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

Capacity Estimation Basics

Estimate traffic, payload sizes, and storage growth early to choose the right bottleneck strategy and avoid accidental over-engineering.
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Why Rough Math Beats Guessing

Capacity estimation is not about perfect forecasting. It is about avoiding blind design. Even basic back-of-the-envelope estimates help you choose sane defaults for caching, databases, queues, and scaling strategy.

Start With Workload Shape

  • Average RPS and peak RPS
  • Burst duration (seconds vs minutes)
  • Read/write ratio
  • Payload sizes (request and response)
  • Hot keys (popular users/items)

Translate Traffic Into Resource Demand

Each request consumes resources: CPU, memory, DB queries, and network. If you know “work per request,” you can estimate bottlenecks.

Example:
Peak traffic: 5,000 RPS
Per request: 1 DB query + 1 cache query

Peak DB QPS ≈ 5,000
Peak cache QPS ≈ 5,000

Database Connection Reality

Databases are often limited by concurrent connections. If each request requires a DB connection for 50ms, the maximum throughput per connection is about 20 ops/sec.

If avg DB time = 50ms:
Ops per connection ≈ 1 / 0.05 = 20 ops/sec

To support 5,000 DB ops/sec:
Required concurrent connections ≈ 5,000 / 20 = 250
Then add headroom (e.g., 30%) -> ~325

Storage Growth

Estimate data growth early to avoid painful migrations. You need to know: daily ingest, retention, and indexing overhead.

If uploads = 50GB/day and retention = 365 days:
Raw storage ≈ 18.25TB/year (before replication and overhead)

Headroom and Safety Margins

Production systems need headroom. Running at 90–100% saturation guarantees tail latency spikes and instability. Target sustainable utilization (often 50–70% for critical bottlenecks) and scale before you hit the cliff.

Production-First Takeaway

Capacity estimation converts requirements into constraints: QPS, concurrency, storage, and bandwidth. You do not need exact numbers—just enough to choose the right bottleneck strategy and avoid accidental over-engineering or under-provisioning.

Bottlenecks and Queues (Little's Law Intuition) →