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[System Design Tech Case Study Pulse #49] 100+ Million Requests per Second : How Amazon Shopping Cart Actually Works
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[System Design Tech Case Study Pulse #49] 100+ Million Requests per Second : How Amazon Shopping Cart Actually Works

With detailed explanation and flow chart....

Naina Chaturvedi's avatar
Naina Chaturvedi
Jan 02, 2025
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[System Design Tech Case Study Pulse #49] 100+ Million Requests per Second : How Amazon Shopping Cart Actually Works
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Hi All,

Amazon's implementation of DynamoDB plays a crucial role in managing their shopping cart system, handling an astounding 100 million+ requests per second during peak times. This feat enables Amazon to provide a seamless, low-latency shopping experience for millions of customers worldwide, even during high-traffic events like Prime Day.

Learn how to System design —Design Lyft

[System Design Tech Case Study Pulse #15] 80 Million Photos Daily : How Instagram Achieves Real Time Photo Sharing

[System Design Tech Case Study Pulse #9] Facebook's News Feed Algorithm Marvel: How it Serves 2.9 Billion Daily Active Users Using PyTorch and Cassandra

Let me dive deep into how Amazon engineered this system, exploring the key architectural decisions, scaling strategies, and optimizations that enable DynamoDB to manage this enormous volume of shopping cart data and requests.


System Overview 

Before we delve into Amazon's DynamoDB architecture for the shopping cart system, let's look at some key metrics that highlight the scale of its operations:

- Requests per second: 100 million+

- Active users: Hundreds of millions

- Items in catalog: 350 million+

- Peak operations: Black Friday, Prime Day

- Data centers: Multiple, globally distributed

- Latency target: < 10 milliseconds

- Availability: 99.999%+

- Supported devices: Web, mobile, smart devices

- DynamoDB tables: Hundreds, purpose-specific

- Total data managed: Petabytes


Learn system design pulses -

[System Design Pulse #1] Understanding Latency and Throughput: Critical Factors in System Design and Performance Tuning

[System Design Pulse #2] Striking the Right Balance: Optimizing Availability and Consistency in Distributed Architectures

[System Design Pulse #3] THE theorem of System Design and why you MUST know it - Brewer theorem

[System Design Pulse #4] How Distributed Message Queues Work?

[System Design Pulse #5] Breaking It Down: The Magic Behind Microservices Architecture

[System Design Pulse #6] Why Availability Patterns Are So Crucial in System Design?

[System Design Pulse #7] How Consistency Patterns helps Design Robust and Efficient Systems?

[System Design Pulse #8] Caching is Crucial : How Caching Slashes Latency and Supercharges Performance

[System Design Pulse #9] Why these Key Components are Crucial for System Design.


How it works ( tech in depth) —

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