Distributed Transaction Patterns

In microservices architecture, we usually have the same problem. How to handle distributed transactions?

As contrary to monolithic applications, where we can use a single database, microservices can have different databases. So managing transactions, rollbacks, and consistency across multiple services becomes a challenge.

Challenges

• Data Consistency: Ensuring that all services have a consistent view of the data.
• Idempotency: Ensuring that operations can be retried without causing unintended side effects.
• Failure Handling: Dealing with partial failures where some services succeed while others fail.

Outbox pattern

It store the event in same database as the original data with a single transaction. Then, create an event with CDC (Change Data Capture) to send the event to the message broker, which later can be consumed by other services.

Raw events pattern

All changes in a table are monitored via CDC, and events are sent to the message broker.

• Reading yourself pattern: services create events, to be consumed for themselves and possible other services.