[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data
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The “People You May Know” (PYMK) recommendation service helps LinkedIn’s members identify other members that they might want to connect to and is the major driver for growing LinkedIn's social network. The principal challenge in developing a service like PYMK is dealing with the sheer scale of computation needed to make precise recommendations with a high recall. PYMK service at LinkedIn has been operational for over a decade, during which it has evolved from an Oracle-backed system that took weeks to compute recommendations to a Hadoop backed system that took a few days to compute recommendations to its most modern embodiment where it can compute recommendations in near real time. This talk will present the evolution of PYMK to its current architecture. We will focus on various systems we built along the way, with an emphasis on systems we built for our most recent architecture, namely Gaia, our real-time graph computing capability, and Venice our online feature store with scoring capability, and how we integrate these individual systems to generate recommendations in a timely and agile manner, while still being cost-efficient. We will briefly talk about the lessons learned about scalability limits of our past and current design choices and how we plan to tackle the scalability challenges for the next phase of growth. https://qcon.ai/qconai2019/presentation/people-you-may-know-fast-recommendations-over-massive-data
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[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data
- 1. People You May Know Fast Recommendations Over Massive Data Jeff Weiner Chief Executive Officer Sumit Rangwala Artificial Intelligence Felix GV Data Infrastructure
- 2. My Professional Network Professional network in real world Sumit Felix Amol GaojiePeter
- 3. My Professional Network Professional network in real world Professional network on LinkedIn Sumit Felix Peter Amol Gaojie Sumit Felix Peter Amol Gaojie
- 4. My Professional Network Professional network in real world Professional network on LinkedIn Sumit Felix Peter Amol Gaojie Sumit Felix Peter Amol Gaojie Predicting real world connections
- 5. Helps grow member’s professional network Recommends people that one might know People You May Know Enables many other LinkedIn services
- 6. Talk Outline People You May Know PYMK: Generating Recommendations PYMK Architecture Evolution PYMK Rebirth Insights and Road Ahead
- 8. PYMK: Prediction Strategy Data Mining • LinkedIn’s Economic Graph • Member’s activities and profile LinkedIn Economic Graph Sumit Felix Peter Amol Gaojie
- 9. PYMK: Prediction Strategy Data Mining • LinkedIn’s Economic Graph • Member’s activities and profile LinkedIn Economic Graph Felix Peter Amol Gaojie Microsoft USC Sumit
- 10. Recommendation System Candidate Generation Feature Generation Scoring
- 11. PYMK: Candidate Generation Using commonalities in economic graph • Friends of my friends (triangle closing) LinkedIn Economic Graph Amol Peter Gaojie Sumit Felix
- 12. PYMK: Candidate Generation Using commonalities in economic graph • Friends of my friends (triangle closing) • Coworkers • Personalized Page Rank LinkedIn Economic Graph Amol Peter Gaojie Felix Microsoft Sumit
- 13. PYMK: Feature Generation Using economic graph characteristics • Number of common friends Using member activities/profile • Common work location LinkedIn Economic Graph Amol Peter Gaojie Felix Microsoft Sumit
- 15. PYMK: Recommendation System Candidate Generation Feature Generation Sumit might know Amol’s friend Felix Sumit and Felix has one common friend Sumit and Felix both work in Bay Area
- 16. PYMK: Recommendation System Candidate Generation Feature Generation Sumit might know Amol’s friend Felix Sumit and Felix has one common friend Sumit and Felix both work in Bay Area Graph processing Data processing
- 18. Pre-compute recommendations A P P R O A C H
- 19. PYMK: The Beginning Problem Space • 10s of millions of members Architecture • Pre-compute using SQL Shortcomings • Staleness of 6 weeks to 6 months • Extraneous computation Oracle
- 20. PYMK: The Beginning Problem Space • 10s of millions of members Architecture • Pre-compute using SQL Shortcomings • Staleness of 6 weeks to 6 months • Extraneous computation Oracle PYMK Service Online service request
- 21. PYMK: Keeping up with Growth Problem space • Low 100s of millions of members Architecture • Pre-compute using Hadoop MR • Push to a key-value store Shortcomings • Staleness of 2-3 days • Extraneous computation Voldemort PYMK Service
- 22. PYMK: Pushing the Technology Limits Problem Space • Mid 100s of millions of members Architecture • Pre-compute using Spark1 • Push to a key-value store Shortcomings • Staleness of 1-2 days • Excessive computation cost Venice [1] Managing Exploding Big Data PYMK Service
- 23. PYMK: Exploring Data Freshness Problem Space • Use up to date member data Architecture • Hybrid offline-online approach Shortcomings • Split-brain design • Didn’t scale Venice Realtime signals PYMK Service
- 25. PYMK Rebirth
- 26. Compute recommendations on demand A P P R O A C H
- 27. PYMK: Recommendation System Candidate Generation Feature Generation Sumit might know Amol’s friend Felix Sumit and Felix has one common friend Sumit and Felix both work in Bay Area Online Graph Traversal Fast Data Access
- 28. An online graph processing system
- 29. G A I A A generic service for executing complex graph algorithms with low latency on massive graphs
- 31. Gaia: Overview Gaia Any kind of graph A snapshot on HDFS
- 32. Gaia: Overview Gaia Any kind of graph Updates to graph A snapshot on HDFS Via Kafka, etc.
- 33. Gaia: Overview Gaia Any kind of graph Updates to graph Graph algorithm code A snapshot on HDFS Via Kafka, etc. Using compute framework e.g., triangle closing, random graph walks
- 34. Design Choice Gaia • Single server architecture with replicas • Full in-memory graph for fast execution
- 35. Gaia: Architecture Server Server Server Gaia
- 36. Gaia: Architecture Server Server Server Algo Algo Algo Gaia
- 37. Gaia: Architecture Graph snapshot on disk Server Server Server Algo Algo Algo Gaia
- 38. Gaia: Architecture Graph snapshot on disk Graph updates via Kafka, etc. Server Server Server Algo Algo Algo Gaia
- 39. PYMK Gaia • Candidate generation using triangle closing and common connection count • 10s of milliseconds (p90)
- 40. A key-value store with scoring capability
- 41. At a glance Venice • Tailored for serving ML jobs’ output • High throughput ingestion • Fast lookups • Self-service onboarding
- 42. Supported Ingestion Modes in Venice Batch Hadoop Push Job
- 43. Supported Ingestion Modes in Venice Batch Incremental Hadoop Push Job Samza Streaming Job
- 44. Supported Ingestion Modes in Venice Batch Incremental Hadoop Push Job Push Job Samza Reprocessing Job (Kappa Architecture) Streaming Job
- 45. Supported Ingestion Modes in Venice Batch Incremental Hadoop Push Job Push Job Samza Reprocessing Job (Kappa Architecture) Streaming Job Hybrid Any Batch Job + Streaming Job (Lambda Architecture)
- 46. Online Feature Retrieval F i r s t P Y M K U s e C a s e
- 47. Requirements Online Feature Retrieval • Millions of lookups / sec at peak • ~1000 keys / query • Thousands of queries / sec • ~80B / value
- 48. Before / After Online Feature Retrieval • Base latency • 4 seconds (p99) • Changed storage engine to RocksDB • 60 ms (p99)
- 49. Embeddings S e c o n d P Y M K U s e C a s e
- 50. Requirements Embeddings • Millions of lookups / sec at peak • ~1000 keys / query • Thousands of queries / sec • ~800B / value • 10x the previous size
- 51. Before / After Embeddings • Base latency • 275 ms (p99) • Server-side computation • 60 ms (p99)
- 52. At a glance Server-side Computation • Simple vector operations • Smaller response size • Big input (vector) • Small output (scalar) • Declarative API • No arbitrary code
- 53. More tuning Fast Avro • Online feature retrieval • 60 to 40 ms (p99) • Embeddings w/ computation • 60 to 35 ms (p99) • Now open-source! • github.com/linkedin/avro-util
- 54. PYMK Today P u t t i n g i t a l l t o g e t h e r
- 55. PYMK: Recommendation System Candidate Generation Sumit might know Amol’s friend, Felix Sumit and Felix have one common friend Sumit and Felix both work in Bay Area PYMK Service Feature Generation Scoring Sumit and Felix likely know each other Venice Gaia
- 56. PYMK: Today Venice PYMK Service Gaia 1. Ingest in Gaia & Venice 2. Candidate gen & graph features from Gaia 4. Final scoring by PYMK Service 3. Member features & partial scoring from Venice Staleness • Seconds to minutes
- 57. Key Learnings • Pre-computation is viable for many products • Scaling RT computation requires moving compute close to data • Infra aware Machine Learning
- 58. Looking Ahead
- 59. • Further scale Gaia & Venice • More candidates • More features • Larger features • More complex computations ML-Aware Infra
- 60. • Continue democratizing access • Easier onboarding to Venice & Gaia • Multi-tenancy for Venice Compute • Integration with other frameworksProductive ML
- 61. Contributors Amol Ghoting Gaojie Liu Kevinjeet Gill Peter Chng Min Huang Yao Chen Hema Raghavan Many othersAshish Singhai
- 62. Thank You