--- title: Setting Up Database Monitoring for Google Cloud SQL managed MySQL description: >- Install and configure Database Monitoring for MySQL managed on Google Cloud SQL. breadcrumbs: >- Docs > Database Monitoring > Setting up MySQL > Setting Up Database Monitoring for Google Cloud SQL managed MySQL --- # 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 1. Grant the Agent access to the database 1. Install and configure the Agent 1. Install the Cloud SQL Integration ## Before you begin{% #before-you-begin %} {% dl %} {% dt %} Supported MySQL versions {% /dt %} {% dd %} 5.6, 5.7, or 8.0+ {% /dd %} {% dt %} Supported Agent versions {% /dt %} {% dd %} 7.36.1+ {% /dd %} {% dt %} RAM Requirement {% /dt %} {% dd %} Datadog Database Monitoring requires at least 16 GB of RAM on the SQL instance to function properly. {% /dd %} {% dt %} Performance impact {% /dt %} {% dd %} 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](https://docs.datadoghq.com/database_monitoring/agent_integration_overhead/?tab=mysql)). {% /dd %} {% dt %} Proxies, load balancers, and connection poolers {% /dt %} {% dd %} The Datadog 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. 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. {% /dd %} {% dt %} Data security considerations {% /dt %} {% dd %} See [Sensitive information](https://docs.datadoghq.com/database_monitoring/data_collected/#sensitive-information) for information about what data the Agent collects from your databases and how to ensure it is secure. {% /dd %} {% /dl %} ## Configure MySQL settings{% #configure-mysql-settings %} Configure the following [Database Flags](https://cloud.google.com/sql/docs/mysql/flags) and then **restart the server** for the settings to take effect: {% tab title="MySQL ≥ 5.7" %} | Parameter | Value | Description | | ---------------------------------------- | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `performance_schema` | `on` | Required. Enables the [Performance Schema](https://dev.mysql.com/doc/refman/8.0/en/performance-schema-quick-start.html). | | `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`. | {% /tab %} {% tab title="MySQL 5.6" %} | Parameter | Value | Description | | -------------------------------------- | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `performance_schema` | `on` | Required. Enables the [Performance Schema](https://dev.mysql.com/doc/refman/8.0/en/performance-schema-quick-start.html). | | `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`. | {% /tab %} ## Grant the Agent access{% #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](https://dev.mysql.com/doc/refman/8.0/en/creating-accounts.html) for more info. {% tab title="MySQL ≥ 5.7" %} Create the `datadog` user and grant basic permissions: ```sql CREATE USER datadog@'%' IDENTIFIED by ''; 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@'%'; ``` {% /tab %} {% tab title="MySQL 5.6" %} Create the `datadog` user and grant basic permissions: ```sql CREATE USER datadog@'%' IDENTIFIED BY ''; GRANT REPLICATION CLIENT ON *.* TO datadog@'%' WITH MAX_USER_CONNECTIONS 5; GRANT PROCESS ON *.* TO datadog@'%'; GRANT SELECT ON performance_schema.* TO datadog@'%'; ``` {% /tab %} Create the following schema: ```sql CREATE SCHEMA IF NOT EXISTS datadog; GRANT EXECUTE ON datadog.* to datadog@'%'; ``` Create the `explain_statement` procedure to enable the Agent to collect explain plans: ```sql 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 `` with your database schema: ```sql DELIMITER $$ CREATE PROCEDURE .explain_statement(IN query TEXT) SQL SECURITY DEFINER BEGIN SET @explain := CONCAT('EXPLAIN FORMAT=json ', query); PREPARE stmt FROM @explain; EXECUTE stmt; DEALLOCATE PREPARE stmt; END $$ DELIMITER ; GRANT EXECUTE ON PROCEDURE .explain_statement TO datadog@'%'; ``` To collect index metrics, grant the `datadog` user an additional privilege: ```sql GRANT SELECT ON mysql.innodb_index_stats TO datadog@'%'; ``` Starting from Agent v7.65, the Datadog Agent can collect schema information from MySQL databases. See the [Collecting schemas](https://docs.datadoghq.com/database_monitoring/setup_mysql/gcsql?tab=mysql57#collecting-schemas) section below for more info on how to grant the Agent permissions for this collection. ### Runtime setup consumers{% #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. ```SQL 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@'%'; ``` ### Securely store your password{% #securely-store-your-password %} Store your password using secret management software such as [Vault](https://www.vaultproject.io/). You can then reference this password as `ENC[]` in your Agent configuration files: for example, `ENC[datadog_user_database_password]`. See [Secrets Management](https://docs.datadoghq.com/agent/configuration/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. ### Verify{% #verify %} Verify the user was created successfully using the following commands, replacing `` with the password you created above: ```shell mysql -u datadog --password= -e "show status" | \ grep Uptime && echo -e "\033[0;32mMySQL user - OK\033[0m" || \ echo -e "\033[0;31mCannot connect to MySQL\033[0m" ``` ```shell mysql -u datadog --password= -e "show slave status" && \ echo -e "\033[0;32mMySQL grant - OK\033[0m" || \ echo -e "\033[0;31mMissing REPLICATION CLIENT grant\033[0m" ``` ## Install and configure the Agent{% #install-and-configure-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](https://app.datadoghq.com/account/settings/agent/latest). {% tab title="Host" %} 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](https://docs.datadoghq.com/agent/configuration/agent-configuration-files/#agent-configuration-directory). See the [sample mysql.d/conf.yaml](https://github.com/DataDog/integrations-core/blob/master/mysql/datadog_checks/mysql/data/conf.yaml.example) for all available configuration options, including those for custom metrics. Add this configuration block to your `mysql.d/conf.yaml` to collect MySQL metrics: ```yaml init_config: instances: - dbm: true host: '' port: 3306 username: datadog password: 'ENC[datadog_user_database_password]' # from the CREATE USER step earlier, stored as a secret # 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: '' instance_id: '' ``` See the [GCP section of the `mysql.conf.yaml` file](https://github.com/DataDog/integrations-core/blob/master/mysql/datadog_checks/mysql/data/conf.yaml.example) for additional information on setting `project_id` and `instance_id` fields. [Restart the Agent](https://docs.datadoghq.com/agent/configuration/agent-commands/#start-stop-and-restart-the-agent) to start sending MySQL metrics to Datadog. {% /tab %} {% tab title="Docker" %} To configure the Database Monitoring Agent running in a Docker container such as in Google Cloud Run, set the [Autodiscovery Integration Templates](https://docs.datadoghq.com/agent/docker/integrations/?tab=docker) 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{% #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: ```bash export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx export DD_AGENT_VERSION=7.36.1 docker run -e "DD_API_KEY=${DD_API_KEY}" \ -v /var/run/docker.sock:/var/run/docker.sock:ro \ -l com.datadoghq.ad.check_names='["mysql"]' \ -l com.datadoghq.ad.init_configs='[{}]' \ -l com.datadoghq.ad.instances='[{ "dbm": true, "host": "", "port": 3306, "username": "datadog", "password": "", "gcp": { "project_id": "", "instance_id": "" } }]' \ registry.datadoghq.com/agent:${DD_AGENT_VERSION} ``` ### Dockerfile{% #dockerfile %} Labels can also be specified in a `Dockerfile`, so you can build and deploy a custom agent without changing any infrastructure configuration: ```Dockerfile FROM registry.datadoghq.com/agent: LABEL "com.datadoghq.ad.check_names"='["mysql"]' LABEL "com.datadoghq.ad.init_configs"='[{}]' LABEL "com.datadoghq.ad.instances"='[{"dbm": true, "host": "", "port": 5432,"username": "datadog","password": "ENC[datadog_user_database_password]", "gcp": {"project_id": "", "instance_id": ""}}]' ``` See the [GCP section of the `mysql.conf.yaml` file](https://github.com/DataDog/integrations-core/blob/master/mysql/datadog_checks/mysql/data/conf.yaml.example) for additional information on setting `project_id` and `instance_id` fields. {% /tab %} {% tab title="Kubernetes" %} If you have a Kubernetes cluster, use the [Datadog Cluster Agent](https://docs.datadoghq.com/containers/cluster_agent/setup/) for Database Monitoring. Follow the instructions to [enable the cluster checks](https://docs.datadoghq.com/containers/cluster_agent/clusterchecks/) 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: ### Operator{% #operator %} Using the [Operator instructions in Kubernetes and Integrations](https://docs.datadoghq.com/containers/kubernetes/integrations/?tab=datadogoperator) as a reference, follow the steps below to set up the MySQL integration: 1. Create or update the `datadog-agent.yaml` file with the following configuration: ```yaml apiVersion: datadoghq.com/v2alpha1 kind: DatadogAgent metadata: name: datadog spec: global: clusterName: site: credentials: apiSecret: secretName: datadog-agent-secret keyName: api-key features: clusterChecks: enabled: true override: nodeAgent: image: name: agent tag: clusterAgent: extraConfd: configDataMap: mysql.yaml: |- cluster_check: true init_config: instances: - host: port: username: datadog password: 'ENC[datadog_user_database_password]' dbm: true gcp: project_id: ' port: username: datadog password: 'ENC[datadog_user_database_password]' gcp: project_id: ' port: username: datadog password: 'ENC[datadog_user_database_password]' gcp: project_id: '' tags.datadoghq.com/service: '' annotations: ad.datadoghq.com/service.check_names: '["mysql"]' ad.datadoghq.com/service.init_configs: '[{}]' ad.datadoghq.com/service.instances: | [ { "dbm": true, "host": "", "port": , "username": "datadog", "password": "ENC[datadog_user_database_password]", "gcp": { "project_id": "", "instance_id": "" } } ] spec: ports: - port: protocol: TCP targetPort: name: mysql ``` 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](https://docs.datadoghq.com/agent/configuration/secrets-management) and declare the password using the `ENC[]` syntax. {% /tab %} ### Validate{% #validate %} [Run the Agent's status subcommand](https://docs.datadoghq.com/agent/configuration/agent-commands/#agent-status-and-information) and look for `mysql` under the Checks section, or see the [Databases](https://app.datadoghq.com/databases) page to get started! ## Example Agent Configurations{% #example-agent-configurations %} ### One agent connecting to multiple hosts{% #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](https://docs.datadoghq.com/database_monitoring/architecture/) for DBM). To connect to multiple hosts, create an entry for each host in the MySQL integration config. {% alert level="info" %} Datadog recommends using one Agent to monitor no more than 30 database instances.Benchmarks show that one Agent running on a t4g.medium EC2 instance (2 CPUs and 4GB of RAM) can successfully monitor 30 RDS db.t3.medium instances (2 CPUs and 4GB of RAM). {% /alert %} ```yaml init_config: instances: - dbm: true host: example-service-primary.example-host.com port: 3306 username: datadog 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 port: 3306 username: datadog password: 'ENC[datadog_user_database_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: 'ENC[datadog_user_database_password]' options: replication: true tags: - 'env:prod' - 'team:team-discovery' - 'service:example-service' [...] ``` ### Running custom queries{% #running-custom-queries %} To collect custom metrics, use the `custom_queries` option. See the sample [mysql.d/conf.yaml](https://github.com/DataDog/integrations-core/blob/master/mysql/datadog_checks/mysql/data/conf.yaml.example) for more details. ```yaml init_config: instances: - dbm: true host: localhost port: 3306 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' ``` ### Collecting schemas{% #collecting-schemas %} {% alert level="danger" %} Datadog Agent v7.65+ is required for MySQL schema collection. {% /alert %} To enable this feature, use the `collect_schemas` option. See the sample [mysql.d/conf.yaml](https://github.com/DataDog/integrations-core/blob/master/mysql/datadog_checks/mysql/data/conf.yaml.example) for more details. ```yaml init_config: instances: - dbm: true host: localhost port: 3306 username: datadog password: 'ENC[datadog_user_database_password]' collect_schemas: enabled: true ``` **Note**: For Agent v7.68 and below, use `schemas_collection` instead of `collect_schemas`. **Note**: To collect schemas for a table, MySQL requires that the Datadog Agent has SELECT access for it. This is a [MySQL-enforced restriction](https://dev.mysql.com/doc/refman/8.4/en/information-schema-introduction.html#information-schema-privileges). Without SELECT access, the table will not appear in metadata queries. The Agent does not use SELECT to access or read your table data. This permission is needed solely to retrieve schema details, due to how MySQL handles metadata visibility. To grant SELECT permissions to a Datadog user, use one or a combination of the following commands: - **All databases**: ```sql GRANT SELECT ON *.* TO datadog@'%'; ``` - **Per database basis**: ```sql GRANT SELECT ON [database name].* TO datadog@'%'; ``` - **Per table basis**: ```sql GRANT SELECT ON [database name].[table name] TO datadog@'%'; ``` - **Per column basis**: ```sql GRANT SELECT ([column name1], [column name 2]) ON [database name].[table name] TO datadog@'%'; ``` ### Working with hosts through a proxy{% #working-with-hosts-through-a-proxy %} If the Agent must connect through a proxy such as the [Cloud SQL Auth proxy](https://cloud.google.com/sql/docs/mysql/connect-admin-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. ```yaml init_config: instances: - dbm: true host: localhost port: 5000 username: datadog password: 'ENC[datadog_user_database_password]' reported_hostname: example-service-primary - dbm: true host: localhost port: 5001 username: datadog password: 'ENC[datadog_user_database_password]' reported_hostname: example-service-replica-1 ``` ## Install the Cloud SQL integration{% #install-the-cloud-sql-integration %} To collect more comprehensive database metrics from Google Cloud, install the [Cloud SQL integration](https://docs.datadoghq.com/integrations/google_cloudsql) (optional). ## Troubleshooting{% #troubleshooting %} If you have installed and configured the integrations and Agent as described and it is not working as expected, see [Troubleshooting](https://docs.datadoghq.com/database_monitoring/troubleshooting/?tab=mysql) ## Further reading{% #further-reading %} - [Basic MySQL Integration](https://docs.datadoghq.com/integrations/mysql/)