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Window aggregates are ubiquitous in stream processing. In Azure StreamAnalytics (ASA), a stream processing service hosted by Microsoft's Azure cloud,we see many customer queries that contain aggregate functions (such as MIN andMAX) over multiple correlated windows (e.g., tumbling windows of length fiveminutes and ten minutes) defined on the same event stream. In this paper, wepresent a cost-based optimization framework for optimizing such queries bysharing computation among multiple windows. In particular, we introduce thenotion of factor windows, which are auxiliary windows that are not in the inputquery but may nevertheless help reduce the overall computation cost, and ourcost-based optimizer can produce rewritten query plans that have lower coststhan the original query plan by utilizing factor windows. Since ouroptimization techniques are at the level of query (plan) rewriting, they can beimplemented on any stream processing system that supports a declarative,SQL-like query language without changing the underlying query execution engine.We formalize the shared computation problem, present the optimizationtechniques in detail, and report evaluation results over both synthetic andreal datasets. Our results show that, compared to the original query plans, therewritten plans output by our cost-based optimizer can yield significantlyhigher (up to 16.8x) throughput.

Factor Windows: Cost-based Query Rewriting for Optimizing Correlated Window Aggregates