As a software developer/solutions architect, navigating the complexities of modernizing legacy applications requires more than just adopting new technologies. It demands a deep understanding of software design patterns that ensure scalable, resilient, and maintainable solutions. Unfortunately, in the rush to modernize, crucial design patterns are often overlooked, leading to technical debt, performance bottlenecks, and security vulnerabilities. This article explores key design patterns frequently ignored by modernization teams, the reasons they are neglected, and the consequences of bypassing them. By integrating these patterns into modernization strategies, developers and architects can build robust, future-proof applications that stand the test of evolving technological landscapes.

Strangler Fig Pattern

The Strangler Fig Pattern is a gradual migration strategy where new functionality is built around the existing legacy system, slowly replacing it until the old system is entirely phased out.

  • Why it’s overlooked: Modernization teams often opt for a full rewrite rather than incremental refactoring, assuming that starting from scratch will be faster and more efficient. However, this can introduce significant risks and delays.
  • Real-world example: A financial institution migrating from a monolithic COBOL-based mainframe to a microservices-based architecture used the Strangler Fig Pattern. They introduced an API layer that progressively handled more transactions while legacy components were retired incrementally.
  • Consequence of ignoring it: A complete system rewrite without this pattern can lead to prolonged development times, business disruptions, and increased failure risks due to untested new implementations.
  • When to use: Use this pattern when modernizing large, complex legacy applications that cannot afford extended downtime or complete overhauls at once.

Saga Pattern

The Saga Pattern manages distributed transactions by breaking them into a series of smaller, compensating transactions.

  • Why it’s overlooked: Modernization teams often assume eventual consistency is automatically handled by microservices frameworks, neglecting explicit transactional workflows. Implementing sagas can be challenging due to the need for handling failures and maintaining consistency. Teams may opt for simpler orchestration mechanisms without considering the potential for distributed transaction failures.
  • A real-world example: An online travel booking system had issues where partial failures left customers with incomplete reservations (e.g., flights booked but hotels not confirmed). Implementing the Saga Pattern ensured rollback mechanisms were in place, maintaining data consistency across services. 
  • Consequence of ignoring it: Without the Saga Pattern, distributed systems suffer from data inconsistencies, orphaned transactions, and poor user experience. Distributed transactions can lead to inconsistencies if not handled properly. Failures in one service can impact the entire transaction.
  • When to use: Use this pattern when dealing with distributed transactions involving multiple services that must maintain consistency and systems with complex business transactions that span multiple services.

Sidecar Pattern

The Sidecar Pattern runs auxiliary services in separate containers alongside main application services, enabling functionalities like logging, monitoring, and security without modifying the core application.

  • Why It’s Overlooked: Modernization teams may prioritize core service development and neglect auxiliary concerns, leading to bloated application code.
  • Real-World Example: A fintech company used the Sidecar Pattern to deploy a separate logging and monitoring service alongside each microservice, simplifying debugging and performance tracking.
  • Consequence of Ignoring It: Neglecting this pattern leads to tightly coupled services, making maintenance difficult and increasing the complexity of scaling and updating auxiliary functions.
  • When to Use: Use this pattern when microservices require independent functionalities like logging, monitoring, or security without modifying the core service logic.

Circuit Breaker Pattern

The Circuit Breaker Pattern prevents a system from continuously making requests to a failing service, reducing unnecessary load and enabling faster recovery.

  • Why it’s overlooked: Modernization teams often assume cloud-native platforms handle failure gracefully, ignoring the need for explicit fault tolerance mechanisms.
  • Real-world example: Netflix employs the Circuit Breaker Pattern to maintain high availability in its microservices architecture. If a particular service fails repeatedly, the circuit breaker trips and prevents further calls until recovery.
  • Consequence of ignoring it: Without circuit breakers, cascading failures can occur, where a single failing microservice can bring down an entire system due to unhandled retries and excessive load.
  • When to use: Use this pattern in distributed systems where service failures must be isolated to prevent widespread outages.

Bulkhead Pattern

The Bulkhead Pattern isolates different components or services so that failures in one do not impact the others.

  • Why it’s overlooked: Many teams focus on horizontal scaling but neglect to compartmentalize workloads, making services susceptible to systemic failures.
  • Real-world example: In e-commerce platforms, checkout, inventory, and recommendation services can be isolated using bulkheads to ensure that failure in one does not affect the others.
  • Consequence of ignoring it: Ignoring this pattern can lead to entire systems going down due to a single point of failure, significantly impacting user experience and revenue.
  • When to use: Use this pattern in microservices architectures where services must operate independently to ensure resilience.

Event Sourcing Pattern

The Event Sourcing Pattern stores changes to an application’s state as a sequence of immutable events rather than modifying records directly.

  • Why it’s overlooked: Teams often prioritize relational database models and transactional consistency, overlooking event-driven architectures that enhance auditability and scalability.
  • Real-world example: Uber uses event sourcing to track rides, payments, and user interactions, ensuring that every action is recorded as an event for consistency and debugging.
  • Consequence of ignoring it: Not using event sourcing can lead to data inconsistencies, loss of historical data, and difficulties in troubleshooting and replaying past transactions.
  • When to use: Use this pattern in applications requiring strong audit trails, historical tracking, and event-driven state management.

CQRS (Command Query Responsibility Segregation) Pattern

CQRS pattern separates read and write operations into different models, optimizing for performance and scalability.

  • Why it’s overlooked: Many teams’ default to CRUD-based architectures without considering read-heavy or write-heavy optimizations.
  • Real-world example: E-commerce platforms like Amazon use CQRS to manage inventory updates separately from customer queries, ensuring high performance under heavy loads.
  • Consequence of ignoring it: Ignoring CQRS can lead to database contention, performance bottlenecks, and inefficient scaling strategies.
  • When to use: Use CQRS in high-performance applications where read and write workloads differ significantly.

Repository Pattern

The Repository Pattern separates the business logic from data access, providing a clean abstraction layer between application logic and database queries.

  • Why It’s Overlooked: Modern ORM (Object-Relational Mapping) frameworks promise simplified data management, leading teams to believe explicit repository layers are unnecessary.
  • Real-World Example: A healthcare software provider initially used direct ORM calls within service classes. As the system scaled, database logic became scattered, leading to maintenance challenges. Refactoring to use the Repository Pattern improved code organization and maintainability.
  • Consequence of Ignoring It: Ignoring this pattern leads to tightly coupled code, making it harder to switch databases, optimize queries, or maintain separation of concerns.
  • When to Use: Use this pattern when dealing with complex domain logic that requires a clean separation between business rules and data access.

API Gateway Pattern

The API Gateway Pattern acts as a single entry point for all client requests, routing them to appropriate backend services while handling cross-cutting concerns like authentication, logging, and rate limiting.

  • Why It’s Overlooked: Teams may assume direct client-to-microservice communication is sufficient, leading to complex client logic and inefficient network calls.
  • Real-World Example: A streaming service adopted an API Gateway to handle authentication, request aggregation, and traffic management across various backend services, improving performance and security.
  • Consequence of Ignoring It: Without an API Gateway, microservices architectures can become fragmented, increasing security risks, inconsistent data access, and complex client-side logic.
  • When to Use: Use this pattern when managing multiple microservices and requiring centralized handling of security, authentication, and request routing.

Modernization efforts should not only focus on adopting new technologies but also on leveraging proven design patterns to ensure scalability, resilience, and maintainability. Patterns like Strangler Fig, Saga, Circuit Breaker, Bulkhead, Event Sourcing, Repository, Sidecar, and CQRS provide essential strategies to ensure a scalable, resilient, and maintainable architecture. By integrating these patterns into modernization efforts, teams can avoid common pitfalls, minimize risks, improve system reliability, and create robust solutions for the future.

A version of this blog was first posted by Karsun expert Lakshman Maruri. Lakshman is an expert in our aviation portfolio. Connect with him on LinkedIn. Our Karsun Cloud Solutions experts use tools like those available in our Cloud Runways and Microservices Toolkits to accelerate transformation and build resilient, scalable architecture. Learn more at https://karsun-llc.com/innovation-center/modernization-and-transformation-toolkits/cloud-runways/