Best practices for Java distributed transaction processing under microservice architecture

Best practices for handling distributed transactions in Java microservice architecture include: choosing an appropriate framework (such as Spring Cloud Sleuth); adopting a two-phase commit protocol; using a compensation mechanism; considering using the Saga pattern; and leveraging distributed locks.

Best Practices for Java Distributed Transaction Processing under Microservice Architecture

Introduction

In microservice architecture, distributed transaction processing is a common and critical challenge. Transactions must ensure data consistency and integrity across multiple services, while also accounting for factors such as network latency, failures, and parallelism. This article describes best practices for handling distributed transactions in a Java microservices architecture.

1. Choose the right distributed transaction framework

There are many distributed transaction frameworks available for Java, including Spring Cloud Sleuth, Apache Kafka, and AxonIQ Axon Framework. It's important to choose a framework that meets your specific needs.

2. Using two-phase commit

Two-phase commit (2PC) is a classic distributed transaction protocol. It consists of two phases:

• Preparation phase: The coordinator asks participants whether they can commit the transaction.
• Commit/Rollback Phase: If all participants are ready, the coordinator instructs them to commit the transaction; otherwise, instructs them to rollback.

3. Use the compensation mechanism

The compensation mechanism involves performing the opposite operation after the transaction fails. This resolves the coordinator single point of failure problem in 2PC.

4. Consider using the Saga pattern

The Saga pattern involves breaking down a transaction into a series of independent steps. Each step is handled by a dedicated service. If any step fails, the system can compensate for the previous steps.

5. Utilize distributed locks

Distributed locks can be used to prevent concurrent transactions from accessing shared resources, resulting in data inconsistency.

Practical case

Consider a system containing three microservices:

• Order service: Create and Manage orders.
• Warehouse service: Manage inventory.
• Payment Services: Process payments.

We can use Spring Cloud Sleuth and 2PC to handle distributed transactions when creating orders:

// 订单服务
@Transactional
public void createOrder(Order order) {
    orderService.save(order);
    warehouseService.reserveInventory(order.getItems());
    paymentService.chargeCustomer(order);
}

// 仓库服务
@Transactional
public void reserveInventory(List<Item> items) {
    for (Item item : items) {
        inventoryRepository.update(item, item.getQuantity() - 1);
    }
}

// 支付服务
@Transactional
public void chargeCustomer(Order order) {
    paymentRepository.update(order, order.getTotalPrice());
}

Conclusion

By following these Best practices you can use to efficiently handle distributed transactions in a Java microservices architecture. These practices help ensure data consistency and integrity and increase system robustness.

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