Li

LightDB – fast embedded DB for Scala

Hacker News

LightDB – fast embedded DB for Scala

I’ve just released version 4.0 of LightDB, a high-performance embedded database for Scala that combines key-value storage, SQL-like querying, graph traversal, and full-text search; all in one JVM library. I built it out of necessity: I needed a fast, in-process database to handle large-scale entity resolution, full-text filtering, and relationship traversal. All without an external DB server. Nothing existing fit that niche, so LightDB was born. What it offers: Key-value access (RocksDB, LMDB, MapDB, etc.) A concise Scala SQL-like DSL A built-in graph traversal engine Lucene-powered full-text search & faceting Runs fully embedded in your app. No servers, no sockets Blazingly fast: processing jobs that used to take days now finish in hours Everything is composable, strongly typed, and designed for performance-critical JVM applications. I’d love to hear feedback, especially if you’ve ever tried to use something like RocksDB, SQLite, or Lucene in a Scala app and found the integration painful.

Visit product View on Hacker NewsMay 31, 2025

Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: just released, exist, existing · Missing: https docs, excited, lua

77%77% 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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% 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

33%33% 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

21%21% 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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