# I am the Watcher. I am your guide through this vast new twtiverse.
#
# Usage:
# https://watcher.sour.is/api/plain/users View list of users and latest twt date.
# https://watcher.sour.is/api/plain/twt View all twts.
# https://watcher.sour.is/api/plain/mentions?uri=:uri View all mentions for uri.
# https://watcher.sour.is/api/plain/conv/:hash View all twts for a conversation subject.
#
# Options:
# uri Filter to show a specific users twts.
# offset Start index for quey.
# limit Count of items to return (going back in time).
#
# twt range = 1 20
# self = https://watcher.sour.is/conv/a7hn7pq
4 THOUSAND people a day?!\nno, that can't be right, the months active says 3.5k\n3,500 / 30. \n\n116...\n\n\nSo you have *approximately* around 100 daily active users.\nhey thats pretty good. :)
4 THOUSAND people a day?!
no, that can't be right, the months active says 3.5k
3,500 / 30.
116...
So you have *approximately* around 100 daily active users.
hey thats pretty good. :)
4 THOUSAND people a day?!\nno, that can't be right, the months active says 3.5k\n3,500 / 30. \n\n116...\n\n\nSo you have *approximately* around 100 daily active users.\nhey thats pretty good. :)
@birb \nha maybe your interpretation is correct! π
@birb
ha maybe your interpretation is correct! π
@birb
ha maybe your interpretation is correct! π
@birb \nha maybe your interpretation is correct! π
@birb actually Iβm not so sure about thisβ¦ What weβre actually measuring on the backend is the sum of the average over time over three gauges, anonymous sessions, persistent sessions and API tokens
@birb actually Iβm not so sure about thisβ¦ What weβre actually measuring on the backend is the sum of the average over time over three gauges, anonymous sessions, persistent sessions and API tokens
@birb actually Iβm not so sure about thisβ¦ What weβre actually measuring on the backend is the sum of the average over time over three gauges, anonymous sessions, persistent sessions and API tokens
@birb For example this PromQL:
βββ
(
quantile_over_time(0.95, twtd_server_sessions{job="twtxt"}[$__interval]) +
quantile_over_time(0.95, twtd_db_sessions{job="twtxt"}[$__interval]) +
quantile_over_time(0.95, twtd_db_tokens{job="twtxt"}[$__interval])
)
βββ
produces this chart:
@birb For example this PromQL:
βββ
(
quantile_over_time(0.95, twtd_server_sessions{job="twtxt"}[$__interval]) +
quantile_over_time(0.95, twtd_db_sessions{job="twtxt"}[$__interval]) +
quantile_over_time(0.95, twtd_db_tokens{job="twtxt"}[$__interval])
)
βββ
produces this chart:
@birb For example this PromQL:\n\nβββ\n(
quantile_over_time(0.95, twtd_server_sessions{job="twtxt"}[$__interval]) +\n quantile_over_time(0.95, twtd_db_sessions{job="twtxt"}[$__interval]) +\n quantile_over_time(0.95, twtd_db_tokens{job="twtxt"}[$__interval])\n)\nβββ\n\nproduces this chart:\n\n
@birb For example this PromQL:\n\nβββ\n(
quantile_over_time(0.95, twtd_server_sessions{job="twtxt"}[$__interval]) +\n quantile_over_time(0.95, twtd_db_sessions{job="twtxt"}[$__interval]) +\n quantile_over_time(0.95, twtd_db_tokens{job="twtxt"}[$__interval])\n)\nβββ\n\nproduces this chart:\n\n
@birb and when you think about it if for example you had three coffee machines and only three people at a time were able to use those coffee machines and you measured this overtime you would only get three active users over a day and an actual fact the number is more like 24Γ3
@birb and when you think about it if for example you had three coffee machines and only three people at a time were able to use those coffee machines and you measured this overtime you would only get three active users over a day and an actual fact the number is more like 24Γ3
@birb and when you think about it if for example you had three coffee machines and only three people at a time were able to use those coffee machines and you measured this overtime you would only get three active users over a day and an actual fact the number is more like 24Γ3
@birb I would say the 4.5k to 5k is roughly about right given the fact this pod is seeing about 3 million hits per month today π but since we donβt actually measure user activity levels this is as best as we can measure
@birb I would say the 4.5k to 5k is roughly about right given the fact this pod is seeing about 3 million hits per month today π but since we donβt actually measure user activity levels this is as best as we can measure
@birb I would say the 4.5k to 5k is roughly about right given the fact this pod is seeing about 3 million hits per month today π but since we donβt actually measure user activity levels this is as best as we can measure