A beginner’s guide to Phantom Read anomaly
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
Database transactions are defined by the four properties known as ACID. The Isolation Level (I in ACID) allows you to trade off data integrity for performance.
The weaker the isolation level, the more anomalies can occur, and in this article, we are going to describe the Phantom Read phenomenon.
A beginner’s guide to Phantom Read anomaly - @vlad_mihalcea https://t.co/TJ6otpwu4V pic.twitter.com/dI9hPZ7gXK
— Java (@java) July 12, 2018
How do find and getReference EntityManager methods work when using JPA and Hibernate
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
While doing my High-Performance Java Persistence training, I realized that not all developers are familiar with the getReference method of the JPA EntityManager and most of them use find almost exclusively.
In this article, we are going to see the difference between the find and getReference method so that it’s clear when to apply them depending on the underlying use case.
See the difference between the find and getReference method in JPA and #Hibernate - @vlad_mihalcea https://t.co/vfnBexGpOt pic.twitter.com/xMqeh8Lf2f
— Java (@java) July 3, 2018
A beginner’s guide to Non-Repeatable Read anomaly
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
Database transactions are defined by the four properties known as ACID. The Isolation Level (I in ACID) allows you to trade off data integrity for performance.
The weaker the isolation level, the more anomalies can occur, and in this article, we are going to describe the Non-Repeatable Read phenomenon.
A beginner’s guide to Non-Repeatable Read anomaly - @vlad_mihalcea https://t.co/AZzNpLPBkf pic.twitter.com/tZsH7XGwlV
— Java (@java) June 25, 2018
MariaDB 10.3 supports database sequences
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
Traditionally, both MySQL and MariaDB relied on AUTO_INCREMENT columns to generate an IDENTITY Primary Key. Although IDENTITY columns are very efficient in generating the Primary Key value, when it comes to using JPA and Hibernate, the IDENTITY generator prevents us from using JDBC batch inserts.
To automatically enroll multiple INSERT, UPDATE or DELETE statements, Hibernate requires delaying the SQL statement until the Persistence Context is flushed. This works very well for the SEQUENCE identifier since the entity identifier can be fetched prior to executing the INSERT statement.
However, for IDENTITY columns, the only way to know the entity identifier is if we execute the SQL INSERT statement. And, Hibernate needs the entity identifier when persisting an entity because otherwise, it cannot build the key which is used for locating an entity in the currently running Persistence Context.
How to improve statement caching efficiency with IN clause parameter padding
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
Recently, I stumbled on the following Twitter thread:
A lesser known jOOQ feature that developers don't know about, but DBAs will rejoice!
— jOOQ (@JavaOOQ) April 11, 2018
Automatic IN-list padding (to prevent contention on execution plan caches / too many hard parses)https://t.co/CNa7kd5rAr
This jOOQ feature is indeed really useful since it reduces the number of SQL statements that have to be generated when varying the IN clause parameters dynamically.
Starting with Hibernate ORM 5.2.18, it’s now possible to use IN clause parameter padding so that you can improve SQL Statement Caching efficiency.
In this article, I’m going to explain how this new mechanism works and why you should definitely consider it when using a relational database system that supports Execution Plan caching.
A beginner’s guide to Dirty Read anomaly
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
Database transactions are defined by the four properties known as ACID. The Isolation Level (I in ACID) allows you to trade off data integrity for performance.
The weaker the isolation level, the more anomalies can occur, and in this article, we are going to describe the Dirty Read phenomenon.
A beginner’s guide to Linearizability
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
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.
How to synchronize bidirectional entity associations with JPA and Hibernate
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
While answering this StackOverflow question, I realized that it’s a good idea to summarize how various bidirectional associations should be synchronized when using JPA and Hibernate.
Therefore, in this article, you are going to learn how and also why you should always synchronize both sides of an entity relationship, no matter if it’s @OneToMany, @OneToOne or @ManyToMany.
How to avoid the Hibernate Query Cache N+1 issue
Are you struggling with performance issues in your Spring, Jakarta EE, or Java EE application?
Imagine having a tool that could automatically detect performance issues in your JPA and Hibernate data access layer long before pushing a problematic change into production!
With the widespread adoption of AI agents generating code in a heartbeat, having such a tool that can watch your back and prevent performance issues during development, long before they affect production systems, can save your company a lot of money and make you a hero!
Hypersistence Optimizer is that tool, and it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, Micronaut, or Play Framework.
So, rather than allowing performance issues to annoy your customers, you are better off preventing those issues using Hypersistence Optimizer and enjoying spending your time on the things that you love!
Introduction
I recently answered this question on the Hibernate forum, and since it’s a very good one, I decided to turn it into an article.
In this post, we will describe how the N+1 query issue is generated when using the second-level Hibernate Query Cache.
How the N+1 query issue is generated when using the second-level #Hibernate Query Cache - @vlad_mihalceahttps://t.co/ysel1ZBYU3 pic.twitter.com/Dg8gzlO6ST
— Java (@java) June 7, 2018
