Database Monitoring provides deep visibility into your Microsoft SQL Server databases by exposing query metrics, query samples, explain plans, database states, failovers, and events.
Do the following steps to enable Database Monitoring with your database:
The Datadog Agent requires read-only access to the database server to collect statistics and queries.
Create a read-only login to connect to your server and grant the required permissions:
USE [master];
CREATE LOGIN datadog WITH PASSWORD = '<PASSWORD>';
GO
--Set context to msdb database and create datadog user
USE [msdb];
CREATE USER datadog FOR LOGIN datadog;
GO
--Switch back to master and grant datadog user server permissions
USE [master];
GRANT VIEW SERVER STATE to datadog;
GRANT VIEW ANY DEFINITION to datadog;
GO
Create the datadog user in each additional application database:
USE [database_name];
CREATE USER datadog FOR LOGIN datadog;
This is required because RDS does not permit granting CONNECT ANY DATABASE. The Datadog Agent needs to connect to each database to collect database-specific file I/O statistics.
Since AWS does not grant direct host access, the Datadog Agent must be installed on a separate host where it is able to talk to the SQL Server host. There are several options for installing and running the Agent.
To start collecting SQL Server telemetry, first install the Datadog Agent.
Create the SQL Server Agent conf file C:\ProgramData\Datadog\conf.d\sqlserver.d\conf.yaml. See the sample conf file for all available configuration options.
init_config:
instances:
- dbm: true
host: '<HOSTNAME>,<SQL_PORT>'
username: datadog
password: '<PASSWORD>'
connector: adodbapi
adoprovider: MSOLEDBSQL
tags: # optional
- 'service:<CUSTOM_SERVICE>'
- 'env:<CUSTOM_ENV>'
To use Windows Authentication, set connection_string: "Trusted_Connection=yes" and omit the username and password fields.
Use the service and env tags to link your database telemetry to other telemetry through a common tagging scheme. See Unified Service Tagging on how these tags are used throughout Datadog.
The recommended ADO provider is Microsoft OLE DB Driver. Ensure the driver is installed on the host where the agent is running.
connector: adodbapi
adoprovider: MSOLEDBSQL
The other two providers, SQLOLEDB and SQLNCLI, are considered deprecated by Microsoft and should no longer be used.
The recommended ODBC driver is Microsoft ODBC Driver. Ensure the driver is installed on the host where the Agent is running.
connector: odbc
driver: '{ODBC Driver 17 for SQL Server}'
Once all Agent configuration is complete, restart the Datadog Agent.
Run the Agent’s status subcommand and look for sqlserver under the Checks section. Navigate to the Databases page in Datadog to get started.
To start collecting SQL Server telemetry, first install the Datadog Agent.
On Linux, the Datadog Agent additionally requires an ODBC SQL Server driver to be installed—for example, the Microsoft ODBC driver. Once an ODBC SQL Server is installed, copy the odbc.ini and odbcinst.ini files into the /opt/datadog-agent/embedded/etc folder.
Use the odbc connector and specify the proper driver as indicated in the odbcinst.ini file.
Create the SQL Server Agent conf file /etc/datadog-agent/conf.d/sqlserver.d/conf.yaml. See the sample conf file for all available configuration options.
init_config:
instances:
- dbm: true
host: '<HOSTNAME>,<SQL_PORT>'
username: datadog
password: '<PASSWORD>'
connector: odbc
driver: '<Driver from the `odbcinst.ini` file>'
tags: # optional
- 'service:<CUSTOM_SERVICE>'
- 'env:<CUSTOM_ENV>'
Use the service and env tags to link your database telemetry to other telemetry through a common tagging scheme. See Unified Service Tagging on how these tags are used throughout Datadog.
Once all Agent configuration is complete, restart the Datadog Agent.
Run the Agent’s status subcommand and look for sqlserver under the Checks section. Navigate to the Databases 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.
Note: The Agent must have read permission on the Docker socket for Autodiscovery of labels to work.
Replace the values to match your account and environment. See the sample conf file for all available configuration options.
export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
export DD_AGENT_VERSION=7.35.0
docker run -e "DD_API_KEY=${DD_API_KEY}" \
-v /var/run/docker.sock:/var/run/docker.sock:ro \
-l com.datadoghq.ad.check_names='["sqlserver"]' \
-l com.datadoghq.ad.init_configs='[{}]' \
-l com.datadoghq.ad.instances='[{
"dbm": true,
"host": "<HOSTNAME>",
"port": <SQL_PORT>,
"connector": "odbc",
"driver": "FreeTDS",
"username": "datadog",
"password": "<PASSWORD>",
"tags": [
"service:<CUSTOM_SERVICE>"
"env:<CUSTOM_ENV>"
]
}]' \
datadoghq/agent:${DD_AGENT_VERSION}
Use the service and env tags to link your database telemetry to other telemetry through a common tagging scheme. See Unified Service Tagging on how these tags are used throughout Datadog.
Run the Agent’s status subcommand and look for sqlserver under the Checks section. Alternatively, navigate to the Databases 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 "clusterAgent.confd.sqlserver\.yaml=cluster_check: true
init_config:
instances:
- dbm: true
host: <HOSTNAME>
port: 1433
username: datadog
password: '<PASSWORD>'
connector: 'odbc'
driver: 'FreeTDS'\
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/sqlserver.yaml:
cluster_check: true # Make sure to include this flag
init_config:
instances:
- dbm: true
host: '<HOSTNAME>'
port: <SQL_PORT>
username: datadog
password: '<PASSWORD>'
connector: "odbc"
driver: "FreeTDS"
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: sqlserver-datadog-check-instances
annotations:
ad.datadoghq.com/service.check_names: '["sqlserver"]'
ad.datadoghq.com/service.init_configs: '[{}]'
ad.datadoghq.com/service.instances: |
[
{
"dbm": true,
"host": "<HOSTNAME>",
"port": <SQL_PORT>,
"username": "datadog",
"password": "<PASSWORD>",
"connector": "odbc",
"driver": "FreeTDS"
}
]
spec:
ports:
- port: 1433
protocol: TCP
targetPort: 1433
name: sqlserver
The Cluster Agent automatically registers this configuration and begins running the SQL Server check.
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.
To collect more comprehensive database metrics and logs from AWS, install the RDS integration.
Additional helpful documentation, links, and articles:
