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LogLens, a fast alternative to grep – jq for structured logs

Hacker News

LogLens, a fast alternative to grep – jq for structured logs

Hi HN, I'm the creator of LogLens. Like many of you, I spend a lot of time digging through massive structured (mostly JSON) log files. I've always relied on grep for its speed and then piped to jq for the actual filtering, but I find this workflow gets slow and complicated, especially with multi-GB files or complex queries. LogLens is my attempt to fix this. It's a single, fast CLI tool written in Rust that's designed specifically for structured logs. It combines a simple SQL-like query language (e.g., loglens query './logs' 'level == "error" && status >= 500') with parallel, memory-mapped file processing to be significantly faster than grep | jq. The Model (Please Read): This is a closed-source, freemium tool. Free Tier: The core features (search, query, fields, compress/decompress) are free to use, forever. My goal is for the free tier to be genuinely useful on its own. Pro Tier: The advanced features (tui, stats, watch, count, etc.) are part of a Pro license. I'm a solo developer, and I'm trying to build a sustainable side income from this. The license is $79 for a year of updates, which includes a perpetual fallback license. This means that after your year is up, you can keep using the last version you downloaded, forever. The Tech: It's written in Rust, using rayon for parallel processing and memmap2 for fast file access. The query engine is a simple, hand-written recursive parser. I'd be grateful for any feedback you have on the tool, the query language, or the business model. Website (with demo GIF): https://www.getloglens.com Docs: https://www.getloglens.com/docs Thanks for checking it out!

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
82%82% 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: model, single, using · Missing: mac, agents, macos
66%66% 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
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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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