How do you handle distributed transactions in microservices?

I think to handle distributed transactions first we need to understand the complexity of transactions and what it is. Once you understand then you will be able to implement design patterns depending on your scenario. If you are dealing with complex transactions scenario where transactions happened within many services then you can go with Saga (Orchestrator) Pattern OR go with Event driven architecture. You should be clear that what are you going to handle and what are the complexities. We will discuss more in details later.

Distributed transactions in microservices bring complexity, performance overhead, and scalability challenges. Ensuring data consistency, implementing rollbacks, and handling availability issues can be problematic. Alternative approaches like compensating transactions or distributed sagas are often used to address these challenges while preserving microservices' benefits.

The real challenge of distributed transactions arises when attempting to maintain the ACID properties (Atomicity, Consistency, Isolation, Durability) across multiple databases or services in a distributed environment. Distributed transactions face various complexities and potential issues that can impact system reliability and performance. Here are some key problems associated with distributed transactions: 1. Consistency Across Services 2. Two-Phase Commit Overhead 3. Blocking and Deadlocks 4. High Latency 5. Single Point of Failure 6. Complexity of Rollbacks 7. Scalability Concerns 8. Network Failures 9. Transactional Boundaries 10. CAP Theorem Trade-offs

Data consistency poses one of the most fundamental challenges in distributed systems. Unlike single-process systems, where localized transactions simplify the management of complex business processes, distributed systems often involve data residing across multiple nodes. This distribution prevents reliance on traditional atomic transactions to encompass an entire business process. The core challenge lies in balancing data consistency, availability, and partition tolerance (as per the CAP theorem) when designing solutions for distributed environments.

TCC (Try-Confirm/Cancel) is a distributed transaction protocol used to ensure consistency and reliability in complex distributed systems. It provides a structured approach for handling distributed transactions across multiple services or components.

This pattern addresses breaking down a request into smaller chunks of work well as long as the request does not involve too many services. Using orchestrated by a central service or choreographed by each service that publishes and subscribes to events often becomes too complex to manage effectively, and maintainability becomes an issue.

Saga is controversial. It brings into your architecture substantial trade offs: - using orchestration you introduce a central component which is fairly tightly coupled with components he orchestrates. This makes system more fragile. - centralized orchestrator likely to become a single point of failure. Not good. - event driven / choreography implementation although addresses single point of failure, might still result in tight coupling between microservices, as well as increased complexity and processing overhead within each step of your distrubuted transaction process. Bottom line, think twice whether your distributed transaction process merits SAGA or perhaps it can be scrapped bare towards more stateless processing.

The SAGA pattern typically revolves around two methods: Orchestration and Choreography. In Orchestration, an orchestrator oversees and directs the sequence of transactions or processes, while in Choreography, an event-based system automates process flows based on respective events. Orchestration may be preferable when there are a limited number of transactions or processes to manage. However, it's important to acknowledge its potential drawbacks, particularly its centralized nature. In the event of an orchestration failure, the entire system could be impacted, contradicting the advantages of Microservices. It's essential to consider this aspect and assess whether it aligns with the needs and behaviours of your microservices architecture.

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: 2. Orchestration:

The SAGA design pattern manages distributed transactions in microservices. It breaks transactions into sagas, each comprising steps that can either complete or compensate. A central coordinator orchestrates saga execution, triggering compensating actions upon failure. Communication occurs through events, promoting loose coupling. Idempotent steps ensure safe retries. SAGA maintains consistency in distributed systems by structuring transaction management across services. Here's how it works: 1. Orchestration: A central coordinator manages the execution of the saga steps across multiple services 2. Compensation: If a step fails, compensating actions are triggered to undo the effects 3. Event-driven 4. Idempotency

I love this pattern because no information is lost if implemented correctly and IMO the benefits it provides are well worth the additional complexity and overhead. It pays many folds on the ROI.

Event Sourcing is a software architecture pattern that revolves around capturing and storing the state of an application as a sequence of events rather than the current state. It enables reconstructing the application's state at any point in time by replaying the stored events. This is commonly used in distributed systems and microservices architectures. The key concepts and principles of the Event Sourcing pattern: Key Concepts: 1. Events 2. Event Store 3. Aggregate Roots 4. Command Handling 5. Event Handlers 6. Projection Workflow: 1. Command Processing 2. Event Persistence 3. Event Replay 4. Projection 5. Querying Benefits 1. Auditability: 2. Temporal Querying 3. Resilience 4. Scalability

Transaction log tailing is a technique used in database systems to capture and process real-time changes made to a database through its transaction log. By "tailing" or monitoring the transaction log, it becomes possible to track and react to individual database transactions as they occur.

Key Concepts of CQRS pattern 1. Command 2. Query 3. Command Handler 4. Aggregate 5. Event 6. Event Handler 7. Read Model Workflow 1. Command Processing 2. Event Emission 3. Event Handling 4. Querying Benefits 1. Separation of Concerns 2. Optimized Read Models 3. Scalability 4. Flexibility 5. Event Sourcing Integration Challenges 1. Complexity 2. Consistency 3. Learning Curve CQRS is a valuable pattern in scenarios where the requirements for reading and writing data significantly differ. It provides flexibility, scalability, and the ability to optimize data models for specific use cases. However, its adoption should be considered based on the complexity and specific needs of the application.

CQRS is often used in conjunction with event sourcing, where all changes to the system's state are captured as a series of events. This pattern can be beneficial for complex, scalable, and maintainable systems but may introduce additional complexity and should be carefully considered for each application's specific requirements.

In my experience the Outbox Pattern is very useful to implement "atomic operations". When you are implementing a microservices architecture an operation made on a given microservice M must be notified to other ones through events, aka Integration Events (IEs). One way to send those IEs is through a message broker like RabbitMQ or similar. Additionally, those IEs are mainly sent when a DB update transaction is performed. So, we have 2 operations here, updating the DB and sending the IEs through a message broker, so we don't have an atomic operation. A way to do it atomic is having a DB table to store the IEs which are stored in the same transaction with the DB changes and a daemon to read from DB and send the IEs, aka the Outbox Pattern.

A polling publisher is a communication pattern commonly used in distributed systems to provide real-time updates or notifications to subscribers. Instead of relying on traditional event-based or push-based mechanisms, a polling publisher allows subscribers to periodically request updates from the publisher by polling it at regular intervals.

The Idempotency Pattern is a design principle in which performing the same operation multiple times has the same effect as performing it once. This ensures that even if an operation is repeated due to network issues or retries, the system remains in a consistent state. It's crucial for building robust and reliable distributed systems, as it reduces the risk of unintended side effects and ensures predictable behavior, especially in scenarios involving network failures or retries.

Benefits of the idempotency pattern is : Robustness: Idempotency enhances the robustness of systems, especially in the face of network issues, retries, and failures. Consistency: The pattern ensures consistency in the system's state, even when dealing with duplicate requests. Error Handling: Idempotency simplifies error handling by allowing clients to retry operations without causing unintended consequences.

I like idempotency because most of time you would be ensuring that operations or messages can be carried out or delivered multiple times without altering the final outcome or causing unintended side effects. Here are the main reasons why you should choose: - Preventing Duplicate Transactions: - Resilience to Failures - Order Independence - Enhanced Reliability - Scalability

The idempotency pattern ensures that repeating an operation multiple times yields the same result. It's crucial for reliability in distributed systems. By assigning unique identifiers to requests and tracking their status, systems can recognize duplicate requests and avoid unintended side effects. Idempotency simplifies error handling and enhances system robustness by allowing retries without adverse consequences, ensuring consistency and reliability in distributed environments. Ex:when User A initiates a $100 transfer to User B, a unique tx ID is generated. If a network issue or user error causes a duplicate request with the same ID, the system recognizes it and returns the original transaction response, ensuring only one transfer occurs.

The saga patern is valuable D.pattern.Breakin transaction into series of local trns'ction,each managed by different services,allow 4 compensating actions in case of failures.This pat'rn,ensures consistency while avoiding resource blocking & locking issues then, Event Sourcing pattern.Storing the state of entity as a sequence of events enables easy Idempotency pattern plays a crucial role in making operations or messages idempotent, so they be executed/delivered multi times without altering the outcome.This ensures resilience 2 failures & retries by employing unique identifiers, ver numbers, or state machines