Random Sampling for Group-By Queries
release_ldh2gg5qn5amdjrmcibtl5yrma
by
Trong Duc Nguyen, Ming-Hung Shih, Sai Sree Parvathaneni, Bojian Xu,
Divesh Srivastava, Srikanta Tirthapura
2019
Abstract
Random sampling has been widely used in approximate query processing on large
databases, due to its potential to significantly reduce resource usage and
response times, at the cost of a small approximation error. We consider random
sampling for answering the ubiquitous class of group-by queries, which first
group data according to one or more attributes, and then aggregate within each
group after filtering through a predicate. The challenge with group-by queries
is that a sampling method cannot focus on optimizing the quality of a single
answer (e.g. the mean of selected data), but must simultaneously optimize the
quality of a set of answers (one per group).
We present CVOPT, a query- and data-driven sampling framework for a set of
group-by queries. To evaluate the quality of a sample, CVOPT defines a metric
based on the norm (e.g. ℓ_2 or ℓ_∞) of the coefficients of
variation (CVs) of different answers, and constructs a stratified sample that
provably optimizes the metric. CVOPT can handle group-by queries on data where
groups have vastly different statistical characteristics, such as frequencies,
means, or variances. CVOPT jointly optimizes for multiple aggregations and
multiple group-by clauses, and provides a way to prioritize specific groups or
aggregates. It can be tuned to cases when partial information about a query
workload is known, such as a data warehouse where queries are scheduled to run
periodically.
Our experimental results show that CVOPT outperforms current state-of-the-art
on sample quality and estimation accuracy for group-by queries. On a set of
queries on two real-world data sets, CVOPT yields relative errors that are 5x
smaller than competing approaches, under the same space budget.
In text/plain
format
Archived Files and Locations
application/pdf 975.7 kB
file_dzoow2ywvbgk7k5hv6n3ffdt4e
|
arxiv.org (repository) web.archive.org (webarchive) |
1909.02629v1
access all versions, variants, and formats of this works (eg, pre-prints)