---
title: Query using conditional logic
description: 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.
url: https://docs.influxdata.com/influxdb/cloud/query-data/flux/conditional-logic/
estimated_tokens: 6908
product: InfluxDB Cloud (TSM)
version: cloud
---

# Query using conditional logic

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](/flux/v0/get-started/) for a conceptual overview of Flux and parts of a Flux query.
-   [Execute queries](/influxdb/cloud/query-data/execute-queries/) to discover a variety of ways to run your queries.

##### Conditional expression syntax

```js
// 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()`](/flux/v0/stdlib/universe/filter/), [`map()`](/flux/v0/stdlib/universe/map/), [`reduce()`](/flux/v0/stdlib/universe/reduce) ).

## Evaluating conditional expressions

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

For example, given the following statement:

```js
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](#conditionally-set-the-value-of-a-variable)
-   [Create conditional filters](#create-conditional-filters)
-   [Conditionally transform column values with map()](#conditionally-transform-column-values-with-map)
-   [Conditionally increment a count with reduce()](#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()`.

```js
dueDate = 2019-05-01
overdue = if dueDate < now() then true else false
```

### Create conditional filters

The following example uses an example `metric` [dashboard variable](/influxdb/cloud/visualize-data/variables/) to change how the query filters data. `metric` has three possible values:

-   Memory
-   CPU
-   Disk

```js
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](/flux/v0/stdlib/universe/map/) to conditionally transform column values. It sets the `level` column to a specific string based on `_value` column.

<!-- Tabbed content: Select one of the following options -->

**No Comments:**

```js
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",
        }),
    )
```

**Comments:**

```js
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",
        }),
    )
```

<!-- End tabbed content -->

### Conditionally increment a count with reduce()

The following example uses the [`aggregateWindow()`](/flux/v0/stdlib/universe/aggregatewindow/) and [`reduce()`](/flux/v0/stdlib/universe/reduce/) functions to count the number of records in every five minute window that exceed a defined threshold.

<!-- Tabbed content: Select one of the following options -->

**No Comments:**

```js
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,
                }),
            ),
    )
```

**Comments:**

```js
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,
                }),
            ),
    )
```

<!-- End tabbed content -->

#### Related

-   [Query fields and tags](/influxdb/cloud/query-data/flux/query-fields/)
-   [filter() function](/flux/v0/stdlib/universe/filter/)
-   [map() function](/flux/v0/stdlib/universe/map/)
-   [reduce() function](/flux/v0/stdlib/universe/reduce/)

[conditionals](/influxdb/cloud/tags/conditionals/) [flux](/influxdb/cloud/tags/flux/)