--- title: Setting Up Database Monitoring for Amazon RDS managed Postgres description: Install and configure Database Monitoring for Postgres on Amazon RDS. breadcrumbs: >- Docs > Database Monitoring > Setting up Postgres > Setting Up Database Monitoring for Amazon RDS managed Postgres --- # Setting Up Database Monitoring for Amazon RDS managed Postgres Database Monitoring provides deep visibility into your Postgres databases by exposing query metrics, query samples, explain plans, database states, failovers, and events. 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 Postgres database: 1. Configure the AWS integration 1. Configure database parameters 1. Grant the Agent access to the database 1. Install and configure the Agent 1. Install the RDS integration {% alert level="info" %} [RDS Quick Install](https://docs.datadoghq.com/database_monitoring/setup_postgres/rds/quick_install) is our recommended installation method for smaller environments (for example 20 database hosts) or those new to DBM and want to try it out quickly. For those managing large fleets of databases where deploying the agent via UI doesn't scale as well we recommend the standard installation, to manually manage the agent yourself or integrate with your automation practices. {% /alert %} ## Before you begin{% #before-you-begin %} {% dl %} {% dt %} Supported PostgreSQL versions {% /dt %} {% dd %} 9.6, 10, 11, 12, 13, 14, 15, 16, 17 {% /dd %} {% dt %} Supported Agent versions {% /dt %} {% dd %} 7.36.1+ {% /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=postgres)). {% /dd %} {% dt %} Proxies, load balancers, and connection poolers {% /dt %} {% dd %} The Datadog Agent must connect directly to the host being monitored. For self-hosted databases, `127.0.0.1` or the socket is preferred. The Agent should not connect to the database through a proxy, load balancer, or connection pooler such as `pgbouncer`. 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 the AWS integration{% #configure-the-aws-integration %} Enable **Resource Collection** in the **Resource Collection** section of your [Amazon Web Services integration tile](https://app.datadoghq.com/integrations/amazon-web-services). ## Configure Postgres settings{% #configure-postgres-settings %} Configure the following [parameters](https://www.postgresql.org/docs/current/config-setting.html) in the [DB parameter group](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_WorkingWithParamGroups.html) and then **restart the server** for the settings to take effect. For more information about these parameters, see the [Postgres documentation](https://www.postgresql.org/docs/current/pgstatstatements.html). | Parameter | Value | Description | | ---------------------------------- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `shared_preload_libraries` | `pg_stat_statements` | Required for `postgresql.queries.*` metrics. Enables collection of query metrics using the [pg_stat_statements](https://www.postgresql.org/docs/current/pgstatstatements.html) extension. | | `track_activity_query_size` | `4096` | Required for collection of larger queries. Increases the size of SQL text in `pg_stat_activity`. If left at the default value then queries longer than `1024` characters will not be collected. | | `pg_stat_statements.track` | `ALL` | Optional. Enables tracking of statements within stored procedures and functions. | | `pg_stat_statements.max` | `10000` | Optional. Increases the number of normalized queries tracked in `pg_stat_statements`. This setting is recommended for high-volume databases that see many different types of queries from many different clients. | | `pg_stat_statements.track_utility` | `off` | Optional. Disables utility commands like PREPARE and EXPLAIN. Setting this value to `off` means only queries like SELECT, UPDATE, and DELETE are tracked. | | `track_io_timing` | `on` | Optional. Enables collection of block read and write times for queries. | ### Enable `auto_explain` (optional){% #enable-auto_explain-optional %} By default, the agent only gathers [`EXPLAIN`](https://www.postgresql.org/docs/current/sql-explain.html) plans for a sampling of in-flight queries. These plans are of a more general nature, especially when application code uses prepared statements. To collect full `EXPLAIN ANALYZE` plans taken from all queries, you need to use [`auto_explain`](https://www.postgresql.org/docs/current/auto-explain.html), a first-party extension bundled with PostgreSQL available in all major providers. *Logging collection is a prerequisite to `auto_explain` collection*, so be sure to enable it before continuing. {% alert level="danger" %} **Important:** `auto_explain` produces log lines that may contain sensitive application data, similar to raw values in non-obfuscated SQL. Use the [`dbm_parameterized_queries_read`](https://docs.datadoghq.com/account_management/rbac/permissions/#database-monitoring) permission to control access to the resulting plans. To restrict visibility of the log lines themselves—which are visible to all users in your Datadog organization by default—also configure [RBAC for Logs](https://docs.datadoghq.com/logs/guide/logs-rbac). Datadog recommends using both permissions to protect sensitive information effectively. {% /alert %} 1. Configure `auto_explain` settings. The log format *must* be `json`, but other settings can vary depending on your application. This example logs an `EXPLAIN ANALYZE` plan for all queries over one second, including buffer information but omitting timing (which can have overhead). | Parameter | Value | Description | | ------------------------------------ | --------------------------------- | ----------------------------------------- | | `shared_preload_libraries` | `pg_stat_statements,auto_explain` | Enables automatic `EXPLAIN ANALYZE` | | `auto_explain.log_format` | `json` | Generates machine-readable plans | | `auto_explain.log_min_duration` | `1000` | Logs plans when queries exceed one second | | `auto_explain.log_analyze` | `on` | Use the `ANALYZE` form of `EXPLAIN` | | `auto_explain.log_buffers` | `on` | Include buffer use in plans | | `auto_explain.log_timing` | `off` | Do not include timing (high overhead) | | `auto_explain.log_triggers` | `on` | Include plans for trigger statement | | `auto_explain.log_verbose` | `on` | Use verbose plan type | | `auto_explain.log_nested_statements` | `on` | Include nested statements | | `auto_explain.sample_rate` | `1` | Explain all queries over duration | Change the `log_line_prefix` to enable richer event correlation. For more information, see the [RDS DB parameter groups](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_LogAccess.Concepts.PostgreSQL.overview.parameter-groups.html) documentation. `auto_explain` ingestion requires this be set to `%m:%r:%u@%d:[%p]:%l:%e:%s:%v:%x:%c:%q%a`. To ensure your RDS instances are forwarding logs to CloudWatch and Datadog, follow the instructions for [Amazon RDS Log Collection](https://docs.datadoghq.com/integrations/amazon-rds/?tab=standard#log-collection). ## Grant the Agent access{% #grant-the-agent-access %} The Datadog Agent requires read-only access to the database server in order to collect statistics and queries. The following SQL commands should be executed on the **primary** database server (the writer) in the cluster if Postgres is replicated. Choose a PostgreSQL database on the server for the Agent to connect to. The Agent can collect telemetry from all databases on the database server regardless of which one it connects to, so a good option is to use the default `postgres` database. Choose a different database only if you need the Agent to run [custom queries against data unique to that database](https://docs.datadoghq.com/integrations/faq/postgres-custom-metric-collection-explained/). Connect to the chosen database as a superuser (or another user with sufficient permissions). For example, if your chosen database is `postgres`, connect as the `postgres` user using [psql](https://www.postgresql.org/docs/current/app-psql.html) by running: ```bash psql -h mydb.example.com -d postgres -U postgres ``` Create the `datadog` user: ```SQL CREATE USER datadog WITH password ''; ``` **Note:** IAM authentication is also supported. Please see [the guide](https://docs.datadoghq.com/database_monitoring/guide/managed_authentication) on how to configure this for your RDS instance. {% tab title="Postgres ≥ 15" %} Give the `datadog` user permission to relevant tables: ```SQL ALTER ROLE datadog INHERIT; ``` Create the following schema **in every database**: ```SQL CREATE SCHEMA datadog; GRANT USAGE ON SCHEMA datadog TO datadog; GRANT USAGE ON SCHEMA public TO datadog; GRANT pg_monitor TO datadog; CREATE EXTENSION IF NOT EXISTS pg_stat_statements schema public; ``` {% /tab %} {% tab title="Postgres ≥ 10" %} Create the following schema **in every database**: ```SQL CREATE SCHEMA datadog; GRANT USAGE ON SCHEMA datadog TO datadog; GRANT USAGE ON SCHEMA public TO datadog; GRANT pg_monitor TO datadog; CREATE EXTENSION IF NOT EXISTS pg_stat_statements schema public; ``` {% /tab %} {% tab title="Postgres 9.6" %} Create the following schema **in every database**: ```SQL CREATE SCHEMA datadog; GRANT USAGE ON SCHEMA datadog TO datadog; GRANT USAGE ON SCHEMA public TO datadog; GRANT SELECT ON pg_stat_database TO datadog; CREATE EXTENSION IF NOT EXISTS pg_stat_statements; ``` Create functions **in every database** to enable the Agent to read the full contents of `pg_stat_activity` and `pg_stat_statements`: ```SQL CREATE OR REPLACE FUNCTION datadog.pg_stat_activity() RETURNS SETOF pg_stat_activity AS $$ SELECT * FROM pg_catalog.pg_stat_activity; $$ LANGUAGE sql SECURITY DEFINER; CREATE OR REPLACE FUNCTION datadog.pg_stat_statements() RETURNS SETOF pg_stat_statements AS $$ SELECT * FROM pg_stat_statements; $$ LANGUAGE sql SECURITY DEFINER; ``` {% /tab %} {% alert level="info" %} For data collection or custom metrics that require querying additional tables, you may need to grant the `SELECT` permission on those tables to the `datadog` user. Example: `grant SELECT on to datadog;`. See [PostgreSQL custom metric collection](https://docs.datadoghq.com/integrations/faq/postgres-custom-metric-collection-explained/) for more information. {% /alert %} Create the function **in every database** to enable the Agent to collect explain plans. ```SQL CREATE OR REPLACE FUNCTION datadog.explain_statement( l_query TEXT, OUT explain JSON ) RETURNS SETOF JSON AS $$ DECLARE curs REFCURSOR; plan JSON; BEGIN SET TRANSACTION READ ONLY; OPEN curs FOR EXECUTE pg_catalog.concat('EXPLAIN (FORMAT JSON) ', l_query); FETCH curs INTO plan; CLOSE curs; RETURN QUERY SELECT plan; END; $$ LANGUAGE 'plpgsql' RETURNS NULL ON NULL INPUT SECURITY DEFINER; ``` ### 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 %} To verify the permissions are correct, run the following commands to confirm the Agent user is able to connect to the database and read the core tables: {% tab title="Postgres ≥ 10" %} ```shell psql -h localhost -U datadog postgres -A \ -c "select * from pg_stat_database limit 1;" \ && echo -e "\e[0;32mPostgres connection - OK\e[0m" \ || echo -e "\e[0;31mCannot connect to Postgres\e[0m" psql -h localhost -U datadog postgres -A \ -c "select * from pg_stat_activity limit 1;" \ && echo -e "\e[0;32mPostgres pg_stat_activity read OK\e[0m" \ || echo -e "\e[0;31mCannot read from pg_stat_activity\e[0m" psql -h localhost -U datadog postgres -A \ -c "select * from pg_stat_statements limit 1;" \ && echo -e "\e[0;32mPostgres pg_stat_statements read OK\e[0m" \ || echo -e "\e[0;31mCannot read from pg_stat_statements\e[0m" ``` {% /tab %} {% tab title="Postgres 9.6" %} ```shell psql -h localhost -U datadog postgres -A \ -c "select * from pg_stat_database limit 1;" \ && echo -e "\e[0;32mPostgres connection - OK\e[0m" \ || echo -e "\e[0;31mCannot connect to Postgres\e[0m" psql -h localhost -U datadog postgres -A \ -c "select * from pg_stat_activity limit 1;" \ && echo -e "\e[0;32mPostgres pg_stat_activity read OK\e[0m" \ || echo -e "\e[0;31mCannot read from pg_stat_activity\e[0m" psql -h localhost -U datadog postgres -A \ -c "select * from pg_stat_statements limit 1;" \ && echo -e "\e[0;32mPostgres pg_stat_statements read OK\e[0m" \ || echo -e "\e[0;31mCannot read from pg_stat_statements\e[0m" ``` {% /tab %} When it prompts for a password, use the password you entered when you created the `datadog` user. ## Install and configure the Agent{% #install-and-configure-the-agent %} To monitor RDS 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](https://app.datadoghq.com/account/settings/agent/latest). {% tab title="Host" %} To configure collecting Database Monitoring metrics for an Agent running on a host, for example when you provision a small EC2 instance for the Agent to collect from an RDS database: 1. Edit the `postgres.d/conf.yaml` file to point to your `host` / `port` and set the masters to monitor. See the [sample postgres.d/conf.yaml](https://github.com/DataDog/integrations-core/blob/master/postgres/datadog_checks/postgres/data/conf.yaml.example) for all available configuration options. ```yaml init_config: instances: - dbm: true host: '' port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' aws: instance_endpoint: '' region: '' tags: - "dbinstanceidentifier:" ## Required for Postgres 9.6: Uncomment these lines to use the functions created in the setup # pg_stat_statements_view: datadog.pg_stat_statements() # pg_stat_activity_view: datadog.pg_stat_activity() ## Optional: Connect to a different database if needed for `custom_queries` # dbname: '' ``` For Agent versions `≤ 7.49`, add the following setting to the instance config where `host` and `port` are specified: ```yaml ssl: allow ``` If you want to authenticate with IAM, specify the `region` and `instance_endpoint` parameters, and set `managed_authentication.enabled` to `true`. **Note**: only enable `managed_authentication` if you want to use IAM authentication. IAM authentication takes precedence over the `password` field. ```yaml init_config: instances: - dbm: true host: '' port: 5432 username: datadog aws: instance_endpoint: '' region: '' managed_authentication: enabled: true tags: - "dbinstanceidentifier:" ## Required for Postgres 9.6: Uncomment these lines to use the functions created in the setup # pg_stat_statements_view: datadog.pg_stat_statements() # pg_stat_activity_view: datadog.pg_stat_activity() ## Optional: Connect to a different database if needed for `custom_queries` # dbname: '' ``` For information on configuring IAM authentication on your RDS instance, see [Connecting with Managed Authentication](https://docs.datadoghq.com/database_monitoring/guide/managed_authentication). 1. [Restart the Agent](https://docs.datadoghq.com/agent/configuration/agent-commands/#start-stop-and-restart-the-agent). {% /tab %} {% tab title="Docker" %} To configure an integration for an Agent running in a Docker container such as in ECS or Fargate, you have a couple of methods available, all of which are covered in detail in the [Docker Configuration Documentation](https://docs.datadoghq.com/containers/docker/integrations/?tab=labels#configuration). The examples below show how to use [Docker Labels](https://docs.docker.com/engine/manage-resources/labels/) and [Autodiscovery Templates](https://docs.datadoghq.com/getting_started/containers/autodiscovery/) to configure the Postgres integration. **Note**: The Agent must have read permission on the Docker socket for Autodiscovery of labels to work. ### Command line{% #command-line %} Run the following command from your [command line](https://docs.datadoghq.com/containers/docker/integrations/?tab=labels#using-docker-run-nerdctl-run-or-podman-run) to start the Agent. Replace the placeholder values with those for your account and environment. ```bash export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx export DD_AGENT_VERSION= docker run -e "DD_API_KEY=${DD_API_KEY}" \ -v /var/run/docker.sock:/var/run/docker.sock:ro \ -l com.datadoghq.ad.checks='{"postgres": { "init_config": {}, "instances": [{ "dbm": true, "host": "", "port": 5432, "username": "datadog", "password": "", "aws": { "instance_endpoint": "", "region": "" }, "tags": ["dbinstanceidentifier:"] }] }}' \ registry.datadoghq.com/agent:${DD_AGENT_VERSION} ``` For Postgres 9.6, add the following settings to the instance config where host and port are specified: ```yaml "pg_stat_statements_view": "datadog.pg_stat_statements()", "pg_stat_activity_view": "datadog.pg_stat_activity()" ``` ### Dockerfile{% #dockerfile %} You can also specify labels in a `Dockerfile`, allowing you to build and deploy a custom Agent without modifying your infrastructure configuration: ```Dockerfile FROM registry.datadoghq.com/agent: LABEL "com.datadoghq.ad.check_names"='["postgres"]' LABEL "com.datadoghq.ad.init_configs"='[{}]' LABEL "com.datadoghq.ad.instances"='[{"dbm": true, "host": "", "port": 5432,"username": "datadog","password": "ENC[datadog_user_database_password]","aws": {"instance_endpoint": "", "region": ""}, "tags": ["dbinstanceidentifier:"]}]' ``` For Postgres 9.6, add the following settings to the instance config where host and port are specified: ```yaml "pg_stat_statements_view": "datadog.pg_stat_statements()", "pg_stat_activity_view": "datadog.pg_stat_activity()" ``` 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. Alternatively, see the [Autodiscovery template variables documentation](https://docs.datadoghq.com/agent/faq/template_variables/) to provide the password as an environment variable. {% /tab %} {% tab title="Kubernetes" %} If you're running a Kubernetes cluster, use the [Datadog Cluster Agent](https://docs.datadoghq.com/containers/cluster_agent/setup/) to enable Database Monitoring. **Note**: Make sure [cluster checks](https://docs.datadoghq.com/containers/cluster_agent/clusterchecks/) are enabled for your Datadog Cluster Agent before proceeding. Below are step-by-step instructions for configuring the Postgres integration using different Datadog Cluster Agent deployment methods. ### 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 Postgres 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: postgres.yaml: |- cluster_check: true init_config: instances: - host: port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' dbm: true aws: instance_endpoint: region: tags: - "dbinstanceidentifier:" ``` **Note**: For Postgres 9.6, add the following lines to the instance config where host and port are specified: ```yaml pg_stat_statements_view: datadog.pg_stat_statements() pg_stat_activity_view: datadog.pg_stat_activity() ``` 1. Apply the changes to the Datadog Operator using the following command: ```shell kubectl apply -f datadog-agent.yaml ``` ### Helm{% #helm %} Using the [Helm instructions in Kubernetes and Integrations](https://docs.datadoghq.com/containers/kubernetes/integrations/?tab=helm) as a reference, follow the steps below to set up the Postgres integration: 1. Update your `datadog-values.yaml` file (used in the Cluster Agent installation instructions) with the following configuration: ```yaml datadog: clusterChecks: enabled: true clusterChecksRunner: enabled: true clusterAgent: enabled: true confd: postgres.yaml: |- cluster_check: true init_config: instances: - dbm: true host: port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' aws: instance_endpoint: region: tags: - "dbinstanceidentifier:" ``` **Note**: For Postgres 9.6, add the following lines to the instance config where host and port are specified: ```yaml pg_stat_statements_view: datadog.pg_stat_statements() pg_stat_activity_view: datadog.pg_stat_activity() ``` 1. Deploy the Agent with the above configuration file using the following command: ```shell helm install datadog-agent -f datadog-values.yaml datadog/datadog ``` {% alert level="info" %} For Windows, append `--set targetSystem=windows` to the `helm install` command. {% /alert %} ### Configure with mounted files{% #configure-with-mounted-files %} To configure a cluster check with a mounted configuration file, mount the configuration file in the Cluster Agent container at the path: `/conf.d/postgres.yaml`: ```yaml cluster_check: true # Make sure to include this flag init_config: instances: - dbm: true host: '' port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' aws: instance_endpoint: region: tags: - "dbinstanceidentifier:" ## Required: For Postgres 9.6, uncomment these lines to use the functions created in the setup # pg_stat_statements_view: datadog.pg_stat_statements() # pg_stat_activity_view: datadog.pg_stat_activity() ``` ### Configure with Kubernetes service annotations{% #configure-with-kubernetes-service-annotations %} Instead of 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 using the following syntax: #### Autodiscovery annotations v2{% #autodiscovery-annotations-v2 %} ```yaml apiVersion: v1 kind: Service metadata: name: postgres labels: tags.datadoghq.com/env: '' tags.datadoghq.com/service: '' annotations: ad.datadoghq.com/service.checks: | { "postgres": { "init_config": , "instances": [ { "dbm": true, "host": "", "port": 5432, "username": "datadog", "password": "ENC[datadog_user_database_password]", "aws": { "instance_endpoint": "", "region": "" }, "tags": [ "dbinstanceidentifier:" ] } ] } } spec: ports: - port: 5432 protocol: TCP targetPort: 5432 name: postgres ``` For more information, see [Autodiscovery Annotations](https://docs.datadoghq.com/containers/kubernetes/integrations/?tab=annotations#configuration). If you're using Postgres 9.6, add the following to the instance configuration: ```json "pg_stat_statements_view": "datadog.pg_stat_statements()", "pg_stat_activity_view": "datadog.pg_stat_activity()" ``` The Cluster Agent automatically registers this configuration and begins running the Postgres 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 `postgres` under the Checks section. Or visit 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 Postgres 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: 5432 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: 5432 username: datadog password: 'ENC[datadog_user_database_password]' tags: - 'env:prod' - 'team:team-discovery' - 'service:example-service' - dbm: true host: example-service–replica-2.example-host.com port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' tags: - 'env:prod' - 'team:team-discovery' - 'service:example-service' [...] ``` ### Monitoring multiple databases on a database host{% #monitoring-multiple-databases-on-a-database-host %} Use the `database_autodiscovery` option to permit the Agent to discover all databases on your host to monitor. You can specify `include` or `exclude` fields to narrow the scope of databases discovered. See the sample [postgres.d/conf.yaml](https://github.com/DataDog/integrations-core/blob/master/postgres/datadog_checks/postgres/data/conf.yaml.example) for more details. ```yaml init_config: instances: - dbm: true host: example-service-primary.example-host.com port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' database_autodiscovery: enabled: true # Optionally, set the include field to specify # a set of databases you are interested in discovering include: - mydb.* - example.* 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 [postgres.d/conf.yaml](https://github.com/DataDog/integrations-core/blob/master/postgres/datadog_checks/postgres/data/conf.yaml.example) for more details. ```yaml init_config: instances: - dbm: true host: localhost port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' custom_queries: - metric_prefix: employee 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' ``` ### Monitoring relation metrics for multiple databases{% #monitoring-relation-metrics-for-multiple-databases %} In order to collect relation metrics (such as `postgresql.seq_scans`, `postgresql.dead_rows`, `postgresql.index_rows_read`, and `postgresql.table_size`), the Agent must be configured to connect to each database (by default, the Agent only connects to the `postgres` database). Specify a single "DBM" instance to collect DBM telemetry from all databases. Use the `database_autodiscovery` option to avoid specifying each database name. ```yaml init_config: instances: # This instance is the "DBM" instance. It will connect to the # all logical databases, and send DBM telemetry from all databases - dbm: true host: example-service-primary.example-host.com port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' database_autodiscovery: enabled: true exclude: - ^users$ - ^inventory$ relations: - relation_regex: .* # This instance only collects data from the `users` database # and collects relation metrics from tables prefixed by "2022_" - host: example-service-primary.example-host.com port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' dbname: users dbstrict: true relations: - relation_regex: 2022_.* relkind: - r - i # This instance only collects data from the `inventory` database # and collects relation metrics only from the specified tables - host: example-service-primary.example-host.com port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' dbname: inventory dbstrict: true relations: - relation_name: products - relation_name: external_seller_products ``` ### Collecting schemas{% #collecting-schemas %} To enable this feature, use the `collect_schemas` option. You must also configure the Agent to connect to each logical database. Use the `database_autodiscovery` option to avoid specifying each logical database. See the sample [postgres.d/conf.yaml](https://github.com/DataDog/integrations-core/blob/master/postgres/datadog_checks/postgres/data/conf.yaml.example) for more details. ```yaml init_config: # This instance only collects data from the `users` database # and collects relation metrics only from the specified tables instances: - dbm: true host: example-service-primary.example-host.com port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' dbname: users dbstrict: true collect_schemas: enabled: true relations: - products - external_seller_products # This instance detects every logical database automatically # and collects relation metrics from every table - dbm: true host: example-service–replica-1.example-host.com port: 5432 username: datadog password: 'ENC[datadog_user_database_password]' database_autodiscovery: enabled: true collect_schemas: enabled: true relations: - relation_regex: .* ``` ### 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 RDS Integration{% #install-the-rds-integration %} To see infrastructure metrics from AWS, such as CPU, alongside the database telemetry in DBM, install the [RDS integration](https://docs.datadoghq.com/integrations/amazon_rds) (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=postgres) ## Further reading{% #further-reading %} - [Basic Postgres Integration](https://docs.datadoghq.com/integrations/postgres/) - [Autodiscovery for RDS](https://docs.datadoghq.com/database_monitoring/guide/rds_autodiscovery) - [Capturing SQL Query Parameter Values](https://docs.datadoghq.com/database_monitoring/guide/parameterized_queries/)