Part 2, Chapter 10 Every new chapter of my book is released right after it’s being completed, so the reader doesn’t have to wait for the whole part to be finished to get access to new material. Table of content This chapter explains the core mapping elements used by Hibernate and details the basic type and the identifier generators.
Why? JPA relies heavily on the persistence.xml configuration file, and the standard API to bootstrap a JPA provider programmatically requires too much boilerplate code. While in a typical enterprise application, providing a persistence.xml file is not really an issue, this requirement doesn’t get along with unit testing, especially when tests are completely isolated and they need to validate different aspects of JPA or Hibernate. That was an issue that I bumped into when writing test cases for the High-Performance Java Persistence book. All my tests need to be isolated, and not all… Read More
Part 2, Chapter 9 Every new chapter of my book is released right after it’s being completed, so the reader doesn’t have to wait for the whole part to be finished to get access to new material. Table of content This chapter explains how to handle connections in Hibernate and how to monitor their usage as well as the statement that gets automatically generated, and the table of contents looks like this:
Second part, Chapter 8 Now that the first part of my book is published, it’s time to focus on the second part, which covers both JPA and Hibernate. From now on, every new chapter is going to be released right after it’s completed, so the reader doesn’t have to wait for the whole part to be finished to get access to new chapters. Table of content This chapter aims to remind the reader why Hibernate has its place in high-performance data access, and the table of contents looks like this:
Introduction In this article, we are going to learn how the JPA and Hibernate Cascade Types work. JPA translates entity state transitions to database DML statements. Because it’s common to operate on entity graphs, JPA allows us to propagate entity state changes from Parents to Child entities. This behavior is configured through the CascadeType mappings.
Recap In my previous post, I explained the benefits of using explicit optimistic locking. As we then discovered, there’s a very short time window in which a concurrent transaction can still commit a Product price change right before our current transaction gets committed. This issue can be depicted as follows: Alice fetches a Product She then decides to order it The Product optimistic lock is acquired The Order is inserted in the current transaction database session The Product version is checked by the Hibernate explicit optimistic locking routine The price engine manages… Read More
Introduction In my previous post I demonstrated how you can scale optimistic locking through write-concerns splitting. Version-less optimistic locking is one lesser-known Hibernate feature. In this post, I’ll explain both the good and the bad parts of this approach. Version-less optimistic locking Optimistic locking is commonly associated with a logical or physical clocking sequence, for both performance and consistency reasons. The clocking sequence points to an absolute entity state version for all entity state transitions. To support legacy database schema optimistic locking, Hibernate added a version-less concurrency control mechanism. To enable this… Read More
Introduction In my previous post I introduced the entity state transitions Object-relational mapping paradigm. All managed entity state transitions are translated to associated database statements when the current Persistence Context gets flushed. Hibernate’s flush behavior is not always as obvious as one might think. Write-behind Hibernate tries to defer the Persistence Context flushing up until the last possible moment. This strategy has been traditionally known as transactional write-behind. The write-behind is more related to Hibernate flushing rather than any logical or physical transaction. During a transaction, the flush may occur multiple times…. Read More
Introduction Hibernate shifts the developer mindset from SQL statements to entity state transitions. Once an entity is actively managed by Hibernate, all changes are going to be automatically propagated to the database. Manipulating domain model entities (along with their associations) is much easier than writing and maintaining SQL statements. Without an ORM tool, adding a new column requires modifying all associated INSERT/UPDATE statements. But Hibernate is no silver bullet either. Hibernate doesn’t free us from ever worrying about the actual executed SQL statements. Controlling Hibernate is not as straightforward as one might… Read More
Introduction In this article, you are going to learn how to automatically detect the N+1 query problem when using JPA and Hibernate using the db-util open-source project. With Hibernate, you manage entity state transitions which are then translated to SQL statements. The number of generated SQL statements is affected by the current fetching strategy, Criteria queries or Collection mappings and you might not always get what you expected. Ignoring SQL statements is risky and it may eventually put a heavy toll on the overall application performance. I’m a strong advocate of peer… Read More