PostgreSQL is a relational database management system. It’s even the world’s most advanced open source one of them. As such, as its core, Postgres solves concurrent access to a set of data and maintains consistency while allowing concurrent operations.

In the PostgreSQL Concurrency series of articles here we did see several aspects of how to handle concurrent use cases of your application design with PostgreSQL. The main thing to remember is that a Database Management System first task is to handle concurrency access to the data for you.

This article fits in the PostgreSQL Concurrency series, where we installed a tweeter like application schema and had all the characters from Shakespeare’s A Midsummer Night’s Dream tweet their own lines in our database in PostgreSQL Concurrency: Data Modification Language.

A previous article in the series covered how to manage concurrent retweets in an efficient way: Computing and Caching, where we learn how to maintain a cache right in your PostgreSQL database, thanks for materialized views. We even went as far as maintaining an external cache in another application layer using PostgreSQL LISTEN and NOTIFY features and a Golang application.

Today’s article is going to address concurrency in the context of updating data in a batch. This activity is quite common, as soon as your system is connected to other systems either internally or with external providers. While it’s pretty easy to ingest new data, and easy enough to update data from an external source when nothing happens in your database, doing the operation safely with concurrent activity is more complex. Once more though, PostgreSQL comes with all the tooling you need to handle that situation.