A Look At The Data Systems Behind The Gameplay For League Of Legends
Data Engineering Podcast
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Reducing Onboarding Overhead and Challenges in Data Platforms and Systems
This chapter discusses the challenges of onboarding new teammates and reducing the overhead in the design and operation of data platforms and systems. The speakers talk about leveraging vendor tools and libraries to ease the onboarding process, using Monte Carlo for anomaly detection and Elation for maintaining an up-to-date catalog, and utilizing internal wikis and training programs to help new engineers become productive. The chapter concludes with the acknowledgement that the onboarding process is an ongoing challenge that requires continuous improvement.
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Summary
The majority of blog posts and presentations about data engineering and analytics assume that the consumers of those efforts are internal business users accessing an environment controlled by the business. In this episode Ian Schweer shares his experiences at Riot Games supporting player-focused features such as machine learning models and recommeder systems that are deployed as part of the game binary. He explains the constraints that he and his team are faced with and the various challenges that they have overcome to build useful data products on top of a legacy platform where they don’t control the end-to-end systems.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Ian Schweer about building the data systems that power League of Legends
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what League of Legends is and the role that data plays in the experience?
- What are the characteristics of the data that you are working with? (e.g. volume/variety/velocity, structured vs. unstructured, real-time vs. batch, etc.)
- What are the biggest data-related challenges that you face (technically or organizationally)?
- Multiplayer games are very sensitive to latency. How does that influence your approach to instrumentation/data collection in the end-user experience?
- Can you describe the current architecture of your data platform?
- What are the notable evolutions that it has gone through over the life of the game/product?
- What are the capabilities that you are optimizing for in your platform architecture?
- Given the longevity of the League of Legends product, what are the practices and design elements that you rely on to help onboard new team members?
- What are the seams that you intentionally build in to allow for evolution of components and use cases?
- What are the most interesting, innovative, or unexpected ways that you have seen data and its derivatives used by Riot Games or your players?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data stack for League of Legends?
- What are the most interesting or informative mistakes that you have made (personally or as a team)?
- What do you have planned for the future of the data stack at Riot Games?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links
- Riot Games
- League of Legends
- Team Fight Tactics
- Wild Rift
- DoorDash
- Decision Science
- Kafka
- Alation
- Airflow
- Spark
- Monte Carlo
- libtorch
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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