Fluid + JindoCache 存储加速系统高阶功能使用§

挂载点在根目录下§

默认使用 JindoRuntime 会在挂载点多一层 / 的目录，如果想挂载在根目录下可以在 dataset 里进行参数指定

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: hbase
spec:
  mounts:
    - mountPoint: oss://test-bucket/
      options:
        fs.oss.accessKeyId: <OSS_ACCESS_KEY_ID>
        fs.oss.accessKeySecret: <OSS_ACCESS_KEY_SECRET>
        fs.oss.endpoint: <OSS_ENDPOINT> 
      name: hbase
      path: /

指定 spec.mounts.path = /，则会将文件挂载在根目录下

Secret 加密 AK 参数§

apiVersion: v1
kind: Secret
metadata:
  name: mysecret
stringData:
  fs.oss.accessKeyId: <OSS_ACCESS_KEY_ID>
  fs.oss.accessKeySecret: <OSS_ACCESS_KEY_SECRET>

在 dataset 里使用 secret

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: hadoop
spec:
  mounts:
  - mountPoint: oss://<OSS_BUCKET>/<OSS_DIRECTORY>/
    name: hadoop
    options:
      fs.oss.endpoint: <OSS_ENDPOINT>
    encryptOptions:
      - name: fs.oss.accessKeyId
        valueFrom:
          secretKeyRef:
            name: mysecret
            key: fs.oss.accessKeyId
      - name: fs.oss.accessKeySecret
        valueFrom:
          secretKeyRef:
            name: mysecret
            key: fs.oss.accessKeySecret

Raft 3 master 模式§

JindoRuntime 模式启动一个 master，可以通过使用 master.replicas 来启动 3 个 master 进行 HA 转换。

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: hadoop
spec:
  replicas: 3
  tieredstore:
    levels:
      - mediumtype: HDD
        path: /mnt/disk1
        quota: 2G
        high: "0.8"
        low: "0.7"
  master:
    replicas: 3
  • master.replicas = 3 : 启动 3 个 master pod

使用 Placement 部署多个 runtime§

JindoRuntime 启动后，默认 worker 节点上只能启动一个 runtime 的worker，属于独占模式，如果想要在同一个节点上支持部署多个 worker，可以使用 shared 模式

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: hbase
spec:
  mounts:
    - mountPoint: oss://test-bucket/
      options:
        fs.oss.accessKeyId: <OSS_ACCESS_KEY_ID>
        fs.oss.accessKeySecret: <OSS_ACCESS_KEY_SECRET>
        fs.oss.endpoint: <OSS_ENDPOINT> 
      name: hbase
  placement: "Shared"

使用 NoseSelector 部署节点§

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: hadoop
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: SSD
        path: /mnt/disk1
        quota: 10Gi
        high: "0.9"
        low: "0.8"
  master:
    nodeSelector:
      kubernetes.io/hostname: cn-hangzhou.10.1.1
  worker:
    nodeSelector:
      kubernetes.io/hostname: cn-hangzhou.10.1.1
  fuse:
    nodeSelector:
      kubernetes.io/hostname: cn-hangzhou.10.1.1

使用 dataset nodeAffinity 功能§

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: hbase
spec:
  mounts:
    - mountPoint: oss://test-bucket/
      options:
        fs.oss.accessKeyId: <OSS_ACCESS_KEY_ID>
        fs.oss.accessKeySecret: <OSS_ACCESS_KEY_SECRET>
        fs.oss.endpoint: <OSS_ENDPOINT> 
      name: hbase
  nodeAffinity:
    required:
      nodeSelectorTerms:
        - matchExpressions:
            - key: hbase-cache
              operator: In
              values:
                - "true"

Worker 个数扩缩容§

使用kubectl scale对 Runtime 的 Worker 数量进行调整

$ kubectl scale jindoruntime <runtime_name> --replicas=<replica_num>

其中 * runtime_name：runtime 的名字 * replica_num：表示扩所容的 Worker 数量

使用 tolerations 功能§

您可以在 dataset 里定义 tolerations 指定 worker 节点的调度

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: hbase
spec:
  mounts:
    - mountPoint: oss://test-bucket/
      options:
        fs.oss.accessKeyId: <OSS_ACCESS_KEY_ID>
        fs.oss.accessKeySecret: <OSS_ACCESS_KEY_SECRET>
        fs.oss.endpoint: <OSS_ENDPOINT> 
      name: hbase
  tolerations:
    - key: hbase 
      operator: Equal 
      value: "true"

也可以为 master/worker/fuse 单独指定 tolerations 调度策略

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: hadoop
spec:
  replicas: 3
  tieredstore:
    levels:
      - mediumtype: HDD
        path: /mnt/disk1
        quota: 2Gi
        high: "0.8"
        low: "0.7"
  master:
    tolerations:
      - key: hbase 
        operator: Equal 
        value: "true"
  worker:
    tolerations:
      - key: hbase 
        operator: Equal 
        value: "true" 
  fuse:
    tolerations:
      - key: hbase 
        operator: Equal 
        value: "true"

Resource 资源§

可以指定 master/worker 等的 resource 资源

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: hadoop
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: SSD
        path: /var/lib/docker/jindo
        quota: 200Gi
        high: "0.9"
        low: "0.8"
  master:
    resources:
      limits:
        cpu: "4"
        memory: "8Gi"
      requests:
        cpu: "2"
        memory: "3Gi"
  worker:
    resources:
      limits:
        cpu: "4"
        memory: "8Gi"
      requests:
        cpu: "2"
        memory: "3Gi"

Fuse 回收策略§

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: oss
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: SSD
        path: /var/lib/docker/jindo
        quota: 200Gi
        high: "0.9"
        low: "0.8"
  fuse:
    cleanPolicy: OnDemand
  • fuse.cleanPolicy: onDemand / OnRuntimeDeleted 分别表示按需启动，任务结束 fuse 也结束和在 runtime 销毁的时候和其他组件一起销毁掉

JindoCache Fuse 客户端相关参数和使用§

参数名称 参数说明 使用范例
auto_unmount fuse进程退出后自动umount挂载节点。 -oauto_unmount
ro 只读挂载，启用参数后不允许写操作。 -oro
direct_io 开启后，读写文件可以绕过page cache。 -odirect_io
kernel_cache 开启后，利用内核缓存优化读性能。 -okernel_cache
auto_cache 默认开启，与kernel_cache 二选一，与kernel_cache不同的是，如果文件大小或修改时间发生变化，缓存就会失效。 -oauto_cache
entry_timeout 默认值，0.1。文件名读取缓存保留时间（秒），用于优化性能。0表示不缓存。 默认-oentry_timeout=60
attr_timeout 默认值，0.1。文件属性缓存保留时间（秒），用于优化性能。0表示不缓存。 默认-oattr_timeout=60
negative_timeout 默认值，0.1。文件名读取失败缓存保留时间（秒），用于优化性能。0表示不缓存。 默认-onegative_timeout=30

配置方式

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: oss
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: SSD
        path: /var/lib/docker/jindo
        quota: 200Gi
        high: "0.9"
        low: "0.8"
  fuse:
    args:
      - -oro
      - -oattr_timeout=60
      - -oentry_timeout=60
      - -onegative_timeout=60

以数组方式写在 spec.fuse.args 里，按照需要填写即可

如单独配置客户端pread参数：

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: oss
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: SSD
        path: /var/lib/docker/jindo
        quota: 200Gi
        high: "0.9"
        low: "0.8"
  fuse:
    args:
      - -oro
      - -ometrics_port=0
      - -okernel_cache
      - -oattr_timeout=7200
      - -oentry_timeout=7200
      - -onegative_timeout=7200
      - -opread

FuseOnly 使用方式§

如您想使用 FuseOnly 方式，可以使用如下配置方式

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: hadoop
spec:
  master:
    disabled: true
  worker:
    disabled: true

多挂载点§

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: data
spec:
  mounts:
    - mountPoint: oss://test-bucket-1/dir1/
      options:
        fs.oss.accessKeyId: <OSS_ACCESS_KEY_ID>
        fs.oss.accessKeySecret: <OSS_ACCESS_KEY_SECRET>
        fs.oss.endpoint: <OSS_ENDPOINT> 
      name: spark
    - mountPoint: oss://test-bucket-2/dir2/
      options:
        fs.oss.accessKeyId: <OSS_ACCESS_KEY_ID>
        fs.oss.accessKeySecret: <OSS_ACCESS_KEY_SECRET>
        fs.oss.endpoint: <OSS_ENDPOINT> 
      name: hadoop

在挂载 pvc 和 fuse 以及做 dataload 的时候，这两个数据源的方式将以 mounts.name 作为区分，比如 /spark 路径下访问 oss://test-bucket-1/dir1/ 下的文件，/hadoop 路径下访问 oss://test-bucket-2/dir2/ 下的文件内容

master元数据持久化§

如果想通过给master挂载volume的方式，让master的元数据持久化到指定的存储上，可以使用如下配置方式

apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: oss
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: SSD
        path: /var/lib/docker/jindo
        quota: 200Gi
        high: "0.9"
        low: "0.8"
  volumes:
    - name: nas
      persistentVolumeClaim:
        claimName: nas-test
  master:
    volumeMounts:
      - name: nas
        subPath: test
        mountPath: /var/cache/
    properties:
      namespace.meta-dir: "/var/cache/"

该例子将元数据目录 namespace.meta-dir 指定到挂载的volume上，并进行持久化，注意挂载的vomlume必须就有读/写/删除权限，不可指定为只读

更多缓存策略§

元数据缓存§

元数据缓存有两种策略，可以 dataset 的定义中进行设置，打开和关闭分别对应 ONCE 和 ALWAYS，默认策略是关闭。

  • 元数据缓存开关
apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: data
spec:
  mounts:
    - mountPoint: oss://test-bucket-1/dir1/
      options:
        metaPolicy: ONCE
        fs.oss.endpoint: <OSS_ENDPOINT>
  • 关闭元数据缓存（默认）
apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: data
spec:
  mounts:
    - mountPoint: oss://test-bucket-1/dir1/
      options:
        metaPolicy: ALWAYS
        fs.oss.endpoint: <OSS_ENDPOINT>

写时落缓存（WRITE_THROUGH）§

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: data
spec:
  mounts:
    - mountPoint: oss://test-bucket-1/dir1/
      options:
        writePolicy: WRITE_THROUGH
        metaPolicy: ONCE # 可以打开或关闭
        fs.oss.endpoint: <OSS_ENDPOINT>

写临时缓存（CACHE_ONLY）§

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: data
spec:
  mounts:
    - mountPoint: oss://test-bucket-1/dir1/
      options:
        writePolicy: CACHE_ONLY
        metaPolicy: ONCE #必须打开元数据缓存
        fs.oss.endpoint: <OSS_ENDPOINT>

DHT 策略（海量小文件只读场景）§

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: data
spec:
  mounts:
    - mountPoint: oss://test-bucket-1/dir1/
      options:
        cacheStrategy: DHT
        metaPolicy: ONCE #必须打开元数据缓存
        fs.oss.endpoint: <OSS_ENDPOINT>