SQL joins are used to combine data from two or more tables based on a related column between them. There are several types of joins, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN. In this tutorial, we will focus on the INNER JOIN and its relationship with the WHERE clause.
What is an INNER JOIN?
An INNER JOIN returns records that have matching values in both tables. It combines rows from two or more tables where the join condition is met. The result is a new table that contains columns from both tables.
Using the WHERE Clause for Joins
Before the introduction of the ANSI SQL standard, joins were performed using the WHERE clause. This method is still supported by most databases, including MySQL. However, it is not recommended as it can lead to confusing and hard-to-maintain queries.
Here is an example of a join using the WHERE clause:
SELECT table1.this, table2.that, table2.somethingelse
FROM table1, table2
WHERE table1.foreignkey = table2.primarykey
AND (some other conditions)
Using the INNER JOIN Clause
The INNER JOIN clause is a more readable and maintainable way to perform joins. It clearly specifies the join condition and makes it easier to add or remove tables from the query.
Here is an example of the same join using the INNER JOIN clause:
SELECT table1.this, table2.that, table2.somethingelse
FROM table1
INNER JOIN table2
ON table1.foreignkey = table2.primarykey
WHERE (some other conditions)
Logical Query Processing Phases
To understand how SQL queries are processed, it’s essential to know the logical query processing phases. These phases include:
- FROM: A Cartesian product (cross join) is performed between the first two tables in the FROM clause.
- ON: The ON filter is applied to the result of the FROM phase.
- OUTER (join): If an OUTER JOIN is specified, rows from the preserved table or tables for which a match was not found are added to the result.
- WHERE: The WHERE filter is applied to the result of the previous phases.
- GROUP BY: The rows from the previous phases are arranged in groups based on the column list specified in the GROUP BY clause.
- HAVING: The HAVING filter is applied to the groups generated by the GROUP BY phase.
- SELECT: The SELECT list is processed, generating a new table.
- DISTINCT: Duplicate rows are removed from the result of the SELECT phase.
- ORDER BY: The rows from the previous phases are sorted according to the column list specified in the ORDER BY clause.
- TOP: The specified number or percentage of rows is selected from the beginning of the sorted result.
Applying Conditional Statements in ON / WHERE
When deciding where to apply conditional statements, it’s essential to consider the order of operations and the performance implications. In general, it’s recommended to apply conditions that filter out a large number of rows as early as possible in the query.
Applying conditions in the ON clause can improve performance by reducing the number of rows that need to be joined. However, the optimizer may rearrange the predicates in the WHERE clause and the INNER JOIN, so the actual execution plan may vary.
Best Practices
- Use explicit joins (INNER JOIN, LEFT JOIN, etc.) instead of implicit joins using the WHERE clause.
- Apply conditions that filter out a large number of rows as early as possible in the query.
- Consider performance implications when deciding where to apply conditional statements.
- Write queries in a readable and maintainable way, even if it means making some compromises on performance.
Example Use Cases
Here is an example of a query that uses both the INNER JOIN and WHERE clauses:
SELECT *
FROM Customers c
INNER JOIN CustomerAccounts ca
ON ca.CustomerID = c.CustomerID
WHERE c.State = 'NY'
This query joins the Customers table with the CustomerAccounts table on the CustomerID column and filters out customers who are not from New York.
In conclusion, understanding SQL joins and the relationship between the INNER JOIN and WHERE clauses is essential for writing efficient and readable queries. By following best practices and considering performance implications, you can write high-quality queries that meet your needs.