Setting Up Database Monitoring for Amazon DocumentDB

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Database Monitoring offers comprehensive insights into your DocumentDB databases by providing access to critical metrics, operation samples, explain plans, and replication state changes. To take advantage of Database Monitoring for Amazon DocumentDB, ensure that the Datadog Agent is installed and configured to connect to your DocumentDB instances. This guide outlines the steps to set up Database Monitoring for Amazon DocumentDB.

Before you begin

Supported Amazon DocumentDB major versions: 4.0, 5.0
Supported Amazon DocumentDB cluster types: Instance-based clusters.

Note: Amazon DocumentDB Elastic Cluster is not supported.
Supported Agent versions: 7.58.0+
Performance impact: The default Agent configuration for Database Monitoring is conservative, but you can adjust settings such as the collection interval and operation sampling rate to better suit your needs. For most workloads, the Agent represents less than one percent of query execution time on the database and less than one percent of CPU.
Connection strings or SRV strings: Although MongoDB connection strings or SRV strings provide many benefits such as automatic failover and load balancing, the Datadog Agent must connect directly to the MongoDB instance being monitored. If the Agent connects to a different MongoDB instance while it is running (as in the case of failover, load balancing, and so on), the Agent calculates the difference in statistics between two hosts, producing inaccurate metrics.
Data security considerations: Read about how Database Management handles sensitive information for information about what data the Agent collects from your databases and how to ensure it is secure.

Setup

To enable Database Monitoring for your database:

Grant the Agent access to your Amazon DocumentDB instances
Install and configure the Agent
(Optional) Install the Amazon DocumentDB integration

Grant the Agent access to your Amazon DocumentDB instances

The Datadog Agent requires read-only access to the Amazon DocumentDB instance to collect statistics and queries.

In a Mongo shell, authenticate to the primary node of the replica set, create a read-only user for the Datadog Agent in the admin database, and grant the required permissions:

# Authenticate as the admin user.

use admin
db.auth("admin", "<YOUR_AMAZON_DOCUMENTDB_ADMIN_PASSWORD>")

# Create the user for the Datadog Agent.

db.createUser({
"user": "datadog",
"pwd": "<UNIQUE_PASSWORD>",
"roles": [
{ role: "read", db: "admin" },
{ role: "read", db: "local" },
{ role: "clusterMonitor", db: "admin" }
]
})

Grant additional permissions to the datadog user in the databases you want to monitor:

db.grantRolesToUser("datadog", [
{ role: "read", db: "mydatabase" },
{ role: "read", db: "myotherdatabase" }
])

Alternatively, you can grant the readAnyDatabase role to the datadog user in the admin database to monitor all databases:

db.grantRolesToUser("datadog", [
{ role: "readAnyDatabase", db: "admin" }
])

For each shard in your cluster, connect to the primary node of the shard, create a read-only user for the Datadog Agent in the admin database, and grant the required permissions:

# Authenticate as the admin user.

use admin
db.auth("admin", "<YOUR_AMAZON_DOCUMENTDB_ADMIN_PASSWORD>")

# Create the user for the Datadog Agent.

db.createUser({
"user": "datadog",
"pwd": "<UNIQUE_PASSWORD>",
"roles": [
{ role: "read", db: "admin" },
{ role: "read", db: "local" },
{ role: "clusterMonitor", db: "admin" }
]
})

Grant additional permissions to the datadog user in the databases you want to monitor:

db.grantRolesToUser("datadog", [
{ role: "read", db: "mydatabase" },
{ role: "read", db: "myotherdatabase" }
])

Alternatively, you can grant the readAnyDatabase role to the datadog user in the admin database to monitor all databases:

db.grantRolesToUser("datadog", [
{ role: "readAnyDatabase", db: "admin" }
])

Follow the same steps and create the same user from a mongos proxy. This action creates the local user in the config servers and allows direct connection.

Securely store your password

Store your password using secret management software such as Vault. You can then reference this password as ENC[<SECRET_NAME>] in your Agent configuration files: for example, ENC[datadog_user_database_password]. See Secrets Management for more information.

The examples on this page use datadog_user_database_password to refer to the name of the secret where your password is stored. It is possible to reference your password in plain text, but this is not recommended.

Install and configure the Agent

To monitor your Amazon DocumentDB Cluster, you must install and configure the Datadog Agent on a host that can remotely access your Amazon DocumentDB Cluster. This host can be a Linux host, a Docker container, or a Kubernetes pod.

Amazon DocumentDB Elastic Clusters is not supported because it only exposes the cluster (mongos) endpoints.

Create the configuration file

To monitor an Amazon DocumentDB replica set, the Agent needs to connect to all members (including the arbiter) of the replica set.

Use the following configuration block as an example to configure the Agent to connect to a replica set member:

init_config:
instances:
    ## @param hosts - required
    ## Specify the hostname, IP address, or UNIX domain socket of
    ## a mongod instance as listed in the replica set configuration.
    ## If the port number is not specified, the default port 27017 is used.
    #
    - hosts:
          - <HOST>:<PORT> # Primary node

      ## @param username - string - optional
      ## The username to use for authentication.
      #
      username: datadog

      ## @param password - string - optional
      ## The password to use for authentication.
      #
      password: 'ENC[datadog_user_database_password]'

      ## @param options - mapping - optional
      ## Connection options. For a complete list, see:
      ## https://docs.mongodb.com/manual/reference/connection-string/#connections-connection-options
      #
      options:
          authSource: admin

      ## @param tls - boolean - required
      ## Required 'true' in Amazon DocumentDB.
      tls: true

      ## @param tls_ca_file - string - required
      ## Path to the CA certificate file used to verify the server certificate.
      tls_ca_file: <CERT_FILE_PATH>

      ## @param dbm - boolean - optional
      ## Set to true to enable Database Monitoring.
      #
      dbm: true

      ## @param cluster_name - string - optional
      ## The unique name of the cluster to which the monitored MongoDB instance belongs.
      ## Used to group MongoDB instances in a MongoDB cluster.
      ## cluster_name should follow Datadog tags naming conventions. See:
      ## https://docs.datadoghq.com/developers/guide/what-best-practices-are-recommended-for-naming-metrics-and-tags/#rules-and-best-practices-for-naming-tags
      ## Required when `dbm` is enabled.
      #
      cluster_name: <MONGO_CLUSTER_NAME>

      ## @param reported_database_hostname - string - optional
      ## Set the reported database hostname for the connected MongoDB instance.
      ## This value overrides the MongoDB hostname detected by the Agent
      ## from the MongoDB admin command serverStatus.host.
      #
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>

      ## @param additional_metrics - list of strings - optional
      ## List of additional metrics to collect. Available options are:
      ## - metrics.commands: Use of database commands
      ## - tcmalloc: TCMalloc memory allocator
      ## - top: Usage statistics for each collection
      ## - collection: Metrics of the specified collections
      #
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']

      ## @param collections_indexes_stats - boolean - optional
      ## Set to true to collect index statistics for the specified collections.
      ## Requires `collections` to be set.
      #
      collections_indexes_stats: true

      ## @param database_autodiscovery - mapping - optional
      ## Enable database autodiscovery to automatically collect metrics from all your MongoDB databases.
      #
      database_autodiscovery:
          ## @param enabled - boolean - required
          ## Enable database autodiscovery.
          #
          enabled: true

          ## @param include - list of strings - optional
          ## List of databases to include in the autodiscovery. Use regular expressions to match multiple databases.
          ## For example, to include all databases starting with "mydb", use "^mydb.*".
          ## By default, include is set to ".*" and all databases are included.
          #
          include:
              - '^mydb.*'

          ## @param exclude - list of strings - optional
          ## List of databases to exclude from the autodiscovery. Use regular expressions to match multiple databases.
          ## For example, to exclude all databases starting with "mydb", use "^mydb.*".
          ## When the exclude list conflicts with include list, the exclude list takes precedence.
          #
          exclude:
              - '^mydb2.*'
              - 'admin$'

          ## @param max_databases - integer - optional
          ## Maximum number of databases to collect metrics from. The default value is 100.
          #
          max_databases: 100

          ## @param refresh_interval - integer - optional
          ## Interval in seconds to refresh the list of databases. The default value is 600 seconds.
          #
          refresh_interval: 600

An example configuration for a replica set with 1 primary and 2 secondaries is as follows:

init_config:
instances:
    - hosts:
          - <HOST_REPLICA_1>:<PORT> # Primary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    - hosts:
          - <HOST_REPLICA_2>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    - hosts:
          - <HOST_REPLICA_3>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true

To monitor a MongoDB sharded cluster, the Agent needs to connect to the mongos router(s) and all members of the shards. If you have multiple mongos routers, you can configure the Agent to connect to them for load balancing.

Use the following configuration block as an example to configure the Agent to connect to a Mongos router:

init_config:
instances:
    ## @param hosts - required
    ## For a sharded cluster, you need one check instance for each mongod instance in
    ## each shard (including the configsvr shard) as well as one additional check instance
    ## that connects to at least one mongos node.

    ## Specify the hostname, IP address, or UNIX domain socket of the mongod or mongos instance.

    ## If the port number is not specified, the default port 27017 is used.
    - hosts:
          - <HOST>:<PORT>

      ## @param username - string - optional
      ## The username to use for authentication.
      #
      username: datadog

      ## @param password - string - optional
      ## The password to use for authentication.
      #
      password: 'ENC[datadog_user_database_password]'

      ## @param options - mapping - optional
      ## Connection options. For a complete list, see:
      ## https://docs.mongodb.com/manual/reference/connection-string/#connections-connection-options
      #
      options:
          authSource: admin

      ## @param tls - boolean - required
      ## Required 'true' in Amazon DocumentDB.
      tls: true

      ## @param tls_ca_file - string - required
      ## Path to the CA certificate file used to verify the server certificate.
      tls_ca_file: <CERT_FILE_PATH>

      ## @param dbm - boolean - optional
      ## Set to true to enable Database Monitoring.
      #
      dbm: true

      ## @param cluster_name - string - optional
      ## The unique name of the cluster to which the monitored MongoDB instance belongs.
      ## Used to group MongoDB instances in a MongoDB cluster.
      ## cluster_name should follow Datadog tags naming conventions. See:
      ## https://docs.datadoghq.com/developers/guide/what-best-practices-are-recommended-for-naming-metrics-and-tags/#rules-and-best-practices-for-naming-tags
      ## Required when `dbm` is enabled.
      #
      cluster_name: <MONGO_CLUSTER_NAME>

      ## @param reported_database_hostname - string - optional
      ## Set the reported database hostname for the connected MongoDB instance.
      ## This value overrides the MongoDB hostname detected by
      ## the Agent from the MongoDB admin command serverStatus.host.
      #
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>

      ## @param additional_metrics - list of strings - optional
      ## List of additional metrics to collect. Available options are:
      ## - metrics.commands: Use of database commands
      ## - tcmalloc: TCMalloc memory allocator
      ## - top: Usage statistics for each collection
      ## - collection: Metrics of the specified collections
      ## - jumbo_chunks: Count and percentage of jumbo chunks. Ignored on mongod instances.
      ## - sharded_data_distribution: Distribution of data in sharded collections.
      #
      additional_metrics:
          ['metrics.commands', 'tcmalloc', 'top', 'collection', 'jumbo_chunks', 'sharded_data_distribution']

      ## @param collections_indexes_stats - boolean - optional
      ## Set to true to collect index statistics for the specified collections.
      ## Requires `collections` to be set.
      #
      collections_indexes_stats: true

      ## @param database_autodiscovery - mapping - optional
      ## Enable database autodiscovery to automatically collect metrics from all your MongoDB databases.
      #
      database_autodiscovery:
          ## @param enabled - boolean - required
          ## Enable database autodiscovery.
          #
          enabled: true

          ## @param include - list of strings - optional
          ## List of databases to include in the autodiscovery. Use regular expressions to match multiple databases.
          ## For example, to include all databases starting with "mydb", use "^mydb.*".
          ## By default, include is set to ".*" and all databases are included.
          #
          include:
              - '^mydb.*'

          ## @param exclude - list of strings - optional
          ## List of databases to exclude from the autodiscovery. Use regular expressions to match multiple databases.
          ## For example, to exclude all databases starting with "mydb", use "^mydb.*".
          ## When the exclude list conflicts with include list, the exclude list takes precedence.
          #
          exclude:
              - '^mydb2.*'
              - 'admin$'

          ## @param max_databases - integer - optional
          ## Maximum number of databases to collect metrics from. The default value is 100.
          #
          max_databases: 100

          ## @param refresh_interval - integer - optional
          ## Interval in seconds to refresh the list of databases. The default value is 600 seconds.
          #
          refresh_interval: 600

Refer to the Replica Set configuration for an example configuration for connecting to members in each shard and the config server.

An example configuration for a sharded cluster with 1 mongos router and 2 shards is as follows:

init_config:
instances:
    ## mongos router
    - hosts:
          - <HOST_MONGOS>:<PORT>
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics:
          ['metrics.commands', 'tcmalloc', 'top', 'collection', 'jumbo_chunks', 'sharded_data_distribution']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    ## Shard1
    - hosts:
          - <HOST_SHARD1_1>:<PORT> # Primary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    - hosts:
          - <HOST_SHARD1_2>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    - hosts:
          - <HOST_SHARD1_3>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    ## Shard 2
    - hosts:
          - <HOST_SHARD2_1>:<PORT> # Primary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    - hosts:
          - <HOST_SHARD2_2>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    - hosts:
          - <HOST_SHARD2_3>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
      additional_metrics: ['metrics.commands', 'tcmalloc', 'top', 'collection']
      collections_indexes_stats: true
      database_autodiscovery:
          enabled: true
    ## Config server
    - hosts:
          - <HOST_CONFIG_1>:<PORT> # Primary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      database: config
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
    - hosts:
          - <HOST_CONFIG_2>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      database: config
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>
      reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
    - hosts:
          - <HOST_CONFIG_3>:<PORT> # Secondary node
      username: datadog
      password: 'ENC[datadog_user_database_password]'
      database: config
      options:
          authSource: admin
      tls: true
      tls_ca_file: <CERT_FILE_PATH>
      dbm: true
      cluster_name: <MONGO_CLUSTER_NAME>

If you installed the Amazon DocumentDB integration to enrich instances with DocumentDB integration telemetry, add this section to your configuration:

## @param aws - mapping - optional
## This block defines the configuration for Amazon DocumentDB instances.
## These values are only applied when `dbm: true` option is set.
#
aws:
    ## @param instance_endpoint - string - optional
    ## Equal to the Endpoint.Address of the instance the Agent is connecting to.
    ## This value is optional if the value of `host` is already configured to the instance endpoint.
    ##
    ## For more information on instance endpoints,
    ## see the AWS docs https://docs.aws.amazon.com/documentdb/latest/developerguide/API_Endpoint.html
    #
    instance_endpoint: <AWS_INSTANCE_ENDPOINT>
    ## @param cluster_identifier - string - optional
    ## Equal to the cluster identifier of the instance the Agent is connecting to.
    ## This value is optional if the value of `cluster_name` is already configured to the cluster identifier.
    ##
    ## For more information on cluster identifiers,
    ## see the AWS docs https://docs.aws.amazon.com/documentdb/latest/developerguide/API_DBCluster.html
    #
    cluster_identifier: <AWS_CLUSTER_IDENTIFIER_ENDPOINT>

Set up the Agent

Place the MongoDB Agent configuration file created in the previous step in /etc/datadog-agent/conf.d/mongo.d/conf.yaml. See the sample conf file for all available configuration options.

Once all Agent configuration is complete, restart the Datadog Agent.

Validate

Run the Agent’s status subcommand and look for mongo under the Checks section. Navigate to the Database Monitoring for MongoDB page in Datadog to get started.

To configure the Database Monitoring Agent running in a Docker container, set the Autodiscovery Integration Templates as Docker labels on your Agent container.

The MongoDB check is included in the Datadog Agent. No additional installation is necessary.

Note: The Agent must have read permission on the Docker socket for autodiscovery of labels to work.

Add the configuration details for the MongoDB check from the previous step in the com.datadoghq.ad.checks label. See the sample conf file for all available configuration options.

export DD_API_KEY=<DD_API_KEY>
export DD_AGENT_VERSION=7.58.0

docker run -e "DD_API_KEY=${DD_API_KEY}" \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -l com.datadoghq.ad.checks='{
    "mongo": {
      "init_config": {},
      "instances": [{
        "hosts": ["<HOST>:<PORT>"],
        "username": "datadog",
        "password": "<UNIQUE_PASSWORD>",
        "options": {
          "authSource": "admin"
        },
        "dbm": true,
        "cluster_name": "<MONGO_CLUSTER_NAME>",
        "reported_database_hostname": "<DATABASE_HOSTNAME_OVERRIDE>",
        "additional_metrics": ["metrics.commands", "tcmalloc", "top", "collection"],
        "collections_indexes_stats": true,
        "database_autodiscovery": {
          "enabled": true
        }
      }]
    }
  }' \
  datadog/agent:${DD_AGENT_VERSION}

Validate

Run the Agent’s status subcommand and look for mongo under the Checks section. Navigate to the Database Monitoring for MongoDB page in Datadog to get started.

If you have a Kubernetes cluster, use the Datadog Cluster Agent for Database Monitoring.

If cluster checks are not already enabled in your Kubernetes cluster, follow the instructions to enable cluster checks. You can configure the Cluster Agent either with static files mounted in the Cluster Agent container, or by using Kubernetes service annotations.

Command line with Helm

Execute the following Helm command to install the Datadog Cluster Agent on your Kubernetes cluster. Replace the values to match your account and environment:

helm repo add datadog https://helm.datadoghq.com
helm repo update

helm install <RELEASE_NAME> \
  --set 'datadog.apiKey=<DATADOG_API_KEY>' \
  --set 'clusterAgent.enabled=true' \
  --set 'clusterChecksRunner.enabled=true' \
  --set 'clusterAgent.confd.mongo\.yaml=cluster_check: true
init_config:
instances:
  - hosts:
      - <HOST>:<PORT>
    username: datadog
    password: <UNIQUE_PASSWORD>
    options:
      authSource: admin
    dbm: true
    cluster_name: <MONGO_CLUSTER_NAME>
    reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
    database_autodiscovery:
      enabled: true
    additional_metrics: ["metrics.commands", "tcmalloc", "top", "collection"]
    collections_indexes_stats: true' \
  datadog/datadog

Configure with mounted files

To configure a cluster check with a mounted configuration file, mount the configuration file in the Cluster Agent container on the path: /conf.d/mongo.yaml:

cluster_check: true  # Make sure to include this flag
init_config:
instances:
  - hosts:
      - <HOST>:<PORT>
    username: datadog
    password: "ENC[datadog_user_database_password]"
    options:
      authSource: admin
    dbm: true
    cluster_name: <MONGO_CLUSTER_NAME>
    reported_database_hostname: <DATABASE_HOSTNAME_OVERRIDE>
    database_autodiscovery:
      enabled: true
    additional_metrics: ["metrics.commands", "tcmalloc", "top", "collection"]
    collections_indexes_stats: true

Configure with Kubernetes service annotations

Rather than mounting a file, you can declare the instance configuration as a Kubernetes Service. To configure this check for an Agent running on Kubernetes, create a Service in the same namespace as the Datadog Cluster Agent:

apiVersion: v1
kind: Service
metadata:
  name: mongodb-datadog-check-instances
  annotations:
    ad.datadoghq.com/mongo.checks: |
    {
      "mongo": {
        "init_config": {},
        "instances": [{
          "hosts": ["<HOST>:<PORT>"],
          "username": "datadog",
          "password": "ENC[datadog_user_database_password]",
          "options": {
            "authSource": "admin"
          },
          "dbm": true,
          "cluster_name": "<MONGO_CLUSTER_NAME>",
          "reported_database_hostname": "<DATABASE_HOSTNAME_OVERRIDE>",
          "additional_metrics": ["metrics.commands", "tcmalloc", "top", "collection"],
          "collections_indexes_stats": true,
          "database_autodiscovery": {
            "enabled": true
          }
        }]
      }
    }    
spec:
  ports:
  - port: 27017
    protocol: TCP
    targetPort: 27017
    name: mongodb

The Cluster Agent automatically registers this configuration and begins running the MongoDB integration.

To avoid exposing the datadog user’s password in plain text, use the Agent’s secret management package and declare the password using the ENC[] syntax.

Validate

Run the Agent’s status subcommand and look for mongo under the Checks section. Navigate to the Database Monitoring for MongoDB page in Datadog to get started.

Install the Amazon DocumentDB integration

To collect more comprehensive database metrics from Amazon DocumentDB, install the Amazon DocumentDB integration (optional).

Data Collected

Metrics

Refer to the MongoDB integration documentation for a comprehensive list of metrics collected by the MongoDB integration.

Operation samples and explain plans

Database Monitoring for Amazon DocumentDB gathers operation samples using the currentOp command. This command provides information about operations that are currently being executed on the MongoDB instance. Additionally, Database Monitoring collects explain plans for the read operation samples using the explain command, offering detailed insights into the query execution plans.

Replication state changes

Database Monitoring for Amazon DocumentDB generates an event each time there is a change in the replication state within the MongoDB instance. This ensures that any changes in replication are promptly detected and reported.

Collection of schemas and indexes

Database Monitoring for Amazon DocumentDB collects inferred schemas and indexes of Amazon DocumentDB collections. This information is used to provide insights into the structure and organization of your collections.

When analyzing Amazon DocumentDB collections, Datadog collects inferred schema information by sampling documents using the $sample aggregation stage. From this analysis, only metadata about the schema is gathered and sent to Datadog, including field names, field prevalence (how often each field appears), and their respective data types. Datadog does not collect or transmit the actual content of documents or any customer business data. This ensures that sensitive data remains protected while still providing valuable insights into the structure and organization of your collections.