Cloud Design Patterns

Cloud Design Patterns

Architects design workloads by integrating platform services, functionality, and code to meet both functional and nonfunctional requirements. To design effective workloads, you must understand these requirements and select topologies and methodologies that address the challenges of your workload's constraints. Cloud design patterns provide solutions to many common challenges.

System design heavily relies on established design patterns. You can design infrastructure, code, and distributed systems by using a combination of these patterns. These patterns are crucial for building reliable, highly secure, cost-optimized, operationally efficient, and high-performing applications in the cloud.

The following cloud design patterns are technology-agnostic, which makes them suitable for any distributed system. You can apply these patterns across Azure, other cloud platforms, on-premises setups, and hybrid environments.

How Cloud Design Patterns Enhance the Design Process

Cloud workloads are vulnerable to the fallacies of distributed computing, which are common but incorrect assumptions about how distributed systems operate. Examples of these fallacies include:

• The network is reliable.
• Latency is zero.
• Bandwidth is infinite.
• The network is secure.
• Topology doesn't change.
• There's one administrator.
• Component versioning is simple.
• Observability implementation can be delayed.

These misconceptions can result in flawed workload designs. Design patterns don't eliminate these misconceptions but help raise awareness, provide compensation strategies, and provide mitigations. Each cloud design pattern has trade-offs. Focus on why you should choose a specific pattern instead of how to implement it.

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References

Reference	When to load
Reliability & Resilience Patterns	Ambassador, Bulkhead, Circuit Breaker, Compensating Transaction, Retry, Health Endpoint Monitoring, Leader Election, Saga, Sequential Convoy
Performance Patterns	Async Request-Reply, Cache-Aside, CQRS, Index Table, Materialized View, Priority Queue, Queue-Based Load Leveling, Rate Limiting, Sharding, Throttling
Messaging & Integration Patterns	Choreography, Claim Check, Competing Consumers, Messaging Bridge, Pipes and Filters, Publisher-Subscriber, Scheduler Agent Supervisor
Architecture & Design Patterns	Anti-Corruption Layer, Backends for Frontends, Gateway Aggregation/Offloading/Routing, Sidecar, Strangler Fig
Deployment & Operational Patterns	Compute Resource Consolidation, Deployment Stamps, External Configuration Store, Geode, Static Content Hosting
Security Patterns	Federated Identity, Quarantine, Valet Key
Event-Driven Architecture Patterns	Event Sourcing
Best Practices & Pattern Selection	Selecting appropriate patterns, Well-Architected Framework alignment, documentation, monitoring
Azure Service Mappings	Common Azure services for each pattern category

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Pattern Categories at a Glance

Category	Patterns	Focus
Reliability & Resilience	9 patterns	Fault tolerance, self-healing, graceful degradation
Performance	10 patterns	Caching, scaling, load management, data optimization
Messaging & Integration	7 patterns	Decoupling, event-driven communication, workflow coordination
Architecture & Design	7 patterns	System boundaries, API gateways, migration strategies
Deployment & Operational	5 patterns	Infrastructure management, geo-distribution, configuration
Security	3 patterns	Identity, access control, content validation
Event-Driven Architecture	1 pattern	Event sourcing and audit trails

External Links

• Cloud Design Patterns - Azure Architecture Center
• Azure Well-Architected Framework