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Version: 2.2.0

Observe chaos impact using Grafana

Chaos Engineering is the discipline of experimenting on a system to build confidence in the system’s capability to withstand turbulent conditions in production. Monitoring a system's resilience and its performance under chaos are one of the fundamental principles of chaos engineering. Litmus has sample chaos interleaved dashboards available on Grafana’s community dashboards as well as provisioned dashboards along with provisioned data sources. Some sample chaos interleaved dashboards can be found here

Before you begin#

The following should be required before integrating Grafana with litmus 2.0:

Grafana setup with provisioned data source amd dashboards using Prometheus deployment with scrape jobs#

The following steps can be followed to set up Grafana with Prometheus for accessing the integrated and interleaved dashboards

  • Clone the litmus repo
git clone https://github.com/litmuschaos/litmus.gitcd litmus/monitoring
  • Create monitoring namespace on the cluster
kubectl create ns monitoring
  • Deploy prometheus components
kubectl -n monitoring apply -f utils/prometheus/prometheus-scrape-configuration/
  • Deploy metrics exporters
kubectl -n monitoring apply -f utils/metrics-exporters/node-exporter/kubectl -n monitoring apply -f utils/metrics-exporters/kube-state-metrics/
  • Deploy chaos-exporter when the cluster is not connected to litmus 2.0 control plane via litmus agent (exporter is installed as a part of the agent bundle)
kubectl -n litmus apply -f utils/metrics-exporters/litmus-metrics/chaos-exporter/
  • Deploy Grafana
kubectl -n monitoring apply -f utils/grafana/

You may access the grafana dashboard via the LoadBalancer (or NodePort) service IP or via a port-forward operation on localhost and then view it from manage dashboards section.

View the services running in the monitoring namespace

kubectl get svc -n monitoring

Now copy the EXTERNAL-IP of grafana and view it in the browser

Default username/password credentials: admin/admin

Screenshots#

Chaos Result selector dropdown:#

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Chaos Engine Context (Target application's NAMESPACE_LABEL) selector dropdown:#

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Chaos Engines with Experiments as Chaos Results:#

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Chaos event annotations:#

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Chaos Result verdict annotations:#

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Interleaved Chaos events:#

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Interleaved Chaos Result verdicts:#

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Chaos Result verdict failure alerts:#

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Chaos Result probe failure alerts:#

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Systems healthy / in steady state OR no alerts to be issued:#

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Systems un-healthy / failed to regain steady state after chaos / meet SLO OR alerts are issued:#

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Alerts issued:#

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Chaos interleaving over infra and application metrics#

Chaos interleaving can be achieved using the litmuschaos_awaited_experiments and litmuschaos_experiment_verdict prometheus metrics which can be transformed using grafana variables and annotations into chaos injection events with metadata and results to monitor the application under test or infrastructure under test.

Sample variable configurations:

Queries:

chaosresult_name

label_values(litmuschaos_awaited_experiments{app=~"chaos-exporter"}, chaosresult_name)

chaosengine_context

label_values(litmuschaos_experiment_verdict{app=~"chaos-exporter"}, chaosengine_context)

Screenshots#

Chaos result name variable
Chaos engine context variable

Sample annotation configurations:

Queries:

LitmusChaos Events

litmuschaos_awaited_experiments{chaosresult_name=~"$chaosresult_name", job="litmus/chaos-exporter", app="chaos-exporter"}

LitmusChaos Metrics

litmuschaos_experiment_verdict{chaosresult_name=~"$chaosresult_name",chaosengine_context=~"$chaosengine_context", job="litmus/chaos-exporter", app="chaos-exporter"}

Screenshots#

Chaos event annotation
Chaos metric annotation

Fault injection and system failure alerts#

These alerts can be configured and triggered based on conditions set on panels using the litmuschaos_awaited_experiments and litmuschaos_experiment_verdict metrics. The same can be issued to various channels registered for the corresponding alerts on Grafana.

Sample alert configuration for chaos result verdict

Query:

litmuschaos_experiment_verdict{job="litmus/chaos-exporter", app="chaos-exporter", chaosresult_verdict="Fail"}

Screenshots#

Experiment verdict failure alert query
Experiment verdict failure alert configuration

Sample alert configuration for probe success percentage

Query:

litmuschaos_experiment_verdict{job="litmus/chaos-exporter", app="chaos-exporter", probe_success_percentage!="100.000000"}

Screenshots#

Probe failure alert query
Probe failure alert configuration

Gauges from metrics for aggregated view of chaos injections#

Metrics from chaos-exporter like litmuschaos_passed_experiments, litmuschaos_failed_experiments and litmuschaos_awaited_experiments when ingested in Prometheus which is connected as a data source can provide an aggregated view of chaos injections on a target agent cluster or namespace.

Queries:

Total Experiments Runs

sum(litmuschaos_passed_experiments{job="litmus/chaos-exporter"} + litmuschaos_failed_experiments{job="litmus/chaos-exporter"})

Passed Experiments

sum(litmuschaos_passed_experiments{job="litmus/chaos-exporter"})

Failed Experiments

sum(litmuschaos_failed_experiments{job="litmus/chaos-exporter"})

Queued Experiments

sum(litmuschaos_awaited_experiments{job="litmus/chaos-exporter"})

Screenshot#

Gauge metrics

Resources#

Observability Considerations in Chaos: The Metrics Story

Learn More#