Use this guide to get started monitoring your Google Cloud environment. This approach simplifies the setup for Google Cloud environments with multiple projects, allowing you to maximize your monitoring coverage.
See the full list of Google Cloud integrations
Datadog's Google Cloud integration collects all Google Cloud metrics. Datadog continually updates the docs to show every dependent integration, but the list of integrations is sometimes behind the latest cloud services metrics and services.
If you don’t see an integration for a specific Google Cloud service, reach out to Datadog Support.
Set up Datadog’s Google Cloud integration to collect metrics and logs from your Google Cloud services.
Prerequisites
If your organization restricts identities by domain, you must add Datadog’s customer identity as an allowed value in your policy. Datadog’s customer identity: C0147pk0i
Service account impersonation and automatic project discovery relies on you having certain roles and APIs enabled to monitor projects. Before you start, ensure the following APIs are enabled for each of the projects you want to monitor:
Allows developers to manage billing for their Google Cloud Platform projects programmatically. See the Cloud Cost Management (CCM) documentation for more information.
Ensure that any projects being monitored are not configured as scoping projects that pull in metrics from multiple other projects.
Metric collection
Installation
Organization-level metric collection is not available for the site.
Organization-level (or folder-level) monitoring is recommended for comprehensive coverage of all projects, including any future projects that may be created in an org or folder.
Note: Your Google Cloud Identity user account must have the Admin role assigned to it at the desired scope to complete the setup in Google Cloud (for example, Organization Admin).
1. Create a Google Cloud service account in the default project
Click Done to complete creating the service account.
2. Add the service account at the organization or folder level
In the Google Cloud console, go to the IAM page.
Select a folder or organization.
To grant a role to a principal that does not already have other roles on the resource, click Grant Access, then enter the email of the service account you created earlier.
Enter the service account’s email address.
Assign the following roles:
Compute Viewer provides read-only access to get and list Compute Engine resources
Monitoring Viewer provides read-only access to the monitoring data availabile in your Google Cloud environment
Note: The Browser role is only required in the default project of the service account. Other projects require only the other listed roles.
3. Add the Datadog principal to your service account
Note: If you previously configured access using a shared Datadog principal, you can revoke the permission for that principal after you complete these steps.
Click Add Google Cloud Account.
If you have no configured projects, you are automatically redirected to this page.
Copy your Datadog principal and keep it for the next section.
Note: Keep this window open for Section 4.
In the Google Cloud console, under the Service Accounts menu, find the service account you created in Section 1.
Go to the Permissions tab and click Grant Access.
Paste your Datadog principal into the New principals text box.
Assign the role of Service Account Token Creator.
Click Save.
4. Complete the integration setup in Datadog
In your Google Cloud console, navigate to the Service Account > Details tab. On this page, find the email associated with this Google service account. It has the format <SA_NAME>@<PROJECT_ID>.iam.gserviceaccount.com.
Copy this email.
Return to the integration configuration tile in Datadog (where you copied your Datadog principal in the previous section).
Paste the email you copied in Add Service Account Email.
Click Verify and Save Account.
Metrics appear in Datadog approximately 15 minutes after setup.
Best practices for monitoring multiple projects
Enable per-project cost and API quota attribution
By default, Google Cloud attributes the cost of monitoring API calls, as well as API quota usage, to the project containing the service account for this integration. As a best practice for Google Cloud environments with multiple projects, enable per-project cost attribution of monitoring API calls and API quota usage. With this enabled, costs and quota usage are attributed to the project being queried, rather than the project containing the service account. This provides visibility into the monitoring costs incurred by each project, and also helps to prevent reaching API rate limits.
To enable this feature:
Ensure that the Datadog service account has the Service Usage Consumer role at the desired scope (folder or organization).
The Datadog Google Cloud integration for the site uses service accounts to create an API connection between Google Cloud and Datadog. Follow the instructions below to create a service account and provide Datadog with the service account credentials to begin making API calls on your behalf.
Give the service account a unique name and optional description.
Click Create and continue.
Add the following roles:
Compute Viewer
Monitoring Viewer
Cloud Asset Viewer
Click Done.
Note: You must be a Service Account Key Admin to select Compute Engine and Cloud Asset roles. All selected roles allow Datadog to collect metrics, tags, events, and user labels on your behalf.
At the bottom of the page, find your service accounts and select the one you just created.
Click Add Key -> Create new key, and choose JSON as the type.
Click Create. A JSON key file is downloaded to your computer. Note where it is saved, as it is needed to complete the installation.
On the Configuration tab, select Upload Key File to integrate this project with Datadog.
Optionally, you can use tags to filter out hosts from being included in this integration. Detailed instructions on this can be found in the configuration section.
Click Install/Update.
If you want to monitor multiple projects, use one of the following methods:
Repeat the process above to use multiple service accounts.
Use the same service account by updating the project_id in the JSON file downloaded in step 10. Then upload the file to Datadog as described in steps 11-14.
This method enables you to monitor all projects visible to a service account by assigning IAM roles in the relevant projects. You can assign these roles to projects individually, or you can configure Datadog to monitor groups of projects by assigning these roles at the organization or folder level. Assigning roles in this way allows Datadog to automatically discover and monitor all projects in the given scope, including any new projects that may be added to the group in the future.
Go to the Permissions tab and click on Grant Access.
Paste your Datadog principal into the New principals text box.
Assign the role of Service Account Token Creator and click SAVE.
Note: If you previously configured access using a shared Datadog principal, you can revoke the permission for that principal after you complete these steps.
3. Complete the integration setup in Datadog
In your Google Cloud console, navigate to the Service Account > Details tab. There, you can find the email associated with this Google service account. It resembles <sa-name>@<project-id>.iam.gserviceaccount.com.
Copy this email.
Return to the integration configuration tile in Datadog (where you copied your Datadog principal in the previous section).
In the box under Add Service Account Email, paste the email you previously copied.
Click on Verify and Save Account.
In approximately fifteen minutes, metrics appear in Datadog.
Validation
To view your metrics, use the left menu to navigate to Metrics > Summary and search for gcp:
Configuration
Limit metric collection by metric namespace
Optionally, you can choose which Google Cloud services you monitor with Datadog. Configuring metrics collection for specific Google services lets you optimize your Google Cloud Monitoring API costs, while retaining visibility into your critical services.
Under the Metric Collection tab in Datadog’s Google Cloud integration page, unselect the metric namespaces to exclude. You can also choose to disable collection of all metric namespaces.
Limit metric collection by tag
By default, you’ll see all your Google Compute Engine (GCE) instances in Datadog’s infrastructure overview. Datadog automatically tags them with GCE host tags and any GCE labels you may have added.
Optionally, you can use tags to limit the instances that are pulled into Datadog. Under a project’s Metric Collection tab, enter the tags in the Limit Metric Collection Filters textbox. Only hosts that match one of the defined tags are imported into Datadog. You can use wildcards (? for single character, * for multi-character) to match many hosts, or ! to exclude certain hosts. This example includes all c1* sized instances, but excludes staging hosts:
Forward logs from your Google Cloud services to Datadog using Google Cloud Dataflow and the Datadog template. This method provides both compression and batching of events before forwarding to Datadog.
You can use the terraform-gcp-datadog-integration module to manage this infrastructure through Terraform, or follow the instructions in this section to:
Create a custom Dataflow worker service account to provide least privilege to your Dataflow pipeline workers
Create a log sink to publish logs to the Pub/Sub topic
Create a Dataflow job using the Datadog template to stream logs from the Pub/Sub subscription to Datadog
You have full control over which logs are sent to Datadog through the logging filters you create in the log sink, including GCE and GKE logs. See Google’s Logging query language page for information about writing filters. For a detailed examination of the created architecture, see Stream logs from Google Cloud to Datadog in the Cloud Architecture Center.
Note: You must enable the Dataflow API to use Google Cloud Dataflow. See Enabling APIs in the Google Cloud documentation for more information.
To collect logs from applications running in GCE or GKE, you can also use the Datadog Agent.
1. Create a Cloud Pub/Sub topic and subscription
Go to the Cloud Pub/Sub console and create a new topic. Select the option Add a default subscription to simplify the setup.
Note: You can also manually configure a Cloud Pub/Sub subscription with the Pull delivery type. If you manually create your Pub/Sub subscription, leave the Enable dead lettering box unchecked. For more details, see Unsupported Pub/Sub features.
Give that topic an explicit name such as export-logs-to-datadog and click Create.
Create an additional topic and default subscription to handle any log messages rejected by the Datadog API. The name of this topic is used within the Datadog Dataflow template as part of the path configuration for the outputDeadletterTopictemplate parameter. When you have inspected and corrected any issues in the failed messages, send them back to the original export-logs-to-datadog topic by running a Pub/Sub to Pub/Sub template job.
Datadog recommends creating a secret in Secret Manager with your valid Datadog API key value, for later use in the Datadog Dataflow template.
Warning: Cloud Pub/Subs are subject to Google Cloud quotas and limitations. If the number of logs you have exceeds those limitations, Datadog recommends you split your logs over several topics. See the Monitor the Pub/Sub Log Forwarding section for information on setting up monitor notifications if you approach those limits.
2. Create a custom Dataflow worker service account
The default behavior for Dataflow pipeline workers is to use your project’s Compute Engine default service account, which grants permissions to all resources in the project. If you are forwarding logs from a Production environment, you should instead create a custom worker service account with only the necessary roles and permissions, and assign this service account to your Dataflow pipeline workers.
Go to the Service Accounts page in the Google Cloud console and select your project.
Click CREATE SERVICE ACCOUNT and give the service account a descriptive name. Click CREATE AND CONTINUE.
Add the roles in the required permissions table and click DONE.
roles/pubsub.publisher Allow this service account to publish failed messages to a separate subscription, which allows for analysis or resending the logs.
roles/storage.objectAdmin Allow this service account to read and write to the Cloud Storage bucket specified for staging files.
Note: If you don’t create a custom service account for the Dataflow pipeline workers, ensure that the default Compute Engine service account has the required permissions above.
Choose Cloud Pub/Sub as the destination and select the Cloud Pub/Sub topic that was created for that purpose. Note: The Cloud Pub/Sub topic can be located in a different project.
Choose the logs you want to include in the sink with an optional inclusion or exclusion filter. You can filter the logs with a search query, or use the sample function. For example, to include only 10% of the logs with a severity level of ERROR, create an inclusion filter with severity="ERROR" AND sample(insertId, 0.1).
Click Create Sink.
Note: It is possible to create several exports from Google Cloud Logging to the same Cloud Pub/Sub topic with different sinks.
Give the job a name and select a Dataflow regional endpoint.
Select Pub/Sub to Datadog in the Dataflow template dropdown, and the Required parameters section appears.
a. Select the input subscription in the Pub/Sub input subscription dropdown.
b. Enter the following in the Datadog Logs API URL field:
https://
Note: Ensure that the Datadog site selector on the right of the page is set to your Datadog site before copying the URL above.
c. Select the topic created to receive message failures in the Output deadletter Pub/Sub topic dropdown.
d. Specify a path for temporary files in your storage bucket in the Temporary location field.
Under Optional Parameters, check Include full Pub/Sub message in the payload.
If you created a secret in Secret Manager with your Datadog API key value as mentioned in step 1, enter the resource name of the secret in the Google Cloud Secret Manager ID field.
See Template parameters in the Dataflow template for details on using the other available options:
apiKeySource=KMS with apiKeyKMSEncryptionKey set to your Cloud KMS key ID and apiKey set to the encrypted API key
Not recommended: apiKeySource=PLAINTEXT with apiKey set to the plaintext API key
If you created a custom worker service account, select it in the Service account email dropdown.
Click RUN JOB.
Note: If you have a shared VPC, see the Specify a network and subnetwork page in the Dataflow documentation for guidelines on specifying the Network and Subnetwork parameters.
Validation
New logging events delivered to the Cloud Pub/Sub topic appear in the Datadog Log Explorer.
gcp.pubsub.subscription.num_undelivered_messages for the number of messages pending delivery
gcp.pubsub.subscription.oldest_unacked_message_age for the age of the oldest unacknowledged message in a subscription
Use the metrics above with a metric monitor to receive alerts for the messages in your input and deadletter subscriptions.
Monitor the Dataflow pipeline
Use Datadog’s Google Cloud Dataflow integration to monitor all aspects of your Dataflow pipelines. You can see all your key Dataflow metrics on the out-of-the-box dashboard, enriched with contextual data such as information about the GCE instances running your Dataflow workloads, and your Pub/Sub throughput.
Expanded BigQuery monitoring provides granular visibility into your BigQuery environments.
BigQuery jobs performance monitoring
To monitor the performance of your BigQuery jobs, grant the BigQuery Resource Viewer role to the Datadog service account for each Google Cloud project.
Notes:
You need to have verified your Google Cloud service account in Datadog, as outlined in the setup section.
You do not need to set up Dataflow to collect logs for expanded BigQuery monitoring.
BigQuery data quality monitoring provides quality metrics from your BigQuery tables (such as freshness and updates to row count and size). Explore the data from your tables in depth on the Data Quality Monitoring page.
To collect quality metrics, grant the BigQuery Metadata Viewer role to the Datadog Service Account for each BigQuery table you are using.
Note: BigQuery Metadata Viewer can be applied at a BigQuery table, dataset, project, or organization level.
For Data Quality Monitoring of all tables within a dataset, grant access at the dataset level.
For Data Quality Monitoring of all datasets within a project, grant access at the project level.
Datadog recommends setting up a new logs index called data-observability-queries, and indexing your BigQuery job logs for 15 days. Use the following index filter to pull in the logs:
Select Enable Resource Collection in the Resource Collection tab of the Google Cloud integration page. This allows you to receive resource events in Datadog when Google’s Cloud Asset Inventory detects changes in your cloud resources.
Then, follow the steps below to forward change events from a Pub/Sub topic to the Datadog Event Explorer.
Enter export-asset-changes-to-datadog for the subscription name.
Select the Cloud Pub/Sub topic previously created.
Select Pull as the delivery type.
Click CREATE.
Grant access
To read from this Pub/Sub subscription, the Google Cloud service account used by the integration needs the pubsub.subscriptions.consume permission for the subscription. A default role with minimal permissions that allows this is the Pub/Sub subscriber role. Follow the steps below to grant this role:
In the info panel on the right of the page, click the Permissions tab. If you don’t see the info panel, click SHOW INFO PANEL.
Click ADD PRINCIPAL.
Enter the service account email used by the Datadog Google Cloud integration. You can find your service accounts listed on the left of the Configuration tab in the Google Cloud integration page in Datadog.
Create an asset feed
Run the command below in Cloud Shell or the gcloud CLI to create a Cloud Asset Inventory Feed that sends change events to the Pub/Sub topic created above.
Private Service Connect is only available for the US5 and EU Datadog sites.
Use the Google Cloud Private Service Connect integration to visualize connections, data transferred, and dropped packets through Private Service Connect. This gives you visibility into important metrics from your Private Service Connect connections, both for producers as well as consumers.
Private Service Connect (PSC) is a Google Cloud networking product that enables you to access Google Cloud services, third-party partner services, and company-owned applications directly from your Virtual Private Cloud (VPC).
Fraction of the allocated CPU that is currently in use on the instance. Note that some machine types allow bursting above 100% usage. Shown as fraction
Cumulative metrics
Cumulative metrics are imported into Datadog with a .delta metric for each metric name. A cumulative metric is a metric where the value constantly increases over time. For example, a metric for sent bytes might be cumulative. Each value records the total number of bytes sent by a service at that time. The delta value represents the change since the previous measurement.
For example:
gcp.gke.container.restart_count is a CUMULATIVE metric. While importing this metric as a cumulative metric, Datadog adds the gcp.gke.container.restart_count.delta metric which includes the delta values (as opposed to the aggregate value emitted as part of the CUMULATIVE metric). See Google Cloud metric kinds for more information.
Events
All service events generated by your Google Cloud Platform are forwarded to your Datadog Events Explorer.
Service Checks
The Google Cloud Platform integration does not include any service checks.
Tags
Tags are automatically assigned based on a variety of Google Cloud Platform and Google Compute Engine configuration options. The project_id tag is added to all metrics. Additional tags are collected from the Google Cloud Platform when available, and varies based on metric type.
Additionally, Datadog collects the following as tags:
Any hosts with <key>:<value> labels.
Custom labels from Google Pub/Sub, GCE, Cloud SQL, and Cloud Storage.
Troubleshooting
Incorrect metadata for user defined gcp.logging metrics?
For non-standard gcp.logging metrics, such as metrics beyond Datadog’s out of the box logging metrics, the metadata applied may not be consistent with Google Cloud Logging.
In these cases, the metadata should be manually set by navigating to the metric summary page, searching and selecting the metric in question, and clicking the pencil icon next to the metadata.
