Status
Stability	beta: metrics
Distributions	contrib, observiq
Issues
Code Owners	@aabmass, @dashpole, @jsuereth, @punya, @damemi, @psx95

This exporter can be used to send metrics (including trace exemplars) to Google Cloud Managed Service for Prometheus. It is one of several supported approaches for sending metrics to Google Cloud Managed Service for Prometheus.

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.
  • compression (optional): Compression format for Metrics gRPC requests. Supported values: [gzip]. Defaults to no compression.
  • grpc_pool_size (optional): Sets the size of the connection pool in the GCP client. Defaults to a single connection.
  • use_insecure (optional): If true, disables gRPC client transport security. Only has applies if Endpoint is not "".
  • add_metric_suffixes (default=true): Add type and unit suffixes to metrics.
  • extra_metrics_config (optional): Enable or disable additional metrics.
    • enable_target_info (default=true): Add target_info metric based on resource.
    • enable_scope_info (default=true): Add otel_scope_info metric and scope_name/scope_version attributes to all other metrics.
  • resource_filters (optional): Provides a list of filters to match resource attributes which will be included in metric labels.
    • prefix (optional): Match resource attribute keys by prefix.
    • regex (optional): Match resource attribute keys by regex.
• 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 = 1000): 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: The sending_queue is provided (and documented) by the Exporter Helper

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]

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.

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-east1"
      action: upsert

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