In a relational database, what is the basic data structure?

In a relational database, the basic data structure is a "two-dimensional table", and the relationship between tables is reflected through the common fields in different tables. A relational database refers to a database that uses a relational model to organize data; the relational model can be simply understood as a two-dimensional table model, that is, a relational database is a data organization composed of two-dimensional tables and the relationships between them.

The operating environment of this tutorial: Windows 7 system, Dell G3 computer.

Relational database refers to a database that uses a relational model to organize data. It stores data in the form of rows and columns to facilitate user understanding. The series of rows and columns in a relational database Known as tables, a group of tables makes up a database. Users retrieve data from a database through queries, which are executable codes that limit certain areas of the database. The relational model can be simply understood as a two-dimensional table model, and a relational database is a data organization composed of two-dimensional tables and the relationships between them.

In a relational database, the basic data structure is a two-dimensional table, and the relationship between tables is reflected through the common fields in different tables. Each relational model is not isolated. They are not a bunch of two-dimensional tables randomly stacked together. To correctly reflect things and the connections between things, careful relational database design is required.

Features:

1. Storage method: traditional relational The database uses a table storage method, and the data is stored in rows and columns, which is very convenient to read and query.

2. Storage structure: Relational databases store data in a structured way. Each data table must define each field (that is, define the structure of the table first), and then store data according to the structure of the table. The advantage of this is that the form and content of the data have been defined before the data is stored, so the reliability and stability of the entire data table are relatively high. However, the problem is that once the data is stored, , it will be very difficult if you need to modify the structure of the data table.

3. Storage specifications: In order to avoid duplication, standardize data and make full use of storage space, relational databases store data in the form of minimal relational tables, so that data management can become clear and clear at a glance. , of course, this is mainly the case of a data table. The situation is different if there are multiple tables. Since the data involves multiple data tables, there are complex relationships between the data tables. As the number of data tables increases, data management will become more and more complex.

4. Extension method: Since relational databases store data in data tables, the bottleneck of data operations appears in the operation of multiple data tables, and the more data tables there are, the more serious the problem is. If you want to alleviate For this problem, we can only improve the processing power, that is, choose a computer with faster speed and higher performance. Although this method can expand the space to a certain extent, the expansion space must be very limited, that is, the relational database only has vertical Expansion capabilities.

5. Query method: Relational databases use structured query language (SQL) to query the database. SQL has long been supported by various database manufacturers and has become a standard in the database industry. It can support database CRUD (add, query, update, delete) operations have very powerful functions. SQL can use index-like methods to speed up query operations.

6. Standardization: During the database design and development process, developers usually face the need to operate on one or more data entities (including arrays, lists, and nested data) at the same time. In this way, in relational databases In , a data entity generally needs to be divided into multiple parts first, and then the divided parts are normalized. After normalization, they are stored in multiple relational data tables respectively. This is a complex process. The good news is that with the development of software technology, quite a few software development platforms provide some simple solutions. For example, you can use the ORM layer (that is, object relational mapping) to map the object model in the database to the SQL-based relational model. database and convert data between different types of systems.

7. Transactional: Relational database emphasizes ACID rules (Atomicity, Consistency, Isolation, Durability), which can meet the higher transactional requirements. High performance or data operations that require complex data queries, and can fully meet the requirements for high performance and operational stability of database operations. And relational databases place great emphasis on strong consistency of data and have good support for transaction operations. Relational databases can control the atomicity of transactions at fine granularity, and can roll back transactions immediately if an operation is incorrect or necessary.

8. Read and write performance: Relational databases place great emphasis on data consistency, and pay a huge price for reducing read and write performance. Although the reliability of relational databases in storing and processing data is very good, once it is faced When processing massive data, the efficiency will become very poor, especially when encountering high concurrent reading and writing, the performance will drop drastically.

9. Authorization method: Common relational databases include Oracle, SQLServer, DB2, and Mysql. Except for Mysql, most relational databases require a high fee to be used, even if they are free. Mysql performance is also subject to many limitations.

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