Monitor databases with Cloud Monitoring

View Firestore with MongoDB compatibility metrics

To view the different Firestore with MongoDB compatibility metrics and create charts, use one the following:

• The Monitoring page in the Firestore section of the Google Cloud console. This page includes a predefined monitoring dashboard. You can also create up to one custom dashboard. To access the Monitoring page for a database, follow these steps:

  1. In the Google Cloud console, open the Firestore Databases page.

     Go to Databases

  2. Select a database from the list.

  3. In the navigation menu, click Monitoring to open a dashboard.

• The metrics explorer within Cloud Monitoring in Google Cloud console. For more information about creating charts, see Create charts with Metrics Explorer.

Monitored Resources

A monitored resource in Cloud Monitoring represents a logical or physical entity, such as a virtual machine, a database, or an application. Monitored resources contain a unique set of metrics that can be explored, reported through a dashboard, or used to create alerts. Each resource also has a set of resource labels, which are key-value pairs that hold additional information about the resource. Resource labels are available for all metrics associated with the resource.

Using the Cloud Monitoring API, Firestore with MongoDB compatibility performance is monitored with the following resource:

Metrics

For a complete list of metrics for Firestore, see Firestore metrics. The following section describe some of the available metrics

Service runtime metrics

The serviceruntime metrics provide a high-level overview of a project's traffic. These metrics are available for most Google Cloud APIs. The consumed_api monitored resource type contains these common metrics. These metrics are sampled every 30 minutes resulting in data being smoothed out.

An important resource label for the serviceruntime metrics is method. This label represents the underlying RPC method called. The SDK method that you call may not necessarily be named the same as the underlying RPC method. The reason is that the SDK provides high-level API abstraction. However, when trying to understand how your application interacts with Firestore, it is important to understand the metrics based on the name of the RPC method.

If you need to know what the underlying RPC method is for a given SDK method, see the API documentation.

Use the following service runtime metrics to monitor your database.

api/request_latencies

The api/request_latencies metric provides latency distributions across all completed requests.

Firestore records metrics from the Firestore Service component. Latency metrics include the time that Firestore receives the request to the time that Firestore finishes sending the response, including interactions with the storage layer. Due to this, round-trip latency (rtt) between the client and the Firestore service is not included in these metrics.

Document operation metrics

Firestore provides read, write, and delete counts. The write metric provides a breakdown between the 'CREATE' and 'UPDATE' operation. These metrics are aligned with CRUD operations.

The following metrics can be used to understand whether your database is read heavy or write heavy, and the rate of new documents versus deleted documents.

• document/delete_ops_count: The number of successful document deletes.
• document/read_ops_count: The number of successful document reads from queries or lookups.
• document/write_ops_count: The number of successful document writes.

Index metrics

Index write rates can be contrasted with the document/write_ops_count metric to understand index fanout.

• index/write_count: Count of index writes.

TTL Metrics

The TTL metrics for Firestore with MongoDB compatibility metrics are used to monitor the effect of the TTL policy enforced.

• document/ttl_deletion_count: Total count of documents deleted by TTL services.
• document/ttl_expiration_to_deletion_delays: Time elapsed between when a document with a TTL expired, and when it was actually deleted.

Create custom Cloud Monitoring dashboards

In Cloud Monitoring, custom dashboards allow you to display information that is relevant to you in an organized way. For example, you might create a dashboard to display the performance metrics and alerting policies for your project in your production environment.

For more information about setting up a custom dashboard, see Manage custom dashboard and Add dashboard widgets.

Create an alerting policy

Cloud Monitoring lets you create alerts to notify you when a change in a metric condition occurs. You can use these alerts to be notified of potential problems before they impact your users.

For more information about creating alerts, see Create metric-threshold alerting policies.

Consider the following example where we create a latency alert policy. The alerting policy checks p99 latency over a 5 minute rolling window. If the p99 latency stays above 250ms for 5 minutes, the alert is triggered.

      fetch consumed_api
      | metric 'serviceruntime.googleapis.com/api/request_latencies'
      | filter (resource.service == 'firestore.googleapis.com')
      | group_by 5m,
          [value_request_latencies_percentile:
            percentile(value.request_latencies, 99)]
      | every 5m
      | condition val() > 0.25 's'