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Merge pull request #986 from influxdata/query/percentages 

Calculating percentages in Flux
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@ -0,0 +1,216 @@
---
title: Calculate percentages with Flux
list_title: Calculate percentages
description: >
Use [`pivot()` or `join()`](/v2.0/query-data/flux/mathematic-operations/#pivot-vs-join)
and the [`map()` function](/v2.0/reference/flux/stdlib/built-in/transformations/map/)
to align operand values into rows and calculate a percentage.
menu:
v2_0:
name: Calculate percentages
parent: Query with Flux
weight: 206
aliases:
- /v2.0/query-data/guides/manipulate-timestamps/
related:
- /v2.0/query-data/flux/mathematic-operations
- /v2.0/reference/flux/stdlib/built-in/transformations/map
- /v2.0/reference/flux/stdlib/built-in/transformations/pivot
- /v2.0/reference/flux/stdlib/built-in/transformations/join
list_query_example: percentages
---
Calculating percentages from queried data is a common use case for time series data.
To calculate a percentage in Flux, operands must be in each row.
Use `map()` to re-map values in the row and calculate a percentage.
**To calculate percentages**
1. Use [`from()`](/v2.0/reference/flux/stdlib/built-in/inputs/from/),
[`range()`](/v2.0/reference/flux/stdlib/built-in/transformations/range/) and
[`filter()`](/v2.0/reference/flux/stdlib/built-in/transformations/filter/) to query operands.
2. Use [`pivot()` or `join()`](/v2.0/query-data/flux/mathematic-operations/#pivot-vs-join)
to align operand values into rows.
3. Use [`map()`](/v2.0/reference/flux/stdlib/built-in/transformations/map/)
to divide the numerator operand value by the denominator operand value and multiply by 100.
{{% note %}}
The following examples use `pivot()` to align operands into rows because
`pivot()` works in most cases and is more performant than `join()`.
_See [Pivot vs join](/v2.0/query-data/flux/mathematic-operations/#pivot-vs-join)._
{{% /note %}}
```js
from(bucket: "example-bucket")
|> range(start: -1h)
|> filter(fn: (r) => r._measurement == "m1" and r._field =~ /field[1-2]/ )
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> map(fn: (r) => ({ r with _value: r.field1 / r.field2 * 100.0 }))
```
## GPU monitoring example
The following example queries data from the gpu-monitor bucket and calculates the
percentage of GPU memory used over time.
Data includes the following:
- **`gpu` measurement**
- **`mem_used` field**: used GPU memory in bytes
- **`mem_total` field**: total GPU memory in bytes
### Query mem_used and mem_total fields
```js
from(bucket: "gpu-monitor")
|> range(start: 2020-01-01T00:00:00Z)
|> filter(fn: (r) => r._measurement == "gpu" and r._field =~ /mem_/)
```
###### Returns the following stream of tables:
| _time | _measurement | _field | _value |
|:----- |:------------:|:------: | ------: |
| 2020-01-01T00:00:00Z | gpu | mem_used | 2517924577 |
| 2020-01-01T00:00:10Z | gpu | mem_used | 2695091978 |
| 2020-01-01T00:00:20Z | gpu | mem_used | 2576980377 |
| 2020-01-01T00:00:30Z | gpu | mem_used | 3006477107 |
| 2020-01-01T00:00:40Z | gpu | mem_used | 3543348019 |
| 2020-01-01T00:00:50Z | gpu | mem_used | 4402341478 |
<p style="margin:-2.5rem 0;"></p>
| _time | _measurement | _field | _value |
|:----- |:------------:|:------: | ------: |
| 2020-01-01T00:00:00Z | gpu | mem_total | 8589934592 |
| 2020-01-01T00:00:10Z | gpu | mem_total | 8589934592 |
| 2020-01-01T00:00:20Z | gpu | mem_total | 8589934592 |
| 2020-01-01T00:00:30Z | gpu | mem_total | 8589934592 |
| 2020-01-01T00:00:40Z | gpu | mem_total | 8589934592 |
| 2020-01-01T00:00:50Z | gpu | mem_total | 8589934592 |
### Pivot fields into columns
Use `pivot()` to pivot the `mem_used` and `mem_total` fields into columns.
Output includes `mem_used` and `mem_total` columns with values for each corresponding `_time`.
```js
// ...
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
```
###### Returns the following:
| _time | _measurement | mem_used | mem_total |
|:----- |:------------:| --------: | ---------: |
| 2020-01-01T00:00:00Z | gpu | 2517924577 | 8589934592 |
| 2020-01-01T00:00:10Z | gpu | 2695091978 | 8589934592 |
| 2020-01-01T00:00:20Z | gpu | 2576980377 | 8589934592 |
| 2020-01-01T00:00:30Z | gpu | 3006477107 | 8589934592 |
| 2020-01-01T00:00:40Z | gpu | 3543348019 | 8589934592 |
| 2020-01-01T00:00:50Z | gpu | 4402341478 | 8589934592 |
### Map new values
Each row now contains the values necessary to calculate a percentage.
Use `map()` to re-map values in each row.
Divide `mem_used` by `mem_total` and multiply by 100 to return the percentage.
{{% note %}}
To return a precise float percentage value that includes decimal points, the example
below casts integer field values to floats and multiplies by a float value (`100.0`).
{{% /note %}}
```js
// ...
|> map(fn: (r) => ({
_time: r._time,
_measurement: r._measurement,
_field: "mem_used_percent",
_value: float(v: r.mem_used) / float(v: r.mem_total) * 100.0
}))
```
##### Query results:
| _time | _measurement | _field | _value |
|:----- |:------------:|:------: | ------: |
| 2020-01-01T00:00:00Z | gpu | mem_used_percent | 29.31 |
| 2020-01-01T00:00:10Z | gpu | mem_used_percent | 31.37 |
| 2020-01-01T00:00:20Z | gpu | mem_used_percent | 30.00 |
| 2020-01-01T00:00:30Z | gpu | mem_used_percent | 35.00 |
| 2020-01-01T00:00:40Z | gpu | mem_used_percent | 41.25 |
| 2020-01-01T00:00:50Z | gpu | mem_used_percent | 51.25 |
### Full query
```js
from(bucket: "gpu-monitor")
|> range(start: 2020-01-01T00:00:00Z)
|> filter(fn: (r) => r._measurement == "gpu" and r._field =~ /mem_/ )
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> map(fn: (r) => ({
_time: r._time,
_measurement: r._measurement,
_field: "mem_used_percent",
_value: float(v: r.mem_used) / float(v: r.mem_total) * 100.0
}))
```
## Examples
#### Calculate percentages using multiple fields
```js
from(bucket: "example-bucket")
|> range(start: -1h)
|> filter(fn: (r) => r._measurement == "example-measurement")
|> filter(fn: (r) =>
r._field == "used_system" or
r._field == "used_user" or
r._field == "total"
)
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> map(fn: (r) => ({ r with
_value: float(v: r.used_system + r.used_user) / float(v: r.total) * 100.0
}))
```
#### Calculate percentages using multiple measurements
1. Ensure measurements are in the same [bucket](/v2.0/reference/glossary/#bucket).
2. Use `filter()` to include data from both measurements.
3. Use `group()` to ungroup data and return a single table.
4. Use `pivot()` to pivot fields into columns.
5. Use `map()` to re-map rows and perform the percentage calculation.
<!-- -->
```js
from(bucket: "example-bucket")
|> range(start: -1h)
|> filter(fn: (r) =>
(r._measurement == "m1" or r._measurement == "m2") and
(r._field == "field1" or r._field == "field2")
)
|> group()
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> map(fn: (r) => ({ r with _value: r.field1 / r.field2 * 100.0 }))
```
#### Calculate percentages using multiple data sources
```js
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: "example-bucket")
|> 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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@ -18,6 +18,7 @@ related:
- /v2.0/reference/flux/stdlib/built-in/transformations/aggregates/reduce/
- /v2.0/reference/flux/language/operators/
- /v2.0/reference/flux/stdlib/built-in/transformations/type-conversions/
- /v2.0/query-data/flux/calculate-percentages/
list_query_example: map_math
---
@ -98,6 +99,7 @@ percent(sample: 20.0, total: 80.0)
To transform multiple values in an input stream, your function needs to:
- [Handle piped-forward data](/v2.0/query-data/flux/custom-functions/#functions-that-manipulate-piped-forward-data).
- Each operand necessary for the calculation exists in each row _(see [Pivot vs join](#pivot-vs-join) below)_.
- Use the [`map()` function](/v2.0/reference/flux/stdlib/built-in/transformations/map) to iterate over each row.
The example `multiplyByX()` function below includes:
@ -178,93 +180,57 @@ bytesToGB = (tables=<-) =>
### Calculate a percentage
To calculate a percentage, use simple division, then multiply the result by 100.
{{% note %}}
Operands in percentage calculations should always be floats.
{{% /note %}}
```js
> 1.0 / 4.0 * 100.0
25.0
```
#### User vs system CPU usage
The example below calculates the percentage of total CPU used by the `user` vs the `system`.
_For an in-depth look at calculating percentages, see [Calculate percentates](/v2.0/query-data/flux/calculate-percentages)._
{{< code-tabs-wrapper >}}
{{% code-tabs %}}
[Comments](#)
[No Comments](#)
{{% /code-tabs %}}
## 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:
{{% code-tab-content %}}
#### 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
```js
// Custom function that converts usage_user and
// usage_system columns to floats
usageToFloat = (tables=<-) =>
tables
|> map(fn: (r) => ({
_time: r._time,
usage_user: float(v: r.usage_user),
usage_system: float(v: r.usage_system)
})
)
data
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> map(fn: (r) => ({ r with
_value: (r.field1 + r.field2) / r.field3 * 100.0
}))
```
// Define the data source and filter user and system CPU usage
// from 'cpu-total' in the 'cpu' measurement
from(bucket: "example-bucket")
##### Join multiple data sources for mathematic calculations
```js
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: "example-bucket")
|> range(start: -1h)
|> filter(fn: (r) =>
r._measurement == "cpu" and
r._field == "usage_user" or
r._field == "usage_system" and
r.cpu == "cpu-total"
r._measurement == "example-measurement" and
r._field == "example-field"
)
// Pivot the output tables so usage_user and usage_system are in each row
|> pivot(rowKey: ["_time"], columnKey: ["_field"], valueColumn: "_value")
// Convert usage_user and usage_system to floats
|> usageToFloat()
// Map over each row and calculate the percentage of
// CPU used by the user vs the system
|> map(fn: (r) => ({
// Preserve existing columns in each row
r with
usage_user: r.usage_user / (r.usage_user + r.usage_system) * 100.0,
usage_system: r.usage_system / (r.usage_user + r.usage_system) * 100.0
})
)
join(tables: {t1: t1, t2: t2}, on: ["id"])
|> map(fn: (r) => ({ r with _value: r._value_t2 / r.available_t1 * 100.0 }))
```
{{% /code-tab-content %}}
{{% code-tab-content %}}
```js
usageToFloat = (tables=<-) =>
tables
|> map(fn: (r) => ({
_time: r._time,
usage_user: float(v: r.usage_user),
usage_system: float(v: r.usage_system)
})
)
from(bucket: "example-bucket")
|> range(start: timeRangeStart, stop: timeRangeStop)
|> filter(fn: (r) =>
r._measurement == "cpu" and
r._field == "usage_user" or
r._field == "usage_system" and
r.cpu == "cpu-total"
)
|> pivot(rowKey: ["_time"], columnKey: ["_field"], valueColumn: "_value")
|> usageToFloat()
|> map(fn: (r) => ({
r with
usage_user: r.usage_user / (r.usage_user + r.usage_system) * 100.0,
usage_system: r.usage_system / (r.usage_user + r.usage_system) * 100.0
})
)
```
{{% /code-tab-content %}}
{{< /code-tabs-wrapper >}}

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@ -332,6 +332,38 @@ moving_average:
| 2020-01-01T00:06:00Z | 1.325 |
| 2020-01-01T00:06:00Z | 1.150 |
percentages:
-
code: |
```js
data
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> map(fn: (r) => ({
_time: r._time,
_field: "used_percent",
_value: float(v: r.used) / float(v: r.total) * 100.0
}))
```
input: |
| _time | _field | _value |
|:----- |:------: | ------:|
| 2020-01-01T00:00:00Z | used | 2.5 |
| 2020-01-01T00:00:10Z | used | 3.1 |
| 2020-01-01T00:00:20Z | used | 4.2 |
| _time | _field | _value |
|:----- |:------: | ------:|
| 2020-01-01T00:00:00Z | total | 8.0 |
| 2020-01-01T00:00:10Z | total | 8.0 |
| 2020-01-01T00:00:20Z | total | 8.0 |
output: |
| _time | _field | _value |
|:----- |:------: | ------:|
| 2020-01-01T00:00:00Z | used_percent | 31.25 |
| 2020-01-01T00:00:10Z | used_percent | 38.75 |
| 2020-01-01T00:00:20Z | used_percent | 52.50 |
quantile:
-
code: |