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Chapter 1. Administrator metrics

1.1. Serverless administrator metrics

Metrics enable cluster administrators to monitor how OpenShift Serverless cluster components and workloads are performing.

You can view different metrics for OpenShift Serverless by navigating to Dashboards in the web console Administrator perspective.

1.1.1. Prerequisites

  • See the OpenShift Container Platform documentation on Managing metrics for information about enabling metrics for your cluster.
  • You have access to an account with cluster administrator access (or dedicated administrator access for OpenShift Dedicated or Red Hat OpenShift Service on AWS).
  • You have access to the Administrator perspective in the web console.
Warning

If Service Mesh is enabled with mTLS, metrics for Knative Serving are disabled by default because Service Mesh prevents Prometheus from scraping metrics.

For information about resolving this issue, see Enabling Knative Serving metrics when using Service Mesh with mTLS.

Scraping the metrics does not affect autoscaling of a Knative service, because scraping requests do not go through the activator. Consequently, no scraping takes place if no pods are running.

1.2. Serverless controller metrics

The following metrics are emitted by any component that implements a controller logic. These metrics show details about reconciliation operations and the work queue behavior upon which reconciliation requests are added to the work queue.

Metric nameDescriptionTypeTagsUnit

work_queue_depth

The depth of the work queue.

Gauge

reconciler

Integer (no units)

reconcile_count

The number of reconcile operations.

Counter

reconciler, success

Integer (no units)

reconcile_latency

The latency of reconcile operations.

Histogram

reconciler, success

Milliseconds

workqueue_adds_total

The total number of add actions handled by the work queue.

Counter

name

Integer (no units)

workqueue_queue_latency_seconds

The length of time an item stays in the work queue before being requested.

Histogram

name

Seconds

workqueue_retries_total

The total number of retries that have been handled by the work queue.

Counter

name

Integer (no units)

workqueue_work_duration_seconds

The length of time it takes to process and item from the work queue.

Histogram

name

Seconds

workqueue_unfinished_work_seconds

The length of time that outstanding work queue items have been in progress.

Histogram

name

Seconds

workqueue_longest_running_processor_seconds

The length of time that the longest outstanding work queue items has been in progress.

Histogram

name

Seconds

1.3. Webhook metrics

Webhook metrics report useful information about operations. For example, if a large number of operations fail, this might indicate an issue with a user-created resource.

Metric nameDescriptionTypeTagsUnit

request_count

The number of requests that are routed to the webhook.

Counter

admission_allowed, kind_group, kind_kind, kind_version, request_operation, resource_group, resource_namespace, resource_resource, resource_version

Integer (no units)

request_latencies

The response time for a webhook request.

Histogram

admission_allowed, kind_group, kind_kind, kind_version, request_operation, resource_group, resource_namespace, resource_resource, resource_version

Milliseconds

1.4. Knative Eventing metrics

Cluster administrators can view the following metrics for Knative Eventing components.

By aggregating the metrics from HTTP code, events can be separated into two categories; successful events (2xx) and failed events (5xx).

1.4.1. Broker ingress metrics

You can use the following metrics to debug the broker ingress, see how it is performing, and see which events are being dispatched by the ingress component.

Metric nameDescriptionTypeTagsUnit

event_count

Number of events received by a broker.

Counter

broker_name, event_type, namespace_name, response_code, response_code_class, unique_name

Integer (no units)

event_dispatch_latencies

The time taken to dispatch an event to a channel.

Histogram

broker_name, event_type, namespace_name, response_code, response_code_class, unique_name

Milliseconds

1.4.2. Broker filter metrics

You can use the following metrics to debug broker filters, see how they are performing, and see which events are being dispatched by the filters. You can also measure the latency of the filtering action on an event.

Metric nameDescriptionTypeTagsUnit

event_count

Number of events received by a broker.

Counter

broker_name, container_name, filter_type, namespace_name, response_code, response_code_class, trigger_name, unique_name

Integer (no units)

event_dispatch_latencies

The time taken to dispatch an event to a channel.

Histogram

broker_name, container_name, filter_type, namespace_name, response_code, response_code_class, trigger_name, unique_name

Milliseconds

event_processing_latencies

The time it takes to process an event before it is dispatched to a trigger subscriber.

Histogram

broker_name, container_name, filter_type, namespace_name, trigger_name, unique_name

Milliseconds

1.4.3. InMemoryChannel dispatcher metrics

You can use the following metrics to debug InMemoryChannel channels, see how they are performing, and see which events are being dispatched by the channels.

Metric nameDescriptionTypeTagsUnit

event_count

Number of events dispatched by InMemoryChannel channels.

Counter

broker_name, container_name, filter_type, namespace_name, response_code, response_code_class, trigger_name, unique_name

Integer (no units)

event_dispatch_latencies

The time taken to dispatch an event from an InMemoryChannel channel.

Histogram

broker_name, container_name, filter_type, namespace_name, response_code, response_code_class, trigger_name, unique_name

Milliseconds

1.4.4. Event source metrics

You can use the following metrics to verify that events have been delivered from the event source to the connected event sink.

Metric nameDescriptionTypeTagsUnit

event_count

Number of events sent by the event source.

Counter

broker_name, container_name, filter_type, namespace_name, response_code, response_code_class, trigger_name, unique_name

Integer (no units)

retry_event_count

Number of retried events sent by the event source after initially failing to be delivered.

Counter

event_source, event_type, name, namespace_name, resource_group, response_code, response_code_class, response_error, response_timeout

Integer (no units)

1.5. Knative Serving metrics

Cluster administrators can view the following metrics for Knative Serving components.

1.5.1. Activator metrics

You can use the following metrics to understand how applications respond when traffic passes through the activator.

Metric nameDescriptionTypeTagsUnit

request_concurrency

The number of concurrent requests that are routed to the activator, or average concurrency over a reporting period.

Gauge

configuration_name, container_name, namespace_name, pod_name, revision_name, service_name

Integer (no units)

request_count

The number of requests that are routed to activator. These are requests that have been fulfilled from the activator handler.

Counter

configuration_name, container_name, namespace_name, pod_name, response_code, response_code_class, revision_name, service_name,

Integer (no units)

request_latencies

The response time in milliseconds for a fulfilled, routed request.

Histogram

configuration_name, container_name, namespace_name, pod_name, response_code, response_code_class, revision_name, service_name

Milliseconds

1.5.2. Autoscaler metrics

The autoscaler component exposes a number of metrics related to autoscaler behavior for each revision. For example, at any given time, you can monitor the targeted number of pods the autoscaler tries to allocate for a service, the average number of requests per second during the stable window, or whether the autoscaler is in panic mode if you are using the Knative pod autoscaler (KPA).

Metric nameDescriptionTypeTagsUnit

desired_pods

The number of pods the autoscaler tries to allocate for a service.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

excess_burst_capacity

The excess burst capacity served over the stable window.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

stable_request_concurrency

The average number of requests for each observed pod over the stable window.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

panic_request_concurrency

The average number of requests for each observed pod over the panic window.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

target_concurrency_per_pod

The number of concurrent requests that the autoscaler tries to send to each pod.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

stable_requests_per_second

The average number of requests-per-second for each observed pod over the stable window.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

panic_requests_per_second

The average number of requests-per-second for each observed pod over the panic window.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

target_requests_per_second

The number of requests-per-second that the autoscaler targets for each pod.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

panic_mode

This value is 1 if the autoscaler is in panic mode, or 0 if the autoscaler is not in panic mode.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

requested_pods

The number of pods that the autoscaler has requested from the Kubernetes cluster.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

actual_pods

The number of pods that are allocated and currently have a ready state.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

not_ready_pods

The number of pods that have a not ready state.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

pending_pods

The number of pods that are currently pending.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

terminating_pods

The number of pods that are currently terminating.

Gauge

configuration_name, namespace_name, revision_name, service_name

Integer (no units)

1.5.3. Go runtime metrics

Each Knative Serving control plane process emits a number of Go runtime memory statistics (MemStats).

Note

The name tag for each metric is an empty tag.

Metric nameDescriptionTypeTagsUnit

go_alloc

The number of bytes of allocated heap objects. This metric is the same as heap_alloc.

Gauge

name

Integer (no units)

go_total_alloc

The cumulative bytes allocated for heap objects.

Gauge

name

Integer (no units)

go_sys

The total bytes of memory obtained from the operating system.

Gauge

name

Integer (no units)

go_lookups

The number of pointer lookups performed by the runtime.

Gauge

name

Integer (no units)

go_mallocs

The cumulative count of heap objects allocated.

Gauge

name

Integer (no units)

go_frees

The cumulative count of heap objects that have been freed.

Gauge

name

Integer (no units)

go_heap_alloc

The number of bytes of allocated heap objects.

Gauge

name

Integer (no units)

go_heap_sys

The number of bytes of heap memory obtained from the operating system.

Gauge

name

Integer (no units)

go_heap_idle

The number of bytes in idle, unused spans.

Gauge

name

Integer (no units)

go_heap_in_use

The number of bytes in spans that are currently in use.

Gauge

name

Integer (no units)

go_heap_released

The number of bytes of physical memory returned to the operating system.

Gauge

name

Integer (no units)

go_heap_objects

The number of allocated heap objects.

Gauge

name

Integer (no units)

go_stack_in_use

The number of bytes in stack spans that are currently in use.

Gauge

name

Integer (no units)

go_stack_sys

The number of bytes of stack memory obtained from the operating system.

Gauge

name

Integer (no units)

go_mspan_in_use

The number of bytes of allocated mspan structures.

Gauge

name

Integer (no units)

go_mspan_sys

The number of bytes of memory obtained from the operating system for mspan structures.

Gauge

name

Integer (no units)

go_mcache_in_use

The number of bytes of allocated mcache structures.

Gauge

name

Integer (no units)

go_mcache_sys

The number of bytes of memory obtained from the operating system for mcache structures.

Gauge

name

Integer (no units)

go_bucket_hash_sys

The number of bytes of memory in profiling bucket hash tables.

Gauge

name

Integer (no units)

go_gc_sys

The number of bytes of memory in garbage collection metadata.

Gauge

name

Integer (no units)

go_other_sys

The number of bytes of memory in miscellaneous, off-heap runtime allocations.

Gauge

name

Integer (no units)

go_next_gc

The target heap size of the next garbage collection cycle.

Gauge

name

Integer (no units)

go_last_gc

The time that the last garbage collection was completed in Epoch or Unix time.

Gauge

name

Nanoseconds

go_total_gc_pause_ns

The cumulative time in garbage collection stop-the-world pauses since the program started.

Gauge

name

Nanoseconds

go_num_gc

The number of completed garbage collection cycles.

Gauge

name

Integer (no units)

go_num_forced_gc

The number of garbage collection cycles that were forced due to an application calling the garbage collection function.

Gauge

name

Integer (no units)

go_gc_cpu_fraction

The fraction of the available CPU time of the program that has been used by the garbage collector since the program started.

Gauge

name

Integer (no units)

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