Calculate percentages with Flux

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

Use from(), range() and filter() to query operands.
Use pivot() or join() to align operand values into rows.
Use map() to divide the numerator operand value by the denominator operand value and multiply by 100.

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.

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

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

_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.

// ...
    |> 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.

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).

// ...
    |> 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

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

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

Ensure measurements are in the same bucket.
Use filter() to include data from both measurements.
Use group() to ungroup data and return a single table.
Use pivot() to pivot fields into columns.
Use map() to re-map rows and perform the percentage calculation.

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

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 example_table",
)

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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