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Scotty: Efficient Window Aggregation for out-of-order Stream Processing
Citation key TraubGCBKRM18
Author Jonas Traub Philipp Grulich Alejandro Rodríıguez Cuéllar Sebastian Breß Asterios Katsifodimos Tilmann Rabl Volker Markl
Pages 1300-1303
Year 2018
Journal International Conference on Data Engineering (ICDE 2018)
Abstract Computing aggregates over windows is at the core of virtually every stream processing job. Typical stream processing applications involve overlapping windows and, therefore, cause redundant computations. Several techniques prevent this redundancy by sharing partial aggregates among windows. However, these techniques do not support out-of-order processing and session windows. Out-of-order processing is a key requirement to deal with delayed tuples in case of source failures such as temporary sensor outages. Session windows are widely used to separate different periods of user activity from each other. In this paper, we present Scotty, a high throughput operator for window discretization and aggregation. Scotty splits streams into non-overlapping slices and computes partial aggregates per slice. These partial aggregates are shared among all concurrent queries with arbitrary combinations of tumbling, sliding, and session windows. Scotty introduces the first slicing technique which (1) enables stream slicing for session windows in addition to umbling and sliding windows and (2) processes out-of-order tuples efficiently. Our technique is generally applicable to a broad group of dataflow systems which use a unified batch and stream processing model. Our experiments show that we achieve a throughput an order of magnitude higher than alternative stateof-the-art solutions.
Link to publication [1] Link to original publication [2] Download Bibtex entry [3]

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