Relational databases handle replication by copying and synchronizing data across multiple database instances to ensure consistency, availability, and fault tolerance. Replication typically involves a primary node (or master) that processes write operations and one or more replica nodes (or slaves) that receive copies of the data. Changes made to the primary node are propagated to replicas through mechanisms like transaction logs or binary logs, which record every write operation. For example, in PostgreSQL, Write-Ahead Logging (WAL) captures changes, and replicas apply these logs to stay in sync. This setup allows read operations to be distributed across replicas, reducing load on the primary node while maintaining data integrity.
Replication strategies vary based on consistency and performance requirements. Synchronous replication ensures data is written to the primary and all replicas before confirming a transaction, guaranteeing strong consistency but introducing latency. Asynchronous replication, used in systems like MySQL’s standard replication, allows the primary to confirm writes before replicas are updated, improving performance at the risk of temporary inconsistencies. Multi-master replication, such as in PostgreSQL’s BDR (Bi-Directional Replication), enables multiple nodes to accept writes, but requires conflict resolution—for example, using timestamps or application-defined rules to resolve conflicting updates. These trade-offs determine the system’s suitability for scenarios like high-read workloads (favoring asynchronous replication) or mission-critical systems (needing synchronous consistency).
Practical implementations often combine replication types to address specific needs. For instance, a financial application might use synchronous replication for transaction records to ensure accuracy, while employing asynchronous read replicas for reporting. Cloud services like Amazon RDS automate replication setup, allowing developers to scale reads with minimal configuration. Tools like MySQL Group Replication or SQL Server AlwaysOn Availability Groups provide built-in failover mechanisms, promoting a replica to primary if the master fails. However, developers must still account for replication lag, potential conflicts, and monitoring to maintain system health. Properly configured replication balances performance, durability, and complexity, making it a foundational tool for scalable and resilient database architectures.