AWS Redshift Type of Subqueries with an Examples

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  • Post last modified:April 3, 2023
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A subquery in a database is a select expression that is enclosed in parentheses as a nested query block in a query statement. The Subquery may return zero to one or more values to its upper select or parent select statements. In this article, we will check Redshift type of subqueries with an examples.

Redshift Type of Subqueries with an Examples
Redshift Subqueries

Page Contents

Introduction to Amazon Redshift Subqueries

A subquery in Redshift is a nested select statement, that return zero or more records to is upper select statement.

The outer query that contains subquery is sometimes referred to as a super or parent query. Subqueries are usually used to calculate or derive the values that will be consumed by the parent or outer query.

You can use these nested query or subquery blocks in any of the following SQL statements:

Redshift Type of Subqueries

There are various types of subqueries that you can use in your query to simplify the complicated logic or calculations. Here are the subquery types in Amazon Redshift

Now, let us check these subqueries in details

Redshift Row Subquery

A single row subquery returns zero or one row to the outer SQL statement. The return value will be associated with the multiple columns in the query. You can use row subquery in a WHERE clause, or a FROM clause of a SELECT statement.

For example, below is the sample example of single row subquery in a SELECT statement:

select *,(select '1') as dummy_value from Patients;
 id | h_id | name | dummy_value
----+------+------+-------------
  2 |  222 | B    | 1
  4 |  444 | D    | 1
  5 |  555 | E    | 1
  1 |  111 | A    | 1
  3 |  333 | C    | 1
(5 rows)

Redshift Table Subquery

A table subquery returns multiple rows and multiple columns. You can use these type of subqueries in a FROM clause or as an argument of an EXISTS, IN, ANY, or ALL clauses.

For examples,

SELECT * FROM patients WHERE id > 0.01 * (SELECT SUM(id) FROM patients);
 id | h_id | name
----+------+------
  2 |  222 | B
  4 |  444 | D
  5 |  555 | E
  1 |  111 | A
  3 |  333 | C
(5 rows)

Redshift Scalar Subqueries

A scalar subquery is a regular SELECT query in parentheses that returns exactly one value: one row with one column. It is similar to the Redshift row subquery.

The query is executed and the returned value is used in the outer query. If the subquery returns zero rows, the value of the subquery is null. If it returns more than one row, Amazon Redshift returns an error.

For example, consider below simple example to verify scalar subquery.

select *,(select avg(id) from patients) as avg_id from Patients;
 id | h_id | name | avg_id
----+------+------+--------
  2 |  222 | B    |      3
  4 |  444 | D    |      3
  5 |  555 | E    |      3
  1 |  111 | A    |      3
  3 |  333 | C    |      3
(5 rows)

Redshift Correlated Subquery

Redshift Correlated subquery is a query within a query that refer the columns from the parent or outer query. This kind of subquery contains one or more correlations between its columns and the columns produced by the outer query.

More more details on correlated subqueries, read my other post: AWS Redshift Correlated Subquery and its Restrictions

Best Practices for Redshift Subqueries

When using subqueries in Redshift, there are some best practices you can follow to optimize performance of your complex queries:

  • Use EXISTS clause instead of IN condition: In your query, if you need to check if a value exists in a subquery, use EXISTS instead of IN. This can significantly improve query performance of your query.
  • Use Limited number of subqueries: Try to limit the number of subqueries in your queries, as multiple subqueries can take additional resources.
  • Use Temporary or CTEs whenever possible: Consider using temporary tables or CTEs to break your query down into smaller steps. Redshift temp tables or CTEs can significantly improve the performance of your query when you are using a same subquery in multiple location of your query.
  • Replace subqueries with INNER JOINs: If you need to join a table with a subquery, consider using an INNER JOIN instead of a subquery.
  • Avoid correlated subqueries in nested queries: Correlated subqueries in Redshift can be very powerful, but they can also be very slow. Try to avoid using them in nested queries.
  • Use LIMIT control subquery results: If your subquery returns a large number of records, consider using LIMIT clause to control the results returned by the subquery.

Conclusion

Amazon Redshift subqueries can pose several advantages and disadvantages. Redshift SQL Subqueries can simplify complex SQL queries by breaking them down into smaller, more readable and manageable components. Subqueries such as correlated subqueries can improve your query performance.

However, subqueries can reduce the query performance by increasing the complexity of your query. Subqueries may not always be the best approach for solving a particular problem, and other techniques such as joins or window functions may be more appropriate.

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