How do cloud-native applications ensure consistency of data across microservices?

Data consistency between microservices refers to maintaining correct synchronization of data operations across different services in distributed cloud-native applications, preventing errors such as duplicate updates or lost transactions. Its importance lies in ensuring transaction integrity, such as guaranteeing consistency between payments and inventory in e-commerce order scenarios, thereby enhancing system reliability and user experience.

The core of this concept is the eventual consistency mechanism, based on event-driven architecture and the Saga transaction pattern. Saga handles asynchronous operations through distributed transaction chains (e.g., triggering the payment service after order inventory deduction), characterized by asynchronous message passing and compensating transactions. In practical applications, the event sourcing mechanism records operation logs to restore consistency, supports loose coupling and elasticity, and strengthens the observability and fault tolerance of microservices.

Implementation steps: 1. Design compensating transactions such as order cancellation logic. 2. Deploy message queues like Kafka to propagate events. 3. Monitor eventual consistency delays. Typical scenarios include inventory updates, with business values such as reducing serial bottlenecks, improving scalability and resilience, and supporting high-concurrency businesses like online transactions.