The CHECK constraint can be defined either during the creation of the table or can be put in place later via an alter statement. To drop a CHECK constraint, we will need to specify the naming convention that was used during the creation of the constraint:.
The default value will be added to all new records if no other value is specified. Tables in a relational database can grow to be extremely long with a great number of rows present in each table, under the circumstances, retrieving information via SQL can sometimes be a very time taking process. By creating an index, the performance of data retrieval queries can be greatly improved. Indexes can be created or dropped at any point in time and do not have to be a part of the table definition at the time of table creation.
If you want to create an index on a combination of columns, you can list the column names within the parentheses, separated by commas:.
They can be values present in a single column of a table or a combination of multiple columns in the table.
This is how information is kept consistent in relational databases when they are broken down into multiple Fact and Dimension tables. The foreign key constraint is used to prevent operations in a relational database that would destroy links between tables. This concludes our list of commonly used SQL constraints. Which one do you think is the most useful? Tell us all about your experience with SQL constraints in the comment section below.
Happy Learning! Remember Me! Great Learning is an ed-tech company that offers impactful and industry-relevant programs in high-growth areas. Know More. Sign in. Log into your account. Forgot your password? Password recovery. For a patient, those attributes might be name, birthdate, address, etc.
To keep data consistent and usable, several constraints are made available to the database designer. Arguably the most important constraint is the primary key constraint, often shortened to PK. Most tables have a primary key. A primary key is a column that serves as the main identifier for data in that table. A table can only have one primary key, and it must be unique for all rows—so no two patients can have the same ID, no two doctors can have the same ID, and so on.
If your table can contain multiple identical objects, for example a books table in a library database, your table may be prone to duplicate data. Having an individual ID for each book would allow you to avoid this issue. For example, while a doctor may have many patients, a patient only has one primary care provider. Foreign key constraint rules would enforce that the DoctorId column in the patients table would have to match up with an existing DoctorId in the doctors table.
If we were to add a new patient to the patients table, the data would be rejected if the DoctorId column were blank or otherwise did not reference an existing doctor. With these rules enforced, it would be a simple matter to see all the patients a specific doctor has—we would simply write a query that retrieves every patient with the relevant value in the DoctorId column. Similarly, a doctor may write many prescriptions over the course of their career, and a patient may have multiple prescriptions at various times.
We could, however, have a DoctorId column and a PatientId column, both constrained by foreign key rules, in our prescriptions table. While other constraints are less vital to the structure of a database, they are no less vital to your data integrity. A column with the not null restraint cannot be empty. For example, the Name columns in our patients and doctors tables should be subject to not null constraints.
Foreign and primary key columns are also implicitly not null. The unique constraint ensures that all values in a given column are unique. This is for data that needs to be unique for each row in a table. We might want to use the unique constraint on the License column from the not null example, or if our patients table has an insurance ID column.
Check is used to make sure a value in a column fulfills a given requirement. Cardinality describes the relationship between two data tables by expressing the minimum and maximum number of entity occurrences associated with one occurrence of a related entity.
In Figure 9. In this figure, the cardinality is 0 zero on the right and 1 one on the left. The outermost symbol of the relationship symbol, on the other hand, represents the connectivity between the two tables. Connectivity is the relationship between two tables, e. The only time it is zero is when the FK can be null.
When it comes to participation, there are three options to the relationship between these entities: either 0 zero , 1 one or many. The left side of this symbol is read as minimum 1 and maximum 1. On the right side, it is read as: minimum 1 and maximum many.
The line that connects two tables, in an ERD, indicates the relationship type between the tables : either identifying or non-identifying. An identifying relationship will have a solid line where the PK contains the FK. A non-identifying relationshi p is indicated by a broken line and does not contain the FK in the PK. See the section in Chapter 8 that discusses weak and strong relationships for more explanation.
In an optional relationship , the FK can be null or the parent table does not need to have a corresponding child table occurrence. The symbol, shown in Figure 9. For example, if you look at the Order table on the right-hand side of Figure 9. In other words, the many side is optional. The relationship symbol in Figure 9. Figure 9. The one side is optional. In a mandatory relationship , one entity occurrence requires a corresponding entity occurrence. The symbol for this relationship shows one and only one as shown in Figure 9.
The one side is mandatory. So far we have seen that the innermost side of a relationship symbol on the left-side of the symbol in Figure 9.
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