Cloud Design Pattern Tuesdays
This collection contains a summary and simple implementation of the cloud design patterns covered by the WAF.
Ambassador Pattern
The Ambassador pattern is a design pattern where a helper service acts as an intermediary between a client and backend services.
It's particularly useful for:
- Offloading responsibilities like logging, routing, retry policies, or monitoring
- Supporting legacy applications or hard-to-change systems
- Building shared client connectivity features
- Offloading connectivity concerns to specialists
This pattern is best suited when you need to abstract cross-cutting concerns and is commonly used in microservices architectures. However, it's not recommended for latency-critical applications or when features require deep client integration.
Read more about the Ambassador Pattern
Anti-Corruption Layer Pattern
The Anti-Corruption Layer pattern introduces a layer between two subsystems to prevent undesirable dependencies and preserve the integrity of the internal model.
It's particularly useful for:
- Integrating legacy systems with modern applications
- Working with external services that have different models or protocols
- Ensuring an application's design isn't limited by external dependencies
- Maintaining clean boundaries between different subsystems
This pattern is best suited when you need to integrate systems with differing semantics, but it's not recommended when there are no significant semantic differences between the systems or when the layer would introduce unnecessary complexity.
Read more about the Anti-Corruption Layer Pattern
Asynchronous Request-Reply Pattern
The Asynchronous Request-Reply pattern decouples backend processing from frontend hosts by allowing the sender to continue processing without waiting for an immediate reply.
It's particularly useful for:
- Decoupling services and increasing system reliability
- Handling long-running operations without blocking
- Buffering workloads and managing system load
- Enabling systems to operate independently
This pattern is best suited for distributed systems and event-driven architectures where immediate feedback isn't critical. However, it's not recommended for real-time systems requiring low latency or for simple operations where the added complexity isn't justified.
Read more about the Asynchronous Request-Reply Pattern
Backends for Frontends Pattern
The Backends for Frontends Pattern creates dedicated backend services for each frontend interface (web, mobile, IoT) to handle their unique requirements and optimize their specific needs.
It's particularly useful for:
- Applications with multiple frontends requiring different data shapes
- Reducing over-fetching and under-fetching of data
- Improving modularity and maintainability
- Simplifying API development for each frontend type
This pattern is best suited when frontends have distinct requirements that would make a shared backend overly complex. However, it's not recommended for applications with simple or uniform frontend requirements where a single API would suffice.
Read more about the Backends for Frontends Pattern
Bulkhead Pattern
The Bulkhead Pattern isolates elements of an application into pools to prevent cascading failures and ensure that a failure in one component doesn't impact others.
It's particularly useful for:
- Preventing cascading failures across microservices
- Isolating critical resources like database connections
- Managing task queues and compute resources
- Improving fault tolerance and recovery strategies
This pattern is best suited for complex applications where component isolation is crucial for reliability. However, it's not recommended for lightweight applications where the isolation complexity would outweigh the benefits, or for services with minimal risk of resource exhaustion.
Read more about the Bulkhead Pattern
Cache-Aside Pattern
The Cache-Aside Pattern is a caching strategy where the application is responsible for managing both reading from and writing to the cache. Data is loaded into the cache on-demand.
It's particularly useful for:
- Read-heavy applications that can tolerate minor data staleness
- Reducing load on the primary data store
- Improving application performance and scalability
- Applications where you need granular control over the cache
This pattern is best suited for scenarios where read performance is a priority and eventual consistency is acceptable. However, it is not ideal for write-heavy workloads or when strong data consistency between the cache and the data store is required.