A plain-English guide to High-Level Design (HLD): what belongs on the big-picture map, how it differs from LLD and requirements, common architecture styles, a worked food-delivery example, pitfalls to avoid, and a reusable template.
A plain-English guide to Low-Level Design: classes, relationships, SOLID principles, design patterns, UML, a worked parking-lot example, and how good LLD turns a big-picture plan into code a team can actually build.
A plain-English guide to the very first move in a system design interview: ask clarifying questions. Includes a five-phase framework, a worked URL shortener example, common mistakes to avoid, and how to practise before the real thing.
A plain-English guide to scoping in system design: what belongs inside the frame, what stays outside, and how the earliest decision on any project quietly shapes every technical choice that follows.
A plain-English guide to bottlenecks in system design: the six kinds, how to spot the warning signs, how architects hunt them down, and the proven strategies for fixing them without shifting the problem elsewhere.
A plain-English guide to asynchronous processing in system design: queues, pub/sub, event streams, retries, dead letter queues, idempotency, eventual consistency, and how the best teams decide what to wait for.
A plain-English tour of Domain-Driven Design: the ubiquitous language, bounded contexts, entities and value objects, aggregates, events, and how the discipline still shapes software teams today.
A calm, thorough guide to the single most important idea in Domain-Driven Design: the “trunk” analogy showing how the same word means different things in different rooms, what a bounded context really is (a modelling boundary from Eric Evans 2003 book paired with a ubiquitous language), why the “one giant shared model” approach turns into a big ball of mud, the three threads of Language, Model, and Boundary, the distinction between a subdomain in the problem space and a bounded context in the solution space, five practical ways to find natural boundaries (vocabulary shifts, overloaded words, event storming, org chart, pace of change), how big a bounded context should be and how to read the warning signs when it drifts too big or too small, teams and ownership through the lens of Conway Law, eight context mapping patterns (Partnership, Shared Kernel, Customer-Supplier, Conformist, Anti-Corruption Layer, Open Host Service, Published Language, Separate Ways), how shared concepts cross contexts through IDs rather than shared classes, the difference between bounded contexts (modelling boundaries) and microservices (deployment boundaries) and the case for modular monoliths, five practices for implementing a boundary well including contract testing, honest pros and cons, six common pitfalls, five real-world examples (retail, banking, healthcare, ride-hailing, edtech), bounded contexts in modern cloud and AI-assisted practice, an FAQ, and a glossary.
A calm, thorough guide to why experienced architects lean on design patterns so heavily: the two-children-building-block-towers and bread-recipe analogies, the surprising origin of pattern thinking in Christopher Alexander 1977 building architecture book and the 1994 Gang of Four adoption, what a design pattern really is (a reusable shape of a solution, not code to copy), five deeper reasons architects reach for them (avoiding reinventing the wheel, shared vocabulary, encoded trade-offs, easier reading, sharpened judgement), the anatomy of every well-documented pattern (Name, Problem, Solution, Consequences), the three families (Creational, Structural, Behavioural), eight notable patterns worth knowing (Singleton, Factory, Builder, Observer, Strategy, Adapter, Decorator, Facade), the difference between design patterns and architectural patterns with a five-row comparison table, patterns as a shared team vocabulary, patterns as captured decisions with the structural-engineer parallel, honest pros and cons, a five-step guide to choosing the right pattern, five common pitfalls, patterns beyond pure code (UI, physical architecture, team process), five real-world examples (Singleton in a print queue, Observer in a weather app, Adapter in international travel, Facade in a car ignition, Strategy in a navigation app), patterns in modern cloud and AI-assisted practice, an FAQ, and a glossary.
A calm, thorough guide to redundancy in system design: the bicycle-with-two-brakes and circus-safety-net analogies, what redundancy really is (deliberate duplication of independent components), why it matters (true cost of downtime, failure as certainty not possibility, and the quiet value of predictability), how to find and eliminate single points of failure including hidden ones like a single expert engineer, the six main types of redundancy (hardware, data, network, geographic, software, power), the choice between active-active and active-passive with a step-by-step walkthrough of what a failover actually involves, the N+1 / N+M / 2N / 2N+1 vocabulary and triple modular redundancy for voting systems, data redundancy through RAID (0/1/5/6) and replication with the family-photo-album analogy plus the consistency-vs-speed trade-off, geographic redundancy across regions and the how-far-is-far-enough question, the difference between redundancy, resiliency, fault tolerance, and high availability, the honest pros and cons and real cost, six common pitfalls (hidden dependencies, untested failover, split-brain, over-engineering, confusing redundancy with backups, letting backup capacity rot), a five-step design process, five real-world examples (aircraft, power grids, online retail, home networking, emergency communication), redundancy in modern cloud, chaos engineering, and AI-assisted practice, an FAQ, and a glossary.