Merge pull request #202 from influxdata/guide/conditional-query
Conditional query guidepull/209/head
Scott Anderson
GitHub
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---
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title: Query using conditional logic
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seotitle: Query using conditional logic in Flux
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description: >
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This guide describes how to use Flux conditional expressions, such as `if`,
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`else`, and `then`, to query and transform data.
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v2.0/tags: [conditionals, flux]
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menu:
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v2_0:
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name: Query using conditionals
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parent: How-to guides
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weight: 209
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---
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Flux provides `if`, `then`, and `else` conditional expressions that allow for powerful and flexible Flux queries.
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##### Conditional expression syntax
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```js
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// Pattern
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if <condition> then <action> else <alternative-action>
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// Example
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if color == "green" then "008000" else "ffffff"
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```
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Conditional expressions are most useful in the following contexts:
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- When defining variables.
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- When using functions that operate on a single row at a time (
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[`filter()`](/v2.0/reference/flux/functions/built-in/transformations/filter/),
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[`map()`](/v2.0/reference/flux/functions/built-in/transformations/map/),
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[`reduce()`](/v2.0/reference/flux/functions/built-in/transformations/aggregations/reduce) ).
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## Examples
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- [Conditionally set the value of a variable](#conditionally-set-the-value-of-a-variable)
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- [Create conditional filters](#create-conditional-filters)
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- [Conditionally transform column values with map()](#conditionally-transform-column-values-with-map)
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- [Conditionally increment a count with reduce()](#conditionally-increment-a-count-with-reduce)
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### Conditionally set the value of a variable
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The following example sets the `overdue` variable based on the
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`dueDate` variable's relation to `now()`.
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```js
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dueDate = 2019-05-01
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overdue = if dueDate < now() then true else false
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```
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### Create conditional filters
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The following example uses an example `metric` [dashboard variable](/v2.0/visualize-data/variables/)
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to change how the query filters data.
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`metric` has three possible values:
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- Memory
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- CPU
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- Disk
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```js
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from(bucket: "example-bucket")
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|> range(start: -1h)
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|> filter(fn: (r) =>
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if v.metric == "Memory"
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then r._measurement == "mem" and r._field == "used_percent"
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else if v.metric == "CPU"
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then r._measurement == "cpu" and r._field == "usage_user"
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else if v.metric == "Disk"
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then r._measurement == "disk" and r._field == "used_percent"
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else r._measurement != ""
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)
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```
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### Conditionally transform column values with map()
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The following example uses the [`map()` function](/v2.0/reference/flux/functions/built-in/transformations/map/)
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to conditionally transform column values.
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It sets the `level` column to a specific string based on `_value` column.
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{{< code-tabs-wrapper >}}
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{{% code-tabs %}}
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[No Comments](#)
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[Comments](#)
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{{% /code-tabs %}}
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{{% code-tab-content %}}
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```js
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from(bucket: "example-bucket")
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|> range(start: -5m)
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|> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent" )
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|> map(fn: (r) => ({
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_time: r._time,
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_value: r._value,
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level:
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if r._value >= 95.0000001 and r._value <= 100.0 then "critical"
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else if r._value >= 85.0000001 and r._value <= 95.0 then "warning"
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else if r._value >= 70.0000001 and r._value <= 85.0 then "high"
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else "normal"
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})
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)
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```
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{{% /code-tab-content %}}
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{{% code-tab-content %}}
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```js
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from(bucket: "example-bucket")
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|> range(start: -5m)
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|> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent" )
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|> map(fn: (r) => ({
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// Retain the _time column in the mapped row
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_time: r._time,
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// Retain the _value column in the mapped row
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_value: r._value,
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// Set the level column value based on the _value column
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level:
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if r._value >= 95.0000001 and r._value <= 100.0 then "critical"
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else if r._value >= 85.0000001 and r._value <= 95.0 then "warning"
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else if r._value >= 70.0000001 and r._value <= 85.0 then "high"
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else "normal"
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})
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)
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```
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{{% /code-tab-content %}}
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{{< /code-tabs-wrapper >}}
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### Conditionally increment a count with reduce()
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The following example uses the [`aggregateWindow()`](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow/)
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and [`reduce()`](/v2.0/reference/flux/functions/built-in/transformations/aggregates/reduce/)
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functions to count the number of records in every five minute window that exceed a defined threshold.
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{{< code-tabs-wrapper >}}
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{{% code-tabs %}}
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[No Comments](#)
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[Comments](#)
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{{% /code-tabs %}}
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{{% code-tab-content %}}
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```js
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threshold = 65.0
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data = from(bucket: "example-bucket")
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|> range(start: -1h)
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|> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent" )
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|> aggregateWindow(
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every: 5m,
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fn: (column, tables=<-) => tables |> reduce(
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identity: {above_threshold_count: 0.0},
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fn: (r, accumulator) => ({
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above_threshold_count:
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if r._value >= threshold then accumulator.above_threshold_count + 1.0
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else accumulator.above_threshold_count + 0.0
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})
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)
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)
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```
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{{% /code-tab-content %}}
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{{% code-tab-content %}}
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```js
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threshold = 65.0
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data = from(bucket: "example-bucket")
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|> range(start: -1h)
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|> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent" )
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// Aggregate data into 5 minute windows using a custom reduce() function
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|> aggregateWindow(
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every: 5m,
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// Use a custom function in the fn parameter.
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// The aggregateWindow fn parameter requires 'column' and 'tables' parameters.
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fn: (column, tables=<-) => tables |> reduce(
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identity: {above_threshold_count: 0.0},
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fn: (r, accumulator) => ({
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// Conditionally increment above_threshold_count if
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// r.value exceeds the threshold
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above_threshold_count:
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if r._value >= threshold then accumulator.above_threshold_count + 1.0
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else accumulator.above_threshold_count + 0.0
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})
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)
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)
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```
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{{% /code-tab-content %}}
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{{< /code-tabs-wrapper >}}
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