Telemetry/LongitudinalExamples
Note: There is good background in the example notebook for the longitudinal data set.
Table structure
Get an overview of the longitudinal data table:
describe longitudinal
That table has a row for each client, with columns for the different parts of the ping. Some properties are directly available to query on:
SELECT count(*) AS count FROM longitudinal WHERE os = 'Linux'
Sampling
While composing queries, it can be helpful to work on small samples to reduce query runtimes:
SELECT * FROM longitudinal LIMIT 1000 ...
Or to look at a 1% sample of the clients:
SELECT * FROM longitudinal WHERE sample_id[1] = 5 ...
The sample_id partitions the clients into stable ~1% samples.
Arrays
Other properties are arrays, which contain one entry for each submission from that client (newest first):
SELECT reason[1] AS newest_reason FROM longitudinal WHERE os = 'Linux'
To expand arrays and maps and work on the data in them row-wise we can use UNNEST(array):
WITH lengths AS (SELECT os, greatest(-1, least(31, sl / (24*60*60))) AS days FROM longitudinal CROSS JOIN UNNEST(session_length, reason) AS t(sl, r) WHERE r = 'shutdown' OR r = 'aborted-session') SELECT os, days, count(*) AS count FROM lengths GROUP BY days, os ORDER BY days ASC
Links:
Maps
Some fields like active_addons or user_prefs are handled as maps, on which you can use the [] operator and special functions:
WITH adp AS
(SELECT active_addons[1]['{d10d0bf8-f5b5-c8b4-a8b2-2b9879e08c5d}']
IS NOT null AS has_adblockplus
FROM longitudinal)
SELECT has_adblockplus, count(*) AS count
FROM adp GROUP BY 1 ORDER BY 2 DESC
Links:
Examples
- Blocklist URLs (extensions.blocklist.url):
SELECT bl, COUNT(bl) FROM (SELECT element_at(settings, 1).user_prefs['extensions.blocklist.url'] AS bl FROM longitudinal) GROUP BY bl
- Blocklist enabled/disabled (extensions.blocklist.enabled) count:
SELECT bl, COUNT(bl) FROM (SELECT element_at(settings, 1).blocklist_enabled AS bl FROM longitudinal) GROUP BY bl