Abstract
Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, and minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics such as properties of aggregation functions (e.g., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), and stream (dis)order. Violating the assumptions of a technique can deem it unusable or drastically reduce its performance. In this paper, we present the first general stream slicing technique for window aggregation. General stream slicing automatically adapts to workload characteristics to improve performance without sacrificing its general applicability. As a prerequisite, we identify workload characteristics which affect the performance and applicability of aggregation techniques. Our experiments show that general stream slicing outperforms alternative concepts by up to one order of magnitude.
Original language | English |
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Title of host publication | Advances in Database Technology - EDBT 2019 |
Subtitle of host publication | 22nd International Conference on Extending Database Technology, Proceedings |
Editors | Melanie Herschel, Helena Galhardas, Carsten Binnig, Zoi Kaoudi, Irini Fundulaki, Berthold Reinwald |
Publisher | OpenProceedings.org |
Pages | 97-108 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-89318-081-3 |
DOIs | |
Publication status | Published - 2019 |
Event | 22nd International Conference on Extending Database Technology, EDBT 2019 - Lisbon, Portugal Duration: 26 Mar 2019 → 29 Mar 2019 |
Conference
Conference | 22nd International Conference on Extending Database Technology, EDBT 2019 |
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Country/Territory | Portugal |
City | Lisbon |
Period | 26/03/19 → 29/03/19 |