Alerting rules

Alerting rules allow you to define alert conditions based on Prometheus expression language expressions and to send notifications about firing alerts to an external service. Whenever the alert expression results in one or more vector elements at a given point in time, the alert counts as active for these elements’ label sets.

Defining alerting rules

Alerting rules are configured in Prometheus in the same way as recording rules.

An example rules file with an alert would be:

  1. groups:
  2. - name: example
  3. rules:
  4. - alert: HighRequestLatency
  5. expr: job:request_latency_seconds:mean5m{job="myjob"} > 0.5
  6. for: 10m
  7. labels:
  8. severity: page
  9. annotations:
  10. summary: High request latency

The optional for clause causes Prometheus to wait for a certain duration between first encountering a new expression output vector element and counting an alert as firing for this element. In this case, Prometheus will check that the alert continues to be active during each evaluation for 10 minutes before firing the alert. Elements that are active, but not firing yet, are in the pending state. Alerting rules without the for clause will become active on the first evaluation.

The labels clause allows specifying a set of additional labels to be attached to the alert. Any existing conflicting labels will be overwritten. The label values can be templated.

The annotations clause specifies a set of informational labels that can be used to store longer additional information such as alert descriptions or runbook links. The annotation values can be templated.

Templating

Label and annotation values can be templated using console templates. The $labels variable holds the label key/value pairs of an alert instance. The configured external labels can be accessed via the $externalLabels variable. The $value variable holds the evaluated value of an alert instance.

  1. # To insert a firing element's label values:
  2. {{ $labels.<labelname> }}
  3. # To insert the numeric expression value of the firing element:
  4. {{ $value }}

Examples:

  1. groups:
  2. - name: example
  3. rules:
  4. # Alert for any instance that is unreachable for >5 minutes.
  5. - alert: InstanceDown
  6. expr: up == 0
  7. for: 5m
  8. labels:
  9. severity: page
  10. annotations:
  11. summary: "Instance {{ $labels.instance }} down"
  12. description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 5 minutes."
  13. # Alert for any instance that has a median request latency >1s.
  14. - alert: APIHighRequestLatency
  15. expr: api_http_request_latencies_second{quantile="0.5"} > 1
  16. for: 10m
  17. annotations:
  18. summary: "High request latency on {{ $labels.instance }}"
  19. description: "{{ $labels.instance }} has a median request latency above 1s (current value: {{ $value }}s)"

Inspecting alerts during runtime

To manually inspect which alerts are active (pending or firing), navigate to the “Alerts” tab of your Prometheus instance. This will show you the exact label sets for which each defined alert is currently active.

For pending and firing alerts, Prometheus also stores synthetic time series of the form ALERTS{alertname="<alert name>", alertstate="<pending or firing>", <additional alert labels>}. The sample value is set to 1 as long as the alert is in the indicated active (pending or firing) state, and the series is marked stale when this is no longer the case.

Sending alert notifications

Prometheus’s alerting rules are good at figuring what is broken right now, but they are not a fully-fledged notification solution. Another layer is needed to add summarization, notification rate limiting, silencing and alert dependencies on top of the simple alert definitions. In Prometheus’s ecosystem, the Alertmanager takes on this role. Thus, Prometheus may be configured to periodically send information about alert states to an Alertmanager instance, which then takes care of dispatching the right notifications.
Prometheus can be configured to automatically discover available Alertmanager instances through its service discovery integrations.

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