Selecting and joining data

In the previous article I have described the principles of the relational databases. This leads to the SQL language which is built upon them and is used by many database systems worldwide. In this article I will describe in more details how individual relations (tables) are connected (joined) together. I assume that you have some basic experience with SQL in querying individual tables.


When working with a database, and especially when working with multiple tables, it is crucial to understand the structure of the database – its database schema. There is a full follow up article about the database design in which I explain how to create the schema. But before you start designing your own databases it is good to know how to work with existing ones.

Reading the Database Schema

The example database has the following schema:

Database Schema

There are eight tables in the schema, each table has its columns and their data types listed in the schema. The schema also shows keys:

  • a column marked with PK is part of a Primary Key,
  • a column marked with FK is part of a Foreign Key,
  • a column marked with K is part of some other Key.

The database schema also shows lines representing relationships between tables (relations) – these provide further details about the defined foreign keys. The relationship endpoints represent cardinality of the relationship – how many records of the relation may reference how many other rows. A bar means one, a circle means zero and a foot means N. Examples:

ERD Legend

Reading that you can see for example that:

  • the person_meeting table has a compound primary key;
  • the person table has a compound key on the combination first_name, last_name, nickname;
  • the column must be unique;
  • the meeting.id_location column references the location.id_location column;
  • a meeting has 1..1 relationship to the location therefore a meeting must have exactly one (at least one and at most one) assigned location
  • a location has 0..N relationship to the meeting therefore a location can be used by zero or more meetings (a location does not have to be used in any meeting at all);
  • a person can have zero or more contacts;
  • a contact must be assigned to exactly one person;
  • a person may have zero or more attendances (person_meeting);
  • an attendance must be assigned to one person.

Selecting Data

I assume you know how to select data from a single table. When joining multiple tables together, it is important to follow some good practices. First it is good practice to be explicit about column names and use dot notation to specify fully qualified column names. So instead of SELECT description FROM ... use SELECT meeting.description FROM ..., otherwise you will run into weird errors in case you join tables which happen to have columns with same name.

Also, avoid using *, at least in the SQL queries used in your application. When you are requesting all columns from a table in an application, you make:

  • the application inefficient (what if there is a column containing a person photograph?),
  • prone to errors which arise from database changes.

Image that you select data from two tables like SELECT meeting.*, person.* FROM .... The column named description will contain the meeting description. When you then add a description column to the person table, that column will override the first one (you will have two columns with the same name) and the query will suddenly start to return the person description! Such errors are very treacherous so it is best to avoid them in the first place.


Alias allows you to rename a table or a column within a query. E.g. to rename a column you would write:

SELECT id_contact_type AS id, name FROM contact_type

The above query will return a table with columns id and name. The keyword AS can be omitted, so the below query is also valid:

SELECT id_contact_type id, name FROM contact_type

Learn to spot when you need aliases. For example you need them when writing SELECT meeting.description, relation.description FROM ... because in the query result, the column name is never fully qualified - i.e. you will have two columns named description. Therefore use SELECT meeting.description AS meeting_description, relation.description FROM ... (or rename both columns to avoid confusion).

Relation vs. Table

All results of SELECT queries are returned as tables. However not all of them are relations, because a relation must have a key (and no duplicates). For managing duplicates, there are two SQL keywords used: ALL and DISTINCT. For example, when you write:

SELECT city FROM location ORDER BY city;

You will obtain a list of all locations, with only their cities listed. This means that there are duplicates and the result is not a relation. To remove the duplicates use the keyword DISTINCT:


Now the query will return each city only once. The keyword ALL is the opposite of DISTINCT and lists all values including duplicates. As that is the default behavior, the keyword ALL is rarely used (sometimes it is used to emphasize that you really need the duplicates). It is important to understand that the DISTINCT keyword refers to the entire table, i.e. the query:

SELECT DISTINCT city, street_name FROM location ORDER BY city;

will return unique combinations of city and street_name, therefore it will return some cities duplicate. The result will be a relation. It is important to be aware of when you may receive duplicates in the query results because they may change the results of joins slightly unexpectedly.


NULL is a SQL keyword which marks a missing value. It is somewhat similar to a null pointer. Because it has no value, it requires some care when comparing it. For example the below query will return no rows:

SELECT * FROM person WHERE height = NULL

As will the one below:

SELECT * FROM person WHERE height != NULL

Only the query below will really return persons without height:


I.e. it means that NULL = NULL is neither true, nor false. Also there are some things you need to be aware of when counting with NULLs. You must not confuse NULL with 0. All expressions with NULL yield NULL, e.g.:

  • 3 + 0 -> 0
  • 3 + NULL -> NULL
  • 3 / 0 -> ERROR: division by zero
  • 3 / NULL -> NULL

This has some important consequences, e.g. when you have a person table:


A query:

SELECT AVG(height) FROM person

will return 5.

SELECT AVG(height) FROM person

will return 7.5. This means that for example for person height, you cannot replace an unknown value with 0. As you can see in the above example, doing so will return wrong results for e.g. average person height. Therefore it is very important to use NULL values where needed.

Joining Tables

The principle of joining tables comes from the Θ-join operator in relational algebra. To join two tables together, you need to provide a condition which will be used to match individual records. The result of the join will contain all columns of the two source tables. Usually the join condition is the condition defined in a foreign key, although it can really be any condition.

All tables used in the query must be specified in the FROM clause:

SELECT [ ALL | DISTINCT ] column_expression, ...
FROM table_expression
    [ WHERE search_condition ]
    [ GROUP BY column_expression [, ... ] ]
    [ HAVING search_condition ]
    [ ORDER BY { column_expression [ ASC | DESC ] }
        [, column_expression [ASC | DESC ], ... ]
  • A table in the table_expression is any of:
    • relation (a physical table in the schema)
    • view (a virtual table in the schema)
    • result of query (a volatile table) – allows recursion
    • result of join (a volatile table) – allows recursion

Tables are joined using the JOIN operator:

... FROM
*table* [AS *alias*]
*table* [AS *alias*]
	[ ON *join_condition* | USING (*column_name*) ]
[ WHERE ...

For example to join a person to their location you can write:

    person JOIN location
    ON person.id_location = location.id_location

The join will return only the rows for which the join condition is true.

Cross JOIN

The join condition corresponds to the foreign key FOREIGN KEY (id_location) REFERENCES location(id_location) which is defined in the person table. This makes sure that the result makes sense. In case you want to join tables where there is no foreign key, or you want to make a more complicated join condition, you can use a cross join (also called cartesian product or cross product):

    person, location

The above query will return all combinations of rows from person and location. This includes combinations which make no sense at all! If you run the query, be patient for the result, because it contains a lot of rows. Do you know how many?

Number of rows in the person table times the number of rows in the location table. A few above 2400 in the sample database.

To obtain a sensible result – list for each person only the location which is assigned to that person – you again need to add that condition:

    person, location
    WHERE person.id_location = location.id_location

Now the result is exactly the same as the one you have obtained with the JOIN operator. The cross join has two uses:

  • When you have no idea about the join condition, it helps you find it.
  • When you really need all combinations of rows from two tables (which is rare, but happens sometimes).

Otherwise I highly discourage you from using it. Putting the join condition into the WHERE statement is functionally equivalent to putting it into the ON statement. But it leads to chaotic SQL queries in which join conditions and search conditions are mixed together. Which ultimately leads to gross errors where the join conditions become overridden by the search condition and the query returns rows which makes no sense at all. So you should avoid using cross joins.


The join I have described above is an inner join – it returns the matching rows from both tables. It is the default type of a join, but if you want to be explicit (which I strongly suggest), you write it as:

    person INNER JOIN location
    ON person.id_location = location.id_location

This is exactly the same as the query not using the INNER keyword.


An outer join allows you to select matching rows from both tables plus some not matching rows. This is useful in cases where some parts of the relation is optional. For example, let’s say you want to list all persons and their addresses. If you use the INNER join, it will give you only those persons that have an address, so it will not give you all the persons!

To obtain all persons, even those that do not match any location. You need to use an outer join – either LEFT or RIGHT:

    person LEFT OUTER JOIN location
    ON person.id_location = location.id_location


    location RIGHT OUTER JOIN person
    ON person.id_location = location.id_location

The two queries above are equivalent. LEFT JOIN takes all the rows from the left table (to the left of the JOIN operator) – person and matches rows from the remaining table. Or I can say that the LEFT JOIN does an INNER JOIN and then adds unmatched rows from the LEFT table. RIGHT JOIN works analogously. Because the order of tables is not important in the join statement, you can use the LEFT or RIGHT join depending on what currently suits you. Also note that the keyword OUTER is optional (when there is a LEFT or RIGHT join, it is always outer).

An INNER JOIN is symmetric – both tables are equally important. A LEFT / RIGHT OUTER JOIN is asymmetric as it makes one table more important. It is quite important to understand what it means for your data.

    person LEFT JOIN location
    ON person.id_location = location.id_location

The above query will select all persons irrespective of whether they have some location or not. It will always give you all persons even if they do not ‘live’ anywhere.

    person RIGHT JOIN location
    ON person.id_location = location.id_location

The above query will give you all locations irrespective of whether some person is using them. It will always give you all locations even if they are not used anywhere. An outer join is typically used when there is something optional (relations 0..1:1:N or 1..1:0..N). It does not make sense to use it in cases where the relationship is required because it cannot add anything not already returned by INNER JOIN.

The answer to the question whether you want to use an inner or outer join fully depends on the application you are writing. Consider these requests and the corresponding type of join:

  • list all customers and their contact addresses (outer join)
  • list all employees enrolled in a course (inner join)
  • list all licenses (including unused ones) and the employees they are assigned to (outer join)
  • list all occupied meeting rooms (inner join)
  • list all customers who bought something and their contact addresses (inner join and outer join)

JOIN Types

The diagram below shows all the useful join types:

Join types Venn diagram

The INNER join is an intersection of the LEFT and RIGHT join. It contains only the rows common to both tables (and to the LEFT and RIGHT join). Note however that it is not the intersection of the tables. Its counter part is FULL OUTER JOIN which is an union of both the LEFT and RIGHT join as it contains the matched rows and unmatched rows from both the left table and the right table. Although there is nothing wrong with it, the full outer join is rarely used, simply it is not often required in applications.

You may also come across the term equijoin. That is no other join type, it is simply a join which uses equality as the join condition. Most joins are equijoins.


There is an alternative shorter way to write the JOIN statement provided that the join condition uses the two columns with the same name. You can write:

    person LEFT JOIN location
    USING (id_location)

Which is equivalent to

    person LEFT JOIN location
    ON person.id_location = location.id_location

The USING statement allows you to simplify a very common type of a join condition. It does in no way affect the way the tables are joined. You can use it with any of the above modifiers. The only difference is that the resulting table will contain only a single column id_location.


You may also encounter a NATURAL join. The natural join is designed for the same purpose as the USING statement, but it takes it one step further – it automatically assumes that the join condition is equality of all columns with identical names. So this:

    person LEFT JOIN location
    USING (id_location)
    ON person.id_location = location.id_location

is equal to:

    person NATURAL LEFT JOIN location

Which is nice and short, but has some catches. First, unless you know the database, it is unclear what is happening because the join condition is not explicit. This is not good for software developed by more people. Second, natural join will bite you from behind. Let’s say that you want to join tables person and location and use natural join. But both the tables have a description column (you can add it and test it!). Running the query:

    person NATURAL LEFT JOIN location

would then be equivalent to:

    person LEFT JOIN location
    USING (id_location)
    ON person.id_location = location.id_location AND person.description = location.description

So it will probably return no rows at all. And this is very likely something you don’t want to do. Therefore I suggest that you don’t use a NATURAL join and are always explicit about the columns you want to use in your condition.

Joining more than two tables

A single JOIN operator can join only two tables. If you want to join more than two tables, you simply take advantage of the rule that a result of a join is a table as well. If you want to select all persons with all their contacts and their contact_types, you would use:

    (person LEFT JOIN contact
    ON person.id_person = contact.id_person)
    LEFT JOIN contact_type
    ON contact.id_contact_type = contact_type.id_contact_type
ORDER BY person.last_name

In the above query, first the person and contact tables are joined. Then the contact_type is join to the result of that join. I have added parentheses for clarity, but they do not have any effect. If the above seems convoluted to you, imagine that it works similarly to plus operator. E.g. person JOIN contact JOIN contact_type is similar to 5 + 8 + 13.


Union really has nothing to do with join. But since I mentioned intersection and union operations in the join types section, let’s see what SQL UNION operator does. UNION in SQL does normal union of sets. This means that it operates on two sets of same elements. Remember that join operates on two different tables with different attributes and merges those attributes together. The UNION operation requires two tables with same (or at least data-type compatible) attributes. For example, assume that you want to select all relations of Sonny Lona (which has id = 21) in the sample database, let’s start with the query:

    relation JOIN person
    ON relation.id_person2 = person.id_person
WHERE relation.id_person1 = '21'

This will return 4 rows, but the problem is that the relations are always between two persons. So the above query returns relations of Sonny Lona to someone, but not relations of someone to Sonny Lona. These would be returned by the query:

    relation JOIN person
    ON relation.id_person1 = person.id_person
WHERE relation.id_person2 = '21'

Note that I have swapped id_person1 and id_person2 and the result is 6 rows. Now I have two tables with the same attributes and I want to merge the results into a single table:

    relation JOIN person
    ON relation.id_person2 = person.id_person
WHERE relation.id_person1 = '21'


    relation JOIN person
    ON relation.id_person1 = person.id_person
WHERE relation.id_person2 = '21'

ORDER BY first_name

The above query will return all 10 rows. Notice there are some oddities with the UNION statement. UNION is an operator which expects a SELECT statement on both the left and the right side. But the first SELECT statement cannot contain an ORDER BY clause, as that can be used only at the end of the entire query. Also note that in the ORDER BY clause, a fully qualified column name is not necessary because there is only a single column first_name in the entire table. By default UNION removes duplicates from the result, if you wan to keep them, use UNION ALL.

Similar result can be obtained by another query without using UNION:

SELECT relation.*, person1.first_name AS first_name_1, person1.last_name AS last_name_1,
    person2.first_name AS first_name_2, person2.last_name AS last_name_2
    relation JOIN person AS person1
        ON relation.id_person1 = person1.id_person
    JOIN person AS person2
        ON relation.id_person2 = person2.id_person
WHERE relation.id_person1 = '21' OR relation.id_person2 = '21'

The above query is quite interesting because it is a query, where you must use aliases on both tables and columns. It also shows joining more than two tables together and it also shows that nothing prevents you from joining the same table more times. In the above query I first join the table person to relation on the column id_person. Then I again join the table person, but on the column id_person2. This is possible because the table relation has two links to the table person and the join person represents a different person each time (once it is the first person, second time it is the other one). Because the same table is used more than once, it must be renamed with alias (it would be sufficient to name one of them), also the column names must be renamed.


In this article I have described how joining tables works in SQL. The join is used to obtain data from multiple tables and merge their column sets together. The join is a very important part of the SQL language, no real application can do without it. I have also shown the UNION statement which can merge data from two compatible tables together. The union is used much less than the join.

Technically, joins are not too complicated. The complex thing about joining tables is that you have to analyze and understand what the application requires and choose the appropriate join type. This is very important and there is no step-by-step procedure for that, it is something you have to figure out on your own.

New Concepts and Terms

  • database schema
  • join
  • fully qualified name
  • inner join
  • outer join
  • left / right join
  • using
  • union