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.
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.
- Filter by the
prometheusmeasurement. - 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")
- Filter by your Prometheus metric name measurement.
- Filter out the
sumandcountfields.
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")
Derive average values from a summary metric
Use the sum and count values provided in Prometheus summary metrics to derive an average summary value.
- Filter by the
prometheusmeasurement. - Filter by the
<metric_name>_countand<metric_name>_sumfields. - Use
pivot()to pivot fields into columns based on time. Each row then contains a<metric_name>_countand<metric_name>_sumcolumn. - Divide the
<metric_name>_sumcolumn by the<metric_name>_countcolumn 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}))
- Filter by your Prometheus metric name measurement.
- Filter by the
countandsumfields. - Use
pivot()to pivot fields into columns. Each row then contains acountandsumcolumn. - Divide the
sumcolumn by thecountcolumn 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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