10 Essential Java Libraries to Reduce Boilerplate Code (Beyond Lombok)

Java has a reputation for being verbose. And let's be honest, it is well-earned. How many times have you written a connector class that looks like a carbon copy of the entity class? Or a manual HTTP client?

Lombok is the first step, but it's not the only tool in the box.

Here are 10 essential libraries that will crush the boilerplate in your application and let you focus on what matters: the business logic.

1. MapStruct: Bean Mapping Done Right

The Problem: You have a UserEntity (database) and a UserDTO (API). Writing dto.setName(entity.getName()) fifty times is soulless work.

The Solution: MapStruct generates this code for you at compile time. It is type-safe, follows your naming conventions, and is zero-overhead at runtime (unlike reflection-based mappers).

@Mapper
public interface UserMapper {
    UserMapper INSTANCE = Mappers.getMapper(UserMapper.class);
 
    @Mapping(source = "email", target = "emailAddress")
    UserDTO userToUserDTO(User user);
}

2. Java Records: The Native Solution

The Problem: Creating a simple immutable data carrier requires private fields, getters, equals(), hashCode(), and toString().

The Solution: Native to Java (JDK 14+), Records reduce this to one line.

// BEFORE (20+ lines of clutter)
public class Point { ... }
 
// AFTER (1 line of beauty)
public record Point(int x, int y) {}

Tip: Records replace Lombok's @Value effectively.

3. Retrofit: Type-Safe HTTP Clients

The Problem: Using standard HttpClient requires manually building URLs, setting headers, and parsing JSON responses. It's repetitive string manipulation.

The Solution: Retrofit turns your HTTP API into a simple Java interface.

public interface GitHubService {
  @GET("users/{user}/repos")
  Call<List<Repo>> listRepos(@Path("user") String user);
}

4. jOOQ: SQL in Java

The Problem: JPA (Hibernate) is great for simple saves, but complex queries require messy String concatenation (HQL) or the confusing Criteria API.

The Solution: jOOQ lets you write SQL in Java that is type-safe. If you rename a DB column, your Java code won't compile.

// No more "Select * from..." strings
create.select(AUTHOR.FIRST_NAME, AUTHOR.LAST_NAME)
      .from(AUTHOR)
      .where(AUTHOR.YEAR_OF_BIRTH.gt(1920))
      .fetch();

5. AssertJ: Assertions Humans Can Read

The Problem: Standard JUnit assertions (assertEquals(expected, actual)) are clunky and don't offer great autocomplete.

The Solution: Fluent assertions that read like English.

// AssertJ
assertThat(user.getName())
  .startsWith("Jo")
  .isEqualToIgnoringCase("john");

6. Vavr: Functional Java

The Problem: Java's standard functional API can be verbose, and handling exceptions in Streams (try-catch inside logic) is ugly.

The Solution: Vavr brings immutable persistent collections and functional control structures like Try, Option, and Either.

// Handling exceptions cleanly
Try.of(() -> 1 / 0)
   .recover(x -> 0)
   .get();

7. Testcontainers: Real Integration Tests

The Problem: Setting up a real database for local tests is hard. Mocking the database often hides real bugs (e.g., Postgres-specific SQL errors).

The Solution: Spins up disposable Docker containers for your tests automatically.

@Container
PostgreSQLContainer container = new PostgreSQLContainer("postgres:15");

8. Immutables: Builders on Steroids

The Problem: You want immutable objects but need "Builders" or "With" methods (copy-on-write) to modify them efficiently.

The Solution: A powerful annotation processor that generates comprehensive immutable implementations.

@Value.Immutable
interface Person {
  String name();
  int age();
}
// Usage: ImmutablePerson.builder().name("John").age(30).build();

9. Apache Commons Lang: The Missing Utils

The Problem: Java code is filled with null checks: if (str != null && !str.isEmpty()).

The Solution: StringUtils, CollectionUtils, and ObjectUtils handle null safety for you.

if (StringUtils.isBlank(userInput)) { 
    // Handles null, empty, and whitespace
}

10. Spring Data JPA: The Repository Pattern

The Problem: Writing DAOs requires implementing the same findById, save, delete methods for every single table.

The Solution: You define the interface, Spring generates the implementation.

// 0 lines of implementation code needed
public interface UserRepository extends JpaRepository<User, Long> {
    List<User> findByLastName(String lastName);
}

Summary

Boilerplate code is where bugs hide. By adopting these libraries, you reduce the surface area for errors and make your codebase easier to read.

Start with MapStruct and Records today—they offer the highest immediate return on investment.

Happy Coding! 🚀