High-Performance Java Persistence – Chapter 13 – Flushing

Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Wouldn’t that be just awesome?

Well, Hypersistence Optimizer is that tool! And it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, or Play Framework.

So, enjoy spending your time on the things you love rather than fixing performance issues in your production system on a Saturday night!

Part 2, Chapter 13

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 inner-workings of the Hibernate Persistence Context implementation.

13. Flushing 
13.1 Flush modes
13.2 Events and the action queue
13.2.1 Flush operation order
13.3 Dirty Checking 
13.3.1 The default dirty checking mechanism Controlling the Persistence Context size 
13.3.2 Bytecode enhancement 

Chapter summary

As explained in the write-based optimizations section, the Persistence Context acts as a transactional write-behind cache. The Hibernate Session is commonly referred to as the first-level cache since every managed entity is stored in a Map, and, once an entity is loaded, any successive request serves it from the cache, therefore avoiding a database roundtrip.
However, aside from caching entities, the Persistence Context acts as an entity state transition buffer.

Like any write-behind cache, the Persistence Context requires flushing in order to synchronize the in-memory persistent state with the underlying database. At flush time, Hibernate can detect if a managed entity has changed since it was loaded and trigger a table row update. This process is called dirty checking, and it greatly simplifies data access layer operations.

However, having an intermediate write-behind cache is not without challenges and the Persistence Context can be subject to data inconsistencies. Since efficiency is meaningless if effectiveness is being compromised, this chapter aims to analyze the inner-workings of the flushing mechanism, so the application developer knows how to optimize it without affecting data consistency.

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