VMAnomalyConfig

The VMAnomalyConfig CRD allows declaratively defining anomaly detection models , schedulers , and queries .

VMAnomalyConfig object updates models, schedulers and reader.queries sections of VMAnomaly configuration by adding items with {metadata.namespace}-{metadata.name} key prefix. If at least one generated item collides with an existing key, only the colliding item is skipped, while other valid items from the same VMAnomalyConfig are still added to the resulting configuration. Check the VMAnomalyConfig status and related events to identify skipped items caused by collisions.

With given VMAnomaly CR:

      apiVersion: operator.victoriametrics.com/v1
kind: VMAnomaly
metadata:
  name: example
  namespace: test
spec:
  replicaCount: 2
  license:
    key: "xx"
  configSelector:
    matchExpressions:
    - key: app
      operator: In
      values: [test]
  configRawYaml: |
    reader:
      queries:
        ingestion_rate:
          expr: 'sum(rate(vm_rows_inserted_total[5m])) by (type) > 0'
          step: '1m'
    schedulers:
      scheduler_periodic_1m:
        class: "periodic"
        # or class: "scheduler.periodic.PeriodicScheduler" until v1.13.0 with class alias support
        infer_every: "1m"
        fit_every: "2m"
        fit_window: "3h"
    models:
      zscore:
        class: 'zscore'
        z_threshold: 2.5
  reader:
    datasourceURL: http://vmsingle-read-example:8428
    samplingPeriod: 10s
  writer:
    datasourceURL: http://vmsingle-write-example:8428
  monitoring:
    push:
      url: http://vmsingle-monitoring-example:8428

and VMAnomalyConfig CR

      apiVersion: operator.victoriametrics.com/v1
kind: VMAnomalyConfig
metadata:
  name: example
  namespace: test
  labels:
    app: test
spec:
  models:
    zscore:
      class: zscore
      z_threshold: 3.0
  schedulers:
    periodic:
      class: "periodic"
      # or class: "scheduler.periodic.PeriodicScheduler" until v1.13.0 with class alias support
      infer_every: "1m"
      fit_every: "2m"
      fit_window: "3h"
  queries:
    delete-rate:
      expr: 'sum(rate(vm_rows_deleted_total[5m])) by (type) > 0'
      step: '1m'

The result anomaly detection configuration is:

      schedulers:
  test-example-periodic:
    class: "periodic"
    infer_every: "1m"
    fit_every: "2m"
    fit_window: "3h"
  scheduler_periodic_1m:
    class: "periodic"
    infer_every: "1m"
    fit_every: "2m"
    fit_window: "3h"
models:
  test-example-zscore:
    class: 'zscore'
    z_threshold: 3.0
reader:
  datasourceURL: http://vmsingle-read-example:8428
  samplingPeriod: 10s
  queries:
    ingestion_rate:
      expr: 'sum(rate(vm_rows_inserted_total[5m])) by (type) > 0'
      step: '1m'
    test-example-delete-rate:
      expr: 'sum(rate(vm_rows_deleted_total[5m])) by (type) > 0'
      step: '1m'
writer:
  datasourceURL: http://vmsingle-write-example:8428

Specification #

You can see the full actual specification of the VMAnomalyConfig resource in the API docs -> VMAnomalyConfig .

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