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This page documents the efforts to measure Go Faster deployments. This is currently in the discovering phase. | This page documents the efforts to measure Go Faster deployments. This is currently in the discovering phase. | ||
= Update 2016-10 = | |||
'''(georg)''' | |||
Here is a quick hack on re:dash, from the longitudinal table (a 1% sample of our clients): | |||
https://sql.telemetry.mozilla.org/queries/1472/source | |||
'''(chutten)''' | |||
To make it useful, I think it would need some changes: | |||
* Constrain it by time. | |||
* Constrain it by release channel. | |||
* Report it as a percentage or proportion to give an impression of scale. | |||
So, if you could say something like "According to a 1% sample of Firefox clients reporting between Date1 and Date2, X% of Firefox release users have this addon installed." | |||
That, I think, would be the most concise, useful thing we could get from the longitudinal dataset. | |||
If what you want is to see rollout of the system addon across the populations, that would be a decent place to start. What you'd want then is to see numbers per day. | |||
So.... | |||
SELECT t.ss_startDate AS d, CASE WHEN element_at(t.addons, '<the addon id>') IS NOT NULL THEN 'has the addon' ELSE 'nopes' END AS has_addon, normalized_channel, COUNT(DISTINCT client_id) AS num | |||
FROM longitudinal | |||
CROSS JOIN UNNEST(subsession_start_date, active_addons) AS t(ss_startDate, addons) | |||
GROUP BY 1, 2, 3 | |||
That... _might_ do it? I'm not sure. But it should give you a list of dates with has/nope counts by channel. Then a Visualization (type: line, x-axis d, y-axis num, group by has_addon or channel or both) should give you the curves you want. | |||
= History = | = History = | ||