Design a simplified SQL database system. Your system should support the following operations:
WHERE
condition. The WHERE
clause only supports equality comparisons (e.g., column = value
).Your design should address the following:
SELECT
operation with the WHERE
clause?JOIN
operation?Provide example SQL statements and the expected output for each operation.
For instance, consider the following scenario:
CREATE TABLE Employees (EmpID INT, Name VARCHAR, DeptID INT); CREATE TABLE Departments (DeptID INT, DeptName VARCHAR);
INSERT INTO Employees (EmpID, Name, DeptID) VALUES (1, 'Alice', 101); INSERT INTO Employees (EmpID, Name, DeptID) VALUES (2, 'Bob', 102); INSERT INTO Departments (DeptID, DeptName) VALUES (101, 'Engineering'); INSERT INTO Departments (DeptID, DeptName) VALUES (102, 'Sales');
SELECT Name FROM Employees WHERE DeptID = 101; // Expected output: Alice SELECT Employees.Name, Departments.DeptName FROM Employees INNER JOIN Departments ON Employees.DeptID = Departments.DeptID; // Expected output: Alice, Engineering and Bob, Sales
Discuss the time and space complexity of each operation in your design. How would you improve performance through indexing?
When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:
For designing a database using a brute force approach, we're essentially trying out every possible database structure. We create all possible tables, columns, and relationships between them. Then, we test if each structure meets our requirements.
Here's how the algorithm would work step-by-step:
def design_sql_brute_force(data_requirements,
performance_criteria):
possible_database_structures = []
working_solutions = []
# Generate all possible table structures.
for table_combination in generate_table_combinations(data_requirements):
# Generate all possible column structures for each table.
for column_combination in generate_column_combinations(table_combination):
# Generate all possible relationships between tables.
for relationship_combination in generate_relationship_combinations(column_combination):
possible_database_structures.append(relationship_combination)
# Test each structure to see if it meets requirements
for database_structure in possible_database_structures:
if test_database_structure(
database_structure, data_requirements, performance_criteria):
working_solutions.append(database_structure)
# Evaluate working solutions based on criteria
best_solution = find_best_solution(working_solutions, performance_criteria)
return best_solution
def generate_table_combinations(data_requirements):
# Placeholder for generating table combinations
# In a real implementation, this function would generate different
# combinations of tables based on the data requirements.
return [["table1", "table2"]]
def generate_column_combinations(table_combination):
# Placeholder for generating column combinations
# In a real implementation, this function would generate different
# combinations of columns for each table.
return [["columnA", "columnB"], ["columnC", "columnD"]]
def generate_relationship_combinations(column_combination):
#Placeholder for generating relationship combinations
#In a real implementation, this would generate different
#relationship combinations such as one-to-many or many-to-many
return [{"table1": "table2", "relationship": "one-to-many"}]
def test_database_structure(
database_structure, data_requirements, performance_criteria):
#Placeholder for testing if the data structure works as expected
#This function would insert, update and query and assess the results
return True
def find_best_solution(working_solutions, performance_criteria):
# Placeholder for evaluating solutions and returning the best one
if working_solutions:
return working_solutions[0]
else:
return None
Designing a SQL database involves carefully organizing information into related tables. The optimal approach focuses on minimizing redundancy and ensuring data integrity through a process called normalization. This involves breaking down large tables into smaller, more manageable ones and defining clear relationships between them.
Here's how the algorithm would work step-by-step:
def design_sql_database():
# Step 1: Identify entities (tables).
customers_table = {}
# Step 2: Define attributes (columns) for customers.
customers_table['customer_id'] = 'INT PRIMARY KEY'
customers_table['customer_name'] = 'VARCHAR(255)'
customers_table['customer_address'] = 'VARCHAR(255)'
orders_table = {}
# Step 3: Define attributes (columns) for orders.
orders_table['order_id'] = 'INT PRIMARY KEY'
orders_table['customer_id'] = 'INT, FOREIGN KEY REFERENCES customers(customer_id)'
orders_table['order_date'] = 'DATE'
products_table = {}
products_table['product_id'] = 'INT PRIMARY KEY'
products_table['product_name'] = 'VARCHAR(255)'
products_table['product_price'] = 'DECIMAL(10, 2)'
order_items_table = {}
order_items_table['order_item_id'] = 'INT PRIMARY KEY'
order_items_table['order_id'] = 'INT, FOREIGN KEY REFERENCES orders(order_id)'
order_items_table['product_id'] = 'INT, FOREIGN KEY REFERENCES products(product_id)'
order_items_table['quantity'] = 'INT'
# Step 4 & 5: Handle repeating groups (e.g., customer phone numbers) by creating a separate table.
customer_phone_numbers_table = {}
customer_phone_numbers_table['phone_number_id'] = 'INT PRIMARY KEY'
customer_phone_numbers_table['customer_id'] = 'INT, FOREIGN KEY REFERENCES customers(customer_id)'
customer_phone_numbers_table['phone_number'] = 'VARCHAR(20)'
# Step 6: Define relationships (foreign keys).
# Orders table has a foreign key referencing Customers table.
# Step 7: Ensure each column directly relates to the primary key.
# Address is related to customer, quantity is related to order item, etc.
# Step 8: Repeat normalization as needed.
database_schema = {
'customers': customers_table,
'orders': orders_table,
'products': products_table,
'order_items': order_items_table,
'customer_phone_numbers': customer_phone_numbers_table
}
return database_schema
Case | How to Handle |
---|---|
Empty SQL query string | Return an error or an empty result set as appropriate for an invalid query. |
SQL query with syntax errors | Raise an exception or return an error message indicating the syntax error and its location. |
Invalid table name in the query | Return an error indicating that the table does not exist or that the user lacks permission to access it. |
Invalid column name in the query | Return an error indicating that the column does not exist in the specified table. |
SQL injection vulnerability | Sanitize user inputs to prevent malicious code from being executed, utilizing parameterized queries. |
Query attempts to access data the user is not authorized to view. | Enforce proper access control mechanisms to prevent unauthorized data access. |
Arithmetic overflow during calculations within the SQL query. | Use data types and checks that prevent overflow and handle potential errors gracefully. |
Query returns a result set exceeding memory limits. | Implement pagination or streaming to handle large result sets efficiently. |