Database Monitoring provides deep visibility into your Microsoft SQL Server databases by exposing query metrics, query samples, explain plans, database states, failovers, and events.

Complete the following steps to enable Database Monitoring with your database:

1. Grant the Agent access to the database
2. Install the Agent
3. Install the Cloud SQL integration

Before you begin

Supported SQL Server versions

2014, 2016, 2017, 2019, 2022

Supported Agent versions

7.41.0+

Performance impact

The default Agent configuration for Database Monitoring is conservative, but you can adjust settings such as the collection interval and query 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.

Database Monitoring runs as an integration on top of the base Agent (see benchmarks).

Proxies, load balancers, and connection poolers

The Datadog Agent must connect directly to the host being monitored. The Agent should not connect to the database through a proxy, load balancer, or connection pooler. If the Agent connects to different hosts 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.

Grant the Agent access

The Datadog Agent requires read-only access to the database server to collect statistics and queries.

Create a datadog user on the Cloud SQL instance.

To maintain read-only access for the agent, remove the datadog user from the default CustomerDbRootRole. Instead, grant only the explicit permissions required by the agent.

GRANT VIEW SERVER STATE to datadog as CustomerDbRootRole;
GRANT VIEW ANY DEFINITION to datadog as CustomerDbRootRole;
ALTER SERVER ROLE CustomerDbRootRole DROP member datadog;

Create the datadog user in each additional application database:

USE [database_name];
CREATE USER datadog FOR LOGIN datadog;

This is required because Google Cloud SQL does not permit granting CONNECT ANY DATABASE. The Datadog Agent needs to connect to each database to collect database-specific file I/O statistics.

Install the Agent

Google Cloud does not grant direct host access, meaning 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.

init_config:
instances:
  - dbm: true
    host: '<HOSTNAME>,<SQL_PORT>'
    username: datadog
    password: '<PASSWORD>'
    connector: adodbapi
    provider: MSOLEDBSQL
    tags:  # Optional
      - 'service:<CUSTOM_SERVICE>'
      - 'env:<CUSTOM_ENV>'
    # After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
    gcp:
      project_id: '<PROJECT_ID>'
      instance_id: '<INSTANCE_ID>'

connector: adodbapi
adoprovider: MSOLEDBSQL19  # Replace with MSOLEDBSQL for versions 18 and lower

connector: odbc
driver: '{ODBC Driver 18 for SQL Server}'

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>'
    # After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
    gcp:
      project_id: '<PROJECT_ID>'
      instance_id: '<INSTANCE_ID>'

export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
export DD_AGENT_VERSION=7.51.0

docker run -e "DD_API_KEY=${DD_API_KEY}" \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -l com.datadoghq.ad.check_names='["sqlserver"]' \
  -l com.datadoghq.ad.init_configs='[{}]' \
  -l com.datadoghq.ad.instances='[{
    "dbm": true,
    "host": "<HOSTNAME>",
    "port": <SQL_PORT>,
    "connector": "odbc",
    "driver": "ODBC Driver 18 for SQL Server",
    "username": "datadog",
    "password": "<PASSWORD>",
    "tags": [
      "service:<CUSTOM_SERVICE>"
      "env:<CUSTOM_ENV>"
    ],
    "gcp": {
      "project_id": "<PROJECT_ID>",
      "instance_id": "<INSTANCE_ID>"
    }
  }]' \
  gcr.io/datadoghq/agent:${DD_AGENT_VERSION}

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: "ODBC Driver 18 for SQL Server"
    tags:  # Optional
      - "service:<CUSTOM_SERVICE>"
      - "env:<CUSTOM_ENV>"
    gcp:
      project_id: "<PROJECT_ID>"
      instance_id: "<INSTANCE_ID>"' \
  datadog/datadog

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: "ODBC Driver 18 for SQL Server"
    tags:  # Optional
      - 'service:<CUSTOM_SERVICE>'
      - 'env:<CUSTOM_ENV>'
    # After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
    gcp:
      project_id: '<PROJECT_ID>'
      instance_id: '<INSTANCE_ID>'

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": "ODBC Driver 18 for SQL Server",
          "tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],  # Optional
          "gcp": {
            "project_id": "<PROJECT_ID>",
            "instance_id": "<INSTANCE_ID>"
          }
        }
      ]      
spec:
  ports:
  - port: 1433
    protocol: TCP
    targetPort: 1433
    name: sqlserver

Example Agent Configurations

Connecting with DSN using the ODBC driver on Linux

1. Locate the odbc.ini and odbcinst.ini files. By default, these are placed in the /etc directory when installing ODBC.

2. Copy the odbc.ini and odbcinst.ini files into the /opt/datadog-agent/embedded/etc folder.

3. Configure your DSN settings as follows:

   odbcinst.ini must provide at least one section header and ODBC driver location.

   Example:

   [ODBC Driver 18 for SQL Server]
   Description=Microsoft ODBC Driver 18 for SQL Server
   Driver=/opt/microsoft/msodbcsql18/lib64/libmsodbcsql-18.3.so.2.1
   UsageCount=1
   

   odbc.ini must provide a section header and a Driver path that matches odbcinst.ini.

   Example:

   [datadog]
   Driver=/opt/microsoft/msodbcsql18/lib64/libmsodbcsql-18.3.so.2.1
   

4. Update the /etc/datadog-agent/conf.d/sqlserver.d/conf.yaml file with your DSN information.

   Example:

   instances:
     - dbm: true
       host: 'localhost,1433'
       username: datadog
       password: '<PASSWORD>'
       connector: 'odbc'
       driver: '{ODBC Driver 18 for SQL Server}' # This is the section header of odbcinst.ini
       dsn: 'datadog' # This is the section header of odbc.ini
   

5. Restart the Agent.

Using AlwaysOn

Note: For AlwaysOn users, the Agent must be installed on a separate server and connected to the cluster through the listener endpoint. This is because information about Availability Group (AG) secondary replicas is collected from the primary replica. Additionally, installing the Agent in this way helps to keep it up and running in the event of a failover.

instances:
  - dbm: true
    host: 'shopist-prod,1433'
    username: datadog
    password: '<PASSWORD>'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    include_ao_metrics: true  # If Availability Groups is enabled
    include_fci_metrics: true   # If Failover Clustering is enabled

One Agent connecting to multiple hosts

It is common to configure a single Agent host to connect to multiple remote database instances (see Agent installation architectures for DBM). To connect to multiple hosts, create an entry for each host in the SQL Server integration config. In these cases, Datadog recommends limiting the number of instances per Agent to a maximum of 10 database instances to guarantee reliable performance.

init_config:
instances:
  - dbm: true
    host: 'example-service-primary.example-host.com,1433'
    username: datadog
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    password: '<PASSWORD>'
    tags:
      - 'env:prod'
      - 'team:team-discovery'
      - 'service:example-service'
  - dbm: true
    host: 'example-service–replica-1.example-host.com,1433'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    username: datadog
    password: '<PASSWORD>'
    tags:
      - 'env:prod'
      - 'team:team-discovery'
      - 'service:example-service'
  - dbm: true
    host: 'example-service–replica-2.example-host.com,1433'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    username: datadog
    password: '<PASSWORD>'
    tags:
      - 'env:prod'
      - 'team:team-discovery'
      - 'service:example-service'
    [...]

Storing passwords securely

While it is possible to declare passwords directly in the Agent configuration files, it is a more secure practice to encrypt and store database credentials elsewhere using secret management software such as Vault. The Agent is able to read these credentials using the ENC[] syntax. Review the secrets management documentation for the required setup to store these credentials. The following example shows how to declare and use those credentials:

init_config:
instances:
  - dbm: true
    host: 'localhost,1433'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    username: datadog
    password: 'ENC[datadog_user_database_password]'

Running custom queries

To collect custom metrics, use the custom_queries option. See the sample sqlserver.d/conf.yaml for more details.

init_config:
instances:
  - dbm: true
    host: 'localhost,1433'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    username: datadog
    password: '<PASSWORD>'
    custom_queries:
    - query: SELECT age, salary, hours_worked, name FROM hr.employees;
      columns:
        - name: custom.employee_age
          type: gauge
        - name: custom.employee_salary
           type: gauge
        - name: custom.employee_hours
           type: count
        - name: name
           type: tag
      tags:
        - 'table:employees'

Working with hosts through a remote proxy

If the Agent must connect to a database host through a remote proxy, all telemetry is tagged with the hostname of the proxy rather than the database instance. Use the reported_hostname option to set a custom override of the hostname detected by the Agent.

init_config:
instances:
  - dbm: true
    host: 'localhost,1433'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    username: datadog
    password: '<PASSWORD>'
    reported_hostname: products-primary
  - dbm: true
    host: 'localhost,1433'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    username: datadog
    password: '<PASSWORD>'
    reported_hostname: products-replica-1

Discovering ports automatically

SQL Server Browser Service, Named Instances, and other services can automatically detect port numbers. You can use this instead of hardcoding port numbers in connection strings. To use the Agent with one of these services, set the port field to 0.

For example, a Named Instance config:

init_config:
instances:
  - host: <hostname\instance name>
    port: 0

Install the Google Cloud SQL integration

To collect more comprehensive database metrics from Google Cloud SQL, install the Google Cloud SQL integration.

Further reading

Documentation, liens et articles supplémentaires utiles:

SQL Server IntegrationDOCUMENTATION