Documentation

Use multiple fields in a calculation

This page documents an earlier version of InfluxDB OSS. InfluxDB 3 Core is the latest stable version.

To use values from multiple fields in a mathematic calculation, complete the following steps:

  1. Filter by fields required in your calculation
  2. Pivot fields into columns
  3. Perform the mathematic calculation

Filter by fields

Use filter() to return only the fields necessary for your calculation. Use the or logical operator to filter by multiple fields.

The following example queries two fields, A and B:

from(bucket: "example-bucket")
    |> range(start: -1m)
    |> filter(fn: (r) => r._field == "A" or r._field == "B")

This query returns one or more tables for each field. For example:

_time_field_value
2021-01-01T00:00:00ZA12.4
2021-01-01T00:00:15ZA12.2
2021-01-01T00:00:30ZA11.6
2021-01-01T00:00:45ZA11.9
_time_field_value
2021-01-01T00:00:00ZB3.1
2021-01-01T00:00:15ZB4.8
2021-01-01T00:00:30ZB2.2
2021-01-01T00:00:45ZB3.3

Pivot fields into columns

Use pivot() to align multiple fields by time.

To correctly pivot on _time, points for each field must have identical timestamps. If timestamps are irregular or do not align perfectly, see Normalize irregular timestamps.

// ...
  |> pivot(rowKey: ["_time"], columnKey: ["_field"], valueColumn: "_value")

Using the queried data above, this pivot() function returns:

_timeAB
2021-01-01T00:00:00Z12.43.1
2021-01-01T00:00:15Z12.24.8
2021-01-01T00:00:30Z11.62.2
2021-01-01T00:00:45Z11.93.3

Perform the calculation

Use map() to perform the mathematic operation using column values as operands.

The following example uses values in the A and B columns to calculate a new _value column:

// ...
    |> map(fn: (r) => ({ r with _value: r.A * r.B }))

Using the pivoted data above, this map() function returns:

_timeAB_value
2021-01-01T00:00:00Z12.43.138.44
2021-01-01T00:00:15Z12.24.858.56
2021-01-01T00:00:30Z11.62.225.52
2021-01-01T00:00:45Z11.93.339.27

Full example query

from(bucket: "example-bucket")
    |> range(start: -1m)
    |> filter(fn: (r) => r._field == "A" or r._field == "B")
    |> pivot(rowKey: ["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> map(fn: (r) => ({r with _value: r.A * r.B}))

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InfluxDB 3.9: Performance upgrade preview

InfluxDB 3 Enterprise 3.9 includes a beta of major performance upgrades with faster single-series queries, wide-and-sparse table support, and more.

InfluxDB 3 Enterprise 3.9 includes a beta of major performance and feature updates.

Key improvements:

  • Faster single-series queries
  • Consistent resource usage
  • Wide-and-sparse table support
  • Automatic distinct value caches for reduced latency with metadata queries

Preview features are subject to breaking changes.

For more information, see:

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Join the Telegraf Enterprise beta to get early access to the Telegraf Controller and provide feedback to help shape the future of Telegraf Enterprise.

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InfluxDB Docker latest tag changing to InfluxDB 3 Core

On May 27, 2026, the latest tag for InfluxDB Docker images will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments.

If using Docker to install and run InfluxDB, the latest tag will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments. For example, if using Docker to run InfluxDB v2, replace the latest version tag with a specific version tag in your Docker pull command–for example:

docker pull influxdb:2