Activity Stream Datasets

This article describes the various BigQuery tables Mozilla uses to store Activity Stream data, along with some examples of how to access them.

Table of Contents

What is Activity Stream?

Activity Stream is the Firefox module which manages the in product content pages for Firefox:

The Activity Stream team has implemented data collection in and around these pages. This data has some overlap with the standard Firefox Telemetry system, however it is a custom system, designed and maintained by that team.

For specific questions about this data, reach out to the #fx-messaging-system Slack channel directly.

Activity Stream Pings

This data is measured in various custom pings that are sent via PingCentre (different from Pingsender).

Accessing Activity Stream Data

Activity Stream pings are stored in BigQuery (like other Firefox Telemetry). There are two datasets: activity_stream and messaging_system.


The activity_stream dataset contains the following tables:

  • events stores user interactions with the about:home and about:newtab pages
  • sessions stores sessions of about:home and about:newtab pages
  • impression_stats stores impression/click/block events for the Pocket recommendations on the about:home and about:newtab pages
  • spoc_fills stores "Pocket Sponsored" recommendation related pings


The messaging_system dataset contains the following tables:

  • cfr stores metrics on user interactions with the CFR (Contextual Feature Recommendation) system
  • moments stores "Moments Pages" related pings
  • onboarding stores metrics on user interactions with onboarding features
  • snippets stores impression/click/dismissal metrics for Firefox Snippets
  • whats_new_panel stores "What's New Panel" related pings
  • undesired_events stores system health related events

Gotchas and Caveats

Since this data collection isn't collected or maintained through our standard Telemetry API, there are a number of "gotchas" to keep in mind when working on this data:

  • Ping send conditions: Activity Stream pings have different send conditions, both from Telemetry pings as well as from each other. For example, AS Session Pings get sent by profiles that entered an Activity Stream session, at the end of that session, regardless of how long that session is. Compare this to main pings, which get sent by all Telemetry enabled profiles upon subsession end (browser shutdown, environment change, or local midnight cutoff).

    Due to these inconsistencies, using data from different sources can be tricky. For example, if we wanted to know how much of DAU (from main pings) had a custom about:home page (available in AS Health Pings), joining on client_id and a date field would only provide information on profiles that started the session on that same day (active profiles on multi-day sessions would be excluded).

  • Population covered: In addition to the usual considerations when looking at a measurement (in what version of Firefox did this measurement start getting collected? In what channels is it enabled in? etc.), when working with this data, there are additional Activity Stream specific conditions to consider when deciding "who is eligible to send this ping?"

    For example, Pocket recommendations are only enabled in the US, CA, UK, and DE countries, for profiles that are on en-US, en-CA, en-GB, and de locales. Furthermore, users can set their about:home and about:newtab page to non-Activity Stream pages. This information can be important when deciding denominators for certain metrics.

  • Different ping types in the same table: The tables in the activity_stream namespace can contain multiple types of pings. For example, the events table contains both AS Page Takeover pings as well as AS User Event pings.

  • Null handling: Some fields in the Activity Stream data encode nulls with a 'N/A' string or a -1 value.

  • Changes in ping behaviors: These pings continue to undergo development and the behavior as well as possible values for a given ping seem to change over time. For example, older versions of the event pings for clicking on a Topsite do not seem to report card_types and icon_types, while newer versions do. Caution is advised.

  • Pocket data: Data related to Pocket interaction and usage in the about:home and about:newtab pages get sent to Pocket via this data collection and pipeline. However, due to privacy reasons, the client_id is omitted in the ping whenever the Pocket recommendation identifiers are included, instead it reports with another user unique identifier impression_id. Though all the Pocket user interactions, such as clicks, dismisses, and save to pocket are still reported as the regular events with the client_id as long as they don't contain the Pocket recommendation identifiers.


Sessions per client_id

Note: only includes client_ids that completed an Activity Stream session that day.

    DATE(submission_timestamp) AS date,
    count(DISTINCT session_id) as num_sessions
    DATE(submission_timestamp) = '20200601'

Topsite clicks and Highlights clicks

    DATE(submission_timestamp) AS date,
    source in ('TOP_SITES', 'HIGHLIGHTS')
    AND event = 'CLICK'
    DATE(submission_timestamp) = '20200601'

Topsite Tile Dismissals: Sponsored and Non-Sponsored

The Topsite Tile Dismiss action corresponds to the BLOCK event which can be taken on a Sponsored or Non-Sponsored Tile reference. When applied to a Non-Sponsored Tile, the BLOCK event prevents the Tile from appearing in TopSites but leaves the browsing history as is. The DELETE event is fired when the user selects Delete from History and is only applicable to Non-Sponsored Tiles. This action deletes the URL from the client's complete browser history and prevents the Tile from appearing in their Topsites. DELETE doesn't apply to Sponsored Tiles as these are not generated by the user's browsing history.

    DATE(submission_timestamp) AS date,
    source = 'TOP_SITES'
    AND event = 'BLOCK'
    AND DATE(submission_timestamp) = '20220101'
    AND value LIKE '%"card_type":"spoc"%'

Snippet impressions, blocks, clicks, and dismissals

Note: Which snippet message a record corresponds to can be identified by the message_id (check with Marketing for snippet recipes published).

    DATE(submission_timestamp) AS date,
    DATE(submission_timestamp) = '20200601'