How Netflix Stores 140 Million Hours of Viewing Data Per Day

Streaming at Netflix scale isn’t just about entertainment—it’s a massive engineering challenge. With 140 million hours of viewing happening daily, Netflix needs to store billions of user interactions without compromising performance.

The Challenges of Storing Large-Scale Viewing Data

Netflix faces three key hurdles:

1️⃣ Sheer Volume – Every pause, rewind, and play is stored, leading to billions of writes per day.

2️⃣ Read vs. Write Imbalance – Write-heavy workloads (9:1 ratio) make traditional database systems inefficient.

3️⃣ Ultra-Low Latency Needs – Users expect instant playback and seamless recommendations.

Netflix’s Data Storage Strategy

To manage these challenges, Netflix employs:

✅ EVCache – A memory-based caching layer that stores frequently accessed data, reducing database strain.

✅ LiveVH vs. CompressedVH – Splitting viewing history into uncompressed (LiveVH) for fast access and compressed (CompressedVH) for archived storage.

✅ Chunking Large Records – Dividing big playback histories into smaller segments for faster retrieval.

✅ Data Sharding – Distributing stored data across servers based on viewing type (full plays, previews, etc.).

Automated Optimization & Performance Scaling

Netflix dynamically moves older data to optimized storage layers, ensuring low-cost scalability without sacrificing performance. The system is self-adjusting, shifting access patterns based on frequency and user behavior.

Final Thoughts

Netflix's multi-layered storage model allows it to stream seamlessly to millions of users while keeping costs, latency, and retrieval speeds optimized.

How do you think other streaming platforms manage their data at scale? Let’s discuss! 🚀