Use Case Track

Real-time driving score service using Flink

SK telecom presents how to build and operate a session-based streaming application using Flink. A driving score service essentially calculates a driving score of a user's driving session considering speeding, rapid acceleration and rapid deceleration during the session. At SK telecom, this service was originally powered by batch ETL using Hive but has recently been migrated to stream processing using Flink. While batch ETL was only capable of letting users know a driving score 24 hours after a session is finished, Flink enables us to inform drivers of driving scores as soon as they reach their destinations. In this presentation, we talk about the dataflow design, trigger customization for emitting early results, exposing job-level metrics and a service discovery mechanism for integration with Prometheus.

Authors

Dongwon Kim
Dongwon Kim
SK Telecom
Dongwon Kim

Dongwon Kim is a big data architect at SK telecom. During his post-doctoral work, he was fascinated by the internal architecture of Flink and gave a talk titled “a comparative performance evaluation of Flink” at Flink Forward 2015. He introduces Flink to SK telecom, SK energy, and SK hynix to fulfill various needs for real-time streaming processing from the companies. Last year at Flink Forward 2017 Berlin, he shared his experience of using Flink in building a solution for Predictive Maintenance. He recently has been adopting Flink to calculate driving scores of millions of users in real time.

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