Microservices Architecture for Webapps

Breaks a large application into small, independently deployable services that communicate via APIs or messaging.

Core Principles

• Each service has a single responsibility

• Each service owns its own database

• Services deploy and scale independently

Benefits

• Loose coupling

• Fault isolation

• Independent scaling

• Faster releases / CI-CD

• Team autonomy

Challenges

• Distributed system complexity

• Observability & monitoring

• Data consistency across services

• Cross-service debugging

• Orchestration (e.g., Kubernetes)

Used by: Netflix, Amazon
Best for: Large, fast-growing platforms with clear domain boundaries.

🧭 Primary–Replica Architecture

(Formerly “Master–Slave”)

One primary node handles writes, and multiple replicas handle reads via replication.

Benefits

• Read scalability

• Redundancy

• Simpler consistency model for writes

Risks

• Primary failure requires failover

• Replication lag → stale reads

Best for: Databases, caching layers, analytics read scaling.

🔗 Peer-to-Peer (P2P) Architecture

A fully decentralized network where each node acts as both client and server.

Benefits

• No single point of failure

• High resilience

• Censorship resistance

Trade-offs

• Trust and integrity management

• NAT traversal

• Performance variability

Examples: BitTorrent, blockchain networks
Best for: File sharing, distributed ledgers.

🎯 Event-Driven Architecture

Components communicate by publishing and subscribing to events through a message broker.

Core Components

• Producers

• Broker (e.g., Apache Kafka, RabbitMQ)

• Consumers

Benefits

• High throughput

• Loose coupling

• Asynchronous processing

• Scalable and reactive

Challenges

• Event ordering

• Idempotency

• Schema evolution

• Debugging distributed flows

Best for: IoT systems, stock trading, audit logs, microservices integration.

📨 Broker Architecture

A central broker mediates communication between components.

Responsibilities

• Routing

• Queueing

• Retries

• Load leveling

Risks

• Broker can become bottleneck

• Requires clustering & high availability

Tools: RabbitMQ, Apache ActiveMQ, NATS
Best for: Workflow orchestration, integration-heavy systems.

🚀 Space-Based Architecture

Distributes processing and in-memory state across nodes to eliminate central database bottlenecks.

Components

• Processing units

• Distributed data grid (e.g., Hazelcast, Apache Ignite, GigaSpaces)

Benefits

• Near real-time response

• Horizontal scaling

• High availability

Challenges

• State consistency

• Partitioning

• Recovery & rebalancing

Best for: High-frequency trading, real-time analytics, ultra-low latency systems.

🧩 Microkernel (Plug-in) Architecture

A small core system with pluggable extensions.

Benefits

• Modular

• Extensible

• Isolated updates

Risks

• Plug-in versioning conflicts

• Dependency management complexity

Examples: Eclipse, Visual Studio Code
Best for: Platforms that evolve via extensions.

🧱 Layered Architecture

Organizes the system into structured layers:

• Presentation

• Business logic

• Application/service

• Data

Benefits

• Clear separation of concerns

• Maintainability

• Replaceable layers

Trade-offs

• Added latency

• Potential over-engineering

Best for: Enterprise apps like CRM, banking, e-commerce systems.

🖥️ Client–Server Architecture

Clients send requests; servers process and return responses.

Benefits

• Centralized management

• Strong security controls

• Scalable with load balancing

Risks

• Server bottlenecks

• Requires redundancy planning

Best for: Web apps, email systems, multiplayer games.

🧪 Pipe–Filter Architecture

Data flows through sequential filters, each transforming or validating data.

Benefits

• Composability

• Reusable components

• Easy unit testing

Risks

• Backpressure

• Throughput bottlenecks

• Error propagation

Best for: ETL pipelines, compilers, streaming data processing.

🎯 Executive Summary Comparison

Good to consider:

• 🔐 A security implications comparison