Category “YeSQL” — 77 articles

A previous article in the PostgreSQL Concurrency series covered how to manage concurrent retweets in an efficient way: in Computing and Caching, we learnt how to maintain a cache right in your PostgreSQL database, using MATERIALIZED VIEWS. We also had a look at how to take care of Batch Updates and Concurrency.

While in the first case we are providing a solution to a technical problem where we want to solve performance issues while keeping the same semantics, in the second case we are actually implementing a part of the application’s Business Logic as a scheduled job.

Today’s article shows a modern technique to handle the scheduling of those business oriented activities that are not tied to any user activity. When thinking about it this way, you certainly don’t want to implement the backbone of your business logic in a shell script that’s directly maintained in the production environment, do 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.

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.