Home Economics Building Evolutionary Architectures: Support Constant Change
Putting Evolutionary Architecture into Practice
Finally, we look at the steps required to implement the ideas around evolutionary architecture. This includes both technical and business concerns, including organization and team impacts. We also suggest where to start and how to sell these ideas to your business.
The impact of software architecture has a surprisingly wide breadth on a variety of factors not normally associated with software, including team impacts, budgeting, and a host of others.
Teams structured around domains rather than technical capabilities have several advantages when it comes to evolutionary architecture and exhibit some common characteristics.
Domain-centric teams tend to be cross-functional, meaning every project role is covered by someone on the project. The goal of a domain-centric team is to eliminate operational friction. In other words, the team has all the roles needed to design, implement, and deploy their service, including traditionally separate roles like operations. But these roles must change to accommodate this new structure, which includes the following roles:
Must coordinate the goals of this service with other services, including other service teams.
Design architecture to eliminate inappropriate coupling that complicates incremental change. Notice this doesn’t require an exotic architecture like microservices. A well-designed modular monolithic application may display the same ability to accommodate incremental change (although architects must design the application explicitly to support this level of change).
Testers must become accustomed to the challenges of integration testing across domains, such as building integration environments, creating and maintaining contracts, and so on.
Slicing up services and deploying them separately (often alongside existing services and deployed continuously) is a daunting challenge for many organizations with traditional IT structures. Naive old school architects believe that component and operational modularity are the same thing, but this is often not the case in the real world. Automating DevOps tasks like machine provisioning and deployment are critical to success.
Database administrators must deal with new granularity, transaction, and system of record issues.
One goal of cross-functional teams is to eliminate coordination friction. On traditional siloed teams, developers often must wait on a DBA to make changes or wait for someone in operations to provide resources. Making all the roles local eliminates the incidental friction of coordination across silos.
While it would be luxurious to have every role filled by qualified engineers on every project, most companies aren’t that lucky. Key skill areas are always constrained by external forces like market demand. So, many companies aspire to create cross-functional teams but cannot because of resources. In those cases, constrained resources may be shared across projects. For example, rather than have one operations engineer per service, perhaps they rotate across several different teams.
By modeling architecture and teams around the domain, the common unit of change is now handled within the same team, reducing artificial friction. A domain-centric architecture may still use layered architecture for its other benefits, such as separation of concerns. For example, the implementation of a particular microservice might depend on a framework that implements the layered architecture, allowing that team to easily swap out a technical layer. Microservices encapsulate the technical architecture inside the domain, inverting the traditional relationship.
FINDING NEW RESOURCES VIA AUTOMATING DEVOPS
Neal once consulted for a company that offered a hosted service. They had a dozen development teams, all with well-defined modules. However, they had an operations group who managed all maintenance, provisioning, monitoring, and other common tasks. The manager commonly received complaints from developers who wanted faster turnaround on needed resources like database and web servers. To alleviate some of the pressure, he started assigning an operations person one day a week to each project. During that day, the developers were happy as can be — no waiting around for resources! Alas, the manager didn’t have enough resources to do that regularly.
Or so he thought. We discerned that much of the manual work performed by operations was accidental complexity: misconfigured machines, a hodgepodge of manufacturers and brands, and many other repairable offenses. Once everything was well cataloged, we helped them automate the provisioning of new machines using Puppet. After this work, the operations team had enough members to permanently embed an operations engineer on each project and still have enough people to manage the automated infrastructure.
They didn’t hire new engineers, nor did they significantly change their job roles. Instead, they applied modern engineering practices to automate things that humans shouldn’t deal with on a regular basis, freeing them to be better partners in development efforts.
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