Today, I stumbled upon a StackOverflow answer that I gave some time ago and realized that it deserves a post of its own.
As previously explained, the
@JoinForuma is a very awesome annotation which allows you to customize the way you join entities beyond JPA
Continue reading “How to map the latest child of a parent entity using Hibernate @JoinFormula”
While doing my High-Performance Java Persistence training, I came to realize that it’s worth explaining how a relational database works, as otherwise, it is very difficult to grasp many transaction-related concepts like atomicity, durability, and checkpoints.
In this post, I’m going to give a high-level explanation of how a relational database works internally while also hinting some database-specific implementation details.
Continue reading “How does a relational database work”
As previously explained, you can run database integration tests 20 times faster! The trick is to map the data directory in memory, and my previous article showed you what changes you need to do when you have a PostgreSQL or MySQL instance on your machine.
In this post, I’m going to expand the original idea, and show you how you can achieve the same goal using Docker and tmpfs.
Continue reading “How to run integration tests at warp speed using Docker and tmpfs”
Relational database systems employ various Concurrency Control mechanisms to provide transactions with ACID property guarantees. While isolation levels are one way of choosing a given Concurrency Control mechanism, you can also use explicit locking whenever you want a finer-grained control to prevent data integrity issues.
As previously explained, there are two types of explicit locking mechanisms: pessimistic (physical) and optimistic (logical). In this post, I’m going to explain how explicit pessimistic locking interacts with non-query DML statements (e.g. insert, update, and delete).
Continue reading “How does database pessimistic locking interact with INSERT, UPDATE, and DELETE SQL statements”
Unlike SQL Server which, by default, relies on the 2PL (Two-Phase Locking) to implement the SQL standard isolation levels, Oracle, PostgreSQL, and MySQL InnoDB engine use MVCC (Multi-Version Concurrency Control).
However, providing a truly Serializable isolation level on top of MVCC is really difficult, and, in this post, I’ll demonstrate that it’s very difficult to prevent the Phantom Read anomaly without resorting to pessimistic locking.
Continue reading “A beginner’s guide to the Phantom Read anomaly, and how it differs between 2PL and MVCC”