Use Cases Track

Scaling Uber’s Realtime Optimization with Apache Flink

Many marketplace products (e.g pricing, positioning etc.) in Uber require intensive realtime optimizations. Such applications help Uber automatically maintain marketplace reliability, generate market insights and improve the network efficiency across more than 600 cities in realtime. Underneath, Uber engineers leverage Apache Flink to build a platform that not only runs compute intensive optimization models, but also very quickly reacts to rapid changes in marketplace. In this talk, I will cover the compute platform that leverages Apache Flink to i.) aggregate billions of realtime and forecasted demand and supply level information across the globe. ii.) trigger on-demand optimization models to respond to changes in marketplace and iii.) scale both horizontally and vertically as we expand the platform to onboard new applications and experiences.

Authors

Xingzhong Xu
Xingzhong Xu
Sr Software Engineer Uber Technologies Inc.
Xingzhong Xu

Xingzhong is a Senior Software Engineer in Uber where he works on dynamic pricing for more than two years.

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