Say No to Venn Diagrams When Explaining JOINs
It's easy to use Venn diagrams to explain JOINs, but when you think about it, they're not quite the right tool to use. That's where JOIN diagrams come in.
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In recent times, there have been a couple of tremendously popular blog posts explaining JOINs using Venn diagrams. After all, relational algebra and SQL are set oriented theories and languages, so it only makes sense to illustrate set operations like JOINs using Venn Diagrams. Right?
Google seems to say so:
Everyone uses Venn Diagrams to explain JOINs. But that's…
Venn Diagrams are perfect to illustrate … actual set operations! SQL knows three of them:
And they can be explained as such:
Most of you use
EXCEPT are more exotic, but do come in handy every now and then.
The point here is: These set operations operate on sets of elements (tuples), which are all of the same type. As in the examples above, all elements are people with first and last names. This is also why
EXCEPT are more exotic, because they're usually not very useful.
JOIN is much more useful. For instance, you want to combine the set of actors with their corresponding set of films.
JOIN is really a Ccartesian product (also cross product) with a filter. Here’s a nice illustration of a cartesian product:
So, What’s a Better Way to Illustrate JOIN Operations?
JOIN diagrams! Let's look at
CROSS JOIN first, because all other
JOIN types can be derived from
Remember, a cross join (in SQL also written with a comma separated table list, historically) is just taking every item on the left side and combining it with every item on the right side. When you
CROSS JOIN a table of three rows with a table of four rows, you will get 3×4=12 result rows. See, I'm using an “x” character to write the multiplication. I.e. a “cross”.
All other joins are still based on cross joins, but with additional filters, and perhaps unions. Here's an explanation of each individual
In plain text, an
INNER JOIN is a
CROSS JOIN in which only those combinations are retained which fulfil a given predicate. For instance:
-- "Classic" ANSI JOIN syntax SELECT * FROM author a JOIN book b ON a.author_id = b.author_id -- "Nice" ANSI JOIN syntax SELECT * FROM author a JOIN book b USING (author_id) -- "Old" syntax using a "CROSS JOIN" SELECT * FROM author a, book b WHERE a.author_id = b.author_id
Remember though, that the
INNER JOIN is still a Cartesian product, i.e. it retains all the "duplicate" rows where the predicate evaluates to true. In the visual example, we can see that there are two resulting blue rows because the right side had two blue rows, and the left blue row is repeated. We get a cross product of 1×2=2 rows.
OUTER JOIN types help where we want to retain those rows from either the
LEFT side or the
RIGHT or both (
FULL) sides, for which there was no matching row where the predicate yielded true.
LEFT OUTER JOIN in relational algebra is defined as such:
Or more verbosely in SQL:
SELECT * FROM author a LEFT JOIN book b USING (author_id)
This will produce all the authors and their books, but if an author doesn't have any books, we still want to get the author with NULL as their only book value. So, it's the same as writing:
SELECT * FROM author a JOIN book b USING (author_id) UNION SELECT a.*, NULL, NULL, NULL, ..., NULL FROM ( SELECT a.* FROM author a EXCEPT SELECT a.* FROM author a JOIN book b USING (author_id) ) a
But no one wants to write that much SQL, so
OUTER JOIN was implemented.
Conclusion: Say NO to Venn Diagrams
JOINs are relatively easy to understand intuitively. And they're relatively easy to explain using Venn diagrams. But whenever you do that, remember, that you’re making a wrong analogy. A
JOIN is not strictly a set operation that can be described with Venn diagrams. A
JOIN is always a cross product with a predicate, and possibly a
UNION to add additional rows to the
OUTER JOIN result.
So, if in doubt, please use
JOIN diagrams rather than Venn Diagrams. They’re more accurate and visually more useful.
Published at DZone with permission of Lukas Eder. See the original article here.
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