Space partitioning method for database table, device and storage medium

Abstract

Disclosed is a space partitioning method for a database table, including: determining a first data amount within a first time period and a second data amount within a second time period of the database table; calling a target network model, inputting the first data amount and the second data amount into the target network model, and outputting a third data amount within the target time period, wherein the target network model is configured to predict a data amount of a next time period based on data amounts of a previous time period and a current time period; and determining a number of target regions based on the third data amount within the target time period, and partitioning, based on the number of target regions, a space in the database table configured to store the data to be stored.

1 . A space partitioning method for a database table, comprising:
 determining a first data amount within a first time period and a second data amount within a second time period of the database table, wherein the first time period and the second time period are prior to a target time period corresponding to data to be stored;   calling a target network model, inputting the first data amount and the second data amount into the target network model, and outputting a third data amount within the target time period, wherein the target network model is configured to predict a data amount of a next time period based on data amounts of a previous time period and a current time period; and   determining a number of target regions based on the third data amount within the target time period, and partitioning, based on the number of target regions, a space in the database table configured to store the data to be stored.   
     
     
         2 . The method according to  claim 1 , wherein the first and the second time periods are adjacent ones, and the first time period is a previous time period of the target time period. 
     
     
         3 . The method according to  claim 1 , wherein before determining the first data amount within the first time period and the second data amount within the second time period of the database table, the method further comprises:
 acquiring a plurality of groups of data by pre-partitioning, based on a time stamp of data in the database table, the data in the database table according to a predetermined period length; and   wherein determining the first data amount within the first time period and the second data amount within the second time period of the database table comprises:   determining the first data amount within the first time period and the second data amount within the second time period of the database table by counting a data amount in the first time period and counting a data amount in the second time period from the plurality of groups of data.   
     
     
         4 . The method according to  claim 1 , wherein determining the number of target regions based on the third data amount within the target time period comprises:
 determining the number of target regions by formula:   
       
         
           
             
               k 
               = 
               
                 ⌈ 
                 
                   n 
                   m 
                 
                 ⌉ 
               
             
           
         
       
       based on the third data amount within the target time period;
 wherein k is the number of target regions, n is the third data amount within the target time period, m indicates a maximum storage capacity of a single area, and “┌ ┐” represents a rounding-up operation. 
 
     
     
         5 . The method according to  claim 1 , wherein the target network model is obtained by training a neural network model based on a data amount within a plurality of time periods and a data amount of one time period upon elapse of each of the plurality of time periods. 
     
     
         6 . A space partitioning device for a database table, comprising:
 a processor and a memory configured to store a computer program, wherein the processor, when running the computer program, is caused to perform a space partitioning method for a database table comprising:   determining a first data amount within a first time period and a second data amount within a second time period of the database table, wherein the first time period and the second time period are prior to a target time period corresponding to data to be stored;   calling a target network model, inputting the first data amount and the second data amount into the target network model, and outputting a third data amount within the target time period, wherein the target network model is configured to predict a data amount of a next time period based on data amounts of a previous time period and a current time period; and   determining a number of target regions based on the third data amount within the target time period, and partitioning, based on the number of target regions, a space in the database table configured to store the data to be stored.   
     
     
         7 . The device according to  claim 6 , wherein the first and the second time periods are adjacent ones, and the first time period is a previous time period of the target time period. 
     
     
         8 . The apparatus device according to  claim 6 , wherein before determining the first data amount within the first time period and the second data amount within the second time period of the database table, the method further comprises:
 acquiring a plurality of groups of data by pre-partitioning, based on a time stamp of data in the database table, the data in the database table according to a predetermined period length; and   wherein determining the first data amount within the first time period and the second data amount within the second time period of the database table comprises:   determining the first data amount within the first time period and the second data amount within the second time period of the database table by counting a data amount in the first time period and counting a data amount in the second time period from the plurality of groups of data.   
     
     
         9 . The device according to  claim 6 , wherein determining the number of target regions based on the third data amount within the target time period comprises:
 determining the number of target regions by formula:   
       
         
           
             
               k 
               = 
               
                 ⌈ 
                 
                   n 
                   m 
                 
                 ⌉ 
               
             
           
         
       
       based on the third data amount within the target time period;
 wherein k is the number of target regions, n is the third data amount within the target time period, m indicates a maximum storage capacity of a single area, and “┌ ┐” represents a rounding-up operation. 
 
     
     
         10 . The device according to  claim 6 , wherein the target network model is obtained by training a neural network model based on a data amount within a plurality of time periods and a data amount of one time period after each of the plurality of time periods. 
     
     
         11 . A non-volatile computer-readable storage medium storing instructions therein, wherein the instructions, when executed by a processor, causes the processor to perform the method as defined in  claim 1 . 
     
     
         12 . A computer device comprising a processor and a memory configured to store a computer program, wherein the processor, when running the computer program, is caused to perform a space partitioning method for a database table, comprising:
 determining a first data amount within a first time period and a second data amount within a second time period of the database table, wherein the first time period and the second time period are prior to a target time period corresponding to data to be stored;   calling a target network model, inputting the first data amount and the second data amount into the target network model, and outputting a third data amount within the target period, wherein the target network model is configured to predict a data amount of a next time period based on data amounts of a previous time period and a current time period; and   determining, a number of target regions based on the third data amount within the target time period, and partitioning, based on the number of target regions, a space in the database table configured to store the data to be stored.   
     
     
         13 . The computer device according to  claim 12 , wherein the first and the second time periods are adjacent ones, and the first time period is a previous time period of the target time period. 
     
     
         14 . The computer device according to  claim 12 , wherein before determining the first data amount within the first time period and the second data amount within the second time period of the database table, the method further comprises:
 acquiring a plurality of groups of data by pre-partitioning, based on a time stamp of data in the database table, the data in the database table according to a predetermined period length; and   wherein determining the first data amount within the first time period and the second data amount within the second time period of the database table comprises:   determining the first data amount within the first time period and the second data amount within the second time period of the database table by counting a data amount in the first time period and counting a data amount in the second time period from the plurality of groups of data.   
     
     
         15 . The computer device according to  claim 12 , wherein determining the number of target regions based on the third data amount within the target time period comprises:
 determining the number of target regions by formula:   
       
         
           
             
               k 
               = 
               
                 ⌈ 
                 
                   n 
                   m 
                 
                 ⌉ 
               
             
           
         
       
       based on the third data amount within the target time period;
 wherein k is the number of target regions, n is the third data amount within the target time period, m indicates a maximum storage capacity of a single area, and “┌ ┐” represents a rounding-up operation. 
 
     
     
         16 . The computer device according to  claim 12 , wherein the target network model is obtained by training a neural network model based on a data amount within a plurality of time periods and a data amount of one time period upon elapse of each of the plurality of time periods. 
     
     
         17 . The non-volatile computer-readable storage medium according to  claim 11 , wherein the first and the second time periods are adjacent ones, and the first time period is a previous time period of the target time period. 
     
     
         18 . The non-volatile computer-readable storage medium according to  claim 11 , wherein before determining the first data amount within the first time period and the second data amount within the second time period of the database table, the method further comprises:
 acquiring a plurality of groups of data by pre-partitioning, based on a time stamp of data in the database table, the data in the database table according to a predetermined period length; and   wherein determining the first data amount within the first time period and the second data amount within the second time period of the database table comprises:   determining the first data amount within the first time period and the second data amount within the second time period of the database table by counting a data amount in the first time period and counting a data amount in the second time period from the plurality of groups of data.   
     
     
         19 . The non-volatile computer-readable storage medium according to  claim 11 , wherein determining the number of target regions based on the third data amount within the target time period comprises:
 determining the number of target regions by formula:   
       
         
           
             
               k 
               = 
               
                 ⌈ 
                 
                   n 
                   m 
                 
                 ⌉ 
               
             
           
         
       
       based on the third data amount within the target time period;
 wherein k is the number of target regions, n is the third data amount within the target time period, m indicates a maximum storage capacity of a single area, and “┌ ┐” represents a rounding-up operation. 
 
     
     
         20 . The non-volatile computer-readable storage medium according to  claim 11 , wherein the target network model is obtained by training a neural network model based on a data amount within a plurality of time periods and a data amount of one time period upon elapse of each of the plurality of time periods.