1. Requirements

Functional Requirements

• Users can:
  • Create paste (text content)
  • Get unique URL for paste
  • Retrieve paste via URL
• Optional:
  • Expiration time (TTL)
  • Public/private pastes
  • Edit/delete pastes

Non-Functional Requirements

• Read-heavy system
• Latency: < 100ms for retrieval
• Scalability: Billions of pastes
• Availability: High (reads should not fail)
• Durability: Pastes must not be lost

2. Estimations

Traffic

• Writes: ~10K/sec
• Reads: ~500K–1M/sec

Storage

Assume:

• Avg paste size = 5KB
• 1B pastes → ~5TB

👉 Requires distributed storage

Read/Write Pattern

• Read-heavy → heavy caching needed

3. API Design

3.1 Create Paste

Request:

Response:

3.2 Get Paste

3.3 Delete Paste

4. Data Storage & Design

4.1 Metadata Store

Use:

• Cassandra / DynamoDB

Schema:

4.2 Content Storage (Important Separation)

Use:

• Object storage (e.g., Amazon S3)

Key:

👉 Reason:

• Large content not suitable for DB
• Cheap, scalable storage

4.3 Cache

Use:

• Redis

Key:

5. High-Level Architecture (HLD)

System consists of:

• Entry Layer
• Read Path
• Write Path

5.1 Entry Layer

• CDN + Load Balancer

Responsibilities:

• Handle read spikes
• Serve cached pastes
• Protect backend

5.2 Read Path

1. Client requests paste
2. CDN:
   • Hit → return content
   • Miss → forward
3. Load Balancer → App Server
4. App Server:
   • Check Redis
   • If miss → fetch metadata + content
5. Return paste

5.3 Write Path

1. Client submits paste
2. App server:
   • Generate paste_id
   • Store metadata in DB
   • Store content in object storage
3. Cache metadata/content

6. Detailed Breakdown

6.1 Paste ID Generation

Approach: Distributed ID + Base62

Use Snowflake-style:

Convert to Base62 → short ID

6.2 Read Optimization

• CDN caches popular pastes
• Redis caches hot data
• Most reads avoid DB

6.3 Write Optimization

• Async writes to object storage
• Metadata stored separately

6.4 Stateless Scaling

• App servers stateless
• Scale horizontally

6.5 Hot Key Handling

Problem:

• Viral paste → heavy reads

Solution:

• CDN absorbs traffic
• Redis replication
• Local cache on app servers

7. Additional Considerations

7.1 Large Paste Handling

• Store large pastes in chunks (optional)
• Stream content instead of loading fully

7.2 Expiration & TTL

• Store expiry in DB
• Object storage lifecycle rules auto-delete

7.3 Cache Eviction

• Redis uses LRU/LFU

7.4 Degraded Mode

Redis Down:

• Fetch from DB + object storage

DB Down:

• Serve cached pastes

Object Storage Down:

• Serve cached data if available

7.5 Security

• Rate limiting (prevent spam)
• Content moderation
• Private pastes require auth

🏁 Final Summary

• Separation of metadata & content improves scalability
• CDN + Redis caching ensures low latency
• Object storage handles large data efficiently
• Stateless services ensure horizontal scaling
• TTL + lifecycle policies prevent storage bloat