From 9330136658e606c5a3f36f64c43bd1f7f9c1b6ab Mon Sep 17 00:00:00 2001 From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com> Date: Fri, 13 Oct 2023 10:55:55 +0100 Subject: [PATCH] Update website/docs/docs/build/cumulative-metrics.md --- website/docs/docs/build/cumulative-metrics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md index d9736ec3695..708045c1f3e 100644 --- a/website/docs/docs/build/cumulative-metrics.md +++ b/website/docs/docs/build/cumulative-metrics.md @@ -39,7 +39,7 @@ metrics: ## Limitations Cumulative metrics are currently under active development and have the following limitations: -1. You can only use the [`metric_time` dimension](/docs/docs/build/dimensions#time) to check cumulative metrics. If you don't use `metric_time` in the query, the cumulative metric will return incorrect results because it won't perform the time spine join. This means you cannot reference time dimensions other than the `metric_time` in the query. +1. You can only use the [`metric_time` dimension](/docs/build/dimensions#time) to check cumulative metrics. If you don't use `metric_time` in the query, the cumulative metric will return incorrect results because it won't perform the time spine join. This means you cannot reference time dimensions other than the `metric_time` in the query. 2. If you use `metric_time` in your query filter but don't include "start_time" and "end_time," cumulative metrics will left-censor the input data. For example, if you query a cumulative metric with a 7-day window with the filter `{{ TimeDimension('metric_time') }} BETWEEN '2023-08-15' AND '2023-08-30' `, the values for `2023-08-15` to `2023-08-20` return missing or incomplete data. This is because we apply the `metric_time` filter to the aggregation input. To avoid this, you must use `start_time` and `end_time` in the query filter.