The avg_over_time is a rollup function. This function calculates results over raw samples on some interval, which starts in the past and ends at the current time. This interval is usually named lookbehind window. For example, avg_over_time(m[1h]) calculates the average value over raw samples for time series m on the interval (t-1h ... t], where t is the current time. If m returns multiple time series, then the rollup function calculates results individually per each time series.

Rollup functions expect time series selector with lookbehind window in square brackets as an argument. If something other is passed to rollup function, then the following cases are possible:

• Prometheus returns expected type range vector in call to function "...", got instant vector if subquery isn't called explicitly. For example, such an error is returned in the following cases avg_over_time(m) or avg_over_time(sum(m)).
• Prometheus returns ranges only allowed for vector selectors if the colon with optional step is missing in subquery's square brackets. For example, such an error is returned in avg_over_time(sum(m)[1h]).
• Subquery execution. For example, avg_over_time(sum(m)[1h:]) would calculate the sum(m) per each point on the graph and then calculate the average over these points with one hour lookbehind window.

VictoriaMetrics doesn't return errors in these cases, since it automatically performs the needed conversions as documented here:

• avg_over_time(m) is automatically converted to the equivalent of avg_over_time(m[$__interval]), where $__interval is the interval between adjacent points on the graph in Grafana.
• avg_over_time(sum(m)) is automatically converted into avg_over_time(sum(default_rollup(m[$__interval]))[$__interval:$__interval])
• avg_over_time(sum(m)[1h]) and avg_over_time(sum(m)[1h:]) are automatically converted into the same query - avg_over_time(sum(default_rollup(m[$__interval]))[1h:$__interval]).