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

Distributed System Fundamentals

Distributed systems trade simplicity for scale and resilience. Learn core properties, time and failure assumptions, and production failure modes like partitions, retries, and split brain.
On this page

What Makes a System Distributed

  • Multiple nodes communicate over a network to provide one service.
  • Network and node failures are normal, not exceptional.
  • State is replicated or partitioned to scale and survive failures.

Core Properties

  • Availability: the system responds to requests.
  • Consistency: all observers see compatible state.
  • Partition tolerance: system continues despite network splits.
  • Latency: network hops dominate performance at scale.

Hard Truths

  • The network is unreliable and can drop, reorder, and duplicate messages.
  • Clocks are not perfectly synchronized and can drift.
  • Failures are partial: one node fails while others continue.
  • Retries can cause duplicates. Idempotency is mandatory.

Time and Ordering

  • Wall clock time is not a safe ordering mechanism.
  • Logical clocks (Lamport, vector clocks) help reason about causality.
  • Monotonicity matters: out of order events create correctness bugs.

Common Building Blocks

  • Replication: leader based, leaderless, quorum reads and writes.
  • Partitioning: consistent hashing, range sharding, rebalancing.
  • Coordination: consensus, membership, leases.
  • Messaging: at most once, at least once, exactly once as a goal.

Production Failure Modes

  • Partition: nodes cannot reach each other, clients see timeouts.
  • Split brain: multiple leaders accept writes and diverge state.
  • Retry storms: clients amplify an outage with aggressive retries.
  • Thundering herd: many nodes recover and hit dependencies at once.
  • Backpressure failure: queues grow without control and collapse latency.

Operational Checklist

  • Idempotency keys for write operations.
  • Timeouts, retry caps, and jittered backoff.
  • Load shedding and backpressure paths.
  • Clear health checks and readiness gating.
  • Observability: tracing, saturation metrics, and error budgets.
CAP Theorem in Practice →