API Priority and Fairness
Kubernetes v1.20 [beta]
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
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.
--max-requests-inflightflag without the API Priority and Fairness feature enabled. API Priority and Fairness does apply to watch requests. When API Priority and Fairness is disabled, watch requests are not subject to the
Enabling/Disabling API Priority and Fairness
The API Priority and Fairness feature is controlled by a feature gate
and is enabled by default. See Feature
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 Group with: (a) a
v1alpha1 version and a
v1beta1 version, disabled by default, and
v1beta3 versions, enabled by default. You can
disable the feature gate and API group beta versions by adding the
following command-line flags to your
kube-apiserver \ --feature-gates=APIPriorityAndFairness=false \ --runtime-config=flowcontrol.apiserver.k8s.io/v1beta2=false,flowcontrol.apiserver.k8s.io/v1beta3=false \ # …and other flags as usual
Alternatively, you can enable the v1alpha1 and v1beta1 versions of the API group
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.
Without APF enabled, overall concurrency in the API server is limited by the
--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.
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.
define the available priority levels, the share of the available concurrency
budget that each can handle, and allow for fine-tuning queuing behavior.
are used to classify individual inbound requests, matching each to a
single PriorityLevelConfiguration. There is also a
of the same API group, and it has the same Kinds with the same syntax and
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-mutating-requests-inflight), and all
PriorityLevelConfigurations will see their maximum allowed concurrency
go up (or down) by the same fraction.
v1beta3the relevant PriorityLevelConfiguration field is named "assured concurrency shares" rather than "nominal concurrency shares". Also, in Kubernetes release 1.25 and earlier there were no periodic adjustments: the nominal/assured limits were always applied without adjustment.
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.
--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:
queuesreduces 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.
queueLengthLimitallows larger bursts of traffic to be sustained without dropping any requests, at the cost of increased latency and memory usage.
handSizeallows 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
handSizemakes 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
handSizealso 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 https://play.golang.org/p/Gi0PLgVHiUg , which computes this table.
|HandSize||Queues||1 elephant||4 elephants||16 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
working upward. The first match wins.
matchingPrecedence. If multiple FlowSchemas with equal
matchingPrecedencematch the same request, the one with lexicographically smaller
namewill win, but it's better not to rely on this, and instead to ensure that no two FlowSchemas have the same
A FlowSchema matches a given request if at least one of its
matches. A rule matches if at least one of its
subjects and at least
one of its
nonResourceRules (depending on whether the
incoming request is for a resource or non-resource URL) match the request.
name field in subjects, and the
nonResourceURLs fields of resource and non-resource rules,
* may be specified to match all values for the given field,
effectively removing it from consideration.
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.
exemptpriority level is used for requests that are not subject to flow control at all: they will always be dispatched immediately. The mandatory
exemptFlowSchema classifies all requests from the
system:mastersgroup into this priority level. You may define other FlowSchemas that direct other requests to this priority level, if appropriate.
catch-allpriority level is used in combination with the mandatory
catch-allFlowSchema 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-defaultpriority level that is installed by default) as appropriate. Because it is not expected to be used normally, the mandatory
catch-allpriority 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:
node-highpriority level is for health updates from nodes.
systempriority level is for non-health requests from the
system:nodesgroup, i.e. Kubelets, which must be able to contact the API server in order for workloads to be able to schedule on them.
leader-electionpriority level is for leader election requests from built-in controllers (in particular, requests for
leasescoming from the
system:kube-schedulerusers and service accounts in the
kube-systemnamespace). 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.
workload-highpriority level is for other requests from built-in controllers.
workload-lowpriority level is for requests from any other service account, which will typically include all requests from controllers running in Pods.
global-defaultpriority level handles all other traffic, e.g. interactive
kubectlcommands 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
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.
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
The question of who controls the object is answered by first looking
for an annotation with key
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
Maintenance of a mandatory or suggested configuration object also
includes ensuring that it has an
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
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
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/v1beta3 kind: FlowSchema metadata: name: health-for-strangers spec: matchingPrecedence: 1000 priorityLevelConfiguration: name: exempt rules: - nonResourceRules: - nonResourceURLs: - "/healthz" - "/livez" - "/readyz" verbs: - "*" subjects: - kind: Group group: name: "system:unauthenticated"
priority_levelwere inconsistently named
priorityLevel, respectively. If you're running Kubernetes versions v1.19 and earlier, you should refer to the documentation for your version.
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_totalis 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
reasonlabel 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_totalis a counter vector (cumulative since server start) of requests that began executing, broken down by
apiserver_current_inqueue_requestsis a gauge vector of recent high water marks of the number of queued requests, grouped by a label named
request_kindwhose value is
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_requestsgauge vector that holds the last window's high water mark of number of requests actively being served.
apiserver_flowcontrol_read_vs_write_current_requestsis 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
request_kind(which takes on the values
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_current_inqueue_requestsis a gauge vector holding the instantaneous number of queued (not executing) requests, broken down by
apiserver_flowcontrol_current_executing_requestsis a gauge vector holding the instantaneous number of executing (not waiting in a queue) requests, broken down by
apiserver_flowcontrol_request_concurrency_in_useis a gauge vector holding the instantaneous number of occupied seats, broken down by
apiserver_flowcontrol_priority_level_request_utilizationis 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
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_utilizationis 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_enqueueis a histogram vector of queue lengths for the queues, broken down by
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.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_limitis 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_nominal_limit_seatsis 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.
apiserver_flowcontrol_lower_limit_seatsis a gauge vector holding the lower bound on each priority level's dynamic concurrency limit.
apiserver_flowcontrol_upper_limit_seatsis a gauge vector holding the upper bound on each priority level's dynamic concurrency limit.
apiserver_flowcontrol_demand_seatsis 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_watermarkis a gauge vector holding, for each priority level, the maximum seat demand seen during the last concurrency borrowing adjustment period.
apiserver_flowcontrol_demand_seats_averageis 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_stdevis 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_smoothedis a gauge vector holding, for each priority level, the smoothed enveloped seat demand determined at the last concurrency adjustment.
apiserver_flowcontrol_target_seatsis a gauge vector holding, for each priority level, the concurrency target going into the borrowing allocation problem.
apiserver_flowcontrol_seat_fair_fracis a gauge holding the fair allocation fraction determined in the last borrowing adjustment.
apiserver_flowcontrol_current_limit_seatsis a gauge vector holding, for each priority level, the dynamic concurrency limit derived in the last adjustment.
apiserver_flowcontrol_request_wait_duration_secondsis a histogram vector of how long requests spent queued, broken down by the labels
executelabel indicates whether the request has started executing.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_secondsis a histogram vector of how long requests took to actually execute, broken down by
apiserver_flowcontrol_watch_count_samplesis a histogram vector of the number of active WATCH requests relevant to a given write, broken down by
apiserver_flowcontrol_work_estimated_seatsis a histogram vector of the number of estimated seats (maximum of initial and final stage of execution) associated with requests, broken down by
apiserver_flowcontrol_request_dispatch_no_accommodation_totalis 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
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.
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
- If possible, the number of seats available across all priority levels for a
kube-apiservercan be increased by increasing the values for the
max-mutating-requests-inflightflags. Alternatively, horizontally scaling the number of
kube-apiserverinstances will increase the total concurrency per priority level across the cluster assuming there is sufficient load balancing of requests.
- 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-specannotation to false.
- 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/v1beta3 kind: FlowSchema metadata: name: list-events-default-service-account spec: distinguisherMethod: type: ByUser matchingPrecedence: 8000 priorityLevelConfiguration: name: catch-all rules: - resourceRules: - apiGroups: - '*' namespaces: - default resources: - events verbs: - list subjects: - kind: ServiceAccount 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.