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Deploy Search & Vector Search

Install and use With MongoDB Community Edition

You can use the Kubernetes Operator and deploy the mongot process resources to run with MongoDB Community Edition v8.3 or later on a Kubernetes cluster. The mongot process supports both MongoDB Search and Vector Search. Optionally, you can enable and configure Vector Search to automatically generate vector embeddings for text data in your collections and queries using a supported Voyage AI embedding model.

Important

Automated embedding is in Preview. The feature and the corresponding documentation might change at any time during the preview period. To learn more, see Preview Features.

The following procedure demonstrates how to deploy and configure MongoDB Search and Vector Search to run with a new or existing replica set in your Kubernetes cluster. The deployment uses TLS certificates to ensure secure communication between MongoDB nodes and the mongot search process.

Prerequisites

To deploy MongoDB Search and Vector Search, you must have the following:

  • A running Kubernetes cluster.

  • Kubernetes command-line tool, kubectl, configured to communicate with your cluster.

  • Helm, the package manager for Kubernetes, to install the Kubernetes Operator.

  • cert-manager or an alternative certificate management solution for TLS certificate provisioning.

  • Bash v5.1 or later for running the commands in this tutorial.

Optionally, to configure Vector Search to automatically generate vector embeddings for text data in your collections and queries, you must create API keys for the embedding service. We recommend creating two keys, one for generating embeddings at index-time for text data in your collection and another for generating embeddings at query-time for your query text. If you don't have the keys, you can create the keys from the Atlas UI.

Procedure

1

Required. Set the environment variables.

Set the environment variables for use in the subsequent steps in this procedure. Copy the following commands, update the values for your environment, and then run them to load the variables:

1# set it to the context name of the k8s cluster
2export K8S_CTX="<local cluster context>"
3
4# the following namespace will be created if not exists
5export MDB_NS="mongodb"
6
7# MongoDBCommunity resource name referenced throughout the guide
8export MDB_RESOURCE_NAME="mdbc-rs"
9# Number of replica set members deployed in the sample MongoDBCommunity
10export MDB_MEMBERS=3
11
12# TLS-related secret names used for MongoDBCommunity and MongoDBSearch
13export MDB_TLS_CA_SECRET_NAME="${MDB_RESOURCE_NAME}-ca"
14export MDB_TLS_SERVER_CERT_SECRET_NAME="${MDB_RESOURCE_NAME}-tls"
15export MDB_SEARCH_TLS_SECRET_NAME="${MDB_RESOURCE_NAME}-search-tls"
16
17export MDB_TLS_CA_CONFIGMAP="${MDB_RESOURCE_NAME}-ca-configmap"
18export MDB_TLS_SELF_SIGNED_ISSUER="${MDB_RESOURCE_NAME}-selfsigned-cluster-issuer"
19export MDB_TLS_CA_CERT_NAME="${MDB_RESOURCE_NAME}-selfsigned-ca"
20export MDB_TLS_CA_ISSUER="${MDB_RESOURCE_NAME}-cluster-issuer"
21
22export MDB_VERSION="8.3.4"
23
24# root admin user for convenience, not used here at all in this guide
25export MDB_ADMIN_USER_PASSWORD="admin-user-password-CHANGE-ME"
26# regular user performing restore and search queries on sample mflix database
27export MDB_USER_PASSWORD="mdb-user-password-CHANGE-ME"
28# user for MongoDB Search to connect to the replica set to synchronise data from
29export MDB_SEARCH_SYNC_USER_PASSWORD="search-sync-user-password-CHANGE-ME"
30
31export OPERATOR_HELM_CHART="mongodb/mongodb-kubernetes"
32# comma-separated key=value pairs for additional parameters passed to the helm-chart installing the operator
33export OPERATOR_ADDITIONAL_HELM_VALUES=""
34
35# TLS is mandatory; connection string must include tls=true
36export MDB_CONNECTION_STRING="mongodb://mdb-user:${MDB_USER_PASSWORD}@${MDB_RESOURCE_NAME}-0.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local:27017/?replicaSet=${MDB_RESOURCE_NAME}&tls=true&tlsCAFile=/tls/ca.crt"
37
38export CERT_MANAGER_NAMESPACE="cert-manager"
39
40# Vector Search auto embedding related configurations
41export AUTO_EMBEDDING_API_KEY_SECRET_NAME="voyage-api-keys"
42export AUTO_EMBEDDING_API_QUERY_KEY="<embedding-model-query-key>"
43export AUTO_EMBEDDING_API_INDEXING_KEY="<embedding-model-indexing-key>"
44export PROVIDER_ENDPOINT="https://ai.mongodb.com/v1/embeddings"
45export EMBEDDING_MODEL="voyage-4"

Note

If you have the API keys to enable Vector Search to automatically generate embeddings, replace the following placeholder values in the environment variables:

AUTO_EMBEDDING_API_QUERY_KEY

API key for generating embeddings for the query text.

AUTO_EMBEDDING_API_INDEXING_KEY

API key for generating embeddings for text data in your collection at index-time.

PROVIDER_ENDPOINT

Embedding model provider's endpoint. Value defaults to https://ai.mongodb.com/v1/embeddings for keys created from the Atlas UI. Replace with https://api.voyageai.com/v1/embeddings if you created the API keys directly from Voyage AI.

Optional

Validate that the environment variables have been set.

To verify that all necessary environment variables are set, run the following code in your terminal:

1required=(
2  K8S_CTX
3  MDB_NS
4  MDB_RESOURCE_NAME
5  MDB_VERSION
6  MDB_MEMBERS
7  CERT_MANAGER_NAMESPACE
8  MDB_TLS_CA_SECRET_NAME
9  MDB_TLS_SERVER_CERT_SECRET_NAME
10  MDB_SEARCH_TLS_SECRET_NAME
11  MDB_ADMIN_USER_PASSWORD
12  MDB_SEARCH_SYNC_USER_PASSWORD
13  MDB_USER_PASSWORD
14  OPERATOR_HELM_CHART
15)
16
17missing_req=()
18for v in "${required[@]}"; do [[ -n "${!v:-}" ]] || missing_req+=("${v}"); done
19
20if (( ${#missing_req[@]} )); then
21  echo "ERROR: Missing required environment variables:" >&2
22  for m in "${missing_req[@]}"; do echo "  - ${m}" >&2; done
23else
24  echo "All required environment variables present."
25fi
2

Conditional. Add the MongoDB Helm repository.

Helm automates the deployment and management of MongoDB instances on Kubernetes. If you already have the Helm repository that contains the Helm chart for installing the Kubernetes Operator operator, or skip this step. Otherwise, add the Helm repository.

To add the Helm repository, copy, paste, and run the following:

1helm repo add mongodb https://mongodb.github.io/helm-charts
2helm repo update mongodb
3helm search repo mongodb/mongodb-kubernetes
3

Conditional. Install the MongoDB Controllers for Kubernetes Operator.

The Kubernetes Operator watches MongoDBCommunity and MongoDBSearch custom resources and manages the lifecycle of your MongoDB deployments. If you already installed the MongoDB Controllers for Kubernetes Operator, skip this step. Otherwise, install the MongoDB Controllers for Kubernetes Operator from the Helm repository you added in the previous step.

To install the MongoDB Controllers for Kubernetes Operator in the mongodb namespace, copy, paste, and run the following commands:

1helm upgrade --install --debug --kube-context "${K8S_CTX}" \
2  --create-namespace \
3  --namespace="${MDB_NS}" \
4  mongodb-kubernetes \
5  ${OPERATOR_ADDITIONAL_HELM_VALUES:+--set ${OPERATOR_ADDITIONAL_HELM_VALUES}} \
6  "${OPERATOR_HELM_CHART}"
4

Optional. Wait for the operator deployment.

Ensure that the Kubernetes Operator is fully operational before proceeding with the MongoDB Search and Vector Search deployment. Run the following command to verify that all operator components are running and available.

1kubectl --context "${K8S_CTX}" -n "${MDB_NS}" rollout status --timeout=2m deployment/mongodb-kubernetes-operator
2echo "Operator deployment in ${MDB_NS} namespace"
3kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get deployments
4echo; echo "Operator pod in ${MDB_NS} namespace"
5kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get pods
5

Required. Create and load the MongoDB user secrets.

MongoDB requires authentication for secure access. In this step, you create three Kubernetes secrets:

  • mdb-admin-user-password: Credentials for the MongoDB administrator.

  • mdb-user-password: Credentials for the user authorized to perform search queries.

  • mdbc-rs-search-sync-source-password: Credentials for a dedicated search user used internally by the mongot process to synchronize data and manage indexes.

Kubernetes Operator uses passwords from those secrets to automatically create the users in the MongoDB database.

To create the secrets, copy, paste, and run the following command:

1# Create admin user secret
2kubectl create secret generic mdb-admin-user-password \
3  --from-literal=password="${MDB_ADMIN_USER_PASSWORD}" \
4  --dry-run=client -o yaml | kubectl apply --context "${K8S_CTX}" --namespace "${MDB_NS}" -f -
5
6# Create search sync source user secret
7kubectl create secret generic "${MDB_RESOURCE_NAME}-search-sync-source-password" \
8  --from-literal=password="${MDB_SEARCH_SYNC_USER_PASSWORD}" \
9  --dry-run=client -o yaml | kubectl apply --context "${K8S_CTX}" --namespace "${MDB_NS}" -f -
10
11# Create regular user secret
12kubectl create secret generic mdb-user-password \
13  --from-literal=password="${MDB_USER_PASSWORD}" \
14  --dry-run=client -o yaml | kubectl apply --context "${K8S_CTX}" --namespace "${MDB_NS}" -f -
15
16echo "User secrets created."
6

Conditional. Install the cert-manager.

The cert-manager is required for managing TLS certificates. If you already have cert-manager installed in your cluster, skip this step. Otherwise, install cert-manager using Helm.

To install cert-manager in the cert-manager namespace, run the following command in your terminal:

1helm upgrade --install \
2  cert-manager \
3  oci://quay.io/jetstack/charts/cert-manager \
4  --kube-context "${K8S_CTX}" \
5  --namespace "${CERT_MANAGER_NAMESPACE}" \
6  --create-namespace \
7  --set crds.enabled=true
8
9for deployment in cert-manager cert-manager-cainjector cert-manager-webhook; do
10  kubectl --context "${K8S_CTX}" \
11    -n "${CERT_MANAGER_NAMESPACE}" \
12    wait --for=condition=Available "deployment/${deployment}" --timeout=300s
13done
14
15echo "cert-manager is ready in namespace ${CERT_MANAGER_NAMESPACE}."
7

Required. Prepare certificate issuer and CA infrastructure.

Create the certificate authority infrastructure that will issue TLS certificates for MongoDB and MongoDBSearch resources. The commands perform the following actions:

  • Create a self-signed ClusterIssuer.

  • Generate a CA certificate.

  • Publish a cluster-wide CA issuer that all namespaces can use.

  • Expose the CA bundle through a ConfigMap so that MongoDB resources can use it.

1# Bootstrap a self-signed ClusterIssuer that will mint the CA material consumed by
2# the MongoDBCommunity deployment.
3kubectl apply --context "${K8S_CTX}" -f - <<EOF_MANIFEST
4apiVersion: cert-manager.io/v1
5kind: ClusterIssuer
6metadata:
7  name: ${MDB_TLS_SELF_SIGNED_ISSUER}
8spec:
9  selfSigned: {}
10EOF_MANIFEST
11
12kubectl --context "${K8S_CTX}" wait --for=condition=Ready clusterissuer "${MDB_TLS_SELF_SIGNED_ISSUER}"
13
14# Create the CA certificate and secret in the cert-manager namespace.
15kubectl apply --context "${K8S_CTX}" -f - <<EOF_MANIFEST
16apiVersion: cert-manager.io/v1
17kind: Certificate
18metadata:
19  name: ${MDB_TLS_CA_CERT_NAME}
20  namespace: ${CERT_MANAGER_NAMESPACE}
21spec:
22  isCA: true
23  commonName: ${MDB_TLS_CA_CERT_NAME}
24  secretName: ${MDB_TLS_CA_SECRET_NAME}
25  privateKey:
26    algorithm: ECDSA
27    size: 256
28  issuerRef:
29    name: ${MDB_TLS_SELF_SIGNED_ISSUER}
30    kind: ClusterIssuer
31EOF_MANIFEST
32
33kubectl --context "${K8S_CTX}" wait --for=condition=Ready -n "${CERT_MANAGER_NAMESPACE}" certificate "${MDB_TLS_CA_CERT_NAME}"
34
35# Publish a cluster-scoped issuer that fronts the generated CA secret so all namespaces can reuse it.
36kubectl apply --context "${K8S_CTX}" -f - <<EOF_MANIFEST
37apiVersion: cert-manager.io/v1
38kind: ClusterIssuer
39metadata:
40  name: ${MDB_TLS_CA_ISSUER}
41spec:
42  ca:
43    secretName: ${MDB_TLS_CA_SECRET_NAME}
44EOF_MANIFEST
45
46kubectl --context "${K8S_CTX}" wait --for=condition=Ready clusterissuer "${MDB_TLS_CA_ISSUER}"
47
48TMP_CA_CERT="$(mktemp)"
49
50kubectl --context "${K8S_CTX}" \
51  get secret "${MDB_TLS_CA_SECRET_NAME}" -n "${CERT_MANAGER_NAMESPACE}" \
52  -o jsonpath="{.data['ca\\.crt']}" | base64 --decode > "${TMP_CA_CERT}"
53
54# Expose the CA bundle through a ConfigMap for workloads and the MongoDBCommunity resource.
55kubectl --context "${K8S_CTX}" create configmap "${MDB_TLS_CA_CONFIGMAP}" -n "${MDB_NS}" \
56  --from-file=ca-pem="${TMP_CA_CERT}" --from-file=mms-ca.crt="${TMP_CA_CERT}" \
57  --from-file=ca.crt="${TMP_CA_CERT}" \
58  --dry-run=client -o yaml | kubectl --context "${K8S_CTX}" apply -f -
59
60echo "Cluster-wide CA issuer ${MDB_TLS_CA_ISSUER} is ready."
8

Required. Issue TLS certificates.

Issue TLS certificates for both the MongoDB server and the MongoDBSearch service. The MongoDB server certificate includes all necessary DNS names for the pod and service communication. Both certificates support server and client authentication.

1server_certificate="${MDB_RESOURCE_NAME}-server-tls"
2search_certificate="${MDB_RESOURCE_NAME}-search-tls"
3
4mongo_dns_names=()
5for ((member = 0; member < MDB_MEMBERS; member++)); do
6  mongo_dns_names+=("${MDB_RESOURCE_NAME}-${member}")
7  mongo_dns_names+=("${MDB_RESOURCE_NAME}-${member}.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local")
8done
9mongo_dns_names+=(
10  "${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local"
11  "*.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local"
12)
13
14search_dns_names=(
15  "*.${MDB_RESOURCE_NAME}-search-0-svc.${MDB_NS}.svc.cluster.local"
16)
17
18render_dns_list() {
19  local dns_list=("$@")
20  for dns in "${dns_list[@]}"; do
21    printf "      - \"%s\"\n" "${dns}"
22  done
23}
24
25kubectl apply --context "${K8S_CTX}" -n "${MDB_NS}" -f - <<EOF_MANIFEST
26apiVersion: cert-manager.io/v1
27kind: Certificate
28metadata:
29  name: ${server_certificate}
30  namespace: ${MDB_NS}
31spec:
32  secretName: ${MDB_TLS_SERVER_CERT_SECRET_NAME}
33  issuerRef:
34    name: ${MDB_TLS_CA_ISSUER}
35    kind: ClusterIssuer
36  duration: 240h0m0s
37  renewBefore: 120h0m0s
38  usages:
39    - digital signature
40    - key encipherment
41    - server auth
42    - client auth
43  dnsNames:
44$(render_dns_list "${mongo_dns_names[@]}")
45---
46apiVersion: cert-manager.io/v1
47kind: Certificate
48metadata:
49  name: ${search_certificate}
50  namespace: ${MDB_NS}
51spec:
52  secretName: ${MDB_SEARCH_TLS_SECRET_NAME}
53  issuerRef:
54    name: ${MDB_TLS_CA_ISSUER}
55    kind: ClusterIssuer
56  duration: 240h0m0s
57  renewBefore: 120h0m0s
58  usages:
59    - digital signature
60    - key encipherment
61    - server auth
62    - client auth
63  dnsNames:
64$(render_dns_list "${search_dns_names[@]}")
65EOF_MANIFEST
66
67kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait --for=condition=Ready certificate "${server_certificate}" --timeout=300s
68kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait --for=condition=Ready certificate "${search_certificate}" --timeout=300s
69
70echo "MongoDB TLS certificates have been issued."
9

Conditional. Create and deploy the MongoDB Community resource.

If you've already deployed the MongoDB Community Edition, skip this step. Otherwise, deploy the MongoDB Community Edition.

To deploy the MongoDB Community Edition, complete the following steps:

  1. Create a MongoDBCommunity custom resource named mdb-rs.

    The resource defines CPU and memory resources for the mongod and mongodb-agent containers, and sets up the following three users:

    mdb-user

    User that can restore database and run search queries. This user uses the mdb-user-password secret to perform these operations.

    search-sync-source

    User that MongoDB Search uses to connect to MongoDB database in order to manage and build indexes. This user uses searchCoordinator role that the Kubernetes operator creates. This uses uses the mdbc-rs-search-sync-source-password secret to connect mongot to mongod.

    admin-user

    Database admin user.

    The Kubernetes Operator uses this resource to configure a MongoDB replica set with 3 members.

    To create the secrets, copy, paste, and run the following commands:

    1kubectl apply --context "${K8S_CTX}" -n "${MDB_NS}" -f - <<EOF
    2apiVersion: mongodbcommunity.mongodb.com/v1
    3kind: MongoDBCommunity
    4metadata:
    5  name: ${MDB_RESOURCE_NAME}
    6spec:
    7  version: ${MDB_VERSION}
    8  type: ReplicaSet
    9  members: ${MDB_MEMBERS}
    10  security:
    11    tls:
    12      enabled: true
    13      certificateKeySecretRef:
    14        name: ${MDB_TLS_SERVER_CERT_SECRET_NAME}
    15      caConfigMapRef:
    16        name: ${MDB_TLS_CA_CONFIGMAP}
    17    authentication:
    18      ignoreUnknownUsers: true
    19      modes:
    20        - SCRAM
    21  agent:
    22    logLevel: DEBUG
    23  statefulSet:
    24    spec:
    25      template:
    26        spec:
    27          containers:
    28            - name: mongod
    29              resources:
    30                limits:
    31                  cpu: "2"
    32                  memory: 2Gi
    33                requests:
    34                  cpu: "1"
    35                  memory: 1Gi
    36            - name: mongodb-agent
    37              resources:
    38                limits:
    39                  cpu: "1"
    40                  memory: 2Gi
    41                requests:
    42                  cpu: "0.5"
    43                  memory: 1Gi
    44  users:
    45    # admin user with root role
    46    - name: mdb-admin
    47      db: admin
    48      # a reference to the secret containing user password
    49      passwordSecretRef:
    50        name: mdb-admin-user-password
    51      scramCredentialsSecretName: mdb-admin-user
    52      roles:
    53        - name: root
    54          db: admin
    55    # user performing search queries
    56    - name: mdb-user
    57      db: admin
    58      # a reference to the secret containing user password
    59      passwordSecretRef:
    60        name: mdb-user-password
    61      scramCredentialsSecretName: mdb-user-scram
    62      roles:
    63        - name: restore
    64          db: sample_mflix
    65        - name: readWrite
    66          db: sample_mflix
    67    # user used by MongoDB Search to connect to MongoDB database to
    68    # synchronize data from.
    69    # For MongoDB <8.2, the operator will be creating the
    70    # searchCoordinator custom role automatically.
    71    # From MongoDB 8.2, searchCoordinator role will be a
    72    # built-in role.
    73    - name: search-sync-source
    74      db: admin
    75      # a reference to the secret that will be used to generate the user's password
    76      passwordSecretRef:
    77        name: ${MDB_RESOURCE_NAME}-search-sync-source-password
    78      scramCredentialsSecretName: ${MDB_RESOURCE_NAME}-search-sync-source
    79      roles:
    80        - name: searchCoordinator
    81          db: admin
    82EOF

  2. Wait for the MongoDBCommunity resource deployment to complete.

    When you apply the MongoDBCommunity custom resource, the Kubernetes operator begins deploying the MongoDB nodes (pods). This step pauses the execution until the mdbc-rs resource's status phase is Running, which indicates that the MongoDB Community replica set is operational.

    1echo "Waiting for MongoDBCommunity resource to reach Running phase..."
    2kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait \
    3  --for=jsonpath='{.status.phase}'=Running mdbc/mdbc-rs --timeout=400s
    4echo; echo "MongoDBCommunity resource"
    5kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get mdbc/mdbc-rs
    6echo; echo "Pods running in cluster ${K8S_CTX}"
    7kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get pods
10

Required. Create and deploy the resource for MongoDB Search and Vector Search.

You can deploy one instance of the search node without any load balancing.

To deploy, complete the following steps:

  1. Create a MongoDBSearch custom resource named mdbc-rs.

    This resource specifies the CPU and memory resource requirements for the search nodes. To learn more about the settings in this custom resource, see MongoDB Search and Vector Search Settings.

    1# create a Kubernetes secret that would have embedding model's API Keys
    2kubectl create secret generic "${AUTO_EMBEDDING_API_KEY_SECRET_NAME}" \
    3  --from-literal=query-key="${AUTO_EMBEDDING_API_QUERY_KEY}" \
    4  --from-literal=indexing-key="${AUTO_EMBEDDING_API_INDEXING_KEY}" --context "${K8S_CTX}" -n "${MDB_NS}"
    5
    6# create MongoDBSearch resource, enabling the auto embedding using the API Keys provided above
    7kubectl apply --context "${K8S_CTX}" -n "${MDB_NS}" -f - <<EOF
    8apiVersion: mongodb.com/v1
    9kind: MongoDBSearch
    10metadata:
    11  name: ${MDB_RESOURCE_NAME}
    12spec:
    13  security:
    14    tls:
    15      certificateKeySecretRef:
    16        name: ${MDB_SEARCH_TLS_SECRET_NAME}
    17  autoEmbedding:
    18    providerEndpoint: ${PROVIDER_ENDPOINT}
    19    embeddingModelAPIKeySecret:
    20      name: ${AUTO_EMBEDDING_API_KEY_SECRET_NAME}
    21  clusters:
    22    - resourceRequirements:
    23        limits:
    24          cpu: "3"
    25          memory: 5Gi
    26        requests:
    27          cpu: "2"
    28          memory: 3Gi
    29EOF

    Note

    Since the Kubernetes Operator only deploys single instance of MongoDB Search, that instance is automatically configured as the embedding materialized View writer.

    1kubectl apply --context "${K8S_CTX}" -n "${MDB_NS}" -f - <<EOF
    2apiVersion: mongodb.com/v1
    3kind: MongoDBSearch
    4metadata:
    5  name: ${MDB_RESOURCE_NAME}
    6spec:
    7  security:
    8    tls:
    9      certificateKeySecretRef:
    10        name: ${MDB_SEARCH_TLS_SECRET_NAME}
    11  clusters:
    12    - resourceRequirements:
    13        limits:
    14          cpu: "3"
    15          memory: 5Gi
    16        requests:
    17          cpu: "2"
    18          memory: 3Gi
    19EOF

  2. Wait for the MongoDBSearch resource deployment to complete.

    When you apply the MongoDBSearch custom resource, the Kubernetes operator begins deploying the search nodes (pods). This step pauses the execution until the mdbc-rs MongoDBSearch resource's status phase is Running, which indicates that the MongoDB Search is operational.

    1echo "Waiting for MongoDBSearch resource to reach Running phase..."
    2kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait \
    3  --for=jsonpath='{.status.phase}'=Running mdbs/"${MDB_RESOURCE_NAME}" --timeout=300s
11

Optional. Verify the MongoDB Community resource status.

Ensure that the MongoDBCommunity resource deployment with MongoDBSearch was successful.

1echo "Waiting for MongoDBCommunity resource to reach Running phase..."
2kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait \
3  --for=jsonpath='{.status.phase}'=Running mdbc/mdbc-rs --timeout=400s
4echo; echo "MongoDBCommunity resource"
5kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get mdbc/mdbc-rs
6echo; echo "Pods running in cluster ${K8S_CTX}"
7kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get pods
12

Optional. View all the running pods in your namespace.

View all the running pods in your namespace pods for the MongoDB replica set members, the MongoDB Controllers for Kubernetes Operator, and the Search nodes.

1echo; echo "MongoDBCommunity resource"
2kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get mdbc/mdbc-rs
3echo; echo "MongoDBSearch resource"
4kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get mdbs/mdbc-rs
5echo; echo "Pods running in cluster ${K8S_CTX}"
6kubectl --context "${K8S_CTX}" -n "${MDB_NS}" get pods

Verify the Deployment

After the procedure completes, confirm the deployment is healthy before running queries.

Checkpoint
Verification Step

MongoDB Community resource running

Run kubectl get mongodbcommunity -n <your-namespace> and confirm the MongoDBCommunity resource shows the Running phase.

MongoDB Search resource running

Run kubectl get mongodbsearch -n <your-namespace> and confirm the MongoDBSearch resource shows the Running phase.

All pods healthy

Run kubectl get pods -n <your-namespace> and confirm all replica set member pods and search pods show a Running status with all containers ready.

Search queries succeed

Connect to the replica set and run a MongoDB Search or Vector Search query to confirm the search index is reachable.

Next Steps

After you deploy MongoDB Search and Vector Search with MongoDB Community Edition, you can:

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