The Explicit Join Advantage: Why It's the SQL Champion for Readable and Maintainable Code

Here's an example of an explicit join:

SELECT student.name, course.title
FROM student
INNER JOIN course ON student.course_id = course.id;

This query selects student names and course titles from two tables: student and course. The INNER JOIN ensures that only rows where the course_id in the student table matches the id in the course table are included.

Implicit Joins, on the other hand, are less clear. They rely on the order of tables listed in the FROM clause and the WHERE clause to define the join. This can be confusing and error-prone, especially for complex queries.

Here's an example of an implicit join (which actually performs a cross join):

SELECT student.name, course.title
FROM student, course
WHERE student.course_id = course.id;

This query might seem similar to the explicit join, but it's actually different. In implicit joins, the comma between tables indicates a cartesian product (all possible combinations of rows from both tables) and the WHERE clause filters the results afterward. This can be much less efficient than an explicit join.

Here's a summary of the key differences:

• Readability: Explicit joins are easier to read and understand.
• Maintainability: Explicit joins are easier to maintain and modify.
• Performance: In some cases, explicit joins can be more performant than implicit joins.
• Standard: Explicit joins are the recommended standard by SQL standards organizations.

• Explicit:

SELECT order.id, customer.name, product.name AS product_name
FROM orders order
INNER JOIN customers customer ON order.customer_id = customer.id
INNER JOIN products product ON order.product_id = product.id;

• Implicit (not recommended):

SELECT order.id, customer.name, product.name AS product_name
FROM orders order, customers customer, products product
WHERE order.customer_id = customer.id AND order.product_id = product.id;

Left Join:

SELECT order.id, customer.name, product.name AS product_name
FROM orders order
LEFT JOIN customers customer ON order.customer_id = customer.id
LEFT JOIN products product ON order.product_id = product.id;

This will include all orders, even those with a missing customer or product ID (resulting in NULL values for those columns).

SELECT order.id, customer.name, product.name AS product_name
FROM orders order, customers customer, products product
WHERE order.customer_id = customer.id OR order.product_id = product.id;

This approach might not be what you want as it would include rows where only one of the joins has a match.

Right Join:

SELECT order.id, customer.name, product.name AS product_name
FROM orders order
RIGHT JOIN customers customer ON order.customer_id = customer.id
RIGHT JOIN products product ON order.product_id = product.id;

This will include all products, even those with no matching order (resulting in NULL values for order details).

• Implicit (not recommended, construction varies depending on database):

This type of join is typically not written using an implicit approach in most databases. There might be workarounds involving subqueries, but explicit joins are preferred for clarity.

Subqueries:

Subqueries are essentially nested queries that can be used to retrieve data from one table and use it as a filter or condition in the main query. This can be an option for complex joins, but subqueries can make the code harder to read and understand.

UNION/UNION ALL:

These operators combine the results of two or more SELECT statements. However, they can only be used when the columns you're selecting from each table are compatible (same data type and order).

Materialized Views (MVs):

MVs are pre-computed snapshots of a query result. While not exactly an alternative to joins on the fly, they can improve performance for frequently used complex joins. However, MVs require maintenance to keep them synchronized with the underlying tables.

Denormalization:

This involves strategically adding redundant data to a table to avoid joins altogether. This can improve performance for simple queries but can lead to data inconsistency issues if not managed carefully.

Here's a general guideline for choosing between joins and alternatives:

• Use joins for most cases: They are the standard way to combine data relationally and are generally well-optimized by database engines.
• Consider subqueries for complex filtering based on data from another table.
• Use UNION/UNION ALL for combining result sets with compatible structures.
• Explore MVs for performance optimization of frequently used complex joins, but be aware of maintenance overhead.
• Denormalization should be a last resort due to potential data inconsistency issues.