What are common use cases of data sync in distributed systems?

Data synchronization in distributed systems ensures consistency and availability across multiple nodes or services. Three common use cases include maintaining high availability through replication, enabling real-time analytics with cached data, and supporting event-driven architectures where services need shared state. Each addresses specific challenges in scalability, fault tolerance, and performance.

First, replication for high availability ensures data remains accessible during failures. For example, a globally distributed e-commerce platform might replicate product inventory data across regional databases. If one region’s database fails, users can still view and purchase products using replicated data from another region. Technologies like Apache Cassandra use tunable consistency levels to balance replication speed and data accuracy. This approach reduces latency for geographically dispersed users while providing fault tolerance.

Second, caching layers often rely on data sync to keep cached data consistent with source systems. A social media app might cache user profiles in Redis to reduce database load. When a user updates their profile, the system must propagate changes to all cached copies to prevent stale data. Techniques like write-through caching (updating cache and database simultaneously) or TTL-based invalidation (automatically refreshing data) are commonly used. Without proper sync, users might see outdated information, leading to a poor experience.

Third, event-driven systems use synchronization to propagate state changes across services. For instance, in a food delivery app, an order placement event might trigger updates to inventory, payment, and delivery services. A message broker like Kafka can stream these events, ensuring all services process the same data in near real time. Conflict resolution strategies, such as version vectors or last-write-wins, handle cases where concurrent updates occur. This approach decouples services while maintaining data consistency, enabling scalable and maintainable architectures.