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references this people table, and we'll need another column that
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is a foreign key that references the flights table, such
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that I can relate those two tables together.
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So that table could look like this.
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This now is a simplified passengers table that only has two columns.
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It has a person id column and a flight id column.
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The idea of this table now is it's known as an association
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table, or a joined table that just associates one value from one table
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with another value from another table.
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This row here, one and one, means the person with an id of one
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is on flight number one.
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I could look up that person inside of the people table,
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look up that flight inside of the flights table,
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and figure out who the person is and what flight they're on.
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Down here, two and four, means whoever the person with an ID of 2
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is on whichever flight happens to have an ID of 4.
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So this now has allowed us to be able to represent
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the types of relationships we want.
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We have a table for airports and a table for flights and any flight
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is going to map to two different airports, one destination one origin.
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And any airport might appear on multiple different flights.
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It's sort of a one to many relationship.
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Then over here, when it comes to passengers,
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we've stored people inside of a separate table,
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and then had a many to many mapping between people and flights
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so that any person could be on multiple different flights.
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Like here, for example, person number two is on both flights one and four.
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Likewise, a flight could have multiple people.
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So in this case flight number six has passengers five and six
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that are on that flight as well.
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We've been able to represent those relationships.
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Of course a byproduct of doing this is that now our tables are a little bit
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messier to look at.
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Messy in the sense that it's not immediately obvious to me,
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when I look at this table, what data I'm looking at.
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I see these numbers, but I don't know what these numbers mean.
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I've separated all these tables into different places.
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Now it's a little harder for me to figure out who is on which flight.
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I have to look at this data, look up people in the people table,
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look up flights in the flights table, and somehow
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associate all of that information back together in order
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to draw any sort of conclusion.
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But luckily, SQL makes it pretty easy for us
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to be able to take data across multiple different tables
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and join them all back together.
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We can do this using a JOIN query that takes multiple tables
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and joins them together.
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So the syntax for a JOIN query might look something like this.
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And here we'll go back to just the two-table setup where
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I have flights and passengers, where every passenger is
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associated with one flight.
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But you could extend this and join multiple tables
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to deal with our more complex example as well.
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But here, I'd like to select every person's first name
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and their origin and their destination.
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I'm going to select that from the flights table,
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but I need to join it with the passengers table.
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Then I say ON to indicate how it is these two
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tables are related to one another.
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In this case, I'm saying the way these two tables are
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related to one another is that the flight id column of the passengers
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table is associated with the id column of the flights table.
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The flights table has an id that uniquely identifies every flight,
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and the passengers table has a flight id column
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that uniquely identifies the flight that we're referring to
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for this particular passenger.
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And so the result I might get is a table that looks like this.
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That gives me everyone's first name, but also
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their origin and their destination.
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Our origin and destination are going to be drawn from that table of flights
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and the first name is going to be drawn from the table of passengers.
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But by using a JOIN query, I've been able to take data
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from two separate tables and join them both back together.
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And there are a number of different types of JOIN queries that I can run.
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What we saw here was just the default JOIN, which
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is otherwise known as an INNER JOIN.
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Effectively, an INNER JOIN will take the two tables,
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it will cross compare them based on the condition that I specified
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and only return back to me the results where there's a match on both sides.
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Where we match a passenger's flight id with an id in the flights table.
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There are various different kinds of outer
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joins if I want to be OK with the idea that maybe
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something on the left table that I'm joining
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doesn't match with anything on the right,
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or maybe something on the right table doesn't match with something
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on the left.
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But just know there are other types of JOIN queries that I can run as well.
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Other strategies that can be helpful when dealing with SQL tables
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are optimizations we can make to make queries more efficient.
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One thing we can do with our tables is to create an index
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on a particular table.
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You can think of an index as kind of like the index in the back of a book,
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for example, where if you wanted to be able to search for a topic in a text
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book, you could open the textbook and just page by page look for every topic
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and just try and find the topic you're looking for.
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But often what you'll be able to do if the table has
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an index is go to the index of the book, find the topic you're looking for,
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and that will quickly give you a reference for how
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to get to the right page in question.
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An index on a table operates in much the same way.
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