tencent cloud


Deploying with Tencent Cloud EKS

Terakhir diperbarui:2021-12-27 12:51:44

    Elastic Kubernetes Service (EKS) is a TKE service mode that allows you to deploy workloads without purchasing any nodes. EKS is fully compatible with native Kubernetes, allowing you to purchase and manage resources natively. This service is billed based on the actual amount of resources used by containers. In addition, EKS provides extended support for Tencent Cloud products, such as storage and network products, and can ensure the secure isolation of containers. EKS is ready to use out-of-the-box.

    Deploying GooseFS with Tencent Cloud EKS can make full use of the elastic computing resources from EKS and construct an on-demand, pay-as-you-go COS access acceleration service billed on a per second basis.


    The figure below shows the general architecture of deploying GooseFS with Tencent EKS.

    As shown in the figure, the entire architecture consists of three parts: EKS Managed Components, User Resource Pool, and COS Server. User Resource Pool is mainly used to deploy GooseFS clusters, and COS Server is used as a remote storage system and can be replaced by CHDFS, a public cloud storage service. During the construction process:

    • Both GooseFS Master and Worker are deployed as Kubernetes StatefulSet.
    • Fluid is used to start a GooseFS cluster.
    • Fuse Client is integrated into the sandbox of the User Pod.
    • The usage method is the same as standard Kubernetes.


    Preparing the environment

    1. Create an EKS cluster. For directions, see Creating a Cluster.
    2. Enable cluster access and select internet access or private network access as appropriate. Refer to Connecting to a Cluster for directions.
    3. Run the kubectl get ns command to make sure the cluster is available:
      -> goosefs kubectl get ns
      default Active 7h31m
      kube-node-lease Active 7h31m
      kube-public Active 7h31m
      kube-system Active 7h31m
    4. Obtain helm. Refer to Helm docs for directions.

    Installing GooseFS

    1. Enter the helm install command to install a chart package and Fluid:
      -> goosefs helm install fluid ./charts/fluid-on-tke
      NAME: fluid
      LAST DEPLOYED: Tue Jul 6 17:41:20 2021
      NAMESPACE: default
      STATUS: deployed
      REVISION: 1
      TEST SUITE: None
    2. View the status of pods related to fluid:
      -> goosefs kubectl -n fluid-system get pod
      alluxioruntime-controller-78877d9d47-p2pv6 1/1 Running 0 59s
      dataset-controller-5f565988cc-wnp7l 1/1 Running 0 59s
      goosefsruntime-controller-6c55b57cd6-hr78j 1/1 Running 0 59s
    3. Create a dataset, modify the relevant variables as appropriate, and run the kubectl apply -f dataset.yaml command to apply the dataset:
      apiVersion: data.fluid.io/v1alpha1
      kind: Dataset
      name: ${dataset-name}
      - mountPoint: cosn://${bucket-name}
      name: ${dataset-name}
      fs.cosn.userinfo.secretKey: XXXXXXX
      fs.cosn.userinfo.secretId: XXXXXXX
      fs.cosn.bucket.region: ap-${region}
      fs.cosn.impl: org.apache.hadoop.fs.CosFileSystem
      fs.AbstractFileSystem.cosn.impl: org.apache.hadoop.fs.CosN
      fs.cos.app.id: ${user-app-id}
    4. Create a GooseFS cluster with yaml below, and run kubectl apply -f runtime.yaml:
      apiVersion: data.fluid.io/v1alpha1
      kind: GooseFSRuntime
      name: slice1
      master.goosefs.eks.tencent.com/model: c6
      worker.goosefs.eks.tencent.com/model: c6
      replicas: 6 # Number of workers. Although the controller can be expanded, GooseFS currently does not support automatic data re-balance.
      replicas: 1 # Number of GooseFS data replicas
      imagePullPolicy: Always
      image: ccr.ccs.tencentyun.com/cosdev/goosefs # Image and version of a GooseFS cluster
      imageTag: v1.0.1
      - mediumtype: MEM # Supports MEM, HDD, and SSD, which represent memory, premium cloud storage, and SSD cloud storage respectively.
      path: /data
      quota: 5G # Both memory and cloud storage will take effect. The minimum capacity of cloud storage is 10 GB.
      high: "0.95"
      low: "0.7"
      goosefs.user.streaming.data.timeout: 5s
      goosefs.job.worker.threadpool.size: "22"
      goosefs.master.journal.type: UFS # UFS or EMBEDDED. UFS for the case of only one master.
      # goosefs.worker.network.reader.buffer.size: 128MB
      goosefs.user.block.size.bytes.default: 128MB
      # goosefs.user.streaming.reader.chunk.size.bytes: 32MB
      # goosefs.user.local.reader.chunk.size.bytes: 32MB
      goosefs.user.metrics.collection.enabled: "false"
      goosefs.user.metadata.cache.enabled: "true"
      goosefs.user.metadata.cache.expiration.time: "2day"
      # A required parameter, which sets the VM specification of pods. Default value: 1c1g
      cpu: 8
      memory: "16Gi"
      cpu: 8
      memory: "16Gi"
      replicas: 1
      # journal:
      # volumeType: pvc
      # storageClass: goosefs-hdd
      - "-Xmx12G"
      - "-XX:+UnlockExperimentalVMOptions"
      - "-XX:ActiveProcessorCount=8"
      - "-Xms10G"
      - "-Xmx28G"
      - "-Xms28G"
      - "-XX:+UnlockExperimentalVMOptions"
      - "-XX:MaxDirectMemorySize=28g"
      - "-XX:ActiveProcessorCount=8"
      cpu: 16
      memory: "32Gi"
      cpu: 16
      memory: "32Gi"
      - "-Xmx4G"
      - "-Xms4G"
      - "-XX:+UseG1GC"
      - "-XX:MaxDirectMemorySize=4g"
      - "-XX:+UnlockExperimentalVMOptions"
      - "-XX:ActiveProcessorCount=24"
    5. Check the statuses of the cluster and PVC:
      -> goosefs kubectl get pod
      slice1-master-0 2/2 Running 0 8m8s
      slice1-worker-0 2/2 Running 0 8m8s
      slice1-worker-1 2/2 Running 0 8m8s
      slice1-worker-2 2/2 Running 0 8m8s
      slice1-worker-3 2/2 Running 0 8m8s
      slice1-worker-4 2/2 Running 0 8m8s
      slice1-worker-5 2/2 Running 0 8m8s
      -> goosefs kubectl get pvc
      slice1 Bound default-slice1 100Gi ROX fluid 7m37s # PVC and dataset share the same name. 100Gi is a dummy value used as a placeholder.

    Loading data

    To preload data, you only need to create a resource with the yaml below (e.g. kubectl apply -f dataload.yaml). A response example after running is as follows:

      apiVersion: data.fluid.io/v1alpha1
      kind: DataLoad
        name: slice1-dataload
        # Configuring the dataset that needs to load data
          name: slice1
          namespace: default

    After creating, you can check the status via kubectl get dataload slice1-dataload.

    Mounting PVC to a service pod

    The user service container should be used in accordance to the K8s instruction. Refer to Kubernetes documentation for details.

    Terminating a GooseFS cluster

    To terminate a GooseFS cluster, you can specify the master and worker nodes to be deleted and run the delete command. This is a high-risk operation. Make sure that there is no I/O operation on GooseFS in the service pod.

      -> goosefs kubectl get sts
      NAME            READY   AGE
      slice1-master   1/1     14m
      slice1-worker   6/6     14m
      -> goosefs kubectl delete sts slice1-master slice1-worker
      statefulset.apps "slice1-master" deleted
      statefulset.apps "slice1-worker" deleted
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