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Cloud Design Patterns, a series on the patterns that keep AWS workloads up under load, and essays on measuring AI-assisted engineering, republished from charlessieg.com.

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Series · 10 posts

Cloud Design Patterns

Ten patterns that keep AWS workloads up under load, each with the AWS services and settings that build it, the cases where it is the wrong tool, and the evidence a Well-Architected Review looks for in live accounts.

Start with the series overview

  1. Part 1: Retries Without Retry Storms: Backoff, Jitter, and Idempotency Keys8 min read
  2. Part 2: Circuit Breakers: Failing Fast So a Slow Dependency Can't Take You Down7 min read
  3. Part 3: Queue-Based Load Leveling: Let the Queue Take the Spike8 min read
  4. Part 4: Load Shedding and Backpressure: Saying No Early Keeps the Lights On7 min read
  5. Part 5: Cache-Aside With Request Coalescing: Surviving the Cold Cache8 min read
  6. Part 6: Event Fan-Out With Dead-Letter Queues: Publish Once, Fail Alone8 min read
  7. Part 7: CQRS With Read Models: Stop Making One Database Do Two Jobs7 min read
  8. Part 8: Static Stability: Designing for the Day the Control Plane Is Down8 min read
  9. Part 9: Cell-Based Architecture: Shrinking the Blast Radius7 min read
  10. Part 10: Observability That Answers Questions: RED, USE, Traces, and SLOs8 min read
Essays

Measuring AI-assisted engineering

Originally published at charlessieg.com and republished here because they are the argument the leverage engagement rests on.

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