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.

翻译：我们重新审视了经典的变化传播框架，该框架用于处理更新下的查询评估。标准框架采用查询计划并物化中间视图，这会在空间和时间上产生高昂的多项式开销，而连接运算符是罪魁祸首。在本文中，我们提出了一种无需连接的新变化传播框架，从而自然地避免了这种多项式爆炸。同时，我们证明了新框架仍然支持增量结果和完整查询结果的常数延迟枚举，这与标准框架相同。此外，我们对其更新成本进行了定量分析，这不仅重现了该问题上许多最近的理论结果，还提供了一种有效的查询计划优化方法。新框架也易于集成到现有的流式数据库系统中。实验结果表明，我们使用Flink DataStream API实现的系统原型在空间、时间和延迟方面显著优于其他系统。