# Transform data with mathematic operations

This page documents an earlier version of InfluxDB.
InfluxDB v2 is the latest stable version.
See the equivalent **InfluxDB v2** documentation: Transform data with mathematic operations.

Flux supports mathematic expressions in data transformations. This article describes how to use Flux arithmetic operators to “map” over data and transform values using mathematic operations.

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.

##### Basic mathematic operations

```
// Examples executed using the Flux REPL
> 9 + 9
18
> 22 - 14
8
> 6 * 5
30
> 21 / 7
3
```

See Flux read-eval-print-loop (REPL).

#### Operands must be the same type

Operands in Flux mathematic operations must be the same data type. For example, integers cannot be used in operations with floats. Otherwise, you will get an error similar to:

```
Error: type error: float != int
```

To convert operands to the same type, use type-conversion functions or manually format operands. The operand data type determines the output data type. For example:

```
100 // Parsed as an integer
100.0 // Parsed as a float
// Example evaluations
> 20 / 8
2
> 20.0 / 8.0
2.5
```

## Custom mathematic functions

Flux lets you create custom functions that use mathematic operations. View the examples below.

###### Custom multiplication function

```
multiply = (x, y) => x * y
multiply(x: 10, y: 12)
// Returns 120
```

###### Custom percentage function

```
percent = (sample, total) => (sample / total) * 100.0
percent(sample: 20.0, total: 80.0)
// Returns 25.0
```

### Transform values in a data stream

To transform multiple values in an input stream, your function needs to:

- Handle piped-forward data.
- Each operand necessary for the calculation exists in each row
*(see Pivot vs join below)*. - Use the
`map()`

function to iterate over each row.

The example `multiplyByX()`

function below includes:

- A
`tables`

parameter that represents the input data stream (`<-`

). - An
`x`

parameter which is the number by which values in the`_value`

column are multiplied. - A
`map()`

function that iterates over each row in the input stream. It uses the`with`

operator to preserve existing columns in each row. It also multiples the`_value`

column by`x`

.

```
multiplyByX = (x, tables=<-) =>
tables
|> map(fn: (r) => ({
r with
_value: r._value * x
})
)
data
|> multiplyByX(x: 10)
```

## Examples

### Convert bytes to gigabytes

To convert active memory from bytes to gigabytes (GB), divide the `active`

field
in the `mem`

measurement by 1,073,741,824.

The `map()`

function iterates over each row in the piped-forward data and defines
a new `_value`

by dividing the original `_value`

by 1073741824.

```
from(bucket: "db/rp")
|> range(start: -10m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "active"
)
|> map(fn: (r) => ({
r with
_value: r._value / 1073741824
})
)
```

You could turn that same calculation into a function:

```
bytesToGB = (tables=<-) =>
tables
|> map(fn: (r) => ({
r with
_value: r._value / 1073741824
})
)
data
|> bytesToGB()
```

#### Include partial gigabytes

Because the original metric (bytes) is an integer, the output of the operation is an integer and does not include partial GBs.
To calculate partial GBs, convert the `_value`

column and its values to floats using the
`float()`

function
and format the denominator in the division operation as a float.

```
bytesToGB = (tables=<-) =>
tables
|> map(fn: (r) => ({
r with
_value: float(v: r._value) / 1073741824.0
})
)
```

### Calculate a percentage

To calculate a percentage, use simple division, then multiply the result by 100.

```
> 1.0 / 4.0 * 100.0
25.0
```

*For an in-depth look at calculating percentages, see Calculate percentages.*

## Pivot vs join

To query and use values in mathematical operations in Flux, operand values must
exists in a single row.
Both `pivot()`

and `join()`

will do this, but there are important differences between the two:

#### Pivot is more performant

`pivot()`

reads and operates on a single stream of data.
`join()`

requires two streams of data and the overhead of reading and combining
both streams can be significant, especially for larger data sets.

#### Use join for multiple data sources

Use `join()`

when querying data from different buckets or data sources.

##### Pivot fields into columns for mathematic calculations

```
data
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> map(fn: (r) => ({ r with
_value: (r.field1 + r.field2) / r.field3 * 100.0
}))
```

##### Join multiple data sources for mathematic calculations

```
import "sql"
import "influxdata/influxdb/secrets"
pgUser = secrets.get(key: "POSTGRES_USER")
pgPass = secrets.get(key: "POSTGRES_PASSWORD")
pgHost = secrets.get(key: "POSTGRES_HOST")
t1 = sql.from(
driverName: "postgres",
dataSourceName: "postgresql://${pgUser}:${pgPass}@${pgHost}",
query:"SELECT id, name, available FROM exampleTable"
)
t2 = from(bucket: "db/rp")
|> range(start: -1h)
|> filter(fn: (r) =>
r._measurement == "example-measurement" and
r._field == "example-field"
)
join(tables: {t1: t1, t2: t2}, on: ["id"])
|> map(fn: (r) => ({ r with _value: r._value_t2 / r.available_t1 * 100.0 }))
```

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