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版本:v2.6.x

在 EKS 上部署 Milvus 群集

本主题介绍如何在亚马逊 EKS 上部署 Milvus 群集。

前提条件

  • 您已在本地 PC 或 Amazon EC2 上安装了 AWS CLI,它将作为您执行本文档所涉及操作的端点。对于 Amazon Linux 2 或 Amazon Linux 2023,已经安装了 AWS CLI 工具。要在本地 PC 上安装 AWS CLi。请参阅如何安装 AWS CLI
  • 您已在首选端点设备上安装了 Kubernetes 和 EKS 工具,包括
  • 已正确授予 AWS IAM 权限。您使用的 IAM 安全负责人必须拥有使用 Amazon EKS IAM 角色、服务相关角色、AWS CloudFormation、VPC 和其他相关资源的权限。您可以采用以下任一方法授予您的委托人适当的权限。
    • (不建议)只需将您使用的用户/角色的关联策略设置为 AWS 受管策略AdministratorAccess
    • (强烈建议)要执行最小权限原则,请执行以下操作:
      • 要设置eksctl 的权限,请参阅 eksctl 的最小权限

      • 要设置创建/删除 AWS S3 存储桶的权限,请参阅以下权限设置:

        {
        "Version": "2012-10-17",
        "Statement": [
        {
        "Sid": "S3BucketManagement",
        "Effect": "Allow",
        "Action": [
        "s3:CreateBucket",
        "s3:PutBucketAcl",
        "s3:PutBucketOwnershipControls",
        "s3:DeleteBucket"
        ],
        "Resource": [
        "arn:aws:s3:::milvus-bucket-*"
        ]
        }
        ]
        }
      • 要设置创建/删除 IAM 策略的权限,请参阅以下权限设置。请将YOUR_ACCOUNT_ID 替换为您自己的权限。

        {
        "Version": "2012-10-17",
        "Statement": [
        {
        "Sid": "IAMPolicyManagement",
        "Effect": "Allow",
        "Action": [
        "iam:CreatePolicy",
        "iam:DeletePolicy"
        ],
        "Resource": "arn:aws:iam::YOUR_ACCOUNT_ID:policy/MilvusS3ReadWrite"
        }
        ]
        }

设置 AWS 资源

您可以使用 AWS 管理控制台、AWS CLI 或 IaC 工具(如 Terraform)设置所需的 AWS 资源,包括 AWS S3 存储桶和 EKS 群集。在本文档中,首选 AWS CLI 来演示如何设置 AWS 资源。

创建亚马逊 S3 存储桶

  • 创建 AWS S3 存储桶。

    阅读 "桶命名规则",并在命名 AWS S3 桶时遵守命名规则。

    milvus_bucket_name="milvus-bucket-$(openssl rand -hex 12)"

    aws s3api create-bucket --bucket "$milvus_bucket_name" --region 'us-east-2' --acl private --object-ownership ObjectWriter --create-bucket-configuration LocationConstraint='us-east-2'

    # Output
    #
    # "Location": "http://milvus-bucket-039dd013c0712f085d60e21f.s3.amazonaws.com/"
  • 创建一个 IAM 策略,用于读取和写入上述桶中的对象。请使用您自己的名称替换桶的名称。

    echo '{
    "Version": "2012-10-17",
    "Statement": [
    {
    "Effect": "Allow",
    "Action": [
    "s3:GetObject",
    "s3:PutObject",
    "s3:ListBucket",
    "s3:DeleteObject"
    ],
    "Resource": [
    "arn:aws:s3:::<bucket-name>",
    "arn:aws:s3:::<bucket-name>/*"
    ]
    }
    ]
    }' > milvus-s3-policy.json

    aws iam create-policy --policy-name MilvusS3ReadWrite --policy-document file://milvus-s3-policy.json

    # Get the ARN from the command output as follows:
    # {
    # "Policy": {
    # "PolicyName": "MilvusS3ReadWrite",
    # "PolicyId": "AN5QQVVPM1BVTFlBNkdZT",
    # "Arn": "arn:aws:iam::12345678901:policy/MilvusS3ReadWrite",
    # "Path": "/",
    # "DefaultVersionId": "v1",
    # "AttachmentCount": 0,
    # "PermissionsBoundaryUsageCount": 0,
    # "IsAttachable": true,
    # "CreateDate": "2023-11-16T06:00:01+00:00",
    # "UpdateDate": "2023-11-16T06:00:01+00:00"
    # }
    # }
  • 将策略附加到您的 AWS 用户。

    aws iam attach-user-policy --user-name <your-user-name> --policy-arn "arn:aws:iam::<your-iam-account-id>:policy/MilvusS3ReadWrite"

创建亚马逊 EKS 群集

  • 按如下方式准备群集配置文件,并将其命名为eks_cluster.yaml

    apiVersion: eksctl.io/v1alpha5
    kind: ClusterConfig

    metadata:
    name: 'milvus-eks-cluster'
    region: 'us-east-2'
    version: "1.27"

    iam:
    withOIDC: true

    serviceAccounts:
    - metadata:
    name: aws-load-balancer-controller
    namespace: kube-system
    wellKnownPolicies:
    awsLoadBalancerController: true

    managedNodeGroups:
    - name: milvus-node-group
    labels: { role: milvus }
    instanceType: m6i.4xlarge
    desiredCapacity: 3
    privateNetworking: true

    addons:
    - name: vpc-cni
    version: latest
    attachPolicyARNs:
    - arn:aws:iam::aws:policy/AmazonEKS_CNI_Policy
    - name: coredns
    version: latest
    - name: kube-proxy
    version: latest
    - name: aws-ebs-csi-driver
    version: latest
    wellKnownPolicies:
    ebsCSIController: true
  • 运行以下命令创建 EKS 群集。

    eksctl create cluster -f eks_cluster.yaml
  • 获取 kubeconfig 文件。

    aws eks update-kubeconfig --region 'us-east-2' --name 'milvus-eks-cluster'
  • 验证 EKS 群集。

    kubectl cluster-info

    kubectl get nodes -A -o wide

创建存储类

Milvus 使用etcd 作为元存储,需要依靠gp3 StorageClass 来创建和管理 PVC。

cat <<EOF | kubectl apply -f -
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: ebs-gp3-sc
annotations:
storageclass.kubernetes.io/is-default-class: "true"
provisioner: ebs.csi.aws.com
volumeBindingMode: WaitForFirstConsumer
parameters:
type: gp3
EOF

将原来的 gp2 StorageClass 设置为非默认。

kubectl patch storageclass gp2 -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"false"}}}'

安装 AWS LoadBalancer 控制器

  • 添加 Helm chars repo。

    helm repo add eks https://aws.github.io/eks-charts
    helm repo update
  • 安装 AWS Load Balancer Controller。

    helm install aws-load-balancer-controller eks/aws-load-balancer-controller \
    -n kube-system \
    --set clusterName='milvus-eks-cluster' \
    --set serviceAccount.create=false \
    --set serviceAccount.name=aws-load-balancer-controller
  • 验证安装

    kubectl get deployment -n kube-system aws-load-balancer-controller

部署 Milvus

在本指南中,我们将使用 Milvus Helm 图表来部署 Milvus 集群。你可以在这里找到图表。

  • 添加 Milvus Helm 图表 repo。

    helm repo add milvus https://zilliztech.github.io/milvus-helm/
    helm repo update
  • 准备好 Milvus 配置文件milvus.yaml ,并用你自己的配置文件替换<bucket-name> <s3-access-key> <s3-secret-key>

    说明
    • 要为你的 Milvus 配置 HA,请参考此计算器以获取更多信息。您可以直接从计算器下载相关配置,并应删除 MinIO 相关配置。
    • 要实现协调器的多副本部署,请将xxCoordinator.activeStandby.enabled 设置为true
    cluster:
    enabled: true

    service:
    type: LoadBalancer
    port: 19530
    annotations:
    service.beta.kubernetes.io/aws-load-balancer-type: external
    service.beta.kubernetes.io/aws-load-balancer-name: milvus-service
    service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing
    service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip

    minio:
    enabled: false

    externalS3:
    enabled: true
    host: "s3.us-east-2.amazonaws.com"
    port: "443"
    useSSL: true
    bucketName: "<bucket-name>"
    useIAM: false
    cloudProvider: "aws"
    iamEndpoint: ""
    accessKey: "<s3-access-key>"
    secretKey: "<s3-secret-key>"
    region: "us-east-2"

    # HA Configurations
    rootCoordinator:
    replicas: 2
    activeStandby:
    enabled: true
    resources:
    limits:
    cpu: 1
    memory: 2Gi

    indexCoordinator:
    replicas: 2
    activeStandby:
    enabled: true
    resources:
    limits:
    cpu: "0.5"
    memory: 0.5Gi

    queryCoordinator:
    replicas: 2
    activeStandby:
    enabled: true
    resources:
    limits:
    cpu: "0.5"
    memory: 0.5Gi

    dataCoordinator:
    replicas: 2
    activeStandby:
    enabled: true
    resources:
    limits:
    cpu: "0.5"
    memory: 0.5Gi

    proxy:
    replicas: 2
    resources:
    limits:
    cpu: 1
    memory: 2Gi
  • 安装 Milvus。

    helm install milvus-demo milvus/milvus -n milvus -f milvus.yaml
  • 等待直到所有 pod 都Running

    kubectl get pods -n milvus
    说明

    Helm 不支持调度服务创建顺序。在etcdpulsar 运行初期,业务 pod 重启一两次是正常的。

  • 获取 Milvus 服务地址。

    kubectl get svc -n milvus

验证安装

您可以按照下面的简单指南验证安装。更多详情,请参阅此示例

  • 下载示例代码。

    wget https://raw.githubusercontent.com/milvus-io/pymilvus/master/examples/hello_milvus.py
  • 将示例代码中的host 参数更改为上述 Milvus 服务地址。

```python
...
connections.connect("default", host="milvus-service-06b515b1ce9ad10.elb.us-east-2.amazonaws.com", port="19530")
...
```
  • 运行示例代码。

    python3 hello_milvus.py

    输出结果应与下图类似:

    === start connecting to Milvus     ===

    Does collection hello_milvus exist in Milvus: False

    === Create collection `hello_milvus` ===

    === Start inserting entities ===

    Number of entities in Milvus: 3000

    === Start Creating index IVF_FLAT ===

    === Start loading ===

    === Start searching based on vector similarity ===

    hit: id: 2998, distance: 0.0, entity: {'random': 0.9728033590489911}, random field: 0.9728033590489911
    hit: id: 1262, distance: 0.08883658051490784, entity: {'random': 0.2978858685751561}, random field: 0.2978858685751561
    hit: id: 1265, distance: 0.09590047597885132, entity: {'random': 0.3042039939240304}, random field: 0.3042039939240304
    hit: id: 2999, distance: 0.0, entity: {'random': 0.02316334456872482}, random field: 0.02316334456872482
    hit: id: 1580, distance: 0.05628091096878052, entity: {'random': 0.3855988746044062}, random field: 0.3855988746044062
    hit: id: 2377, distance: 0.08096685260534286, entity: {'random': 0.8745922204004368}, random field: 0.8745922204004368
    search latency = 0.4693s

    === Start querying with `random > 0.5` ===

    query result:
    -{'embeddings': [0.20963514, 0.39746657, 0.12019053, 0.6947492, 0.9535575, 0.5454552, 0.82360446, 0.21096309], 'pk': '0', 'random': 0.6378742006852851}
    search latency = 0.9407s
    query pagination(limit=4):
    [{'random': 0.6378742006852851, 'pk': '0'}, {'random': 0.5763523024650556, 'pk': '100'}, {'random': 0.9425935891639464, 'pk': '1000'}, {'random': 0.7893211256191387, 'pk': '1001'}]
    query pagination(offset=1, limit=3):
    [{'random': 0.5763523024650556, 'pk': '100'}, {'random': 0.9425935891639464, 'pk': '1000'}, {'random': 0.7893211256191387, 'pk': '1001'}]

    === Start hybrid searching with `random > 0.5` ===

    hit: id: 2998, distance: 0.0, entity: {'random': 0.9728033590489911}, random field: 0.9728033590489911
    hit: id: 747, distance: 0.14606499671936035, entity: {'random': 0.5648774800635661}, random field: 0.5648774800635661
    hit: id: 2527, distance: 0.1530652642250061, entity: {'random': 0.8928974315571507}, random field: 0.8928974315571507
    hit: id: 2377, distance: 0.08096685260534286, entity: {'random': 0.8745922204004368}, random field: 0.8745922204004368
    hit: id: 2034, distance: 0.20354536175727844, entity: {'random': 0.5526117606328499}, random field: 0.5526117606328499
    hit: id: 958, distance: 0.21908017992973328, entity: {'random': 0.6647383716417955}, random field: 0.6647383716417955
    search latency = 0.4652s

    === Start deleting with expr `pk in ["0" , "1"]` ===

    query before delete by expr=`pk in ["0" , "1"]` -> result:
    -{'random': 0.6378742006852851, 'embeddings': [0.20963514, 0.39746657, 0.12019053, 0.6947492, 0.9535575, 0.5454552, 0.82360446, 0.21096309], 'pk': '0'}
    -{'random': 0.43925103574669633, 'embeddings': [0.52323616, 0.8035404, 0.77824664, 0.80369574, 0.4914803, 0.8265614, 0.6145269, 0.80234545], 'pk': '1'}

    query after delete by expr=`pk in ["0" , "1"]` -> result: []

    === Drop collection `hello_milvus` ===

清理成功

万一需要通过卸载 Milvus、销毁 EKS 群集、删除 AWS S3 buckets 和相关 IAM 策略来恢复环境。

  • 卸载 Milvus。

    helm uninstall milvus-demo -n milvus
  • 销毁 EKS 群集。

    eksctl delete cluster --name milvus-eks-cluster --region us-east-2
  • 删除 AWS S3 存储桶和相关 IAM 策略。

    您应该用自己的名称和策略 ARN 替换水桶名称和策略 ARN。

    aws s3 rm s3://milvus-bucket-039dd013c0712f085d60e21f --recursive

    aws s3api delete-bucket --bucket milvus-bucket-039dd013c0712f085d60e21f --region us-east-2

    aws iam detach-user-policy --user-name <your-user-name> --policy-arn "arn:aws:iam::12345678901:policy/MilvusS3ReadWrite"

    aws iam delete-policy --policy-arn 'arn:aws:iam::12345678901:policy/MilvusS3ReadWrite'

下一步

如果你想了解如何在其他云上部署 Milvus: