PostgreSQL Tutorials

PostgreSQL is an open source, object-relational database built with a focus on extensibility, data integrity, and speed. Its concurrency support makes it fully ACID compliant, and it supports dynamic loading and catalog-driven operations to let users customize its data types, functions, and more.


SQL databases come installed with all the commands you need to add, modify, delete, and query your data. This cheat sheet-style guide provides a quick reference to some of the most commonly-used SQL commands.
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This guide outlines how to install client programs for a variety of database management systems (DBMSs) — including PostgreSQL, MySQL, Redis, and MongoDB — on an Ubuntu 18.04 server. It will also explain how to use these programs to connect to a managed database instance.
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This tutorial uses pgbench, PostgreSQL’s built-in benchmarking tool, to run load tests on a DigitalOcean Managed PostgreSQL Database. It explains how connections pools work, shows how to create one using the DigitalOcean Control Panel, and finally uses results from the pgbench tests to show how connection pools can be an inexpensive method of increasing database throughput.
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This tutorial provides instructions on how to install pgLoader and use it to migrate a remote MySQL database to PostgreSQL over an SSL connection. It also briefly touches on a few different migration scenarios where pgLoader may be useful.
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In this tutorial, you will set up PostgreSQL database monitoring in Nagios Core using the check_postgres Nagios plugin and set up Slack-based alerting. In the end, you’ll have a monitoring system in place for your managed PostgreSQL database, and will be notified of status changes of various functionality immediately.
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In this tutorial, you import database metrics generated by the PostgreSQL statistics collector into Elasticsearch via Logstash. This entails configuring Logstash to pull data from the database using the PostgreSQL JDBC connector to send it to Elasticsearch for indexing immediately afterward. The imported data can later be analyzed and visualized in Kibana. Then, if your database is brand new, you’ll use pgbench, a PostgreSQL benchmarking tool, to create more interesting visualizations. In the end, you’ll have an automated system pulling in PostgreSQL statistics for later analysis.
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In this tutorial, you’ll build an address book API service with Node.js. The API exposes a simple RESTful API interface to create, delete, and find people in the database. You’ll use Git to push the code to GitHub. Then you’ll use Semaphore to test the application, build a Docker image, and deploy it to a DigitalOcean Kubernetes cluster. For the database, you’ll create a PostgreSQL cluster using DigitalOcean Managed Databases.
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