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A very minimal standalone log ingestion and querying platform with builtin client library for instrumentation.

3 starsGo

LogsGo - an experimental log ingestion/query project I built to learn

by SaumyaCodes·Apr 18, 2026·2 points·0 comments
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AI Analysis

●MidShip It

Honest learning project in a solved category — Grafana Loki and Datadog already dominate.

Strengths
  • •Multi-tiered storage with memory, BadgerDB, and S3 shows thoughtful architecture decisions.
  • •Skiplist-based in-memory store enables O(log n) insertion and query performance.
  • •Author openly seeks design feedback — genuine learning mindset, not marketing hype.
Weaknesses
  • •Logging infrastructure is extremely crowded — Loki, Splunk, Datadog solve this at scale.
  • •Learning project by author's admission — not production-ready or differentiated.
Target Audience

Backend developers, DevOps engineers learning distributed systems

Similar To

Grafana Loki · Datadog · Splunk

Post Description

I built LogsGo as a learning project to explore log ingestion, querying, and storage tradeoffs.

It’s a small Go-based system where logs come in over gRPC, land in memory first, then flush into local storage and optionally S3-compatible object storage. I also added a simple query language plus a small UI to inspect log occurrences over time. This wasn’t built because I think the world needed “another logging system” or because I’m an expert here. I mostly wanted to learn by building something end to end: ingestion paths, storage layering, querying, retention, auth/TLS, and some UI work.

Repo: https://github.com/Saumya40-codes/LogsGO

I’d genuinely appreciate feedback, including “this design is wrong for X reason” type feedback. If parts of it feel overengineered / naive / badly thought through, that’s useful for me too.

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