Key points are not available for this paper at this time.
As modern main-memory optimized data systems increasingly rely on fast scans, lightweight indexes that allow for data skipping play a crucial role in data filtering to reduce system I/O. Scans benefit from data skipping when the data order is sorted, semi-sorted, or comprised of clustered values. However data skipping loses effectiveness over arbitrary data distributions. Applying data skipping techniques over non-sorted data can significantly decrease query performance since the extra cost of metadata reads result in no corresponding scan performance gains. We introduce adaptive data skipping as a framework for structures and techniques that respond to a vast array of data distributions and query workloads. We reveal an adaptive zonemaps design and implementation on a main-memory column store prototype to demonstrate that adaptive data skipping has potential for 1.4X speedup.
Building similarity graph...
Analyzing shared references across papers
Loading...
Qin et al. (Thu,) studied this question.
synapsesocial.com/papers/6a02270d8d267ec217d8d6cf — DOI: https://doi.org/10.1145/2882903.2914836
Wilson Qin
Harvard University Press
Stratos Idreos
Harvard University
Harvard University Press
Building similarity graph...
Analyzing shared references across papers
Loading...