Which Is Not a Complex Data Type in Hive?

When working with Hive, it is essential to understand the different data types available. Hive supports both primitive and complex data types.

While primitive data types include integers, strings, and booleans, complex data types allow you to store structured or nested data within a single column. These complex data types include arrays, maps, and structs.

Arrays

An array is an ordered collection of elements of the same type. It allows you to store multiple values in a single column. In Hive, arrays are defined using square brackets [] and can contain any valid Hive data type.

Example:

CREATE TABLE employees (
  id INT,
  names ARRAY<STRING>
);

Maps

A map is an unordered collection of key-value pairs. Each key in the map must be unique, while values can be of any valid Hive data type. Maps are defined using angle brackets <> and the keyword MAP.

Example:

CREATE TABLE employee_details (
  id INT,
  attributes MAP<STRING, STRING>
);

Structs

A struct is a collection of named fields that can have different data types. It allows you to group related fields together into a single column. Structs are defined using parentheses () and the keyword STRUCT.

Example:

CREATE TABLE customer (
  id INT,
  info STRUCT<name: STRING, age: INT>
);

The Non-Complex Data Type

Among these complex data types (arrays, maps, and structs), there is no non-complex or simple data type in Hive. All other available data types in Hive are considered primitive or simple data types. This means that they store single values and cannot contain structured or nested data.

Primitive data types supported by Hive include:

• BOOLEAN: Represents a boolean value (true or false).
• TINYINT: Represents a 1-byte signed integer.
• SMALLINT: Represents a 2-byte signed integer.
• INT: Represents a 4-byte signed integer.
• BIGINT: Represents an 8-byte signed integer.
• FLOAT: Represents a single-precision floating-point number.
• DOUBLE: Represents a double-precision floating-point number.
• STRING: Represents textual data stored as UTF-8 encoded characters.

In conclusion, when working with Hive, it is crucial to understand the complex data types such as arrays, maps, and structs. These complex data types allow you to store structured or nested data within a single column.

However, there is no non-complex or simple data type in Hive. All other available data types in Hive are considered primitive or simple data types that store single values only. By understanding these distinctions, you can effectively utilize the appropriate data type for your needs in Hive.

I hope this article has provided you with a clear understanding of the complex and non-complex data types in Hive!