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:

1. Configure database parameters
2. Grant the Agent access to the database
3. Install the Agent
4. 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 Google Cloud 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:

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 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 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@'%';

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.

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 Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
    gcp:
      project_id: '<PROJECT_ID>'
      instance_id: '<INSTANCE_ID>'

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}

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>"}}]'

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

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 Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
    gcp:
      project_id: '<PROJECT_ID>'
      instance_id: '<INSTANCE_ID>'

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

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

Install the Cloud SQL integration

To collect more comprehensive database metrics from Google Cloud, install the Cloud SQL integration (optional).

Troubleshooting

If you have installed and configured the integrations and Agent as described and it is not working as expected, see Troubleshooting

Further reading

Additional helpful documentation, links, and articles:

Basic MySQL IntegrationDOCUMENTATION