Fix slow analytical queries. Essential for production performance with large datasets.

Netflix - Optimizing Viewer Engagement Analytics for 200M+ Users

Netflix processes billions of viewing events daily to calculate user engagement metrics, content recommendations, and trending analysis. Window functions are used for calculating rolling averages, user ranking, and content performance metrics. The challenge was optimizing these calculations to run within strict SLA requirements for real-time recommendations.

E-commerce Analytics

Netflix implemented a multi-tier optimization strategy: 1) Partitioned tables by date and region for better data locality, 2) Created specialized composite indexes for common window function patterns (user_id, timestamp), 3) Used materialized views for frequently accessed metrics like daily/weekly engagement scores, 4) Implemented batch processing with Apache Spark for large-scale window operations, 5) Used approximate algorithms for real-time dashboards where exact precision wasn't critical.

-- Optimized viewer engagement calculation

WITH user_viewing_metrics AS (

SELECT

user_id,

content_id,

viewing_date,

watch_duration,

-- Optimized window functions with proper indexing

AVG(watch_duration) OVER (

PARTITION BY user_id

ORDER BY viewing_date

ROWS BETWEEN 6 PRECEDING AND CURRENT ROW

) as avg_7day_duration,

RANK() OVER (

PARTITION BY content_id, DATE_TRUNC('day', viewing_date)

ORDER BY watch_duration DESC

) as daily_engagement_rank

FROM viewing_events

WHERE viewing_date >= CURRENT_DATE - INTERVAL '30 days'

)

SELECT

user_id,

content_id,

avg_7day_duration,

daily_engagement_rank,

CASE

WHEN avg_7day_duration >= 45 AND daily_engagement_rank <= 100 THEN 'HIGH_ENGAGEMENT'

WHEN avg_7day_duration >= 20 THEN 'MEDIUM_ENGAGEMENT'

ELSE 'LOW_ENGAGEMENT'

END as engagement_level

FROM user_viewing_metrics

WHERE daily_engagement_rank <= 1000;

Reduced analytics processing time from 6 hours to 45 minutes, enabled real-time recommendation updates, and supported 10x increase in concurrent analytical queries. This optimization directly improved user engagement by 15% through faster, more accurate content recommendations.

PostgreSQL

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