Query examples

    Return all time series with the metric :

    Return all time series with the metric http_requests_total and the given job and handler labels:

    1. http_requests_total{job="apiserver", handler="/api/comments"}

    Return a whole range of time (in this case 5 minutes) for the same vector, making it a range vector:

    1. 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.

      All regular expressions in Prometheus use .

      To select all HTTP status codes except 4xx ones, you could run:

      Return the 5-minute rate of the http_requests_total metric for the past 30 minutes, with a resolution of 1 minute.

      1. rate(http_requests_total[5m])[30m:1m]

      This is an example of a nested subquery. The subquery for the function uses the default resolution. Note that using subqueries unnecessarily is unwise.

      1. max_over_time(deriv(rate(distance_covered_total[5s])[30s:5s])[10m:])
      1. 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:

      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):

      1. (instance_memory_limit_bytes - instance_memory_usage_bytes) / 1024 / 1024

      The same expression, but summed by application, could be written like this:

      1. sum by (app, proc) (
      2. ) / 1024 / 1024

      If the same fictional cluster scheduler exposed CPU usage metrics like the following for every instance:

      1. instance_cpu_time_ns{app="lion", proc="web", rev="34d0f99", env="prod", job="cluster-manager"}
      2. instance_cpu_time_ns{app="elephant", proc="worker", rev="34d0f99", env="prod", job="cluster-manager"}
      3. instance_cpu_time_ns{app="turtle", proc="api", rev="4d3a513", env="prod", job="cluster-manager"}
      4. instance_cpu_time_ns{app="fox", proc="widget", rev="4d3a513", env="prod", job="cluster-manager"}

      Assuming this metric contains one time series per running instance, you could count the number of running instances per application like this:

      1. count by (app) (instance_cpu_time_ns)