Linear data structure query efficiency improving method

Abstract

The present invention relates to a linear data structure query efficiency improving method. The method comprises the following steps: taking an ID of a linear storage data object as a KEY of a red-black tree node, and taking an address of the data object in a storage medium as a data domain of the red-black tree node, so as to construct a red-black tree; and inquiring the KEY of the red-black tree in the red-black tree, and when the key corresponding to the ID of the same data object is inquired, obtaining a to-be-searched object from data directing storage addresses. Thus, query in linear storage elements is converted into query in the red-black tree, so that search time is significantly saved.

Description

technical field [0001] The invention relates to the field of query methods, in particular to a method for querying in a shared memory. In the method, data objects in the shared memory are sequentially and linearly stored. Background technique [0002] In the field of programming, there is a concept called an array. In implementation, an array is a linear storage space with continuous addresses such as figure 1 Shown: [0003] This piece of space is divided into equal parts, for each part, it can be expressed as follows: [0004] arr[i] [0005] where i is 0~(n-1) [0006] We call arr[i] an element of this array, and i is called a subscript. [0007] For the search of the array, each element of the array is traversed and then compared, for example: [0008] [0009] The time complexity is a function of n: [0010] f(n)=O(n) [0011] If there are m values ​​to be compared, such as [0012] [0013] Then the time complexity is: [0014] f(n)=O(m*n) [0015] It ...

AI Technical Summary

Problems solved by technology

[0015] It can be seen that as the number of band searches (m) and the length of the array (n) increase, the time complexity of linear search increases sharply

Images