MySQL implements the data analysis function of the ordering system

MySQL is a commonly used relational database management system that is widely used in various applications. In ordering systems, data analysis functions are very important for restaurant operators. This article will introduce how to use MySQL to implement the data analysis function of the ordering system, and attach specific code examples.

1. Create a data table
First, we need to create a database and corresponding data table. Assume that our ordering system has the following main data tables:

1. Orders table (orders): stores information related to customer orders, including order number, customer ID, order time, total amount, etc.

   CREATE TABLE orders ( order_id INT PRIMARY KEY AUTO_INCREMENT, customer_id INT, order_time DATETIME, total_amount DECIMAL(10, 2) );

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2. Dishes: Stores information about all dishes, including dish ID, dish name, price, etc.

   CREATE TABLE dishes ( dish_id INT PRIMARY KEY AUTO_INCREMENT, dish_name VARCHAR(50), price DECIMAL(10, 2) );

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3. Order details table (order_details): records the dishes and their quantities included in each order.

   CREATE TABLE order_details ( order_id INT, dish_id INT, quantity INT, PRIMARY KEY (order_id, dish_id) );

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2. Insert test data
Next, we need to insert some test data into the data table for data analysis. Suppose we have the following test data:

Orders table (orders):

INSERT INTO orders (customer_id, order_time, total_amount) VALUES (1, '2021-01-01', 25.50), (2, '2021-01-02', 50.00), (3, '2021-01-03', 35.75);

Dishes table (dishes):

INSERT INTO dishes (dish_name, price) VALUES ('宫保鸡丁', 18.00), ('鱼香肉丝', 16.50), ('红烧肉', 23.80);

Order details table (order_details):

INSERT INTO order_details (order_id, dish_id, quantity) VALUES (1, 1, 2), (1, 2, 1), (2, 2, 3), (3, 1, 1), (3, 3, 2);

3. Basic data statistics
When using MySQL for data analysis, we can obtain the required data through some basic SQL query statements. The following are some commonly used data statistics query examples:

1. Statistics on order quantity and total sales amount

   SELECT COUNT(*) AS order_count, SUM(total_amount) AS total_sales FROM orders;

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2. Statistics on total sales quantity of each dish and total sales amount

   SELECT dishes.dish_name, SUM(order_details.quantity) AS total_quantity, SUM(order_details.quantity * dishes.price) AS total_sales FROM dishes JOIN order_details ON dishes.dish_id = order_details.dish_id GROUP BY dishes.dish_id;

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3. Query a customer’s order quantity and total consumption amount

   SELECT customer_id, COUNT(*) AS order_count, SUM(total_amount) AS total_expense FROM orders WHERE customer_id = 1;

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4. Advanced data analysis
Except In addition to basic data statistics functions, if we want to perform more complex data analysis, we can use MySQL's aggregate functions, conditional filtering, sorting and other features in combination. The following are some query examples for advanced data analysis:

1. Query the most popular dishes (with the most sales)

   SELECT dishes.dish_name, SUM(order_details.quantity) AS total_quantity FROM dishes JOIN order_details ON dishes.dish_id = order_details.dish_id GROUP BY dishes.dish_id ORDER BY total_quantity DESC LIMIT 3;

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   This query will return the top 3 items with the most sales A dish.

2. Query the consumption amount ranking of each customer

   SELECT customer_id, SUM(total_amount) AS total_expense, RANK() OVER (ORDER BY SUM(total_amount) DESC) AS expense_rank FROM orders GROUP BY customer_id;

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   This query will return the consumption amount and ranking of each customer.

3. Query the total sales amount and average sales amount per day

   SELECT DATE(order_time) AS order_date, SUM(total_amount) AS total_sales, AVG(total_amount) AS average_sales FROM orders GROUP BY DATE(order_time);

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   This query will return the total sales amount and average sales amount per day.

To sum up, by using various functions and syntax of MySQL, we can realize the data analysis function of the ordering system. In practical applications, we can conduct further data analysis and optimization based on specific needs and business scenarios. MySQL provides powerful tools and functions to meet our various data analysis needs.

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