Pros of ModelMapper

• Faster mapping performance, especially for large datasets
• More intuitive API with less configuration required
• Better support for complex object graphs and nested mappings

Cons of ModelMapper

• Less extensive documentation compared to Dozer
• Fewer built-in converters for handling specific data types
• May require more manual configuration for very complex mappings

Code Comparison

ModelMapper:

ModelMapper modelMapper = new ModelMapper();
DestinationType dest = modelMapper.map(source, DestinationType.class);

Dozer:

Mapper mapper = DozerBeanMapperBuilder.buildDefault();
DestinationType dest = mapper.map(source, DestinationType.class);

Both libraries offer similar basic usage, but ModelMapper often requires less configuration for complex mappings. Dozer may need more explicit mapping definitions in its XML configuration or through its API for intricate scenarios.

ModelMapper excels in performance and ease of use for most common mapping scenarios, while Dozer provides more fine-grained control and extensive documentation. The choice between the two depends on the specific requirements of your project, such as performance needs, complexity of mappings, and desired level of customization.

Pros of MapStruct

• Compile-time code generation, resulting in faster execution and earlier error detection
• Type-safe mapping with minimal runtime overhead
• Extensive IDE support for code completion and refactoring

Cons of MapStruct

• Steeper learning curve due to annotation-based configuration
• Less flexibility for complex mappings compared to runtime-based solutions

Code Comparison

MapStruct:

@Mapper
public interface CarMapper {
    @Mapping(source = "numberOfSeats", target = "seatCount")
    CarDto carToCarDto(Car car);
}

Dozer:

<mapping>
  <class-a>com.example.Car</class-a>
  <class-b>com.example.CarDto</class-b>
  <field>
    <a>numberOfSeats</a>
    <b>seatCount</b>
  </field>
</mapping>

MapStruct uses Java annotations for mapping configuration, while Dozer typically uses XML or API-based configuration. MapStruct generates mapping code at compile-time, whereas Dozer performs mappings at runtime using reflection.

Both libraries aim to simplify object mapping in Java applications, but they differ in their approach and performance characteristics. MapStruct is generally faster and provides stronger type safety, while Dozer offers more flexibility for complex mapping scenarios.

Pros of Orika

• Generally faster performance, especially for large datasets
• More flexible API with support for custom mappers and converters
• Better support for generic types and collections

Cons of Orika

• Steeper learning curve due to more complex API
• Less extensive documentation compared to Dozer
• Fewer built-in type conversions out of the box

Code Comparison

Dozer:

DozerBeanMapper mapper = new DozerBeanMapper();
DestinationObject destObject = mapper.map(sourceObject, DestinationObject.class);

Orika:

MapperFactory mapperFactory = new DefaultMapperFactory.Builder().build();
mapperFactory.classMap(SourceClass.class, DestinationClass.class).byDefault().register();
MapperFacade mapper = mapperFactory.getMapperFacade();
DestinationObject destObject = mapper.map(sourceObject, DestinationObject.class);

Both Dozer and Orika are popular Java bean mapping libraries, each with its own strengths. Dozer is known for its simplicity and ease of use, making it a good choice for straightforward mapping scenarios. Orika, on the other hand, offers better performance and more advanced features, making it suitable for complex mapping requirements and high-performance applications. The choice between the two depends on the specific needs of your project, considering factors such as performance requirements, complexity of mappings, and team expertise.