The SQL language is very intuitive. Until it isn’t.

Over the years, a lot of people have criticised the SQL language for a variety of reasons. For instance: IDEs cannot easily guess what auto completion options to offer, because as long as you don’t specify the FROM clause, there are no tables in scope (yet):

-- Don't you wish this would be completed to first_name?
SELECT first_na...

-- Aaah, now it works:
SELECT first_na...
FROM customer

These things are weird, because the lexical order of operations does not match the logical order of operations. We humans may sometimes (often) intuitively understand this ordering difference. E.g. we know that we’re about to select from the customer table. But the IDE doesn’t know this.

GROUP BY contributes the most confusion

When a junior developer / SQL beginner starts working with SQL, quite quickly, they will find out about aggregation and GROUP BY. And they’ll quickly write things like:

SELECT count(*)
FROM customer

Yay, we have 200 customers!

And then:

SELECT count(*)
FROM customer
WHERE first_name = 'Steve'

Wow, 90 of them are called Steve! Interesting. Let’s find out how many we have per name…

SELECT first_name, count(*)
FROM customer
GROUP BY first_name

Ahaa!

FIRST_NAME   COUNT
------------------
Steve        90
Jane         80
Joe          20
Janet        10

Very nice. But are they all the same? Let’s check out the last name, too

SELECT first_name, last_name, count(*)
FROM customer
GROUP BY first_name

Oops!

ORA-00979: not a GROUP BY expression

Jeez, what does it mean? (note, unfortunately, MySQL users that do not use the STRICT mode will still get a result here with arbitrary last names!, so a new MySQL user won’t understand their mistake)

How do you easily explain this to a SQL newbie? It seems obvious to “pros”, but is it really obvious? Is it obvious enough that you can explain it easily to a junior? Think about it. Why are each one of these statements semantically correct or wrong?

-- Wrong
SELECT first_name, count(*)
FROM customer
WHERE count(*) > 1
GROUP BY first_name

-- Correct
SELECT first_name, count(*)
FROM customer
GROUP BY first_name
HAVING count(*) > 1

-- Correct
SELECT first_name, count(*)
FROM customer
GROUP BY first_name
ORDER BY count(*) DESC

-- Wrong
SELECT first_name, last_name, count(*)
FROM customer
GROUP BY first_name

-- Correct
SELECT first_name, MAX(last_name), count(*)
FROM customer
GROUP BY first_name

-- Wrong
SELECT first_name || ' ' || last_name, count(*)
FROM customer
GROUP BY first_name

-- Correct
SELECT first_name || ' ' || MAX(last_name), count(*)
FROM customer
GROUP BY first_name

-- Correct
SELECT MAX(first_name || ' ' || last_name), count(*)
FROM customer
GROUP BY first_name

The problem is syntax related

The SQL syntax works in a similar way like the English language. It is a command. We start commands with verbs. The verb is SELECT (or INSERT, UPDATE, DELETE, CREATE, DROP, etc. etc.)

Unfortunately, human language is incredibly ill-suited for the much more formal world of programming. While it offers some consolation to new users (possibly non-programmers) who are absolute beginners, it just makes stuff hard for everyone else. All the different SQL clauses have extremely complex interdependencies. For instance:

• In the presence of a GROUP BY clause, only expressions built from GROUP BY expressions (or functional dependencies thereof), or aggregate functions can be used in HAVING, SELECT, and ORDER BY clauses.
• For simplicity reasons, let’s not even talk about GROUPING SETS
• In fact, there are even a few cases in which GROUP BY is implied. E.g. if you write a “naked” HAVING clause
• A single aggregate function in the SELECT clause (in the absence of GROUP BY) will force aggregation into a single row
• In fact, this can also be implied by putting that aggregate function in ORDER BY (for whatever reason)
• You can ORDER BY quite a few expressions that reference any columns from the FROM clause without SELECTing them. But that’s no longer true if you write SELECT DISTINCT
• …

The list is endless. If you’re interested, you can read the SQL standard documents and check out how many weird and complicated inter-dependencies there exist between the many clauses of the SELECT statement.

Can this ever be understood?

Luckily, yes! There’s a simple trick, which I’m always explaining to the delegates that visit my SQL Masterclass. The lexical (syntactical) order of SQL operations (clauses) does not correspond at all to the logical order of operations (although, sometimes, they do coincidentally). Thanks to modern optimisers, the order also doesn’t correspond to the actual order of operations, so we really have: syntactical -> logical -> actual order, but let’s leave that aside for now.

The logical order of operations is the following (for “simplicity” I’m leaving out vendor specific things like CONNECT BY, MODEL, MATCH_RECOGNIZE, PIVOT, UNPIVOT and all the others):

• FROM: This is actually the first thing that happens, logically. Before anything else, we’re loading all the rows from all the tables and join them. Before you scream and get mad: Again, this is what happens first logically, not actually. The optimiser will very probably not do this operation first, that would be silly, but access some index based on the WHERE clause. But again, logically, this happens first. Also: all the JOIN clauses are actually part of this FROM clause. JOIN is an operator in relational algebra. Just like + and - are operators in arithmetics. It is not an independent clause, like SELECT or FROM
• WHERE: Once we have loaded all the rows from the tables above, we can now throw them away again using WHERE
• GROUP BY: If you want, you can take the rows that remain after WHERE and put them in groups or buckets, where each group contains the same value for the GROUP BY expression (and all the other rows are put in a list for that group). In Java, you would get something like: Map<String, List<Row>>. If you do specify a GROUP BY clause, then your actual rows contain only the group columns, no longer the remaining columns, which are now in that list. Those columns in the list are only visible to aggregate functions that can operate upon that list. See below.
• aggregations: This is important to understand. No matter where you put your aggregate function syntactically (i.e. in the SELECT clause, or in the ORDER BY clause), this here is the step where aggregate functions are calculated. Right after GROUP BY. (remember: logically. Clever databases may have calculated them before, actually). This explains why you cannot put an aggregate function in the WHERE clause, because its value cannot be accessed yet. The WHERE clause logically happens before the aggregation step. Aggregate functions can access columns that you have put in “this list” for each group, above. After aggregation, “this list” will disappear and no longer be available. If you don’t have a GROUP BY clause, there will just be one big group without any key, containing all the rows.
• HAVING: … but now you can access aggregation function values. For instance, you can check that count(*) > 1 in the HAVING clause. Because HAVING is after GROUP BY (or implies GROUP BY), we can no longer access columns or expressions that were not GROUP BY columns.
• WINDOW: If you’re using the awesome window function feature, this is the step where they’re all calculated. Only now. And the cool thing is, because we have already calculated (logically!) all the aggregate functions, we can nest aggregate functions in window functions. It’s thus perfectly fine to write things like sum(count(*)) OVER () or row_number() OVER (ORDER BY count(*)). Window functions being logically calculated only now also explains why you can put them only in the SELECT or ORDER BY clauses. They’re not available to the WHERE clause, which happened before. Note that PostgreSQL and Sybase SQL Anywhere have an actual WINDOW clause!
• SELECT: Finally. We can now use all the rows that are produced from the above clauses and create new rows / tuples from them using SELECT. We can access all the window functions that we’ve calculated, all the aggregate functions that we’ve calculated, all the grouping columns that we’ve specified, or if we didn’t group/aggregate, we can use all the columns from our FROM clause. Remember: Even if it looks like we’re aggregating stuff inside of SELECT, this has happened long ago, and the sweet sweet count(*) function is nothing more than a reference to the result.
• DISTINCT: Yes! DISTINCT happens after SELECT, even if it is put before your SELECT column list, syntax-wise. But think about it. It makes perfect sense. How else can we remove distinct rows, if we don’t know all the rows (and their columns) yet?
• UNION, INTERSECT, EXCEPT: This is a no-brainer. A UNION is an operator that connects two subqueries. Everything we’ve talked about thus far was a subquery. The output of a union is a new query containing the same row types (i.e. same columns) as the first subquery. Usually. Because in wacko Oracle, the penultimate subquery is the right one to define the column name. Oracle database, the syntactic troll 😉
• ORDER BY: It makes total sense to postpone the decision of ordering a result until the end, because all other operations might use hashmaps, internally, so any intermediate order might be lost again. So we can now order the result. Normally, you can access a lot of rows from the ORDER BY clause, including rows (or expressions) that you did not SELECT. But when you specified DISTINCT, before, you can no longer order by rows / expressions that were not selected. Why? Because the ordering would be quite undefined.
• OFFSET: Don’t use offset
• LIMIT, FETCH, TOP: Now, sane databases put the LIMIT (MySQL, PostgreSQL) or FETCH (DB2, Oracle 12c, SQL Server 2012) clause at the very end, syntactically. In the old days, Sybase and SQL Server thought it would be a good idea to have TOP as a keyword in SELECT. As if the correct ordering of SELECT DISTINCT wasn’t already confusing enough.

There, we have it. It makes total sense. And if you ever want to do something that is not in the “right order”, the simplest trick is always to resort to a derived table. E.g. when you want to group on a window function:

-- Doesn't work, cannot put window functions in GROUP BY
SELECT ntile(4) ORDER BY (age) AS bucket, MIN(age), MAX(age)
FROM customer
GROUP BY ntile(4) ORDER BY (age)

-- Works:
SELECT bucket, MIN(age), MAX(age)
FROM (
  SELECT age, ntile(4) ORDER BY (age) AS bucket
  FROM customer
) c
GROUP BY bucket

Why does it work? Because:

• In the derived table, FROM happens first, and then the WINDOW is calculated, then the bucket is SELECTed.
• The outer SELECT can now treat the result of this window function calculation like any ordinary table in the FROM clause, then GROUP BY an ordinary column, then aggregate, then SELECT

Let’s review our original examples with an explanation why they work or why they don’t.

-- Wrong: Because aggregate functions are calculated
-- *after* GROUP BY, and WHERE is applied *before* GROUP BY
SELECT first_name, count(*)
FROM customer
WHERE count(*) > 1
GROUP BY first_name

-- logical order         -- available columns after operation
-------------------------------------------------------------
FROM customer            -- customer.*
WHERE ??? > 1            -- customer.* (count not yet available!)
GROUP BY first_name      -- first_name (customer.* for aggs only)
<aggregate> count(*)     -- first_name, count
SELECT first_name, count -- first_name, count



-- Correct: Because aggregate functions are calculated
-- *after* GROUP BY but *before* HAVING, so they're 
-- available to the HAVING clause.
SELECT first_name, count(*)
FROM customer
GROUP BY first_name
HAVING count(*) > 1

-- logical order         -- available columns after operation
-------------------------------------------------------------
FROM customer            -- customer.*
GROUP BY first_name      -- first_name (customer.* for aggs only)
<aggregate> count(*)     -- first_name, count
HAVING count > 1         -- first_name, count
SELECT first_name, count -- first_name, count



-- Correct: Both SELECT and ORDER BY are applied *after*
-- the aggregation step, so aggregate function results are 
-- available
SELECT first_name, count(*)
FROM customer
GROUP BY first_name
ORDER BY count(*) DESC

-- logical order         -- available columns after operation
-------------------------------------------------------------
FROM customer            -- customer.*
GROUP BY first_name      -- first_name (customer.* for aggs only)
<aggregate> count(*)     -- first_name, count
SELECT first_name, count -- first_name, count
ORDER BY count DESC      -- first_name, count



-- Wrong: Because the GROUP BY clause creates groups of
-- first names, and all the remaining customer columns
-- are aggregated into a list, which is only visiblbe to
-- aggregate functions
SELECT first_name, last_name, count(*)
FROM customer
GROUP BY first_name

-- logical order         -- available columns after operation
-----------------------------------------------------------------
FROM customer            -- customer.*
GROUP BY first_name      -- first_name (customer.* for aggs only)
<aggregate> count(*)     -- first_name, count
                         -- first_name, count (last_name removed)
SELECT first_name, ???, count 



-- Correct: Because now, we're using an aggregate function
-- to access one of the columns that have been put into that
-- list of columns that are otherwise no longer available
-- after the GROUP BY clause
SELECT first_name, MAX(last_name), count(*)
FROM customer
GROUP BY first_name

-- logical order         -- available columns after operation
-----------------------------------------------------------------
FROM customer            -- customer.*
GROUP BY first_name      -- first_name (customer.* for aggs only)
                         -- first_name, max, count
<aggregate> MAX(last_name), count(*) 
                         -- first_name, max, count
SELECT first_name, max, count



-- Wrong: Because we still cannot access the last name column
-- which is in that list after the GROUP BY clause.
SELECT first_name || ' ' || last_name, count(*)
FROM customer
GROUP BY first_name

-- logical order         -- available columns after operation
-----------------------------------------------------------------
FROM customer            -- customer.*
GROUP BY first_name      -- first_name (customer.* for aggs only)
<aggregate> count(*)     -- first_name, count
                         -- first_name, count (last_name removed)
SELECT first_name || ' ' || ???, count 




-- Correct: Because we can access the last name column from
-- aggregate functions, which can see that list
SELECT first_name || ' ' || MAX(last_name), count(*)
FROM customer
GROUP BY first_name

-- logical order         -- available columns after operation
-----------------------------------------------------------------
FROM customer            -- customer.*
GROUP BY first_name      -- first_name (customer.* for aggs only)
                         -- first_name, max, count
<aggregate> MAX(last_name), count(*)  
                         -- first_name, max, count (no last_name)
SELECT first_name || ' ' || max, count




-- Correct: Because both GROUP BY columns and aggregated
-- columns are available to aggregate functions
SELECT MAX(first_name || ' ' || last_name), count(*)
FROM customer
GROUP BY first_name

-- logical order         -- available columns after operation
-----------------------------------------------------------------
FROM customer            -- customer.*
GROUP BY first_name      -- first_name (customer.* for aggs only)
                         -- first_name, max, count
<aggregate> MAX(first_name || ' ' || last_name), count(*)
SELECT max, count        -- first_name, max, count

Always think about the logical order of operations

If you’re not a frequent SQL writer, the syntax can indeed be confusing. Especially GROUP BY and aggregations “infect” the rest of the entire SELECT clause, and things get really weird. When confronted with this weirdness, we have two options:

• Get mad and scream at the SQL language designers
• Accept our fate, close our eyes, forget about the snytax and remember the logical operations order

I generally recommend the latter, because then things start making a lot more sense, including the beautiful cumulative daily revenue calculation below, which nests the daily revenue (SUM(amount) aggregate function) inside of the cumulative revenue (SUM(...) OVER (...) window function):

SELECT
  payment_date,
  SUM(SUM(amount)) OVER (ORDER BY payment_date) AS revenue
FROM payment
GROUP BY payment_date

… because aggregations logically happen before window functions.

Want to learn more? We also have these articles for you to read:

• SQL GROUP BY and Functional Dependencies: A Very Useful Feature
• How SQL GROUP BY Should Have Been Designed – Like Neo4j’s Implicit GROUP BY
• How to Translate SQL GROUP BY and Aggregations to Java 8
• Do You Really Understand SQL’s GROUP BY and HAVING clauses?
• GROUP BY ROLLUP / CUBE