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Version: v0.9

Quick Start

This document mainly describes how to deploy Fluid with Helm, and use Fluid to create a dataset and speed up your application.

Requirements​

  1. Kubernetes 1.18+

    If you don't have a Kubernetes now, we highly recommend you use a cloud Kubernetes service. Usually, with a few steps, you can get your own Kubernetes Cluster. Here's some of the certified cloud Kubernetes services:

    Note: While convenient, Minikube is not recommended to deploy Fluid due to its limited functionalities.

  2. Kubectl 1.18+

    Please make sure your kubectl is properly configured to interact with your Kubernetes environment.

  3. Helm 3

    In the following steps, we'll deploy Fluid with Helm 3

Deploy Fluid​

  1. Create namespace for Fluid

    $ kubectl create ns fluid-system

  2. Add Fluid repository to Helm repos and keep it up-to-date

    $ helm repo add fluid https://fluid-cloudnative.github.io/charts
    $ helm repo update

  3. Deploy Fluid with Helm

    $ helm install fluid fluid/fluid
    NAME: fluid
    LAST DEPLOYED: Tue Jul  7 11:22:07 2020
    NAMESPACE: default
    STATUS: deployed
    REVISION: 1
    TEST SUITE: None

  4. Check running status of Fluid

    $ kubectl get po -n fluid-system
    NAME                                         READY   STATUS    RESTARTS   AGE
    alluxioruntime-controller-64948b68c9-zzsx2   1/1     Running   0          108s
    csi-nodeplugin-fluid-2mfcr                   2/2     Running   0          108s
    csi-nodeplugin-fluid-l7lv6                   2/2     Running   0          108s
    dataset-controller-5465c4bbf9-5ds5p          1/1     Running   0          108s

Create a Dataset​

Fluid provides cloud-native data acceleration and management capabilities, and use dataset as a high-level abstraction to facilitate user management. Here we will show you how to create a dataset with Fluid.

  1. Create a Dataset object through the CRD file, which describes the source of the dataset.

    $ cat<<EOF >dataset.yaml
    apiVersion: data.fluid.io/v1alpha1
    kind: Dataset
    metadata:
      name: demo
    spec:
      mounts:
        - mountPoint: https://mirrors.bit.edu.cn/apache/spark/
          name: spark
    EOF
    kubectl create -f dataset.yaml

  2. Create an AlluxioRuntime CRD object to support the dataset we created. We use Alluxio as its runtime here.

    $ cat<<EOF >runtime.yaml
    apiVersion: data.fluid.io/v1alpha1
    kind: AlluxioRuntime
    metadata:
      name: demo
    spec:
      replicas: 1
      tieredstore:
        levels:
          - mediumtype: MEM
            path: /dev/shm
            quota: 2Gi
            high: "0.95"
            low: "0.7"
    EOF

    Create Alluxio Runtime with kubectl

    kubectl create -f runtime.yaml

  3. Next, we create an application to access this dataset. Here we will access the same data multiple times and compare the time consumed by each access.

    $ cat<<EOF >app.yaml
    apiVersion: v1
    kind: Pod
    metadata:
      name: demo-app
    spec:
      containers:
        - name: demo
          image: nginx
          volumeMounts:
            - mountPath: /data
              name: demo
      volumes:
        - name: demo
          persistentVolumeClaim:
            claimName: demo
    EOF

    Create Pod with kubectl

    $ kubectl create -f app.yaml

  4. Dive into the container to access data, the first access will take longer.

    $ kubectl exec -it demo-app -- bash
    $ du -sh /data/spark/spark-3.0.3/spark-3.0.3-bin-without-hadoop.tgz
    150M	/data/spark/spark-3.0.3/spark-3.0.3-bin-without-hadoop.tgz
    $ time cp /data/spark/spark-3.0.3/spark-3.0.3-bin-without-hadoop.tgz /dev/null
    real	0m13.171s
    user	0m0.002s
    sys	0m0.028s

  5. In order to avoid the influence of other factors like page cache, we will delete the previous container, create the same application, and try to access the same file. Since the file has been cached by alluxio at this time, you can see that it takes significantly less time now.

    $ kubectl delete -f app.yaml && kubectl create -f app.yaml
    $ kubectl exec -it demo-app -- bash
    $ time cp /data/spark/spark-3.0.3/spark-3.0.3-bin-without-hadoop.tgz /dev/null
    real	0m0.344s
    user	0m0.002s
    sys	0m0.020s

We've created a dataset and did some management in a very simple way. For more detail about Fluid, we provide several tutorial docs for you: