INNER JOIN ON vs WHERE clause

Solution 1:

INNER JOIN is ANSI syntax that you should use.

It is generally considered more readable, especially when you join lots of tables.

It can also be easily replaced with an OUTER JOIN whenever a need arises.

The WHERE syntax is more relational model oriented.

A result of two tables JOINed is a cartesian product of the tables to which a filter is applied which selects only those rows with joining columns matching.

It's easier to see this with the WHERE syntax.

As for your example, in MySQL (and in SQL generally) these two queries are synonyms.

Also, note that MySQL also has a STRAIGHT_JOIN clause.

Using this clause, you can control the JOIN order: which table is scanned in the outer loop and which one is in the inner loop.

You cannot control this in MySQL using WHERE syntax.

Solution 2:

Others have pointed out that INNER JOIN helps human readability, and that's a top priority, I agree.
Let me try to explain why the join syntax is more readable.

A basic SELECT query is this:

SELECT stuff
FROM tables
WHERE conditions

The SELECT clause tells us what we're getting back; the FROM clause tells us where we're getting it from, and the WHERE clause tells us which ones we're getting.

JOIN is a statement about the tables, how they are bound together (conceptually, actually, into a single table).

Any query elements that control the tables - where we're getting stuff from - semantically belong to the FROM clause (and of course, that's where JOIN elements go). Putting joining-elements into the WHERE clause conflates the which and the where-from, that's why the JOIN syntax is preferred.

Solution 3:

Applying conditional statements in ON / WHERE

Here I have explained the logical query processing steps.

Reference: Inside Microsoft® SQL Server™ 2005 T-SQL Querying
Publisher: Microsoft Press
Pub Date: March 07, 2006
Print ISBN-10: 0-7356-2313-9
Print ISBN-13: 978-0-7356-2313-2
Pages: 640

Inside Microsoft® SQL Server™ 2005 T-SQL Querying

(8)  SELECT (9) DISTINCT (11) TOP <top_specification> <select_list>
(1)  FROM <left_table>
(3)       <join_type> JOIN <right_table>
(2)       ON <join_condition>
(4)  WHERE <where_condition>
(5)  GROUP BY <group_by_list>
(6)  WITH {CUBE | ROLLUP}
(7)  HAVING <having_condition>
(10) ORDER BY <order_by_list>

The first noticeable aspect of SQL that is different than other programming languages is the order in which the code is processed. In most programming languages, the code is processed in the order in which it is written. In SQL, the first clause that is processed is the FROM clause, while the SELECT clause, which appears first, is processed almost last.

Each step generates a virtual table that is used as the input to the following step. These virtual tables are not available to the caller (client application or outer query). Only the table generated by the final step is returned to the caller. If a certain clause is not specified in a query, the corresponding step is simply skipped.

Brief Description of Logical Query Processing Phases

Don't worry too much if the description of the steps doesn't seem to make much sense for now. These are provided as a reference. Sections that come after the scenario example will cover the steps in much more detail.

1. FROM: A Cartesian product (cross join) is performed between the first two tables in the FROM clause, and as a result, virtual table VT1 is generated.

2. ON: The ON filter is applied to VT1. Only rows for which the <join_condition> is TRUE are inserted to VT2.

3. OUTER (join): If an OUTER JOIN is specified (as opposed to a CROSS JOIN or an INNER JOIN), rows from the preserved table or tables for which a match was not found are added to the rows from VT2 as outer rows, generating VT3. If more than two tables appear in the FROM clause, steps 1 through 3 are applied repeatedly between the result of the last join and the next table in the FROM clause until all tables are processed.

4. WHERE: The WHERE filter is applied to VT3. Only rows for which the <where_condition> is TRUE are inserted to VT4.

5. GROUP BY: The rows from VT4 are arranged in groups based on the column list specified in the GROUP BY clause. VT5 is generated.

6. CUBE | ROLLUP: Supergroups (groups of groups) are added to the rows from VT5, generating VT6.

7. HAVING: The HAVING filter is applied to VT6. Only groups for which the <having_condition> is TRUE are inserted to VT7.

8. SELECT: The SELECT list is processed, generating VT8.

9. DISTINCT: Duplicate rows are removed from VT8. VT9 is generated.

10. ORDER BY: The rows from VT9 are sorted according to the column list specified in the ORDER BY clause. A cursor is generated (VC10).

11. TOP: The specified number or percentage of rows is selected from the beginning of VC10. Table VT11 is generated and returned to the caller.

(Applying conditional statements in ON / WHERE will not make much difference in few cases. This depends on how many tables you have joined and the number of rows available in each join tables)

Solution 4:

The implicit join ANSI syntax is older, less obvious, and not recommended.

In addition, the relational algebra allows interchangeability of the predicates in the WHERE clause and the INNER JOIN, so even INNER JOIN queries with WHERE clauses can have the predicates rearranged by the optimizer.

I recommend you write the queries in the most readable way possible.

Sometimes this includes making the INNER JOIN relatively "incomplete" and putting some of the criteria in the WHERE simply to make the lists of filtering criteria more easily maintainable.

For example, instead of:

SELECT *
FROM Customers c
INNER JOIN CustomerAccounts ca
    ON ca.CustomerID = c.CustomerID
    AND c.State = 'NY'
INNER JOIN Accounts a
    ON ca.AccountID = a.AccountID
    AND a.Status = 1

Write:

SELECT *
FROM Customers c
INNER JOIN CustomerAccounts ca
    ON ca.CustomerID = c.CustomerID
INNER JOIN Accounts a
    ON ca.AccountID = a.AccountID
WHERE c.State = 'NY'
    AND a.Status = 1

But it depends, of course.

Solution 5:

Implicit joins (which is what your first query is known as) become much much more confusing, hard to read, and hard to maintain once you need to start adding more tables to your query. Imagine doing that same query and type of join on four or five different tables ... it's a nightmare.

Using an explicit join (your second example) is much more readable and easy to maintain.