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Data Infrastructure at LinkedIn

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This talk was given by Jun Rao (Staff Software Engineer at LinkedIn) and Sam Shah (Senior Engineering Manager at LinkedIn) at the Analytics@Webscale Technical Conference (June 2013).

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Data Infrastructure at LinkedIn

  1. 1. Data Infrastructure at Linkedin Jun Rao and Sam Shah LinkedIn Confidential ©2013 All Rights Reserved
  2. 2. Outline LinkedIn Confidential ©2013 All Rights Reserved 2 1. LinkedIn introduction 2. Online/nearline infrastructure overview 3. Infrastructure for data mining 4. Conclusion
  3. 3. The World‟s Largest Professional Network Members Worldwide 2 new Members Per Second 100M+ Monthly Unique Visitors 200M+ 2M+ Company Pages Connecting Talent  Opportunity. At scale… LinkedIn Confidential ©2013 All Rights Reserved 3
  4. 4. 4 Member Profiles Large dataset Medium writes Very high reads Freshness <1s
  5. 5. People You May Know 5 Large dataset Compute intensive High reads Freshness ~hrs
  6. 6. LinkedIn Today 6 Moving dataset High writes High reads Freshness ~mins
  7. 7. The Big-Data Feedback Loop LinkedIn Confidential ©2013 All Rights Reserved 7 Value  Insights  Scale  Product ScienceData Member Engagement  Virality  Signals  Refinement  Infrastructure Analytics 
  8. 8. LinkedIn Data Infrastructure: Three-Phase Abstraction LinkedIn Confidential ©2013 All Rights Reserved 8 Users Online Data Infra Near-Line Infra Application Offline Data Infra Infrastructure Latency & Freshness Requirements Products Online Activity that should be reflected immediately • Member Profiles • Company Profiles • Connections • Messages • Endorsements • Skills Near-Line Activity that should be reflected soon • Activity Streams • Profile Standardization • News • Recommendations • Search • Messages Offline Activity that can be reflected later • People You May Know • Connection Strength • News • Recommendations • Next best idea…
  9. 9. LinkedIn Data Infrastructure: Sample Stack 9 Infra challenges in 3-phase ecosystem are diverse, complex and specific Some off-the-shelf. Significant investment in home-grown, deep and interesting platforms
  10. 10. Streaming Transactions 10
  11. 11. Databus : Timeline-Consistent Change Data Capture LinkedIn Data Infrastructure Solutions
  12. 12. Databus at LinkedIn 12 DB Bootstrap Capture Changes On-line Changes On-line Changes DB Consistent Snapshot at U  Transport independent of data source: Oracle, MySQL, …  Transactional semantics  In order, at least once delivery  Tens of relays  Hundreds of sources  Low latency - milliseconds Consumer 1 Consumer n Client Databus ClientLib Consumer 1 Consumer n Databus ClientLib Client Relay Event Win
  13. 13. Scaling Core Databases 13 RO RO RO
  14. 14. Voldemort: Highly-Available Distributed KV Store LinkedIn Data Infrastructure Solutions 14
  15. 15. • Pluggable components • Tunable consistency / availability • Key/value model, server side “views” • 10 clusters, 100+ nodes • Largest cluster – 10K+ qps • Avg latency: 3ms • Hundreds of Stores • Largest store – 2.8TB+ Voldemort: Architecture
  16. 16. Streaming Non-transactional Events 16 Offline Nearline Processing
  17. 17. Kafka: High-Volume Low-Latency Messaging System LinkedIn Data Infrastructure Solutions 17
  18. 18. Kafka Architecture Producer Consumer Producer Consumer Zookeeper topic1-part1 topic2-part2 topic2-part1 topic1-part2 topic2-part2 topic2-part1 topic1-part1 topic1-part2 topic1-part1 topic1-part2 topic2-part2 topic2-part1 Broker 1 Broker 2 Broker 3 Broker 4 Key features • Scale-out architecture • High throughput • Automatic load balancing • Intra-cluster replication Per day stats • writes: 10+ billion messages • reads: 50+ billion messages
  19. 19. Filling in the Data Store Gap 19 Text Search
  20. 20. Espresso: Indexed Timeline-Consistent Distributed Data Store LinkedIn Data Infrastructure Solutions 20
  21. 21. Application View 21 Hierarchical data model Rich functionality on resources  Conditional updates  Partial updates  Atomic counters Rich functionality within resource groups  Transactions  Secondary index  Text search
  22. 22. Espresso: System Components 22 • Partitioning/replication • Timeline consistency • Change propagation
  23. 23. Generic Cluster Manager: Helix • Generic Distributed State Model • Config Management • Automatic Load Balancing • Fault tolerance • Cluster expansion and rebalancing • Espresso, Databus and Search • Open Source Apr 2012 • https://github.com/linkedin/helix 23
  24. 24. Infrastructure challenges in large-scale data mining Putting it together
  25. 25. Top complaints from data scientists 1 Getting the data in (Ingress ETL) 2 Getting the data out (Egress) 3 Workflow management 4 Model of computation 5 …
  26. 26. Top complaints from data scientists 1 Getting the data in (Ingress ETL) 2 Getting the data out (Egress) 3 Workflow management 4 Model of computation 5 …
  27. 27. LinkedIn circa 2010 LinkedIn Confidential ©2013 All Rights Reserved 27
  28. 28. O(n2) data integration complexity
  29. 29. Infrastructure fragility • Can‟t get all data • Hard to operate • Multi-hour delay • Labor intensive • Slow • Does it work?
  30. 30. Process fragility • Labor intensive • One man‟s cleaning… FE MT BE DT FE Dev BE Dev ETL Team ETL DW/ Hadoop
  31. 31. Data model { tracking_code=null, session_id=42, tracking_time=Tue Jul 31 07:27:25 PDT 2010, error_key=null, locale=en_us, browser_id=ddc61a81-5311-4859-be42-ca7dc7b941e3, member_id=1213, page_key=profile, tracking_info=Viewee=1214,lnl=f,nd=1,o=1214,^SP=pId- 'pro_stars',rslvd=t,vs=v,vid=1214,ps=EDU|EXP|SKIL|, error_id=null, page_type=FULL_PAGE, request_path=view ... }
  32. 32. Data model (cont‟d) { article_id=5560874437395353942, title=Five Good Reasons to Hire the Unemployed, language=en_US, article_source=bit.ly, url=aHR0cDovL3d3dy5vbmV0aGluZ25ldy5jb20vaW5kZXgucGhwL3dvcmsvMTAyLWZpdmUtZ29v ZC1yZWFzb25zLXRvLWhpcmUtdGhlLXVuZW1wbG95ZWQK, ... }
  33. 33. Problems 1 Data integration across systems 2 Fragile infrastructure 3 Lack of proper data models (ad-hoc)
  34. 34. LinkedIn 2013 LinkedIn Confidential ©2013 All Rights Reserved 34
  35. 35. O(n) data integration
  36. 36. Publish/subscribe commit log
  37. 37. Data model  Hundreds of message types  Thousands of fields  What do they all mean?  What happens when they change?
  38. 38. Data model 1 Education 2 Push data cleanliness upstream 3 O(1) ETL 4 Evidence-based correctness
  39. 39. Data model  DDL for data definition and schema  Central versioned registry of all schemas  Schema review  Programmatic compatibility model – Schema changes handled transparently
  40. 40. Workflow 1 Check in schema 2 Code review 3 Ship Seamless data load into downstream systems
  41. 41. Audit trail
  42. 42. Result: complete, verified copy of all data available
  43. 43. Top complaints from data scientists 1 Getting the data in (Ingress ETL) 2 Getting the data out (Egress) 3 Workflow management 4 Model of computation 5 …
  44. 44. Egress store DATA into „kafka://…‟ using Stream();
  45. 45. Top complaints from data scientists 1 Getting the data in (Ingress ETL) 2 Getting the data out (Egress) 3 Workflow management 4 Model of computation 5 …
  46. 46. Workflows 46 Job A Job B Job C
  47. 47. Workflows 47 Job A Job B Job C Push to Production
  48. 48. Workflows 48 Job A Job B Job C Push to Production Job X
  49. 49. Workflows 49 Job A Job B Job C Push to Production Job X Push to QA
  50. 50. Real workflows are complicated 50
  51. 51. Workflow management: Azkaban 51  Dependency management  Diverse job types (Pig, Hive, Java, . . . )  Scheduling  Monitoring  Configuration  Retry/restart on failure  Resource locking  Log collection  Historical information
  52. 52. Workflow management: Azkaban 52
  53. 53. Workflow management: Azkaban 53
  54. 54. Top complaints from data scientists 1 Getting the data in (Ingress ETL) 2 Getting the data out (Egress) 3 Workflow management 4 Model of computation 5 …
  55. 55. Model of computation • Alternating Direction Method of Multipliers (ADMM) • Distributed Conjugate Gradient Descent (DCGD) • Distributed L-BFGS • Bayesian Distributed Learning (BDL) Graphs Distributed learning Near-line processing
  56. 56. LinkedIn Data Infrastructure: A few take-aways LinkedIn Confidential ©2013 All Rights Reserved 56 1. Building infrastructure in a hyper-growth environment is challenging. 2. Few vs Many: Balance over-specialized (agile) vs generic efforts (leverage-able) platforms (*) 3. Balance open-source products with home- grown platforms (**) 4. Data Model and Integration e2e are key (*)
  57. 57. 57 Learning more data.linkedin.com

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