sql-ecommerce-project/02-data.sql at master · jmHan001/sql-ecommerce-project · GitHub

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-- ============================================
-- 测试数据（15个用户 + 5个分类 + 10个商品 + 30条订单）
-- ============================================

USE ecommerce;

-- 用户（5个南宁 + 5个南昌 + 5个其他）
INSERT INTO t_user (username, phone, city) VALUES
('张三', '13800000001', '南宁'),
('李四', '13800000002', '南宁'),
('王五', '13800000003', '南宁'),
('赵六', '13800000004', '南宁'),
('孙七', '13800000005', '南宁'),
('周八', '13800000006', '南昌'),
('吴九', '13800000007', '南昌'),
('郑十', '13800000008', '南昌'),
('冯一', '13800000009', '南昌'),
('陈二', '13800000010', '南昌'),
('褚三', '13800000011', '桂林'),
('卫四', '13800000012', '柳州'),
('蒋五', '13800000013', '北海'),
('沈六', '13800000014', '玉林'),
('韩七', '13800000015', '梧州');

-- 商品分类
INSERT INTO t_category (id, name, pid) VALUES
(1, '手机数码', 0),
(2, '电脑办公', 0),
(3, '家用电器', 0),
(4, '手机', 1),
(5, '笔记本', 2);

-- 商品
INSERT INTO t_product (name, category_id, price, stock) VALUES
('iPhone 15', 4, 5999.00, 100),
('华为Mate 60', 4, 4999.00, 80),
('小米14', 4, 3999.00, 120),
('OPPO Find X7', 4, 3499.00, 60),
('MacBook Pro 14', 5, 12999.00, 30),
('联想ThinkPad X1', 5, 8999.00, 25),
('戴尔XPS 15', 5, 7999.00, 20),
('格力空调1.5匹', 3, 2999.00, 50),
('美的冰箱', 3, 2499.00, 40),
('海尔洗衣机', 3, 1999.00, 35);

-- 订单（30条，模拟现实购买场景）
INSERT INTO t_order (user_id, product_id, quantity, total_price, order_time) VALUES
(1, 1, 1, 5999.00, '2026-01-15 10:30:00'),
(1, 3, 2, 7998.00, '2026-02-20 14:00:00'),
(2, 2, 1, 4999.00, '2026-01-18 09:00:00'),
(2, 5, 1, 12999.00, '2026-03-05 16:30:00'),
(3, 3, 1, 3999.00, '2026-01-22 11:00:00'),
(3, 4, 3, 10497.00, '2026-04-10 10:00:00'),
(4, 1, 1, 5999.00, '2026-02-01 08:30:00'),
(4, 8, 2, 5998.00, '2026-05-15 13:00:00'),
(5, 2, 1, 4999.00, '2026-02-10 15:00:00'),
(5, 6, 1, 8999.00, '2026-06-01 09:00:00'),
(6, 3, 1, 3999.00, '2026-01-28 10:00:00'),
(6, 7, 1, 7999.00, '2026-03-18 14:30:00'),
(7, 4, 1, 3499.00, '2026-02-14 12:00:00'),
(7, 9, 1, 2499.00, '2026-04-20 16:00:00'),
(8, 1, 2, 11998.00, '2026-03-01 09:00:00'),
(8, 10, 1, 1999.00, '2026-05-01 11:00:00'),
(9, 2, 1, 4999.00, '2026-03-10 10:30:00'),
(9, 5, 1, 12999.00, '2026-06-10 14:00:00'),
(10, 3, 1, 3999.00, '2026-04-01 08:00:00'),
(10, 8, 3, 8997.00, '2026-05-20 17:00:00'),
(11, 4, 1, 3499.00, '2026-04-15 09:30:00'),
(12, 6, 1, 8999.00, '2026-05-05 10:00:00'),
(13, 7, 1, 7999.00, '2026-05-10 11:30:00'),
(14, 9, 2, 4998.00, '2026-06-05 13:00:00'),
(15, 10, 1, 1999.00, '2026-06-15 15:00:00'),
(1, 10, 1, 1999.00, '2026-06-20 08:00:00'),
(2, 4, 1, 3499.00, '2026-06-21 09:30:00'),
(6, 5, 1, 12999.00, '2026-06-22 14:00:00'),
(8, 2, 1, 4999.00, '2026-06-22 16:00:00'),
(9, 3, 2, 7998.00, '2026-06-23 10:00:00');