# Find percentile and quantile values

This page documents an earlier version of InfluxDB OSS.
InfluxDB OSS v2 is the latest stable version.
See the equivalent **InfluxDB v2** documentation: Find percentile and quantile values.

Use the `quantile()`

function
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

**(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:

**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 |

`estimate_tdigest`

returns:

_value |
---|

1.5 |

### 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:

**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 |

`exact_mean`

returns:

_value |
---|

1.5 |

### 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:

**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 |

`exact_selector`

returns:

_time | _value |
---|---|

2020-01-01T00:02:00Z | 1.0 |

The examples below use the example data variable.

## 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()`

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")
)
```

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