分析kubernetes中的事件机制-源码解析
我们通过 kubectl describe [资源] 命令，可以在看到Event输出，并且经常依赖event进行问题定位，从event中可以分析整个POD的运行轨迹，为服务的客观测性提供数据来源，由此可见，event在Kubernetes中起着举足轻重的作用。 event并不只是kubelet中都有的，关于event的操作被封装在client-go/tools/record包，我们完全可以在写入自定义的event。 现在让我们来一步步揭开event的面纱。 Event定义 其实event也是一个资源对象，并且通过apiserver将event存储在etcd中，所以我们也可以通过 kubectl get event 命令查看对应的event对象。 以下是一个event的yaml文件： apiVersion: v1 count: 1 eventTime: null firstTimestamp: "2020-03-02T13:08:22Z" involvedObject: apiVersion: v1 kind: Pod name: example-foo-d75d8587c-xsf64 namespace: default resourceVersion: "429837" uid: ce611c62-6c1a-4bd8-9029-136a1adf7de4 kind: Event lastTimestamp: "2020-03-02T13:08:22Z" message: Pod sandbox changed, it will be killed and re-created. metadata: creationTimestamp: "2020-03-02T13:08:30Z" name: example-foo-d75d8587c-xsf64.15f87ea1df862b64 namespace: default resourceVersion: "479466" selfLink: /api/v1/namespaces/default/events/example-foo-d75d8587c-xsf64.15f87ea1df862b64 uid: 9fe6f72a-341d-4c49-960b-e185982d331a reason: SandboxChanged reportingComponent: "" reportingInstance: "" source: component: kubelet host: minikube type: Normal 主要字段说明：** involvedObject： 触发event的资源类型 lastTimestamp：最后一次触发的时间 message：事件说明 metadata :event的元信息，name，namespace等 reason：event的原因 source：上报事件的来源，比如kubelet中的某个节点 type:事件类型，Normal或Warning event字段定义可以看这里：types.go#L5078 接下来我们来看看，整个event是如何下入的。 写入事件 1、这里以kubelet为例，看看是如何进行事件写入的 2、文中代码以Kubernetes 1.17.3为例进行分析 先以一幅图来看下整个的处理流程 创建操作事件的客户端：kubelet/app/server.go#L461 // makeEventRecorder sets up kubeDeps.Recorder if it's nil. It's a no-op otherwise. func makeEventRecorder(kubeDeps *kubelet.Dependencies, nodeName types.NodeName) { if kubeDeps.Recorder != nil { return } //事件广播 eventBroadcaster := record.NewBroadcaster() //创建EventRecorder kubeDeps.Recorder = eventBroadcaster.NewRecorder(legacyscheme.Scheme, v1.EventSource{Component: componentKubelet, Host: string(nodeName)}) //发送event至log输出 eventBroadcaster.StartLogging(klog.V(3).Infof) if kubeDeps.EventClient != nil { klog.V(4).Infof("Sending events to api server.") //发送event至apiserver eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: kubeDeps.EventClient.Events("")}) } else { klog.Warning("No api server defined - no events will be sent to API server.") } } 通过 makeEventRecorder 创建了 EventRecorder 实例，这是一个事件广播器，通过它提供了StartLogging和StartRecordingToSink两个事件处理函数，分别将event发送给log和apiserver。NewRecorder创建了 EventRecorder 的实例，它提供了 Event ，Eventf 等方法供事件记录。 EventBroadcaster 我们来看下EventBroadcaster接口定义：event.go#L113 // EventBroadcaster knows how to receive events and send them to any EventSink, watcher, or log. type EventBroadcaster interface { // StartEventWatcher(eventHandler func(*v1.Event)) watch.Interface StartRecordingToSink(sink EventSink) watch.Interface StartLogging(logf func(format string, args ...interface{})) watch.Interface NewRecorder(scheme *runtime.Scheme, source v1.EventSource) EventRecorder Shutdown() } 具体实现是通过 eventBroadcasterImpl struct来实现了各个方法。 其中StartLogging 和 StartRecordingToSink 其实就是完成了对事件的消费，EventRecorder实现对事件的写入，中间通过channel实现了生产者消费者模型。 EventRecorder 我们先来看下EventRecorder 接口定义：event.go#L88，提供了一下4个方法 // EventRecorder knows how to record events on behalf of an EventSource. type EventRecorder interface { // Event constructs an event from the given information and puts it in the queue for sending. // 'object' is the object this event is about. Event will make a reference-- or you may also // pass a reference to the object directly. // 'type' of this event, and can be one of Normal, Warning. New types could be added in future // 'reason' is the reason this event is generated. 'reason' should be short and unique; it // should be in UpperCamelCase format (starting with a capital letter). "reason" will be used // to automate handling of events, so imagine people writing switch statements to handle them. // You want to make that easy. // 'message' is intended to be human readable. // // The resulting event will be created in the same namespace as the reference object. Event(object runtime.Object, eventtype, reason, message string) // Eventf is just like Event, but with Sprintf for the message field. Eventf(object runtime.Object, eventtype, reason, messageFmt string, args ...interface{}) // PastEventf is just like Eventf, but with an option to specify the event's 'timestamp' field. PastEventf(object runtime.Object, timestamp metav1.Time, eventtype, reason, messageFmt string, args ...interface{}) // AnnotatedEventf is just like eventf, but with annotations attached AnnotatedEventf(object runtime.Object, annotations map[string]string, eventtype, reason, messageFmt string, args ...interface{}) } 主要参数说明： object 对应event资源定义中的 involvedObject eventtype 对应event资源定义中的type，可选Normal，Warning. reason ：事件原因 message ：事件消息 我们来看下当我们调用 Event(object runtime.Object, eventtype, reason, message string) 的整个过程。发现最终都调用到了 generateEvent 方法：event.go#L316 func (recorder *recorderImpl) generateEvent(object runtime.Object, annotations map[string]string, timestamp metav1.Time, eventtype, reason, message string) { ..... event := recorder.makeEvent(ref, annotations, eventtype, reason, message) event.Source = recorder.source go func() { // NOTE: events should be a non-blocking operation defer utilruntime.HandleCrash() recorder.Action(watch.Added, event) }() } 最终事件在一个 goroutine 中通过调用 recorder.Action 进入处理，这里保证了每次调用event方法都是非阻塞的。其中 makeEvent 的作用主要是构造了一个event对象，事件name根据InvolvedObject中的name加上时间戳生成： 注意看：对于一些非namespace资源产生的event，event的namespace是default func (recorder *recorderImpl) makeEvent(ref *v1.ObjectReference, annotations map[string]string, eventtype, reason, message string) *v1.Event { t := metav1.Time{Time: recorder.clock.Now()} namespace := ref.Namespace if namespace == "" { namespace = metav1.NamespaceDefault } return &v1.Event{ ObjectMeta: metav1.ObjectMeta{ Name: fmt.Sprintf("%v.%x", ref.Name, t.UnixNano()), Namespace: namespace, Annotations: annotations, }, InvolvedObject: *ref, Reason: reason, Message: message, FirstTimestamp: t, LastTimestamp: t, Count: 1, Type: eventtype, } } 进一步跟踪Action方法，apimachinery/blob/master/pkg/watch/mux.go#L188:23 // Action distributes the given event among all watchers. func (m *Broadcaster) Action(action EventType, obj runtime.Object) { m.incoming <- Event{action, obj} } 将event写入到了一个channel里面。注意：这个Action方式是apimachinery包中的方法，因为实现的sturt recorderImpl将 *watch.Broadcaster 作为一个匿名struct，并且在 NewRecorder 进行 Broadcaster 赋值，这个Broadcaster其实就是 eventBroadcasterImpl 中的Broadcaster。 到此，基本清楚了event最终被写入到了 Broadcaster 中的 incoming channel中，下面看下是怎么进行消费的。 消费事件 在 makeEventRecorder 调用的 StartLogging 和 StartRecordingToSink 其实就是完成了对事件的消费。 StartLogging直接将event输出到日志 StartRecordingToSink将事件写入到apiserver 两个方法内部都调用了 StartEventWatcher 方法，并且传入一个 eventHandler 方法对event进行处理 func (e *eventBroadcasterImpl) StartEventWatcher(eventHandler func(*v1.Event)) watch.Interface { watcher := e.Watch() go func() { defer utilruntime.HandleCrash() for watchEvent := range watcher.ResultChan() { event, ok := watchEvent.Object.(*v1.Event) if !ok { // This is all local, so there's no reason this should // ever happen. continue } eventHandler(event) } }() return watcher } 其中 watcher.ResultChan 方法就拿到了事件，这里是在一个goroutine中通过func (m *Broadcaster) loop() ==>func (m *Broadcaster) distribute(event Event) 方法调用将event又写入了broadcasterWatcher.result 主要看下 StartRecordingToSink 提供的的eventHandler， recordToSink 方法： func recordToSink(sink EventSink, event *v1.Event, eventCorrelator *EventCorrelator, sleepDuration time.Duration) { // Make a copy before modification, because there could be multiple listeners. // Events are safe to copy like this. eventCopy := *event event = &eventCopy result, err := eventCorrelator.EventCorrelate(event) if err != nil { utilruntime.HandleError(err) } if result.Skip { return } tries := 0 for { if recordEvent(sink, result.Event, result.Patch, result.Event.Count > 1, eventCorrelator) { break } tries++ if tries >= maxTriesPerEvent { klog.Errorf("Unable to write event '%#v' (retry limit exceeded!)", event) break } // Randomize the first sleep so that various clients won't all be // synced up if the master goes down. // 第一次重试增加随机性，防止 apiserver 重启的时候所有的事件都在同一时间发送事件 if tries == 1 { time.Sleep(time.Duration(float64(sleepDuration) * rand.Float64())) } else { time.Sleep(sleepDuration) } } } 其中event被经过了一个 eventCorrelator.EventCorrelate(event) 方法做预处理，主要是聚合相同的事件（避免产生的事件过多，增加 etcd 和 apiserver 的压力，也会导致查看 pod 事件很不清晰） 下面一个for循环就是在进行重试，最大重试次数是12次，调用 recordEvent 方法才真正将event写入到了apiserver。 事件处理 我们来看下EventCorrelate方法： // EventCorrelate filters, aggregates, counts, and de-duplicates all incoming events func (c *EventCorrelator) EventCorrelate(newEvent *v1.Event) (*EventCorrelateResult, error) { if newEvent == nil { return nil, fmt.Errorf("event is nil") } aggregateEvent, ckey := c.aggregator.EventAggregate(newEvent) observedEvent, patch, err := c.logger.eventObserve(aggregateEvent, ckey) if c.filterFunc(observedEvent) { return &EventCorrelateResult{Skip: true}, nil } return &EventCorrelateResult{Event: observedEvent, Patch: patch}, err } 分别调用了 aggregator.EventAggregate ， logger.eventObserve ， filterFunc 三个方法，分别作用是： aggregator.EventAggregate：聚合event，如果在最近 10 分钟出现过 10 个相似的事件（除了 message 和时间戳之外其他关键字段都相同的事件），aggregator 会把它们的 message 设置为 (combined from similar events)+event.Message logger.eventObserve：它会把相同的事件以及包含 aggregator 被聚合了的相似的事件，通过增加 Count 字段来记录事件发生了多少次。 filterFunc: 这里实现了一个基于令牌桶的限流算法，如果超过设定的速率则丢弃，保证了apiserver的安全。 我们主要来看下aggregator.EventAggregate方法： func (e *EventAggregator) EventAggregate(newEvent *v1.Event) (*v1.Event, string) { now := metav1.NewTime(e.clock.Now()) var record aggregateRecord // eventKey is the full cache key for this event //eventKey 是将除了时间戳外所有字段结合在一起 eventKey := getEventKey(newEvent) // aggregateKey is for the aggregate event, if one is needed. //aggregateKey 是除了message和时间戳外的字段结合在一起，localKey 是message aggregateKey, localKey := e.keyFunc(newEvent) // Do we have a record of similar events in our cache? e.Lock() defer e.Unlock() //从cache中根据aggregateKey查询是否存在，如果是相同或者相类似的事件会被放入cache中 value, found := e.cache.Get(aggregateKey) if found { record = value.(aggregateRecord) } //判断上次事件产生的时间是否超过10分钟，如何操作则重新生成一个localKeys集合（集合中存放message） maxInterval := time.Duration(e.maxIntervalInSeconds) * time.Second interval := now.Time.Sub(record.lastTimestamp.Time) if interval > maxInterval { record = aggregateRecord{localKeys: sets.NewString()} } // Write the new event into the aggregation record and put it on the cache //将locakKey也就是message放入集合中，如果message相同就是覆盖了 record.localKeys.Insert(localKey) record.lastTimestamp = now e.cache.Add(aggregateKey, record) // If we are not yet over the threshold for unique events, don't correlate them //判断localKeys集合中存放的类似事件是否超过10个， if uint(record.localKeys.Len()) < e.maxEvents { return newEvent, eventKey } // do not grow our local key set any larger than max record.localKeys.PopAny() // create a new aggregate event, and return the aggregateKey as the cache key // (so that it can be overwritten.) eventCopy := &v1.Event{ ObjectMeta: metav1.ObjectMeta{ Name: fmt.Sprintf("%v.%x", newEvent.InvolvedObject.Name, now.UnixNano()), Namespace: newEvent.Namespace, }, Count: 1, FirstTimestamp: now, InvolvedObject: newEvent.InvolvedObject, LastTimestamp: now, //这里会对message加个前缀：(combined from similar events): Message: e.messageFunc(newEvent), Type: newEvent.Type, Reason: newEvent.Reason, Source: newEvent.Source, } return eventCopy, aggregateKey } aggregator.EventAggregate方法中其实就是判断了通过cache和localKeys判断事件是否相似，如果最近 10 分钟出现过 10 个相似的事件就合并并加上前缀，后续通过logger.eventObserve方法进行count累加，如果message也相同，肯定就是直接count++。 总结 好了，event处理的整个流程基本就是这样，我们可以概括一下，可以结合文中的图对比一起看下： 创建 EventRecorder 对象，通过其提供的 Event 等方法，创建好event对象 将创建出来的对象发送给 EventBroadcaster 中的channel中 EventBroadcaster 通过后台运行的goroutine，从管道中取出事件，并广播给提前注册好的handler处理 当输出log的handler收到事件就直接打印事件 当 EventSink handler收到处理事件就通过预处理之后将事件发送给apiserver 其中预处理包含三个动作，1、限流 2、聚合 3、计数 apiserver收到事件处理之后就存储在etcd中 回顾event的整个流程，可以看到event并不是保证100%事件写入（从预处理的过程来看），这样做是为了后端服务etcd的可用性，因为event事件在整个集群中产生是非常频繁的，尤其在服务不稳定的时候，而相比Deployment,Pod等其他资源，又没那么的重要。所以这里做了个取舍。 参考文档： https://cizixs.com/2017/06/22/kubelet-source-code-analysis-part4-event/ 关注公众号，获取最新文章推送
