Use Case Track

Lessons learned from Migrating to a Stateful Streaming Framework

In modern applications of streaming frameworks, stateful streaming is arguably one of the most important usage cases. Flink, as a well-supported streaming framework for stateful streaming, readily helps developers spend less efforts on system deployment and focus more on the business logic. Nevertheless, upgrading from an existing production system to a new one with stateful streaming can still be a challenging task for any development team. In this talk, we will share our experience in migrating an existing system at Appier (an AI-based startup specialized with B2B solutions) to stateful streaming with Flink. We will first discuss how stateful streaming matches our business logic and its potential benefits. Then, we review the obstacles that we have encountered during migration, and present our solutions to conquer them. We hope that our experience and tips shared in this talk hints future users to prepare themselves towards applying Flink in their production systems more painlessly.

Authors

Wei-Che (Tony) Wei
Wei-Che (Tony) Wei
Appier
Wei-Che (Tony) Wei

Wei-Che(Tony) Wei is a softwore engineer on Data Platform Team at Appier. He works on providing general facilities for internal users to access data by leveraging different open sources, such as Flink, Spark and Kafka. Recently, he focuses on building a streaming platform to let users benefit from the advantage of stateful streaming framework. And he has been contributing to Flink as well.

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