Understanding the HAVING Clause in SQL

Introduction

The HAVING clause is a powerful tool in SQL that allows you to filter groups of rows based on conditions. In this article, we will explore how to use the HAVING clause in the SELECT statement and provide examples to illustrate its usage.

The Problem at Hand

We are given two tables: businesses and transactions. We want to write a single SQL query that calculates the number of unique customers for each business and whether there is more than one transaction for each customer. The original queries use subqueries, which can be inefficient.

Original Queries

Let’s examine the original queries:

-- Query 1: Counting unique customers without HAVING clause
SELECT b.name, (SELECT COUNT(1) 
               FROM (SELECT COUNT(1) num 
                       FROM pos_transactions pt WHERE pt.business_id = b.id GROUP BY user_id) x) 
       AS the_number_of_unique_customers_num 
FROM businesses b

-- Query 2: Counting unique customers with HAVING clause
SELECT b.name, (SELECT COUNT(1) 
               FROM (SELECT COUNT(1) num 
                       FROM pos_transactions pt WHERE pt.business_id = b.id GROUP BY user_id 
                       HAVING num > 1) x) 
       AS the_number_of_unique_customers_have_had_more_than_one_trans 
FROM businesses b

Both queries are similar, but the second query adds a HAVING clause to filter out businesses with only one transaction.

Solution

We can achieve the desired result using a single SQL query with a left join and two levels of aggregation. The first level groups by business ID, user ID, and counts the number of transactions for each group. The second level calculates the total count and checks if there is more than one transaction for each customer.

SELECT b.name, 
       COUNT(*) AS the_number_of_unique_customers_num,
       SUM(COUNT(*) > 1) AS the_number_of_unique_customers_have_had_more_than_one_trans
FROM (
  SELECT b.id, b.name, t.user_id, COUNT(*) counter
  FROM businesses b LEFT JOIN transactions t ON t.business_id = b.id
  GROUP BY b.id, b.name, t.user_id
) t
GROUP BY id, name

Alternatively, for MySQL versions 8.0 and above, we can use aggregation and window functions:

SELECT DISTINCT b.name, 
       COUNT(*) OVER (PARTITION BY b.id) AS the_number_of_unique_customers_num,
       SUM(COUNT(*) > 1) OVER (PARTITION BY b.id) AS the_number_of_unique_customers_have_had_more_than_one_trans
FROM businesses b LEFT JOIN transactions t ON t.business_id = b.id

Explanation

Let’s break down the first query:

SELECT b.name, 
       COUNT(*) AS the_number_of_unique_customers_num,
       SUM(COUNT(*) > 1) AS the_number_of_unique_customers_have_had_more_than_one_trans
FROM (
  SELECT b.id, b.name, t.user_id, COUNT(*) counter
  FROM businesses b LEFT JOIN transactions t ON t.business_id = b.id
  GROUP BY b.id, b.name, t.user_id
) t
GROUP BY id, name

The subquery groups by business ID, user ID, and counts the number of transactions for each group. The outer query then calculates two aggregate values:

1. the_number_of_unique_customers_num: This is the total count of unique customers across all businesses.
2. the_number_of_unique_customers_have_had_more_than_one_trans: This is a boolean value indicating whether there is more than one transaction for each customer.

The second query uses window functions to achieve the same result:

SELECT DISTINCT b.name, 
       COUNT(*) OVER (PARTITION BY b.id) AS the_number_of_unique_customers_num,
       SUM(COUNT(*) > 1) OVER (PARTITION BY b.id) AS the_number_of_unique_customers_have_had_more_than_one_trans
FROM businesses b LEFT JOIN transactions t ON t.business_id = b.id

This query uses two window functions:

1. COUNT(*) OVER (PARTITION BY b.id): This returns the total count of unique customers for each business.
2. SUM(COUNT(*) > 1) OVER (PARTITION BY b.id): This returns a boolean value indicating whether there is more than one transaction for each customer.

Conclusion

The HAVING clause is a powerful tool in SQL that allows you to filter groups of rows based on conditions. By using the HAVING clause, we can write efficient and effective queries to analyze data.

Last modified on 2023-07-12