Query using conditional logic
This page documents an earlier version of InfluxDB. InfluxDB v2.7 is the latest stable version. View this page in the v2.7 documentation.
Flux provides if, then, and else conditional expressions that allow for powerful and flexible Flux queries.
If you’re just getting started with Flux queries, check out the following:
- Get started with Flux for a conceptual overview of Flux and parts of a Flux query.
- Execute queries to discover a variety of ways to run your 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 false
Create conditional filters
The following example uses an example metric dashboard variable
to change how the query filters data.
metric has three possible values:
- Memory
- CPU
- Disk
from(bucket: "example-bucket")
|> 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: "example-bucket")
|> 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: "example-bucket")
|> 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
data = from(bucket: "example-bucket")
|> 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: "example-bucket")
|> 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,
}),
),
)
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