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FluxLang 101 Cheat Sheet by

The basic functionalities of Flux Language

A Table of Measure

Measur­ement
Sensor
Temper­ature
Timestamp
iot-oven
S1
290
163395­360­000­0000000
iot-oven
S2
105
163395­361­500­0000000
iot-oven
S1
305
163395­366­000­0000000
iot-oven
S2
120
163395­367­500­0000000

Basic Filtering

from(bucket: "training")
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => r._measurement == "iot-oven")
  |> filter(fn: (r) => r._field == "temperature")
  |> filter(fn: (r) => r.sensor == "S2")
  |> filter(fn: (r) => r._value < 100)
The range clause allows to filter by time creating a time windodw
The filter clause reduces the ammount of records and can be applied on the measur­ements, fields, tags, and field/tag keys.

Functions

Flux transf­orm­ations take a stream of tables as input, transform the data in some way, and output a stream of tables.

Aggreg­ation

from(bucket: "training")
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => r._measurement == "iot-oven")
  |> group(columns: ["_field"])
  |> mean()
Flux aggregate functions are transf­orm­ations aggregate values from input tables in some way.

Selector

from(bucket: "training")
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => r._measurement == "iot-oven")
  |> filter(fn: (r) => r.sensor == "S1")
  |> group(columns: ["_field"])
  |> last()
Flux selector functions are transf­orm­ations that return one or more record per input table.

Condit­ional Expres­sions

... |> map(fn: (r) => ({
      r with _value:
                    if r._value == true then 1
                    else 0  }))
Condit­ional expres­sions evaluate a boolea­n-v­alued condition. If the result is true, the expression that follows the then keyword is evaluated and returned.
  

Line Protocol

Advanced Windowing

from(bucket: "training")
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => r._measurement == "iot-oven")
  |> filter(fn: (r) => r._field == "temperature")
  |> filter(fn: (r) => r.sensor == "S1")
  |> aggregateWindow(every: 2m, fn: mean)
aggreg­ate­Win­dow() downsa­mples data by grouping data into fixed windows of time and applying an aggregate or selector function to each window.

NOTE The flag 
create­Empty: false
can be used to consider only the windows that contains data (its default value is 
true
)

Map and Custom Functions

from(bucket: "training") ...  
        |> map(fn: (r) => ({ r with correctValue: r._value - 5.0 }))
Note the 
r with
clause maintains alla the original columns and adds the new one.

Custom pipe forwar­dable function

adjValues = (tables=<-, x) =>
  tables
    |> map(fn: (r) => ({ r with correctValue: r._value + x}))

from(bucket: "training")
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => r._measurement == "iot-oven")
  |> adjValues(x:-5.0)
Most Flux functions manipulate data piped-­forward into the function. In order for a custom function to process piped-­forward data, one of the function parameters must capture the input tables using the 
<-
pipe-r­eceive expres­sion.

Joins

from(bucket: "training")
...
join(tables: {s1: hs1, s2: hs2}, on: ["_time"], method: "inner")
The join() function merges two or more input streams, whose values are equal on a set of common columns, into a single output stream.
   
 

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  • Published: 13th October, 2022

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