Skip to content

Keptn Metrics Operator

The Keptn Metrics Operator collects, processes, and analyzes metrics data from a variety of sources. Once collected, this data can be used to generate a variety of reports and dashboards that provide insights into the health and performance of the application and infrastructure.

While Kubernetes has ways to extend its metrics APIs, there are limitations, especially that they allow you to use only a single observability platform such as Prometheus, Thanos, Cortex, Dynatrace or Datadog. The Keptn Metrics Operator solves this problem by providing a single entry point for all your metrics data, regardless of its source. This means that data multiple observability platforms are available via a single access point.

Keptn metrics are integrated with the Kubernetes Custom Metrics API, so they are compatible with the Kubernetes HorizontalPodAutoscaler (HPA), which enables the horizontal scaling of workloads based on metrics collected from observability platforms. See Using the HorizontalPodAutoscaler for instructions.

The Metrics Operator consists of the following components:

  • Metrics Controller
  • Analysis Controller
  • Metrics Adapter
graph TD;
K((CRs)) -- apply --> L[Kubernetes API]
X[Metrics Adapter] <--> L
Y[Metrics Controller] <--> L
Z[Analysis Controller] <--> L

P3((<svg height="80" width="80"><image href="https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Prometheus_software_logo.svg/2066px-Prometheus_software_logo.svg.png" height="80" width="80" /></svg>))
P1[<svg height="80" width="100"><image href="https://imgix.datadoghq.com/img/about/presskit/usage/logousage_white.png" height="100" width="100" /></svg>]  <--> Y
P3 <--> Y
P3 <--> Z
P2[<svg height="70" width="100"><image href="https://seeklogo.com/images/D/dynatrace-logo-0B89594073-seeklogo.com.png" height="70" width="100" /></svg>] <--> Z

style L fill:#006bb8,stroke:#fff,stroke-width:px,color:#fff
style Y fill:#d8e6f4,stroke:#fff,stroke-width:px,color:#006bb8
style Z fill:#d8e6f4,stroke:#fff,stroke-width:px,color:#006bb8
style X fill:#d8e6f4,stroke:#fff,stroke-width:px,color:#006bb8
style K fill:#fff,stroke:#123,stroke-width:px,color:#006bb8
style P1 fill:#fff,stroke:#fff,stroke-width:px,color:#fff
style P2 fill:#fff,stroke:#fff,stroke-width:px,color:#fff
style P3 fill:#fff,stroke:#fff,stroke-width:px,color:#fff

The Metrics adapter exposes custom metrics from an application to external monitoring and alerting tools. The adapter exposes custom metrics on a specific endpoint where external monitoring and alerting tools can scrape them. It is an important component of the metrics operator as it allows for the collection and exposure of custom metrics, which can be used to gain insight into the behavior and performance of applications running on a Kubernetes cluster.

The Metrics controller fetches metrics from an SLI provider. The controller reconciles a KeptnMetric resource and updates its status with the metric value provided by the selected metric provider. Each KeptnMetric is identified by name and is associated with an instance of an observability platform that is defined in a KeptnMetricsProvider resource.

The steps in which the controller fetches metrics are given below:

  1. When a KeptnMetric resource is found or modified, the controller checks whether the metric has been updated within the interval that is defined in the spec.fetchintervalseconds field.

  2. If not, it skips the reconciliation process and queues the request for later.

  3. The controller attempts to fetch the provider defined in the spec.provider.name field.

  4. If this is not possible, the controller reconciles and queues the request for later.

  5. If the provider is found, the controller loads the provider and evaluates the query defined in the spec.query field.

  6. If the evaluation is successful, it stores the fetched value in the status field of the KeptnMetric object.
  7. If the evaluation fails, the error and reason is written to the KeptnMetricStatus resource. The error is described in both human-readable language and as raw data to help identify the source of the problem (such as a forbidden code).

Comments