Core Code
Core Code Modern Dev Handbook
SYSTEM-DESIGN Contents
Constraints & Core Metrics
What Is System Design (Production-First) Functional vs Non-Functional Requirements Constraints, Assumptions, and Design Inputs Latency vs Throughput Tail Latency (p95/p99) and Why It Dominates UX SLI, SLO, SLA: The Reliability Contract Capacity Estimation Basics Bottlenecks and Queues (Little's Law Intuition)
Scaling & Traffic Management
Vertical vs Horizontal Scaling Load Balancing Fundamentals Caching Strategies Overview Cache Invalidation and Consistency Rate Limiting at Scale Backpressure: Preventing Overload Timeouts and Retries (Safe Defaults) Graceful Degradation and Traffic Shedding
Data & Consistency
Database Scaling Basics Replication: Read Replicas and Failover Consistency Models (Strong vs Eventual) Sharding Basics Partitioning and Hotspot Avoidance Idempotency and Deduplication Eventual Consistency and UX Schema Evolution and Backward Compatibility
Reliability Patterns
Timeouts: Bounding Damage Retry Strategies (Jitter, Caps, Budgets) Circuit Breaker Bulkhead Pattern Fallbacks and Graceful Degradation Load Shedding and Admission Control Backups and Restore Strategy Disaster Recovery (RPO/RTO)
Observability & Control
Golden Signals: Latency, Traffic, Errors, Saturation Metrics: SLIs and SLO Targets Logging at Scale (Structure and Cost) Correlation IDs and Context Propagation Distributed Tracing Overview Alerting and Runbooks Capacity Planning and Saturation Postmortems and Continuous Improvement
Case Studies
Case Study: URL Shortener Case Study: File Upload and Serving Case Study: Notification System Case Study: Chat System Overview Case Study: Rate Limiter Service Case Study: Social Feed (Fan-out Trade-offs) Case Study: Search Autocomplete Case Study: Analytics Ingestion Pipeline
Distributed Systems Foundations
CAP Theorem Consensus Basics Leader Election Distributed Locking Clock Skew Split Brain Quorum Reads and Writes Eventual vs Strong Consistency (Practical)
Service Communication Patterns
Synchronous vs Asynchronous Request-Response Pattern Message Queues Pub/Sub Pattern Event-Driven Architecture Saga Pattern CQRS Overview Outbox Pattern API Gateway Pattern Backend For Frontend (BFF)
Data at Scale Advanced
Index Design Deep Dive Query Planning Multi-Region Databases Read-After-Write Guarantees Global Databases Data Locality Large Table Strategies Background Jobs Design
Multi-Region Architecture
Multi-Region Basics Active-Active Active-Passive Geo DNS Cross-Region Replication Global Latency Optimization Regional Failover
Cost-Aware System Design
Cost vs Performance Storage Cost Trade-offs Cache Cost Analysis Overprovisioning vs Risk Cloud Pricing Mental Model Right-Sizing Instances
Security Architecture
Zero Trust Service-to-Service Auth Secrets Management Encryption at Rest Encryption in Transit DDoS Protection Multi-Tenant Data Isolation
System Design Interview
Design Twitter Design YouTube Design Uber Design Payment System Design E-commerce Platform Design Distributed Cache

Correlation IDs and Context Propagation

Correlation IDs connect logs across services. Propagate request context reliably to make debugging fast and incident response actionable.
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Correlation IDs Connect the Dots

In distributed systems, a single user request can span multiple services. Correlation IDs (or trace IDs) allow you to follow a request end-to-end across logs and services.

Propagation Strategy

Generate a request ID at the edge and propagate it through headers to downstream services. Every service logs the same ID.

Incoming request:
X-Request-Id: 7f9e...

Service A logs requestId
Service A calls Service B with same header
Service B logs same requestId

Why It Matters

Without correlation IDs, debugging requires guesswork and timestamp matching. With them, you can reconstruct the exact execution path quickly.

Production-First Takeaway

Make correlation IDs mandatory for all services. Enforce propagation at gateways and middleware layers.

Distributed Tracing Overview →