Ensuring Data Consistency in Microservices Architecture: Challenges and Solutions

In the world of microservices, services are divided based on different responsibilities or logics. When combined, these services form a cohesive flow, serving the domain purpose, much like a real-time business process flow that passes through multiple stages or departments. Each stage is divided, but together they efficiently serve the business.

Importance of Data Consistency

Data consistency across various stages in a process flow is crucial. Inconsistency in data can lead to business failures. In software development, data inconsistency or failure at any stage holds the same importance as it does in business.

When building an application using microservices architecture, handling data consistency and failures becomes more critical. This is because microservices involve different isolated processes, each working with its own database. In a monolithic architecture, data consistency is easier to achieve because the application works around a single code base. However, in a microservices scenario, achieving data consistency is challenging as we have to synchronize data and flow across multiple services. So, how do we solve this?

Solutions for Data Consistency in Microservices

There are several solutions for ensuring data consistency in microservices, including SAGA, Event Sourcing, and Two-Phase Commit (2PC).

Let's delve into the most famous pattern: the SAGA Pattern.

SAGA Pattern

The SAGA pattern ensures data consistency across multiple services by coordinating a sequence of local transactions. Each local transaction updates the database and publishes an event or message to trigger the next transaction. If a transaction fails, compensating transactions are executed to undo the changes made by previous transactions.

Types of SAGA Pattern

1. Choreography-based SAGA: This can be achieved using a messaging system like Kafka. In this approach, each service involved in the SAGA performs its transaction and then publishes an event. Other services listen for these events and perform their respective transactions in response. If a transaction fails, the service publishes another event to trigger compensating transactions to undo the changes made by previous transactions.

How Choreography-based SAGA Works:

• Service A performs its transaction and publishes an event.
• Service B listens to the event, performs its transaction, and publishes its event.
• The process continues with other services.
• If any service fails, it publishes a compensating event to undo its transaction.

2. Orchestration-based SAGA: In this approach, a central orchestrator manages the entire transaction process. The orchestrator sends commands to each service to perform its transaction and waits for a response. If a service fails, the orchestrator sends commands to execute compensating transactions to undo the changes.

How Orchestration-based SAGA Works:

• The orchestrator starts the SAGA by sending a command to Service A.
• Service A performs its transaction and replies to the orchestrator.
• The orchestrator then sends a command to Service B, and so on.
• If any service fails, the orchestrator sends compensating commands to the previously successful services.

Here Are Two Other Solutions for Data Consistency

Event Sourcing: This approach ensures data consistency by storing the state of a business entity as a sequence of events. Instead of updating a database directly, each change is represented as an event and stored in an event store. The current state of an entity can be reconstructed by replaying these events. This guarantees that all changes are logged and can be tracked or rolled back if necessary.

Two-Phase Commit (2PC): 2PC is a distributed algorithm that ensures all or none of the participating services commit a transaction. It involves two phases:

1. Prepare Phase: The coordinator asks all participating services if they are ready to commit the transaction. Each service performs the necessary operations and responds with a 'yes' or 'no'.
2. Commit Phase: If all services agree, the coordinator sends a commit command. If any service disagrees, the coordinator sends a rollback command to all services to undo the transaction. This ensures atomicity but can be slow and may cause blocking issues.

Understanding and implementing these patterns can help maintain data consistency in a microservices architecture, ensuring reliable and efficient business processes.

Stay tuned for more articles on microservices and other tech insights. Bye for now!