Manager, Developer Education
Databases are a key component of many websites and applications, and are at the core of how data is stored and exchanged across the internet. One of the most important aspects of database management is the practice of retrieving data from a database, whether it’s on an ad hoc basis or part of a process that’s been coded into an application. There are several ways to retrieve information from a database, but one of the most commonly-used methods is performed through submitting queries through the command line.
In relational database management systems, a query is any command used to retrieve data from a table. In Structured Query Language (SQL), queries are almost always made using the SELECT
statement.
In this guide, we will discuss the basic syntax of SQL queries as well as some of the more commonly-employed functions and operators. We will also practice making SQL queries using some sample data in a PostgreSQL database.
PostgreSQL, often shortened to “Postgres,” is a relational database management system with an object-oriented approach, meaning that information can be represented as objects or classes in PostgreSQL schemas. PostgreSQL aligns closely with standard SQL, although it also includes some features not found in other relational database systems.
In general, the commands and concepts presented in this guide can be used on any Linux-based operating system running any SQL database software. However, it was written specifically with an Ubuntu 18.04 server running PostgreSQL in mind. To set this up, you will need the following:
With this setup in place, we can begin the tutorial.
Before we can begin making queries in SQL, we will first create a database and a couple tables, then populate these tables with some sample data. This will allow you to gain some hands-on experience when you begin making queries later on.
For the sample database we’ll use throughout this guide, imagine the following scenario:
You and several of your friends all celebrate your birthdays with one another. On each occasion, the members of the group head to the local bowling alley, participate in a friendly tournament, and then everyone heads to your place where you prepare the birthday-person’s favorite meal.
Now that this tradition has been going on for a while, you’ve decided to begin tracking the records from these tournaments. Also, to make planning dinners easier, you decide to create a record of your friends’ birthdays and their favorite entrees, sides, and desserts. Rather than keep this information in a physical ledger, you decide to exercise your database skills by recording it in a PostgreSQL database.
To begin, open up a PostgreSQL prompt as your postgres superuser:
- sudo -u postgres psql
Note: If you followed all the steps of the prerequisite tutorial on Installing PostgreSQL on Ubuntu 18.04, you may have configured a new role for your PostgreSQL installation. In this case, you can connect to the Postgres prompt with the following command, substituting sammy
with your own username:
- sudo -u sammy psql
Next, create the database by running:
- CREATE DATABASE birthdays;
Then select this database by typing:
- \c birthdays
Next, create two tables within this database. We’ll use the first table to track your friends’ records at the bowling alley. The following command will create a table called tourneys
with columns for the name
of each of your friends, the number of tournaments they’ve won (wins
), their all-time best
score, and what size bowling shoe they wear (size
):
- CREATE TABLE tourneys (
- name varchar(30),
- wins real,
- best real,
- size real
- );
Once you run the CREATE TABLE
command and populate it with column headings, you’ll receive the following output:
OutputCREATE TABLE
Populate the tourneys
table with some sample data:
- INSERT INTO tourneys (name, wins, best, size)
- VALUES ('Dolly', '7', '245', '8.5'),
- ('Etta', '4', '283', '9'),
- ('Irma', '9', '266', '7'),
- ('Barbara', '2', '197', '7.5'),
- ('Gladys', '13', '273', '8');
You’ll receive the following output:
OutputINSERT 0 5
Following this, create another table within the same database which we’ll use to store information about your friends’ favorite birthday meals. The following command creates a table named dinners
with columns for the name
of each of your friends, their birthdate
, their favorite entree
, their preferred side
dish, and their favorite dessert
:
- CREATE TABLE dinners (
- name varchar(30),
- birthdate date,
- entree varchar(30),
- side varchar(30),
- dessert varchar(30)
- );
Similarly for this table, you’ll receive feedback verifying that the table was created:
OutputCREATE TABLE
Populate this table with some sample data as well:
- INSERT INTO dinners (name, birthdate, entree, side, dessert)
- VALUES ('Dolly', '1946-01-19', 'steak', 'salad', 'cake'),
- ('Etta', '1938-01-25', 'chicken', 'fries', 'ice cream'),
- ('Irma', '1941-02-18', 'tofu', 'fries', 'cake'),
- ('Barbara', '1948-12-25', 'tofu', 'salad', 'ice cream'),
- ('Gladys', '1944-05-28', 'steak', 'fries', 'ice cream');
OutputINSERT 0 5
Once that command completes successfully, you’re done setting up your database. Next, we’ll go over the basic command structure of SELECT
queries.
As mentioned in the introduction, SQL queries almost always begin with the SELECT
statement. SELECT
is used in queries to specify which columns from a table should be returned in the result set. Queries also almost always include FROM
, which is used to specify which table the statement will query.
Generally, SQL queries follow this syntax:
- SELECT column_to_select FROM table_to_select WHERE certain_conditions_apply;
By way of example, the following statement will return the entire name
column from the dinners
table:
- SELECT name FROM dinners;
Output name
---------
Dolly
Etta
Irma
Barbara
Gladys
(5 rows)
You can select multiple columns from the same table by separating their names with a comma, like this:
- SELECT name, birthdate FROM dinners;
Output name | birthdate
---------+------------
Dolly | 1946-01-19
Etta | 1938-01-25
Irma | 1941-02-18
Barbara | 1948-12-25
Gladys | 1944-05-28
(5 rows)
Instead of naming a specific column or set of columns, you can follow the SELECT
operator with an asterisk (*
) which serves as a placeholder representing all the columns in a table. The following command returns every column from the tourneys
table:
- SELECT * FROM tourneys;
Output name | wins | best | size
---------+------+------+------
Dolly | 7 | 245 | 8.5
Etta | 4 | 283 | 9
Irma | 9 | 266 | 7
Barbara | 2 | 197 | 7.5
Gladys | 13 | 273 | 8
(5 rows)
WHERE
is used in queries to filter records that meet a specified condition, and any rows that do not meet that condition are eliminated from the result. A WHERE
clause typically follows this syntax:
- . . . WHERE column_name comparison_operator value
The comparison operator in a WHERE
clause defines how the specified column should be compared against the value. Here are some common SQL comparison operators:
Operator | What it does |
---|---|
= |
tests for equality |
!= |
tests for inequality |
< |
tests for less-than |
> |
tests for greater-than |
<= |
tests for less-than or equal-to |
>= |
tests for greater-than or equal-to |
BETWEEN |
tests whether a value lies within a given range |
IN |
tests whether a row’s value is contained in a set of specified values |
EXISTS |
tests whether rows exist, given the specified conditions |
LIKE |
tests whether a value matches a specified string |
IS NULL |
tests for NULL values |
IS NOT NULL |
tests for all values other than NULL |
For example, if you wanted to find Irma’s shoe size, you could use the following query:
- SELECT size FROM tourneys WHERE name = 'Irma';
Output size
------
7
(1 row)
SQL allows the use of wildcard characters, and these are especially handy when used in WHERE
clauses. Percentage signs (%
) represent zero or more unknown characters, and underscores (_
) represent a single unknown character. These are useful if you’re trying to find a specific entry in a table, but aren’t sure of what that entry is exactly. To illustrate, let’s say that you’ve forgotten the favorite entree of a few of your friends, but you’re certain this particular entree starts with a “t.” You could find its name by running the following query:
- SELECT entree FROM dinners WHERE entree LIKE 't%';
Output entree
-------
tofu
tofu
(2 rows)
Based on the output above, we see that the entree we have forgotten is tofu
.
There may be times when you’re working with databases that have columns or tables with relatively long or difficult-to-read names. In these cases, you can make these names more readable by creating an alias with the AS
keyword. Aliases created with AS
are temporary, and only exist for the duration of the query for which they’re created:
- SELECT name AS n, birthdate AS b, dessert AS d FROM dinners;
Output n | b | d
---------+------------+-----------
Dolly | 1946-01-19 | cake
Etta | 1938-01-25 | ice cream
Irma | 1941-02-18 | cake
Barbara | 1948-12-25 | ice cream
Gladys | 1944-05-28 | ice cream
(5 rows)
Here, we have told SQL to display the name
column as n
, the birthdate
column as b
, and the dessert
column as d
.
The examples we’ve gone through up to this point include some of the more frequently-used keywords and clauses in SQL queries. These are useful for basic queries, but they aren’t helpful if you’re trying to perform a calculation or derive a scalar value (a single value, as opposed to a set of multiple different values) based on your data. This is where aggregate functions come into play.
Oftentimes, when working with data, you don’t necessarily want to see the data itself. Rather, you want information about the data. The SQL syntax includes a number of functions that allow you to interpret or run calculations on your data just by issuing a SELECT
query. These are known as aggregate functions.
The COUNT
function counts and returns the number of rows that match a certain criteria. For example, if you’d like to know how many of your friends prefer tofu for their birthday entree, you could issue this query:
- SELECT COUNT(entree) FROM dinners WHERE entree = 'tofu';
Output count
-------
2
(1 row)
The AVG
function returns the average (mean) value of a column. Using our example table, you could find the average best score amongst your friends with this query:
- SELECT AVG(best) FROM tourneys;
Output avg
-------
252.8
(1 row)
SUM
is used to find the total sum of a given column. For instance, if you’d like to see how many games you and your friends have bowled over the years, you could run this query:
- SELECT SUM(wins) FROM tourneys;
Output sum
-----
35
(1 row)
Note that the AVG
and SUM
functions will only work correctly when used with numeric data. If you try to use them on non-numerical data, it will result in either an error or just 0
, depending on which RDBMS you’re using:
- SELECT SUM(entree) FROM dinners;
OutputERROR: function sum(character varying) does not exist
LINE 1: select sum(entree) from dinners;
^
HINT: No function matches the given name and argument types. You might need to add explicit type casts.
MIN
is used to find the smallest value within a specified column. You could use this query to see what the worst overall bowling record is so far (in terms of number of wins):
- SELECT MIN(wins) FROM tourneys;
Output min
-----
2
(1 row)
Similarly, MAX
is used to find the largest numeric value in a given column. The following query will show the best overall bowling record:
- SELECT MAX(wins) FROM tourneys;
Output max
-----
13
(1 row)
Unlike SUM
and AVG
, the MIN
and MAX
functions can be used for both numeric and alphabetic data types. When run on a column containing string values, the MIN
function will show the first value alphabetically:
- SELECT MIN(name) FROM dinners;
Output min
---------
Barbara
(1 row)
Likewise, when run on a column containing string values, the MAX
function will show the last value alphabetically:
- SELECT MAX(name) FROM dinners;
Output max
------
Irma
(1 row)
Aggregate functions have many uses beyond what was described in this section. They’re particularly useful when used with the GROUP BY
clause, which is covered in the next section along with several other query clauses that affect how result sets are sorted.
In addition to the FROM
and WHERE
clauses, there are several other clauses which are used to manipulate the results of a SELECT
query. In this section, we will explain and provide examples for some of the more commonly-used query clauses.
One of the most frequently-used query clauses, aside from FROM
and WHERE
, is the GROUP BY
clause. It’s typically used when you’re performing an aggregate function on one column, but in relation to matching values in another.
For example, let’s say you wanted to know how many of your friends prefer each of the three entrees you make. You could find this info with the following query:
- SELECT COUNT(name), entree FROM dinners GROUP BY entree;
Output count | entree
-------+---------
1 | chicken
2 | steak
2 | tofu
(3 rows)
The ORDER BY
clause is used to sort query results. By default, numeric values are sorted in ascending order, and text values are sorted in alphabetical order. To illustrate, the following query lists the name
and birthdate
columns, but sorts the results by birthdate:
- SELECT name, birthdate FROM dinners ORDER BY birthdate;
Output name | birthdate
---------+------------
Etta | 1938-01-25
Irma | 1941-02-18
Gladys | 1944-05-28
Dolly | 1946-01-19
Barbara | 1948-12-25
(5 rows)
Notice that the default behavior of ORDER BY
is to sort the result set in ascending order. To reverse this and have the result set sorted in descending order, close the query with DESC
:
- SELECT name, birthdate FROM dinners ORDER BY birthdate DESC;
Output name | birthdate
---------+------------
Barbara | 1948-12-25
Dolly | 1946-01-19
Gladys | 1944-05-28
Irma | 1941-02-18
Etta | 1938-01-25
(5 rows)
As mentioned previously, the WHERE
clause is used to filter results based on specific conditions. However, if you use the WHERE
clause with an aggregate function, it will return an error, as is the case with the following attempt to find which sides are the favorite of at least three of your friends:
- SELECT COUNT(name), side FROM dinners WHERE COUNT(name) >= 3;
OutputERROR: aggregate functions are not allowed in WHERE
LINE 1: SELECT COUNT(name), side FROM dinners WHERE COUNT(name) >= 3...
The HAVING
clause was added to SQL to provide functionality similar to that of the WHERE
clause while also being compatible with aggregate functions. It’s helpful to think of the difference between these two clauses as being that WHERE
applies to individual records, while HAVING
applies to group records. To this end, any time you issue a HAVING
clause, the GROUP BY
clause must also be present.
The following example is another attempt to find which side dishes are the favorite of at least three of your friends, although this one will return a result without error:
- SELECT COUNT(name), side FROM dinners GROUP BY side HAVING COUNT(name) >= 3;
Output count | side
-------+-------
3 | fries
(1 row)
Aggregate functions are useful for summarizing the results of a particular column in a given table. However, there are many cases where it’s necessary to query the contents of more than one table. We’ll go over a few ways you can do this in the next section.
More often than not, a database contains multiple tables, each holding different sets of data. SQL provides a few different ways to run a single query on multiple tables.
The JOIN
clause can be used to combine rows from two or more tables in a query result. It does this by finding a related column between the tables and sorts the results appropriately in the output.
SELECT
statements that include a JOIN
clause generally follow this syntax:
- SELECT table1.column1, table2.column2
- FROM table1
- JOIN table2 ON table1.related_column=table2.related_column;
Note that because JOIN
clauses compare the contents of more than one table, the previous example specifies which table to select each column from by preceding the name of the column with the name of the table and a period. You can specify which table a column should be selected from like this for any query, although it’s not necessary when selecting from a single table, as we’ve done in the previous sections. Let’s walk through an example using our sample data.
Imagine that you wanted to buy each of your friends a pair of bowling shoes as a birthday gift. Because the information about your friends’ birthdates and shoe sizes are held in separate tables, you could query both tables separately then compare the results from each. With a JOIN
clause, though, you can find all the information you want with a single query:
- SELECT tourneys.name, tourneys.size, dinners.birthdate
- FROM tourneys
- JOIN dinners ON tourneys.name=dinners.name;
Output name | size | birthdate
---------+------+------------
Dolly | 8.5 | 1946-01-19
Etta | 9 | 1938-01-25
Irma | 7 | 1941-02-18
Barbara | 7.5 | 1948-12-25
Gladys | 8 | 1944-05-28
(5 rows)
The JOIN
clause used in this example, without any other arguments, is an inner JOIN
clause. This means that it selects all the records that have matching values in both tables and prints them to the results set, while any records that aren’t matched are excluded. To illustrate this idea, let’s add a new row to each table that doesn’t have a corresponding entry in the other:
- INSERT INTO tourneys (name, wins, best, size)
- VALUES ('Bettye', '0', '193', '9');
- INSERT INTO dinners (name, birthdate, entree, side, dessert)
- VALUES ('Lesley', '1946-05-02', 'steak', 'salad', 'ice cream');
Then, re-run the previous SELECT
statement with the JOIN
clause:
- SELECT tourneys.name, tourneys.size, dinners.birthdate
- FROM tourneys
- JOIN dinners ON tourneys.name=dinners.name;
Output name | size | birthdate
---------+------+------------
Dolly | 8.5 | 1946-01-19
Etta | 9 | 1938-01-25
Irma | 7 | 1941-02-18
Barbara | 7.5 | 1948-12-25
Gladys | 8 | 1944-05-28
(5 rows)
Notice that, because the tourneys
table has no entry for Lesley and the dinners
table has no entry for Bettye, those records are absent from this output.
It is possible, though, to return all the records from one of the tables using an outer JOIN
clause. Outer JOIN
clauses are written as either LEFT JOIN
, RIGHT JOIN
, or FULL JOIN
.
A LEFT JOIN
clause returns all the records from the “left” table and only the matching records from the right table. In the context of outer joins, the left table is the one referenced by the FROM
clause, and the right table is any other table referenced after the JOIN
statement.
Run the previous query again, but this time use a LEFT JOIN
clause:
- SELECT tourneys.name, tourneys.size, dinners.birthdate
- FROM tourneys
- LEFT JOIN dinners ON tourneys.name=dinners.name;
This command will return every record from the left table (in this case, tourneys
) even if it doesn’t have a corresponding record in the right table. Any time there isn’t a matching record from the right table, it’s returned as a blank value or NULL
, depending on your RDBMS:
Output name | size | birthdate
---------+------+------------
Dolly | 8.5 | 1946-01-19
Etta | 9 | 1938-01-25
Irma | 7 | 1941-02-18
Barbara | 7.5 | 1948-12-25
Gladys | 8 | 1944-05-28
Bettye | 9 |
(6 rows)
Now run the query again, this time with a RIGHT JOIN
clause:
- SELECT tourneys.name, tourneys.size, dinners.birthdate
- FROM tourneys
- RIGHT JOIN dinners ON tourneys.name=dinners.name;
This will return all the records from the right table (dinners
). Because Lesley’s birthdate is recorded in the right table, but there is no corresponding row for her in the left table, the name
and size
columns will return as blank values in that row:
Output name | size | birthdate
---------+------+------------
Dolly | 8.5 | 1946-01-19
Etta | 9 | 1938-01-25
Irma | 7 | 1941-02-18
Barbara | 7.5 | 1948-12-25
Gladys | 8 | 1944-05-28
| | 1946-05-02
(6 rows)
Note that left and right joins can be written as LEFT OUTER JOIN
or RIGHT OUTER JOIN
, although the OUTER
part of the clause is implied. Likewise, specifying INNER JOIN
will produce the same result as just writing JOIN
.
There is a fourth join clause called FULL JOIN
available for some RDBMS distributions, including PostgreSQL. A FULL JOIN
will return all the records from each table, including any null values:
- SELECT tourneys.name, tourneys.size, dinners.birthdate
- FROM tourneys
- FULL JOIN dinners ON tourneys.name=dinners.name;
Output name | size | birthdate
---------+------+------------
Dolly | 8.5 | 1946-01-19
Etta | 9 | 1938-01-25
Irma | 7 | 1941-02-18
Barbara | 7.5 | 1948-12-25
Gladys | 8 | 1944-05-28
Bettye | 9 |
| | 1946-05-02
(7 rows)
Note: As of this writing, the FULL JOIN
clause is not supported by either MySQL or MariaDB.
As an alternative to using FULL JOIN
to query all the records from multiple tables, you can use the UNION
clause.
The UNION
operator works slightly differently than a JOIN
clause: instead of printing results from multiple tables as unique columns using a single SELECT
statement, UNION
combines the results of two SELECT
statements into a single column.
To illustrate, run the following query:
- SELECT name FROM tourneys UNION SELECT name FROM dinners;
This query will remove any duplicate entries, which is the default behavior of the UNION
operator:
Output name
---------
Irma
Etta
Bettye
Gladys
Barbara
Lesley
Dolly
(7 rows)
To return all entries (including duplicates) use the UNION ALL
operator:
- SELECT name FROM tourneys UNION ALL SELECT name FROM dinners;
Output name
---------
Dolly
Etta
Irma
Barbara
Gladys
Bettye
Dolly
Etta
Irma
Barbara
Gladys
Lesley
(12 rows)
The names and number of the columns in the results table reflect the name and number of columns queried by the first SELECT
statement. Note that when using UNION
to query multiple columns from more than one table, each SELECT
statement must query the same number of columns, the respective columns must have similar data types, and the columns in each SELECT
statement must be in the same order. The following example shows what might result if you use a UNION
clause on two SELECT
statements that query a different number of columns:
- SELECT name FROM dinners UNION SELECT name, wins FROM tourneys;
OutputERROR: each UNION query must have the same number of columns
LINE 1: SELECT name FROM dinners UNION SELECT name, wins FROM tourne...
Another way to query multiple tables is through the use of subqueries. Subqueries (also known as inner or nested queries) are queries enclosed within another query. These are useful in cases where you’re trying to filter the results of a query against the result of a separate aggregate function.
To illustrate this idea, say you want to know which of your friends have won more matches than Barbara. Rather than querying how many matches Barbara has won then running another query to see who has won more games than that, you can calculate both with a single query:
- SELECT name, wins FROM tourneys
- WHERE wins > (
- SELECT wins FROM tourneys WHERE name = 'Barbara'
- );
Output name | wins
--------+------
Dolly | 7
Etta | 4
Irma | 9
Gladys | 13
(4 rows)
The subquery in this statement was run only once; it only needed to find the value from the wins
column in the same row as Barbara
in the name
column, and the data returned by the subquery and outer query are independent of one another. There are cases, though, where the outer query must first read every row in a table and compare those values against the data returned by the subquery in order to return the desired data. In this case, the subquery is referred to as a correlated subquery.
The following statement is an example of a correlated subquery. This query seeks to find which of your friends have won more games than is the average for those with the same shoe size:
- SELECT name, size FROM tourneys AS t
- WHERE wins > (
- SELECT AVG(wins) FROM tourneys WHERE size = t.size
- );
In order for the query to complete, it must first collect the name
and size
columns from the outer query. Then, it compares each row from that result set against the results of the inner query, which determines the average number of wins for individuals with identical shoe sizes. Because you only have two friends that have the same shoe size, there can only be one row in the result set:
Output name | size
------+------
Etta | 9
(1 row)
As mentioned earlier, subqueries can be used to query results from multiple tables. To illustrate this with one final example, say you wanted to throw a surprise dinner for the group’s all-time best bowler. You could find which of your friends has the best bowling record and return their favorite meal with the following query:
- SELECT name, entree, side, dessert
- FROM dinners
- WHERE name = (SELECT name FROM tourneys
- WHERE wins = (SELECT MAX(wins) FROM tourneys));
Output name | entree | side | dessert
--------+--------+-------+-----------
Gladys | steak | fries | ice cream
(1 row)
Notice that this statement not only includes a subquery, but also contains a subquery within that subquery.
Issuing queries is one of the most commonly-performed tasks within the realm of database management. There are a number of database administration tools, such as phpMyAdmin or pgAdmin, that allow you to perform queries and visualize the results, but issuing SELECT
statements from the command line is still a widely-practiced workflow that can also provide you with greater control.
If you’re new to working with SQL, we encourage you to use our SQL Cheat Sheet as a reference and to review the official PostgreSQL documenation. Additionally, if you’d like to learn more about SQL and relational databases, the following tutorials may be of interest to you:
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Thank you, a lot of useful material, but I read it all to the end.