Google Managed Service for Prometheus Exporter

Status
Stability	beta
Supported pipeline types	metrics
Distributions	contrib

This exporter can be used to send metrics and traces to Google Cloud Managed Service for Prometheus. The difference between this exporter and the googlecloud exporter is that metrics sent with this exporter are queried using promql, rather than standard the standard MQL.

This exporter is not the standard method of ingesting metrics into Google Cloud Managed Service for Prometheus, which is built on a drop-in replacement for the Prometheus server: https://github.com/GoogleCloudPlatform/prometheus. This exporter does not support the full range of Prometheus functionality, including the UI, recording and alerting rules, and can't be used with the GMP Operator, but does support sending metrics.

Configuration Reference

The following configuration options are supported:

• project (optional): GCP project identifier.
• user_agent (optional): Override the user agent string sent on requests to Cloud Monitoring (currently only applies to metrics). Specify {{version}} to include the application version number. Defaults to opentelemetry-collector-contrib {{version}}.
• metric(optional): Configuration for sending metrics to Cloud Monitoring.
  • endpoint (optional): Endpoint where metric data is going to be sent to. Replaces endpoint.
• use_insecure (optional): If true, use gRPC as their communication transport. Only has effect if Endpoint is not "".
• retry_on_failure (optional): Configuration for how to handle retries when sending data to Google Cloud fails.
  • enabled (default = false)
  • initial_interval (default = 5s): Time to wait after the first failure before retrying; ignored if enabled is false
  • max_interval (default = 30s): Is the upper bound on backoff; ignored if enabled is false
  • max_elapsed_time (default = 120s): Is the maximum amount of time spent trying to send a batch; ignored if enabled is false
• sending_queue (optional): Configuration for how to buffer traces before sending.
  • enabled (default = true)
  • num_consumers (default = 10): Number of consumers that dequeue batches; ignored if enabled is false
  • queue_size (default = 5000): Maximum number of batches kept in memory before data; ignored if enabled is false; User should calculate this as num_seconds * requests_per_second where:
    • num_seconds is the number of seconds to buffer in case of a backend outage
    • requests_per_second is the average number of requests per seconds.

Note: These retry_on_failure and sending_queue are provided (and documented) by the Exporter Helper

Example Configuration

receivers:
    prometheus:
        config:
          scrape_configs:
            # Add your prometheus scrape configuration here.
            # Using kubernetes_sd_configs with namespaced resources (e.g. pod)
            # ensures the namespace is set on your metrics.
            - job_name: 'kubernetes-pods'
                kubernetes_sd_configs:
                - role: pod
                relabel_configs:
                - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
                action: keep
                regex: true
                - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
                action: replace
                target_label: __metrics_path__
                regex: (.+)
                - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
                action: replace
                regex: (.+):(?:\d+);(\d+)
                replacement: $$1:$$2
                target_label: __address__
                - action: labelmap
                regex: __meta_kubernetes_pod_label_(.+)
processors:
    batch:
        # batch metrics before sending to reduce API usage
        send_batch_max_size: 200
        send_batch_size: 200
        timeout: 5s
    memory_limiter:
        # drop metrics if memory usage gets too high
        check_interval: 1s
        limit_percentage: 65
        spike_limit_percentage: 20
    resourcedetection:
        # detect cluster name and location
        detectors: [gcp]
        timeout: 10s
    transform:
      # "location", "cluster", "namespace", "job", "instance", and "project_id" are reserved, and 
      # metrics containing these labels will be rejected.  Prefix them with exported_ to prevent this.
      metric_statements:
      - context: datapoint
        statements:
        - set(attributes["exported_location"], attributes["location"])
        - delete_key(attributes, "location")
        - set(attributes["exported_cluster"], attributes["cluster"])
        - delete_key(attributes, "cluster")
        - set(attributes["exported_namespace"], attributes["namespace"])
        - delete_key(attributes, "namespace")
        - set(attributes["exported_job"], attributes["job"])
        - delete_key(attributes, "job")
        - set(attributes["exported_instance"], attributes["instance"])
        - delete_key(attributes, "instance")
        - set(attributes["exported_project_id"], attributes["project_id"])
        - delete_key(attributes, "project_id")

exporters:
    googlemanagedprometheus:

service:
  pipelines:
    metrics:
      receivers: [prometheus]
      processors: [batch, memory_limiter, transform, resourcedetection]
      exporters: [googlemanagedprometheus]

Resource Attribute Handling

The Google Managed Prometheus exporter maps metrics to the prometheus_target monitored resource. The logic for mapping to monitored resources is designed to be used with the prometheus receiver, but can be used with other receivers as well. To avoid collisions (i.e. "duplicate timeseries enountered" errors), you need to ensure the prometheus_target resource uniquely identifies the source of metrics. The exporter uses the following resource attributes to determine monitored resource:

• location: [location, cloud.availability_zone, cloud.region]
• cluster: [cluster, k8s.cluster.name]
• namespace: [namespace, k8s.namespace.name]
• job: [service.name + service.namespace]
• instance: [service.instance.id]

In the configuration above, cloud.availability_zone, cloud.region, and k8s.cluster.name are detected using the resourcedetection processor with the gcp detector. The prometheus receiver sets service.name to the configured job_name, and service.instance.id is set to the scrape target's instance. The prometheus receiver sets k8s.namespace.name when using role: pod.

Manually Setting location, cluster, or namespace

In GMP, the above attributes are used to identify the prometheus_target monitored resource. As such, it is recommended to avoid writing metric or resource labels that match these keys. Doing so can cause errors when exporting metrics to GMP or when trying to query from GMP. So, the recommended way to set them is with the resourcedetection processor.

If you still need to set location, cluster, or namespace labels (such as when running in non-GCP environments), you can do so with the resource processor like so:

processors:
  resource:
    attributes:
    - key: "location"
      value: "us-east-1"
      action: upsert

Setting cluster, location or namespace using metric labels

This example copies the location metric attribute to a new exported_location attribute, then deletes the original location. It is recommended to use the exported_* prefix, which is consistent with GMP's behavior.

You can also use the groupbyattrs processor to move metric labels to resource labels. This is useful in situations where, for example, an exporter monitors multiple namespaces (with each namespace exported as a metric label). One such example is kube-state-metrics.

Using groupbyattrs will promote that label to a resource label and associate those metrics with the new resource. For example:

processors:
  groupbyattrs:
    keys:
    - namespace
    - cluster
    - location