This is where we seek for some automation which can easily convert the Java beans. This involves boilerplate code and introduces overheads that can’t be overlooked, especially when dealing with large data shapes. Still, the DTO pattern heavily depends on the mappers or the logic that converts the incoming data into DTO or vice-versa. Thus client applications can accept a data object in a custom-defined DTO with required fields. The problem discussed above can be reduced by introducing the DTO (Data Transfer Object) pattern, which requires defining simple classes to transfer data between layers.Ī server can define a DTO that would return the API response payload which can be different from the persisted Entity objects so that it doesn’t end up exposing the schema of the Data Access Object layer. This article is accompanied by a working code example on GitHub.
But if we have an object that accepts more than 20-30 fields and many nested objects with a good amount of fields again within it, then this code becomes quite tedious. If we have an object or payload that takes few fields, then this boilerplate code would be fine to implement once. The interactions between each layer become quite tedious and cumbersome.Ĭonsider a client-server application that requires us to pass different objects at different layers, then it would require a lot of boilerplate code to handle the interactions, data-type conversions, etc. When we define multi-layered architectures, we often tend to represent data differently at each layer.
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