Handling Distributed Transactions in Microservices

Handling distributed transactions in microservices is one of the trickiest challenges in modern architecture—because microservices are designed to be autonomous, loosely coupled, and independently scalable. Traditional ACID transactions don’t scale well across service boundaries, so we rely on patterns that embrace eventual consistency and resilience.

Common Strategies

1. Saga Pattern 🔄

This is the go-to approach for managing distributed transactions without locking resources. A sequence of local transactions, where each transaction updates data within a single service and publishes an event that triggers the next step in the saga. If a step fails, compensating transactions are executed to undo the changes made by previous successful steps.

Two Saga Types 🧩

• Choreography: Each service listens for events and reacts accordingly. No central coordinator.
  • ✅ Pros: Simple, scalable.
  • ❌ Cons: Harder to track and debug.
• Orchestration: A central orchestrator (like a workflow engine) directs the transaction flow.
  • ✅ Pros: Easier to manage and visualize.
  • ❌ Cons: Adds coupling to the orchestrator.

Tools 🛠

• Camunda
• Axon
• Temporal
• MassTransit (for .NET)

2. Event-Driven Architecture 📨

Services publish and subscribe to events. Each service updates its own state based on events received.

• Ensures eventual consistency
• Works well with Kafka, RabbitMQ, or Azure Service Bus

3. Compensation Transactions 🧠

Instead of rolling back, you define compensating actions to undo the effects of a failed step.

Example: If a payment succeeds but order creation fails, you issue a refund event.

Services achieve consistency over time rather than immediately. When a service completes an action, it publishes an event. Other services react to these events, performing their own operations. If a failure occurs, compensation logic is implemented to reverse or correct the state.

4. Outbox Pattern 🧪

Used to ensure reliable messaging between services.

• Write the event to a local "outbox" table within the same transaction as the business data.
• A background process reads from the outbox and publishes the event.

This avoids the dual-write problem and ensures atomicity at the service level.

5. Distributed Workflow Engines 🧰

For complex business flows, use engines like:

• Durable Functions (Azure)
• Temporal.io
• Camunda BPM

These tools manage state, retries, and compensation logic across services.

Best Practices 🧱

• Design for eventual consistency, not strict ACID.
• Use idempotent operations to handle retries safely.
• Implement timeouts, circuit breakers, and fallbacks for resilience.
• Monitor with tools like Jaeger, Zipkin, or OpenTelemetry.

Handling Distributed Transactions in .NET Core

Handling distributed transactions in a microservice architecture using .NET Core primarily involves patterns that ensure data consistency across multiple, independent services, as traditional two-phase commit (2PC) mechanisms are often not suitable due to their blocking nature and tight coupling.

Saga Pattern Implementation

• Can be orchestrated using a centralized orchestrator service or via choreography (event-driven communication).
• Message brokers like RabbitMQ or Azure Service Bus are commonly used to facilitate event exchange.
• Frameworks like MassTransit or NServiceBus can simplify saga implementation.

Eventual Consistency with Compensation

• Relies heavily on event-driven architecture.
• Services publish domain events after successful operations, and other services subscribe and react.
• Requires careful design of event schemas and robust error handling/retry mechanisms.

Two-Phase Commit (2PC) – With Caveats

• Concept: A protocol where a transaction coordinator ensures all participating services either commit or abort a transaction. It has a prepare phase and a commit phase.
• Implementation: While TransactionScope and IDbTransaction exist, truly distributed XA transactions with automatic 2PC coordination across diverse data sources are complex and often rely on external transaction managers or specific database capabilities. It's generally less favored in microservices due to its blocking nature and impact on performance and scalability.

Outbox Pattern Implementation

• Involves adding an outbox table to your service's database.
• Use a background service (e.g., using IHostedService) to process and publish events from this table.

Choosing an Approach

The best approach depends on the specific requirements of your application regarding consistency, performance, and fault tolerance. Saga and eventual consistency patterns are generally preferred in microservices for their flexibility and resilience. The Outbox pattern can be combined with Saga or Eventual Consistency to ensure reliable event publishing.