Learn High Level Design (HLD) fundamentals

Master the Fundamentals of Distributed Systems

Dive into the world of distributed systems with this comprehensive curriculum, covering essential concepts and practical design patterns.

Understanding CAP Theorem: Explore the fundamental trade-offs between Consistency, Availability, and Partition Tolerance in distributed environments. Learn how to make informed decisions when designing resilient systems.

Scaling from 0 to Millions: Gain expertise in designing systems that can seamlessly scale from a small user base to handle millions of users. Discover strategies for horizontal scaling and performance optimization.

Consistent Hashing: Implement consistent hashing to distribute data evenly across multiple servers, maximizing scalability and ensuring fault tolerance. Learn how this technique minimizes data movement during server additions or removals.

Design a URL Shortening Service: Build a scalable URL shortening service, similar to Bitly, that generates unique, concise links. This hands-on project covers data structures, algorithms, and distributed system design.

Back-of-the-Envelope Estimation: Develop the ability to quickly estimate resource requirements (storage, bandwidth, request load) to validate the feasibility of your system designs. A crucial skill for early-stage planning.

Design a Key-Value Store: Implement the core functionality of a key-value store, the foundation of many distributed databases like Redis and DynamoDB. Understand data structures, consistency models, and replication strategies.

SQL vs NoSQL: Compare and contrast SQL (relational) and NoSQL databases. Learn to choose the right database type for your specific application needs, considering factors like data structure, scalability, and consistency requirements.

Design WhatsApp System: Architect a real-time chat application, mirroring the functionality of WhatsApp, including message delivery, group chats, and media sharing. A challenging project covering real-time communication and distributed data management.

Design Rate Limiter: Implement a rate limiter to protect your APIs from abuse and ensure fair usage. Learn various rate limiting algorithms and strategies for preventing denial-of-service attacks.

Design Idempotent POST API: Design idempotent POST APIs to handle potential retries gracefully. Ensure that repeated requests have the same effect as a single request, preventing unintended side effects.

Design High Availability System: Build systems that maintain continuous uptime through redundancy, data replication, and automated failover mechanisms. Essential for mission-critical applications.

Kafka (Distributed Messaging Queue): Learn how Kafka, a powerful distributed messaging queue, enables reliable, high-throughput, event-driven communication between services. Explore its use cases in microservices architectures.

Proxy vs Reverse Proxy: Understand the differences between proxies and reverse proxies. Learn how to use them to control outbound requests and manage inbound traffic, respectively, for enhanced security and performance.

Load Balancer and Its Algorithms: Efficiently distribute client requests using load balancers. Explore various load balancing algorithms, including round-robin, least connections, and IP hash, to optimize performance and availability.

Distributed Caching System: Improve application performance by implementing a distributed caching system that caches frequently accessed data across multiple servers. Reduce latency and database load.

Distributed Transactions (2PC, 3PC, SAGA): Coordinate updates across multiple services while maintaining data consistency using distributed transactions. Explore protocols like 2PC, 3PC, and SAGA patterns.

Database Indexing: Optimize query performance by strategically organizing data using database indexing. Learn different indexing techniques and their impact on query speed.

Distributed Concurrency Control: Manage concurrent updates across multiple nodes without compromising data consistency. Explore techniques for distributed concurrency control and preventing race conditions.

Two-Phase Locking: Ensure transaction serializability using two-phase locking, a locking protocol that guarantees data integrity during concurrent operations.