Handling Distributed Transactions In Microservices

Contemporary thinking and development efforts are heavily focused on Microservices, and I am no exception. Microservices, at their essence and in their genuine context, constitute a distributed system.

So, what exactly is a distributed transaction? Distributed Transactions refer to transactions that extend across multiple physical systems or computers connected via a network. In the realm of microservices, a transaction becomes distributed as it spans multiple services. These services are invoked in a specific sequence to collectively execute the entire transaction. Microservices architecture is known for its decentralized and independent nature, where applications are divided into smaller, loosely coupled services. One of the challenges in such an architecture is handling transactions, especially distributed transactions that involve multiple microservices. The traditional ACID properties (Atomicity, Consistency, Isolation, Durability) may become challenging to achieve in a distributed environment due to factors like network failures, latency, and potential inconsistencies.

Here are common approaches for implementing distributed transactions in .NET Core microservices:

 1. Two-Phase Commit (2PC):

Two-Phase Commit is a classic approach where a coordinator service manages the distributed transaction across microservices. The Two-Phase Commit (2PC) operates in two distinct stages: a prepare phase and a commit phase. This mechanism orchestrates a collection of distributed transactions as a cohesive, atomic unit, ensuring consistency in their execution. The process involves a Transaction Coordinator responsible for managing the lifecycle of the transaction, overseeing its preparation and subsequent commitment.

- Coordinator Service:

  - Initiates the transaction.

  - Communicate with all participating services to ensure they can commit.

  - If all agree, send a commit message; otherwise, send a rollback message.

- Participant Services:

  - Receive a prepared message from the coordinator.

  - Perform the local transaction.

  - Send an acknowledgment back to the coordinator.

Challenges with a 2-Phase Commit Implementation:

Once a system acknowledges the prepare command, it must ensure the ability to commit the transaction upon receiving the commit command. This necessitates the locking of all altered information until the commit or abort command is received. This locking mechanism prevents any changes to the information, creating a bottleneck that can significantly impede system performance, especially in a microservices environment with numerous deployed services.

While this issue also existed in distributed monolithic applications, the smaller scope of microservices and the increased number of deployed services exacerbate the problem. The complexity and performance impact of required locks escalates with each additional external system involved in the transaction.

Benefits of 2-Phase Commit:

1. Atomicity Guarantee: 2PC ensures the atomicity of the transaction, concluding with either all microservices being successful or none having changed.

2. Isolation of Objects: Changes to objects are not visible until the transaction coordinator commits the changes, ensuring isolation.

3. Synchronous Call: The approach involves a synchronous call, notifying the client of success or failure.

Drawbacks of 2-Phase Commit:

1. High Latency: 2PC transactions are relatively slow compared to transactions within a single service, especially during high loads. They are highly dependent on the transaction coordinator, impacting system responsiveness.

2. Lock Performance Bottleneck: The use of locks can become a performance bottleneck, slowing down the system. Additionally, there is a risk of deadlocks where two transactions mutually lock each other, causing a standstill.

 2. Saga Pattern:

In this methodology, every business transaction is designed and implemented as a saga. A saga is essentially a sequence of local transactions, where each microservice involved updates its respective database and emits a message or event. This event serves as a trigger for the subsequent local transaction in the saga. Should a local transaction encounter a failure due to a violation of a business rule, the saga initiates a sequence of compensating transactions. These compensating transactions effectively undo the changes made by the preceding local transactions.

There are two primary approaches for coordinating sagas:

1. Choreography:

   - In a choreography-based coordination, each local transaction is responsible for publishing domain events that, in turn, trigger local transactions in other services.

   - The progression of the saga relies on the implicit understanding and collaboration between participating microservices.

2. Orchestration:

   - Orchestrated sagas involve an orchestrator object that directs the participants on which local transactions to execute.

   - The orchestrator actively manages the flow of the saga, guiding the sequence of local transactions among the microservices.

These coordinating mechanisms, whether through choreography or orchestration, provide flexibility in managing the flow of transactions within a distributed system, offering different trade-offs based on the requirements of the application.

- Saga Orchestrator:

  - Coordinates the series of local transactions.

  - Manages compensation transactions for rollback.

- Compensation Transactions:

  - Reverse the effects of a previous transaction if needed.

Saga pattern allows for more flexibility, but it requires careful design to handle compensating transactions and possible inconsistencies.

 3. Distributed Transaction Coordinator (DTC):

A Distributed Transaction Coordinator (DTC) is a service that manages distributed transactions across multiple databases or systems to ensure the ACID (Atomicity, Consistency, Isolation, Durability) properties are maintained. While DTC can provide certain benefits, it also comes with drawbacks. Let's explore both aspects:

 Benefits of Distributed Transaction Coordinator (DTC):

1. Atomic Transactions:

   - DTC ensures that distributed transactions are atomic, meaning that either all parts of the transaction commit successfully or none do. This helps maintain data consistency.

2. Consistency:

   - DTC helps enforce consistency by coordinating transactions and ensuring that all involved databases or systems reach a consistent state.

3. Isolation:

   - DTC ensures isolation by managing the concurrent execution of transactions, preventing interference, and maintaining the integrity of each transaction.

4. Durability:

   - Transactions coordinated by DTC are designed to be durable, meaning that once committed, the changes are permanent and survive system failures.

5. Centralized Coordination:

   - DTC provides a centralized coordination mechanism for distributed transactions, simplifying the development and management of transactional workflows.

 Drawbacks of Distributed Transaction Coordinator (DTC):

•  Performance Overhead:

- Implementing distributed transactions with DTC introduces performance overhead due to the need for coordination and communication between participating systems. This can impact overall system performance.

• Scalability Challenges:

- DTC may face scalability challenges, especially in scenarios where the number of participating systems or the volume of transactions is high. The centralized coordination model may become a bottleneck.

• Complexity:

- Implementing and managing distributed transactions with DTC can be complex. Developers need to carefully handle issues such as deadlocks, timeouts, and transactional boundaries.

• Network Dependency:

- Distributed transactions are highly dependent on network communication. Network failures or delays can lead to uncertainties in the state of transactions.

• Vendor Dependency:

- The implementation and behavior of DTC may vary between different database management systems or platforms. This introduces vendor dependencies, making it challenging to switch or upgrade systems.

• Two-Phase Commit Limitations:

- DTC often uses the Two-Phase Commit (2PC) protocol, which, while providing transactional consistency, has drawbacks such as increased latency and the potential for blocking.

• Lack of Support in Some Environments:

- Not all environments or platforms support DTC, limiting its applicability in certain scenarios or when transitioning to modern cloud-native architectures.

 

- Configuration:

  - Ensure each database involved supports DTC.

  - Configure DTC on participating microservices.

- TransactionScope:

  - Use `TransactionScope` in your .NET Core code to enlist in distributed transactions.

While DTC provides a straightforward way to manage distributed transactions, it may not be suitable for all scenarios, especially in cloud-native or containerized environments.

 4. Event Sourcing and CQRS:

Event Sourcing involves capturing all changes to an application state as a sequence of events. CQRS (Command Query Responsibility Segregation) separates the read and write operations.

- Write-Side (Command):

  - Generates events and publishes them to a message broker.

- Read-Side (Query):

  - Listens to events and updates read models.

5. Retry and Circuit Breaker Patterns: 

Implementing retry mechanisms and circuit breaker patterns can enhance the resilience of microservices during transient failures. This is crucial for scenarios where a microservice might not be available temporarily.

  6. Consistent Data Models: 

   Ensuring that microservices share a common understanding of the data model can simplify interactions and reduce the need for complex distributed transactions.

This approach promotes eventual consistency and scalability but might not provide immediate consistency.

 Important Considerations:

- Idempotency: Ensure that operations can be repeated without causing additional side effects.  

- Compensating Actions: Design compensating transactions for rollback scenarios.

- Message Brokers: Use a reliable message broker to communicate between microservices.

- Retry Mechanisms: Implement retry mechanisms to handle transient failures.

- Consistent Hashing: Consider consistent hashing for data partitioning to avoid hotspots.

- Eventual Consistency: Accept eventual consistency and design around it.

Choose an approach based on your specific use case, taking into account factors such as complexity, performance, and the nature of your business requirements. Each approach has its trade-offs, and the best choice depends on your specific use case and requirements.

Asynchronous Communication

In a microservices architecture, asynchronous communication is commonly facilitated through message-oriented protocols. Two popular protocols for handling asynchronous calls in microservices are:

1. Message Queues and Publish-Subscribe Systems:

   - Message Queues (MQ): Message queues provide a mechanism for asynchronous communication between microservices. Services can send messages to a queue, and other services can consume messages from the queue. This pattern is particularly useful when you want to decouple the sender and receiver, allowing them to operate independently.

   - Examples: RabbitMQ, Apache Kafka, Amazon SQS (Simple Queue Service), and Microsoft Azure Service Bus Queues.

   - Use Cases: Task offloading, event-driven architectures, and communication between microservices where the sender does not require an immediate response.

2. Publish-Subscribe Systems:

   - Publish-Subscribe: In a publish-subscribe model, a microservice (publisher) publishes events to a topic, and other microservices (subscribers) interested in those events subscribe to the topic. This enables broadcasting events to multiple subscribers asynchronously.

   - Examples: Apache Kafka (supports both message queues and publish-subscribe), MQTT (Message Queuing Telemetry Transport), and some features in RabbitMQ and Azure Service Bus.

   - Use Cases: Event-driven architectures, real-time updates, and scenarios where multiple services need to be notified of specific events.

Choosing the Right Protocol:

The choice between message queues and publish-subscribe depends on the specific requirements of your microservices application.

- Message Queues: Use when you need a point-to-point communication pattern, where a message is sent from one service to exactly one other service.

- Publish-Subscribe: Use when you want to broadcast events to multiple subscribers, and the sender does not need to know the identity of the subscribers.

Benefits of Asynchronous Communication in Microservices:

1. Decoupling: Asynchronous communication helps decouple microservices, allowing them to evolve independently.

  

2. Resilience: Microservices can continue to operate even if some components are temporarily unavailable, as they do not rely on immediate responses.

3. Scalability: Asynchronous communication supports better scalability by distributing workloads and enabling parallel processing.

4. Flexibility: Different microservices can be written in different programming languages and deployed independently, as long as they adhere to the agreed-upon message formats.

5. Event-Driven Architecture: Asynchronous communication is fundamental to building event-driven architectures, where services react to events in a loosely coupled manner.

In summary, both message queues and publish-subscribe systems play crucial roles in enabling effective asynchronous communication between microservices, providing the flexibility and scalability required in distributed systems. The choice between them depends on the specific use case and communication pattern that best fits your application's requirements.

  Conclusion:

Handling transactions in a microservices architecture requires thoughtful consideration of the application's requirements, business logic, and the trade-offs associated with various patterns. The choice of a specific approach often depends on factors such as the level of consistency required, the likelihood of failures, and the overall system complexity. As with any architectural decision, it's essential to carefully evaluate the pros and cons of each approach in the context of your specific use case.