Documentation

Work with Prometheus summaries

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

A summary samples observations (usually things like request durations and response sizes). While it also provides a total count of observations and a sum of all observed values, it calculates configurable quantiles over a sliding time window.

Prometheus metric types

Example summary metric in Prometheus data
# HELP task_executor_run_duration The duration in seconds between a run starting and finishing.
# TYPE task_executor_run_duration summary
example_summary_duration{label="foo",quantile="0.5"} 4.147907251
example_summary_duration{label="foo",quantile="0.9"} 4.147907251
example_summary_duration{label="foo",quantile="0.99"} 4.147907251
example_summary_duration_sum{label="foo"} 2701.367126714001
example_summary_duration_count{label="foo"} 539

The examples below include example data collected from the InfluxDB OSS 2.x /metrics endpoint 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.

Visualize summary metric quantile values

Prometheus summary metrics provide quantile values that can be visualized without modification.

  1. Filter by the prometheus measurement.
  2. Filter by your Prometheus metric name field.
from(bucket: "example-bucket")
  |> range(start: -1m)
  |> filter(fn: (r) => r._measurement == "prometheus")
  |> filter(fn: (r) => r._field == "go_gc_duration_seconds")
  1. Filter by your Prometheus metric name measurement.
  2. Filter out the sum and count fields.
from(bucket: "example-bucket")
  |> range(start: -1m)
  |> filter(fn: (r) => r._measurement == "go_gc_duration_seconds")
  |> filter(fn: (r) => r._field != "count" and r._field != "sum")
Visualize Prometheus summary quantiles

Derive average values from a summary metric

Use the sum and count values provided in Prometheus summary metrics to derive an average summary value.

  1. Filter by the prometheus measurement.
  2. Filter by the <metric_name>_count and <metric_name>_sum fields.
  3. Use pivot() to pivot fields into columns based on time. Each row then contains a <metric_name>_count and <metric_name>_sum column.
  4. Divide the <metric_name>_sum column by the <metric_name>_count column to produce a new _value.
from(bucket: "example-bucket")
  |> range(start: -1m)
  |> filter(fn: (r) => r._measurement == "prometheus")
  |> filter(fn: (r) =>
    r._field == "go_gc_duration_seconds_count" or
    r._field == "go_gc_duration_seconds_sum"
  )
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
  |> map(fn: (r) => ({ r with
    _value: r.go_gc_duration_seconds_sum / r.go_gc_duration_seconds_count
  }))
  1. Filter by your Prometheus metric name measurement.
  2. Filter by the count and sum fields.
  3. Use pivot() to pivot fields into columns. Each row then contains a count and sum column.
  4. Divide the sum column by the count column to produce a new _value.
from(bucket: "example-bucket")
  |> range(start: -1m)
  |> filter(fn: (r) => r._measurement == "go_gc_duration_seconds")
  |> filter(fn: (r) => r._field == "count" or r._field == "sum")
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
  |> map(fn: (r) => ({ r with _value: r.sum / r.count }))

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