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High-Performance Java Persistence – Part One
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!
The journey
Four months, one week and two days and 114 pages; that’s how much it took to write the first part of the High-Performance Java Persistence book.
As previously stated, the book is developed in an Agile fashion. Each part represents a milestone, which is accompanied by a release. This way, the readers can get access to the book content prior to finishing the whole book (which might take a year or so).
A beginner’s guide to Read and Write Skew phenomena
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 article about ACID and database transactions, I introduced the three phenomena described by the SQL standard:
- dirty read
- non-repeatable read
- phantom read
While these are good to differentiate the four isolation levels (Read Uncommitted, Read Committed, Repeatable Read and Serializable), in reality, there are more phenomena to take into consideration as well. The 1995 paper (A Critique of ANSI SQL Isolation Levels) introduces the other phenomena that are omitted from the standard specification.
In my High-Performance Java Persistence book, I decided to insist on the Transaction chapter as it is very important for both data-access effectiveness and efficiency.
Two years of blogging
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!
Keep on writing
It’s been two years since I started this blog and so it’s time to draw a line and see how it went since last year.
Page Views
In these two years, I got 500.000 page views and the last month was the first one to break the 50.000 views threshold.
How does the MySQL JDBC driver handle prepared statements
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!
Prepared statement types
While researching for the Statement Caching chapter in my High-Performance Java Persistence book, I got the chance to compare how Oracle, SQL Server, PostgreSQL and MySQL handle prepare statements.
Thanks to Jess Balint (MySQL JDBC driver contributor), who gave a wonderful answer on StackOverflow, I managed to get a better understanding of how MySQL handles prepared statements from a database performance point of view.
A paranoid’s guide to backing up a working folder
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!
Oops time
Leanpub supports multiple storage engines, and a private GitHub repository is probably the safest way to the backing up your working folder. I chose Dropbox as I didn’t envision anything wrong with the automatic synchronization mechanism.
While working on my book, I accidentally managed to wipe out half of my diagrams, and all changes were instantly synchronized by Dropbox. The free-of-charge Dropbox account doesn’t offer folder-level versioning, so deleted files are simply gone. Fortunately, IntelliJ IDEA Local History saved the day, and the diagrams were properly restored.
How does FlexyPool support both Connection proxies and decorators
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!
Proxies
FlexyPool monitors connection pool usage and so it needs to intercept the connection close method call.
For simplicity sake, the first version was relying on dynamic proxies for this purpose:
private static class ConnectionInvocationHandler
implements InvocationHandler {
public static final String CLOSE_METHOD_NAME = "close";
private final Connection target;
private final ConnectionCallback callback;
public ConnectionInvocationHandler(
Connection target,
ConnectionCallback callback) {
this.target = target;
this.callback = callback;
}
@Override
public Object invoke(
Object proxy,
Method method,
Object[] args) throws Throwable {
if (CLOSE_METHOD_NAME.equals(method.getName())) {
callback.close();
}
return method.invoke(target, args);
}
}
As straightforward as it may be, a proxy invocation is slower than a decorator, which calls the target method using a direct invocation.
Because all connection pools use proxies anyway, adding another proxy layer only adds more call-time overhead and so now FlexyPool supports connection decorators as well.
Why you should always use connection pooling with Oracle XE
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
Oracle Express Edition is the free version of Oracle Enterprise Edition and its smaller size makes it very convenient for testing various Oracle functionalities.
According to Oracle documentation, the Express Edition can use at most one CPU and 1 GB of RAM, but in reality, there are other limitations that are not always obvious.
How to translate WordPress blog posts to Leanpub Markdown
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!
Unix tools to the rescue
I am a big fan of Unix utilities as they help me out on a daily basis. When the Leanbub WordPress export tool failed me, I knew I had to write my own import script. Without an automatic script, I’d had to spend more than half an hour to fix broken source codes or tables and migrate image references to the book repository folder structure.
But the The High-Performance Java Persistence book is using info that’s scattered across over hundreds of blog posts, which would have taken me months to manually import.
Speedment ORM – deliberate enterprise caching
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!
A small company with big dreams
I first heard of Speedment while watching a Hazelcast webinar about a RDBMS Change Data Capture approach for updating the in-memory data grid.
In this article, we will have the pleasure of talking to Per-Åke Minborg, who is the CTO and one of the founders of Speedment AB.
How does FlexyPool support the Dropwizard Metrics package renaming
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
FlexyPool relies heavily on Dropwizard (previously Codahale) Metrics for monitoring the connection pool usage. Being integrated into Dropwizard, the package name was bound to be renamed.
So instead of com.codahale.metrics the 4.0.0 release will use the io.dropwizard.metrics package name.