Setting Up Database Monitoring for MongoDB Atlas

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

Before you begin

Supported MongoDB major versions: 4.4, 5.0, 6.0, 7.0, 8.0
Supported MongoDB Atlas cluster tiers: M10 and higher

Note: MongoDB Atlas Serverless instances or shared clusters (M0 Sandbox, M2, M5) are 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 MongoDB Atlas Cluster
Install and configure the Agent
(Optional) Install the MongoDB Atlas integration

Grant the Agent access to your MongoDB Atlas Cluster

The Datadog Agent requires read-only access to the MongoDB Atlas Cluster to collect statistics and queries.

Create a custom monitoring role

In the MongoDB Atlas UI, navigate to the Database Access tab.
On the Custom Roles tab, click Add New Custom Role.
Enter a Custom Role Name, such as datadog.
Add the following permissions to the custom role:
- read on the admin database
- read on the local database
- read on the config database (Sharded Cluster only)
- clusterMonitor on the admin database
- read on the user created databases you want to monitor, or readAnyDatabase to monitor all databases
Click Add Custom Role.

Create a monitoring user with the custom monitoring role

In the MongoDB Atlas UI, navigate to the Database Access tab.
On the Database Users tab, click Add New Database User.
Under the Authentication Method, select Password.
Enter the username and password.
Under Database User Privileges, expand Custom Roles and select the custom monitoring role you created in the previous step.
Click Add User.
Note the username and password for the monitoring user, so you can configure the Agent.

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

Get the individual MongoDB instance hostname and port from the SRV connection string

Applications usually connect to MongoDB Atlas using an SRV connection string, but the Datadog Agent must connect directly to the individual MongoDB instance being monitored. If the Agent connects to 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.

To get the individual MongoDB instance hostname and port, you can use network utility command line tools like dig in Linux or nslookup in Windows to resolve the SRV connection string.

Replica set members

For a non-sharded (replica set) cluster with the SRV connection string mongodb+srv://XXXXX.XXX.mongodb.net/:

Use dig in Linux to resolve the SRV connection string:

dig +short SRV _mongodb._tcp.XXXXX.XXX.mongodb.net

The output should be similar to:

0 27017 XXXXX-00-00.4zh9o.mongodb.net.
0 27017 XXXXX-00-01.4zh9o.mongodb.net.
0 27017 XXXXX-00-02.4zh9o.mongodb.net.

Use nslookup in Windows to resolve the SRV connection string:

nslookup -type=SRV _mongodb._tcp.XXXXX.XXX.mongodb.net

The output should be similar to:

_mongodb._tcp.XXXXX.XXX.mongodb.net service = 0 0 27017 XXXXX-00-00.4zh9o.mongodb.net.
_mongodb._tcp.XXXXX.XXX.mongodb.net service = 0 0 27017 XXXXX-00-01.4zh9o.mongodb.net.
_mongodb._tcp.XXXXX.XXX.mongodb.net service = 0 0 27017 XXXXX-00-02.4zh9o.mongodb.net.

In this example, the individual MongoDB instances <HOST>:<PORT> from the replica set are:

XXXXX-00-00.4zh9o.mongodb.net:27017
XXXXX-00-01.4zh9o.mongodb.net:27017
XXXXX-00-02.4zh9o.mongodb.net:27017

You can use the <HOST>:<PORT> retrieved from the SRV connection string to configure the Agent.

mongos routers

For a sharded cluster with the SRV connection string mongodb+srv://XXXXX.XXX.mongodb.net/:

Use dig in Linux to resolve the SRV connection string:

dig +short SRV _mongodb._tcp.XXXXX.XXX.mongodb.net

The output should be similar to:

0 27016 XXXXX-00-00.4zh9o.mongodb.net.
0 27016 XXXXX-00-01.4zh9o.mongodb.net.
0 27016 XXXXX-00-02.4zh9o.mongodb.net.

Use nslookup in Windows to resolve the SRV connection string:

nslookup -type=SRV _mongodb._tcp.XXXXX.XXX.mongodb.net

The output should be similar to:

_mongodb._tcp.XXXXX.XXX.mongodb.net service = 0 0 27016 XXXXX-00-00.4zh9o.mongodb.net.
_mongodb._tcp.XXXXX.XXX.mongodb.net service = 0 0 27016 XXXXX-00-01.4zh9o.mongodb.net.
_mongodb._tcp.XXXXX.XXX.mongodb.net service = 0 0 27016 XXXXX-00-02.4zh9o.mongodb.net.

In this example, the individual mongos routers are:

XXXXX-00-00.4zh9o.mongodb.net:27016
XXXXX-00-01.4zh9o.mongodb.net:27016
XXXXX-00-02.4zh9o.mongodb.net:27016.

You can use the <HOST>:<PORT> retrieved from the SRV connection string to configure the Agent.

Shard members

To get the individual MongoDB instances for each shard, you can connect to the mongos router and run the following command:

use admin
db.runCommand("getShardMap")

The output should be similar to:

{
"map" : {
"shard-0": "shard-0/XXXXX-00-00.4zh9o.mongodb.net:27017,XXXXX-00-01.4zh9o.mongodb.net:27017,XXXXX-00-02.4zh9o.mongodb.net:27017",
"shard-1": "shard-1/XXXXX-01-00.4zh9o.mongodb.net:27017,XXXXX-01-01.4zh9o.mongodb.net:27017,XXXXX-01-02.4zh9o.mongodb.net:27017"
},
"hosts": {
"XXXXX-00-00.4zh9o.mongodb.net:27017": "shard-0",
"XXXXX-00-01.4zh9o.mongodb.net:27017": "shard-0",
"XXXXX-00-02.4zh9o.mongodb.net:27017": "shard-0",
"XXXXX-01-00.4zh9o.mongodb.net:27017": "shard-1",
"XXXXX-01-01.4zh9o.mongodb.net:27017": "shard-1",
"XXXXX-01-02.4zh9o.mongodb.net:27017": "shard-1",
"XXXXX-00-00-config.4zh9o.mongodb.net:27017": "config",
"XXXXX-00-01-config.4zh9o.mongodb.net:27017": "config",
"XXXXX-00-02-config.4zh9o.mongodb.net:27017": "config"
},
"ok" : 1
}

In this example, the individual MongoDB instances for shard-0 are:

XXXXX-00-00.4zh9o.mongodb.net:27017
XXXXX-00-01.4zh9o.mongodb.net:27017
XXXXX-00-02.4zh9o.mongodb.net:27017

For shard-1, they are:

XXXXX-01-00.4zh9o.mongodb.net:27017
XXXXX-01-01.4zh9o.mongodb.net:27017
XXXXX-01-02.4zh9o.mongodb.net:27017

For the config server, they are:

XXXXX-00-00-config.4zh9o.mongodb.net:27017
XXXXX-00-01-config.4zh9o.mongodb.net:27017
XXXXX-00-02-config.4zh9o.mongodb.net:27017

You can use one of these hostnames to configure the Agent.

Create the configuration file

To monitor a MongoDB 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>

    ## @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 - optional
    ## Set to true to connect to the MongoDB instance using TLS.
    #
    tls: true

    ## @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
    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
    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
    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 - optional
    ## Set to true to connect to the MongoDB instance using TLS.
    #
    tls: true

    ## @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
    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
    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
    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
    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
    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
    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
    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
    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
    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
    dbm: true
    cluster_name: <MONGO_CLUSTER_NAME>

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/service.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 MongoDB Atlas integration

To collect more comprehensive database metrics from MongoDB Atlas, install the MongoDB Atlas integration (optional).

Data Collected

Metrics

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

Slow operations

Database Monitoring for MongoDB captures slow operations from either MongoDB slow query logs or the system.profile collection. Slow operations are defined as those taking longer than the slowms threshold set in your MongoDB configuration.

With Database Profiling Enabled: When profiling is enabled at levels 1 or 2, Database Monitoring collects slow operations from the system.profile collection.
With Database Profiling Disabled: If profiling is disabled, Database Monitoring relies on MongoDB slow query logs to gather slow operations.

Operation samples and explain plans

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

When analyzing MongoDB 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.