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Milvus Cluster Setup with Kubernetes in on-premises

Contributors kevin-hoke nkarthik

This section discusses the milvus cluster setup for the vector database solution for NetApp.

Milvus cluster setup with Kubernetes in on-premises

Customer challenges to scale independently on storage and compute, effective infrastructure management and data management,
Kubernetes and vector databases together form a powerful, scalable solution for managing large data operations. Kubernetes optimizes resources and manages containers, while vector databases efficiently handle high-dimensional data and similarity searches. This combination enables swift processing of complex queries on large datasets and seamlessly scales with growing data volumes, making it ideal for big data applications and AI workloads.

  1. In this section, we detail the process of installing a Milvus cluster on Kubernetes, utilizing a NetApp storage controller for both cluster data and customer data.

  2. To install a Milvus cluster, Persistent Volumes (PVs) are required for storing data from various Milvus cluster components. These components include etcd (three instances), pulsar-bookie-journal (three instances), pulsar-bookie-ledgers (three instances), and pulsar-zookeeper-data (three instances).

    Note In milvus cluster, we can use either pulsar or kafka for the underlying engine supporting Milvus cluster's reliable storage and publication/subscription of message streams. For Kafka with NFS,NetApp has made improvements in ONTAP 9.12.1 and later, and these enhancements, along with NFSv4.1 and Linux changes that are included in RHEL 8.7 or 9.1 and higher, resolve the "silly rename" issue that can occur when running Kafka over NFS. if you interested in more in-depth information on the topic of running kafka with netapp NFS solution, please check - this link.
  3. We created a single NFS volume from NetApp ONTAP and established 12 persistent volumes, each with 250GB of storage. The storage size can vary depending on the cluster size; for instance, we have another cluster where each PV has 50GB. Please refer below to one of the PV YAML files for more details; we had 12 such files in total. In each file, the storageClassName is set to 'default', and the storage and path are unique to each PV.

    root@node2:~# cat sai_nfs_to_default_pv1.yaml
    apiVersion: v1
    kind: PersistentVolume
      name: karthik-pv1
        storage: 250Gi
      volumeMode: Filesystem
      - ReadWriteOnce
      persistentVolumeReclaimPolicy: Retain
      storageClassName: default
        path: /vectordbsc/milvus/milvus1
          - matchExpressions:
            - key:
              operator: In
              - node2
              - node3
              - node4
              - node5
              - node6
  4. Execute the 'kubectl apply' command for each PV YAML file to create the Persistent Volumes, and then verify their creation using ‘kubectl get pv’

    root@node2:~# for i in $( seq 1 12 ); do kubectl apply -f sai_nfs_to_default_pv$i.yaml; done
    persistentvolume/karthik-pv1 created
    persistentvolume/karthik-pv2 created
    persistentvolume/karthik-pv3 created
    persistentvolume/karthik-pv4 created
    persistentvolume/karthik-pv5 created
    persistentvolume/karthik-pv6 created
    persistentvolume/karthik-pv7 created
    persistentvolume/karthik-pv8 created
    persistentvolume/karthik-pv9 created
    persistentvolume/karthik-pv10 created
    persistentvolume/karthik-pv11 created
    persistentvolume/karthik-pv12 created
  5. For storing customer data, Milvus supports object storage solutions such as MinIO, Azure Blob, and S3. In this guide, we utilize S3. The following steps apply to both ONTAP S3 and StorageGRID object store. We use Helm to deploy the Milvus cluster. Download the configuration file, values.yaml, from the Milvus download location. Please refer to the appendix for the values.yaml file we used in this document.

  6. Ensure that the 'storageClass' is set to 'default' in each section, including those for the log, etcd, zookeeper, and bookkeeper.

  7. In the MinIO section, disable MinIO.

  8. Create a NAS bucket from ONTAP or StorageGRID object storage and include them in an External S3 with the object storage credentials.

    # External S3
    # - these configs are only used when `externalS3.enabled` is true
      enabled: true
      host: ""
      port: "80"
      accessKey: "24G4C1316APP2BIPDE5S"
      secretKey: "Zd28p43rgZaU44PX_ftT279z9nt4jBSro97j87Bx"
      useSSL: false
      bucketName: "milvusdbvol1"
      rootPath: ""
      useIAM: false
      cloudProvider: "aws"
      iamEndpoint: ""
      region: ""
      useVirtualHost: false
  9. Before creating the Milvus cluster, ensure that the PersistentVolumeClaim (PVC) does not have any pre-existing resources.

    root@node2:~# kubectl get pvc
    No resources found in default namespace.
  10. Utilize Helm and the values.yaml configuration file to install and start the Milvus cluster.

    root@node2:~# helm upgrade --install my-release milvus/milvus --set global.storageClass=default  -f values.yaml
    Release "my-release" does not exist. Installing it now.
    NAME: my-release
    LAST DEPLOYED: Thu Mar 14 15:00:07 2024
    NAMESPACE: default
    STATUS: deployed
    TEST SUITE: None
  11. Verify the status of the PersistentVolumeClaims (PVCs).

    root@node2:~# kubectl get pvc
    NAME                                                             STATUS   VOLUME         CAPACITY   ACCESS MODES   STORAGECLASS   AGE
    data-my-release-etcd-0                                           Bound    karthik-pv8    250Gi      RWO            default        3s
    data-my-release-etcd-1                                           Bound    karthik-pv5    250Gi      RWO            default        2s
    data-my-release-etcd-2                                           Bound    karthik-pv4    250Gi      RWO            default        3s
    my-release-pulsar-bookie-journal-my-release-pulsar-bookie-0      Bound    karthik-pv10   250Gi      RWO            default        3s
    my-release-pulsar-bookie-journal-my-release-pulsar-bookie-1      Bound    karthik-pv3    250Gi      RWO            default        3s
    my-release-pulsar-bookie-journal-my-release-pulsar-bookie-2      Bound    karthik-pv1    250Gi      RWO            default        3s
    my-release-pulsar-bookie-ledgers-my-release-pulsar-bookie-0      Bound    karthik-pv2    250Gi      RWO            default        3s
    my-release-pulsar-bookie-ledgers-my-release-pulsar-bookie-1      Bound    karthik-pv9    250Gi      RWO            default        3s
    my-release-pulsar-bookie-ledgers-my-release-pulsar-bookie-2      Bound    karthik-pv11   250Gi      RWO            default        3s
    my-release-pulsar-zookeeper-data-my-release-pulsar-zookeeper-0   Bound    karthik-pv7    250Gi      RWO            default        3s
  12. Check the status of the pods.

    root@node2:~# kubectl get pods -o wide
    NAME                                            READY   STATUS      RESTARTS        AGE    IP              NODE    NOMINATED NODE   READINESS GATES
    <content removed to save page space>

    Please make sure the pods status are ‘running’ and working as expected

  13. Test data writing and reading in Milvus and NetApp object storage.

    • Write data using the "" Python program.

      root@node2:~# date;python3 ;date
      Thu Apr  4 04:15:35 PM UTC 2024
      === start connecting to Milvus     ===
      === Milvus host: localhost         ===
      Does collection hello_milvus_ntapnew_update2_sc exist in Milvus: False
      === Drop collection - hello_milvus_ntapnew_update2_sc ===
      === Drop collection - hello_milvus_ntapnew_update2_sc2 ===
      === Create collection `hello_milvus_ntapnew_update2_sc` ===
      === Start inserting entities       ===
      Number of entities in hello_milvus_ntapnew_update2_sc: 3000
      Thu Apr  4 04:18:01 PM UTC 2024
    • Read the data using the "" Python file.

      root@node2:~# python3
      === start connecting to Milvus     ===
      === Milvus host: localhost         ===
      Does collection hello_milvus_ntapnew_update2_sc exist in Milvus: True
      {'auto_id': False, 'description': 'hello_milvus_ntapnew_update2_sc', 'fields': [{'name': 'pk', 'description': '', 'type': <DataType.INT64: 5>, 'is_primary': True, 'auto_id': False}, {'name': 'random', 'description': '', 'type': <DataType.DOUBLE: 11>}, {'name': 'var', 'description': '', 'type': <DataType.VARCHAR: 21>, 'params': {'max_length': 65535}}, {'name': 'embeddings', 'description': '', 'type': <DataType.FLOAT_VECTOR: 101>, 'params': {'dim': 16}}]}
      Number of entities in Milvus: hello_milvus_ntapnew_update2_sc : 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: 2600, distance: 0.602496862411499, entity: {'random': 0.3098157043984633}, random field: 0.3098157043984633
      hit: id: 1831, distance: 0.6797959804534912, entity: {'random': 0.6331477114129169}, random field: 0.6331477114129169
      hit: id: 2999, distance: 0.0, entity: {'random': 0.02316334456872482}, random field: 0.02316334456872482
      hit: id: 2524, distance: 0.5918987989425659, entity: {'random': 0.285283165889066}, random field: 0.285283165889066
      hit: id: 264, distance: 0.7254047393798828, entity: {'random': 0.3329096143562196}, random field: 0.3329096143562196
      search latency = 0.4533s
      === Start querying with `random > 0.5` ===
      query result:
      -{'random': 0.6378742006852851, 'embeddings': [0.20963514, 0.39746657, 0.12019053, 0.6947492, 0.9535575, 0.5454552, 0.82360446, 0.21096309, 0.52323616, 0.8035404, 0.77824664, 0.80369574, 0.4914803, 0.8265614, 0.6145269, 0.80234545], 'pk': 0}
      search latency = 0.4476s
      === Start hybrid searching with `random > 0.5` ===
      hit: id: 2998, distance: 0.0, entity: {'random': 0.9728033590489911}, random field: 0.9728033590489911
      hit: id: 1831, distance: 0.6797959804534912, entity: {'random': 0.6331477114129169}, random field: 0.6331477114129169
      hit: id: 678, distance: 0.7351570129394531, entity: {'random': 0.5195484662306603}, random field: 0.5195484662306603
      hit: id: 2644, distance: 0.8620758056640625, entity: {'random': 0.9785952878381153}, random field: 0.9785952878381153
      hit: id: 1960, distance: 0.9083120226860046, entity: {'random': 0.6376039340439571}, random field: 0.6376039340439571
      hit: id: 106, distance: 0.9792704582214355, entity: {'random': 0.9679994241326673}, random field: 0.9679994241326673
      search latency = 0.1232s
      Does collection hello_milvus_ntapnew_update2_sc2 exist in Milvus: True
      {'auto_id': True, 'description': 'hello_milvus_ntapnew_update2_sc2', 'fields': [{'name': 'pk', 'description': '', 'type': <DataType.INT64: 5>, 'is_primary': True, 'auto_id': True}, {'name': 'random', 'description': '', 'type': <DataType.DOUBLE: 11>}, {'name': 'var', 'description': '', 'type': <DataType.VARCHAR: 21>, 'params': {'max_length': 65535}}, {'name': 'embeddings', 'description': '', 'type': <DataType.FLOAT_VECTOR: 101>, 'params': {'dim': 16}}]}

      Based on the above validation, the integration of Kubernetes with a vector database, as demonstrated through the deployment of a Milvus cluster on Kubernetes using a NetApp storage controller, offers customers a robust, scalable, and efficient solution for managing large-scale data operations. This setup provides customers with the ability to handle high-dimensional data and execute complex queries rapidly and efficiently, making it an ideal solution for big data applications and AI workloads. The use of Persistent Volumes (PVs) for various cluster components, along with the creation of a single NFS volume from NetApp ONTAP, ensures optimal resource utilization and data management. The process of verifying the status of PersistentVolumeClaims (PVCs) and pods, as well as testing data writing and reading, provides customers with the assurance of reliable and consistent data operations. The use of ONTAP or StorageGRID object storage for customer data further enhances data accessibility and security. Overall, this setup empowers customers with a resilient and high-performing data management solution that can seamlessly scale with their growing data needs.