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Cloud Service Monitoring

Backend

Using the cloud_service_monitoring backend, you can use the Cloud Service Monitoring API to manage your SLOs.

backends:
  cloud_service_monitoring:
    project_id: "${WORKSPACE_PROJECT_ID}"

SLOs are created from standard metrics available in Cloud Monitoring and the data is stored in Cloud Service Monitoring API (see docs).

The following methods are available to compute SLOs with the cloud_service_monitoring backend:

  • basic to create standard SLOs for Google App Engine, Google Kubernetes Engine, and Cloud Endpoints.
  • good_bad_ratio for metrics of type DELTA or CUMULATIVE.
  • distribution_cut for metrics of type DELTA and unit DISTRIBUTION.

Basic

The basic method is used to let the Cloud Service Monitoring API automatically generate standardized SLOs for the following GCP services:

  • Google App Engine
  • Google Kubernetes Engine (with Istio)
  • Google Cloud Endpoints

The SLO configuration uses Cloud Monitoring GCP metrics and only requires minimal configuration compared to custom SLOs.

Example config (App Engine availability):

backend: cloud_service_monitoring
method: basic
service_level_indicator:
  app_engine:
    project_id: ${GAE_PROJECT_ID}
    module_id: ${GAE_MODULE_ID}
  availability: {}

For details on filling the app_engine fields, see AppEngine spec.

→ Full SLO config

Example config (Cloud Endpoint latency):

backend: cloud_service_monitoring
method: basic
service_level_indicator:
  cloud_endpoints:
    service_name: ${ENDPOINT_URL}
  latency:
    threshold: 724 # ms

For details on filling the cloud_endpoints fields, see CloudEndpoint spec.

Example config (Istio service latency):

backend: cloud_service_monitoring
method: basic
service_level_indicator:
  mesh_istio:
    mesh_uid: ${GKE_MESH_UID}
    service_namespace: ${GKE_SERVICE_NAMESPACE}
    service_name: ${GKE_SERVICE_NAME}
  latency:
    threshold: 500 # ms

For details on filling the mesh_istio fields, see MeshIstio spec.

→ Full SLO config

Example config (Istio service latency) [DEPRECATED]:

backend: cloud_service_monitoring
method: basic
service_level_indicator:
  cluster_istio:
    project_id: ${GKE_PROJECT_ID}
    location: ${GKE_LOCATION}
    cluster_name: ${GKE_CLUSTER_NAME}
    service_namespace: ${GKE_SERVICE_NAMESPACE}
    service_name: ${GKE_SERVICE_NAME}
  latency:
    threshold: 500 # ms

For details on filling the cluster_istio fields, see ClusterIstio spec.

→ Full SLO config

Good / bad ratio

The good_bad_ratio method is used to compute the ratio between two metrics:

  • Good events, i.e events we consider as 'good' from the user perspective.
  • Bad or valid events, i.e events we consider either as 'bad' from the user perspective, or all events we consider as 'valid' for the computation of the SLO.

This method is often used for availability SLOs, but can be used for other purposes as well (see examples).

Example config:

backend: cloud_service_monitoring
method: good_bad_ratio
service_level_indicator:
  filter_good:  >
    project="${GAE_PROJECT_ID}"
    metric.type="appengine.googleapis.com/http/server/response_count"
    resource.type="gae_app"
    metric.labels.response_code >= 200
    metric.labels.response_code < 500
  filter_valid: >
    project="${GAE_PROJECT_ID}"
    metric.type="appengine.googleapis.com/http/server/response_count"

You can also use the filter_bad field which identifies bad events instead of the filter_valid field which identifies all valid events.

→ Full SLO config

Distribution cut

The distribution_cut method is used for Cloud distribution-type metrics, which are usually used for latency metrics.

A distribution metric records the statistical distribution of the extracted values in histogram buckets. The extracted values are not recorded individually, but their distribution across the configured buckets are recorded, along with the count, mean, and sum of squared deviation of the values.

Example config:

backend: cloud_service_monitoring
method: distribution_cut
service_level_indicator:
  filter_valid: >
    project=${GAE_PROJECT_ID}
    metric.type=appengine.googleapis.com/http/server/response_latencies
    metric.labels.response_code >= 200
    metric.labels.response_code < 500
  range_min: 0
  range_max: 724 # ms

The range_min and range_max are used to specify the latency range that we consider 'good'.

→ Full SLO config

Service Monitoring API considerations

Tracking objects

Since Cloud Service Monitoring API persists Service and ServiceLevelObjective objects, we need ways to keep our local SLO YAML configuration synced with the remote objects.

Auto-imported

Some services are auto-imported by the Service Monitoring API: they correspond to SLO configurations using the basic method.

The following conventions are used by the Service Monitoring API to give a unique id to an auto-imported Service:

  • App Engine:

    gae:{project_id}_{module_id}

    → Make sure that the app_engine block in your config has the correct fields corresponding to your App Engine service.

  • Cloud Endpoints:

    ist:{project_id}-{service}

    → Make sure that the cloud_endpoints block in your config has the correct fields corresponding to your Cloud Endpoint service.

  • Mesh Istio [NOT YET RELEASED]:

    ist:{project_id}-{mesh_uid}-{service_namespace}-{service_name}

    → Make sure that the mesh_istio block in your config has the correct fields corresponding to your Istio service.

  • Cluster Istio [DEPRECATED SOON]:

    ist:{project_id}-{suffix}-{location}-{cluster_name}-{service_namespace}-{service_name}

    → Make sure that the cluster_istio block in your config has the correct fields corresponding to your Istio service.

You cannot import an existing ServiceLevelObjective object, since they use a random id.

Custom

Custom services are the ones you create yourself using the Cloud Service Monitoring API and the slo-generator.

The following conventions are used by the slo-generator to give a unique id to a custom Service and Service Level Objective objects:

  • service_id = ${metadata.service_name}-${metadata.feature_name}

  • slo_id = ${metadata.service_name}-${metadata.feature_name}-${metadata.slo_name}-${window}

To keep track of those, do not update any of the following fields in your configs:

  • metadata.service_name, metadata.feature_name and metadata.slo_name in the SLO config.

  • window in the Error Budget Policy.

If you need to make updates to any of those fields, first run the slo-generator with the -d (delete) option (see #deleting-objects), then re-run normally.

To import an existing custom Service objects, find out your service id from the API and fill the service_id in the service_level_indicator configuration.

Deleting objects

To delete an SLO object in Cloud Monitoring API using the cloud_service_monitoring class, run the slo-generator with the -d (or --delete) flag:

slo-generator -f <SLO_CONFIG_PATH> -b <ERROR_BUDGET_POLICY> --delete

Alerting

See the Cloud Service Monitoring docs for instructions on alerting.

Examples

Complete SLO samples using Cloud Service Monitoring are available in samples/cloud_service_monitoring. Check them out!