A 10-Step Roadmap to Systematically Master System Design

Introduction

I have summarized the steps to systematically learn system design into 10 points.

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1. Foundations

The first things to grasp are the concepts that form the basis of all discussions on system design.

• Scalability: Understanding the difference between vertical scaling (upgrading server specs) and horizontal scaling (increasing the number of servers).
• Reliability and Fault Tolerance: Designing a system that continues to operate even if some components fail.
• High Availability: Redundant configurations to minimize downtime.
• Latency vs. Throughput: The trade-off between response speed and the amount of processing per unit of time.
• CAP Theorem: In a distributed system, you cannot simultaneously satisfy Consistency, Availability, and Partition Tolerance. Furthermore, the PACELC theorem, an extension of the CAP theorem, indicates that even during normal operation without network partitions, there is a trade-off between latency and consistency.

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2. Networking and Protocols

Understanding how communication between systems works.

• HTTP / HTTPS: The fundamental protocols of the Web, including the mechanism of encryption via TLS.
• TCP vs UDP: Choosing between TCP, which prioritizes reliability, and UDP, which prioritizes speed.
• DNS (Domain Name System): The mechanism for resolving domain names to IP addresses.
• Load Balancer (L4 vs L7): L4 performs distribution at the transport layer, while L7 performs it at the application layer.

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3. Databases

How data is stored and managed is the core of system design.

• RDB (PostgreSQL, MySQL): Standardized data structures and transaction management.
• NoSQL (MongoDB, Cassandra, DyanamoDB): Flexible schema and excels at horizontal scaling.
• NewSQL (Cloud Spanner, TiDB): A new generation of databases that achieves both SQL consistency and horizontal scalability.
• Database Indexing: Mechanisms to improve query performance.
• Database Replication: Copying data across multiple nodes to increase availability.
• Database Sharding: A horizontal partitioning method that splits data and distributes it across multiple databases.

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4. Caching

An essential technique for resolving performance bottlenecks.

• In-Memory Cache (Redis, Memcached): Keeps data in memory for high-speed data access.
• Cache Invalidation Strategy: How to maintain consistency between the cache and the database.
• Write-through / Write-back Cache: The difference in timing for updating the cache during writes.
• CDN (Content Delivery Network): Delivers content from servers geographically closer to the user.

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5. Message Queues

Realizes asynchronous processing and loose coupling between services.

• Kafka: A high-throughput distributed streaming platform.
• RabbitMQ: A message broker capable of flexible routing.
• Amazon SQS: A managed queue service provided by AWS.
• Google Cloud Pub/Sub: A fully managed messaging service provided by Google Cloud.
• Event-Driven Architecture: A loosely coupled processing flow triggered by the occurrence of events.

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6. Microservices

Divides a monolith to increase flexibility in development and deployment.

• Inter-service Communication (REST, gRPC): Choosing between HTTP-based REST and high-performance gRPC.
• API Gateway: A single entry point that routes requests from clients to each service.
• Service Discovery: A mechanism to automatically resolve dynamically changing service IP addresses.
• Circuit Breaker: A fallback mechanism to prevent the chain reaction of failures.

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7. Containers and Orchestration

Streamlines application deployment and operations.

• Docker: Packages an application and its dependencies into a container.
• Kubernetes: An orchestration tool that automates the deployment, scaling, and management of containers.
• Container Networking: Mechanisms to control communication between containers.
• Service Mesh (Istio): An infrastructure layer that manages and controls communication between microservices.

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8. Observability

Mechanisms for grasping the state of systems in production and discovering issues early.

• Logging: Mechanisms for collecting, storing, and searching logs from applications and infrastructure.
• Monitoring (Prometheus, Grafana, New Relic, Datadog): Metric collection and visualization.
• Distributed Tracing (OpenTelemetry): Tracking requests across microservices.
• Alerting System: A mechanism to notify when anomalies are detected.

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9. Security

Basic knowledge for designing secure systems.

• Authentication and Authorization: Verifying who the user is (authentication) and controlling what they can do (authorization).
• OAuth / JWT: Standard authentication flows and token-based authorization.
• Encryption (TLS, HTTPS): Encrypting communication channels to protect data.
• Rate Limiting and DDoS Protection: Protecting the system from malicious access and overloading.
• SQL Injection: An attack where unauthorized SQL is executed via user input. Prevented by using prepared statements and input validation.
• Cross-Site Scripting (XSS): An attack where malicious scripts are embedded into web pages. Prevented by output escaping and Content Security Policy settings.
• Cross-Site Request Forgery (CSRF): An attack where requests unintended by the user are sent from another site. Prevented by CSRF tokens and SameSite Cookie attributes.

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10. Data Processing

Patterns for processing large volumes of data efficiently.

• Batch Processing: A method of processing a set amount of data at once.
• Stream Processing: Sequentially processes data as it flows in real-time.
• Data Pipeline: A flow that automates data collection, transformation, and storage.
• Event Sourcing: A pattern where all state changes are recorded as events.

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Summary

Firmly understanding the concepts in each step is the shortest route to mastering system design.

I plan to follow this roadmap for my learning as I aim to level up as an engineer.