Setting Up Database Monitoring for SQL Server on Amazon RDS

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:

  1. Configure the AWS integration
  2. Grant the Agent access
  3. Install the Agent
  4. Install the RDS 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.

Configure the AWS integration

Enable Standard Collection in the Resource Collection section of your Amazon Web Services integration tile.

Grant the Agent access

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;
-- If not using either of Log Shipping Monitoring (available in Agent v7.50+), comment out the next line:
GRANT SELECT ON dbo.log_shipping_monitor_primary to datadog;
GRANT SELECT ON dbo.log_shipping_monitor_secondary to datadog;
-- If not using SQL Server Agent Monitoring (available in Agent v7.57+), comment out the next three lines:
GRANT SELECT ON dbo.sysjobs to datadog;
GRANT SELECT ON dbo.sysjobhistory TO datadog;
GRANT SELECT ON dbo.sysjobactivity to 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.

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 the Agent

Because 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.

    Note: For AlwaysOn users with cloud-hosted instances, the recommended approach is to install the Agent on a remote instance and connect to each individual replica. Telemetry for the relevant availability groups will be collected from each replica the agent is connected to. As a fallback method, the Agent can be installed on a separate server and connected to the cluster through the listener endpoint.

    To start collecting SQL Server telemetry, install the Datadog Agent, then create the SQL Server Agent conf file at C:\ProgramData\Datadog\conf.d\sqlserver.d\conf.yaml. Refer to the sample conf file for all available configuration options.

    init_config:
instances:
  - dbm: true
    host: '<HOSTNAME>,<PORT>'
    username: datadog
    password: 'ENC[datadog_user_database_password]'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    tags:  # Optional
      - 'service:<CUSTOM_SERVICE>'
      - 'env:<CUSTOM_ENV>'
    # After adding your instance endpoint, configure the Datadog AWS integration to pull additional cloud data such as CPU, Memory, etc.
    aws:
      instance_endpoint: '<INSTANCE_ENDPOINT>'

    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 for details on how these tags are used across Datadog.

    Supported Drivers

    Microsoft ADO

    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: MSOLEDBSQL19  # Replace with MSOLEDBSQL for versions 18 and lower

    The other two providers, SQLOLEDB and SQLNCLI, are considered deprecated by Microsoft and should not be used.

    ODBC

    The recommended ODBC driver is Microsoft ODBC Driver. Starting with Agent 7.51, ODBC Driver 18 for SQL Server is included by default in the Linux Agent. For Windows, ensure the driver is installed on the host where the Agent is running.

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

    Once all Agent configuration is complete, restart the Datadog Agent.

    Validate

    Run the Agent’s status subcommand and look for sqlserver under the Checks section. Navigate to the Databases page in Datadog to get started.

    Note: For AlwaysOn users with cloud-hosted instances, the recommended approach is to install the Agent on a remote instance and connect to each individual replica. Telemetry for the relevant availability groups will be collected from each replica the agent is connected to. As a fallback method, the Agent can be installed on a separate server and connected to the cluster through the listener endpoint.

    To start collecting SQL Server telemetry, first install the Datadog Agent.

    On Linux, you must also install an ODBC SQL Server driver, such as Microsoft ODBC driver. After installation, 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>,<PORT>'
    username: datadog
    password: 'ENC[datadog_user_database_password]'
    connector: odbc
    driver: '<Driver from the `odbcinst.ini` file>'
    tags:  # Optional
      - 'service:<CUSTOM_SERVICE>'
      - 'env:<CUSTOM_ENV>'
    # After adding your instance endpoint, configure the Datadog AWS integration to pull additional cloud data such as CPU, Memory, etc.
    aws:
      instance_endpoint: '<INSTANCE_ENDPOINT>'

    Use the service and env tags to link your database telemetry to other telemetry through a common tagging scheme. See Unified Service Tagging for details on how these tags are used across Datadog.

    Once all Agent configuration is complete, restart the Datadog Agent.

    Validate

    Run the Agent’s status subcommand and look for sqlserver under the Checks section. Navigate to the Databases page in Datadog to get started.

    Note: For AlwaysOn users with cloud-hosted instances, the recommended approach is to install the Agent on a remote instance and connect to each individual replica. Telemetry for the relevant availability groups will be collected from each replica the agent is connected to. As a fallback method, the Agent can be installed on a separate server and connected to the cluster through the listener endpoint.

    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=<AGENT_VERSION>

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

    Validate

    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.

    Note: For AlwaysOn users with cloud-hosted instances, the recommended approach is to install the Agent on a remote instance and connect to each individual replica. Telemetry for the relevant availability groups will be collected from each replica the agent is connected to. As a fallback method, the Agent can be installed on a separate server and connected to the cluster through the listener endpoint.

    If you’re running a Kubernetes cluster, use the Datadog Cluster Agent to enable Database Monitoring. If cluster checks aren’t already enabled, follow these instructions to enable them before proceeding.

    Operator

    Follow the steps below to set up the SQL Server integration, using the Operator instructions in Kubernetes and Integrations as a reference.

    1. Create or update the datadog-agent.yaml file with the following configuration:

      apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
  name: datadog
spec:
  global:
    clusterName: <CLUSTER_NAME>
    site: <DD_SITE>
    credentials:
      apiSecret:
        secretName: datadog-agent-secret
        keyName: api-key

  features:
    clusterChecks:
      enabled: true

  override:
    nodeAgent:
      image:
        name: agent
        tag: <AGENT_VERSION>

    clusterAgent:
      extraConfd:
        configDataMap:
          sqlserver.yaml: |-
            cluster_check: true # Required for cluster checks
            init_config:
            instances:
            - host: <HOSTNAME>,<PORT>
              username: datadog
              password: 'ENC[datadog_user_database_password]'
              connector: 'odbc'
              driver: 'ODBC Driver 18 for SQL Server'
              dbm: true
              # Optional: For additional tags
              tags:
                - 'service:<CUSTOM_SERVICE>'
                - 'env:<CUSTOM_ENV>'
              # After adding your instance endpoint, configure the Datadog AWS integration to pull additional cloud data such as CPU, Memory, etc.
              aws:
                instance_endpoint: <INSTANCE_ENDPOINT>

    2. Apply the changes to the Datadog Operator using the following command:

      kubectl apply -f datadog-agent.yaml

    Helm

    Complete the following steps to install the Datadog Cluster Agent on your Kubernetes cluster. Replace the values to match your account and environment.

    1. Complete the Datadog Agent installation instructions for Helm.

    2. Update your YAML configuration file (datadog-values.yaml in the Cluster Agent installation instructions) to include the following:

      clusterAgent:
  confd:
    sqlserver.yaml: |-
      cluster_check: true # Required for cluster checks
      init_config:
      instances:
      - dbm: true
        host: <HOSTNAME>,<PORT>
        username: datadog
        password: 'ENC[datadog_user_database_password]'
        connector: 'odbc'
        driver: 'ODBC Driver 18 for SQL Server'
        # Optional: For additional tags
        tags:
          - 'service:<CUSTOM_SERVICE>'
          - 'env:<CUSTOM_ENV>'
        # After adding your instance endpoint, configure the Datadog AWS integration to pull additional cloud data such as CPU, Memory, etc.
        aws:
          instance_endpoint: <INSTANCE_ENDPOINT>      

clusterChecksRunner:
  enabled: true

    3. Deploy the Agent with the above configuration file from the command line:

      helm install datadog-agent -f datadog-values.yaml datadog/datadog
    For Windows, append --set targetSystem=windows to the helm install command.

    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/sqlserver.yaml:

    cluster_check: true  # Required for cluster checks
init_config:
instances:
  - dbm: true
    host: <HOSTNAME>,<PORT>
    username: datadog
    password: 'ENC[datadog_user_database_password]'
    connector: 'odbc'
    driver: 'ODBC Driver 18 for SQL Server'
    # Optional: For additional tags
    tags:
      - 'service:<CUSTOM_SERVICE>'
      - 'env:<CUSTOM_ENV>'
    # After adding your instance endpoint, configure the Datadog AWS integration to pull additional cloud data such as CPU, Memory, etc.
    aws:
      instance_endpoint: <INSTANCE_ENDPOINT>

    Configure with Kubernetes service annotations

    Instead of 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>",
          "username": "datadog",
          "password": "ENC[datadog_user_database_password]",
          "connector": "odbc",
          "driver": "ODBC Driver 18 for SQL Server",
          "tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],
          "aws": {
            "instance_endpoint": "<INSTANCE_ENDPOINT>"
          }
        }
      ]      
spec:
  ports:
  - port: 1433
    protocol: TCP
    targetPort: 1433
    name: sqlserver

    See the SQL Server integration spec for additional information on setting deployment_type and name fields.

    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.

    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: 'ENC[datadog_user_database_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

    For AlwaysOn users, the Agent should be installed on each replica server and connected directly to each replica. The full set of AlwaysOn telemetry is collected from each individual replica, in addition to host-based telemetry (CPU, disk, memory, and so on) for each server.

    instances:
  - dbm: true
    host: 'shopist-prod,1433'
    username: datadog
    password: 'ENC[datadog_user_database_password]'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    database_metrics:
      # If Availability Groups is enabled
      ao_metrics:
        enabled: true
      # If Failover Clustering is enabled
      fci_metrics:
        enabled: true

    Monitoring SQL Server Agent Jobs

    To enable monitoring of SQL Server Agent jobs, the Datadog Agent must have access to the [msdb] database.
    SQL Server Agent Jobs monitoring is not available for Azure SQL Database.

    Monitoring of SQL Server Agent jobs is supported on SQL Server versions 2016 and newer. Starting from Agent v7.57, the Datadog Agent can collect SQL Server Agent job metrics and histories. To enable this feature, set enabled to true in the agent_jobs section of the SQL Server integration configuration file. The collection_interval and history_row_limit fields are optional.

    instances:
  - dbm: true
    host: 'shopist-prod,1433'
    username: datadog
    password: '<PASSWORD>'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    agent_jobs:
      enabled: true
      collection_interval: 15
      history_row_limit: 10000

    Collecting schemas

    Starting from Agent v7.56, the Datadog Agent can collect schema information from SQLServer databases running SQLServer 2017 or higher. To enable this feature, use the schemas_collection option. Schemas are collected on databases for which the Agent has CONNECT access.

    To collect schema information from RDS instances, you must grant the datadog user explicit CONNECT access to each database on the instance. For more information, see Grant the Agent access.

    Use the database_autodiscovery option to avoid specifying each logical database. See the sample sqlserver.d/conf.yaml for more details.

    init_config:
instances:
 # This instance detects every logical database automatically
  - dbm: true
        host: 'shopist-prod,1433'
    username: datadog
    password: 'ENC[datadog_user_database_password]'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    database_autodiscovery: true
    schemas_collection:
      enabled: true
    database_metrics:
      # Optional: enable metric collection for indexes
      index_usage_metrics:
        enabled: true
# This instance only collects schemas and index metrics from the `users` database
  - dbm: true
        host: 'shopist-prod,1433'
    username: datadog
    password: 'ENC[datadog_user_database_password]'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    database: users
    schemas_collection:
      enabled: true
    database_metrics:
      # Optional: enable metric collection for indexes
      index_usage_metrics:
        enabled: true

    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. An agent with 4 CPUs and 16GB of RAM can monitor up to 30 databases with the same resources each.

    init_config:
instances:
  - dbm: true
    host: 'example-service-primary.example-host.com,1433'
    username: datadog
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    password: 'ENC[datadog_user_database_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: 'ENC[datadog_user_database_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: 'ENC[datadog_user_database_password]'
    tags:
      - 'env:prod'
      - 'team:team-discovery'
      - 'service:example-service'
    [...]

    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: 'ENC[datadog_user_database_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: 'ENC[datadog_user_database_password]'
    reported_hostname: products-primary
  - dbm: true
    host: 'localhost,1433'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    username: datadog
    password: 'ENC[datadog_user_database_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 RDS integration

    To collect more comprehensive database metrics and logs from AWS, install the RDS integration.

    Further reading

    Additional helpful documentation, links, and articles:

    Basic SQL Server IntegrationDOCUMENTATION more more Troubleshoot Common IssuesDOCUMENTATION more more Configure Deadlock MonitoringDOCUMENTATION more more