An Active User is defined as a client who has
total_daily_uri >= 5 URI for a given date.
Dates are defined by
submission_date(not by client activity date).
total_daily_uriis defined as their sum of
scalar_parent_browser_engagement_total_uri_countfor a given date
Active DAU (aDAU) is the number of Active Users on a given day.
Active MAU (aMAU) is the number of unique clients who have been an Active User on any day in the last 28 days. In other words, any client that contributes to aDAU in the last 28 days would also contribute to aMAU for that day. Note that this is not simply the sum of aDAU over 28 days, since any particular client could be active on many days.
Active WAU (aWAU) is the number of unique clients who have been an Active User on any day in the last 7 days. Caveats above for aMAU also apply to aWAU.
To make the time boundaries more clear, let's consider a particular date 2019-01-28. The aDAU number assigned to 2019-01-28 should consider all main pings received during 2019-01-28 UTC. We cannot observe the full data until 2019-01-28 closes (and in practice we need to wait a bit longer since we are usually referencing derived datasets like
clients_daily that are updated once per day over several hours following midnight UTC), so the earliest we can calculate this value is on 2019-01-29. If plotted as a time series, this value should always be plotted at the point labeled 2019-01-28. Likewise, aMAU for 2019-01-28 should consider a 28 day range that includes main pings received on 2019-01-28 and back to beginning of day UTC 2019-01-01. Again, the earliest we can calculate the value is on 2019-01-29.
For quick analysis, using
firefox_desktop_exact_mau28_by_dimensions is recommended. Below is an example query for getting MAU, WAU, and DAU for 2018 using
SELECT submission_date, SUM(visited_5_uri_mau) AS visited_5_uri_mau, SUM(visited_5_uri_wau) AS visited_5_uri_wau, SUM(visited_5_uri_dau) AS visited_5_uri_dau FROM telemetry.firefox_desktop_exact_mau28_by_dimensions WHERE submission_date_s3 >= '2018-01-01' AND submission_date_s3 < '2019-01-01' GROUP BY submission_date ORDER BY submission_date
For analysis of dimensions not available in
clients_last_seen is recommended. Below is an example query for getting aMAU, aWAU, and aDAU by
app_version for 2018 using
SELECT submission_date, app_version, -- days_since_* values are always < 28 or null, so aMAU could also be -- calculated with COUNT(days_since_visited_5_uri) COUNTIF(days_since_visited_5_uri < 28) AS visited_5_uri_mau, COUNTIF(days_since_visited_5_uri < 7) AS visited_5_uri_wau, -- days_since_* values are always >= 0 or null, so aDAU could also be -- calculated with COUNTIF(days_since_visited_5_uri = 0) COUNTIF(days_since_visited_5_uri < 1) AS visited_5_uri_dau FROM telemetry.clients_last_seen WHERE submission_date_s3 >= '2018-01-01' AND submission_date_s3 < '2019-01-01' GROUP BY submission_date, app_version ORDER BY submission_date, app_version
For analysis of only aDAU, using
clients_daily is more efficient than
clients_last_seen. Getting aMAU and aWAU from
clients_daily is not recommended. Below is an example query for getting aDAU for 2018 using
SELECT submission_date_s3, COUNT(*) AS visited_5_uri_dau FROM telemetry.clients_daily WHERE scalar_parent_browser_engagement_total_uri_count_sum >= 5 -- In BigQuery use yyyy-MM-DD, e.g. '2018-01-01' AND submission_date_s3 >= '20180101' AND submission_date_s3 < '20190101' GROUP BY submission_date_s3 ORDER BY submission_date_s3
main_summary can also be used for getting aDAU. Below is an example query using a 1% sample over March 2018 using
**1**: Note, the probe measuring `scalar_parent_browser_engagement_total_uri_count` only exists in clients with Firefox 50 and up. Clients on earlier versions of Firefox won't be counted as an Active User (regardless of their use). Similarly, `scalar_parent_browser_engagement_total_uri_count` doesn't increment when a client is in Private Browsing mode, so that won't be included as well.
SELECT submission_date_s3, COUNT(*) * 100 AS visited_5_uri_dau FROM ( SELECT submission_date_s3, client_id, SUM(scalar_parent_browser_engagement_total_uri_count) >= 5 AS visited_5_uri FROM telemetry.main_summary WHERE sample_id = '51' -- In BigQuery use yyyy-MM-DD, e.g. '2018-03-01' AND submission_date_s3 >= '20180301' AND submission_date_s3 < '20180401' GROUP BY submission_date_s3, client_id) WHERE visited_5_uri GROUP BY submission_date_s3 ORDER BY submission_date_s3