I wrote a concurrent log parser in Go to learn about concurrency

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I wanted to learn about using Go for concurrent tasks (e.g. using goroutines and channels), so I built a tool to solve a real problem I had at work. I wanted to parse CLF (e.g. Apache or Nginx) logs and store them in SQLite so I would be able to perform further data analysis on them without having to resort to "heavier" tools like Grafana. It is mostly meant as a practice project but maybe someone else could also find it handy someday. It is a little rough around the edges but overall I achieved with it what I set out to do and working on it over the last few weeks has taught me a lot about Golang as a newbie. Coming from the overcomplicated world of Node.js (both on the frontend and backend), I've been loving the simplicity of Go! https://github.com/thevxn/xilt

Visit product View on Hacker NewsMar 27, 2025

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2points

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

74%74% 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 · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, tasks, using · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% 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

10%10% 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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