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MASTER SPINE (1–94)0 / 94
FOCUS NEXT · STEP 01 OF 94Thinking in Systems →
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01Fundamentals
▶
01–02 · Design Methodology▶
·01. Thinking in Systems·02. Back-of-the-Envelope Estimation
03–05 · SLOs, Skew & ADRs▶
·03. Availability, SLOs, and Downtime Budgets·04. Workload Modeling: Zipfian Skew, Fan-Out & Capacity Mathematics·05. System Architecture Decision-Making: Trade-offs & ADRs
·06. Computer Hardware
07–08 · Concurrency & Lock-Free▶
·07. Concurrency, Memory Models & Lock-Free Data Structures·08. Lock-Free Concurrency: Memory Barriers, CAS & Hazard Pointers
09–10 · OS I/O & Kernel Bypass▶
·09. Operating System & Network I/O: Epoll, Event Loops, Direct I/O, and Linux io_uring·10. High-Performance Network I/O: io_uring, Kernel-Bypass & XDP
·11. Storage Media
12–13 · Traffic Ingress▶
·12. Traffic Ingress Architecture: L4 Transport vs. L7 Application Load Balancing·13. Reverse Proxies, SSL/TLS Termination Architectures & API Gateways
14–15 · Single-Node Capacity▶
·14. Single-Node Capacity Engineering, CPU Saturation Formulas & Little's Law·15. Systems Benchmarking, the Saturation Knee & Vertical vs. Horizontal Scaling Decisions
02Building Blocks
▶
16–20 · Data Structures & Indexes▶
·16. B+ Trees & Disk-Based Indexing Internals·17. LSM-Trees and SSTables: Write-Optimized Storage Architecture·18. Bloom Filters·19. Consistent Hashing·20. Probabilistic Data Structures: HyperLogLog & Count-Min Sketch
21–22 · Storage Engines & MVCC▶
·21. Database Storage Engines: Slotted Pages, Buffer Pool Management, and WAL/ARIES Recovery·22. Database Transaction Isolation Levels, MVCC, and Concurrency Anomalies
23–24 · Distributed Fundamentals▶
·23. The Fallacies of Distributed Computing·24. Time, Clocks, and Ordering: From Physical Drift to Vector Clocks and TrueTime
·25. API Architecture·26. Event & Outbox Patterns·27. Caching Architecture·28. Stream Foundations·29. Application Topology
30–34 · Replication & Consensus▶
·30. Distributed Replication Strategies: Single-Leader, Multi-Leader & Leaderless Quorums·31. Advanced Replication: Chain Replication, Multi-Leader Topologies & Conflict Resolution·32. Distributed Consistency Models, Linearizability, and Quorum Anomalies·33. Distributed Consensus: Multi-Paxos, Raft Invariants & Byzantine Fault Tolerance·34. Advanced Consensus: Multi-Paxos, Fast Paxos & Byzantine Fault Tolerance (BFT)
35–38 · Sharding & Transactions▶
·35. Partitioning Strategies, Rebalancing & Consistent Hashing Topologies·36. Dynamic Rebalancing & Auto-Sharding: Range Splits & Heat Mitigation·37. Distributed Transactions: 2PC, 3PC, Sagas & TCC·38. Database Sharding, Partitioning & Distributed Transactions: 2PC vs. Sagas
39–42 · Resilience & Security▶
·39. Resilience Engineering: Circuit Breakers, Bulkheads & Adaptive Rate Limiting·40. Global Scalability & Multi-Region: BGP Anycast & Active-Active·41. Multi-Datacenter Consistency & WAN Replication: Spanner TrueTime vs CockroachDB HLC·42. Distributed Security: Zero Trust, mTLS & Multi-Tenant Isolation
03Workload Engines
▶
43–44 · Commit Logs & Streams▶
·43. Distributed Log Architecture: Kafka Internals, Zero-Copy I/O, and Replication·44. Distributed Stream Processing: Watermarks, Windowing, and Chandy-Lamport Checkpoints
45–46 · Consensus & Leases▶
·45. The Raft Consensus Algorithm: Leader Election, Quorums, and Replicated State Machines·46. Distributed Coordination & Consensus with etcd: Leases, Revision Watches, and Fencing Tokens
47–48 · Dynamo & Multi-Raft SQL▶
·47. Distributed Key-Value Stores: Dynamo-Style Gossip, Tunable Quorums, and Anti-Entropy·48. Distributed SQL, Multi-Raft Consensus & Two-Phase Commit
49–50 · Object & Search Engines▶
·49. Distributed Object Storage: Reed-Solomon Erasure Coding & Ceph CRUSH·50. Distributed Search & Analytics Engines: Inverted Indexes, Columnar Segments & Distributed Aggregations
04Design Studio
▶
51–53 · Read, Blob & Media▶
·51. Distributed URL Shortener: Base62 Token Generation, Range Allocation, and Low-Latency Redirection·52. Distributed Object Storage, Multipart Uploads & Global CDN Architecture·53. Global Video Streaming Architecture: HLS/DASH, Distributed Transcoding & Edge CDNs
54–56 · Real-Time & Collab▶
·54. Distributed Notification Platform: Multi-Channel Delivery, Rate Limiting, Deduplication, and Fan-Out Queues·55. Real-Time Chat & Presence Systems: WebSockets, Message Ordering, and Ephemeral Heartbeats·56. Real-Time Collaborative Editing: Operational Transformation vs. CRDT Architecture
57–58 · Feeds & Autocomplete▶
·57. Social Feed & Activity Stream Architecture: Fan-Out, Caching, and Cursor Pagination·58. Distributed Search & Typeahead Autocomplete Architecture: Inverted Indexes, FSTs, and BM25 Ranking
59–62 · Ledgers, SaaS & Global▶
·59. Financial Ledger Architecture: Double-Entry Bookkeeping and Distributed Idempotency·60. High-Concurrency Flash Sales & Ticket Booking: Inventory Reservation, Redis Lua, and Virtual Waiting Rooms·61. Multi-Tenant Enterprise SaaS Architecture: Isolation, Metering & Resource Quotas·62. Global Multi-Region Active-Active Architecture: Geo-Routing, Residency & Failover
05Systems Library
▶
63–64 · Traffic & Edge▶
·63. Envoy Proxy & Istio Service Mesh·64. NGINX, HAProxy & Modern API Gateways (Kong & APISIX)
65–66 · Distributed DBs▶
·65. Apache Cassandra & Dynamo Ring·66. Distributed SQL: TiDB, TiKV & CockroachDB Architecture Study
·67. Messaging & Logs·68. Stream Engines
69–70 · Consensus Engines▶
·69. Apache ZooKeeper & Ratis: Consensus, Znodes and Coordination Primitives·70. etcd Internals — MVCC, Bbolt & Raft Consensus
71–73 · Storage Internals▶
·71. Large-Scale Storage Architecture: Vitess Sharding & ClickHouse Columnar Analytics·72. Ceph — RADOS, CRUSH Map & BlueStore NVMe Engine·73. Apache HDFS & HBase — Distributed Storage & Wide-Column Engines
06Production Engineering
▶
·74. Observability & Tracing·75. Debugging & RCA·76. Chaos Engineering·77. Deployments & DR·78. Performance & eBPF·79. FinOps & Cost·80. Cluster Operations
07AI & ML Systems
▶
·81. GPU Memory & Sharding·82. GPU Networking
83–84 · LLM Inference▶
·83. LLM Inference: Prefill, Decode and KV Cache·84. High-Throughput LLM Inference Serving: Continuous Batching, PagedAttention, and Chunked Prefill
·85. Vector Databases·86. GPU Scheduling·87. AI Data Systems·88. RLHF & Alignment·89. Agentic & Multimodal·90. AI Observability
08Capstones
▶
·91. Flash-Sale Capstone·92. Distributed KV Capstone·93. LLM Inference Capstone·94. Enterprise RAG Capstone
CHOOSE YOUR PATHWAY

Learning Paths & Specializations

All pathways share the same rigorous engineering foundation. Specialize in large-scale applications, distributed data infrastructure, or distributed AI systems.

01

Common foundation

The shared engineering foundation: reasoning, computer systems, data structures and distributed systems.

12 Stages · 59 Topics ↗
02

Large-scale application systems

Design payments, messaging, search, booking, social and multi-tenant applications.

8 Chapters · 13 Topics ↗
03

Distributed data and infrastructure

Understand databases, messaging platforms, storage systems, coordination and distributed compute.

8 Chapters · 10 Topics ↗
04

Distributed AI and ML systems

Study distributed training, inference, GPU orchestration, vector retrieval and AI platforms.

8 Chapters · 12 Topics ↗
Made for curious engineers.About this notebook · Curriculum