Hive Data Types

Data Types in Hive define the kind of values that can be stored in each column of a table. For example,

1. A name should be stored as text (STRING).
2. A price should be stored as a number (DOUBLE).
3. A date of birth should be stored as a DATE.

Choosing the right data type ensures that your queries work correctly and that Hive can store your data efficiently.

Types of Data Types -

Hive offers three broad categories of data types:

• Primitive Data Types
• Collection Data Types
• Complex Data Types

Primitive Data Types -

These are the basic building blocks of data in Hive. They store simple values like numbers, text, or dates.

Data Type	Description	Length	Range
TINYINT	Very small integer	1 Byte	-128 to 127
SMALLINT	Small integer	2 Byte	-32768 to 32767
INT	Regular integer	4 Byte	-2147483648 .. 2147483647
BIGINT	Large integer	8 Byte	9223372036854775808 .. 9223372036854775807
FLOAT	Single precision decimal	4 Byte	1.40129846432481707e-45 .. 3.40282346638528860e+38
DOUBLE	Double precision decimal	8 Byte	4.94065645841246544e-324d .. 1.79769313486231570e+308
DECIMAL(p,s)	Precise decimal values	p, s	DECIMAL(10,2) stores 12345.67
STRING	Text values	Max 2GB	32,767
VARCHAR(n)	Text up to ‘n’ length	n
CHAR(n)	Fixed-length text	n	CHAR(4) stores 'Test'
BOOLEAN	True or False	True or False	TRUE or FLASE
DATE	Calendar date		1400-01-01 to 9999-12-31
TIMESTAMP	Date with time		1400-01-01 00:00:00 to 9999-12-31 00:00:00

Here is the conversion table for all primitive data types:

• "Y" Represents the data type can convert from source to destination.
• "N" represents the data type can’t be converted from source to destination.

Examples -

Scenario: Using different primitive types:

CREATE TABLE product_data (
  product_id INT,
  product_name STRING,
  price DOUBLE,
  available BOOLEAN
);

This stores product information, where each field has the right data type for its value.

Collection Data Types -

Sometimes, you need to store multiple values inside one field. That’s where collection types come in. They allow you to group multiple pieces of information together.

Complex Type	Description	Example
Array	Stores a list of values of the same type	ARRAY<STRING> → ['Apple', 'Banana']
Map	Stores key-value pairs	MAP<STRING, INT> → {'Math': 90, 'English': 85}
Structs	Stores multiple fields as a single object	STRUCT<name:STRING, age:INT>

Here is the conversion table for all collection data types:

• "Y" Represents the data type can convert from source to destination.
• "N" represents the data type can’t be converted from source to destination.

	Array	Map	Structs
Array	Y	N	N
Map	N	Y	N
Structs	Y	N	N

Examples -

Scenario: Using ARRAY and MAP:

CREATE TABLE student_data (
  student_id INT,
  student_name STRING,
  subjects ARRAY<STRING>,
  marks MAP<STRING, INT>
);

This table can store multiple subjects and their marks for each student in one row.

Complex Data Types -

Complex types are nested versions of the collection types where you combine them to create more advanced structures.

Examples -

CREATE TABLE company_data (
  company_id INT,
  company_name STRING,
  employees ARRAY<STRUCT<emp_name:STRING, emp_salary:DOUBLE>>
);

This means each company can have an array of employees, and for each employee, you store both name and salary.

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