kubernetes
/
website
mirror of https://github.com/kubernetes/website.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

promql highlighting 

Signed-off-by: Laura Lorenz <lauralorenz@google.com>
pull/51818/head
Laura Lorenz 2025-08-06 19:36:32 +00:00
parent 75d0139f49
commit 51d3214096
1 changed files with 27 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

47
content/en/docs/concepts/cluster-administration/dra.md
View File

@ -116,16 +116,16 @@ your deployment by monitoring the following metrics.
The following metrics look closely at the internal ResourceClaim controller
managed by the `kube-controller-manager` component.
* Workqueue Add Rate: Monitor
`sum(rate(workqueue_adds_total{name="resource_claim"}[5m]))` to gauge how
quickly items are added to the ResourceClaim controller.
* Workqueue Add Rate: Monitor {{< highlight promql
>}}sum(rate(workqueue_adds_total{name="resource_claim"}[5m])){{< /highlight
>}} to gauge how quickly items are added to the ResourceClaim controller.
* Workqueue Depth: Track
`sum(workqueue_depth{endpoint="kube-controller-manager",
name="resource_claim"})` to identify any backlogs in the ResourceClaim
{{< highlight promql >}}sum(workqueue_depth{endpoint="kube-controller-manager",
name="resource_claim"}){{< /highlight >}} to identify any backlogs in the ResourceClaim
controller.
* Workqueue Work Duration: Observe `histogram_quantile(0.99,
* Workqueue Work Duration: Observe {{< highlight promql >}}histogram_quantile(0.99,
sum(rate(workqueue_work_duration_seconds_bucket{name="resource_claim"}[5m]))
by (le))` to understand the speed at which the ResourceClaim controller
by (le)){{< /highlight >}} to understand the speed at which the ResourceClaim controller
processes work.
If you are experiencing low Workqueue Add Rate, high Workqueue Depth, and/or
@ -148,12 +148,14 @@ that the end-to-end metrics are ultimately influenced by the
`kube-controller-manager`'s performance in creating ResourceClaims from
ResourceClainTemplates in deployments that heavily use ResourceClainTemplates.
* Scheduler End-to-End Duration: Monitor `histogram_quantile(0.99,
* Scheduler End-to-End Duration: Monitor {{< highlight promql
>}}histogram_quantile(0.99,
sum(increase(scheduler_pod_scheduling_sli_duration_seconds_bucket[5m])) by
(le))`.
* Scheduler Algorithm Latency: Track `histogram_quantile(0.99,
(le)){{< /highlight >>}}.
* Scheduler Algorithm Latency: Track {{< highlight promql
>}}histogram_quantile(0.99,
sum(increase(scheduler_scheduling_algorithm_duration_seconds_bucket[5m])) by
(le))`.
(le)){{< /highlight >}}.
### `kubelet` metrics
@ -162,12 +164,14 @@ the `NodePrepareResources` and `NodeUnprepareResources` methods of the DRA
driver. You can observe this behavior from the kubelet's point of view with the
following metrics.
* Kubelet NodePrepareResources: Monitor `histogram_quantile(0.99,
* Kubelet NodePrepareResources: Monitor {{< highlight promql
>}}histogram_quantile(0.99,
sum(rate(dra_operations_duration_seconds_bucket{operation_name="PrepareResources"}[5m]))
by (le))`.
* Kubelet NodeUnprepareResources: Track `histogram_quantile(0.99,
by (le)){{< /highlight >}}.
* Kubelet NodeUnprepareResources: Track {{< highlight promql
>}}histogram_quantile(0.99,
sum(rate(dra_operations_duration_seconds_bucket{operation_name="UnprepareResources"}[5m]))
by (le))`.
by (le)){{< /highlight >}}.
### DRA kubeletplugin operations
@ -178,14 +182,17 @@ which surfaces its own metric for the underlying gRPC operation
behavior from the point of view of the internal kubeletplugin with the following
metrics.
* DRA kubeletplugin gRPC NodePrepareResources operation: Observe `histogram_quantile(0.99,
* DRA kubeletplugin gRPC NodePrepareResources operation: Observe {{< highlight
promql >}}histogram_quantile(0.99,
sum(rate(dra_grpc_operations_duration_seconds_bucket{method_name=~".*NodePrepareResources"}[5m]))
by (le))`
* DRA kubeletplugin gRPC NodeUnprepareResources operation: Observe `histogram_quantile(0.99,
by (le)){{< /highlight >}} .
* DRA kubeletplugin gRPC NodeUnprepareResources operation: Observe {{< highlight
promql >}}histogram_quantile(0.99,
sum(rate(dra_grpc_operations_duration_seconds_bucket{method_name=~".*NodeUnprepareResources"}[5m]))
by (le))`.
by (le)){{< /highlight >}}.
## {{% heading "whatsnext" %}}
* [Learn more about DRA](/docs/concepts/scheduling-eviction/dynamic-resource-allocation)
* [Learn more about
DRA](/docs/concepts/scheduling-eviction/dynamic-resource-allocation)