What is an ACID transaction in distributed databases?

An ACID transaction in distributed databases ensures reliable data operations across multiple nodes or systems. ACID stands for Atomicity, Consistency, Isolation, and Durability. In a distributed setup, these properties address challenges like network failures, node crashes, or concurrent access. Atomicity guarantees that a transaction either fully completes or has no effect—no partial updates. For example, transferring funds between accounts in different regions must update both or neither. Consistency ensures the database remains in a valid state before and after the transaction, adhering to predefined rules like foreign keys or uniqueness constraints. Isolation prevents transactions from interfering with each other, even when executed concurrently. Durability ensures committed changes survive system failures, typically through replication or persistent storage.

A practical example is an e-commerce system handling inventory and orders globally. Suppose a user in Europe purchases an item from a warehouse in Asia. The transaction must deduct stock in Asia, charge the user’s European account, and log the order. Atomicity ensures all three steps succeed or roll back if one fails (e.g., payment rejection). Consistency prevents overselling by validating stock levels before committing. Isolation stops other users from seeing intermediate states (like reserved-but-unpaid items). Durability replicates the final state across servers to survive regional outages. Without ACID, partial updates could corrupt data—for example, charging a user without reserving inventory.

Implementing ACID in distributed databases introduces trade-offs. Traditional approaches like two-phase commit (2PC) coordinate nodes but add latency. Modern systems like Google Spanner or CockroachDB use hybrid methods, combining atomic clocks (for global timestamps) and partitioning data to balance consistency and performance. For instance, Spanner’s TrueTime API synchronizes nodes to enable strict isolation without excessive locking. However, developers must still weigh the cost: strict ACID can limit scalability, while relaxed models (like eventual consistency) sacrifice guarantees. Choosing the right isolation level (e.g., serializable vs. read-committed) and replication strategy (synchronous vs. asynchronous) is critical for optimizing distributed transactions.