This check lets you track the performance of your SQL Server instances. It collects metrics for number of user connections, rate of SQL compilations, and more.
You can also create your own metrics by having the check run custom queries.
Follow the instructions below to install and configure this check for an Agent running on a host. For containerized environments, see the Autodiscovery Integration Templates for guidance on applying these instructions.
The SQL Server check is included in the Datadog Agent package, so you don’t need to install anything else on your SQL Server instances.
Make sure that your SQL Server instance supports SQL Server authentication by enabling “SQL Server and Windows Authentication mode” in the server properties. Server Properties -> Security -> SQL Server and Windows Authentication mode
Create a read-only login to connect to your server:
CREATE LOGIN datadog WITH PASSWORD = '<PASSWORD>'; CREATE USER datadog FOR LOGIN datadog; GRANT SELECT on sys.dm_os_performance_counters to datadog; GRANT VIEW SERVER STATE to datadog;
init_config: instances: - host: "<SQL_HOST>,<SQL_PORT>" username: datadog password: "<YOUR_PASSWORD>" connector: odbc # alternative is 'adodbapi' driver: SQL Server
See the example check configuration for a comprehensive description of all options, including how to use custom queries to create your own metrics.
Note: The (default) provider
SQLOLEDB is being deprecated. To use the newer
MSOLEDBSQL provider, set the
adoprovider variable to
MSOLEDBSQL in your
sqlserver.d/conf.yaml file after having downloaded the new provider from Microsoft. It is also possible to use the Windows Authentication and not specify the username/password with:
Restart the Agent to start sending SQL Server metrics to Datadog.
Available for Agent versions >6.0
Collecting logs is disabled by default in the Datadog Agent, enable it in your
Add this configuration block to your
sqlserver.d/conf.yaml file to start collecting your SQL Server logs:
logs: - type: file path: "<LOG_FILE_PATH>" source: sqlserver service: "<SERVICE_NAME>"
service parameter values based on your environment. See the sample sqlserver.d/conf.yaml for all available configuration options.
See Datadog’s documentation for additional information on how to configure the Agent for log collection in Kubernetes environments.
Extra configuration steps are required to get the SQL Server integration running on a Linux host:
odbcinst.inifiles into the
conf.yamlfile to use the
odbcconnector and specify the proper driver as specified in the
Run the Agent’s
status subcommand and look for
sqlserver under the Checks section.
|The ratio of data pages found and read from the buffer cache over all data page requests.|
Shown as fraction
|Duration that a page resides in the buffer pool.|
Shown as second
|The number of batch requests per second.|
Shown as request
|The number of SQL compilations per second.|
Shown as operation
|The number of SQL re-compilations per second.|
Shown as operation
|The number of user connections.|
Shown as connection
|The number of times per second that SQL Server is unable to retain a lock right away for a resource.|
Shown as lock
|The number of page splits per second.|
Shown as operation
|The number of processes blocked.|
Shown as process
|The number of pages flushed to disk per second by a checkpoint or other operation that require all dirty pages to be flushed.|
Shown as page
Most of these metrics come from your SQL Server’s
The SQL server check does not include any events.
Returns CRITICAL if the Agent cannot connect to SQL Server to collect metrics, otherwise OK.
Need help? Contact Datadog support.
See the main documentation for more details about how to test and develop Agent based integrations.
To run the tests on Windows, an instance of MSSQL is expected to run on the host. The name of the database instance and the credentials reflect what we have on the CI environment, so that might not work out of the box on a local development environment.
On Linux, a Docker container running a MSSQL instance is automatically started before running the tests. We use unixODBC and FreeTDS to talk to the database so, depending on the Linux distribution, you need to install additional dependencies on your local dev environment before running the tests. For example these are the installation steps for Ubuntu 14.04:
sudo apt-get install unixodbc unixodbc-dev tdsodbc
brew install unixodbc freetds