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Query using conditional logic

Query using conditional logic in Flux

Conditional logic

This guide describes how to use Flux conditional expressions, such as `if`, `else`, and `then`, to query and transform data. **Flux evaluates statements from left to right and stops evaluating once a condition matches.**

enterprise_influxdb_v1

name	parent
Conditional logic	Query with Flux

/influxdb/v2/query-data/flux/conditional-logic/

```js if color == "green" then "008000" else "ffffff" ```

Flux provides if, then, and else conditional expressions that allow for powerful and flexible Flux queries.

Conditional expression syntax

// Pattern
if <condition> then <action> else <alternative-action>

// Example
if color == "green" then "008000" else "ffffff"

Conditional expressions are most useful in the following contexts:

When defining variables.
When using functions that operate on a single row at a time ( filter(), map(), reduce() ).

Evaluating conditional expressions

Flux evaluates statements in order and stops evaluating once a condition matches.

For example, given the following statement:

if r._value > 95.0000001 and r._value <= 100.0 then
    "critical"
else if r._value > 85.0000001 and r._value <= 95.0 then
    "warning"
else if r._value > 70.0000001 and r._value <= 85.0 then
    "high"
else
    "normal"

When r._value is 96, the output is "critical" and the remaining conditions are not evaluated.

Examples

Conditionally set the value of a variable
Create conditional filters
Conditionally transform column values with map()
Conditionally increment a count with reduce()

Conditionally set the value of a variable

The following example sets the overdue variable based on the dueDate variable's relation to now().

dueDate = 2019-05-01T00:00:00Z
overdue = if dueDate < now() then true else false

Create conditional filters

The following example uses an example metric variable to change how the query filters data. metric has three possible values:

Memory
CPU
Disk

metric = "Memory"

from(bucket: "telegraf/autogen")
    |> range(start: -1h)
    |> filter(
        fn: (r) => if v.metric == "Memory" then
            r._measurement == "mem" and r._field == "used_percent"
        else if v.metric == "CPU" then
            r._measurement == "cpu" and r._field == "usage_user"
        else if v.metric == "Disk" then
            r._measurement == "disk" and r._field == "used_percent"
        else
            r._measurement != "",
    )

Conditionally transform column values with map()

The following example uses the map() function to conditionally transform column values. It sets the level column to a specific string based on _value column.

{{< code-tabs-wrapper >}} {{% code-tabs %}} No Comments Comments {{% /code-tabs %}} {{% code-tab-content %}}

from(bucket: "telegraf/autogen")
    |> range(start: -5m)
    |> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent")
    |> map(
        fn: (r) => ({r with
            level: if r._value >= 95.0000001 and r._value <= 100.0 then
                "critical"
            else if r._value >= 85.0000001 and r._value <= 95.0 then
                "warning"
            else if r._value >= 70.0000001 and r._value <= 85.0 then
                "high"
            else
                "normal",
        }),
    )

from(bucket: "telegraf/autogen")
    |> range(start: -5m)
    |> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent")
    |> map(
        fn: (r) => ({
            // Retain all existing columns in the mapped row
            r with
            // Set the level column value based on the _value column
            level: if r._value >= 95.0000001 and r._value <= 100.0 then
                "critical"
            else if r._value >= 85.0000001 and r._value <= 95.0 then
                "warning"
            else if r._value >= 70.0000001 and r._value <= 85.0 then
                "high"
            else
                "normal",
        }),
    )

Conditionally increment a count with reduce()

The following example uses the aggregateWindow() and reduce() functions to count the number of records in every five minute window that exceed a defined threshold.