Introduction to Backend Architectures (Part 2)

In Part 1, we explored the fundamental principles behind backend architecture: what it is, when it matters, and how to approach its design and implementation. Now in Part 2, let’s break down the three most commonly used backend architecture patterns and understand their strengths, weaknesses, and ideal use cases.

The three we'll cover:

• Monolithic Architecture
• Distributed Architecture (Service-Oriented & Microservices)
• Serverless Architecture

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🔹 1. Monolithic Architecture

A Monolithic Architecture is a traditional model where all components—UI, business logic, and data access—are packaged into a single deployable unit. It’s often the go-to structure for early-stage applications due to its simplicity.

✅ Pros

• Simplicity: Development, testing, and deployment are straightforward since everything is contained in one codebase.
• Consistency: All modules share the same libraries and runtime.
• Efficiency: Internal method calls are faster than network requests.

❌ Cons

• Limited Scalability: You can’t scale individual components; the whole system must scale together.
• Rigid Deployments: Any change, even a minor one, often requires redeploying the entire application.
• Growing Complexity: As the app grows, code becomes harder to manage and onboard new devs becomes slower.

🛠️ Use Cases

• MVPs and small-scale applications
• Applications with stable, well-defined domains
• High-performance systems where low-latency communication is crucial (e.g., trading engines)

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🔹 2. Distributed Architectures (Service-Oriented)

Distributed architectures break applications into smaller services, each responsible for a specific business capability. This category includes generic service-based architectures and microservices.

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2.1 Generic Services

Generic services are independent modules that provide specific capabilities. They may run within the same app or as separate services in a distributed setup.

• Example: An authentication service used by multiple products.
• Often used in legacy systems or as a transition stage from monoliths to microservices.

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2.2 Microservices Architecture

A Microservices Architecture is a refined form of service-oriented design where each service:

• Is independently deployable
• Runs in its own process
• Communicates via HTTP/REST, gRPC, or message queues (like RabbitMQ or Kafka)

The core principle? Autonomy and isolation, enabling scalability, agility, and resilience.

✅ Pros

• Independent Development & Deployment: Teams can ship updates to their own services independently.
• Fault Isolation: A failing service won’t bring down the whole system.
• Polyglot Freedom: Each service can be built using the best-fit language or tech stack.

❌ Cons

• Operational Complexity: Requires monitoring, service discovery, and distributed logging.
• Data Management: Ensuring consistency across services is challenging (eventual consistency, distributed transactions).
• Harder Testing: End-to-end and integration testing becomes more complicated.

🛠️ Use Cases

• E-commerce platforms with modular domains (catalog, cart, payment)
• Streaming platforms (recommendations, playback, user profiles)
• Scalable social networks and multiplayer games

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🔹 3. Serverless Architecture

Serverless Architecture abstracts away infrastructure management entirely. Applications run in ephemeral containers managed by cloud platforms—typically via Function-as-a-Service (FaaS).

You write the code, define when it should run (trigger), and the provider handles:

• Provisioning
• Scaling
• Availability

✅ Pros

• No server management: Focus solely on business logic.
• Auto-scaling: Effortless scaling based on workload.
• Pay-as-you-go: You’re billed only for execution time and resources used.

❌ Cons

• Cold Start Latency: Especially in low-traffic scenarios, startup time may affect performance.
• Debugging & Testing Challenges: Difficult to emulate and troubleshoot locally.
• Cost Creep: For long-running tasks or high-traffic systems, costs can become unpredictable.

🛠️ Use Cases

• Real-time file processing (e.g., image resizing on upload to S3 via AWS Lambda)
• Real-time analytics (e.g., stream processing using AWS Kinesis + Lambda)
• Data pipelines (ETL jobs)
• Static websites and JAMstack apps (e.g., deployed via Netlify or Cloudflare Pages)

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🧠 Choosing the Right Architecture

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📚 Final Thoughts

Understanding backend architecture patterns is essential for making informed technical decisions. Each architecture has its trade-offs, and the "best" choice depends on your team size, budget, scalability needs, and application complexity.

In summary:

• Monoliths are great to start with—simple and efficient.
• Microservices shine at scale—modular and robust.
• Serverless is the future for many—agile and cost-efficient.

Use this guide to weigh your options carefully and architect with confidence.

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Inspired by Erik Reinert’s Backend Architecture course on Frontend Masters.