Generic windowing support for extensible stream processing systems

Abstract

Stream processing applications process high volume, continuous feeds from live data sources, employ data-in-motion analytics to analyze these feeds, and produce near real-time insights with low latency. One of the fundamental characteristics of such applications is the on-the-fly nature of the computation, which does not require access to disk resident data. Stream processing applications store the most recent history of streams in memory and use it to perform the necessary modeling and analysis tasks. This recent history is often managed using windows. All data stream management systems provide some form of windowing functionality. Windowing makes it possible to implement streaming versions of the traditionally blocking relational operators, such as streaming aggregations, joins, and sorts, as well as any other analytic operator that requires keeping the most recent tuples as state, such as time series analysis operators and signal processing operators.