Setting Up Database Monitoring for Google Cloud SQL managed 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. Do the following setup to enable Database Monitoring with your MySQL database:

Configure database parameters
Grant the Agent access to the database
Install the Agent
Install the Cloud SQL Integration

Before you begin

Supported MySQL versions: 5.6, 5.7, or 8.0+
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 IP address provided in the GCP console. 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 Database Flags and then restart the server for the settings to take effect:

Parameter	Value	Description
`performance_schema`	`on`	Required. Enables the Performance Schema.
`max_digest_length`	`4096`	Required for collection of larger queries. Increases the size of SQL digest text in `events_statements_*` tables. If left at the default value then queries longer than `1024` characters will not be collected.
`performance_schema_max_digest_length`	`4096`	Must match `max_digest_length`.

Parameter	Value	Description
`performance_schema`	`on`	Required. Enables the Performance Schema.
`max_digest_length`	`4096`	Required for collection of larger queries. Increases the size of SQL digest text in `events_statements_*` tables. If left at the default value then queries longer than `1024` characters will not be collected.
`performance_schema_max_digest_length`	`4096`	Must match `max_digest_length`.
`performance_schema_max_sql_text_length`	`4096`	Must match `max_digest_length`.

Grant the Agent access

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

The following instructions grant the Agent permission to login from any host using datadog@'%'. You can restrict the datadog user to be allowed to login only from localhost by using datadog@'localhost'. See the MySQL documentation for more info.

Create the datadog user and grant basic permissions:

CREATE USER datadog@'%' IDENTIFIED WITH mysql_native_password 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 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@'%';

Runtime setup consumers

Datadog recommends that you create the following procedure to give the Agent the ability to enable performance_schema.events_* consumers at runtime.

DELIMITER $$
CREATE PROCEDURE datadog.enable_events_statements_consumers()
    SQL SECURITY DEFINER
BEGIN
    UPDATE performance_schema.setup_consumers SET enabled='YES' WHERE name LIKE 'events_statements_%';
    UPDATE performance_schema.setup_consumers SET enabled='YES' WHERE name = 'events_waits_current';
END $$
DELIMITER ;
GRANT EXECUTE ON PROCEDURE datadog.enable_events_statements_consumers TO datadog@'%';

Verify

Verify the user was created successfully using the following commands, replacing <UNIQUEPASSWORD> with the password you created above:

mysql -u datadog --password=<UNIQUEPASSWORD> -e "show status" | \
grep Uptime && echo -e "\033[0;32mMySQL user - OK\033[0m" || \
echo -e "\033[0;31mCannot connect to MySQL\033[0m"

mysql -u datadog --password=<UNIQUEPASSWORD> -e "show slave status" && \
echo -e "\033[0;32mMySQL grant - OK\033[0m" || \
echo -e "\033[0;31mMissing REPLICATION CLIENT grant\033[0m"

Install the Agent

To monitor Cloud SQL hosts, install the Datadog Agent in your infrastructure and configure it to connect to each instance 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 GCE instance for the Agent to collect from a Google Cloud SQL database:

Edit the mysql.d/conf.yaml file, in the conf.d/ folder at the root of your Agent’s configuration directory. 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: '<INSTANCE_ADDRESS>'
    port: 3306
    username: datadog
    password: '<UNIQUEPASSWORD>' # from the CREATE USER step earlier

    # After adding your project and instance, configure the Datadog GCP integration to pull additional cloud data such as CPU, Memory, etc.
    gcp:
      project_id: '<PROJECT_ID>'
      instance_id: '<INSTANCE_ID>'

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

See the MySQL integration spec for additional information on setting project_id and instance_id fields.

Restart the Agent to start sending MySQL metrics to Datadog.

To configure the Database Monitoring Agent running in a Docker container such as in Google Cloud Run, 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

Get up and running quickly by executing 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=${DD_API_KEY}" \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -l com.datadoghq.ad.check_names='["mysql"]' \
  -l com.datadoghq.ad.init_configs='[{}]' \
  -l com.datadoghq.ad.instances='[{
    "dbm": true,
    "host": "<INSTANCE_ADDRESS>",
    "port": 3306,
    "username": "datadog",
    "password": "<UNIQUEPASSWORD>",
    "gcp": {
      "project_id": "<PROJECT_ID>",
      "instance_id": "<INSTANCE_ID>"
    }
  }]' \
  gcr.io/datadoghq/agent:${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 gcr.io/datadoghq/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": "<INSTANCE_ADDRESS>", "port": 5432,"username": "datadog","password": "<UNIQUEPASSWORD>", "gcp": {"project_id": "<PROJECT_ID>", "instance_id": "<INSTANCE_ID>"}}]'

See the MySQL integration spec for additional information on setting project_id and instance_id 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 either with static files mounted in the Cluster Agent container or 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>"
    gcp:
      project_id: "<PROJECT_ID>"
      instance_id: "<INSTANCE_ID>" \
  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: '<INSTANCE_ADDRESS>'
    port: 3306
    username: datadog
    password: '<UNIQUEPASSWORD>'
    # After adding your project and instance, configure the Datadog GCP integration to pull additional cloud data such as CPU, Memory, etc.
    gcp:
      project_id: '<PROJECT_ID>'
      instance_id: '<INSTANCE_ID>'

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": "<INSTANCE_ADDRESS>",
          "port": 3306,
          "username": "datadog",
          "password": "<UNIQUEPASSWORD>",
          "gcp": {
            "project_id": "<PROJECT_ID>",
            "instance_id": "<INSTANCE_ID>"
          }
        }
      ]      
spec:
  ports:
  - port: 3306
    protocol: TCP
    targetPort: 3306
    name: mysql

See the MySQL integration spec for additional information on setting project_id and instance_id fields.

The Cluster Agent automatically registers this configuration and begin 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.