Relational databases handle distributed transactions using protocols that ensure atomicity across multiple systems. The most common approach is the two-phase commit protocol (2PC). In 2PC, a coordinator node manages the transaction across all participating databases. During the first phase (prepare), the coordinator asks each participant if they can commit the transaction. Each participant validates the operation locally and responds “yes” or “no.” If all agree, the coordinator proceeds to the second phase (commit), instructing all participants to finalize the transaction. If any participant rejects the prepare phase, the coordinator aborts the transaction. For example, transferring funds between two bank accounts in separate databases would use 2PC to ensure either both transfers succeed or neither does.
While 2PC guarantees atomicity, it has limitations. Blocking issues can occur if the coordinator fails after the prepare phase, leaving participants in an uncertain state until recovery. To address this, some systems use optimizations like three-phase commit (3PC), which adds a pre-commit phase to reduce blocking, though this increases complexity. Alternatively, databases might implement compensating transactions (e.g., reversing a payment if a later step fails) or adopt patterns like Sagas, which break a transaction into smaller, reversible steps. For instance, an e-commerce system might deduct inventory first, then process payment, and automatically refund if the payment fails. These approaches trade strict atomicity for flexibility in distributed environments.
Developers must also consider latency and failure modes in distributed transactions. Network partitions or slow nodes can delay 2PC phases, leading to timeouts and retries. To mitigate this, databases often include logging mechanisms (e.g., transaction logs) to track progress and recover from crashes. Tools like XA transactions in MySQL or PostgreSQL’s two-phase commit support provide built-in interfaces for managing distributed transactions. However, manual intervention may still be needed for edge cases, such as resolving stuck transactions after a coordinator failure. Proper monitoring, idempotent operations, and clear rollback strategies are critical to maintaining reliability in these scenarios.