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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.

Today’s article shows how to maintain an external cache in another application layer. In this article we are going to maintain an in-memory cache in a Golang service, using PostgreSQL LISTEN and NOTIFY features.



In the previous article of the series Modeling for Concurrency, we saw how to model your application for highly concurrent activity. It was a follow-up to the article entitled PostgreSQL Concurrency: Isolation and Locking, which was a primer on PostgreSQL isolation and locking properties and behaviors.

Today’s article takes us a step further and builds on what we did in the previous articles in our series. After having 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, and having had them like and retweet a lot in PostgreSQL Concurrency: Isolation and Locking, we saw how to manage concurrent retweets in an efficient way in Computing and Caching.

What we did implement in the previous article is a cache system, all with its necessary cache invalidation policy. Sometimes though, the processing of an event needs to happen within the same transaction where the event is registered in your system. PostgreSQL makes it possible to maintain a summary table transactionally thanks to its trigger support. Today, we’re going to dive in how to maintain a summary table with triggers, and its impact on concurrency.


Let’s continue to dive in PostgreSQL Concurrency. In the previous article of the series, Modeling for Concurrency, we saw how to model your application for highly concurrent activity. It was a follow-up to the article entitled PostgreSQL Concurrency: Isolation and Locking, which was a primer on PostgreSQL isolation and locking properties and behaviors.

Today’s article takes us a step further and builds on what we did in the previous articles in our series. After having 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, and having had them like a retweet a lot in PostgreSQL Concurrency: Isolation and Locking, it’s time to think about how to display our counters in an efficient way.

In this article, we’re going to think about when we should compute results and when we should cache them for instant retrieval, all within the SQL tooling. The SQL tooling for handling cache is a MATERIALIZED VIEW, and it comes with cache invalidation routines, of course.


Let’s continue to dive in PostgreSQL Concurrency. Last week’s article PostgreSQL Concurrency: Isolation and Locking was a primer on PostgreSQL isolation and locking properties and behaviors.

Today’s article takes us a step further and builds on what we did last week, in particular the database modeling for a tweet like application. After having 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, it’s time for them to do some actions on the tweets: likes and retweet.

Of course, we’re going to put concurrency to the test, so we’re going to have to handle very very popular tweets from the play!


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.

This article is a primer on PostgreSQL Isolation and Locking properties and behaviors. You might be interested into the previous article in the series: PostgreSQL Concurrency: Data Modification Language.

Dimitri Fontaine

PostgreSQL Major Contributor

Open Source Software Engineer

France