As a well-established stream processing engine, Flink has also been keeping expanding its horizon into the batch processing world. Among all the differences between batch and streaming applications, an important one is query pattern. Continous query is the most native query pattern of stream processing. Stream applications draw a static DAG to describe the query logic and submit it as a long running job. In contrast, batch processing heavily relies on interactive queries. That means later query logic may vary depending on the output of earlier queries. Hence the application logic is difficult to be described as a static DAG.
In this short talk we will introduce the new Flink features we introduced to better support interactive queries. Including the API semantics, the intermediate results caching and metadata management mechanism, as well as potential use cases. From this talk, the audience will learn how Flink supports batch and stream processing at the same time, and how interactive queries can be used to help the users write their batch applications.
