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Update aggregation operator docs (#6240)

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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,
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
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:
sum(
sum by (app, proc) (
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
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
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
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)
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>)]
@ -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
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:
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**
applications, we could simply write: