Method for realizing self-adaptive vertical divided relational database and system thereof

Abstract

The invention discloses a method for realizing a self-adaptive vertical divided relational database, belonging to the field of a relational database, comprising the following steps of: S1, for a list, continuously collecting and counting correlation column occurring times related by user inquiring based on a column storage structure, calculating thresholds of the correlation column occurring times based on the historic data in the predetermined time window, educing the correlation columns greater than the thresholds for vertically dividing and obtaining the vertically divided correlation column sets; S2, creating the connection data based on the vertically divided correlation column sets; and S3, based on the vertically divided correlation column sets, adopting a predetermined policy for distributing different correlation columns of the list to different segments to realize parallel execution of inquiry. The invention can improve the rationality of the storage structure and realize parallel inquiry and improve the expandability.

Description

technical field [0001] The invention relates to the field of relational databases, in particular to a method and system for realizing self-adaptive vertically partitioned relational databases. Background technique [0002] Traditional relational database systems are based on row storage, and the recorded data is stored in the same data block. This method is very suitable for read and write operations and meets the application scenarios with frequent short transactions. The disadvantage is that additional columns may be read, complex queries are slow, indexing costs are high, system maintenance is complex, and continuous system optimization is required. The characteristic of the database system based on column storage (referred to as column database) is to store the columns of records separately, and then use the connection data to reconstruct the records. The design of column storage brings many advantages, such as data compression and index optimization processing for data...

AI Technical Summary

Problems solved by technology

This method has three problems. The first problem is that all columns are stored separately. Although all column values ​​​​can be sorted and indexed, it will affect performance when reconstructing records; the second problem is the storage of value data blocks. Although the serial number can alleviate the update problem of the column storage, it affects the performance of the read operation. Since only the serial number of the value data block is stored, a binary search is required when querying the column value; the third problem is that the table is not considered Data partitioning and distributed processing affect the parallel query capability and scalability of the system

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