144 lines
5.2 KiB
Markdown
144 lines
5.2 KiB
Markdown
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# Sub Queries
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A subquery is a SQL query nested within a larger query. This powerful technique allows you to build complex data operations, treating the output of the inner query as input for the outer one.
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Subqueries are crucial for filtering, aggregation, and data manipulation tasks that simple queries cannot address effectively.
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Subqueries can appear in several key clauses:
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- SELECT clause
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- FROM clause
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- WHERE clause
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- HAVING clause
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The inner query executes first, its results informing the execution of the parent query. This execution order is fundamental to understanding subquery behavior.
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Employ subqueries within SELECT, INSERT, UPDATE, or DELETE statements to:
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- Compare an expression against the result of another query.
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- Ascertain if an expression is present within a query's results.
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- Verify if a query returns any rows.
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## Subqueries with SELECT
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Subqueries frequently appear in the `WHERE` clause of `SELECT` statements to filter data based on criteria derived from another query's result set.
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Consider the following `CUSTOMERS` table:
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| ID | NAME | AGE | ADDRESS | SALARY |
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|-----|----------|-----|-----------|-----------|
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| 1 | Ramesh | 35 | Ahmedabad | 2000.0 0 |
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| 2 | Khilan | 25 | Delhi | 1500.00 |
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| 3 | Kaushik | 23 | Kota | 2000.00 |
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| 4 | Chaitali | 25 | Mumbai | 6500.00 |
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| 5 | Hardik | 27 | Bhopal | 8500.00 |
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| 6 | Komal | 22 | MP | 4500.00 |
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| 7 | Muffy | 24 | Indore | 10000.00 |
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Example: Retrieve customers with a salary greater than 4500.
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```sql
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SELECT *
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FROM CUSTOMERS
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WHERE ID IN (
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SELECT ID
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FROM CUSTOMERS
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WHERE SALARY > 4500
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);
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```
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This query first identifies the `ID`s of customers earning more than 4500. The outer query then selects all information for customers whose `ID` is in that list.
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Result:
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| ID | NAME | AGE | ADDRESS | SALARY |
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|-----|----------|-----|---------|---------- |
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| 4 | Chaitali | 25 | Mumbai | 6500.00 |
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| 5 | Hardik | 27 | Bhopal | 8500 .00 |
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| 7 | Muffy | 24 | Indore | 10000.00 |
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## Subqueries with UPDATE
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Subqueries can update single or multiple columns based on criteria determined by the subquery's result.
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Assuming a `CUSTOMERS_BKP` table exists as a backup. The following example updates the `SALARY` in the `CUSTOMERS` table by multiplying it by 0 .25 for customers whose `AGE` is 27 or greater, based on the `CUSTOMERS_BKP` table.
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```sql
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UPDATE CUSTOMERS
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SET SALARY = SALARY * 0.25
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WHERE AGE IN (
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SELECT AGE
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FROM CUSTOMERS_BKP
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WHERE AGE >= 27
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);
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```
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This statement identifies ages from `CUSTOMERS_BKP` that are 27 or greater. The outer `UPDATE` then applies the salary adjustment to rows in `CUSTOMERS` with matching ages.
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Initial `CUSTOMERS` table:
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| ID | NAME | AGE | ADDRESS | SALARY |
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|-----|----------|-----|-----------|----------- |
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| 1 | Ramesh | 35 | Ahmedabad | 2000.00 |
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| 2 | Khilan | 25 | Delhi | 15 00.00 |
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| 3 | Kaushik | 23 | Kota | 2000.00 |
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| 4 | Chaitali | 25 | Mumbai | 6500.00 |
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| 5 | Hardik | 27 | Bhopal | 8500.00 |
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| 6 | Komal | 22 | MP | 4500.00 |
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| 7 | Muffy | 24 | Indore | 10000. 00 |
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After executing the `UPDATE`, the `CUSTOMERS` table will contain:
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| ID | NAME | AGE | ADDRESS | SALARY |
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|-----|----------|-----|-----------|-----------|
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| 1 | Ramesh | 35 | Ahmedabad | 500.00 |
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| 2 | Khilan | 25 | Delhi | 1500.00 |
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| 3 | Kaushik | 23 | Kota | 2000.00 |
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| 4 | Chaitali | 25 | Mumbai | 65 00.00 |
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| 5 | Hardik | 27 | Bhopal | 2125.00 |
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| 6 | Komal | 22 | MP | 4500.00 |
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| 7 | Muffy | 24 | Indore | 10000.00 |
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Note: Ramesh (Age 35) and Hardik (Age 27) are affected as their age is >= 27.
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## Subqueries with DELETE
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Subqueries enable deleting rows based on conditions derived from the subquery's output .
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Assuming a `CUSTOMERS_BKP` table is available. The following example deletes records from the `CUSTOMERS` table for customers whose `AGE` is 27 or greater, as found in the `CUSTOMERS_BKP` table.
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```sql
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DELETE FROM CUSTOMERS
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WHERE AGE IN (
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SELECT AGE
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FROM CUSTOMERS_BKP
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WHERE AGE >= 27
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);
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```
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This query identifies ages from `CUSTOMERS_BKP` that meet the condition. The outer `DELETE` then removes rows from `CUSTOMERS` with those corresponding ages.
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Initial `CUSTOMERS` table:
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| ID | NAME | AGE | ADDRESS | SALARY |
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|-----|----------|-----|-----------|-----------|
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| 1 | Ramesh | 35 | Ahmedabad | 2000.00 |
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| 2 | Khilan | 25 | Delhi | 1500.00 |
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| 3 | Kaushik | 23 | Kota | 2 000.00 |
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| 4 | Chaitali | 25 | Mumbai | 6500.00 |
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| 5 | Hardik | 27 | Bhopal | 8500.00 |
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| 6 | Komal | 22 | MP | 4500.00 |
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| 7 | Muffy | 24 | Indore | 10000.00 |
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After executing the `DELETE`, the `CUSTOMERS` table will contain:
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| ID | NAME | AGE | ADDRESS | SALARY |
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|-----|----------|-----|---------|----------|
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| 2 | Khilan | 25 | Delhi | 1500.00 |
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| 3 | Kaushik | 23 | Kota | 2000.00 |
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| 4 | Chaitali | 25 | Mumbai | 6500.0 0 |
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| 6 | Komal | 22 | MP | 4500.00 |
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| 7 | Muffy | 24 | Indore | 10000.00 |
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Rows with Age 35 (Ramesh) and 27 (Hardik) are removed.
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