Multiple Rows to Columns: A SQL Query Solution

As a database enthusiast, I’ve encountered various scenarios where transforming multiple rows into columns becomes necessary. In this article, we’ll delve into the world of SQL and explore how to create a list of IDs that are missing one or more types.

Understanding the Problem

The problem statement describes a table with an ID column and multiple type columns, each containing a value. Each ID appears multiple times in the table, and each ID has one or more types associated with it. The task is to identify the unique IDs of instances that are missing at least one type.

For instance, consider the following table:

In this example, the IDs 11112 and 11113 are missing one or more types. The goal is to create a list of these missing IDs.

The Solution: SQL Query

To solve this problem, we can employ a combination of subqueries, cross joins, and filtering techniques.

Subquery: Selecting Required Types

First, let’s identify the required types by selecting distinct values from the Type column. We’ll assume that all types present in the data are required.

SELECT DISTINCT 'type3' AS type FROM dual UNION ALL
SELECT DISTINCT 'type4' AS type FROM dual;

This subquery returns a list of required types, which we’ll use later.

Main Query: Cross Join and Filtering

Next, let’s create a cross join between the id column and the required_types table. This will generate all possible combinations of IDs and types.

SELECT i.id, rt.type
FROM (SELECT DISTINCT id FROM t) i CROSS JOIN
     required_types rt;

The CROSS JOIN keyword generates Cartesian products of the two tables.

Now, let’s join this result with the original table (t) on the condition that the id column matches and the type column is equal to one of the required types. We’ll use a LEFT JOIN to ensure that all IDs are included in the result, even if they don’t have matching records in the original table.

SELECT i.id, rt.type
FROM (SELECT DISTINCT id FROM t) i CROSS JOIN
     required_types rt LEFT JOIN
     t
     ON t.id = i.id AND t.type = rt.type
WHERE t.id IS NULL;

This query filters out the IDs that have matching records in the original table.

Final Result

The final result is a list of IDs that are missing one or more types. The id column contains the ID values, and the type column contains the required type values.

+-------+--------+
| id    | type   |
+-------+--------+
| 11112 | type3  |
| 11113 | type4  |
+-------+--------+

In this example, the IDs 11112 and 11113 are missing one or more types.

Alternative Solution: Using NOT IN Operator

If all types present in the data are required, you can use the NOT IN operator to filter out the IDs that have matching records in the original table.

SELECT DISTINCT i.id
FROM (SELECT DISTINCT id FROM t) i
WHERE i.id NOT IN (
  SELECT T.id
  FROM t AS T
  WHERE T.type IN ('type1', 'type2')
);

This query uses a subquery to select IDs that have only Type1 or Type2, and then filters out these IDs from the main result.

Conclusion

Transforming multiple rows into columns can be challenging, but SQL provides various techniques to achieve this. In this article, we explored how to create a list of IDs that are missing one or more types by using a combination of subqueries, cross joins, and filtering techniques. We also provided an alternative solution using the NOT IN operator.

By mastering these techniques, you’ll be better equipped to handle complex data transformation challenges in your database projects.

Last modified on 2023-11-11