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Struct Data Type

Conceptually, a STRUCT column contains an ordered list of columns called "entries". The entries are referenced by name using strings. This document refers to those entry names as keys. Each row in the STRUCT column must have the same keys. The names of the struct entries are part of the schema. Each row in a STRUCT column must have the same layout. The names of the struct entries are case-insensitive.

STRUCTs are typically used to nest multiple columns into a single column, and the nested column can be of any type, including other STRUCTs and LISTs.

STRUCTs are similar to PostgreSQL's ROW type. The key difference is that DuckDB STRUCTs require the same keys in each row of a STRUCT column. This allows DuckDB to provide significantly improved performance by fully utilizing its vectorized execution engine, and also enforces type consistency for improved correctness. DuckDB includes a row function as a special way to produce a STRUCT, but does not have a ROW data type. See an example below and the nested functions docs for details.

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

Creating Structs

Structs can be created using the struct_pack(name := expr, ...) function or the equivalent array notation {'name': expr, ...} notation. The expressions can be constants or arbitrary expressions.

Struct of integers:

SELECT {'x': 1, 'y': 2, 'z': 3};

Struct of strings with a NULL value:

SELECT {'yes': 'duck', 'maybe': 'goose', 'huh': NULL, 'no': 'heron'};

Struct with a different type for each key:

SELECT {'key1': 'string', 'key2': 1, 'key3': 12.345};

Struct using the struct_pack function. Note the lack of single quotes around the keys and the use of the := operator:

SELECT struct_pack(key1 := 'value1', key2 := 42);

Struct of structs with NULL values:

        {'yes': 'duck', 'maybe': 'goose', 'huh': NULL, 'no': 'heron'},
        {'yes':'frog', 'maybe': 'salamander', 'huh': 'dragon', 'no':'toad'}

Create a struct from columns and/or expressions using the row function:

This returns {'': 1, '': 2, '': a}:

SELECT row(x, x + 1, y) FROM (SELECT 1 AS x, 'a' AS y);

If using multiple expressions when creating a struct, the row function is optional:

This also returns {'': 1, '': 2, '': a}:

SELECT (x, x + 1, y) FROM (SELECT 1 AS x, 'a' AS y);

Adding Field(s)/Value(s) to Structs

Add to a Struct of integers:

SELECT struct_insert({'a': 1, 'b': 2, 'c': 3}, d := 4);

Retrieving from Structs

Retrieving a value from a struct can be accomplished using dot notation, bracket notation, or through struct functions like struct_extract.

Use dot notation to retrieve the value at a key's location. In the following query, the subquery generates a struct column a, which we then query with a.x.

SELECT a.x FROM (SELECT {'x': 1, 'y': 2, 'z': 3} AS a);

If a key contains a space, simply wrap it in double quotes (").

SELECT a."x space" FROM (SELECT {'x space': 1, 'y': 2, 'z': 3} AS a);

Bracket notation may also be used. Note that this uses single quotes (') since the goal is to specify a certain string key and only constant expressions may be used inside the brackets (no expressions):

SELECT a['x space'] FROM (SELECT {'x space': 1, 'y': 2, 'z': 3} AS a);

The struct_extract function is also equivalent. This returns 1:

SELECT struct_extract({'x space': 1, 'y': 2, 'z': 3}, 'x space');


Rather than retrieving a single key from a struct, star notation (*) can be used to retrieve all keys from a struct as separate columns. This is particularly useful when a prior operation creates a struct of unknown shape, or if a query must handle any potential struct keys.

All keys within a struct can be returned as separate columns using *:

FROM (SELECT {'x': 1, 'y': 2, 'z': 3} AS a);
x y z
1 2 3

Dot Notation Order of Operations

Referring to structs with dot notation can be ambiguous with referring to schemas and tables. In general, DuckDB looks for columns first, then for struct keys within columns. DuckDB resolves references in these orders, using the first match to occur:

No Dots

SELECT part1
FROM tbl;
  1. part1 is a column

One Dot

SELECT part1.part2
FROM tbl;
  1. part1 is a table, part2 is a column
  2. part1 is a column, part2 is a property of that column

Two (or More) Dots

SELECT part1.part2.part3
FROM tbl;
  1. part1 is a schema, part2 is a table, part3 is a column
  2. part1 is a table, part2 is a column, part3 is a property of that column
  3. part1 is a column, part2 is a property of that column, part3 is a property of that column

Any extra parts (e.g., .part4.part5, etc.) are always treated as properties

Creating Structs with the row Function

The row function can be used to automatically convert multiple columns to a single struct column. When using row the keys will be empty strings allowing for easy insertion into a table with a struct column. Columns, however, cannot be initialized with the row function, and must be explicitly named. For example:

Inserting values into a struct column using the row function:


The table will contain a single entry:

{'v': a, 'i': 42}

The following produces the same result as above:


Initializing a struct column with the row function will fail:

Invalid Input Error: A table cannot be created from an unnamed struct

When casting structs, the names of fields have to match. Therefore, the following query will fail:

    (SELECT {'x': 42} AS a);
Mismatch Type Error: Type STRUCT(x INTEGER) does not match with STRUCT(y INTEGER). Cannot cast STRUCTs - element "x" in source struct was not found in target struct

A workaround for this would be to use struct_pack instead:

SELECT struct_pack(y := a.x) AS b
    (SELECT {'x': 42} AS a);

This behavior was introduced in DuckDB v0.9.0. Previously, this query ran successfully and returned struct {'y': 42} as column b.

Comparison Operators

Nested types can be compared using all the comparison operators. These comparisons can be used in logical expressions for both WHERE and HAVING clauses, as well as for creating BOOLEAN values.

The ordering is defined positionally in the same way that words can be ordered in a dictionary. NULL values compare greater than all other values and are considered equal to each other.

Up to DuckDB 0.10.1, nested NULL values were compared as follows. At the top level, nested NULL values obey standard SQL NULL comparison rules: comparing a nested NULL value to a nested non-NULL value produces a NULL result. Comparing nested value members, however, uses the internal nested value rules for NULLs, and a nested NULL value member will compare above a nested non-NULL value member. DuckDB 0.10.2 introduced a breaking change in semantics, described below.

Nested NULL values are compared following Postgres' semantics, i.e., lower nested levels are used for tie-breaking.


See Nested Functions.

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Last modified: 2024-06-18