Thanks for the reply. I did run up a sample database with some time weighted
samples and unfortunately the moving average ignores the time that the data is
stored so doesn't help.
I also had a look at pull 6113 and there is a lot of code in a language &
platform I'm unfamiliar with, and its difficult to work out what routines to
There are 2 ways to implement a time weighted average, firstly to interpolate
like how openTSDB does it, which for some IoT sensor scenarios is useful if
there is lost data or data resolution needs to be more granular. However the
other common IoT scenario I'm trying to solve for is when the sensor reacts to
changes - a better model as it reduces network bandwidth, storage and CPU usage
as well as increases battery life for remote sensors compared to regular time
interval data collection.
To implement a time weighted average from irregular time samples shouldn't be
that tough, I have it implemented previously at the application layer,
1) You want the <window> parameter in the moving average query function to be
time based, not number of points.
2) Each sample is weighted by the time delta between itself and the previous
sample as a proportion to the time window size.
3) Add up these weighted samples to get the average.
4) Would also work as continuous query.
Keep up the good work on influxdb - I like the approach you have taken. I will
be using it for my IoT solution that will be open sourced soon although I need
to find an alternative solution to the time weighted average at some point.
Hopefully you can see the benefit in a time weighted average function for a
time series database targeted for IoT scenarios - I would have a try at writing
this function but would need help with Go & your platform code.
Remember to include the version number!
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