dazl – the Go logging framework of frameworks

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A few years ago, my team moved from Java to Go. Working on Go projects, we came across a variety of logging frameworks with different APIs outputting messages in different formats. Go seemed to be lacking a logging abstraction like slf4j, which has been so invaluable to the Java ecosystem. Without that abstraction layer, the APIs for configuring logging vary wildly across projects, and libraries must either add a dependency on one of those frameworks or simply avoid structured logging altogether. Dazl is a logging abstraction layer that decouples the logging API from specific Go logging libraries, providing a pluggable logging backend with support for popular frameworks like zap and zerolog. Add logging to your Go library without forcing a particular logging dependency on your users. Use dazl to make logging configurable (via YAML) in your Go application. Dazl is designed to make logging easier for Go developers and their users by providing a unified interface to establish consistency across Go libraries and logging frameworks. Dazl was originally developed in open source at the Open Networking Foundation but remained somewhat hidden away within the subdirectories of an obscure repo. My team has since moved to Intel where this project is still in use in today. I’m sharing it now in the hopes Go developers will get the same value from it we have.

Visit product View on Hacker NewsApr 21, 2023

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

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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: users, way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

29%29% 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

12%12% 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.

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