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chaos-observability-flow-overview Overview Overview

Observability in Litmus serves a two-fold cause:

  1. To provide the right hooks to APM platforms so as to enable visualization and understand the behavior of application/microservices under chaotic conditions.

  2. Ability to gather, record & factor in data provided by standard observability frameworks as part of SLO validation in automated chaos experiment runs - the results of which can be stored & analyzed as experiment “verdicts” or “metadata”.

Chaos Observability in Litmus can be sectioned into the following:

  1. Visualising Chaos Scenario (Visualization)
  • Chaos Scenario Execution Graph
  1. Fetching Logs (Logging)
  • Litmus Checker Logs
  • Experiment Logs
  • Non-Chaos Scenario Logs
  1. Monitoring Systems in Real Time During Chaos (Monitoring)
  • Metrics
  • Events
  1. Viewing Experiment Verdict and Summary (Summarisation)
  • Chaos Result
  1. Post-Chaos Scenario Analytics (Analytics)
  • Chaos Scenario Statistics and Information