Welcome to SQL 4: Aggregate FunctionsBecome more intimate with your data: use aggregate functions to explore the traits which make your data unique and beautiful.

Todd BirchardBlockedUnblockFollowFollowingMar 13Aggregate functions in SQL are super dope.

When combining these functions with clauses such as GROUP BY and HAVING, we discover ways to view our data from completely new perspectives.

Instead of looking at the same old endless flat table, we can use these functions to give us entirely new insights; aggregate functions help us to understand bigger-picture things.

Those things might include finding outliers in datasets, or simply figuring out which employee with a family to feed should be terminated, based on some arbitrary metric such as sales numbers.

With the basics of JOINs under our belts, this is when SQL starts feel really, really powerful.

Our plain two-dimensional tables suddenly gain this power to be combined, aggregated, folded on to themselves, expand infinitely outward as the universe itself, and even transcend into the fourth dimension.

*Our Base Aggregation FunctionsFirst up, let’s see what we mean by “aggregate functions” anyway.

These simple functions provide us with a way to mathematically quantify what exactly is in our database.

Aggregate functions are performed on table columns to give us the make-up of said column.

On their own, they seem quite simple:AVG: The average of a set of values in a column.

COUNT: Number of rows a column contains in a specified table or view.

MIN: The minimum value in a set of values.

MAX: The maximum value in a set of values.

SUM: The sum of values.

DISTINCT AggregationsA particularly useful way of using aggregate functions on their own is when we’d like to know the number of DISTINCT values.

While aggregate values take all records into account, using DISTINCT limits the data returned to specifically refer to unique values.

COUNT(column_name) will return the number of all records in a column, where COUNT(DISTINCT column_name) will ignore counting records where the value in the counted column is repeated.

Using GROUP BYThe GROUP BY statement is often used with aggregate functions (COUNT, MAX, MIN, SUM, AVG) to group the result-set by one or more columns.

To demonstrate how aggregate functions work moving forward, I’ll be using a familiar database: the database which contains all the content for this very blog.

Let’s look at how we can use aggregate functions to find which authors have been posting most frequently:SELECT COUNT(title), author_id FROM posts GROUP BY author_id;And the result:Oh look, a real-life data problem to solve!.It seems like authors are represented in Ghost’s posts table simply by their IDs.

This isn’t very useful.

Luckily, we’ve already learned enough about JOINs to know we can fill in the missing information from the users table!SELECT COUNT(title), author_idFROM postsGROUP BY author_id;Let’s see the results this time around:Now that’s more like it!.Matt is crushing the game with his Lynx Roundup series, with myself in second place.

Max had respectable numbers for a moment but has presumably moved on to other hobbies, such as living his life.

For the remainder, well, I’ve got nothing to say other than we’re hiring.

We don’t pay though.

In fact, there’s probably zero benefits to joining us.

Conditional Grouping With “HAVING”HAVING is like the WHERE of aggregations.

We can't use WHERE on aggregate values, so that's why HAVING exists.

HAVING can't accept any conditional value, but instead, it must accept a numerical conditional derived from a GROUP BY.

Perhaps this would be easier to visualize in a query:SELECT COUNT(posts.

title), users.

nameFROM postsLEFT JOIN usersON (posts.

author_id = users.

id)GROUP BY users.

idORDER BY COUNT(posts.

title) DESC;In this scenario, we want to see which tags on our blog have the highest number of associated posts.

The query is very similar to the one we made previously, only this time we have a special guest:HAVING COUNT(DISTINCT posts_tags.

post_id) > 10This usage of HAVING only gives us tags which have ten posts or more.

This should clean up our report by letting Darwinism takes its course.

Here's how it worked out:As expected, Matt’s roundup posts take the lead (and if we compare this to previous data, we can see Matt has made a total of 17 non-Lynx posts: meaning Max and Matt are officially TIED).

If we hadn’t included our HAVING statement, this list would be much longer, filled with tags nobody cares about.

Thanks to explicit omission, now we don't need to experience the dark depression that comes when confronting those sad pathetic tags.

Out of sight, out of mind.

Get CreativeAggregate functions aren’t just about counting values.

Especially in Data Science, these functions are critical to drawing any statistical conclusions from data.

That said, attention spans only last so long, and I’m not a scientist.

Perhaps that can be your job.

Originally published at hackersandslackers.

com on March 14, 2019.

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