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API Priority and Fairness

FEATURE STATE: Kubernetes v1.18 alpha
This feature is currently in a alpha state, meaning:

  • The version names contain alpha (e.g. v1alpha1).
  • Might be buggy. Enabling the feature may expose bugs. Disabled by default.
  • Support for feature may be dropped at any time without notice.
  • The API may change in incompatible ways in a later software release without notice.
  • Recommended for use only in short-lived testing clusters, due to increased risk of bugs and lack of long-term support.

Controlling the behavior of the Kubernetes API server in an overload situation is a key task for cluster administrators. The kube-apiserverControl plane component that serves the Kubernetes API. has some controls available (i.e. the --max-requests-inflight and --max-mutating-requests-inflight command-line flags) to limit the amount of outstanding work that will be accepted, preventing a flood of inbound requests from overloading and potentially crashing the API server, but these flags are not enough to ensure that the most important requests get through in a period of high traffic.

The API Priority and Fairness feature (APF) is an alternative that improves upon aforementioned max-inflight limitations. APF classifies and isolates requests in a more fine-grained way. It also introduces a limited amount of queuing, so that no requests are rejected in cases of very brief bursts. Requests are dispatched from queues using a fair queuing technique so that, for example, a poorly-behaved controllerA control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state. ) need not starve others (even at the same priority level).

Caution: Requests classified as “long-running” — primarily watches — are not subject to the API Priority and Fairness filter. This is also true for the --max-requests-inflight flag without the API Priority and Fairness feature enabled.

Enabling API Priority and Fairness

The API Priority and Fairness feature is controlled by a feature gate and is not enabled by default. See Feature Gates for a general explanation of feature gates and how to enable and disable them. The name of the feature gate for APF is “APIPriorityAndFairness”. This feature also involves an API GroupA set of related paths in the Kubernetes API. that must be enabled. You can do these things by adding the following command-line flags to your kube-apiserver invocation:

kube-apiserver \
--feature-gates=APIPriorityAndFairness=true \ \
 # …and other flags as usual

The command-line flag --enable-priority-and-fairness=false will disable the API Priority and Fairness feature, even if other flags have enabled it.


There are several distinct features involved in the API Priority and Fairness feature. Incoming requests are classified by attributes of the request using FlowSchemas, and assigned to priority levels. Priority levels add a degree of isolation by maintaining separate concurrency limits, so that requests assigned to different priority levels cannot starve each other. Within a priority level, a fair-queuing algorithm prevents requests from different flows from starving each other, and allows for requests to be queued to prevent bursty traffic from causing failed requests when the average load is acceptably low.

Priority Levels

Without APF enabled, overall concurrency in the API server is limited by the kube-apiserver flags --max-requests-inflight and --max-mutating-requests-inflight. With APF enabled, the concurrency limits defined by these flags are summed and then the sum is divided up among a configurable set of priority levels. Each incoming request is assigned to a single priority level, and each priority level will only dispatch as many concurrent requests as its configuration allows.

The default configuration, for example, includes separate priority levels for leader-election requests, requests from built-in controllers, and requests from Pods. This means that an ill-behaved Pod that floods the API server with requests cannot prevent leader election or actions by the built-in controllers from succeeding.


Even within a priority level there may be a large number of distinct sources of traffic. In an overload situation, it is valuable to prevent one stream of requests from starving others (in particular, in the relatively common case of a single buggy client flooding the kube-apiserver with requests, that buggy client would ideally not have much measurable impact on other clients at all). This is handled by use of a fair-queuing algorithm to process requests that are assigned the same priority level. Each request is assigned to a flow, identified by the name of the matching FlowSchema plus a flow distinguisher — which is either the requesting user, the target resource’s namespace, or nothing — and the system attempts to give approximately equal weight to requests in different flows of the same priority level.

After classifying a request into a flow, the API Priority and Fairness feature then may assign the request to a queue. This assignment uses a technique known as shuffle shardingA technique for assigning requests to queues that provides better isolation than hashing modulo the number of queues. , which makes relatively efficient use of queues to insulate low-intensity flows from high-intensity flows.

The details of the queuing algorithm are tunable for each priority level, and allow administrators to trade off memory use, fairness (the property that independent flows will all make progress when total traffic exceeds capacity), tolerance for bursty traffic, and the added latency induced by queuing.

Exempt requests

Some requests are considered sufficiently important that they are not subject to any of the limitations imposed by this feature. These exemptions prevent an improperly-configured flow control configuration from totally disabling an API server.


The Priority and Fairness feature ships with a suggested configuration that should suffice for experimentation; if your cluster is likely to experience heavy load then you should consider what configuration will work best. The suggested configuration groups requests into five priority classes:

  • The system priority level is for requests from the system:nodes group, i.e. Kubelets, which must be able to contact the API server in order for workloads to be able to schedule on them.

  • The leader-election priority level is for leader election requests from built-in controllers (in particular, requests for endpoints, configmaps, or leases coming from the system:kube-controller-manager or system:kube-scheduler users and service accounts in the kube-system namespace). These are important to isolate from other traffic because failures in leader election cause their controllers to fail and restart, which in turn causes more expensive traffic as the new controllers sync their informers.

  • The workload-high priority level is for other requests from built-in controllers.

  • The workload-low priority level is for requests from any other service account, which will typically include all requests from controllers runing in Pods.

  • The global-default priority level handles all other traffic, e.g. interactive kubectl commands run by nonprivileged users.

Additionally, there are two PriorityLevelConfigurations and two FlowSchemas that are built in and may not be overwritten:

  • The special exempt priority level is used for requests that are not subject to flow control at all: they will always be dispatched immediately. The special exempt FlowSchema classifies all requests from the system:masters group into this priority level. You may define other FlowSchemas that direct other requests to this priority level, if appropriate.

  • The special catch-all priority level is used in combination with the special catch-all FlowSchema to make sure that every request gets some kind of classification. Typically you should not rely on this catch-all configuration, and should create your own catch-all FlowSchema and PriorityLevelConfiguration (or use the global-default configuration that is installed by default) as appropriate. To help catch configuration errors that miss classifying some requests, the mandatory catch-all priority level only allows one concurrency share and does not queue requests, making it relatively likely that traffic that only matches the catch-all FlowSchema will be rejected with an HTTP 429 error.


The flow control API involves two kinds of resources. PriorityLevelConfigurations define the available isolation classes, the share of the available concurrency budget that each can handle, and allow for fine-tuning queuing behavior. FlowSchemas are used to classify individual inbound requests, matching each to a single PriorityLevelConfiguration.


A PriorityLevelConfiguration represents a single isolation class. Each PriorityLevelConfiguration has an independent limit on the number of outstanding requests, and limitations on the number of queued requests.

Concurrency limits for PriorityLevelConfigurations are not specified in absolute number of requests, but rather in “concurrency shares.” The total concurrency limit for the API Server is distributed among the existing PriorityLevelConfigurations in proportion with these shares. This allows a cluster administrator to scale up or down the total amount of traffic to a server by restarting kube-apiserver with a different value for --max-requests-inflight (or --max-mutating-requests-inflight), and all PriorityLevelConfigurations will see their maximum allowed concurrency go up (or down) by the same fraction.

Caution: With the Priority and Fairness feature enabled, the total concurrency limit for the server is set to the sum of –max-requests-inflight and –max-mutating-requests-inflight. There is no longer any distinction made between mutating and non-mutating requests; if you want to treat them separately for a given resource, make separate FlowSchemas that match the mutating and non-mutating verbs respectively.

When the volume of inbound requests assigned to a single PriorityLevelConfiguration is more than its permitted concurrency level, the type field of its specification determines what will happen to extra requests. A type of Reject means that excess traffic will immediately be rejected with an HTTP 429 (Too Many Requests) error. A type of Queue means that requests above the threshold will be queued, with the shuffle sharding and fair queuing techniques used to balance progress between request flows.

The queuing configuration allows tuning the fair queuing algorithm for a priority level. Details of the algorithm can be read in the enhancement proposal, but in short:

  • Increasing queues reduces the rate of collisions between different flows, at the cost of increased memory usage. A value of 1 here effectively disables the fair-queuing logic, but still allows requests to be queued.

  • Increasing queueLengthLimit allows larger bursts of traffic to be sustained without dropping any requests, at the cost of increased latency and memory usage.

  • Changing handSize allows you to adjust the probability of collisions between different flows and the overall concurrency available to a single flow in an overload situation.

    Note: A larger handSize makes it less likely for two individual flows to collide (and therefore for one to be able to starve the other), but more likely that a small number of flows can dominate the apiserver. A larger handSize also potentially increases the amount of latency that a single high-traffic flow can cause. The maximum number of queued requests possible from a single flow is handSize * queueLengthLimit.

Following is a table showing an interesting collection of shuffle sharding configurations, showing for each the probability that a given mouse (low-intensity flow) is squished by the elephants (high-intensity flows) for an illustrative collection of numbers of elephants. See , which computes this table.

Example Shuffle Sharding Configurations
HandSizeQueues1 elephant4 elephants16 elephants


A FlowSchema matches some inbound requests and assigns them to a priority level. Every inbound request is tested against every FlowSchema in turn, starting with those with numerically lowest — which we take to be the logically highest — matchingPrecedence and working onward. The first match wins.

Caution: Only the first matching FlowSchema for a given request matters. If multiple FlowSchemas match a single inbound request, it will be assigned based on the one with the highest matchingPrecedence. If multiple FlowSchemas with equal matchingPrecedence match the same request, the one with lexicographically smaller name will win, but it’s better not to rely on this, and instead to ensure that no two FlowSchemas have the same matchingPrecedence.

A FlowSchema matches a given request if at least one of its rules matches. A rule matches if at least one of its subjects and at least one of its resourceRules or nonResourceRules (depending on whether the incoming request is for a resource or non-resource URL) matches the request.

For the name field in subjects, and the verbs, apiGroups, resources, namespaces, and nonResourceURLs fields of resource and non-resource rules, the wildcard * may be specified to match all values for the given field, effectively removing it from consideration.

A FlowSchema’s distinguisherMethod.type determines how requests matching that schema will be separated into flows. It may be either ByUser, in which case one requesting user will not be able to starve other users of capacity, or ByNamespace, in which case requests for resources in one namespace will not be able to starve requests for resources in other namespaces of capacity, or it may be blank (or distinguisherMethod may be omitted entirely), in which case all requests matched by this FlowSchema will be considered part of a single flow. The correct choice for a given FlowSchema depends on the resource and your particular environment.


Every HTTP response from an API server with the priority and fairness feature enabled has two extra headers: X-Kubernetes-PF-FlowSchema-UID and X-Kubernetes-PF-PriorityLevel-UID, noting the flow schema that matched the request and the priority level to which it was assigned, respectively. The API objects’ names are not included in these headers in case the requesting user does not have permission to view them, so when debugging you can use a command like

kubectl get flowschemas -o custom-columns="uid:{metadata.uid},name:{}"
kubectl get prioritylevelconfigurations -o custom-columns="uid:{metadata.uid},name:{}"

to get a mapping of UIDs to names for both FlowSchemas and PriorityLevelConfigurations.


When you enable the API Priority and Fairness feature, the kube-apiserver exports additional metrics. Monitoring these can help you determine whether your configuration is inappropriately throttling important traffic, or find poorly-behaved workloads that may be harming system health.

  • apiserver_flowcontrol_rejected_requests_total counts requests that were rejected, grouped by the name of the assigned priority level, the name of the assigned FlowSchema, and the reason for rejection. The reason will be one of the following:

    • queue-full, indicating that too many requests were already queued,
    • concurrency-limit, indicating that the PriorityLevelConfiguration is configured to reject rather than queue excess requests, or
    • time-out, indicating that the request was still in the queue when its queuing time limit expired.
  • apiserver_flowcontrol_dispatched_requests_total counts requests that began executing, grouped by the name of the assigned priority level and the name of the assigned FlowSchema.

  • apiserver_flowcontrol_current_inqueue_requests gives the instantaneous total number of queued (not executing) requests, grouped by priority level and FlowSchema.

  • apiserver_flowcontrol_current_executing_requests gives the instantaneous total number of executing requests, grouped by priority level and FlowSchema.

  • apiserver_flowcontrol_request_queue_length_after_enqueue gives a histogram of queue lengths for the queues, grouped by priority level and FlowSchema, as sampled by the enqueued requests. Each request that gets queued contributes one sample to its histogram, reporting the length of the queue just after the request was added. Note that this produces different statistics than an unbiased survey would.

    Note: An outlier value in a histogram here means it is likely that a single flow (i.e., requests by one user or for one namespace, depending on configuration) is flooding the API server, and being throttled. By contrast, if one priority level’s histogram shows that all queues for that priority level are longer than those for other priority levels, it may be appropriate to increase that PriorityLevelConfiguration’s concurrency shares.

  • apiserver_flowcontrol_request_concurrency_limit gives the computed concurrency limit (based on the API server’s total concurrency limit and PriorityLevelConfigurations’ concurrency shares) for each PriorityLevelConfiguration.

  • apiserver_flowcontrol_request_wait_duration_seconds gives a histogram of how long requests spent queued, grouped by the FlowSchema that matched the request, the PriorityLevel to which it was assigned, and whether or not the request successfully executed.

    Note: Since each FlowSchema always assigns requests to a single PriorityLevelConfiguration, you can add the histograms for all the FlowSchemas for one priority level to get the effective histogram for requests assigned to that priority level.

  • apiserver_flowcontrol_request_execution_seconds gives a histogram of how long requests took to actually execute, grouped by the FlowSchema that matched the request and the PriorityLevel to which it was assigned.

What's next

For background information on design details for API priority and fairness, see the enhancement proposal. You can make suggestions and feature requests via SIG API Machinery.