- 필수 기능
- 시작하기
- Glossary
- 표준 속성
- Guides
- Agent
- 통합
- 개방형텔레메트리
- 개발자
- Administrator's Guide
- API
- Datadog Mobile App
- CoScreen
- Cloudcraft
- 앱 내
- 서비스 관리
- 인프라스트럭처
- 애플리케이션 성능
- APM
- Continuous Profiler
- 스팬 시각화
- 데이터 스트림 모니터링
- 데이터 작업 모니터링
- 디지털 경험
- 소프트웨어 제공
- 보안
- AI Observability
- 로그 관리
- 관리
Database Monitoring offers comprehensive insights into your Amazon DocumentDB (with MongoDB compatibility) 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 Amazon DocumentDB instances. This guide outlines the steps to set up Database Monitoring for Amazon DocumentDB.
To enable Database Monitoring for your database:
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" }
])
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.
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.
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/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
If you installed the Amazon DocumentDB integration to enrich instances with Amazon 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: <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>
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.
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}
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.
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
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
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.
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 collect more comprehensive database metrics from Amazon DocumentDB, install the Amazon DocumentDB integration (optional).
Refer to the integration documentation for a comprehensive list of metrics collected by the integration.
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.
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.
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.