Community Blog Optimize Hybrid Cloud Data Access Based on ACK Fluid (4): Mount Third-party Storage Directories to Kubernetes

Optimize Hybrid Cloud Data Access Based on ACK Fluid (4): Mount Third-party Storage Directories to Kubernetes

Part 4 of this 5-part series focuses on using ACK Fluid to implement Kubernetes-based mounting of third-party storage host directories.

By Yang Che

This article series describes how to support and optimize hybrid cloud data access scenarios based on ACK Fluid. For more information about related articles, please see:

In the previous article, I discussed how to accelerate read access to third-party storage to achieve better performance, lower costs, and reduce reliance on leased line stability.

In some scenarios, customers may not use standard CSI interfaces for connecting with cloud services due to historical reasons and the development and maintenance cost of container storage interfaces. Instead, they use non-containerized methods such as automated scripts. However, when migrating to the cloud, it becomes necessary to consider how to connect with cloud services based on standard interfaces.

This article focuses on using ACK Fluid to implement Kubernetes-based mounting of third-party storage host directories. This approach is more standardized and can improve efficiency.



Many enterprises have on-premises storage that does not support the CSI protocol and can only be mounted as host directories using tools like Ansible. This poses challenges for connecting with the standardized Kubernetes platform. Additionally, similar performance and cost issues as mentioned in the previous article need to be addressed:

Lack of standards and difficulties in migrating to the cloud: Host directory mounting cannot be perceived and scheduled by Kubernetes, making it challenging to use and manage with containerized workloads.

Lack of data isolation: When the entire directory is mounted on the host, data becomes globally visible and can be accessed by all workloads.

Similar requirements for cost, performance, and availability as Scenario 2, which I won't repeat here.

ACK Fluid provides the capability to accelerate PV host directories based on JindoRuntime [1]. It supports direct host directory mounting, enabling data access acceleration through distributed caching in a native, simple, quick, and secure manner.

Here are the key benefits:

  1. Migrate the traditional architecture to a cloud-native adaptive architecture: Change the host directory mounting mode to PV volumes under the CSI protocol, which can be managed by Kubernetes and easily integrated with public cloud services through standardized protocols.
  2. Low-cost migration of traditional architecture: By mounting the host directory and converting the Hostpath protocol to PV storage volumes during deployment, you can start using them immediately without additional development.
  3. Data isolation: Use the subdataset mode of Fluid to isolate the visibility of different users to different directories of offline storage.
  4. Provides the same benefits as mentioned in the previous article, such as performance, cost, automation, and cache-free data persistence.

Summary: ACK Fluid offers out-of-the-box benefits, including high performance, low cost, automation, and no data persistence, for accessing host directories of third-party storage in a cloud computing environment.


1. Prerequisites

• Create an ACK Pro cluster with a version of 1.18 or later. For more information, see Create an ACK Pro cluster [2].

• Cloud-native AI suite is installed, and ack-fluid components are deployed. Note: If you have installed open source Fluid, uninstall it and then deploy ack-fluid components.

• If the cloud-native AI suite is not installed, enable Fluid Data Acceleration during installation. For more information, see Install the cloud-native AI suite [3].

• If the cloud-native AI suite is installed, deploy the ack-fluid on the Cloud-native AI Suite page in the Container Service console.

• A kubectl client is connected to the cluster. For more information, see Use kubectl to connect to a cluster [4].

• Create PV volumes and PVC volume claims that require access to the storage system. Different storage systems in a Kubernetes environment have different methods for creating storage volumes. To ensure stable connections between the storage systems and the Kubernetes clusters, follow the official documentation of the corresponding storage system for preparation.

2. Prepare the Mount Point of the Host Directory

In this example, sshfs is used to simulate converting third-party storage into a data volume claim through Fluid and accelerating access to the data volume claim.

2.1 First log on to the three machines,, and and install the sshfs service respectively. In this example, CentOS is used and run the following command:

$ sudo yum install sshfs -y

2.2 Log on to the sshfs server Run the following command to create a new subdirectory under the /mnt directory as the mount point of the host directory, and create a test file.

$ mkdir /mnt/demo-remote-fs
$ cd /mnt/demo-remote-fs
$ dd if=/dev/zero of=/mnt/demo-remote-fs/allzero-demo count=1024 bs=10M

2.3 Run the following command to create the corresponding host directories for the client nodes and of the sshfs.

$ mkdir /mnt/demo-remote-fs
$ sshfs /mnt/demo-remote-fs
$ ls /mnt/demo-remote-fs

2.4 Run the following command to add tags to the nodes and The tag demo-remote-fs=true is used to configure node scheduling constraints for the Master and Worker components of JindoRuntime.

$ kubectl label node demo-remote-fs=true$ 
kubectl label node demo-remote-fs=true

2.5 Select node and run the following command to access data and evaluate the file access performance. It takes 1 minute and 5.889 seconds to copy 10 GB of files.

$  ls -lh /mnt/demo-remote-fs/
total 10G
-rwxrwxr-x 1 root root 10G Aug 13 10:07 allzero-demo
$ time cat /mnt/demo-remote-fs/allzero-demo > /dev/null

real  1m5.889s
user  0m0.086s
sys  0m3.281s

3. Create a Fluid Dataset and a JindoRuntime

Use the following YAML to create a dataset.yaml file:

The dataset.yaml configuration file contains two Fluid resource objects to be created: Dataset and JindoRuntime.

Dataset: describes the information about the host directory to be mounted.

JindoRuntime: the configurations of the JindoFS distributed cache system to be started, including the number of worker component replicas and the maximum available cache capacity for each worker component.

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
  name: hostpath-demo-dataset
    - mountPoint: local:///mnt/demo-remote-fs
      name: data
      path: /
    - ReadOnlyMany
apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
  name: hostpath-demo-dataset
      demo-remote-fs: "true"
      demo-remote-fs: "true"
      demo-remote-fs: "true"
  replicas: 2
      - mediumtype: MEM
        path: /dev/shm
        quota: 10Gi
        high: "0.99"
        low: "0.99"

The following table describes the object parameters in the configuration file.

Parameter Description
Dataset.spec.mounts[*].mountPoint The information about the data source to be mounted. When you mount a host directory as a data source, the local://<path> format is supported. path is the mounted host directory and needs to be set to an absolute path.
Dataset.spec.nodeAffinity Set constraints on node scheduling for the Master and Worker components of JindoRuntime, with fields consistent with Pod.Spec.Affinity.NodeAffinity.
JindoRuntime.spec.replicas The number of workers for the JindoFS cache system. It can be adjusted as needed.
JindoRuntime.spec.tieredstore.levels[*].mediu mtype The type of cache. Only HDD (Mechanical Hard Disk Drive), SSD (Solid State Drive), and MEM (Memory) are supported. In AI training scenarios, we recommend that you use MEM. When MEM is used, the cache data storage directory specified by path needs to be set to the memory file system. For example, you can specify a temporary mount point and mount a temporary file system (TMPFS) file system to the mount point.
JindoRuntime.spec.tieredstore.levels[*].path The directory used by JindoFS workers to cache data. For optimal data access performance, we recommend that you use /dev/shm or other paths where the memory file system is mounted.
JindoRuntime.spec.tieredstore.levels[*].quota The maximum cache size that each worker can use. You can modify the number based on your requirements.

3.1 Run the following commands to create the Dataset and JindoRuntime resource objects:

$ kubectl create -f dataset.yaml

3.2 Run the following command to view the deployment of the Dataset:

$ kubectl get dataset hostpath-demo-dataset

Expected output:

hostpath-demo-dataset   10.00GiB         0.00B    20.00GiB         0.0%                Bound   47s

3.3 If the Dataset is in the Bound state, the JindoFS cache system is started in the cluster. Application pods can access the data defined in the Dataset.

4. Create a DataLoad to Perform cache preheating

The data access efficiency of application pods may be low because the first access cannot hit the data cache. Fluid provides the DataLoad cache preheating operation to improve the efficiency of the first data access.

4.1 Create a dataload.yaml file. Sample code:

apiVersion: data.fluid.io/v1alpha1
kind: DataLoad
  name: dataset-warmup
    name: hostpath-demo-dataset
    namespace: default
  loadMetadata: true
    - path: /
      replicas: 1

The following table describes the object parameters.

Parameter Description
spec.dataset.name The name of the Dataset object to be preheated.
spec.dataset.namespace The namespace to which the Dataset object belongs. The namespace must be the same as the namespace of the DataLoad object.
spec.loadMetadata Specifies whether to synchronize metadata before preheating. This parameter must be set to true for JindoRuntime.
spec.target[*].path The path or file to be preheated. The path must be a relative path of the mount point specified in the Dataset object. For example, if the data source mounted to the Dataset is pvc://my-pvc/mydata, setting the path to /test will preheat the /mydata/test directory under my-pvc's corresponding storage system.
spec.target[*].replicas The number of worker pods created to cache the preheated path or file.

4.2 Run the following command to create a DataLoad object:

$ kubectl create -f dataload.yaml

4.3 Run the following command to check the DataLoad status:

$ kubectl get dataload dataset-warmup

Expected output:

NAME             DATASET                 PHASE      AGE   DURATION
dataset-warmup   hostpath-demo-dataset   Complete   96s   1m2s

4.4 Run the following command to check the data cache status:

$ kubectl get dataset

Expected output:

hostpath-demo-dataset   10.00GiB         10.00GiB   20.00GiB         100.0%              Bound   157m

After the DataLoad cache preheating is performed, the cached data volume of the dataset has been updated to the size of the entire dataset, which means that the entire dataset has been cached, and the cached percentage is 100.0%.

5. View the Access Performance After Data Preheating

5.1 Use the following YAML to create a pod.yaml file, and modify the claimName in the YAML file to be the same as the name of the dataset that is created in this example.

apiVersion: v1
kind: Pod
  name: nginx
    - name: nginx
      image: nginx
      - "bash"
      - "-c"
      - "sleep inf"
        - mountPath: /data
          name: data-vol
    - name: data-vol
        claimName: hostpath-demo-dataset # The name must be the same as the Dataset. 

5.2 Run the following command to create an application pod:

kubectl create -f pod.yaml

5.3 Run the following command to log on to the pod and access the data:

$ kubectl exec -it nginx bash

The expected output is that the time required to copy 10 GB of files is 8.629 seconds, which is one-eighth of the time required for sshfs direct remote copy (1 minute 5.889 seconds):

root@nginx:/# ls -lh /data
total 10G
-rwxrwxr-x 1 root root 10G Aug 13 10:07 allzero-demo
root@nginx:/# time cat /data/allzero-demo > /dev/null

real  0m8.629s
user  0m0.031s
sys  0m3.594s

5.4 Cleanup application pods

$ kubectl delete po nginx


[1] The general acceleration capability of the PV host directory
[2] Create an ACK Pro cluster
[3] Install the cloud-native AI suite
[4] Use kubectl to connect to a cluster

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