Distributed transactions face significant challenges due to their reliance on coordinating multiple independent systems. The primary issues stem from maintaining consistency, handling failures, and managing performance trade-offs. These challenges arise because operations span different services, databases, or networks, each with their own states and potential points of failure. Ensuring that all parts of a transaction either commit or roll back atomically becomes complex in such environments.
One major challenge is achieving atomicity and consistency across systems. In a single database, transactions use locks and a transaction manager to enforce ACID properties. However, in distributed systems, coordinating commits or rollbacks requires protocols like Two-Phase Commit (2PC). For example, if a payment service and an inventory service must update together, 2PC ensures both agree to commit. However, this protocol introduces latency and blocking: if one system fails during the “prepare” phase, others remain locked until recovery. This blocking behavior can degrade performance and create bottlenecks, especially in high-throughput systems.
Another challenge is handling partial failures and network issues. Distributed systems must account for scenarios like network partitions, timeouts, or crashed nodes. For instance, if a payment service successfully charges a user but the inventory service crashes before reducing stock, the system must decide whether to retry, roll back, or proceed with inconsistent data. Implementing retries or compensating transactions (e.g., refunds) adds complexity. Developers often use patterns like Sagas, which break transactions into smaller steps with compensating actions, but this shifts the problem to managing business logic for undo operations, which may not always be feasible.
Finally, performance and scalability are critical concerns. Distributed transactions require coordination across services, which increases latency. For example, a 2PC protocol adds round-trip delays for each phase. Locking resources across systems also risks contention, especially in systems requiring low latency. Alternatives like eventual consistency reduce coordination but introduce temporary inconsistencies, requiring applications to tolerate stale data. This trade-off forces developers to choose between strict consistency (with higher overhead) or eventual consistency (with application-layer reconciliation logic), depending on the use case. Balancing these factors is key to designing robust distributed systems.