Understanding the Problem and Requirements

In this blog post, we’ll delve into the world of SQL queries, focusing on a specific problem that involves joining two tables: mobiles and reviews. The goal is to select the count of records in the reviews table for each corresponding mobile ID from the mobiles table. We’ll explore how to achieve this using SQL joins and aggregations.

Table Structures

Let’s start by examining the structure of our two tables:

Mobiles Table

+---------+-------------+
| mobile_id | mobile_name |
+---------+-------------+
| 1        | iPhone 13   |
| 2        | Samsung S22 |
| 3        | Google Pixel|
| 4        | Apple iPhone|
| 5        | OnePlus 9  |
+---------+-------------+

Reviews Table

+---------+----------+--------------+
| review_id | mobile_id | review_body  |
+---------+----------+--------------+
| 1        | 1        | "Excellent device!"|
| 2        | 1        | "Great camera!" |
| 3        | 2        | "Nice design!"   |
| 4        | 2        | "Good performance"|
| 5        | 3        | "Love it!"      |
+---------+----------+--------------+

The Existing Query

The original query provided by the user attempts to solve this problem. Let’s break down its components:

SELECT c.*, p.review_body
FROM   ((select mobile_id, mobile_name from mobiles 
WHERE brand_id=1 limit 0,5) c)
left JOIN
(
   SELECT  mobile_id,
   MAX(review_id) MaxDate
   FROM    reviews
   GROUP BY mobile_id
) MaxDates ON c.mobile_id = MaxDates.mobile_id left JOIN
reviews p ON   MaxDates.mobile_id = p.mobile_id
AND MaxDates.MaxDate = p.review_id

This query uses two main join types:

1. Subquery: The first SELECT statement inside the outer join is a subquery, which retrieves only the first 5 mobiles with a brand_id of 1.
2. Left Join: The query then joins this result set (c) with another subquery using a left join.

Modifying the Query to Include Review Count

The provided answer suggests modifying the query to include the review count for each mobile device:

SELECT  c.*, p.review_body, MaxDates.review_count
FROM   ((select mobile_id, mobile_name from mobiles 
WHERE brand_id=1 limit 0,5) c)
left JOIN
(
   SELECT  mobile_id,
   COUNT(review_id) review_count,
   MAX(review_id) MaxDate
   FROM    reviews
   GROUP BY mobile_id
) MaxDates ON c.mobile_id = MaxDates.mobile_id left JOIN
reviews p ON   MaxDates.mobile_id = p.mobile_id
AND MaxDates.MaxDate = p.review_id

This modified query introduces a new column, review_count, which is calculated using the COUNT aggregation function.

How it Works

To understand this query better, let’s break down its components:

1. Inner Join: The first join (((select mobile_id, mobile_name from mobiles ...) joins with reviews) creates a temporary result set that includes all reviews for the specified mobile devices.
2. Outer Join: The second join combines this temporary result set with another query using an outer join. This join retrieves the latest review date and count for each mobile device.

SQL Joins and Aggregations

SQL joins are used to combine data from multiple tables based on relationships between them. There are several types of joins, including:

• Inner Join: Retrieves only the rows that have matching values in both tables.
• Left Join (or Left Outer Join): Retrieves all the rows from the left table and matching rows from the right table. If there’s no match, it returns NULL on the right side.
• Right Join (or Right Outer Join): Similar to a left join but retrieves data from the right table first.

SQL aggregations are used to summarize large datasets. Common aggregation functions include:

• COUNT(): Returns the number of rows that match a condition.
• MAX(), MIN(), SUM(), and AVG(): Return the maximum, minimum, sum, or average value in a column.

Conclusion

In this blog post, we explored how to solve a common SQL problem involving joins and aggregations. We examined the structure of two tables: mobiles and reviews. We then delved into an existing query that attempted to solve a similar problem and modified it to include review count. By understanding SQL joins and aggregations, developers can efficiently retrieve data from multiple tables.

Additional Tips

• When working with large datasets, use efficient indexing strategies to improve query performance.
• Familiarize yourself with different SQL dialects (e.g., MySQL, PostgreSQL) and learn their unique features and limitations.
• Practice writing SQL queries to become proficient in handling various data modeling scenarios.

Last modified on 2025-01-24