N+1 Problem and Pagination with One-To-Many Relationship in Java

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Introduction

The N+1 problem is a common issue in object-oriented programming, particularly when dealing with relationships between entities. In this article, we’ll explore how to paginate entities with one-to-many relationships using Hibernate’s fetch types without warning firstResult/maxResults specified.

Background

Hibernate, a popular Java persistence framework, provides several ways to manage relationships between entities. However, when it comes to pagination and fetching related data, things can get complex. In this article, we’ll delve into the N+1 problem, its causes, and solutions using Hibernate’s fetch types.

The N+1 Problem

The N+1 problem occurs when an entity is queried, and for each instance of that entity, a separate query is executed to retrieve related data. This can lead to performance issues, especially when dealing with large datasets or complex relationships.

In our case, we have a one-to-many relationship between Customer and Figure. When fetching all customers, we want to include their related figures in the response. However, due to the N+1 problem, Hibernate will execute multiple queries, leading to unnecessary database access and potential performance issues.

Example Use Case

Suppose we have two entities: Customer and Figure. The Customer entity has a one-to-many relationship with Figure, as shown in our example:

@OneToMany(mappedBy = "createdBy",
        cascade = {CascadeType.MERGE, CascadeType.PERSIST},
        fetch = FetchType.LAZY,
        orphanRemoval = true)
@ToString.Exclude
private Set<Figure> figures = new HashSet<>();

When fetching all customers, we want to include their related figures in the response. However, due to the N+1 problem, Hibernate will execute multiple queries, leading to unnecessary database access.

Fetch Types and Lazy Loading

To mitigate the N+1 problem, Hibernate provides several fetch types that allow us to control how related data is loaded.

• FetchType.EAGER: Loads all related data upfront. This can improve performance but increases memory usage.
• FetchType.LAZY: Loads related data only when needed. This is the default behavior for one-to-many relationships.
• FetchType.PRELOAD: A combination of EAGER and LAZY fetching.

Using Fetch Type LAZY

By setting the fetch type to LAZY, we can avoid the N+1 problem. However, this means that related data will only be loaded when needed, which might lead to performance issues if the data is large or complex.

@OneToMany(mappedBy = "createdBy",
        cascade = {CascadeType.MERGE, CascadeType.PERSIST},
        fetch = FetchType.LAZY,
        orphanRemoval = true)
@ToString.Exclude
private Set<Figure> figures = new HashSet<>();

Using Entity Graphs

Another approach to mitigate the N+1 problem is by using entity graphs. An entity graph allows us to specify a set of related entities that should be loaded together.

@EntityGraph(type = "subgraph")
@OrderBy("id DESC")
public class Customer {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    // getters and setters
}

Using Spring Data and Query Methods

Spring Data provides a powerful way to query data using JPA query methods. By using @Query annotations, we can define complex queries that fetch related data in a single statement.

public interface CustomerRepository extends JpaRepository<Customer, Long> {

    @Query("SELECT c FROM Customer c LEFT JOIN FETCH c.figures")
    List<Customer> findAllWithRelatedData();
}

Blaze-Persistence Entity Views

Blaze-Persistence is a Java library that allows us to easily map between JPA models and custom interface or abstract class defined models. It provides an alternative approach to handling relationships and pagination.

By using Blaze-Persistence, we can define our target structure (DTO model) the way we like and map attributes via JPQL expressions to the entity model.

@EntityView(Customer.class)
public interface CustomerDTO {
    @IdMapping
    Long getId();
    String getName();
    String getSurname();
    String getLogin();
    @Mapping("SIZE(figures)")
    Integer getNumberOfFigures();
    Role getRole();
}

Querying is a matter of applying the entity view to a query, and Spring Data integration allows us to use it almost like Spring Data Projections.

The best part about Blaze-Persistence Entity Views is that it will only fetch the state that is actually necessary!

Conclusion

Pagination with one-to-many relationships can be challenging when dealing with Hibernate’s fetch types. By understanding the N+1 problem and using various techniques, such as fetch type LAZY, entity graphs, Spring Data query methods, or Blaze-Persistence Entity Views, we can improve performance and reduce unnecessary database access.

In our example, we used Blaze-Persistence Entity Views to define a custom DTO model that maps attributes via JPQL expressions. By applying the entity view to a query, we can fetch related data in a single statement, reducing the N+1 problem and improving performance.

Remember to choose the right approach for your specific use case, taking into account factors such as performance requirements, memory usage, and database complexity.

Last modified on 2023-06-19