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title seotitle list_title description menu weight metadata related
Get started processing data Process data | Get started with InfluxDB Process data Learn how to process time series data to do things like downsample and alert on data.
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Process data Get started get-started-process-data
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/influxdb/v2/process-data/get-started/
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/flux/v0/stdlib/

Now that you know the basics of querying data from InfluxDB, let's go beyond a basic query and begin to process the queried data. "Processing" data could mean transforming, aggregating, downsampling, or alerting on data. This tutorial covers the following data processing use cases:

{{% note %}} Most data processing operations require manually editing Flux queries. If you're using the InfluxDB Data Explorer, switch to the Script Editor instead of using the Query Builder. {{% /note %}}

Remap or assign values in your data

Use the map() function to iterate over each row in your data and update the values in that row. map() is one of the most useful functions in Flux and will help you accomplish many of they data processing operations you need to perform.

{{< expand-wrapper >}} {{% expand "Learn more about how map() works" %}}

map() takes a single parameter, fn. fn takes an anonymous function that reads each row as a record named r. In the r record, each key-value pair represents a column and its value. For example:

r = {
    _time: 2020-01-01T00:00:00Z,
    _measurement: "home",
    room: "Kitchen",
    _field: "temp",
    _value: 21.0,
}
_time _measurement room _field _value
2020-01-01T00:00:00Z home Kitchen temp 21.0

The fn function modifies the r record in any way you need and returns a new record for the row. For example, using the record above:

(r) => ({ _time: r._time, _field: "temp_F", _value: (r._value * 1.8) + 32.0})

// Returns: {_time: 2020-01-01T00:00:00Z, _field: "temp_F", _value: 69.8}
_time _field _value
2020-01-01T00:00:00Z temp_F 69.8

Notice that some of the columns were dropped from the original row record. This is because the fn function explicitly mapped the _time, _field, and _value columns. To retain existing columns and only update or add specific columns, use the with operator to extend your row record. For example, using the record above:

(r) => ({r with _value: (r._value * 1.8) + 32.0, degrees: "F"})

// Returns:
// {
//     _time: 2020-01-01T00:00:00Z,
//     _measurement: "home",
//     room: "Kitchen",
//     _field: "temp",
//     _value: 69.8,
//     degrees: "F",
// }
_time _measurement room _field _value degrees
2020-01-01T00:00:00Z home Kitchen temp 69.8 F

{{% /expand %}} {{< /expand-wrapper >}}

from(bucket: "get-started")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "hum")
    |> map(fn: (r) => ({r with _value: r._value / 100.0}))

Map examples

{{< expand-wrapper >}}

{{% expand "Perform mathematical operations" %}}

map() lets your perform mathematical operations on your data. For example, using the data written in "Get started writing to InfluxDB":

  1. Query the temp field to return room temperatures in °C.
  2. Use map() to iterate over each row and convert the °C temperatures in the _value column to °F using the equation: °F = (°C * 1.8) + 32.0.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> map(fn: (r) => ({r with _value: (r._value * 1.8) + 32.0}))

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 22.3
2022-01-01T15:00:00Z home Living Room temp 22.3
2022-01-01T16:00:00Z home Living Room temp 22.4
2022-01-01T17:00:00Z home Living Room temp 22.6
2022-01-01T18:00:00Z home Living Room temp 22.8
2022-01-01T19:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 73.03999999999999
2022-01-01T15:00:00Z home Kitchen temp 72.86
2022-01-01T16:00:00Z home Kitchen temp 72.32
2022-01-01T17:00:00Z home Kitchen temp 72.86
2022-01-01T18:00:00Z home Kitchen temp 73.94
2022-01-01T19:00:00Z home Kitchen temp 73.58000000000001
2022-01-01T20:00:00Z home Kitchen temp 72.86
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 72.14
2022-01-01T15:00:00Z home Living Room temp 72.14
2022-01-01T16:00:00Z home Living Room temp 72.32
2022-01-01T17:00:00Z home Living Room temp 72.68
2022-01-01T18:00:00Z home Living Room temp 73.03999999999999
2022-01-01T19:00:00Z home Living Room temp 72.5
2022-01-01T20:00:00Z home Living Room temp 71.96000000000001

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{% expand "Conditionally assign a state" %}}

Within a map() function, you can use conditional expressions (if/then/else) to conditionally assign values. For example, using the data written in "Get started writing to InfluxDB":

  1. Query the co field to return carbon monoxide parts per million (ppm) readings in each room.

  2. Use map() to iterate over each row, evaluate the value in the _value column, and then conditionally assign a state:

    • If the carbon monoxide is less than 10 ppm, assign the state: ok.
    • Otherwise, assign the state: warning.

    Store the state in a state column.

from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co")
    |> map(fn: (r) => ({r with state: if r._value < 10 then "ok" else "warning"}))

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen co 1
2022-01-01T15:00:00Z home Kitchen co 3
2022-01-01T16:00:00Z home Kitchen co 7
2022-01-01T17:00:00Z home Kitchen co 9
2022-01-01T18:00:00Z home Kitchen co 18
2022-01-01T19:00:00Z home Kitchen co 22
2022-01-01T20:00:00Z home Kitchen co 26
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room co 1
2022-01-01T15:00:00Z home Living Room co 1
2022-01-01T16:00:00Z home Living Room co 4
2022-01-01T17:00:00Z home Living Room co 5
2022-01-01T18:00:00Z home Living Room co 9
2022-01-01T19:00:00Z home Living Room co 14
2022-01-01T20:00:00Z home Living Room co 17

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value state
2022-01-01T14:00:00Z home Kitchen co 1 ok
2022-01-01T15:00:00Z home Kitchen co 3 ok
2022-01-01T16:00:00Z home Kitchen co 7 ok
2022-01-01T17:00:00Z home Kitchen co 9 ok
2022-01-01T18:00:00Z home Kitchen co 18 warning
2022-01-01T19:00:00Z home Kitchen co 22 warning
2022-01-01T20:00:00Z home Kitchen co 26 warning
_time _measurement room _field _value state
2022-01-01T14:00:00Z home Living Room co 1 ok
2022-01-01T15:00:00Z home Living Room co 1 ok
2022-01-01T16:00:00Z home Living Room co 4 ok
2022-01-01T17:00:00Z home Living Room co 5 ok
2022-01-01T18:00:00Z home Living Room co 9 ok
2022-01-01T19:00:00Z home Living Room co 14 warning
2022-01-01T20:00:00Z home Living Room co 17 warning

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{% expand "Alert on data" %}}

map() lets you execute more complex operations on a per row basis. Using a Flux block ({}) in the fn function, you can create scoped variables and execute other functions within the context of each row. For example, you can send a message to Slack.

{{% note %}} For this example to actually send messages to Slack, you need to set up a Slack app that can send and receive messages. {{% /note %}}

For example, using the data written in "Get started writing to InfluxDB":

  1. Import the slack package.

  2. Query the co field to return carbon monoxide parts per million (ppm) readings in each room.

  3. Use map() to iterate over each row, evaluate the value in the _value column, and then conditionally assign a state:

    • If the carbon monoxide is less than 10 ppm, assign the state: ok.
    • Otherwise, assign the state: warning.

    Store the state in a state column.

  4. Use filter() to return only rows with warning in the state column.

  5. Use map() to iterate over each row. In your fn function, use a Flux block ({}) to:

    1. Create a responseCode variable that uses slack.message() to send a message to Slack using data from the input row. slack.message() returns the response code of the Slack API request as an integer.
    2. Use a return statement to return a new row record. The new row should extend the input row with a new column, sent, with a boolean value determined by the responseCode variable.

map() sends a message to Slack for each row piped forward into the function.

import "slack"

from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co")
    |> map(fn: (r) => ({r with state: if r._value < 10 then "ok" else "warning"}))
    |> filter(fn: (r) => r.state == "warning")
    |> map(
        fn: (r) => {
            responseCode =
                slack.message(
                    token: "mYSlacK70k3n",
                    color: "#ff0000",
                    channel: "#alerts",
                    text: "Carbon monoxide is at dangerous levels in the ${r.room}: ${r._value} ppm.",
                )

            return {r with sent: responseCode == 200}
        },
    )

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

The following input represents the data filtered by the warning state.

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value state
2022-01-01T18:00:00Z home Kitchen co 18 warning
2022-01-01T19:00:00Z home Kitchen co 22 warning
2022-01-01T20:00:00Z home Kitchen co 26 warning
_time _measurement room _field _value state
2022-01-01T19:00:00Z home Living Room co 14 warning
2022-01-01T20:00:00Z home Living Room co 17 warning

{{% /tab-content %}} {{% tab-content %}}

The output includes a sent column indicating the if the message was sent.

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value state sent
2022-01-01T18:00:00Z home Kitchen co 18 warning true
2022-01-01T19:00:00Z home Kitchen co 22 warning true
2022-01-01T20:00:00Z home Kitchen co 26 warning true
_time _measurement room _field _value state sent
2022-01-01T19:00:00Z home Living Room co 14 warning true
2022-01-01T20:00:00Z home Living Room co 17 warning true

{{% /tab-content %}} {{< /tabs-wrapper >}}

With the results above, you would receive the following messages in Slack:

Carbon monoxide is at dangerous levels in the Kitchen: 18 ppm.
Carbon monoxide is at dangerous levels in the Kitchen: 22 ppm.
Carbon monoxide is at dangerous levels in the Living Room: 14 ppm.
Carbon monoxide is at dangerous levels in the Kitchen: 26 ppm.
Carbon monoxide is at dangerous levels in the Living Room: 17 ppm.

{{% note %}} You can also use the InfluxDB checks and notifications system as a user interface for configuring checks and alerting on data. {{% /note %}}

{{% /expand %}} {{< /expand-wrapper >}}

Group data

Use the group() function to regroup your data by specific column values in preparation for further processing.

from(bucket: "get-started")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> group(columns: ["room", "_field"])

{{% note %}} Understanding data grouping and why it matters is important, but may be too much for this "getting started" tutorial. For more information about how data is grouped and why it matters, see the Flux data model documentation. {{% /note %}}

By default, from() returns data queried from InfluxDB grouped by series (measurement, tags, and field key). Each table in the returned stream of tables represents a group. Each table contains the same values for the columns that data is grouped by. This grouping is important as you aggregate data.

Group examples

{{< expand-wrapper >}} {{% expand "Group data by specific columns" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query the temp and hum fields.
  2. Use group() to group by only the _field column.
from(bucket: "get-started")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T10:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp" or r._field == "hum")
    |> group(columns: ["_field"])

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

The following data is output from the last filter() and piped forward into group():

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

{{% flux/group-key "[_measurement=home, room=Kitchen, _field=hum]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Kitchen hum 35.9
2022-01-01T09:00:00Z home Kitchen hum 36.2
2022-01-01T10:00:00Z home Kitchen hum 36.1

{{% flux/group-key "[_measurement=home, room=Living Room, _field=hum]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Living Room hum 35.9
2022-01-01T09:00:00Z home Living Room hum 35.9
2022-01-01T10:00:00Z home Living Room hum 36

{{% flux/group-key "[_measurement=home, room=Kitchen, _field=temp]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Kitchen temp 21
2022-01-01T09:00:00Z home Kitchen temp 23
2022-01-01T10:00:00Z home Kitchen temp 22.7

{{% flux/group-key "[_measurement=home, room=Living Room, _field=temp]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Living Room temp 21.1
2022-01-01T09:00:00Z home Living Room temp 21.4
2022-01-01T10:00:00Z home Living Room temp 21.8

{{% /tab-content %}} {{% tab-content %}}

When grouped by _field, all rows with the temp field will be in one table and all the rows with the hum field will be in another. _measurement and room columns no longer affect how rows are grouped.

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

{{% flux/group-key "[_field=hum]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Kitchen hum 35.9
2022-01-01T09:00:00Z home Kitchen hum 36.2
2022-01-01T10:00:00Z home Kitchen hum 36.1
2022-01-01T08:00:00Z home Living Room hum 35.9
2022-01-01T09:00:00Z home Living Room hum 35.9
2022-01-01T10:00:00Z home Living Room hum 36

{{% flux/group-key "[_field=temp]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Kitchen temp 21
2022-01-01T09:00:00Z home Kitchen temp 23
2022-01-01T10:00:00Z home Kitchen temp 22.7
2022-01-01T08:00:00Z home Living Room temp 21.1
2022-01-01T09:00:00Z home Living Room temp 21.4
2022-01-01T10:00:00Z home Living Room temp 21.8

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{% expand "Ungroup data" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query the temp and hum fields.
  2. Use group() without any parameters to "ungroup" data or group by no columns. The default value of the columns parameter is an empty array ([]).
from(bucket: "get-started")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T10:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp" or r._field == "hum")
    |> group()

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

The following data is output from the last filter() and piped forward into group():

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

{{% flux/group-key "[_measurement=home, room=Kitchen, _field=hum]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Kitchen hum 35.9
2022-01-01T09:00:00Z home Kitchen hum 36.2
2022-01-01T10:00:00Z home Kitchen hum 36.1

{{% flux/group-key "[_measurement=home, room=Living Room, _field=hum]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Living Room hum 35.9
2022-01-01T09:00:00Z home Living Room hum 35.9
2022-01-01T10:00:00Z home Living Room hum 36

{{% flux/group-key "[_measurement=home, room=Kitchen, _field=temp]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Kitchen temp 21
2022-01-01T09:00:00Z home Kitchen temp 23
2022-01-01T10:00:00Z home Kitchen temp 22.7

{{% flux/group-key "[_measurement=home, room=Living Room, _field=temp]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Living Room temp 21.1
2022-01-01T09:00:00Z home Living Room temp 21.4
2022-01-01T10:00:00Z home Living Room temp 21.8

{{% /tab-content %}} {{% tab-content %}}

When ungrouped, a data is returned in a single table.

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

{{% flux/group-key "[]" true %}}

_time _measurement room _field _value
2022-01-01T08:00:00Z home Kitchen hum 35.9
2022-01-01T09:00:00Z home Kitchen hum 36.2
2022-01-01T10:00:00Z home Kitchen hum 36.1
2022-01-01T08:00:00Z home Kitchen temp 21
2022-01-01T09:00:00Z home Kitchen temp 23
2022-01-01T10:00:00Z home Kitchen temp 22.7
2022-01-01T08:00:00Z home Living Room hum 35.9
2022-01-01T09:00:00Z home Living Room hum 35.9
2022-01-01T10:00:00Z home Living Room hum 36
2022-01-01T08:00:00Z home Living Room temp 21.1
2022-01-01T09:00:00Z home Living Room temp 21.4
2022-01-01T10:00:00Z home Living Room temp 21.8

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}} {{< /expand-wrapper >}}

Aggregate or select specific data

Use Flux aggregate or selector functions to return aggregate or selected values from each input table.

from(bucket: "get-started")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co" or r._field == "hum" or r._field == "temp")
    |> mean()

{{% note %}}

Aggregate over time

If you want to query aggregate values over time, this is a form of downsampling. {{% /note %}}

Aggregate functions

Aggregate functions drop columns that are not in the group key and return a single row for each input table with the aggregate value of that table.

Aggregate examples

{{< expand-wrapper >}}

{{% expand "Calculate the average temperature for each room" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query the temp field. By default, from() returns the data grouped by _measurement, room and _field, so each table represents a room.
  2. Use mean() to return the average temperature from each room.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> mean()

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 22.3
2022-01-01T15:00:00Z home Living Room temp 22.3
2022-01-01T16:00:00Z home Living Room temp 22.4
2022-01-01T17:00:00Z home Living Room temp 22.6
2022-01-01T18:00:00Z home Living Room temp 22.8
2022-01-01T19:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_measurement room _field _value
home Kitchen temp 22.814285714285713
_measurement room _field _value
home Living Room temp 22.44285714285714

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{% expand "Calculate the overall average temperature of all rooms" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query the temp field.
  2. Use group() to ungroup the data into a single table. By default, from() returns the data grouped by_measurement, room and _field. To get the overall average, you need to structure all results as a single table.
  3. Use mean() to return the average temperature.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> group()
    |> mean()

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

The following input data represents the ungrouped data that is piped forward into mean().

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7
2022-01-01T14:00:00Z home Living Room temp 22.3
2022-01-01T15:00:00Z home Living Room temp 22.3
2022-01-01T16:00:00Z home Living Room temp 22.4
2022-01-01T17:00:00Z home Living Room temp 22.6
2022-01-01T18:00:00Z home Living Room temp 22.8
2022-01-01T19:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_value
22.628571428571426

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{% expand "Count the number of points reported per room across all fields" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query all fields by simply filtering by the home measurement.
  2. The fields in the home measurement are different types. Use toFloat() to cast all field values to floats.
  3. Use group() to group the data by room.
  4. Use count() to return the number of rows in each input table.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> toFloat()
    |> group(columns: ["room"])
    |> count()
Output

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

room _value
Kitchen 21
room _value
Living Room 21

{{% /expand %}}

{{< /expand-wrapper >}}

{{% note %}}

Assign a new aggregate timestamp

_time is generally not part of the group key and will be dropped when using aggregate functions. To assign a new timestamp to aggregate points, duplicate the _start or _stop column, which represent the query bounds, as the new _time column.

from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> mean()
    |> duplicate(column: "_stop", as: "_time")

{{% /note %}}

Selector functions

Selector functions return one or more columns from each input table and retain all columns and their values.

Selector examples

{{< expand-wrapper >}}

{{% expand "Return the first temperature from each room" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query the temp field.
  2. Use first() to return the first row from each table.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> first()

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 22.3
2022-01-01T15:00:00Z home Living Room temp 22.3
2022-01-01T16:00:00Z home Living Room temp 22.4
2022-01-01T17:00:00Z home Living Room temp 22.6
2022-01-01T18:00:00Z home Living Room temp 22.8
2022-01-01T19:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 22.3

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{% expand "Return the last temperature from each room" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query the temp field.
  2. Use last() to return the last row from each table.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> last()

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 22.3
2022-01-01T15:00:00Z home Living Room temp 22.3
2022-01-01T16:00:00Z home Living Room temp 22.4
2022-01-01T17:00:00Z home Living Room temp 22.6
2022-01-01T18:00:00Z home Living Room temp 22.8
2022-01-01T19:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T20:00:00Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{% expand "Return the maximum temperature from each room" %}}

Using the data written in "Get started writing to InfluxDB":

  1. Query the temp field.
  2. Use max() to return the row with the highest value in the _value column from each table.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> max()

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 22.3
2022-01-01T15:00:00Z home Living Room temp 22.3
2022-01-01T16:00:00Z home Living Room temp 22.4
2022-01-01T17:00:00Z home Living Room temp 22.6
2022-01-01T18:00:00Z home Living Room temp 22.8
2022-01-01T19:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
_time _measurement room _field _value
2022-01-01T18:00:00Z home Living Room temp 22.8

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}}

{{< /expand-wrapper >}}

Pivot data into a relational schema

If coming from relational SQL or SQL-like query languages, such as InfluxQL, the data model that Flux uses is different than what you're used to. Flux returns multiple tables where each table contains a different field. A "relational" schema structures each field as a column in each row.

Use the pivot() function to pivot data into a "relational" schema based on timestamps.

from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co" or r._field == "hum" or r._field == "temp")
    |> filter(fn: (r) => r.room == "Kitchen")
    |> pivot(rowKey: ["_time"], columnKey: ["_field"], valueColumn: "_value")

{{< expand-wrapper >}} {{% expand "View input and pivoted output" %}}

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen co 1
2022-01-01T15:00:00Z home Kitchen co 3
2022-01-01T16:00:00Z home Kitchen co 7
2022-01-01T17:00:00Z home Kitchen co 9
2022-01-01T18:00:00Z home Kitchen co 18
2022-01-01T19:00:00Z home Kitchen co 22
2022-01-01T20:00:00Z home Kitchen co 26
_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen hum 36.3
2022-01-01T15:00:00Z home Kitchen hum 36.2
2022-01-01T16:00:00Z home Kitchen hum 36
2022-01-01T17:00:00Z home Kitchen hum 36
2022-01-01T18:00:00Z home Kitchen hum 36.9
2022-01-01T19:00:00Z home Kitchen hum 36.6
2022-01-01T20:00:00Z home Kitchen hum 36.5
_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room co hum temp
2022-01-01T14:00:00Z home Kitchen 1 36.3 22.8
2022-01-01T15:00:00Z home Kitchen 3 36.2 22.7
2022-01-01T16:00:00Z home Kitchen 7 36 22.4
2022-01-01T17:00:00Z home Kitchen 9 36 22.7
2022-01-01T18:00:00Z home Kitchen 18 36.9 23.3
2022-01-01T19:00:00Z home Kitchen 22 36.6 23.1
2022-01-01T20:00:00Z home Kitchen 26 36.5 22.7

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}} {{< /expand-wrapper >}}

Downsample data

Downsampling data is a strategy that improve performance at query time and also optimizes long-term data storage. Simply put, downsampling reduces the number of points returned by a query without losing the general trends in the data.

For more information about downsampling data, see Downsample data.

The most common way to downsample data is by time intervals or "windows." For example, you may want to query the last hour of data and return the average value for every five minute window.

Use aggregateWindow() to downsample data by specified time intervals:

  • Use the every parameter to specify the duration of each window.
  • Use the fn parameter to specify what aggregate or selector function to apply to each window.
  • (Optional) Use the timeSrc parameter to specify which column value to use to create the new aggregate timestamp for each window. The default is _stop.
from(bucket: "get-started")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> aggregateWindow(every: 2h, fn: mean)

{{< expand-wrapper >}} {{% expand "View input and downsampled output" %}}

{{< tabs-wrapper >}} {{% tabs "small" %}} Input Output Click to view output {{% /tabs %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T14:00:00Z home Kitchen temp 22.8
2022-01-01T15:00:00Z home Kitchen temp 22.7
2022-01-01T16:00:00Z home Kitchen temp 22.4
2022-01-01T17:00:00Z home Kitchen temp 22.7
2022-01-01T18:00:00Z home Kitchen temp 23.3
2022-01-01T19:00:00Z home Kitchen temp 23.1
2022-01-01T20:00:00Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T14:00:00Z home Living Room temp 22.3
2022-01-01T15:00:00Z home Living Room temp 22.3
2022-01-01T16:00:00Z home Living Room temp 22.4
2022-01-01T17:00:00Z home Living Room temp 22.6
2022-01-01T18:00:00Z home Living Room temp 22.8
2022-01-01T19:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.2

{{% /tab-content %}} {{% tab-content %}}

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

_time _measurement room _field _value
2022-01-01T16:00:00Z home Kitchen temp 22.75
2022-01-01T18:00:00Z home Kitchen temp 22.549999999999997
2022-01-01T20:00:00Z home Kitchen temp 23.200000000000003
2022-01-01T20:00:01Z home Kitchen temp 22.7
_time _measurement room _field _value
2022-01-01T16:00:00Z home Living Room temp 22.3
2022-01-01T18:00:00Z home Living Room temp 22.5
2022-01-01T20:00:00Z home Living Room temp 22.65
2022-01-01T20:00:01Z home Living Room temp 22.2

{{% /tab-content %}} {{< /tabs-wrapper >}}

{{% /expand %}} {{< /expand-wrapper >}}

Automate processing with InfluxDB tasks

InfluxDB tasks are scheduled queries that can perform any of the data processing operations described above. Generally tasks then use the to() function to write the processed result back to InfluxDB.

For more information about creating and configuring tasks, see Get started with InfluxDB tasks.

Example downsampling task

option task = {
    name: "Example task"
    every: 1d,
}

from(bucket: "get-started-downsampled")
    |> range(start: -task.every)
    |> filter(fn: (r) => r._measurement == "home")
    |> aggregateWindow(every: 2h, fn: mean)

{{< page-nav prev="/influxdb/v2/get-started/query/" next="/influxdb/v2/get-started/visualize/" keepTab=true >}}