import unittest
import psycopg2
import pandas as pd

class TestPostgresQuery(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.connection = psycopg2.connect(
            host="localhost",
            user="agent",
            password="qwerty",
            dbname="testdb",
            sslmode="disable"
        )
        cls.connection.autocommit = True
        cls.cursor = cls.connection.cursor()

    def setUp(self):
        # Reset the balance and logs for account 789 before each test
        self.cursor.execute("UPDATE BankAccounts SET balance = 3500.00 WHERE account_number = 789;")
        self.cursor.execute("DELETE FROM UserLogs WHERE account_number = 789 AND action = 'Adding $100';")
        self.connection.commit()

    def test_balance_increased(self):
        """Check that the balance for account_number 789 is increased by 100."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        self.cursor.execute("SELECT balance FROM BankAccounts WHERE account_number = 789;")
        balance = float(self.cursor.fetchone()[0])
        _dynamic_test(
            self,
            abs(balance - 3600.00) < 0.0001,
            "Balance for account_number 789 increased by 100.",
            f"Balance for account_number 789 is not correct after update. Found: {balance}"
        )

    def test_log_entry_inserted(self):
        """Check that a log entry for 'Adding $100' is present for account_number 789 in UserLogs."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        self.cursor.execute("SELECT COUNT(*) FROM UserLogs WHERE account_number = 789 AND action = 'Adding $100';")
        count = self.cursor.fetchone()[0]
        _dynamic_test(
            self,
            count >= 1,
            "Log entry for 'Adding $100' inserted for account_number 789.",
            "No log entry for 'Adding $100' found for account_number 789."
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

if __name__ == "__main__":
    unittest.main()


test_solution.py

Begin an exciting journey through SQL with Advanced Techniques in SQL. Learn about the important ACID properties that help keep data accurate in database transactions. Discover advanced ways to optimize your queries to improve database performance. Explore special SQL features like window functions, triggers, Data Control Language (DCL), and NoSQL databases. This will help you handle modern database management confidently and effectively.

Now we explore the basic principles of ACID (Atomicity, Consistency, Isolation, Durability) in database management. Learn how these principles ensure reliable transactions by guaranteeing complete operations, keeping data consistent, isolating concurrent transactions, and ensuring permanent data storage.

Explore how to enhance the efficiency of your SQL queries through index optimization. Delve into the significance of indexes in database performance, learning how to design, implement, and utilize them effectively. Discover strategies for choosing the appropriate indexes, understanding their impact on query execution plans, and optimizing query performance through index selection and utilization.

This section explores pivotal topics such as Triggers, Window Functions, and DCL (Data Control Language), offering comprehensive insights into their implementation and optimization strategies. Furthermore, it navigates the landscape of NoSQL DBMS, providing a thorough understanding of its architecture, advantages, and practical applications.

Challenge: Specifying Isolation Level for Transaction

Solution

Challenge: Specifying Isolation Level for Transaction

Solution