Documentation

Work with arrays

An array type is an ordered sequence of values of the same type.

Array syntax

An array literal contains a sequence of values (also known as elements) enclosed in square brackets ([]). Values are comma-separated and must be the same type.

Example arrays
["1st", "2nd", "3rd"]

[1.23, 4.56, 7.89]

[10, 25, -15]

Reference values in an array

Use bracket notation to reference reference a value in an array. Flux arrays use zero-based indexing. Provide the index of the value to reference.

arr = ["1st", "2nd", "3rd"]

arr[0]
// Returns 1st

arr[2]
// Returns 3rd

Operate on arrays

Iterate over an array

  1. Import the experimental/array package.
  2. Use array.map to iterate over elements in an array, apply a function to each element, and then return a new array.
import "experimental/array"

a = [
    {fname: "John", lname: "Doe", age: 42},
    {fname: "Jane", lname: "Doe", age: 40},
    {fname: "Jacob", lname: "Dozer", age: 21},
]

a |> array.map(fn: (x) => ({statement: "${x.fname} ${x.lname} is ${x.age} years old."}))

// Returns
// [
//     {statement: "John Doe is 42 years old."},
//     {statement: "Jane Doe is 40 years old."},
//     {statement: "Jacob Dozer is 21 years old."}
// ]

Check if a value exists in an array

Use the contains function to check if a value exists in an array.

names = ["John", "Jane", "Joe", "Sam"]

contains(value: "Joe", set: names)
// Returns true

Get the length of an array

Use the length function to get the length of an array (number of elements in the array).

names = ["John", "Jane", "Joe", "Sam"]

length(arr: names)
// Returns 4

Create a stream of tables from an array

  1. Import the array package.
  2. Use array.from() to return a stream of tables. The input array must be an array of records. Each key-value pair in the record represents a column and its value.
import "array"

arr = [
    {fname: "John", lname: "Doe", age: "37"},
    {fname: "Jane", lname: "Doe", age: "32"},
    {fname: "Jack", lname: "Smith", age: "56"},
]

array.from(rows: arr)
Output
fnamelnameage
JohnDoe37
JaneDoe32
JackSmith56

Compare arrays

Use the == comparison operator to check if two arrays are equal. Equality is based on values, their type, and order.

[1,2,3,4] == [1,3,2,4]
// Returns false

[12300.0, 34500.0] == [float(v: "1.23e+04"), float(v: "3.45e+04")]
// Returns true

Filter an array

  1. Import the experimental/array package.
  2. Use array.filter to iterate over and evaluate elements in an array with a predicate function and then return a new array with only elements that match the predicate.
import "experimental/array"

a = [1, 2, 3, 4, 5]

a |> array.filter(fn: (x) => x >= 3)
// Returns [3, 4, 5]

Merge two arrays

  1. Import the experimental/array package.
  2. Use array.concat to merge two arrays.
import "experimental/array"

a = [1, 2, 3]
b = [4, 5, 6]

a |> array.concat(v: b)
// Returns [1, 2, 3, 4, 5, 6]

Return the string representation of an array

Use display() to return Flux literal representation of an array as a string.

arr = [1, 2, 3]

display(v: arr)

// Returns "[1, 2, 3]"

Include the string representation of an array in a table

Use display() to return Flux literal representation of an array as a string and include it as a column value.

import "sampledata"

sampledata.string()
    |> map(fn: (r) => ({_time: r._time, exampleArray: display(v: [r.tag, r._value])}))

Output

_time (time)exampleArray (string)
2021-01-01T00:00:00Z[t1, smpl_g9qczs]
2021-01-01T00:00:10Z[t1, smpl_0mgv9n]
2021-01-01T00:00:20Z[t1, smpl_phw664]
2021-01-01T00:00:30Z[t1, smpl_guvzy4]
2021-01-01T00:00:40Z[t1, smpl_5v3cce]
2021-01-01T00:00:50Z[t1, smpl_s9fmgy]
2021-01-01T00:00:00Z[t2, smpl_b5eida]
2021-01-01T00:00:10Z[t2, smpl_eu4oxp]
2021-01-01T00:00:20Z[t2, smpl_5g7tz4]
2021-01-01T00:00:30Z[t2, smpl_sox1ut]
2021-01-01T00:00:40Z[t2, smpl_wfm757]
2021-01-01T00:00:50Z[t2, smpl_dtn2bv]

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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 1.9

Explorer 1.9 is now available with InfluxQL support, an AI-assisted Flux to SQL converter (beta), and new live sample data simulators.

View Explorer 1.9 release notes

Explorer 1.9 includes new features and improvements that make it easier to query, visualize, and manage data.

Highlights:

  • Flux to SQL converter (beta): Convert Flux queries to SQL with an AI-assisted converter.
  • InfluxQL support: Query data with InfluxQL in the Data Explorer and dashboards, and save and load InfluxQL queries.
  • InfluxQL visualizations: Render line and bar charts from InfluxQL results with per-tag series grouping.
  • Query error history: Review a history of query errors in the query tool.
  • Live sample data simulators: Generate continuous live sample data with new bird data and signal generator simulators.

For more details, see Explorer 1.9 release notes

InfluxDB 3.10 is now available

InfluxDB 3 Core 3.10 adds an automatic catalog format upgrade, a configurable query-concurrency limit, and processing engine improvements.

Key updates in InfluxDB 3 Core 3.10:

  • Catalog format upgrade: the on-disk catalog automatically upgrades from format v2 to v3 on first 3.10 startup. Migration is one-way—back up your catalog before upgrading.
  • --max-concurrent-queries: limit concurrent queries (adjustable at runtime).
  • GET /ready endpoint for readiness probes.
  • Processing engine: cross-database queries and trigger lockdown flags.

For more information, see the InfluxDB 3 Core release notes.

InfluxDB 3.10 is now available

InfluxDB 3 Enterprise 3.10 adds automated backup and restore, row-level deletions, and user management, with an automatic catalog format upgrade and performance preview improvements.

Key updates in InfluxDB 3 Enterprise 3.10:

  • Catalog format upgrade: the on-disk catalog automatically upgrades from format v2 to v3 on first 3.10 startup. Migration is one-way—back up your catalog before upgrading.
  • Automated backup and restore (beta)
  • Row-level deletions
  • User management (authentication and RBAC) — preview
  • Performance preview improvements

Backup and restore, row-level deletions, and the performance preview require the Enterprise storage engine upgrade (opt-in beta). Beta and preview features are subject to breaking changes and aren’t recommended for production use.

For more information, see the InfluxDB 3 Enterprise release notes

Telegraf Enterprise is now generally available

Telegraf Enterprise is now generally available, along with Telegraf Controller v1.0.

Telegraf Enterprise combines Telegraf Controller, a centralized management console for Telegraf, with official support from InfluxData. Manage configurations, monitor fleet health, and operate tens of thousands of Telegraf agents from a single system.

InfluxDB Docker latest tag changing to InfluxDB 3 Core

On September 15, 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