apache/skywalking

APM, Application Performance Monitoring System

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Summary Information

Updated 2 minutes ago
Added to GitGenius on September 2nd, 2026
Created on November 7th, 2015
Open Issues & Pull Requests: 40 (+0)
GitHub issues: Enabled
Number of forks: 6,634
Total Stargazers: 24,942 (+0)
Total Subscribers: 810 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.2 hours
Mean response time: 20.4 days
90th percentile: 27.7 days
Tracked items: 520

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 77% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 8% of issues opened in the past year have been closed. Three people close 91% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 35
New in 7 days: 4
Closed in 7 days: 5
Avg open age: 247 days
Stale 30+ days: 9
Stale 90+ days: 5

Recent activity

Opened in 7 days: 4
Closed in 7 days: 3
Comments in 7 days: 6
Events in 7 days: 28

Top labels

  • database (272)
  • feature (260)
  • bug (228)
  • backend (66)
  • agent (35)
  • question (34)
  • UI (30)
  • enhancement (29)

Detailed Description

Apache SkyWalking is an application performance monitoring system designed for microservices, cloud-native, and container-based architectures.

SkyWalking addresses the challenge of observing distributed systems by providing distributed tracing, metrics collection, and log management in a single platform. It works by deploying language-specific agents across your services to capture telemetry data, which flows into a central cluster for analysis and storage. The system can process over a hundred billion telemetry data points from a single cluster and supports multiple data sources including Zipkin, OpenTelemetry, Prometheus, Zabbix, and Fluentd, normalizing them through a consistent pipeline. The tool includes BanyanDB, a purpose-built observability database for ingesting and analyzing telemetry data, and offers eBPF-based monitoring through its Rover agent for Kubernetes deployments to diagnose CPU and network performance.

SkyWalking suits organizations running microservices architectures who need comprehensive observability across services, deployments, and APIs. It provides agents for Java, .Net Core, PHP, NodeJS, Golang, Lua, Rust, C++, JavaScript, and Python, making it applicable to polyglot environments. The platform distinguishes itself through service-centric alerting rules, a log management pipeline with sampling policies and metric extraction, and AI-powered capabilities that recognize HTTP URI patterns and calculate metric baselines for intelligent anomaly detection. Teams should choose this tool if they want a unified system combining tracing, metrics, and logs rather than managing separate tools, and if they need to monitor Kubernetes deployments with eBPF-based profiling.

The project maintains active development across multiple language agents and ecosystem integrations. The codebase shows ongoing work on core observability features including distributed tracing enhancements, new agent implementations, and telemetry pipeline improvements. Development activity spans both the main platform and complementary projects like the Horizon UI for 3D infrastructure visualization and the SkyWalking Showcase for deployment examples.