![]() ![]() We can demonstrate each of these by doing a COUNT(*) and showing how many rows are in each dataset. Returns matches and all rows from both tables Returns matches and all rows from the right listed table Returns matches and all rows from the left listed table JOIN VisualĭEFAULT: returns only the rows where matches were found These Venn diagrams are a nice way of demonstrating what data is returned in these joins. There are a few different types of JOINs, each which specifies a different way for the database to handle data that doesn’t match the join condition. We can even join all 3 tables together if we’d like using multiple JOIN commands JOIN types Try for yourself to JOIN the tracks and albums tables. We can get the joined data from both tables by running the following query: In the above figure we can see that their relationship is defined by the artist_id in the albums table acting as a foreign key to the id column in the artists table. ![]() Let’s join the artists and albums tables. Note that the order of table1 and table2 and the keys really doesn’t matter. So let’s get to it! To specify how we join two tables we use the following format The world (and data) works better with relationships. It gets messy already even for our small example, and just wouldn’t be realistic for real company implementation. That’s a lot of duplicate data to store, and if a parameter in any of that changes, you’d have to update it in many different rows. If we didn’t have relationships we’d have to keep all the data in one giant table like the one in the figure here.Įach track for example would have to hold all of the information on it’s album and on the artist. The foreign key column could be of any type and link to any column in another table as long as that other column is a Primary Key uniquely identifying a single row. It is very common for foreign key to be named in the format of _id as album_id and artist_id are, but again it’s not required. ![]() We also looked up which albums had an id of 89 and found that the tracks referred to the album “American Idiot”. Earlier in this tutorial we looked up all the tracks with an album_id of 89. It contains a value of the id of the correct artist that produced that album.Īnother example is the album_id in the tracks database. A great example of this is the artist_id column in the albums table. It can be email, username, or any other column as long as it can be counted on to uniquely identify that row of data in the table.įoreign keys are columns in a table that specify a link to a primary key in another table. It is very common for databases to have a column named id (short for identification number) as an enumerated Primary Key for each table. The relationships for the 3 tables we’ve been using so far are visualized here:Ī primary key is a column (or sometimes set of columns) in a table that is a unique identifier for each row. Relationships are defined in each tables by connecting Foreign Keys from one table to a Primary Key in another. PostgreSQL is a Relational Database, which means it stores data in tables that can have relationships (connections) to other tables. There are a couple of key concepts to describe before we start JOINing data: Relationships So far we’ve been working with each table separately, but as you may have guessed by the tables being named tracks, albums, and artists and some of the columns having names like album_id, it is possible to JOIN these tables together to fetch results from both! ![]()
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