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Spring request-level memoization
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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
Memoization is a method-level caching technique for speeding-up consecutive invocations.
This post will demonstrate how you can achieve request-level repeatable reads for any data source, using Spring AOP only.
Spring Caching
Spring offers a very useful caching abstracting, allowing you do decouple the application logic from the caching implementation details.
Spring Caching uses an application-level scope, so for a request-only memoization we need to take a DIY approach.
Java Performance Workshop with Peter Lawrey
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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!
Peter Lawrey at IT Days
I’ve just come back from a Java Performance Workshop held by Peter Lawrey at Cluj-Napoca IT Days.
Peter Lawrey is a well-known Java StackOverflow user and the creator of Java Chronicle open-source library.
Of Java and low latency
Little’s Law defines concurrency as:
To increase throughput we can either:
- increase server resources (scaling vertically or horizontally)
- decrease latency (improve performance)
While enterprise applications are usually designed for scaling, trading systems focus on lowering latencies.
As surprising as it may sound, most trading systems, I’ve heard of, are actually written in Java. Java automatic memory management is a double-edged sword, since it trades simplicity for flexibility.
A low latency system worse nightmare is a stop-of-the-world process, like a garbage collector major collection. So in order to avoid such situations, you must go off-heap.
How to address the OptimisticLockException in JPA and Hibernate
Last modified:
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
Application-level repeatable reads are suitable for preventing lost updates in web conversations. Enabling entity-level optimistic locking is fairly easy. You just have to mark one logical-clock property (usually an integer counter) with the JPA @Version annotation and Hibernate takes care of the rest.
The catch
Optimistic locking discards all incoming changes that are relative to an older entity version. But everything has a cost and optimistic locking makes no difference.
The optimistic concurrency control mechanism takes an all-or-nothing approach even for non-overlapping changes. If two concurrent transactions are changing distinct entity property subsets, then there’s no risk of losing updates.
Two concurrent updates, starting from the same entity version are always going to collide. It’s only the first update that’s going to succeed, the second one failing with an optimistic locking exception. This strict policy acts as if all changes are overlapping. For highly concurrent write scenarios, this single-version check strategy can lead to a large number of roll-backed updates.
Hibernate collections optimistic locking
Last modified:
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
Hibernate provides an optimistic locking mechanism to prevent lost updates even for long-conversations. In conjunction with an entity storage, spanning over multiple user requests (extended persistence context or detached entities) Hibernate can guarantee application-level repeatable-reads.
The dirty checking mechanism detects entity state changes and increments the entity version. While basic property changes are always taken into consideration, Hibernate collections are more subtle in this regard.
Owned vs Inverse collections
In relational databases, two records are associated with a foreign key reference. In this relationship, the referenced record is the parent while the referencing row (the foreign key side) is the child. A non-null foreign key may only reference an existing parent record.
In the Object-oriented space, this association can be represented in both directions. We can have a many-to-one reference from a child to parent and the parent can also have a one-to-many children collection.
Because both sides could potentially control the database foreign key state, we must ensure that only one side is the owner of this association. Only the owning side state changes are propagated to the database. The non-owning side has been traditionally referred as the inverse side.
Next, I’ll describe the most common ways of modeling this association.
How does Hibernate guarantee application-level repeatable reads
Last modified:
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
In my previous post I described how application-level transactions offer a suitable concurrency control mechanism for long conversations.
All entities are loaded within the context of a Hibernate Session, acting as a transactional write-behind cache.
A Hibernate persistence context can hold one and only one reference to a given entity. The first level cache guarantees session-level repeatable reads.
If the conversation spans over multiple requests we can have application-level repeatable reads. Long conversations are inherently stateful so we can opt for detached objects or long persistence contexts. But application-level repeatable reads require an application-level concurrency control strategy such as optimistic locking.
The catch
But this behavior may prove unexpected at times.
MongoDB Incremental Migration Scripts
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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
An incremental software development process requires an incremental database migration strategy.
I remember working on an enterprise application where the hibernate.hbm2ddl.auto was the default data migration tool.
Updating the production environment required intensive preparation and the migration scripts were only created on-the-spot. An unforeseen error could have led production data corruption.
Incremental updates to the rescue
The incremental database update is a technical feature that needs to be addressed in the very first application development iterations.
We used to develop our own custom data migration implementations and spending time on writing/supporting frameworks is always working against your current project budget.
A project must be packed with both application code and all associated database schema/data updates scripts. Using incremental migration scripts allows us to automate the deployment process and to take advantage of continuous delivery.
Nowadays you don’t have to implement data migration tools, Flyway does a better job than all our previous custom frameworks. All database schema and data changes have to be recorded in incremental update scripts following a well-defined naming convention.
A RDBMS migration plan addresses both schema and data changes. It’s always good to separate schema and data changes. Integration tests might only use the schema migration scripts in conjunction with test-time related data.
Flyway supports all major relational database systems but for NoSQL (e.g. MongoDB) you need to look somewhere else.
One year of blogging
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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!
Teaching is my way of learning
Exactly one year ago today, I wrote my very first blog post. It’s been such a long journey ever since, so it’s time to draw a line and review all my technical writing accomplishments.
I realized that sharing knowledge is a way of pushing myself to reason thoroughly on a particular subject. So, both my readers and I have something to learn from my writing. Finding time to think of future blog topics, researching particular subjects, writing code snippets and the ever-present pre-publishing reviews is worth the hassle.
Under the umbrella
The Internet is huge, so being heard is not something you would leave to chance. From the start, I knew that I needed to do more than writing high-quality articles. When nobody knows anything about you, your only chance is strategic marketing.
Several well-received articles, and Allen Coin proposed me for the Dev of the Week column.