docs-v2/content/influxdb/v2.1/query-data/flux/percentile-quantile.md

title	seotitle	list_title	description	weight	menu	influxdb/v2.1/tags	related	list_query_example
Find percentile and quantile values	Query percentile and quantile values in Flux	Percentile & quantile	Use the `quantile()` function to return all values within the `q` quantile or percentile of input data.	210	influxdb_2_1 parent name Query with Flux Percentile & quantile	query percentile quantile	/influxdb/v2.1/query-data/flux/query-median/ /{{< latest "flux" >}}/stdlib/universe/quantile/	quantile

Use the [quantile() function](/{{< latest "flux" >}}/stdlib/universe/quantile/) to return a value representing the q quantile or percentile of input data.

Percentile versus quantile

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:

• estimate_tdigest
• exact_mean
• exact_selector

estimate_tdigest

(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 0.5 quantile or 50th percentile:

{{< flex >}} {{% flex-content %}} Given the following input table:

_time	_value
2020-01-01T00:01:00Z	1.0
2020-01-01T00:02:00Z	1.0
2020-01-01T00:03:00Z	2.0
2020-01-01T00:04:00Z	3.0
{{% /flex-content %}}
{{% flex-content %}}
estimate_tdigest returns:

_value
1.5
{{% /flex-content %}}
{{< /flex >}}

exact_mean

An aggregate method that takes the average of the two points closest to the quantile value. Output tables consist of a single row containing the calculated quantile.

If calculating the 0.5 quantile or 50th percentile:

{{< flex >}} {{% flex-content %}} Given the following input table:

_time	_value
2020-01-01T00:01:00Z	1.0
2020-01-01T00:02:00Z	1.0
2020-01-01T00:03:00Z	2.0
2020-01-01T00:04:00Z	3.0
{{% /flex-content %}}
{{% flex-content %}}
exact_mean returns:

_value
1.5
{{% /flex-content %}}
{{< /flex >}}

exact_selector

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:

{{< flex >}} {{% flex-content %}} Given the following input table:

_time	_value
2020-01-01T00:01:00Z	1.0
2020-01-01T00:02:00Z	1.0
2020-01-01T00:03:00Z	2.0
2020-01-01T00:04:00Z	3.0
{{% /flex-content %}}
{{% flex-content %}}
exact_selector returns:

_time	_value
2020-01-01T00:02:00Z	1.0
{{% /flex-content %}}
{{< /flex >}}

{{% note %}} The examples below use the example data variable. {{% /note %}}

Find the value representing the 99th percentile

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.

data
    |> quantile(q: 0.99)

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:

data
    |> quantile(q: 0.99, method: "exact_mean")

Find the point with the quantile value

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 0.99 quantile:

data
    |> quantile(q: 0.99, method: "exact_selector")

Use quantile() with aggregateWindow()

[aggregateWindow()](/{{< latest "flux" >}}/stdlib/universe/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 downsample data.

To specify the quantile calculation method in aggregateWindow(), use the [full function syntax](/{{< latest "flux" >}}/stdlib/universe/aggregatewindow/#specify-parameters-of-the-aggregate-function):

data
    |> aggregateWindow(
        every: 5m,
        fn: (tables=<-, column) => tables
            |> quantile(q: 0.99, method: "exact_selector"),
    )