6 research outputs found
Random Sampling for Group-By Queries
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. ℓ2 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
Hillview:A trillion-cell spreadsheet for big data
Hillview is a distributed spreadsheet for browsing very large datasets that
cannot be handled by a single machine. As a spreadsheet, Hillview provides a
high degree of interactivity that permits data analysts to explore information
quickly along many dimensions while switching visualizations on a whim. To
provide the required responsiveness, Hillview introduces visualization
sketches, or vizketches, as a simple idea to produce compact data
visualizations. Vizketches combine algorithmic techniques for data
summarization with computer graphics principles for efficient rendering. While
simple, vizketches are effective at scaling the spreadsheet by parallelizing
computation, reducing communication, providing progressive visualizations, and
offering precise accuracy guarantees. Using Hillview running on eight servers,
we can navigate and visualize datasets of tens of billions of rows and
trillions of cells, much beyond the published capabilities of competing
systems