Multi-library separation method and system based on business function intelligent analysis and medium

Abstract

The invention discloses a multi-library separation method based on business data intelligent analysis. The method comprises the following steps of obtaining an access request of the front-end businessdata, performing semantic analysis on the access request, analyzing the access object condition, generating an access strategy, executing the corresponding access strategy according to the type of the statement, recording the statement access object information, and generating an access frequency table, regularly and intelligently analyzing an object in the access frequency table to generate a migration strategy, analyzing the service data to determine an object needing to be migrated in the access frequency table, migrating the determined object needing to be migrated into a corresponding database, and after the data migration is completed, updating a data access strategy, and performing data cleaning on the database before the migration. The method classifies and intelligently analyzesthe data accessed by the business, can automatically analyze and detect the access frequency and the access content of the business processing database object, completes the automatic migration and cleaning of the object, and achieves the physical separation and read-write separation of the data of multiple databases.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a multi-database separation method, system, terminal and medium through intelligent analysis of business functions. Background technique [0002] At present, the core information trading system of large enterprises is mainly based on relational databases. Complex business logic is realized by adding, deleting, modifying and querying business data through relational databases. The database capacity of the information system basically exceeds TB level, and even reaches PB. class. The single-table data in the core database can reach more than 100G, and some even reach more than TB level. The query of large tables often affects the query efficiency of the database, affects the business process, and greatly reduces the system performance. [0003] In the early stage of system design, the database storage capacity planning lacked effective data basis, and the information syste...

AI Technical Summary

Problems solved by technology

The single-table data in the core database can reach more than 100G, and some even reach more than TB level. Large-scale query often affects the query efficiency of the database, affects the business process, and greatly reduces the system performance.

[0003] In the early stage of system design, the database storage capacity planning lacked effective data basis, and the information system designers did not accurately evaluate the later data growth of the business, resulting in the storage capacity of the database being unable to carry business data

In the preliminary planning, the storage hardware is relatively old, the capacity supported by the storage hardware is limited, and the database cannot be expanded for storage. Therefore, it is necessary to manually dump or clean up the production database, which requires a lot of manpower and time costs.

At the same time, it also needs to bear the loss caused by human misoperation; after the data migration, the front-end business needs to access the data through DBLINK across databases, which not only increases the maintenance difficulty of the database administrator, but also seriously affects the performance of the business system

[0004] Manual data dump of database table data requires business personnel to evaluate the pre-dumped table, and judge whether the table meets the dumping conditions according to the business access frequency and the front-end business query cycle, and the number of tables in the core database As many as hundreds of tables, all tables cannot be evaluated, so only a small number of tables can be dumped

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