The two-phase commit (2PC) protocol is a distributed algorithm used to ensure atomicity in transactions across multiple nodes in a system. Atomicity means that all parts of a transaction either complete successfully (commit) or fail entirely (abort), with no partial outcomes. This is critical in distributed systems where a transaction spans multiple databases, services, or resources. The protocol involves a coordinator node that manages the process and participant nodes that execute the transaction steps. The goal is to guarantee consistency even if failures occur during the transaction.
The protocol operates in two phases. In the prepare phase, the coordinator sends a request to all participants, asking if they can commit the transaction. Each participant performs checks (e.g., validating data, locking resources) and responds with “yes” or “no.” If all participants agree, the coordinator moves to the commit phase, instructing them to finalize the transaction. If any participant votes “no” or fails to respond, the coordinator aborts the transaction and informs all participants to roll back. For example, consider a banking system transferring funds between two accounts stored on separate databases. The coordinator ensures both databases confirm they can deduct and add funds (prepare phase) before finalizing the changes (commit phase). If one database is unavailable, the transaction is canceled, preserving consistency.
While 2PC ensures atomicity, it has trade-offs. A key limitation is blocking: if the coordinator fails after the prepare phase, participants remain in a waiting state until the coordinator recovers, which can stall the system. Additionally, locking resources during the prepare phase can reduce performance in high-throughput scenarios. Despite these drawbacks, 2PC is widely used in distributed databases (e.g., Apache Kafka transactions) and middleware systems requiring strong consistency. Alternatives like the Saga pattern avoid blocking but require compensating actions for rollbacks, making 2PC a simpler choice for scenarios where temporary delays are acceptable compared to inconsistent data. Developers should weigh these factors when designing distributed systems.