How do you implement data synchronization across microservices?

Data synchronization in a microservices architecture refers to ensuring that data replicas maintained by different services meet consistency requirements, which is crucial for business correctness (e.g., order-inventory). It addresses the data silo problem caused by independent databases in distributed systems and serves as the foundation for core scenarios such as inventory deduction and cross-service queries.

Core solutions are divided into synchronous and asynchronous categories:

1. Synchronous: Directly reading and writing each other's data through API calls (e.g., REST/gRPC), which is simple but highly coupled and prone to cascading failures;

2. Asynchronous event-driven (mainstream): Services publish domain events (e.g., ""OrderCreated"") to message brokers (Kafka/RabbitMQ), and subscribers update local data after consumption, achieving decoupling with eventual consistency. Common technologies include Change Data Capture (CDC) and Saga transaction patterns (compensation transactions to handle failures).

Implementation steps:

1. Evaluate consistency requirements: Choose Saga for strong consistency scenarios (e.g., payments) and CDC for weak consistency scenarios (e.g., user profile updates);

2. Deploy event bus: Deploy Kafka clusters via Operators in Kubernetes, configure Topics and ACLs;

3. Integrate frameworks: Embed Debezium (CDC) or Axon Framework in applications to publish events, and have consumers listen for updates;

4. Monitor and replay: Use Prometheus to track message latency, and leverage Kafka's retained logs to support failure replay. Value: Improves system resilience and supports independent scalability.