Setting Up Database Monitoring for Azure Database for MySQL

Docs > Database Monitoring > Setting up MySQL > Setting Up Database Monitoring for Azure Database for MySQL

Database Monitoring provides deep visibility into your MySQL databases by exposing query metrics, query samples, explain plans, connection data, system metrics, and telemetry for the InnoDB storage engine.

The Agent collects telemetry directly from the database by logging in as a read-only user. Complete the following steps to enable Database Monitoring with your MySQL database:

Configure database parameters
Grant the Agent access to the database
Install the Agent
Install the Azure MySQL integration

Before you begin

Supported MySQL versions: 5.7, or 8.0+
Supported Azure MySQL deployment types: MySQL on Azure VMs, Single Server, Flexible Server
Supported Agent versions: 7.36.1+
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 Agent must connect directly to the host being monitored, preferably through the instance endpoint. The Agent should not connect to the database through a proxy, load balancer, or connection pooler. While this can be an anti-pattern for client applications, each Agent must have knowledge of the underlying hostname and should stick to a single host for its lifetime, even in cases of failover. If the Datadog Agent connects to different hosts while it is running, the values of metrics will be incorrect.
Data security considerations: See Sensitive information for information about what data the Agent collects from your databases and how to ensure it is secure.

Configure MySQL settings

Configure the following in the server parameters and then restart the server for the settings to take effect:

Parameter	Value	Description
`performance_schema`	`ON`	Required. Enables the Performance Schema.

The agent also requires performance_schema.events_statements_* consumers to be set to ON to collect currently running queries. By default, Azure MySQL Database enables performance schema consumers so no additional configuration is required.

Grant the Agent access

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

Create the datadog user and grant basic permissions:

CREATE USER datadog@'%' IDENTIFIED by '<UNIQUEPASSWORD>';
ALTER USER datadog@'%' WITH MAX_USER_CONNECTIONS 5;
GRANT REPLICATION CLIENT ON *.* TO datadog@'%';
GRANT PROCESS ON *.* TO datadog@'%';
GRANT SELECT ON performance_schema.* TO datadog@'%';

Create the datadog user and grant basic permissions:

CREATE USER datadog@'%' IDENTIFIED BY '<UNIQUEPASSWORD>';
GRANT REPLICATION CLIENT ON *.* TO datadog@'%' WITH MAX_USER_CONNECTIONS 5;
GRANT PROCESS ON *.* TO datadog@'%';
GRANT SELECT ON performance_schema.* TO datadog@'%';

Create the following schema:

CREATE SCHEMA IF NOT EXISTS datadog;
GRANT EXECUTE ON datadog.* to datadog@'%';
GRANT CREATE TEMPORARY TABLES ON datadog.* TO datadog@'%';

Create the explain_statement procedure to enable the Agent to collect explain plans:

DELIMITER $$
CREATE PROCEDURE datadog.explain_statement(IN query TEXT)
    SQL SECURITY DEFINER
BEGIN
    SET @explain := CONCAT('EXPLAIN FORMAT=json ', query);
    PREPARE stmt FROM @explain;
    EXECUTE stmt;
    DEALLOCATE PREPARE stmt;
END $$
DELIMITER ;

Additionally, create this procedure in every schema from which you want to collect explain plans. Replace <YOUR_SCHEMA> with your database schema:

DELIMITER $$
CREATE PROCEDURE <YOUR_SCHEMA>.explain_statement(IN query TEXT)
    SQL SECURITY DEFINER
BEGIN
    SET @explain := CONCAT('EXPLAIN FORMAT=json ', query);
    PREPARE stmt FROM @explain;
    EXECUTE stmt;
    DEALLOCATE PREPARE stmt;
END $$
DELIMITER ;
GRANT EXECUTE ON PROCEDURE <YOUR_SCHEMA>.explain_statement TO datadog@'%';

Install the Agent

To monitor Azure hosts, install the Datadog Agent in your infrastructure and configure it to connect to each instance endpoint remotely. The Agent does not need to run on the database, it only needs to connect to it. For additional Agent installation methods not mentioned here, see the Agent installation instructions.

To configure this check for an Agent running on a host, for example when you provision a small virtual machine for the Agent to collect from the database:

Edit the mysql.d/conf.yaml file, in the conf.d/ folder at the root of your Agent’s configuration directory to start collecting your MySQL metrics. See the sample mysql.d/conf.yaml for all available configuration options, including those for custom metrics.

Add this configuration block to your mysql.d/conf.yaml to collect MySQL metrics:

init_config:

instances:
  - dbm: true
    host: '<AZURE_INSTANCE_ENDPOINT>'
    port: 3306
    username: datadog
    password: '<YOUR_CHOSEN_PASSWORD>' # from the CREATE USER step earlier

    # After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU and Memory.
    azure:
      deployment_type: '<DEPLOYMENT_TYPE>'
      fully_qualified_domain_name: '<AZURE_INSTANCE_ENDPOINT>'

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

Note: Wrap your password in single quotes in case a special character is present.

Restart the Agent to start sending MySQL metrics to Datadog.

To configure the Database Monitoring Agent running in a Docker container, you can 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.

Command line

Execute the following command to run the agent from your command line. Replace the values to match your account and environment:

export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
export DD_AGENT_VERSION=7.36.1

docker run -e "DD_API_KEY=${DD_API_KEY}" \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -l com.datadoghq.ad.check_names='["mysql"]' \
  -l com.datadoghq.ad.init_configs='[{}]' \
  -l com.datadoghq.ad.instances='[{
    "dbm": true,
    "host": "<AZURE_INSTANCE_ENDPOINT>",
    "port": 3306,
    "username": "datadog",
    "password": "<UNIQUEPASSWORD>",
    "azure": {
      "deployment_type": "<DEPLOYMENT_TYPE>",
      "fully_qualified_domain_name": "<AZURE_INSTANCE_ENDPOINT>"
    }
  }]' \
  gcr.io/datadoghq/agent:${DD_AGENT_VERSION}

Dockerfile

Labels can also be specified in a Dockerfile, so you can build and deploy a custom agent without changing any infrastructure configuration:

FROM datadog/agent:7.36.1

LABEL "com.datadoghq.ad.check_names"='["mysql"]'
LABEL "com.datadoghq.ad.init_configs"='[{}]'
LABEL "com.datadoghq.ad.instances"='[{"dbm": true, "host": "<AZURE_INSTANCE_ENDPOINT>", "port": 3306,"username": "datadog","password": "<UNIQUEPASSWORD>", "azure": {"deployment_type": "<DEPLOYMENT_TYPE>", "fully_qualified_domain_name": "<AZURE_INSTANCE_ENDPOINT>"}}]'

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

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, or see the Autodiscovery template variables documentation on how to pass in the password as an environment variable.

If you have a Kubernetes cluster, use the Datadog Cluster Agent for Database Monitoring.

Follow the instructions to enable the cluster checks if not already enabled in your Kubernetes cluster. You can declare the MySQL configuration with static files mounted in the Cluster Agent container, or by using service annotations:

Command line with Helm

Execute the following Helm command to install the Datadog Cluster Agent on your Kubernetes cluster. Replace the values to match your account and environment:

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.mysql\.yaml=cluster_check: true
init_config:
instances:
  - dbm: true
    host: <INSTANCE_ADDRESS>
    port: 3306
    username: datadog
    password: "<UNIQUEPASSWORD>"
    azure:
      deployment_type: "<DEPLOYMENT_TYPE>"
      fully_qualified_domain_name: "<AZURE_INSTANCE_ENDPOINT>"' \
  datadog/datadog

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/mysql.yaml:

cluster_check: true  # Make sure to include this flag
init_config:
instances:
  - dbm: true
    host: '<AZURE_INSTANCE_ENDPOINT>'
    port: 3306
    username: datadog
    password: '<UNIQUEPASSWORD>'
    # 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>'

Configure with Kubernetes service annotations

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:

apiVersion: v1
kind: Service
metadata:
  name: mysql
  labels:
    tags.datadoghq.com/env: '<ENV>'
    tags.datadoghq.com/service: '<SERVICE>'
  annotations:
    ad.datadoghq.com/service.check_names: '["mysql"]'
    ad.datadoghq.com/service.init_configs: '[{}]'
    ad.datadoghq.com/service.instances: |
      [
        {
          "dbm": true,
          "host": "<AZURE_INSTANCE_ENDPOINT>",
          "port": 3306,
          "username": "datadog",
          "password": "<UNIQUEPASSWORD>",
          "azure": {
            "deployment_type": "<DEPLOYMENT_TYPE>",
            "fully_qualified_domain_name": "<AZURE_INSTANCE_ENDPOINT>"
          }
        }
      ]      
spec:
  ports:
  - port: 3306
    protocol: TCP
    targetPort: 3306
    name: mysql

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

The Cluster Agent automatically registers this configuration and begins running the MySQL 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.

Validate

Run the Agent’s status subcommand and look for mysql under the Checks section. Or visit the Databases page to get started.

Example Agent Configurations

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 MySQL 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
    port: 3306
    username: datadog
    password: '<PASSWORD>'
    tags:
      - 'env:prod'
      - 'team:team-discovery'
      - 'service:example-service'
  - dbm: true
    host: example-service-replica-1.example-host.com
    port: 3306
    username: datadog
    password: '<PASSWORD>'
    options:
      replication: true
    tags:
      - 'env:prod'
      - 'team:team-discovery'
      - 'service:example-service'
  - dbm: true
    host: example-service-replica-2.example-host.com
    port: 3306
    username: datadog
    password: '<PASSWORD>'
    options:
      replication: true
    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
    port: 3306
    username: datadog
    password: 'ENC[datadog_user_database_password]'

Running custom queries

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

init_config:
instances:
  - dbm: true
    host: localhost
    port: 3306
    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 proxy

If the Agent must connect through a proxy such as the Cloud SQL Auth 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
    port: 5000
    username: datadog
    password: '<PASSWORD>'
    reported_hostname: example-service-primary
  - dbm: true
    host: localhost
    port: 5001
    username: datadog
    password: '<PASSWORD>'
    reported_hostname: example-service-replica-1