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Nullable types are a feature of some programming languages which allow a value to be set to the special value NULL instead of the usual possible values of the data type.In statically typed languages, a nullable type is an option type, [citation needed] while in dynamically typed languages (where values have types, but variables do not), equivalent behavior is provided by having a single null ...
The SQL:1999 standard introduced a BOOLEAN data type as an optional feature (T031). When restricted by a NOT NULL constraint, a SQL BOOLEAN behaves like Booleans in other languages, which can store only TRUE and FALSE values. However, if it is nullable, which is the default like all other SQL data types, it can have the special null value also.
Each column in an SQL table declares the type(s) that column may contain. ANSI SQL includes the following data types. [14] Character strings and national character strings. CHARACTER(n) (or CHAR(n)): fixed-width n-character string, padded with spaces as needed; CHARACTER VARYING(n) (or VARCHAR(n)): variable-width string with a maximum size of n ...
SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Edgar F. Codd [12] in the early 1970s. [13] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM's original quasirelational database management system, System R, which a group at IBM San ...
SQLAlchemy is an open-source Python library that provides an SQL toolkit (called "SQLAlchemy Core") and an Object Relational Mapper (ORM) for database interactions. It allows developers to work with databases using Python objects, enabling efficient and flexible database access.
An SQL UPDATE statement changes the data of one or more records in a table. Either all the rows can be updated, or a subset may be chosen using a condition. The UPDATE statement has the following form: [1] UPDATE table_name SET column_name = value [, column_name = value ...] [WHERE condition]
The purpose of DQL commands is to get the schema relation based on the query passed to it. Although often considered part of DML, the SQL SELECT statement is strictly speaking an example of DQL. When adding FROM or WHERE data manipulators to the SELECT statement the statement is then considered part of the DML.
Selecting only certain columns to load: (or selecting null columns not to load). For example, if the source data has three columns (aka "attributes"), roll_no, age, and salary, then the selection may take only roll_no and salary. Or, the selection mechanism may ignore all those records where salary is not present (salary = null).