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18 KiB
18 KiB
P4-ReplicaSet Controller
前言
在上一篇文章中，对deployment controller的工作模式进行了详细地分析:
分析后得知，deployment controller更多的是对每个相应版本的replicaset副本数进行管理，而不涉及直接对pod的管理，因此，承接上节，本章来分析replicaSet Controller的源码.
ReplicaSet Controller
初始化
参照上节一样，直接来到各类controller初始化的函数:
cmd/kube-controller-manager/app/controllermanager.go:343
controllers["replicaset"] = startReplicaSetController
==> cmd/kube-controller-manager/app/apps.go:69
go replicaset.NewReplicaSetController(
// replicaSet controller只关注ReplicaSets和Pod这两种资源。
ctx.InformerFactory.Apps().V1().ReplicaSets(),
ctx.InformerFactory.Core().V1().Pods(),
ctx.ClientBuilder.ClientOrDie("replicaset-controller"),
replicaset.BurstReplicas,
).Run(int(ctx.ComponentConfig.ReplicaSetController.ConcurrentRSSyncs), ctx.Stop)
创建ReplicaSetController
先来看看NewReplicaSetController创建的过程:
==> pkg/controller/replicaset/replica_set.go:109
func NewReplicaSetController(rsInformer appsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, kubeClient clientset.Interface, burstReplicas int) *ReplicaSetController {
eventBroadcaster := record.NewBroadcaster()
eventBroadcaster.StartLogging(klog.Infof)
eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: kubeClient.CoreV1().Events("")})
// NewBaseController方法往下看
return NewBaseController(rsInformer, podInformer, kubeClient, burstReplicas,
apps.SchemeGroupVersion.WithKind("ReplicaSet"),
"replicaset_controller",
"replicaset",
controller.RealPodControl{
KubeClient: kubeClient,
Recorder: eventBroadcaster.NewRecorder(scheme.Scheme, v1.EventSource{Component: "replicaset-controller"}),
},
)
}
// NewBaseController is the implementation of NewReplicaSetController with additional injected
// parameters so that it can also serve as the implementation of NewReplicationController.
func NewBaseController(rsInformer appsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, kubeClient clientset.Interface, burstReplicas int,
gvk schema.GroupVersionKind, metricOwnerName, queueName string, podControl controller.PodControlInterface) *ReplicaSetController {
if kubeClient != nil && kubeClient.CoreV1().RESTClient().GetRateLimiter() != nil {
metrics.RegisterMetricAndTrackRateLimiterUsage(metricOwnerName, kubeClient.CoreV1().RESTClient().GetRateLimiter())
}
rsc := &ReplicaSetController{
GroupVersionKind: gvk,
kubeClient: kubeClient,
podControl: podControl,
burstReplicas: burstReplicas,
expectations: controller.NewUIDTrackingControllerExpectations(controller.NewControllerExpectations()),
queue: workqueue.NewNamedRateLimitingQueue(workqueue.DefaultControllerRateLimiter(), queueName),
}
rsInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{
AddFunc: rsc.enqueueReplicaSet,
UpdateFunc: rsc.updateRS,
DeleteFunc: rsc.enqueueReplicaSet,
})
rsc.rsLister = rsInformer.Lister()
// informer会同步待操作的资源到本地的queue中，HasSynced方法就是用来判断判断queue是否已同步的
rsc.rsListerSynced = rsInformer.Informer().HasSynced
podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{
AddFunc: rsc.addPod,
UpdateFunc: rsc.updatePod,
DeleteFunc: rsc.deletePod,
})
rsc.podLister = podInformer.Lister()
// informer会同步待操作的资源到本地的queue中，HasSynced方法就是用来判断判断queue是否已同步的
rsc.podListerSynced = podInformer.Informer().HasSynced
rsc.syncHandler = rsc.syncReplicaSet
return rsc
}
NewBaseController这里主要关注AddEventHandler为资源的informer增加的curd方法，例如pod相关的addPod、updatePod、deletePod方法。
ReplicaSetController Run方法
接着往下，创建好ReplicaSetController对象后，看它的运行过程，即Run方法。
==> pkg/controller/replicaset/replica_set.go:177
// Run begins watching and syncing.
func (rsc *ReplicaSetController) Run(workers int, stopCh <-chan struct{}) {
defer utilruntime.HandleCrash()
defer rsc.queue.ShutDown()
controllerName := strings.ToLower(rsc.Kind)
klog.Infof("Starting %v controller", controllerName)
defer klog.Infof("Shutting down %v controller", controllerName)
// 判断各个informer的缓存是否已经同步完毕的函数
if !controller.WaitForCacheSync(rsc.Kind, stopCh, rsc.podListerSynced, rsc.rsListerSynced) {
return
}
// worker的数量默认是5个，开启5个worker，每个worker间隔1s运行一次rsc.worker函数，来检查并收敛rs的状态
for i := 0; i < workers; i++ {
go wait.Until(rsc.worker, time.Second, stopCh)
}
<-stopCh
}
来到了这里，可发现ReplicaSetController.Run()函数和上一节的DeploymentController.Run()函数非常地相似。所以，从这里开始，各类controller之间代码相似的步骤可能会跳过，不再每个地方都重复详细说明。
往上溯源，可以找到，worker的数量配置默认为5个，参见这里:
pkg/controller/apis/config/v1alpha1/defaults.go:219
func SetDefaults_ReplicaSetControllerConfiguration(obj *kubectrlmgrconfigv1alpha1.ReplicaSetControllerConfiguration) {
if obj.ConcurrentRSSyncs == 0 {
obj.ConcurrentRSSyncs = 5
}
}
wait.Until()函数是很有意思的，上节也做过仔细分析，可以再回顾一下这里:
[waituntil循环计时器函数](https://github.com/yinwenqin/kubeSourceCodeNote/blob/master/controller/Kubernetes源码学习-Controller-P3-Controller分类与Deployment Controller.md#waituntil循环计时器函数)
好，直接进入主题，开始分析rsc.worker工作函数.
工作逻辑
pkg/controller/replicaset/replica_set.go:190
for i := 0; i < workers; i++ {
go wait.Until(rsc.worker, time.Second, stopCh)
}
==> pkg/controller/replicaset/replica_set.go:432
// processNextWorkItem()函数的作用是把informer work queue工作队列里的对象取出，按照申明的要求来处理它们，标记它们。
func (rsc *ReplicaSetController) worker() {
for rsc.processNextWorkItem() {
}
}
==> pkg/controller/replicaset/replica_set.go:437
func (rsc *ReplicaSetController) processNextWorkItem() bool {
// work queue中取出队首元素
key, quit := rsc.queue.Get()
if quit {
return false
}
defer rsc.queue.Done(key)
// syncHandler每一个队列对象，强保证同一时间只会有一个go协程处理它(无并发竞争)。所谓sync，意思是将work queue中待操作的对象，同步实现到运行环境中。
err := rsc.syncHandler(key.(string))
if err == nil {
rsc.queue.Forget(key)
return true
}
utilruntime.HandleError(fmt.Errorf("Sync %q failed with %v", key, err))
rsc.queue.AddRateLimited(key)
return true
}
主要函数是这个syncHandler，接着追溯，可以在这里找到这个结构体属性函数的赋值：
pkg/controller/replicaset/replica_set.go:163
// NewBaseController is the implementation of NewReplicaSetController with additional injected
// parameters so that it can also serve as the implementation of NewReplicationController.
func NewBaseController(rsInformer appsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, kubeClient clientset.Interface, burstReplicas int,
gvk schema.GroupVersionKind, metricOwnerName, queueName string, podControl controller.PodControlInterface) *ReplicaSetController {
// ... 省略
rsc.syncHandler = rsc.syncReplicaSet
return rsc
}
接着便可以找到ReplicaSetController.syncReplicaSet函数：
pkg/controller/replicaset/replica_set.go:562
// syncReplicaSet will sync the ReplicaSet with the given key if it has had its expectations fulfilled,
// meaning it did not expect to see any more of its pods created or deleted. This function is not meant to be
// invoked concurrently with the same key.
func (rsc *ReplicaSetController) syncReplicaSet(key string) error {
startTime := time.Now()
defer func() {
klog.V(4).Infof("Finished syncing %v %q (%v)", rsc.Kind, key, time.Since(startTime))
}()
// key的字符串格式是这样的: ${NAMESPACE}/${NAME}
namespace, name, err := cache.SplitMetaNamespaceKey(key)
if err != nil {
return err
}
// 获取到rs对象
rs, err := rsc.rsLister.ReplicaSets(namespace).Get(name)
if errors.IsNotFound(err) {
klog.V(4).Infof("%v %v has been deleted", rsc.Kind, key)
rsc.expectations.DeleteExpectations(key)
return nil
}
if err != nil {
return err
}
// 判断rs是否实现所声明的期望状态，这里SatisfiedExpectations是使用expectations机制来判断这个rs是否满足期望状态。
rsNeedsSync := rsc.expectations.SatisfiedExpectations(key)
selector, err := metav1.LabelSelectorAsSelector(rs.Spec.Selector)
if err != nil {
utilruntime.HandleError(fmt.Errorf("Error converting pod selector to selector: %v", err))
return nil
}
// list all pods to include the pods that don't match the rs`s selector
// anymore but has the stale controller ref.
// TODO: Do the List and Filter in a single pass, or use an index.
// 取出所有的的pod，labels.Everything()取到的是空selector，即不使用label selector，取全部pod
allPods, err := rsc.podLister.Pods(rs.Namespace).List(labels.Everything())
if err != nil {
return err
}
// Ignore inactive pods.
// 去除 inactive状态的pod
filteredPods := controller.FilterActivePods(allPods)
// 根据rs和selector来选择受此rs版本管理的pod
filteredPods, err = rsc.claimPods(rs, selector, filteredPods)
if err != nil {
return err
}
var manageReplicasErr error
// 如果rs未达到期望状态，则对副本进行管理，以使rs满足声明的期望状态
if rsNeedsSync && rs.DeletionTimestamp == nil {
// 最重要的函数manageReplicas，未达期望时，管理rs对应的pod(新增/删除)
manageReplicasErr = rsc.manageReplicas(filteredPods, rs)
}
rs = rs.DeepCopy()
newStatus := calculateStatus(rs, filteredPods, manageReplicasErr)
// 只要有对应pod的更新，则需要更新rs的status字段
updatedRS, err := updateReplicaSetStatus(rsc.kubeClient.AppsV1().ReplicaSets(rs.Namespace), rs, newStatus)
if err != nil {
// Multiple things could lead to this update failing. Requeuing the replica set ensures
// Returning an error causes a requeue without forcing a hotloop
return err
}
// 当指定了MinReadySeconds时，即使pod 已经是ready状态了，但也不会视为Available，需要等待MinReadySeconds后再来刷新rs的状态。因此，enqueueReplicaSetAfter方法，异步等待MinReadySeconds后，把该rs重新压入work queue队列中
if manageReplicasErr == nil && updatedRS.Spec.MinReadySeconds > 0 &&
updatedRS.Status.ReadyReplicas == *(updatedRS.Spec.Replicas) &&
updatedRS.Status.AvailableReplicas != *(updatedRS.Spec.Replicas) {
rsc.enqueueReplicaSetAfter(updatedRS, time.Duration(updatedRS.Spec.MinReadySeconds)*time.Second)
}
return manageReplicasErr
}
划重点，两个重要的函数：SatisfiedExpectations(判断是否满足sync条件) / manageReplicas(sync后续的副本pod新增、删除操作)。分别来看看
SatisfiedExpectations函数
在此之前，必须先了解一下rs controller(后面简称rsc)的Expectations机制。rsc会将每一个rs的期望状态(比如期望新增3个副本)保存在本地缓存中，在sync执行之前，会对期望状态进行条件判断，满足条件才会真正进行sync操作。
来看看SatisfiedExpectations函数的逻辑：
pkg/controller/controller_utils.go:181
func (r *ControllerExpectations) SatisfiedExpectations(controllerKey string) bool {
// 若此key存在Expectations期望状态
if exp, exists, err := r.GetExpectations(controllerKey); exists {
// Expectations期望状态达成或者过期，则需要sync
if exp.Fulfilled() {
klog.V(4).Infof("Controller expectations fulfilled %#v", exp)
return true
} else if exp.isExpired() {
klog.V(4).Infof("Controller expectations expired %#v", exp)
return true
} else {
// 存在期望状态但未达成，则无需sync。因为后面的handler在处理资源增删的时候会来新建和修改Expectations，说明当前正在接近期望状态中，所以本次无需再sync
klog.V(4).Infof("Controller still waiting on expectations %#v", exp)
return false
}
}
// 不存在Expectations(新增的资源对象)，或者获取Expectations出错，则视为需要执行sync
else if err != nil {
klog.V(2).Infof("Error encountered while checking expectations %#v, forcing sync", err)
} else {
klog.V(4).Infof("Controller %v either never recorded expectations, or the ttl expired.", controllerKey)
}
return true
}
manageReplicas函数
==> pkg/controller/replicaset/replica_set.go:459
func (rsc *ReplicaSetController) manageReplicas(filteredPods []*v1.Pod, rs *apps.ReplicaSet) error {
// rs当前管理的pod数量 与 rs声明指定pod的数量 的差量
diff := len(filteredPods) - int(*(rs.Spec.Replicas))
rsKey, err := controller.KeyFunc(rs)
if err != nil {
utilruntime.HandleError(fmt.Errorf("Couldn't get key for %v %#v: %v", rsc.Kind, rs, err))
return nil
}
// 当 rs当前管理的pod数量 小于 rs声明指定pod的数量 时，说明应该继续增加pod
if diff < 0 {
diff *= -1
// 每次新增数量以突发增加数量burstReplicas为上限
if diff > rsc.burstReplicas {
diff = rsc.burstReplicas
}
// 创建ExpectCreations期望
rsc.expectations.ExpectCreations(rsKey, diff)
klog.V(2).Infof("Too few replicas for %v %s/%s, need %d, creating %d", rsc.Kind, rs.Namespace, rs.Name, *(rs.Spec.Replicas), diff)
// slowStartBatch用来以指数级批量启动pod, 其中controller.SlowStartInitialBatchSize默认值为1，作为底数。
successfulCreations, err := slowStartBatch(diff, controller.SlowStartInitialBatchSize, func() error {
// 创建单个pod的函数 CreatePodsWithControllerRef
err := rsc.podControl.CreatePodsWithControllerRef(rs.Namespace, &rs.Spec.Template, rs, metav1.NewControllerRef(rs, rsc.GroupVersionKind))
if err != nil && errors.IsTimeout(err) {
return nil
}
return err
})
if skippedPods := diff - successfulCreations; skippedPods > 0 {
klog.V(2).Infof("Slow-start failure. Skipping creation of %d pods, decrementing expectations for %v %v/%v", skippedPods, rsc.Kind, rs.Namespace, rs.Name)
for i := 0; i < skippedPods; i++ {
// Decrement the expected number of creates because the informer won't observe this pod
rsc.expectations.CreationObserved(rsKey)
}
}
return err
// 当 rs当前管理的pod数量 大于 rs声明指定pod的数量 时，说明应该减少pod
} else if diff > 0 {
if diff > rsc.burstReplicas {
diff = rsc.burstReplicas
}
klog.V(2).Infof("Too many replicas for %v %s/%s, need %d, deleting %d", rsc.Kind, rs.Namespace, rs.Name, *(rs.Spec.Replicas), diff)
// 获取需要删除的pod
podsToDelete := getPodsToDelete(filteredPods, diff)
// 修改rs的期望状态，在期望中剔除将要删除的pod
rsc.expectations.ExpectDeletions(rsKey, getPodKeys(podsToDelete))
errCh := make(chan error, diff)
var wg sync.WaitGroup
wg.Add(diff)
// 并发删除目标pod
for _, pod := range podsToDelete {
go func(targetPod *v1.Pod) {
defer wg.Done()
if err := rsc.podControl.DeletePod(rs.Namespace, targetPod.Name, rs); err != nil {
// Decrement the expected number of deletes because the informer won't observe this deletion
podKey := controller.PodKey(targetPod)
klog.V(2).Infof("Failed to delete %v, decrementing expectations for %v %s/%s", podKey, rsc.Kind, rs.Namespace, rs.Name)
rsc.expectations.DeletionObserved(rsKey, podKey)
errCh <- err
}
}(pod)
}
wg.Wait()
select {
case err := <-errCh:
// all errors have been reported before and they're likely to be the same, so we'll only return the first one we hit.
if err != nil {
return err
}
default:
}
}
return nil
}
这个函数即是实际操控管理pod副本数量的函数，其中的slowStartBatch批量启动pod的功能比较有意思，来看看。
批量启动pod
pkg/controller/replicaset/replica_set.go:658
func slowStartBatch(count int, initialBatchSize int, fn func() error) (int, error) {
// 剩余要执行的数量
remaining := count
// 累计成功执行的数量
successes := 0
// batchSize是每次批量执行的数量，从initialBatchSize(1)和剩余数量中取最小值。每次循环执行成功后，batchSize乘以2，以指数级扩充。
for batchSize := integer.IntMin(remaining, initialBatchSize); batchSize > 0; batchSize = integer.IntMin(2*batchSize, remaining) {
errCh := make(chan error, batchSize)
var wg sync.WaitGroup
wg.Add(batchSize)
for i := 0; i < batchSize; i++ {
go func() {
defer wg.Done()
if err := fn(); err != nil {
errCh <- err
}
}()
}
wg.Wait()
curSuccesses := batchSize - len(errCh)
successes += curSuccesses
// 某一轮循环出错时，跳出循环，后续的不再执行。
if len(errCh) > 0 {
return successes, <-errCh
}
remaining -= batchSize
}
return successes, nil
}
ReplicaSetController工作流程总结
总结一下，在出现新版本的rs后，rsc按照以下步骤进行工作：
1.通过SatisfiedExpectations函数，发现expectations期望状态本地缓存中不存在此rs key，因此返回true，需要sync
2.通过manageReplicas管理pod，新增或删除
3.判断pod副本数是多了还是少了，多则要删，少则要增
4.增删之前创建expectations对象并设置add / del值
5.slowStartBatch新增 / 并发删除 pod
6.更新expection
expections缓存机制，在运行的pod副本数在向声明指定的副本数收敛之时，很好地避免了频繁的informer数据查询，以及可能随之而来的数据更新不及时的问题，这个机制设计巧妙贯穿整个rsc工作过程，也是不太易于理解之处。