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Version: 2.11.0

Chaos Scenarios Statistics


Chaos injections often tends to disrupt tightly coupled micro-services and processes. Visualizing the results and plotting analytical graphs prove to be useful under such circumstances. An analytical overview of chaos scenarios for an entire month or a year can help in benchmarking release cycles and building a viable cloud-native product. Also a comparative study over time or rather just being able to observe and plot resiliency scores across different types of chaos scenarios on different subsystems provide a conclusive summary of the reliability metrics for an application under test (AUT) and the supporting platform or infrastructure.

Prerequisites

The following should be required before knowing about chaos scenario statistics:

Data flow architecture

The chaos center automatically detects scheduled chaos scenario runs on all connected chaos delegates for a project and provides statistical graphs and visualizations. Data for chaos scenarios runs and chaos results from all the chaos delegates are stored in a mongoDB database which is then ingested into analytical pipelines in the control plane server to transform the raw data into meaningful insights for browsing and reporting.

Data flow for statistical analysis

Chaos engine context

The context is a user defined label for a chaos engine to indicate the intent or the target of chaos. Some of it's uses are for naming AUT, micro-service, infrastructure resource etc. Engine context can be added or updated by the user via the UI. It is used to filter chaos experiments, results, tests during statistical analysis and for filtering chaos injection events during real time monitoring of application or infrastructure metrics interleaved with chaos. It defaults to the target application label and namespace separated by _ while using chaos center for scheduling chaos scenarios.

Chaos scenario subject

The subject is a user defined label for a chaos scenario to indicate the intent or the target of chaos. Some of it's uses are for naming AUT, micro-service, infrastructure resource etc. Chaos Scenarios subject can be added or updated by the user via the UI. It is used to filter chaos scenarios during statistical analysis and is stored as a metadata for referencing to a particular application group or version on a given target cluster with chaos delegate. It defaults to the target application name and namespace separated by _ while using chaos center for scheduling chaos scenarios.

Summary

Statistics of a chaos scenarios schedule across its runs and analyzing application performance across chaos scenarios on a target cluster's AUT are facilitated via the data stored in the persistent storage (mongoDB), collected by the connected chaos delegate plane components like subscriber and chaos-exporter. Engine context and Chaos Scenario subject are meant to provide the user with more granular control over the target of chaos while analyzing results or monitoring system or application metrics in real-time under stress or chaos.

Resources

Analyzing chaos scenarios

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