Taking System Architecture Diagrams One Step Further
An example of how you can quickly improve a diagram to be more developer-friendly
👋 Hi, this is Thomas. Welcome to a new edition of Beyond Runtime, where I dive into the messy, fascinating world of distributed systems, debugging, AI, and system design. All through the lens of a CTO with 20+ years in the backend trenches.
QUOTE OF THE WEEK:
“Computer science is swimming in abstractions. So many, in fact, that it’s easy to overlook what’s missing.” - George Fairbanks
Software development is inherently collaborative, requiring a shared understanding of a system’s structure, limitations, and future evolution. Achieving this alignment is essential for eliminating ambiguity and ensuring cohesive team progress.
Developers often rely on diagrams to effectively communicate the complexities of distributed software systems, since they are powerful visual aid tools: they can bypass potential misunderstandings and delays that can arise from solely relying on text or verbal explanations.
After all, humans are inherently visual thinkers—it’s not by chance that most cultures have a variation of the expression “a picture is worth a thousand words”.
However, not all system diagrams are equally effective.
A few tweaks can transform a standard diagram into a developer-friendly one. Consider this example:
At first glance, it provides a clean, high-level overview of the system, using proximity to group related elements and lines to show interactions.
But upon closer inspection, we see it also exhibits several common “diagramming anti-patterns.” This diagram includes unnamed or ambiguously named elements, minimal use of color, and lacks annotations. Without arrows, it’s difficult to track data flow, and those unfamiliar with the symbols might struggle to interpret certain components.
Let’s take this diagram one step further by making the following enhancements:
Add labels and annotations for clarity.
Use arrows to indicate data flow directions.
Break the diagram into smaller, logically grouped modules.
Employ color to visually link related elements.
After these adjustments, the improved diagram conveys a more complete understanding of the system.
To take it a step further, we could connect the diagram directly to our live software system to access real-time updates and contextual data on components, APIs, and dependencies.
For an even more comprehensive view, we could include records of architectural decisions, trade-offs, and requirements. This will ensure that a system is well-understood, thoughtfully designed, and can be clearly communicated to others.
In conclusion, while diagrams are very powerful tools to quickly convey a complex concept (and bypass the potential misunderstandings), dynamic diagrams with additional system design information are much more helpful when building a software systems.
💜 This newsletter is sponsored by Multiplayer.app. AI-powered debugging for distributed systems.
I originally wrote about this topic in this article:
I explored these topics:
Key components of a system architecture diagram
Using standard conventions
Visualize architectures with different types of diagrams
Utilize automated tooling
📚 Interesting Articles & Resources
“Event-Driven Architecture (EDA) is exciting until you deal with event versioning” - Raul Junco
Event-Driven Architecture (EDA) is a design approach where systems communicate through events, enabling more scalability and flexibility. This architecture is particularly suited for distributed systems as it decouples services, allowing them to react asynchronously. Check this article for the benefits of using an EDA and tips on how to implement it.
“How to design a system for scale” - Franco Fernando
Designing a system for scale requires a focus on four key areas: data partitioning, asynchronous processing, statelessness, and caching. By incorporating these principles, you can build systems that maintain performance and reliability as they grow.
“How Hootsuite Slashed Infrastructure Costs by 40% - Hiren Dhaduk”
Hootsuite managed to reduce costs by 40% by restructuring its backend architecture, specifically through multi-tenancy, autoscaling, and containerization. By consolidating multiple tenants onto shared servers, using autoscaling to optimize resources based on demand, and adopting containers for better resource efficiency, they streamlined operations and significantly cut costs.