Gaurav Sen System Design Info

Using Redis or Memcached to store frequent query results. Famous Case Studies

When tackling a system design problem in an interview or a real-world project, Sen suggests following a structured, step-by-step framework:

Never start drawing architecture immediately. Spend the first 5 minutes defining functional requirements (what the system does) and non-functional requirements (availability, consistency, latency). gaurav sen system design

: Identify single points of failure, scaling issues, and introduce optimization layers (Caching, Sharding, Message Queues). 2. Core Building Blocks of Scalable Architecture

: Always define functional and non-functional requirements (e.g., latency, availability) first. High-Level Design : Deconstruct real-world apps like to see underlying patterns. Communication is Key Using Redis or Memcached to store frequent query results

: Data is written to the cache and the database simultaneously. Highly consistent, but introduces write latency.

Next, you learn how these building Blocks communicate and fail together: : Identify single points of failure, scaling issues,

If you enroll in his System Design course or watch his playlist, you will encounter a specific progression of topics. Here are the essential modules that define his teaching.

Implementing Write-Through, Write-Back, and Cache-Aside patterns, alongside eviction policies like LRU (Least Recently Used).

In a distributed database or caching layer, traditional hashing ( server = hash(key) % number_of_servers ) fails miserably when a server is added or removed, causing massive data migration. Sen’s deep dives into explain how mapping both servers and data keys to a circular ring minimizes data movement to only a fraction of the keys ( 1/n ) during cluster resizing. Message Queues and Event-Driven Architecture

When a single database instance chokes on data volume, you must scale horizontally.