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]

Was this page helpful?

Thank you for your feedback!


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
Download Telegraf Controller v0.0.7-beta

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