This Confluence has been LDAP enabled, if you are an ASF Committer, please use your LDAP Credentials to login. Any problems file an INFRA jira ticket please.

Child pages
  • LanguageManual Types
Skip to end of metadata
Go to start of metadata

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 8 Next »

Hive Data Types

Column types

These are all the supported column types in Hive:

Primitive types:

  • TINYINT
  • SMALLINT
  • INT
  • BIGINT
  • BOOLEAN
  • FLOAT
  • DOUBLE
  • STRING
  • BINARY (Note: Only available starting with Hive 0.8.0)
  • TIMESTAMP (Note: Only available starting with Hive 0.8.0)
  • DECIMAL (Note: Only available starting with Hive 0.11.0)

Complex types:

  • arrays: ARRAY<data_type>
  • maps: MAP<primitive_type, data_type>
  • structs: STRUCT<col_name : data_type [COMMENT col_comment], ...>

  • union: UNIONTYPE<data_type, data_type, ...>

Timestamps

Supports traditional UNIX timestamp with optional nanosecond precision.

Supported conversions:

  • Integer numeric types: Interpreted as UNIX timestamp in seconds
  • Floating point numeric types: Intepreted as UNIX timestamp in seconds with decimal precision
  • Strings: JDBC compliant java.sql.Timestamp format "YYYY-MM-DD HH:MM:SS.fffffffff" (9 decimal place precision)

Timestamps are interpreted to be timezoneless and stored as an offset from the UNIX epoch. Convenience UDFs for conversion to and from timezones are provided (to_utc_timestamp, from_utc_timestamp).
All existing datetime UDFs (month, day, year, hour, etc.) work with the TIMESTAMP data type.

Decimal datatype

The Decimal datatype in Hive based on Java's BigDecimal which is used for representing immutable arbitrary precision decimal numbers in Java.

With the present implementation, the Decimal datatype has unlimited precision. While this has absolutely no impact on usability, it's worth noting for performance reasons. HIVE-3796 is tracking the progress of allowing users to specify scale and precision when creating tables with decimal datatype.

Union types

Union types can at any one point hold exactly one of their specified data types. You can create an instance of this type using the create_union UDF:

CREATE TABLE union_test(foo UNIONTYPE<int, double, array<string>, struct<a:int,b:string>>);
SELECT foo FROM union_test;

{0:1}
{1:2.0}
{2:["three","four"]}
{3:{"a":5,"b":"five"}}
{2:["six","seven"]}
{3:{"a":8,"b":"eight"}}
{0:9}
{1:10.0}

The first part in the deserialized union is the tag which lets us know which part of the union is being used. In this example 0 means the first data_type from the definition which is an int and so on.

To create a union you have to provide this tag to the create_union UDF:

SELECT create_union(0, key), create_union(if(key<100, 0, 1), 2.0, value), create_union(1, "a", struct(2, "b")) FROM src LIMIT 2;

{0:"238"}	{1:"val_238"}	{1:{"col1":2,"col2":"b"}}
{0:"86"}	{0:2.0}	{1:{"col1":2,"col2":"b"}}

Literals

Integral types

Integral literals are assumed to be INT by default, unless the number exceeds the range of INT in which case it is interpreted as a BIGINT, or if one of the following postfixes is present on the number.

Type

Postfix

Example

TINYINT

Y

100Y

SMALLINT

S

100S

BIGINT

L

100L

String types

String literals can be expressed with either single quotes (') or double quotes ("). Hive uses C-style escaping within the strings.

Floating point types

Floating point literals are assumed to be DOUBLE. Scientific notation is not yet supported.

Handling of NULL Values

Missing values are represented by the special value NULL. To import data with NULL fields, check documentation of the SerDe used by the table. (The default Text Format uses LazySimpleSerDe which interprets the string \N as NULL when importing.)

  • No labels