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1.0 (stable)
Data Types

General-Purpose Data Types

The table below shows all the built-in general-purpose data types. The alternatives listed in the aliases column can be used to refer to these types as well, however, note that the aliases are not part of the SQL standard and hence might not be accepted by other database engines.

Name Aliases Description
BIGINT INT8, LONG signed eight-byte integer
BIT BITSTRING string of 1s and 0s
BLOB BYTEA, BINARY, VARBINARY variable-length binary data
BOOLEAN BOOL, LOGICAL logical boolean (true/false)
DATE   calendar date (year, month day)
DECIMAL(prec, scale) NUMERIC(prec, scale) fixed-precision number with the given width (precision) and scale, defaults to prec = 18 and scale = 3
DOUBLE FLOAT8, double precision floating-point number (8 bytes)
HUGEINT   signed sixteen-byte integer
INTEGER INT4, INT, SIGNED signed four-byte integer
INTERVAL   date / time delta
REAL FLOAT4, FLOAT single precision floating-point number (4 bytes)
SMALLINT INT2, SHORT signed two-byte integer
TIME   time of day (no time zone)
TIMESTAMP WITH TIME ZONE TIMESTAMPTZ combination of time and date that uses the current time zone
TIMESTAMP DATETIME combination of time and date
TINYINT INT1 signed one-byte integer
UBIGINT   unsigned eight-byte integer
UHUGEINT   unsigned sixteen-byte integer
UINTEGER   unsigned four-byte integer
USMALLINT   unsigned two-byte integer
UTINYINT   unsigned one-byte integer
UUID   UUID data type
VARCHAR CHAR, BPCHAR, TEXT, STRING variable-length character string

Implicit and explicit typecasting is possible between numerous types, see the Typecasting page for details.

Nested / Composite Types

DuckDB supports five nested data types: ARRAY, LIST, MAP, STRUCT, and UNION. Each supports different use cases and has a different structure.

Name Description Rules when used in a column Build from values Define in DDL/CREATE
ARRAY An ordered, fixed-length sequence of data values of the same type. Each row must have the same data type within each instance of the ARRAY and the same number of elements. [1, 2, 3] INTEGER[3]
LIST An ordered sequence of data values of the same type. Each row must have the same data type within each instance of the LIST, but can have any number of elements. [1, 2, 3] INTEGER[]
MAP A dictionary of multiple named values, each key having the same type and each value having the same type. Keys and values can be any type and can be different types from one another. Rows may have different keys. map([1, 2], ['a', 'b']) MAP(INTEGER, VARCHAR)
STRUCT A dictionary of multiple named values, where each key is a string, but the value can be a different type for each key. Each row must have the same keys. {'i': 42, 'j': 'a'} STRUCT(i INTEGER, j VARCHAR)
UNION A union of multiple alternative data types, storing one of them in each value at a time. A union also contains a discriminator "tag" value to inspect and access the currently set member type. Rows may be set to different member types of the union. union_value(num := 2) UNION(num INTEGER, text VARCHAR)

Updating Values of Nested Types

When performing updates on values of nested types, DuckDB performs a delete operation followed by an insert operation. When used in a table with ART indexes (either via explicit indexes or primary keys/unique constraints), this can lead to unexpected constraint violations. For example:

INSERT INTO students VALUES (1, 'Student 1');

  SET j = [2]
  WHERE i = 1;
Constraint Error: Duplicate key "i: 1" violates primary key constraint.
If this is an unexpected constraint violation please double check with the known index limitations section in our documentation (


ARRAY, LIST, MAP, STRUCT, and UNION types can be arbitrarily nested to any depth, so long as the type rules are observed.

Struct with LISTs:

SELECT {'birds': ['duck', 'goose', 'heron'], 'aliens': NULL, 'amphibians': ['frog', 'toad']};

Struct with list of MAPs:

SELECT {'test': [map([1, 5], [42.1, 45]), map([1, 5], [42.1, 45])]};

A list of UNIONs:

SELECT [union_value(num := 2), union_value(str := 'ABC')::UNION(str VARCHAR, num INTEGER)];

Performance Implications

The choice of data types can have a strong effect on performance. Please consult the Performance Guide for details.

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Last modified: 2024-07-10