Pattern: Event-driven architecture

pattern  

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Context

You have applied the Database per Service pattern. Each service has its own database. Some business transactions, however, span multiple service so you need a mechanism to ensure data consistency across services. For example, lets imagine that you are building an e-commerce store where customers have a credit limit. The application must ensure that a new order will not exceed the customer’s credit limit. Since Orders and Customers are in different databases the application cannot simply use a local ACID transaction.

Problem

How to maintain data consistency across services?

Forces

• 2PC is not an option

Solution

Use an event-driven, eventually consistent approach. Each service publishes an event whenever it update its data. Other service subscribe to events. When an event is received, a service updates its data.

Example

An e-commerce application that uses this approach would work as follows:

1. The Order Service creates an Order in a pending state and publishes an OrderCreated event.
2. The Customer Service receives the event and attempts to reserve credit for that Order. It then publishes either a Credit Reserved event or a CreditLimitExceeded event.
3. The Order Service receives the event from the Customer Service and changes the state of the order to either approved or cancelled

Resulting context

This pattern has the following benefits:

• It enables an application to maintain data consistency across multiple services without using distributed transactions

This solution has the following drawbacks:

• The programming model is more complex

There are also the following issues to address:

• In order to be reliable, an application must atomically update its database and publish an event. It cannot use the traditional mechanism of a distributed transaction that spans the database and the message broker. Instead, it must use one the patterns listed below.

Related patterns

• The Database per Service pattern creates the need for this pattern
• The following patterns are ways to atomically update state and publish events:
  • Event sourcing
  • Transactional Outbox
  • Database triggers
  • Transaction log tailing

See also

The article Event-Driven Data Management for Microservices by @crichardson describes this pattern