API Priority and Fairness

FEATURE STATE: Kubernetes v1.29 [stable]

Controlling the behavior of the Kubernetes API server in an overload situation is a key task for cluster administrators. The kube-apiserver 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 controller need not starve others (even at the same priority level).

This feature is designed to work well with standard controllers, which use informers and react to failures of API requests with exponential back-off, and other clients that also work this way.

Enabling/Disabling API Priority and Fairness

The API Priority and Fairness feature is controlled by a command-line flag and is enabled by default. See Options for a general explanation of the available kube-apiserver command-line options and how to enable and disable them. The name of the command-line option for APF is "--enable-priority-and-fairness". This feature also involves an API Group with: (a) a stable v1 version, introduced in 1.29, and enabled by default (b) a v1beta3 version, enabled by default, and deprecated in v1.29. You can disable the API group beta version v1beta3 by adding the following command-line flags to your kube-apiserver invocation:

kube-apiserver \
--runtime-config=flowcontrol.apiserver.k8s.io/v1beta3=false \
 # …and other flags as usual

The command-line flag --enable-priority-and-fairness=false will disable the API Priority and Fairness feature.

Recursive server scenarios

API Priority and Fairness must be used carefully in recursive server scenarios. These are scenarios in which some server A, while serving a request, issues a subsidiary request to some server B. Perhaps server B might even make a further subsidiary call back to server A. In situations where Priority and Fairness control is applied to both the original request and some subsidiary ones(s), no matter how deep in the recursion, there is a danger of priority inversions and/or deadlocks.

One example of recursion is when the kube-apiserver issues an admission webhook call to server B, and while serving that call, server B makes a further subsidiary request back to the kube-apiserver. Another example of recursion is when an APIService object directs the kube-apiserver to delegate requests about a certain API group to a custom external server B (this is one of the things called "aggregation").

When the original request is known to belong to a certain priority level, and the subsidiary controlled requests are classified to higher priority levels, this is one possible solution. When the original requests can belong to any priority level, the subsidiary controlled requests have to be exempt from Priority and Fairness limitation. One way to do that is with the objects that configure classification and handling, discussed below. Another way is to disable Priority and Fairness on server B entirely, using the techniques discussed above. A third way, which is the simplest to use when server B is not kube-apisever, is to build server B with Priority and Fairness disabled in the code.


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 particular limit 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.

The concurrency limits of the priority levels are periodically adjusted, allowing under-utilized priority levels to temporarily lend concurrency to heavily-utilized levels. These limits are based on nominal limits and bounds on how much concurrency a priority level may lend and how much it may borrow, all derived from the configuration objects mentioned below.

Seats Occupied by a Request

The above description of concurrency management is the baseline story. Requests have different durations but are counted equally at any given moment when comparing against a priority level's concurrency limit. In the baseline story, each request occupies one unit of concurrency. The word "seat" is used to mean one unit of concurrency, inspired by the way each passenger on a train or aircraft takes up one of the fixed supply of seats.

But some requests take up more than one seat. Some of these are list requests that the server estimates will return a large number of objects. These have been found to put an exceptionally heavy burden on the server. For this reason, the server estimates the number of objects that will be returned and considers the request to take a number of seats that is proportional to that estimated number.

Execution time tweaks for watch requests

API Priority and Fairness manages watch requests, but this involves a couple more excursions from the baseline behavior. The first concerns how long a watch request is considered to occupy its seat. Depending on request parameters, the response to a watch request may or may not begin with create notifications for all the relevant pre-existing objects. API Priority and Fairness considers a watch request to be done with its seat once that initial burst of notifications, if any, is over.

The normal notifications are sent in a concurrent burst to all relevant watch response streams whenever the server is notified of an object create/update/delete. To account for this work, API Priority and Fairness considers every write request to spend some additional time occupying seats after the actual writing is done. The server estimates the number of notifications to be sent and adjusts the write request's number of seats and seat occupancy time to include this extra work.


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. To enable distinct handling of distinct instances, controllers that have many instances should authenticate with distinct usernames

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 sharding, 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 flow control API involves two kinds of resources. PriorityLevelConfigurations define the available priority levels, 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 priority level. Each PriorityLevelConfiguration has an independent limit on the number of outstanding requests, and limitations on the number of queued requests.

The nominal concurrency limit for a PriorityLevelConfiguration is not specified in an absolute number of seats, but rather in "nominal concurrency shares." The total concurrency limit for the API Server is distributed among the existing PriorityLevelConfigurations in proportion to these shares, to give each level its nominal limit in terms of seats. 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.

The bounds on how much concurrency a priority level may lend and how much it may borrow are expressed in the PriorityLevelConfiguration as percentages of the level's nominal limit. These are resolved to absolute numbers of seats by multiplying with the nominal limit / 100.0 and rounding. The dynamically adjusted concurrency limit of a priority level is constrained to lie between (a) a lower bound of its nominal limit minus its lendable seats and (b) an upper bound of its nominal limit plus the seats it may borrow. At each adjustment the dynamic limits are derived by each priority level reclaiming any lent seats for which demand recently appeared and then jointly fairly responding to the recent seat demand on the priority levels, within the bounds just described.

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.

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 https://play.golang.org/p/Gi0PLgVHiUg , 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 FlowSchemas, starting with those with the numerically lowest matchingPrecedence and working upward. The first match wins.

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) match 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 ByUser, in which one requesting user will not be able to starve other users of capacity; ByNamespace, in which requests for resources in one namespace will not be able to starve requests for resources in other namespaces of capacity; or blank (or distinguisherMethod may be omitted entirely), in which 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.


Each kube-apiserver maintains two sorts of APF configuration objects: mandatory and suggested.

Mandatory Configuration Objects

The four mandatory configuration objects reflect fixed built-in guardrail behavior. This is behavior that the servers have before those objects exist, and when those objects exist their specs reflect this behavior. The four mandatory objects are as follows.

  • The mandatory exempt priority level is used for requests that are not subject to flow control at all: they will always be dispatched immediately. The mandatory 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 mandatory catch-all priority level is used in combination with the mandatory 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 suggested global-default priority level that is installed by default) as appropriate. Because it is not expected to be used normally, the mandatory catch-all priority level has a very small concurrency share and does not queue requests.

Suggested Configuration Objects

The suggested FlowSchemas and PriorityLevelConfigurations constitute a reasonable default configuration. You can modify these and/or create additional configuration objects if you want. If your cluster is likely to experience heavy load then you should consider what configuration will work best.

The suggested configuration groups requests into six priority levels:

  • The node-high priority level is for health updates from nodes.

  • The system priority level is for non-health 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 running in Pods.

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

The suggested FlowSchemas serve to steer requests into the above priority levels, and are not enumerated here.

Maintenance of the Mandatory and Suggested Configuration Objects

Each kube-apiserver independently maintains the mandatory and suggested configuration objects, using initial and periodic behavior. Thus, in a situation with a mixture of servers of different versions there may be thrashing as long as different servers have different opinions of the proper content of these objects.

Each kube-apiserver makes an initial maintenance pass over the mandatory and suggested configuration objects, and after that does periodic maintenance (once per minute) of those objects.

For the mandatory configuration objects, maintenance consists of ensuring that the object exists and, if it does, has the proper spec. The server refuses to allow a creation or update with a spec that is inconsistent with the server's guardrail behavior.

Maintenance of suggested configuration objects is designed to allow their specs to be overridden. Deletion, on the other hand, is not respected: maintenance will restore the object. If you do not want a suggested configuration object then you need to keep it around but set its spec to have minimal consequences. Maintenance of suggested objects is also designed to support automatic migration when a new version of the kube-apiserver is rolled out, albeit potentially with thrashing while there is a mixed population of servers.

Maintenance of a suggested configuration object consists of creating it --- with the server's suggested spec --- if the object does not exist. OTOH, if the object already exists, maintenance behavior depends on whether the kube-apiservers or the users control the object. In the former case, the server ensures that the object's spec is what the server suggests; in the latter case, the spec is left alone.

The question of who controls the object is answered by first looking for an annotation with key apf.kubernetes.io/autoupdate-spec. If there is such an annotation and its value is true then the kube-apiservers control the object. If there is such an annotation and its value is false then the users control the object. If neither of those conditions holds then the metadata.generation of the object is consulted. If that is 1 then the kube-apiservers control the object. Otherwise the users control the object. These rules were introduced in release 1.22 and their consideration of metadata.generation is for the sake of migration from the simpler earlier behavior. Users who wish to control a suggested configuration object should set its apf.kubernetes.io/autoupdate-spec annotation to false.

Maintenance of a mandatory or suggested configuration object also includes ensuring that it has an apf.kubernetes.io/autoupdate-spec annotation that accurately reflects whether the kube-apiservers control the object.

Maintenance also includes deleting objects that are neither mandatory nor suggested but are annotated apf.kubernetes.io/autoupdate-spec=true.

Health check concurrency exemption

The suggested configuration gives no special treatment to the health check requests on kube-apiservers from their local kubelets --- which tend to use the secured port but supply no credentials. With the suggested config, these requests get assigned to the global-default FlowSchema and the corresponding global-default priority level, where other traffic can crowd them out.

If you add the following additional FlowSchema, this exempts those requests from rate limiting.

apiVersion: flowcontrol.apiserver.k8s.io/v1
kind: FlowSchema
  name: health-for-strangers
  matchingPrecedence: 1000
    name: exempt
    - nonResourceRules:
      - nonResourceURLs:
          - "/healthz"
          - "/livez"
          - "/readyz"
          - "*"
        - kind: Group
            name: "system:unauthenticated"



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.

Maturity level BETA

  • apiserver_flowcontrol_rejected_requests_total is a counter vector (cumulative since server start) of requests that were rejected, broken down by the labels flow_schema (indicating the one that matched the request), priority_level (indicating the one to which the request was assigned), and reason. The reason label will be one of the following values:

    • 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.
    • time-out, indicating that the request was still in the queue when its queuing time limit expired.
    • cancelled, indicating that the request is not purge locked and has been ejected from the queue.
  • apiserver_flowcontrol_dispatched_requests_total is a counter vector (cumulative since server start) of requests that began executing, broken down by flow_schema and priority_level.

  • apiserver_flowcontrol_current_inqueue_requests is a gauge vector holding the instantaneous number of queued (not executing) requests, broken down by priority_level and flow_schema.

  • apiserver_flowcontrol_current_executing_requests is a gauge vector holding the instantaneous number of executing (not waiting in a queue) requests, broken down by priority_level and flow_schema.

  • apiserver_flowcontrol_current_executing_seats is a gauge vector holding the instantaneous number of occupied seats, broken down by priority_level and flow_schema.

  • apiserver_flowcontrol_request_wait_duration_seconds is a histogram vector of how long requests spent queued, broken down by the labels flow_schema, priority_level, and execute. The execute label indicates whether the request has started executing.

  • apiserver_flowcontrol_nominal_limit_seats is a gauge vector holding each priority level's nominal concurrency limit, computed from the API server's total concurrency limit and the priority level's configured nominal concurrency shares.

Maturity level ALPHA

  • apiserver_current_inqueue_requests is a gauge vector of recent high water marks of the number of queued requests, grouped by a label named request_kind whose value is mutating or readOnly. These high water marks describe the largest number seen in the one second window most recently completed. These complement the older apiserver_current_inflight_requests gauge vector that holds the last window's high water mark of number of requests actively being served.

  • apiserver_current_inqueue_seats is a gauge vector of the sum over queued requests of the largest number of seats each will occupy, grouped by labels named flow_schema and priority_level.

  • apiserver_flowcontrol_read_vs_write_current_requests is a histogram vector of observations, made at the end of every nanosecond, of the number of requests broken down by the labels phase (which takes on the values waiting and executing) and request_kind (which takes on the values mutating and readOnly). Each observed value is a ratio, between 0 and 1, of the number of requests divided by the corresponding limit on the number of requests (queue volume limit for waiting and concurrency limit for executing).

  • apiserver_flowcontrol_request_concurrency_in_use is a gauge vector holding the instantaneous number of occupied seats, broken down by priority_level and flow_schema.

  • apiserver_flowcontrol_priority_level_request_utilization is a histogram vector of observations, made at the end of each nanosecond, of the number of requests broken down by the labels phase (which takes on the values waiting and executing) and priority_level. Each observed value is a ratio, between 0 and 1, of a number of requests divided by the corresponding limit on the number of requests (queue volume limit for waiting and concurrency limit for executing).

  • apiserver_flowcontrol_priority_level_seat_utilization is a histogram vector of observations, made at the end of each nanosecond, of the utilization of a priority level's concurrency limit, broken down by priority_level. This utilization is the fraction (number of seats occupied) / (concurrency limit). This metric considers all stages of execution (both normal and the extra delay at the end of a write to cover for the corresponding notification work) of all requests except WATCHes; for those it considers only the initial stage that delivers notifications of pre-existing objects. Each histogram in the vector is also labeled with phase: executing (there is no seat limit for the waiting phase).

  • apiserver_flowcontrol_request_queue_length_after_enqueue is a histogram vector of queue lengths for the queues, broken down by priority_level and flow_schema, as sampled by the enqueued requests. Each request that gets queued contributes one sample to its histogram, reporting the length of the queue immediately after the request was added. Note that this produces different statistics than an unbiased survey would.

  • apiserver_flowcontrol_request_concurrency_limit is the same as apiserver_flowcontrol_nominal_limit_seats. Before the introduction of concurrency borrowing between priority levels, this was always equal to apiserver_flowcontrol_current_limit_seats (which did not exist as a distinct metric).

  • apiserver_flowcontrol_lower_limit_seats is a gauge vector holding the lower bound on each priority level's dynamic concurrency limit.

  • apiserver_flowcontrol_upper_limit_seats is a gauge vector holding the upper bound on each priority level's dynamic concurrency limit.

  • apiserver_flowcontrol_demand_seats is a histogram vector counting observations, at the end of every nanosecond, of each priority level's ratio of (seat demand) / (nominal concurrency limit). A priority level's seat demand is the sum, over both queued requests and those in the initial phase of execution, of the maximum of the number of seats occupied in the request's initial and final execution phases.

  • apiserver_flowcontrol_demand_seats_high_watermark is a gauge vector holding, for each priority level, the maximum seat demand seen during the last concurrency borrowing adjustment period.

  • apiserver_flowcontrol_demand_seats_average is a gauge vector holding, for each priority level, the time-weighted average seat demand seen during the last concurrency borrowing adjustment period.

  • apiserver_flowcontrol_demand_seats_stdev is a gauge vector holding, for each priority level, the time-weighted population standard deviation of seat demand seen during the last concurrency borrowing adjustment period.

  • apiserver_flowcontrol_demand_seats_smoothed is a gauge vector holding, for each priority level, the smoothed enveloped seat demand determined at the last concurrency adjustment.

  • apiserver_flowcontrol_target_seats is a gauge vector holding, for each priority level, the concurrency target going into the borrowing allocation problem.

  • apiserver_flowcontrol_seat_fair_frac is a gauge holding the fair allocation fraction determined in the last borrowing adjustment.

  • apiserver_flowcontrol_current_limit_seats is a gauge vector holding, for each priority level, the dynamic concurrency limit derived in the last adjustment.

  • apiserver_flowcontrol_request_execution_seconds is a histogram vector of how long requests took to actually execute, broken down by flow_schema and priority_level.

  • apiserver_flowcontrol_watch_count_samples is a histogram vector of the number of active WATCH requests relevant to a given write, broken down by flow_schema and priority_level.

  • apiserver_flowcontrol_work_estimated_seats is a histogram vector of the number of estimated seats (maximum of initial and final stage of execution) associated with requests, broken down by flow_schema and priority_level.

  • apiserver_flowcontrol_request_dispatch_no_accommodation_total is a counter vector of the number of events that in principle could have led to a request being dispatched but did not, due to lack of available concurrency, broken down by flow_schema and priority_level.

  • apiserver_flowcontrol_epoch_advance_total is a counter vector of the number of attempts to jump a priority level's progress meter backward to avoid numeric overflow, grouped by priority_level and success.

Good practices for using API Priority and Fairness

When a given priority level exceeds its permitted concurrency, requests can experience increased latency or be dropped with an HTTP 429 (Too Many Requests) error. To prevent these side effects of APF, you can modify your workload or tweak your APF settings to ensure there are sufficient seats available to serve your requests.

To detect whether requests are being rejected due to APF, check the following metrics:

  • apiserver_flowcontrol_rejected_requests_total: the total number of requests rejected per FlowSchema and PriorityLevelConfiguration.
  • apiserver_flowcontrol_current_inqueue_requests: the current number of requests queued per FlowSchema and PriorityLevelConfiguration.
  • apiserver_flowcontrol_request_wait_duration_seconds: the latency added to requests waiting in queues.
  • apiserver_flowcontrol_priority_level_seat_utilization: the seat utilization per PriorityLevelConfiguration.

Workload modifications

To prevent requests from queuing and adding latency or being dropped due to APF, you can optimize your requests by:

  • Reducing the rate at which requests are executed. A fewer number of requests over a fixed period will result in a fewer number of seats being needed at a given time.
  • Avoid issuing a large number of expensive requests concurrently. Requests can be optimized to use fewer seats or have lower latency so that these requests hold those seats for a shorter duration. List requests can occupy more than 1 seat depending on the number of objects fetched during the request. Restricting the number of objects retrieved in a list request, for example by using pagination, will use less total seats over a shorter period. Furthermore, replacing list requests with watch requests will require lower total concurrency shares as watch requests only occupy 1 seat during its initial burst of notifications. If using streaming lists in versions 1.27 and later, watch requests will occupy the same number of seats as a list request for its initial burst of notifications because the entire state of the collection has to be streamed. Note that in both cases, a watch request will not hold any seats after this initial phase.

Keep in mind that queuing or rejected requests from APF could be induced by either an increase in the number of requests or an increase in latency for existing requests. For example, if requests that normally take 1s to execute start taking 60s, it is possible that APF will start rejecting requests because requests are occupying seats for a longer duration than normal due to this increase in latency. If APF starts rejecting requests across multiple priority levels without a significant change in workload, it is possible there is an underlying issue with control plane performance rather than the workload or APF settings.

Priority and fairness settings

You can also modify the default FlowSchema and PriorityLevelConfiguration objects or create new objects of these types to better accommodate your workload.

APF settings can be modified to:

  • Give more seats to high priority requests.
  • Isolate non-essential or expensive requests that would starve a concurrency level if it was shared with other flows.

Give more seats to high priority requests

  1. If possible, the number of seats available across all priority levels for a particular kube-apiserver can be increased by increasing the values for the max-requests-inflight and max-mutating-requests-inflight flags. Alternatively, horizontally scaling the number of kube-apiserver instances will increase the total concurrency per priority level across the cluster assuming there is sufficient load balancing of requests.
  2. You can create a new FlowSchema which references a PriorityLevelConfiguration with a larger concurrency level. This new PriorityLevelConfiguration could be an existing level or a new level with its own set of nominal concurrency shares. For example, a new FlowSchema could be introduced to change the PriorityLevelConfiguration for your requests from global-default to workload-low to increase the number of seats available to your user. Creating a new PriorityLevelConfiguration will reduce the number of seats designated for existing levels. Recall that editing a default FlowSchema or PriorityLevelConfiguration will require setting the apf.kubernetes.io/autoupdate-spec annotation to false.
  3. You can also increase the NominalConcurrencyShares for the PriorityLevelConfiguration which is serving your high priority requests. Alternatively, for versions 1.26 and later, you can increase the LendablePercent for competing priority levels so that the given priority level has a higher pool of seats it can borrow.

Isolate non-essential requests from starving other flows

For request isolation, you can create a FlowSchema whose subject matches the user making these requests or create a FlowSchema that matches what the request is (corresponding to the resourceRules). Next, you can map this FlowSchema to a PriorityLevelConfiguration with a low share of seats.

For example, suppose list event requests from Pods running in the default namespace are using 10 seats each and execute for 1 minute. To prevent these expensive requests from impacting requests from other Pods using the existing service-accounts FlowSchema, you can apply the following FlowSchema to isolate these list calls from other requests.

Example FlowSchema object to isolate list event requests:

apiVersion: flowcontrol.apiserver.k8s.io/v1
kind: FlowSchema
  name: list-events-default-service-account
    type: ByUser
  matchingPrecedence: 8000
    name: catch-all
    - resourceRules:
      - apiGroups:
          - '*'
          - default
          - events
          - list
        - kind: ServiceAccount
            name: default
            namespace: default
  • This FlowSchema captures all list event calls made by the default service account in the default namespace. The matching precedence 8000 is lower than the value of 9000 used by the existing service-accounts FlowSchema so these list event calls will match list-events-default-service-account rather than service-accounts.
  • The catch-all PriorityLevelConfiguration is used to isolate these requests. The catch-all priority level has a very small concurrency share and does not queue requests.

What's next

Last modified July 02, 2024 at 1:35 PM PST: fixes typo (856c384387)