content/shared/influxdb-v2/query-data/flux/percentile-quantile.md
Use the quantile() function
to return a value representing the q quantile or percentile of input data.
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 one of the following methods to calculate the quantile:
(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 >}} |
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 >}} |
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 %}}
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)
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")
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")
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:
data
|> aggregateWindow(
every: 5m,
fn: (tables=<-, column) => tables
|> quantile(q: 0.99, method: "exact_selector"),
)