Understanding the Problem and Requirements

As a technical blogger, it’s essential to break down complex problems into manageable parts. In this article, we’ll explore a real-world scenario where an individual wants to retrieve the total business count of employees in each category, such as doctors, lawyers, educators, professionals, restaurants, and others.

Background and Context

We start with two tables: employees and doctorsrating. The employees table contains information about each employee, including their unique identifier (emp_bioid). The doctorsrating table is a many-to-many relationship between doctors (or other professionals) and employees. Each doctor has an emp_bioid foreign key referencing the employees table.

**Table Structure:**
### Employees Table

| Column Name | Data Type | Description |
| --- | --- | --- |
| emp_bioid | int | Unique employee identifier |
| name | varchar(50) | Employee name |
| ... | ... | Additional columns |

### DoctorsRating Table

| Column Name | Data Type | Description |
| --- | --- | --- |
| emp_bioid | int | Foreign key referencing the employees table |
| doctor_rating | float | Doctor rating (1-10) |
| ... | ... | Additional columns |

The Original Query and Limitations

The original query provided in the question aims to retrieve the total count of doctors, lawyers, educators, professionals, restaurants, and others for each employee. However, this approach has limitations.

**Original Query:**
```markdown
SELECT 
    emp_bioid, 
    COUNT(*) as Doctor, 
    (SELECT emp_name 
     FROM employees 
     WHERE emp_bioid = doctorsrating.emp_bioid
     ) AS emp_name 
FROM doctorsrating 
WHERE countryid=$countryid 
AND createdAt between '$fromDate' AND '$toDate'
GROUP BY emp_bioid;

Extending the Query for Multiple Categories

To overcome the limitations of the original query, we need to extend it to include multiple categories. We can achieve this by using a single query with multiple COUNT() functions.

**Extended Query:**
```markdown
SELECT 
    emp_bioid, 
    COUNT(*) as Doctor,
    COUNT(*) as Lawyer,
    COUNT(*) as Educations,
    COUNT(*) as Professionals, 
    (SELECT emp_name 
     FROM employees 
     WHERE emp_bioid = doctorsrating.emp_bioid
       OR emp_bioid = lawyers.emp_bioid
       OR emp_bioid = educations.emp_bioid
       OR emp_bioid = professionals.emp_bioid
    ) AS emp_name 
FROM doctorsrating 
WHERE countryid=$countryid 
AND createdAt between '$fromDate' AND '$toDate'
GROUP BY emp_bioid;

However, this query has a significant drawback: it requires multiple SELECT statements to retrieve the count for each category. We can improve this by using a single COUNT() function with conditional logic.

Using Conditional Count Functions

We can use the following approach to achieve our goal:

**Conditional Count Query:**
```markdown
SELECT 
    emp_bioid, 
    COUNT(CASE WHEN table_name = 'doctors' THEN 1 END) as Doctor,
    COUNT(CASE WHEN table_name = 'lawyers' THEN 1 END) as Lawyer,
    COUNT(CASE WHEN table_name = 'educations' THEN 1 END) as Educations,
    COUNT(CASE WHEN table_name = 'professionals' THEN 1 END) as Professionals, 
    (SELECT emp_name 
     FROM employees 
     WHERE emp_bioid IN (
         SELECT doctorsrating.emp_bioid
         FROM doctorsrating
         WHERE countryid=$countryid AND createdAt between '$fromDate' AND '$toDate'
         GROUP BY emp_bioid HAVING COUNT(*) >= 1
     )
   OR emp_bioid IN (
         SELECT lawyers.emp_bioid
         FROM lawyers
         WHERE countryid=$countryid AND createdAt between '$fromDate' AND '$toDate'
         GROUP BY emp_bioid HAVING COUNT(*) >= 1
     )
   OR emp_bioid IN (
         SELECT educations.emp_bioid
         FROM educations
         WHERE countryid=$countryid AND createdAt between '$fromDate' AND '$toDate'
         GROUP BY emp_bioid HAVING COUNT(*) >= 1
     )
   OR emp_bioid IN (
         SELECT professionals.emp_bioid
         FROM professionals
         WHERE countryid=$countryid AND createdAt between '$fromDate' AND '$toDate'
         GROUP BY emp_bioid HAVING COUNT(*) >= 1
     )
    ) AS emp_name 
FROM doctorsrating
WHERE countryid=$countryid 
AND createdAt between '$fromDate' AND '$toDate';

This query uses CASE statements within the COUNT() function to conditionally count rows for each category. The subquery retrieves the unique emp_bioid values for each table.

Note that this approach assumes that the number of employees in each category is at least 1, which may not always be the case.

Conclusion

In conclusion, finding the total business count of employees in each category requires a well-thought-out query with conditional logic. By using techniques such as conditional count functions and subqueries, we can create efficient queries that meet our requirements. However, it’s essential to consider potential edge cases and optimize our queries for optimal performance.

Additional Considerations

When working with complex data and multiple tables, it’s crucial to:

• Regularly back up your database to prevent data loss.
• Use indexing to improve query performance.
• Optimize queries using techniques like caching, partitioning, or reordering joins.
• Consider using denormalization to reduce the number of joins required.

By following these best practices and staying up-to-date with the latest SQL features and technologies, you can create efficient and scalable queries that meet your specific needs.

Last modified on 2023-08-07