Learn about the Wavefront Google Dataproc Integration.

Google Cloud Platform Integration

The Google Cloud Platform integration is full-featured native integration offering agentless data ingestion of GCP metric data, as well as pre-defined dashboards and alert conditions for certain GCP services.

Metrics Configuration

Wavefront ingests Google Cloud Platform metrics using the v3 Stackdriver Monitoring APIs. For details on the metrics, see the metrics documentation.

Metrics originating from Google Cloud Platform are prefixed with gcp. within Wavefront. Once the integration has been set up, you can browse the available GCP metrics in the metrics browser.


Wavefront provides Google Cloud Platform dashboards for the following services:

  • Google App Engine
  • Google BigQuery
  • Google Cloud Bigtable
  • Google Cloud Billing
  • Google Cloud Datastore
  • Google Cloud Dataproc
  • Google Cloud Functions
  • Google Cloud Logging
  • Google Cloud Pub/Sub
  • Google Cloud Router
  • Google Cloud Spanner
  • Google Cloud Storage
  • Google Cloud VPN
  • Google Compute Engine
  • Google Container Engine
  • Google Firebase
  • Google Kubernetes Engine
  • Google ML Engine


The Google Cloud Platform integration dashboard contains predefined alert conditions. These conditions are embedded as queries in the dashboard’s charts. For example:


To create the alert, click the Create Alert link under the query and configure the alert properties (notification targets, condition checking frequency, etc.).

Google Cloud Platform Integration

Add a GCP Integration

Adding a Google Cloud Platform (GCP) integration requires establishing a trust relationship between GCP and Tanzu Observability by Wavefront. Minimum required permissions you need depend on the services that you are using. See Google Cloud Platform Overview and Permissions for details.

The overall process involves the following:

  • Creating a service account
  • Giving that account viewer privileges
  • Downloading a JSON private key

To register a Google Cloud Platform integration:

  1. In the Name text box, enter a meaningful name.
  2. In the JSON key text box, enter your JSON key to give read-only access to a GCP project. Note: The JSON key is securely stored and never exposed except for read-only access to the GCP APIs.
  3. (Optional) Select the categories to fetch.
  4. (Optional) In the Metric Allow List text box, you can add metrics to an allow list by entering a regular expression.

    For example, to monitor all the CPU metrics coming from the Compute Engine, enter ^gcp.compute.instance.cpu.*$.

  5. (Optional) In the Additional Metric Prefixes text box, enter a comma separated list of additional metrics prefixes. The metrics names that start with these prefixes will be imported in addition to what you have selected as categories.
  6. (Optional) Change the Service Refresh Rate. The default is 5 minutes.
  7. (Optional) Select to enable Detailed Histogram Metrics, Delta Counts, and Pricing & Billing information. Note: Enabling Detailed Histogram Metrics and Delta Counts will increase your ingestion rate and costs.

    If you select to enable the Pricing & Billing information, you must also provide an API key.

  8. Click Register.
Metric Name Description
gcp.dataproc.cluster.hdfs.datanodes Indicates the number of HDFS DataNodes that are running inside a cluster.
gcp.dataproc.cluster.hdfs.storage_capacity Indicates the capacity of HDFS system running on the cluster, in GB.
gcp.dataproc.cluster.hdfs.storage_utilization The percentage of HDFS storage currently used.
gcp.dataproc.cluster.hdfs.unhealthy_blocks Indicates the number of unhealthy blocks inside the cluster.
gcp.dataproc.cluster.job.completion_time The time jobs took to complete from the time the user submits a job to the time Dataproc reports it is completed.
gcp.dataproc.cluster.job.duration The time jobs have spent in a given state.
gcp.dataproc.cluster.job.failed_count Indicates the number of jobs that have failed on a cluster.
gcp.dataproc.cluster.job.running_count Indicates the number of jobs that are running on a cluster.
gcp.dataproc.cluster.job.submitted_count Indicates the number of jobs that have been submitted to a cluster.
gcp.dataproc.cluster.operation.completion_time The time operations took to complete from the time the user submits an operation to the time Dataproc reports it is completed.
gcp.dataproc.cluster.operation.duration The time operations have spent in a given state.
gcp.dataproc.cluster.operation.failed_count Indicates the number of operations that have failed on a cluster.
gcp.dataproc.cluster.operation.running_count Indicates the number of operations that are running on a cluster.
gcp.dataproc.cluster.operation.submitted_count Indicates the number of operations that have been submitted to a cluster.
gcp.dataproc.cluster.yarn.allocated_memory_percentage The percentage of YARN memory that is allocated.
gcp.dataproc.cluster.yarn.apps Indicates the number of active YARN applications.
gcp.dataproc.cluster.yarn.containers Indicates the number of YARN containers.
gcp.dataproc.cluster.yarn.memory_size Indicates the YARN memory size in GB.
gcp.dataproc.cluster.yarn.nodemanagers Indicates the number of YARN NodeManagers running inside the cluster.
gcp.dataproc.cluster.yarn.pending_memory_size The current memory request, in GB, that is pending to be fulfilled by the scheduler.
gcp.dataproc.cluster.yarn.virtual_cores Indicates the number of virtual cores in YARN.