mirror of
https://github.com/prometheus/prometheus.git
synced 2025-11-04 02:11:01 +01:00
cf9e934036
* Examples: link to `rate` for new users Signed-off-by: Ted Robertson 10043369+tredondo@users.noreply.github.com Co-authored-by: Bryan Boreham <bjboreham@gmail.com>
98 lines
3.6 KiB
Markdown
98 lines
3.6 KiB
Markdown
---
|
|
title: Querying examples
|
|
nav_title: Examples
|
|
sort_rank: 4
|
|
---
|
|
|
|
# Query examples
|
|
|
|
## Simple time series selection
|
|
|
|
Return all time series with the metric `http_requests_total`:
|
|
|
|
http_requests_total
|
|
|
|
Return all time series with the metric `http_requests_total` and the given
|
|
`job` and `handler` labels:
|
|
|
|
http_requests_total{job="apiserver", handler="/api/comments"}
|
|
|
|
Return a whole range of time (in this case 5 minutes up to the query time)
|
|
for the same vector, making it a [range vector](../basics/#range-vector-selectors):
|
|
|
|
http_requests_total{job="apiserver", handler="/api/comments"}[5m]
|
|
|
|
Note that an expression resulting in a range vector cannot be graphed directly,
|
|
but viewed in the tabular ("Console") view of the expression browser.
|
|
|
|
Using regular expressions, you could select time series only for jobs whose
|
|
name match a certain pattern, in this case, all jobs that end with `server`:
|
|
|
|
http_requests_total{job=~".*server"}
|
|
|
|
All regular expressions in Prometheus use [RE2
|
|
syntax](https://github.com/google/re2/wiki/Syntax).
|
|
|
|
To select all HTTP status codes except 4xx ones, you could run:
|
|
|
|
http_requests_total{status!~"4.."}
|
|
|
|
## Subquery
|
|
|
|
Return the 5-minute [rate](./functions.md#rate) of the `http_requests_total` metric for the past 30 minutes, with a resolution of 1 minute.
|
|
|
|
rate(http_requests_total[5m])[30m:1m]
|
|
|
|
This is an example of a nested subquery. The subquery for the `deriv` function uses the default resolution. Note that using subqueries unnecessarily is unwise.
|
|
|
|
max_over_time(deriv(rate(distance_covered_total[5s])[30s:5s])[10m:])
|
|
|
|
## Using functions, operators, etc.
|
|
|
|
Return the per-second rate for all time series with the `http_requests_total`
|
|
metric name, as measured over the last 5 minutes:
|
|
|
|
rate(http_requests_total[5m])
|
|
|
|
Assuming that the `http_requests_total` time series all have the labels `job`
|
|
(fanout by job name) and `instance` (fanout by instance of the job), we might
|
|
want to sum over the rate of all instances, so we get fewer output time series,
|
|
but still preserve the `job` dimension:
|
|
|
|
sum by (job) (
|
|
rate(http_requests_total[5m])
|
|
)
|
|
|
|
If we have two different metrics with the same dimensional labels, we can apply
|
|
binary operators to them and elements on both sides with the same label set
|
|
will get matched and propagated to the output. For example, this expression
|
|
returns the unused memory in MiB for every instance (on a fictional cluster
|
|
scheduler exposing these metrics about the instances it runs):
|
|
|
|
(instance_memory_limit_bytes - instance_memory_usage_bytes) / 1024 / 1024
|
|
|
|
The same expression, but summed by application, could be written like this:
|
|
|
|
sum by (app, proc) (
|
|
instance_memory_limit_bytes - instance_memory_usage_bytes
|
|
) / 1024 / 1024
|
|
|
|
If the same fictional cluster scheduler exposed CPU usage metrics like the
|
|
following for every instance:
|
|
|
|
instance_cpu_time_ns{app="lion", proc="web", rev="34d0f99", env="prod", job="cluster-manager"}
|
|
instance_cpu_time_ns{app="elephant", proc="worker", rev="34d0f99", env="prod", job="cluster-manager"}
|
|
instance_cpu_time_ns{app="turtle", proc="api", rev="4d3a513", env="prod", job="cluster-manager"}
|
|
instance_cpu_time_ns{app="fox", proc="widget", rev="4d3a513", env="prod", job="cluster-manager"}
|
|
...
|
|
|
|
...we could get the top 3 CPU users grouped by application (`app`) and process
|
|
type (`proc`) like this:
|
|
|
|
topk(3, sum by (app, proc) (rate(instance_cpu_time_ns[5m])))
|
|
|
|
Assuming this metric contains one time series per running instance, you could
|
|
count the number of running instances per application like this:
|
|
|
|
count by (app) (instance_cpu_time_ns)
|