The general guideline is to store enough data that all measures (mean, quantile, min, max) are commutative over different queries by ensuring that measure(Xs) =

The general guideline is to store enough data that all measures (mean, quantile, min, max) are commutative over different queries by ensuring that measure(Xs) = measure(map(measure(x), arbitrary_partition(Xs))) for any set of metrics Xs and partitioning function. In other words, no matter how you measure different distinct subsets of an original metric the measure of all those subsets gives the same result as measuring the whole set. This is only possible if enough data is stored in the metric about the underlying values in the set. This allows aggregation for efficiency, usually either by partitioning the data by short time windows (1 second or more usually) and storing measures over each duration or by storing a sampled subset of original values, both of which can later be queried to produce measures over arbitrary periods of time. A few measures can be supported exactly with pre-aggregation (min, max, mean, variance), some can be supported with bounded error (quantiles) or statistical error (pretty much any measure over sampled values).