Cloud Design Patterns

Performance Patterns

Asynchronous Request-Reply Pattern

Problem: Client applications expect synchronous responses, but back-end processing is asynchronous.

Solution: Decouple back-end processing from a front-end host where back-end processing must be asynchronous, but the front end requires a clear response.

When to Use:

• Long-running back-end operations

• Client applications can't wait for synchronous responses

• Offloading compute-intensive operations from web tier

Implementation Considerations:

• Return HTTP 202 (Accepted) with location header for status checking

• Implement status endpoint for clients to poll

• Consider webhooks for callback notifications

• Use correlation IDs to track requests

• Implement timeouts for long-running operations

Cache-Aside Pattern

Problem: Applications repeatedly access the same data from a data store.

Solution: Load data on demand into a cache from a data store when needed.

When to Use:

• Frequently accessed, read-heavy data

• Data that changes infrequently

• Reducing load on primary data store

Implementation Considerations:

• Check cache before accessing data store

• Load data into cache on cache miss (lazy loading)

• Set appropriate cache expiration policies

• Implement cache invalidation strategies

• Handle cache failures gracefully (fallback to data store)

• Consider cache coherency in distributed scenarios

CQRS (Command Query Responsibility Segregation) Pattern

Problem: Read and write workloads have different requirements and scaling needs.

Solution: Separate operations that read data from those that update data by using distinct interfaces.

When to Use:

• Read and write workloads have vastly different performance characteristics

• Different teams work on read and write sides

• Need to prevent merge conflicts in collaborative scenarios

• Complex business logic differs between reads and writes

Implementation Considerations:

• Separate read and write models

• Use event sourcing to synchronize models

• Scale read and write sides independently

• Consider eventual consistency implications

• Implement appropriate security for commands vs queries

Index Table Pattern

Problem: Queries frequently reference fields that aren't indexed efficiently.

Solution: Create indexes over the fields in data stores that queries frequently reference.

When to Use:

• Improving query performance

• Supporting multiple query patterns

• Working with NoSQL databases without native indexing

Implementation Considerations:

• Create separate tables/collections optimized for specific queries

• Maintain indexes asynchronously using events or triggers

• Consider storage overhead of duplicate data

• Handle index update failures and inconsistencies

Materialized View Pattern

Problem: Data is poorly formatted for required query operations.

Solution: Generate prepopulated views over the data in one or more data stores when the data isn't ideally formatted for query operations.

When to Use:

• Complex queries over normalized data

• Improving read performance for complex joins/aggregations

• Supporting multiple query patterns efficiently

Implementation Considerations:

• Refresh views asynchronously using background jobs or triggers

• Consider staleness tolerance for materialized data

• Balance between storage cost and query performance

• Implement incremental refresh where possible

Priority Queue Pattern

Problem: Some requests need faster processing than others.

Solution: Prioritize requests sent to services so that requests with a higher priority are processed more quickly.

When to Use:

• Providing different service levels to different customers

• Processing critical operations before less important ones

• Managing mixed workloads with varying importance

Implementation Considerations:

• Use message priority metadata

• Implement multiple queues for different priority levels

• Prevent starvation of low-priority messages

• Monitor queue depths and processing times per priority

Queue-Based Load Leveling Pattern

Problem: Intermittent heavy loads can overwhelm services.

Solution: Use a queue as a buffer between a task and a service to smooth intermittent heavy loads.

When to Use:

• Protecting services from traffic spikes

• Decoupling producers and consumers

• Enabling asynchronous processing

Implementation Considerations:

• Choose appropriate queue technology (Azure Storage Queue, Service Bus, etc.)

• Monitor queue length to detect saturation

• Implement auto-scaling based on queue depth

• Set appropriate message time-to-live (TTL)

• Handle poison messages with dead-letter queues

Rate Limiting Pattern

Problem: Service consumption must be controlled to prevent resource exhaustion.

Solution: Control the consumption of resources by applications, tenants, or services to prevent resource exhaustion and throttling.

When to Use:

• Protecting backend services from overload

• Implementing fair usage policies

• Preventing one tenant from monopolizing resources

Implementation Considerations:

• Implement token bucket, leaky bucket, or fixed window algorithms

• Return HTTP 429 (Too Many Requests) when limits exceeded

• Provide Retry-After headers to clients

• Consider different limits for different clients/tiers

• Make limits configurable and monitorable

Sharding Pattern

Problem: A single data store may have limitations in storage capacity and performance.

Solution: Divide a data store into a set of horizontal partitions or shards.

When to Use:

• Scaling beyond single database limits

• Improving query performance by reducing dataset size

• Distributing load across multiple databases

Implementation Considerations:

• Choose appropriate shard key (hash, range, or list-based)

• Avoid hot partitions by selecting balanced shard keys

• Handle cross-shard queries carefully

• Plan for shard rebalancing and splitting

• Consider operational complexity of managing multiple shards

Throttling Pattern

Problem: Resource consumption must be limited to prevent system overload.

Solution: Control the consumption of resources used by an application, tenant, or service.

When to Use:

• Ensuring system operates within defined capacity

• Preventing resource exhaustion during peak load

• Enforcing SLA-based resource allocation

Implementation Considerations:

• Implement at API gateway or service level

• Use different strategies: reject requests, queue, or degrade service

• Return appropriate HTTP status codes (429, 503)

• Provide clear feedback to clients about throttling

• Monitor throttling metrics to adjust capacity