Documentation

Operate on columns

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

API token hashing is enabled by default in InfluxDB OSS 2.9.0

Stronger token security: tokens are stored as hashes on disk, so a copy of the database file doesn’t expose usable tokens. Existing tokens are hashed on first startup and the original strings can’t be recovered afterward — capture any plaintext tokens you still need before you upgrade.

For more information, see Token hashing.

Use the following common queries to operate on columns:

These examples use NOAA water sample data.

Find and count unique values in a column

Find and count the number of unique values in a specified column. The following examples find and count unique locations where data was collected.

Find unique values

This query:

  • Uses group() to ungroup data and return results in a single table.
  • Uses keep() and unique() to return unique values in the specified column.
from(bucket: "noaa")
    |> range(start: -30d)
    |> group()
    |> keep(columns: ["location"])
    |> unique(column: "location")

Example results

location
coyote_creek
santa_monica

Count unique values

This query:

  • Uses group() to ungroup data and return results in a single table.
  • Uses keep(), unique(), and then count() to count the number of unique values.
from(bucket: "noaa")
    |> group()
    |> unique(column: "location")
    |> count(column: "location")

Example results

location
2

Recalculate the _values column

To recalculate the _value column, use the with operator in map() to overwrite the existing _value column.

The following query:

  • Uses filter() to filter the average_temperature measurement.
  • Uses map() to convert Fahrenheit temperature values into Celsius.

from(bucket: "noaa")
    |> filter(fn: (r) => r._measurement == "average_temperature")
    |> range(start: -30d)
    |> map(fn: (r) => ({r with _value: (float(v: r._value) - 32.0) * 5.0 / 9.0} ))
_field_measurement_start_stop_timelocation_value
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:00:00Zcoyote_creek27.77777777777778
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:06:00Zcoyote_creek22.77777777777778
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:12:00Zcoyote_creek30
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:18:00Zcoyote_creek31.666666666666668
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:24:00Zcoyote_creek25
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:30:00Zcoyote_creek21.11111111111111
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:36:00Zcoyote_creek28.88888888888889
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:42:00Zcoyote_creek24.444444444444443
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:48:00Zcoyote_creek29.444444444444443
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T00:54:00Zcoyote_creek26.666666666666668
degreesaverage_temperature1920-03-05T22:10:01Z2020-03-05T22:10:01Z2019-08-17T01:00:00Zcoyote_creek21.11111111111111
•••••••••••••••••••••

Calculate a new column

To use values in a row to calculate and add a new column, use map(). This example below converts temperature from Fahrenheit to Celsius and maps the Celsius value to a new celsius column.

The following query:

  • Uses filter() to filter the average_temperature measurement.
  • Uses map() to create a new column calculated from existing values in each row.
from(bucket: "noaa")
    |> filter(fn: (r) => r._measurement == "average_temperature")
    |> range(start: -30d)
    |> map(fn: (r) => ({r with celsius: (r._value - 32.0) * 5.0 / 9.0}))

Example results

_start_stop_field_measurementlocation_time_valuecelsius
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:00:00Z8227.78
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:06:00Z7322.78
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:12:00Z8630.00
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:18:00Z8931.67
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:24:00Z7725.00
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:30:00Z7021.11
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:36:00Z8428.89
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:42:00Z7624.44
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:48:00Z8529.44
1920-03-05T22:10:01Z2020-03-05T22:10:01Zdegreesaverage_temperaturecoyote_creek2019-08-17T00:54:00Z8026.67
••••••••••••••••••••••••

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InfluxDB OSS 2.9.0: API tokens are hashed by default

Stronger token security in InfluxDB OSS 2.9.0 — tokens are hashed on disk by default. Existing tokens are hashed on first startup and can’t be recovered afterward. Capture any plaintext tokens you still need before you upgrade.

View InfluxDB OSS 2.9.0 release notes

Hashed tokens authenticate exactly like unhashed tokens — clients and integrations keep working.

Also new in 2.9.0:

  • Configurable backup compression
  • Restore support for backups containing hashed tokens
  • Tighter Edge Data Replication queue validation
  • Flux upgrade
  • Compaction reliability improvements

Key enhancements in Explorer 1.8

Explorer 1.8 is now available with streaming data subscriptions (beta), line protocol preview, and query history & saved queries.

View Explorer 1.8 release notes

Explorer 1.8 includes new features and improvements that make it easier to ingest, explore, and manage data.

Highlights:

  • Streaming data subscriptions (beta): Stream data into Explorer from MQTT, Kafka, and AMQP sources.
  • Line protocol preview: Preview line protocol, schema, and parse errors before data is written.
  • Custom sample data: Generate custom sample datasets with line protocol and schema preview.
  • Query history and saved queries: Browse query history and save/re-run named queries.
  • Retention period management: Set, update, or clear retention periods on databases and tables.

For more details, see Explorer 1.8 release notes

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:

Telegraf Enterprise now in public beta

Get early access to the Telegraf Controller and provide feedback to help shape the future of Telegraf Enterprise.

See the Blog Post

The upcoming Telegraf Enterprise offering is for organizations running Telegraf at scale and is comprised of two key components:

  • Telegraf Controller: A control plane (UI + API) that centralizes Telegraf configuration management and agent health visibility.
  • Telegraf Enterprise Support: Official support for Telegraf Controller and Telegraf plugins.

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

For more information:

Telegraf Controller v0.0.7-beta now available

Telegraf Controller v0.0.7-beta is now available with new features, improvements, bug fixes, and an important breaking change.

View the release notes
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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