How do you manage data consistency and integrity across microservices?

In a microservices architecture, data consistency and integrity mean that the results of transactional operations across multiple independent databases (each microservice typically has its own exclusive data storage) must satisfy business rules. Its importance lies in the difficulty of implementing traditional ACID transactions in a distributed environment; improper handling can lead to data corruption, invalidation of business logic, and is particularly fatal in critical business scenarios such as e-commerce and finance.

Achieving strong data consistency across microservices is extremely complex, and the eventual consistency model is usually adopted. Core methods include: the Saga pattern (implemented through a series of local transactions and compensating transaction rollbacks), event-driven architecture (utilizing event sourcing, CDC, and reliable event buses to achieve asynchronous propagation and subscription of state changes), and the TCC (Try-Confirm-Cancel) pattern. These mechanisms rely on idempotency, retries, deduplication, and transaction logs to ensure the reliability and traceability of operations while reducing tight coupling between services.

Implementation steps typically involve: 1) Identifying distributed transaction boundaries and determining compensation logic; 2) Selecting and implementing a transaction coordination mechanism (such as Saga Orchestrator or Choreography); 3) Deploying a reliable messaging system (e.g., Kafka/Pulsar) to deliver events; 4) Ensuring idempotent processing on both the server and consumer sides; 5) Implementing retries, timeouts, and circuit breakers in conjunction with API gateways and service meshes. Its value lies in significantly improving the overall resilience, fault tolerance, and horizontal scalability of the system, allowing partial services to recover consistency even after temporary failures.