Flux is a lightweight scripting language for querying databases (like InfluxDB) and working with data. It's part of InfluxDB 1.7 and 2.0, but can be run independently of those. This repo represents the language definition and an implementation of the language core.
NOTE: We plan to provide a
fluxcommand line program that exposes a REPL and talks to various data sources. In the meantime see theinfluxcommand in this repo as it has working Flux installation against the 2.0 InfluxDB database.
A complete specification can be found in SPEC.md. The specification contains many examples to start learning Flux.
Currently Flux is only avaliable via InfluxDB, see http://docs.influxdata.com/flux/ for getting started.
Here are a few examples of the language to get an idea of the syntax.
// This line is a comment
// Support for traditional math operators
1 + 1
// Several data types are built-in
true // a boolean true value
1 // an int
1.0 // a float
"this is a string" // a string literal
1h5m // a duration of time representing 1 hour and 5 minutes
2018-10-10 // a time starting at midnight for the default timezone on Oct 10th 2018
2018-10-10T10:05:00 // a time at 10:05 AM for the default timezone on Oct 10th 2018
[1,1,2] // an array of integers
{foo: "str", bar: false} // an object with two keys and their values
// Values can be assigned to identifers
x = 5.0
x + 3.0 // 8.0
// Import libraries
import "math"
// Call functions always using keyword arguments
math.pow(base: 5, exponent: 3) // 5^3 = 125
// Functions are defined by assigning them to identifers
add = (a, b) => a + b
// Call add using keyword arguments
add(a: 5, b: 3) // 8
// Functions are polymorphic
add(a: 5.5, b: 2.5) // 8.0
// Access data from a database and store it as an identifer
import "influxdb"
data = influxdb.from(bucket:"telegraf/autogen")
// Chain more transformation functions to further specify the desired data
cpu = data
// only get the last 5m of data
|> range(start: -5m)
// only get the "usage_user" data from the _measurement "cpu"
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_user")
// Return the data to the client
cpu |> yield()
// Group an aggregate along different dimensions
cpu
// organize data into groups by host and region
|> group(columns:["host","region"])
// compute the mean of each group
|> mean()
// yield this result to the client
|> yield()
// Window an aggregate over time
cpu
// organize data into groups of 1 minute
// compute the mean of each group
|> aggregateWindow(every: 1m, fn: mean)
// yield this result to the client
|> yield()
// Gather different data
mem = data
// only get the last 5m of data
|> range(start: -5m)
// only get the "used_percent" data from the _measurement "mem"
|> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent")
// Join data to create wider tables and map a function over the result
join(tables: {cpu:cpu, mem:mem}, on:["_time", "host"])
// compute the ratio of cpu usage to mem used_percent
|> map(fn:(r) => {_time: r._time, _value: r._value_cpu / r._value_mem)
// again yield this result to the client
|> yield()
The above examples give only a taste of what is possible with Flux. See the complete documentation for more complete examples and installation instructions.