get_started.md

Get Started With Fluid

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 1.14+

   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:

   • Amazon Elastic Kubernetes Service
   • Google Kubernetes Engine
   • Azure Kubernetes Service
   • Aliyun Container Service for Kubernetes

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

2. Kubectl 1.14+

   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. Download the latest Fluid from Github release page

3. Deploy Fluid with Helm

   $ helm install fluid fluid-<version>.tgz
   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-3.0.1-bin-without-hadoop.tgz
   150M	/data/spark/spark-3.0.1-bin-without-hadoop.tgz
   $ time cp /data/spark/spark-3.0.1-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-3.0.1-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 sample docs for you:

• Speed Up Accessing Remote Files
• Cache Co-locality for Workload Scheduling
• Accelerate Machine Learning Training with Fluid