chaos-mesh/chaos-mesh
Chaos Mesh
Chaos Mesh is a cloud-native fault injection tool and Kubernetes chaos engineering platform designed to verify system resilience. It functions as a testing framework for designing and executing automated failure scenarios to evaluate how containerized workloads recover from disruptions.
The project acts as a multi-cluster chaos orchestrator, providing a centralized control plane to manage and monitor experiments across multiple remote Kubernetes clusters from a single interface. It includes a dashboard for the visual scheduling of experiments and the coordination of complex failure scenarios.
The platform covers a broad range of fault injection categories, including network latency, pod termination, and resource stress for CPU and memory. It also provides capabilities for simulating hardware and system failures, such as IO errors and system time manipulation, as well as infrastructure-level disruptions across cloud providers and JVM-specific faults.
Observability is handled through metrics monitoring and system health verification to evaluate stability during injections. Security is managed via dashboard authentication and injection scope restrictions based on namespace annotations.
Features
- Chaos Engineering Frameworks - Provides an integrated framework for designing, scheduling, and orchestrating the injection of failures to measure system resilience.
- Fault Injection Testing - A comprehensive tool for introducing network, hardware, and application disruptions to test the stability of distributed systems.
- Custom Resource Definitions - Employs Kubernetes custom resource definitions to manage the desired state and orchestration of chaos experiments.
- Kernel Fault Injection - Manipulates system behavior by interacting with Linux kernel modules, namespaces, and network traffic control tools.
- Application-Layer Fault Injection - Introduces errors into software stacks or network connections to test the resilience of specific applications.
- Cloud Infrastructure Faults - Simulates regional outages and hardware failures across cloud providers to ensure high availability.
- Container Termination - Kills containers and services within a cluster to test failover mechanisms and high-availability configurations.
- Network Faults - Simulates network latency and connectivity failures to test how microservices handle communication disruptions.
- Orchestrator Faults - Introduces controlled disruptions into Kubernetes clusters to verify that applications and infrastructure maintain resilience.
- Multi-Cluster Orchestrators - Provides a centralized control plane to coordinate chaos experiments across multiple remote Kubernetes clusters.
- Resiliency Testing Loops - Injects controlled faults into Kubernetes clusters through automated testing loops to verify application recovery.
- Network - Introduces network latency, DNS failures, and connectivity disruptions to test microservice communication resilience.
- Stress Testing - Creates abnormal CPU and memory consumption to identify performance degradation under extreme conditions.
- System Resource Stressing - Exhausts CPU, memory, and I/O resources to identify performance breaking points under extreme load.
- Chaos Metrics - Collects and exports telemetry on experiment duration and resource usage for system components.
- Multi-Cloud Validation - Simulates regional outages and hardware failures across cloud providers to ensure high availability and service continuity.
- Node-Level Injection Agents - Deploys agents to every node via DaemonSets to execute low-level fault injections directly on the host.
- Runtime Process Faults - Simulates crashes or performance degradation in Java Virtual Machine processes to test application handling of failures.
- Physical Hardware Fault Injection - Extends fault injection capabilities beyond virtual containers to affect physical hardware.
- Storage IO Fault Injection - Injects faults into storage and input/output operations to verify the availability of distributed data systems.
- Fault Injection Sidecars - Injects failures into target processes by attaching helper sidecar containers or utilizing process-level instrumentation.
- System Time Virtualization - Provides capabilities to introduce time skews and offsets to the system clock for testing time-sensitive logic.
- Kubernetes-Native Workflows - Uses custom resource definitions to model and coordinate sequences of chaos experiments.
- Container Execution Interruptions - Simulates the failure of specific containers within a group to verify system recovery and availability.
- Cluster Health Monitoring - Collects telemetry from the cluster and targeted pods to verify system stability during and after fault injection.
- System Health Monitors - Performs status checks on the environment to evaluate whether the system remains stable during fault injection.
- Fault Scenario Modeling - Groups multiple experiments into a single scenario to manage complex failure sequences and generate reports.
- Chaos Experiment Dashboards - Provides a centralized user interface for the visual scheduling and real-time monitoring of experiments across clusters.
- Chaos Engineering - Chaos engineering platform designed for Kubernetes.
- Reliability And Chaos - Platform for orchestrating chaos experiments on Kubernetes clusters.
- Chaos Engineering - Chaos engineering platform for Kubernetes environments.
- Chaos Engineering Tools - Cloud-native platform for orchestrating complex Kubernetes chaos experiments.
Star history
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