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LITMUS ROADMAP

This document captures only the high level roadmap items. For the detailed backlog, see issues list.

Completed

  • Declarative Chaos Intent via custom resources
  • Chaos Operator to orchestrate chaos experiments
  • Off the shelf / ready chaos experiments for general Kubernetes chaos
  • Self sufficient, Centralized Hub for chaos experiments
  • Per-experiment minimal RBAC permissions definition
  • Creation of 'scenarios' involving multiple faults via Argo-based Chaos Workflows (with examples for microservices apps like podtato-head and sock-shop)
  • Cross-Cloud Control Plane (Litmus Portal) to perform chaos against remote clusters
  • Helm charts for LitmusChaos control plane
  • Helm Chart for LitmusChaos execution Plane
  • Support for admin mode (centralized chaos management) as well as namespaced mode (multi-tenant clusters)
  • Continuous chaos via flexible schedules, with support to halt/resume or (manual/conditional) abort experiments
  • Provide complete workflow termination/abort capability
  • Generation of observability data via Prometheus metrics and Kubernetes chaos events for experiments
  • Steady-State hypothesis validation before, during and after chaos injection via different probe types
  • Support for Docker, Containerd & CRI-O runtime
  • Support for scheduling policies (nodeSelector, tolerations) and resource definitions for chaos pods
  • ChaosHub refactor for 2.x user flow
  • Support for ARM64 nodes
  • Minimized role permissions for Chaos Service Accounts
  • Scaffolding scripts (SDK) to help bootstrap a new chaos experiment in Go, Python, Ansible
  • Support orchestration of non-native chaos libraries via the BYOC (Bring-Your-Own-Chaos) model
  • Support for OpenShift platform
  • Workflow YAML linter addition
  • Integration tests & e2e framework creation for control plane components and chaos experiments
  • Documentation (usage guide for chaos operator, resources & developer guide for new experiment creation)
  • Improved documentation and tutorials for Litmus Portal based execution flow
  • Add architecture details & design resources
  • Define community sync up cadence and structure

In-Progress (Under Design OR Active Development)

  • Native Chaos Workflows with redesigned subscriber to improve resource delegation, enabling seamless and efficient execution of chaos workflows within Kubernetes clusters.
  • Introduce transient runners to improve resource efficiency during chaos experiments by dynamically creating and cleaning up chaos runner instances.
  • Implement Kubernetes connectors to enable streamlined integration with Kubernetes clusters, providing simplified authentication and configuration management.
  • Integrate with tools like K8sGPT to generate insightful reports that identify potential weaknesses in your Kubernetes environment before executing chaos experiments.
  • Add Terraform support for defining and executing chaos experiments on infrastructure components, enabling infrastructure-as-code-based chaos engineering.
  • Add SDK support for Python and Java, with potential extensions to other programming languages based on community interest.
  • Include in-product documentation, such as tooltips, to improve user experience and ease of adoption.
  • Implement the litmus-java-sdk with a targeted v1.0.0 release by Q1.
  • Integrate distributed tracing by adding attributes or events to spans, and create an OpenTelemetry demo showcasing chaos engineering observability.
  • Enhance the exporter to function as an OpenTelemetry collector, providing compatibility with existing observability pipelines.
  • Add support for DocumentDB by replacing certain MongoDB operations, improving flexibility for database chaos.
  • Upgrade Kubernetes SDK from version 1.21 to 1.26 to stay aligned with the latest Kubernetes features and enhancements.
  • Refactor the chaos charts to:
    • Replace latest tags with specific, versioned image tags.
    • Consolidate multiple images into a single optimized image.
  • Update GraphQL and authentication API documentation for improved clarity and user guidance.
  • Add comprehensive unit and fuzz tests to enhance code reliability and robustness.
  • Implement out-of-the-box Slack integration for better collaboration and monitoring during chaos experiments.

Backlog

  • Validation support for all ChaosEngine schema elements within workflow wizard
  • Chaos-center users account to chaosService account map
  • Cross-hub experiment support within a Chaos Workflow
  • Enhanced CRD schema for ChaosEngine to support advanced CommandProbe configuration
  • Support for S3 artifact sink (helps performance/benchmark runs)
  • Chaos experiments against virtual machines and cloud infrastructure (AWS, GCP, Azure, VMWare, Baremetal)
  • Off the shelf chaos-integrated monitoring dashboards for application chaos categories
  • Support for user defined chaos experiment result definition
  • Increased fault injection types (IOChaos, HTTPChaos, JVMChaos)
  • Special Interest Groups (SIGs) around specific areas in the project to take the roadmap forward