Dongwon Kim
Kakao Mobility
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Dongwon Kim is working on streaming data at Kakao Mobility. 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. Before joining Kakao Mobility, he attended Flink Forward three times as a speaker to share his experience of adopting Flink at SK telecom with “Predictive Maintenance with Apache Flink” at Flink Forward Berlin 2017, “Real-time driving score service using Flink” at Flink Forward Berlin 2018, and “Do Flink on Web with FLOW” at Flink Forward Europe 2019.
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Session-based Flink pipeline with Redis and Cassandra
Kakao Mobility provides taxi, proxy driver, e-bike, shuttle bus, and navigation services all through a single mobile app. We run a Flink pipeline for the services to deliver seamless customer experiences for distance-based fare estimation, usage-based insurance, and trip summary upon user trip completion. The pipeline performs the following tasks in sequence on streams of GPS points from the app:
(1) group GPS points from each trip into a session
(2) trigger aggregation immediately upon trip completion without waiting for timeout to happen
(3) perform map-matching on each session to get the correct sequence of road segments
(4) enrich each road segment with road-specific attributes from Redis
(5) compute results from the enriched data and write to Cassandra
In this talk, we share our distilled experience from developing and operating the session-based pipeline. We highlight why and how we use Redis and Cassandra in the pipeline.
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