When you use Database per Service design you will need to consider Saga for distributed transactions to maintain data consistency across multiple services.
A Saga is a sequence of local transactions and each transaction will publish messages or events that trigger the next local transaction.
If something goes wrong to a participating microservice, there should be a compensating transaction to undo the changes that were made by the preceding local transactions.
There are two popular Saga patterns:
- Choreography - Where microservices publish a message/event from a local transaction and trigger subscribers or participating microservices for the next local transaction.
- Orchestration - Where microservices have an orchestrator to command what participating microservices should trigger the next local transaction and should receive a reply from that local transaction.
CQRS is a popular architectural pattern that separates write (Command) part and read (Query) part to boost system overall performance and promotes better response delivery.
When the system has an overwhelming read activity than write activity this pattern helps lessen the database workloads and avoid system bottleneck.
It also amplify scalability as read databases or replicas data sources can be placed in a geolocation where data consumer can access information with reduced response latency.
See python CQRS example here.
Event Sourcing is a powerful tool in building distributed systems in software architecture.
In the traditional approach the create, read, update, and delete (CRUD) usually update the data directly to the data source and a new value represents the current state of that application without leaving previous states or records of how it got to the current state.
Event sourcing pattern stores changes of an application as a sequence of immutable events to a persistent data store called Event Store in an append-only style where we can maintain history of events so we are able to reconstruct past events and enable compensating transaction if necessary.
It works well with CQRS architecture, since a write model and a read model can work together to separate the Event Store for append-only (write) operation from the Projector which you can use to query (read) suitable data for the UI presentation. Moreover consumers can subscribe to the events and initiate tasks that complete an operation. Although CQRS is not required for the Event Sourcing design pattern.
See python Event Sourcing example here.
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