System Design Roadmap
Complete system design path
It provides a structured journey — from foundational knowledge to advanced distributed system design — focused on scalability, resilience, and performance.
1. The Foundations
"You can’t scale what you don’t understand."
🎯 Goal
Understand how data flows in and out of a system — the basic building blocks before scaling.
🧩 Topics
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Networking & HTTP
- Learn how data moves: requests, responses, latency, and throughput.
- Understand HTTP methods, headers, cookies, caching, and RESTful design.
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Databases & Storage
- Learn relational vs non-relational trade-offs.
- Master indexing, normalization (up to BCNF), and ACID transactions.
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Caching
- What to cache, when to invalidate, and how TTL & eviction policies affect performance.
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Load Balancing & Reverse Proxies
- Distribute requests, maintain session consistency, and avoid single points of failure.
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APIs & Communication
- REST, gRPC, and GraphQL — know when to use each and their trade-offs.
2. The Mechanics
"Now you’re building systems, not just services."
🎯 Goal
Build systems that survive real-world conditions — latency, failures, and concurrency.
🧩 Topics
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Scalability Patterns
- Vertical vs horizontal scaling, sharding, partitioning strategies.
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Asynchronous Communication
- Use message queues (Kafka, RabbitMQ, MQTT) to decouple services.
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Data Consistency & Transactions
- Apply distributed transaction patterns: SAGA, 2PC, Outbox, CDC.
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Observability
- Logging, tracing (OpenTelemetry, Sentry), and meaningful metrics.
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Availability & Reliability
- Understand SLAs, SLIs, and SLOs.
- Design for graceful degradation, not blind uptime.
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Security & Authentication
- TLS, JWTs, OAuth2, rate limiting, and least-privilege access.
3. Advanced Concepts
"You’re no longer asking how to build it, but how it behaves under stress."
🎯 Goal
Design systems that adapt, recover, and scale predictably as complexity grows.
🧩 Topics
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Distributed Systems Theory
- CAP, PACELC, idempotency, and eventual consistency.
- Understand when each trade-off is acceptable.
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Event-Driven Architectures
- Fully decoupled systems using events as the source of truth.
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Data Modeling at Scale
- Polyglot persistence, schema evolution, analytical pipelines.
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System Evolution
- Blue-green deployments, feature flags, and graceful migrations.
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Performance Optimization
- Identify bottlenecks: database, cache, network, serialization, I/O.
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Resilience Engineering
- Circuit breakers, retries with backoff, chaos testing, and bulkheads.
4. Mini-Projects & Practice
🧱 Foundation Projects
- Build a simple HTTP server.
- Create REST & gRPC APIs with rate limiting.
- Implement Redis caching for a blog API.
⚙️ Mechanics Projects
- Design a scalable message queue (using Kafka or RabbitMQ).
- Implement distributed transactions (Outbox or SAGA pattern).
- Add observability with OpenTelemetry + Grafana dashboards.
🚀 Advanced Projects
- Build an event-driven order processing system.
- Design a microservice-based e-commerce backend.
- Implement blue-green deployments with feature flags.
- Run chaos experiments to test resilience.
5. Recommended Resources
📘 Books
- Designing Data-Intensive Applications — Martin Kleppmann
- Site Reliability Engineering — Google SRE Team
- The Art of Scalability — Abbott & Fisher
Courses
Tools & Frameworks
- Monitoring: Prometheus, Grafana, OpenTelemetry
- Messaging: Kafka, RabbitMQ
- Caching: Redis, Memcached
- Resilience: Hystrix, Resilience4j
- Testing: k6, Locust, Chaos Mesh
6. Skills Checklist
- Understand how HTTP, caching, and load balancing work
- Master data modeling and database trade-offs
- Build scalable REST/gRPC APIs
- Implement asynchronous messaging and queues
- Apply distributed transaction patterns
- Design for high availability and graceful degradation
- Add observability and meaningful monitoring
- Apply security best practices (TLS, OAuth2, JWTs)
- Optimize performance and identify bottlenecks
- Build resilient systems using chaos engineering and retries