Hadoop Hive Data Types With Examples

Hadoop Hive Data Types With Examples

Hadoop Hive Data Types With Examples are mainly divided into 2 types

Primitive Data Types

primitive Data Types also divide into 4 types there are mentioned below

  • Numeric Data Type
  • String Data Type
  • Date/Time Data Type
  • Miscellaneous Data Type

Here is the common thing between Java,Sql and Hive is The Data types and sizes of Hadoop hive data types is similar to Java and SQL Primitive Data types

Numeric Data Type

Numerical Data types are mainly divided into 2 types

  • Integral Data Types
  • Floating Data Types
Integral Data Types 
  • TINYINT (This TINYINT is equal to Java’s BYTE data type)
  • SMALLINT (This SMALLINT is equal to Java’s SHORT data type)
  • INT (This INT is equal to Java’s INT data type)
  • BIGINT (This BIGINTis equal to Java’s LONG data type)
Floating Data Types
  • FLOAT (This FLOAT is equal to Java’s FLOAT data type )
  • DOUBLE (This DOUBLE is equal to Java’s DOUBLE data type)
  • DECIMAL (This DECIMAL is equal to SQL’s DECIMAL data type)

DECIMAL(4,3) represents total of 4 digits, out of which 3 are decimal digits. Below is the chart for all numeric types with their ranges and examples.

Hadoop Hive Data Types With Examples

By default in Hive integral values are taken as a INT data type unless the value of integral values range are cross the range of INT values (Please check the above table for values).Suppose if we need to use a low integral value like 100 it to be treated as TINYINT or SMALLINT or BIGINT then we need to suffix the value with Y, S or L respectively.

Examples: 100Y – TINYINT, 100S – SMALLINT, 100L – BIGINT

String Data Types

In Hive String Data Types are Mainly divided into 3 types there are mentioned below

  • CHAR

This String Data types are supported in Hive from only 0.14 and above versions only

Hive String Data types

What is Difference between CHAR vs VARCHAR

  • CHAR is fixed length
  • VARCHAR is is of variable length but we need to specify the max length of the field (example : name VARCHAR(64)). If the values are less than the max length specified then remaining space will be freed out.
  • Maximum value of CHAR is 255 but Maximum value of VARCHAR is 65355
  • In VARCHAR we can optimized the Space/Storage by using released the unused bytes but in CHAR we can optimize the unused Space CHAR unused bytes will  not be released but filled with spaces.
  • If a string value being assigned to a VARCHAR value exceeds the length specified, then the string is silently truncated.

DATE/TIME Data Types

Date/Time Data types are mainly Divide into 2 types

  • DATE

This hive date/time data types in UNIX time stamp format for date/time related fields in hive.


Represented format is YYYY-­MM-­DD

Example  : DATE ‘2014-­12-­07’

Date ranges allowed are 0000-­01-­01 to 9999-­12-­31


TIMESTAMP use the format yyyy-mm-dd hh:mm:ss[.f...]. 

Cast Type Result
cast(date as date) Same date value
cast(date as string) Date is formatted as a string in the form ‘YYYY-MM-DD’.
cast(date as timestamp) Midnight of the year/month/day of the date value is returned as timestamp.
cast(string as date) If the string is in the form ‘YYYY-MM-DD’, then a date value corresponding to that is returned. If the string value does not match this format, then NULL is returned.
cast(timestamp as date) The year/month/day of the timestamp is returned as a date value.

Miscellaneous Types

Hive Supports 2 more primitive Data types


Hive BOOLEAN is similar to Java’s BOOLEAN types,it can stores true or false values only

BINARY is an array of Bytes and similar to VARBINARY in many RDBMSs. BINARY columns are stored within the record, not separately like BLOBs

Implicit Conversion Between Primitive Data Types


TINYINT can be converted to any other numeric data type but BIGINT can only be converted to FLOAT or DOUBLE but the reverse

Boolean & Binary data types will not be  converted to any other data type implicitly.

Explicit Conversion Between Primitive Data Types

Explicit type conversion can be done using the cast operator only.

Example: CAST(‘500’ AS INT) will convert the string ‘500’ to the integer value 500.  But If cast is used incorrectly as in CAST(‘Hello’ AS INT) , then cast operation will fail and returns NULL .

Complex Data Types

The Above Primitive Data Types are almost same in all other Databases,Here hive also supported the hive complex data types which are not available in many relational database management systems.

Hive Presently supported 4 complex data types

  • MAP

ARRAY Data Type

Same as Array in java,An Ordered sequences of similar type elements that are index using zero-based integers

Example                      array(‘John’, ‘Doe’)

The second value is accused by using array[1]

MAP Data Type

Collection of key-value pairs. Fields are accessed using array notation of keys (e.g., [‘key’]).

Example                      map(‘first’, ‘John’,’last’, ‘Doe’)

if a column  name is of type MAP with key→value pairs  ‘first’→’John’ and ‘last’→’Doe’, then the last  name can be referenced using name[‘last’].

STRUCT Data Type

  • It is similar to STRUCT in C language. It is a record type which encapsulates a set of named fields that can be any primitive data type. Elements in STRUCT type are accessed using the DOT (.) notation.

Example – For a column c of type STRUCT {a INT; b INT} the a field is accessed by the expression c.a


UNIONTYPE is collection of Heterogeneous data types.It is similar to Unions in C. At any point of time, an Union Type can hold any one (exactly one) data type from its specified data types


UNIONTYPE<int, double, array<string>, struct<a:int,b:string>>


Hive Complex Data types Example

This is the detailed information about Hadoop Hive Data Types With Examples

Also Read Introduction To Hive Partition Big Data

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  1. Perfect explanation about Hadoop hive data types with examples.I want to clearly know about Union data type which is in the list of complex data types and when this Union data type has added in hive complex data types ?

  2. Would u plz detailed me about map datatype

  3. mahesh chimmiri says:

    defiantly We will add more complex data type real time examples in our future post

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