Grokking the System Design Interview

Ace your system design interview with the original Grokking the System Design Interview course by Design Gurus – ex-FAANG hiring managers. With 30K+ 5★ reviews, this course will help you master scalability, distributed systems, and more.
Level:
Beginner
Study Time:
20h
Lessons:
65
4.7
(37,216 ratings)
109,659 learners

Course Overview

Grokking the System Design Interview is the original and most comprehensive course for system design interviews. Created by ex-FAANG hiring managers from Google, Facebook, Amazon, and Microsoft, it distills years of real-world experience into a proven step-by-step framework that has helped over 100,000 software engineer to gain a deep mastery of system desig...

What you'll learn

  • A proven approach to systematically tackle any system design question — from requirements gathering to final detailed solution.
  • Master the core concepts of scalability, reliability, and performance to build robust, web-scale systems.
  • Dive into caching strategies, load balancing methods, and database sharding to handle complex, large-scale scenarios.
  • Get insider tips from ex-Google, Facebook, Amazon, and Microsoft interviewers on what top tech companies expect in system design interviews.
  • Master the art of requirements engineering — from clarifying requirements to understanding constraints — for large-scale systems.
  • Learn system design with real-world patterns: microservices, event-driven architectures, data partitioning, and more.
  • Explore hands-on examples like designing Instagram, Uber-like services, and messaging platforms to gain practical experience.
  • Understand how to choose the right technology stack, communication protocol (REST vs. gRPC), and hosting strategy (cloud vs. on-prem).

Course Content

5 Chapters • 65 Lessons
2.

Glossary of System Design Basics

19 lessons
System Design Basics
Get familiar with core system design concepts, key architectural components, and building blocks of scalable, distributed software systems.
Key Characteristics of Distributed Systems
Explore key characteristics of distributed systems, including scalability, reliability, availability, efficiency, fault tolerance, and manageability.
Load Balancing
Learn how load balancing improves scalability and availability by distributing traffic using smart algorithms, health checks, and redundancy techniques.
Caching
Learn caching strategies to improve system performance, including cache types, eviction policies, read/write methods, and cache invalidation techniques.
Data Partitioning
Learn data partitioning techniques—horizontal, vertical, hybrid—and partitioning criteria to scale databases, improve performance, and balance workloads.
Indexes
Understand how database indexes optimize query performance, enable efficient lookups, and impact write operations in large-scale systems.
Proxies
Learn how forward and reverse proxies improve security, caching, and traffic control by mediating communication between clients and servers.
Redundancy and Replication
Learn how redundancy and replication improve system reliability and availability through failover strategies and synchronous, asynchronous, and semi-synchronous replication.
SQL vs. NoSQL
Compare relational and non-relational databases to understand schema flexibility, scalability, ACID compliance, and ideal use cases for each.
CAP Theorem
Understand why distributed systems must trade off between consistency, availability, and partition tolerance—only two of the three can be guaranteed at any time.
PACELC Theorem
Learn how PACELC extends CAP by showing that even without partitions, distributed systems must balance latency and consistency in replicated environments.
Consistent Hashing
Learn the architecture of scalable systems using consistent hashing for efficient data partitioning, replication, and dynamic node management.
Long-Polling vs WebSockets vs Server-Sent Events
Understand the system design of real-time communication protocols for scalable push-based updates between client and server.
Bloom Filters
Explore space-efficient system design using Bloom filters for fast, probabilistic membership checks with minimal memory and no false negatives.
Quorum
Design highly available distributed systems using quorum-based consistency models to coordinate read/write operations across replicated nodes.
Leader and Follower
Understand the architecture of leader-based replication in distributed systems for consistent writes, fault tolerance, and coordinated data synchronization.
Heartbeat
Learn how distributed systems use heartbeat signals to detect server failures and maintain reliable request routing and availability.
Checksum
Explore how distributed systems ensure data integrity by using checksums to detect and prevent data corruption during transmission.
Quiz
Test your understanding of system design basic concepts.
3.

System Design Trade-offs

New
22 lessons
Importance of Discussing Trade-offs
Demonstrating trade-offs shows maturity, critical thinking, and real-world design skills crucial for scalable, practical system architecture.
Strong vs Eventual Consistency
Explore consistency models in distributed systems, comparing strong and eventual consistency in terms of latency, availability, accuracy, and scalability trade-offs.
Latency vs Throughput
Understand the difference between latency and throughput, and explore strategies to optimize response time and data processing capacity in systems.
ACID vs BASE Properties in Databases
Understand key differences between ACID and BASE database models, balancing consistency, availability, scalability, and fault tolerance in distributed systems.
Read-Through vs Write-Through Cache
Explore read-through vs write-through caching strategies for improving read performance, data consistency, and integrity in scalable systems.
Batch Processing vs Stream Processing
Compare batch vs stream processing in system design—complexity, resource efficiency, continuous vs scheduled data flow, and real-time responsiveness.
Load Balancer vs. API Gateway
Understand differences between Load Balancer and API Gateway in scalable architectures—traffic distribution, request routing, API management, and availability.
API Gateway vs Direct Service Exposure
Explore API Gateway vs Direct Service Exposure in distributed systems—compare centralized routing and security with direct access and reduced latency.
Proxy vs. Reverse Proxy
Understand how proxies and reverse proxies manage client and server traffic in distributed system design to improve security, caching, and scalability.
API Gateway vs. Reverse Proxy
Learn the difference between API Gateways and Reverse Proxies in distributed systems, focusing on routing, security, load balancing, and orchestration.
SQL vs. NoSQL
Compare SQL vs NoSQL databases based on schema structure, scalability, ACID compliance, flexibility, and best use cases in system design.
Primary-Replica vs Peer-to-Peer Replication
Understand Primary-Replica vs Peer-to-Peer replication models, including data flow, consistency, scalability, fault tolerance, and use cases.
Data Compression vs Data Deduplication
Learn the differences between data compression and deduplication for optimizing storage—focus, scope, efficiency, and best-use scenarios.
Server-Side Caching vs Client-Side Caching
Understand server-side vs client-side caching—how they differ in location, control, performance impact, and best use across web apps.
REST vs RPC
Learn the architectural differences between REST and RPC, including stateless resource handling vs. procedure calls, flexibility, scalability, and performance trade-offs.
Polling vs. Long-Polling vs. WebSockets vs. Webhooks
Compare real-time delivery strategies—Polling, Long-Polling, WebSockets, and Webhooks—for scalable, event-driven system communication.
CDN Usage vs Direct Server Serving
Learn when to use a Content Delivery Network vs direct server hosting based on traffic scale, geographic distribution, and caching efficiency.
Serverless Architecture vs Traditional Server-based
Learn serverless system design benefits like dynamic scaling and reduced ops versus traditional server hosting with full control and overhead.
Stateful vs Stateless Architecture
Explore the architecture of stateful vs stateless systems, comparing session handling, scalability, and design trade-offs for APIs and web services.
Hybrid Cloud Storage vs All-Cloud Storage
Explore hybrid cloud vs all-cloud storage architectures, balancing scalability, compliance, and control for secure, flexible enterprise data strategies.
Token Bucket vs Leaky Bucket
Learn how Token Bucket and Leaky Bucket algorithms manage network traffic shaping, rate limiting, and handling of bursty data.
Read Heavy vs Write Heavy System
Explore how system design varies for read-heavy and write-heavy workloads with techniques like batching, replication, and data partitioning.
5.

Appendix

2 lessons

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FAQs

A system design interview tests your ability to design scalable, efficient, and maintainable systems. It involves creating a high-level architecture for real-world applications like social media platforms, e-commerce sites, or messaging systems. The focus is on your problem-solving, trade-off analysis, and design choices.

The best way to prepare is to learn key concepts like scalability, databases, caching, and APIs. Practice designing systems like URL shorteners or chat apps, and explain your design choices. Use resources like "Grokking the System Design Interview" by DesignGurus.io for a structured approach.

No, system design interviews typically don’t involve coding. These interviews focus on high-level architecture, system components, and design choices rather than writing code. However, you may need to discuss algorithms or pseudocode to explain certain design decisions.

A system design interview is typically 45-60 minutes long. During this time, you'll analyze requirements, propose a design, discuss trade-offs, and answer follow-up questions. Efficient time management is crucial to cover all aspects of the design.

To nail system design interviews, understand the problem requirements thoroughly, break the system into components, and focus on scalability, reliability, and trade-offs. Practice designing real-world systems and use structured resources like "Grokking the System Design Interview" to build your skills.

To answer a system design question, start by clarifying the requirements, then outline the system's high-level architecture. Break the design into key components (e.g., database, APIs, caching) and explain trade-offs. Address scalability, fault tolerance, and performance to show a complete understanding.

Prepare for a system design interview by learning fundamentals like databases, caching, load balancing, and distributed systems. Practice designing systems like chat apps or e-commerce sites. Use resources like "Grokking the System Design Interview" for examples and guidance.

Yes, system design interviews can be challenging because they test complex topics like scalability, distributed systems, and trade-offs. However, with practice and understanding of common system design patterns, they become manageable. Structured resources like "Grokking the System Design Interview" can simplify preparation.

To solve system design problems, start by understanding the requirements and constraints. Break the system into components like storage, APIs, and communication layers. Focus on scalability, fault tolerance, and efficiency while discussing trade-offs and justifying your design choices.

Practice system design concepts by studying real-world applications like URL shorteners, social media platforms, and ride-sharing apps. Break down their architectures, identify components, and explore trade-offs. Use resources like "Grokking the System Design Interview" and participate in mock interviews for hands-on experience.

In a system design interview, you're expected to analyze requirements, design a scalable and reliable architecture, and explain your choices. Key topics include databases, APIs, caching, load balancing, and trade-offs. Clear communication and a structured approach are crucial.

To answer a system design interview, clarify requirements first. Propose a high-level architecture, divide the system into components, and explain how they interact. Discuss scalability, reliability, and trade-offs to demonstrate a thorough understanding of the design.

To get good at system design, focus on understanding core concepts like scalability, databases, caching, and distributed systems. Practice solving real-world design problems and learn from examples in resources like "Grokking the System Design Interview." Consistent practice and feedback are key.

No, a system design interview is different from a coding interview. Coding interviews focus on solving algorithmic problems with code, while system design interviews evaluate your ability to create scalable and efficient architectures for real-world applications. Both test different skill sets.

Design interviews often include questions like designing a URL shortener, a messaging app, a ride-sharing platform, or an e-commerce system. You'll be asked to address scalability, database design, API structure, caching, and trade-offs in your solutions.

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