arl/statsviz

Visualise Go runtime metrics in real time

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 19th, 2026
Created on August 14th, 2020
Open Issues & Pull Requests: 10 (+0)
GitHub issues: Enabled
Number of forks: 124
Total Stargazers: 3,646 (+0)
Total Subscribers: 25 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 4.2 hours
Mean response time: 75.6 days
90th percentile: 344.0 days
Tracked items: 8

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Issue Activity (beta)

Open issues: 3
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 803 days
Stale 30+ days: 3
Stale 90+ days: 3

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • enhancement (2)
  • frontend (2)
  • good first issue (2)
  • backend (1)
  • bug (1)
  • documentation (1)
  • waiting-for-info (1)

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Detailed Description

Statsviz is a Go library that visualizes runtime metrics in real time through an interactive web interface.

The tool solves the problem of monitoring Go program performance by exposing runtime metrics—heap usage, garbage collection pauses, goroutine counts, scheduler activity, and object allocations—as live plots accessible through a browser. It works by registering HTTP handlers with your application's HTTP server, then collecting and streaming runtime metrics data to a web UI that displays them as real-time charts.

Statsviz suits any Go application where developers need visibility into runtime behavior during development or debugging. It integrates with standard Go HTTP servers and popular frameworks including Echo, Fiber, Gin, and FastHTTP. The tool can be registered at custom paths, served over HTTPS, and placed behind middleware, making it flexible for different deployment scenarios. The README does not compare it to alternative monitoring solutions.

The project maintains active test coverage across Linux and other platforms. Examples are provided for multiple HTTP frameworks and configuration patterns, indicating ongoing attention to real-world usage scenarios. The tool supports custom user-defined plots alongside its built-in metrics visualizations.