StampedeCon 2015 - Building a Player Focused Data Pipeline
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At the StampedeCon 2015 Big Data Conference: Riot Games’ mission statement is to become the most player focused company in the world. With over 67 million players battling on the fields of justice every month, League of Legends generates more than 45 terabytes of data on a daily basis. From game events to store transactions, data comes in from thousands of sources around the world. The big data engineering team at Riot Games is responsible for collecting this data and exposing it through a variety of tools to assist in delivering value to the players. This talk will span the past, present, and future of our data ecosystem, covering the reasons behind the decisions we made and the lessons we learned along the way.
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StampedeCon 2015 - Building a Player Focused Data Pipeline
- 1. #StampedeCon 2015 - Riot Games BUILDING A PLAYER FOCUSED DATA PIPELINE RYAN TABORA @ryantabora SEAN MALONEY @sean_seannery
- 2. SEAN MALONEY ENGINEER WHO WE ARE SMALONEY@ RIOTGAMES.COM @SEAN_SEANNERY WORKING ON RIOT’S ETL TOOLS FAVORITE ACTIVITY: ATTEMPTING TO GROW FACIAL HAIR BUT FAILING MISERABLY
- 3. RYAN TABORA ENGINEER WHO WE ARE WORKING ON RIOT’S INGESTION PIPELINE. FAVORITE ACTIVITY: EATING MAC + CHEESE WHILE LISTENING TO DEATH METAL. RTABORA@ RIOTGAMES.COM @RYANTABORA
- 4. OUR DATA PLATFORM (THEN) 5 THINGS YOU NEED RIOT GAMES SCALE AGENDA 3 THINGS WE STILL NEED (AND YOU MAY WANT ALSO) OUR DATA PLATFORM (NOW)
- 7. LEAGUE OF LEGENDS 2009 LAUNCH ONLINE MULTI- PLAYER WINDOWS / OSX 40-50 MIN. GAMES
- 8. LEAGUE OF LEGENDS STATS 7.5 MILLION PEAK CONCURRENT PLAYERS STATS RELEASED JANUARY 2014 67 MILLION MONTHLY ACTIVE PLAYERS MORE THAN MORE THAN 27 MILLION DAILY ACTIVE PLAYERS MORE THAN
- 11. 2013
- 13. 5 THINGS YOU NEED 1 2 3 4 5
- 14. Auditing ETLs can use queries with custom injected data. Ad-Hoc Data Requests Extend with new connection types and custom etls easily Self-Service Architecture The big data team is small. We can’t manage all the ETLS ourselves. Support Multiple Datacenters One task will execute on different database servers around the world. A.K.A. 5 THINGS WE DIDN’ T HAVE Multiple Data Access Patterns Extend with new connection types and custom etls easily
- 15. 5 THINGS YOU NEED SELF-SERVICE ARCHITECTURE 1 2 3 4 5
- 16. Need Backup!
- 19. User Documentation No one likes doing it, but it helps a lot. Onboard training Get new coworkers in-the-know Familiar Protocols Use REST or RPC so developers are on the same page Focus on UX Your tools need to be easy for non-technical people to use. SELF SERVICE HOW?
- 20. 5 THINGS YOU NEED A PLAN FOR MULTIPLE DATACENTERS 1 2 3 4 5
- 22. NA Korea Russia
- 23. Sqoop / Oozie OUTGROWING OUR TOOLS Pentaho DMExpress
- 24. Templating ETLs can use queries with custom injected data. Scale Horizontally As the data grows, the tool should be able to handle it. Empower Users The big data team is small. We can’t manage all the ETLS ourselves. Support One ETL - Many Sources One task will execute on different database servers around the world. YOUR ETL TOOL SHOULD...
- 25. Distributed ETL Software written in Ruby. Candidate for Riot open sourcing Same ETL applied to multiple regions / datacenters Self-Service UI with SQL query templating.
- 26. Create an ETL
- 27. Create an ETL
- 28. DynamoDB S3 SQS (S)FTP Hive Microsoft SQL Server Vertica Redshift Mysql REST websites FUETL CAN CONNECT TO
- 29. Create an ETL
- 32. FuETL SCALING
- 33. FuETL SCALING
- 35. FuETL STATISTICS 14 TB DATA MOVED DAILY 5213 ACTIVE REGIONAL ETLS 23125 DAILY ETL RUNS
- 37. 5 THINGS YOU NEED AUDITING TOOLS 1 2 3 4 5
- 38. Network Blips WHAT THE @&!#$? Improper shutdowns (deploys / ec2 reboots) HOW ETLS FAIL
- 39. REST micro-service built with Java and docker. Reports and visualizations we can use to find problems. Source and target comparison. Warehouse Auditing Service Platform
- 40. HOW TO AUDIT
- 41. HOW TO AUDIT
- 42. HOW TO AUDIT
- 43. VISUALIZING
- 44. VISUALIZING
- 45. 5.5 THINGS YOU NEED STANDARDIZED TIMEZONE!! 1 2 3.5 4 5
- 46. AUDITING ENABLES Single source of truth Recovering from data loss Good source of metadata
- 47. 5 THINGS YOU NEED MULTIPLE ACCESS PATTERNS 1 2 3 4 5
- 49. BATCH
- 50. BATCH OLAP
- 52. WASTING RESOURCES Fast Aggregates Slow Lookups
- 53. Returns one row in less than one second Java web service Simple abstraction, backed by DynamoDB Point Data Service
- 54. ABSTRACTION
- 55. MIRRORING HIVE
- 56. Full duplicate of the transactional data copied to DynamoDB Data load powered by Fuetl and ad- hoc EMR cluster Audited by WASP Point Data Service
- 57. CHOOSE TECHNOLOGY BASED ON NEEDS, AND DON’T FORCE IT
- 58. 5 THINGS YOU NEED AD-HOC DATA CRUNCHING 1 2 3 4 5
- 59. Easily scale our resources Both vertically (metastore) and horizontally (clusters) Support intensive ad-hoc tasks. We can spin up temporary dedicated clusters for big projects. We own our infrastructure Before, the game servers team got all the love. Can now join our data! One task will execute on different database servers around the world. TO THE CLOUD!
- 61. 2013
- 64. 3 THINGS WE STILL NEED(AND YOU MIGHT ALSO WANT) 1 2 3 4 5
- 66. OPEN SOURCE
- 67. RIOTERS WANTED!