Core Code
Core Code Modern Dev Handbook
REACT Contents
React Fundamentals
React Architecture Overview JSX and Rendering Model Component Design Principles Props and Data Flow State Management with useState Side Effects with useEffect Event Handling Patterns Conditional Rendering Strategies Lists, Keys, and Reconciliation Forms and Controlled Components Component Composition Lifting State and Shared State Basic Routing with React Router Failure Modes in Beginner React Apps
React + TypeScript
Typing Functional Components Typing Props and Children useState and Type Inference useReducer with Type Safety Typing Event Handlers Generic Components Utility Types in React Discriminated Unions for UI State Strict Mode and Null Safety API Response Typing Strategies Form Typing Patterns Avoiding any and Unsafe Casts Type-Safe Context API Failure Modes in React + TypeScript
React Advanced Patterns
Custom Hooks Architecture Context vs Prop Drilling Tradeoffs Compound Components Pattern Controlled vs Uncontrolled Abstractions Render Props and Pattern Alternatives State Machines in React Performance Optimization Playbook Memoization Strategies Code Splitting and Lazy Loading Error Boundaries and Recovery Portal Architecture Large Scale Folder Structure Testing Strategy: Unit and Integration Common React Anti Patterns
React Production
CSR vs SSR vs SSG Hydration and Mismatch Failures Next.js Architecture Overview App Router Deep Dive Authentication Patterns Token Storage Security Role Based UI Control API Integration Patterns Rate Limiting and UI Resilience Frontend Security Checklist XSS in Modern React Observability in Frontend Bundle Optimization Production Incident Playbook
Distributed Systems
Distributed System Fundamentals CAP Theorem in Practice Consistency Models Replication Strategies Leader Election Concepts Consensus Algorithms Overview Event Driven Architectures Message Queues and Delivery Guarantees Idempotency Patterns Saga Pattern Circuit Breakers Backpressure and Flow Control Observability in Distributed Systems Failure Scenarios and Postmortems

Message Queues and Delivery Guarantees

Delivery semantics define correctness under retries and failures. Learn at most once, at least once, and exactly once goals, plus ordering, redelivery, and operational controls like DLQ and backpressure.
On this page

Why Delivery Guarantees Matter

  • Networks fail and brokers retry. Messages can be duplicated or lost.
  • Correctness depends on what the system promises and what consumers assume.
  • Production rule: design for duplicates and define acceptable loss explicitly.

At Most Once

  • Message is delivered zero or one time.
  • Duplicates are avoided, but loss can occur.
  • Fits non critical telemetry and best effort notifications.

At Least Once

  • Message is delivered one or more times.
  • Duplicates are expected, consumers must be idempotent.
  • Most common production default for business events.

Exactly Once as a Goal

  • True end to end exactly once is extremely hard.
  • Systems approximate it with transactions, idempotency, and deduplication.
  • Production rule: treat exactly once as an application level property, not a broker slogan.

Acknowledgements and Redelivery

  • Consumers ack after processing completes, not before.
  • If processing fails or ack is not received, broker redelivers.
  • Visibility timeout and lease concepts define how long a message is in flight.

Ordering Reality

  • Ordering is usually guaranteed only per partition or per queue.
  • Parallelism can break ordering if not keyed carefully.
  • Use keys that align with business invariants to preserve required order.

Backpressure and Load Shedding

  • Queue depth is a signal of pressure.
  • Scale consumers up to a limit, then shed or degrade non critical work.
  • Protect downstream dependencies with circuit breakers and rate limits.

Failure Modes

  • Duplicate side effects when consumers are not idempotent.
  • Hot partition where one key dominates throughput and lags.
  • Poison pill message blocks progress without DLQ handling.
  • Unbounded retries create storms and amplify outages.

Operational Checklist

  • Define retry policy with caps and jitter.
  • Dead letter queue with alerting and replay procedure.
  • Consumer lag dashboards and autoscaling thresholds.
  • Idempotency keys and dedupe strategy for side effects.
  • Runbooks for broker outage and backlog drain.
Idempotency Patterns →