  We revisit the classical change propagation framework for query evaluation
under updates. The standard framework takes a query plan and materializes the
intermediate views, which incurs high polynomial costs in both space and time,
with the join operator being the culprit. In this paper, we propose a new
change propagation framework without joins, thus naturally avoiding this
polynomial blowup. Meanwhile, we show that the new framework still supports
constant-delay enumeration of both the deltas and the full query results, the
same as in the standard framework. Furthermore, we provide a quantitative
analysis of its update cost, which not only recovers many recent theoretical
results on the problem, but also yields an effective approach to optimizing the
query plan. The new framework is also easy to be integrated into an existing
streaming database system. Experimental results show that our system prototype,
implemented using Flink DataStream API, significantly outperforms other systems
in terms of space, time, and latency.
