Scaling Up: Microservices, Communication Protocols, and the Bigger Picture
Where We've Been
Part 1 showed how you're already using distributed systems. Part 2 explored what happens when you split a simple system into specialised roles, introducing domain boundaries, eventual consistency, and the CAP theorem. Now let's see how this scales further.
Breaking Down the Boundaries
As restaurants grow, they subdivide roles even further. Instead of one waiter handling everything front-of-house, you might have:
- Someone to seat customers and take initial orders
- A sommelier to recommend wines
- Food runners to deliver meals
- Different people handling different sections
Each subdivision creates new boundaries. What was once customer → waiter → chef → waiter → customer becomes a complex chain of handoffs. Each step adds latency and potential failure points.
In software terms, this is the evolution from monolith to microservices to nano-services. "Micro" or "Nano" doesn't relate to the lines of code, it relates to the scope of the job being performed. Whether that job is generalised or highly specialised depends on your scale.
The Kitchen Gets Complex Too
The kitchen also subdivides. Instead of one or two chefs doing everything, you get:
- Grill specialists
- A sauce chef
- Vegetable preparation
- Pastry specialist
- Plating and presentation
All these must coordinate perfectly. If the vegetables are ready but the meat isn't, the dish fails. If the sauce chef is running behind, everyone waits. Multiple dishes for the same table must finish simultaneously.
This is service orchestration in action. Each service (chef) handles its speciality, but coordination from the head chef becomes critical.
Communication Protocols Matter
Restaurants solved this long ago with strict communication protocols. Orders must be legible and contain essential information like table numbers. Missing information? The kitchen rejects it immediately rather than guessing.
Customers don't walk into the kitchen to order directly - there's an interface (waitstaff) that validates and routes requests appropriately. Waiters don't take food directly from the grill - they wait for the chef's signal that it's ready.
In software, this translates to API contracts and service boundaries. Each microservice owns its database. Other services cannot access that data directly - they must go through the service's API. This ensures data integrity and proper validation.
Failure Handling
Restaurants also demonstrate excellent failure handling. If the grill breaks down, they might temporarily remove grilled items from the menu rather than shutting down entirely. If a waiter calls in sick, others cover their sections.
This is graceful degradation - continuing to operate with reduced functionality rather than complete failure. The system remains available (remember CAP theorem?) even when parts fail.
The Bigger Distributed System
Here's the crucial point I want to leave you with: restaurants themselves are just nodes in a much larger distributed system. They have supply chains for ingredients, laundry services for table linens, cleaning companies, payment processors, reservation systems.
Each external dependency introduces the same challenges we've discussed:
- Communication delays
- Potential failures
- Eventual consistency
- Need for fault tolerance
The restaurant must handle supplier delays, maintain backup vendors, and design around external service failures. Sound familiar?
Your Software Systems
Your applications exist in this same reality. That database call? Network communication with potential failure. That payment API? External service dependency. User authentication through OAuth? You're part of someone else's distributed system.
Even a "simple" web application typically involves:
- Load balancers
- Web servers
- Application servers
- Databases
- Caching layers
- External APIs
- CDNs for static assets
- DNS resolution
Each introduces the distributed systems concepts we've covered.
Patterns Everywhere
The beautiful thing is these patterns repeat everywhere. Whether it's restaurant operations, football teams, military units, or software architecture, the same principles apply:
- Specialisation improves efficiency but increases coordination complexity
- Communication takes time and can fail
- You cannot have perfect consistency, availability, and partition tolerance simultaneously
- Interfaces and protocols prevent chaos
- Graceful degradation beats complete failure
- Everything is part of something bigger
Wrapping Up
Distributed systems aren't magical - they're the natural evolution of any system that grows beyond what one component can handle. You already understand the concepts because you navigate distributed systems every day.
The next time you're designing a system, remember the restaurant analogy (or whatever system you know inside out) and try and translate their solutions to these problems to your use case. Start simple, add complexity only when needed, design for failure, and remember that everything is connected to everything else.
Whether you realised it or not, you've been working with distributed systems all along. Now you know why they work the way they do.
Top comments (0)