Mastering Distributed Transactions: From CAP Theory to .NET CAP Solution

Preface

We start by discussing distributed transactions, a technical difficulty in enterprise integration and micro‑service architectures.

Database Transaction

Before distributed transactions, recall the ACID properties of a database transaction. When a database crashes during commit, it uses redo/undo logs (e.g., SQL Server writes to a log file before modifying data) to recover and ensure strong consistency.

Distributed Theory

When a single database becomes a performance bottleneck, it may be partitioned across multiple servers, making the original ACID guarantees insufficient. The CAP theorem (Consistency, Availability, Partition tolerance) describes the trade‑offs in such clustered environments.

CAP Theorem

Consistency: all clients see the same data simultaneously.

Availability: every request receives a response.

Partition tolerance: the system continues operating despite network partitions.

In practice, a system can only guarantee two of the three properties.

BASE Theory

BASE (Basically Available, Soft state, Eventually consistent) extends CAP by accepting weaker consistency for higher availability.

Distributed Transaction Solutions

Two‑Phase Commit (2PC)

2PC follows the XA protocol: a coordinator first asks all participants to pre‑commit, then asks them to commit. If any participant aborts, all roll back. This approach sacrifices availability and adds complexity.

Compensating Transaction (TCC)

TCC splits a transaction into Try, Confirm, and Cancel phases. It registers a compensation operation for each action, offering simpler implementation but weaker consistency.

Local Message Table (Asynchronous Assurance)

The local message table pattern records messages in the same database transaction as business data. A consumer processes the messages later; failures trigger retries. This achieves eventual consistency and is widely used.

MQ Transactional Message

Some MQs (e.g., RocketMQ) support transactional messages using a prepare‑then‑commit flow, similar to 2PC, but many mainstream MQs (RabbitMQ, Kafka) do not.

Sagas Transaction Model

Sagas decompose a long‑running distributed transaction into a series of local transactions coordinated by a workflow engine. If a step fails, compensating actions are executed in reverse order.

SagaBuilder saga = SagaBuilder.newSaga("trip")
        .activity("Reserve car", ReserveCarAdapter.class)
        .compensationActivity("Cancel car", CancelCarAdapter.class)
        .activity("Book hotel", BookHotelAdapter.class)
        .compensationActivity("Cancel hotel", CancelHotelAdapter.class)
        .activity("Book flight", BookFlightAdapter.class)
        .compensationActivity("Cancel flight", CancelFlightAdapter.class)
        .end()
        .triggerCompensationOnAnyError();

camunda.getRepositoryService().createDeployment()
        .addModelInstance(saga.getModel())
        .deploy();

Distributed Transaction Solution: CAP

The author created an open‑source .NET solution named CAP (not the CAP theorem). It provides a visual dashboard, supports multiple databases (SQL Server, MySQL, PostgreSQL) and message queues (RabbitMQ, Kafka), offers real‑time metrics, integrates with Consul for service discovery, and is MIT‑licensed.

Conclusion

The article covered CAP and BASE theories, compared several distributed transaction approaches (2PC, TCC, local message tables, MQ transactional messages, Sagas), and introduced the .NET CAP framework as a practical solution that balances consistency, availability, and scalability.