• DOI:10.14778/3425879.3425880
Corpus ID: 220042053

Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads

@article{Ding2020TsunamiAL,
  title={Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads},
  author={J. Ding and V. Nathan and M. Alizadeh and T. Kraska},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.13282}
}

J. Ding, V. Nathan, +1 author T. Kraska

Published 2020

Computer Science

ArXiv

Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing… CONTINUE READING

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