Streams and Tables: Two Sides of the Same Coin.

Stream processing has emerged as a paradigm for applications that require low-latency evaluation of operators over unbounded sequences of data. Defining the semantics of stream processing is challenging in the presence of distributed data sources. The physical and logical order of data in a stream may become inconsistent in such a setting. Existing models either neglect these inconsistencies or handle them by means of data buffering and reordering techniques, thereby compromising processing latency. In this paper, we introduce the Dual Streaming Model to reason about physical and logical order in data stream processing. This model presents the result of an operator as a stream of successive updates, which induces a duality of results and streams. As such, it provides a natural way to cope with inconsistencies between the physical and logical order of streaming data in a continuous manner, without explicit buffering and reordering. We further discuss the trade-offs and challenges faced when implementing this model in terms of correctness, latency, and processing cost. A case study based on Apache Kafka illustrates the effectiveness of our model in the light of real-world requirements.