Using Google Cloud Monitoring in Grafana

To access Google Cloud Monitoring settings, hover your mouse over the Configuration (gear) icon, then click Data Sources, and click Add data source, then click the Google Cloud Monitoring data source.

For authentication options and configuration details, see the documentation.

The following APIs need to be enabled first:

• Cloud Resource Manager API

Click on the links above and click the Enable button:

Using GCP Service Account Key File

The GCP Service Account must have the Monitoring Viewer role as shown in the image below:

Using GCE Default Service Account

If Grafana is running on a Google Compute Engine (GCE) virtual machine, the service account in use must have access to the Cloud Monitoring API scope.

The Google Cloud Monitoring query editor allows you to build two types of queries - Metric and Service Level Objective (SLO). Both types return time series data.

Metric Queries

The metric query editor allows you to select metrics, group/aggregate by labels and by time, and use filters to specify which time series you want in the results.

To create a metric query, follow these steps:

1. Choose the option Metrics in the Query Type dropdown
2. Choose a project from the Project dropdown
3. Choose a Google Cloud Platform service from the Service dropdown
4. Choose a metric from the Metric dropdown.

Google Cloud Monitoring supports different kinds of metrics like GAUGE, DELTA, and CUMULATIVE. They support different aggregation options, for example, reducers and aligners. The Grafana query editor displays the list of available aggregation methods for a selected metric and sets a default reducer and aligner when you select the metric.

Filter

To add a filter, click the plus icon and choose a field to filter by and enter a filter value e.g. instance_name = grafana-1. You can remove the filter by clicking on the trash icon.

Simple wildcards

When the operator is set to = or != it is possible to add wildcards to the filter value field. E.g us-* will capture all values that starts with “us-” and *central-a will capture all values that ends with “central-a”. *-central-* captures all values that has the substring of -central-. Simple wildcards are less expensive than regular expressions.

Regular expressions

When the operator is set to =~ or !=~ it is possible to add regular expressions to the filter value field. E.g us-central[1-3]-[af] would match all values that starts with “us-central”, is followed by a number in the range of 1 to 3, a dash and then either an “a” or an “f”. Leading and trailing slashes are not needed when creating regular expressions.

Pre-processing

Preprocessing options are displayed in the UI when the selected metric has a metric kind of delta or cumulative. If the selected metric has a metric kind of gauge, no pre-processing option will be displayed.

If you select Rate, data points are aligned and converted to a rate per time series. If you select Delta, data points are aligned by their delta (difference) per time series.

Grouping

You can reduce the amount of data returned for a metric by combining different time series. To combine multiple time series, specify a grouping and a function.

Group by

Group by resource or metric labels to reduce the number of time series and to aggregate the results by a group. For example, group by instance_name to view an aggregated metric for a Compute instance.

Metadata labels

There’s no API for retrieving metadata labels. As a result, you cannot populate the group by list with the metadata labels that are available for the selected service and metric. However, the Group By field list comes with a pre-defined set of common system labels.

User labels cannot be predefined, but you can enter them manually in the Group By field. If a metadata label, user label, or system label is included in the Group By segment, then you can create filters based on it and expand its value on the Alias field.

Group by function

Select a grouping function to combine the time series in the group into a single time series.

Alignment

The process of alignment consists of collecting all data points received in a fixed length of time, applying a function to combine those data points, and assigning a timestamp to the result.

Alignment function

During alignment, all data points are received in a fixed interval. Within each interval (determined by the alignment period) and for each time series, the data is aggregated into a single point. The value of that point is determined by the type of alignment function used. For more information on alignment functions, refer to alignment metric selector.

Alignment period

The Alignment Period groups a metric by time if an aggregation is chosen. The default is to use the GCP Google Cloud Monitoring default groupings (which allows you to compare graphs in Grafana with graphs in the Google Cloud Monitoring UI). The option is called cloud monitoring auto and the defaults are:

• 1m for time ranges < 23 hours
• 5m for time ranges >= 23 hours and < 6 days
• 1h for time ranges >= 6 days

The other automatic option is grafana auto. This will automatically set the group by time depending on the time range chosen and the width of the time series panel. For more information about grafana auto, refer to the interval variable.

You can also choose fixed time intervals to group by, like 1h or 1d.

Alias patterns

The Alias By field allows you to control the format of the legend keys. The default is to show the metric name and labels. This can be long and hard to read. Using the following patterns in the alias field, you can format the legend key the way you want it.

Metric type patterns

Label patterns

In the Group By dropdown, you can see a list of metric and resource labels for a metric. These can be included in the legend key using alias patterns.

Example Alias By: {{metric.type}} - {{metric.label.instance_name}}

Example Result: compute.googleapis.com/instance/cpu/usage_time - server1-prod

It is also possible to resolve the name of the Monitored Resource Type.

Example Alias By: {{resource.type}} - {{metric.type}}

Example Result: gce_instance - compute.googleapis.com/instance/cpu/usage_time

Deep linking from Grafana panels to the Metrics Explorer in Google Cloud Console

Click on a time series in the panel to see a context menu with a link to View in Metrics Explorer in Google Cloud Console. Clicking that link opens the Metrics Explorer in the Google Cloud Console and runs the query from the Grafana panel there. The link navigates the user first to the Google Account Chooser and after successfully selecting an account, the user is redirected to the Metrics Explorer. The provided link is valid for any account, but it only displays the query if your account has access to the GCP project specified in the query.

Automatic unit detection

Grafana issues one query to the Cloud Monitoring API per query editor row, and each API response includes a unit. Grafana will attempt to convert the returned unit into a unit that is understood by the Grafana time series panel. If the conversion was successful, then the unit will be displayed on the Y-axis on the panel. If the query editor rows returned different units, then the unit from the last query editor row is used in the time series panel.

How to create an SLO query

To create an SLO query, follow these steps:

1. Choose the option Service Level Objectives (SLO) in the Query Type dropdown.
2. Choose a project from the Project dropdown.
3. Choose an SLO service from the Service dropdown.
4. Choose an from the SLO dropdown.
5. Choose a time series selector from the Selector dropdown.

The friendly names for the time series selectors are shown in Grafana. Here is the mapping from the friendly name to the system name that is used in the Service Monitoring documentation:

Alias patterns for SLO queries

The Alias By field allows you to control the format of the legend keys for SLO queries too.

Alignment period/group by time for SLO queries

SLO queries use the same .

MQL (Monitoring Query Language) queries

The MQL query builder in the Google Cloud Monitoring data source allows you to display MQL results in time series format. To get an understanding of the basic concepts in MQL, refer to .

Create an MQL query

To create an MQL query, follow these steps:

1. In the Query Type list, select Metrics.
2. Click <> Edit MQL right next to the Query Type field. This will toggle the metric query builder mode so that raw MQL queries can be used.
3. Choose a project from the Project list.
4. Add the query of your choice in the text area.

Alias patterns for MQL queries

MQL queries use the same alias patterns as .

{{metric.service}} is not supported. {{metric.type}} and {{metric.name}} show the time series key in the response.

Instead of hard-coding things like server, application and sensor name in your metric queries you can use variables in their place. Variables are shown as dropdown select boxes at the top of the dashboard. These dropdowns make it easy to change the data being displayed in your dashboard.

Check out the documentation for an introduction to the templating feature and the different types of template variables.

Variable of the type Query allows you to query Google Cloud Monitoring for various types of data. The Google Cloud Monitoring data source plugin provides the following Query Types.

Using variables in queries

Refer to the variable syntax documentation.

allow you to overlay rich event information on top of graphs. You add annotation queries via the Dashboard menu / Annotations view. Annotation rendering is expensive so it is important to limit the number of rows returned. There is no support for showing Google Cloud Monitoring annotations and events yet but it works well with custom metrics in Google Cloud Monitoring.

With the query editor for annotations, you can select a metric and filters. The Title and Text fields support templating and can use data returned from the query. For example, the Title field could have the following text:

{{metric.type}} has value: {{metric.value}}

Example Result: monitoring.googleapis.com/uptime_check/http_status has this value: 502

You can provision CloudWatch data source by modifying Grafana’s configuration files. To learn more about provisioning and all the settings you can set, see the

Here is a provisioning example using the JWT (Service Account key file) authentication type.

Here is a provisioning example using GCE Default Service Account authentication.

apiVersion: 1
datasources:
  - name: Google Cloud Monitoring
    type: stackdriver
    access: proxy
    jsonData:
      authenticationType: gce