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---
title: Aggregate data with SQL
seotitle: Aggregate or apply selector functions with SQL in InfluxDB Clustered
description: >
Use aggregate and selector functions to perform aggregate operations on your
time series data.
menu:
influxdb3_clustered:
name: Aggregate data
parent: Query with SQL
identifier: query-sql-aggregate
weight: 203
influxdb3/clustered/tags: [query, sql]
related:
- /influxdb3/clustered/reference/sql/functions/aggregate/
- /influxdb3/clustered/reference/sql/functions/selector/
list_code_example: |
##### Aggregate fields by groups
```sql
SELECT
mean(field1) AS mean,
selector_first(field2)['value'] as first,
tag1
FROM home
GROUP BY tag
```
##### Aggregate by time-based intervals
```sql
SELECT
DATE_BIN(INTERVAL '1 hour', time, '2022-01-01T00:00:00Z'::TIMESTAMP) AS time,
mean(field1),
sum(field2),
tag1
FROM home
GROUP BY 1, tag1
```
---
An SQL query that aggregates data includes the following clauses:
{{< req type="key" >}}
- {{< req "\*">}} `SELECT`: Specify fields, tags, and calculations to output from a
measurement or use the wildcard alias (`*`) to select all fields and tags
from a measurement.
- {{< req "\*">}} `FROM`: Specify the measurement to query data from.
- `WHERE`: Only return data that meets the specified conditions--for example, falls within
a time range, contains specific tag values, or contains a field value outside a specified range.
- `GROUP BY`: Group data that have the same values for specified columns and expressions (for example, an aggregate function result).
{{% note %}}
For simplicity, the term **"aggregate"** in this guide refers to applying
both aggregate and selector functions to a dataset.
{{% /note %}}
Learn how to apply aggregate operations to your queried data:
- [Aggregate and selector functions](#aggregate-and-selector-functions)
- [Aggregate functions](#aggregate-functions)
- [Selector functions](#selector-functions)
- [Example aggregate queries](#example-aggregate-queries)
## Aggregate and selector functions
Both aggregate and selector functions return a single row from each SQL partition
or group. For example, if you `GROUP BY room` and perform an aggregate operation
in your `SELECT` clause, results include an aggregate value for each unique
value of `room`.
### Aggregate functions
Use **aggregate functions** to aggregate values in a specified column for each
group and return a single row per group containing the aggregate value.
[View aggregate functions](/influxdb3/clustered/reference/sql/functions/aggregate/)
##### Basic aggregate query
```sql
SELECT AVG(co) from home
```
### Selector functions
Use **selector functions** to "select" a value from a specified column.
The available selector functions are designed to work with time series data.
[View selector functions](/influxdb3/clustered/reference/sql/functions/selector/)
Each selector function returns a Rust _struct_ (similar to a JSON object)
representing a single time and value from the specified column in the each group.
What time and value get returned depend on the logic in the selector function.
For example, `selector_first` returns the value of specified column in the first row of the group.
`selector_max` returns the maximum value of the specified column in the group.
#### Selector struct schema
The struct returned from a selector function has two properties:
- **time**: `time` value in the selected row
- **value**: value of the specified column in the selected row
```js
{time: 2023-01-01T00:00:00Z, value: 72.1}
```
#### Use selector functions
Each selector function has two arguments:
- The first is the column to operate on.
- The second is the time column to use in the selection logic.
In your `SELECT` statement, execute a selector function and use bracket notation
to reference properties of the [returned struct](#selector-struct-schema) to
populate the column value:
```sql
SELECT
selector_first(temp, time)['time'] AS time,
selector_first(temp, time)['value'] AS temp,
room
FROM home
GROUP BY room
```
## Example aggregate queries
- [Perform an ungrouped aggregation](#perform-an-ungrouped-aggregation)
- [Group and aggregate data](#group-and-aggregate-data)
- [Downsample data by applying interval-based aggregates](#downsample-data-by-applying-interval-based-aggregates)
- [Query rows based on aggregate values](#query-rows-based-on-aggregate-values)
{{% note %}}
#### Sample data
The following examples use the sample data written in the
[Get started writing data guide](/influxdb3/clustered/get-started/write/).
To run the example queries and return results,
[write the sample data](/influxdb3/clustered/get-started/write/#write-line-protocol-to-influxdb)
to your {{% product-name %}} database before running the example queries.
{{% /note %}}
### Perform an ungrouped aggregation
To aggregate _all_ queried values in a specified column:
- Use aggregate or selector functions in your `SELECT` statement.
- Do not include a `GROUP BY` clause to leave your data ungrouped.
```sql
SELECT avg(co) AS 'average co' from home
```
{{< expand-wrapper >}}
{{% expand "View example results" %}}
| average co |
| :---------------: |
| 5.269230769230769 |
{{% /expand %}}
{{< /expand-wrapper >}}
### Group and aggregate data
To apply aggregate or selector functions to grouped data:
- Use aggregate or selector functions in your `SELECT` statement.
- Include columns to group by in your `SELECT` statement.
- Include a `GROUP BY` clause with a comma-delimited list of columns and expressions to group by.
Keep the following in mind when using `GROUP BY`:
- `GROUP BY` can use column aliases that are defined in the `SELECT` clause.
- `GROUP BY` can't use an alias named `time`. If you include `time` in `GROUP BY`, it always uses the measurement `time` column.
```sql
SELECT
room,
avg(temp) AS 'average temp'
FROM home
GROUP BY room
```
{{< expand-wrapper >}}
{{% expand "View example results" %}}
| room | average temp |
| :---------- | -----------------: |
| Living Room | 22.16923076923077 |
| Kitchen | 22.623076923076926 |
{{% /expand %}}
{{< /expand-wrapper >}}
#### Downsample data by applying interval-based aggregates
A common use case when querying time series is downsampling data by applying
aggregates to time-based groups. To group and aggregate data into time-based
groups:
- In your `SELECT` clause:
- Use the [`DATE_BIN` function](/influxdb3/clustered/reference/sql/functions/time-and-date/#date_bin)
to calculate time intervals and output a column that contains the start of the interval nearest to the `time` timestamp in each row--for example,
the following clause calculates two-hour intervals starting at `1970-01-01T00:00:00Z` and returns a new `time` column that contains the start of the interval
nearest to `home.time`:
```sql
SELECT
DATE_BIN(INTERVAL '2 hours', time, '1970-01-01T00:00:00Z'::TIMESTAMP) AS time
FROM home
...
```
Given a `time` value
{{% influxdb/custom-timestamps-span %}}`2023-03-09T13:00:50.000Z`{{% /influxdb/custom-timestamps-span %}},
the output `time` column contains
{{% influxdb/custom-timestamps-span %}}`2023-03-09T12:00:00.000Z`{{% /influxdb/custom-timestamps-span %}}.
- Use [aggregate](/influxdb3/clustered/reference/sql/functions/aggregate/) or [selector](/influxdb3/clustered/reference/sql/functions/selector/) functions on specified columns.
- In your `GROUP BY` clause:
- Specify the `DATE_BIN(...)` column ordinal reference (`1`).
- Specify other columns (for example, `room`) that are specified in the `SELECT` clause and aren't used in a selector function.
```sql
SELECT
DATE_BIN(INTERVAL '2 hours', time, '1970-01-01T00:00:00Z'::TIMESTAMP) AS time
...
GROUP BY 1, room
...
```
To reference the `DATE_BIN(...)` result column by _name_ in the `GROUP BY` clause, assign an alias other than "time" in the `SELECT` clause--for example:
```sql
SELECT
DATE_BIN(INTERVAL '2 hours', time, '1970-01-01T00:00:00Z'::TIMESTAMP) AS _time
FROM home
...
GROUP BY _time, room
```
- Include an `ORDER BY` clause with columns to sort by.
The following example retrieves unique combinations of time intervals and rooms with their minimum, maximum, and average temperatures.
```sql
SELECT
DATE_BIN(INTERVAL '2 hours', time, '1970-01-01T00:00:00Z'::TIMESTAMP) AS time,
room,
selector_max(temp, time)['value'] AS 'max temp',
selector_min(temp, time)['value'] AS 'min temp',
avg(temp) AS 'average temp'
FROM home
GROUP BY 1, room
ORDER BY room, 1
```
{{< expand-wrapper >}}
{{% expand "View example results" %}}
{{% influxdb/custom-timestamps %}}
| time | room | max temp | min temp | average temp |
| :------------------- | :---------- | -------: | -------: | -----------------: |
| 2022-01-01T08:00:00Z | Kitchen | 23 | 21 | 22 |
| 2022-01-01T10:00:00Z | Kitchen | 22.7 | 22.4 | 22.549999999999997 |
| 2022-01-01T12:00:00Z | Kitchen | 22.8 | 22.5 | 22.65 |
| 2022-01-01T14:00:00Z | Kitchen | 22.8 | 22.7 | 22.75 |
| 2022-01-01T16:00:00Z | Kitchen | 22.7 | 22.4 | 22.549999999999997 |
| 2022-01-01T18:00:00Z | Kitchen | 23.3 | 23.1 | 23.200000000000003 |
| 2022-01-01T20:00:00Z | Kitchen | 22.7 | 22.7 | 22.7 |
| 2022-01-01T08:00:00Z | Living Room | 21.4 | 21.1 | 21.25 |
| 2022-01-01T10:00:00Z | Living Room | 22.2 | 21.8 | 22 |
| 2022-01-01T12:00:00Z | Living Room | 22.4 | 22.2 | 22.299999999999997 |
| 2022-01-01T14:00:00Z | Living Room | 22.3 | 22.3 | 22.3 |
| 2022-01-01T16:00:00Z | Living Room | 22.6 | 22.4 | 22.5 |
| 2022-01-01T18:00:00Z | Living Room | 22.8 | 22.5 | 22.65 |
| 2022-01-01T20:00:00Z | Living Room | 22.2 | 22.2 | 22.2 |
{{% /influxdb/custom-timestamps %}}
{{% /expand %}}
{{< /expand-wrapper >}}
{{% note %}}
#### GROUP BY time
In the `GROUP BY` clause, the name "time" always refers to the `time` column in the source table.
If you want to reference a calculated time column by name, use an alias different from "time"--for example:
```sql
SELECT
DATE_BIN(INTERVAL '2 hours', time, '1970-01-01T00:00:00Z'::TIMESTAMP)
AS _time,
room,
selector_max(temp, time)['value'] AS 'max temp',
selector_min(temp, time)['value'] AS 'min temp',
avg(temp) AS 'average temp'
FROM home
GROUP BY _time, room
ORDER BY room, _time
```
{{% /note %}}
### Query rows based on aggregate values
To query data based on values after an aggregate operation, include a `HAVING`
clause with defined predicate conditions such as a value threshold.
Predicates in the `WHERE` clause are applied _before_ data is aggregated.
Predicates in the `HAVING` clause are applied _after_ data is aggregated.
```sql
SELECT
room,
avg(co) AS 'average co'
FROM home
GROUP BY room
HAVING "average co" > 5
```
{{< expand-wrapper >}}
{{% expand "View example results" %}}
| room | average co |
| :------ | -----------------: |
| Kitchen | 6.6923076923076925 |
{{% /expand %}}
{{< /expand-wrapper >}}
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