冗余查询 Java 冗余数据库

转载

mob64ca140caeb2 2024-04-10 09:06:19

文章标签 冗余查询 Java 数据库 java mysql python 文章分类 Java 后端开发

dbms数据库管理系统

Redundancy takes place when there are more than one or multiple copies of the same relation in a database. Simply the storage of same or similar value more than once in the field is referred to as Redundancy.

冗余。简单地说，在现场多次存储相同或相似值的存储被称为冗余

For e.g. – Let's consider this Employee Table.

例如–让我们考虑这个雇员表。

EmpID	EmpName	Age	Salary	Department
1	Avdhesh	30	45000	IT
2	Dinesh	30	45000	IT
3	Ramesh	30	45000	IT
4	Suresh	30	45000	IT

EmpID	EmpName	年龄	薪水	部门
1个	佛陀	30	45000	它
2	Dinesh	30	45000	它
3	拉梅什	30	45000	它
4	苏雷什	30	45000	它

The similarity among the field values of the age and salary comes under the Redundancy in the database. Redundancy can lead to three types of issues:

年龄和薪水字段值之间的相似性在数据库的“冗余”下。冗余可能导致三种类型的问题：

Insertion Anomaly
插入异常
Updation Anomaly
更新异常
Deletion Anomaly
删除异常

(1) Insertion Anomaly )

Insertion Anomaly occurs when a particular detail is needed to be inserted in a relationship but the detail has some missing data, due to which it is not able to be inserted inside the database.

当需要在关系中插入特定的详细信息但该详细信息有一些丢失的数据时，会发生插入异常，因此，无法将其插入数据库中。

For e.g. – In the table of employee, suppose we want to enter this data.

例如–在雇员表中，假设我们要输入此数据。

EmpID	EmpName	Age	Salary	Department
4	Suresh	33	45000

EmpID	EmpName	年龄	薪水	部门
4	苏雷什	33	45000

Now, unless the department is specified for this particular employee, it is not possible to alter the database with this data.

现在，除非为该特定员工指定部门，否则无法使用此数据来更改数据库。

Insertion Anomaly mostly occurs when some additional data is missing from the detail.

当明细中缺少一些其他数据时，通常会发生插入异常。

(2) Updation Anomaly )

In Updation Anomaly, the updation of particular redundant field value consumes a lot of time and it is costly too.

在更新异常中，特定冗余字段值的更新会消耗大量时间，并且代价也很高。

For e.g. – Let us consider in the table of employee, we want to update the salary of all the employees from 40000 to 42000. That will take time.

例如–让我们在员工表中考虑，我们想要将所有员工的薪水从40000更新到42000。这将需要一些时间。

Since mostly the database is large, it takes a lot of time in order to update any data if they are redundant. It is costly as well, as the many databases need to get updated together. If anyone of the database left out, then it results in the inconsistency of the data.

由于大多数数据库很大，因此如果有多余的数据就需要花费很多时间来更新任何数据。这也是昂贵的，因为许多数据库需要一起更新。如果遗漏了数据库中的任何人，则将导致数据不一致。

(3) Deletion Anomaly )

In Deletion Anomaly, some unrelated data also get deleted with the preferred one. This result in the loss of data which we don't want to lose.

在“删除异常”中，一些不相关的数据也会被首选数据删除。这导致丢失我们不希望丢失的数据。

For e.g. – Let us consider in the table of an employee, we want to delete the employee record, and then with that, we will also lose the department details as well.

例如–让我们在员工表中考虑，我们想要删除员工记录，然后，我们也将丢失部门详细信息。

Deletion of some unwanted data results into the deletion of the data stored as a record in the database as some additional important information. This happens in Deletion Anomaly.

删除某些不需要的数据会导致删除作为记录存储在数据库中的数据，作为一些其他重要信息。这发生在删除异常中。