Configurer Database Monitoring pour Amazon DocumentDB

Database Monitoring offre des informations détaillées sur vos bases de données Amazon DocumentDB (avec compatibilité MongoDB) en donnant accès à des métriques essentielles, des échantillons d’opérations, des plans d’explication et des changements d’état de réplication. Pour tirer parti de Database Monitoring pour Amazon DocumentDB, assurez-vous que l’Agent Datadog est installé et configuré pour se connecter à vos instances Amazon DocumentDB. Ce guide décrit les étapes de configuration de Database Monitoring pour Amazon DocumentDB.

Avant de commencer

Versions majeures d’Amazon DocumentDB prises en charge
4.0.0, 5.0.0
Types de clusters Amazon DocumentDB pris en charge
Clusters basés sur des instances.

Remarque : les clusters élastiques Amazon DocumentDB ne sont pas pris en charge.
Supported Agent versions
7.59.0+
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 Amazon DocumentDB connection strings provide many benefits such as automatic failover and load balancing, the Datadog Agent must connect directly to the DocumentDB instance being monitored. If the Agent connects to a different DocumentDB 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.

Configuration

Pour activer Database Monitoring pour votre base de données :

  1. Accorder à l’Agent l’accès à vos instances Amazon DocumentDB
  2. Installer et configurer l’Agent
  3. (Facultatif) Installer l’intégration Amazon DocumentDB

Accorder à l’Agent l’accès à vos instances Amazon DocumentDB

L’Agent Datadog nécessite un accès en lecture seule à l’instance Amazon DocumentDB pour collecter des statistiques et des requêtes.

Dans un shell Mongo, authentifiez-vous sur le nœud primaire du replica set, créez un utilisateur en lecture seule pour l’Agent Datadog dans la base de données admin et accordez les autorisations requises :

# Authentifiez-vous en tant qu'utilisateur admin.

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

# Créez l'utilisateur pour l'Agent Datadog.

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

Accordez des autorisations supplémentaires à l’utilisateur datadog dans les bases de données que vous souhaitez surveiller :

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

Vous pouvez également accorder le rôle readAnyDatabase à l’utilisateur datadog dans la base de données admin pour surveiller toutes les bases de données :

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

Stocker votre mot de passe de manière sécurisée

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.

Installer et configurer l’Agent

Pour surveiller votre cluster Amazon DocumentDB, vous devez installer et configurer l’Agent Datadog sur un host pouvant accéder à distance à votre cluster Amazon DocumentDB. Ce host peut être un host Linux, un conteneur Docker ou un pod Kubernetes.

Créer le fichier de configuration

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>

      ## @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/extend/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

Si vous avez installé l’intégration Amazon DocumentDB pour enrichir les instances avec les données de télémétrie de l’intégration Amazon DocumentDB, ajoutez cette section à votre 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: <AMAZON_DOCUMENTDB_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: <AMAZON_DOCUMENTDB_CLUSTER_IDENTIFIER>

Configurer l’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.

Installer l’intégration Amazon DocumentDB

Pour collecter des métriques de base de données plus complètes depuis Amazon DocumentDB, installez l’intégration Amazon DocumentDB (facultatif).

Données collectées

Métriques

Consultez la documentation de l’intégration pour obtenir la liste complète des métriques collectées par l’intégration.

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 DocumentDB 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 DocumentDB 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.