Documentation

Work with Prometheus gauges

Use Flux to query and transform Prometheus gauge metrics stored in InfluxDB.

A gauge is a metric that represents a single numerical value that can arbitrarily go up and down.

Prometheus metric types

Example gauge metric in Prometheus data
# HELP example_gauge_current Current number of items as example gauge metric
# TYPE example_gauge_current gauge
example_gauge_current 128

Generally gauge metrics can be used as they are reported and don’t require any additional processing.

The examples below include example data collected from the InfluxDB OSS 2.x /metrics endpoint using prometheus.scrape() and stored in InfluxDB.

Prometheus metric parsing formats

Query structure depends on the Prometheus metric parsing format used to scrape the Prometheus metrics. Select the appropriate metric format version below.

Calculate the rate of change in gauge values

  1. Filter results by the prometheus measurement and counter metric name field.
  2. Use derivative() to calculate the rate of change between gauge values. By default, derivative() returns the rate of change per second. Use the unit parameter to customize the rate unit. To replace negative derivatives with null values, set the nonNegative parameter to true.
from(bucket: "example-bucket")
    |> range(start: -1m)
    |> filter(fn: (r) => r._measurement == "prometheus" and r._field == "go_goroutines")
    |> derivative(nonNegative: true)
Raw Prometheus gauge metric in InfluxDB
Derivative of Prometheus gauge metrics in InfluxDB

View example input and output data

  1. Filter results by the counter metric name measurement and gauge field.
  2. Use derivative() to calculate the rate of change between gauge values. By default, derivative() returns the rate of change per second. Use the unit parameter to customize the rate unit. To replace negative derivatives with null values, set the nonNegative parameter to true.
from(bucket: "example-bucket")
    |> range(start: -1m)
    |> filter(fn: (r) => r._measurement == "go_goroutines" and r._field == "gauge")
    |> derivative(nonNegative: true)
Raw Prometheus gauge metric in InfluxDB
Derivative of Prometheus gauge metrics in InfluxDB

View example input and output data

Calculate the average rate of change in specified time windows

  1. Import the experimental/aggregate package.

  2. Filter results by the prometheus measurement and counter metric name field.

  3. Use aggregate.rate() to calculate the average rate of change per time window.

    • Use the every parameter to define the time window interval.
    • Use the unit parameter to customize the rate unit. By default, aggregate.rate() returns the per second (1s) rate of change.
    • Use the groupColumns parameter to specify columns to group by when performing the aggregation.
import "experimental/aggregate"

from(bucket: "example-bucket")
    |> range(start: -1m)
    |> filter(fn: (r) => r._measurement == "prometheus" and r._field == "go_goroutines")
    |> aggregate.rate(every: 10s, unit: 1s)
Raw Prometheus gauge metric in InfluxDB
Calculate the average rate of change of Prometheus gauge metrics per time window with Flux

View example input and output data

  1. Import the experimental/aggregate package.

  2. Filter results by the counter metric name measurement and gauge field.

  3. Use aggregate.rate() to calculate the average rate of change per time window.

    • Use the every parameter to define the time window interval.
    • Use the unit parameter to customize the rate unit. By default, aggregate.rate() returns the per second (1s) rate of change.
    • Use the groupColumns parameter to specify columns to group by when performing the aggregation.
import "experimental/aggregate"

from(bucket: "example-bucket")
    |> range(start: -1m)
    |> filter(fn: (r) => r._measurement == "go_goroutines" and r._field == "gauge")
    |> aggregate.rate(every: 10s, unit: 1s)
Raw Prometheus gauge metric in InfluxDB
Calculate the average rate of change of Prometheus gauge metrics per time window with Flux

View example input and output data


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InfluxDB Cloud backed by InfluxDB TSM

All InfluxDB Cloud organizations created on or after January 31, 2023 are backed by the new InfluxDB IOx storage engine which enables nearly unlimited series cardinality and SQL query support. Check the right column of your InfluxDB Cloud organization homepage to see which InfluxDB storage engine you’re using.

If powered by TSM, this is the correct documentation.

If powered by IOx, see the IOx-based InfluxDB Cloud documentation.

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