Managing distributed transactions in a document database requires strategies that balance consistency, availability, and scalability. Document databases, such as MongoDB or Couchbase, often prioritize scalability and flexible data models over strict transactional guarantees. However, modern document databases now support limited ACID transactions within a single cluster or shard. For cross-database or cross-service transactions, developers typically rely on patterns like Sagas or application-level compensation to handle partial failures and maintain data integrity.
One common approach is the Saga pattern, which breaks a distributed transaction into a sequence of local transactions. Each local transaction updates data within a single service or database and emits an event to trigger the next step. If a step fails, compensating transactions undo previous changes. For example, in an e-commerce system, placing an order might involve reserving inventory (document database) and charging a payment service. If payment fails, a compensating transaction releases the reserved inventory. Sagas avoid locking resources across services, improving scalability, but require careful design to handle rollbacks and ensure idempotency (retrying steps safely).
Some document databases, like MongoDB, support multi-document ACID transactions within a sharded cluster, allowing atomic updates across documents. However, these transactions have limitations—such as timeout thresholds and performance overhead—and may not extend to external systems. For broader distributed scenarios, combining database-level transactions with application patterns is often necessary. Tools like event sourcing (storing state changes as events) or frameworks like Apache Kafka can help orchestrate Sagas by tracking steps and triggering compensations. Developers must evaluate trade-offs: database-native transactions simplify code but may limit scalability, while application-level patterns offer flexibility at the cost of increased complexity.