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Understanding Player Behaviour

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An introduction to analytics, map/reduce and DynamoDB on AWS. Slides from the 'Powering games with Amazon Web Services' event in London.

An introduction to analytics, map/reduce and DynamoDB on AWS. Slides from the 'Powering games with Amazon Web Services' event in London.

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Transcript

  • 1. UnderstandingPlayer Behaviour
  • 2. Player behaviour is priceless
  • 3. Play statistics
  • 4. Social graph
  • 5. Monitor and iterate
  • 6. Increase playability
  • 7. IncreaseDLC sales
  • 8. Increase advertising engagement
  • 9. Ask questionsof player behaviour
  • 10. Ask questionsof player behaviour data
  • 11. Step 1: data collection
  • 12. Database
  • 13. Player data is complex
  • 14. Player data is plentiful
  • 15. Player data is fast moving
  • 16. Capturing andmanaging player data is hard
  • 17. Database canbecome bottleneck
  • 18. DynamoDB
  • 19. Step 2: analytics
  • 20. Hadoop
  • 21. Elastic MapReduce
  • 22. Managed
  • 23. Flexible
  • 24. Java(or Ruby, Python etc)
  • 25. Data warehouse
  • 26. S3Input data
  • 27. S3 Input dataCode Elastic MapReduce
  • 28. S3 Input dataCode Elastic Name MapReduce node
  • 29. S3 Input dataCode Elastic Name MapReduce node Elastic cluster
  • 30. S3 Input dataCode Elastic Name MapReduce node HDFS Elastic cluster
  • 31. S3 Input dataCode Elastic Name MapReduce node Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
  • 32. S3 Input dataCode Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
  • 33. DynamoDB integrateswith Elastic MapReduce
  • 34. Combine
  • 35. HiveQL queries
  • 36. Backup and restore
  • 37. Data movement
  • 38. Import/Export
  • 39. Multipart upload
  • 40. Multipart, parallel results delivery
  • 41. Direct Connect
  • 42. Scale control
  • 43. Resize running job flows
  • 44. 14 hoursTime remaining: 14 hours
  • 45. 14 hoursTime remaining: 7 hours
  • 46. Time remaining: 3 hours
  • 47. Balance cost and performance
  • 48. Resize based on usage patterns
  • 49. Steady state Steady state Batch processing
  • 50. Perfect for Spot
  • 51. Cluster types
  • 52. Small
  • 53. High memory High CPU or both
  • 54. HPC
  • 55. Click stream analysis for Best Buy 3.5 billion records 71 million unique cookies 1.7 million targeted ads 13 Tb of clickstream logs Each day
  • 56. Click stream analysis for Madden Workflow time from 2 days to 8 hoursProcurement time from 2 months to 5 minutes $13k per month500% increase return on advertising spend
  • 57. Web log analysis and recommendation engine $29.9 million in sales 842 million page views 434 Gb of page logs 97 million ‘favourites’