The LazyInitializationException is a common occurrence when using Hibernate. It typically happens when you access a lazy-loaded association outside of the persistence context in which it was loaded. I will show you how to prevent this exception without compromising the performance of your application. Through the lens of Spring Data, I will explain how to resolve this challenge with a handy annotation: @EntityGraph. Implementing this strategy allows you to optimize both for efficiency and effectiveness, avoiding one of the common pitfalls in Hibernate.
Table of Contents
- LazyInitializationException in Hibernate
- EntityGraph Annotation as a Solution
- Using EntityGraph Annotation
- Conclusion
LazyInitializationException in Hibernate
This exception is thrown because Hibernate tries to proxy collections and associations for lazy loading by default. When you try to access these proxies after the session has closed, Hibernate cannot load the actual data and throws the LazyInitializationException.
For example, consider two entities Employee and Department, where an Employee entity might have a many-to-one association to the Department entity:
import jakarta.persistence.*
@Entity
public class Employee {
...
@ManyToOne(fetch = FetchType.LAZY)
@JoinColumn(name="department_id")
private Department department;
...
}Since the employee’s department object isn’t prefetched (FetchType.LAZY), if we try to access the Department of an Employee after the session is closed, we will encounter a LazyInitializationException.
A naive solution for this issue would be to eagerly fetch the department data every time we load an employee’s record from the database. However, such an approach can quickly lead to performance degradation. Essentially, this leads to an excessive number of queries and handling large objects in memory. Entity relationships in real-life projects can become complex and deeply interconnected, further compounding these challenges.
EntityGraph Annotation as a Solution
One of the solutions, if you use JPA with Spring Data, is to apply JPA’s EntityGraph annotation. @EntityGraph allows you to define a graph of entities that should be fetched from the database in a single selection for a specific use case. We can define an EntityGraph on the repository method to eagerly fetch the required associations. This makes sure the data is already loaded when the session is open, preventing the LazyInitializationException. Yet, we preserve the benefits of lazy loading, since we have complete control over when the eager fetch is applied.
Using EntityGraph Annotation
The following example demonstrates using EntityGraph annotation in Spring Data:
import org.springframework.data.jpa.repository.JpaRepository
import org.springframework.data.jpa.repository.EntityGraph
public interface EmployeeRepository extends JpaRepository<Employee, Long> {
Optional<Employee> findById(Long id);
}
...
public interface EmployeeRepositoryWithEagerFetch extends JpaRepository<Employee, Long> {
@EntityGraph(attributePaths = {"department"})
Optional<Employee> findById(Long id);
}In the above code, @EntityGraph(attributePaths = {"department"}) tells Hibernate to eagerly fetch the Department association when it loads the Employee. This offers you control over when the eager fetch applies, a point I have demonstrated by defining two distinct repositories. The EmployeeRepository ensures no relationships are loaded by default. Conversely, the EmployeeRepositoryWithEagerFetch has been designed for use as needed. Specifically when we need to access both the department data and the corresponding employee details simultaneously.
Conclusion
The LazyInitializationException is one of the most common challenges faced by developers while working with Hibernate. The EntityGraph annotation is a useful tool to address this issue by determining what associated entity mappings need to be fetched from the database during the execution of a query. This helps avoid accessing an uninitialized collection. Moreover, it allows us to establish control over when eager fetching is applied. This means we can enjoy the benefits of lazy loading where advantageous, making our data model more efficient and flexible.
