• Sagas allow for the implementation of long-running, distributed business transactions, executing a set of operations across multiple microservices, applying consistent all-or-nothing semantics.
• For the sake of decoupling, communication between microservices should preferably happen asynchronously, for instance using distributed commit logs like Apache Kafka.
• The outbox pattern provides a solution for service authors to perform writes to their local database and send messages via Apache Kafka, without relying on unsafe “dual writes.”
• Debezium, a distributed open-source change data capture platform, provides a robust and flexible foundation for orchestrating Saga flows using the outbox pattern.

When moving to microservices, one of the first things to realize is that individual services don’t exist in isolation. While the goal is to create loosely coupled, independent services with as little interaction as possible, chances are high that one service needs a particular data set owned by another service, or that multiple services need to act in concert to achieve a consistent outcome of an operation in the domain of our business.

The outbox pattern,  implemented via change data capture, is a proven approach for addressing the concern of data exchange between microservices; avoiding any unsafe “dual writes” to multiple resources, e.g., a database and a message broker, the outbox pattern achieves eventually consistent data exchange, without depending on the synchronous availability of all participants, and not requiring complex protocols such as XA (a widely  used standard for distributed transaction processing defined by  The Open Group) either.

In this article, I’d like to explore how to take the outbox pattern to the next level and use it for implementing Sagas—potentially long-running business transactions that span across multiple microservices. One common example is that of booking a trip comprising multiple parts: either all flight legs and accommodation should be booked together or none of them. Sagas split up one such overarching business transaction into a series of multiple local database transactions, that are executed by the participating services.

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