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Subqueries are parenthesized query expressions that appear as part of a larger, outer query. Subqueries are usually based on SELECT ... FROM, but in DuckDB other query constructs such as PIVOT can also appear as a subquery.

Scalar Subquery

Scalar subqueries are subqueries that return a single value. They can be used anywhere where a regular expression can be used. If a scalar subquery returns more than a single value, the first value returned will be used.

Consider the following table:


grade course
7 Math
9 Math
8 CS
CREATE TABLE grades (grade INTEGER, course VARCHAR);
INSERT INTO grades VALUES (7, 'Math'), (9, 'Math'), (8, 'CS');

We can run the following query to obtain the minimum grade:

SELECT min(grade) FROM grades;

By using a scalar subquery in the WHERE clause, we can figure out for which course this grade was obtained:

SELECT course FROM grades WHERE grade = (SELECT min(grade) FROM grades);

Subquery Comparisons: ALL, ANY and SOME

In the section on scalar subqueries, a scalar expression was compared directly to a subquery using the equality comparison operator (=). Such direct comparisons only make sense with scalar subqueries.

Scalar expressions can still be compared to single-column subqueries returning multiple rows by specifying a quantifier. Available quantifiers are ALL, ANY and SOME. The quantifiers ANY and SOME are equivalent.


The ALL quantifier specifies that the comparison as a whole evaluates to true when the individual comparison results of the expression at the left hand side of the comparison operator with each of the values from the subquery at the right hand side of the comparison operator all evaluate to true:

SELECT 6 <= ALL (SELECT grade FROM grades) AS adequate;



because 6 is less than or equal to each of the subquery results 7, 8 and 9.

However, the following query

SELECT 8 >= ALL (SELECT grade FROM grades) AS excellent;



because 8 is not greater than or equal to the subquery result 7. And thus, because not all comparisons evaluate true, >= ALL as a whole evaluates to false.


The ANY quantifier specifies that the comparison as a whole evaluates to true when at least one of the individual comparison results evaluates to true. For example:

SELECT 5 >= ANY (SELECT grade FROM grades) AS fail;



because no result of the subquery is less than or equal to 5.

The quantifier SOME maybe used instead of ANY: ANY and SOME are interchangeable.

In DuckDB, and contrary to most SQL implementations, a comparison of a scalar with a single-column subquery returning multiple values still executes without error. However, the result is unstable, as the final comparison result is based on comparing just one (non-deterministically selected) value returned by the subquery.


The EXISTS operator tests for the existence of any row inside the subquery. It returns either true when the subquery returns one or more records, and false otherwise. The EXISTS operator is generally the most useful as a correlated subquery to express semijoin operations. However, it can be used as an uncorrelated subquery as well.

For example, we can use it to figure out if there are any grades present for a given course:

SELECT EXISTS (SELECT * FROM grades WHERE course = 'Math') AS math_grades_present;
SELECT EXISTS (SELECT * FROM grades WHERE course = 'History') AS history_grades_present;


The NOT EXISTS operator tests for the absence of any row inside the subquery. It returns either true when the subquery returns an empty result, and false otherwise. The NOT EXISTS operator is generally the most useful as a correlated subquery to express antijoin operations. For example, to find Person nodes without an interest:

CREATE TABLE interest (PersonId BIGINT, topic VARCHAR);

INSERT INTO Person VALUES (1, 'Jane'), (2, 'Joe');
INSERT INTO interest VALUES (2, 'Music');

FROM Person
WHERE NOT EXISTS (SELECT * FROM interest WHERE interest.PersonId =;
id name
1 Jane

DuckDB automatically detects when a NOT EXISTS query expresses an antijoin operation. There is no need to manually rewrite such queries to use LEFT OUTER JOIN ... WHERE ... IS NULL.

IN Operator

The IN operator checks containment of the left expression inside the result defined by the subquery or the set of expressions on the right hand side (RHS). The IN operator returns true if the expression is present in the RHS, false if the expression is not in the RHS and the RHS has no NULL values, or NULL if the expression is not in the RHS and the RHS has NULL values.

We can use the IN operator in a similar manner as we used the EXISTS operator:

SELECT 'Math' IN (SELECT course FROM grades) AS math_grades_present;

Correlated Subqueries

All the subqueries presented here so far have been uncorrelated subqueries, where the subqueries themselves are entirely self-contained and can be run without the parent query. There exists a second type of subqueries called correlated subqueries. For correlated subqueries, the subquery uses values from the parent subquery.

Conceptually, the subqueries are run once for every single row in the parent query. Perhaps a simple way of envisioning this is that the correlated subquery is a function that is applied to every row in the source data set.

For example, suppose that we want to find the minimum grade for every course. We could do that as follows:

FROM grades grades_parent
WHERE grade =
    (SELECT min(grade)
     FROM grades
     WHERE grades.course = grades_parent.course);
grade course
7 Math
8 CS

The subquery uses a column from the parent query (grades_parent.course). Conceptually, we can see the subquery as a function where the correlated column is a parameter to that function:

SELECT min(grade)
FROM grades
WHERE course = ?;

Now when we execute this function for each of the rows, we can see that for Math this will return 7, and for CS it will return 8. We then compare it against the grade for that actual row. As a result, the row (Math, 9) will be filtered out, as 9 <> 7.

Returning Each Row of the Subquery as a Struct

Using the name of a subquery in the SELECT clause (without referring to a specific column) turns each row of the subquery into a struct whose fields correspond to the columns of the subquery. For example:

FROM (SELECT unnest(generate_series(41, 43)) AS x, 'hello' AS y) t;
{‘x’: 41, ‘y’: hello}
{‘x’: 42, ‘y’: hello}
{‘x’: 43, ‘y’: hello}
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Last modified: 2024-06-15