This derived dataset makes it easier to analyze the Firefox event ping.
It has the following advantages over accessing the raw ping table (
- There is no need to
eventscolumn: this is already done for you.
- You don't have to know which process type emitted your event. If you care, you can query the
- It is clustered on the
event_categorycolumn, which can dramatically speed up your query.
The events dataset contains one row for each event submitted in an event ping for that day.
extra fields of the event
are mapped to columns named
To access the
event_map_values, you can use the
SELECT mozfun.map.get_key(event_map_values, "branch") AS branch FROM telemetry.events.
Please note that
event_timestamp refers to the time in milliseconds when the event was recorded relative to the main process start time (
session_start_time), while the
timestamp column refers to the time the ping was ingested.
event_timestamp is useful for determining relative order of events within a single session. Adding
session_start_time will allow you to approximate the absolute time an event occurred, subject to client clock skew and other factors.
This query gets the count of the number of times the user initiated the
learn_more_breach actions. Note the use of the
event_category to optimize the query:
for this example, this reduces the amount of data scanned from 450 GB to 52 MB.
SELECT countif(event_method = 'dismiss_breach_alert') AS n_dismissing_breach_alert, countif(event_method = 'learn_more_breach') AS n_learn_more FROM telemetry.events_v1 WHERE event_category = 'pwmgr' AND submission_date='2020-04-20' AND sample_id=0
This dataset is generated by BigQuery ETL. The query that generates the dataset is
Firefox has an API to record events, which are then submitted through the main ping. The format and mechanism of event collection in Firefox is documented in the Firefox source documentation.
The full events data pipeline is documented in the event pipeline documentation.