![]() tags (optional): add this if you’d like to tag your custom metrics with additional metadataįor example, you can send a custom query to the pg_stat_activity view to continuously gauge the number of applications connected to each of your backends, broken down by application name and user.Alternatively, you can set the type to tag to tag the metric with the data contained in this column. The type can be the metric type (gauge, count, rate, etc.), or submission method for the queried metric. ![]() ![]() Each item in the list should have a name (the custom metric name that will get appended to the metric_prefix) and a type. columns (required): an ordered list of every column returned by the query above.query (required): the SQL query to run on your database.metric_prefix (required): the prefix to use across every custom metric name (by default, this is postgresql).In the custom_queries section of the Datadog Agent’s example PostgreSQL configuration file, you’ll see some guidelines about the components you’ll need to provide: You also have the option to add custom tag(s) to your PostgreSQL metrics, and to limit metric collection to specific schemas, if desired.ĭownload now Collecting custom PostgreSQL metrics with Datadogĭatadog’s PostgreSQL integration provides you with an option to collect custom metrics that are mapped to specific queries. The example below instructs the Agent to access metrics locally through port 5432, using the datadog user and password we just created. Now you can customize the config file to provide Datadog with the correct information and any tags you’d like to add to your metrics. The location of this file varies according to your OS and platform consult the documentation for more details.Ĭopy the example config file ( postgres.d/) and save it as conf.yaml. Configure the Agent to collect PostgreSQL metricsĪfter you’ve installed the Agent on each of your PostgreSQL servers, you’ll need to create a configuration file that provides the Agent with the information it needs in order to begin collecting PostgreSQL data. You’ll be prompted to enter the password you just created for your datadog user once you’ve done so, you should see the following output: Postgres connection - OK. Psql -h localhost -U datadog postgres -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" Basically, you’ll need to log into a psql session as a user who has CREATEROLE privileges, create a datadog user and password, and grant it read access to pg_stat_database: Next, you’ll need to give the Agent permission to access statistics from the pg_stat_database view, by following the instructions in our documentation. GRANT the Agent permission to monitor PostgreSQL Installing the Agent usually takes just a single command-to get started, follow the instructions for your platform here. The Datadog Agent is open source software that aggregates and reports metrics from your servers, so that you can graph and alert on them in real time. Instead of querying PostgreSQL metrics manually through the utilities covered in Part 2 of this series, you can use the Datadog Agent to automatically aggregate these metrics and make them visible in a customizable template dashboard that shows you how these metrics evolve over time. Try it free Datadog’s PostgreSQL integration Last but not least, we’ll discuss how to identify bottlenecks in your code by tracing application requests (including PostgreSQL queries) with Datadog APM. We’ll also show you how you can leverage Datadog Database Monitoring to get query-level insights into your databases. In this post, we’ll walk through the process of installing Datadog on your PostgreSQL servers, so you can visualize database performance in an out-of-the-box screenboard like the one shown below. And, because Datadog integrates with more thanĥ00 other technologies, you’ll be able to correlate metrics from your PostgreSQL servers with other services throughout your environment. If you’ve already read Parts 1 and 2 of this series, you’ve learned about the key metrics to monitor in PostgreSQL, and how to start collecting this data with native and open source tools.ĭatadog’s PostgreSQL integration helps you automatically collect PostgreSQL data from the statistics collector, so that you can monitor everything in one place.
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