In this case multiple rows are related to multiple rows. In a relational database this can be implemented with the first table having a second_table_id column that says to which row of the second table that row is related. In a many-to-one kind of relationship, one row of the first table can be related to one single row of the second table, and one row of the second table can be related to multiple rows of the first table. In a relational database this can be implemented with the second table having a first_table_id column that says to which row of the first table that row is related. In a one-to-many kind of relationship, one row of the first table can be related to multiple rows of the second table. When you have tables that are related to each other, their relationships could be one of various types. Let's talk a moment about the relationships you can have between tables and why you might want to join three tables together. To do that you add a second INNER JOIN statement and a second ON statement to indicate the third table and the second relationship. ON table2.id = table3.id Generic INNER JOIN statement between three tables It is possible to use multiple join statements together to join more than one table at the same time. In this case the two tables are joined using the relationship table1.id = table2.id. How the two tables should be joined is written in the ON statement. FROM statement indicates which is the first table, then the second table name is written just after the INNER JOIN keywords. ON table1.id = table2.id Generic INNER JOIN statement between two tables Join is a statement that lets you put together two tables, matching rows that are related to each other, and keeping only the rows that can be matched, not keeping unpaired rows. I have already written about SQL joins here and here, but let's take a moment to review how a join works first, and particularly the syntax specific to MySQL. Always use explain to analyze query execution plans and optimize your queries for performance.When you're working with your database, you might need to put together data from a few different tables. By properly indexing the related columns between tables, you can significantly improve the performance of your queries. Indexing is critical when it comes to joins in MySQL. > Single-row index lookup on actor using PRIMARY (id=film_actor.actor_id) (cost=0.08 rows=1) > Index scan on film_actor using PRIMARY (cost=54.93 rows=5462) > Filter: (film.id Index range scan on film using PRIMARY (cost=3.02 rows=10) Next, let's turn the index off (by making it invisible) and run the same query again: ALTER TABLE film_actor ALTER INDEX idx_film_id INVISIBLE įilm.id Nested loop left join (cost=12347.65 rows=54620) > Single-row index lookup on actor using PRIMARY (id=film_actor.actor_id) (cost=0.84 rows=1) > Covering index lookup on film_actor using idx_fk_film_id (film_id=film.id) (cost=1.06 rows=5) > Filter: (film.id Index range scan on film using PRIMARY (cost=2.82 rows=10) > Nested loop left join (cost=10.25 rows=55) LEFT JOIN actor ON actor.id = film_actor.actor_idįilm.id Nested loop left join (cost=29.42 rows=55) LEFT JOIN film_actor ON film_actor.film_id = film.id Here's the query we'll use: SELECTįilm.title, actor.first_name, actor.last_name We'll use the explain statement to see the query execution plan and the cost of each query.Let's say we want to get a list of the first 10 films and all the actors that were in those movies. To illustrate the impact of indexing, let's run some queries with and without indexes. This table has a composite primary key made up of actor_id and film_id. To get a list of all the movies and the actors that were in those movies, we need to use the film_actor table as the joining table. We have a film table full of movies and an actor table full of actors. To link these two entities, you need a joining table with composite primary keys. For example, in a film database, an actor can be in many movies and a movie can have many actors.

Sometimes you'll come across a many-to-many relationship between tables. The better way is to use an index on the related columns, which allows MySQL to quickly retrieve the matching rows and combine them. One way to do this is by doing a full table scan, which is slow and inefficient. When MySQL joins tables together, it needs to figure out which rows from one table match which rows from the other table. In this post, we're going to take a deeper dive into indexing joins and how it can affect the performance of your queries. In our previous video, we talked about joins and how they work in MySQL. The importance of indexing joins in MySQL

Mysql join across databases