gitlab-org--gitlab-foss/doc/development/telemetry/snowplow.md

stage	group	info
Growth	Telemetry	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

Snowplow Guide

This guide provides an overview of how Snowplow works, and implementation details.

For more information about Telemetry, see:

• Telemetry Guide
• Usage Ping Guide

More useful links:

• Telemetry Direction
• Data Analysis Process
• Data for Product Managers
• Data Infrastructure

What is Snowplow

Snowplow is an enterprise-grade marketing and product analytics platform which helps track the way users engage with our website and application.

Snowplow consists of the following loosely-coupled sub-systems:

• Trackers fire Snowplow events. Snowplow has 12 trackers, covering web, mobile, desktop, server, and IoT.
• Collectors receive Snowplow events from trackers. We have three different event collectors, synchronizing events either to Amazon S3, Apache Kafka, or Amazon Kinesis.
• Enrich cleans up the raw Snowplow events, enriches them and puts them into storage. We have an Hadoop-based enrichment process, and a Kinesis-based or Kafka-based process.
• Storage is where the Snowplow events live. We store the Snowplow events in a flat file structure on S3, and in the Redshift and PostgreSQL databases.
• Data modeling is where event-level data is joined with other data sets and aggregated into smaller data sets, and business logic is applied. This produces a clean set of tables which make it easier to perform analysis on the data. We have data models for Redshift and Looker.
• Analytics are performed on the Snowplow events or on the aggregate tables.

Snowplow schema

We have many definitions of Snowplow's schema. We have an active issue to standardize this schema including the following definitions:

• Frontend and backend taxonomy as listed below
• Feature instrumentation taxonomy
• Self describing events
• Iglu schema
• Snowplow authored events

Enabling Snowplow

Tracking can be enabled at:

• The instance level, which enables tracking on both the frontend and backend layers.
• User level, though user tracking can be disabled on a per-user basis. GitLab tracking respects the Do Not Track standard, so any user who has enabled the Do Not Track option in their browser is not tracked at a user level.

We utilize Snowplow for the majority of our tracking strategy and it is enabled on GitLab.com. On a self-managed instance, Snowplow can be enabled by navigating to:

• Admin Area > Settings > Integrations in the UI.
• admin/application_settings/integrations in your browser.

The following configuration is required:

Name	Value
Collector	snowplow.trx.gitlab.net
Site ID	gitlab
Cookie domain	.gitlab.com

Snowplow request flow

The following example shows a basic request/response flow between the following components:

• Snowplow JS / Ruby Trackers on GitLab.com
• GitLab.com Snowplow Collector
• GitLab's S3 Bucket
• GitLab's Snowflake Data Warehouse
• Sisense:

sequenceDiagram
    participant Snowplow JS (Frontend)
    participant Snowplow Ruby (Backend)
    participant GitLab.com Snowplow Collector
    participant S3 Bucket
    participant Snowflake DW
    participant Sisense Dashboards
    Snowplow JS (Frontend) ->> GitLab.com Snowplow Collector: FE Tracking event
    Snowplow Ruby (Backend) ->> GitLab.com Snowplow Collector: BE Tracking event
    loop Process using Kinesis Stream
      GitLab.com Snowplow Collector ->> GitLab.com Snowplow Collector: Log raw events
      GitLab.com Snowplow Collector ->> GitLab.com Snowplow Collector: Enrich events
      GitLab.com Snowplow Collector ->> GitLab.com Snowplow Collector: Write to disk
    end
    GitLab.com Snowplow Collector ->> S3 Bucket: Kinesis Firehose
    S3 Bucket->>Snowflake DW: Import data
    Snowflake DW->>Snowflake DW: Transform data using dbt
    Snowflake DW->>Sisense Dashboards: Data available for querying

Implementing Snowplow JS (Frontend) tracking

GitLab provides Tracking, an interface that wraps the Snowplow JavaScript Tracker for tracking custom events. There are a few ways to utilize tracking, but each generally requires at minimum, a category and an action. Additional data can be provided that adheres to our Feature instrumentation taxonomy.

field	type	default value	description
category	string	document.body.dataset.page	Page or subsection of a page that events are being captured within.
action	string	'generic'	Action the user is taking. Clicks should be click and activations should be activate, so for example, focusing a form field would be activate_form_input, and clicking a button would be click_button.
data	object	{}	Additional data such as label, property, value, and context as described in our Feature Instrumentation taxonomy.

Tracking in HAML (or Vue Templates)

When working within HAML (or Vue templates) we can add data-track-* attributes to elements of interest. All elements that have a data-track-event attribute automatically have event tracking bound on clicks.

Below is an example of data-track-* attributes assigned to a button:

%button.btn{ data: { track: { event: "click_button", label: "template_preview", property: "my-template" } } }

<button class="btn"
  data-track-event="click_button"
  data-track-label="template_preview"
  data-track-property="my-template"
/>

Event listeners are bound at the document level to handle click events on or within elements with these data attributes. This allows them to be properly handled on re-rendering and changes to the DOM. Note that because of the way these events are bound, click events should not be stopped from propagating up the DOM tree. If for any reason click events are being stopped from propagating, you need to implement your own listeners and follow the instructions in Tracking in raw JavaScript.

Below is a list of supported data-track-* attributes:

attribute	required	description
data-track-event	true	Action the user is taking. Clicks must be prepended with click and activations must be prepended with activate. For example, focusing a form field would be activate_form_input and clicking a button would be click_button.
data-track-label	false	The label as described in our Feature Instrumentation taxonomy.
data-track-property	false	The property as described in our Feature Instrumentation taxonomy.
data-track-value	false	The value as described in our Feature Instrumentation taxonomy. If omitted, this is the element's value property or an empty string. For checkboxes, the default value is the element's checked attribute or false when unchecked.
data-track-context	false	The context as described in our Feature Instrumentation taxonomy.

Tracking within Vue components

There's a tracking Vue mixin that can be used in components if more complex tracking is required. To use it, first import the Tracking library and request a mixin.

import Tracking from '~/tracking';
const trackingMixin = Tracking.mixin({ label: 'right_sidebar' });

You can provide default options that are passed along whenever an event is tracked from within your component. For instance, if all events within a component should be tracked with a given label, you can provide one at this time. Available defaults are category, label, property, and value. If no category is specified, document.body.dataset.page is used as the default.

You can then use the mixin normally in your component with the mixin Vue declaration. The mixin also provides the ability to specify tracking options in data or computed. These override any defaults and allow the values to be dynamic from props, or based on state.

export default {
  mixins: [trackingMixin],
  // ...[component implementation]...
  data() {
    return {
      expanded: false,
      tracking: {
        label: 'left_sidebar'
      }
    };
  },
}

The mixin provides a track method that can be called within the template, or from component methods. An example of the whole implementation might look like the following.

export default {
  mixins: [Tracking.mixin({ label: 'right_sidebar' })],
  data() {
    return {
      expanded: false,
    };
  },
  methods: {
    toggle() {
      this.expanded = !this.expanded;
      this.track('click_toggle', { value: this.expanded })
    }
  }
};

And if needed within the template, you can use the track method directly as well.

<template>
  <div>
    <a class="toggle" @click.prevent="toggle">Toggle</a>
    <div v-if="expanded">
      <p>Hello world!</p>
      <a @click.prevent="track('click_action')">Track an event</a>
    </div>
  </div>
</template>

Tracking in raw JavaScript

Custom event tracking and instrumentation can be added by directly calling the Tracking.event static function. The following example demonstrates tracking a click on a button by calling Tracking.event manually.

import Tracking from '~/tracking';

const button = document.getElementById('create_from_template_button');
button.addEventListener('click', () => {
  Tracking.event('dashboard:projects:index', 'click_button', {
    label: 'create_from_template',
    property: 'template_preview',
    value: 'rails',
  });
})

Tests and test helpers

In Jest particularly in Vue tests, you can use the following:

import { mockTracking } from 'helpers/tracking_helper';

describe('MyTracking', () => {
  let spy;

  beforeEach(() => {
    spy = mockTracking('_category_', wrapper.element, jest.spyOn);
  });

  it('tracks an event when clicked on feedback', () => {
    wrapper.find('.discover-feedback-icon').trigger('click');

    expect(spy).toHaveBeenCalledWith('_category_', 'click_button', {
      label: 'security-discover-feedback-cta',
      property: '0',
    });
  });
});

In obsolete Karma tests it's used as below:

import { mockTracking, triggerEvent } from 'spec/helpers/tracking_helper';

describe('my component', () => {
  let trackingSpy;

  beforeEach(() => {
    trackingSpy = mockTracking('_category_', vm.$el, spyOn);
  });

  const triggerEvent = () => {
    // action which should trigger a event
  };

  it('tracks an event when toggled', () => {
    expect(trackingSpy).not.toHaveBeenCalled();

    triggerEvent('a.toggle');

    expect(trackingSpy).toHaveBeenCalledWith('_category_', 'click_edit_button', {
      label: 'right_sidebar',
      property: 'confidentiality',
    });
  });
});

Implementing Snowplow Ruby (Backend) tracking

GitLab provides Gitlab::Tracking, an interface that wraps the Snowplow Ruby Tracker for tracking custom events.

Custom event tracking and instrumentation can be added by directly calling the GitLab::Tracking.event class method, which accepts the following arguments:

argument	type	default value	description
category	string	'application'	Area or aspect of the application. This could be HealthCheckController or Lfs::FileTransformer for instance.
action	string	'generic'	The action being taken, which can be anything from a controller action like create to something like an Active Record callback.
data	object	{}	Additional data such as label, property, value, and context as described in Instrumentation at GitLab. These are set as empty strings if you don't provide them.

Tracking can be viewed as either tracking user behavior, or can be utilized for instrumentation to monitor and visualize performance over time in an area or aspect of code.

For example:

class Projects::CreateService < BaseService
  def execute
    project = Project.create(params)

    Gitlab::Tracking.event('Projects::CreateService', 'create_project',
      label: project.errors.full_messages.to_sentence,
      value: project.valid?
    )
  end
end

Unit testing

Use the expect_snowplow_event helper when testing backend Snowplow events. See testing best practices for details.

Performance

We use the AsyncEmitter when tracking events, which allows for instrumentation calls to be run in a background thread. This is still an active area of development.

Developing and testing Snowplow

There are several tools for developing and testing Snowplow Event

Testing Tool	Frontend Tracking	Backend Tracking	Local Development Environment	Production Environment	Production Environment
Snowplow Analytics Debugger Chrome Extension	{check-circle}	{dotted-circle}	{check-circle}	{check-circle}	{check-circle}
Snowplow Inspector Chrome Extension	{check-circle}	{dotted-circle}	{check-circle}	{check-circle}	{check-circle}
Snowplow Micro	{check-circle}	{check-circle}	{check-circle}	{dotted-circle}	{dotted-circle}
Snowplow Mini	{check-circle}	{check-circle}	{dotted-circle}	{status_preparing}	{status_preparing}

Legend

{check-circle} Available, {status_preparing} In progress, {dotted-circle} Not Planned

Preparing your MR for Review

1. For frontend events, in the MR description section, add a screenshot of the event's relevant section using the Snowplow Analytics Debugger Chrome browser extension.
2. For backend events, please use Snowplow Micro and add the output of the Snowplow Micro good events GET http://localhost:9090/micro/good.

Snowplow Analytics Debugger Chrome Extension

Snowplow Analytics Debugger is a browser extension for testing frontend events. This works on production, staging and local development environments.

1. Install the Snowplow Analytics Debugger Chrome browser extension.
2. Open Chrome DevTools to the Snowplow Analytics Debugger tab.
3. Learn more at Igloo Analytics.

Snowplow Inspector Chrome Extension

Snowplow Inspector Chrome Extension is a browser extension for testing frontend events. This works on production, staging and local development environments.

1. Install Snowplow Inspector.
2. Open the Chrome extension by pressing the Snowplow Inspector icon beside the address bar.
3. Click around on a webpage with Snowplow and you should see JavaScript events firing in the inspector window.

Snowplow Micro

Snowplow Micro is a very small version of a full Snowplow data collection pipeline: small enough that it can be launched by a test suite. Events can be recorded into Snowplow Micro just as they can a full Snowplow pipeline. Micro then exposes an API that can be queried.

Snowplow Micro is a Docker-based solution for testing frontend and backend events in a local development environment. You need to modify GDK using the instructions below to set this up.

• Read Introducing Snowplow Micro
• Look at the Snowplow Micro repository
• Watch our installation guide recording

1. Install Snowplow Micro:

   docker run --mount type=bind,source=$(pwd)/example,destination=/config -p 9090:9090 snowplow/snowplow-micro:latest --collector-config /config/micro.conf --iglu /config/iglu.json
   

2. Install snowplow micro by cloning the settings in this project:

   git clone git@gitlab.com:a_akgun/snowplow-micro.git
   ./snowplow-micro.sh
   

3. Update port in SQL to set 9090:

   gdk psql -d gitlabhq_development
   update application_settings set snowplow_collector_hostname='localhost:9090', snowplow_enabled=true, snowplow_cookie_domain='.gitlab.com';
   

4. Update app/assets/javascripts/tracking.js to remove this line:

   forceSecureTracker: true
   

5. Update lib/gitlab/tracking.rb to add these lines:

   protocol: 'http',
   port: 9090,
   

6. Update lib/gitlab/tracking.rb to change async emitter from https to http:

   SnowplowTracker::AsyncEmitter.new(Gitlab::CurrentSettings.snowplow_collector_hostname, protocol: 'http'),
   

7. Enable Snowplow in the admin area, Settings::Integrations::Snowplow to point to: http://localhost:3000/admin/application_settings/integrations#js-snowplow-settings.

8. Restart GDK:

   `gdk restart`
   

9. Send a test Snowplow event from the Rails console:

   Gitlab::Tracking.self_describing_event('iglu:com.gitlab/pageview_context/jsonschema/1-0-0', { page_type: 'MY_TYPE' }, context: nil )
   

Snowplow Mini

Snowplow Mini is an easily-deployable, single-instance version of Snowplow.

Snowplow Mini can be used for testing frontend and backend events on a production, staging and local development environment.

For GitLab.com, we're setting up a QA and Testing environment using Snowplow Mini.