Kubelet源码分析(三):Garbage Collection

1

源码版本

kubernetes version: v1.3.0

kubelet GC介绍

在分析kubelet启动流程时,老是会碰到各类GC,这里单独提出来做下较详细的分析。
kubelet's Garbage Collection主要由两部分组成:

  • containerGC: 使用指定的container回收策略,删除那些已经结束的containers

  • imageManager: k8s所有images的生命周期管理就是通过imageManager来实现的,其实该imageManager也是依赖了cAdvisor。

imageManager

策略初始化

imageManager的回收策略结构如下:

type ImageGCPolicy struct {
    // Any usage above this threshold will always trigger garbage collection.
    // This is the highest usage we will allow.
    HighThresholdPercent int

    // Any usage below this threshold will never trigger garbage collection.
    // This is the lowest threshold we will try to garbage collect to.
    LowThresholdPercent int

    // Minimum age at which a image can be garbage collected.
    MinAge time.Duration
}

该结构的出厂设置在cmd/kubelet/app/server.go中的UnsecuredKubeletConfig()接口进行。

func UnsecuredKubeletConfig(s *options.KubeletServer) (*KubeletConfig, error) {
...
    imageGCPolicy := kubelet.ImageGCPolicy{
            MinAge:               s.ImageMinimumGCAge.Duration,
            HighThresholdPercent: int(s.ImageGCHighThresholdPercent),
            LowThresholdPercent:  int(s.ImageGCLowThresholdPercent),
        }
...
}

赋值的KubeletServer的几个参数的初始化在cmd/kubelet/app/options/options.go中的NewKubeletServer()接口中进行:

func NewKubeletServer() *KubeletServer {
    return &KubeletServer{
        ...
        ImageMinimumGCAge:            unversioned.Duration{Duration: 2 * time.Minute},
        ImageGCHighThresholdPercent:  90,
        ImageGCLowThresholdPercent:   80,
        ...
    }
}

从上面的初始化过程可以得出:

  • 在磁盘的占用率高于90%时,imageGC将一直被触发

  • 在磁盘的占用率低于80%时,imageGC将不会触发

  • imageGC会尝试先delete最少使用的image,但是如果该image的创建时间才低于2min,将不会被删除。

imageManager初始化

上面介绍的都是imageManager的回收策略参数初始化,下面开始介绍imageManager。
结构所在目录:pkg/kubelet/image_manager.go
结构如下:

type imageManager interface {
    // Applies the garbage collection policy. Errors include being unable to free
    // enough space as per the garbage collection policy.
    GarbageCollect() error

    // Start async garbage collection of images.
    Start() error

    GetImageList() ([]kubecontainer.Image, error)

    // TODO(vmarmol): Have this subsume pulls as well.
}

可以看到imageManager是个interface,实际初始化的结构体是realImageManager:

type realImageManager struct {
    // Container runtime
    runtime container.Runtime

    // Records of images and their use.
    imageRecords     map[string]*imageRecord
    imageRecordsLock sync.Mutex

    // The image garbage collection policy in use.
    policy ImageGCPolicy

    // cAdvisor instance.
    cadvisor cadvisor.Interface

    // Recorder for Kubernetes events.
    recorder record.EventRecorder

    // Reference to this node.
    nodeRef *api.ObjectReference

    // Track initialization
    initialized bool
}

该接口的初始化需要先回到pkg/kubelet/kubelet.go中的NewMainKubelet()接口中:

func NewMainKubelet(
    hostname string,
    nodeName string,
...
) (*Kubelet, error) {
...
    // setup containerGC
    containerGC, err := kubecontainer.NewContainerGC(klet.containerRuntime, containerGCPolicy)
    if err != nil {
        return nil, err
    }
    klet.containerGC = containerGC

    // setup imageManager
    imageManager, err := newImageManager(klet.containerRuntime, cadvisorInterface, recorder, nodeRef, imageGCPolicy)
    if err != nil {
        return nil, fmt.Errorf("failed to initialize image manager: %v", err)
    }
    klet.imageManager = imageManager
...
}

可以看到上面的接口中对containerGC和imageManager都进行了初始化,这里先介绍imageManager,containerGC留到下面再讲。
newImageManager()接口如下:

func newImageManager(runtime container.Runtime, cadvisorInterface cadvisor.Interface, recorder record.EventRecorder, nodeRef *api.ObjectReference, policy ImageGCPolicy) (imageManager, error) {
    // 检查policy参数有效性
    if policy.HighThresholdPercent < 0 || policy.HighThresholdPercent > 100 {
        return nil, fmt.Errorf("invalid HighThresholdPercent %d, must be in range [0-100]", policy.HighThresholdPercent)
    }
    if policy.LowThresholdPercent < 0 || policy.LowThresholdPercent > 100 {
        return nil, fmt.Errorf("invalid LowThresholdPercent %d, must be in range [0-100]", policy.LowThresholdPercent)
    }
    if policy.LowThresholdPercent > policy.HighThresholdPercent {
        return nil, fmt.Errorf("LowThresholdPercent %d can not be higher than HighThresholdPercent %d", policy.LowThresholdPercent, policy.HighThresholdPercent)
    }
    // 初始化realImageManager结构
    im := &realImageManager{
        runtime:      runtime,
        policy:       policy,
        imageRecords: make(map[string]*imageRecord),
        cadvisor:     cadvisorInterface,
        recorder:     recorder,
        nodeRef:      nodeRef,
        initialized:  false,
    }

    return im, nil
}

查看上面的初始化接口,可以看出该imageManager跟容器runtime、cAdvisor、EventRecorder、nodeRef、Policy都有关。
这里可以进行大胆的猜测:

  • runtime用于进行image的删除操作

  • cAdvisor用于获取image占用磁盘的情况

  • EventRecorder用于发送具体的回收事件

  • Policy就是具体的回收策略了

  • nodeRef干嘛的?猜不到,还是后面继续看源码吧!

imageManager启动

所有的参数初始化结束后,需要开始进入真正的GC启动流程,该步骤还是需要查看CreateAndInitKubelet()接口。
接口目录:cmd/kubelet/app/server.go
接口调用流程:main -> app.Run -> run -> RunKubelet -> CreateAndInitKubelet
接口如下:

func CreateAndInitKubelet(kc *KubeletConfig) (k KubeletBootstrap, 
    pc *config.PodConfig, err error) {
...
    k.StartGarbageCollection()

    return k, pc, nil
}

该接口调用了启动GC的接口StartGarbageCollection(),具体实现如下:

func (kl *Kubelet) StartGarbageCollection() {
    go wait.Until(func() {
        if err := kl.containerGC.GarbageCollect(kl.sourcesReady.AllReady()); err != nil {
            glog.Errorf("Container garbage collection failed: %v", err)
        }
    }, ContainerGCPeriod, wait.NeverStop)

    go wait.Until(func() {
        if err := kl.imageManager.GarbageCollect(); err != nil {
            glog.Errorf("Image garbage collection failed: %v", err)
        }
    }, ImageGCPeriod, wait.NeverStop)
}

上面的接口分别启动了containerGC和imageManager的协程,可以看出containerGC是每1分钟触发回收,imageManager是每5分钟触发回收。
该GarbageCollect()接口需要根据之前参数初始化时的realImageManager结构进行查看,进入kl.imageManager.GarbageCollect()一看究竟:

func (im *realImageManager) GarbageCollect() error {
    // 获取节点上所存在的images的磁盘占用率
    fsInfo, err := im.cadvisor.ImagesFsInfo()
    if err != nil {
        return err
    }
    // 容量及可利用的空间
    capacity := int64(fsInfo.Capacity)
    available := int64(fsInfo.Available)
    if available > capacity {
        glog.Warningf("available %d is larger than capacity %d", available, capacity)
        available = capacity
    }

    // Check valid capacity.
    if capacity == 0 {
        err := fmt.Errorf("invalid capacity %d on device %q at mount point %q", capacity, fsInfo.Device, fsInfo.Mountpoint)
        im.recorder.Eventf(im.nodeRef, api.EventTypeWarning, container.InvalidDiskCapacity, err.Error())
        return err
    }

    // 查看images的磁盘占用率是否大于等于HighThresholdPercent
    usagePercent := 100 - int(available*100/capacity)
    if usagePercent >= im.policy.HighThresholdPercent {
        // 尝试去回收images的占用率到LowThresholdPercent之下
        amountToFree := capacity*int64(100-im.policy.LowThresholdPercent)/100 - available
        glog.Infof("[ImageManager]: Disk usage on %q (%s) is at %d%% which is over the high threshold (%d%%). Trying to free %d bytes", fsInfo.Device, fsInfo.Mountpoint, usagePercent, im.policy.HighThresholdPercent, amountToFree)
        // 真正的回收接口
        freed, err := im.freeSpace(amountToFree, time.Now())
        if err != nil {
            return err
        }

        if freed < amountToFree {
            err := fmt.Errorf("failed to garbage collect required amount of images. Wanted to free %d, but freed %d", amountToFree, freed)
            im.recorder.Eventf(im.nodeRef, api.EventTypeWarning, container.FreeDiskSpaceFailed, err.Error())
            return err    ·
        }
    }

    return nil
}

这里最关键的接口便是im.freeSpace(),该接口才是真正进行资源回收的接口。
该接口有两个参数:第一个便是设置这次打算回收的空间,第二个是传入调用回收接口的当前时间。
具体的回收,我们进入接口继续细看:

func (im *realImageManager) freeSpace(bytesToFree int64, freeTime time.Time) (int64, error) {
    // 用im.runtime遍历现存的所有的images,并更新im.imageRecords,下面会用到。
    err := im.detectImages(freeTime)
    if err != nil {
        return 0, err
    }
    // 操作imageRecords的锁
    im.imageRecordsLock.Lock()
    defer im.imageRecordsLock.Unlock()

    // 获取所有的images
    images := make([]evictionInfo, 0, len(im.imageRecords))
    for image, record := range im.imageRecords {
        images = append(images, evictionInfo{
            id:          image,
            imageRecord: *record,
        })
    }
    sort.Sort(byLastUsedAndDetected(images))

    // 下面的循环将尝试删除images,直到满足需要删除的空间为止
    var lastErr error
    spaceFreed := int64(0)
    for _, image := range images {
        glog.V(5).Infof("Evaluating image ID %s for possible garbage collection", image.id)
        // Images that are currently in used were given a newer lastUsed.
        if image.lastUsed.Equal(freeTime) || image.lastUsed.After(freeTime) {
            glog.V(5).Infof("Image ID %s has lastUsed=%v which is >= freeTime=%v, not eligible for garbage collection", image.id, image.lastUsed, freeTime)
            break
        }

        // Avoid garbage collect the image if the image is not old enough.
        // In such a case, the image may have just been pulled down, and will be used by a container right away.
        // 查看该image的空闲时间是否够久,不够久的话将不删除
        // 这个时间在GC的策略中有配置
        if freeTime.Sub(image.firstDetected) < im.policy.MinAge {
            glog.V(5).Infof("Image ID %s has age %v which is less than the policy's minAge of %v, not eligible for garbage collection", image.id, freeTime.Sub(image.firstDetected), im.policy.MinAge)
            continue
        }

        // 调用runtime(即Docker)的接口删除指定的image
        glog.Infof("[ImageManager]: Removing image %q to free %d bytes", image.id, image.size)
        err := im.runtime.RemoveImage(container.ImageSpec{Image: image.id})
        if err != nil {
            lastErr = err
            continue
        }
        // 将删除的镜像从imageRecords中去除,所以前面需要加锁
        delete(im.imageRecords, image.id)
        // 增加已经删除的image的size
        spaceFreed += image.size
        // 如果已经删除的image的大小已经满足要求,则退出回收流程
        if spaceFreed >= bytesToFree {
            break
        }
    }

    return spaceFreed, lastErr
}

基本的imageManager模块流程差不多就这样了,这里还可以继续深入学习下cAdvisor和docker runtime的接口实现。

containerGC

策略初始化

containerGC回收策略相关结构如下:

type ContainerGCPolicy struct {
    // Minimum age at which a container can be garbage collected, zero for no limit.
    MinAge time.Duration

    // Max number of dead containers any single pod (UID, container name) pair is
    // allowed to have, less than zero for no limit.
    MaxPerPodContainer int

    // Max number of total dead containers, less than zero for no limit.
    MaxContainers int
}

该结构的初始化是在cmd/kubelet/app/kubelet.go文件中的CreateAndInitKubelet()接口中进行。
调用流程:main --> app.Run --> RunKubelet --> CreateAndInitKubelet

func CreateAndInitKubelet(kc *KubeletConfig) (k KubeletBootstrap, pc *config.PodConfig, err error) {
    var kubeClient clientset.Interface
    if kc.KubeClient != nil {
        kubeClient = kc.KubeClient
        // TODO: remove this when we've refactored kubelet to only use clientset.
    }

    // containerGC的回收策略初始化
    gcPolicy := kubecontainer.ContainerGCPolicy{
        MinAge:             kc.MinimumGCAge,
        MaxPerPodContainer: kc.MaxPerPodContainerCount,
        MaxContainers:      kc.MaxContainerCount,
    }
...
}

可以看到实际的参数来源于kc结构,而该结构的初始化是在cmd/kubelet/app/kubelet.go文件中的UnsecuredKubeletConfig()接口中进行。
调用流程:main --> app.Run --> UnsecuredKubeletConfig

func UnsecuredKubeletConfig(s *options.KubeletServer) (*KubeletConfig, error) {
...
        MaxContainerCount:            int(s.MaxContainerCount),
        MaxPerPodContainerCount:      int(s.MaxPerPodContainerCount),
        MinimumGCAge:                 s.MinimumGCAge.Duration,
...
}

最开始的参数都来源于KubeletServer中的KubeletConfiguration结构,相关的参数如下:

type KubeletConfiguration struct {
...
    // containerGC会回收已经结束的container,但是该container结束后必须要停留
    // 大于MinimumGCAge时间才能被回收。 default: 1min
    MinimumGCAge unversioned.Duration `json:"minimumGCAge"`
    // 用于指定每个已经结束的Pod最多可以存在containers的数量,default: 2
    MaxPerPodContainerCount int32 `json:"maxPerPodContainerCount"`
    // 集群最大支持的container数量
    MaxContainerCount int32 `json:"maxContainerCount"`
    
}

而该入参的初始化还是需要回到cmd/kubelet/app/options/options.go中的NewKubeletServer()接口,实际初始化如下:

func NewKubeletServer() *KubeletServer {
...
            MaxContainerCount:            240,
            MaxPerPodContainerCount:      2,
            MinimumGCAge:                 unversioned.Duration{Duration: 1 * time.Minute},

从上面的初始化可以看出:

  • 该节点可以创建的最大container数量是240

  • 每个Pod最大可以容纳2个containers

  • container结束之后,至少需要在1分钟之后才能被containerGC回收

所以基本的containerGC策略就明白了。

containerGC初始化

策略结构初始化完之后,还要进行最后一步containerGC结构初始化,需要进入pkg/kubelet/kubelet.go的NewMainKubelet()接口查看:

func NewMainKubelet(...) {
...
    // setup containerGC
    containerGC, err := kubecontainer.NewContainerGC(klet.containerRuntime, containerGCPolicy)
    if err != nil {
        return nil, err
    }
    klet.containerGC = containerGC
...
}

继续查看NewContainerGC(),该接口在pkg/kubelet/container/container_gc.go中,看下干了啥:

func NewContainerGC(runtime Runtime, policy ContainerGCPolicy) (ContainerGC, error) {
    if policy.MinAge < 0 {
        return nil, fmt.Errorf("invalid minimum garbage collection age: %v", policy.MinAge)
    }

    return &realContainerGC{
        runtime: runtime,
        policy:  policy,
    }, nil
}

接口很简单,根据之前的策略结构体又初始化了一个realContainerGC结构,可以看出该接口就比较完整了,可以想象一下需要进行container的回收的话,必须要用到runtime的接口(比如查看当前容器状态,删除容器等操作),所以结构中带入实际使用的runtime是必然的。
可以关注下该对象支持的方法,后面会用到。

containerGC启动

所有的参数初始化结束后,需要开始进入真正的GC启动流程,该步骤上面讲imageManager时已经提及,这里直接进入正题。
启动containerGC的接口是StartGarbageCollection(),具体实现如下:

func (kl *Kubelet) StartGarbageCollection() {
    go wait.Until(func() {
        if err := kl.containerGC.GarbageCollect(kl.sourcesReady.AllReady()); err != nil {
            glog.Errorf("Container garbage collection failed: %v", err)
        }
    }, ContainerGCPeriod, wait.NeverStop)

    go wait.Until(func() {
        if err := kl.imageManager.GarbageCollect(); err != nil {
            glog.Errorf("Image garbage collection failed: %v", err)
        }
    }, ImageGCPeriod, wait.NeverStop)
}

接下来我们一起看下containerGC的GarbageCollect()接口,但要找到这个接口的话,我们得回到之前初始化containerGC的步骤。
实际初始化containerGC时真正返回的是realContainerGC结构,所以GarbageCollect()是该结构的方法:

func (cgc *realContainerGC) GarbageCollect(allSourcesReady bool) error {
    return cgc.runtime.GarbageCollect(cgc.policy, allSourcesReady)
}

看到这里,发现containerGC的套路跟imageManager一样,所以一招鲜吃遍天。
我们使用的runtime就是docker,所以需要去找docker的GarbageCollect()接口实现,具体runtime的初始化可以查看之前一篇文章<Kubelet源码分析(二) DockerClient>的介绍,这里就不讲具体的初始化了,直接进入正题。
Docker的GarbageCollect()接口在pkg/kubelet/dockertools/container_gc.go中:

func (cgc *containerGC) GarbageCollect(gcPolicy kubecontainer.ContainerGCPolicy, allSourcesReady bool) error {
    // 从所有的容器中分离出那些可以被回收的contianers
    // evictUnits: 可以识别的但已经dead,并且创建时间大于回收策略中的minAge的containers
    // unidentifiedContainers: 无法识别的containers
    evictUnits, unidentifiedContainers, err := cgc.evictableContainers(gcPolicy.MinAge)
    if err != nil {
        return err
    }

    // 先删除无法识别的containers
    for _, container := range unidentifiedContainers {
        glog.Infof("Removing unidentified dead container %q with ID %q", container.name, container.id)
        err = cgc.client.RemoveContainer(container.id, dockertypes.ContainerRemoveOptions{RemoveVolumes: true})
        if err != nil {
            glog.Warningf("Failed to remove unidentified dead container %q: %v", container.name, err)
        }
    }

    // 所有资源都已经准备好之后,可以删除那些已经dead的containers
    if allSourcesReady {
        for key, unit := range evictUnits {
            if cgc.isPodDeleted(key.uid) {
                cgc.removeOldestN(unit, len(unit)) // Remove all.
                delete(evictUnits, key)
            }
        }
    }

    // 检查所有的evictUnits, 删除每个Pod中超出的containers
    if gcPolicy.MaxPerPodContainer >= 0 {
        cgc.enforceMaxContainersPerEvictUnit(evictUnits, gcPolicy.MaxPerPodContainer)
    }

    // 确保节点的最大containers数量
    // 检查节点containers数量是否超出了最大限制,是的话就删除多出来的containers
    // 优先删除最先创建的containers
    if gcPolicy.MaxContainers >= 0 && evictUnits.NumContainers() > gcPolicy.MaxContainers {
        // 计算每个单元最多可以有几个containers
        numContainersPerEvictUnit := gcPolicy.MaxContainers / evictUnits.NumEvictUnits()
        if numContainersPerEvictUnit < 1 {
            numContainersPerEvictUnit = 1
        }
        // 
        cgc.enforceMaxContainersPerEvictUnit(evictUnits, numContainersPerEvictUnit)

        // 需要删除containers的话,优先删除最老的containers
        numContainers := evictUnits.NumContainers()
        if numContainers > gcPolicy.MaxContainers {
            flattened := make([]containerGCInfo, 0, numContainers)
            for uid := range evictUnits {
                // 先整合所有的containers
                flattened = append(flattened, evictUnits[uid]...)
            }
            sort.Sort(byCreated(flattened))
            // 删除numContainers-gcPolicy.MaxContainers个最先创建的contianers
            cgc.removeOldestN(flattened, numContainers-gcPolicy.MaxContainers)
        }
    }

    // 删除containers之后,需要清除对应的软连接
    logSymlinks, _ := filepath.Glob(path.Join(cgc.containerLogsDir, fmt.Sprintf("*.%s", LogSuffix)))
    for _, logSymlink := range logSymlinks {
        if _, err = os.Stat(logSymlink); os.IsNotExist(err) {
            err = os.Remove(logSymlink)
            if err != nil {
                glog.Warningf("Failed to remove container log dead symlink %q: %v", logSymlink, err)
            }
        }
    }

    return nil
}

User Configuration

上面通过源码介绍了imageManager和containerGC的实现,里面也涉及到了GC Policy的配置,我们也可以通过手动修改kubelet的flags来改变参数默认值。

imageManager相关配置

  • image-gc-high-threshold: 该值表示磁盘占用率达到该值后会触发image garbage collection。默认值是90%

  • image-gc-low-threshold: 该值表示image GC尝试以回收的方式来达到的磁盘占用率,若磁盘占用率原本就小于该值,不会触发GC。默认值是80%

containerGC相关配置

  • minimum-container-ttl-duration: 表示container结束后多长时间可以被GC回收,默认是1min

  • maximum-dead-containers-per-container: 表示每个已经结束的Pod中最多可以存在多少个containers,默认值是2个

  • maximum-dead-containers: 表示kubelet所在节点最多可以保留已经结束的containers的数量,默认值是240

容器在停止工作后是可以被garbage collection进行回收,但是我们也需要对containers进行保留,因为有些containers可能是异常停止的,而container可以保留有logs或者别的游泳的数据用于开发进行问题定位。
根据上面的需求,我们就可以通过maximum-dead-containers-per-container和maximum-dead-containers很好的来实现这个目标。


如果觉得我的文章对你有用,请随意赞赏

你可能感兴趣的

不是很懂你们这些 · 2017-11-18

写得很详细!虽然代码看不懂,不过流程已经对我帮助很大了!工作需要偶然看到,支持支持!

回复

0

多谢多谢!

Robinly 作者 · 2017-11-19
载入中...