Around the turn of this century, enterprise software hit a significant snag. During this time, the complexities of distributed systems and the need for efficient data transfer across different layers and systems began hitting critical mass.
Fortunately, help arrived in the form of data transfer objects (DTOs), formally introduced and popularized in Martin Fowler’s 2002 book Patterns of Enterprise Application Architecture. Novel at the time, Fowler described DTOs as objects that carry data between processes to reduce the number of method calls, particularly in remote interfaces.
DTOs soon became a common design pattern in Java Enterprise applications. They quickly evolved from their extensive, initial use in Enterprise JavaBeans (EJB) architecture to helping define boundaries and contracts between different services with the rise of service-oriented architecture (SOA) and microservices architecture through the decade.
As RESTful APIs and JSON became popular, developers found DTOs to be a natural fit for transferring data across HTTP in a lightweight and efficient manner. As a result, these popular patterns played a significant role in object-relational mapping (ORM), helping to transfer data between applications and database layers without exposing schemas directly.
Now, over two decades later, DTOs are widely used in modern microservices-based architecture and API development. But this wide use isn’t without criticism and complications. To understand why, we’ll clearly define DTOs, review their benefits and drawbacks, and discuss how to maximize the former while mitigating the latter.
Data transfer objects are design patterns—conceptual frameworks—used by software developers to create efficiencies in object-oriented programming-based software applications.
These patterns are often created as plain old Java objects (POJOS) and are referred to as DTO classes when implemented in code. A DTO class is a container that uses accessors (getters and setters) to manipulate data fields to simplify and aggregate data for transport between software application subsystems or layers.
This makes DTO classes especially useful tools in scenarios where software developers, architects, or system integrators need to aggregate and encapsulate data for network communication (e.g., client-server interactions).
In practice, DTO classes often share common characteristics:
Reduced network calls: As mentioned in our introduction, using DTOs can reduce the number of calls made across a network. This particular characteristic is especially beneficial in distributed systems or in applications where minimizing network traffic plays a big role in overall performance, like large healthcare, financial, and telecommunications companies.
Simplification and aggregation: While they can be used in various scenarios, DTO classes truly shine when used to deliver streamlined, simplified representations of data. For example, a given application may have a complex internal data structure or business logic—complexities that aren’t necessary or useful for another client or layer that’s requesting information. Here, before serialization occurs, a developer could use a constructor to create an instance of a DTO class, populating it with only the necessary data in a simplified and aggregated form.
Decoupling: This simplification and aggregation allows DTOs to decouple different layers of an application. In this way, a presentation layer could use DTO classes to receive information from the business layer without needing to understand the complex domain models of the latter.
Data format consistency: The fact that DTO classes provide a consistent method of transferring data makes them additionally useful when working with different types of clients or when data needs to be serialized in a specific format for network transmission.
Integration with other patterns: In enterprise organizations, DTOs are commonly used along with other frameworks, such as MVC (model-view-controller), CQRS (command query responsibility segregation), repository, facade, and factory patterns.
Security and validation: Finally, the use of DTOs can contribute to organizational security and data validation. When creating a DTO class, developers choose which pieces of data are included. This means they can actively exclude sensitive information that should not be exposed to certain layers or clients. Alternatively, they can transform or omit internal data representations that shouldn’t be exposed to external systems. Developers can also validate data before a DTO class is created, checking correctness, adherence, and completeness to relevant rules or constraints.
In general, data transfer objects can be valuable to any large organization that benefits from a more streamlined and secure data handling process. No surprise that DTOs are being put to work in such a surprisingly diverse amount of enterprise sectors.
The following 10 examples help to illustrate their nearly universal utility:
For telecoms managing vast networks, more efficient data transfer is particularly valuable. DTOs help consolidate network status data from various sources into a single object, reducing server requests and enhancing monitoring efficiency.
Healthcare systems need ways to ensure that patient data is handled in ways that maintain confidentiality and privacy. The simplification process inherent to DTOs can help ensure only relevant information about a patient is shared between electronic health record systems and external analysis tools.
Banks can utilize data transfer objects as a means of transferring customer transaction data between internal processing systems and customer-facing applications. This secures sensitive information like account balances, credit scores, and social security numbers.
To help maintain consistent product information formats, ecommerce platforms can employ DTOs for exchanging data between their inventory management systems and online storefronts. In doing so, product displays can remain both accurate and consistent.
Arguably, scalability is one of the most important features of businesses that provide cloud services. Therefore, the data management efficiencies DTOs provide can prove valuable as organizations work to make sure their distributed computing resources are hyper-responsive to the demands of fluctuating workloads.
Large enterprise software applications (e.g., Microsoft 365 Apps for Enterprise, Quickbooks Enterprise, etc.) can’t forgo updates and maintenance in order to avoid expensive disruptions. The ability for data transfer objects to decouple a system’s user interface from its business logic enables vital work to take place without needing to impact the overall system.
DTOs allow logistics companies to standardize data exchange between their internal tracking system and a myriad of external vendor systems. This allows these companies to accommodate differing data formats without needing to change any underlying systems.
DTOs also allow mobile app developers to deliver data in formats optimized for mobile display and interaction. By doing so, they can reduce the processing load on the client side which, in turn, can enhance the user experience.
A subset of networked environments that alone supported over three billion collective users in 2022, online gaming companies rely heavily on all manner of ways to keep the transfer of player data across servers in near real-time as efficient as possible. By using DTOs to minimize latency, these companies ensure loyal gamers around the globe can quickscope to their heart’s content.
In the automotive industry, DTOs can standardize data communication between different manufacturing systems and quality control. While this sounds like a simple benefit, in manufacturing this contributes to reducing (or eliminating) workplace hazards and inefficiencies while ensuring maximum production output.
Despite the advantages they provide to so many industries, data transfer objects have several potential drawbacks and can create significant issues when used inappropriately.
Understanding the more problematic side of DTOs is essential for those seeking to use them.
Common challenges and drawbacks include the following:
Performance considerations: The rise of DTOs was fueled by the need for performance improvements in network scenarios. However, the haphazard use of DTOs in large organizations can negatively impact network performance by actually increasing the payload size of network calls.
Learning curve and misuse: Adverse performance considerations highlight why it’s vital to understand how and when to use DTOs most effectively. Data transfer objects come with a hefty learning curve, a point sometimes missed by less experienced developers.
Loss of context or logic: Often by design, DTO classes will not contain business logic as part of the simplified and aggregated data serialized for transfer. While efficient, this results in a loss of context, which means important business rules and constraints may need to be enforced elsewhere. This contextual displacement can lead to errors and inconsistencies.
Overhead in object mapping: DTO classes require mapping back and forth from domain models or database entities. While this mapping can be done manually or through automated mapping tools, this need can introduce additional code overhead and complexity, especially in large applications with a variety of data types.
Data duplication: DTO classes are separate from domain models which can lead to data duplication. This redundancy can become an issue due to memory usage and the need to keep data in sync between DTOs and domain models.
Increased codebase size: In large organizations, data transfer operations are often numerous and diverse. The common practice of using a different DTO class for each distinct data transfer operation can result in a large codebase with high overhead that’s challenging for developers to understand and navigate.
Potential for stale data: In many of the industries that benefit the most from DTOs, real-time or near real-time data accuracy is critical. In these organizations where data changes frequently, systematic delays or lags create the risk that data encapsulated in DTO classes can become outdated quickly.
Complexity in object construction: Aggregating data from multiple sources can be resource-intensive, especially if the sources are large or if the aggregation logic is complex. In these cases, harmonizing different data models into a single DTO class can present a challenge. Once accomplished, the logic that was required to construct the object may be difficult to manage and maintain.
Tight coupling with external systems: External interfaces, like APIs, change frequently due to ever-evolving requirements and expectations. Therefore, DTOs designed specifically for these interfaces often require frequent modifications, causing a tight coupling that can be difficult to manage.
Versioning issues: Large distributed systems necessitate a constellation of different applications and services, each developed at different times, using different technologies, featuring its own set of requirements. In these environments, the need to ensure compatibility by maintaining different versions of DTOs can grow complex and challenging.
The problems DTOs may create do throw a bit of cold water on all the benefits they provide. But, thankfully, data contracts can be leveraged to get the most out of data transfer objects while helping to mitigate their many potential downsides. Here’s how:
A data contract, properly drafted and enforced, will provide a formal definition of the data structure for a DTO class. The clarity provided ensures all parties involved in subsequent data exchanges understand precisely what data is included in each DTO class, its format, and how that class should be implemented.
Having a data contract in place increases the likelihood that DTOs will be consistent and in compliance with the organization’s agreed-upon data structure. Consistent and compliant DTOs help maintain data integrity and reduce errors in the data exchange, an exceptionally important benefit when DTOs are used as part of systems that handle highly sensitive information like biometric, health, or authentication-related data.
A data contract will allow for controlled versioning of DTO classes. And, as data requirements evolve, the data contract can be updated in a managed way. This helps developers handle changes without breaking existing integrations.
The precision inherent in a good data contract helps organizations minimize unnecessary data duplication in DTOs. Keeping DTOs lean and focused reduces the overhead in data mapping and transfer as a result.
Data contracts also provide clear rules and structures for how data should be mapped to and from DTOs. This clarity, in turn, facilitates the use of automated mapping tools which can reduce the manual coding effort required to implement DTO classes.
Enforced rules and structures also dictate which data should and should not be included in each DTO class. Over time, this prevents unnecessarily complex or large DTOs from creating performance issues at scale.
As part of the contract drafting process, an organization’s data security requirements are also clearly defined. As such, developers address specific needs like encryption and masking when simplifying and aggregating data, ensuring DTOs comply and adhere to all security protocols.
The larger an organization gets, the more important standardization across its multitude of services and applications becomes. By standardizing how DTOs are used throughout an organization, data contracts prevent DTOs from eroding interoperability and increasing operational complexity.
By providing a buffer layer that defines the data exchange, data contracts reduce the risks of tight coupling between DTOs and external systems. This provides developers with more flexibility in internal implementation and the ability to facilitate the transfer of data without requiring clients to understand the complex domain objects of the business layer.
Data contracts also serve as a potent documentation source for developers, providing clear guidelines for how to implement and use DTO classes correctly. This helps to flatten the DTO learning curve while dissuading misuse overall.
On the whole, data contracts complement the use of DTOs by providing structure, clarity, and standardization. These factors are crucial for effective data management and exchange, especially in complex and distributed systems. They help in leveraging the strengths of DTOs while addressing some of their inherent challenges.
And, believe it or not, data contracts can do so much more. Find out more by joining our product waitlist at Gable.ai.