分析kubernetes中的事件机制

  • Z时代
  • 2024-01-10
  • 分类：综合

编程

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。

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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

**<br />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。

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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：<br />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。<br />NewRecorder创建了 EventRecorder 的实例，它提供了 Event ，Eventf 等方法供事件记录。

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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。

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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。<br />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。<br />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。<br />注意：<br />这个Action方式是apimachinery包中的方法，因为实现的sturt recorderImpl<br />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。

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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。

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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：

  1. 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

  2. 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。

  3. 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++。

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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：

  1. 创建 EventRecorder 对象，通过其提供的 Event 等方法，创建好event对象

  2. 将创建出来的对象发送给 EventBroadcaster 中的channel中

  3. 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

  4. 当输出log的handler收到事件就直接打印事件

  5. 当 EventSink handler收到处理事件就通过预处理之后将事件发送给apiserver

  6. 其中预处理包含三个动作，1、限流 2、聚合 3、计数

  7. 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/

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