From Monolith to Microservices: A Beginner's Guide to Architectural Patterns

Modern applications aren’t built the way they used to be. The shift from monolithic architectures to microservices has transformed how developers design, deploy, and scale software. In this guide, we’ll explore that evolution and uncover how the Saga pattern elegantly solves one of the toughest challenges in distributed systems: maintaining data consistency across multiple services.

Introduction

In the early stages of software development, most applications were built as one big unit, simple to deploy but hard to scale or maintain. As systems grew, developers started breaking them into smaller, more manageable parts.

This evolution led to the rise of software architecture patterns, reusable, high-level design blueprints that guide how we structure, organize, and connect our systems.
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Monolithic Architecture

A monolithic architecture means everything (UI, business logic, and database) is bundled together in a single deployable unit.

Let’s take an order management system as an example:

• OrderTable - stores order details
• StockTable - manages inventory
• CustomerTable - holds customer information

All of these exist in one database and are managed by one application. When a customer places an order, we can use a single transaction to ensure consistency:

• If everything succeeds → COMMIT
• If something fails → ROLLBACK

Pros

• Simple to develop and test
• Easier to use transactions
• Good for small or early-stage projects

Cons

• Difficult to scale specific modules
• Harder to maintain as the system grows
• Tight coupling - a small change can require redeploying the whole system
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Microservices Architecture

As applications grow, scaling becomes harder. That’s where microservices come in.

Instead of one big system, we create independent services, each responsible for a single business function.

For our same Order Management System:

• Order Service → manages orders
• Inventory Service → manages stock
• Customer Service → manages customers

Each service has its own database, API, and deployment lifecycle.

This independence improves flexibility and scalability. However, it also introduces a new problem.
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The Distributed Transaction Problem

In a monolithic app, we can wrap operations in one database transaction.
In microservices, however, each service owns its own database, meaning there’s no single transaction across services.

Example:

1. Order Service creates an order.
2. Inventory Service reduces stock.
3. Payment Service charges the customer.

If step 3 fails (payment error), we can’t simply “rollback” the order because each step happened in a different service and database.

So how do we maintain consistency across distributed services?
That’s where the Saga pattern comes in.
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Introducing the Saga Pattern

A Saga is a sequence of local transactions where each service performs its own operation and publishes an event to trigger the next one.

If something goes wrong, Saga triggers compensating actions to undo previous steps.

Saga = managing distributed transactions through events and compensations with eventual consistency.

It’s important to note that Saga doesn’t make all microservices perfectly consistent at the same time. Instead, it ensures that the system reaches a consistent state eventually, accepting temporary inconsistencies during the process.
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The Two Saga Types

1. Orchestrator Pattern

Here, a central coordinator (Orchestrator) controls the workflow.
It tells each service what to do and handles errors when something fails.

Example Flow:

1. Order Service sends request → Orchestrator
2. Orchestrator → calls Inventory Service to reduce stock
3. Orchestrator → calls Payment Service to charge customer
4. If payment fails → Orchestrator tells Inventory to restore stock

Pros

• Centralized control → easier to debug
• Clear flow of logic

Cons

• Single point of failure (the orchestrator)
• Adds coupling between orchestrator and services
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2. Choreography Pattern

In this version, there’s no central controller.
Services communicate via events.

Example Flow:

1. Order Service creates order → publishes OrderCreated event
2. Inventory Service listens → reduces stock → publishes StockReduced event
3. Payment Service listens → charges customer → publishes PaymentSuccess
4. Order Service listens → marks order as completed

Everything flows naturally through events, like a chain reaction.

Pros

• No single point of failure
• Loosely coupled services
• Highly scalable

Cons

• Harder to trace and debug
• Requires solid understanding of event-driven architecture
• No single place to view the full workflow

Event-Driven Communication

The heart of the choreography Saga is event-driven communication, made possible by message brokers such as:

• RabbitMQ
• Kafka
• Azure Service Bus

How it works:

1. Services publish events when something happens
2. Other services subscribe to those events
3. The broker stores and delivers messages reliably

Example:

OrderService → publishes "OrderCreated"
InventoryService → subscribes to "OrderCreated"
PaymentService → subscribes to "StockReduced"

Even if a service is temporarily down, the broker keeps the message until it’s back online, ensuring eventual consistency.

When Not to Use Saga (and Alternatives)

While Saga is powerful, it’s not always the right tool. For smaller systems or cases needing strict atomicity, consider other strategies:

When Not to Use Saga

• When services are tightly coupled and rarely fail.
• When data consistency must be immediate (e.g., financial transactions).
• When the system is still small enough for monolithic simplicity.
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Alternatives to Saga

• Two-Phase Commit (2PC): Coordinates transactions across systems (simpler, but not very scalable).
• Distributed Locks: Ensure exclusive access to shared resources.
• Database Sharding: Split large datasets across nodes to improve scalability.
• CQRS (Command Query Responsibility Segregation): Separate models for write and read operations for complex systems.

Each technique has trade-offs. The key is choosing the right one for your use case.

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Conclusion

Every architecture pattern has trade-offs.

• Monolithic → simple but less flexible
• Microservices → scalable but complex
• Saga → enables eventual consistency in distributed systems