Cache synchronization using jOOQ and PostgreSQL functions
Introduction In this article, we are going to see how we can achieve cache synchronization with the help of jOOQ and PostgreSQL functions. By using Change Data Capture, we can track how table records change over time and synchronize the application-level cache entries that were built from the table records in question.
A beginner’s guide to Serializability
Introduction In this article, we are going to see what Serializability means and what guarantees does it offer. Relational database systems provide a Serializable isolation level that’s supposed to provide transaction Serializability. However, as you will soon see, some databases even provide Strict Serializability, which is a combination of Serializability and Linearizability.
PostgreSQL trigger consistency check
Introduction In this article, we are going to see how to implement a non-trivial consistency check using a PostgreSQL INSERT and UPDATE trigger. By using a database trigger after executing an INSERT or UPDATE, we can ensure that the sum of salaries in a given department does not exceed the maximum budget allocated for the given department.
A beginner’s guide to Linearizability
Introduction Linearizability is a lesser-known, yet omnipresent property of a data registry in the context of read and write operations that might happen concurrently. This article aims to explain what linearizability consists of, and why it’s more prevalent that you might have previously thought.
Things to consider before jumping to application-level caching
Introduction Relational database transactions are ACID and the strong consistency model simplifies application development. Because enabling Hibernate caching is one configuration away, it’s very appealing to turn to caching whenever the data access layer starts showing performance issues. Adding a caching layer can indeed improve application performance, but it has its price and you need to be aware of it.
