Abstract
In-memory database systems have to keep base data as well as intermediate results generated during query processing in main memory. In addition, the effort to access intermediate results is equivalent to the effort to access the base data. Therefore, the optimization of intermediate results is interesting and has a high impact on the performance of the query execution. For this domain, we propose the continuous use of lightweight compression methods for intermediate results and have the aim of developing a balanced query processing approach based on compressed intermediate results. To minimize the overall query execution time, it is important to find a balance between the reduced transfer times and the increased computational effort. This paper provides an overview and presents a system design for our vision. Our system design addresses the challenge of integrating a large and evolving corpus of lightweight data compression algorithms in an in-memory column store. In detail, we present our model-driven approach and describe ongoing research topics to realize our compression-aware query processing vision.
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Hildebrandt, J., Habich, D., Damme, P., Lehner, W. (2017). Compression-Aware In-Memory Query Processing: Vision, System Design and Beyond. In: Blanas, S., Bordawekar, R., Lahiri, T., Levandoski, J., Pavlo, A. (eds) Data Management on New Hardware. ADMS IMDM 2016 2016. Lecture Notes in Computer Science(), vol 10195. Springer, Cham. https://doi.org/10.1007/978-3-319-56111-0_3
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