Retrieve Data Using SQL

Window functions are often used to (among other things):

• calculate cumulative sums

• order rows within a partition.

As you will see, there are many similarities between aggregations and window functions.

Let’s wave our little magic want over this... :magicien:

SELECT incorporation_date, min(id) OVER() FROM entity ;

Tada! No more error! When we insert  OVER()  , the aggregate function  min()  returns as many rows as there are in entity. This is not normal behavior for an aggregate function!

Window Functions

Yes, it’s true... there are many similarities between aggregations and window functions. Both are executed in two phases, and they differ only in respect to phase two:

Window functions are defined using an OVER clause. The OVER clause has two capabilities, each with its own behavior! Here are the corresponding syntaxes:

• OVER (PARTITION BY ...)

• OVER (PARTITION BY ... ORDER BY ...)

OVER (PARTITION BY...)

Let’s analyze these two queries, which use the nb_entities table from the previous chapter:

-- REQUETE n°1SELECT sum(cnt_entities) FROM nb_entities GROUP BY id_intermediary ;-- REQUETE n°2SELECT sum(cnt_entities) OVER (PARTITION BY id_intermediary) FROM nb_entities ;

Query no. 2 functions almost like the aggregation in query 1; the only thing that’s different is what  sum  returns.

In query no. 2, if  sum  receives as input the list of values  [10, 1, 5]  , it won’t return  16  ; it will return  [16, 16, 16].

In both cases, we are calculating the total number of companies created by each intermediary. But here is how the result is presented using a window function:

SELECT id_intermediary,     jurisdiction,     cnt_entities,     sum(cnt_entities) OVER (PARTITION BY id_intermediary) AS entities_by_intermediary FROM nb_entities ;

OVER (PARTITION BY... ORDER BY...)

With an aggregate function

When we add  ORDER BY  to the  OVER  clause, the aggregate function behaves in still another way!

Indeed, it considers these values to be ranked. As before, the aggregate function is going to return as many rows as it received as input. But it’s going to do this:

1. Perform a calculation on the 1st value, and return the result;

2. Perform a calculation on the 1st and 2nd values, and return the result;

3. Perform a calculation on the 1st, 2nd, and 3rd values, and return the result;

4. And so on.

Thus, if the  sum  function receives [10, 5, 1], it will return [10, 15, 16], because

• 10 = 10

• 10 + 5 = 15

• 10 + 5 + 1 = 16

Here,  sum  behaves like a cumulative sum!

With a ranking function

If we use a ranking function (for example,  rank  ), it will simply return the order of the rows. These rows have been previously sorted, thanks to  ORDER BY .

A little example: we are in a race, and have recorded the time it took each runner to finish the course. To find out who will be on the podium, we need to rank the athletes according to their time:

SELECT     firstname,     time,     rank() OVER (ORDER BY temps) FROM course ;

Cumulative Sums and Ranking Numbers

So let’s assign a rank to each jurisdiction for each intermediary. Let’s call our ranking  rank  . To assign a  rank  to a jurisdiction, I sort  cnt_entities  in descending order. Since I want to obtain a ranking for each intermediary, I write  PARTITION BY id_intermediary  .

We can also calculate a cumulative sum! Let’s calculate the cumulative sum of   cnt_entities  for each intermediary, sorting the rows in descending order of   cnt_entities  . Let’s call our cumulative sum  cum_sum  .

SELECT id_intermediary,    jurisdiction,    cnt_entities,    rank()        OVER(PARTITION BY id_intermediary ORDER BY cnt_entities DESC) AS rank,    sum(cnt_entities)         OVER(PARTITION BY id_intermediary ORDER BY cnt_entities DESC) AS cum_sumFROM nb_entities ;

Here's the result:

Summary

• Window functions are very similar to aggregations, except they don’t change the number of rows.

• The  OVER  keyword can be used to form groups of rows (aggregate functions) or to order rows within a partition (ranking functions).

• The keyword  OVER  can be used to calculate  cumulative sums  or  determine rankings .