-
-
- - {{i.name}} - -
- Simple time series selection -
++ Simple time series selection +
-Return all time series with the metric http_requests_total:
Return all time series with the metric http_requests_total:
http_requests_totalhttp_requests_totalReturn all time series with the metric http_requests_total and the givenjob and handler labels:
Return all time series with the metric http_requests_total and the givenjob and handler labels:
http_requests_total{job="apiserver", handler="/api/comments"}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:
+Return a whole range of time (in this case 5 minutes) for the same vector, + making it a range vector:
-http_requests_total{job="apiserver", handler="/api/comments"}[5m]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.
+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:
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"}http_requests_total{job=~".*server"}All regular expressions in Prometheus use RE2 syntax.
+All regular expressions in Prometheus use RE2 syntax.
-To select all HTTP status codes except 4xx ones, you could run:
+To select all HTTP status codes except 4xx ones, you could run:
-http_requests_total{status!~"4.."}http_requests_total{status!~"4.."}- Subquery -
++ Subquery +
-Return the 5-minute rate of the http_requests_total metric for the past 30 minutes, with a resolution of 1 minute.
Return the 5-minute 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]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.
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:])max_over_time(deriv(rate(distance_covered_total[5s])[30s:5s])[10m:])- Using functions, operators, etc. -
++ 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:
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])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:
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]))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):
+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(instance_memory_limit_bytes - instance_memory_usage_bytes) / 1024 / 1024The same expression, but summed by application, could be written like this:
+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 / 1024sum by (app, proc) (instance_memory_limit_bytes - instance_memory_usage_bytes) / 1024 / 1024If the same fictional cluster scheduler exposed CPU usage metrics like the following for every instance:
+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="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:
+ ...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])))
+ 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:
+ 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)
+ count by (app) (instance_cpu_time_ns)
+