In the realm of Java development, the pursuit of the right Persistence framework can be akin to choosing the right vehicle for a long journey. Many developers, including myself, have long relied on JPA (Java Persistence API) for database interactions. However, after years of working with Hibernate and recognizing its complexities, we ventured into the expansive landscape of non-JPA SQL mapping frameworks, discovering a more robust means of managing data. This article walks you through various non-JPA frameworks such as **Spring JDBCTemplate**, **jOOQ**, and others, ultimately guiding you to make an informed decision on which could best serve your next development project.

Why Only Non-JPA?

Our journey began with JPA, which often felt like a labyrinth with too many corridors and unexpected turns. Over the years, the magic behind Hibernate turned into what we termed as “bloatware,” creating intricacies rather than simplifying tasks. Therefore, we decided to drop JPA completely and experiment with alternatives like **Spring JDBCTemplate**, resulting in quicker delivery times, improved performance, and manageable code. Cumulatively, it feels refreshing, leading us to explore and share our findings regarding the other non-JPA options available for SQL mapping.

The Methodology Behind Our Exploration

In understanding these frameworks better, we set out to explore practical everyday scenarios with a focus on usability rather than performance. Our intent was to create a point of reference with templates showcasing common uses across different frameworks. Below, we outline our comparison methodology:

• Implemented common data-centric application tasks
• Evaluated frameworks based on their readability and efficiency
• Documented best practices and welcomed community feedback

Frameworks Compared

Our selection criteria led us to evaluate several frameworks based on three core aspects:

• Embracing SQL language and RDBMS usage
• Maturity for enterprise-level applications
• Optional use of JPA annotations without being a full JPA implementation

The frameworks we dive into are:

• Spring JDBCTemplate
• jOOQ
• MyBatis
• EBean
• JDBI (version 2.77)

Understanding Common Scenarios

We chose real-world scenarios to implement using these frameworks, each focusing on a critical function commonly required within a data-centric application:

1. Fetch single entity based on primary key
2. Fetch list of entities based on condition
3. Save new single entity and return primary key
4. Batch insert multiple entities
5. Update a single existing entity
6. Perform complex selects with joins
7. Manage one-to-many and many-to-one relationships

Each scenario has its tangible implementation in our Scenarios class, which also describes how to best approach them.

How to Get Started

1. Clone the repository from GitHub
2. Configure your PostgreSQL connection details in application.properties
3. Create necessary tables and data by executing create-script.sql
4. Run register_employee.sql to set up a stored procedure
5. Run JUnit tests via Gradle, ensuring that you also set up EBean enhancer for your IDE for unit tests to run smoothly
6. Test the scenarios by executing the provided test classes!

Troubleshooting and Recommendations

As you navigate through different frameworks, you might encounter various challenges. Here are some tips for seamless sailing:

• Consult the documentation carefully to understand any setup requirements unique to each framework.
• Review any error logs, as they often provide insight into what’s gone wrong during execution.
• If you encounter issues with JUnit tests not passing, double-check your configuration settings.
• For community discussions or detailed insights, feel free to reach out.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

The journey into non-JPA frameworks has opened our eyes to a world of possibilities with SQL mapping in Java. While our findings may lean towards a subjective standpoint, they certainly serve as a guiding light for any developer grappling with the decisions of framework selection.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.