## Do You Really Understand SQL’s GROUP BY and HAVING clauses?

There are some things in SQL that we simply take for granted without thinking about them properly.

One of these things are the `GROUP BY` and the less popular `HAVING` clauses. Let’s look at a simple example. For this example, we’ll reiterate the example database we’ve seen in this previous article about the awesome LEAD(), LAG(), FIRST_VALUE(), LAST_VALUE() functions:

```CREATE TABLE countries (
code CHAR(2) NOT NULL,
year INT NOT NULL,
gdp_per_capita DECIMAL(10, 2) NOT NULL,
govt_debt DECIMAL(10, 2) NOT NULL
);
```

Before there were window functions, aggregations were made only with `GROUP BY`. A typical question that we could ask our database using SQL is:

What are the top 3 average government debts in percent of the GDP for those countries whose GDP per capita was over 40’000 dollars in every year in the last four years

In SQL (PostgreSQL dialect), we would write:

```select code, avg(govt_debt)
from countries
where year > 2010
group by code
having min(gdp_per_capita) >= 40000
order by 2 desc
limit 3
```

```-- The average government debt
select code, avg(govt_debt)

-- for those countries
from countries

-- in the last four years
where year > 2010

-- yepp, for the countries
group by code

-- whose GDP p.c. was over 40'000 in every year
having min(gdp_per_capita) >= 40000

-- The top 3
order by 2 desc
limit 3
```

The result being:

```code     avg
------------
JP    193.00
US     91.95
DE     56.00
```

Remember the 10 easy steps to a complete understanding of SQL:

1. `FROM` generates the data set
2. `WHERE` filters the generated data set
3. `GROUP BY` aggregates the filtered data set
4. `HAVING` filters the aggregated data set
5. `SELECT` transforms the filters aggregated data set
6. `ORDER BY` sorts the transformed data set
7. `LIMIT .. OFFSET` frames the sorted data set

## The empty GROUP BY clause

A very special case of `GROUP BY` is the explicit or implicit empty `GROUP BY` clause. Here’s a question that we could ask our database:

Are there any countries at all with a GDP per capita of more than 50’000 dollars?

And in SQL, we’d write:

```select true answer
from countries
having max(gdp_per_capita) >= 50000
```

The result being

```answer
------
t
```

You could of course have used the `EXISTS` clause instead (please don’t use `COUNT(*)` in these cases):

```select exists(
select 1
from countries
where gdp_per_capita >= 50000
);
```

And we would get, again:

```answer
------
t
```

… but let’s focus on the plain `HAVING` clause.

Not everyone knows that `HAVING` can be used all by itself, or what it even means to have `HAVING` all by itself. Already the SQL 1992 standard allowed for the use of `HAVING` without `GROUP BY`, but it wasn’t until the introduction of `GROUPING SETS` in SQL:1999, when the semantics of this syntax was retroactively unambiguously defined:

7.10 <having clause>

```<having clause> ::= HAVING <search condition>
```

Syntax Rules

1) Let HC be the <having clause>. Let TE be the <table expression> that immediately contains
HC. If TE does not immediately contain a <group by clause>, then GROUP BY ( ) is implicit.

That’s interesting. There is an implicit `GROUP BY ( )`, if we leave out the explicit `GROUP BY` clause. If you’re willing to delve into the SQL standard a bit more, you’ll find:

```<group by clause> ::=
GROUP BY <grouping specification>

<grouping specification> ::=
<grouping column reference>
| <rollup list>
| <cube list>
| <grouping sets list>
| <grand total>
| <concatenated grouping>

<grouping set> ::=
<ordinary grouping set>
| <rollup list>
| <cube list>
| <grand total>

<grand total> ::= <left paren> <right paren>
```

So, `GROUP BY ( )` is essentially grouping by a “grand total”, which is what’s intuitively happening, if we just look for the highest ever GDP per capita:

```select max(gdp_per_capita)
from countries;
```

Which yields:

```     max
--------
52409.00
```

The above query is also implicitly the same as this one (which isn’t supported by PostgreSQL):

```select max(gdp_per_capita)
from countries
group by ();
```

(but beware, this isn’t always the case – read this interesting article by Glenn Paulley for details)

## The awesome GROUPING SETs

In this section of the article, we’ll be leaving PostgreSQL land, entering SQL Server land, as PostgreSQL shamefully doesn’t implement any of the following (yet).

Now, we cannot understand the grand total (empty `GROUP BY ( )` clause), without having a short look at the SQL:1999 standard `GROUPING SETS`. Some of you may have heard of `CUBE()` or `ROLLUP()` grouping functions, which are just syntactic sugar for commonly used `GROUPING SETS`. Let’s try to answer this question in a single query:

What are the highest GDP per capita values per year OR per country

In SQL, we’ll write:

```select code, year, max(gdp_per_capita)
from countries
group by grouping sets ((code), (year))
```

Which yields two concatenated sets of records:

```code    year    max
------------------------
NULL    2009    46999.00 <- grouped by year
NULL    2010    48358.00
NULL    2011    51791.00
NULL    2012    52409.00

CA      NULL    52409.00 <- grouped by code
DE      NULL    44355.00
FR      NULL    42578.00
GB      NULL    38927.00
IT      NULL    36988.00
JP      NULL    46548.00
RU      NULL    14091.00
US      NULL    51755.00
```

That’s kind of nice, isn’t it? It’s essentially just the same thing as this query with `UNION ALL`

```select code, null, max(gdp_per_capita)
from countries
group by code
union all
select null, year, max(gdp_per_capita)
from countries
group by year;
```

In fact, it’s exactly the same thing, as the latter explicitly concatenates two sets of grouped records… i.e. two `GROUPING SETS`. This SQL Server documentation page also explains it very nicely.

## And the most powerful of them all: CUBE()

Now, imagine, you’d like to add the “grand total”, and also the highest value per country AND year, producing four different concatenated sets. To limit the results, we’ll also filter out GDPs of less than 48000 for this example:

```select
code, year, max(gdp_per_capita),
grouping_id(code, year) grp
from countries
where gdp_per_capita >= 48000
group by grouping sets (
(),
(code),
(year),
(code, year)
)
order by grp desc;
```

This nice-looking query will now produce all the possible grouping combinations that we can imagine, including the grand total, in order to produce:

```code    year    max         grp
---------------------------------
NULL    NULL    52409.00    3 <- grand total

NULL    2012    52409.00    2 <- group by year
NULL    2010    48358.00    2
NULL    2011    51791.00    2

CA      NULL    52409.00    1 <- group by code
US      NULL    51755.00    1

US      2010    48358.00    0 <- group by code and year
CA      2012    52409.00    0
US      2012    51755.00    0
CA      2011    51791.00    0
US      2011    49855.00    0
```

And because this is quite a common operation in reporting and in OLAP, we can simply write the same by using the `CUBE()` function:

```select
code, year, max(gdp_per_capita),
grouping_id(code, year) grp
from countries
where gdp_per_capita >= 48000
group by cube(code, year)
order by grp desc;
```

## Compatibility

While the first couple of queries also worked on PostgreSQL, the ones that are using `GROUPING SETS` will work only on 4 out of 17 RDBMS currently supported by jOOQ. These are:

• DB2
• Oracle
• SQL Server
• Sybase SQL Anywhere

jOOQ also fully supports the previously mentioned syntaxes. The `GROUPING SETS` variant can be written as such:

```// Countries is an object generated by the jOOQ
// code generator for the COUNTRIES table.
Countries c = COUNTRIES;

ctx.select(
c.CODE,
c.YEAR,
max(c.GDP_PER_CAPITA),
groupingId(c.CODE, c.YEAR).as("grp"))
.from(c)
.where(c.GDP_PER_CAPITA.ge(new BigDecimal("48000")))
.groupBy(groupingSets(new Field[][] {
{},
{ c.CODE },
{ c.YEAR },
{ c.CODE, c.YEAR }
}))
.orderBy(fieldByName("grp").desc())
.fetch();
```

… or the `CUBE()` version:

```ctx.select(
c.CODE,
c.YEAR,
max(c.GDP_PER_CAPITA),
groupingId(c.CODE, c.YEAR).as("grp"))
.from(c)
.where(c.GDP_PER_CAPITA.ge(new BigDecimal("48000")))
.groupBy(cube(c.CODE, c.YEAR))
.orderBy(fieldByName("grp").desc())
.fetch();
``` … and in the future, we’ll emulate `GROUPING SETS` by their equivalent `UNION ALL` queries in those databases that do not natively support `GROUPING SETS`.