Query using conditional logic
This page documents an earlier version of InfluxDB OSS. InfluxDB 3 Core is the latest stable version.
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 = 2019-05-01 overdue = if dueDate < now() then true else falseCreate 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?
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