DAU and MAU

For the purposes of DAU, a profile is considered active if it sends any main ping.

  • Dates are defined by submission_date_s3 or submission_date.

DAU is the number of clients sending a main ping on a given day.

MAU is the number of unique clients who have been a DAU on any day in the last 28 days. In other words, any client that contributes to DAU in the last 28 days would also contribute to MAU for that day. Note that this is not simply the sum of DAU over 28 days, since any particular client could be active on many days.

WAU is the number of unique clients who have been a DAU on any day in the last 7 days. Caveats above for MAU also apply to WAU.

To make the time boundaries more clear, let's consider a particular date 2019-01-28. The DAU 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, MAU 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 firefox_desktop_exact_mau28_by_dimensions.

SELECT
  submission_date,
  SUM(mau) AS mau,
  SUM(wau) AS wau,
  SUM(dau) AS 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 firefox_desktop_exact_mau28_by_dimensions, using clients_last_seen is recommended. Below is an example query for getting MAU, WAU, and DAU by app_version for 2018 using clients_last_seen.

SELECT
  submission_date,
  app_version,
  -- days_since_seen is always between 0 and 28, so MAU could also be
  -- calculated with COUNT(days_since_seen) or COUNT(*)
  COUNTIF(days_since_seen < 28) AS mau,
  COUNTIF(days_since_seen < 7) AS wau,
  -- days_since_* values are always between 0 and 28 or null, so DAU could also
  -- be calculated with COUNTIF(days_since_seen = 0)
  COUNTIF(days_since_seen < 1) AS 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 DAU, using clients_daily is more efficient than clients_last_seen. Getting MAU and WAU from clients_daily is not recommended. Below is an example query for getting DAU for 2018 using clients_daily.

SELECT
  submission_date_s3,
  COUNT(*) AS dau
FROM
  telemetry.clients_daily
WHERE
  -- In BigQuery use yyyy-MM-DD, e.g. '2018-01-01'
  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 DAU. Below is an example query using a 1% sample over March 2018 using main_summary:

SELECT
  submission_date_s3,
  -- Note: this does not include NULL client_id in count where above methods do
  COUNT(DISTINCT client_id) * 100 AS DAU
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
ORDER BY
  submission_date_s3