List Type

A LIST column encodes lists of values. Fields in the column can have values with different lengths, but they must all have the same underlying type. LISTs are typically used to store arrays of numbers, but can contain any uniform data type, including other LISTs and STRUCTs.

LISTs are similar to PostgreSQL’s ARRAY type. DuckDB uses the LIST terminology, but some array functions are provided for PostgreSQL compatibility.

See the data types overview for a comparison between nested data types.

For storing fixed-length lists, DuckDB uses the ARRAY type.

Creating Lists

Lists can be created using the list_value(expr, ...) function or the equivalent bracket notation [expr, ...]. The expressions can be constants or arbitrary expressions. To create a list from a table column, use the list aggregate function.

-- List of integers
SELECT [1, 2, 3];
-- List of strings with a NULL value
SELECT ['duck', 'goose', NULL, 'heron'];
-- List of lists with NULL values
SELECT [['duck', 'goose', 'heron'], NULL, ['frog', 'toad'], []];
-- Create a list with the list_value function
SELECT list_value(1, 2, 3);
-- Create a table with an integer list column and a varchar list column
CREATE TABLE list_table (int_list INTEGER[], varchar_list VARCHAR[]);

Retrieving from Lists

Retrieving one or more values from a list can be accomplished using brackets and slicing notation, or through list functions like list_extract. Multiple equivalent functions are provided as aliases for compatibility with systems that refer to lists as arrays. For example, the function array_slice.

We wrap the list creation in parenthesis so that it happens first. This is only needed in our basic examples here, not when working with a list column. For example, this can’t be parsed: SELECT ['a', 'b', 'c'][1].

Example	Result
`SELECT (['a', 'b', 'c'])[3]`	`'c'`
`SELECT (['a', 'b', 'c'])[-1]`	`'c'`
`SELECT (['a', 'b', 'c'])[2 + 1]`	`'c'`
`SELECT list_extract(['a', 'b', 'c'], 3)`	`'c'`
`SELECT (['a', 'b', 'c'])[1:2]`	`['a', 'b']`
`SELECT (['a', 'b', 'c'])[:2]`	`['a', 'b']`
`SELECT (['a', 'b', 'c'])[-2:]`	`['b', 'c']`
`SELECT list_slice(['a', 'b', 'c'], 2, 3)`	`['b', 'c']`

Ordering

The ordering is defined positionally. NULL values compare greater than all other values and are considered equal to each other.

Null Comparisons

At the top level, NULL nested values obey standard SQL NULL comparison rules: comparing a NULL nested value to a non-NULL nested value produces a NULL result. Comparing nested value members, however, uses the internal nested value rules for NULLs, and a NULL nested value member will compare above a non-NULL nested value member.

Functions

See Nested Functions.

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Last modified: 2024-04-24