answer:def show_menu(): # Определение функции show_menu menu = { # Создание словаря menu, где ключи - названия товаров, значения - список из цены и количества товара "Товар 1": [10, 10], "Товар 2": [20, 0], "Товар 3": [30, 1] } print("Меню магазина:") # Вывод строки "Меню магазина:" for key, value in menu.items(): # Для каждого ключа и значения в словаре menu item_name = key # Присваивание переменной item_name текущего ключа price = value[0] # Присваивание переменной price первого значения текущего значения в списке quantity = value[1] # Присваивание переменной quantity второго значения текущего значения в списке print(f"{key} - Цена: {price}, осталось: {quantity}") # Вывод строки с названием, ценой и количеством товара для каждого ключа

answer:```python # Создание словаря товаров с их ценами и остатками на складе items = { "Товар 1": [10, 10], "Товар 2": [20, 0], "Товар 3": [30, 1] } # Функция для вывода меню магазина def show_menu(): print("Меню магазина:") # Перебор элементов словаря для вывода названия, цены и остатка товара for key, value in items.items(): print(f"{key} - Цена: {value[0]}, осталось: {value[1]}") # Функция для получения выбора пользователя def get_user_choice(): choice = input("Выберите товар (введите название): ") return choice # Функция расчета общей стоимости выбранного количества товара def calculate_total_price(item, quantity): # Проверка, есть ли достаточно товара на складе if quantity > items[item][1]: print("Количество товара не хватает") return None else: total_price = quantity * items[item][0] return total_price # Функция размещения заказа def place_order(item, quantity): # Проверка, есть ли достаточно товара на складе if quantity > items[item][1]: print("Количество товара не хватает") else: total_price = calculate_total_price(item, quantity) if total_price: print("Ваш заказ размещен!") print(f"Общая стоимость заказа: {total_price}") update_stock(item, quantity) # Функция проверки наличия товара на складе def check_stock(item): if items[item][1] == 0: print("Товара нет в наличии") return False else: return True # Функция обновления количества товара на складе def update_stock(item, quantity): items[item][1] -= quantity # Функция для поиска товаров по названию в меню магазина def search_item(): search_input = input("Введите название товара: ") for key in items.keys(): if search_input.lower() in key.lower(): print(f"Найден товар: {key} - Цена: {items[key][0]}, осталось: {items[key][1]}") # Функция для добавления товара в корзину def add_to_cart(item, quantity, cart): if item in cart: cart[item] += quantity else: cart[item] = quantity # Функция для вывода товаров в корзине def show_cart(cart): print("Товары в корзине:") for item, quantity in cart.items(): print(f"{item} - Количество: {quantity}") # Основная функция программы def main(): # Переменная для хранения выбранного товара selected_item = "" # Переменная для хранения количества товара quantity = 0 # Словарь для хранения товаров в корзине cart = {} # Вывод меню магазина show_menu() # Получение выбора пользователя selected_item = get_user_choice() # Проверка, есть ли выбранный товар на складе if check_stock(selected_item): # Ввод количества товара quantity = int(input("Введите количество единиц товара: ")) # Размещение заказа place_order(selected_item, quantity) add_to_cart(selected_item, quantity, cart) show_cart(cart) # Вызов основной функции программы main() ```

answer:There are several ways to optimize and accelerate recursive requests to a database with SQLAlchemy: 1. Implement efficient indexing: Ensure that the tables involved in the recursive query have proper indexes. Indexes can significantly improve the performance of queries by allowing the database to quickly locate the required data. 2. Use eager loading: When performing recursive queries involving relationships, consider using SQLAlchemy's eager loading feature. This allows you to fetch all the necessary related objects in a single query instead of making multiple queries for each level of recursion. 3. Use CTE (Common Table Expressions): SQLAlchemy supports Common Table Expressions (CTE) which can be used to perform recursive queries. CTEs allow you to define temporary tables and reference them multiple times in the query, simplifying recursive operations. 4. Optimize database schema: Review and optimize the database schema to ensure that it is designed in a way that supports efficient recursive operations. This can involve denormalizing tables, using appropriate data types, and eliminating unnecessary relationships. 5. Implement caching: If the data being queried is relatively static or changes infrequently, consider implementing a caching mechanism. This can help reduce the number of database requests and improve overall performance. 6. Use database-specific optimizations: Different databases have their own specific optimizations for recursive queries. Consult the documentation and resources specific to your database to explore additional techniques for optimization. 7. Tune SQLAlchemy configuration: Depending on the specific use case, adjusting SQLAlchemy's configuration options such as connection pooling, query batching, and prefetching can help optimize recursive queries. It is important to note that the effectiveness of these techniques may vary depending on the specific use case and dataset. Profiling and benchmarking the application can help identify further areas of improvement.

answer:To perform parallel queries to a PostgreSQL database using SQLAlchemy, you can take advantage of Python's multiprocessing module. Here's an example of how you can execute multiple queries in parallel: ```python import multiprocessing from sqlalchemy import create_engine, text def query_func(query, result_list): """ This function will be executed in parallel to execute the query and store the result in the result_list. """ engine = create_engine('postgresql://username:password@localhost:5432/db_name') with engine.connect() as conn: result = conn.execute(query) result_list.append(result.fetchall()) if __name__ == '__main__': queries = [ 'SELECT * FROM table1', 'SELECT * FROM table2', 'SELECT * FROM table3' ] # Create a list to store the results results = [] # Create a multiprocessing pool with the desired number of workers pool = multiprocessing.Pool(processes=len(queries)) # Execute queries in parallel using the pool.map() function pool.map(query_func, [(query, results) for query in queries]) # Close the pool and wait for all processes to complete pool.close() pool.join() # Access the results of each query for result in results: print(result) ``` In this example, the `query_func()` function is defined to execute a single query and store the result in a shared list. The `multiprocessing.Pool` class is used to create a pool of worker processes, and the `pool.map()` function is used to execute the `query_func()` function in parallel for each query. Make sure to replace the PostgreSQL connection string with the appropriate values for your database. Note that parallel execution of queries may not always result in better performance, and it's important to consider database resources and limitations. It's recommended to monitor the performance and adjust the number of parallel queries accordingly.