The System Design Encyclopedia: 250 Articles Covering Every Core Topic

250 articles. What started as a single post about load balancing has grown into a comprehensive system design library. This milestone article is your organized reference — every topic grouped by category so you can find exactly what you need.

By the Numbers#

Every article includes practical examples, trade-off analysis, and production recommendations. No filler — each piece targets a specific concept you will encounter in real systems or interviews.

Fundamentals#

The building blocks every engineer should know cold.

• Load Balancing — distribute traffic across servers using round-robin, least connections, consistent hashing, and L4/L7 strategies
• Caching — reduce latency and database load with Redis, Memcached, CDN caching, and cache invalidation patterns
• Horizontal vs Vertical Scaling — when to scale up a single machine versus scaling out to many
• CAP Theorem — the fundamental trade-off between consistency, availability, and partition tolerance
• Latency and Throughput — measuring, benchmarking, and optimizing system performance
• DNS and Networking — how requests travel from browser to server and back
• API Design — REST, GraphQL, gRPC, and WebSocket patterns for clean interfaces
• Rate Limiting — protecting systems from abuse with token bucket, sliding window, and distributed rate limiters
• Idempotency — designing operations that are safe to retry without side effects
• Pagination — cursor-based, offset, and keyset pagination for large result sets
• Back-of-the-Envelope Estimation — quick math to validate system design decisions
• Proxies and Reverse Proxies — forwarding requests, SSL termination, and traffic shaping
• Content Delivery Networks — edge caching, cache invalidation, and global content distribution
• Hashing Algorithms — MD5, SHA, and when to use cryptographic vs non-cryptographic hashes
• TCP vs UDP — reliable vs fast delivery and when each protocol matters
• HTTP/2 and HTTP/3 — multiplexing, server push, QUIC, and modern protocol improvements
• Serialization Formats — JSON, Protocol Buffers, Avro, MessagePack, and trade-offs
• Webhooks — push-based integrations, retry logic, and security considerations

Distributed Systems#

The hard problems that emerge when you add a network between components.

• Consensus Algorithms — Raft, Paxos, and how distributed nodes agree on state
• Service Discovery — how microservices find each other with Consul, etcd, ZooKeeper, and Kubernetes DNS
• Distributed Transactions — two-phase commit, saga patterns, and eventual consistency
• Event-Driven Architecture — using events to decouple services with Kafka, RabbitMQ, and SNS/SQS
• Message Queues — reliable async communication between services
• Leader Election — choosing a coordinator in a distributed cluster
• Consistent Hashing — distributing data across nodes with minimal redistribution on changes
• Vector Clocks and CRDTs — tracking causality and resolving conflicts without coordination
• Gossip Protocols — how nodes share state in large decentralized clusters
• Circuit Breakers — preventing cascading failures when downstream services degrade
• Distributed Locking — coordinating exclusive access across multiple nodes with Redlock and ZooKeeper
• Write-Ahead Logs — durability and replication through append-only log structures
• Bulkhead Pattern — isolating failures to prevent system-wide outages
• Backpressure — handling overload by signaling producers to slow down
• Quorum Reads and Writes — tunable consistency with R + W > N guarantees
• Crashing vs Byzantine Failures — failure models and what your system should tolerate
• Cluster Membership — detecting joins, leaves, and failures in dynamic clusters
• Partitioned Logs — Kafka-style ordered, durable, partitioned event streams
• Anti-Entropy and Merkle Trees — detecting and repairing data inconsistencies between replicas
• Conflict Resolution — last-write-wins, merge functions, and application-level strategies

Architecture Patterns#

Structural approaches for organizing large systems.

• Microservices vs Monolith — when to split and when to stay together
• CQRS — separating read and write models for performance and scalability
• Event Sourcing — storing state as a sequence of events instead of current snapshots
• Domain-Driven Design — bounded contexts, aggregates, and ubiquitous language
• Hexagonal Architecture — ports and adapters for testable, framework-independent code
• Strangler Fig Pattern — incrementally migrating from monolith to microservices
• Sidecar and Ambassador Patterns — extending service functionality without code changes
• API Gateway — centralized entry point for routing, auth, rate limiting, and transformation
• BFF (Backend for Frontend) — tailored APIs for different client types
• Saga Pattern — managing distributed transactions through orchestration or choreography
• Cell-Based Architecture — isolating blast radius with independent, self-contained cells
• Multi-Tenancy — sharing infrastructure between tenants with proper isolation
• Feature Flags — decoupling deployment from release with progressive rollouts
• Clean Architecture — dependency inversion and layered boundaries for maintainable code
• Modular Monolith — monolith structure with clear module boundaries as a stepping stone
• Outbox Pattern — reliable event publishing from transactional databases
• Throttling and Debouncing — controlling request frequency at the application layer
• Plugin Architecture — extensible systems with runtime-loadable modules
• Service Mesh — infrastructure-layer networking with Istio, Linkerd, and Consul Connect

Interview Questions#

System design problems commonly asked in technical interviews.

• Design a URL Shortener — hashing, base62 encoding, read-heavy optimization
• Design a Chat System — WebSockets, message ordering, presence, and offline delivery
• Design a Rate Limiter — algorithms, distributed coordination, and edge cases
• Design a Notification System — multi-channel delivery, templating, preferences, and retries
• Design a News Feed — fan-out on write vs read, ranking, and caching strategies
• Design a Search Autocomplete — trie data structures, ranking, and latency optimization
• Design a File Storage System — chunking, deduplication, metadata, and CDN distribution
• Design a Metrics and Monitoring System — time-series storage, aggregation, and alerting
• Design a Payment System — idempotency, state machines, reconciliation, and PCI compliance
• Design a Video Streaming Platform — transcoding, adaptive bitrate, CDN, and DRM
• Design a Ride-Sharing Service — geospatial indexing, matching, pricing, and ETA
• Design a Distributed Cache — partitioning, eviction, replication, and consistency
• Design a Web Crawler — politeness, deduplication, frontier management, and distributed crawling
• Design a Ticket Booking System — seat locking, race conditions, overbooking prevention
• Design a Social Graph — friend-of-friend queries, graph storage, and privacy controls
• Design a Location-Based Service — geohashing, proximity search, and real-time tracking
• Design a Collaborative Editor — operational transforms, CRDTs, and real-time sync
• Design an Ad Serving System — auction mechanics, targeting, real-time bidding, and analytics

Infrastructure#

The platform layer that keeps everything running.

• Kubernetes — container orchestration, pod networking, and autoscaling
• CI/CD Pipelines — automated build, test, and deploy workflows
• Infrastructure as Code — Terraform, Pulumi, and declarative infrastructure management
• Container Networking — overlay networks, service mesh, and network policies
• Observability — logs, metrics, traces, and the three pillars of understanding production
• Chaos Engineering — intentionally breaking things to build resilience
• Blue-Green and Canary Deployments — safe release strategies with instant rollback
• Database Migration Strategies — zero-downtime schema changes and data migrations
• Auto-Scaling — CPU, queue depth, and custom metric-based scaling policies
• Connection Pooling — PgBouncer, ProxySQL, and managing database connections at scale
• Edge Computing — moving compute closer to users for latency-sensitive workloads
• GitOps — using Git as the single source of truth for infrastructure state
• Service Level Objectives — defining SLIs, SLOs, and SLAs with error budgets
• Incident Management — on-call rotations, runbooks, postmortems, and blameless culture
• Load Testing — stress testing with k6, Locust, and Gatling to find breaking points
• DNS and Traffic Management — weighted routing, failover, and geo-based DNS strategies
• Serverless Architecture — Lambda, Cloud Functions, and event-driven compute without servers
• Multi-Region Deployment — active-active, active-passive, and data replication across regions

Security#

Protecting systems, data, and users.

• Zero Trust Architecture — never trust, always verify — identity-based security for every request
• OAuth 2.0 and OIDC — modern authentication and authorization flows
• API Security — protecting APIs with authentication, encryption, and input validation
• Secrets Management — Vault, AWS Secrets Manager, and rotating credentials safely
• DDoS Protection — rate limiting, WAF, and traffic scrubbing at scale
• mTLS — mutual TLS for service-to-service encryption and authentication
• RBAC and ABAC — role-based and attribute-based access control models
• Supply Chain Security — securing dependencies, container images, and build pipelines
• Data Encryption — at rest, in transit, and application-layer encryption patterns
• CORS and CSP — browser security headers and cross-origin resource policies
• Penetration Testing — methodologies, tools, and integrating security into CI/CD
• JWT Security — token signing, rotation, revocation, and common pitfalls

Data#

Storage, processing, and movement of data at scale.

• Data Partitioning and Sharding — hash, range, directory, and geo sharding strategies
• Database Replication — leader-follower, multi-leader, and leaderless replication
• SQL vs NoSQL — choosing the right data model for your access patterns
• Time-Series Databases — storing and querying metrics, IoT, and financial data
• Data Lakes and Warehouses — centralized analytics storage with Snowflake, BigQuery, and Delta Lake
• Change Data Capture — streaming database changes with Debezium and Kafka Connect
• ETL and Data Pipelines — batch and streaming data transformation workflows
• Graph Databases — modeling relationships with Neo4j, Neptune, and Dgraph
• Bloom Filters and Probabilistic Data Structures — space-efficient membership testing
• LSM Trees and B-Trees — the storage engine foundations behind modern databases
• Data Governance — lineage, cataloging, quality, and compliance at scale
• Object Storage — S3, GCS, MinIO, and designing for unstructured data at petabyte scale
• Full-Text Search — Elasticsearch, OpenSearch, and inverted index architectures
• Data Versioning — tracking dataset changes for reproducibility and rollback
• Stream Processing — Flink, Spark Streaming, and real-time event transformation

AI/ML Systems#

The infrastructure behind machine learning in production.

• ML System Design — training pipelines, feature stores, model serving, and monitoring
• RAG Architecture — retrieval-augmented generation for grounded LLM applications
• Vector Databases — storing and querying embeddings with Pinecone, Weaviate, and pgvector
• Feature Stores — centralized feature management for training and serving consistency
• Model Serving — real-time inference, batching, A/B testing, and canary rollouts
• LLM Infrastructure — hosting, fine-tuning, prompt management, and cost optimization
• AI Gateway Patterns — routing, caching, fallback, and rate limiting for AI APIs
• Embedding Pipelines — generating, storing, and indexing vector embeddings at scale
• ML Observability — monitoring model performance, drift detection, and retraining triggers
• GPU Infrastructure — scheduling, multi-tenancy, and cost optimization for training workloads
• Data Labeling Pipelines — human-in-the-loop, active learning, and quality assurance
• A/B Testing for ML — experiment design, statistical significance, and model comparison
• Prompt Engineering Patterns — chain-of-thought, few-shot, and structured output techniques
• AI Agent Architecture — tool use, planning loops, memory, and orchestration frameworks

How to Use This Encyclopedia#

If you are preparing for interviews: Start with Fundamentals, then work through the Interview Questions section. Use the Architecture Patterns and Distributed Systems categories to deepen your answers.

If you are building production systems: Jump to the specific topic you need. Each article includes practical code examples, trade-off analysis, and real-world recommendations.

If you are learning system design from scratch: Read Fundamentals front to back, then branch into whichever category interests you most.

Recommended Learning Paths#

Path 1: Interview Prep (4-6 weeks)#

1. Fundamentals (week 1-2) — load balancing, caching, CAP theorem, API design
2. Architecture Patterns (week 3) — microservices, CQRS, event sourcing
3. Distributed Systems (week 4) — consensus, consistent hashing, circuit breakers
4. Interview Questions (week 5-6) — practice end-to-end designs with trade-off discussions

Path 2: Production Engineering (ongoing)#

1. Infrastructure — Kubernetes, CI/CD, observability, auto-scaling
2. Security — zero trust, mTLS, secrets management
3. Data — partitioning, replication, CDC, stream processing
4. Distributed Systems — deep dive into failure modes and recovery

Path 3: AI/ML Engineering#

1. Fundamentals — API design, caching, rate limiting
2. Data — vector databases, search, stream processing
3. AI/ML Systems — RAG, model serving, embedding pipelines, AI gateways
4. Infrastructure — GPU scheduling, serverless, observability

What Comes Next#

250 articles is a milestone, not a finish line. System design evolves as infrastructure evolves — new patterns emerge, old patterns get refined, and the community keeps pushing the boundaries of what distributed systems can do.

The next 250 will go deeper: more production war stories, more code-level implementations, more diagrams, and more coverage of the AI/ML infrastructure wave reshaping how we build systems.

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