If you’re a developer who wants to learn how real-world systems are built, this roadmap will walk you through every key concept — from basics to advanced topics — in the right order. Whether you're preparing for interviews, architecting systems, or just curious about how things work at scale, this guide is your starting point.
To effectively learn and master system design, you can follow a structured roadmap that typically progresses from foundational concepts to advanced topics and practical application through case studies. This comprehensive approach is essential for building scalable and robust applications and for excelling in system design interviews, particularly for senior engineer roles or higher. I will be creating articles for these all system design concepts with practical examples in the coming days.
1. Understanding the Fundamentals and Prerequisites
Before diving deep into system design, it is highly recommended to have a solid grasp of core computer science subjects, including Data Structures and Algorithms (DSA), Operating Systems (OS), and Computer Networks (CN). These foundational areas provide the necessary context for understanding how systems work and how to design them effectively.
Key Fundamental Concepts:
System Design Definition and Approach: Understand what system design entails—defining system elements, their interactions, and relationships to solve a problem. It involves gathering requirements, understanding constraints, identifying scope, and outlining main components.
Performance vs. Scalability: Differentiate between a system being performant (fast for a single user) and scalable (fast for a single user but remains performant under heavy load).
Latency vs. Throughput: Understand that latency is the time for a request's turnaround, while throughput is the number of requests served per unit of time. Reducing latency can involve deploying services across multiple data centres.
Availability vs. Consistency (CAP Theorem): A critical concept stating that in a distributed system with a network partition, one must choose between Consistency (every read gets the most recent write) and Availability (every request receives a response, even if not the latest data). The choice depends on the system's type (e.g., payment systems favour consistency, while chat apps may prioritise availability).
2. Core System Design Concepts (High-Level Design - HLD)
High-Level Design (HLD) focuses on designing the overall system architecture, identifying different components like databases, message queues, and caches, without coding.
Scaling Strategies:
Vertical Scaling (Scaling Up): Increasing resources (CPU, RAM, storage) of a single server. It's easy but limited and introduces downtime during upgrades.
Horizontal Scaling (Scaling Out): Adding more servers (replicas) to handle a subset of requests, distributing the workload. This is more powerful, offers redundancy and fault tolerance, but is more complex.
Networking Essentials:
Client-Server Model: How clients (e.g., browsers, mobile apps) initiate requests and servers accept and fulfil them.
IP Addresses & DNS: How IP addresses uniquely identify devices, and how the Domain Name System (DNS) translates human-readable domain names (e.g., neetcode.io) into IP addresses. DNS can also be used for load balancing.
TCP/IP & UDP: Understanding the Internet Protocol Suite, including TCP (a reliable protocol ensuring packet reassembly and retransmission, on which HTTP and WebSockets are built) and UDP (used when some packet loss is acceptable, like live streaming).
HTTP/HTTPS: HTTP is an application-level protocol for web communication. HTTPS is its secure version, encrypting data using SSL/TLS. Different HTTP versions (1, 2, 3) have varying implementations.
Load Balancing:
A reverse proxy or server that directs incoming requests to appropriate servers to distribute traffic evenly.
Algorithms include Round Robin, Least Connections, and Hashing.
Understanding Layer 4 vs. Layer 7 load balancers.
Content Delivery Networks (CDNs): A network of servers globally located that cache static files (images, videos, HTML, CSS, JavaScript) from an origin server. CDNs ensure faster delivery to users by serving content from the nearest location, reducing latency and server load.
Caching: Creating copies of data for faster retrieval.
Can occur at various levels: browser disk, computer memory, CPU cache, CDN, web server, database, application level.
Caching strategies (e.g., cache-aside, write-through, write-back, write-around) and eviction policies (e.g., LRU, LFU, FIFO).
Popular tools include Redis and Memcached.
Databases:
SQL (Relational Databases - RDBMS): Organise data into tables with predefined schemas. They are typically ACID compliant (Atomicity, Consistency, Isolation, Durability), making them suitable for strong consistency needs like banking systems. Examples: MySQL, PostgreSQL.
NoSQL (Non-Relational Databases): Drop consistency constraints to allow for easier horizontal scaling. They are schema-less and include types like key-value stores, document stores, and graph databases. Examples: MongoDB, Cassandra, Neo4j, DynamoDB.
Database Scaling Techniques:
Replication: Creating read-only copies of a database (leader-follower replication) to scale read operations and add fault tolerance. Leader-leader replication allows writes to any replica but can lead to inconsistency.
Sharding (Horizontal Partitioning): Breaking up a database into smaller, manageable pieces (shards) distributed across different machines, typically using a shard key. This scales read and write performance.
Vertical Partitioning: Splitting a database by columns to optimise queries for specific data patterns.
Indexing: Creating efficient lookup tables to speed up read queries by avoiding full table scans.
Denormalisation: Combining related data into a single table to reduce joins and improve read performance, often at the cost of increased storage and update complexity.
API Patterns:
REST (Representational State Transfer): A popular standardisation around HTTP APIs that are stateless and follow consistent guidelines (e.g., 200 for success, 400 for bad request, 500 for server error).
GraphQL: Introduced by Facebook in 2015, allows clients to make a single query to fetch exactly the resources they need, avoiding overfetching.
gRPC: A framework by Google (2016), mainly for server-to-server communication, offering performance benefits through binary protocol buffers over JSON.
WebSockets: Support bi-directional communication, pushing messages immediately, unlike HTTP polling. Useful for real-time applications like chat.
Webhooks: Allow a server to send an HTTP request to another server as soon as an event occurs, avoiding constant polling.
Message Queues/Brokers:
Used for asynchronous communication and handling non-critical tasks.
Decouple different parts of an application and persist data before processing.
Popular options include Kafka and RabbitMQ.
Implement publisher-subscriber models.
Concepts like Dead Letter Queues (DLQ) for failed messages.
Monitoring & Logging: Essential for identifying problems, understanding system health, and tracking metrics. Tools include AWS CloudWatch, Grafana, Prometheus, Splunk.
Security: Implementing strong authentication and authorisation (e.g., using tokens, OAuth, Access Control Lists), and encryption. This also includes preventing DDoS attacks through rate limiting.
3. Advanced System Design Concepts
Microservices vs. Monoliths: Transitioning from a single, large codebase (monolith) to smaller, independent services (microservices) for better scalability, management, and deployment. Concepts include containerisation (e.g., Docker) and container orchestration.
Service Discovery: How microservices locate each other.
Distributed Systems: Handling complexities like consensus algorithms and transactions across distributed systems.
Disaster Recovery & High Availability: Strategies to ensure system resilience and recovery from failures (e.g., geographical replication, geo-redundancy).
Cloud Services: Understanding platforms like AWS, Azure, GCP, and their offerings (EC2, S3, Lambda, Virtual Private Cloud, Auto Scaling) for deploying and managing scalable systems.
Advanced Design Patterns: Exploring patterns like Saga pattern, Anti-Corruption Layer, Choreography/Orchestration, and Backend for Frontend specific to distributed systems.
4. Low-Level Design (LLD)
Low-Level Design focuses on the actual machine coding, API design, and class diagrams. This involves writing well-structured, maintainable, and extensible code.
Object-Oriented Programming (OOP) Principles: Covering the four pillars: Abstraction, Encapsulation, Inheritance, and Polymorphism.
SOLID Principles: A set of five design principles that help develop maintainable and scalable software: Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion.
Design Patterns: Understanding creational, structural, and behavioral patterns (e.g., Factory, Singleton, Decorator, Strategy, Observer, Command).
Concurrency & Thread Safety: Learning about multi-threading, race conditions, deadlocks, and synchronisation mechanisms.
UML Diagrams: Creating diagrams like class diagrams and component diagrams to visualise system structure.
Clean Code Principles: Adhering to principles like DRY (Don't Repeat Yourself) and avoiding "God Classes".
5. Practice and Application
The most crucial step is practice, practice, and more practice. Apply the learned concepts by solving real-world system design problems and case studies.
Common Case Studies: Design systems like:
Netflix / YouTube (video streaming, content delivery, recommendation engines).
WhatsApp / Facebook Messenger (chat applications, real-time messaging, group chats).
Twitter / Social Media Feeds (posting updates, timeline generation, handling millions of users).
Uber / Ola (ride-sharing services, ride matching, tracking, fare estimation).
URL Shortener (e.g., Bitly).
E-commerce Website (e.g., Amazon, Flipkart) (product search, authentication, online payment, flash sales).
Book My Show (reservation/booking systems).
Dropbox (distributed file storage).
Zomato (food delivery networks).
Design Thinking: Before designing, clarify requirements (functional and non-functional), identify bottlenecks, and define data flow.
Trade-offs: Always consider and justify the trade-offs in your design choices, as there's no single "perfect" solution.
Presentation Skills: Learn to present your solution in an organised manner, asking clarifying questions and explaining your thought process.
Hands-on Projects: Build personal projects to apply concepts and showcase your skills.
I will be creating the articles for these concepts in the upcoming days. Excited to share my knowledge with you all.
Happy Learning!!!!
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