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Loosely coupled teams core

Research from the DevOps Research and Assessment (DORA) team shows that effective organizational and technical structures are predictors for achieving continuous delivery. Whether you’re using Kubernetes or mainframes, your architecture enables teams to adopt practices that foster higher levels of software delivery performance.

When teams adopt continuous delivery practices, adopting the following architectural practices drives successful outcomes:

  • Teams can make large-scale changes to the design of their systems without the permission of somebody outside the team or depending on other teams.
  • Teams are able to complete work without needing fine-grained communication and coordination with people outside the team.
  • Teams deploy and release their product or service on demand, independently of the services it depends on or of other services that depend on it.
  • Teams do most of their testing on demand, without requiring an integrated test environment.
  • Teams can deploy during normal business hours with negligible downtime.

It’s possible to achieve these outcomes with mainframe technologies. It’s also possible to fail to achieve them even when using the latest, most trendy technologies. Many organizations invest lots of time and effort in adopting technologies, but fail to achieve critical software delivery outcomes, due to limitations imposed by architecture.

When the architecture of the system is designed to enable teams to test, deploy, and change systems without dependencies on other teams, teams require little communication to get work done. In other words, both the architecture and the teams are loosely coupled.

This connection between communication bandwidth and systems architecture was first discussed by Melvin Conway, who said, “organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations.” To counteract tightly-coupled architectures and help support better communication patterns, teams and organizations can use the Inverse Conway Maneuver, whereby team structures and patterns are designed to promote the expected architectural state. In this way, team communication patterns support and enforce the architectural patterns that are built.

With a tightly coupled architecture, small changes can result in large-scale, cascading failures. As a result, anyone working in one part of the system must constantly coordinate with anyone else working in another part of the system, including navigating complex and bureaucratic change management processes.

Microservices architectures are supposed to enable these outcomes, as should any true service-oriented architecture. In practice, many so-called service-oriented architectures don’t permit testing and deploying services independently of each other, and thus won’t let teams achieve higher software delivery performance. It’s essential to be strict about these outcomes when implementing service-oriented and microservice architectures.

How to implement architectures for continuous delivery

Consider the major architectural archetypes. Randy Shoup, formerly an Engineering Director for App Engine and VP of Engineering at WeWork, observed the following:

“There is no one perfect architecture for all products and all scales. Any architecture meets a particular set of goals or range of requirements and constraints, such as time to market, ease of developing functionality, scaling, etc. The functionality of any product or service will almost certainly evolve over time—it should not be surprising that our architectural needs will change as well. What works at scale 1x rarely works at scale 10x or 100x.”

Given the pros and cons of architectural archetypes, each fits a different evolutionary need for an organization.

Archetype Pros Cons
Monolithic v1
(all functionality in one application)
  • Simple at first
  • Low interprocess latencies
  • Single codebase, one deployment unit
  • Resource-efficient at small scales
  • Coordination overhead increases as team grows
  • Poor enforcement of modularity
  • Poor scaling
  • All-or-nothing deploy (downtime failures)
  • Long build times
Monolithic v2
(set of monolithic tiers: frontend presentation, application server, database layer)
  • Simple at first
  • Join queries are easy
  • Single schema deployment
  • Resource-efficient at small scales
  • Tendency for increased coupling over time
  • Poor scaling and redundancy (all or nothing, vertical only)
  • Difficult to tune properly
  • All-or-nothing schema management
(modular, independent, graph relationship or tiers, isolated persistence)
  • Each unit is simple
  • Independent scaling and performance
  • Independent testing and deployment
  • Can optimally tune performance (caching, replication, etc.)
  • Many cooperating units
  • Many small repos
  • Requires more sophisticated tooling and dependency management
  • Network latencies

As the table shows, a monolithic architecture that supports a lean product development effort (for example, rapid prototyping of new features, and potential pivots or large changes in strategies) is different from an architecture that needs hundreds of teams of developers, each of whom must be able to independently deliver value to the customer. By allowing the architecture to evolve, you can ensure that your architecture always serves the current needs of the organization. Regardless of the archetype, when architecting to facilitate continuous delivery, teams must be empowered to achieve the capabilities discussed in the introduction to this document.

Building cross-functional teams, with representation from across the organization (product, dev, test, and operations) enables teams to work independently and facilitates building around team boundaries. When your teams are cross-functional, they can function autonomously, experiment with ideas, and choose their own tools. To help with cross-team communication and testing, it can be helpful to have well-defined contracts between services.

Team independence is important, as is the independence of their products and services. Services need to be testable on demand. Adopting techniques around mocking and stubbing of external services helps reduce the impact of external dependencies and lets teams quickly create test environments. Also, implementing contract testing of external services helps ensure that dependencies on their service or other services are still met. To truly achieve continuous delivery, an individual team’s product or service must be independently acceptance tested and deployed from the services it depends on.

To enable deploy-anytime capabilities, consider implementing blue/green or rolling deployment models, with high degrees of automation. With these models, at least two or more versions of the product or service are running simultaneously. These deployment models allow teams to validate changes and deploy to production with little or no downtime. An important consideration is how data upgrades are performed, meaning data and schema must be done in a backward-compatible manner.

In order to aid the independent deployment of components, we recommend that you create backward-compatible versioned APIs. Ensuring backward compatibility for APIs adds complexity to systems, but the flexibility you gain in terms of ease of deployment pays for the added complexity many times over.

Service-oriented and microservice architectures enable these capabilities because they use bounded contexts and APIs as a way to decouple large domains into smaller, more loosely coupled units and the use of test doubles and virtualization as a way to test services or components in isolation.

Common pitfalls in architectures

  • Simultaneously releasing many services. In teams where testability and deployability are not prioritized, most testing requires the use of complex and expensive integrated environments. In many cases, deployments require that you simultaneously release many services due to complex interdependencies. These “big-bang” deployments require teams to orchestrate their work, with many hand-offs and dependencies between hundreds or thousands of tasks. Big-bang deployments typically take many hours or even days, and require scheduling significant downtime.

  • Integrating changes with the changes from hundreds, or even thousands, of other developers. Those developers, in turn, might have dependencies on tens, hundreds, or thousands of interconnected systems. Testing is done in scarce integration test environments, which often require weeks to obtain and configure. These environments are typically not representative of production, reducing the value and accuracy of the testing. The result is not only long lead times for changes (typically measured in weeks or months) but also low developer productivity and poor deployment outcomes.

  • Creating bottlenecks in the software delivery process. Example bottlenecks could be a single team that many others rely on either from a manual process standpoint (testing, deployment, and so on) or from a service operation standpoint. In both examples, those bottlenecks create single points of failure and demand that those teams or services scale to meet the demands of the many dependent teams.

Ways to improve your architecture

With an architecture that enables small teams of developers to independently implement, test, and deploy code into production safely and quickly, you can increase developer productivity and improve deployment outcomes. A key feature of service-oriented and microservice architectures is that they’re composed of loosely coupled services with bounded contexts. One popular set of patterns for modern web architecture based on these principles is the twelve-factor app.

Randy Shoup observed the following:

“Organizations with these types of service-oriented architectures, such as Google and Amazon, have incredible flexibility and scalability. These organizations have tens of thousands of developers where small teams can still be incredibly productive.”

In many organizations, services are distinctly hard to test and deploy. Rather than re-architecting everything, we recommend an iterative approach to improving the design of your enterprise system. This approach is known as evolutionary architecture. In this method, it’s given that successful products and services will require re-architecting during their lifecycle due to the changing requirements placed on them.

One valuable pattern in this context is the strangler fig application. In this pattern, you iteratively replace a monolithic architecture with a more componentized one by ensuring that new work is done following the principles of a service-oriented architecture. You accept that the new architecture might well delegate to the system it is replacing. Over time, as more and more functionality is performed in the new architecture, the old system is “strangled.”

Replacing a monolithic architecture with a more componentized one.

Product and service architectures continually evolve. There are many ways to decide what should be a new module or service, and the process is iterative. When deciding whether to make a piece of functionality into a service, consider if it has the following traits:

  • Implements a single business function or capability.
  • Performs its function with minimal interaction with other services.
  • Is built, scaled, and deployed independently from other services.
  • Interacts with other services by using lightweight communication methods, for example, a message bus or HTTP endpoints.
  • Can be implemented with different tools, programming languages, data stores, and so on.

Moving to microservices or a service-oriented architecture also changes many things through the organization as a whole. In his platform rant, Steve Yegge presents several critical lessons learned from moving to a SOA:

  • Metrics and monitoring become more important and escalations become more difficult because an issue surfaced in one service could be from a service many service calls away.
  • Internal services can produce Denial of Service (DOS) type problems, so quotas and message throttling are important in every service.
  • QA and monitoring begin to blend, because monitoring must be comprehensive and must exercise the business logic and data of the service.
  • When there are many services, having a service-discovery mechanism becomes important for efficient operation of the system.
  • Without a universal standard for running a service in a debuggable environment, debugging issues in other people’s services is much harder.

Case study: Datastore

A tightly coupled architecture can impede everyone’s productivity and ability to safely make changes. In contrast, a loosely coupled architecture promotes productivity and safety with well-defined interfaces that enforce how modules connect with each other. A loosely coupled architecture lets small and productive teams make changes that can be deployed safely and independently. And because each service also has a well-defined API, it enables easier testing of services and the creation of contracts and service level agreements (SLAs) between teams.

Loosely coupled teams.

Randy Shoup describes this architecture as follows:

“This type of architecture has served Google extremely well, for a service like Gmail, there’s five or six other layers of services underneath it, each very focused on a very specific function. Each service is supported by a small team, who builds it and runs their functionality, with each group potentially making different technology choices. Another example is the Datastore service, which is one of the largest NoSQL services in the world, and yet it is supported by a team of only about eight people, largely because it is based on layers upon layers of dependable services built upon each other.”

This kind of service-oriented architecture allows small teams to work on smaller and simpler units of development that each team can deploy independently, quickly, and safely.

Ways to measure architectural improvement

Whether on a mainframe or in microservices, facilitating the practices required for architectural improvement is essential for improving software delivery performance (increased deployment frequency with reduced lead time for changes, time to restore service, and change failure rate). As your services and products become less tightly coupled, your deployment frequency should increase. When measuring improvement, consider using deployment rate rather than just count, because deployment count naturally increases as services are added. Lastly, you should see a reduction in time to detect and recover from problems and in the time for changes to reach production.

Aside from taking these deployment and service measures, teams that operate more independently demonstrate improvements in job satisfaction and team experimentation, and tend to select different technologies and tools based on their needs.

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