In SQLite, both
INTEGER are used to define integer data types for columns in database tables. SQLite is a lightweight and self-contained relational database management system (RDBMS) that follows a dynamic typing system, which means that it is not strict about data types. However, it does provide some data type affinity for columns to help optimize storage and query performance. INT and INTEGER are examples of such data types with the same affinity.
Data Type Affinity
INTEGER have the same data type affinity, which is INTEGER. This means that when you define a column as INT or INTEGER, SQLite treats it as an integer data type and stores the data accordingly.
In terms of storage, there is no difference between
INTEGER. Both data types will use the same amount of storage space to store integer values. SQLite uses a dynamic storage system, so the actual space consumed may vary depending on the size of the integer value.
INTEGER support the same range of integer values. In SQLite, integer values are typically stored as signed 64-bit integers, which allows for values ranging from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807.
From a querying perspective, there is no practical difference between
INTEGER. You can perform arithmetic operations, comparisons, and filtering on columns of either data type in the same way.
INTEGER are supported in SQLite and are widely used interchangeably. SQLite’s flexible data type system allows you to use either of these data type names when defining your table schema.
INTEGER are valid choices, it’s considered good practice to be consistent in your choice of data type names within your database schema. Consistency makes the code more readable and easier to maintain.
INTEGER in SQLite are functionally equivalent and can be used interchangeably to define integer columns in your database tables. Both have the same data type affinity, storage characteristics, and support the same range of integer values. Choosing between them is primarily a matter of coding style and consistency within your database schema.