BigQuery in Social Gaming

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Our presentation at Google's cloud platform road show London, about our usage of BigQuery in our MMORPG Here Be Monsters.

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BigQuery in Social Gaming

  1. 1. BigQuery in Social Gaming Yan Cui, Senior Developer Davinder Pank, Social BI Manager
  2. 2. Who is Gamesys? • Founded in 2001 • #1 in the UK • Handle $5 Billion in turnover annually • First company to launch real money gaming on Facebook • Employ 1,000 globally
  3. 3. Travel, Collect, Craft!
  4. 4. Trap Monsters
  5. 5. Events driven Analysis Enables deeper ad hoc analysis Analysis goes only as far as the data Finer the grain, bigger the volume
  6. 6. Be Ready for Success Jackpotjoy Slots Bingo Lane Here Be Monsters DAU 600,000+ 150,000+ 40,000+ Events (monthly) 5 billion+ 500 million+ 700 million+ 1 TB+ 200 GB+ 500 GB+
  7. 7. • Ensure the health of our defined KPIs across our products – Jackpotjoy Slots, Bingo Lane and Here Be Monsters • Develop player insights to better improve the depth with which users engage with our games Social Gaming Business Intelligence, Gamesys BigQuery
  8. 8. Why BigQuery? Scales Managed Fully managed architecture, allows instant project startup, and rapid time to insight Easy to learn, minimal transition period, especially for those moving from traditional relational databases SQL Grows with your project, scales horizontally from 100 thousands to 100 Bn's of rows with no loss of performance on interactive queries
  9. 9. Monitoring KPI Health Reporting Apps Script Spreadsheets Cloud Storage Big Query ETL
  10. 10. KPI Dashboards in Google Spreadsheets
  11. 11. Custom Dashboards in Google Spreadsheets
  12. 12. Developing Player Insights Big Joins No need for temporary tables, let’s us get to the results we need in as few steps as possible How BigQuery features and functions allow us to better explore our data
  13. 13. Developing Player Insights • Our analysis hinges on being able to compare behaviour amongst players with similar in-game maturity to one another • Where we gain some of our most invaluable insights are where changes in player behaviour lie outside the norm of what we would expect How BigQuery features and functions allow us to better explore our data
  14. 14. Developing Player Insights Window Functions Rank and partition allow us to compare fairly player engagement and monetisation metrics across players with the same in- game maturity. Reduces dependence on external tools. Lead and lag allow us to easily identify segments exhibiting interesting changes in behaviour How BigQuery features and functions allow us to better explore our data
  15. 15. Reporting Apps Script Spreadsheets Cobra App Engine Cloud Storage Big Query ETL Mailman App Engine email mobile push notifications 4 facebook notifications Beyond BigQuery
  16. 16. In the near future.. • Move from nightly ETL to real-time – BigQuery streaming insert • Predication API
  17. 17. Thank You!
  18. 18. JackpotJoy Slots http://apps.facebook.com/jackpotjoyslots Bingo Lane http://apps.facebook.com/bingolane Here Be Monsters http://apps.facebook.com/herebemonsters Building a MMORPG http://bit.ly/1hjqoL8 http://slidesha.re/18MD4XY Google I/O 2013 – Here Be BigQuery http://bit.ly/1fHjbce

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