Optimize Hybrid Cloud Data Access Based on ACK Fluid (5): Automated Across-regional Center Data Distribution

Part 5 of this 5-part series describes how to use the scheduled warm-up mechanism of ACK Fluid to update data accessible to compute clusters in different regions.

By Yang Che

In the previous articles, we discussed Day 1 of combining Kubernetes and data in hybrid cloud scenarios, focusing on solving data access problems and connecting cloud computing with offline storage. Building upon this, ACK Fluid further addresses the cost and performance issues related to data access. Moving on to Day 2, when users actually implement this solution in a production environment, the main challenge lies in how the operations and maintenance team handles data synchronization for multi-region clusters.

Overview

Many enterprises establish multiple computing clusters in different regions for various reasons such as performance, security, stability, and resource isolation. These clusters require remote access to a centralized data storage center. For example, with the growing popularity of large language models, supporting multi-region inference services based on these models has become a necessity for many businesses. However, this specific scenario presents several challenges:

• Manual synchronization of data across data centers on multiple computing clusters, which is time-consuming.

• Complexity in managing large language models due to the large number of parameters and files. Different businesses choose different basic models and business data, resulting in different final models.

• Continuous and frequent updates of model data based on business inputs.

• Slow startup and file retrieval time for model inference services. Large language models have extensive parameter sizes, often in the hundreds of GBs, which leads to significant time consumption for pulling parameters into GPU memory and extremely long startup times.

• Synchronous updating of models across all regions, which negatively impacts the performance of an overloaded storage cluster.

In addition to the acceleration capabilities of common storage clients, ACK Fluid provides scheduled and triggered data migration and preheating capabilities to simplify data distribution.

• Reduce network and computing costs: Cross-region traffic costs are significantly reduced, computing time is shortened, and the cost of computing clusters is slightly increased. Furthermore, costs can be further reduced through elasticity.

• Accelerate application data updates: By performing data access within the same data center or zone, latency is reduced, and cache throughput concurrency can be linearly scaled.

• Streamline complex data synchronization operations: Customizable policies can control data synchronization operations, reducing contention for data access and automating O&M complexity.

Demo

This presentation describes how to use the scheduled warm-up mechanism of ACK Fluid to update data accessible to compute clusters in different regions.

Prerequisites

• Create an ACK Pro cluster with a version of 1.18 or later. For more information, see Create an ACK Pro cluster [1].

• Cloud-native AI suite is installed, and ack-fluid components are deployed. Important: If you have open source Fluid installed, 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 Deploy the cloud-native AI suite [2].

• If the cloud-native AI suite is installed, deploy the ack-fluid on the Cloud-native AI Suite page [3] in the Container Service console.

• A Kubernetes cluster is connected by using kubectl. For more information, see Use kubectl to connect to a cluster [4].

Background Information

Prepare the Kubernetes and OSS environments. It only takes about 10 minutes to deploy the JindoRuntime environment.

Step 1: Upload Data to OSS Bucket

1. Run the following command to download a copy of test data:

$ wget https://archive.apache.org/dist/hbase/2.5.2/RELEASENOTES.md

2. Upload the downloaded test data to the corresponding bucket of Alibaba Cloud OSS. The upload method can use ossutil, a client tool provided by OSS. For more information, see Install ossutil [5].

$ ossutil cp RELEASENOTES.md oss://<bucket>/<path>/RELEASENOTES.md

Step 2: Create a Dataset and a JindoRuntime

1. Before creating a Dataset, you can create a mySecret.yaml file to store the accessKeyId and accessKeySecret of OSS.

Create the mySecret.yaml file by using the following YAML template:

apiVersion: v1
kind: Secret
metadata:
  name: mysecret
stringData:
  fs.oss.accessKeyId: xxx
  fs.oss.accessKeySecret: xxx

2. Run the following command to generate a Secret:

$ kubectl create -f mySecret.yaml

3. Use the following sample YAML file to create a dataset.yaml file that contains two parts:

• Create a Dataset that describes information about the remote storage dataset and UFS.

• Create a JindoRuntime to enable JindoFS for data caching in the cluster.

apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: demo
spec:
  mounts:
    - mountPoint: oss://<bucket-name>/<path>
      options:
        fs.oss.endpoint: <oss-endpoint>
      name: hbase
      path: "/"
      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
  accessModes:
    - ReadOnlyMany
---
apiVersion: data.fluid.io/v1alpha1
kind: JindoRuntime
metadata:
  name: demo
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: MEM
        path: /dev/shm
        quota: 2Gi
        high: "0.99"
        low: "0.8"
  fuse:
   args:
    - -okernel_cache
    - -oro
    - -oattr_timeout=60
- -oentry_timeout=60
- -onegative_timeout=60

The following table describes the parameters in the YAML template.

Parameter	Description
mountPoint	`oss://<oss_bucket>/<path>` indicates the path where the UFS is mounted. The path does not need to contain endpoint information.
fs.oss.endpoint	The public or internal endpoint of the OSS bucket.
accessModes	The access mode of a Dataset.
replicas	The number of workers in the JindoFS clusters.
mediumtype	The cache type. When you create a JindoRuntime template, JindoFS temporarily supports one of the following cache types: HDD, SSD, and MEM.
path	The storage path. You can specify only one path. If you set mediumtype to MEM, you must specify a local path to store data, such as logs.
quota	The maximum size of cached data. Unit: GB. The cache capacity can be configured based on the UFS data size.
high	The upper limit of the storage capacity.
low	The lower limit of the storage capacity.
fuse.args	The optional fuse client mount parameter. It is usually used with Dataset access mode. When the Dataset access mode is set to ReadOnlyMany, we enable kernel_cache to use kernel cache to optimize read performance. In this case, you can set attr_timeout (cache retention period of file attribute ), entry_timeout (cache retention period of file name read), and negative_timeout (cache retention period of file name read failure). The default value is 7200s. When the Dataset access mode is set to ReadWriteMany, we recommend that you use the default configuration. In this case, the parameters are as follows: `- -oauto_cache- -oattr_timeout=0- -oentry_timeout=0- -onegative_timeout=0`. Use auto_cache to ensure that the cache becomes invalid if the file size or modification time changes. Set the timeout period to 0.

4. Run the following commands to create a JindoRuntime and a Dataset:

$ kubectl create -f dataset.yaml

5. Run the following command to check the deployment of the Dataset:

$ kubectl get dataset

Expected output:

NAME    UFS TOTAL SIZE   CACHED      CACHE CAPACITY   CACHED PERCENTAGE   PHASE   AGE
demo    588.90KiB        0.00B       10.00GiB         0.0%                Bound   2m7s

Step 3: Create a Dataload That Supports Scheduled Running

1. Use the following sample YAML file to create a file named dataload.yaml.

apiVersion: data.fluid.io/v1alpha1
kind: DataLoad
metadata:
  name: cron-dataload
spec:
  dataset:
    name: demo
    namespace: default
  policy: Cron
schedule: "*/2 * * * *" # Run every 2 min

The following table describes the parameters in the YAML template.

Parameter	Description
dataset	The name and namespace of the dataset where the dataload is executed.
policy	The execution policy. Currently, Once and Cron are supported. Here, create a scheduled dataload task.
shcedule	The policy that triggers the dataload.

schedule uses the following cron format:

# ┌───────────── Minute (0 to 59)
# │ ┌───────────── Hour (0 to 23)
# │ │ └ ─────────────── A day of a month (1 - 31)
# │ │ │ ┌───────────── Month (1 to 12)
# │ │ │ │ │ │ ───────────── A day of a week (0 - 6) (Sunday to Monday; in some systems, 7 also represents Sunday)
# │ │ │ │ │                          or sun, mon, tue, web, thu, fri, sat
# │ │ │ │ │ │
# │ │ │ │ │ │
# * * * * *

Meanwhile, Cron supports the following operators:

• A comma (,) indicates an enumeration. For example, 1, 3, 4, 7 indicates that Dataload is executed at the 1st, 3rd, 4th, and 7th minute per hour.

• The conjunction (-) indicates a range. For example, 1-6 indicates that the Dataload is executed every minute from the 1st to 6th minute per hour.

• The asterisk (*) represents any possible value. For example, the asterisk in the "hour domain" is equivalent to "every hour".

• A percent sign (%) indicates "every". For example, %10 indicates that the Dataload is run every 10 minutes.

• A slash (/) is used to describe the increment of the range. For example, /2 * indicates that the Dataload is run every 2 minutes.

You can also find more information here.

For more information about advanced configurations of Dataload, see the following configuration file:

apiVersion: data.fluid.io/v1alpha1
kind: DataLoad
metadata:
  name: cron-dataload
spec:
  dataset:
    name: demo
    namespace: default
  policy: Cron # including Once, Cron
  schedule: * * * * * # only set when policy is cron
  loadMetadata: true
  target:
    - path: <path1>
      replicas: 1
    - path: <path2>
      replicas: 2

The following table describes the parameters in the YAML template.

Parameter	Description
policy	The dataload execution policy, including [Once, Cron].
schedule	The schedule used by Cron. This parameter is valid only when the policy is set to Cron.
loadMetadata	Indicates whether to synchronize metadata before dataload.
target	The target of the dataload. You can specify multiple targets.
path	The path where the dataload is executed.
replicas	The number of cached replicas.

6. Run the following command to create a Dataload:

$ kubectl apply -f dataload.yaml

7. Run the following command to check the Dataload status:

$ kubectl get dataload

Expected output:

NAME             DATASET   PHASE      AGE     DURATION
cron-dataload    demo      Complete   3m51s   2m12s

8. After the Dataload status is Complete, run the following command to check the current dataset status:

$ kubectl get dataset

Expected output:

NAME    UFS TOTAL SIZE   CACHED      CACHE CAPACITY   CACHED PERCENTAGE   PHASE   AGE
demo    588.90KiB        588.90KiB   10.00GiB         100.0%              Bound   5m50s

It can be seen that all files in OSS have been loaded into the cache.

Step 4: Create an Application Container to Access Data in OSS

This article will create an application container to access the above file to view the effect of scheduled Dataload.

1. Create a file named app.yaml by using the following YAML template:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  containers:
    - name: nginx
      image: nginx
      volumeMounts:
        - mountPath: /data
          name: demo-vol
  volumes:
    - name: demo-vol
      persistentVolumeClaim:
        claimName: demo

2. Run the following command to create an application container.

$ kubectl create -f app.yaml

3. Wait for the application container to be ready. Run the following command to view the data in OSS:

$ kubectl exec -it nginx -- ls -lh /data

Expected output:

total 589K
-rwxrwxr-x 1 root root 589K Jul 31 04:20 RELEASENOTES.md

4. To verify the effect of dataload updating the underlying file regularly, we modify the RELEASENOTES.md content and upload it again before the scheduled dataload is triggered.

$ echo "hello, crondataload." >> RELEASENOTES.md

Upload the file to OSS again.

$ ossutil cp RELEASENOTES.md oss://<bucket-name>/<path>/RELEASENOTES.md

5. Wait for the dataload task to be triggered. When the Dataload task is complete, run the following command to check the running status of the Dataload job:

$ kubectl describe dataload cron-dataload

Expected output:

...
Status:
  Conditions:
    Last Probe Time:       2023-07-31T04:30:07Z
    Last Transition Time:  2023-07-31T04:30:07Z
    Status:                True
    Type:                  Complete
  Duration:                5m54s
  Last Schedule Time:      2023-07-31T04:30:00Z
  Last Successful Time:    2023-07-31T04:30:07Z
  Phase:                   Complete
...

The Last Schedule Time in Status is the scheduling time of the last dataload job, and the Last Successful Time is the completion time of the last dataload job.

In this case, you can run the following command to check the current Dataset status:

$ kubectl get dataset

Expected output:

NAME    UFS TOTAL SIZE   CACHED      CACHE CAPACITY   CACHED PERCENTAGE   PHASE   AGE
demo    588.90KiB        1.15MiB     10.00GiB         100.0%              Bound   10m

You can see that the updated file has also been loaded into the cache.

6. Run the following command to view the updated file in the application container:

$ kubectl exec -it nginx -- tail /data/RELEASENOTES.md

Expected output:

  \<name\>hbase.config.read.zookeeper.config\</name\>
  \<value\>true\</value\>
  \<description\>
        Set to true to allow HBaseConfiguration to read the
        zoo.cfg file for ZooKeeper properties. Switching this to true
        is not recommended, since the functionality of reading ZK
        properties from a zoo.cfg file has been deprecated.
  \</description\>
\</property\>
hello, crondataload.

As you can see from the last line, the application container can already access the updated file.

Clear the Environment

If you no longer use the data acceleration function, clear the environment.

Run the following command to delete the JindoRuntime and application container:

$ kubectl delete -f app.yaml

$ kubectl delete -f dataset.yaml

Summary

The discussion on optimizing data access in hybrid cloud scenarios using ACK Fluid concludes here. The Alibaba Cloud Container Service team will continue to iterate and optimize it in this scenario for users. As the practice deepens, this series will be continuously updated.

Reference

[1] Create an ACK Pro cluster
https://www.alibabacloud.com/help/en/doc-detail/176833.html#task-skz-qwk-qfb
[2] Deploy the cloud-native AI suite
https://www.alibabacloud.com/help/zh/ack/cloud-native-ai-suite/user-guide/deploy-the-cloud-native-ai-suite#task-2038811
[3] Container Service console
https://account.aliyun.com/login/login.htm?oauth_callback=https%3A%2F%2Fcs.console.aliyun.com%2F
[4] Use kubectl to connect to a cluster
https://www.alibabacloud.com/help/zh/ack/ack-managed-and-ack-dedicated/user-guide/obtain-the-kubeconfig-file-of-a-cluster-and-use-kubectl-to-connect-to-the-cluster#task-ubf-lhg-vdb
[5] Install ossutil
https://www.alibabacloud.com/help/zh/oss/developer-reference/install-ossutil#concept-303829

Community

Optimize Hybrid Cloud Data Access Based on ACK Fluid (5): Automated Across-regional Center Data Distribution

Overview

Demo

Prerequisites

Background Information

Step 1: Upload Data to OSS Bucket

Step 2: Create a Dataset and a JindoRuntime

Step 3: Create a Dataload That Supports Scheduled Running

Step 4: Create an Application Container to Access Data in OSS

Clear the Environment

Summary

Reference

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