saga-orchestration

Distributed transactions across microservices are managed using saga patterns to coordinate multi-step workflows and execute compensating transactions for graceful rollback. This approach maintains data consistency across services without relying on traditional two-phase commits.

Orchestration uses a central coordinator to command each service step, while choreography relies on event publishing for autonomous service reactions. This skill supports both coordination models to manage distributed transactions based on your workflow complexity.

Failure rollbacks in microservice workflows are handled by executing compensating transactions. When a step in the distributed transaction fails, the saga automatically triggers compensating actions to undo previous steps and restore data consistency.

Yes, you can implement long-running business processes with Python sagas. This approach facilitates state management and event publishing to coordinate cross-aggregate workflows that span multiple microservices over extended periods.

The best way to coordinate an order fulfillment process is using saga orchestration to chain inventory reservation, payment processing, and shipping steps. If any step fails, compensating transactions automatically undo prior actions to prevent inconsistent state.

You should avoid saga patterns when strict real-time ACID consistency is required across all aggregates. Sagas prioritize eventual consistency through compensating transactions, making them unsuitable for workflows that cannot tolerate temporary data inconsistency.