Documentation

Summary and notes for learning algorithms and system design. The book is organized into nine parts, ordered so each part builds on the one before it.

Essentials

Prerequisite material: memory, growth of algorithms, maths, concepts, templates, mindmap, and references.

• Essentials
• Prerequisites
• How Memory Works
• Growth of Algorithm
• Maths
• Concepts
• Memory Works (Templates)
• Mindmap
• References

Part I: Fundamental Data Structures & Core Algorithms

• Part I: Fundamental Data Structures & Core Algorithms
• Chapter 1: Linear Data Structures & Memory Models
• Chapter 2: Hierarchical Data Structures, Search Trees & Spatial Indexing
• Chapter 3: Core Algorithmic Paradigms & Parallel Computing
• Chapter 3B: Decision Theory, Game Theory & Bayesian Systems
• Chapter 4: Graph Theory & Network Algorithms
• Chapter 4A: Computational Theory & Advanced Algorithms

Part II: System Design Foundations & High-Scale Architecture

• Part II: System Design Foundations & High-Scale Architecture
• Chapter 5: System Design Fundamentals & Infrastructure Security
• Chapter 5B: Software Architecture Patterns
• Chapter 6: Caching Strategies & Edge Acceleration

Part III: Messaging, Notifications & Real-Time Communication

• Part III: Messaging, Notifications & Real-Time Communication
• Chapter 7: Asynchronous Messaging & Pub/Sub Systems
• Chapter 7A: Data Engineering & Stream Processing
• Chapter 8: Real-Time Communication & Notification Systems

Part IV: Distributed Systems & Database Architecture

• Part IV: Distributed Systems & Database Architecture
• Chapter 9: Distributed Systems Principles, Consensus & Decentralized Ledger Technology
• Chapter 10: Database Engineering, Replication & Scaling

Part V: Cloud Architecture, Operating Systems & DevOps Engineering

• Part V: Cloud Architecture, Operating Systems & DevOps Engineering
• Chapter 11: Cloud Primitives, Edge Systems & IoT Engineering
• Chapter 12: Containerization, Orchestration & CI/CD Pipelines
• Chapter 12A: Operating Systems & Kernel Mechanics

Part VI: Machine Learning Systems & Scalable AI Infrastructure

• Part VI: Machine Learning Systems & Scalable AI Infrastructure
• Chapter 13: Machine Learning & Deep Learning Foundations
• Chapter 14: MLOps & Production Machine Learning Systems
• Chapter 15: Generative AI Infrastructure & Large Scale LLM Orchestration

Part VII: Financial Systems, Identity & Compliance

• Part VII: Financial Systems, Identity & Compliance
• Chapter 16: Banking & Payment Infrastructure
• Chapter 17: Identity, KYC & Decentralized Identity
• Chapter 18: Trading Systems & Market Infrastructure
• Chapter 19: DAOs & On-Chain Governance
• Chapter 20: Compliance, Risk & Regulatory Systems
• Chapter 21: Search, Ranking & Recommendation Systems

Part VIII: Web3 Security, Multi-Tenancy & Embedded Finance

• Part VIII: Web3 Security, Multi-Tenancy & Embedded Finance
• Chapter 22: Crypto Custody, Privacy Cryptography & Web3 Security
• Chapter 23: Multi-Tenant SaaS & Licensing
• Chapter 24: Embedded Finance & Lending
• Chapter 25: Resilience, Testing, SRE & Privacy-Preserving ML
• Chapter 26: Embedded Systems & Edge Sync

Part IX: High-Scale System Design Case Studies & Applied Infrastructure

• Part IX: High-Scale System Design Case Studies & Applied Infrastructure
• Chapter 27: Fundamental Distributed Utilities
• Chapter 28: Content Ingestion, Search & Storage Engines
• Chapter 29: Feeds, Notifications & Real-Time Messaging
• Chapter 30: Geospatial & Location Services
• Chapter 31: Event Processing, Analytics & Gaming Engines
• Chapter 32: High-Concurrency Financial & Transactional Systems
• Chapter 33: Enterprise Operations & Monitoring Infrastructure
• Chapter 34: Media Streaming & Cloud Storage Systems
• Chapter 35: Modular Systems & Plugin Architecture