Implement stable max-times, which are useful for highlighting spikes, e.g. moving_max().
Current behavior:
Without this feature, there are currently three options are available:
moving_average() (or kaufmans_adaptive_moving_average()) which flatten out your spikes, essentially hiding them.
Display without filtering. Visually representing spikes with a lot of jitter is suboptimal (for more reasons that I care to explain here).
Increase the time period in the GUI. This throws out information, and makes the graph less appealing.
None of the above are satisfactory.
Desired behavior:
SELECT moving_max(..., 10) FROM ... would result in the maximum values including the previous 9 datapoints per each datapoint. This is calculated similarly to a moving_average(), except taking the maximum instead of the mean in the moving window.
Pseudo code:
This is what you'd write in Python/Pandas for a moving window size of 3:
Many people would use this to display maximum response times in http and db calls for example, as they frequently vary depending on input parameters. A frequently used function will normally have a bunch of cheap uses, and a few costly ones. This function would really help visualization of the costly ones, while more or less filtering out the cheap ones (depending on the moving window size).
BastiJoe
commented
1 year ago
Proposal:
Implement stable max-times, which are useful for highlighting spikes, e.g.
moving_max().Current behavior:
Without this feature, there are currently three options are available:
moving_average()(orkaufmans_adaptive_moving_average()) which flatten out your spikes, essentially hiding them.None of the above are satisfactory.
Desired behavior:
SELECT moving_max(..., 10) FROM ...would result in the maximum values including the previous 9 datapoints per each datapoint. This is calculated similarly to amoving_average(), except taking the maximum instead of the mean in the moving window.Pseudo code:
This is what you'd write in Python/Pandas for a moving window size of 3:
Use case:
Many people would use this to display maximum response times in http and db calls for example, as they frequently vary depending on input parameters. A frequently used function will normally have a bunch of cheap uses, and a few costly ones. This function would really help visualization of the costly ones, while more or less filtering out the cheap ones (depending on the moving window size).