Find percentile and quantile values

Percentiles and quantiles are very similar, differing only in the number used to calculate return values. A percentile is calculated using numbers between 0 and 100. A quantile is calculated using numbers between 0.0 and 1.0. For example, the 0.5 quantile is the same as the 50th percentile.

Select a method for calculating the quantile

Select one of the following methods to calculate the quantile:

• exact_mean

(Default) An aggregate method that uses a t-digest data structure to compute a quantile estimate on large data sources. Output tables consist of a single row containing the calculated quantile.

If calculating the quantile or 50th percentile:

Given the following input table:

estimate_tdigest returns:

If calculating the 0.5 quantile or 50th percentile:

Given the following input table:

exact_mean returns:

A selector method that returns the data point for which at least q points are less than. Output tables consist of a single row containing the calculated quantile.

If calculating the 0.5 quantile or 50th percentile:

Given the following input table:

The examples below use the .

Use the default method, "estimate_tdigest", to return all rows in a table that contain values in the 99th percentile of data in the table.

Find the average of values closest to the quantile

Use the exact_mean method to return a single row per input table containing the average of the two values closest to the mathematical quantile of data in the table. For example, to calculate the 0.99 quantile:

Use the exact_selector method to return a single row per input table containing the value that q * 100% of values in the table are less than. For example, to calculate the quantile:

Use quantile() with aggregateWindow()

aggregateWindow() segments data into windows of time, aggregates data in each window into a single point, and then removes the time-based segmentation. It is primarily used to .

To specify the quantile calculation method in aggregateWindow(), use the :

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