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Distributed Transaction Management in a Microservices Architecture

Posted by Sandeep Chanda on Jul 22, 2016

Distributed transaction management is a key architectural consideration that needs to be addressed whenever you are proposing a microservices deployment model to a customer. For developers from the monolithic relational database world, it is not an easy idea to grasp. In a monolithic application with a relational data store, transactions are ACID (atomic, consistent, isolated, and durable) in nature and follow a simple pessimistic control for data consistency. Distributed transactions are always guided by a two-phase commit protocol, where first all the changes are temporarily applied and then committed to, or rolled back, depending on whether or not all the transactions were successful or there was any error. You could easily write queries that fetched data from different relational sources and the distributed transaction coordinator model supported transaction control across disparate relational data sources.

In today's world, however, the focus is on modular, self-contained APIs. To make these services self-contained and aligned with the microservices deployment paradigm, the data is also self-contained within the module, loosely coupled from other APIs. Encapsulating the data allows the services to grow independently, but a major problem is dealing with keeping data consistent across the services.

A microservices architecture promotes availability over consistency, hence leveraging a distributed transaction coordinator with a two-phase commit protocol is usually not an option. It gets even more complicated with the idea that these loosely-coupled data repositories might not necessarily all be relational stores and could be a polyglot with a mix of relational and non-relational data stores. The idea of having an immediately consistent state is difficult. What you should look forward to, however, is an architecture that promotes eventual consistency like CQRS and Event Sourcing. An event driven architecture can support a BASE (basic availability, soft-state, and eventual consistency) transaction model. This is a great technique to solve the problem of handling transactions across services and customers can be convinced to negotiate the contract towards a BASE model since usually transactional consistency across functional boundaries is mostly about frequent change of states, and consistency rules can be relaxed to let the final state be eventually visible to the consumers.



TAGS:

data management, APIs, relational databases, Microservices, Distributed Transaction Processing, Software architectures, loose coupling, microservice architectures


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