MongoDB Community Search on Kubernetes - Quick Start

This guide provides instructions for deploying MongoDB Community Edition along with its Search capabilities onto a Kubernetes cluster. By following these steps, you will set up a MongoDB instance and configure search indexes to perform full-text search queries against your data.

Prerequisites

Community Search is currently in private preview, and access to the image requires a secret to pull the search container image from Quay.io. This secret is specified during the first step of the process below, and must be obtained from MongoDB when requesting access to the private preview.

Before you begin, ensure you have the following tools and configurations in place:

Kubernetes cluster: A running Kubernetes cluster (e.g., Minikube, Kind, GKE, EKS, AKS).
kubectl: The Kubernetes command-line tool, configured to communicate with your cluster.
Helm: The package manager for Kubernetes, used here to install the MongoDB Kubernetes Operator.
Bash 5.1+: All shell commands in this guide are intended to be run in Bash. Scripts in this guide are automatically tested on Linux with Bash 5.1.

Setup Steps

The following steps guide you through deploying MongoDB Community with Search. Each step provides a shell script. It is important to first source the env_variables.sh script provided and customize its values for your environment. The subsequent script snippets rely on the environment variables defined in env_variables.sh. You should copy and paste each script into your Bash terminal.

1. Configure Environment Variables

First, you need to set up your environment. The env_variables.sh script, shown below, contains variables for the subsequent steps. You should create this file locally or use the linked one.

Download or copy the content of env_variables.sh: env_variables.sh

# set it to the context name of the k8s cluster
export K8S_CLUSTER_0_CONTEXT_NAME="<local cluster context>"

# At the private preview stage the community search image is accessible only from a private repository.
# Please contact MongoDB Support to get access.
export PRIVATE_PREVIEW_IMAGE_PULLSECRET="<.dockerconfigjson>"

# the following namespace will be created if not exists
export MDB_NAMESPACE="mongodb"

export MDB_ADMIN_USER_PASSWORD="admin-user-password-CHANGE-ME"
export MDB_SEARCH_USER_PASSWORD="search-user-password-CHANGE-ME"

export OPERATOR_HELM_CHART="mongodb/mongodb-kubernetes"
# comma-separated key=value pairs for additional parameters passed to the helm-chart installing the operator
export OPERATOR_ADDITIONAL_HELM_VALUES=""

This will load the variables into your current shell session, making them available for the commands in the following steps.

2. Add MongoDB Helm Repository

First, add the MongoDB Helm repository. This repository contains the Helm chart required to install the MongoDB Kubernetes Operator. The operator automates the deployment and management of MongoDB instances (both Community and Enterprise editions) on Kubernetes.

code_snippets/090_helm_add_mogodb_repo.sh

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

3. Install MongoDB Kubernetes Operator

Next, install the MongoDB Kubernetes Operator from the Helm repository you just added. The Operator will watch for MongoDBCommunity and MongoDBSearch custom resources and manage the lifecycle of your MongoDB deployments.

code_snippets/0100_install_operator.sh

helm upgrade --install --debug --kube-context "${K8S_CLUSTER_0_CONTEXT_NAME}" \
  --create-namespace \
  --namespace="${MDB_NAMESPACE}" \
  mongodb-kubernetes \
  --set "${OPERATOR_ADDITIONAL_HELM_VALUES:-"dummy=value"}" \
  "${OPERATOR_HELM_CHART}"

This command installs the operator in the mongodb namespace (creating it if it doesn't exist) and names the release community-operator.

4. Configure Pull Secret for MongoDB Community Search

To use MongoDB Search, your Kubernetes cluster needs to pull the necessary container images. This step creates a Kubernetes secret named community-private-preview-pullsecret. This secret stores the credentials required to access the image repository for MongoDB Search. The script then patches the mongodb-kubernetes-database-pods service account to include this pull secret, allowing pods managed by this service account to pull the required images.

code_snippets/0200_configure_community_search_pullsecret.sh

kubectl apply --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: community-private-preview-pullsecret
data:
  .dockerconfigjson: "${PRIVATE_PREVIEW_IMAGE_PULLSECRET}"
type: kubernetes.io/dockerconfigjson
EOF

pull_secrets=$(kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" \
  get sa mongodb-kubernetes-database-pods -n "${MDB_NAMESPACE}" -o=jsonpath='{.imagePullSecrets[*]}')

if [[ "${pull_secrets}" ]]; then
  kubectl patch --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" \
    sa mongodb-kubernetes-database-pods  \
    --type=json -p='[{"op": "add", "path": "/imagePullSecrets/-", "value": {"name": "community-private-preview-pullsecret"}}]'
else
  kubectl patch --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" \
    sa mongodb-kubernetes-database-pods  \
    --type=merge -p='{"imagePullSecrets": [{"name": "community-private-preview-pullsecret"}]}'
fi
echo "ServiceAccount mongodb-kubernetes-database-pods has been patched: "

kubectl get --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" -o yaml sa mongodb-kubernetes-database-pods

This script creates a community-private-preview-pullsecret secret in your Kubernetes namespace and associates it with the service account used for MongoDB pods.

5. Verify Pull Secret Configuration

Confirm that the community-private-preview-pullsecret has been successfully added to the mongodb-kubernetes-database-pods service account. This ensures that Kubernetes can authenticate with the container registry when pulling images for MongoDB Search pods.

code_snippets/0210_verify_community_search_pullsecret.sh

echo "Verifying mongodb-kubernetes-database-pods contains proper pull secret"
if ! kubectl get --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" -o json \
  sa mongodb-kubernetes-database-pods -o=jsonpath='{.imagePullSecrets[*]}' | \
    grep community-private-preview-pullsecret; then
  echo "ERROR: mongodb-kubernetes-database-pods service account doesn't contain necessary pullsecret"
  kubectl get --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" -o json \
    sa mongodb-kubernetes-database-pods -o=yaml
  return 1
fi

This command checks the mongodb-kubernetes-database-pods service account to confirm the presence of community-private-preview-pullsecret.

Creating a MongoDB Community Search Deployment

With the prerequisites and initial setup complete, you can now deploy MongoDB Community Edition and enable Search.

6. Create MongoDB User Secrets

MongoDB requires authentication for secure access. This step creates two Kubernetes secrets: admin-user-password and search-user-password. These secrets store the credentials for the MongoDB administrative user and a dedicated search user, respectively. These secrets will be mounted into the MongoDB pods.

code_snippets/0305_create_mongodb_community_user_secrets.sh

kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" --namespace "${MDB_NAMESPACE}" \
  create secret generic admin-user-password \
  --from-literal=password="${MDB_ADMIN_USER_PASSWORD}"

kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" --namespace "${MDB_NAMESPACE}" \
  create secret generic search-user-password \
  --from-literal=password="${MDB_SEARCH_USER_PASSWORD}"

Ensure these secrets are created in the same namespace where you plan to deploy MongoDB.

7. Create MongoDB Community Resource

Now, deploy MongoDB Community by creating a MongoDBCommunity custom resource named mdbc-rs. This resource definition instructs the MongoDB Kubernetes Operator to configure a MongoDB replica set with 3 members, running version 8.0.6. MongoDB Community Search is supported only from MongoDB Community Server version 8.0. It also defines CPU and memory resources for the mongod and mongodb-agent containers, and sets up two users (admin-user and search-user) with their respective roles and password secrets. User search-user will be used to restore, connect and perform search queries on the sample_mflix database.

code_snippets/0310_create_mongodb_community_resource.sh

kubectl apply --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" -f - <<EOF
apiVersion: mongodbcommunity.mongodb.com/v1
kind: MongoDBCommunity
metadata:
  name: mdbc-rs
spec:
  version: 8.0.6
  type: ReplicaSet
  members: 3
  security:
    authentication:
      ignoreUnknownUsers: true
      modes:
        - SCRAM
  agent:
    logLevel: INFO
  statefulSet:
    spec:
      template:
        spec:
          containers:
            - name: mongod
              resources:
                limits:
                  cpu: "3"
                  memory: 5Gi
                requests:
                  cpu: "2"
                  memory: 5Gi
            - name: mongodb-agent
              resources:
                limits:
                  cpu: "2"
                  memory: 5Gi
                requests:
                  cpu: "1"
                  memory: 5Gi
  users:
    - name: admin-user
      passwordSecretRef:
        name: admin-user-password
      roles:
        - db: admin
          name: clusterAdmin
        - db: admin
          name: userAdminAnyDatabase
      scramCredentialsSecretName: admin-user
    - name: search-user
      passwordSecretRef:
        name: search-user-password
      roles:
        - db: sample_mflix
          name: dbOwner
      scramCredentialsSecretName: search-user
EOF

8. Wait for MongoDB Community Resource to be Ready

After applying the MongoDBCommunity custom resource, the operator begins deploying the MongoDB nodes (pods). This step uses kubectl wait to pause execution until the mdbc-rs resource's status phase becomes Running, indicating that the MongoDB Community replica set is operational.

code_snippets/0315_wait_for_community_resource.sh

echo "Waiting for MongoDBCommunity resource to reach Running phase..."
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" wait --for=jsonpath='{.status.phase}'=Running mdbc/mdbc-rs --timeout=400s
echo; echo "MongoDBCommunity resource"
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" get mdbc/mdbc-rs
echo; echo "Pods running in cluster ${K8S_CLUSTER_0_CONTEXT_NAME}"
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" get pods

9. Create MongoDB Search Resource

Once your MongoDB deployment is ready, enable Search capabilities by creating a MongoDBSearch custom resource, also named mdbc-rs to associate it with the MongoDB instance. This resource specifies the CPU and memory resource requirements for the search nodes.

Note: Private preview of MongoDB Community Search comes with some limitations, and it is not suitable for production use:

TLS cannot be enabled in MongoDB Community deployment (MongoD communicates with MongoT with plain text).
Only one node of search node is supported (load balancing not supported)

code_snippets/0320_create_mongodb_search_resource.sh

kubectl apply --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" -f - <<EOF
apiVersion: mongodb.com/v1
kind: MongoDBSearch
metadata:
  name: mdbc-rs
spec:
  resourceRequirements:
    limits:
      cpu: "3"
      memory: 5Gi
    requests:
      cpu: "2"
      memory: 3Gi
EOF

The MongoDBSearch.spec fields are supported:

spec.source.mongodbResourceRef.name - omitted in the example as the MongoDBSearch CR has the same name as MongoDBCommunity CR allowing to integrate both using naming convention. While keeping the same name is recommended (you cannot have more than one MongoDBSearch resources referencing the same MongoDBCommunity resource - it's 1:1 relationship) it's not enforced. The name can be different, but then you must explicitly point to the MongoDBCommunity you would like to enable search in. Note that you enable search capabilities by deploying search component (with MongoDBSearch CR) and nothing is necessary to define in MongoDBCommunity CR to configure it for search - it will be configured automatically by recognising there is related MongoDBSearch pointing to it.
spec.version: Version of mongodb-community-search. By default, the operator chooses the MongoDB Search version automatically, but it is possible to specify it explicitly. Currently, the default value is 1.47.0.
spec.statefulSet: Optional statefulset overrides, which are applied last to the mongot's statefulset. It is possible to adjust any statefulset configuration that was create by the operator (the overrides are applied last). The type of the field is apps/v1/StatefulSet and both spec.statefulSet.spec and spec.statefulSet.metadata fields are supported.
spec.persistence.single: optional storage configuration for MongoDB Search persistence volume containing storing search indexes. See here for more information about storage settings. MongoDBSearch reuses the same persistence type as in other custom resources (e.g. MongoDB), but supports only single persistence field. If not set, the operator sets spec.persistence.single.storage = 10G.
spec.resourceRequirements - resource requests and limits for mongodb-search container. It's recommended to use this field to customize resource allocations instead of overriding it via spec.statefulSet overrides. If not set, the operator sets the following values (no limits, only requests):

requests:
    cpu: 2
    memory: 2G

10. Wait for Search Resource to be Ready

Similar to the MongoDB deployment, the Search deployment needs time to initialize. This step uses kubectl wait to pause until the MongoDBSearch resource mdbc-rs reports a Running status in its .status.phase field, indicating that the search nodes are operational and integrated.

code_snippets/0325_wait_for_search_resource.sh

echo "Waiting for MongoDBSearch resource to reach Running phase..."
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" wait --for=jsonpath='{.status.phase}'=Running mdbs/mdbc-rs --timeout=300s

This command polls the status of the MongoDBSearch resource mdbc-rs.

11. Verify MongoDB Community Resource Status

Double-check the status of your MongoDBCommunity resource to ensure it remains healthy and that the integration with the Search resource is reflected if applicable.

code_snippets/0330_wait_for_community_resource.sh

echo "Waiting for MongoDBCommunity resource to reach Running phase..."
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" wait --for=jsonpath='{.status.phase}'=Running mdbc/mdbc-rs --timeout=400s

This provides a final confirmation that the core database is operational.

12. List Running Pods

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

code_snippets/0335_show_running_pods.sh

echo; echo "MongoDBCommunity resource"
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" get mdbc/mdbc-rs
echo; echo "MongoDBSearch resource"
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" get mdbs/mdbc-rs
echo; echo "Pods running in cluster ${K8S_CLUSTER_0_CONTEXT_NAME}"
kubectl --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" get pods

Using MongoDB Search

Now that your MongoDB Community database with Search is deployed, you can start using its search capabilities.

13. Deploy MongoDB Tools Pod

To interact with your MongoDB deployment, this step deploys a utility pod named mongodb-tools-pod. This pod runs a MongoDB Community Server image and is kept running with a sleep infinity command, allowing you to use kubectl exec to run MongoDB client tools like mongosh and mongorestore from within the Kubernetes cluster. Running steps in a pod inside the cluster simplifies connectivity to mongodb without neeeding to expose the database externally (provided steps directly connect to the *.cluster.local hostnames).

code_snippets/0410_run_mongodb_tools_pod.sh

#!/bin/bash

kubectl apply -n "${MDB_NAMESPACE}" --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -f - <<EOF
apiVersion: v1
kind: Pod
metadata:
  name: mongodb-tools-pod
  labels:
    app: mongodb-tools
spec:
  containers:
  - name: mongodb-tools
    image: mongodb/mongodb-community-server:8.0.6-ubi9
    command: ["/bin/bash", "-c"]
    args: ["sleep infinity"]
  restartPolicy: Never
EOF

echo "Waiting for the mongodb-tools to be ready..."
kubectl wait --for=condition=Ready pod/mongodb-tools-pod -n "${MDB_NAMESPACE}" --context "${K8S_CLUSTER_0_CONTEXT_NAME}" --timeout=60s

14. Import Sample Data

To test the search functionality, this step imports the sample_mflix.movies collection. It downloads the sample dataset and uses mongorestore to load the data into the sample_mflix database in your MongoDB deployment, connecting as the search-user.

code_snippets/0420_import_movies_mflix_database.sh

#!/bin/bash

kubectl exec -n "${MDB_NAMESPACE}" --context "${K8S_CLUSTER_0_CONTEXT_NAME}" mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF
echo "Downloading sample database archive..."
curl https://atlas-education.s3.amazonaws.com/sample_mflix.archive -o /tmp/sample_mflix.archive
echo "Restoring sample database"
mongorestore --archive=/tmp/sample_mflix.archive --verbose=1 --drop --nsInclude 'sample_mflix.*' --uri="mongodb://search-user:${MDB_SEARCH_USER_PASSWORD}@mdbc-rs-0.mdbc-rs-svc.${MDB_NAMESPACE}.svc.cluster.local:27017/?replicaSet=mdbc-rs"
EOF
)"

This command uses mongorestore from the mongodb-tools-pod to load data from the downloaded sample_mflix.archive file.

15. Create Search Index

Before performing search queries, create a search index. This step uses kubectl exec to run mongosh in the mongodb-tools-pod. It connects to the sample_mflix database as search-user and calls db.movies.createSearchIndex() to create a search index named "default" with dynamic mappings on the movies collection. Dynamic mapping automatically indexes all fields with supported types. MongoDB Search offers flexible index definitions, allowing for dynamic and static field mappings, various analyzer types (standard, language-specific, custom), and features like synonyms and faceted search.

code_snippets/0430_create_search_index.sh

#!/bin/bash

kubectl exec --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" mongodb-tools-pod -- \
  mongosh --quiet "mongodb://search-user:${MDB_SEARCH_USER_PASSWORD}@mdbc-rs-0.mdbc-rs-svc.${MDB_NAMESPACE}.svc.cluster.local:27017/?replicaSet=mdbc-rs" \
    --eval "use sample_mflix" \
    --eval 'db.movies.createSearchIndex("default", { mappings: { dynamic: true } });'

16. Wait for Search Index to be Ready

Creating a search index is an asynchronous operation. This script polls periodically the status by executing db.movies.getSearchIndexes("default").

code_snippets/0440_wait_for_search_index_ready.sh

#!/bin/bash

for _ in $(seq 0 10); do
  search_index_status=$(kubectl exec --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" mongodb-tools-pod -- \
      mongosh --quiet "mongodb://search-user:${MDB_SEARCH_USER_PASSWORD}@mdbc-rs-0.mdbc-rs-svc.${MDB_NAMESPACE}.svc.cluster.local:27017/?replicaSet=mdbc-rs" \
        --eval "use sample_mflix" \
        --eval 'db.movies.getSearchIndexes("default")[0]["status"]')

  if [[ "${search_index_status}" == "READY" ]]; then
    echo "Search index is ready."
    break
  fi
  echo "Search index is not ready yet: status=${search_index_status}"
  sleep 2
done

if [[ "${search_index_status}" != "READY" ]]; then
  echo "Error waiting for the search index to be ready"
  return 1
fi

17. Execute a Search Query

Once the search index is ready, execute search queries using the $search aggregation pipeline stage. MongoDB Search supports a query language, allowing for various types of queries such as text search, autocomplete, faceting, and more. You can combine $search with other aggregation stages to further refine and process your results.

code_snippets/0450_execute_search_query.sh

#!/bin/bash

mdb_script=$(cat <<'EOF'
use sample_mflix;
db.movies.aggregate([
  {
    $search: {
      "compound": {
        "must": [ {
          "text": {
            "query": "baseball",
            "path": "plot"
          }
        }],
        "mustNot": [ {
          "text": {
            "query": ["Comedy", "Romance"],
            "path": "genres"
          }
        } ]
      },
      "sort": {
        "released": -1
      }
    }
  },
  {
    $limit: 3
  },
  {
    $project: {
      "_id": 0,
      "title": 1,
      "plot": 1,
      "genres": 1,
      "released": 1
    }
  }
]);
EOF
)

kubectl exec --context "${K8S_CLUSTER_0_CONTEXT_NAME}" -n "${MDB_NAMESPACE}" mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF
echo '${mdb_script}' > /tmp/mdb_script.js
mongosh --quiet "mongodb://search-user:${MDB_SEARCH_USER_PASSWORD}@mdbc-rs-0.mdbc-rs-svc.${MDB_NAMESPACE}.svc.cluster.local:27017/?replicaSet=mdbc-rs" < /tmp/mdb_script.js
EOF
)"

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README.md

README.md

MongoDB Community Search on Kubernetes - Quick Start

Prerequisites

Setup Steps

1. Configure Environment Variables

2. Add MongoDB Helm Repository

3. Install MongoDB Kubernetes Operator

4. Configure Pull Secret for MongoDB Community Search

5. Verify Pull Secret Configuration

Creating a MongoDB Community Search Deployment

6. Create MongoDB User Secrets

7. Create MongoDB Community Resource

8. Wait for MongoDB Community Resource to be Ready

9. Create MongoDB Search Resource

10. Wait for Search Resource to be Ready

11. Verify MongoDB Community Resource Status

12. List Running Pods

Using MongoDB Search

13. Deploy MongoDB Tools Pod

14. Import Sample Data

15. Create Search Index

16. Wait for Search Index to be Ready

17. Execute a Search Query

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MongoDB Community Search on Kubernetes - Quick Start

Prerequisites

Setup Steps

1. Configure Environment Variables

2. Add MongoDB Helm Repository

3. Install MongoDB Kubernetes Operator

4. Configure Pull Secret for MongoDB Community Search

5. Verify Pull Secret Configuration

Creating a MongoDB Community Search Deployment

6. Create MongoDB User Secrets

7. Create MongoDB Community Resource

8. Wait for MongoDB Community Resource to be Ready

9. Create MongoDB Search Resource

10. Wait for Search Resource to be Ready

11. Verify MongoDB Community Resource Status

12. List Running Pods

Using MongoDB Search

13. Deploy MongoDB Tools Pod

14. Import Sample Data

15. Create Search Index

16. Wait for Search Index to be Ready

17. Execute a Search Query