SQL operators execute directly on RLE, index, and dictionary-compressed columns on GPUs, with order-of-magnitude speedups for pre-sorted production workloads but only 2x mean gains on general public datasets.
MorphStore: Analytical Query Engine with a Holistic Compression-Enabled Processing Model
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abstract
In this paper, we present MorphStore, an open-source in-memory columnar analytical query engine with a novel holistic compression-enabled processing model. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory column-store database systems, but mainly for base data. In particular, during query processing, these systems only keep the data compressed until an operator cannot process the compressed data directly, whereupon the data is decompressed, but not recompressed. Thus, the full potential of compression during query processing is not exploited. To overcome that, we developed a novel compression-enabled processing model as presented in this paper. As we are going to show, the continuous usage of compression for all base data and all intermediates is very beneficial to reduce the overall memory footprint as well as to improve the query performance.
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GPU Acceleration of SQL Analytics on Compressed Data
SQL operators execute directly on RLE, index, and dictionary-compressed columns on GPUs, with order-of-magnitude speedups for pre-sorted production workloads but only 2x mean gains on general public datasets.