Statistical method and statistical device for big data in database

Abstract

The invention discloses a statistical method and a statistical device for big data in a database. The method comprises the following steps: inquiring whether a log list in the database has a log record of an appointed data or not, if so, exiting; otherwise, compiling a service statistical script in an SQL format according to a service demand and storing the script in the database or a configuration file of a code to execute the next step; calling the service statistical script to summarize and perform statistics on the service report table data of the appointed data and storing the obtained service report table data to a summarized data table in the database for a user to directly inquire the service summarized data of the appointed data; splitting original detailed data of the appointed data into corresponding data split sub-tables according to a splitting rule; and storing the executing result of the appointed data to the log record in the database. According to the method and the device disclosed by the invention, the time of the user for inquiry can be greatly shortened, the efficiency of statistical query can be greatly improved, and wastes of the hardware resources and costs can be greatly reduced.

Description

technical field [0001] The invention relates to the field of big data statistics, in particular to a statistical method and device for big data in a database. Background technique [0002] General enterprise business data will be stored in the database to ensure data integrity and availability. With the growth of business, the data of a single data table will become larger and larger. When the data of a single data table reaches tens of millions or hundreds of millions, the speed of user statistical query will become slower and slower, and it will take several minutes. The statistical efficiency is low, so that the needs of users cannot be met. The current common practice is to upgrade the server hardware configuration to meet business needs. As the amount of data increases, the consumption of hardware resources will also increase. In this way, when the business does not grow month-on-month, it is necessary to continuously upgrade hardware resources to meet the continuous ...

AI Technical Summary

Problems solved by technology

With the growth of business, the data of a single data table will become larger and larger. When the data of a single data table reaches tens of millions or hundreds of millions, the speed of user statistical query will become slower and slower, and it will take several minutes. The statistical efficiency is low, so it cannot meet the needs of users

At present, the common practice is to upgrade the server hardware configuration to meet the needs of the business. As the amount of data increases, the consumption of hardware resources will also increase.

In this way, when the business does not grow month-on-month, it is necessary to continuously upgrade hardware resources to meet the continuous accumulation of data requirements, which will cause waste of hardware resources and costs

Images