def design_sql_brute_force(requirements):
    best_design = None
    best_query = None
    best_performance = float('inf')

    # Consider all possible table structures
    possible_table_structures = generate_table_structures()

    for table_structure in possible_table_structures:
        # Define how the tables relate to each other
        table_relationships = generate_table_relationships(table_structure)

        for relationship in table_relationships:
            # Imagine all the queries
            possible_queries = generate_possible_queries(table_structure, relationship, requirements)

            for query in possible_queries:
                # Evaluate if the query fulfills the requirements
                results = execute_query(table_structure, relationship, query)

                # Check if the results are acceptable
                if check_results(results, requirements):
                    # Calculate performance of the query
                    performance = calculate_performance(query, table_structure)

                    # Keep track of the best design
                    if performance < best_performance:
                        best_performance = performance
                        best_design = table_structure
                        best_query = query

    return best_design, best_query

def generate_table_structures():
    # Placeholder function
    # Returns a list of possible table structures
    return [{"table1": ["col1", "col2"], "table2": ["col3", "col4"]} ]

def generate_table_relationships(table_structure):
    # Placeholder function
    # Returns a list of possible relationships between tables
    return [{"table1": "col1", "table2": "col3"}]

def generate_possible_queries(table_structure, relationship, requirements):
    # Placeholder function
    # Returns a list of possible queries
    return ["SELECT * FROM table1", "SELECT col1 FROM table1"]

def execute_query(table_structure, relationship, query):
    # Placeholder function
    # Execute query against the table structure and relationship
    return [{"col1": "value1", "col2": "value2"}]

def check_results(results, requirements):
    # Placeholder function
    # Check the results against the requirements
    return True

def calculate_performance(query, table_structure):
    # Placeholder function
    # Return the performance of the query, such as time it took
    return 1.0

# The following are example drivers for the function above.

# Define requirements
requirements = {"data_needed": ["col1", "col2"]}

# Consider all possible table structures

# Find the best design
best_table_design, best_sql_query = design_sql_brute_force(requirements)

# Print best table design and best SQL query
print(f'Best Table Design:
{best_table_design}')
print(f'Best SQL Query:
{best_sql_query}')

class SQLDesign:
    def __init__(self):
        pass

    def design_database(self):
        # Define tables as dictionaries.
        tables = {}

        # Create the customers table.
        tables['customers'] = {
            'customer_id': 'INT PRIMARY KEY',
            'customer_name': 'VARCHAR(255)',
            'customer_email': 'VARCHAR(255)'
        }

        # Create the orders table.
        tables['orders'] = {
            'order_id': 'INT PRIMARY KEY',
            'customer_id': 'INT',
            'order_date': 'DATE',
            'total_amount': 'DECIMAL(10, 2)'
        }

        # Establish foreign key relationship.
        # Orders table relates to customers table using customer_id.
        tables['orders']['customer_id'] = 'INT FOREIGN KEY REFERENCES customers(customer_id)'

        tables['products'] = {
            'product_id': 'INT PRIMARY KEY',
            'product_name': 'VARCHAR(255)',
            'product_description': 'TEXT',
            'product_price': 'DECIMAL(10, 2)'
        }

        tables['order_items'] = {
            'order_item_id': 'INT PRIMARY KEY',
            'order_id': 'INT',
            'product_id': 'INT',
            'quantity': 'INT',
            'item_price': 'DECIMAL(10, 2)'
        }

        # Establish foreign key relationships.
        # Order items relate to orders and products tables.
        tables['order_items']['order_id'] = 'INT FOREIGN KEY REFERENCES orders(order_id)'

        # Products need to be linked for item details
        tables['order_items']['product_id'] = 'INT FOREIGN KEY REFERENCES products(product_id)'

        # Returns the data model as a dictionary.
        return tables