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Chapter 1. Administrator metrics

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

To view different metrics for OpenShift Serverless, you can check the OpenShift Container Platform monitoring documentation.

1.1.1. Prerequisites for OpenShift Serverless administrator metrics
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  • You have enabled 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).
Warning

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

To resolve this issue, see the Enabling Knative Serving metrics when using Service Mesh with mTLS section in the Service Mesh integration documentation.

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

1.2. Serverless controller metrics
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Any component that implements controller logic emits the following metrics. These metrics show details about reconciliation operations and the work queue behavior that adds reconciliation requests to the queue.

Expand
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
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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.

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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
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Cluster administrators can view the following metrics for Knative Eventing components. By aggregating the metrics from HTTP code, you can separate the events into two categories; successful events (2xx) and failed events (5xx).

1.4.1. Broker ingress metrics
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You can use the following metrics to debug the broker ingress, evaluate its performance, and identify that events the ingress component dispatches.

Expand
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
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You can use the following metrics to debug broker filters, evaluate their performance, and confirm that the filters dispatch events. You can also measure the latency of event filtering.

Expand
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 required to process an event before dispatching it to a trigger subscriber.

Histogram

broker_name, container_name, filter_type, namespace_name, trigger_name, unique_name

Milliseconds

1.4.3. InMemoryChannel dispatcher metrics
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You can use the following metrics to debug InMemoryChannel channels, evaluate their performance, and identify the events that the channels dispatch.

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

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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

The event source retries and sends events that failed during the initial delivery trial.

Counter

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

Integer (no units)

1.4.5. Knative Kafka broker metrics
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You can use the following metrics to debug and visualize the performance of Kafka broker.

Expand
Metric nameDescriptionTypeTagsUnit

event_count_1_total{job="kafka-broker-receiver-sm-service", namespace="knative-eventing"}

Number of events received by a broker

Counter

name
broker name
namespace_name
broker namespace
event_type
event type
response_code
HTTP response code returned by the broker
response_code_class
HTTP response code class returned by the broker: 2xx, 3xx, 4xx, 5xx

Dimensionless

event_dispatch_latencies_ms_bucket{job="kafka-broker-receiver-sm-service", namespace="knative-eventing"}

The time spent dispatching an event to a Kafka cluster

Histogram

name
broker name
namespace_name
broker namespace
event_type
event type
response_code
HTTP response code returned by the broker
response_code_class
HTTP response code class returned by the broker: 2xx, 3xx, 4xx, 5xx

Milliseconds

kafka_broker_controller_consumer_group_expected_replicas

Number of expected replicas for a given Kafka consumer group resource

Gauge

consumer_name
resource name
namespace_name
resource namespace
consumer_kind
resource Kind, enum: KafkaSource, Trigger, Subscription
Note

In this context, resources refer to user facing entities such as Kafka source, trigger, and subscription. Avoid using internal or generated names when using these resources.

Dimensionless

kafka_broker_controller_consumer_group_ready_replicas

Number of ready replicas for a given Kafka consumer group resource

Gauge

consumer_name
resource name
namespace_name
resource namespace
consumer_kind
resource Kind, enum: KafkaSource, Trigger, Subscription
Note

In this context, resources refer to user facing entities such as Kafka source, trigger, and subscription. Avoid using internal or generated names when using these resources.

Dimensionless

1.4.6. Knative Kafka trigger metrics
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You can use the following metrics to debug and visualize the performance of Kafka triggers.

Expand
Metric nameDescriptionTypeTagsUnit

event_count_1_total{job="kafka-broker-dispatcher-sm-service", namespace="knative-eventing"}

Number of events dispatched by a trigger to a subscriber

Counter

consumer_name
trigger name
namespace_name
trigger namespace
name
broker name
event_type
event type
response_code
HTTP response code returned by the trigger subscriber service
response_code_class
HTTP response code class returned by the trigger subscriber service: 2xx, 3xx, 4xx, 5xx

Dimensionless

event_dispatch_latencies_ms_bucket{job="kafka-broker-dispatcher-sm-service", namespace="knative-eventing"}

The time spent dispatching an event to a subscriber

Histogram

consumer_name
trigger name
namespace_name
trigger namespace
name
broker name
event_type
event type
response_code
HTTP response code returned by the trigger subscriber service
response_code_class
HTTP response code class returned by the trigger subscriber service: 2xx, 3xx, 4xx, 5xx

Milliseconds

event_processing_latencies_ms_bucket{job="kafka-broker-dispatcher-sm-service", namespace="knative-eventing"}

The time spent processing and filtering an event

Histogram

consumer_name
trigger name
namespace_name
trigger namespace
name
broker name
event_type
event type

Milliseconds

1.4.7. Knative Kafka channel metrics
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You can use the following metrics to debug and visualize the performance of Kafka channel.

Expand
Metric nameDescriptionTypeTagsUnit

event_count_1_total{job="kafka-channel-receiver-sm-service", namespace="knative-eventing"}

Number of events received by a Kafka channel

Counter

name
Kafka channel name
namespace_name
Kafka channel namespace
event_type
event type
response_code
HTTP response code returned by the Kafka channel
response_code_class
HTTP response code class returned by the Kafka channel: 2xx, 3xx, 4xx, 5xx

Dimensionless

event_dispatch_latencies_ms_bucket{job="kafka-channel-receiver-sm-service", namespace="knative-eventing"}

The time spent dispatching an event to a Kafka cluster

Histogram

name
Kafka channel name
namespace_name
Kafka channel namespace
event_type
event type
response_code
HTTP response code returned by the Kafka channel
response_code_class
HTTP response code class returned by the Kafka channel: 2xx, 3xx, 4xx, 5xx

Milliseconds

1.4.8. Knative Kafka subscription metrics
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You can use the following metrics to debug and visualize the performance of subscriptions associated with the Kafka channel.

Expand
Metric nameDescriptionTypeTagsUnit

event_count_1_total{job="kafka-channel-dispatcher-sm-service", namespace="knative-eventing"}

Number of events dispatched by a subscription to a subscriber

Counter

consumer_name
Subscription name
namespace_name
Subscription namespace
name
KafkaChannel name
event_type
event type
response_code
HTTP response code returned by the Subscription subscriber service
response_code_class
HTTP response code class returned by the Subscription subscriber service: 2xx, 3xx, 4xx, 5xx

Dimensionless

event_dispatch_latencies_ms_bucket{job="kafka-channel-dispatcher-sm-service", namespace="knative-eventing"}

The time spent dispatching an event to a subscriber

Histogram

consumer_name
Subscription name
namespace_name
Subscription namespace
name
KafkaChannel name
event_type
event type
response_code
HTTP response code returned by the Subscription subscriber service
response_code_class
HTTP response code class returned by the Subscription subscriber service: 2xx, 3xx, 4xx, 5xx

Milliseconds

event_processing_latencies_ms_bucket{job="kafka-channel-dispatcher-sm-service", namespace="knative-eventing"}

The time spent processing an event

Histogram

consumer_name
Subscription name
namespace_name
Subscription namespace
name
KafkaChannel name
event_type
event type

Dimensionless

1.4.9. Knative Kafka source metrics
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You can use the following metrics to debug and visualize the performance of Kafka sources.

Expand
Metric nameDescriptionTypeTagsUnit

event_count_1_total{job="kafka-source-dispatcher-sm-service", namespace="knative-eventing"}

Number of events dispatched by a Kafka source

Counter

consumer_name
Kafka source name
namespace_name
Kafka source namespace
name
Kafka source name
event_type
event type
response_code
HTTP response code returned by the Kafka source sink service
response_code_class
HTTP response code class returned by the Kafka source sink service: 2xx, 3xx, 4xx, 5xx

Dimensionless

event_dispatch_latencies_ms_bucket{job="kafka-source-dispatcher-sm-service", namespace="knative-eventing"}

The time spent dispatching an event to a sink

Histogram

consumer_name
Kafka source name
namespace_name
Kafka source namespace
name
Kafka source name
event_type
event type
response_code
HTTP response code returned by the Kafka source sink service
response_code_class
HTTP response code class returned by the Kafka source sink service: 2xx, 3xx, 4xx, 5xx

Milliseconds

event_processing_latencies_ms_bucket{job="kafka-source-dispatcher-sm-service", namespace="knative-eventing"}

The time spent processing an event

Histogram

consumer_name
Kafka source name
namespace_name
Kafka source namespace
name
Kafka source name
event_type
event type

Milliseconds

kafka_broker_controller_consumer_group_expected_replicas

Number of expected replicas for a given Kafka consumer group resource

Gauge

consumer_name
resource name
namespace_name
resource namespace
consumer_kind
resource Kind, enum: KafkaSource, Trigger, Subscription
Note

In this context, resources refer to user facing entities such as Kafka source,trigger, and subscription. Avoid using internal or generated names when using these resources.

Dimensionless

kafka_broker_controller_consumer_group_ready_replicas

Number of ready replicas for a given Kafka consumer group resource

Gauge

consumer_name
resource name
namespace_name
resource namespace
consumer_kind
resource Kind, enum: KafkaSource, Trigger, Subscription
Note

In this context, resources refer to user facing entities such as Kafka source,trigger, and subscription. Avoid using internal or generated names when using these resources.

Dimensionless

1.4.10. Knative Kafka sink metrics
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You can use the following metrics to debug and visualize the performance of Kafka sinks.

Expand
Metric nameDescriptionTypeTagsUnit

event_count_1_total{job="kafka-sink-receiver-sm-service", namespace="knative-eventing"}

Number of events received by a broker

Counter

name
Kafka sink name
namespace_name
Kafka sink namespace
event_type
event type
response_code
HTTP response code returned by the Kafka sink
response_code_class
HTTP response code class returned by the Kafka sink: 2xx, 3xx, 4xx, 5xx

Dimensionless

event_dispatch_latencies_ms_bucket{job="kafka-sink-receiver-sm-service", namespace="knative-eventing"}

The time spent dispatching an event to a Kafka cluster

Histogram

name
Kafka sink name
namespace_name
Kafka sink namespace
event_type
event type
response_code
HTTP response code returned by the Kafka sink
response_code_class
HTTP response code class returned by the Kafka sink: 2xx, 3xx, 4xx, 5xx

Milliseconds

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

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

Expand
Metric nameDescriptionTypeTagsUnit

request_concurrency

The number of concurrent requests that routes 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 route to the activator. The activator handler processes these requests.

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
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The autoscaler component exposes several metrics related to autoscaler behavior for each revision. For example, you can monitor the number of pods that the autoscaler targets for a service. You can also monitor the average requests per second during the stable window and whether the autoscaler enters panic mode when using the Knative pod autoscaler (KPA).

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Metric nameDescriptionTypeTagsUnit

desired_pods

The number of pods the autoscaler tries to assign 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 the system allocates that are currently ready.

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
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Each Knative Serving control plane process emits many Go runtime memory statistics, as defined in MemStats.

Note

The name tag for each metric is an empty tag.

Expand
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 are free.

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.

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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