We’re trying to optimize
actionDecl metrics that call
rollupMetric over a number of sources, on time-consuming compound metrics.
If I understand correctly, there are three aggregation levels:
AGGTis an aggregation
avg, over consecutive time intervals
AGGSis an aggregation
sum, over timeseries sharing a header/source
AGGLis an aggregation
sum, over multiple sources (
Q1. Is there a way to transform 3. above into 2. above by somehow creating a virtual common source (without destroying existing connections)?
Q2. Would a Q1-based solution be faster than
Q3. Is there a way to use Expression Engine’s
rollup and collect the timeseries arguments like it is done for
rollupMetric using its
Q4. Would a
MetricFunctionLibrary be possible and faster than
rollupMetric parallelize the aggregation to end up with logarithmic time complexity in the number of timeseries aggregated upon?
evalInstrumentation be made to penetrate an
actionDecl that calls
rollupMetric on a
CompoundMetric? At the moment, we only see one line with total time taken; and using
cache property does not seem to help.