Software Engineering

Understand the concepts

• What is idempotency? API retries explained with examples

  Understand idempotency through an API timeout example: repeated effects, request keys, and why the same response is not the same as the same outcome.

• CAP theorem explained through one network partition

  Understand consistency, availability and partition tolerance with a two-replica example. See why CAP is more precise than simply choosing two out of three.

• Backpressure explained: why a bigger queue is not enough

  Follow a growing queue to understand backpressure, buffering and load shedding. See what happens when a producer sends work faster than a consumer can process it.

• What is eventual consistency? Replicas that agree, eventually

  Eventual consistency explained with a worked example: why a read can return stale data after a write, when replicas converge, and how to still guarantee a user sees their own change.

• What is a race condition? The lost update explained

  A race condition explained with a worked lost-update example: why concurrent read-modify-write can drop an update, and how atomic operations, locks and constraints prevent it.

• What is rate limiting? Controlling how often clients can call

  Rate limiting explained with a worked token-bucket example: why APIs cap requests, how bursts and sustained rates are handled, and how it differs from a fixed window.

• Cache invalidation explained: why your app shows stale data

  Follow a stale product price through a cache. Compare TTL expiry and explicit invalidation, then see why a concurrent read can put an old value back.

What you’ll learn

• Why distributed systems fail, and how they stay reliable
• What actually happens behind APIs, databases and caches
• Concurrency, consistency and the trade-offs between them
• The mechanisms behind retries, idempotency and backpressure

Concepts you’ll explore