データベースモニタリングは、クエリメトリクス、クエリサンプル、実行計画、データベースの状態、フェイルオーバー、イベントを公開することで、Microsoft SQL Server データベースを詳細に可視化します。

データベースでデータベースモニタリングを有効にするには、以下の手順を実行します。

1. Agent にデータベースへのアクセスを付与する
2. Agent のインストールと構成
3. Azure インテグレーションをインストールする

はじめに

サポートされている SQL Server バージョン

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.

Agent にアクセスを付与する

Datadog Agent が統計やクエリを収集するためには、データベースサーバーへの読み取り専用のアクセスが必要となります。

CREATE LOGIN datadog WITH PASSWORD = '<PASSWORD>';
CREATE USER datadog FOR LOGIN datadog;
ALTER SERVER ROLE ##MS_ServerStateReader## ADD MEMBER datadog;
ALTER SERVER ROLE ##MS_DefinitionReader## ADD MEMBER datadog;
-- Log Shipping Monitoring (Agent v7.50 以降で利用可能) を使用するには、次の 3 行のコメントを外します。
-- USE msdb;
-- CREATE USER datadog FOR LOGIN datadog;
-- GRANT SELECT to datadog;

CREATE USER datadog FOR LOGIN datadog;

init_config:
instances:
  - host: '<SERVER_NAME>.database.windows.net,1433'
    database: '<DATABASE_1>'
    username: datadog
    password: '<PASSWORD>'
    # プロジェクトとインスタンスを追加した後、CPU、メモリなどの追加のクラウドデータをプルするために Datadog Azure インテグレーションを構成します。
    azure:
      deployment_type: 'sql_database'
      fully_qualified_domain_name: '<SERVER_NAME>.database.windows.net'

  - host: '<SERVER_NAME>.database.windows.net,1433'
    database: '<DATABASE_2>'
    username: datadog
    password: '<PASSWORD>'
    # プロジェクトとインスタンスを追加した後、CPU、メモリなどの追加のクラウドデータをプルするために Datadog Azure インテグレーションを構成します。
    azure:
      deployment_type: 'sql_database'
      fully_qualified_domain_name: '<SERVER_NAME>.database.windows.net'

CREATE LOGIN datadog WITH PASSWORD = '<PASSWORD>';
CREATE USER datadog FOR LOGIN datadog;
GRANT CONNECT ANY DATABASE to datadog;
GRANT VIEW SERVER STATE to datadog;
GRANT VIEW ANY DEFINITION to datadog;
-- Log Shipping Monitoring (Agent v7.50 以降で利用可能) を使用するには、次の 3 行のコメントを外します。
-- USE msdb;
-- CREATE USER datadog FOR LOGIN datadog;
-- GRANT SELECT to datadog;

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.

Agent のインストールと構成

Azure はホストへの直接アクセスを許可しないため、Datadog Agent は SQL Server ホストと通信可能な別のホストにインストールする必要があります。Agent のインストールと実行には、いくつかのオプションがあります。

init_config:
instances:
  - dbm: true
    host: '<HOSTNAME>,<SQL_PORT>'
    username: datadog
    password: 'ENC[datadog_user_database_password]'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    tags:  # Optional
      - 'service:<CUSTOM_SERVICE>'
      - 'env:<CUSTOM_ENV>'
    # After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
    azure:
      deployment_type: '<DEPLOYMENT_TYPE>'
      fully_qualified_domain_name: '<AZURE_INSTANCE_ENDPOINT>'

connector: adodbapi
adoprovider: MSOLEDBSQL19  # バージョン 18 以下の MSOLEDBSQL に置き換えます

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

init_config:
instances:
  - dbm: true
    host: '<HOSTNAME>,<SQL_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 project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
    azure:
      deployment_type: '<DEPLOYMENT_TYPE>'
      fully_qualified_domain_name: '<AZURE_ENDPOINT_ADDRESS>'

export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
export DD_AGENT_VERSION=7.51.0

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

cluster_check: true  # Make sure to include this flag
init_config:
instances:
  - dbm: true
    host: '<HOSTNAME>,<SQL_PORT>'
    username: datadog
    password: 'ENC[datadog_user_database_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 Azure integration to pull additional cloud data such as CPU, Memory, etc.
    azure:
      deployment_type: '<DEPLOYMENT_TYPE>'
      fully_qualified_domain_name: '<AZURE_ENDPOINT_ADDRESS>'

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>,<SQL_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>"],  # Optional
          "azure": {
            "deployment_type": "<DEPLOYMENT_TYPE>",
            "fully_qualified_domain_name": "<AZURE_ENDPOINT_ADDRESS>"
          }
        }
      ]      
spec:
  ports:
  - port: 1433
    protocol: TCP
    targetPort: 1433
    name: sqlserver

Agent の構成例

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

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: 'ENC[datadog_user_database_password]'
    connector: adodbapi
    adoprovider: MSOLEDBSQL
    include_ao_metrics: true  # If Availability Groups is enabled
    include_fci_metrics: true   # If Failover Clustering is enabled

Monitoring SQL Server Agent Jobs

Note: For monitoring SQL Server Agent jobs, the Datadog Agent must have access to the [msdb] 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. To enable this feature, use the schemas_collection option. Schemas are collected on databases for which the Agent has CONNECT access.

Note: For schema collection on RDS instances, it is necessary to grant explicit CONNECT access to the datadog user for each database on the instance. See Grant the Agent access for more details.

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:
      enabled: true
    schemas_collection:
      enabled: true
    # Optional: enable metric collection for indexes
    include_index_usage_metrics: 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
    include_index_usage_metrics: 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. 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: '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

Azure インテグレーションをインストールする

Azure からより包括的なデータベースメトリクスとログを収集するには、Azure インテグレーションをインストールします。

参考資料

お役に立つドキュメント、リンクや記事:

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