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

Replication: Read Replicas and Failover

Replication improves read throughput and availability, but introduces lag and requires clear rules for reads-after-writes behavior and failover safety.
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Replication Improves Availability and Read Capacity

Replication copies data from a primary database to one or more replicas. This allows read traffic to be distributed and provides redundancy in case the primary fails.

Replication Lag

Replication is not instantaneous. Under load, replicas may fall behind. This creates eventual consistency behavior for read-after-write scenarios.

Failover Behavior

When the primary fails, a replica can be promoted. However, failover is not free: clients must reconnect, in-flight transactions may fail, and temporary downtime may occur.

Read Strategies

  • All reads from primary (strong consistency, limited scale)
  • Read-heavy endpoints from replicas (better throughput, possible staleness)
  • Read-after-write from primary only

Operational Concerns

  • Monitoring replication lag
  • Testing failover regularly
  • Ensuring backups align with replication topology

Production-First Takeaway

Replication increases availability and read throughput but introduces lag. Design read paths with explicit consistency expectations and monitor lag continuously.

Consistency Models (Strong vs Eventual) →