Scale Determines Capability: Differences in SQL Processing Among Language Models

1. Introduction

As artificial intelligence technology continues to advance rapidly, language models are increasingly evaluated by their ability to generate and understand SQL, the core language for database operations. Different language models with varying parameter sizes exhibit significant differences in handling SQL tasks. This article explores how model parameter size impacts SQL processing capabilities, using case studies to uncover the reasons behind these differences.

2. Relationship Between Model Parameters and Capability

The number of parameters in a language model is closely tied to its ability to understand and generate text. Small-scale models (with parameters in the hundreds of thousands to millions) struggle with complex tasks due to limited computational resources and training data. In contrast, large-scale models (with parameters in the billions or even tens of billions) excel at handling logically rigorous and highly structured tasks, demonstrating superior language modeling and contextual understanding.

3. Performance of Different-Scale Models

3.1 Small-Scale Models (Million-Level Parameters)

Small-scale models can handle simple SQL queries like basic SELECT statements but often fail with complex operations such as nested queries or multi-table joins due to limited parameter capacity.

3.2 Medium-Scale Models (Billion-Level Parameters)

Billion-parameter models show improved performance, handling single-table queries and simple multi-table joins. However, they may still encounter logical errors in more complex scenarios involving GROUP BY or HAVING clauses.

3.3 Large-Scale Models (Tens of Billion-Level Parameters and Above)

Models with tens of billions or even hundreds of billions of parameters (like GPT-4 or similar models) demonstrate remarkable SQL processing capabilities. They can generate complex queries, understand context, and even optimize SQL statements, thanks to their advanced language modeling and reasoning abilities.

4. Case Study: Complex SQL Query Execution

4.1 Compared Models

We tested three distilled models:

• DeepSeek-R1-Distill-Qwen-32B
• DeepSeek-R1-Distill-Qwen-7B
• DeepSeek-R1-Distill-Qwen-1.5B

4.2 Task

Simulate a MySQL 8.0 database system to execute a complex SQL query and provide the execution process and results.

Table Creation Statements

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CREATE TABLE departments (
    dept_id INT PRIMARY KEY,
    dept_name VARCHAR(50)
);

CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(50),
    department_id INT,
    salary DECIMAL(10, 2),
    hire_date DATE
);

Data Insertion

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INSERT INTO departments (dept_id, dept_name) VALUES
(10, 'Engineering'),
(20, 'Sales'),
(30, 'Marketing'),
(40, 'HR'),
(50, 'Finance'),
(60, 'IT'),
(70, 'Operations'),
(80, 'Research'),
(90, 'Support'),
(100, 'Design');

INSERT INTO employees (id, name, department_id, salary, hire_date) VALUES
(1, 'Alice', 10, 62000.00, '2023-02-10'),
(2, 'Bob', 20, 78000.00, '2022-07-15'),
(3, 'Charlie', 30, 45000.00, '2023-09-05'),
(4, 'David', 40, 55000.00, '2021-12-20'),
(5, 'Eve', 50, 67000.00, '2022-03-18'),
(6, 'Frank', 60, 72000.00, '2023-06-25'),
(7, 'Grace', 70, 49000.00, '2022-11-30'),
(8, 'Hank', 80, 83000.00, '2023-04-12'),
(9, 'Ivy', NULL, 59000.00, '2022-08-22'),
(10, 'Jack', 90, 64000.00, '2023-01-08');

Query SQL

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SELECT d.dept_name, COUNT(e.id) AS emp_count, AVG(e.salary) AS avg_salary
FROM departments d
LEFT JOIN (
    SELECT id, name, department_id, salary, hire_date
    FROM employees
    WHERE salary > (SELECT AVG(salary) FROM employees WHERE hire_date >= '2022-01-01')
) e ON d.dept_id = e.department_id
WHERE e.hire_date >= '2022-01-01'
GROUP BY d.dept_name
HAVING COUNT(e.id) > 0
ORDER BY avg_salary DESC;

4.3 Rationale for Task Selection

1. Differences are clear: This task is complex enough to clearly contrast differences among different models.
2. Can assess SQL syntax understanding: The model needs to accurately parse keywords, operators, and structures in SQL statements to ensure the generated queries comply with syntax standards.
3. Can assess database understanding: The test requires the model to understand and execute table creation and insertion statements in the task for correct processing.
4. Can assess SQL processing capability: SQL queries involve operations such as filtering, aggregation, sorting, subqueries, and JOINs. The test can evaluate whether the model understands these operations and can execute them correctly to return the expected results.
5. Can assess logical reasoning ability: The test requires first creating tables and inserting data, and then executing SQL step by step to obtain the correct data, with each step being essential.

4.4 Expected Answer

Temperature：0.3

5. Results Comparison

5.1 ❘❙❚ 32B Model Performance

Query Results

Execution Process

1. Calculate the average salary in the subquery:

   • Filter employees with hire_date >= '2022-01-01', totaling 9 employees.
   • Calculate the average salary for these employees as approximately 64,333.33.

2. Filter employees in subquery e:

   • Select employees with salaries higher than 64,333.33 from the employees table, resulting in employees 2, 5, 6, and 8.

3. Left join departments and subquery e:

   • Join based on department_id to obtain employee information for each department.

4. Filter and group:

   • Filter employees with hire_date >= '2022-01-01' (already ensured by the subquery).
   • Group by department and calculate the number of employees and average salary.

5. Filter and sort:

   • Retain departments with more than 0 employees.
   • Sort by average salary in descending order to obtain the results.

Final Results

The departments Research, Sales, IT, and Finance each have one employee with average salaries of 83,000.00, 78,000.00, 72,000.00, and 67,000.00, respectively, sorted in descending order.

The 32B large model correctly obtained the results and clearly listed the execution process and intermediate results, indicating that the model can accurately understand the task and perform reasoning.

5.2 ❘❙❚ 7B Model Performance

Query Results

Detailed Steps

1. Calculate the average employee salary: The average salary of all employees in the employees table is 63,400.00.
2. Filter employees meeting the conditions: Filter employees with salaries greater than 63,400 and hire_date >= '2022-01-01', totaling 5 employees.
3. Left join to the departments table: Join these employees to the departments table, with department IDs of 20, 50, 60, 80, and 90.
4. Group by department: Calculate the number of employees and average salary for each department.
5. Filter non-empty departments: Exclude departments without employees.
6. Sort: Sort by average salary in descending order.

Result Explanation

• Research (80): 1 employee, average salary 83,000.00.
• Sales (20): 1 employee, average salary 78,000.00.
• IT (60): 1 employee, average salary 72,000.00.
• Finance (50): 1 employee, average salary 67,000.00.
• Support (90): 1 employee, average salary 64,000.00.

The results are sorted by average salary in descending order.

Compared to the 32B model, the execution steps are relatively concise, but the query results include an extra data entry (Support, 1, 64000).

The reason is that the 7B model did not filter the average salary calculation step by hire date, directly calculating the average salary for the entire table.

This indicates that the model can understand the task but is prone to errors in reasoning.

5.3 ❘❙❚ 1.5B Model Performance

Final Results

Department names: Engineering, Sales, Marketing, HR, Finance, IT, Operations, Research, Support, Design

Average salaries: 62,000.00, 78,000.00, 45,000.00, 59,000.00, 67,000.00, 72,000.00, 49,000.00, 83,000.00, 59,000.00, 64,000.00

Sorting: Engineering, Sales, Marketing, HR, Finance, IT, Operations, Research, Support, Design

Execution Process Summary

1. Data preparation: Generate the departments and employees tables to ensure data correctness.
2. Subquery to calculate average salary: Use SELECT FROM employees WHERE salary > (SELECT AVG(salary) FROM employees WHERE hire_date >= '2022-01-01') to calculate the average salary.
3. Left outer join: Connect the departments and employees tables using LEFT JOIN to ensure that the dept_id in the departments table matches the department_id in the employees table.
4. Main query filtering conditions: Filter employees who meet the following conditions:
   • e.hire_date >= '2022-01-01'
   • d.dept_id = e.department_id
5. Grouping and sorting: Group the results by emp_count and avg_salary, then sort by avg_salary in descending order.
6. Execute the query: Combine the above steps to execute the entire query statement and obtain the final results.

The execution steps provided are correct, but the model failed to provide intermediate results, and the final output is nonsensical.

Clearly, this model can understand the task but cannot perform reasoning.

5.4 ❘❙❚ Comparison Results

As the number of parameters increases, the model shows significant improvements in accuracy, completeness, and reasoning for SQL statements.

1.5B Model: Cannot complete the task but can provide correct execution steps, indicating it can understand SQL structure and handle simple scenarios like generating query statements from text.

7B Model: Possesses some reasoning ability but makes mistakes in details, leading to incorrect answers. It can handle simple reasoning tasks.

32B Model: Provides correct answers and can handle more scenarios, including complex SQL tasks, query diagnostics, and optimizations.

6. Summary

The number of model parameters plays a critical role in enhancing SQL processing capabilities. Small-scale models are suitable for simple tasks, medium-scale models can handle moderate complexity, and large-scale models excel in complex SQL tasks. However, even large models may struggle with extremely complex queries or lack domain knowledge, suggesting potential for fine-tuning. Larger models also introduce higher latency and resource consumption, necessitating a balance between performance and cost.

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