In my previous post, we discussed basic SQL queries. In this chapter, we'll delve into more advanced SQL concepts. SQL offers a rich set of tools for interacting with data, such as JOINs, and subqueries. This article will cover the intricacies of these SQL concepts and also highlight some of the best practices for writing SQL joins and subqueries.

SQL Joins

SQL JOIN operations allow you to combine data from two or more tables based on a common field.

Imagine you're tasked with analyzing sales data for an e-commerce website. The information you need is scattered across various database tables: customer data in one table, product information in another, and sales transactions in yet another. To make sense of this data and derive valuable insights, you must bring these tables together. This is where SQL joins come into play.

Types of SQL Queries

SQL offers a variety of join types, each with its own specific purpose. In this article, we will discuss FOUR of the most common join types

1. Inner join

   The INNER JOIN retrieves rows from both tables where the specified condition is met. If there's no match, the rows are excluded from the result. Thus if no match was found, no rows will be returned.

2. Left join (Left outer join)

   The LEFT JOIN returns all rows from the left table and the matched rows from the right table. If there's no match, NULL values are returned for columns from the right table.

3. Right join (Right outer join)

   The RIGHT JOIN is the opposite of the LEFT JOIN. It returns all rows from the right table and the matched rows from the left table. Non-matching rows from the left table receive NULL values.

4. Full join

   The FULL JOIN returns all rows when there is a match in either the left or the right table. If there's no match, NULL values are returned for columns from the table without a match.

Syntax and Examples

We are going to use the table structure for the following

INNER JOIN

Syntax:

SELECT column_name(s)
FROM table1
INNER JOIN table2
ON table1.column_name = table2.column_name;

SELECT Orders.order_id, Customers.customer_name, Orders.order_date
FROM Orders
INNER JOIN Customers ON Orders.customer_id = Customers.customer_id;

Explanation:

• We use an INNER JOIN to combine rows from the Orders table with rows from the Customers table where the customer_id matches.

• The result will give you a list of order IDs, customer names, and order dates for all orders where there's a matching customer.

LEFT JOIN (LEFT OUTER JOIN)

Syntax:

SELECT column_name(s)
FROM table1
LEFT JOIN table2
ON table1.column_name = table2.column_name;

SELECT Customers.customer_name, Orders.order_id, Orders.order_date
FROM Customers
LEFT JOIN Orders ON Customers.customer_id = Orders.customer_id;

Explanation:

• We use a LEFT JOIN to retrieve all rows from the Customers table, even if there are no matching rows in the Orders table.

• The result will give you a list of customer names and, if available, their associated order IDs and order dates. Customers without orders will have NULL values in the order-related columns.

RIGHT JOIN (RIGHT OUTER JOIN)

Syntax:

SELECT column_name(s)
FROM table1
RIGHT JOIN table2
ON table1.column_name = table2.column_name;

SELECT Orders.order_id, Customers.customer_name, Orders.order_date
FROM Orders
RIGHT JOIN Customers ON Orders.customer_id = Customers.customer_id;

Explanation:

• We use a RIGHT JOIN to retrieve all rows from the Orders table, even if there are no matching rows in the Customers table.

• The result will give you a list of order IDs, customer names, and order dates. Orders without associated customers will have NULL values in the customer-related columns.

FULL JOIN

Syntax:

SELECT column_name(s)
FROM table1
FULL JOIN table2
ON table1.column_name = table2.column_name;

SELECT Customers.customer_name, Orders.order_id, Orders.order_date
FROM Customers
FULL OUTER JOIN Orders ON Customers.customer_id = Orders.customer_id;

Explanation:

• We use a FULL OUTER JOIN to retrieve all rows from both the Customers and Orders tables.

• The result will include a list of customer names and their associated order IDs and order dates. It will also include orders without customers and customers without orders, with NULL values where there are no matches.

Best Practices For SQL Joins

When working with SQL joins, keep the following best practices in mind:

• Use descriptive column aliases: This means giving meaningful names to the columns in your query results, especially when you are joining multiple tables. This will make your query results easier to understand and read.

• Optimize query performance: You can improve the performance of your queries by ensuring that your tables have indexes on the columns that you are using in your JOIN conditions. Indexes are data structures that help the database to find rows in a table more quickly.

• Be mindful of data types: When you are joining two tables, the data types of the columns that you are joining must be the same or must be able to be implicitly converted to the same type. Otherwise, you may get unexpected results.

• Test queries incrementally: When you are writing complex JOIN queries, it is a good idea to build and test them incrementally. This means starting with a simple query and adding complexity to it gradually. This will help you to identify and resolve any issues more easily.

• Document your queries: It is important to document your SQL queries, especially if they involve multiple joins or complex logic. This will make your queries easier to maintain and understand by others.

SQL Subqueries

A SQL subquery is a query that is nested inside another query. Subqueries can be used in the SELECT, FROM, and WHERE clauses of SQL statements. They can also be nested inside other subqueries.

Subqueries are often used to filter the results of a query. For example, you could use a subquery to find all customers who have placed an order in the last month, or to find all products that are out of stock.

SELECT *
FROM customers
WHERE customer_id IN (
  SELECT customer_id
  FROM orders
  WHERE order_date > CURRENT_DATE - INTERVAL 1 MONTH
);

The subquery in this example returns a list of all customer IDs for orders that were placed in the last month. The outer query then selects all customers whose IDs are in that list.

Explanation:

• SELECT : This part of the query instructs the database to select all columns (represented by the asterisk ) from the customers table. We're interested in retrieving complete customer records.

• FROM customers: Here, we specify the table we are retrieving data from, which is the customers table. This is where customer information is stored.

• WHERE customer_id IN (...): This is where we start filtering the results to meet our specific criteria. We're telling the database that we want to include only those customer records that satisfy a particular condition. The condition is defined inside the parentheses, but let's delve into that further.

• SELECT customer_id FROM orders: Inside the parentheses, we have another query. This subquery selects customer_id from the orders table. It's like asking, "Give me the IDs of customers who have placed orders."

• WHERE order_date > CURRENT_DATE - INTERVAL 1 MONTH: In this part of the subquery, we filter orders based on their order date. We're looking for orders that were placed within the last month.

  • CURRENT_DATE represents the current date.

  • INTERVAL 1 MONTH is used to subtract one month from the current date. So, it calculates the date that was one month ago from today.

Another practical example of subquery is to select customer names who have placed at least two orders.

SELECT customer_name
FROM Customers
WHERE customer_id IN (
    SELECT customer_id
    FROM Orders
    GROUP BY customer_id
    HAVING COUNT(*) >= 2
);

• SELECT customer_name: This part of the query tells the database that we want to retrieve the customer_name from the Customers table. We are interested in finding the names of customers.

• FROM Customers: Here, we specify the table we are working with, which is the Customers table. This is where we'll find the customer names.

• WHERE customer_id IN (...): This is where things get interesting. We're telling the database that we only want to select customer names that meet a specific condition. The condition is defined inside the parentheses, but let's break it down further.

  • SELECT customer_id FROM Orders: Inside the parentheses, we have another query. This subquery selects the customer_id from the Orders table. It's like asking, "Give me the IDs of customers who have placed orders.

  • GROUP BY customer_id: This part of the subquery groups the results by customer_id. It means we want to count how many orders each customer has placed.

  • HAVING COUNT(*) >= 2: Here, we specify that we only want to consider groups (customers) who have at least 2 orders. So, if a customer has placed two or more orders, they will satisfy this condition.

Tips for Using Subqueries

Here are a few tips for using subqueries effectively:

• Make sure that the subquery is properly correlated to the outer query. This means that the subquery must be able to access the columns in the outer query.

• Avoid using subqueries in the SELECT clause unless you need to. Subqueries can slow down your queries, so it is best to use them only when necessary.

• Be careful when using subqueries in nested queries. Nested queries can be difficult to read and write, so it is important to test them thoroughly before using them in production.

• It's important to note that modern SQL database engines are highly optimized and can often rewrite subqueries into equivalent join-based queries for performance reasons.

Conclusion

SQL joins and subqueries, functions are essential tools in the data analyst's toolkit. By mastering these techniques and understanding how to use them in combination, you can extract valuable insights and perform complex data transformations. Whether you're working with large datasets or conducting detailed data exploration, these SQL features provide the flexibility and power needed to meet your analytical goals. So, dive into the world of SQL and unleash the full potential of your data analysis endeavors.