How to improve statement caching efficiency with IN clause parameter padding
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!
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
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!
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
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!
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
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!
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
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!
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
9 High-Performance Tips when using PostgreSQL with JPA and Hibernate
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!
Introduction
To get the most out of the relational database in use, you need to make sure the data access layer resonates with the underlying database system.
In this article, we are going to see what you can do to boost up performance when using PostgreSQL with JPA and Hibernate.
PostgreSQL reWriteBatchedInserts configuration property
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!
Introduction
The PostgreSQL JDBC Driver has been adding a lot of very useful optimizations, and one of the lesser-known ones is the reWriteBatchedInserts configuration property.
In this article, you will see how the reWriteBatchedInserts JDBC configuration property works in PostgreSQL, and how it allows you to rewrite INSERT statements into a multi-VALUE INSERT.
How to map an immutable entity with JPA and Hibernate
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!
Introduction
In this article, you are going to find out how to map an immutable entity when using JPA and Hibernate.
If your Domain Model requires that a given entity should not be modified by the data access logic, Hibernate can enforce this requirement if the entity is marked with the @Immutable annotation.
PostgreSQL SERIAL or IDENTITY column and Hibernate IDENTITY generator
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!
Introduction
When using PostgreSQL, it’s tempting to use a SERIAL or BIGSERIAL column type to auto-increment Primary Keys.
PostgreSQL 10 also added support for IDENTITY, which behaves in the same way as the legacy SERIAL or BIGSERIAL type.
This article will show you that SERIAL, BIGSERIAL, and IDENTITY are not a very good idea when using JPA and Hibernate.
