# Rel Primer: Aggregations, Group-By, and Joins

This Primer covers aggregations, group-by aggregations, and joins in Rel.

## Introduction

This Rel Primer section focuses on common database operations in Rel: aggregations, group-by aggregations, and joins.

See Basic Syntax for an introduction to the basic syntax of Rel, a prerequisite for this Rel Primer section.

## Aggregations

To demonstrate aggregations, you will import some basic information about soccer players from Spring 2021,
store these data in a base relation (`player_csv`

),
and *install* a few derived relations, making them available for future use.

First, you can import the data and save them in a base relation:

update

```
// update
def config:data = """
name,salary,age,plays_for,nationality
Messi,70,32,BFC,Argentina
Griezmann,45,28,BFC,France
Busquets,15,31,BFC,Spain
Pique,12,32,BFC,Spain
Dembele,12,22,BFC,France
Umtiti,12,25,BFC,France
Cortois,7,28,RM,Belgium
Carvajal,7,28,RM,Spain
Ramos,15,34,RM,Spain
Varane,7,27,RM,France
Marcelo,7,32,RM,Brazil
Kroos,10,30,RM,Germany
Modric,10,35,RM,Croatia
"""
def config:schema = {
:name, "string";
:salary, "int";
:age, "int";
:plays_for, "string";
:nationality, "string"
}
def csv = load_csv[config]
def delete[:player_csv] = player_csv
def insert[:player_csv] = csv
def output = player_csv
```

In practice, `config:data`

would be a file uploaded to a notebook as a string relation, or `config:path`

would be specified
to point to a CSV file location. See CSV Import for details.

Now you can install some derived relations:

install

```
// install
def player(prop, name, val) =
player_csv(prop, row, val)
and player_csv(:name, row, name) from row
def name(x) = player(_, x, _)
def salary = player:salary
def age = player:age
def plays_for = player:plays_for
def nationality = player:nationality
def team(t) = player:plays_for(_, t) // set of teams
```

Once installed, these definitions are available for querying — see Installing Models.
For an explanation of `_`

, which helps collect the set of teams in the `team`

relation,
see the Projection section below.
For an explanation of `from`

, see below.
Expressions like `player:salary`

use the Rel module syntax.

`player_csv`

is a base relation, created by the *update* transaction that loaded the CSV data and inserted them
into `player_csv`

, above. The relations `player`

, `name`

, `salary`

, etc. are derived relations,
sometimes known as *views*, based on the base relation data. The *install* transaction above makes them available to
subsequent queries.

Since `player`

is now installed, you can query it:

```
// query
def output = player
```

### The Last Argument

When writing a table, it is natural to put the *keys* first and the *values* last:
players and their age (as above); players and their salary; or graph edges and their weight.

Rel provides a number of common operations that operate on the last argument of a relation.
For example,
the Standard Library includes utilities for the
max, min,
sum, and average of a relation,
taking the last argument of the relation as the value to be aggregated.
The basic salary stats for the installed `player`

relation
can be computed as follows:

```
// query
def output = { (:sum, sum[salary]);
(:average, average[salary]);
(:count, count[salary]);
(:argmax, argmax[salary]) }
```

Note that these two numbers are different:

```
// query
def output = count[salary], count[x : salary(_, x)]
```

`count[salary]`

is the number of rows in the `salary`

relation.
The relation `x : salary(_, x)`

contains the *unique* values found in the second argument.
As different players have the same salary, the second number is smaller.
This is important to keep in mind when computing averages and sums, as you will see below in
the section on group-by aggregations.

When computing aggregations, make sure you include keys, to avoid conflating equal values.

### Aggregating Over Empty Relations

In most cases, aggregating over an empty set gives an empty set, rather than, say, 0.
For example, `count[x in name : salary[x] < 0]`

is `{}`

, rather than `{0}`

.
This is a design choice that simplifies the semantics of the language, and often results in more sparse intermediate data,
where the default (0) does not have to be represented.

If you want to include the default, you can use the *override* operator,
left_override, also known as `<++`

,
from the Rel Standard Library.

For example:

```
// query
def output = c in {"RM"; "BFC"; "Chelsea"} :
sum[p where plays_for(p, c) : salary[p] ] <++ 0
```

Without `<++ 0`

, the row for “Chelsea” would not be included in the results.
For more on `<++`

,
see Advanced Syntax.

## Bindings

Many Rel expressions result in a relation that has value keys first, and one or more metrics that follow. When writing these expressions in a Rel query or model, you can choose to have the bindings go first and the values — or metrics — go last, or vice versa, depending on what feels more natural. This will not change the result, as shown below.

Consider an example from Basic Syntax, which uses `:`

, so the bindings go first and the values go last:

```
// query
def mydomain = range[1, 5, 1]
def output = x in mydomain, y in mydomain where x + y = 5 : x-y, x+y, x*y
```

You can move the bindings, which include the `where`

and `in`

constraints, to the other side of the `:`

and still
have an equivalent expression.
The following section shows how to do this.

### Using `for`

or `|`

The Rel construct `for`

can be used instead of `:`

to put the values first and the bindings last, which sometimes
makes things easier to read — for example, when the `where`

condition is itself a complex expression.
Note that the result is exactly the same.

```
// query
def mydomain = range[1, 5, 1]
def output = x-y, x+y, x*y for x in mydomain, y in mydomain where x+y = 5
```

Even though they read differently, these two definitions are equivalent.
In both cases, the values of `x`

and `y`

will appear first in the result tuples.

Going back to aggregations, suppose you want to compute the average salary of players under 30.

By choosing to use `:`

or `for`

,
Rel lets you put the condition first and the metric
— the value being aggregated, in this case, `salary`

— last,
or the metric first and the condition last.

Condition first, metric second, using `:`

:

```
// query
def output = average[x in name where age[x] < 30 : salary[x] ]
```

Metric first, condition last, using `for`

(or its alias `|`

) :

```
// query
def output = average[salary[x] for x in name where age[x] < 30]
```

The results will be the same.

In general, for an expression `Expr`

and bindings `b`

,
`Expr for b`

is equivalent to `b : Expr`

.
For a more mathematical notation, `Expr for b`

can also be written as `Expr | b`

. For example:

```
// query
def output = 100 * (x + y) | x in {1; 2}, y in {1; 3} where x + y = 3
```

You can read `|`

as “such that”, remembering that the bound variables are included at the *beginning* of the resulting tuples,
which is what you want for correct aggregation results.

In aggregations, use `b : e`

(bindings at the left) or `e for b`

(or `e | b`

, bindings at the right)
to make sure you are not conflating keys that have the same metric value.
Use `from`

only if you want to quantify away keys, and possibly remove duplicate values as a result.

Summary:
`e | b`

is equivalent to `e for b`

, which is equivalent to `b : e`

.
In all three cases, the result keeps the tuples from `b`

, making it safe for aggregation.

## Group-By

The `:`

operator lets you do group-by aggregations easily. For example,
to see the average age for each team:

```
// query
def output = x in team : average[p where plays_for(p, x): age[p] ]
```

Note: The `in team`

clause is not really needed, since `plays_for`

constrains the values of `x`

in the same way.

If you prefer, you can write this relation as:

`def output[x in team] = average[p where plays_for(p, x): age[p] ]`

As this is more readable, this Primer adopts this style below.

To see the average salary and count, grouped by age:

```
// query
def output[a] = average[p where age(p, a) : salary[p]], count[p : age(p, a)]
```

Average salary and count, grouped by nationality:

```
// query
def output[n] = average[p where nationality(p, n) : salary[p]],
count[p : nationality(p, n)]
```

`for`

and `from`

When aggregating, you will usually need `for`

, or its equivalent `|`

. For example:

```
// query
def output:right = sum[salary[x] for x in name
where plays_for(x, "RM") and age[x] < 30]
def output:wrong = sum[salary[x] from x in name
where plays_for(x, "RM") and age[x] < 30]
```

Module notation is used here to make `right`

and `wrong`

subrelations of `output`

.
See Modules.

There are three players satisfying the condition, all with a salary of 7. The first aggregation takes the sum of this relation:

```
// query
def output = salary[x] for x in name where plays_for(x, "RM") and age[x] < 30
```

The second aggregation, which existentially quantifies away the player, just takes the sum of the relation `{7}`

.

Use `for`

to keep the variables and `from`

to existentially quantify them away.

## Joining Relations

In database parlance, a *join* combines columns from different tables to build a new one, based on common elements in the rows of each table.
In Rel, this corresponds to defining new relations,
based on common elements between the tuples in the joined relations.

It is simple to join relations in Rel: `and`

or `,`

will suffice.

For example, to get a list of players with their team and nationality,
you can join the `plays_for`

and `nationality`

relations:

```
// query
def output(player, team, country) = plays_for(player, team) and
nationality(player, country)
```

You can use `from`

to existentially quantify away variables you do not want in the result —
see the following section.
For example, if you just want to see the nationalities playing for each team, you can write:

```
// query
def output(team, country) = plays_for(player, team) and
nationality(player, country) from player
```

## Projection

When manipulating relations, we often want to remove one or more columns and keep the others.
We have already seen a special case of this,
the partial relational application operator
(`[]`

),
which additionally restricts the removed columns to have particular values.
In general, you can use `exists`

:

```
// query
def myrel = { (1, "a", 2); (1, "b", 3); (3, "a", 6); (4, "b", 12) }
def output(y) = exists(x, z : myrel(x, y, z))
```

There are two other ways to indicate existential quantification: `_`

(underscore) and `from`

.

```
// query
def myrel = { (1, "a", 2); (1, "b", 3); (3, "a", 6); (4, "b", 12) }
def output(x) = myrel(_, x, _)
```

You can think of `_`

as a “wildcard” variable, which will match anything. More than one `_`

can be used, unrelated to each other.

The `from`

construct also existentially quantifies away variables. This definition is equivalent to the previous one:

```
// query
def myrel = { (1, "a", 2); (1, "b", 3); (3, "a", 6); (4, "b", 12) }
def output(y) = myrel(x, y, z) from x, z
```

## Universal Quantification

Rel also supports universal quantification, provided the variable quantified over has a finite domain:

```
// query
def output("yes") = forall(x in {4;5;6} : x < 10)
```

In general, to restrict the domain being quantified over, use `in`

to restrict single variables,
and `where`

to restrict combinations of variables:

```
// query
def output = if
forall(x in {1; 2; 3}, y in {4; 5; 6} where x + y < 10 : x + y < 8)
then "yes" else "no" end
```

Following the rules of logic, if the domain is empty (`false`

), the result is always `true`

:

```
// query
def mydomain = {1; 2; 3; 4}
def output = if
forall(x where mydomain(x) and x < 0 : 5 < 4)
then "yes" else "no" end
```

## Summary

This article has covered common database operations as expressed in Rel: aggregations, group-by aggregations, and joins. For more in this Rel Primer series, see Advanced Syntax.