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Apache SkyWalking - 开源分布式系统性能监控APM

Apache SkyWalking is an open-source Application Performance Monitoring (APM) system designed for distributed systems and microservices, helping developers and operations teams understand the performance and health of their applications.

Java
Added on 2025年6月10日
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Apache SkyWalking - 开源分布式系统性能监控APM preview
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Project Introduction

Summary

Apache SkyWalking is a leading APM tool providing observability for cloud-native applications, microservices, and distributed systems. It offers tracing, metrics, and logging aggregation, analysis, and visualization capabilities.

Problem Solved

In complex distributed systems and microservice architectures, understanding performance bottlenecks, diagnosing failures, and monitoring the health of interconnected services is challenging. SkyWalking provides necessary observability tools to tackle these issues.

Core Features

Distributed Tracing

Provides end-to-end distributed tracing for requests across multiple services, visualizing transaction paths.

Service & Instance Monitoring

Collects and aggregates various metrics like service response times, throughput, and error rates.

Logging Integration

Supports collection and analysis of logs, often integrating with tracing data for better context.

Powerful Visualization UI

Offers a comprehensive web UI for visualization, analysis, and configuration.

Tech Stack

Java
gRPC
Kafka
Elasticsearch
TiDB
OAP (Observability Analysis Platform)
UI (GraphQL, Vue.js)

使用场景

SkyWalking is indispensable for environments requiring comprehensive visibility into application behavior and performance across numerous services.

Scenario 1: Monitoring Microservice Performance

Details

Monitor the performance of individual microservices, track requests flowing between them, and identify which service is causing latency.

User Value

Quickly pinpoint performance bottlenecks in a distributed architecture and improve service responsiveness.

Scenario 2: Troubleshooting Production Issues

Details

When a user reports an error or slow transaction, use tracing to follow the exact path of that request across all involved services to find the source of the problem.

User Value

Reduce Mean Time To Resolution (MTTR) for production incidents by providing detailed context and root cause analysis.

Scenario 3: Capacity Planning & Optimization

Details

Analyze historical performance data and traffic patterns to understand resource usage and plan for future capacity needs.

User Value

Make informed decisions about infrastructure scaling and optimize resource allocation based on actual usage data.

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