Database adaption method for big data analysis engines

Abstract

The invention discloses a database adaptation method for big data analysis engines. The database adaptation method includes steps of defining relational databases capable of being accessed by database access statements; acquiring database query table identification information from received database access instructions; judging operation objects of the database access instructions according to the database query table identification information and correspondingly processing the operation objects; judging whether access operation corresponding to database query statements in the access instructions is query class operation or not, converting the access instructions of the query class operation according to database grammar to obtain corresponding database query instructions if the access operation is the query class operation and accessing the corresponding databases by the aid of the database query instructions. The database adaptation method has the advantage that the problems of severe dependence of massive application programs on particular database structures, low universality and poor expandability of software and the like can be solved by the aid of the database adaptation method.

Description

technical field [0001] The invention relates to the technical field of big data analysis and processing, in particular to database query technology, and in particular to a database adaptation method for a big data analysis engine. Background technique [0002] Non-relational databases improve database performance by reducing infrequently used functions. They are often used to deal with messy unstructured data, such as highly concurrent web page dynamic access information. In actual program development, the application front end has many business requirements and needs to match different database functions. Using a non-relational database with higher performance and lower cost can improve the scalability of the database. Most of the business operation support systems, such as power operation, bank transactions, telecom customer management and e-commerce and other operating systems, are mostly built based on relational databases, which involve the management and query of a lar...

AI Technical Summary

Problems solved by technology

[0003] A large number of application platforms, due to differences in data storage, are often inefficient when accessing databases. For example, a large amount of spatial data in geographic information systems needs to be accessed and updated quickly, while relational databases read and update spatial data. The storage efficiency is low, the scalability is poor, and the physical resources are seriously consumed, so it is not suitable for

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