Gaurav Sen System Design [top] Jun 2026

When breaking down large-scale systems, Gaurav Sen frequently emphasizes several critical architectural layers: Load Balancing and Consistent Hashing

Calculate the scale of the system. Estimate daily active users (DAU), read/write ratios, network bandwidth, and storage requirements for the next 5 years.

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The course is meticulously structured into several key modules, ensuring no stone is left unturned.

Sen frequently illustrates that choosing a technology is an act of sacrificing one benefit for another. For instance, using a consistent database (CP system) might sacrifice availability during a network partition, while an available database (AP system) might serve stale data. By constantly returning to the question, "What is the bottleneck?" or "What happens if this server crashes?", he trains engineers to anticipate failure. He popularized the understanding that system design is essentially risk management. Whether it is choosing between Strong Consistency and Eventual Consistency, or deciding between a relational SQL database and a NoSQL store, the Gaurav Sen method teaches that the justification of the choice is far more important than the choice itself. gaurav sen system design

) changes the mapping of almost all keys, causing a massive cache miss storm.Consistent Hashing solves this by mapping both servers and keys to a circular ring. When a server is added or removed, only a small fraction of the keys need to be remapped, making it indispensable for distributed caching and NoSQL databases like Cassandra. 3. Caching Strategies and Data Management

This article unpacks his philosophy, his core curriculum (specifically the System Design Interview course), and the key components that make his approach different from reading a textbook or memorizing mock interviews. The course is meticulously structured into several key

What features must the system support? (e.g., "Users can upload videos" vs. "Users can comment on videos").

: Don't over-engineer. Do not use microservices or Kafka if a simple monolithic app and a PostgreSQL database can handle your current scale. By constantly returning to the question, "What is

Every read receives the most recent write or an error.

To reduce database load and cut latency, caching is non-negotiable. Sen explains how to implement caches at multiple layers (Client, CDN, Application, and Database). He dives deep into eviction policies like LRU (Least Recently Used) and write policies such as Write-Through and Write-Back. Deconstructing Famous Architectures