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Intro: Handling Data Skew in Production ML Pipelines Roku
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Classroom Contents
Taming Data Skew in Production ML Pipelines
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- 1 Intro: Handling Data Skew in Production ML Pipelines Roku
- 2 Roku Scale & the Mystery of Suddenly Slower Spark Jobs
- 3 How Skew Shows Up in Spark: Stragglers, Shuffle Spills, Idle Executors
- 4 What Data Skew Really Is and Why Parallelism Breaks
- 5 Real-World Example: Power Users, Hot Keys, and Power-Law Data
- 6 Why It Matters: Technical Bottlenecks + Business Cost Blowups
- 7 Where Skew Hits ML Pipelines: Recs, Classification, Computer Vision
- 8 Root Causes of Skew #1: Natural Imbalance from Real-World Events
- 9 Root Causes of Skew #2: Join-Key & Aggregation Skew in Feature Engineering
- 10 Root Causes of Skew #3: Computational Skew NLP, Embeddings, Heavy Transforms
- 11 Mitigation Step 1: Repartitioning—When It Works and Its Limits
- 12 Mitigation Step 2: Key Salting to Split Hot Keys Big Runtime Wins
- 13 Mitigation Step 3: Broadcast Joins to Avoid Massive Shuffles
- 14 Wrap-Up: Choosing the Right Fix + AI to Predict Skew Before It Happens
- 15 Closing & How to Connect
