• How Product Analytics works
• Enable product analytics
• Product analytics dashboards
  • View project dashboards
  • Define a dashboard
• Funnel analysis
  • Example funnel definition
  • Query a funnel
• Raw data export
  • Export raw data with Cube queries
  • Caveats

Product analytics

This page is a work in progress, and we’re updating the information as we add more features. For more information, see the group direction page.

How Product Analytics works

Product Analytics uses several tools:

• Jitsu - A web and app event collection platform that provides a consistent API to collect user data and pass it through to Clickhouse.
• Clickhouse - A database suited to store, query, and retrieve analytical data.
• Cube.js - An analytical graphing library that provides an API to run queries against the data stored in Clickhouse.

Enable product analytics

You can enable and configure product analytics to track events within your project applications on a self-managed instance.

Prerequisite:

• You must be an administrator of a self-managed GitLab instance.

1. On the top bar, select Main menu > Admin.
2. On the left sidebar, select Settings > General.
3. Expand the Product analytics section.

4. Select Enable product analytics and enter the configuration values. The following table shows the required configuration parameters and example values:

   Name	Value
   Jitsu host	https://jitsu.gitlab.com
   Jitsu project ID	g0maofw84gx5sjxgse2k
   Jitsu administrator email	jitsu.admin@gitlab.com
   Jitsu administrator password	<your_password>
   Clickhouse URL	https://<username>:<password>@clickhouse.gitlab.com:8123
   Cube API URL	https://cube.gitlab.com
   Cube API key	25718201b3e9...ae6bbdc62dbb

5. Select Save changes.

Product analytics dashboards

Each project can define an unlimited number of dashboards. These dashboards are defined using our YAML schema and stored in the .gitlab/product_analytics/dashboards/ directory of a project repository. The name of the file is the name of the dashboard, and visualizations are shared across dashboards.

Project maintainers can enforce approval rules on dashboard changes using features such as code owners and approval rules. Dashboards are versioned in source control with the rest of a project’s code.

View project dashboards

To view a list of product analytics dashboards for a project:

1. On the top bar, select Main menu > Projects and find your project.
2. On the left sidebar, select Analytics > Dashboards.

Define a dashboard

To define a dashboard:

1. In .gitlab/product_analytics/dashboards/, create a directory named like the dashboard. Each dashboard should have its own directory.
2. In the new directory, create a .yaml file with the same name as the directory. This file contains the dashboard definition, and must conform to the JSON schema defined in ee/app/validators/json_schemas/product_analytics_dashboard.json.
3. In the .gitlab/product_analytics/dashboards/visualizations/ directory, create a yaml file. This file defines the visualization type for the dashboard, and must conform to the schema in ee/app/validators/json_schemas/product_analytics_visualization.json.

The example below includes three dashboards and one visualization that applies to all dashboards.

.gitlab/product_analytics/dashboards
├── conversion_funnels
│  └── conversion_funnels.yaml
├── demographic_breakdown
│  └── demographic_breakdown.yaml
├── north_star_metrics
|  └── north_star_metrics.yaml
├── visualizations
│  └── example_line_chart.yaml

Funnel analysis

Funnel analysis can be used to understand the flow of users through your application and where users drop out of a predefined flow (for example, a checkout process or ticket purchase).

Each product can also define an unlimited number of funnels. These funnels are defined using our YAML schema and stored in the .gitlab/product_analytics/funnels/ directory of a project repository.

Funnel definitions must include the keys name, seconds_to_convert, and an array of steps.

Each step must include the keys name, target, and action.

Example funnel definition

name: completed_purchase
seconds_to_convert: 3600
steps:
  - name: view_page_1
    target: '/page1.html'
    action: 'pageview'
  - name: view_page_2
    target: '/page2.html'
    action: 'pageview'
  - name: view_page_3
    target: '/page3.html'
    action: 'pageview'

Query a funnel

You can query the funnel data with the REST API. To do this, you can use the example query body below, where you need to replace FUNNEL_NAME with your funnel’s name.

{
  "query": {
      "measures": [
        "FUNNEL_NAME.count"
      ],
      "order": {
        "completed_purchase.count": "desc"
      },
      "filters": [
        {
          "member": "FUNNEL_NAME.date",
          "operator": "beforeDate",
          "values": [
            "2023-02-01"
          ]
        }
      ],
      "dimensions": [
        "FUNNEL_NAME.step"
      ]
    }
}

Raw data export

Exporting the raw event data from the underlying storage engine can help you debug and create datasets for data analysis.

Export raw data with Cube queries

You can query the raw data with the REST API and convert the JSON output to any required format.

You can export the raw data for a specific dimension by passing a list of dimensions to the dimensions key. For example, the following query outputs the raw data for the attributes listed:

POST /api/v4/projects/PROJECT_ID/product_analytics/request/load?queryType=multi

{
  "dimensions": [
    "TrackedEvents.docEncoding",
    "TrackedEvents.docHost",
    "TrackedEvents.docPath",
    "TrackedEvents.docSearch",
    "TrackedEvents.eventType",
    "TrackedEvents.idsAjsAnonymousId",
    "TrackedEvents.localTzOffset",
    "TrackedEvents.pageTitle",
    "TrackedEvents.src",
    "TrackedEvents.utcTime",
    "TrackedEvents.vpSize"
  ],
  "order": {
    "TrackedEvents.apiKey": "asc"
  }
}

If the request is successful, the returned JSON includes an array of rows of results.

Caveats

Because Cube acts as an abstraction layer between the raw data and the API, the exported raw data has some caveats:

• Data is grouped by the selected dimensions. Therefore, the exported data might be incomplete, unless including both utcTime and userAnonymousId.
• Data is by default limited to 10,000 rows, but you can increase the limit to maximum 50,000 rows. If your dataset has more than 50,000 rows, you need to paginate through the results by using the limit and offset parameters.
• Data is always returned in JSON format. If you need it in a different format, you need to convert the JSON to the required format using a scripting language of your choice.
• Issue 391683 tracks the implementation of a more scalable export solution.