OLAP query processing method based on array storage and vector processing

A processing method and vector technology, applied in the field of database management, can solve the problems of increasing the complexity of hash grouping and aggregation calculation, and achieve the effect of improving query processing efficiency

Active Publication Date: 2014-03-12
RENMIN UNIVERSITY OF CHINA
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Problems solved by technology

For example, when the aggregate function includes both simple aggregate functions such as SUM and COUNT, as well as...

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  • OLAP query processing method based on array storage and vector processing
  • OLAP query processing method based on array storage and vector processing
  • OLAP query processing method based on array storage and vector processing

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Embodiment Construction

[0037] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] The invention provides an OLAP query processing method based on array storage and vector processing. This method simplifies the storage and processing of the complex multidimensional data model of the data warehouse. According to the referential integrity constraints in the data warehouse model, the array storage of data is completed at the bottom layer of the data warehouse design. The array storage uses array subscripts as implicit or explicit The primary key, the primary-foreign key reference relationship defined by the referential integrity constraint relationship is converted into an array subscript reference relationship between the table where the foreign key is located and the table where the primary key is located, so that the dimension table and the fact table pass the primary-foreign key reference relationship Direct...

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Abstract

The invention discloses an OLAP query processing method based on array storage and vector processing. In the method, array subscript mapping is carried out on major keys and foreign keys according to referential integrity constraint in a data warehouse model, the major keys of a dimension table and the foreign keys of a fact table are directly mapped to be array subscript addresses, the complex model is converted into a virtual materialized table, and a unified data access view is provided for different queries. The OLAP query is simplified to be three stages of predicate processing, grouping processing and gathering processing. In the stage of predicate processing, selection vectors are used for processing a selection result, on the fact table, of a plurality of predicates many times; in the stage of grouping processing, grouping vectors are used for distributing grouping array subscripts for each fact table record for meeting the predicate condition; in the stage of gathering processing, gathering vectors are used for providing an aggregator based on an array for an aggregate function. The OLAP query processing method based on array storage and vector processing simplifies the OLAP query processing process under a complex mode, eliminates the connection operation between the tables, and improves the code execution efficiency when the OLAP query processing is carried out.

Description

technical field [0001] The invention relates to an OLAP (Online Analytical Processing) query processing method, in particular to an OLAP query processing method based on array storage and vector processing, and belongs to the technical field of database management. Background technique [0002] Database technology can be divided into two main types: transactional query processing (on-line transaction processing, OLTP) and analytical query processing (on-line analytical processing, OLAP). Snowflake model) is represented by data warehouse and multidimensional analysis processing OLAP. [0003] There are massive data stored in the data warehouse, with complex patterns (a large number of dimension tables and complex multi-dimensional data structures), analytical queries have complex multi-table join operations and more grouping and aggregation operations, and its data storage access efficiency and Query processing performance is a key issue restricting data warehouse applicatio...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/283
Inventor 张宇张延松王珊周烜
Owner RENMIN UNIVERSITY OF CHINA
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