Kubernetes源码学习-Controller-P3-Controller分类与Deployment Controller

喜欢ヅ旅行 2023-07-03 02:41 31阅读 0赞

# P3-Controller分类与Deployment Controller #

## 前言 ##

**K8s源码分析完整系列一键直达：[k8s源码笔记-项目地址][k8s_-]，若有帮助，欢迎star，谢谢~**

Controller部分的第一篇文章中，我们从cobra启动命令入口开始，进入到了多实例leader选举部分的代码，对leader选举流程做了详细地分析：

[Controller-P1-多实例leader选举][Controller-P1-_leader]

接着在第二篇中，文字和图解简单描述了controller是如何结合client-go模块中的informer工作的，为本篇及后面的几篇作铺垫：

[Controller-P2-Controller与informer][Controller-P2-Controller_informer]

那么本篇，就接着第一篇往下，继续看代码。

## Controller的分类 ##

**启动**

承接篇一，在cobra入口之下，controller的启动入口在这里：

`cmd/kube-controller-manager/app/controllermanager.go:191`

run := func(ctx context.Context) { }

==> `cmd/kube-controller-manager/app/controllermanager.go:217`，重点是这里的**NewControllerInitializers**函数。

if err := StartControllers(controllerContext, saTokenControllerInitFunc, NewControllerInitializers(controllerContext.LoopMode), unsecuredMux); err != nil { 
       klog.Fatalf("error starting controllers: %v", err)
    }

==> `cmd/kube-controller-manager/app/controllermanager.go:343`

![20200127122642.png][]

可以看到，controller会对不同的资源，分别初始化相应的controller，包含我们常见的deployment、statefulset、endpoint、pvc等等资源，controller种类有多达30余个。因此，在controller整个章节中，不会对它们逐一分析，只会抽取几个常见有代表性地进行深入，本篇就来看看deployment controller吧。

## Deployment Controller ##

### 初始化 ###

`cmd/kube-controller-manager/app/controllermanager.go:354`

controllers["deployment"] = startDeploymentController

==> `cmd/kube-controller-manager/app/apps.go:82`

func startDeploymentController(ctx ControllerContext) (http.Handler, bool, error) { 
      if !ctx.AvailableResources[schema.GroupVersionResource{ Group: "apps", Version: "v1", Resource: "deployments"}] { 
        return nil, false, nil
      }
      dc, err := deployment.NewDeploymentController(
        // deployment主要关注这3个资源: Deployment/ReplicaSet/Pod，deployment通过replicaSet来管理Pod
        // 这3个函数会返回相应资源的informer
        ctx.InformerFactory.Apps().V1().Deployments(),
        ctx.InformerFactory.Apps().V1().ReplicaSets(),
        ctx.InformerFactory.Core().V1().Pods(),
        ctx.ClientBuilder.ClientOrDie("deployment-controller"),
      )
      if err != nil { 
        return nil, true, fmt.Errorf("error creating Deployment controller: %v", err)
      
      // deployment controller 运行函数
      go dc.Run(int(ctx.ComponentConfig.DeploymentController.ConcurrentDeploymentSyncs), ctx.Stop)
      return nil, true, nil
    }

dc.Run()函数，第一个参数是worker的数量，默认值是5个，在这里定义的：`pkg/controller/apis/config/v1alpha1/defaults.go:48`，第二个参数是空结构体，让go协程接收异常停止的信号。

==> `pkg/controller/deployment/deployment_controller.go:148`

// Run begins watching and syncing.
    func (dc *DeploymentController) Run(workers int, stopCh <-chan struct{ }) { 
      defer utilruntime.HandleCrash()
      defer dc.queue.ShutDown()
    
      klog.Infof("Starting deployment controller")
      defer klog.Infof("Shutting down deployment controller")
      // 判断各个informer的缓存是否已经同步完毕的函数
      if !controller.WaitForCacheSync("deployment", stopCh, dc.dListerSynced, dc.rsListerSynced, dc.podListerSynced) { 
        return
      }
      // 启动多个worker开始工作
      for i := 0; i < workers; i++ { 
        go wait.Until(dc.worker, time.Second, stopCh)
      }
    
      <-stopCh
    }

controller.WaitForCacheSync函数是用来检测各个informer是否本地缓存已经同步完毕的函数，返回值是bool类型。前面第二章讲到过，informer为了加速和减轻apiserver的负担，设计了local storage缓存，因此这里做了一步缓存是否已同步的检测。

默认是5个worker，每个worker，调用wait.Until()方法，每间隔1s，循环执行dc.worker函数，运行deployment controller的工作逻辑。wait.Until()这个循环调用的计时器函数还是挺有意思的，展开看下。

#### wait.Until循环计时器函数 ####

`pkg/controller/deployment/deployment_controller.go:160`

==> `vendor/k8s.io/apimachinery/pkg/util/wait/wait.go:88`

==>`vendor/k8s.io/apimachinery/pkg/util/wait/wait.go:130`

func JitterUntil(f func(), period time.Duration, jitterFactor float64, sliding bool, stopCh <-chan struct{ }) { 
      var t *time.Timer
      var sawTimeout bool
    
      for { 
        select { 
        case <-stopCh:
          return
        default:
        }
    
        jitteredPeriod := period
        if jitterFactor > 0.0 { 
          jitteredPeriod = Jitter(period, jitterFactor)
        }
        // sliding这个布尔值的意思是是否将执行函数f()的执行时间计入执行间隔时间内，如果为否，则在f执行前就开始计时，如果为是，则在f执行后再开始计时。
        if !sliding { 
          t = resetOrReuseTimer(t, jitteredPeriod, sawTimeout)
        }
    
        func() { 
          defer runtime.HandleCrash()
          f()
        }()
    
        if sliding { 
          t = resetOrReuseTimer(t, jitteredPeriod, sawTimeout)
        }
    
        // select 下各个分支的权重是公平的，因此，stop信号的处理，在循环开始之前和循环开始之后都分别判断了一次
        select { 
        case <-stopCh:
          return
        // Timer.C是Timer结构体内部的一个channel，计时器在到达指定的时间后会往此channel发送一个事件，channel同时也可以被接收，来触发其他的逻辑。这里的逻辑是判断：如果f()执行超过计时器的超时时间，那么加一个超时的标记sawTimeout。
        case <-t.C:
          sawTimeout = true
        }
      }
    }

resetOrReuseTimer函数：

func resetOrReuseTimer(t *time.Timer, d time.Duration, sawTimeout bool) *time.Timer { 
      if t == nil { 
        return time.NewTimer(d)
      }
      if !t.Stop() && !sawTimeout { 
        <-t.C
      }
      t.Reset(d)
      return t
    }

概括一下，这个函数是对timer模块的一个再封装，重复利用timer计时器，来每秒执行一次dc.worker().

#### dc.worker函数 ####

`pkg/controller/deployment/deployment_controller.go:460`

==> `pkg/controller/deployment/deployment_controller.go:464`

func (dc *DeploymentController) processNextWorkItem() bool { 
      // 从队列头部取出对象
      key, quit := dc.queue.Get()
      if quit { 
        return false
      }
      defer dc.queue.Done(key)
      // 处理对象
      err := dc.syncHandler(key.(string))
      dc.handleErr(err, key)
    
      return true
    }

Deployment controller 的worker函数就是不断地调用processNextWorkItem函数，processNextWorkItem函数是从work queue中获取待处理的对象(第二篇中informer图解中的第7-第8步)，如果存在，那么执行相应后续的增删改查逻辑，如果不存在，那么就退出。

其中dc.queue.Get()接口方法的实现在这里：

`vendor/k8s.io/client-go/util/workqueue/queue.go:140`

func (q *Type) Get() (item interface{ }, shutdown bool) { 
      q.cond.L.Lock()
      defer q.cond.L.Unlock()
      for len(q.queue) == 0 && !q.shuttingDown { 
        q.cond.Wait()
      }
      if len(q.queue) == 0 { 
        // We must be shutting down.
        return nil, true
      }
      // 取出队列的队首
      item, q.queue = q.queue[0], q.queue[1:]
     
      q.metrics.get(item)
      // 对象加入正在处理中map
      q.processing.insert(item)
      // dirty map去除对象(dirty map中保存的是等待处理的对象)
      q.dirty.delete(item)
    
      return item, false
    }

其中的`dc.syncHandler()`方法在这里：

pkg/controller/deployment/deployment\_controller.go:135

dc.syncHandler = dc.syncDeployment

==> pkg/controller/deployment/deployment\_controller.go:560

所有的增删改(滚动更新)查操作，全部都在这个函数内部处理。

func (dc *DeploymentController) syncDeployment(key string) error { 
       startTime := time.Now()
       klog.V(4).Infof("Started syncing deployment %q (%v)", key, startTime)
       defer func() { 
          klog.V(4).Infof("Finished syncing deployment %q (%v)", key, time.Since(startTime))
       }()
    
       namespace, name, err := cache.SplitMetaNamespaceKey(key)
       if err != nil { 
          return err
       }
       deployment, err := dc.dLister.Deployments(namespace).Get(name)
       if errors.IsNotFound(err) { 
          klog.V(2).Infof("Deployment %v has been deleted", key)
          return nil
       }
       if err != nil { 
          return err
       }
    
       // Deep-copy otherwise we are mutating our cache.
       // TODO: Deep-copy only when needed.
       d := deployment.DeepCopy()
    
       everything := metav1.LabelSelector{ }
       if reflect.DeepEqual(d.Spec.Selector, &everything) { 
          // deployment必须包含selector标签
          dc.eventRecorder.Eventf(d, v1.EventTypeWarning, "SelectingAll", "This deployment is selecting all pods. A non-empty selector is required.")
          if d.Status.ObservedGeneration < d.Generation { 
             d.Status.ObservedGeneration = d.Generation
             dc.client.AppsV1().Deployments(d.Namespace).UpdateStatus(d)
          }
          return nil
       }
    
       // 获取deployment所控制的replicaSet
       rsList, err := dc.getReplicaSetsForDeployment(d)
       if err != nil { 
          return err
       }
      
       // 获取所有的pod，map结构，按replicaSet分组，key是rs。
       // 检查deployment在重建的过程中是否还存在旧版本(未更新)的pod
       podMap, err := dc.getPodMapForDeployment(d, rsList)
       if err != nil { 
          return err
       }
    
       if d.DeletionTimestamp != nil { 
          return dc.syncStatusOnly(d, rsList)
       }
    
    
       // 检查deployment是否为pause暂停状态，pause状态则调用sync方法同步deployment
       if err = dc.checkPausedConditions(d); err != nil { 
          return err
       }
    
       if d.Spec.Paused { 
          return dc.sync(d, rsList)
       }
      
       // 判断本次deployment事件是否是一个回滚事件
       // 一旦底层的rs更新到了一个新的版本，就无法自动执行回滚了，因此，直到下一次队列中再次出现此deployment且不为rollback状态时，才能无虞地触发更新rs。所以，这里再进行一次判断，如果deployment带有回滚标记，那么先执行rs的回滚。
       if getRollbackTo(d) != nil { 
          return dc.rollback(d, rsList)
       }
      
       // 判断本次deployment事件是否是一个scale事件，是则调用sync方法同步deployment
       scalingEvent, err := dc.isScalingEvent(d, rsList)
       if err != nil { 
          return err
       }
       if scalingEvent { 
          return dc.sync(d, rsList)
       }
       
       // 更新deployment，视Deployment.Spec.Strategy指定的更新策略类型来执行相应的更新操作
       // 1.如果是rolloutRecreate类型，则一次性杀死pod再重建
       // 2.如果是rolloutRolling类型，则滚动更新pod
       switch d.Spec.Strategy.Type { 
       case apps.RecreateDeploymentStrategyType:
          return dc.rolloutRecreate(d, rsList, podMap)
       case apps.RollingUpdateDeploymentStrategyType:
          return dc.rolloutRolling(d, rsList)
       }
       return fmt.Errorf("unexpected deployment strategy type: %s", d.Spec.Strategy.Type)
    }

### 暂停和扩(缩)容(/删除) ###

dc.sync方法这里出现了两次，分别在pause状态和scaling状态调用，比较关键，分析一下sync方法的内容。

`pkg/controller/deployment/sync.go:48`

func (dc *DeploymentController) sync(d *apps.Deployment, rsList []*apps.ReplicaSet) error { 
      // 展开查看代码，可以知道，这里的newRS，指的是找到模板hash值与当前的d Deployment 模板hash值相同的rs，oldRSs则是所有的历史版本的rs
       newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, false)
       if err != nil { 
          return err
       }
      // 对比最新的rs和之前的rs，如果需要scale缩扩容，则执行scale方法
       if err := dc.scale(d, newRS, oldRSs); err != nil { 
          // If we get an error while trying to scale, the deployment will be requeued
          // so we can abort this resync
          return err
       }
    
      // pause状态，且不处于回滚状态的deployment，进行清理(根据指定的保存历史版本数上限，清理超出限制的历史版本)
       if d.Spec.Paused && getRollbackTo(d) == nil { 
          if err := dc.cleanupDeployment(oldRSs, d); err != nil { 
             return err
          }
       }
      
       allRSs := append(oldRSs, newRS)
       // 同步deployment状态
       return dc.syncDeploymentStatus(allRSs, newRS, d)
    }

来看看dc.scale()方法：

`pkg/controller/deployment/sync.go:289`

func (dc *DeploymentController) scale(deployment *apps.Deployment, newRS *apps.ReplicaSet, oldRSs []*apps.ReplicaSet) error { 
      // FindActiveOrLatest方法返回值：如果此时只有一个活跃的rs，那么就返回这个rs，如果不止，那么就找出revision最新的rs返回
       if activeOrLatest := deploymentutil.FindActiveOrLatest(newRS, oldRSs); activeOrLatest != nil {  
          if *(activeOrLatest.Spec.Replicas) == *(deployment.Spec.Replicas) { 
            // 如果rs已经和deployment指定的副本数一致，直接return
             return nil
          }
          _, _, err := dc.scaleReplicaSetAndRecordEvent(activeOrLatest, *(deployment.Spec.Replicas), deployment)
          return err
       }
    
       // 如果最新的rs的已经收敛到了deployment的期望状态，则旧rs需要被完全scale down缩容删除掉。
       if deploymentutil.IsSaturated(deployment, newRS) { 
          for _, old := range controller.FilterActiveReplicaSets(oldRSs) { 
             if _, _, err := dc.scaleReplicaSetAndRecordEvent(old, 0, deployment); err != nil { 
                return err
             }
          }
          return nil
       }
      
        
       // 在滚动更新的过程中，需要控制旧rs与新rs所控制的模板pod的数量的总和，多出的pod数量不能超过MaxSurge数，因此是滚动更新的过程中，旧rs和新rs控制得pod数量必然是一个此消彼长的过程
       if deploymentutil.IsRollingUpdate(deployment) { 
          allRSs := controller.FilterActiveReplicaSets(append(oldRSs, newRS))
          allRSsReplicas := deploymentutil.GetReplicaCountForReplicaSets(allRSs)
    
          allowedSize := int32(0)
          if *(deployment.Spec.Replicas) > 0 { 
             allowedSize = *(deployment.Spec.Replicas) + deploymentutil.MaxSurge(*deployment)
          }
    
          // 可以增加或删除的pod数量，结果正数则代表可以继续新增pod，结果为负数则代表需要删除pod了
          deploymentReplicasToAdd := allowedSize - allRSsReplicas
    
          var scalingOperation string
          switch { 
          case deploymentReplicasToAdd > 0:
             // 如果是扩容，那么把所有的rs按时间从最新到最旧的顺序排序
             sort.Sort(controller.ReplicaSetsBySizeNewer(allRSs))
             scalingOperation = "up"
    
          case deploymentReplicasToAdd < 0:
             // 如果是缩容，那么把所有的rs按时间从最旧到最新的顺序排序
             sort.Sort(controller.ReplicaSetsBySizeOlder(allRSs))
             scalingOperation = "down"
          }
    
         // 遍历每一个rs, 用map保存此rs应该达到的pod的数量(等于当前数量+需scale数量）
          deploymentReplicasAdded := int32(0)
          nameToSize := make(map[string]int32)
          for i := range allRSs { 
             rs := allRSs[i]
    
             if deploymentReplicasToAdd != 0 { 
                // 计算当前rs需scale的数量
                proportion := deploymentutil.GetProportion(rs, *deployment, deploymentReplicasToAdd, deploymentReplicasAdded)
                // 总pod数量等于当前数量+scale数量
                nameToSize[rs.Name] = *(rs.Spec.Replicas) + proportion
                deploymentReplicasAdded += proportion
             } else { 
                nameToSize[rs.Name] = *(rs.Spec.Replicas)
             }
          }
    
          // Update all replica sets
          for i := range allRSs { 
             rs := allRSs[i]
    
             // Add/remove any leftovers to the largest replica set.
            // 如果还有各rs加起来都未消化完的pod，则交给上面排序后的第一个rs(最新或最旧的rs)。
             if i == 0 && deploymentReplicasToAdd != 0 { 
                leftover := deploymentReplicasToAdd - deploymentReplicasAdded
                nameToSize[rs.Name] = nameToSize[rs.Name] + leftover
                if nameToSize[rs.Name] < 0 { 
                   nameToSize[rs.Name] = 0
                }
             }
    
             // 把这个rs scale到它应该达到的数量
             if _, _, err := dc.scaleReplicaSet(rs, nameToSize[rs.Name], deployment, scalingOperation); err != nil { 
                // Return as soon as we fail, the deployment is requeued
                return err
             }
          }
       }
       return nil
    }

#### syncDeploymentStatus函数 ####

在完成rs的scale和pause状态的逻辑处理后，deployment的状态也需要与最新的rs同步，因此这个函数就是用来同步deployment的状态的。

func (dc *DeploymentController) syncDeploymentStatus(allRSs []*apps.ReplicaSet, newRS *apps.ReplicaSet, d *apps.Deployment) error { 
       newStatus := calculateStatus(allRSs, newRS, d)
    
       if reflect.DeepEqual(d.Status, newStatus) { 
          return nil
       }
    
       newDeployment := d
       newDeployment.Status = newStatus
       _, err := dc.client.AppsV1().Deployments(newDeployment.Namespace).UpdateStatus(newDeployment)
       return err
    }

这个函数主要用来更新deployment的status字段的内容，例如版本、副本数、可用副本数、更新副本数等等。

整个扩容的过程涉及所有rs的操作，可能很容易混淆，但其实只要记住在99%的情况下，deployment只有一个活跃状态的rs，即newRS，大部分操作都是针对这个newRS做的，那么上面的过程就容易理解很多了。

### 滚动更新 ###

Deployment更新策略分为滚动更新和一次性更新，更新方式其实都是类似，只是一个是分批式，一个是全量式，这里看下滚动更新的代码。

deployment 的spec字段内的内容一旦发生变化，就会触发rs的更新，生成新版本的rs，并且基于新rs进行副本扩容，旧版本的rs则会缩容。

`pkg/controller/deployment/deployment_controller.go:644`

==> `pkg/controller/deployment/rolling.go:31`

func (dc *DeploymentController) rolloutRolling(d *apps.Deployment, rsList []*apps.ReplicaSet) error { 
       // 获取新的rs，如果没有新的rs则创建newRS 
       newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, true)
       if err != nil { 
          return err
       }
       allRSs := append(oldRSs, newRS)
    
      // 对比判断newRS是否需要扩容(新rs管理的pod是否已达到目标数量)
       scaledUp, err := dc.reconcileNewReplicaSet(allRSs, newRS, d)
       if err != nil { 
          return err
       }
       if scaledUp { 
          // 扩容完毕，更新deployment的status
          return dc.syncRolloutStatus(allRSs, newRS, d)
       }
    
      // 对比判断oldRS是否需要缩容(旧rs管理的pod是否已经全部终结)
       scaledDown, err := dc.reconcileOldReplicaSets(allRSs, controller.FilterActiveReplicaSets(oldRSs), newRS, d)
       if err != nil { 
          return err
       }
       if scaledDown { 
          // 缩容完毕，更新deployment的status
          return dc.syncRolloutStatus(allRSs, newRS, d)
       }
      
       // deployment 进入complete状态，根据revision历史版本数限制，清除旧的rs
       if deploymentutil.DeploymentComplete(d, &d.Status) { 
          if err := dc.cleanupDeployment(oldRSs, d); err != nil { 
             return err
          }
       }
    
       // 更新deployment的status
       return dc.syncRolloutStatus(allRSs, newRS, d)
    }

reconcileNewReplicaSet函数：

这个函数返回bool值，即是否应该扩容newRS的bool值

func (dc *DeploymentController) reconcileNewReplicaSet(allRSs []*apps.ReplicaSet, newRS *apps.ReplicaSet, deployment *apps.Deployment) (bool, error) { 
       if *(newRS.Spec.Replicas) == *(deployment.Spec.Replicas) { 
          // deployment replicas 和newRS replicas相等，则说明new rs已经无需扩容
          return false, nil
       }
       if *(newRS.Spec.Replicas) > *(deployment.Spec.Replicas) { 
          // newRS replicas > deployment replicas,则说明newRS需要缩容，返回值scaled此时值应当是false
          scaled, _, err := dc.scaleReplicaSetAndRecordEvent(newRS, *(deployment.Spec.Replicas), deployment)
          return scaled, err
       }
       // newRS replicas < deployment replicas,则使用NewRSNewReplicas方法计算newRS此时应用拥有的pod副本的数量
       newReplicasCount, err := deploymentutil.NewRSNewReplicas(deployment, allRSs, newRS)
       if err != nil { 
          return false, err
       }
       // 返回值scaled此时值应当是true
       scaled, _, err := dc.scaleReplicaSetAndRecordEvent(newRS, newReplicasCount, deployment)
       return scaled, err
    }

NewRSNewReplicas函数：

计算newRS此时应该有的副本数量的函数

func NewRSNewReplicas(deployment *apps.Deployment, allRSs []*apps.ReplicaSet, newRS *apps.ReplicaSet) (int32, error) { 
       switch deployment.Spec.Strategy.Type { 
       // 滚动更新时
       case apps.RollingUpdateDeploymentStrategyType:
          // Check if we can scale up.
          maxSurge, err := intstrutil.GetValueFromIntOrPercent(deployment.Spec.Strategy.RollingUpdate.MaxSurge, int(*(deployment.Spec.Replicas)), true)
          if err != nil { 
             return 0, err
          }
         // 当前的副本数(当前值) = 所有版本的rs管理的pod数量的总和
          currentPodCount := GetReplicaCountForReplicaSets(allRSs)
         // 最多允许同时存在的副本数(最大值) = 指定副本数 + maxSurge的副本数(整数或者比例计算)
          maxTotalPods := *(deployment.Spec.Replicas) + int32(maxSurge)
          // 如果当前值比最大值还大，那么说明不能再扩容了，直接返回最新的newRS.Spec.Replicas
          if currentPodCount >= maxTotalPods { 
             return *(newRS.Spec.Replicas), nil
          }
          // 否则，可扩容值 = 最大值 - 当前值 
          scaleUpCount := maxTotalPods - currentPodCount
          // 但每一个版本的rs管理的副本数量，不能超过deployment所指定的副本数量，只有新旧版本的rs加起来的副本数可以突破到maxSurge的上限。因此，这里的可扩容值要取这两个值之间的最小值。
          scaleUpCount = int32(integer.IntMin(int(scaleUpCount), int(*(deployment.Spec.Replicas)-*(newRS.Spec.Replicas))))
          // 此时newRS应有的副本数 = 当前值 + 可扩容值
          return *(newRS.Spec.Replicas) + scaleUpCount, nil
       case apps.RecreateDeploymentStrategyType:
          // 非滚动更新时，newRS的应用副本数 = deployment.Spec.Replicas,无弹性
          return *(deployment.Spec.Replicas), nil
       default:
          return 0, fmt.Errorf("deployment type %v isn't supported", deployment.Spec.Strategy.Type)
       }
    }

reconcileOldReplicaSets函数

这个函数返回bool值，即是否应该缩容oldRSs的bool值

func (dc *DeploymentController) reconcileOldReplicaSets(allRSs []*apps.ReplicaSet, oldRSs []*apps.ReplicaSet, newRS *apps.ReplicaSet, deployment *apps.Deployment) (bool, error) { 
       oldPodsCount := deploymentutil.GetReplicaCountForReplicaSets(oldRSs)
       if oldPodsCount == 0 { 
          // 已经缩容完毕，直接返回
          return false, nil
       }
      // 当前所有的pod的数量(当前值)
       allPodsCount := deploymentutil.GetReplicaCountForReplicaSets(allRSs)
       klog.V(4).Infof("New replica set %s/%s has %d available pods.", newRS.Namespace, newRS.Name, newRS.Status.AvailableReplicas)
      // deployment 指定的最大不可用的副本数(最大不可用值)
       maxUnavailable := deploymentutil.MaxUnavailable(*deployment)
    
       // Check if we can scale down. We can scale down in the following 2 cases:
       // * Some old replica sets have unhealthy replicas, we could safely scale down those unhealthy replicas since that won't further
       // increase unavailability.
       // * New replica set has scaled up and it's replicas becomes ready, then we can scale down old replica sets in a further step.
       //
       // maxScaledDown := allPodsCount - minAvailable - newReplicaSetPodsUnavailable
       // take into account not only maxUnavailable and any surge pods that have been created, but also unavailable pods from
       // the newRS, so that the unavailable pods from the newRS would not make us scale down old replica sets in a further
       // step(that will increase unavailability).
       //
       // Concrete example:
       //
       // * 10 replicas
       // * 2 maxUnavailable (absolute number, not percent)
       // * 3 maxSurge (absolute number, not percent)
       //
       // case 1:
       // * Deployment is updated, newRS is created with 3 replicas, oldRS is scaled down to 8, and newRS is scaled up to 5.
       // * The new replica set pods crashloop and never become available.
       // * allPodsCount is 13. minAvailable is 8. newRSPodsUnavailable is 5.
       // * A node fails and causes one of the oldRS pods to become unavailable. However, 13 - 8 - 5 = 0, so the oldRS won't be scaled down.
       // * The user notices the crashloop and does kubectl rollout undo to rollback.
       // * newRSPodsUnavailable is 1, since we rolled back to the good replica set, so maxScaledDown = 13 - 8 - 1 = 4. 4 of the crashlooping pods will be scaled down.
       // * The total number of pods will then be 9 and the newRS can be scaled up to 10.
       //
       // case 2:
       // Same example, but pushing a new pod template instead of rolling back (aka "roll over"):
       // * The new replica set created must start with 0 replicas because allPodsCount is already at 13.
       // * However, newRSPodsUnavailable would also be 0, so the 2 old replica sets could be scaled down by 5 (13 - 8 - 0), which would then
       // allow the new replica set to be scaled up by 5.
      // Available指的是就绪探针结果为true的副本，若默认未指定就绪探针，则pod running之后自动视就绪为true
      // 最小可用副本数(至少可用数)
       minAvailable := *(deployment.Spec.Replicas) - maxUnavailable
       // newRs不可用数
       newRSUnavailablePodCount := *(newRS.Spec.Replicas) - newRS.Status.AvailableReplicas
      // 最大可缩容数 = 总数 - 最小可用数 - newRS不可用数(为了保证最小可用数，因此此时newRS的不可用副本不能参与这个计算)
       maxScaledDown := allPodsCount - minAvailable - newRSUnavailablePodCount
       if maxScaledDown <= 0 { 
          return false, nil
       }
    
       // oldRS里不健康的副本，无论如何都是需要清除的
       // and cause timeout. See https://github.com/kubernetes/kubernetes/issues/16737
       oldRSs, cleanupCount, err := dc.cleanupUnhealthyReplicas(oldRSs, deployment, maxScaledDown)
       if err != nil { 
          return false, nil
       }
       klog.V(4).Infof("Cleaned up unhealthy replicas from old RSes by %d", cleanupCount)
    
       // 还要对比最大可缩容数和deployment指定的最大同时不可用副本数，这两者之间的最小值，才是可缩容数量
       allRSs = append(oldRSs, newRS)
       scaledDownCount, err := dc.scaleDownOldReplicaSetsForRollingUpdate(allRSs, oldRSs, deployment)
       if err != nil { 
          return false, nil
       }
       klog.V(4).Infof("Scaled down old RSes of deployment %s by %d", deployment.Name, scaledDownCount)
      // oldRS里不健康的副本，无论如何都是需要清除的
       totalScaledDown := cleanupCount + scaledDownCount
       // 判断缩容数是否大于0
       return totalScaledDown > 0, nil
    }

中间的英文注释里的举例说明非常详细，可以看一下注释。

#### syncRolloutStatus函数 ####

这个函数主要用于更新deployment的status字段和其中的condition字段。

func (dc *DeploymentController) syncRolloutStatus(allRSs []*apps.ReplicaSet, newRS *apps.ReplicaSet, d *apps.Deployment) error { 
       newStatus := calculateStatus(allRSs, newRS, d)
    
       if !util.HasProgressDeadline(d) { 
          util.RemoveDeploymentCondition(&newStatus, apps.DeploymentProgressing)
       }
    
       currentCond := util.GetDeploymentCondition(d.Status, apps.DeploymentProgressing)
       /** 判断deployment是否为complete状态，条件有多个： 1. newRS.replicas = newRS.Status.UpdatedReplicas 说明newRS的副本更新已全部完成 2. newRS.status.condition.reason = miniumReplicasAvailable **/
       isCompleteDeployment := newStatus.Replicas == newStatus.UpdatedReplicas && currentCond != nil && currentCond.Reason == util.NewRSAvailableReason
       // 未达到complete状态的deployment，才进行下面的检查
       if util.HasProgressDeadline(d) && !isCompleteDeployment { 
          switch { 
          case util.DeploymentComplete(d, &newStatus):
             // Update the deployment conditions with a message for the new replica set that
             // was successfully deployed. If the condition already exists, we ignore this update.
             msg := fmt.Sprintf("Deployment %q has successfully progressed.", d.Name)
             if newRS != nil { 
                msg = fmt.Sprintf("ReplicaSet %q has successfully progressed.", newRS.Name)
             }
             condition := util.NewDeploymentCondition(apps.DeploymentProgressing, v1.ConditionTrue, util.NewRSAvailableReason, msg)
             util.SetDeploymentCondition(&newStatus, *condition)
    
          case util.DeploymentProgressing(d, &newStatus):
             // If there is any progress made, continue by not checking if the deployment failed. This
             // behavior emulates the rolling updater progressDeadline check.
             msg := fmt.Sprintf("Deployment %q is progressing.", d.Name)
             if newRS != nil { 
                msg = fmt.Sprintf("ReplicaSet %q is progressing.", newRS.Name)
             }
             condition := util.NewDeploymentCondition(apps.DeploymentProgressing, v1.ConditionTrue, util.ReplicaSetUpdatedReason, msg)
             // Update the current Progressing condition or add a new one if it doesn't exist.
             // If a Progressing condition with status=true already exists, we should update
             // everything but lastTransitionTime. SetDeploymentCondition already does that but
             // it also is not updating conditions when the reason of the new condition is the
             // same as the old. The Progressing condition is a special case because we want to
             // update with the same reason and change just lastUpdateTime iff we notice any
             // progress. That's why we handle it here.
             if currentCond != nil { 
                if currentCond.Status == v1.ConditionTrue { 
                   condition.LastTransitionTime = currentCond.LastTransitionTime
                }
                util.RemoveDeploymentCondition(&newStatus, apps.DeploymentProgressing)
             }
             util.SetDeploymentCondition(&newStatus, *condition)
    
          case util.DeploymentTimedOut(d, &newStatus):
             // Update the deployment with a timeout condition. If the condition already exists,
             // we ignore this update.
             msg := fmt.Sprintf("Deployment %q has timed out progressing.", d.Name)
             if newRS != nil { 
                msg = fmt.Sprintf("ReplicaSet %q has timed out progressing.", newRS.Name)
             }
             condition := util.NewDeploymentCondition(apps.DeploymentProgressing, v1.ConditionFalse, util.TimedOutReason, msg)
             util.SetDeploymentCondition(&newStatus, *condition)
          }
       }

DeploymentCondition在这里面反复出现，便于理解，参照一个正常状态的deployment condition样例:

![20200130125357.png][]

#### 总结 ####

滚动更新过程中主要是通过调用`reconcileNewReplicaSet`函数对 newRS 扩容，调用 `reconcileOldReplicaSets`函数 对 oldRSs缩容，按照 `maxSurge` 和 `maxUnavailable` 的约束，计时器间隔1s反复执行、收敛、修正，最终达到期望状态，完成更新。

## 总结 ##

Deployment的回滚、扩(缩)容、暂停、更新等操作，主要是通过修改rs来完成的。其中，rs的版本控制、replicas数量控制是其最核心也是难以理解的地方，但是只要记住99%的时间里deployment对应的活跃的rs只有一个，只有更新时才会出现2个rs，极少数情况下(短时间重复更新)才会出现2个以上的rs，对于上面源码的理解就会容易许多。

另外，从上面这么多步骤的拆解也可以发现，deployment的更新实际基本不涉及对pod的直接操作，因此，本章后续的章节会分析一下replicaSet controller是怎么和pod进行管理交互的。

[k8s_-]: https://github.com/yinwenqin/kubeSourceCodeNote
[Controller-P1-_leader]: https://blog.upweto.top/gitbooks/kubeSourceCodeNote/controller/Kubernetes%E6%BA%90%E7%A0%81%E5%AD%A6%E4%B9%A0-Controller-P1-%E5%A4%9A%E5%AE%9E%E4%BE%8Bleader%E9%80%89%E4%B8%BE.html
[Controller-P2-Controller_informer]: https://blog.upweto.top/gitbooks/kubeSourceCodeNote/controller/Kubernetes%E6%BA%90%E7%A0%81%E5%AD%A6%E4%B9%A0-Controller-P2-Controller%E4%B8%8Einformer.html
[20200127122642.png]: http://mycloudn.upweto.top/20200127122642.png
[20200130125357.png]: http://mycloudn.upweto.top/20200130125357.png