🏗️ System Design Patterns & Python Implementation

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🚀 What's Inside?

1. Python Essentials for Scale

• Concurrency: Threads vs Processes, AsyncIO Event Loops, and GIL limitations.
• Thread Safety: Locks, Semaphores, and Deadlock simulations (Dining Philosophers).

2. High Level Design (HLD) & Resiliency

• Scalability: Vertical vs Horizontal Scaling, Load Balancing algorithms.
• Distributed Data: CAP Theorem, Replication Lag, Sharding (Range vs Hash), and Saga Pattern.
• Resiliency: Circuit Breakers (Netflix Hystrix), Retry with Jitter, and Chaos Engineering.
• Advanced Structures: Bloom Filters, HyperLogLog, and Geospatial QuadTrees (Uber).

3. Advanced AI Architecture (SOTA)

• Production Inference: vLLM (Continuous Batching), KV Caching (PagedAttention), and LoRA Adapters.
• Recommender Systems: Two-Tower Architecture (YouTube/Netflix style).
• RAG Deep Dive: Vector Indexing (HNSW), Chunking strategies, and Embeddings.

4. ⚔️ Interview Prep & Experiments

A set of common interview questions implemented with both "Naive" (Bad) and "Optimal" (Scaled) approaches to demonstrate the difference.

Component	The "Naive" Way (Fail)	The "Optimal" Way (Pass)
Q4. WhatsApp	Polling DB every 1s	WebSockets + Store-and-Forward
Q6. News Feed	Pull Model (Slow Read)	Push Model (Fan-out on Write)
Q7. Google Drive	Re-uploading 1GB Files	Block-level Deduplication
Q8. YouTube	Sending full 4K.mp4	Adaptive Bitrate (HLS/DASH)
Q9. Leaderboard	ORDER BY score	Redis Sorted Sets (SkipList)
Q10. Ticketmaster	Race Condition (Oversell)	Optimistic Locking / Lua Scripts
Q11. Analytics	INSERT every click	Stream Windows (Flink/Spark)
Q12. Trending	HashMap (OOM)	Count-Min Sketch (Probabilistic)
Q13. Kafka	In-Memory Queue	Distributed Append-Only Log
Q15. Payments	Retrying on Timeout	Idempotency Keys

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🏢 Real World Architecture References

I included references to where these patterns are used in production systems:

• Uber/Google Maps: QuadTrees & Geohashing.
• Netflix: Chaos Monkey & Hystrix.
• Discord: Consistent Hashing (Ring).
• Amazon: Saga Pattern for Order Fulfillment.
• Apple Intelligence: LoRA Adapters for On-Device AI.
• YouTube: Two-Tower Neural Networks for Recommendations.

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🛠️ How to Run

1. Setup:

   make setup

2. Start Learning:

   make run

   (Navigate the CLI menu to choose lessons)

3. Run Tests:

   make test

4. Clean:

   make clean

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🤝 Contributing

Contributions are welcome. Please read CONTRIBUTING.md for details on the workflow.

1. Fork the repo
2. Create your feature branch (git checkout -b feat/amazing-feature)
3. Run tests (make test)
4. Commit your changes (git commit -m 'feat: Add amazing feature')
5. Push to the branch (git push origin feat/amazing-feature)
6. Open a Pull Request

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Created for self-education and shared for the community