System Design

--- name: system-design description: Structured approach to designing scalable distributed systems using Alex Xu methodology version: 1.0.0 author: wondelai (adapted) platforms: [claude-code, cursor] license: MIT source: https://github.com/wondelai/skills based_on: "System Design Interview by Alex Xu" --- # System Design Apply a systematic framework for designing large-scale distributed systems. ## Scoring System Rate the design 0-10 on scalability, reliability, and completeness. Show: current score → gaps → improved design → trade-offs. ## The 4-Step Framework ### Step 1: Understand the Problem (5-10 min) Ask clarifying questions before designing anything: - **Scale**: DAU, QPS, data size, read/write ratio - **Scope**: which features to include - **Constraints**: latency requirements, consistency model, budget **Back-of-envelope estimates:** - 1 DAU = ~0.3 QPS average, ~3 QPS peak - 1 server handles ~10K-100K QPS (depends on workload) - SQL DB: ~1K-10K QPS; Redis: ~100K-1M QPS - 1 image ≈ 300KB; 1 min video ≈ 50MB ### Step 2: High-Level Design Draw the major components: ``` Client → CDN → Load Balancer → API Gateway → Services → Database ↓ Message Queue → Workers → Object Storage ``` ### Step 3: Deep Dive Focus on the hard parts: - **Database**: SQL vs NoSQL, sharding strategy, replication - **Caching**: where (CDN, Redis, in-memory), TTL, eviction policy - **API Design**: REST vs GraphQL vs gRPC - **Consistency**: strong vs eventual, CAP theorem implications ### Step 4: Wrap Up — Handle Edge Cases - Single points of failure - Hotspots (celebrity problem) - Geographic distribution - Monitoring and alerting - Graceful degradation ## Key Patterns | Problem | Pattern | |---------|---------| | High read load | Read replicas + cache | | Large data set | Horizontal sharding | | Multiple services | API Gateway | | Async processing | Message queue (Kafka/RabbitMQ) | | Static content | CDN | | Real-time | WebSockets or Server-Sent Events | | Global users | Multi-region + GeoDNS | ## Numbers to Know - L1 cache: 0.5ns; L2: 7ns; RAM: 100ns; SSD: 150μs; Network: 150ms - Throughput: SSD 500MB/s; Network 1Gbps - Storage cost: $0.023/GB/month (S3), $0.10/GB/month (SSD)

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Спроектируй систему аналитики в реальном времени для 10M DAU
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Как лучше организовать хранение и обработку пользовательских файлов?
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