Start with physical limits and capacity reasoning, then advance through infrastructure, distributed-system mechanics, HLD case studies, LLD, and a repeatable interview method.
Your Course Track
Begin with Phase 1: Foundations & Mental Models to master hardware latency numbers and throughput calculations.
33 lessons available now · Advanced tracks coming soon.
A gentle introduction to the four questions behind every system design: how fast is one request, how many requests arrive, what happens when demand grows, and what must remain correct?
Architectural Mental Model
A system is a set of waiting lines connected by networks and storage. Start by asking how much work arrives, how quickly each step serves it, and which promises the user cannot lose.
Lessons in this module
Internalize CPU register speeds, L1/L2/L3 cache misses, DRAM access times, NVMe vs SATA throughput, and datacenter vs intercontinental network latencies.
Compare TCP, UDP, QUIC (HTTP/3), gRPC Protocol Buffers, WebSockets, Server-Sent Events, and Long Polling for high-performance and real-time architectures.
Master Little’s Law for concurrency sizing, unmask the danger of arithmetic averages, and mitigate the Dean & Barroso tail latency amplification effect.
Understand horizontal vs vertical scale limits, Amdahl’s Law for parallelization, and Neil Gunther’s Universal Scalability Law accounting for crosstalk and contention.
Quantify uptime math (99.9% vs 99.99%), calculate serial vs parallel system reliability, and establish actionable SRE error budget burn rates.
Deconstruct database transaction guarantees from Serializable isolation down to Eventual consistency, exploring snapshot isolation, dirty reads, and write skew.
Formalize Gilbert & Lynch’s CAP proof, dissect why network partitions cannot be avoided, and adopt Daniel Abadi’s PACELC model for real-world databases.
The internal mechanics, scaling boundaries, and failure modes of infrastructure primitives: load balancers, API gateways, caches, CDNs, storage engines, and message brokers.
Architectural Mental Model
Infrastructure components are specialized trade-off engines. Caches trade memory for read latency; message brokers trade immediate execution for durable decoupling; load balancers trade hop latency for horizontal throughput.
Lessons in this module
Deep dive into Layer 4 (TCP) vs Layer 7 (HTTP) traffic balancing, routing algorithms (Round Robin, Least Connections, P2C), and active vs passive health checks.
Master edge gateway responsibilities: JWT/OAuth2 authentication, request transformation, dynamic route lookup, rate limiting, and distributed tracing propagation.
Implement cache-aside, read-through, write-through, and write-back patterns while mitigating stampedes, avalanches, penetrations, and hot-key skew.
Distribute keys across dynamic server clusters with minimal remapping on node joins or departures using hash rings and virtual nodes.
Learn how BGP Anycast and Geo-DNS route users to edge points of presence (POPs), static vs dynamic asset caching, origin shielding, and stale-while-revalidate headers.
Unpack the low-level disk mechanics of in-place B+Trees (PostgreSQL/MySQL) versus append-only Log-Structured Merge-Trees (RocksDB/Cassandra), comparing write amplification and compaction.
A decision framework for selecting between Relational (Postgres), Key-Value (Redis), Document (Mongo), Wide-Column (Cassandra), Columnar/OLAP (ClickHouse), and Graph (Neo4j).
Master horizontal database scaling: Single-Leader vs Leaderless replication, replication lag anomalies, sharding strategies, and Consistent Hashing with virtual nodes.
Dissect traditional message queues (RabbitMQ/SQS) versus distributed partitioned append-only event logs (Kafka/Redpanda), exploring zero-copy socket transfer and consumer offsets.
Compare rate limiting algorithms (Token Bucket, Leaky Bucket, Fixed Window, Sliding Window Counter), and design a high-throughput distributed limiter using Redis Lua scripts.
The core mechanics of distributed computing: Raft consensus, physical clock drift vs TrueTime, distributed locks with fencing tokens, Saga transactions, idempotency, and multi-region disaster recovery.
Architectural Mental Model
In a distributed system, partial failure is not an exception; it is the constant operating environment. You cannot trust physical time, single-point locks, or synchronous network acknowledgements.
End-to-end planetary-scale architectural case studies derived from first principles with back-of-the-envelope capacity estimation, data schemas, API contracts, and failure mitigations.
Architectural Mental Model
Start simple, measure hardware and network bounds, identify the first bottleneck, and introduce components only when mathematical evidence demands them.
The foundational object-oriented mechanics, SOLID design principles in production, Gang of Four design patterns, Java Memory Model concurrency theory, UML modeling, and Domain-Driven Design primitives.
Architectural Mental Model
Good low-level design minimizes the blast radius of future changes. We design to abstractions, protect invariants with encapsulation, and compose small, cohesive components.
Production-grade implementations of classic machine coding interview problems: Parking Lot, Elevator Control, In-Memory LRU/LFU Cache, Rate Limiter Library, Chess, Notification Service, and Splitwise.
Architectural Mental Model
In machine coding rounds, clean contracts, SOLID adherence, concurrency safety, and testability triumph over raw algorithmic speed.
Repeatable, time-tested blueprints to excel in both 45-minute High-Level Design (HLD) and 90-minute Low-Level Design (LLD) machine coding interviews, featuring capacity math mental models and live production incident triage.
Architectural Mental Model
An interview is not an oral exam where you wait for questions; it is a collaborative design session where you drive the technical agenda, demonstrate trade-off awareness, and proactively highlight failure modes.
Academic & production standards
Every lesson is cross-verified against established distributed-systems papers, official specifications, and production postmortems.
Put the framework to work
Move from requirements and estimates to APIs, data models, failure paths, and explicit trade-offs in one guided workflow.