Bank system historical data cleaning method and device

Abstract

The invention provides a bank system historical data cleaning method and device, used for cleaning historical data of a database data table. The bank system historical data cleaning method comprises the following steps: formulating a data cleaning rule according to the life cycle and association relationship of the data table of the database; screening data and key values of the data table to be cleaned according to the data cleaning rule, and determining the cleaned data index of the data table to be cleaned; cleaning the data of the data table of the database according to the data cleaning index, and writing the key values of the cleaned data into a backup index file; and generating a cleaning statistics report and recording the cleaned data in the file to save for a set time according to the backup index file, so as to support recovering the file from the file as needed. Through the bank system historical data method and device disclosed by the invention, the cleaning of the historical data can be realized, and efficient and safe operation of the production environment system can be ensured; therefore, the impact of the historical data cleaning process on normal business applications is avoided to the greatest extent, and the probability of affecting the normal applications in the normal production environment after the data cleaning is reduced.

Description

technical field [0001] The invention relates to the field of bank database storage, in particular to a method and device for cleaning historical data of a bank system. Background technique [0002] The core banking system has a large number of businesses and transactions every day. As time goes by, the background database business and transaction data continue to increase. After the life cycle of various data entities in the banking system ends, the related data will no longer be processed. However, they are still kept in the system database, occupying a large amount of storage space after a long period of accumulation, increasing the pressure of system data processing, affecting the execution efficiency of the system, and posing hidden dangers to the safe operation of the system. At present, the commonly used historical data processing methods include: do not process, keep in the database; directly clean up, run batch programs directly on the production environment, locate ...

AI Technical Summary

Problems solved by technology

However, the above-mentioned solutions all have the following problems or parts: the historical data is not cleaned up, and for a system with a large transaction volume and a rapid increase in the database data volume, the pressure on the database is increased, which affects the operating efficiency of the system; the direct cleaning solution has low cleaning efficiency and is not compatible with online transactions The contention for resources (CPU, database) is serious, which affects the normal application of the system; each table is cleaned up separately, due to differences in the cleaning speed and progress of different data tables, especially associated data tables, it will bring data inconsistency to the system

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