You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
When looking at larger intervals of time, Kubernetes PR-Time-To-Merge uses this calculation:
greatest(percentile_disc(0.5) within group (order by open_to_lgtm asc), 0) as m_o2l_a,
This is wrong; it leads to having a greater value the larger your time interval is, which is deceptive. For aggregating a median, there's two reasonable possibilities: a median of medians, or an average of medians. In practice, those two values are rarely that divergent, and an average is faster to calculate.
Therefore it should be:
avg(percentile_disc(0.5) within group (order by open_to_lgtm asc), 0) as m_o2l_a,
This mistake exists across all cacluations in this view.
Assigning to myself, will submit a PR later.
/assign
The text was updated successfully, but these errors were encountered:
When looking at larger intervals of time, Kubernetes PR-Time-To-Merge uses this calculation:
greatest(percentile_disc(0.5) within group (order by open_to_lgtm asc), 0) as m_o2l_a,
This is wrong; it leads to having a greater value the larger your time interval is, which is deceptive. For aggregating a median, there's two reasonable possibilities: a median of medians, or an average of medians. In practice, those two values are rarely that divergent, and an average is faster to calculate.
Therefore it should be:
avg(percentile_disc(0.5) within group (order by open_to_lgtm asc), 0) as m_o2l_a,
This mistake exists across all cacluations in this view.
Assigning to myself, will submit a PR later.
/assign
The text was updated successfully, but these errors were encountered: