Architectural Patterns

In the world of software engineering, architecture defines how an application is structured, deployed, and scaled. Over the years, systems have evolved from monolithic structures to distributed ecosystems – and eventually to microservices, which power most large-scale platforms today.

1 Monolithic Architecture: The Classic All-in-One Approach

What It Is

A monolithic architecture is a single, unified application where all components – user interface, business logic, and data access – exist in one codebase and are deployed as a single unit.

Example:

Imagine an e-commerce app:

• User authentication
• Product listing
• Shopping cart
• Order management
• Payment gateway

All of these are part of one big project, built and deployed together (e.g., a single .jar or .war file, or a Laravel/Django application).

Pros:

1. Simple to develop initially
2. Easy to debug and test
3. Single deployment pipeline
4. Great for early-stage startups

Cons:

1. Hard to scale individual parts – you must scale the entire app.
2. If one module fails, it can bring the whole system down.
3. Slow deployment – any small change requires full redeployment.
4. Difficult to adopt new tech stacks for specific modules.

Analogy:

Think of a restaurant kitchen where everything – cooking, cleaning, billing, and delivery – is done by the same chef. It works fine for a small cafe, but collapses when orders start flooding in.

2 Distributed Architecture: Splitting the Workload

What It Is:

A distributed architecture breaks the system into multiple independent components that run on separate servers or machines.

Each component performs a specific role, and all communicate over a network (usually via APIs).

Example:

The same e-commerce app could be split like this:

• User Service (login, registration)
• Product Service
• Order Service
• Payment Service

Each service might live on its own server and communicate via HTTP, gRPC, or a message queue.

Pros:

1. Scalability – scale each component based on load
2. Fault isolation – one service can fail without affecting others
3. Easier maintenance and modular development

Cons:

1. Requires robust networking and DevOps setup
2. More complexity (latency, retries, service discovery)
3. Data consistency challenges

Analogy:

Now, our restaurant has separate stations – one chef for grilling, one for desserts, one for billing. They communicate through tickets (orders).

It's more organized, but needs coordination and timing.

Subtypes of Distributed Architecture

Distributed systems come in many specialized patterns:

• Microservice Architecture: Small, independently deployable services.
• Event-Driver Architecture: Services communicate via events instead of direct calls.
• Serverless Architecture: functions triggered by events, fully managed by cloud.
• Peer-to-Peer Architecture: Nodes act as both client and server (like BitTorrent).

3 Microservices Architecture: The Modern Distributed system

What It Is:

Microservices architecture is an evolution of distributed systems – it takes modularity to the next level. It's actually a specialized form of distributed architecture.

Here, an application is composed of many small, autonomous and loosely coupled services, each focusing on a single business capability.

Each service:

• Has its own database
• Runs in its own process or container
• Can be developed, deployed, and scaled independently
• Communicates via lightweight APIs or message brokers

Example:

Continuing our e-commerce analogy:

Each of these can be deployed separately and scaled based on usage.

Why Microservices Became So Popular

1 Scalability:

You can scale only what's needed.

If your “Order Service” is getting 10x more traffic, you scale just that – not the entire app.

2 Independent Deployment

Teams can deploy updates without waiting for a “big release”.

This enables continuous integration and delivery (CI/CD).

3 Tech Flexibility

Each service can use the best technology for its purpose (Go for performance-heavy parts, Python for ML, etc.).

4 Fault Isolation

If the Payment Service crashes, the rest of the app (like product browsing) still works.

5 Faster Development

Teams can work in parallel – no more merge conflicts in a giant codebase.

Challenges of Microservices

But it's not all sunshine – microservices bring operational complexity.

Network Overhead:

Communication now happens over APIs -> increased latency.

Data Consistency:

Maintaining consistent data across services is tricky.

Complex Deployment:

Requires containerization (Docker), orchestration (Kubernetes), service discovery, and monitoring.

Testing Difficulty:

End-to-end testing across service needs careful planning.