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[System Design Tech Case Study Pulse #18] Tinder 1.5 Billion Swipes per Day : How Tinder Real Time Matching Actually Works
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[System Design Tech Case Study Pulse #18] Tinder 1.5 Billion Swipes per Day : How Tinder Real Time Matching Actually Works

With detailed explanation and flow chart....

Naina Chaturvedi's avatar
Naina Chaturvedi
Oct 19, 2024
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[System Design Tech Case Study Pulse #18] Tinder 1.5 Billion Swipes per Day : How Tinder Real Time Matching Actually Works
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Hi All,

Tinder's implementation of Amazon DynamoDB plays a crucial role in their real-time match processing system, handling an astounding 1.5 billion swipes per day. This feat enables Tinder to provide instant matching and real-time interactions for millions of users worldwide.

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 Tinder engineered this system, exploring the key architectural decisions, scaling strategies, and optimizations that enable DynamoDB to manage this enormous volume of swipes and matches.


System Overview 

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

- Daily swipes: 1.5 billion+

- Active users: 75 million+

- Matches created daily: 26 million+

- Peak swipe rate: Millions per minute

- Data centers: Multiple, globally distributed

- Latency target: < 100 milliseconds

- Availability: 99.99%+

- Supported platforms: iOS, Android, Web

- DynamoDB tables: Multiple, purpose-specific

- Total data managed: Petabytes

- User interactions: Swipes, matches, messages


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 Tinder works ( Tech in depth) —

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