Multi-dimensional OLAP (On Line Analytical Processing) inquiry processing method facing column storage data warehouse

Abstract

The invention discloses a multi-dimensional OLAP (On Line Analytical Processing) inquiry processing method facing a column storage data warehouse. In the multi-dimensional OLAP inquiry processing method, the OLAP inquiry is decomposed into a bitmap filtering operation, a grouping operation and an aggregation operation. In the bitmap filtering operation, firstly, the predication is executed on a dimension table and a predicate vector bitmap is generated; and a connection operation is converted into a direct dimension table record access operation by surrogate key address mapping so as to implement access according to the positions. In the grouping operation, the pre-generation of grouping units is carried out on fact table records which meet the filtering conditions according to grouping attributes in an SQL (Structured Query Language) command and increasing IDs (identity) are distributed. In the aggregation operation, the grouping aggregation calculation which is carried out according to a grouping item of a grouping filtering vector of a fact table is implemented by carrying out column scanning on the metric attribute of the fact table for once. According to the invention, all OLAP processing tasks can be completed only by carrying out column scanning on the fact table for once, so that the cost of repeatedly scanning is avoided.

Description

technical field [0001] The invention relates to an OLAP (Online Analytical Processing) query processing method, in particular to a column-oriented storage data warehouse and a multi-dimensional OLAP query processing method based on a star connection model, which belongs to the technical field of database management. Background technique [0002] The design goal of On-Line Analytical Processing (OLAP for short) is to meet the specific query and report requirements in decision support or multi-dimensional environment. Data warehouse and OLAP are important supporting technologies for enterprise-level decision-making. [0003] However, the OLAP performance on massive data is difficult to meet the growing demand for real-time high-performance analytical processing. The bottleneck of its performance is mainly reflected in two aspects: one is the low hard disk I / O performance, and the other is that the query processing technology is not optimized enough. The mainstream technology...

AI Technical Summary

Problems solved by technology

Traditional hash joins generate a large number of hash tables under the load of concurrent query processing, and these hash tables will exhaust the memory resources of the system, resulting in poor performance of hash joins

The selection rate of OLAP queries is relatively high, so concurrent query processing faces a greater performance bottleneck, and it is difficult to obtain ideal performance in practice

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