7e8f0450d6
This commit: - Enables Kraken to leverage kube-burner to scrape metrics from Prometheus and index them into Elasticsearch. This way we can take a look at the metrics in Grafana long term even after the cluster is terminated. - Enables separation of operations based on distribution with OpenShift as the default option. One of the use cases is to capture Prometheus instance details as it's installed by default while it's optional for Kubernetes.
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Kraken
Chaos and resiliency testing tool for Kubernetes and OpenShift. Kraken injects deliberate failures into Kubernetes/OpenShift clusters to check if it is resilient to turbulent conditions.
Workflow
Installation and usage
Instructions on how to setup, configure and run Kraken can be found at Installation.
Config
Instructions on how to setup the config and the options supported can be found at Config.
Kubernetes/OpenShift chaos scenarios supported
Kraken supports pod, node, time/date and litmus based scenarios.
Kraken scenario pass/fail criteria and report
It's important to make sure to check if the targeted component recovered from the chaos injection and also if the Kubernetes/OpenShift cluster is healthy as failures in one component can have an adverse impact on other components. Kraken does this by:
- Having built in checks for pod and node based scenarios to ensure the expected number of replicas and nodes are up. It also supports running custom scripts with the checks.
- Leveraging Cerberus to monitor the cluster under test and consuming the aggregated go/no-go signal to determine pass/fail. It is highly recommended to turn on the Cerberus health check feature avaliable in Kraken. Instructions on installing and setting up Cerberus can be found here. Once Cerberus is up and running, set cerberus_enabled to True and cerberus_url to the url where Cerberus publishes go/no-go signal in the Kraken config file.
Performance monitoring
Monitoring the Kubernetes/OpenShift cluster to observe the impact of Kraken chaos scenarios on various components is key to find out the bottlenecks as it's important to make sure the cluster is healthy in terms if both recovery as well as performance during/after the failure has been injected. Instructions on enabling it can be found here.
Scraping and storing metrics long term
Kraken supports capturing metrics for the duration of the scenarios defined in the config and indexes then into Elasticsearch to be able to store and evaluate the state of the runs long term. The indexed metrics can be visualized with the help of Grafana. It uses Kube-burner under the hood. The metrics to capture need to be defined in a metrics profile which Kraken consumes to query prometheus ( installed by default in OpenShift ) with the start and end timestamp of the run. Information on enabling and leveraging this feature can be found here.
Blogs and other useful resources
- Blog post on introduction to Kraken: https://www.openshift.com/blog/introduction-to-kraken-a-chaos-tool-for-openshift/kubernetes
- Discussion and demo on how Kraken can be leveraged to ensure OpenShift is reliable, performant and scalable: https://www.youtube.com/watch?v=s1PvupI5sD0&ab_channel=OpenShift
- Blog post emphasizing the importance of making Chaos part of Performance and Scale runs to mimic the production environments: https://www.openshift.com/blog/making-chaos-part-of-kubernetes/openshift-performance-and-scalability-tests
Contributions
We are always looking for more enhancements, fixes to make it better, any contributions are most welcome. Feel free to report or work on the issues filed on github.
More information on how to Contribute
Community
Key Members(slack_usernames): paigerube14, rook, mffiedler, mohit, dry923, rsevilla, ravielluri