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Null and not null in SQL


NULL and NOT NULL restrictions are critical in database design. These constraints control the presence or absence of values in a database table's columns, which affects data integrity and query results. This article examines the relevance of NULL and NOT NULL constraints in SQL databases, including their practical implementations.

Null Constraint

A NULL value in SQL denotes the lack of data in a column. It means that the data needs to be included, unknown, or undefined.Columns that accept NULL values are deemed nullable. Most columns in SQL databases allow NULL values by default unless the NOT NULL constraint is used to specify otherwise.

Example of Null Constraint:

Consider this scenario: you have a table named Students that stores student information. Let's build this table with some columns, including one that accepts NULL values:

In this example:

  • StudentID, FirstName, and LastName columns are specified as NOT NULL, ensuring that every student record must have values for these columns.
  • Age and Address columns allow NULL values, indicating that age and address information may not be available for all students.

Inserting data with null values:

Let's add some example data to the Student's table.

In this data,insert

  • The first record has values for every column.
  • The second entry has NULL values for Age and Address.
  • The third entry has a NULL value in the Address field.

Querying the Student's table

This query will return the following output:

Null and not null in SQL

Explanation for the output:

  • Each row indicates a student's record.
  • The StudentID, FirstName, LastName, Age, and Address columns are shown.
  • The Age column accepts NULL values hence, the second and third records contain NULL Age values.

Not Null Constraint

In contrast, the NOT NULL constraint enforces the requirement that a column cannot contain NULL values. Every row in a table must include a value for that particular field. Columns that are marked as NOT NULL are considered non-nullable.

Example of NOT NULL Constraint:

Let's modify the Students table to make the Age column NOT NULL:

The Age field will no longer accept NULL entries, guaranteeing that each student record has an age indicated.

Attempting to make this change will result in an error since the second record has a NULL value in the Age field. However, let us assume we omit this step and continue to query the Students table:

Querying the Students table after setting the field to NOT NULL:

The preceding error prevents the query from executing correctly. Nonetheless, after the problem is fixed by either updating the second item with a proper age or deleting the record, the query will return the following results:

Null and not null in SQL

Explanation for the output:

  • The second record, which had a NULL value in the Age column, has been changed or deleted to meet the NOT NULL requirement.
  • Only records with valid ages are saved in the table.

Practical Implications

The use of NULL and NOT NULL constraints has major ramifications for database architecture, data integrity, and query results.

  • Data Integrity: NOT NULL restrictions guarantee that critical data is always present, lowering the possibility of data inconsistency and mistakes. NULLs, on the other hand, provide greater flexibility when dealing with missing or optional information, but they must be handled carefully to avoid unexpected query results.
  • Query Results: SQL queries that include columns with NULL values require particular treatment. If NULLs are not correctly handled, operations like comparisons and arithmetic might provide unexpected results. SQL has methods like IS NULL and IS NOT NULL that deal particularly with NULL data in queries.
  • Indexing and Performance: NULL values might influence query and index performance. Some database systems evaluate NULL values differently than non-NULL values, which may influence index utilization and query optimization tactics.

Best Practices

  • Use NOT NULL restrictions on necessary data fields to maintain data integrity and consistency.
  • Allowing NULL values should be done with caution, especially in columns that are used for crucial actions or restrictions.
  • Include NULL handling principles and recommendations in the database schema documentation to maintain uniformity across applications and development teams.
  • Use the right SQL methods and operators to handle NULL values in queries to avoid unexpected behaviour.

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