism.I started this because every time I needed data to flow — from core to edge, or between datacenters — I ended up gluing together a KV database + CDC + Kafka.It worked, but it always felt like overkill: too many moving parts for even small workloads, and too little determinism.What is it?UnisonDB unifies storage and streaming into a single log-based core. Every write is: • Durable (appended to the WAL), • Ordered (globally sequenced for safety), • Streamable (available to any follower in real time).It combines B+Tree storage (predictable reads, no LSM compaction storms) with WAL-based replication (sub-second fan-out to 100+ nodes).Key Ideas1. Storage + Streaming = One System — no CDC, no Kafka, no sidecar pipelines2. B+Tree-Backed — predictable reads, zero compaction overhead3. Multi-Model — KV, wide-column, and large objects (LOB) in one atomic transaction4. Replication-Native — WAL streams via gRPC; followers tail in real time5. Reactive by Design — every write emits a ZeroMQ notification6. Edge-Friendly — replicas can go offline and resync instantlyPerformance & Tradeoffs 1. Write throughput is lower than pure LSM stores (e.g. BadgerDB) — because writes are globally ordered for replication safety. Deliberate tradeoff: consistency > raw write speed.2. Still ~2× faster than BoltDB with replication enabled.Tech DetailsWritten in GoFlatBuffers for zero-copy serializationgRPC for streaming replicationGitHub: https://github.com/ankur-anand/unisondb

HN · launched Nov 1, 2025 · 17 upvotes (top 35%) · 0 views

Niche: database management systems and engines · 162 launches, 6 in the last 7 days ▼ -25% · Explore this niche →

Category and tags

  • Horizontal
  • Data infrastructure
  • Developer
  • Not AI
  • Commercial product

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Could you build this?

No Building a distributed, log-native key-value store with CRDT replication, crash-safe write-ahead logs, and multi-datacenter consistency requires distributed systems and database storage engine expertise.

What it would actually take: The architecture requires implementing an LSM-tree or append-only WAL storage engine in Go/Rust with fsync durability guarantees, linked-list records for atomic large writes, and corruption detection checksums. It requires distributed consensus or CRDT mechanisms to handle concurrent writes, gRPC streaming protocols, and object-storage syncing with conflict resolution across network partitions. This necessitates deep systems programming, database internals, and distributed systems engineering expertise.

Discussion

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Competitors

Other products that read as similar to this one : 225 launches clear the similarity bar, closest 8 shown.

Attention rank: #96 of 226 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 2 days after the earliest competitor.

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