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---
title: API 优先级和公平性
content_type: concept
min-kubernetes-server-version: v1.18
---
<!-- overview -->
{{< feature-state state="alpha" for_k8s_version="v1.18" >}}
<!--
Controlling the behavior of the Kubernetes API server in an overload situation
is a key task for cluster administrators. The {{< glossary_tooltip
term_id="kube-apiserver" text="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.
-->
对于集群管理员来说，控制 Kubernetes API 服务器在过载情况下的行为是一项关键任务。
{{< glossary_tooltip term_id="kube-apiserver" text="kube-apiserver" >}}
有一些控件（例如：命令行标志 `--max-requests-inflight` 和 `--max-mutating-requests-inflight` ）,
可以限制将要接受的未处理的请求，从而防止过量请求入站，潜在导致 API 服务器崩溃。
但是这些标志不足以保证在高流量期间，最重要的请求仍能被服务器接受。
<!--
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
{{< glossary_tooltip text="controller" term_id="controller" >}} need not
starve others (even at the same priority level).
-->
API 优先级和公平性（ APF ）是一种替代方案，可提升上述最大并发限制。
APF 以更细粒度的方式对请求进行分类和隔离。
它还引入了空间有限的排队机制，因此在非常短暂的突发情况下，API 服务器不会拒绝任何请求。
通过使用公平排队技术从队列中分发请求，这样，
一个行为不佳的 {{< glossary_tooltip text="控制器" term_id="controller" >}}
就不会饿死其他控制器（即使优先级相同）。
<!--
{{< 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.
{{< /caution >}}
-->
{{< caution >}}
属于“长时间运行”类型的请求（主要是 watch ）不受 API 优先级和公平性过滤器的约束。
如果未启用 APF 特性，即便设置 `--max-requests-inflight` 标志，该类请求也不受约束。
{{< /caution >}}
<!-- body -->
<!-- ## Enabling API Priority and Fairness -->
## 启用 API 优先级和公平性 {#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](/docs/reference/command-line-tools-reference/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 {{< glossary_tooltip term_id="api-group"
text="API Group" >}} that must be enabled. You can do these
things by adding the following command-line flags to your
`kube-apiserver` invocation:
-->
APF 特性由特性门控控制，默认情况下不启用。有关如何启用和禁用特性门控的描述，
请参见[特性门控](/docs/reference/command-line-tools-reference/feature-gates/)。
APF 的特性门控叫做 `APIPriorityAndFairness` 。
此特性要求必须启用某个 {{< glossary_tooltip term_id="api-group" text="API Group" >}}。
你可以在启动 `kube-apiserver` 时，添加以下命令行标志来完成这些操作：
```shell
kube-apiserver \
--feature-gates=APIPriorityAndFairness=true \
--runtime-config=flowcontrol.apiserver.k8s.io/v1alpha1=true \
# …其他配置与之前相同
```
<!--
The command-line flag `--enable-priority-and-fairness=false` will disable the
API Priority and Fairness feature, even if other flags have enabled it.
-->
命令行标志 `--enable-priority-fairness=false` 将彻底禁用 APF 特性，即使其他标志启用它也是无效。
<!--
## Concepts
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.
-->
## 概念 {#concepts}
APF 特性包含几个不同的功能。
传入的请求通过 _流模式_ 按照其属性分类，并分配优先级。
每个优先级维护自定义的并发限制，加强了隔离度，这样不同优先级的请求，就不会相互饿死。
在同一个优先级内，公平排队算法可以防止来自不同 _流_ 的请求相互饿死。
该算法将请求排队，通过排队机制，防止在平均负载较低时，通信量突增而导致请求失败。
<!--
### 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.
-->
### 优先级 {#Priority-Levels}
如果未启用 APF ，API 服务器中的整体并发量将受到 `kube-apiserver` 的参数
`--max-requests-inflight` 和 `--max-mutating-requests-inflight` 的限制。
启用 APF 后，将对这些参数定义的并发限制进行求和，然后将总和分配到一组可配置的 _优先级_ 中。
每个传入的请求都会分配一个优先级，每个优先级仅分发其配置允许的并发请求数。
<!--
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.
-->
例如，默认配置包括针对领导者选举请求，内置控制器请求和 Pod 请求，都单独设置优先级。
这表示即使异常的 Pod 向 API 服务器发送大量请求，但无法阻止领导者选举或内置控制器的操作执行成功。
<!--
### Queuing
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.
-->
### 排队 {#Queuing}
即使在同一优先级内，也可能存在大量不同的流量源。
在过载情况下，防止一个请求流饿死其他流是非常有价值的
（尤其是在一个较为常见的场景中，一个 bug 客户端会疯狂请求 kube-apiserver ，
理想情况下，该 bug 客户端不会对其他客户端产生太大的影响）。
公平排队算法在处理具有相同优先级的请求时，实现了上述场景。
该算法给每个请求都分配一个 _流_ ，该 _流_ 由匹配的 _流模式_ 的名称加上一个 _流区分器_
（可以是发出请求的用户、目标资源的名称空间或什么都不是）标识，
并且系统尝试为不同流中具有相同优先级的的请求，赋予近似相等的权重。
<!--
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 {{< glossary_tooltip term_id="shuffle-sharding"
text="shuffle sharding" >}}, which makes relatively efficient use of
queues to insulate low-intensity flows from high-intensity flows.
-->
将请求划分到流中之后，APF 功能将请求分配到队列中。
分配时使用一种称为 {{< glossary_tooltip term_id="shuffle-sharding" text="混洗分片" >}} 的技术，
该技术可以相对有效地利用队列，隔离低强度流与高强度流。
<!--
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.
-->
排队算法的细节可针对每个优先等级进行调整，并允许管理员在内存占用、
公平性（当总流量超标时，独立流将都会继续前进）、
突发流量的容忍度以及排队引发的额外延迟之间进行权衡。
<!--
### 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.
-->
### 豁免请求 {#Exempt-requests}
某些特别重要的请求不受制于此特性施加的任何限制。这些豁免可防止不当的流控配置完全禁用 API 服务器。
<!--
## Defaults
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:
-->
## 默认值 {#Defaults}
APF 特性附带推荐配置，该配置足够进行实验；
如果你的集群有可能承受较大的负载，那么你应该考虑哪种配置最有效。
推荐配置将请求分为五个优先级：
<!--
* 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.
-->
* `system` 优先级用于 `system:nodes` 组（即 Kubelets ）的请求，
kubelets 必须能连上 API 服务器，以便工作负载能够调度到其上。
<!--
* 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.
-->
* `leader-election` 优先级用于内置控制器的领导选举的请求
（特别是来自 `system:kube-controller-manager` 的 `endpoints` 、 `configmaps` 或 `leases` 的请求，
或者在 `kube-system` 命名空间下的 `system:kube-scheduler` 的用户和服务帐户）。
隔离其他流量对这些请求非常重要，因为领导者选举失败会导致控制器发生故障并重新启动，
这反过来会导致新启动的控制器在同步信息时，流量开销更大。
<!--
* The `workload-high` priority level is for other requests from built-in
controllers.
-->
* `workload-high` 优先级用于内置控制器的请求。
<!--
* The `workload-low` priority level is for requests from any other service
account, which will typically include all requests from controllers running in
Pods.
-->
* `workload-low` 优先级适用于来自任何服务帐户的请求，通常包括来自 Pods 中运行的控制器的所有请求。
<!--
* The `global-default` priority level handles all other traffic, e.g.
interactive `kubectl` commands run by nonprivileged users.
-->
* `global-default` 优先级可处理所有其他流量，例如：非特权用户运行的交互式 `kubectl` 命令。
<!--
Additionally, there are two PriorityLevelConfigurations and two FlowSchemas that
are built in and may not be overwritten:
-->
此外，内置了两个优先级配置和两个流模式，它们可能不在上述五个等级之内：
<!--
* 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.
-->
* 特殊的 `exempt` 优先级的请求完全不受流控限制：它们总是立刻被分发。
特殊的 `exempt` 流模式把 `system:masters` 组的所有请求都归类该优先级。
如果合适，你可以自定义流模式，将其他请求定向到该优先级。
<!--
* 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.
-->
* 特殊的 `catch-all` 优先级与特殊的 `catch-all` 流模式结合使用，以确保每个请求都分类。
一般地，你不应该依赖于 `catch-all` 的配置，而应适当地创建自己的 `catch-all` 流模式和优先级配置
（或使用默认安装的 `global-default` 配置）。
为了帮助捕获部分请求未分类的配置错误，强制要求 `catch-all` 优先级仅允许一个并发份额，
并且不对请求进行排队，使得仅与 `catch-all` 流模式匹配的流量被拒绝的可能性更高，并显示 HTTP 429 错误。
<!-- ## Health check concurrency exemption -->
## 健康检查并发豁免 {#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.
-->
推荐配置没有对本地 kubelet 在 kube-apiserver 上运行健康检查请求进行任何特殊处理
——它们倾向于使用安全端口，但不提供凭据。
通过推荐配置，这些请求将分配 `global-default` 流模式和 `global-default` 优先级，
这样其他流量可以排除健康检查。
<!--
If you add the following additional FlowSchema, this exempts those
requests from rate limiting.
-->
如果添加以下流模式，健康检查请求不受速率限制。
{{< caution >}}
<!--
Making this change also allows any hostile party to then send
health-check requests that match this FlowSchema, at any volume they
like. If you have a web traffic filter or similar external security
mechanism to protect your cluster's API server from general internet
traffic, you can configure rules to block any health check requests
that originate from outside your cluster.
-->
进行此更改后，任何敌对方都可以发送与此流模式匹配的任意数量的健康检查请求。
如果你有 Web 流量过滤器或类似的外部安全机制保护集群的 API 服务器免受常规网络流量的侵扰，
则可以配置规则，阻止所有来自集群外部的健康检查请求。
{{< /caution >}}
{{< codenew file="priority-and-fairness/health-for-strangers.yaml" >}}
<!--
## Resources
The flow control API involves two kinds of resources.
[PriorityLevelConfigurations](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#prioritylevelconfiguration-v1alpha1-flowcontrol-apiserver-k8s-io)
define the available isolation classes, the share of the available concurrency
budget that each can handle, and allow for fine-tuning queuing behavior.
[FlowSchemas](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#flowschema-v1alpha1-flowcontrol-apiserver-k8s-io)
are used to classify individual inbound requests, matching each to a single
PriorityLevelConfiguration.
-->
## 资源 {#Resources}
流控 API 涉及两种资源。
[优先级配置](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#prioritylevelconfiguration-v1alpha1-flowcontrol-apiserver-k8s-io)
定义隔离类型和可处理的并发预算量，还可以微调排队行为。
[流模式](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#flowschema-v1alpha1-flowcontrol-apiserver-k8s-io)
用于对每个入站请求进行分类，并匹配一个优先级配置。
<!--
### 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.
-->
### 优先级配置 {#PriorityLevelConfiguration}
一个优先级配置表示单个隔离类型。每个优先级配置对未完成的请求数都有独立的限制，对排队中的请求数也有限制。
<!--
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.
-->
优先级配置的并发限制不是指定请求绝对数量，而是在“并发份额”中指定。
API 服务器的总并发量限制通过这些份额按例分配到现有优先级配置中。
集群管理员可以更改 `--max-requests-inflight` （或 `--max-mutating-requests-inflight` ）的值，
再重新启动 `kube-apiserver` 来增加或减小服务器的总流量，
然后所有的优先级配置将看到其最大并发增加（或减少）了相同的比例。
<!--
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.
-->
{{< caution >}}
启用 APF 功能后，服务器的总并发量限制将设置为
`--max-requests-inflight` 和 `--max-mutating-requests-inflight` 之和。
可变请求和不可变请求之间不再有任何区别；
如果对于某种资源，你需要区别对待不同请求，请创建不同的流模式分别匹配可变请求和不可变请求。
{{< /caution >}}
<!--
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.
-->
当入站请求的数量大于分配的优先级配置中允许的并发级别时， `type` 字段将确定对额外请求的处理方式。
`Reject` 类型，表示多余的流量将立即被 HTTP 429（请求过多）错误所拒绝。
`Queue` 类型，表示对超过阈值的请求进行排队，将使用阈值分片和公平排队技术来平衡请求流之间的进度。
<!--
The queuing configuration allows tuning the fair queuing algorithm for a
priority level. Details of the algorithm can be read in the [enhancement
proposal](#whats-next), but in short:
-->
公平排队算法支持通过排队配置对优先级微调。 可以在[增强建议](#whats-next)中阅读算法的详细信息，但总之：
<!--
* 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.
-->
* `queues` 递增能减少不同流之间的冲突概率，但代价是增加了内存使用量。
值为1时，会禁用公平排队逻辑，但仍允许请求排队。
<!--
* Increasing `queueLengthLimit` allows larger bursts of traffic to be
sustained without dropping any requests, at the cost of increased
latency and memory usage.
-->
* `queueLengthLimit` 递增可以在不丢弃任何请求的情况下支撑更大的突发流量，
但代价是增加了等待时间和内存使用量。
<!--
* 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`.
{{< /note >}}
-->
* 修改 `handSize` 允许你调整过载情况下不同流之间的冲突概率以及单个流可用的整体并发性。
{{< note >}}
较大的 `handSize` 使两个单独的流程发生碰撞的可能性较小（因此，一个流可以饿死另一个流），
但是更有可能的是少数流可以控制 apiserver。
较大的 `handSize` 还可能增加单个高并发流的延迟量。
单个流中可能排队的请求的最大数量为 `handSize *queueLengthLimit` 。
{{< /note >}}
<!--
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.
-->
下表显示了有趣的随机分片配置集合，
每行显示给定的老鼠（低强度流）被不同数量的大象挤压（高强度流）的概率。
表来源请参阅： https://play.golang.org/p/Gi0PLgVHiUg
{{< table caption = "Example Shuffle Sharding Configurations" >}}
<!-- HandSize | Queues | 1 elephant | 4 elephants | 16 elephants -->
随机分片 | 队列数 | 1个大象 | 4个大象 | 16个大象
|----------|-----------|------------|----------------|--------------------|
| 12 | 32 | 4.428838398950118e-09 | 0.11431348830099144 | 0.9935089607656024 |
| 10 | 32 | 1.550093439632541e-08 | 0.0626479840223545 | 0.9753101519027554 |
| 10 | 64 | 6.601827268370426e-12 | 0.00045571320990370776 | 0.49999929150089345 |
| 9 | 64 | 3.6310049976037345e-11 | 0.00045501212304112273 | 0.4282314876454858 |
| 8 | 64 | 2.25929199850899e-10 | 0.0004886697053040446 | 0.35935114681123076 |
| 8 | 128 | 6.994461389026097e-13 | 3.4055790161620863e-06 | 0.02746173137155063 |
| 7 | 128 | 1.0579122850901972e-11 | 6.960839379258192e-06 | 0.02406157386340147 |
| 7 | 256 | 7.597695465552631e-14 | 6.728547142019406e-08 | 0.0006709661542533682 |
| 6 | 256 | 2.7134626662687968e-12 | 2.9516464018476436e-07 | 0.0008895654642000348 |
| 6 | 512 | 4.116062922897309e-14 | 4.982983350480894e-09 | 2.26025764343413e-05 |
| 6 | 1024 | 6.337324016514285e-16 | 8.09060164312957e-11 | 4.517408062903668e-07 |
{{< /table >}}
<!-- ### FlowSchema -->
### 流模式 {#FlowSchema}
<!--
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.
-->
流模式匹配一些入站请求，并将它们分配给优先级。
每个入站请求都会对所有流模式测试是否匹配，
首先从 `matchingPrecedence` 数值最低的匹配开始（我们认为这是逻辑上匹配度最高），
然后依次进行，直到首个匹配出现。
{{< 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`.
-->
对一个请求来说，只有首个匹配的流模式才有意义。
如果一个入站请求与多个流模式匹配，则将基于 `matchingPrecedence` 值最高的请求进行筛选。
如果一个请求匹配多个流模式且 `matchingPrecedence` 的值相同，则按 `name` 的字典序选择最小，
但是最好不要依赖它，而是确保不存在两个流模式具有相同的 `matchingPrecedence` 值。
{{< /caution >}}
<!--
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.
-->
当给定的请求与某个流模式的 `rules` 的其中一条匹配，那么就认为该请求与该流模式匹配。
判断规则与该请求是否匹配，**不仅**要求该条规则的 `subjects` 字段至少存在一个与该请求相匹配，
**而且**要求该条规则的 `resourceRules` 或 `nonResourceRules`
（取决于传入请求是针对资源URL还是非资源URL）字段至少存在一个与该请求相匹配。
<!--
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.
-->
对于 `subjects` 中的 `name` 字段和资源和非资源规则的
`verbs`，`apiGroups`，`resources`，`namespaces` 和 `nonResourceURLs` 字段，
可以指定通配符 `*` 来匹配任意值，从而有效地忽略该字段。
<!--
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.
-->
流模式的 `distinguisherMethod.type` 字段决定了如何把与该模式匹配的请求分散到各个流中。
可能是 `ByUser` ，在这种情况下，一个请求用户将无法饿死其他容量的用户；
或者是 `ByNamespace` ，在这种情况下，一个名称空间中的资源请求将无法饿死其它名称空间的资源请求；
或者它可以为空（或者可以完全省略 `distinguisherMethod` ），
在这种情况下，与此流模式匹配的请求将被视为单个流的一部分。
资源和你的特定环境决定了如何选择正确一个流模式。
<!--
## Diagnostics
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
-->
## 诊断程序 {#Diagnostics}
启用了 APF 的 API 服务器，它每个 HTTP 响应都有两个额外的 HTTP 头：
`X-Kubernetes-PF-FlowSchema-UID` 和 `X-Kubernetes-PF-PriorityLevel-UID`，
注意与请求匹配的流模式和已分配的优先级。
如果请求用户没有查看这些对象的权限，则这些 HTTP 头中将不包含 API 对象的名称，
因此在调试时，你可以使用类似如下的命令：
```shell
kubectl get flowschemas -o custom-columns="uid:{metadata.uid},name:{metadata.name}"
kubectl get prioritylevelconfigurations -o custom-columns="uid:{metadata.uid},name:{metadata.name}"
```
<!--
to get a mapping of UIDs to names for both FlowSchemas and
PriorityLevelConfigurations.
-->
来获取 UID 到流模式的名称和 UID 到优先级配置的名称的映射。
<!-- ## Observability -->
## 可观察性 {#Observability}
<!-- ### Metrics -->
### 指标 {#Metrics}
<!--
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.
-->
当你开启了 APF 后，kube-apiserver 会暴露额外指标。
监视这些指标有助于判断你的配置是否不当地限制了重要流量，
或者发现可能会损害系统健康的，行为不良的工作负载。
<!--
* `apiserver_flowcontrol_rejected_requests_total` is a counter vector
(cumulative since server start) of requests that were rejected,
broken down by the labels `flowSchema` (indicating the one that
matched the request), `priorityLevel` (indicating the one to which
the request was assigned), and `reason`. The `reason` label will be
have 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, or
* `time-out`, indicating that the request was still in the queue
when its queuing time limit expired.
-->
* `apiserver_flowcontrol_rejected_requests_total` 是一个计数器向量，
记录被拒绝的请求数量（自服务器启动以来累计值），
由标签 `flowSchema` （表示与请求匹配的流模式）， `priorityLevel` （表示分配给请该求的优先级）和 `reason` 拆分。
`reason` 标签将具有以下值之一：
* `queue-full` ，表明已经有太多请求排队，
* `concurrency-limit` ，表示将 PriorityLevelConfiguration 配置为 `Reject` 而不是 `Queue` ，或者
* `time-out`, 表示在其排队时间超期的请求仍在队列中。
<!--
* `apiserver_flowcontrol_dispatched_requests_total` is a counter
vector (cumulative since server start) of requests that began
executing, broken down by the labels `flowSchema` (indicating the
one that matched the request) and `priorityLevel` (indicating the
one to which the request was assigned).
-->
* `apiserver_flowcontrol_dispatched_requests_total` 是一个计数器向量，
记录开始执行的请求数量（自服务器启动以来的累积值），
由标签 `flowSchema` （表示与请求匹配的流模式）和 `priorityLevel`（表示分配给该请求的优先级）拆分。
<!--
* `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_requests` 是一个表向量，
记录最近排队请求数量的高水位线，
由标签 `request_kind` 分组，标签的值为 `mutating` 或 `readOnly`。
这些高水位线表示在最近一秒钟内看到的最大数字。
它们补充说明了老的表向量 `apiserver_current_inflight_requests`
（该量保存了最后一个窗口中，正在处理的请求数量的高水位线）。
<!--
* `apiserver_flowcontrol_read_vs_write_request_count_samples` is a
histogram vector of observations of the then-current 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`). The observations are made
periodically at a high rate.
-->
* `apiserver_flowcontrol_read_vs_write_request_count_samples` 是一个直方图向量，
记录当前请求数量的观察值，
由标签 `phase` （取值为 `waiting` 和 `executing` ）和 `request_kind` （取值 `mutating` 和 `readOnly` ）拆分。
定期以高速率观察该值。
<!--
* `apiserver_flowcontrol_read_vs_write_request_count_watermarks` is a
histogram vector of high or low water marks 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`); the label `mark` takes on
values `high` and `low`. The water marks are accumulated over
windows bounded by the times when an observation was added to
`apiserver_flowcontrol_read_vs_write_request_count_samples`. These
water marks show the range of values that occurred between samples.
-->
* `apiserver_flowcontrol_read_vs_write_request_count_watermarks` 是一个直方图向量，
记录请求数量的高/低水位线，
由标签 `phase` （取值为 `waiting` 和 `executing` ）和 `request_kind` （取值为 `mutating` 和 `readOnly` ）拆分；
标签 `mark` 取值为 `high` 和 `low` 。
`apiserver_flowcontrol_read_vs_write_request_count_samples` 向量观察到有值新增，则该向量累积。
这些水位线显示了样本值的范围。
<!--
* `apiserver_flowcontrol_current_inqueue_requests` is a gauge vector
holding the instantaneous number of queued (not executing) requests,
broken down by the labels `priorityLevel` and `flowSchema`.
-->
* `apiserver_flowcontrol_current_inqueue_requests` 是一个表向量，
记录包含排队中的（未执行）请求的瞬时数量，
由标签 `priorityLevel` 和 `flowSchema` 拆分。
<!--
* `apiserver_flowcontrol_current_executing_requests` is a gauge vector
holding the instantaneous number of executing (not waiting in a
queue) requests, broken down by the labels `priorityLevel` and
`flowSchema`.
-->
* `apiserver_flowcontrol_current_executing_requests` 是一个表向量，
记录包含执行中（不在队列中等待）请求的瞬时数量，
由标签 `priorityLevel` 和 `flowSchema` 拆分。
<!--
* `apiserver_flowcontrol_priority_level_request_count_samples` is a
histogram vector of observations of the then-current number of
requests broken down by the labels `phase` (which takes on the
values `waiting` and `executing`) and `priorityLevel`. Each
histogram gets observations taken periodically, up through the last
activity of the relevant sort. The observations are made at a high
rate.
-->
* `apiserver_flowcontrol_priority_level_request_count_samples` 是一个直方图向量，
记录当前请求的观测值，由标签 `phase` （取值为`waiting` 和 `executing`）和 `priorityLevel` 拆分。
每个直方图都会定期进行观察，直到相关类别的最后活动为止。观察频率高。
<!--
* `apiserver_flowcontrol_priority_level_request_count_watermarks` is a
histogram vector of high or low water marks of the number of
requests broken down by the labels `phase` (which takes on the
values `waiting` and `executing`) and `priorityLevel`; the label
`mark` takes on values `high` and `low`. The water marks are
accumulated over windows bounded by the times when an observation
was added to
`apiserver_flowcontrol_priority_level_request_count_samples`. These
water marks show the range of values that occurred between samples.
-->
* `apiserver_flowcontrol_priority_level_request_count_watermarks` 是一个直方图向量，
记录请求数的高/低水位线，由标签 `phase` （取值为 `waiting` 和 `executing` ）和 `priorityLevel` 拆分；
标签 `mark` 取值为 `high` 和 `low` 。
`apiserver_flowcontrol_priority_level_request_count_samples` 向量观察到有值新增，则该向量累积。
这些水位线显示了样本值的范围。
<!--
* `apiserver_flowcontrol_request_queue_length_after_enqueue` is a
histogram vector of queue lengths for the queues, broken down by
the labels `priorityLevel` 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.
-->
* `apiserver_flowcontrol_request_queue_length_after_enqueue` 是一个直方图向量，
记录请求队列的长度，由标签 `priorityLevel` 和 `flowSchema` 拆分。
每个排队中的请求都会为其直方图贡献一个样本，并在添加请求后立即上报队列的长度。
请注意，这样产生的统计数据与无偏调查不同。
{{< 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.
-->
直方图中的离群值在这里表示单个流（即，一个用户或一个名称空间的请求，具体取决于配置）正在疯狂请求 API 服务器，并受到限制。
相反，如果一个优先级的直方图显示该优先级的所有队列都比其他优先级的队列长，则增加优先级配置的并发份额是比较合适的。
{{< /note >}}
<!--
* `apiserver_flowcontrol_request_concurrency_limit` is a gauge vector
hoding the computed concurrency limit (based on the API server's
total concurrency limit and PriorityLevelConfigurations' concurrency
shares), broken down by the label `priorityLevel`.
-->
* `apiserver_flowcontrol_request_concurrency_limit` 是一个表向量，
记录并发限制的计算值（基于 API 服务器的总并发限制和优先级配置的并发份额），
并按标签 `priorityLevel` 拆分。
<!--
* `apiserver_flowcontrol_request_wait_duration_seconds` is a histogram
vector of how long requests spent queued, broken down by the labels
`flowSchema` (indicating which one matched the request),
`priorityLevel` (indicating the one to which the request was
assigned), and `execute` (indicating whether the request started
executing).
-->
* `apiserver_flowcontrol_request_wait_duration_seconds` 是一个直方图向量，
记录请求排队的时间，
由标签 `flowSchema` （表示与请求匹配的流模式），
`priorityLevel` （表示分配该请求的优先级）
和 `execute` （表示请求是否开始执行）拆分。
{{< 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.
-->
由于每个流模式总会给请求分配优先级配置，因此你可以为一个优先级添加所有流模式的直方图，以获取分配给该优先级的请求的有效直方图。
{{< /note >}}
<!--
* `apiserver_flowcontrol_request_execution_seconds` is a histogram
vector of how long requests took to actually execute, broken down by
the labels `flowSchema` (indicating which one matched the request)
and `priorityLevel` (indicating the one to which the request was
assigned).
-->
* `apiserver_flowcontrol_request_execution_seconds` 是一个直方图向量，
记录请求实际执行需要花费的时间，
由标签 `flowSchema` （表示与请求匹配的流模式）和 `priorityLevel` （表示分配给该请求的优先级）拆分。
<!-- ### Debug endpoints -->
### 调试端点 {#Debug-endpoints}
<!--
When you enable the API Priority and Fairness feature,
the kube-apiserver serves the following additional paths at its HTTP[S] ports.
-->
启用 APF 特性后， kube-apiserver 会在其 HTTP/HTTPS 端口提供以下路径：
<!--
- `/debug/api_priority_and_fairness/dump_priority_levels` - a listing of all the priority levels and the current state of each.
You can fetch like this:
-->
- `/debug/api_priority_and_fairness/dump_priority_levels` —— 所有优先级及其当前状态的列表。你可以这样获取：
```shell
kubectl get --raw /debug/api_priority_and_fairness/dump_priority_levels
```
<!-- The output is similar to this: -->
输出类似于：
```
PriorityLevelName, ActiveQueues, IsIdle, IsQuiescing, WaitingRequests, ExecutingRequests,
workload-low, 0, true, false, 0, 0,
global-default, 0, true, false, 0, 0,
exempt, <none>, <none>, <none>, <none>, <none>,
catch-all, 0, true, false, 0, 0,
system, 0, true, false, 0, 0,
leader-election, 0, true, false, 0, 0,
workload-high, 0, true, false, 0, 0,
```
<!--
- `/debug/api_priority_and_fairness/dump_queues` - a listing of all the queues and their current state.
You can fetch like this:
-->
- `/debug/api_priority_and_fairness/dump_queues` ——所有队列及其当前状态的列表。你可以这样获取：
```shell
kubectl get --raw /debug/api_priority_and_fairness/dump_queues
```
<!-- The output is similar to this: -->
输出类似于：
```
PriorityLevelName, Index, PendingRequests, ExecutingRequests, VirtualStart,
workload-high, 0, 0, 0, 0.0000,
workload-high, 1, 0, 0, 0.0000,
workload-high, 2, 0, 0, 0.0000,
...
leader-election, 14, 0, 0, 0.0000,
leader-election, 15, 0, 0, 0.0000,
```
<!--
- `/debug/api_priority_and_fairness/dump_requests` - a listing of all the requests that are currently waiting in a queue.
You can fetch like this:
-->
- `/debug/api_priority_and_fairness/dump_requests` ——当前正在队列中等待的所有请求的列表。你可以这样获取：
```shell
kubectl get --raw /debug/api_priority_and_fairness/dump_requests
```
<!-- The output is similar to this: -->
输出类似于：
```
PriorityLevelName, FlowSchemaName, QueueIndex, RequestIndexInQueue, FlowDistingsher, ArriveTime,
exempt, <none>, <none>, <none>, <none>, <none>,
system, system-nodes, 12, 0, system:node:127.0.0.1, 2020-07-23T15:26:57.179170694Z,
```
<!--
In addition to the queued requests,
the output includeas one phantom line for each priority level that is exempt from limitation.
-->
除了排队的请求外，对于每个优先级，输出还包括一条不受限制的虚拟线。
<!-- You can get a more detailed listing with a command like this: -->
你可以使用以下命令获得更详细的清单：
```shell
kubectl get --raw '/debug/api_priority_and_fairness/dump_requests?includeRequestDetails=1'
```
<!-- The output is similar to this: -->
输出类似于：
```
PriorityLevelName, FlowSchemaName, QueueIndex, RequestIndexInQueue, FlowDistingsher, ArriveTime, UserName, Verb, APIPath, Namespace, Name, APIVersion, Resource, SubResource,
system, system-nodes, 12, 0, system:node:127.0.0.1, 2020-07-23T15:31:03.583823404Z, system:node:127.0.0.1, create, /api/v1/namespaces/scaletest/configmaps,
system, system-nodes, 12, 1, system:node:127.0.0.1, 2020-07-23T15:31:03.594555947Z, system:node:127.0.0.1, create, /api/v1/namespaces/scaletest/configmaps,
```
## {{% heading "whatsnext" %}}
<!--
For background information on design details for API priority and fairness, see the
[enhancement proposal](https://github.com/kubernetes/enhancements/blob/master/keps/sig-api-machinery/20190228-priority-and-fairness.md).
You can make suggestions and feature requests via
[SIG API Machinery](https://github.com/kubernetes/community/tree/master/sig-api-machinery).
-->
有关API优先级和公平性的设计细节的背景信息，
请参阅[增强建议](https://github.com/kubernetes/enhancements/blob/master/keps/sig-api-machinery/20190228-priority-and-fairness.md)。
你可以通过 [SIG APIMachinery](https://github.com/kubernetes/community/tree/master/sig-api-machinery) 提出建议和特性请求。

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@ -0,0 +1,20 @@
apiVersion: flowcontrol.apiserver.k8s.io/v1alpha1
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