1. Introduction
In this blog, you will learn how to use the JOIN clause to combine data from multiple PostgreSQL tables. You will learn about the different types of joins and how to write them in SQL.
Joining tables is a common operation in relational databases, as it allows you to query data from multiple tables as if they were a single table. For example, you might have a table of customers and a table of orders, and you want to find out which customers placed which orders. By joining the two tables, you can get a result set that shows the customer name, order date, and order amount for each order.
There are four main types of joins that you can use in PostgreSQL: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN. Each type of join has a different way of handling the rows that do not match between the tables. In this blog, you will see how each type of join works and what are the benefits and drawbacks of using them.
To follow along with this blog, you will need to have PostgreSQL installed and running on your system. You will also need to create some sample tables and insert some data into them. You can find the instructions on how to do that in the next section.
Are you ready to learn how to join PostgreSQL tables? Let’s get started!
2. What is a JOIN clause?
A JOIN clause is a SQL keyword that allows you to combine data from two or more PostgreSQL tables based on a common column or condition. The result of a join is a new table that contains the columns and rows from both tables that satisfy the join condition.
For example, suppose you have two tables: customers and orders. The customers table has the columns customer_id, name, and email. The orders table has the columns order_id, customer_id, date, and amount. The customer_id column is the common column between the two tables, as it identifies which customer placed which order.
If you want to get the name and email of each customer along with their order details, you can use a join clause to combine the two tables. The join clause will match the rows from the customers table with the rows from the orders table based on the customer_id column. The result will be a new table that has the columns name, email, order_id, date, and amount.
The syntax of a join clause is as follows:
SELECT column_list FROM table1 JOIN table2 ON join_condition;
The column_list specifies which columns you want to select from the joined tables. The table1 and table2 are the names of the tables you want to join. The join_condition is the expression that defines how the tables are matched. Usually, the join_condition is an equality comparison between a column from each table.
For example, the following query uses a join clause to combine the customers and orders tables:
SELECT name, email, order_id, date, amount FROM customers JOIN orders ON customers.customer_id = orders.customer_id;
The output of this query will look something like this:
| name | order_id | date | amount | |
|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 |
| David | david@example.com | 4 | 2024-01-25 | 400 |
As you can see, the join clause has created a new table that contains the information from both tables that are related by the customer_id column.
However, not all joins are as simple as this. Depending on the type of join you use, you might get different results. In the next section, you will learn about the different types of joins and how they affect the output of the join clause.
2.1. Syntax of a JOIN clause
In this section, you will learn the basic syntax of a JOIN clause in PostgreSQL. You will also see how to use different keywords to specify the type of join you want to perform.
The general syntax of a join clause is as follows:
SELECT column_list FROM table1 [JOIN_TYPE] JOIN table2 ON join_condition;
The column_list specifies which columns you want to select from the joined tables. The table1 and table2 are the names of the tables you want to join. The join_condition is the expression that defines how the tables are matched. Usually, the join_condition is an equality comparison between a column from each table.
The JOIN_TYPE is an optional keyword that indicates the type of join you want to perform. There are four main types of joins in PostgreSQL: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN. Each type of join has a different way of handling the rows that do not match between the tables. You will learn more about each type of join in the following sections.
If you omit the JOIN_TYPE keyword, PostgreSQL will assume that you want to perform an INNER JOIN by default. Therefore, the following two queries are equivalent:
SELECT column_list FROM table1 JOIN table2 ON join_condition; SELECT column_list FROM table1 INNER JOIN table2 ON join_condition;
However, it is a good practice to always specify the JOIN_TYPE keyword to avoid confusion and make your code more readable.
Now that you know the syntax of a join clause, let’s see some examples of how to use it to combine data from multiple tables.
2.2. Types of joins
In this section, you will learn about the different types of joins that you can use in PostgreSQL to combine data from multiple tables. You will see how each type of join affects the output of the join clause and what are the advantages and disadvantages of using them.
As you learned in the previous section, a JOIN clause allows you to match rows from two or more tables based on a common column or condition. However, not all rows from the tables may have a matching row in the other table. For example, you might have some customers who have not placed any orders, or some orders that have no customer information. How do you handle these cases?
The answer depends on the type of join you use. There are four main types of joins in PostgreSQL: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN. Each type of join has a different way of dealing with the rows that do not match between the tables. Here is a brief overview of each type of join:
- INNER JOIN: This is the default type of join that you get when you use the JOIN keyword without specifying the join type. An INNER JOIN returns only the rows that have a matching row in both tables. Any rows that do not match are excluded from the result. This type of join is useful when you want to find the intersection of two tables.
- LEFT JOIN: This type of join returns all the rows from the left table (the first table in the join clause) and the matching rows from the right table (the second table in the join clause). If there is no matching row in the right table, the left table row is still included in the result, but the columns from the right table are filled with NULL values. This type of join is useful when you want to find all the rows from the left table and see if they have any corresponding rows in the right table.
- RIGHT JOIN: This type of join is the opposite of a LEFT JOIN. It returns all the rows from the right table and the matching rows from the left table. If there is no matching row in the left table, the right table row is still included in the result, but the columns from the left table are filled with NULL values. This type of join is useful when you want to find all the rows from the right table and see if they have any corresponding rows in the left table.
- FULL JOIN: This type of join returns all the rows from both tables, regardless of whether they have a matching row in the other table or not. If there is no matching row in either table, the columns from the other table are filled with NULL values. This type of join is useful when you want to find the union of two tables and see the differences between them.
To better understand how each type of join works, let’s look at some examples in the next sections.
3. INNER JOIN
In this section, you will learn how to use an INNER JOIN to combine data from two or more PostgreSQL tables. You will see how an INNER JOIN returns only the rows that have a matching row in both tables and how to write an INNER JOIN query in SQL.
An INNER JOIN is the default type of join that you get when you use the JOIN keyword without specifying the join type. An INNER JOIN matches the rows from the first table with the rows from the second table based on the join condition. If the join condition is satisfied, the columns from both tables are included in the result. If the join condition is not satisfied, the row is excluded from the result.
An INNER JOIN is useful when you want to find the intersection of two tables, that is, the rows that have a common value in both tables. For example, you might want to find the customers who have placed orders, or the orders that have customer information.
To perform an INNER JOIN, you need to specify the JOIN keyword followed by the name of the second table and the ON keyword followed by the join condition. The join condition is usually an equality comparison between a column from each table. For example, the following query uses an INNER JOIN to combine the customers and orders tables based on the customer_id column:
SELECT name, email, order_id, date, amount FROM customers INNER JOIN orders ON customers.customer_id = orders.customer_id;
The output of this query will look something like this:
| name | order_id | date | amount | |
|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 |
| David | david@example.com | 4 | 2024-01-25 | 400 |
As you can see, the INNER JOIN has returned only the rows that have a matching customer_id in both tables. Any rows that do not have a matching customer_id are excluded from the result. For example, if there is a customer who has not placed any order, or an order that has no customer information, they will not appear in the output.
An INNER JOIN can also be used to join more than two tables, as long as there is a common column or condition that links them. For example, if you have a third table called products that has the columns product_id, name, and price, and you want to get the name and price of each product that was ordered by each customer, you can use an INNER JOIN to combine the three tables. The query will look something like this:
SELECT customers.name, customers.email, orders.order_id, orders.date, orders.amount, products.name, products.price FROM customers INNER JOIN orders ON customers.customer_id = orders.customer_id INNER JOIN products ON orders.product_id = products.product_id;
The output of this query will look something like this:
| customers.name | customers.email | orders.order_id | orders.date | orders.amount | products.name | products.price |
|---|---|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 | Book | 10 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 | Laptop | 100 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 | Phone | 50 |
| David | david@example.com | 4 | 2024-01-25 | 400 | Tablet | 80 |
In this case, the INNER JOIN has returned only the rows that have a matching customer_id, order_id, and product_id in all three tables. Any rows that do not have a matching value in any of the tables are excluded from the result.
An INNER JOIN is a powerful way to combine data from multiple tables and find the common values between them. However, it also has some drawbacks that you should be aware of. In the next section, you will learn about the benefits and drawbacks of using an INNER JOIN and how to avoid some common pitfalls.
3.1. Example of an INNER JOIN
In this section, you will see an example of how to use an INNER JOIN to combine data from two PostgreSQL tables. You will also learn how an INNER JOIN returns only the rows that have a matching row in both tables and how to write an INNER JOIN query in SQL.
Suppose you have two tables: customers and orders. The customers table has the columns customer_id, name, and email. The orders table has the columns order_id, customer_id, date, and amount. The customer_id column is the common column between the two tables, as it identifies which customer placed which order.
If you want to get the name and email of each customer along with their order details, you can use an INNER JOIN to combine the two tables. The INNER JOIN will match the rows from the customers table with the rows from the orders table based on the customer_id column. If the customer_id values are equal, the columns from both tables are included in the result. If the customer_id values are not equal, the row is excluded from the result.
To perform an INNER JOIN, you need to specify the JOIN keyword followed by the name of the second table and the ON keyword followed by the join condition. The join condition is usually an equality comparison between a column from each table. For example, the following query uses an INNER JOIN to combine the customers and orders tables based on the customer_id column:
SELECT name, email, order_id, date, amount FROM customers INNER JOIN orders ON customers.customer_id = orders.customer_id;
The output of this query will look something like this:
| name | order_id | date | amount | |
|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 |
| David | david@example.com | 4 | 2024-01-25 | 400 |
As you can see, the INNER JOIN has returned only the rows that have a matching customer_id in both tables. Any rows that do not have a matching customer_id are excluded from the result. For example, if there is a customer who has not placed any order, or an order that has no customer information, they will not appear in the output.
An INNER JOIN is a simple and effective way to combine data from multiple tables and find the common values between them. However, it also has some limitations that you should be aware of. In the next section, you will learn about the benefits and drawbacks of using an INNER JOIN and how to avoid some common pitfalls.
3.2. Benefits and drawbacks of an INNER JOIN
In this section, you will learn about the benefits and drawbacks of using an INNER JOIN to combine data from two or more PostgreSQL tables. You will also learn how to avoid some common pitfalls and errors when using an INNER JOIN.
An INNER JOIN is a simple and effective way to find the common values between two tables. It can help you to answer questions such as:
- Which customers have placed orders?
- Which products have been ordered by which customers?
- Which orders have customer and product information?
An INNER JOIN can also help you to improve the performance and efficiency of your queries, as it reduces the number of rows that need to be processed and returned. By using an INNER JOIN, you can avoid unnecessary data and focus on the relevant information.
However, an INNER JOIN also has some limitations and drawbacks that you should be aware of. Some of the possible disadvantages of using an INNER JOIN are:
- An INNER JOIN can cause data loss, as it excludes any rows that do not have a matching row in both tables. This means that you might miss some important information that is only available in one of the tables. For example, if you use an INNER JOIN to combine the customers and orders tables, you will not see any customers who have not placed any orders, or any orders that have no customer information. This might lead to inaccurate or incomplete results.
- An INNER JOIN can cause duplicate rows, as it returns all the rows that have a matching row in both tables. This means that you might see the same information repeated multiple times in the result. For example, if you use an INNER JOIN to combine the customers and orders tables, and a customer has placed more than one order, you will see the same customer information repeated for each order. This might lead to redundant or unnecessary data.
- An INNER JOIN can cause errors, as it requires a valid join condition that links the two tables. If you do not specify a join condition, or if you specify an invalid or incorrect join condition, you will get an error or a wrong result. For example, if you use an INNER JOIN to combine the customers and orders tables, and you forget to specify the customer_id column as the join condition, you will get an error. If you specify a different column as the join condition, such as the name column, you will get a wrong result, as the name column might not be unique or consistent in both tables.
To avoid these drawbacks and errors, you should always use an INNER JOIN with caution and care. Here are some tips and best practices to follow when using an INNER JOIN:
- Always specify the join type as INNER JOIN, even if it is the default type. This will make your code more clear and readable, and avoid confusion with other types of joins.
- Always specify a valid and correct join condition that links the two tables. Use the ON keyword followed by an equality comparison between a column from each table. Make sure that the column you use as the join condition is the same or compatible data type in both tables, and that it has a meaningful relationship between the tables.
- Always check the output of your query and verify that it matches your expectations. Use the COUNT(*) function to see how many rows are returned by your query, and compare it with the number of rows in each table. Use the DISTINCT keyword to eliminate any duplicate rows from your result. Use the WHERE clause to filter out any unwanted or irrelevant rows from your result.
An INNER JOIN is a powerful and useful tool to combine data from multiple tables and find the common values between them. However, it also has some drawbacks and limitations that you should be aware of. By following the tips and best practices in this section, you can use an INNER JOIN effectively and avoid some common pitfalls and errors.
4. LEFT JOIN
In this section, you will learn how to use a LEFT JOIN to combine data from two or more PostgreSQL tables. You will see how a LEFT JOIN returns all the rows from the left table and the matching rows from the right table and how to write a LEFT JOIN query in SQL.
A LEFT JOIN is a type of join that returns all the rows from the left table (the first table in the join clause) and the matching rows from the right table (the second table in the join clause). If there is no matching row in the right table, the left table row is still included in the result, but the columns from the right table are filled with NULL values.
A LEFT JOIN is useful when you want to find all the rows from the left table and see if they have any corresponding rows in the right table. For example, you might want to find all the customers and their order details, even if some customers have not placed any orders.
To perform a LEFT JOIN, you need to specify the LEFT JOIN keyword followed by the name of the second table and the ON keyword followed by the join condition. The join condition is usually an equality comparison between a column from each table. For example, the following query uses a LEFT JOIN to combine the customers and orders tables based on the customer_id column:
SELECT name, email, order_id, date, amount FROM customers LEFT JOIN orders ON customers.customer_id = orders.customer_id;
The output of this query will look something like this:
| name | order_id | date | amount | |
|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 |
| David | david@example.com | 4 | 2024-01-25 | 400 |
| Eve | eve@example.com | NULL | NULL | NULL |
| Frank | frank@example.com | NULL | NULL | NULL |
As you can see, the LEFT JOIN has returned all the rows from the customers table and the matching rows from the orders table. Any rows from the customers table that do not have a matching row in the orders table are still included in the result, but the columns from the orders table are filled with NULL values. For example, Eve and Frank are customers who have not placed any orders, so their order details are NULL.
A LEFT JOIN can also be used to join more than two tables, as long as there is a common column or condition that links them. For example, if you have a third table called products that has the columns product_id, name, and price, and you want to get the name and price of each product that was ordered by each customer, even if some customers have not ordered any products, you can use a LEFT JOIN to combine the three tables. The query will look something like this:
SELECT customers.name, customers.email, orders.order_id, orders.date, orders.amount, products.name, products.price FROM customers LEFT JOIN orders ON customers.customer_id = orders.customer_id LEFT JOIN products ON orders.product_id = products.product_id;
The output of this query will look something like this:
| customers.name | customers.email | orders.order_id | orders.date | orders.amount | products.name | products.price |
|---|---|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 | Book | 10 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 | Laptop | 100 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 | Phone | 50 |
| David | david@example.com | 4 | 2024-01-25 | 400 | Tablet | 80 |
| Eve | eve@example.com | NULL | NULL | NULL | NULL | NULL |
| Frank | frank@example.com | NULL | NULL | NULL | NULL | NULL |
In this case, the LEFT JOIN has returned all the rows from the customers table and the matching rows from the orders and products tables. Any rows from the customers table that do not have a matching row in the orders or products tables are still included in the result, but the columns from the other tables are filled with NULL values.
A LEFT JOIN is a powerful way to combine data from multiple tables and find all the rows from the left table and their corresponding rows in the right table. However, it also has some drawbacks that you should be aware of. In the next section, you will learn about the benefits and drawbacks of using a LEFT JOIN and how to avoid some common pitfalls.
4.1. Example of a LEFT JOIN
In this section, you will see an example of how to use a LEFT JOIN to combine data from two PostgreSQL tables. You will also learn how a LEFT JOIN returns all the rows from the left table and the matching rows from the right table and how to write a LEFT JOIN query in SQL.
A LEFT JOIN is a type of join that returns all the rows from the left table (the first table in the join clause) and the matching rows from the right table (the second table in the join clause). If there is no matching row in the right table, the left table row is still included in the result, but the columns from the right table are filled with NULL values.
A LEFT JOIN is useful when you want to find all the rows from the left table and see if they have any corresponding rows in the right table. For example, you might want to find all the customers and their order details, even if some customers have not placed any orders.
To perform a LEFT JOIN, you need to specify the LEFT JOIN keyword followed by the name of the second table and the ON keyword followed by the join condition. The join condition is usually an equality comparison between a column from each table. For example, the following query uses a LEFT JOIN to combine the customers and orders tables based on the customer_id column:
SELECT name, email, order_id, date, amount FROM customers LEFT JOIN orders ON customers.customer_id = orders.customer_id;
The output of this query will look something like this:
| name | order_id | date | amount | |
|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 |
| David | david@example.com | 4 | 2024-01-25 | 400 |
| Eve | eve@example.com | NULL | NULL | NULL |
| Frank | frank@example.com | NULL | NULL | NULL |
As you can see, the LEFT JOIN has returned all the rows from the customers table and the matching rows from the orders table. Any rows from the customers table that do not have a matching row in the orders table are still included in the result, but the columns from the orders table are filled with NULL values. For example, Eve and Frank are customers who have not placed any orders, so their order details are NULL.
A LEFT JOIN can also be used to join more than two tables, as long as there is a common column or condition that links them. For example, if you have a third table called products that has the columns product_id, name, and price, and you want to get the name and price of each product that was ordered by each customer, even if some customers have not ordered any products, you can use a LEFT JOIN to combine the three tables. The query will look something like this:
SELECT customers.name, customers.email, orders.order_id, orders.date, orders.amount, products.name, products.price FROM customers LEFT JOIN orders ON customers.customer_id = orders.customer_id LEFT JOIN products ON orders.product_id = products.product_id;
The output of this query will look something like this:
| customers.name | customers.email | orders.order_id | orders.date | orders.amount | products.name | products.price |
|---|---|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 | Book | 10 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 | Laptop | 100 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 | Phone | 50 |
| David | david@example.com | 4 | 2024-01-25 | 400 | Tablet | 80 |
| Eve | eve@example.com | NULL | NULL | NULL | NULL | NULL |
| Frank | frank@example.com | NULL | NULL | NULL | NULL | NULL |
In this case, the LEFT JOIN has returned all the rows from the customers table and the matching rows from the orders and products tables. Any rows from the customers table that do not have a matching row in the orders or products tables are still included in the result, but the columns from the other tables are filled with NULL values.
A LEFT JOIN is a powerful way to combine data from multiple tables and find all the rows from the left table and their corresponding rows in the right table. However, it also has some drawbacks that you should be aware of. In the next section, you will learn about the benefits and drawbacks of using a LEFT JOIN and how to avoid some common pitfalls.
4.2. Benefits and drawbacks of a LEFT JOIN
A LEFT JOIN is a type of join that combines data from two PostgreSQL tables and returns all the rows from the left table, regardless of whether they have a matching row in the right table or not. If there is no match, the columns from the right table will have null values in the result set.
One of the benefits of using a LEFT JOIN is that it allows you to retrieve all the data from the left table, even if some of it is not related to the data in the right table. This can be useful when you want to analyze the data in the left table and see how it relates to the data in the right table, if at all. For example, you might want to see which customers have not placed any orders, or which products have not been sold.
Another benefit of using a LEFT JOIN is that it preserves the order of the rows in the left table, which can be important when you want to maintain the original sequence of the data. For example, you might want to see the chronological order of the customer registrations, or the alphabetical order of the product names.
However, a LEFT JOIN also has some drawbacks that you should be aware of. One of the drawbacks is that it can produce a large result set that contains many null values, which can make the data less readable and more difficult to process. For example, if you have a large left table with many rows that do not have a match in the right table, you will end up with a lot of rows that have null values in the right table columns.
Another drawback of using a LEFT JOIN is that it can affect the performance of your query, as it requires more processing time and memory than an INNER JOIN. This is because a LEFT JOIN has to scan the entire left table and compare each row with the right table, while an INNER JOIN only has to scan the rows that have a match. Therefore, you should use a LEFT JOIN only when you need to get all the data from the left table, and not when you only need the data that has a match in the right table.
In summary, a LEFT JOIN is a useful tool to combine data from two PostgreSQL tables and get all the data from the left table, regardless of whether it has a match in the right table or not. However, you should also consider the drawbacks of using a LEFT JOIN, such as the potential size of the result set, the presence of null values, and the impact on the query performance.
5. RIGHT JOIN
A RIGHT JOIN is a type of join that combines data from two PostgreSQL tables and returns all the rows from the right table, regardless of whether they have a matching row in the left table or not. If there is no match, the columns from the left table will have null values in the result set.
One of the benefits of using a RIGHT JOIN is that it allows you to retrieve all the data from the right table, even if some of it is not related to the data in the left table. This can be useful when you want to analyze the data in the right table and see how it relates to the data in the left table, if at all. For example, you might want to see which orders have not been assigned to any customer, or which products have been sold but not delivered.
Another benefit of using a RIGHT JOIN is that it preserves the order of the rows in the right table, which can be important when you want to maintain the original sequence of the data. For example, you might want to see the chronological order of the order dates, or the alphabetical order of the product categories.
However, a RIGHT JOIN also has some drawbacks that you should be aware of. One of the drawbacks is that it can produce a large result set that contains many null values, which can make the data less readable and more difficult to process. For example, if you have a large right table with many rows that do not have a match in the left table, you will end up with a lot of rows that have null values in the left table columns.
Another drawback of using a RIGHT JOIN is that it can affect the performance of your query, as it requires more processing time and memory than an INNER JOIN. This is because a RIGHT JOIN has to scan the entire right table and compare each row with the left table, while an INNER JOIN only has to scan the rows that have a match. Therefore, you should use a RIGHT JOIN only when you need to get all the data from the right table, and not when you only need the data that has a match in the left table.
In summary, a RIGHT JOIN is a useful tool to combine data from two PostgreSQL tables and get all the data from the right table, regardless of whether it has a match in the left table or not. However, you should also consider the drawbacks of using a RIGHT JOIN, such as the potential size of the result set, the presence of null values, and the impact on the query performance.
5.1. Example of a RIGHT JOIN
In this section, you will see an example of how to use a RIGHT JOIN to combine data from two PostgreSQL tables. You will use the same sample tables as in the previous sections: customers and orders.
A RIGHT JOIN returns all the rows from the right table, regardless of whether they have a matching row in the left table or not. If there is no match, the columns from the left table will have null values in the result set.
For example, suppose you want to get the order details along with the customer name and email, if any. You can use a RIGHT JOIN to combine the customers and orders tables, as follows:
SELECT name, email, order_id, date, amount FROM customers RIGHT JOIN orders ON customers.customer_id = orders.customer_id;
The output of this query will look something like this:
| name | order_id | date | amount | |
|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 |
| David | david@example.com | 4 | 2024-01-25 | 400 |
| null | null | 5 | 2024-01-30 | 500 |
| null | null | 6 | 2024-02-05 | 600 |
As you can see, the RIGHT JOIN has returned all the rows from the orders table, including the ones that do not have a matching customer_id in the customers table. The name and email columns for those rows have null values, indicating that there is no corresponding customer for those orders.
This way, you can use a RIGHT JOIN to get all the data from the right table, even if some of it is not related to the data in the left table.
5.2. Benefits and drawbacks of a RIGHT JOIN
A RIGHT JOIN is a type of join that combines data from two PostgreSQL tables and returns all the rows from the right table, regardless of whether they have a matching row in the left table or not. If there is no match, the columns from the left table will have null values in the result set.
One of the benefits of using a RIGHT JOIN is that it allows you to retrieve all the data from the right table, even if some of it is not related to the data in the left table. This can be useful when you want to analyze the data in the right table and see how it relates to the data in the left table, if at all. For example, you might want to see which orders have not been assigned to any customer, or which products have been sold but not delivered.
Another benefit of using a RIGHT JOIN is that it preserves the order of the rows in the right table, which can be important when you want to maintain the original sequence of the data. For example, you might want to see the chronological order of the order dates, or the alphabetical order of the product categories.
However, a RIGHT JOIN also has some drawbacks that you should be aware of. One of the drawbacks is that it can produce a large result set that contains many null values, which can make the data less readable and more difficult to process. For example, if you have a large right table with many rows that do not have a match in the left table, you will end up with a lot of rows that have null values in the left table columns.
Another drawback of using a RIGHT JOIN is that it can affect the performance of your query, as it requires more processing time and memory than an INNER JOIN. This is because a RIGHT JOIN has to scan the entire right table and compare each row with the left table, while an INNER JOIN only has to scan the rows that have a match. Therefore, you should use a RIGHT JOIN only when you need to get all the data from the right table, and not when you only need the data that has a match in the left table.
In summary, a RIGHT JOIN is a useful tool to combine data from two PostgreSQL tables and get all the data from the right table, regardless of whether it has a match in the left table or not. However, you should also consider the drawbacks of using a RIGHT JOIN, such as the potential size of the result set, the presence of null values, and the impact on the query performance.
6. FULL JOIN
A FULL JOIN is a type of join that combines data from two PostgreSQL tables and returns all the rows from both tables, regardless of whether they have a matching row in the other table or not. If there is no match, the columns from the other table will have null values in the result set.
One of the benefits of using a FULL JOIN is that it allows you to retrieve all the data from both tables, even if some of it is not related to the data in the other table. This can be useful when you want to analyze the data in both tables and see how they relate to each other, if at all. For example, you might want to see which customers have placed orders, which orders have been assigned to customers, and which customers and orders have no relation at all.
Another benefit of using a FULL JOIN is that it preserves the order of the rows in both tables, which can be important when you want to maintain the original sequence of the data. For example, you might want to see the chronological order of the customer registrations and the order dates, or the alphabetical order of the customer names and the product names.
However, a FULL JOIN also has some drawbacks that you should be aware of. One of the drawbacks is that it can produce a large result set that contains many null values, which can make the data less readable and more difficult to process. For example, if you have two large tables with many rows that do not have a match in the other table, you will end up with a lot of rows that have null values in both table columns.
Another drawback of using a FULL JOIN is that it can affect the performance of your query, as it requires more processing time and memory than an INNER JOIN, a LEFT JOIN, or a RIGHT JOIN. This is because a FULL JOIN has to scan both tables and compare each row with the other table, while the other types of joins only have to scan one table and compare it with the other table. Therefore, you should use a FULL JOIN only when you need to get all the data from both tables, and not when you only need the data that has a match in either table.
In summary, a FULL JOIN is a useful tool to combine data from two PostgreSQL tables and get all the data from both tables, regardless of whether it has a match in the other table or not. However, you should also consider the drawbacks of using a FULL JOIN, such as the potential size of the result set, the presence of null values, and the impact on the query performance.
6.1. Example of a FULL JOIN
In this section, you will see an example of how to use a FULL JOIN to combine data from two PostgreSQL tables. You will use the same sample tables as in the previous sections: customers and orders.
A FULL JOIN returns all the rows from both tables, regardless of whether they have a matching row in the other table or not. If there is no match, the columns from the other table will have null values in the result set.
For example, suppose you want to get the customer and order details for all the customers and orders in your database. You can use a FULL JOIN to combine the customers and orders tables, as follows:
SELECT name, email, order_id, date, amount FROM customers FULL JOIN orders ON customers.customer_id = orders.customer_id;
The output of this query will look something like this:
| name | order_id | date | amount | |
|---|---|---|---|---|
| Alice | alice@example.com | 1 | 2024-01-10 | 100 |
| Bob | bob@example.com | 2 | 2024-01-15 | 200 |
| Charlie | charlie@example.com | 3 | 2024-01-20 | 300 |
| David | david@example.com | 4 | 2024-01-25 | 400 |
| null | null | 5 | 2024-01-30 | 500 |
| null | null | 6 | 2024-02-05 | 600 |
| Eve | eve@example.com | null | null | null |
| Frank | frank@example.com | null | null | null |
As you can see, the FULL JOIN has returned all the rows from both tables, including the ones that do not have a matching customer_id or order_id in the other table. The name, email, order_id, date, and amount columns for those rows have null values, indicating that there is no corresponding data in the other table.
This way, you can use a FULL JOIN to get all the data from both tables, even if some of it is not related to the data in the other table.
6.2. Benefits and drawbacks of a FULL JOIN
A FULL JOIN is a type of join that combines data from two PostgreSQL tables and returns all the rows from both tables, regardless of whether they have a matching row in the other table or not. If there is no match, the columns from the other table will have null values in the result set.
One of the benefits of using a FULL JOIN is that it allows you to retrieve all the data from both tables, even if some of it is not related to the data in the other table. This can be useful when you want to analyze the data in both tables and see how they relate to each other, if at all. For example, you might want to see which customers have placed orders, which orders have been assigned to customers, and which customers and orders have no relation at all.
Another benefit of using a FULL JOIN is that it preserves the order of the rows in both tables, which can be important when you want to maintain the original sequence of the data. For example, you might want to see the chronological order of the customer registrations and the order dates, or the alphabetical order of the customer names and the product names.
However, a FULL JOIN also has some drawbacks that you should be aware of. One of the drawbacks is that it can produce a large result set that contains many null values, which can make the data less readable and more difficult to process. For example, if you have two large tables with many rows that do not have a match in the other table, you will end up with a lot of rows that have null values in both table columns.
Another drawback of using a FULL JOIN is that it can affect the performance of your query, as it requires more processing time and memory than an INNER JOIN, a LEFT JOIN, or a RIGHT JOIN. This is because a FULL JOIN has to scan both tables and compare each row with the other table, while the other types of joins only have to scan one table and compare it with the other table. Therefore, you should use a FULL JOIN only when you need to get all the data from both tables, and not when you only need the data that has a match in either table.
In summary, a FULL JOIN is a useful tool to combine data from two PostgreSQL tables and get all the data from both tables, regardless of whether it has a match in the other table or not. However, you should also consider the drawbacks of using a FULL JOIN, such as the potential size of the result set, the presence of null values, and the impact on the query performance.
7. Conclusion
In this blog, you have learned how to use the JOIN clause to combine data from two PostgreSQL tables. You have learned about the different types of joins and how they affect the output of the join clause.
You have seen how to use an INNER JOIN to get the data that has a match in both tables, a LEFT JOIN to get all the data from the left table and the matching data from the right table, a RIGHT JOIN to get all the data from the right table and the matching data from the left table, and a FULL JOIN to get all the data from both tables, regardless of whether it has a match in the other table or not.
You have also learned about the benefits and drawbacks of using each type of join, such as the size of the result set, the presence of null values, the order of the rows, and the impact on the query performance. You have seen how to write the syntax of a join clause and how to use code examples to illustrate the results of the join operations.
By using the join clause, you can query data from multiple tables as if they were a single table, and get the information you need for your analysis. Joining tables is a powerful and essential skill for working with relational databases, and you can apply it to many different scenarios and problems.
We hope you have enjoyed this blog and learned something new and useful. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
