debian-mirror-gitlab/doc/user/project/integrations/prometheus.md

stage	group	info
Monitor	APM	To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#designated-technical-writers

Prometheus integration

Introduced in GitLab 9.0.

GitLab offers powerful integration with Prometheus for monitoring key metrics of your apps, directly within GitLab. Metrics for each environment are retrieved from Prometheus, and then displayed within the GitLab interface.

There are two ways to set up Prometheus integration, depending on where your apps are running:

• For deployments on Kubernetes, GitLab can automatically deploy and manage Prometheus.
• For other deployment targets, simply specify the Prometheus server.

Once enabled, GitLab will automatically detect metrics from known services in the metric library. You can also add your own metrics.

Enabling Prometheus Integration

Managed Prometheus on Kubernetes

Introduced in GitLab 10.5.

GitLab can seamlessly deploy and manage Prometheus on a connected Kubernetes cluster, making monitoring of your apps easy.

Requirements

• A connected Kubernetes cluster
• Helm Tiller installed by GitLab

Getting started

Once you have a connected Kubernetes cluster with Helm installed, deploying a managed Prometheus is as easy as a single click.

1. Go to the Operations > Kubernetes page to view your connected clusters
2. Select the cluster you would like to deploy Prometheus to
3. Click the Install button to deploy Prometheus to the cluster

Getting metrics to display on the Metrics Dashboard

After completing the steps above, you will also need deployments in order to view the Operations > Metrics page. Setting up Auto DevOps will help you to quickly create a deployment:

1. Navigate to your project's Operations > Kubernetes page, and ensure that, in addition to "Prometheus" and "Helm Tiller", you also have "Runner" and "Ingress" installed. Once "Ingress" is installed, copy its endpoint.
2. Navigate to your project's Settings > CI/CD page. In the Auto DevOps section, select a deployment strategy and save your changes.
3. On the same page, in the Variables section, add a variable named KUBE_INGRESS_BASE_DOMAIN with the value of the Ingress endpoint you have copied in the previous step. Leave the type as "Variable".
4. Navigate to your project's CI/CD > Pipelines page, and run a pipeline on any branch.
5. When the pipeline has run successfully, graphs will be available on the Operations > Metrics page.

Using the Metrics Dashboard

Select an environment

The Environment dropdown box above the dashboard displays the list of all environments. It enables you to search as you type through all environments and select the one you're looking for.

Select a dashboard

The dashboard dropdown box above the dashboard displays the list of all dashboards available for the project. It enables you to search as you type through all dashboards and select the one you're looking for.

Mark a dashboard as favorite

Introduced in GitLab 13.0.

When viewing a dashboard, click the empty Star dashboard {star-o} button to mark a dashboard as a favorite. Starred dashboards display a solid star {star} button, and appear at the top of the dashboard select list.

To remove dashboard from the favorites list, click the solid Unstar Dashboard {star} button.

About managed Prometheus deployments

Prometheus is deployed into the gitlab-managed-apps namespace, using the official Helm chart. Prometheus is only accessible within the cluster, with GitLab communicating through the Kubernetes API.

The Prometheus server will automatically detect and monitor nodes, pods, and endpoints. To configure a resource to be monitored by Prometheus, simply set the following Kubernetes annotations:

• prometheus.io/scrape to true to enable monitoring of the resource.
• prometheus.io/port to define the port of the metrics endpoint.
• prometheus.io/path to define the path of the metrics endpoint. Defaults to /metrics.

CPU and Memory consumption is monitored, but requires naming conventions in order to determine the environment. If you are using Auto DevOps, this is handled automatically.

The NGINX Ingress that is deployed by GitLab to clusters, is automatically annotated for monitoring providing key response metrics: latency, throughput, and error rates.

Manual configuration of Prometheus

Requirements

Integration with Prometheus requires the following:

1. GitLab 9.0 or higher
2. Prometheus must be configured to collect one of the supported metrics
3. Each metric must be have a label to indicate the environment
4. GitLab must have network connectivity to the Prometheus server

Getting started

Installing and configuring Prometheus to monitor applications is fairly straightforward.

1. Install Prometheus
2. Set up one of the supported monitoring targets
3. Configure the Prometheus server to collect their metrics

Configuration in GitLab

The actual configuration of Prometheus integration within GitLab is very simple. All you will need is the domain name or IP address of the Prometheus server you'd like to integrate with.

1. Navigate to the Integrations page.
2. Click the Prometheus service.
3. Provide the domain name or IP address of your server, for example http://prometheus.example.com/ or http://192.0.2.1/.
4. Click Save changes.

Thanos configuration in GitLab

You can configure Thanos as a drop-in replacement for Prometheus with GitLab. You will need the domain name or IP address of the Thanos server you'd like to integrate with.

1. Navigate to the Integrations page.
2. Click the Prometheus service.
3. Provide the domain name or IP address of your server, for example http://thanos.example.com/ or http://192.0.2.1/.
4. Click Save changes.

Precedence with multiple Prometheus configurations

Although you can enable both a manual configuration and auto configuration of Prometheus, only one of them will be used:

• If you have enabled a Prometheus manual configuration and a managed Prometheus on Kubernetes, the manual configuration takes precedence and is used to run queries from dashboards and custom metrics.
• If you have managed Prometheus applications installed on Kubernetes clusters at different levels (project, group, instance), the order of precedence is described in Cluster precedence.
• If you have managed Prometheus applications installed on multiple Kubernetes clusters at the same level, the Prometheus application of a cluster with a matching environment scope is used.

Monitoring CI/CD Environments

Once configured, GitLab will attempt to retrieve performance metrics for any environment which has had a successful deployment.

GitLab will automatically scan the Prometheus server for metrics from known servers like Kubernetes and NGINX, and attempt to identify individual environments. The supported metrics and scan process is detailed in our Prometheus Metrics Library documentation.

You can view the performance dashboard for an environment by clicking on the monitoring button.

Adding custom metrics

• Introduced in GitLab Premium 10.6.
• Moved to GitLab Core 12.10.

Custom metrics can be monitored by adding them on the monitoring dashboard page. Once saved, they will be displayed on the environment performance dashboard provided that either:

• A connected Kubernetes cluster with the environment scope of * is used and Prometheus installed on the cluster
• Prometheus is manually configured.

A few fields are required:

• Name: Chart title
• Type: Type of metric. Metrics of the same type will be shown together.
• Query: Valid PromQL query.
• Y-axis label: Y axis title to display on the dashboard.
• Unit label: Query units, for example req / sec. Shown next to the value.

Multiple metrics can be displayed on the same chart if the fields Name, Type, and Y-axis label match between metrics. For example, a metric with Name Requests Rate, Type Business, and Y-axis label rec / sec would display on the same chart as a second metric with the same values. A Legend label is suggested if this feature is used.

Query Variables

Predefined variables

GitLab supports a limited set of CI variables in the Prometheus query. This is particularly useful for identifying a specific environment, for example with ci_environment_slug. The supported variables are:

• ci_environment_slug
• kube_namespace
• ci_project_name
• ci_project_namespace
• ci_project_path
• ci_environment_name

NOTE: Note: Variables for Prometheus queries must be lowercase.

User-defined variables

Variables can be defined in a custom dashboard YAML file.

Using variables

Variables can be specified using double curly braces, such as "{{ci_environment_slug}}" (added in GitLab 12.7).

Support for the "%{ci_environment_slug}" format was removed in GitLab 13.0. Queries that continue to use the old format will show no data.

Query Variables from URL

Introduced in GitLab 13.0.

GitLab supports setting custom variables through URL parameters. Surround the variable name with double curly braces ({{example}}) to interpolate the variable in a query:

avg(sum(container_memory_usage_bytes{container_name!="{{pod}}"}) by (job)) without (job)  /1024/1024/1024'

The URL for this query would be:

http://gitlab.com/<user>/<project>/-/environments/<environment_id>/metrics?dashboard=.gitlab%2Fdashboards%2Fcustom.yml&pod=POD

Editing additional metrics from the dashboard

Introduced in GitLab 12.9.

You can edit existing additional custom metrics by clicking the {ellipsis_v} More actions dropdown and selecting Edit metric.

Defining custom dashboards per project

Introduced in GitLab 12.1.

By default, all projects include a GitLab-defined Prometheus dashboard, which includes a few key metrics, but you can also define your own custom dashboards.

You may create a new file from scratch or duplicate a GitLab-defined Prometheus dashboard.

NOTE: Note: The metrics as defined below do not support alerts, unlike custom metrics.

Adding a new dashboard to your project

You can configure a custom dashboard by adding a new YAML file into your project's .gitlab/dashboards/ directory. In order for the dashboards to be displayed on the project's Operations > Metrics page, the files must have a .yml extension and should be present in the project's default branch.

For example:

1. Create .gitlab/dashboards/prom_alerts.yml under your repository's root directory with the following contents:

   dashboard: 'Dashboard Title'
   panel_groups:
     - group: 'Group Title'
       panels:
       - type: area-chart
         title: "Chart Title"
         y_label: "Y-Axis"
         y_axis:
           format: number
           precision: 0
         metrics:
         - id: my_metric_id
           query_range: 'http_requests_total'
           label: "Instance: {{instance}}, method: {{method}}"
           unit: "count"
   

   The above sample dashboard would display a single area chart. Each file should define the layout of the dashboard and the Prometheus queries used to populate data.

2. Save the file, commit, and push to your repository. The file must be present in your default branch.

3. Navigate to your project's Operations > Metrics and choose the custom dashboard from the dropdown.

NOTE: Note: Configuration files nested under subdirectories of .gitlab/dashboards are not supported and will not be available in the UI.

Duplicating a GitLab-defined dashboard

• Introduced in GitLab 12.7.
• From GitLab 12.8 onwards, custom metrics are also duplicated when you duplicate a dashboard.

You can save a complete copy of a GitLab defined dashboard along with all custom metrics added to it. Resulting .yml file can be customized and adapted to your project. You can decide to save the dashboard .yml file in the project's default branch or in a new branch.

1. Click Duplicate dashboard in the dashboard dropdown.

   NOTE: Note: You can duplicate only GitLab-defined dashboards.

2. Enter the file name and other information, such as the new commit's message, and click Duplicate.

If you select your default branch, the new dashboard becomes immediately available. If you select another branch, this branch should be merged to your default branch first.

Dashboard YAML properties

Dashboards have several components:

• Templating variables.
• Panel groups, which consist of panels.
• Panels, which support one or more metrics.

The following tables outline the details of expected properties.

Dashboard (top-level) properties

Property	Type	Required	Description
dashboard	string	yes	Heading for the dashboard. Only one dashboard should be defined per file.
panel_groups	array	yes	The panel groups which should be on the dashboard.
templating	Hash	no	Top level key under which templating related options can be added.

Templating (templating) properties

Property	Type	Required	Description
variables	Hash	no	Variables can be defined here.

Read the documentation on templating.

Panel group (panel_groups) properties

Property	Type	Required	Description
group	string	required	Heading for the panel group.
priority	number	optional, defaults to order in file	Order to appear on the dashboard. Higher number means higher priority, which will be higher on the page. Numbers do not need to be consecutive.
panels	array	required	The panels which should be in the panel group.

Panel (panels) properties

Property	Type	Required	Description
type	enum	no, defaults to area-chart	Specifies the chart type to use, can be: area-chart, line-chart or anomaly-chart.
title	string	yes	Heading for the panel.
y_label	string	no, but highly encouraged	Y-Axis label for the panel.
y_axis	string	no	Y-Axis configuration for the panel.
max_value	number	no	Denominator value used for calculating percentile based results
weight	number	no, defaults to order in file	Order to appear within the grouping. Lower number means higher priority, which will be higher on the page. Numbers do not need to be consecutive.
metrics	array	yes	The metrics which should be displayed in the panel. Any number of metrics can be displayed when type is area-chart or line-chart, whereas only 3 can be displayed when type is anomaly-chart.

Axis (panels[].y_axis) properties

Property	Type	Required	Description
name	string	no, but highly encouraged	Y-Axis label for the panel. Replaces y_label if set.
format	string	no, defaults to engineering	Unit format used. See the full list of units.
precision	number	no, defaults to 2	Number of decimal places to display in the number.

Metrics (metrics) properties

Property	Type	Required	Description
id	string	no	Used for associating dashboard metrics with database records. Must be unique across dashboard configuration files. Required for alerting (support not yet enabled, see relevant issue).
unit	string	yes	Defines the unit of the query's return data.
label	string	no, but highly encouraged	Defines the legend-label for the query. Should be unique within the panel's metrics. Can contain time series labels as interpolated variables.
query	string	yes if query_range is not defined	Defines the Prometheus query to be used to populate the chart/panel. If defined, the query endpoint of the Prometheus API will be utilized.
query_range	string	yes if query is not defined	Defines the Prometheus query to be used to populate the chart/panel. If defined, the query_range endpoint of the Prometheus API will be utilized.
step	number	no, value is calculated if not defined	Defines query resolution step width in float number of seconds. Metrics on the same panel should use the same step value.

Dynamic labels

Dynamic labels are useful when multiple time series are returned from a Prometheus query.

When a static label is used and a query returns multiple time series, then all the legend items will be labeled the same, which makes identifying each time series difficult:

metrics:
  - id: my_metric_id
    query_range: 'http_requests_total'
    label: "Time Series"
    unit: "count"

This may render a legend like this:

For labels to be more explicit, using variables that reflect time series labels is a good practice. The variables will be replaced by the values of the time series labels when the legend is rendered:

metrics:
  - id: my_metric_id
    query_range: 'http_requests_total'
    label: "Instance: {{instance}}, method: {{method}}"
    unit: "count"

The resulting rendered legend will look like this:

There is also a shorthand value for dynamic dashboard labels that make use of only one time series label:

metrics:
  - id: my_metric_id
    query_range: 'http_requests_total'
    label: "Method"
    unit: "count"

This works by lowercasing the value of label and, if there are more words separated by spaces, replacing those spaces with an underscore (_). The transformed value is then checked against the labels of the time series returned by the Prometheus query. If a time series label is found that is equal to the transformed value, then the label value will be used and rendered in the legend like this:

Panel types for dashboards

The below panel types are supported in monitoring dashboards.

Area or Line Chart

To add an area chart panel type to a dashboard, look at the following sample dashboard file:

dashboard: 'Dashboard Title'
panel_groups:
  - group: 'Group Title'
    panels:
      - type: area-chart # or line-chart
        title: 'Area Chart Title'
        y_label: "Y-Axis"
        y_axis:
          format: number
          precision: 0
        metrics:
          - id: area_http_requests_total
            query_range: 'http_requests_total'
            label: "Instance: {{instance}}, Method: {{method}}"
            unit: "count"

Note the following properties:

Property	Type	Required	Description
type	string	no	Type of panel to be rendered. Optional for area panel types
query_range	string	required	For area panel types, you must use a range query

Starting in version 12.8, the y-axis values will automatically scale according to the data. Previously, it always started from 0.

Anomaly chart

Introduced in GitLab 12.5.

To add an anomaly chart panel type to a dashboard, add a panel with exactly 3 metrics.

The first metric represents the current state, and the second and third metrics represent the upper and lower limit respectively:

dashboard: 'Dashboard Title'
panel_groups:
  - group: 'Group Title'
    panels:
      - type: anomaly-chart
        title: "Chart Title"
        y_label: "Y-Axis"
        metrics:
          - id: anomaly_requests_normal
            query_range: 'http_requests_total'
            label: "# of Requests"
            unit: "count"
        metrics:
          - id: anomaly_requests_upper_limit
            query_range: 10000
            label: "Max # of requests"
            unit: "count"
        metrics:
          - id: anomaly_requests_lower_limit
            query_range: 2000
            label: "Min # of requests"
            unit: "count"

Note the following properties:

Property	Type	Required	Description
type	string	required	Must be anomaly-chart for anomaly panel types
query_range	yes	required	For anomaly panel types, you must use a range query in every metric.

Bar chart

To add a bar chart to a dashboard, look at the following sample dashboard file:

dashboard: 'Dashboard Title'
panel_groups:
  - group: 'Group title'
    panels:
      - type: bar
        title: "Http Handlers"
        x_label: 'Response Size'
        y_axis:
          name: "Handlers"
        metrics:
          - id: prometheus_http_response_size_bytes_bucket
            query_range: "sum(increase(prometheus_http_response_size_bytes_bucket[1d])) by (handler)"
            unit: 'Bytes'

Note the following properties:

Property	Type	Required	Description
type	string	yes	Type of panel to be rendered. For bar chart types, set to bar
query_range	yes	yes	For bar chart, you must use a range query

Column chart

To add a column panel type to a dashboard, look at the following sample dashboard file:

dashboard: 'Dashboard Title'
panel_groups:
  - group: 'Group title'
    panels:
      - title: "Column"
        type: "column"
        metrics:
        - id: 1024_memory
          query: 'avg(sum(container_memory_usage_bytes{container_name!="POD",pod_name=~"^%{ci_environment_slug}-([^c].*|c([^a]|a([^n]|n([^a]|a([^r]|r[^y])))).*|)-(.*)",namespace="%{kube_namespace}"}) by (job)) without (job) / count(avg(container_memory_usage_bytes{container_name!="POD",pod_name=~"^%{ci_environment_slug}-([^c].*|c([^a]|a([^n]|n([^a]|a([^r]|r[^y])))).*|)-(.*)",namespace="%{kube_namespace}"}) without (job)) /1024/1024'
          unit: MB
          label: "Memory Usage"

Note the following properties:

Property	Type	Required	Description
type	string	yes	Type of panel to be rendered. For column panel types, set to column
query_range	yes	yes	For column panel types, you must use a range query

Stacked column

Introduced in GitLab 12.8.

To add a stacked column panel type to a dashboard, look at the following sample dashboard file:

dashboard: 'Dashboard title'
priority: 1
panel_groups:
- group: 'Group Title'
  priority: 5
  panels:
  - type: 'stacked-column'
    title: "Stacked column"
    y_label: "y label"
    x_label: 'x label'
    metrics:
      - id: memory_1
        query_range: 'memory_query'
        label: "memory query 1"
        unit: "count"
        series_name: 'group 1'
      - id: memory_2
        query_range: 'memory_query_2'
        label: "memory query 2"
        unit: "count"
        series_name: 'group 2'

Property	Type	Required	Description
type	string	yes	Type of panel to be rendered. For stacked column panel types, set to stacked-column
query_range	yes	yes	For stacked column panel types, you must use a range query

Single Stat

To add a single stat panel type to a dashboard, look at the following sample dashboard file:

dashboard: 'Dashboard Title'
panel_groups:
  - group: 'Group Title'
    panels:
      - title: "Single Stat"
        type: "single-stat"
        metrics:
        - id: 10
          query: 'max(go_memstats_alloc_bytes{job="prometheus"})'
          unit: MB
          label: "Total"

Note the following properties:

Property	Type	Required	Description
type	string	yes	Type of panel to be rendered. For single stat panel types, set to single-stat
query	string	yes	For single stat panel types, you must use an instant query

Percentile based results

Introduced in GitLab 12.8.

Query results sometimes need to be represented as a percentage value out of 100. You can use the max_value property at the root of the panel definition:

dashboard: 'Dashboard Title'
panel_groups:
  - group: 'Group Title'
    panels:
      - title: "Single Stat"
        type: "single-stat"
        max_value: 100
        metrics:
        - id: 10
          query: 'max(go_memstats_alloc_bytes{job="prometheus"})'
          unit: '%'
          label: "Total"

For example, if you have a query value of 53.6, adding % as the unit results in a single stat value of 53.6%, but if the maximum expected value of the query is 120, the value would be 44.6%. Adding the max_value causes the correct percentage value to display.

Heatmaps

Introduced in GitLab 12.5.

To add a heatmap panel type to a dashboard, look at the following sample dashboard file:

dashboard: 'Dashboard Title'
panel_groups:
  - group: 'Group Title'
    panels:
      - title: "Heatmap"
        type: "heatmap"
        metrics:
        - id: 10
          query: 'sum(rate(nginx_upstream_responses_total{upstream=~"%{kube_namespace}-%{ci_environment_slug}-.*"}[60m])) by (status_code)'
          unit: req/sec
          label: "Status code"

Note the following properties:

Property	Type	Required	Description
type	string	yes	Type of panel to be rendered. For heatmap panel types, set to heatmap
query_range	yes	yes	For area panel types, you must use a range query

Templating variables for metrics dashboards

Templating variables can be used to make your metrics dashboard more versatile.

Templating variable types

templating is a top-level key in the dashboard YAML. Define your variables in the variables key, under templating. The value of the variables key should be a hash, and each key under variables defines a templating variable on the dashboard.

A variable can be used in a Prometheus query in the same dashboard using the syntax described here.

text variable type

CAUTION: Warning: This variable type is an alpha feature, and is subject to change at any time without prior notice!

For each text variable defined in the dashboard YAML, there will be a free text box on the dashboard UI, allowing you to enter a value for each variable.

The text variable type supports a simple and a full syntax.

Simple syntax

This example creates a variable called variable1, with a default value of default value:

templating:
  variables:
    variable1: 'default value'     # `text` type variable with `default value` as its default.

Full syntax

This example creates a variable called variable1, with a default value of default. The label for the text box on the UI will be the value of the label key:

templating:
  variables:
    variable1:                       # The variable name that can be used in queries.
      label: 'Variable 1'            # (Optional) label that will appear in the UI for this text box.
      type: text
      options:
        default_value: 'default'     # (Optional) default value.

custom variable type

CAUTION: Warning: This variable type is an alpha feature, and is subject to change at any time without prior notice!

Each custom variable defined in the dashboard YAML creates a dropdown selector on the dashboard UI, allowing you to select a value for each variable.

The custom variable type supports a simple and a full syntax.

Simple syntax

This example creates a variable called variable1, with a default value of value1. The dashboard UI will display a dropdown with value1, value2 and value3 as the choices.

templating:
  variables:
    variable1: ['value1', 'value2', 'value3']

Full syntax

This example creates a variable called variable1, with a default value of var1_option_2. The label for the text box on the UI will be the value of the label key. The dashboard UI will display a dropdown with Option 1 and Option 2 as the choices.

If you select Option 1 from the dropdown, the variable will be replaced with value option 1. Similarly, if you select Option 2, the variable will be replaced with value_option_2:

templating:
  variables:
    variable1:                           # The variable name that can be used in queries.
      label: 'Variable 1'                # (Optional) label that will appear in the UI for this dropdown.
      type: custom
      options:
        values:
        - value: 'value option 1'        # The value that will replace the variable in queries.
          text: 'Option 1'               # (Optional) Text that will appear in the UI dropdown.
        - value: 'value_option_2'
          text: 'Option 2'
          default: true                  # (Optional) This option should be the default value of this variable.

View and edit the source file of a custom dashboard

Introduced in GitLab 12.5.

When viewing a custom dashboard of a project, you can view the original .yml file by clicking on the Edit dashboard button.

Chart Context Menu

From each of the panels in the dashboard, you can access the context menu by clicking the {ellipsis_v} More actions dropdown box above the upper right corner of the panel to take actions related to the chart's data.

The options are:

• Expand panel
• View logs
• Download CSV
• Copy link to chart
• Alerts

Dashboard Annotations

• Introduced in GitLab 12.10 (enabled by feature flag metrics_dashboard_annotations).
• Feature flag removed in GitLab 13.0.

You can use Metrics Dashboard Annotations to mark any important events on every metrics dashboard by adding annotations to it. While viewing a dashboard, annotation entries assigned to the selected time range will be automatically fetched and displayed on every chart within that dashboard. On mouse hover, each annotation presents additional details, including the exact time of an event and its description.

You can create annotations by making requests to the Metrics dashboard annotations API

Expand panel

Introduced in GitLab 13.0.

To view a larger version of a visualization, expand the panel by clicking the {ellipsis_v} More actions icon and selecting Expand panel.

To return to the metrics dashboard, click the Back button in your browser, or pressing the Esc key.

View Logs (ULTIMATE)

Introduced in GitLab 12.8.

If you have Logs enabled, you can navigate from the charts in the dashboard to view Logs by clicking on the context menu in the upper-right corner.

If you use the Timeline zoom function at the bottom of the chart, logs will narrow down to the time range you selected.

Timeline zoom and URL sharing

Introduced in GitLab 12.8.

You can use the Timeline zoom function at the bottom of a chart to zoom in on a date and time of your choice. When you click and drag the sliders to select a different beginning or end date of data to display, GitLab adds your selected start and end times to the URL, enabling you to share specific timeframes more easily.

Downloading data as CSV

Data from Prometheus charts on the metrics dashboard can be downloaded as CSV.

Setting up alerts for Prometheus metrics

Managed Prometheus instances

Introduced in GitLab Ultimate 11.2 for custom metrics, and 11.3 for library metrics.

For managed Prometheus instances using auto configuration, alerts for metrics can be configured directly in the performance dashboard.

To set an alert:

1. Click on the ellipsis icon in the top right corner of the metric you want to create the alert for.
2. Choose Alerts
3. Set threshold and operator.
4. Click Add to save and activate the alert.

To remove the alert, click back on the alert icon for the desired metric, and click Delete.

External Prometheus instances

• Introduced in GitLab Ultimate 11.8.
• Moved to GitLab Core in 12.10.

For manually configured Prometheus servers, a notify endpoint is provided to use with Prometheus webhooks. If you have manual configuration enabled, an Alerts section is added to Settings > Integrations > Prometheus. This contains the URL and Authorization Key. The Reset Key button will invalidate the key and generate a new one.

To send GitLab alert notifications, copy the URL and Authorization Key into the webhook_configs section of your Prometheus Alertmanager configuration:

receivers:
  name: gitlab
  webhook_configs:
  - http_config:
      bearer_token: 9e1cbfcd546896a9ea8be557caf13a76
    send_resolved: true
    url: http://192.168.178.31:3001/root/manual_prometheus/prometheus/alerts/notify.json
  ...

In order for GitLab to associate your alerts with an environment, you need to configure a gitlab_environment_name label on the alerts you set up in Prometheus. The value of this should match the name of your Environment in GitLab.

Taking action on incidents (ULTIMATE)

• Introduced in GitLab Ultimate 11.11.
• From GitLab Ultimate 12.5, when GitLab receives a recovery alert, it will automatically close the associated issue.

Alerts can be used to trigger actions, like opening an issue automatically (enabled by default since 12.1). To configure the actions:

1. Navigate to your project's Settings > Operations > Incidents.
2. Enable the option to create issues.
3. Choose the issue template to create the issue from.
4. Optionally, select whether to send an email notification to the developers of the project.
5. Click Save changes.

Once enabled, an issue will be opened automatically when an alert is triggered which contains values extracted from alert's payload:

• Issue author: GitLab Alert Bot
• Issue title: Extract from annotations/title, annotations/summary or labels/alertname
• Alert Summary: A list of properties
  • starts_at: Alert start time via startsAt
  • full_query: Alert query extracted from generatorURL
  • Optional list of attached annotations extracted from annotations/*
• Alert GFM: GitLab Flavored Markdown from annotations/gitlab_incident_markdown

When GitLab receives a Recovery Alert, it will automatically close the associated issue. This action will be recorded as a system message on the issue indicating that it was closed automatically by the GitLab Alert bot.

To further customize the issue, you can add labels, mentions, or any other supported quick action in the selected issue template, which will apply to all incidents. To limit quick actions or other information to only specific types of alerts, use the annotations/gitlab_incident_markdown field.

Since version 12.2, GitLab will tag each incident issue with the incident label automatically. If the label does not yet exist, it will be created automatically as well.

If the metric exceeds the threshold of the alert for over 5 minutes, an email will be sent to all Maintainers and Owners of the project.

Determining the performance impact of a merge

• Introduced in GitLab 9.2.
• GitLab 9.3 added the numeric comparison of the 30 minute averages.

Developers can view the performance impact of their changes within the merge request workflow.

NOTE: Note: Requires Kubernetes metrics.

When a source branch has been deployed to an environment, a sparkline and numeric comparison of the average memory consumption will appear. On the sparkline, a dot indicates when the current changes were deployed, with up to 30 minutes of performance data displayed before and after. The comparison shows the difference between the 30 minute average before and after the deployment. This information is updated after each commit has been deployed.

Once merged and the target branch has been redeployed, the metrics will switch to show the new environments this revision has been deployed to.

Performance data will be available for the duration it is persisted on the Prometheus server.

Embedding metric charts within GitLab Flavored Markdown

Embedding GitLab-managed Kubernetes metrics

Introduced in GitLab 12.2.

It is possible to display metrics charts within GitLab Flavored Markdown fields such as issue or merge request descriptions. The maximum number of embedded charts allowed in a GitLab Flavored Markdown field is 100.

This can be useful if you are sharing an application incident or performance metrics to others and want to have relevant information directly available.

NOTE: Note: Requires Kubernetes metrics.

To display metric charts, include a link of the form https://<root_url>/<project>/-/environments/<environment_id>/metrics:

GitLab unfurls the link as an embedded metrics panel:

You can also embed a single chart. To get a link to a chart, click the {ellipsis_v} More actions menu in the upper right corner of the chart, and select Copy link to chart, as shown in this example:

The following requirements must be met for the metric to unfurl:

• The <environment_id> must correspond to a real environment.
• Prometheus must be monitoring the environment.
• The GitLab instance must be configured to receive data from the environment.
• The user must be allowed access to the monitoring dashboard for the environment (Reporter or higher).
• The dashboard must have data within the last 8 hours.

If all of the above are true, then the metric will unfurl as seen below:

Metric charts may also be hidden:

You can open the link directly into your browser for a detailed view of the data.

Embedding metrics in issue templates

It is also possible to embed either the default dashboard metrics or individual metrics in issue templates. For charts to render side-by-side, links to the entire metrics dashboard or individual metrics should be separated by either a comma or a space.

Embedding metrics based on alerts in incident issues

For GitLab-managed alerting rules, the issue will include an embedded chart for the query corresponding to the alert. The chart displays an hour of data surrounding the starting point of the incident, 30 minutes before and after.

For manually configured Prometheus instances, a chart corresponding to the query can be included if these requirements are met:

• The alert corresponds to an environment managed through GitLab.
• The alert corresponds to a range query.
• The alert contains the required attributes listed in the chart below.

Attributes	Required	Description
annotations/gitlab_environment_name	Yes	Name of the GitLab-managed environment corresponding to the alert
One of annotations/title, annotations/summary, labels/alertname	Yes	Will be used as the chart title
annotations/gitlab_y_label	No	Will be used as the chart's y-axis label

Embedding Cluster Health Charts (ULTIMATE)

Introduced in GitLab Ultimate 12.9.

Cluster Health Metrics can also be embedded in GitLab-flavored Markdown.

To embed a metric chart, include a link to that chart in the form https://<root_url>/<project>/-/cluster/<cluster_id>?<query_params> anywhere that GitLab-flavored Markdown is supported. To generate and copy a link to the chart, follow the instructions in the Cluster Health Metric documentation.

The following requirements must be met for the metric to unfurl:

• The <cluster_id> must correspond to a real cluster.
• Prometheus must be monitoring the cluster.
• The user must be allowed access to the project cluster metrics.
• The dashboards must be reporting data on the Cluster Health Page

If the above requirements are met, then the metric will unfurl as seen below.

Embedding Grafana charts

Grafana metrics can be embedded in GitLab Flavored Markdown.

Embedding charts via Grafana Rendered Images

It is possible to embed live Grafana charts in issues, as a direct linked rendered image.

The sharing dialog within Grafana provides the link, as highlighted below.

NOTE: Note: For this embed to display correctly, the Grafana instance must be available to the target user, either as a public dashboard, or on the same network.

Copy the link and add an image tag as inline HTML in your Markdown. You may tweak the query parameters as required. For instance, removing the &from= and &to= parameters will give you a live chart. Here is example markup for a live chart from GitLab's public dashboard:

<img src="https://dashboards.gitlab.com/d/RZmbBr7mk/gitlab-triage?orgId=1&refresh=30s&var-env=gprd&var-environment=gprd&var-prometheus=prometheus-01-inf-gprd&var-prometheus_app=prometheus-app-01-inf-gprd&var-backend=All&var-type=All&var-stage=main&from=1580444107655&to=1580465707655"/>

This will render like so:

Embedding charts via integration with Grafana HTTP API

Introduced in GitLab 12.5.

Each project can support integration with one Grafana instance. This configuration allows a user to copy a link to a panel in Grafana, then paste it into a GitLab Markdown field. The chart will be rendered in the GitLab chart format.

Prerequisites for embedding from a Grafana instance:

1. The datasource must be a Prometheus instance.
2. The datasource must be proxyable, so the HTTP Access setting should be set to Server.

Setting up the Grafana integration

1. Generate an Admin-level API Token in Grafana.
2. In your GitLab project, navigate to Settings > Operations > Grafana Authentication.
3. To enable the integration, check the "Active" checkbox.
4. For "Grafana URL", enter the base URL of the Grafana instance.
5. For "API Token", enter the Admin API Token you just generated.
6. Click Save Changes.

Generating a link to a chart

1. In Grafana, navigate to the dashboard you wish to embed a panel from.
2. In the upper-left corner of the page, select a specific value for each variable required for the queries in the chart.
3. In Grafana, click on a panel's title, then click Share to open the panel's sharing dialog to the Link tab. If you click the dashboard's share panel instead, GitLab will attempt to embed the first supported panel on the dashboard (if available).
4. If your Prometheus queries use Grafana's custom template variables, ensure that the "Template variables" option is toggled to On. Of Grafana global template variables, only $__interval, $__from, and $__to are currently supported. Toggle On the "Current time range" option to specify the time range of the chart. Otherwise, the default range will be the last 8 hours.
5. Click Copy to copy the URL to the clipboard.
6. In GitLab, paste the URL into a Markdown field and save. The chart will take a few moments to render.

Metrics dashboard visibility

Introduced in GitLab 13.0.

You can set the visibility of the metrics dashboard to Only Project Members or Everyone With Access. When set to Everyone with Access, the metrics dashboard is visible to authenticated and non-authenticated users.

Troubleshooting

When troubleshooting issues with a managed Prometheus app, it is often useful to view the Prometheus UI.

"No data found" error on Metrics dashboard page

If the "No data found" screen continues to appear, it could be due to:

• No successful deployments have occurred to this environment.
• Prometheus does not have performance data for this environment, or the metrics are not labeled correctly. To test this, connect to the Prometheus server and run a query, replacing $CI_ENVIRONMENT_SLUG with the name of your environment.
• You may need to re-add the GitLab predefined common metrics. This can be done by running the import common metrics Rake task.