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Understanding Player Behaviour
 

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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Understanding Player Behaviour Understanding Player Behaviour Presentation Transcript

  • UnderstandingPlayer Behaviour
  • Player behaviour is priceless
  • Play statistics
  • Social graph
  • Monitor and iterate
  • Increase playability
  • IncreaseDLC sales
  • Increase advertising engagement
  • Ask questionsof player behaviour
  • Ask questionsof player behaviour data
  • Step 1: data collection
  • Database
  • Player data is complex
  • Player data is plentiful
  • Player data is fast moving
  • Capturing andmanaging player data is hard
  • Database canbecome bottleneck
  • DynamoDB
  • Step 2: analytics
  • Hadoop
  • Elastic MapReduce
  • Managed
  • Flexible
  • Java(or Ruby, Python etc)
  • Data warehouse
  • S3Input data
  • S3 Input dataCode Elastic MapReduce
  • S3 Input dataCode Elastic Name MapReduce node
  • S3 Input dataCode Elastic Name MapReduce node Elastic cluster
  • S3 Input dataCode Elastic Name MapReduce node HDFS Elastic cluster
  • S3 Input dataCode Elastic Name MapReduce node Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
  • S3 Input dataCode Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
  • DynamoDB integrateswith Elastic MapReduce
  • Combine
  • HiveQL queries
  • Backup and restore
  • Data movement
  • Import/Export
  • Multipart upload
  • Multipart, parallel results delivery
  • Direct Connect
  • Scale control
  • Resize running job flows
  • 14 hoursTime remaining: 14 hours
  • 14 hoursTime remaining: 7 hours
  • Time remaining: 3 hours
  • Balance cost and performance
  • Resize based on usage patterns
  • Steady state Steady state Batch processing
  • Perfect for Spot
  • Cluster types
  • Small
  • High memory High CPU or both
  • HPC
  • 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
  • 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
  • Web log analysis and recommendation engine $29.9 million in sales 842 million page views 434 Gb of page logs 97 million ‘favourites’