Datediff athena aws series#

Returns the maximum value of the specified measure for a given time granularity (for instance, a quarter) up to a point in time. Function NameĬalculates the average of a measure for a given time granularity (for instance, a quarter) up to a point in time.Ĭalculates the count of a dimension or measure for a given time granularity (for instance, a quarter) up to a point in time.Ĭalculates the maximum of a measure or date for a given time granularity (for instance, a quarter) up to a point in time.Ĭalculates the minimum of a measure or date for a given time granularity (for instance, a quarter) up to a point in time.Ĭalculates the sum of a measure for a given time granularity (for instance, a quarter) up to a point in time.Īverages the set of numbers in the specified measure for a given time granularity (for instance, a quarter) up to a point in time.Ĭalculates the number of values in a dimension or measure for a given time granularity (for instance, a quarter) up to a point in time, including duplicates. The following table summarizes the period to date functions. If left blank, the default endDate takes now(), which is the moment when users load the dashboard. Aggregation functions, which output an aggregated value for a fixed period-to-date time range.Į.g., periodtoDateSum with YEAR granularity returns a single value for the total of the metric from the beginning of the year to the endDate provided in the formula.

Datediff athena aws series#

“OverTime” functions, which are table calculations and return outputs for each row in the visual.Į.g., you can use periodToDateCountOverTime with WEEK granularity to compute a series of week-to-date new customer counts to track the fluctuation of customer engagement.There are two main types of cumulative functions. You can use period to date functions to calculate metrics within a given period-to-date window. Function NameĬalculates the difference of a measure over two different time periods as specified by period granularity and offset.Ĭalculates the last (previous) value of a measure from a previous time period as specified by period granularity and offset.Ĭalculates the percent difference of a measure over two different time periods as specified by period granularity and offset. The following table summarizes the three available period over period functions. Note that period and offset have to be both specified or both left empty. For instance, a period of a quarter with an offset of 2 means comparing against the previous two quarters. You can also use the offset argument to specify how many periods apart you want to compute the comparison. If the period argument is left empty, the calculation changes based on the period granularity that is chosen (in the field well) to be displayed in the visual. The granularity of YEAR means year-over-year computation, Quarter means quarter-over-quarter, and so on. You can use the period argument in the function to define the period granularity of the calculation.

For example, you can compute a year-over-year increase in sales, or week-over-week percentage revenue changes.Ī typical comparative period function has the syntax periodOverPeriodDifference(measure, date, period, offset), with two optional arguments: period and offset. You can use period over period functions to compare measures at different time periods, such as year, quarter, and month. Comparative (period over period) functions We can divide period functions into two main groups: comparative (period over period) functions and cumulative (period to date) functions. New period functionsīefore we demonstrate use cases, let’s go over the new period function suite and see what new functions we now support. We also discuss several scenarios to extend the usage of the period functions, which will be useful in more advanced situations. In this post, we introduce the new period functions and their capabilities, and demonstrate several typical use cases. This allows authors in QuickSight to implement advanced calculations without having to use complicated date offsets in calculations to achieve such datetime-aware comparisons. Amazon QuickSight recently added native support for comparative (e.g., year-over-year) and cumulative (e.g., year-to-date) period functions which allow you to easily introduce these calculations in business reporting, trend analysis and time series analysis.