Announcement

Free to view yesterday and today
Customer Service: cat_manager

Chaos Monkey - 云环境弹性测试工具

Chaos Monkey is a resiliency tool that helps applications tolerate random instance failures, actively testing system robustness.

Go
Added on 2025年5月6日
View on GitHub
Chaos Monkey - 云环境弹性测试工具 preview
15,812
Stars
1,203
Forks
Go
Language

Project Introduction

Summary

Chaos Monkey is a tool from Netflix's Simian Army designed to test system resilience by simulating infrastructure failures in production and staging environments.

Problem Solved

Ensures that applications are truly resilient to unexpected failures by regularly and randomly injecting chaos, preventing engineers from becoming complacent about failure scenarios.

Core Features

Instance Termination

Randomly terminates instances (e.g., virtual machines, containers) in a configurable environment.

Configurable Scheduling and Rules

Allows defining schedules, targeting rules, and exclusion lists to control the blast radius and timing of failures.

Cloud Provider & Platform Integration

Can be integrated with cloud provider APIs (like AWS, GCP, Azure) and deployment platforms (like Spinnaker).

Tech Stack

Go
Spinnaker (Integration)
AWS (Integration)
GCP (Integration)
Azure (Integration)

使用场景

Chaos Monkey is applied in various scenarios to continuously verify the fault tolerance of systems:

场景一:生产环境弹性验证

Details

Regularly terminate instances in production to ensure automatic healing, load balancing, and failover mechanisms work as expected under pressure.

User Value

Increases confidence in production system stability and reduces the likelihood of major outages due to single instance failures.

场景二:持续集成中的弹性测试

Details

Integrate Chaos Monkey into CI/CD pipelines to automatically test the resilience of new service versions before deployment.

User Value

Catches resilience issues early in the development cycle, reducing the cost and risk of fixing them later.

Recommended Projects

You might be interested in these projects

open-telemetryopentelemetry.io

This project aims to simplify task processing through automation, significantly boosting efficiency and accuracy. It's suitable for developers and analysts handling large datasets.

JavaScript
7221433
View Details

TsudaKageyuminhook

This project provides a robust and scalable solution for processing large datasets, offering significant improvements in speed and efficiency compared to traditional methods. Ideal for data engineers and scientists.

C
4814936
View Details

isaac-simIsaacLab

IsaacLab is a unified framework built on NVIDIA Isaac Sim, designed to accelerate robot learning research and development through high-fidelity simulation and scalable training environments.

Python
42441953
View Details