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

Backpressure and Flow Control

Backpressure prevents overload by controlling demand when capacity is saturated. Learn queue based flow control, load shedding, concurrency limits, and failure modes like buffer bloat and thundering herd.
On this page

What Backpressure Solves

  • Without flow control, queues grow, latency explodes, and systems collapse.
  • Backpressure ensures producers slow down when consumers are saturated.
  • Production rule: do not rely on infinite buffering, it only delays failure.

Signals of Saturation

  • Queue depth and consumer lag.
  • Thread pool or worker utilization.
  • Latency percentile spikes and timeout rate increases.
  • Memory pressure and GC pause growth.

Flow Control Techniques

  • Concurrency limits: cap in flight requests per node.
  • Token buckets: rate limit producers with burst control.
  • Credit based: consumers grant credits to producers.
  • Queue buffering: absorb bursts but enforce size limits.

Load Shedding

  • Reject or degrade non critical requests under pressure.
  • Return fast errors instead of slow timeouts.
  • Prefer shedding at the edge to protect core services.

Bulkheads

  • Isolate resources: separate pools for critical and non critical work.
  • Prevent one noisy workload from starving others.
  • Apply per tenant limits to avoid noisy neighbor issues.

Failure Modes

  • Buffer bloat: large queues hide overload until latency becomes unusable.
  • Thundering herd: recovery causes many workers to resume simultaneously.
  • Queue collapse: memory exhaustion from unbounded buffering.
  • Head of line blocking: one slow job blocks many behind it.

Operational Checklist

  • Bound all queues and define drop or spill policies.
  • Set concurrency limits and validate under load tests.
  • Implement load shedding and return fast errors.
  • Separate pools for critical paths and background work.
  • Monitor saturation signals and automate scaling with caps.
Observability in Distributed Systems →