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Afraid of reopened issues?
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
Reopened issues and developer feelings don’t mix well, a recurrent phenomenon I’ve seen on all projects I’ve worked on. Some might feel they’ve worked “in vain”, being reluctant to restart it all over again.
Reopened issues are bound to happen
There is a thin line between taking ownership of your current project and remaining professionally detached at all times. The only thing that matters is the value the customer gets for any given issue, even if it takes more steps than you previously anticipated. In software development, the change is the only thing that never changes, that’s why you’ll always have to deal with reopened issues. Reopening an issue is not necessarily a bad thing, as you’ll soon find out.
Choosing a leader like an agilist
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 leader as a captain
I recently read Petri Kainulainen’s article on sharing leadership among team members and I am on the same wavelength in this regard, since the Agile methods emphasizes the importance of “motivated individuals, who should be trusted”.
While a team leader could be regarded as a reminiscence of the old rigid organization structures, I still see many benefits of having such a captain. When it comes to improving my people skills, I like to get inspired by other domains of activity that have been struggling with the very same challenges for decades. Think about the greatest sports teams, where all team members are stars, and yet they always have a captain for:
- the captain is the role-model for every team member
- he leads by example
- he always mediates conflicts, knowing that prevention is always better than curing
- he welcomes and facilitates new team members integration
- he never loses his temper
- in times of trouble, he is the voice of the team
- he is constantly goal-oriented, making sure the team is on the right track
Caching best practices
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
There is an irresistible attraction to writing custom caching solutions since it seems to be the easiest path to “improving” the overall application performance. Well, caching is a great technique, but there are few steps to consider before even considering it.
The regex that broke a server
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
I’ve never thought I would see an unresponsive server due to a bad regex matcher but that’s just happened to one of our services, yielding it unresponsive.
Let’s assume we parse some external dealer car info. We are trying to find all those cars with “no air conditioning” among various available input patterns (but without matching patterns such as “mono air conditioning”).
A beginner’s guide to Git feature branches
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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!
Why Git
The proprietary software shaped the Version Control Systems (VCS) to fit its requirements:
- the project has a strict release schedule
- the team is collocated
- the sprint goals are well-defined and the focus goes to a limited number of stories
- branching is usually reserved for releases or risky development features
- the centralized server is hidden from the outside world
This is the context in which centralized Version Control Systems (e.g. Subversion) have emerged, but that’s not a good fit for open-source projects because:
- releases are not imposed by deadlines
- the contributors may be scattered all over the globe
- new ideas are welcomed, even if radical or time-consuming
- branching becomes mandatory as developers work on features rather than sprints
- the code is available to the whole world
JOOQ Facts: SQL functions made easy
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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
The JDBC API has always been cumbersome and error-prone and I’ve never been too fond of using it. The first major improvement was brought by the Spring JDBC framework which simply revitalized the JDBC usage with its JdbcTemplate or the SqlFunction classes, to name a few. But Spring JDBC doesn’t address the shortcoming of using string function or input parameters names and this opened the door for type-safe SQL wrappers such as jOOQ.
JOOQ is the next major step towards a better JDBC API and ever since I started using it I knew there was no turning back. JOOQ became my number one choice for building dynamic queries and recently it became my standard SQL function wrapper.
Code review best practices
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!
Code review is a great software instrument and you should definitely use it to improve the quality of your code. But like any other tool, it may be misused sometimes. That’s why I came up with a list of best practices to guide you when reviewing your peers’ code.
- Code review is not testing: Code review is a developer-to-developer business and it doesn’t involve any testing. Code review should check if the task requirements are met in the cleanest possible way.
- You don’t tell what to code review: The same way you don’t tell a tester what to test, you should never tell your peer what to review. The magic of peer review comes from your peer own perspective on the current task design and implementation. Two minds are always better than one.
- You should always check all changes: Bugs may be hidden anywhere and you should search for them diligently. To have the whole picture you need to go through all changes.
- Requirements first: Requirements are the most important driving force. After all, that’s what the customer is paying for. If the current changes are suboptimal you need to reopen the issue. If you happen to spot other code sections that need to be refactored you should create new issues instead of reopening the current one. The “single responsibility principle” applies to tasks as well as to coding.
- One-to-many activity: If you can’t make sure you grasp the code change intention it’s safer to ask somebody else to review it further.
- A way of learning: Code review is a great learning technique especially on large projects. Ideally, you should become familiar with every aspect of your project, but if the project is too large you can at least specialize in multiple modules.
Happy code reviewing!
How to detect the Hibernate N+1 query problem during testing
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 this article, you are going to learn how to automatically detect the N+1 query problem when using JPA and Hibernate using the Hypersistence Utils open-source project.
With Hibernate, you manage entity state transitions, which are then translated to SQL statements. The number of generated SQL statements is affected by the current fetching strategy, Criteria queries, or Collection mappings, and you might not always get what you expected. Ignoring SQL statements is risky, and it may eventually put a heavy toll on the overall application performance.
I’m a strong advocate of peer review, but that’s not “sine qua non” for detecting bad Hibernate usage. Subtle changes may affect the SQL statement count and pass unnoticed through the reviewing process. Not least, when it comes to “guessing” the JPA SQL statements, I feel like I can use any extra help. I’m for as much automation as possible, and that’s why I came up with a mechanism for enforcing the SQL statement count expectations.
JSON pattern matching with sed, perl and regular expressions
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!
Why VIM?
Sooner or later there comes the day when your easy-to-use IDE becomes useless for handling huge files. There aren’t many editors capable of working with very large files, like production logs for instance.
I’ve recently had to analyze a 100 MB one-line JSON file and once more VIM saved the day. VIM, like many other Unix utilities, is both tough and brilliant.
Git interactive rebase uses VIM by default, so it’s worth knowing VIM.
MongoDB and the fine art of data modeling
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
This is the third part of our MongoDB time series tutorial, and this post will emphasize the importance of data modeling. You might want to check the first part of this series, to get familiar with our virtual project requirements and the second part talking about common optimization techniques.
When you first start using MongoDB, you’ll immediately notice it’s schema-less data model. But schema-less doesn’t mean skipping proper data modeling (satisfying your application business and performance requirements). As opposed to a SQL database, a NoSQL document model is more focused towards querying than to data normalization. That’s why your design won’t be finished unless it addresses your data querying patterns.