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Topic: Advanced Indexing Techniques for Database Optimization
Sample Question 1: Indexing Mastery
Question: Consider a large database with millions of records. Discuss the impact of using B-tree indexing versus bitmap indexing in terms of performance and space utilization. Provide a real-world scenario where each type of indexing would be most beneficial.
Answer: In scenarios where high cardinality is essential, B-tree indexing shines due to its ability to maintain order efficiently. On the other hand, bitmap indexing excels when dealing with low cardinality attributes, conserving space and providing faster query performance for specific types of queries. For instance, in a customer database, B-tree indexing on a unique customer ID might be preferable, while bitmap indexing on a binary attribute like gender could optimize certain query types.
Sample Question 2: Query Optimization Challenge
Question: Write an optimized SQL query to retrieve the top 5 customers who have made the highest total purchases in the last month. Consider both performance and readability in your solution.
Answer: Certainly! Here's a sample SQL query that efficiently retrieves the desired information:
sqlSELECT customer_id, SUM(purchase_amount) as total_purchases
FROM purchases
WHERE purchase_date >= NOW() - INTERVAL 1 MONTH
GROUP BY customer_id
ORDER BY total_purchases DESC
LIMIT 5;
This query aggregates the purchase amounts for each customer within the specified time frame, orders them by total purchases in descending order, and limits the result to the top 5 customers.
Sample Question 3: Coding Challenge - Python and SQLite
Question: Create a Python script that connects to an SQLite database and performs the following tasks:
- Creates a table named 'employees' with columns: employee_id (integer), employee_name (text), and salary (real).
- Inserts three sample records into the 'employees' table.
- Retrieves and prints all records from the 'employees' table.
Answer: Certainly! Below is a Python script using the SQLite library to accomplish the tasks:
pythonimport sqlite3
# Connect to the SQLite database (creates a new file if it doesn't exist)
conn = sqlite3.connect('sample.db')
# Create a cursor object to execute SQL queries
cursor = conn.cursor()
# Create 'employees' table
cursor.execute('''
CREATE TABLE employees (
employee_id INTEGER PRIMARY KEY,
employee_name TEXT,
salary REAL
)
''')
# Insert sample records
cursor.executemany('INSERT INTO employees VALUES (?, ?, ?)', [
(1, 'John Doe', 50000.0),
(2, 'Jane Smith', 60000.0),
(3, 'Bob Johnson', 55000.0)
])
# Commit changes
conn.commit()
# Retrieve and print all records
cursor.execute('SELECT * FROM employees')
for row in cursor.fetchall():
print(row)
# Close the connection
conn.close()
This script establishes a connection, creates the table, inserts records, retrieves data, and prints the results.
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