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This article mainly introduces "what is the basic concept of database data structure". In daily operation, I believe many people have doubts about what is the basic concept of database data structure. I have consulted all kinds of materials and sorted out simple and easy operation methods. I hope to help you answer the doubts of "what is the basic concept of database data structure"! Next, please follow the small series to learn together!

Classification of data structures

As can be seen from the above figure, there are only so many knowledge systems for the whole data structure and algorithm research. Remember the time efficiency and space efficiency? Both logical structures and storage structures serve it. Data processing is a form of time and space efficiency.

II. Analysis of data structure

The relationships between data are called logical structures. For example, sets, linear structures (one-to-one), tree structures (one-to-many), graph structures (many-to-many).

The form in which data is stored in a computer is called a storage structure.

Sequential storage: it is the relative position of data elements in memory to represent the logical relationship between data elements

Chain storage: Add a pointer to each data element to store the address, and use the pointer to indicate the logical relationship between the elements.

Time complexity and spatial complexity

Time efficiency and space efficiency were described at the beginning of the article, so how should they be quantified using data? Or how do we measure time efficiency versus space efficiency? This involves time complexity and space complexity.

First of all, let's look at the time complexity:

To understand time complexity, you need to understand time frequency first. The time taken by an algorithm is proportional to the number of times the statements in the algorithm are executed, and the algorithm that executes the most statements takes the most time. The number of statements executed in an algorithm is called statement frequency or time frequency. Note T(n).

Then we introduce the concept of time complexity. Let's look at the official definition: In general, the number of times the basic operation is repeated in the algorithm is a function of the problem size n, denoted by T(n). If there is some auxiliary function f(n), such that when n approaches infinity, the limit value of T(n)/f(n) is not equal to zero, then f(n) is called a function of the same order of magnitude as T(n). Let T(n)=O(f(n)), and call O(f(n)) the asymptotic time complexity of the algorithm. Time complexity is the scale of time, for example, time frequency is T(n), time complexity is O(n). Time complexity is O (n), and time complexity is O(n). that is, his time scale is at this level n.

The relationship between the time complexity of common algorithms is:

O(1)

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