How Spring Boot handles distributed transactions in microservices

To dive deep into how Spring Boot handles distributed transactions in microservices, let’s break it down systematically. This will include conceptual understanding, challenges, design approaches, and a practical example.

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Conceptual Overview

Distributed transactions span multiple independent systems or services, often involving:

1. Multiple Microservices: Each with its own database.
2. Different Resource Managers: Such as relational databases, NoSQL, or message brokers.
3. ACID Compliance Challenges: Ensuring atomicity, consistency, isolation, and durability across systems.

Challenges in Distributed Transactions

1. Network Failures: Services may fail mid-transaction.
2. Data Consistency: Ensuring all services either commit or roll back their operations.
3. Latency: Increased communication overhead.
4. Scalability Impact: Distributed locks or coordination can reduce scalability.

Spring Boot’s Approaches to Distributed Transactions

Spring Boot does not natively support distributed transactions out of the box but provides tools to implement them effectively. Common approaches include:

1. Two-Phase Commit (2PC)

• How it works: A coordinator ensures that all participants agree to commit or roll back.
• Tools: Use of JTA (Java Transaction API) and transaction managers like Atomikos or Bitronix.
• Drawbacks: Slower and less resilient to failures due to coordination overhead.

2. Eventual Consistency with Saga Pattern

• How it works: The saga is a sequence of local transactions. Each transaction updates a service and publishes an event for the next service.
• Implementation : There are 2 ways first Choreography where Services communicate via events (e.g., Kafka, RabbitMQ), and the other one is Orchestration where A central orchestrator coordinates transactions.
• Tools : Spring Boot’s integration with message brokers. Saga frameworks like Axon Framework or Eventuate.

3. Transactional Outbox Pattern

• How it works: Changes and events are written to the same database in a single transaction. A process asynchronously publishes events to a broker.
• Tools: Debezium or custom publishers.

Practical Example: Saga Pattern with Spring Boot

Use Case: Booking a trip (Flight, Hotel, and Car Rental services).

Architecture

• Services:
• FlightService
• HotelService
• CarRentalService
• Message Broker: Apache Kafka or RabbitMQ.

Workflow

• The user starts a trip booking.
• Services handle their local transactions and publish success/failure events.
• Other services react to events to proceed or compensate.

Architecture Diagram — link

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Sequence Diagram — link

Step-by-Step Implementation

1. Set up the Microservices

Each service has:

• Its own database.
• Spring Boot configuration with Kafka or RabbitMQ.

Example of FlightService:

@RestController
@RequestMapping("/flights")
public class FlightController {
    @Autowired
    private KafkaTemplate<String, String> kafkaTemplate;
    @PostMapping("/book")
    public ResponseEntity<String> bookFlight(@RequestBody FlightBookingRequest request) {
        // Save booking in local DB
        String bookingId = saveBookingToDB(request);
        // Publish event
        kafkaTemplate.send("booking-topic", "FlightBooked: " + bookingId);
        return ResponseEntity.ok("Flight booked successfully with ID: " + bookingId);
    }
    private String saveBookingToDB(FlightBookingRequest request) {
        // Simulate saving to DB
        return UUID.randomUUID().toString();
    }
}

2. Event Handling in Other Services

HotelService listens to booking-topic:

@KafkaListener(topics = "booking-topic", groupId = "hotel-group")
public void handleBookingEvent(String event) {
    if (event.startsWith("FlightBooked")) {
        // Extract bookingId and proceed with hotel booking
        String bookingId = extractBookingId(event);
        processHotelBooking(bookingId);
    }
}

private void processHotelBooking(String bookingId) {
    // Simulate hotel booking
    System.out.println("Hotel booked for flight booking ID: " + bookingId);
    
}

3. Compensating Transactions

If CarRentalService fails:

• Publish a CarBookingFailed event.
• Other services react to roll back their transactions.

@KafkaListener(topics = "car-booking-failed-topic", groupId = "flight-group")
public void handleCarBookingFailure(String event) {
    // Rollback flight booking
    rollbackFlightBooking(extractBookingId(event));
}

private void rollbackFlightBooking(String bookingId) {
    System.out.println("Rolling back flight booking with ID: " + bookingId);
}

Running the Example

Start the services: FlightService, HotelService, and CarRentalService.

Test the workflow:

• Trigger a trip booking from a client application.
• Monitor the events and database states.

Simulate Failures:

• Intentionally throw an exception in CarRentalService and verify rollback in other services.

Key Points for Interview and Real-World Use

When to Use Saga:

• High scalability requirements.
• Failure tolerance over strict ACID compliance.

Monitoring:

• Use tools like New Relic or Prometheus to track distributed flows.

Design Considerations:

• Handle idempotency for retries.
• Ensure data integrity across systems.

This approach highlights Spring Boot’s ability to leverage the Saga Pattern and event-driven architecture for handling distributed transactions efficiently in microservices.

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