The Complete System Design Reference: 350-Article Library, Learning Roadmap & Interview Strategy

This is article number 350. What started as a handful of system design explainers has grown into a comprehensive library covering every major area of distributed systems, software architecture, and infrastructure. This capstone article organizes the entire collection into a structured learning roadmap, interview strategy, and practice plan.

The Library at a Glance#

350 articles across 10 major categories. Below is a guided tour of the library with top picks from each category.

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1. Fundamentals of Distributed Systems#

The building blocks: CAP theorem, consistency models, failure modes, and distributed consensus.

• CAP Theorem Explained — Consistency, Availability, Partition Tolerance
• Consensus Algorithms — Raft, Paxos, and Practical BFT
• Distributed Clocks — Lamport Timestamps and Vector Clocks
• Failure Detection — Heartbeats, Phi Accrual, and SWIM
• Consistency Models — Eventual, Causal, Strong, and Linearizability
• The Two Generals Problem and Its Real-World Implications
• Split-Brain Resolution Strategies

Start here if you are new to distributed systems. These concepts appear in every system design interview.

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2. Databases and Storage#

From relational databases to distributed key-value stores, time-series databases, and data lakes.

• SQL vs NoSQL — When to Choose What
• Database Sharding Strategies — Hash, Range, and Directory-Based
• Write-Ahead Logging and Crash Recovery
• LSM Trees vs B-Trees — Storage Engine Internals
• Distributed Transactions — Two-Phase Commit and Saga Pattern
• Time-Series Databases — InfluxDB, TimescaleDB, and Prometheus
• Data Lake Architecture — Medallion Pattern and Lakehouse
• Change Data Capture with Debezium

Key insight: most system design questions revolve around data modeling and storage tradeoffs. Master this category thoroughly.

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3. Caching and Performance#

Caching layers, eviction policies, cache invalidation, and CDN architecture.

• Caching Strategies — Cache-Aside, Write-Through, Write-Behind
• Redis Deep Dive — Data Structures, Persistence, and Clustering
• Cache Invalidation — The Hardest Problem in Computer Science
• CDN Architecture — Edge Caching, Origin Shielding, and Purging
• Bloom Filters for Cache Optimization
• Connection Pooling and Keep-Alive Strategies

Interview tip: always discuss caching in your design. It shows you think about latency and cost.

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4. Networking and Protocols#

HTTP, gRPC, WebSockets, DNS, load balancing, and service mesh.

• HTTP/2 and HTTP/3 — Multiplexing, Header Compression, and QUIC
• gRPC — Protocol Buffers, Streaming, and Deadlines
• WebSocket Architecture for Real-Time Systems
• DNS Architecture — Resolution, Caching, and GeoDNS
• Load Balancing — L4 vs L7, Consistent Hashing, and Health Checks
• Service Mesh — Istio, Linkerd, and Sidecar Proxy Pattern
• API Gateway Patterns — Rate Limiting, Auth, and Routing

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5. Messaging and Event-Driven Architecture#

Queues, streams, event sourcing, and pub/sub at scale.

• Apache Kafka — Partitions, Consumer Groups, and Exactly-Once
• Event Sourcing and CQRS — When and How
• Message Queue Comparison — RabbitMQ, SQS, Kafka, and Pulsar
• Dead Letter Queues and Retry Strategies
• Saga Pattern for Distributed Transactions
• Schema Registry and Schema Evolution
• Idempotency in Event-Driven Systems

Key insight: event-driven architecture is the backbone of modern microservices. Every senior engineer should be fluent here.

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6. System Design Interviews#

Concrete system designs: URL shortener to global-scale social networks.

• Designing a URL Shortener — End to End
• Designing a Chat System — WhatsApp Scale
• Designing a News Feed — Facebook and Twitter
• Designing a Rate Limiter — Token Bucket to Sliding Window
• Designing a Notification System — Push, Email, and SMS
• Designing a Search Autocomplete System
• Designing a Video Streaming Platform — Netflix Architecture
• Designing a Ride-Sharing Platform — Uber and Lyft
• Designing a Distributed File Storage — Google Drive
• Designing a Payment System — Stripe Architecture

Practice plan: work through two designs per week. Sketch the architecture, identify bottlenecks, then read the article to compare.

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7. Infrastructure and DevOps#

Containers, orchestration, CI/CD, infrastructure as code, and cloud-native patterns.

• Kubernetes Architecture — Pods, Services, and the Control Plane
• Container Networking — CNI, Service Discovery, and DNS
• CI/CD Pipeline Design — GitHub Actions, ArgoCD, and Flux
• Infrastructure as Code — Terraform, Pulumi, and CDK
• GitOps — Principles, Tools, and Production Patterns
• Blue-Green and Canary Deployment Strategies
• Feature Flags and Progressive Rollouts

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8. Observability and Reliability#

Monitoring, tracing, logging, SLOs, incident response, and chaos engineering.

• The Three Pillars of Observability — Logs, Metrics, and Traces
• OpenTelemetry Instrumentation Guide
• SLOs, SLIs, and Error Budgets — A Practical Guide
• Distributed Tracing — Jaeger, Zipkin, and Tempo
• Chaos Engineering — Principles, Tools, and Game Days
• On-Call Best Practices and Incident Response
• Alerting Strategy — Signal vs Noise

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9. Security and Authentication#

Auth protocols, encryption, zero trust, and secure system design.

• OAuth 2.0 and OpenID Connect — The Complete Guide
• JWT — Structure, Signing, Validation, and Common Pitfalls
• Zero Trust Architecture — Beyond the Perimeter
• API Security — OWASP Top 10 for APIs
• Encryption at Rest and in Transit
• Secrets Management — Vault, AWS Secrets Manager, and SOPS
• Rate Limiting and DDoS Mitigation

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10. Architecture Patterns and Principles#

Microservices, monoliths, domain-driven design, and emerging patterns.

• Microservices vs Monolith — A Practical Decision Framework
• Domain-Driven Design — Bounded Contexts and Aggregates
• Strangler Fig Pattern — Incremental Migration
• CQRS — Command Query Responsibility Segregation
• Hexagonal Architecture — Ports and Adapters
• Cell-Based Architecture for Blast Radius Reduction
• Multi-Tenancy Patterns — Shared vs Isolated

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The Learning Roadmap#

Phase 1: Foundations (Weeks 1-4)#

Focus on categories 1, 2, and 3. Build a solid mental model of distributed systems, understand storage tradeoffs, and learn caching patterns. These three areas form the foundation for every system design discussion.

Phase 2: Communication and Events (Weeks 5-8)#

Move to categories 4 and 5. Understand how services communicate — synchronous (HTTP, gRPC) vs asynchronous (Kafka, queues). This is where most architectural decisions diverge.

Phase 3: Real Designs (Weeks 9-14)#

Work through category 6 systematically. For each design problem: spend 45 minutes sketching your own solution before reading the article. Compare your approach and note what you missed.

Phase 4: Production Engineering (Weeks 15-18)#

Cover categories 7, 8, and 9. These topics separate junior from senior engineers. Understanding deployment, observability, and security shows production maturity.

Phase 5: Architecture Mastery (Weeks 19-20)#

Finish with category 10. At this point you have enough context to appreciate the tradeoffs between architectural styles.

Interview Strategy#

Before the Interview#

1. Pick 10 system designs from category 6 and practice them end to end
2. Build a template: requirements, estimation, API design, data model, high-level architecture, deep dive, bottlenecks
3. Prepare tradeoff discussions — interviewers care more about why than what

During the Interview#

1. Clarify requirements — spend the first 3-5 minutes asking questions
2. Start with the happy path — get a working design on the board before optimizing
3. Quantify — back-of-envelope calculations show engineering rigor
4. Discuss tradeoffs explicitly — "We could use X, which gives us Y but costs Z"
5. Address failure modes — what happens when a node goes down, a network partitions, or traffic spikes 10x

Common Mistakes#

• Jumping into the solution without gathering requirements
• Over-engineering for scale that does not exist
• Ignoring data consistency requirements
• Forgetting about operational concerns (monitoring, deployment, rollback)

Practice Plan#

What Comes Next#

350 articles is a milestone, not a finish line. Distributed systems continue to evolve — new consensus protocols, new database architectures, new infrastructure primitives. The library will keep growing.

If you have read even a fraction of these articles and practiced the designs, you are well-prepared — not just for interviews, but for building real systems at scale.

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350 articles on system design at codelit.io/blog.