How Tinder Works: Location-Based Matching at Scale

Tinder's Technical Challenge

Tinder processes 2 billion swipes per day and has made over 70 billion matches. The core challenge: efficiently matching people based on location, preferences, and mutual interest in real-time.

Core Features

1. Profile Discovery

• Show profiles near user
• Filter by preferences (age, gender, distance)
• Optimize for engagement
• Avoid showing same profiles repeatedly

2. Swipe Mechanism

• Left (pass) or Right (like)
• Super Like (priority matching)
• Record decisions quickly
• Check for mutual matches

3. Matching

• Detect mutual likes
• Notify both users
• Enable chat

4. Messaging

• Real-time chat
• Media sharing
• Read receipts

Geospatial Architecture

The Problem:

Finding users within X miles of a location is computationally expensive.

Solution: Geospatial Indexing

1. Geohash

• Encode lat/long as string
• Nearby locations share prefix
• "9q8yy" → all users in same ~1km grid

2. Quad-tree

• Divide space recursively
• Query by region
• Efficient for variable density

3. PostGIS / MongoDB Geospatial

• Native geospatial indexes
• 2D sphere queries
• Efficient radius searches

Tinder's Approach:

• Grid-based partitioning
• Users assigned to cells
• Nearby cells queried for discovery

Recommendation Engine

Ranking Factors:

• Distance (closer = better)
• Activity (recent users preferred)
• Popularity (Elo-like scoring)
• Preference match (age, etc.)
• Engagement prediction

Elo-like Scoring:

• Users have hidden scores
• Swiped right on? Score increases
• Swiped left on? Score decreases
• Similar scores matched more

Card Stack Generation:

1. Query nearby users (geospatial)
2. Filter by preferences
3. Remove already-seen
4. Rank by algorithm
5. Return top N as stack

Swipe Processing

When You Swipe:

Left Swipe (Pass):

1. Record decision in activity log
2. Update seen list
3. No immediate action needed
4. Batch process for analytics

Right Swipe (Like):

1. Record like in database
2. Check if other user liked you
3. If match: create match record, notify both
4. If no match: await other user's decision

Data Model:

• likes(user_id, liked_user_id, timestamp)
• matches(user1_id, user2_id, timestamp)
• passes(user_id, passed_user_id, timestamp)

Match Detection

Naive Approach:

• On each like, query: "Did they like me?"
• Works but slow at scale

Optimized Approach:

• Maintain "likes received" cache per user
• On like, check cache first
• Cache in Redis for fast lookup

Match Notification:

1. Match detected
2. Push notification sent to both
3. Chat enabled
4. Match surfaces in UI

Scaling Considerations

Read-Heavy Workload:

• Profile views >> profile updates
• Heavy caching of profiles
• CDN for images

Write Patterns:

• Swipes are high-volume writes
• Batch and async processing
• Eventual consistency acceptable

Geographic Sharding:

• Shard by region
• Users mostly interact locally
• Cross-region matching rare

Messaging System

Real-time Chat:

• WebSocket connections
• Message queue (Kafka) for reliability
• Read receipts and typing indicators

Media Handling:

• Images uploaded to S3/CDN
• Thumbnails generated async
• Moderation pipeline

Anti-Abuse Measures

Spam Prevention:

• Rate limiting swipes
• Profile verification
• Machine learning for fake profiles

Safety Features:

• Photo verification
• Block and report
• Safety tips
• Location hiding option

Performance Optimizations

Profile Caching:

• Hot profiles in Redis
• Pre-computed card stacks
• Background refresh

Image Optimization:

• Multiple resolutions
• Progressive loading
• CDN distribution

Battery/Data:

• Efficient polling intervals
• Delta updates
• Compression

Key Metrics

User Metrics:

• Swipes per session
• Match rate
• Message rate
• Conversion to chat

System Metrics:

• Discovery latency
• Match detection time
• Message delivery time
• API response times

Interview Application

When designing a dating/matching app:

Core Components:

• Geospatial user discovery
• Preference filtering
• Swipe recording
• Match detection
• Real-time messaging

Key Challenges:

• Efficient geospatial queries
• High-volume write handling
• Real-time match notifications
• Scale with user growth

Trade-offs:

• Accuracy vs speed (geospatial precision)
• Fresh vs cached (recommendation staleness)
• Push vs pull (notification strategy)

Tinder's architecture shows how to build location-aware, real-time matching systems at massive scale.