Michelle Krejci on Moving to Microservices: Visualising Technical Debt, Kubernetes, and GraphQL

In this podcast, Daniel Bryant spoke to Michelle Krejci, service engineer lead at Pantheon, about the Drupal and Wordpress webops-based company’s move to a microservices architecture. Michelle is a well-known conference speaker in the space of technical leadership and continuous integration, and she shared her lessons learned over the past four years of the migration. Why listen to this podcast: - The backend for the Pantheon webops platform began as a Python-based monolith with a Cassandra data store. This architecture choice initially enabled rapid feature development as the company searched for product/market fit. However, as the company found success and began scaling their engineering teams, the ability to add new functionality rapidly to the monolith became challenging. - Conceptual debt and technical debt greatly impact the ability to add new features to an application. Moving to microservices does not eliminate either of these forms of debt, but use of this architectural pattern can make it easier to identify and manage the debt, for example by creating well-defined APIs and boundaries between modules. - Technical debt -- and the associated engineering toil -- is real debt, with a dollar value, and should be tracked and made visible to everyone. Establishing “quick wins” during the early stages of the migration towards microservices was essential. Building new business-focused services using asynchronous “fire and forget” event-driven integrations with the monolith helped greatly with this goal. - Using containers and Kubernetes provided the foundations for rapidly deploying, releasing, and rolling back new versions of a service. Running multiple Kubernetes namespaces also allowed engineers to clone the production namespace and environment (without data) and perform development and testing within an individually owned sandboxed namespace. - Using the Apollo GraphQL platform allowed schema-first development. Frontend and backend teams collaborated on creating a GraphQL schema, and then individually built their respective services using this as a contract. Using GraphQL also allowed easy mocking during development. Creating backward compatible schema allowed the deployment and release of functionality to be decoupled.