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[mq] working branch - merge 97f2b64 on top of master at b927965
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2 parents 3a17838 + 97f2b64 commit 8daa9fb

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‎content/en/database_monitoring/setup_sql_server/azure.md

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@@ -64,19 +64,23 @@ When configuring the Datadog Agent, specify one check instance for each applicat
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```yaml
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init_config:
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instances:
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- host: '<SERVER_NAME>.database.windows.net,1433'
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- host: '<SERVER_NAME>.database.windows.net,<PORT>'
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database: '<DATABASE_1>'
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username: datadog
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password: '<PASSWORD>'
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connector: 'odbc'
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driver: 'ODBC Driver 18 for SQL Server'
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# After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
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azure:
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deployment_type: 'sql_database'
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fully_qualified_domain_name: '<SERVER_NAME>.database.windows.net'
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- host: '<SERVER_NAME>.database.windows.net,1433'
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- host: '<SERVER_NAME>.database.windows.net,<PORT>'
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database: '<DATABASE_2>'
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username: datadog
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password: '<PASSWORD>'
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connector: 'odbc'
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driver: 'ODBC Driver 18 for SQL Server'
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# After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
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azure:
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deployment_type: 'sql_database'
@@ -142,7 +146,7 @@ Create the SQL Server Agent conf file `C:\ProgramData\Datadog\conf.d\sqlserver.d
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init_config:
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instances:
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- dbm: true
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host: '<HOSTNAME>,<SQL_PORT>'
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host: '<HOSTNAME>,<PORT>'
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username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: adodbapi
@@ -180,7 +184,7 @@ The recommended ODBC driver is [Microsoft ODBC Driver][8]. Starting with Agent 7
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```yaml
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connector: odbc
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driver: '{ODBC Driver 18 for SQL Server}'
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driver: 'ODBC Driver 18 for SQL Server'
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```
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Once all Agent configuration is complete, [restart the Datadog Agent][9].
@@ -215,7 +219,7 @@ Create the SQL Server Agent conf file `/etc/datadog-agent/conf.d/sqlserver.d/con
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init_config:
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instances:
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- dbm: true
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host: '<HOSTNAME>,<SQL_PORT>'
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host: '<HOSTNAME>,<PORT>'
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username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: odbc
@@ -258,15 +262,15 @@ Replace the values to match your account and environment. See the [sample conf f
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```bash
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export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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export DD_AGENT_VERSION=7.51.0
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export DD_AGENT_VERSION=<AGENT_VERSION>
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docker run -e "DD_API_KEY=${DD_API_KEY}" \
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-v /var/run/docker.sock:/var/run/docker.sock:ro \
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-l com.datadoghq.ad.check_names='["sqlserver"]' \
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-l com.datadoghq.ad.init_configs='[{}]' \
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-l com.datadoghq.ad.instances='[{
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"dbm": true,
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"host": "<HOSTNAME>,<SQL_PORT>",
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"host": "<HOSTNAME>,<PORT>",
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"connector": "odbc",
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"driver": "ODBC Driver 18 for SQL Server",
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"username": "datadog",
@@ -300,9 +304,66 @@ Use the `service` and `env` tags to link your database telemetry to other teleme
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[6]: https://app.datadoghq.com/databases
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{{% /tab %}}
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{{% tab "Kubernetes" %}}
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If you have a Kubernetes cluster, use the [Datadog Cluster Agent][1] for Database Monitoring.
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If you're running a Kubernetes cluster, use the [Datadog Cluster Agent][1] to enable Database Monitoring. If cluster checks aren’t already enabled, [follow these instructions][2] to enable them before proceeding.
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If cluster checks are not already enabled in your Kubernetes cluster, follow the instructions to [enable cluster checks][2]. You can configure the Cluster Agent either with static files mounted in the Cluster Agent container, or by using Kubernetes service annotations:
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### Operator
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Follow the steps below to set up the SQL Server integration, using the [Operator instructions in Kubernetes and Integrations][6] as a reference.
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1. Create or update the `datadog-agent.yaml` file with the following configuration:
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```yaml
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apiVersion: datadoghq.com/v2alpha1
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kind: DatadogAgent
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metadata:
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name: datadog
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spec:
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global:
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clusterName: <CLUSTER_NAME>
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site: <DD_SITE>
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credentials:
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apiSecret:
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secretName: datadog-agent-secret
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keyName: api-key
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features:
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clusterChecks:
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enabled: true
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override:
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nodeAgent:
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image:
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name: agent
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tag: <AGENT_VERSION>
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clusterAgent:
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extraConfd:
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configDataMap:
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sqlserver.yaml: |-
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cluster_check: true # Make sure to include this flag
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init_config:
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instances:
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- host: <HOSTNAME>,<PORT>
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username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: 'odbc'
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driver: 'ODBC Driver 18 for SQL Server'
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dbm: true
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# Optional: For additional tags
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tags:
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- 'service:<CUSTOM_SERVICE>'
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- 'env:<CUSTOM_ENV>'
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# After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
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azure:
358+
deployment_type: '<DEPLOYMENT_TYPE>'
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fully_qualified_domain_name: '<AZURE_ENDPOINT_ADDRESS>'
360+
```
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362+
2. Apply the changes to the Datadog Operator using the following command:
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```shell
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kubectl apply -f datadog-agent.yaml
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```
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### Helm
308369

@@ -314,19 +375,20 @@ Complete the following steps to install the [Datadog Cluster Agent][1] on your K
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clusterAgent:
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confd:
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sqlserver.yaml: |-
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cluster_check: true
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cluster_check: true # Required for cluster checks
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init_config:
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instances:
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- dbm: true
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host: <HOSTNAME>,1433
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host: <HOSTNAME>,<PORT>
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username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: 'odbc'
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driver: '{ODBC Driver 18 for SQL Server}'
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include_ao_metrics: true # Optional: For AlwaysOn users
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tags: # Optional
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driver: 'ODBC Driver 18 for SQL Server'
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# Optional: For additional tags
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tags:
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- 'service:<CUSTOM_SERVICE>'
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- 'env:<CUSTOM_ENV>'
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# After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
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azure:
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deployment_type: '<DEPLOYMENT_TYPE>'
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fully_qualified_domain_name: '<AZURE_ENDPOINT_ADDRESS>'
@@ -353,12 +415,13 @@ cluster_check: true # Make sure to include this flag
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init_config:
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instances:
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- dbm: true
356-
host: '<HOSTNAME>,<SQL_PORT>'
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host: <HOSTNAME>,<PORT>
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username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: "odbc"
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driver: '{ODBC Driver 18 for SQL Server}'
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tags: # Optional
421+
connector: 'odbc'
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driver: 'ODBC Driver 18 for SQL Server'
423+
# Optional: For additional tags
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tags:
362425
- 'service:<CUSTOM_SERVICE>'
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- 'env:<CUSTOM_ENV>'
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# After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
@@ -371,7 +434,6 @@ instances:
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372435
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:
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```yaml
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apiVersion: v1
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kind: Service
@@ -384,12 +446,12 @@ metadata:
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[
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{
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"dbm": true,
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"host": "<HOSTNAME>,<SQL_PORT>",
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"host": "<HOSTNAME>,<PORT>",
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"username": "datadog",
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"password": "ENC[datadog_user_database_password]",
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"connector": "odbc",
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"driver": "ODBC Driver 18 for SQL Server",
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"tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"], # Optional
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"tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],
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"azure": {
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"deployment_type": "<DEPLOYMENT_TYPE>",
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"fully_qualified_domain_name": "<AZURE_ENDPOINT_ADDRESS>"
@@ -416,6 +478,7 @@ To avoid exposing the `datadog` user's password in plain text, use the Agent's [
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[3]: /containers/kubernetes/installation/?tab=helm#installation
417479
[4]: https://github.com/DataDog/integrations-core/blob/master/sqlserver/assets/configuration/spec.yaml#L353-L383
418480
[5]: /agent/configuration/secrets-management
481+
[6]: /containers/kubernetes/integrations/?tab=datadogoperator
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{{% /tab %}}
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{{< /tabs >}}
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‎content/en/database_monitoring/setup_sql_server/gcsql.md

Lines changed: 77 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -61,7 +61,7 @@ Create the SQL Server Agent conf file `C:\ProgramData\Datadog\conf.d\sqlserver.d
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init_config:
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instances:
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- dbm: true
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host: '<HOSTNAME>,<SQL_PORT>'
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host: '<HOSTNAME>,<PORT>'
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username: datadog
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password: '<PASSWORD>'
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connector: adodbapi
@@ -102,7 +102,7 @@ The recommended ODBC driver is [Microsoft ODBC Driver][8]. Starting with Agent 7
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```yaml
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connector: odbc
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driver: '{ODBC Driver 18 for SQL Server}'
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driver: 'ODBC Driver 18 for SQL Server'
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```
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Once all Agent configuration is complete, [restart the Datadog Agent][9].
@@ -137,7 +137,7 @@ Create the SQL Server Agent conf file `/etc/datadog-agent/conf.d/sqlserver.d/con
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init_config:
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instances:
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- dbm: true
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host: '<HOSTNAME>,<SQL_PORT>'
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host: '<HOSTNAME>,<PORT>'
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username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: odbc
@@ -180,7 +180,7 @@ Replace the values to match your account and environment. See the [sample conf f
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```bash
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export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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export DD_AGENT_VERSION=7.51.0
183+
export DD_AGENT_VERSION=<AGENT_VERSION>
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docker run -e "DD_API_KEY=${DD_API_KEY}" \
186186
-v /var/run/docker.sock:/var/run/docker.sock:ro \
@@ -222,9 +222,66 @@ Use the `service` and `env` tags to link your database telemetry to other teleme
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[6]: https://app.datadoghq.com/databases
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{{% /tab %}}
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{{% tab "Kubernetes" %}}
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If you have a Kubernetes cluster, use the [Datadog Cluster Agent][1] for Database Monitoring.
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If you're using a Kubernetes cluster, use the [Datadog Cluster Agent][1] for Database Monitoring. If cluster checks aren’t already enabled, [follow these instructions][2] to enable them before proceeding..
226226

227-
If cluster checks are not already enabled in your Kubernetes cluster, follow the instructions to [enable cluster checks][2]. You can configure the Cluster Agent either with static files mounted in the Cluster Agent container, or by using Kubernetes service annotations:
227+
### Operator
228+
229+
Follow the steps below to set up the SQL Server integration, using the [Operator instructions in Kubernetes and Integrations][6] as a reference.
230+
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1. Create or update the `datadog-agent.yaml` file with the following configuration:
232+
233+
```yaml
234+
apiVersion: datadoghq.com/v2alpha1
235+
kind: DatadogAgent
236+
metadata:
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name: datadog
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spec:
239+
global:
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clusterName: <CLUSTER_NAME>
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site: <DD_SITE>
242+
credentials:
243+
apiSecret:
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secretName: datadog-agent-secret
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keyName: api-key
246+
247+
features:
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clusterChecks:
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enabled: true
250+
251+
override:
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nodeAgent:
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image:
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name: agent
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tag: <AGENT_VERSION>
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257+
clusterAgent:
258+
extraConfd:
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configDataMap:
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sqlserver.yaml: |-
261+
cluster_check: true # Required for cluster checks
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init_config:
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instances:
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- host: <HOSTNAME>,<PORT>
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username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: 'odbc'
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driver: 'ODBC Driver 18 for SQL Server'
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dbm: true
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# Optional: For additional tags
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tags:
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- 'service:<CUSTOM_SERVICE>'
273+
- 'env:<CUSTOM_ENV>'
274+
# After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
275+
gcp:
276+
project_id: '<PROJECT_ID>'
277+
instance_id: '<INSTANCE_ID>'
278+
```
279+
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2. Apply the changes to the Datadog Operator using the following command:
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```shell
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kubectl apply -f datadog-agent.yaml
284+
```
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229286
### Helm
230287

@@ -240,15 +297,16 @@ Complete the following steps to install the [Datadog Cluster Agent][1] on your K
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init_config:
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instances:
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- dbm: true
243-
host: <HOSTNAME>
244-
port: 1433
300+
host: <HOSTNAME>,<PORT>
245301
username: datadog
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password: 'ENC[datadog_user_database_password]'
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connector: 'odbc'
248-
driver: '{ODBC Driver 18 for SQL Server}'
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tags: # Optional
304+
driver: 'ODBC Driver 18 for SQL Server'
305+
# Optional: For additional tags
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tags:
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- 'service:<CUSTOM_SERVICE>'
251308
- 'env:<CUSTOM_ENV>'
309+
# After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
252310
gcp:
253311
project_id: '<PROJECT_ID>'
254312
instance_id: '<INSTANCE_ID>'
@@ -266,10 +324,6 @@ Complete the following steps to install the [Datadog Cluster Agent][1] on your K
266324
For Windows, append <code>--set targetSystem=windows</code> to the <code>helm install</code> command.
267325
</div>
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[1]: https://app.datadoghq.com/organization-settings/api-keys
270-
[2]: /getting_started/site
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[3]: /containers/kubernetes/installation/?tab=helm#installation
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### Configure with mounted files
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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`:
@@ -279,13 +333,13 @@ cluster_check: true # Make sure to include this flag
279333
init_config:
280334
instances:
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- dbm: true
282-
host: '<HOSTNAME>'
283-
port: <SQL_PORT>
336+
host: <HOSTNAME>,<PORT>
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username: datadog
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password: 'ENC[datadog_user_database_password]'
286-
connector: "odbc"
287-
driver: '{ODBC Driver 18 for SQL Server}'
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tags: # Optional
339+
connector: 'odbc'
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driver: 'ODBC Driver 18 for SQL Server'
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# Optional: For additional tags
342+
tags:
289343
- 'service:<CUSTOM_SERVICE>'
290344
- 'env:<CUSTOM_ENV>'
291345
# After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
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[
312366
{
313367
"dbm": true,
314-
"host": "<HOSTNAME>",
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"port": <SQL_PORT>,
368+
"host": "<HOSTNAME>,<PORT>",
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"username": "datadog",
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"password": "ENC[datadog_user_database_password]",
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"connector": "odbc",
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"driver": "ODBC Driver 18 for SQL Server",
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"tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"], # Optional
373+
"tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],
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"gcp": {
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"project_id": "<PROJECT_ID>",
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"instance_id": "<INSTANCE_ID>"
@@ -340,9 +393,10 @@ To avoid exposing the `datadog` user's password in plain text, use the Agent's [
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341394
[1]: /agent/cluster_agent
342395
[2]: /agent/cluster_agent/clusterchecks/
343-
[3]: https://helm.sh
396+
[3]: /containers/kubernetes/installation/?tab=helm#installation
344397
[4]: https://github.com/DataDog/integrations-core/blob/master/sqlserver/assets/configuration/spec.yaml#L324-L351
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[5]: /agent/configuration/secrets-management
399+
[6]: /containers/kubernetes/integrations/?tab=datadogoperator
346400
{{% /tab %}}
347401
{{< /tabs >}}
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