Spark Summit 2016 talk by Colin Borys (Riot Games) and Xiaoyang Yang (Riot Games)

1. VIDEO GAMES
AT SCALE
Improving the
gaming experience
with Spark

3. Choose
From over 120 champions, each
having a unique backstory and
abilities
Compete
With your team to complete
objectives and battle the enemy
team.
Win
Take down defenses and destroy
the enemy nexus

5. What can data tell us?
How do we interact with these data?
WHY SPARK? USE CASES FUTUREDATA@RIOTAGENDA
Data

6. Game Balance
How is Draven’s damage
output since the patch?

7. Network Performance
Player noted lag during
gameplay. Is his ISP having
trouble?

8. Customization
Are there customizations
similar to Star Guardian Lux
this player may enjoy?

9. Players & Data
W O R L D W I D E
Statistics released Jan 2014
67+ million
monthly active players
500+ billion
data points per day
26 petabytes
data collected since beta

10. THE DATA SCIENCE
TOOLKIT
● Scheduled ETLs
● Ad Hoc Queries
● Data pulls
● Recent
● 106
events/sec
● Monitoring
● Fraud/Anomaly
● Desktops
● R/Python
● Tableau

11. THE DATA SCIENCE
TOOLKIT
● Scheduled ETLs
● Ad Hoc Queries
● Data pulls
● Recent
● 106
events/sec
● Monitoring
● Fraud/Anomaly
● Desktops
● R/Python
● Tableau

12. Our data and ecosystem are scaling fine,
but our analytic tools were not.

13. WHY SPARK? USE CASES FUTUREDATA@RIOTAGENDA
Data Science
At Scale

14. Why ?

15. Spark SQL

16. Spark Streaming
Spark SQL

17. Spark Streaming
Spark ML
Spark SQL

18. Spark Streaming
Spark ML
Spark SQL
Empowerment

19. WHY SPARK? USE CASES FUTUREDATA@RIOTAGENDA
How did we
apply this?

20. 1How to Play
(with data)
SparkSQL

21. Iterative query development
Pain PointsFOR DATA EXPLORATION + REPORTING

22. Iterative query development
Data pulls are slow
Pain PointsFOR DATA EXPLORATION + REPORTING

23. Workflow is inefficient.
Data is separated from tools.

24. Integrated
Interactive
Faster
Data, easier.

25. Head-to-head
tests between
EMR and
SparkSQL .
(equal cost clusters)
Minutes

26. Data exploration efficiency increased
Performant SQL queries
Self managed cluster deploys
Managed/SparkSQL
TAKEAWAY

27. 2
Winning the war
on lag
With Spark Streaming

28. Gameplay highly dependent on
network connection.

30. The existing network limits our players.

31. So we built our own!

32. Riot Direct

33. Peering

34. But we need to find them!
We have this awesome tool to fix problems.

35. Scope of Data
W O R L D W I D E
17,000+
Unique ISPs
171,000+
City/ISP combinations
250,000+
Network stat messages per
second

36. Model this
PlayersLatency
Days

37. Model this
...so we can
detect this
Days
PlayersLatency

38. Model this
...so we can
detect this
...or this.
Days
PlayersLatency

39. Model Building/
Evaluation
HIVE
(stores aggregated data)
Kafka
Consume/
Aggregate
Alerts
Spark
Elasticsearch
Dashboards

40. Works
Intuitive
Will be a lot easier in 2.0!
Spark Streaming
TAKEAWAY

41. 3The Recommendation System
Secret Store

47. Why personalized
recommendations?

48. Why personalized
recommendations?

49. Why personalized
recommendations?

51. 67+ millionmonthly active players
500+ billiondata points per day
~1000champion/skin combo
Players
Champ/Skins
Champ/Skins
Players

52. Modeling/
Evaluation
HIVE
Explore/
Feature
engineering
Recommendation
Game Server
Data
Feature
SparkSQL MLlib
Spark

53. Fast prototyping
Works on big matrix!
Easy automation
Recommendation System
TAKEAWAY

54. WHY SPARK? USE CASES FUTUREDATA@RIOTAGENDA
Now what?

55. With Spark, we think that we’re
just getting started.

56. Wanna learn more?
(we’re hiring!)
Colin Borys
CBORYS @ RIOTGAMES.COM
Xiaoyang Yang
XYANG @ RIOTGAMES.COM