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

A technology of data warehouse and processing method, which is applied in the field of database management, can solve problems such as poor hash connection performance, exhaustion of system memory resources, and difficulty in obtaining performance, so as to reduce complexity, improve performance and efficiency, and avoid costs Effect

Active Publication Date: 2012-09-12
RENMIN UNIVERSITY OF CHINA
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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 perfor

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

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Embodiment Construction

[0038] The core of OLAP query processing is star-join, that is, grouping and aggregation calculation is performed on the joining results on the basis of joining the fact table with multiple dimension tables. On this basis, the present invention provides a multi-dimensional OLAP query processing method. This method implements high-performance memory star join processing on the basis of column-stored data warehouses. All OLAP query processing tasks can be completed by performing a column scan on the fact table, and is especially suitable for use on multi-core processor platforms. Based on the above-mentioned characteristics of the OLAP query processing method, the inventors abbreviate it as CDDTA-JOIN (Column Directly Dimensional Tuple Accessing-JOIN) method. The following is a detailed description of this.

[0039] figure 1It shows the data structure and query processing flow of the CDDTA-JOIN method provided by the present invention. The core technical idea of ​​this method...

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/24556G06F16/221G06F16/24549
Inventor 张延松王珊
Owner RENMIN UNIVERSITY OF CHINA
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