Tutorial de uniones SQL: unión cruzada, unión externa completa, unión interna, unión izquierda y unión derecha.

Las uniones SQL permiten que nuestros sistemas de administración de bases de datos relacionales sean, bueno, relacionales.

Las uniones nos permiten reconstruir nuestras tablas de base de datos separadas en las relaciones que impulsan nuestras aplicaciones.

En este artículo, veremos cada uno de los diferentes tipos de combinación en SQL y cómo usarlos.

Esto es lo que cubriremos:

  • ¿Qué es una unión?
  • Configurando su base de datos
  • CROSS JOIN
  • Configurando nuestros datos de ejemplo (directores y películas)
  • FULL OUTER JOIN
  • INNER JOIN
  • LEFT JOIN / RIGHT JOIN
  • Filtrar usando LEFT JOIN
  • Múltiples combinaciones
  • Se une con condiciones extra
  • La realidad de escribir consultas con combinaciones

( Alerta de spoiler : cubriremos cinco tipos diferentes, ¡pero realmente solo necesita conocer dos de ellos!)

¿Qué es una unión?

Una combinación es una operación que combina dos filas juntas en una fila.

Estas filas suelen ser de dos tablas diferentes, pero no es necesario que lo sean.

Antes de ver cómo escribir la combinación en sí, veamos cómo se vería el resultado de una combinación.

Tomemos, por ejemplo, un sistema que almacena información sobre los usuarios y sus direcciones.

Las filas de la tabla que almacena la información del usuario podrían tener este aspecto:

 id | name | email | age ----+--------------+---------------------+----- 1 | John Smith | [email protected] | 25 2 | Jane Doe | [email protected] | 28 3 | Xavier Wills | [email protected] | 3 ... (7 rows)

Y las filas de la tabla que almacena la información de la dirección podrían verse así:

 id | street | city | state | user_id ----+-------------------+---------------+-------+--------- 1 | 1234 Main Street | Oklahoma City | OK | 1 2 | 4444 Broadway Ave | Oklahoma City | OK | 2 3 | 5678 Party Ln | Tulsa | OK | 3 (3 rows)

Podríamos escribir consultas separadas para recuperar tanto la información del usuario como la información de la dirección, pero lo ideal sería escribir una consulta y recibir a todos los usuarios y sus direcciones en el mismo conjunto de resultados.

¡Esto es exactamente lo que nos permite hacer una unión!

Pronto veremos cómo escribir estas uniones, pero si unimos nuestra información de usuario a la información de nuestra dirección, podríamos obtener un resultado como este:

 id | name | email | age | id | street | city | state | user_id ----+--------------+---------------------+-----+----+-------------------+---------------+-------+--------- 1 | John Smith | [email protected] | 25 | 1 | 1234 Main Street | Oklahoma City | OK | 1 2 | Jane Doe | [email protected] | 28 | 2 | 4444 Broadway Ave | Oklahoma City | OK | 2 3 | Xavier Wills | [email protected] | 35 | 3 | 5678 Party Ln | Tulsa | OK | 3 (3 rows) 

Aquí vemos a todos nuestros usuarios y sus direcciones en un buen conjunto de resultados.

Además de producir un conjunto de resultados combinado, otro uso importante de las uniones es extraer información adicional en nuestra consulta que podamos filtrar.

Por ejemplo, si quisiéramos enviar algún correo físico a todos los usuarios que viven en la ciudad de Oklahoma, podríamos usar este conjunto de resultados combinado y filtrar según la citycolumna.

Ahora que conocemos el propósito de las uniones, ¡comencemos a escribir algunas!

Configurando su base de datos

Antes de que podamos escribir nuestras consultas, necesitamos configurar nuestra base de datos.

Para estos ejemplos usaremos PostgreSQL, pero las consultas y conceptos que se muestran aquí se traducirán fácilmente a cualquier otro sistema de base de datos moderno (como MySQL, SQL Server, etc.).

Para trabajar con nuestra base de datos PostgreSQL, podemos usar psqlel programa interactivo de línea de comandos PostgreSQL. Si tiene otro cliente de base de datos con el que disfruta trabajar, también está bien.

Para empezar, creemos nuestra base de datos. Con PostgreSQL ya instalado, podemos ejecutar el comando createdb en nuestra terminal para crear una nueva base de datos. Llamé mía fcc:

$ createdb fcc 

A continuación, iniciemos la consola interactiva usando el comando psqly conectemos a la base de datos que acabamos de crear usando \c :

$ psql psql (11.5) Type "help" for help. john=# \c fcc You are now connected to database "fcc" as user "john". fcc=# 
Nota: He limpiado el psqlresultado de estos ejemplos para que sea más fácil de leer, así que no se preocupe si el resultado que se muestra aquí no es exactamente lo que ha visto en su terminal.

Le animo a que siga estos ejemplos y ejecute estas consultas usted mismo. Aprenderá y recordará mucho más trabajando con estos ejemplos en lugar de simplemente leerlos.

¡Ahora a las uniones!

CROSS JOIN

El tipo más simple de unimos podemos hacer es un CROSS JOINo "producto cartesiano."

Esta combinación toma cada fila de una tabla y la une con cada fila de la otra tabla.

Si tuviéramos dos listas, una que contenga 1, 2, 3y la otra que contenga A, B, C, el producto cartesiano de esas dos listas sería este:

1A, 1B, 1C 2A, 2B, 2C 3A, 3B, 3C 

Cada valor de la primera lista se empareja con cada valor de la segunda lista.

Escribamos este mismo ejemplo como una consulta SQL.

Primero, creemos dos tablas muy simples e insertemos algunos datos en ellas:

CREATE TABLE letters( letter TEXT ); INSERT INTO letters(letter) VALUES ('A'), ('B'), ('C'); CREATE TABLE numbers( number TEXT ); INSERT INTO numbers(number) VALUES (1), (2), (3); 

Nuestras dos tablas, lettersy numbers, solo tienen una columna: un campo de texto simple.

Ahora unámoslos con un CROSS JOIN:

SELECT * FROM letters CROSS JOIN numbers; 
 letter | number --------+-------- A | 1 A | 2 A | 3 B | 1 B | 2 B | 3 C | 1 C | 2 C | 3 (9 rows) 

Este es el tipo de combinación más simple que podemos hacer, pero incluso en este ejemplo simple podemos ver la combinación en funcionamiento: las dos filas separadas (una desde lettersy otra desde numbers) se han unido para formar una fila.

Si bien este tipo de unión a menudo se discute como un mero ejemplo académico, tiene al menos un buen caso de uso: cubrir rangos de fechas.

CROSS JOIN con rangos de fechas

Un buen caso de uso de a CROSS JOINes tomar cada fila de una tabla y aplicarla a todos los días dentro de un rango de fechas.

Say for example you were building an application that tracked daily tasks—things like brushing your teeth, eating breakfast, or showering.

If you wanted to generate a record for every task and for each day of the past week, you could use a CROSS JOIN against a date range.

To make this date range, we can use the generate_series function:

SELECT generate_series( (CURRENT_DATE - INTERVAL '5 day'), CURRENT_DATE, INTERVAL '1 day' )::DATE AS day; 

The generate_series function takes three parameters.

The first parameter is the starting value. In this example we use CURRENT_DATE - INTERVAL '5 day'. This returns the current date minus five days—or "five days ago."

The second parameter is the current date (CURRENT_DATE).

The third parameter is the "step interval"—or how much we want to increment the value each time. Since these are daily tasks we'll use the interval of one day (INTERVAL '1 day').

Putting it all together, this generates a series of dates starting five days ago, ending today, and going one day at a time.

Finally we remove the time portion by casting the output of these values to a date using ::DATE, and we alias this column using AS day to make the output a little nicer.

The output of this query is the past five days plus today:

 day ------------ 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 (6 rows) 

Going back to our tasks-per-day example, let's create a simple table to hold the tasks we want to complete and insert a few tasks:

CREATE TABLE tasks( name TEXT ); INSERT INTO tasks(name) VALUES ('Brush teeth'), ('Eat breakfast'), ('Shower'), ('Get dressed'); 

Our tasks table just has one column, name, and we inserted four tasks into this table.

Now let's CROSS JOIN our tasks with the query to generate the dates:

SELECT tasks.name, dates.day FROM tasks CROSS JOIN ( SELECT generate_series( (CURRENT_DATE - INTERVAL '5 day'), CURRENT_DATE, INTERVAL '1 day' )::DATE AS day ) AS dates 

(Since our date generation query is not an actual table we just write it as a subquery.)

From this query we return the task name and the day, and the result set looks like this:

 name | day ---------------+------------ Brush teeth | 2020-08-19 Brush teeth | 2020-08-20 Brush teeth | 2020-08-21 Brush teeth | 2020-08-22 Brush teeth | 2020-08-23 Brush teeth | 2020-08-24 Eat breakfast | 2020-08-19 Eat breakfast | 2020-08-20 Eat breakfast | 2020-08-21 Eat breakfast | 2020-08-22 ... (24 rows) 

Like we expected, we get a row for each task for every day in our date range.

The CROSS JOIN is the simplest join we can do, but to look at the next few types we'll need a more-realistic table setup.

Creating directors and movies

To illustrate the following join types, we'll use the example of movies and movie directors.

In this situation, a movie has one director, but a movie isn't required to have a director—imagine a new movie being announced but the choice for director hasn't yet been confirmed.

Our directors table will store the name of each director, and the movies table will store the name of the movie as well as a reference to the director of the movie (if it has one).

Let's create those two tables and insert some data into them:

CREATE TABLE directors( id SERIAL PRIMARY KEY, name TEXT NOT NULL ); INSERT INTO directors(name) VALUES ('John Smith'), ('Jane Doe'), ('Xavier Wills') ('Bev Scott'), ('Bree Jensen'); CREATE TABLE movies( id SERIAL PRIMARY KEY, name TEXT NOT NULL, director_id INTEGER REFERENCES directors ); INSERT INTO movies(name, director_id) VALUES ('Movie 1', 1), ('Movie 2', 1), ('Movie 3', 2), ('Movie 4', NULL), ('Movie 5', NULL); 

We have five directors, five movies, and three of those movies have directors assigned to them. Director ID 1 has two movies, and director ID 2 has one.

FULL OUTER JOIN

Now that we have some data to work with let's look at the FULL OUTER JOIN.

A FULL OUTER JOIN has some similarities to a CROSS JOIN, but it has a couple key differences.

The first difference is that a FULL OUTER JOIN requires a join condition.

A join condition specifies how the rows between the two tables are related to each other and on what criteria they should be joined together.

In our example, our movies table has a reference to the director via the director_id column, and this column matches the id column of the directors table. These are the two columns that we will use as our join condition.

Here's how we write this join between our two tables:

SELECT * FROM movies FULL OUTER JOIN directors ON directors.id = movies.director_id; 

Notice the join condition we specified that matches the movie to its director: ON movies.director_id = directors.id.

Our result set looks like an odd Cartesian product of sorts:

 id | name | director_id | id | name ------+---------+-------------+------+-------------- 1 | Movie 1 | 1 | 1 | John Smith 2 | Movie 2 | 1 | 1 | John Smith 3 | Movie 3 | 2 | 2 | Jane Doe 4 | Movie 4 | NULL | NULL | NULL 5 | Movie 5 | NULL | NULL | NULL NULL | NULL | NULL | 5 | Bree Jensen NULL | NULL | NULL | 4 | Bev Scott NULL | NULL | NULL | 3 | Xavier Wills (8 rows) 

The first rows we see are ones where the movie had a director, and our join condition evaluated to true.

Sin embargo, después de esas filas, vemos cada una de las filas restantes de cada tabla, pero con NULLvalores en los que la otra tabla no coincide.

Nota: si no está familiarizado con los NULLvalores, consulte mi explicación aquí en este tutorial del operador SQL.

También vemos otra diferencia entre el CROSS JOINy FULL OUTER JOINaquí. A FULL OUTER JOINdevuelve una fila distinta de cada tabla, a diferencia de la CROSS JOINque tiene múltiples.

INNER JOIN

El siguiente tipo de combinación INNER JOIN, es uno de los tipos de combinación más utilizados.

Una combinación interna solo devuelve filas donde la condición de combinación es verdadera.

En nuestro ejemplo, una unión interna entre nuestras tablas moviesy directorssolo devolvería registros en los que se haya asignado un director a la película.

The syntax is basically the same as before:

SELECT * FROM movies INNER JOIN directors ON directors.id = movies.director_id; 

Our result shows the three movies that have a director:

 id | name | director_id | id | name ----+---------+-------------+----+------------ 1 | Movie 1 | 1 | 1 | John Smith 2 | Movie 2 | 1 | 1 | John Smith 3 | Movie 3 | 2 | 2 | Jane Doe (3 rows) 

Since an inner join only includes rows that match the join condition, the order of the two tables in the join don't matter.

If we reverse the order of the tables in the query we get same result:

SELECT * FROM directors INNER JOIN movies ON movies.director_id = directors.id; 
 id | name | id | name | director_id ----+------------+----+---------+------------- 1 | John Smith | 1 | Movie 1 | 1 1 | John Smith | 2 | Movie 2 | 1 2 | Jane Doe | 3 | Movie 3 | 2 (3 rows) 

Since we listed the directors table first in this query and we selected all columns (SELECT *), we see the directors column data first and then the columns from movies—but the resulting data is the same.

This is a useful property of inner joins, but it's not true for all join types—like our next type.

LEFT JOIN / RIGHT JOIN

These next two join types use a modifier (LEFT or RIGHT) that affects which table's data is included in the result set.

Nota: el LEFT JOINy RIGHT JOINtambién puede ser referido como LEFT OUTER JOINy RIGHT OUTER JOIN.

Estas uniones se utilizan en consultas en las que queremos devolver todos los datos de una tabla en particular y, si existe , también los datos de la tabla asociada.

Si los datos asociados no existen, aún recuperamos todos los datos de la tabla "primaria".

Es una consulta de información sobre algo en particular e información adicional si existe esa información adicional.

Esto será fácil de entender con un ejemplo. Busquemos todas las películas y sus directores, pero no nos importa si tienen director o no, es una ventaja:

SELECT * FROM movies LEFT JOIN directors ON directors.id = movies.director_id; 

La consulta sigue nuestro mismo patrón que antes; acabamos de especificar la combinación como LEFT JOIN.

In this example, the movies table is the "left" table.

If we write the query on one line it makes this a little easier to see:

... FROM movies LEFT JOIN directors ... 

A left join returns all records from the "left" table.

A left join returns any rows from the "right" table that match the join condition.

Rows from the "right" table that don't match the join condition are returned as NULL.

 id | name | director_id | id | name ----+---------+-------------+------+------------ 1 | Movie 1 | 1 | 1 | John Smith 2 | Movie 2 | 1 | 1 | John Smith 3 | Movie 3 | 2 | 2 | Jane Doe 4 | Movie 4 | NULL | NULL | NULL 5 | Movie 5 | NULL | NULL | NULL (5 rows) 

Looking at that result set, we can see why this type of join is useful for "all of this and, if it exists, some of that" type queries.

RIGHT JOIN

The RIGHT JOIN works exactly like the LEFT JOIN—except the rules about the two tables are reversed.

In a right join, all of the rows from the "right" table are returned. The "left" table is conditionally returned based on the join condition.

Let's use the same query as above but substitute LEFT JOIN for RIGHT JOIN:

SELECT * FROM movies RIGHT JOIN directors ON directors.id = movies.director_id; 
 id | name | director_id | id | name ------+---------+-------------+----+-------------- 1 | Movie 1 | 1 | 1 | John Smith 2 | Movie 2 | 1 | 1 | John Smith 3 | Movie 3 | 2 | 2 | Jane Doe NULL | NULL | NULL | 5 | Bree Jensen NULL | NULL | NULL | 4 | Bev Scott NULL | NULL | NULL | 3 | Xavier Wills (6 rows) 

Our result set now returns every directors row and, if it exists, the movies data.

All we've done is switch which table we're considering the "primary" one—the table we want to see all of the data from regardless of if its associated data exists.

LEFT JOIN / RIGHT JOIN in production applications

In a production application, I only ever use LEFT JOIN and I never use RIGHT JOIN.

I do this because, in my opinion, a LEFT JOIN makes the query easier to read and understand.

When I'm writing queries I like to think of starting with a "base" result set, say all movies, and then bring in (or subtract out) groups of things from that base.

Because I like to start with a base, the LEFT JOIN fits this line of thinking. I want all of the rows from my base table (the "left" table), and I conditionally want the rows from the "right" table.

In practice, I don't think I've ever even seen a RIGHT JOIN in a production application. There's nothing wrong with a RIGHT JOIN—I just think it makes the query more difficult to understand.

Re-writing RIGHT JOIN

If we wanted to flip our scenario above and instead return all directors and conditionally their movies, we can easily re-write the RIGHT JOIN into a LEFT JOIN.

Todo lo que tenemos que hacer es cambiar el orden de las tablas en la consulta y cambiar RIGHTa LEFT:

SELECT * FROM directors LEFT JOIN movies ON movies.director_id = directors.id; 
Nota: Me gusta colocar la tabla que se está uniendo (la tabla "derecha", en el ejemplo anterior movies) primero en la condición de unión ( ON movies.director_id = ...), pero esa es solo mi preferencia personal.

Filtrar usando LEFT JOIN

Hay dos casos de uso para usar un LEFT JOIN(o RIGHT JOIN).

El primer caso de uso que ya hemos cubierto: devolver todas las filas de una tabla y condicionalmente de otra.

El segundo caso de uso es devolver filas de la primera tabla donde los datos de la segunda tabla no están presentes.

El escenario se vería así: busque directores que no pertenezcan a una película.

To do this we'll start with a LEFT JOIN and our directors table will be the primary or "left" table:

SELECT * FROM directors LEFT JOIN movies ON movies.director_id = directors.id; 

For a director that doesn't belong to a movie, the columns from the movies table are NULL:

 id | name | id | name | director_id ----+--------------+------+---------+------------- 1 | John Smith | 1 | Movie 1 | 1 1 | John Smith | 2 | Movie 2 | 1 2 | Jane Doe | 3 | Movie 3 | 2 5 | Bree Jensen | NULL | NULL | NULL 4 | Bev Scott | NULL | NULL | NULL 3 | Xavier Wills | NULL | NULL | NULL (6 rows) 

In our example, director ID 3, 4, and 5 don't belong to a movie.

To filter our result set just to these rows, we can add a WHERE clause to only return rows where the movie data is NULL:

SELECT * FROM directors LEFT JOIN movies ON movies.director_id = directors.id WHERE movies.id IS NULL; 
 id | name | id | name | director_id ----+--------------+------+------+------------- 5 | Bree Jensen | NULL | NULL | NULL 4 | Bev Scott | NULL | NULL | NULL 3 | Xavier Wills | NULL | NULL | NULL (3 rows) 

And there are our three movie-less directors!

It's common to use the id column of the table to filter against (WHERE movies.id IS NULL), but all columns from the movies table are NULL—so any of them would work.

(Since we know that all the columns from the movies table will be NULL, in the query above we could just write SELECT directors.* instead of SELECT * to just return all of the director's information.)

Using LEFT JOIN to find matches

In our previous query we found directors that didn't belong to movies.

Using our same structure, we could find directors that do belong to movies by changing our WHERE condition to look for rows where the movie data is notNULL:

SELECT * FROM directors LEFT JOIN movies ON movies.director_id = directors.id WHERE movies.id IS NOT NULL; 
 id | name | id | name | director_id ----+------------+----+---------+------------- 1 | John Smith | 1 | Movie 1 | 1 1 | John Smith | 2 | Movie 2 | 1 2 | Jane Doe | 3 | Movie 3 | 2 (3 rows) 

This may seem handy, but we've actually just re-implemented INNER JOIN!

Multiple joins

We've seen how to join two tables together, but what about multiple joins in a row?

It's actually quite simple, but to illustrate this we need a third table: tickets.

This table will represent tickets sold for a movie:

CREATE TABLE tickets( id SERIAL PRIMARY KEY, movie_id INTEGER REFERENCES movies NOT NULL ); INSERT INTO tickets(movie_id) VALUES (1), (1), (3); 

The tickets table just has an id and a reference to the movie: movie_id.

We've also inserted two tickets sold for movie ID 1, and one ticket sold for movie ID 3.

Now, let's join directors to movies—and then movies to tickets!

SELECT * FROM directors INNER JOIN movies ON movies.director_id = directors.id INNER JOIN tickets ON tickets.movie_id = movies.id; 

Since these are inner joins, the order in which we write the joins doesn't matter. We could have started with tickets, then joined on movies, and then joined on directors.

It again comes down to what you're trying to query and what makes the query the most understandable.

In our result set, we'll notice that we've further narrowed down the rows that are returned:

 id | name | id | name | director_id | id | movie_id ----+------------+----+---------+-------------+----+---------- 1 | John Smith | 1 | Movie 1 | 1 | 1 | 1 1 | John Smith | 1 | Movie 1 | 1 | 2 | 1 2 | Jane Doe | 3 | Movie 3 | 2 | 3 | 3 (3 rows) 

This makes sense because we've added another INNER JOIN. In effect this adds another "AND" condition to our query.

Our query essentially says: "return all directors that belong to movies that also have ticket sales."

If instead we wanted to find directors that belong to movies that may not have ticket sales yet, we could substitute our last INNER JOIN for a LEFT JOIN:

SELECT * FROM directors JOIN movies ON movies.director_id = directors.id LEFT JOIN tickets ON tickets.movie_id = movies.id; 

We can see that Movie 2 is now back in the result set:

 id | name | id | name | director_id | id | movie_id ----+------------+----+---------+-------------+------+---------- 1 | John Smith | 1 | Movie 1 | 1 | 1 | 1 1 | John Smith | 1 | Movie 1 | 1 | 2 | 1 2 | Jane Doe | 3 | Movie 3 | 2 | 3 | 3 1 | John Smith | 2 | Movie 2 | 1 | NULL | NULL (4 rows) 

This movie didn't have any ticket sales, so it was previously excluded from the result set due to the INNER JOIN.

I'll leave this an Exercise For The Reader™, but how would you find directors that belong to movies that don't have any ticket sales?

Join execution order

In the end, we don't really care in what order the joins are executed.

One of the key differences between SQL and other modern programming languages is that SQL is a declarative language.

This means that we specify the outcome we want, but we don't specify the execution details—those details are left up to the database query planner. We specify the joins we want and the conditions on them and the query planner handles the rest.

But, in reality, the database is not joining three tables together at the same time. Instead, it will likely join the first two tables together into one intermediary result, and then join that intermediary result set to the third table.

(Note: This is a somewhat simplified explanation.)

So, as we're working with multiple joins in queries we can just think of them as a series of joins between two tables—although one of those tables can get quite large.

Joins with extra conditions

The last topic we'll cover is a join with extra conditions.

Similar to a WHERE clause, we can add as many conditions as we want to our join conditions.

For example, if we wanted to find movies with directors that are notnamed"John Smith", we could add that extra condition to our join with an AND:

SELECT * FROM movies INNER JOIN directors ON directors.id = movies.director_id AND directors.name  'John Smith';

We can use any operators we would put in a WHERE clause in this join condition.

We also get the same result from this query if we put the condition in a WHERE clause instead:

SELECT * FROM movies INNER JOIN directors ON directors.id = movies.director_id WHERE directors.name  'John Smith';

There are some subtle differences happening under the hood here, but for the purpose of this article the result set is the same.

(If you're unfamiliar with all of the ways you can filter a SQL query, check out the previously mentioned article here.)

The reality about writing queries with joins

In reality, I find myself only using joins in three different ways:

INNER JOIN

The first use case is records where the relationship between two tables does exist. This is fulfilled by the INNER JOIN.

These are situations like finding "movies that have directors" or "users with posts".

LEFT JOIN

The second use case is records from one table—and if the relationship exists—records from a second table. This is fulfilled by the LEFT JOIN.

These are situations like "movies with directors if they have one" or "users with posts if they have some."

LEFT JOIN exclusion

The third most common use case is our second use case for a LEFT JOIN: finding records in one table thatdon'thave a relationship in the second table.

These are situations like "movies without directors" or "users without posts."

Two very useful join types

I don't think I've ever used a FULL OUTER JOIN or a RIGHT JOIN in a production application. The use case just doesn't come up often enough or the query can be written in a clearer way (in the case of RIGHT JOIN).

I have occasionally used a CROSS JOIN for things like spreading records across a date range (like we looked at the beginning), but that scenario also doesn't come up too often.

So, good news! There's really only two types of joins you need to understand for 99.9% of the use cases you'll come across: INNER JOIN and LEFT JOIN!

If you liked this post, you can follow me on twitter where I talk about database things and all other topics related to development.

Thanks for reading!

John

P.S. an extra tip for reading to the end: most database systems will let you just write JOIN in the place of INNER JOIN—it'll save you a little extra typing. :)