How can you manage data consistency when using microservices instead of a monolith?

When working with microservices, it's typical to steer clear of database sharing to avoid tight coupling, which can create a single point of failure. Instead, you might choose a database-per-service pattern, where each microservice has its own database. This method allows each service to evolve independently, but it necessitates thoughtful synchronization strategies. For data consistency, contemplate employing distributed transactions or adopting a Saga pattern, where a sequence of local transactions is coordinated to ensure either all succeed or compensation actions are executed if needed.

Transitioning from monolithic architectures to microservices introduces significant challenges in maintaining data consistency. The database-per-service method reduces tight coupling by assigning each microservice its own database, promoting independence while complicating system-wide data coherence. To address this, strategies such as the Saga pattern orchestrate local transactions across services to ensure overall consistency, with mechanisms like compensatory transactions to manage failures, and tools like 2PC or eventual consistency models to support distributed transactions depending on business needs. This thoughtful integration of patterns and tools enables effective data management within a microservices framework.

Managing data consistency in microservices can be addressed with these strategies: 1. **Database per Service**: Each microservice controls its own database. 2. **Eventual Consistency**: Updates are asynchronous, ensuring data aligns over time. 3. **SAGA Pattern**: Sequences of local transactions manage data across services. 4. **API Composition**: Combines data from multiple services via APIs. 5. **CQRS**: Splits read and write operations for scalability. 6. **Two-Phase Commit**: Coordinates complex transactions across services, though less common due to performance concerns. 7. **Outbox Pattern**: Safeguards event publishing by storing events in a database until confirmed.

When adopting a microservices architecture with separate databases, ensuring data consistency becomes paramount. Strategies like distributed transactions, Saga pattern, event-driven architecture, Change Data Capture (CDC), and Command Query Responsibility Segregation (CQRS) provide viable approaches. Each strategy presents trade-offs in factors such as latency, complexity, and scalability, necessitating careful consideration based on the system's requirements and constraints. Ultimately, selecting the appropriate strategy is crucial for establishing a robust and dependable microservices architecture.

Transitioning from monolithic to microservices complicates data consistency management. Each microservice should have its database, minimizing system-wide risks. Embracing eventual consistency allows gradual data alignment. The Saga pattern orchestrates transactions across services for overall consistency. API composition combines data when needed, and CQRS splits read/write operations for scalability. While Two-Phase Commit coordinates transactions, it's less common due to performance concerns. The Outbox pattern stores events for reliable inter-service communication. These strategies ensure data consistency in microservices, enhancing reliability across systems.

Event sourcing is a potent approach for managing state in microservices by recording each system state change as an event in an immutable log, enabling the reconstruction of the application's state at any point for robust auditing and easier debugging. It enhances microservices architecture by decoupling services through event-based communication, ensuring consistency across services, and providing replay capabilities for new features or corrections. When combined with CQRS (Command Query Responsibility Segregation), event sourcing improves performance and scalability, though it demands meticulous management of event storage and processing to boost system reliability and flexibility in distributed environments.

To diffuse data all over the organization we use CQRS pattern to tackle read capacity. We have a big monolith that implements all the business rules and is linked to a transactional database. The monolith publishes all of its Business events in a broker which are later integrated within a "follower" relational database to guarantee the best data integrity possible. The database is then replicated through multiple regions and multiple cloud providers. Clients can now reach the data through the API or the topics depending on their needs. But by using this pattern we accepted that we will have Data Quality problems and for important business events we systematically implement DQ probes to guarantee consistency over the replicas.

It's essential to carefully evaluate the trade-offs associated with each approach and choose the one that best fits the specific requirements and constraints of your application. Additionally, implementing robust monitoring and error-handling mechanisms is crucial for detecting and resolving consistency issues in a timely manner.

Event-driven architecture (EDA) is crucial for maintaining data consistency in microservices. EDA uses events to communicate changes, ensuring that all services have consistent data. This approach decouples services, allowing them to evolve independently and improving scalability and fault tolerance. In an example e-commerce application, the Order Service publishes an "OrderPlaced" event, which the Payment Service subscribes to for payment processing. If payment fails, a "PaymentFailed" event is published, triggering actions like order cancellation and inventory updates. EDA thus enables reliable and consistent data management in complex microservices environments.

I like you have microservices independent from each other. So I determine the source of truth for all data and isolate it into a single microservice.