Introduction to Netflix Impressions
At Netflix, every image interaction during browsing is transformed into valuable data points called ‘impressions.’ These impressions are fundamental to creating personalized viewing experiences for millions of users worldwide, processing billions of interactions daily.
The Importance of Impression History
Impression history serves multiple critical functions in the Netflix ecosystem:
- Enhanced Personalization: Tracks user content exposure to deliver fresh recommendations
- Frequency Capping: Prevents content over-exposure and maintains engagement
- New Release Management: Monitors initial user interactions for optimal content promotion
- Analytics Support: Provides insights for platform performance and merchandising strategies
Technical Architecture
The system’s foundation is built on a robust Source-of-Truth (SOT) dataset, utilizing various modern technologies:
- Apache Kafka for real-time event processing
- Apache Iceberg for long-term data storage
- Apache Flink for stream processing
- Avro schema for data structuring
System Configuration and Scale
The infrastructure handles an impressive 1-1.5 million events per second globally. Each Flink deployment includes:
- 8 task managers per region
- 8 CPU cores and 32GB memory per manager
- Parallelism of 48
- Regional isolation using the ‘island model’
Quality Assurance and Future Development
Quality control is maintained through comprehensive column-level metrics and a tiered alerting system. Future improvements focus on:
- Enhanced schema management for unschematized events
- Automated performance tuning with autoscalers
- Advanced data quality monitoring and alerting systems
This sophisticated system represents Netflix’s commitment to delivering personalized content discovery at scale, ensuring every user interaction contributes to a better viewing experience.
For more detailed information about Netflix’s Impression System, visit the Netflix Tech Blog