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

Circuit Breaker

Circuit breakers stop calling failing dependencies and give systems time to recover, protecting latency and preventing cascading failures.
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Circuit Breakers Prevent Cascading Failures

A circuit breaker stops calling a dependency that is failing and allows it time to recover. Without a circuit breaker, your system keeps sending traffic into failure, increasing load, increasing queues, and making recovery harder.

States: Closed, Open, Half-Open

  • Closed: calls flow normally
  • Open: calls are rejected fast (or routed to fallback)
  • Half-open: limited probe calls test if the dependency has recovered

What Signals Should Trip the Breaker

  • High error rate (5xx, timeouts)
  • Rising latency (p95/p99)
  • Connection pool exhaustion

Fast Failure Is a Feature

When a dependency is down, failing fast protects the rest of your system. It preserves resources for critical paths and avoids long queueing delays. Users may see errors, but the system stays responsive and recoverable.

Fallbacks and Degradation

Circuit breakers are often paired with fallbacks: return cached data, partial results, or a degraded response. Fallbacks must be explicitly safe and should not silently violate correctness for critical flows.

Operational Considerations

  • Log breaker state changes (open/close events)
  • Expose metrics: open duration, reject counts, probe success rate
  • Separate breakers per dependency and sometimes per endpoint

Production-First Takeaway

Circuit breakers turn repeated failure into controlled behavior. Trip on meaningful signals, fail fast, probe recovery carefully, and instrument breaker behavior so on-call can see what is happening.

Bulkhead Pattern →