Get started with Flux

Flux is InfluxData’s functional data scripting language designed for querying, analyzing, and acting on data.

This multi-part getting started guide walks through important concepts related to Flux. It covers querying time series data from InfluxDB using Flux, and introduces Flux syntax and functions.

Flux design principles

Flux is designed to be usable, readable, flexible, composable, testable, contributable, and shareable. Its syntax is largely inspired by 2018’s most popular scripting language, Javascript, and takes a functional approach to data exploration and processing.

The following example illustrates querying data stored from the last hour, filtering by the cpu measurement and the cpu=cpu-total tag, windowing the data in 1 minute intervals, and calculating the average of each window:

  1. from(bucket:"example-bucket")
  2.   |> range(start:-1h)
  3.   |> filter(fn:(r) =>
  4.     r._measurement == "cpu" and
  5.     r.cpu == "cpu-total"
  6.   )
  7.   |> aggregateWindow(every: 1m, fn: mean)

Key concepts

Flux introduces important new concepts you should understand as you get started.

Pipe-forward operator

Flux uses pipe-forward operators (|>) extensively to chain operations together. After each function or operation, Flux returns a table or collection of tables containing data. The pipe-forward operator pipes those tables into the next function or operation where they are further processed or manipulated. This makes it easy to chain together functions to build sophisticated queries.

Tables

Flux structures all data in tables. When data is streamed from data sources, Flux formats it as annotated comma-separated values (CSV), representing tables. Functions then manipulate or process them and output new tables.

Group keys

Every table has a group key which describes the contents of the table. It’s a list of columns for which every row in the table will have the same value. Columns with unique values in each row are not part of the group key.

As functions process and transform data, each modifies the group keys of output tables. Understanding how tables and group keys are modified by functions is key to properly shaping your data for the desired output.

Example group key
  1. Group key: [_start, _stop, _field]
  2.                    _start:time                      _stop:time           _field:string                      _time:time                  _value:float
  3. ------------------------------  ------------------------------  ----------------------  ------------------------------  ----------------------------
  4. 2019-04-25T17:33:55.196959000Z  2019-04-25T17:34:55.196959000Z            used_percent  2019-04-25T17:33:56.000000000Z             65.55318832397461
  5. 2019-04-25T17:33:55.196959000Z  2019-04-25T17:34:55.196959000Z            used_percent  2019-04-25T17:34:06.000000000Z             65.52391052246094
  6. 2019-04-25T17:33:55.196959000Z  2019-04-25T17:34:55.196959000Z            used_percent  2019-04-25T17:34:16.000000000Z             65.49603939056396
  7. 2019-04-25T17:33:55.196959000Z  2019-04-25T17:34:55.196959000Z            used_percent  2019-04-25T17:34:26.000000000Z             65.51754474639893
  8. 2019-04-25T17:33:55.196959000Z  2019-04-25T17:34:55.196959000Z            used_percent  2019-04-25T17:34:36.000000000Z              65.536737442016

Note that _time and _value are excluded from the example group key because they are unique to each row.

Tools for working with Flux

The Execute queries guide walks through the different tools available for querying InfluxDB with Flux.

Introduction to Flux Query InfluxDB with Flux

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