Netflix Recommendations - Fact Store

1. Kedar Sadekar, Netflix
Nitin Sharma, Netflix
Fact Store - Netflix
Recommendations
#DevSAIS11

2. Agenda
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3. #DevSAIS11
Recommendations at Netflix
● Personalized Homepage for each member
○ Goal: Quickly help members find content they’d like to
watch
○ Risk: Member may lose interest and abandon the service
○ Challenge: Recommendations at Scale

4. #DevSAIS11
Scale @ Netflix
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5. #DevSAIS11
Experimentation Cycle @ Netflix
Design a New Experiment to Test Out Different Ideas
Offline
Experiment
Online
System
Design Experiment Model Testing
Collect Label Data
Offline Feature
Generation
Model Training Model Validation Metrics
Online A/B Testing

6. #DevSAIS11
ML Feature Engineering - Architectural View
Online Feature
Generation
Microservices
Online Scoring
Offline Feature
Generation
Shared Feature
Encoders
Model
Training
Deploy Models
Online SystemOffline Experiment
Features
Facts

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What is a Fact?
● Fact
○ Input data for feature encoders. Used to construct a feature
○ Example: Viewing history of member, my list of a member
● Historical Version of a fact
○ Rewindable - State of the world at that time
● Temporal
■ Facts are temporal i.e. they change with time
■ Each online scoring service uses the latest value of a fact

8. #DevSAIS11
Online
Scoring
Predictor
Fact Microservices
Features
Facts
Log these
Online
Scoring
Predictor
Fact Microservices
Features
Facts
Log these
Recommendations Recommendations
Feature Logging Fact Logging

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Fact Logging - Pull Architecture
Pull
● Daily snapshots of key facts
● Storage
○ S3 & Parquet
● Api to access the data
○ RDD & DataFrames
● Cons
○ Lacks temporal accuracy
○ Load on Microservices
○ Missing Experiment specific facts
Capture
Snapshots
Fact Microservices
Stratified
Member sets
Snapshots

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Fact Logging - Push Architecture
● Compute engines
themselves control
what to log
● Stratification
● Temporal accuracy
Compute Services
Fact Store
Fact Transformer
Fact Fetcher
Fact Logger
ML Workflows
Feature
Generation
Model
Training

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Fact Logger
Precompute Live Compute
Fact
Logger
● Library
● Facts
○ User Related
○ Video Related
○ Computation Specific
● Serialization
● Stratification Service
● Fact Stream
● Storage
Base Fact Tables
Stratification

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Fact Logging - Scalability
Precompute Live Compute
Fact Logger
● 5-10x increase in data through
Kafka
● SLA Impact; Cost Increase
● Compression - 70% decrease

13. Storage & Access
Fact Store
Fact Transformer
Deduplication
Precompute Live Compute
Fact Logger
● Pipeline load
○ Repeated facts
● Aggressive or not
○ Loss threshold
Conditional push
● Spark Job
○ Fact pointers
○ SLA
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14. #DevSAIS11
API Lookback
Member ID My List View History Thumbs
122312 My List Value View History Pointer Thumbs Value
254637 My List Pointer View History Pointer Thumbs Pointer
Member n My List Pointer View History Value Thumbs Pointer
My List
Partition 1
Values
Partition m
Values
View
History
Partition 1
Values
Partition m
Values
Thumbs
Partition 1
Values
Partition m
Values
log_time - x
log_time - y
log_time - z

15. Storage & Access
Fact Store
Fact Transformer
Read API
Precompute Live Compute
Fact Logger
● Query performance
○ Slow moving facts
● Point query
○ Connector
● Query time reduction
○ Hours to minutes
Deduplication
Conditional push
Write
Read
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16. Performance: Storage
• Partitioning scheme
– Noisy neighbor
• Storage format
– Exploratory vs production
• Fast & Slow lane
– Lookback limit
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17. Performance: Spark reads
• Bloom Filters
– Reduce scan
• Cache Access
– EVCache, Spectator
• MapPartitions vs UDF
– Eager vs Lazy
– SPARK-11438, SPARK-11469,
SPARK-20586
Application
ML Library
Read API
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18. Future Work
• Structured with schema evolution
– Best of both (POJO & Spark SQL), Iceberg
• Streaming vs Batch
– Multiple lanes, accountability, independent scale
• Duplication
– Storage vs Runtime cost
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Questions?