Systems Architecture & Distributed Systems Topics
Large-scale distributed system design, service architecture, microservices patterns, global distribution strategies, scalability, and fault tolerance at the service/application layer. Covers microservices decomposition, caching strategies, API design, eventual consistency, multi-region systems, and architectural resilience patterns. Excludes storage and database optimization (see Database Engineering & Data Systems), data pipeline infrastructure (see Data Engineering & Analytics Infrastructure), and infrastructure platform design (see Cloud & Infrastructure).
Caching Strategies and Distributed Caching
Using caches to reduce latency and load: cache-aside, read-through, write-through, and write-behind patterns, TTLs, eviction policies, and distributed caches such as Redis or Memcached. Covers cache invalidation, stampede and thundering-herd protection, and the consistency tradeoffs of caching. Focuses on where and how to cache across tiers.
Multi-Tenancy and Isolation
Serving many tenants from shared infrastructure: tenancy models (silo, pool, bridge), data isolation, per-tenant data residency, noisy-neighbor mitigation, per-tenant limits, and security boundaries between tenants. Covers the cost, isolation, and blast-radius tradeoffs of shared versus dedicated resources, and business continuity: per-tenant backup, disaster recovery, and compliant tenant offboarding and deletion. The architecture layer specific to SaaS and platform products.
API and Interface Design for Distributed Services
Designing the contracts between services and clients: REST, gRPC, and GraphQL tradeoffs, versioning and backward compatibility, pagination, rate limiting, and idempotent endpoints. Covers request/response modeling, error contracts, and API gateway responsibilities. Focuses on the interface layer that ties distributed components together, not internal data schemas.
System Design Methodology and Trade-off Analysis
The end-to-end approach to an open-ended design problem and the judgment that resolves it: clarifying scope and constraints, gathering functional and non-functional requirements, capacity and back-of-envelope estimation, and mapping requirements to a high-level architecture, then reasoning explicitly about competing options on cost, complexity, latency, and reliability to defend a choice. Covers driving a design interview from ambiguity to a proposal, trade-off frameworks, decision-making under uncertainty and incomplete information, reversible-versus-irreversible decisions, and defending choices under scrutiny. The process-and-judgment skill underneath every system-design case study.
Architecture Documentation and Communication
Making an architecture legible to others: architecture decision records (templates, alternatives and consequences, lifecycle, supersession, tagging and discoverability), diagramming and visualization (C4, sequence, deployment, data-flow and trust-boundary diagrams, notation conventions), and communicating designs to technical and non-technical stakeholders. Covers capturing rationale, documenting what a system does under failure, load and consistency trade-offs, recording observability, SLO and security content, running design reviews, keeping docs and diagrams current, and presenting a system clearly under time pressure. The communication skill that separates a good design from an understood one.
Event-Driven Architecture and Asynchronous Messaging
Designing systems around events and message passing: publish/subscribe, message queues, event streaming, choreography versus orchestration, and decoupling producers from consumers. Covers delivery semantics (at-least-once, at-most-once), ordering, backpressure, dead-letter handling, and the operational tradeoffs of asynchronous flows. Includes async processing patterns for offloading slow work.
Load Balancing and Traffic Management
Distributing requests across capacity: load-balancing algorithms (round-robin, least-connections, consistent hashing), L4 versus L7 balancing, health checks, and traffic shaping. Covers sticky sessions, canary and blue-green routing, rate limiting, and graceful draining. The traffic-distribution layer that keeps a scaled system balanced and available.
Distributed Systems Fundamentals
Core theory that underpins any multi-node system: the CAP and PACELC theorems, consistency models (strong, causal, eventual), partitioning, replication, and the fundamental tradeoffs between latency, availability, and consistency. Covers how network partitions, clock skew, and partial failure change the reasoning compared to single-node systems. This is the vocabulary layer every distributed design question builds on.
Scalability Patterns and Techniques
Scaling a system to handle growth in traffic and data: horizontal versus vertical scaling, statelessness, sharding and partitioning strategies, read replicas, and connection pooling. Covers capacity estimation, identifying bottlenecks, and the tradeoffs each scaling axis introduces. The general toolkit for taking a design from thousands to millions of users.