As developers navigate the world of microservices, they often encounter the common rule that services should not share a data source. This rule is rooted in the idea that each service must own its data and have the freedom to change its schema without disrupting its external API. However, it is crucial to differentiate between sharing a data source and sharing data itself. In this blog, we will explore the reasons why sharing a data source is problematic and discuss strategies for sharing data effectively between microservices.

1. Why Sharing a Data Source is Problematic : When it comes to sharing a data source between microservices, problems can arise. Let's consider an example where the Products service owns the products table and all its records. This service exposes this data to other teams via an API, allowing them to access product information. However, direct access to the database should be strictly prohibited to maintain the freedom to make changes to the schema. Sharing a data source in this manner limits the service's ability to evolve and hampers its autonomy.
2. The Benefits of Sharing Data: While sharing a data source poses challenges, sharing data between microservices can be beneficial. Microservices often need access to data from other services to fulfill specific requests. For example, a Trip service may require access to passenger and driver data from separate services to provide trip overviews. By requesting data synchronously from the respective services, the Trip service can ensure that it has fresh and consistent data, enhancing the overall user experience.
3. The Limitations of Synchronicity and Strong Consistency:Although synchronous request/response models for data sharing may appear natural, they do not scale well in complex architectures. As new services emerge and require data from existing services, the dependency chain becomes increasingly tangled. Additionally, each hop in the chain introduces latency and increases the likelihood of failure. To mitigate these downsides, it is crucial to assess whether services truly require up-to-date data.
4. Introducing Eventual Consistency:Achieving eventual consistency can address scalability challenges in microservices architecture. Rather than relying on synchronous requests, services can maintain a local copy of data from other services in their own database tables. Although this data may be temporarily stale, it will eventually be updated. Services can leverage this approach to reduce dependencies and improve system robustness.
5. Leveraging Eventual Consistency in Practice : To illustrate the concept of eventual consistency, let's revisit the Challenge example. The Leaderboard service can maintain a local copy of user data, allowing it to avoid frequent requests to the User service. Similarly, the Challenge and Notification services can also materialize user data locally, ensuring they have the necessary information without relying heavily on the User service. By combining event sourcing and caching techniques, services can achieve a more resilient and scalable architecture.
   
   In the world of microservices, striking the right balance between data ownership and data sharing is essential. While services should not share a data source, sharing data itself can improve efficiency and scalability. By understanding the limitations of synchronicity and strong consistency, developers can embrace eventual consistency and reduce dependencies in their architectures. With careful planning and implementation, microservices can effectively share data while maintaining autonomy and scalability.
   Remember, sharing data, not just a data source, is the key to success in microservices architecture. Embrace eventual consistency and design your services for scalability and autonomy.