Do You Really Understand SQL’s GROUP BY and HAVING clauses?
lukaseder
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
Whew. Some (academic) business requirements.
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
Or, with inline comments
-- 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
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:
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 ();
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;
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;
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();
