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Yash BhanushaliSoftware Engineerauthor linkedin

Best Cloud Native Architecture Patterns

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The cloud has revolutionized how we design and deploy software. Cloud native architecture patterns are the building blocks for this new era of development, offering reusable solutions to common challenges faced in the cloud environment. By embracing these patterns, you can craft applications that are resilient, scalable, and agile – perfectly suited for the dynamic nature of the cloud.


The world of cloud-native technology is always changing, so we need architectures that can grow and change easily. These architectures should work well in distributed environments and use microservices and containers. Cloud-native architecture patterns offer reliable ways to build strong and efficient applications.


Why Cloud Native Patterns Matter


Traditional applications, designed for physical servers, often struggle in the cloud. Cloud-native patterns address this by leveraging the core strengths of cloud computing:


  • Elasticity: Scale resources up or down on demand to meet fluctuating workloads.
  • Automation: Automate infrastructure provisioning, deployment, and management for faster development cycles.
  • Scalability: Easily handle increased traffic or data volume without performance degradation.


These patterns empower you to build applications that are:

  • Highly Available and Resilient: Withstand failures and recover quickly, ensuring minimal downtime.
  • Agile and Maintainable: Easier to develop, deploy, and update, fostering a DevOps culture


Cloud Native Development


Refer this source


In this article, we’ll explore the most popular and used cloud-native architecture patterns you should know to build efficient & reliant applications.


  1. Sidecar Pattern
  2. Ambassador Pattern
  3. Database per service
  4. Backends for Frontends (BFF)
  5. CQRS (Command Query Responsibility Segregation)
  6. Event Sourcing
  7. Saga Pattern

Sidecar Pattern


sidercar pattern


In cloud native deployments, the Sidecar pattern tackles extending application functionality without modifying the core logic. A dedicated container, the sidecar, runs alongside the main application container, providing services like logging, monitoring, security, or API mediation.


This modular design promotes loose coupling, allowing independent scaling and updates for both parts. Sidecars offer flexibility as they can be written in any language and centralize observability through consolidated logging and tracing. Common use cases include log management, service mesh implementation, and resiliency features. By leveraging sidecars, developers can build cleaner, more scalable, and easier-to-maintain cloud-native applications


Example : Consider a mobile game with a real-time chat feature. The core game logic resides in a container, handling player movement, physics, and level rendering.  A separate sidecar container can be deployed alongside the game container. This sidecar could be written in Python for its ease in data analysis.


The sidecar intercepts chat messages between players, filters out inappropriate language, and translates messages if needed. It then sends the processed messages to a central chat server. This keeps the game container focused on delivering a smooth gaming experience, while the sidecar ensures a safe and inclusive chat environment. The sidecar pattern promotes loose coupling, allowing the game to be updated with new features without affecting the chat functionality, and vice versa.


Ambassador Pattern


ambasaddor pattern


The Ambassador pattern in cloud native deployments acts like a diplomatic facade for your microservices. A dedicated Ambassador service sits before your core services, centralizing functionalities like security, traffic management, and client-side tasks. This offers significant advantages such as security policies are enforced at a single entry point, the Ambassador scales independently to handle surges in traffic, and it can distribute requests for optimal performance across various microservice instances.


Additionally, the Ambassador can offload common tasks like authentication, authorization, and encryption, allowing microservices to focus on their core logic. This pattern is particularly useful for securing public APIs, integrating legacy systems, and managing different versions of microservices. By employing the Ambassador pattern, developers can build cloud-native applications that are secure, scalable, and efficient, much like a well-functioning embassy streamlining communication and protecting a nation's interests.


Example : Consider a social media platform built on microservices. Each microservice handles specific tasks: user authentication, timeline management, and post creation. Traditionally, each service would need to implement its own security measures and handle user authentication.


Database per service


database per service


The database per service pattern is a cornerstone of cloud native development for microservices applications. It dictates that each microservice owns and manages its dedicated database, rather than relying on a single shared one. This approach offers significant advantages such as loose coupling between microservices fosters faster development and independent scaling based on specific data storage needs.


Additionally, clear data ownership simplifies maintenance and troubleshooting within each microservice. However, managing multiple databases can increase complexity, and ensuring data consistency across these distributed databases requires careful consideration. Complex business logic spanning multiple microservices might necessitate intricate distributed transactions, which can be error-prone. This pattern is ideal for applications with well-defined boundaries between microservices and minimal data sharing.


It also proves beneficial when different database technologies are needed for specific tasks, like a document database for user profiles and a relational database for order processing. In essence, the database per service pattern empowers you to build modular, scalable, and maintainable cloud-native applications.


Example: Imagine an e-commerce app. The product catalog microservice has its own database for product details, while a separate database managed by the shopping cart microservice stores user-specific cart items. This ensures loose coupling and simplifies scaling based on individual needs.


Backends for Frontends (BFF)



bff pattern


The Backend for Frontend (BFF) pattern tackles the challenge of efficiently serving various user interfaces (UIs) in cloud-native applications. Traditionally, a single API might struggle to cater to the diverse needs of web apps, mobile apps, or voice assistants. The BFF solution introduces a separate backend layer, acting as a middleman for each unique UI. This BFF creates a tailored API specifically designed for that UI, eliminating unnecessary data transfer and streamlining the user experience.


Additionally, BFFs can pre-process and combine data from multiple backend microservices, reducing the number of calls needed by the UI and boosting performance. Importantly, BFFs decouple UIs from the complexities of backend logic, shielding them from changes in microservices and enabling faster development cycles. In essence, the BFF pattern, like a restaurant menu curated for each dining area, ensures efficient, performant, and adaptable UIs within the ever-changing world of cloud-native backends.


Example: Consider a social media app. The web BFF might fetch a user's entire profile for display, while the mobile BFF prioritizes smaller chunks of data for faster loading on smartphones. Both leverage the same backend for user data, but cater to the specific needs of each UI.


CQRS (Command Query Responsibility Segregation)


cqrs pattern


The CQRS (Command Query Responsibility Segregation) pattern in cloud-native architecture separates the world of reading data (queries) from modifying data (commands). Imagine a library with distinct sections for borrowing books (commands) and browsing the collection (queries). CQRS offers significant advantages such as dedicated models for reads and writes allow for optimizations tailored to each task. Queries can be faster as they don't consider data consistency during updates.


Additionally, the read and write sides can be scaled independently based on their workloads, allowing you to handle surges in updates without impacting queries, and vice versa. Development is also simplified as CQRS enforces clean code separation between read and write logic. However, maintaining consistency between separate read and write models requires careful design and can introduce some complexity.


CQRS is particularly valuable for applications with a high read-to-write ratio or those requiring powerful and flexible querying capabilities. By strategically implementing CQRS, you can build cloud-native applications that are performant, scalable, and easier to manage, much like a well-organized library keeps both borrowing and browsing efficient.


Example : In a real-time collaborative document editing application, CQRS can be a game-changer. One database, optimized for writes, handles user edits and ensures data consistency.  A separate read model, potentially a materialized view or a cache, stores the latest document version for fast retrieval. This allows users to see changes almost instantly without compromising the performance of ongoing edits. This separation keeps the collaborative editing experience smooth and responsive.


Event Sourcing Pattern


Event Sourcing Pattern


The Event Sourcing pattern in cloud-native architecture redefines data persistence by focusing on the "why" instead of just the "what."  Unlike traditional methods that store the current state of your data, Event Sourcing meticulously records every change (event) that has ever happened to that data, similar to a historian documenting a nation's journey.


This complete record resides in an append-only event store, forming an immutable and auditable chain of events. To see the current data state, you replay this event stream and rebuild it into a temporary representation called a materialized view. Event Sourcing offers significant advantages such as a complete history allows for easy auditing and understanding data evolution, while the ability to replay the event stream enables powerful debugging and disaster recovery.


Additionally, event stores are typically designed for horizontal scaling, making them ideal for handling massive volumes of data changes. However, this approach also presents challenges. Reasoning about the current state can be complex, requiring replays, and retrieving it might impact performance for real-time applications.


Event Sourcing shines in scenarios like domain-driven design, where events are core concepts, or financial transactions where the immutable record is crucial for compliance. Similar to a film director's detailed production log, Event Sourcing empowers you to build cloud-native applications with strong auditability, reproducibility, and scalability.


Example: In a fitness tracking application, every workout session is recorded as an event in the event store. This allows users to see their complete training history (auditability) and even revisit specific workouts (reproducibility) for analysis. However, retrieving real-time calorie burn might require rebuilding a temporary view from the event stream.


Saga Pattern

saga pattern


The Saga pattern in cloud-native architecture tackles the intricate challenge of managing complex transactions that span multiple microservices. Imagine planning a vacation – booking a flight, reserving a hotel, renting a car – each step handled by a separate travel service.


The Saga pattern ensures all these steps succeed in unison or the entire trip gets cancelled. Here's how it works: a user initiates the saga (booking the trip), then each microservice (flight booking, hotel reservation) performs its local transaction. A central coordinator, the Saga Orchestrator, monitors progress and sends commands to each service. If all local transactions succeed, the saga commits and the trip is confirmed.


However, if any step fails, the Saga Orchestrator springs into action, triggering compensating transactions to undo previous actions (like cancelling the hotel reservation). This pattern offers significant advantages such as data consistency is guaranteed across microservices as the entire transaction succeeds or rolls back completely, and the system becomes resilient by recovering from individual service failures without jeopardizing the overall transaction.


However, implementing Sagas can introduce complexity, especially for long-running transactions or numerous microservices involved. Careful design and implementation are crucial for managing distributed coordination and compensating transactions.


Example: In a video editing application, the Saga pattern ensures a smooth project publishing workflow. If uploading a video to the cloud storage fails, the Saga cancels the thumbnail generation process (compensation) to avoid creating orphaned data. This guarantees data consistency and prevents wasted resources.


Conclusion


Summarizing this article, cloud-native architecture patterns offer a range of strategies for designing and deploying modern applications in the cloud. By embracing principles like microservices, containers, serverless computing, and infrastructure as code, organizations can build applications that are more scalable, resilient, and agile. These patterns enable developers to take full advantage of cloud environments, making it easier to innovate, iterate, and deliver value to customers at scale.