System Design Framework

A structured approach to designing large-scale distributed systems. Apply these principles when architecting new services, reviewing system designs, estimating capacity, or preparing for system design discussions.

Core Principle

Start with requirements, not solutions. Every system design begins by clarifying what you are building, for whom, and at what scale. Jumping to architecture before understanding constraints produces over-engineered or under-engineered systems.

The foundation: Scalable systems are not invented from scratch -- they are assembled from well-understood building blocks (load balancers, caches, queues, databases, CDNs) connected by clear data flows. The skill lies in choosing the right blocks, sizing them correctly, and understanding the tradeoffs each choice introduces. A four-step process -- scope, high-level design, deep dive, wrap-up -- keeps the design focused and communicable.

Scoring

Goal: 10/10. When reviewing or creating system designs, rate them 0-10 based on adherence to the principles below. A 10/10 means the design clearly states requirements, includes back-of-the-envelope estimates, uses appropriate building blocks, addresses scaling and reliability, and acknowledges tradeoffs. Lower scores indicate gaps to address. Always provide the current score and specific improvements needed to reach 10/10.

The System Design Framework

Six areas for building reliable, scalable distributed systems:

1. The Four-Step Process

Core concept: Every system design follows four stages: (1) understand the problem and establish design scope, (2) propose a high-level design and get buy-in, (3) dive deep into critical components, (4) wrap up with tradeoffs and future improvements.

Why it works: Without a structured process, designs either stay too abstract or get lost in premature detail. The four-step approach ensures you invest time proportionally -- broad strokes first, depth where it matters.

Key insights:

• Step 1 consumes ~5-10 minutes: ask clarifying questions, list functional and non-functional requirements, agree on scale (DAU, QPS, storage)
• Step 2 consumes ~15-20 minutes: draw a high-level diagram with APIs, services, data stores, and data flow arrows
• Step 3 consumes ~15-20 minutes: pick 2-3 components that are hardest or most critical and design them in detail
• Step 4 consumes ~5 minutes: summarize tradeoffs, identify bottlenecks, suggest future improvements
• Never skip Step 1 -- ambiguity in scope leads to wasted design effort
• Get explicit agreement on assumptions before proceeding

Code applications:

See: references/four-step-process.md

2. Back-of-the-Envelope Estimation

Core concept: Use powers of two, latency numbers, and simple arithmetic to estimate QPS, storage, bandwidth, and server count before committing to an architecture.

Why it works: Estimation prevents two failure modes: over-provisioning (wasting money) and under-provisioning (outages under load). A 2-minute calculation can save weeks of rework.

Key insights:

• Know the powers of two: 2^10 = 1 thousand, 2^20 = 1 million, 2^30 = 1 billion, 2^40 = 1 trillion
• Memory read ~100 ns, SSD read ~100 us, disk seek ~10 ms, round-trip same datacenter ~0.5 ms, cross-continent ~150 ms
• Availability nines: 99.9% = 8.77 hours downtime/year, 99.99% = 52.6 minutes/year
• QPS estimation: DAU x average-actions-per-day / 86,400 seconds; peak QPS is typically 2-5x average
• Storage estimation: records-per-day x record-size x retention-period
• Always round aggressively -- the goal is order of magnitude, not precision

Code applications:

See: references/estimation-numbers.md

3. Building Blocks

Core concept: Scalable systems are assembled from a standard toolkit: DNS, CDN, load balancers, reverse proxies, application servers, caches, message queues, and consistent hashing.

Why it works: Each block solves a specific scaling or reliability problem. Knowing when and why to introduce each block prevents both premature complexity and avoidable bottlenecks.

Key insights:

• DNS resolves domain names; CDN caches static assets at edge locations close to users
• Load balancers distribute traffic -- L4 (transport layer, fast, simple) vs L7 (application layer, content-aware routing)
• Caching layers: client-side, CDN, web server, application (e.g., Redis/Memcached), database query cache
• Cache strategies: cache-aside (app manages), read-through (cache manages reads), write-through (cache manages writes synchronously), write-behind (cache writes asynchronously)
• Message queues (Kafka, RabbitMQ, SQS) decouple producers from consumers, absorb traffic spikes, and enable async processing
• Consistent hashing distributes keys across nodes with minimal redistribution when nodes are added or removed

Code applications:

See: references/building-blocks.md

4. Database Design and Scaling

Core concept: Choose SQL vs NoSQL based on data shape and access patterns, then scale vertically first, horizontally (replication and sharding) when vertical limits are reached.

Why it works: The database is usually the first bottleneck. Understanding replication, sharding strategies, and denormalization tradeoffs lets you delay expensive re-architectures and plan growth deliberately.

Key insights:

• Vertical scaling (bigger machine) is simpler but has a ceiling; horizontal scaling (more machines) is harder but nearly unlimited
• Replication: leader-follower (one writer, many readers) for read-heavy; multi-leader for multi-region writes
• Sharding strategies: hash-based (even distribution, hard range queries), range-based (efficient range queries, risk of hotspots), directory-based (flexible, extra lookup)
• SQL when you need ACID transactions, complex joins, and a well-defined schema; NoSQL when you need flexible schema, horizontal scale, or very high write throughput
• Denormalization trades storage and write complexity for faster reads -- use it when read performance is critical and data doesn't change frequently
• Celebrity/hotspot problem: if one shard gets disproportionate traffic, add a secondary partition or cache layer

Code applications:

See: references/database-scaling.md

5. Common System Designs

Core concept: Most systems are variations of a small set of well-known designs: URL shortener, rate limiter, notification system, news feed, chat system, search autocomplete, web crawler, and unique ID generator.

Why it works: Studying common designs builds a mental library of patterns and tradeoffs. When a new problem arrives, you recognize which known design it most resembles and adapt rather than invent from scratch.

Key insights:

• URL shortener: base62 encoding, key-value store, 301 vs 302 redirect tradeoff, analytics via redirect logging
• Rate limiter: token bucket or sliding window algorithm, placed at API gateway or middleware, return 429 with Retry-After header
• News feed: fanout-on-write (push to followers' caches at post time) vs fanout-on-read (pull and merge at read time); hybrid for celebrity accounts
• Chat system: WebSocket for real-time bidirectional communication, message queue for delivery guarantees, presence service via heartbeat
• Search autocomplete: trie data structure, top-k frequent queries, precompute and cache results for popular prefixes
• Web crawler: BFS with URL frontier, politeness (robots.txt, rate limiting per domain), deduplication via content hash
• Unique ID generator: UUID (simple, no coordination) vs Snowflake (time-sortable, 64-bit, datacenter-aware)

Code applications:

See: references/common-designs.md

6. Reliability and Operations

Core concept: A system is only as good as its ability to stay up, recover from failures, and be observed. Health checks, monitoring, logging, and deployment strategies are not afterthoughts -- they are first-class design concerns.

Why it works: Production systems fail in ways that design diagrams never predict. Operational readiness -- metrics, alerts, rollback plans, and redundancy -- determines whether a failure becomes a minor blip or a major outage.

Key insights:

• Health checks: liveness (is the process alive?) and readiness (can it serve traffic?) -- Kubernetes uses both
• Monitoring stack: metrics (Prometheus, Datadog), logging (ELK, CloudWatch), tracing (Jaeger, Zipkin) -- the three pillars of observability
• Deployment strategies: rolling (gradual replacement), blue-green (two identical environments, instant switch), canary (small percentage first, then expand)
• Disaster recovery: RPO (how much data can you lose) and RTO (how long until recovery) define your backup and failover strategy
• Multi-datacenter: active-passive (failover) or active-active (both serving); active-active requires data synchronization and conflict resolution
• Autoscaling: scale on CPU, memory, queue depth, or custom metrics; always set both min and max instance counts

Code applications:

See: references/reliability-operations.md

Common Mistakes

Quick Diagnostic

Reference Files

• four-step-process.md: The complete four-step process with time allocation, example questions, and tips for each stage
• estimation-numbers.md: Powers of two, latency numbers, availability nines, QPS/storage/bandwidth estimation with worked examples
• building-blocks.md: DNS, CDN, load balancers, caching strategies, message queues, consistent hashing
• database-scaling.md: SQL vs NoSQL, replication, sharding strategies, denormalization, database selection guide
• common-designs.md: URL shortener, rate limiter, news feed, chat system, search autocomplete, web crawler, unique ID generator
• reliability-operations.md: Health checks, monitoring, logging, deployment strategies, disaster recovery, autoscaling