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Aggregation

Table of contents

Introduction

Aggregate functions operate on sets of values. They are often used with a GROUP BY clause to group values into subsets.

GROUP BY Clause

Description

The GROUP BY expression could be

  1. Identifier
  2. Ordinal
  3. Expression

Identifier

The group by expression could be identifier:

od> SELECT gender, sum(age) FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+------------+
| gender   | sum(age)   |
|----------+------------|
| F        | 28         |
| M        | 101        |
+----------+------------+

Ordinal

The group by expression could be ordinal:

od> SELECT gender, sum(age) FROM accounts GROUP BY 1;
fetched rows / total rows = 2/2
+----------+------------+
| gender   | sum(age)   |
|----------+------------|
| F        | 28         |
| M        | 101        |
+----------+------------+

Expression

The group by expression could be expression:

od> SELECT abs(account_number), sum(age) FROM accounts GROUP BY abs(account_number);
fetched rows / total rows = 4/4
+-----------------------+------------+
| abs(account_number)   | sum(age)   |
|-----------------------+------------|
| 1                     | 32         |
| 13                    | 28         |
| 18                    | 33         |
| 6                     | 36         |
+-----------------------+------------+

Aggregation

Description

  1. The aggregation could be used select.
  2. The aggregation could be used as arguments of expression.
  3. The aggregation could has expression as arguments.

Aggregation in Select

The aggregation could be used select:

od> SELECT gender, sum(age) FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+------------+
| gender   | sum(age)   |
|----------+------------|
| F        | 28         |
| M        | 101        |
+----------+------------+

Expression over Aggregation

The aggregation could be used as arguments of expression:

od> SELECT gender, sum(age) * 2 as sum2 FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+--------+
| gender   | sum2   |
|----------+--------|
| F        | 56     |
| M        | 202    |
+----------+--------+

Expression as argument of Aggregation

The aggregation could has expression as arguments:

od> SELECT gender, sum(age * 2) as sum2 FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+--------+
| gender   | sum2   |
|----------+--------|
| F        | 56     |
| M        | 202    |
+----------+--------+

COUNT Aggregations

Besides regular identifiers, COUNT aggregate function also accepts arguments such as * or literals like 1. The meaning of these different forms are as follows:

  1. COUNT(field) will count only if given field (or expression) is not null or missing in the input rows.
  2. COUNT(*) will count the number of all its input rows.
  3. COUNT(1) is same as COUNT(*) because any non-null literal will count.

HAVING Clause

Description

A HAVING clause can serve as aggregation filter that filters out aggregated values satisfy the condition expression given.

HAVING with GROUP BY

Aggregate expressions or its alias defined in SELECT clause can be used in HAVING condition.

  1. It's recommended to use non-aggregate expression in WHERE although it's allowed to do this in HAVING clause.
  2. The aggregation in HAVING clause is not necessarily same as that on select list. As extension to SQL standard, it's also not restricted to involve identifiers only on group by list.

Here is an example for typical use of HAVING clause:

od> SELECT
...  gender, sum(age)
... FROM accounts
... GROUP BY gender
... HAVING sum(age) > 100;
fetched rows / total rows = 1/1
+----------+------------+
| gender   | sum(age)   |
|----------+------------|
| M        | 101        |
+----------+------------+

Here is another example for using alias in HAVING condition. Note that if an identifier is ambiguous, for example present both as a select alias and an index field, preference is alias. This means the identifier will be replaced by expression aliased in SELECT clause:

od> SELECT
...  gender, sum(age) AS s
... FROM accounts
... GROUP BY gender
... HAVING s > 100;
fetched rows / total rows = 1/1
+----------+-----+
| gender   | s   |
|----------+-----|
| M        | 101 |
+----------+-----+

HAVING without GROUP BY

Additionally, a HAVING clause can work without GROUP BY clause. This is useful because aggregation is not allowed to be present in WHERE clause:

od> SELECT
...  'Total of age > 100'
... FROM accounts
... HAVING sum(age) > 100;
fetched rows / total rows = 1/1
+------------------------+
| 'Total of age > 100'   |
|------------------------|
| Total of age > 100     |
+------------------------+

FILTER Clause

Description

A FILTER clause can set specific condition for the current aggregation bucket, following the syntax aggregation_function(expr) FILTER(WHERE condition_expr). If a filter is specified, then only the input rows for which the condition in the filter clause evaluates to true are fed to the aggregate function; other rows are discarded. The aggregation with filter clause can be use in SELECT clause only.

FILTER with GROUP BY

The group by aggregation with FILTER clause can set different conditions for each aggregation bucket. Here is an example to use FILTER in group by aggregation:

od> SELECT avg(age) FILTER(WHERE balance > 10000) AS filtered, gender FROM accounts GROUP BY gender
fetched rows / total rows = 2/2
+------------+----------+
| filtered   | gender   |
|------------+----------|
| 28.0       | F        |
| 32.0       | M        |
+------------+----------+

FILTER without GROUP BY

The FILTER clause can be used in aggregation functions without GROUP BY as well. For example:

od> SELECT
...   count(*) AS unfiltered,
...   count(*) FILTER(WHERE age > 34) AS filtered
... FROM accounts
fetched rows / total rows = 1/1
+--------------+------------+
| unfiltered   | filtered   |
|--------------+------------|
| 4            | 1          |
+--------------+------------+