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Markdown
360 lines
11 KiB
Markdown
# SELECT
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As we briefly covered in the previous chapter, the `SELECT` statement allows us to retrieve data from single or multiple tables on the database. In this chapter, we will be performing the query on a single table.
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It corresponds to the projection operation of Relational Algebra.
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You can use `SELECT` to get all of your users or a list of users that match a certain criteria.
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Before we dive into the `SELECT` statement let's quickly create a database:
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```sql
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CREATE DATABASE sql_demo;
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```
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Switch to that database:
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```sql
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USE sql_demo;
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```
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Create a new users table:
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```sql
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CREATE TABLE users
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(
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id INT PRIMARY KEY AUTO_INCREMENT,
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username VARCHAR(255) NOT NULL,
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about TEXT,
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email VARCHAR(255),
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birthday DATE,
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active BOOL
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);
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```
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Insert some data that we could work with:
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```sql
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INSERT INTO users
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( username, email, active )
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VALUES
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('bobby', 'b@devdojo.com', true),
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('devdojo', 'd@devdojo.com', false),
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('tony', 't@devdojo.com', true);
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```
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Output:
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```
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Query OK, 3 rows affected (0.00 sec)
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Records: 3 Duplicates: 0 Warnings: 0
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```
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We are going to learn more about the `INSERT` statement in the following chapters.
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## SELECT all columns
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Now that we've got some data in the `users` table, let's go ahead and retrieve all of the entries from that table:
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```sql
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SELECT * FROM users;
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```
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Rundown of the statement:
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* `SELECT`: First, we specify the action that we want to execute, in our case, we want to select or get some data from the database.
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* `*`: The star here indicates that we want to get all of the columns associated with the table that we are selecting from.
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* `FROM`: The from statement tells MySQL which table we want to select the data from. You need to keep in mind that you can select from multiple tables, but this is a bit more advanced, and we are going to cover this in the next few chapters.
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* `users`: This is the table name that we want to select the data from.
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This will return all of the entries in the `users` table along with all of the columns:
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```
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+----+----------+-------+----------+--------+---------------+
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| id | username | about | birthday | active | email |
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+----+----------+-------+----------+--------+---------------+
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| 1 | bobby | NULL | NULL | 1 | b@devdojo.com |
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| 2 | devdojo | NULL | NULL | 0 | d@devdojo.com |
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| 3 | tony | NULL | NULL | 1 | t@devdojo.com |
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+----+----------+-------+----------+--------+---------------+
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3 rows in set (0.00 sec)
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```
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As you can see, we get a list of the 3 users that we've just created, including all of the columns in that table. In some cases, the table might have a lot of columns, and you might not want to see all of them. For example, we have the `about` and `birthday` columns that are all `NULL` at the moment. So let's see how we could limit that and get only a list of specific columns.
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## Pattern matching
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SQL pattern matching let's you to search for patterns if you don't know the exact word or phrase you are looking for. To do this, we use so-called wildcard characters to match a pattern together with LIKE and ILIKE operators.
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Two of the most common wildcard characters are `_` and `%`.
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`_` matches any single character and `%` matches an arbitrary number of characters.
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Let's see an example how you would look for a `username` ending with `y`:
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```sql
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SELECT * FROM users WHERE username LIKE '%y';
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```
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Output:
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```
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+----+----------+-------+----------+--------+---------------+
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| id | username | about | birthday | active | email |
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+----+----------+-------+----------+--------+---------------+
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| 1 | bobby | NULL | NULL | 1 | b@devdojo.com |
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| 3 | tony | NULL | NULL | 1 | t@devdojo.com |
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+----+----------+-------+----------+--------+---------------+
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```
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As you can see above, we used `%` to match any number of characters preceding the character `y`.
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If we know the exact number of characters we want to match, we can use `_`. Each `_` represents a single character.
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So, if we want to look up an username that has `e` as its second character, we would do something like this:
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```sql
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SELECT * FROM users WHERE username LIKE '_e%';
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```
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Output:
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```
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+----+----------+-------+----------+--------+---------------+
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| id | username | about | birthday | active | email |
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+----+----------+-------+----------+--------+---------------+
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| 2 | devdojo | NULL | NULL | 0 | d@devdojo.com |
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+----+----------+-------+----------+--------+---------------+
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```
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Please, keep in mind that `LIKE` operator is case sensitive, meaning it won't mach capital letters with lowercase letters and vice versa. If you wish to ignore capitalization, use `ILIKE` operator instead.
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## Formatting
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As we mentioned in the previous chapters, each SQL statement needs to end with a semi-colon: `;`. Alternatively, rather than using a semi-colon, you could use the `\G` characters which would format the output in a list rather than a table.
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The syntax is absolutely the same but you just change the `;` with `\G`:
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```sql
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SELECT * FROM users \G
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```
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The output will be formatted like this:
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```
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*************************** 1. row ***************************
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id: 1
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username: bobby
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about: NULL
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birthday: NULL
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active: 1
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email: b@devdojo.com
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*************************** 2. row ***************************
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id: 2
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username: devdojo
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about: NULL
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birthday: NULL
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active: 0
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email: d@devdojo.com
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...
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```
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This is very handy whenever your table consists of a large number of columns and they can't fit on the screen, which makes it very hard to read the result set.
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## SELECT specific columns only
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You could limit this to a specific set of columns. Let's say that you only needed the `username` and the `active` columns. In this case, you would change the `*` symbol with the columns that you want to select divided by a comma:
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```sql
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SELECT username,active FROM users;
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```
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Output:
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```
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+----------+--------+
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| username | active |
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+----------+--------+
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| bobby | 1 |
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| devdojo | 0 |
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| tony | 1 |
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+----------+--------+
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```
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As you can see, we are getting back only the 2 columns that we've specified in the `SELECT` statement.
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> **NOTE:** _SQL names are case insensitive. For example, username ≡ USERNAME ≡ userName._
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## SELECT with no FROM Clause
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In a SQL statement, a column can be a literal with no `FROM` clause.
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```sql
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SELECT 'Sunil' as username;
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```
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Output:
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```
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+----------+
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| username |
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+----------+
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| Sunil |
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+----------+
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```
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## SELECT with Arithmetic Operations
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The select clause can contain arithmetic expressions involving the operation +, –, *, and /.
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```sql
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SELECT username, active*5 as new_active FROM users;
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```
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Output:
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```
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+----------+------------+
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| username | new_active |
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+----------+------------+
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| bobby | 5 |
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| devdojo | 0 |
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| tony | 5 |
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+----------+------------+
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```
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## LIMIT
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The `LIMIT` clause is very handy in case that you want to limit the number of results that you get back. For example, at the moment, we have 3 users in our database, but let's say that you only wanted to get 1 entry back when you run the `SELECT` statement.
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This can be achieved by adding the `LIMIT` clause at the end of your statement, followed by the number of entries that you want to get. For example, let's say that we wanted to get only 1 entry back. We would run the following query:
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```sql
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SELECT * FROM users LIMIT 1;
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```
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Output:
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```
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+----+----------+-------+----------+--------+---------------+
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| id | username | about | birthday | active | email |
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+----+----------+-------+----------+--------+---------------+
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| 2 | bobby | NULL | NULL | 1 | b@devdojo.com |
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+----+----------+-------+----------+--------+---------------+
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```
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If you wanted to get 2 entries, you would change `LIMIT 2` and so on.
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## COUNT
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In case that you wanted to get only the number of entries in a specific column, you could use the `COUNT` function. This is a function that I personally use very often.
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The syntax is the following:
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```sql
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SELECT COUNT(*) FROM users;
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```
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Output:
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```
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+----------+
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| COUNT(*) |
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+----------+
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| 3 |
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+----------+
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```
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## MIN, MAX, AVG, and SUM
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Another useful set of functions similar to `COUNT` that would make your life easier are:
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* `MIN`: This would give you the smallest value of a specific column. For example, if you had an online shop and you wanted to get the lowest price, you would use the `MIN` function. In our case, if we wanted to get the lowest user ID, we would run the following:
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```sql
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SELECT MIN(id) FROM users;
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```
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This would return `1` as the lowest user ID that we have is 1.
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* `MAX`: Just like `MIN`, but it would return the highest value:
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```sql
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SELECT MAX(id) FROM users;
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```
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In our case, this would be `3` as we have only 3 users, and the highest value of the `id` column is 3.
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* `AVG`: As the name suggests, it would sum up all of the values of a specific column and return the average value. As we have 3 users with ids 1, 2, and 3, the average would be 6 divided by 3 users which is 2.
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```sql
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SELECT AVG(id) FROM users;
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```
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* `SUM`: This function takes all of the values from the specified column and sums them up:
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```sql
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SELECT SUM(id) FROM users;
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```
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## DISTINCT
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In some cases, you might have duplicate entries in a table, and in order to get only the unique values, you could use `DISTINCT`.
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To better demonstrate this, let's run the insert statement one more time so that we could duplicate the existing users and have 6 users in the users table:
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```sql
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INSERT INTO users
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( username, email, active )
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VALUES
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('bobby', 'b@devdojo.com', true),
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('devdojo', 'd@devdojo.com', false),
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('tony', 't@devdojo.com', true);
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```
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Now, if you run `SELECT COUNT(*) FROM users;` you would get `6` back.
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Let's also select all users and show only the `username` column:
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```sql
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SELECT username FROM users;
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```
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Output:
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```
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+----------+
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| username |
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+----------+
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| bobby |
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| devdojo |
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| tony |
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| bobby |
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| devdojo |
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| tony |
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+----------+
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```
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As you can see, each name is present multiple times in the list. We have `bobby`, `devdjo` and `tony` showing up twice.
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If we wanted to show only the unique `usernames`, we could add the `DISTINCT` keyword to our select statement:
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```sql
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SELECT DISTINCT username FROM users;
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```
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Output:
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```
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+----------+
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| username |
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+----------+
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| bobby |
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| devdojo |
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| tony |
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+----------+
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```
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As you can see, the duplicate entries have been removed from the output.
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## Conclusion
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The `SELECT` statement is essential whenever working with SQL. In the next chapter, we are going to learn how to use the `WHERE` clause and take the `SELECT` statements to the next level.
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