Documentation

Query using conditional logic

See the equivalent InfluxDB v2 documentation: Query using conditional logic.

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

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.

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.

threshold = 65.0

from(bucket: "telegraf/autogen")
    |> range(start: -1h)
    |> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent")
    |> aggregateWindow(
        every: 5m,
        fn: (column, tables=<-) => tables
            |> reduce(
                identity: {above_threshold_count: 0.0},
                fn: (r, accumulator) => ({
                    above_threshold_count: if r._value >= threshold then
                        accumulator.above_threshold_count + 1.0
                    else
                        accumulator.above_threshold_count + 0.0,
                }),
            ),
    )
threshold = 65.0

from(bucket: "telegraf/autogen")
    |> range(start: -1h)
    |> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent")
    // Aggregate data into 5 minute windows using a custom reduce() function
    |> aggregateWindow(
        every: 5m,
        // Use a custom function in the fn parameter.
        // The aggregateWindow fn parameter requires 'column' and 'tables' parameters.
        fn: (column, tables=<-) => tables
            |> reduce(
                identity: {above_threshold_count: 0.0},
                fn: (r, accumulator) => ({
                    // Conditionally increment above_threshold_count if
                    // r.value exceeds the threshold
                    above_threshold_count: if r._value >= threshold then
                        accumulator.above_threshold_count + 1.0
                    else
                        accumulator.above_threshold_count + 0.0,
                }),
            ),
    )

Was this page helpful?

Thank you for your feedback!


Introducing InfluxDB Clustered

A highly available InfluxDB 3.0 cluster on your own infrastructure.

InfluxDB Clustered is a highly available InfluxDB 3.0 cluster built for high write and query workloads on your own infrastructure.

InfluxDB Clustered is currently in limited availability and is only available to a limited group of InfluxData customers. If interested in being part of the limited access group, please contact the InfluxData Sales team.

Learn more
Contact InfluxData Sales

The future of Flux

Flux is going into maintenance mode. You can continue using it as you currently are without any changes to your code.

Flux is going into maintenance mode and will not be supported in InfluxDB 3.0. This was a decision based on the broad demand for SQL and the continued growth and adoption of InfluxQL. We are continuing to support Flux for users in 1.x and 2.x so you can continue using it with no changes to your code. If you are interested in transitioning to InfluxDB 3.0 and want to future-proof your code, we suggest using InfluxQL.

For information about the future of Flux, see the following: