A Cardinality Estimation Approach Based on Two Level Histograms.

For the mainstream relational database management systems, histograms play important roles in cardinality estimation. The main histogram-based cardinality estimation approaches can be classified into two categories: proactive approaches and reactive approaches. For the former, histograms are constructed and updated by periodical data scan which is also the essential reason affecting the accuracy and performance of this kind of approaches. Data scan is avoided in the latter, as an alternative, query feedback records (QPRs) are collected to construct and update histograms. But some time-consuming algorithms such as the effective QFR set calculation, the hole drilling algorithm and the iterative scaling algorithm are used by reactive approaches, which makes it inefficient. In this paper, we propose a novel cardinality estimation approach by combining proactive approach with QFRs. In our approach, data scan will be executed only once to construct the initial first-level histogram. And then, corresponding to all buckets of the first-level histogram, second-level histograms will be constructed and updated based on QFRs. The existence of second-level histograms and the elaborated mechanism dealing with the data update problem can improve the accuracy of cardinality estimation remarkably. Different from the traditional histogram-based approaches, we do not construct only one big histogram covering the whole value range of an attribute, but construct a serials of small second-level histograms covering different parts of the whole value range. These second-level histograms can be constructed and updated independently over time to ensure the performance of the approach. Extensive comparison experiments fully demonstrate the advantages of our approach in accuracy and performance.