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Update aggregation operator docs (#6240) 

Update the aggregation operator documentation.
* Include before expression style syntax as valid.
* Update examples to show before style.

Signed-off-by: Ben Kochie <superq@gmail.com>
This commit is contained in:
Ben Kochie 2019-10-28 17:37:07 +01:00 committed by Julius Volz
parent 3b39f6ae45
commit 8340db5614
2 changed files with 15 additions and 8 deletions
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12
docs/querying/examples.md
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@ -59,7 +59,9 @@ Assuming that the `http_requests_total` time series all have the labels `job`
want to sum over the rate of all instances, so we get fewer output time series, want to sum over the rate of all instances, so we get fewer output time series,
but still preserve the `job` dimension: but still preserve the `job` dimension:
sum(rate(http_requests_total[5m])) by (job) sum by (job) (
rate(http_requests_total[5m])
)
If we have two different metrics with the same dimensional labels, we can apply 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 binary operators to them and elements on both sides with the same label set
@ -71,9 +73,9 @@ scheduler exposing these metrics about the instances it runs):
The same expression, but summed by application, could be written like this: The same expression, but summed by application, could be written like this:
sum( sum by (app, proc) (
instance_memory_limit_bytes - instance_memory_usage_bytes instance_memory_limit_bytes - instance_memory_usage_bytes
) by (app, proc) / 1024 / 1024 ) / 1024 / 1024
If the same fictional cluster scheduler exposed CPU usage metrics like the If the same fictional cluster scheduler exposed CPU usage metrics like the
following for every instance: following for every instance:
@ -87,9 +89,9 @@ following for every instance:
...we could get the top 3 CPU users grouped by application (`app`) and process ...we could get the top 3 CPU users grouped by application (`app`) and process
type (`proc`) like this: type (`proc`) like this:
topk(3, sum(rate(instance_cpu_time_ns[5m])) by (app, proc)) topk(3, sum by (app, proc) (rate(instance_cpu_time_ns[5m])))
Assuming this metric contains one time series per running instance, you could Assuming this metric contains one time series per running instance, you could
count the number of running instances per application like this: count the number of running instances per application like this:
count(instance_cpu_time_ns) by (app) count by (app) (instance_cpu_time_ns)

11
docs/querying/operators.md
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@ -196,7 +196,12 @@ vector of fewer elements with aggregated values:
* `quantile` (calculate φ-quantile (0 ≤ φ ≤ 1) over dimensions) * `quantile` (calculate φ-quantile (0 ≤ φ ≤ 1) over dimensions)
These operators can either be used to aggregate over **all** label dimensions These operators can either be used to aggregate over **all** label dimensions
or preserve distinct dimensions by including a `without` or `by` clause. or preserve distinct dimensions by including a `without` or `by` clause. These
clauses may be used before or after the expression.
<aggr-op> [without|by (<label list>)] ([parameter,] <vector expression>)
or
<aggr-op>([parameter,] <vector expression>) [without|by (<label list>)] <aggr-op>([parameter,] <vector expression>) [without|by (<label list>)]
@ -221,11 +226,11 @@ If the metric `http_requests_total` had time series that fan out by
`application`, `instance`, and `group` labels, we could calculate the total `application`, `instance`, and `group` labels, we could calculate the total
number of seen HTTP requests per application and group over all instances via: number of seen HTTP requests per application and group over all instances via:
sum(http_requests_total) without (instance) sum without (instance) (http_requests_total)
Which is equivalent to: Which is equivalent to:
sum(http_requests_total) by (application, group) sum by (application, group) (http_requests_total)
If we are just interested in the total of HTTP requests we have seen in **all** If we are just interested in the total of HTTP requests we have seen in **all**
applications, we could simply write: applications, we could simply write: