Efficient execution of sliding-window queries over data streams

Emerging data stream processing systems rely on windowing to enable on-the-fly processing of continuous queries over unbounded streams. As a result, several recent efforts have developed window-aware implementations of query operators such as joins and aggregates. This focus on individual operators, however, ignores the larger issue of how to coordinate the pipelined execution of such operators when combined into a full windowed query plan. In this paper, we first show how the straightforward application of traditional pipelined query processing techniques to sliding window queries can result in inefficient and incorrect behavior. We then present three alternative execution techniques that guarantee correct behavior for pipelined sliding window queries and develop new algorithms for correctly evaluating window-based duplicate- elimination, Group-By and Set operators in this context. We implemented all of these techniques in a prototype data stream system and report the results of a detailed performance study of the system.