How to efficiently solve the field mapping in system docking through the MapStruct tool?

Field Mapping Challenges in System Integration with MapStruct Solutions

During system integration, we often face the problem of mapping one system interface field to another system interface field. Although the fields have the same meaning, there are differences in name, type, date format, etc., such as:

• Inconsistent field naming
• Data type differences
• Conversion of string date and Date type date
• The correspondence between single and multi-fields
• The difference between the fields exists or not
• Differences in data structures (e.g., parallel structures in system A, nested structures in system B)

Faced with these complex situations, manually writing set and get methods is inefficient and error-prone. The MapStruct tool came into being, which allows field mapping to be completed in one go through configuration files.

MapStruct is easy to use:

For mappings with different field names, it can be implemented by configuring source and target properties. For example, the A system field oldName maps to the B system field newName :

@Mapping(source = "oldName", target = "newName")

For type conversion, you can customize the method. For example, convert a string date to Date type:

@Mappings({
    @Mapping(source = "stringDate", target = "date", qualifiedByName = "stringToDate")
})

And define the conversion method in the same class:

@Named("stringToDate")
public static Date stringToDate(String stringDate) {
    // string to Date conversion logic return convertedDate;
}

MapStruct automatically generates mapping code based on configuration files, significantly reducing the workload of field mapping, and can be efficiently completed whether it is simple name mapping or complex type conversion.

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