Cost-based Query Rewriting Techniques for Optimizing Aggregates Over Correlated Windows
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Window aggregates are ubiquitous in stream processing. In Azure Stream Analytics (ASA), a stream processing service hosted by Microsoft's Azure cloud, we see many customer queries that contain aggregate functions (such as MIN and MAX) over multiple correlated windows (e.g., tumbling windows of length five minutes and ten minutes) defined on the same event stream. In this paper, we present a cost-based optimization framework for optimizing such queries by sharing computation among multiple windows. Since our optimization techniques are at the level of query rewriting, they can be implemented 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 optimization techniques in detail, and report evaluation results over synthetic workloads.
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