大数据技术之Hive SQL题库-中级

Posted 2023-03-24 骚戴

tags:

篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了大数据技术之Hive SQL题库-中级相关的知识，希望对你有一定的参考价值。

第1章环境准备

1.1 用户信息表

1）表结构

user_id(用户id)	gender(性别)	birthday(生日)
101	男	1990-01-01
102	女	1991-02-01
103	女	1992-03-01
104	男	1993-04-01

2）建表语句

hive>

DROP TABLE IF EXISTS user_info;
CREATE table user_info(
    user_id string comment'用户id',
    gender string comment'性别',
    birthday string comment'生日'
)comment'用户信息表'
row format delimited fields terminated by '\\t';

3）数据装载

hive>

insert overwrite table user_info
values ('101', '男', '1990-01-01'),
       ('102', '女', '1991-02-01'),
       ('103', '女', '1992-03-01'),
       ('104', '男', '1993-04-01'),
       ('105', '女', '1994-05-01'),
       ('106', '男', '1995-06-01'),
       ('107', '女', '1996-07-01'),
       ('108', '男', '1997-08-01'),
       ('109', '女', '1998-09-01'),
       ('1010', '男', '1999-10-01');

1.2 商品信息表

1）表结构

sku_id (商品id)	name (商品名称)	category_id (分类id)	from_date (上架日期)	price (商品价格)
1	xiaomi 10	1	2020-01-01	2000
6	洗碗机	2	2020-02-01	2000
9	自行车	3	2020-01-01	1000

2）建表语句

hive>

DROP TABLE IF EXISTS sku_info;
CREATE TABLE sku_info(
    `sku_id`      string COMMENT '商品id',
    `name`        string COMMENT '商品名称',
    `category_id` string COMMENT '所属分类id',
    `from_date`   string COMMENT '上架日期',
    `price`       double COMMENT '商品单价'
) COMMENT '商品属性表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

insert overwrite table sku_info
values ('1', 'xiaomi 10', '1', '2020-01-01', 2000),
       ('2', '手机壳', '1', '2020-02-01', 10),
       ('3', 'apple 12', '1', '2020-03-01', 5000),
       ('4', 'xiaomi 13', '1', '2020-04-01', 6000),
       ('5', '破壁机', '2', '2020-01-01', 500),
       ('6', '洗碗机', '2', '2020-02-01', 2000),
       ('7', '热水壶', '2', '2020-03-01', 100),
       ('8', '微波炉', '2', '2020-04-01', 600),
       ('9', '自行车', '3', '2020-01-01', 1000),
       ('10', '帐篷', '3', '2020-02-01', 100),
       ('11', '烧烤架', '3', '2020-02-01', 50),
       ('12', '遮阳伞', '3', '2020-03-01', 20);

1.3 商品分类信息表

1）表结构

category_id(分类id)	category_name(分类名称)
1	数码
2	厨卫
3	户外

2）建表语句

hive>

DROP TABLE IF EXISTS category_info;
create table category_info(
    `category_id`   string,
    `category_name` string
) COMMENT '品类表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

insert overwrite table category_info
values ('1','数码'),
       ('2','厨卫'),
       ('3','户外');

1.4 订单信息表

1）表结构

order_id

(订单id)

user_id

(用户id)

create_date

(下单日期)

total_amount

(订单金额)

101

2021-09-30

29000.00

103

2020-10-02

28000.00

2）建表语句

hive>

DROP TABLE IF EXISTS order_info;
create table order_info(
    `order_id`     string COMMENT '订单id',
    `user_id`      string COMMENT '用户id',
    `create_date`  string COMMENT '下单日期',
    `total_amount` decimal(16, 2) COMMENT '订单总金额'
) COMMENT '订单表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

insert overwrite table order_info
values ('1', '101', '2021-09-27', 29000.00),
       ('2', '101', '2021-09-28', 70500.00),
       ('3', '101', '2021-09-29', 43300.00),
       ('4', '101', '2021-09-30', 860.00),
       ('5', '102', '2021-10-01', 46180.00),
       ('6', '102', '2021-10-01', 50000.00),
       ('7', '102', '2021-10-01', 75500.00),
       ('8', '102', '2021-10-02', 6170.00),
       ('9', '103', '2021-10-02', 18580.00),
       ('10', '103', '2021-10-02', 28000.00),
       ('11', '103', '2021-10-02', 23400.00),
       ('12', '103', '2021-10-03', 5910.00),
       ('13', '104', '2021-10-03', 13000.00),
       ('14', '104', '2021-10-03', 69500.00),
       ('15', '104', '2021-10-03', 2000.00),
       ('16', '104', '2021-10-03', 5380.00),
       ('17', '105', '2021-10-04', 6210.00),
       ('18', '105', '2021-10-04', 68000.00),
       ('19', '105', '2021-10-04', 43100.00),
       ('20', '105', '2021-10-04', 2790.00),
       ('21', '106', '2021-10-04', 9390.00),
       ('22', '106', '2021-10-05', 58000.00),
       ('23', '106', '2021-10-05', 46600.00),
       ('24', '106', '2021-10-05', 5160.00),
       ('25', '107', '2021-10-05', 55350.00),
       ('26', '107', '2021-10-05', 14500.00),
       ('27', '107', '2021-10-06', 47400.00),
       ('28', '107', '2021-10-06', 6900.00),
       ('29', '108', '2021-10-06', 56570.00),
       ('30', '108', '2021-10-06', 44500.00),
       ('31', '108', '2021-10-07', 50800.00),
       ('32', '108', '2021-10-07', 3900.00),
       ('33', '109', '2021-10-07', 41480.00),
       ('34', '109', '2021-10-07', 88000.00),
       ('35', '109', '2020-10-08', 15000.00),
       ('36', '109', '2020-10-08', 9020.00),
       ('37', '1010', '2020-10-08', 9260.00),
       ('38', '1010', '2020-10-08', 12000.00),
       ('39', '1010', '2020-10-08', 23900.00),
       ('40', '1010', '2020-10-08', 6790.00);

1.5 订单明细表

1）表结构

order_detail_id (订单明细id)	order_id (订单id)	sku_id (商品id)	create_date (下单日期)	price (商品单价)	sku_num (商品件数)
1	1	1	2021-09-30	2000.00	2
2	1	3	2021-09-30	5000.00	5
22	10	4	2020-10-02	6000.00	1
23	10	5	2020-10-02	500.00	24
24	10	6	2020-10-02	2000.00	5

2）建表语句

hive>

DROP TABLE IF EXISTS order_detail;
CREATE TABLE order_detail
(
    `order_detail_id` string COMMENT '订单明细id',
    `order_id`        string COMMENT '订单id',
    `sku_id`          string COMMENT '商品id',
    `create_date`     string COMMENT '下单日期',
    `price`           decimal(16, 2) COMMENT '下单时的商品单价',
    `sku_num`         int COMMENT '下单商品件数'
) COMMENT '订单明细表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

INSERT overwrite table order_detail
values ('1', '1', '1', '2021-09-27', 2000.00, 2),
       ('2', '1', '3', '2021-09-27', 5000.00, 5),
       ('3', '2', '4', '2021-09-28', 6000.00, 9),
       ('4', '2', '5', '2021-09-28', 500.00, 33),
       ('5', '3', '7', '2021-09-29', 100.00, 37),
       ('6', '3', '8', '2021-09-29', 600.00, 46),
       ('7', '3', '9', '2021-09-29', 1000.00, 12),
       ('8', '4', '12', '2021-09-30', 20.00, 43),
       ('9', '5', '1', '2021-10-01', 2000.00, 8),
       ('10', '5', '2', '2021-10-01', 10.00, 18),
       ('11', '5', '3', '2021-10-01', 5000.00, 6),
       ('12', '6', '4', '2021-10-01', 6000.00, 8),
       ('13', '6', '6', '2021-10-01', 2000.00, 1),
       ('14', '7', '7', '2021-10-01', 100.00, 17),
       ('15', '7', '8', '2021-10-01', 600.00, 48),
       ('16', '7', '9', '2021-10-01', 1000.00, 45),
       ('17', '8', '10', '2021-10-02', 100.00, 48),
       ('18', '8', '11', '2021-10-02', 50.00, 15),
       ('19', '8', '12', '2021-10-02', 20.00, 31),
       ('20', '9', '1', '2021-09-30', 2000.00, 9),
       ('21', '9', '2', '2021-10-02', 10.00, 5800),
       ('22', '10', '4', '2021-10-02', 6000.00, 1),
       ('23', '10', '5', '2021-10-02', 500.00, 24),
       ('24', '10', '6', '2021-10-02', 2000.00, 5),
       ('25', '11', '8', '2021-10-02', 600.00, 39),
       ('26', '12', '10', '2021-10-03', 100.00, 47),
       ('27', '12', '11', '2021-10-03', 50.00, 19),
       ('28', '12', '12', '2021-10-03', 20.00, 13000),
       ('29', '13', '1', '2021-10-03', 2000.00, 4),
       ('30', '13', '3', '2021-10-03', 5000.00, 1),
       ('31', '14', '4', '2021-10-03', 6000.00, 5),
       ('32', '14', '5', '2021-10-03', 500.00, 47),
       ('33', '14', '6', '2021-10-03', 2000.00, 8),
       ('34', '15', '7', '2021-10-03', 100.00, 20),
       ('35', '16', '10', '2021-10-03', 100.00, 22),
       ('36', '16', '11', '2021-10-03', 50.00, 42),
       ('37', '16', '12', '2021-10-03', 20.00, 7400),
       ('38', '17', '1', '2021-10-04', 2000.00, 3),
       ('39', '17', '2', '2021-10-04', 10.00, 21),
       ('40', '18', '4', '2021-10-04', 6000.00, 8),
       ('41', '18', '5', '2021-10-04', 500.00, 28),
       ('42', '18', '6', '2021-10-04', 2000.00, 3),
       ('43', '19', '7', '2021-10-04', 100.00, 55),
       ('44', '19', '8', '2021-10-04', 600.00, 11),
       ('45', '19', '9', '2021-10-04', 1000.00, 31),
       ('46', '20', '11', '2021-10-04', 50.00, 45),
       ('47', '20', '12', '2021-10-04', 20.00, 27),
       ('48', '21', '1', '2021-10-04', 2000.00, 2),
       ('49', '21', '2', '2021-10-04', 10.00, 39),
       ('50', '21', '3', '2021-10-04', 5000.00, 1),
       ('51', '22', '4', '2021-10-05', 6000.00, 8),
       ('52', '22', '5', '2021-10-05', 500.00, 20),
       ('53', '23', '7', '2021-10-05', 100.00, 58),
       ('54', '23', '8', '2021-10-05', 600.00, 18),
       ('55', '23', '9', '2021-10-05', 1000.00, 30),
       ('56', '24', '10', '2021-10-05', 100.00, 27),
       ('57', '24', '11', '2021-10-05', 50.00, 28),
       ('58', '24', '12', '2021-10-05', 20.00, 53),
       ('59', '25', '1', '2021-10-05', 2000.00, 5),
       ('60', '25', '2', '2021-10-05', 10.00, 35),
       ('61', '25', '3', '2021-10-05', 5000.00, 9),
       ('62', '26', '4', '2021-10-05', 6000.00, 1),
       ('63', '26', '5', '2021-10-05', 500.00, 13),
       ('64', '26', '6', '2021-10-05', 2000.00, 1),
       ('65', '27', '7', '2021-10-06', 100.00, 30),
       ('66', '27', '8', '2021-10-06', 600.00, 19),
       ('67', '27', '9', '2021-10-06', 1000.00, 33),
       ('68', '28', '10', '2021-10-06', 100.00, 37),
       ('69', '28', '11', '2021-10-06', 50.00, 46),
       ('70', '28', '12', '2021-10-06', 20.00, 45),
       ('71', '29', '1', '2021-10-06', 2000.00, 8),
       ('72', '29', '2', '2021-10-06', 10.00, 57),
       ('73', '29', '3', '2021-10-06', 5000.00, 8),
       ('74', '30', '4', '2021-10-06', 6000.00, 3),
       ('75', '30', '5', '2021-10-06', 500.00, 33),
       ('76', '30', '6', '2021-10-06', 2000.00, 5),
       ('77', '31', '8', '2021-10-07', 600.00, 13),
       ('78', '31', '9', '2021-10-07', 1000.00, 43),
       ('79', '32', '10', '2021-10-07', 100.00, 24),
       ('80', '32', '11', '2021-10-07', 50.00, 30),
       ('81', '33', '1', '2021-10-07', 2000.00, 8),
       ('82', '33', '2', '2021-10-07', 10.00, 48),
       ('83', '33', '3', '2021-10-07', 5000.00, 5),
       ('84', '34', '4', '2021-10-07', 6000.00, 10),
       ('85', '34', '5', '2021-10-07', 500.00, 44),
       ('86', '34', '6', '2021-10-07', 2000.00, 3),
       ('87', '35', '8', '2020-10-08', 600.00, 25),
       ('88', '36', '10', '2020-10-08', 100.00, 57),
       ('89', '36', '11', '2020-10-08', 50.00, 44),
       ('90', '36', '12', '2020-10-08', 20.00, 56),
       ('91', '37', '1', '2020-10-08', 2000.00, 2),
       ('92', '37', '2', '2020-10-08', 10.00, 26),
       ('93', '37', '3', '2020-10-08', 5000.00, 1),
       ('94', '38', '6', '2020-10-08', 2000.00, 6),
       ('95', '39', '7', '2020-10-08', 100.00, 35),
       ('96', '39', '8', '2020-10-08', 600.00, 34),
       ('97', '40', '10', '2020-10-08', 100.00, 37),
       ('98', '40', '11', '2020-10-08', 50.00, 51),
       ('99', '40', '12', '2020-10-08', 20.00, 27);

1.6 登录明细表

1）表结构

user_id(用户id)	ip_address(ip地址)	login_ts(登录时间)	logout_ts(登出时间)
101	180.149.130.161	2021-09-21 08:00:00	2021-09-27 08:30:00
102	120.245.11.2	2021-09-22 09:00:00	2021-09-27 09:30:00
103	27.184.97.3	2021-09-23 10:00:00	2021-09-27 10:30:00

2）建表语句

hive>

DROP TABLE IF EXISTS user_login_detail;
CREATE TABLE user_login_detail
(
    `user_id`    string comment '用户id',
    `ip_address` string comment 'ip地址',
    `login_ts`   string comment '登录时间',
    `logout_ts`  string comment '登出时间'
) COMMENT '用户登录明细表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

INSERT overwrite table user_login_detail
VALUES ('101', '180.149.130.161', '2021-09-21 08:00:00', '2021-09-27 08:30:00'),
       ('101', '180.149.130.161', '2021-09-27 08:00:00', '2021-09-27 08:30:00'),
       ('101', '180.149.130.161', '2021-09-28 09:00:00', '2021-09-28 09:10:00'),
       ('101', '180.149.130.161', '2021-09-29 13:30:00', '2021-09-29 13:50:00'),
       ('101', '180.149.130.161', '2021-09-30 20:00:00', '2021-09-30 20:10:00'),
       ('102', '120.245.11.2', '2021-09-22 09:00:00', '2021-09-27 09:30:00'),
       ('102', '120.245.11.2', '2021-10-01 08:00:00', '2021-10-01 08:30:00'),
       ('102', '180.149.130.174', '2021-10-01 07:50:00', '2021-10-01 08:20:00'),
       ('102', '120.245.11.2', '2021-10-02 08:00:00', '2021-10-02 08:30:00'),
       ('103', '27.184.97.3', '2021-09-23 10:00:00', '2021-09-27 10:30:00'),
       ('103', '27.184.97.3', '2021-10-03 07:50:00', '2021-10-03 09:20:00'),
       ('104', '27.184.97.34', '2021-09-24 11:00:00', '2021-09-27 11:30:00'),
       ('104', '27.184.97.34', '2021-10-03 07:50:00', '2021-10-03 08:20:00'),
       ('104', '27.184.97.34', '2021-10-03 08:50:00', '2021-10-03 10:20:00'),
       ('104', '120.245.11.89', '2021-10-03 08:40:00', '2021-10-03 10:30:00'),
       ('105', '119.180.192.212', '2021-10-04 09:10:00', '2021-10-04 09:30:00'),
       ('106', '119.180.192.66', '2021-10-04 08:40:00', '2021-10-04 10:30:00'),
       ('106', '119.180.192.66', '2021-10-05 21:50:00', '2021-10-05 22:40:00'),
       ('107', '219.134.104.7', '2021-09-25 12:00:00', '2021-09-27 12:30:00'),
       ('107', '219.134.104.7', '2021-10-05 22:00:00', '2021-10-05 23:00:00'),
       ('107', '219.134.104.7', '2021-10-06 09:10:00', '2021-10-06 10:20:00'),
       ('107', '27.184.97.46', '2021-10-06 09:00:00', '2021-10-06 10:00:00'),
       ('108', '101.227.131.22', '2021-10-06 09:00:00', '2021-10-06 10:00:00'),
       ('108', '101.227.131.22', '2021-10-06 22:00:00', '2021-10-06 23:00:00'),
       ('109', '101.227.131.29', '2021-09-26 13:00:00', '2021-09-27 13:30:00'),
       ('109', '101.227.131.29', '2021-10-06 08:50:00', '2021-10-06 10:20:00'),
       ('109', '101.227.131.29', '2021-10-08 09:00:00', '2021-10-08 09:10:00'),
       ('1010', '119.180.192.10', '2021-09-27 14:00:00', '2021-09-27 14:30:00'),
       ('1010', '119.180.192.10', '2021-10-09 08:50:00', '2021-10-09 10:20:00');

1.7 商品价格变更明细表

1）表结构

sku_id(商品id)	new_price(本次变更之后的价格)	change_date(变更日期)
1	1900.00	2021-09-25
1	2000.00	2021-09-26
2	80.00	2021-09-29
2	10.00	2021-09-30

2）建表语句

hive>

DROP TABLE IF EXISTS sku_price_modify_detail;
CREATE TABLE sku_price_modify_detail
(
    `sku_id`      string comment '商品id',
    `new_price`   decimal(16, 2) comment '更改后的价格',
    `change_date` string comment '变动日期'
) COMMENT '商品价格变更明细表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

insert overwrite table sku_price_modify_detail
values ('1', 1900, '2021-09-25'),
       ('1', 2000, '2021-09-26'),
       ('2', 80, '2021-09-29'),
       ('2', 10, '2021-09-30'),
       ('3', 4999, '2021-09-25'),
       ('3', 5000, '2021-09-26'),
       ('4', 5600, '2021-09-26'),
       ('4', 6000, '2021-09-27'),
       ('5', 490, '2021-09-27'),
       ('5', 500, '2021-09-28'),
       ('6', 1988, '2021-09-30'),
       ('6', 2000, '2021-10-01'),
       ('7', 88, '2021-09-28'),
       ('7', 100, '2021-09-29'),
       ('8', 800, '2021-09-28'),
       ('8', 600, '2021-09-29'),
       ('9', 1100, '2021-09-27'),
       ('9', 1000, '2021-09-28'),
       ('10', 90, '2021-10-01'),
       ('10', 100, '2021-10-02'),
       ('11', 66, '2021-10-01'),
       ('11', 50, '2021-10-02'),
       ('12', 35, '2021-09-28'),
       ('12', 20, '2021-09-29');

1.8 配送信息表

1）表结构

delivery_id （运单id）	order_id （订单id）	user_id （用户id）	order_date （下单日期）	custom_date （期望配送日期）
1	1	101	2021-09-27	2021-09-29
2	2	101	2021-09-28	2021-09-28
3	3	101	2021-09-29	2021-09-30

2）建表语句

hive>

DROP TABLE IF EXISTS delivery_info;
CREATE TABLE delivery_info
(
    `delivery_id` string comment '配送单id',
    `order_id`    string comment '订单id',
    `user_id`     string comment '用户id',
    `order_date`  string comment '下单日期',
    `custom_date` string comment '期望配送日期'
) COMMENT '邮寄信息表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

insert overwrite table delivery_info
values ('1', '1', '101', '2021-09-27', '2021-09-29'),
       ('2', '2', '101', '2021-09-28', '2021-09-28'),
       ('3', '3', '101', '2021-09-29', '2021-09-30'),
       ('4', '4', '101', '2021-09-30', '2021-10-01'),
       ('5', '5', '102', '2021-10-01', '2021-10-01'),
       ('6', '6', '102', '2021-10-01', '2021-10-01'),
       ('7', '7', '102', '2021-10-01', '2021-10-03'),
       ('8', '8', '102', '2021-10-02', '2021-10-02'),
       ('9', '9', '103', '2021-10-02', '2021-10-03'),
       ('10', '10', '103', '2021-10-02', '2021-10-04'),
       ('11', '11', '103', '2021-10-02', '2021-10-02'),
       ('12', '12', '103', '2021-10-03', '2021-10-03'),
       ('13', '13', '104', '2021-10-03', '2021-10-04'),
       ('14', '14', '104', '2021-10-03', '2021-10-04'),
       ('15', '15', '104', '2021-10-03', '2021-10-03'),
       ('16', '16', '104', '2021-10-03', '2021-10-03'),
       ('17', '17', '105', '2021-10-04', '2021-10-04'),
       ('18', '18', '105', '2021-10-04', '2021-10-06'),
       ('19', '19', '105', '2021-10-04', '2021-10-06'),
       ('20', '20', '105', '2021-10-04', '2021-10-04'),
       ('21', '21', '106', '2021-10-04', '2021-10-04'),
       ('22', '22', '106', '2021-10-05', '2021-10-05'),
       ('23', '23', '106', '2021-10-05', '2021-10-05'),
       ('24', '24', '106', '2021-10-05', '2021-10-07'),
       ('25', '25', '107', '2021-10-05', '2021-10-05'),
       ('26', '26', '107', '2021-10-05', '2021-10-06'),
       ('27', '27', '107', '2021-10-06', '2021-10-06'),
       ('28', '28', '107', '2021-10-06', '2021-10-07'),
       ('29', '29', '108', '2021-10-06', '2021-10-06'),
       ('30', '30', '108', '2021-10-06', '2021-10-06'),
       ('31', '31', '108', '2021-10-07', '2021-10-09'),
       ('32', '32', '108', '2021-10-07', '2021-10-09'),
       ('33', '33', '109', '2021-10-07', '2021-10-08'),
       ('34', '34', '109', '2021-10-07', '2021-10-08'),
       ('35', '35', '109', '2021-10-08', '2021-10-10'),
       ('36', '36', '109', '2021-10-08', '2021-10-09'),
       ('37', '37', '1010', '2021-10-08', '2021-10-10'),
       ('38', '38', '1010', '2021-10-08', '2021-10-10'),
       ('39', '39', '1010', '2021-10-08', '2021-10-09'),
       ('40', '40', '1010', '2021-10-08', '2021-10-09');

1.9 好友关系表

1）表结构

user1_id（用户1 id）	user2_id（用户2 id）
101	1010
101	108
101	106

注：表中一行数据中的两个user_id，表示两个用户互为好友。

2）建表语句

hive>

DROP TABLE IF EXISTS friendship_info;
CREATE TABLE friendship_info(
    `user1_id` string comment '用户1id',
    `user2_id` string comment '用户2id'
) COMMENT '用户关系表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

insert overwrite table friendship_info
values ('101', '1010'),
       ('101', '108'),
       ('101', '106'),
       ('101', '104'),
       ('101', '102'),
       ('102', '1010'),
       ('102', '108'),
       ('102', '106'),
       ('102', '104'),
       ('102', '102'),
       ('103', '1010'),
       ('103', '108'),
       ('103', '106'),
       ('103', '104'),
       ('103', '102'),
       ('104', '1010'),
       ('104', '108'),
       ('104', '106'),
       ('104', '104'),
       ('104', '102'),
       ('105', '1010'),
       ('105', '108'),
       ('105', '106'),
       ('105', '104'),
       ('105', '102'),
       ('106', '1010'),
       ('106', '108'),
       ('106', '106'),
       ('106', '104'),
       ('106', '102'),
       ('107', '1010'),
       ('107', '108'),
       ('107', '106'),
       ('107', '104'),
       ('107', '102'),
       ('108', '1010'),
       ('108', '108'),
       ('108', '106'),
       ('108', '104'),
       ('108', '102'),
       ('109', '1010'),
       ('109', '108'),
       ('109', '106'),
       ('109', '104'),
       ('109', '102'),
       ('1010', '1010'),
       ('1010', '108'),
       ('1010', '106'),
       ('1010', '104'),
       ('1010', '102');

1.10 收藏信息表

1）表结构

user_id(用户id)	sku_id(商品id)	create_date(收藏日期)
101	3	2021-09-23
101	12	2021-09-23
101	6	2021-09-25

2）建表语句

hive>

DROP TABLE IF EXISTS favor_info;
CREATE TABLE favor_info
(
    `user_id`     string comment '用户id',
    `sku_id`      string comment '商品id',
    `create_date` string comment '收藏日期'
) COMMENT '用户收藏表'
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t';

3）数据装载

hive>

insert overwrite table favor_info
values ('101', '3', '2021-09-23'),
       ('101', '12', '2021-09-23'),
       ('101', '6', '2021-09-25'),
       ('101', '10', '2021-09-21'),
       ('101', '5', '2021-09-25'),
       ('102', '1', '2021-09-24'),
       ('102', '2', '2021-09-24'),
       ('102', '8', '2021-09-23'),
       ('102', '12', '2021-09-22'),
       ('102', '11', '2021-09-23'),
       ('102', '9', '2021-09-25'),
       ('102', '4', '2021-09-25'),
       ('102', '6', '2021-09-23'),
       ('102', '7', '2021-09-26'),
       ('103', '8', '2021-09-24'),
       ('103', '5', '2021-09-25'),
       ('103', '6', '2021-09-26'),
       ('103', '12', '2021-09-27'),
       ('103', '7', '2021-09-25'),
       ('103', '10', '2021-09-25'),
       ('103', '4', '2021-09-24'),
       ('103', '11', '2021-09-25'),
       ('103', '3', '2021-09-27'),
       ('104', '9', '2021-09-28'),
       ('104', '7', '2021-09-28'),
       ('104', '8', '2021-09-25'),
       ('104', '3', '2021-09-28'),
       ('104', '11', '2021-09-25'),
       ('104', '6', '2021-09-25'),
       ('104', '12', '2021-09-28'),
       ('105', '8', '2021-10-08'),
       ('105', '9', '2021-10-07'),
       ('105', '7', '2021-10-07'),
       ('105', '11', '2021-10-06'),
       ('105', '5', '2021-10-07'),
       ('105', '4', '2021-10-05'),
       ('105', '10', '2021-10-07'),
       ('106', '12', '2021-10-08'),
       ('106', '1', '2021-10-08'),
       ('106', '4', '2021-10-04'),
       ('106', '5', '2021-10-08'),
       ('106', '2', '2021-10-04'),
       ('106', '6', '2021-10-04'),
       ('106', '7', '2021-10-08'),
       ('107', '5', '2021-09-29'),
       ('107', '3', '2021-09-28'),
       ('107', '10', '2021-09-27'),
       ('108', '9', '2021-10-08'),
       ('108', '3', '2021-10-10'),
       ('108', '8', '2021-10-10'),
       ('108', '10', '2021-10-07'),
       ('108', '11', '2021-10-07'),
       ('109', '2', '2021-09-27'),
       ('109', '4', '2021-09-29'),
       ('109
大数据技术之 Hive (小白入门)

Hive 基本概念 
什么是 Hive 
hive 简介 
Hive：由 Facebook 开源用于解决海量结构化日志的数据统计工具。
 Hive: 是基于 Hadoop 的一个数据仓库工具，可以将结构化的数据文件映射为一张表，并提供类 SQL 查询功能。 
Hive 安装 
1）下载hive Hive 官网地址：http://hive.apache.org/
 2) 把 apache-hive-3.1.2-bin.tar.gz 上传到 linux 的/opt/software 目录下
 3）解压 apache-hive-3.1.2-bin.tar.gz 到/opt/module/目录下面 
 tar -zxvf /opt/software/apache-hive-3.1.2-bin.tar.gz -C /opt/module/
 
4）修改 apache-hive-3.1.2-bin.tar.gz 的名称为 hive 
mv /opt/module/apache-hive-3.1.2-bin /opt/module/hive
 
5）修改/etc/profile.d/my_env.sh，添加环境变量 
#HIVE_HOME
export HIVE_HOME=/opt/module/hive
export PATH=$PATH:$HIVE_HOME/bin
 
6）解决日志 Jar 包冲突 
mv $HIVE_HOME/lib/log4j-slf4j-impl-
2.10.0.jar $HIVE_HOME/lib/log4j-slf4j-impl-2.10.0.bak
 
7）初始化元数据库 
 进入hive 安装目录 bin/schematool -dbType derby -initSchema
 
启动并使用 Hive 
1）启动 Hive 
 bin/hive
 
2）使用 Hive 
 show databases;
 show tables;
 create table test(id int);
 insert into test values(1);
 select * from test;
 
MySQL 安装 
1）检查当前系统是否安装过 MySQL 
rpm -qa|grep mariadb
//如果存在通过如下命令卸载
sudo rpm -e --nodeps mariadb-libs
 
2）将 MySQL 安装包拷贝到/opt/software 目录下 解压 MySQL 安装包 
tar -xf mysql-5.7.28-1.el7.x86_64.rpmbundle.tar
 
3）在安装目录下执行 rpm 安装 
sudo rpm -ivh mysql-community-common-5.7.28-1.el7.x86_64.rpm
sudo rpm -ivh mysql-community-libs-5.7.28-1.el7.x86_64.rpm
sudo rpm -ivh mysql-community-libs-compat-5.7.28-1.el7.x86_64.rpm
sudo rpm -ivh mysql-community-client-5.7.28-1.el7.x86_64.rpm
sudo rpm -ivh mysql-community-server-5.7.28-1.el7.x86_64.rpm
 
注意:按照顺序依次执行
 如果 Linux 是最小化安装的，在安装 mysql-community-server-5.7.28-1.el7.x86_64.rpm 时
 可能会出现如下错误 
警告：mysql-community-server-5.7.28-1.el7.x86_64.rpm: 头 V3 DSA/SHA1 
Signature, 密钥 ID 5072e1f5: NOKEY
错误：依赖检测失败：
 libaio.so.1()(64bit) 被 mysql-community-server-5.7.28-1.el7.x86_64 
需要
 libaio.so.1(LIBAIO_0.1)(64bit) 被 mysql-community-server-5.7.28-
1.el7.x86_64 需要
 libaio.so.1(LIBAIO_0.4)(64bit) 被 mysql-community-server-5.7.28-
1.el7.x86_64 需要
 
通过 yum 安装缺少的依赖,然后重新安装 mysql-community-server-5.7.28-1.el7.x86_64 即 可
 4）删除/etc/my.cnf 文件中 datadir 指向的目录下的所有内容
 5）初始化数据库 
sudo mysqld --initialize --user=mysql
 
6）查看临时生成的 root 用户的密码 
sudo cat /var/log/mysqld.log
 
7）启动 MySQL 服务 
sudo systemctl start mysqld
 
8）登录 MySQL 数据库 
mysql -uroot -p
 
9）必须先修改 root 用户的密码,否则执行其他的操作会报错 
set password = password("新密码");
 
10）修改 mysql 库下的 user 表中的 root 用户允许任意 ip 连接 
update mysql.user set host='%' where user='root';
flush privileges; //刷新
 
Hive 元数据配置到 MySQL 
1)拷贝驱动,将 MySQL 的 JDBC 驱动拷贝到 Hive 的 lib 目录下 
 cp /opt/software/mysql-connector-java-
5.1.37.jar $HIVE_HOME/lib
 
2)配置 Metastore 到 MySQL,在$HIVE_HOME/conf 目录下新建 hive-site.xml 文件 
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
 <!-- jdbc 连接的 URL -->
 <property>
 <name>javax.jdo.option.ConnectionURL</name>
 <value>jdbc:mysql://hadoop102:3306/metastore?useSSL=false</value>
</property>
 <!-- jdbc 连接的 Driver-->
 <property>
 <name>javax.jdo.option.ConnectionDriverName</name>
 <value>com.mysql.jdbc.Driver</value>
</property>
<!-- jdbc 连接的 username-->
 <property>
 <name>javax.jdo.option.ConnectionUserName</name>
 <value>root</value>
 </property>
 <!-- jdbc 连接的 password -->
 <property>
 <name>javax.jdo.option.ConnectionPassword</name>
 <value>000000</value>
</property>
 <!-- Hive 元数据存储版本的验证 -->
 <property>
 <name>hive.metastore.schema.verification</name>
 <value>false</value>
</property>
 <!--元数据存储授权-->
 <property>
 <name>hive.metastore.event.db.notification.api.auth</name>
 <value>false</value>
 </property>
 <!-- Hive 默认在 HDFS 的工作目录 -->
 <property>
  <name>hive.metastore.warehouse.dir</name>
 <value>/user/hive/warehouse</value>
 </property>
</configuration>
 
3）登陆 MySQL,新建 Hive 元数据库 
 create database metastore;
 
4） 初始化 Hive 元数据库 
 schematool -initSchema -dbType mysql -verbose
 
使用元数据服务的方式访问 Hive 
1）在 hive-site.xml 文件中添加如下配置信息 
<!-- 指定存储元数据要连接的地址 -->
 <property>
 <name>hive.metastore.uris</name>
 <value>thrift://hadoop102:9083</value>
 </property>
 
Hive 常用交互命令 
usage: hive
-d,--define <key=value> Variable subsitution to apply to hive
 commands. e.g. -d A=B or --define A=B
 --database <databasename> Specify the database to use
-e <quoted-query-string> SQL from command line
-f <filename> SQL from files
-H,--help Print help information
 --hiveconf <property=value> Use value for given property
 --hivevar <key=value> Variable subsitution to apply to hive
 commands. e.g. --hivevar A=B
-i <filename> Initialization SQL file
-S,--silent Silent mode in interactive shell
-v,--verbose Verbose mode (echo executed SQL to the 
console)
 
下一篇hive的基本使用方法（小白入门）
以上是关于大数据技术之Hive SQL题库-中级的主要内容，如果未能解决你的问题，请参考以下文章 
 大数据之Hive：Hive调优全方位指南
 Hive sql大数据有道之Hive sql去重
 干货｜大数据技术之争：PIG对Hive
 大数据系列之数据仓库Hive原理
 好程序员大数据学习路线之hive表的查询
 大数据之Hive：space函数

大数据技术之Hive SQL题库-中级

第1章 环境准备

1.1 用户信息表

1.2 商品信息表

1.3 商品分类信息表

1.4 订单信息表

1.5 订单明细表

1.6 登录明细表

1.7 商品价格变更明细表

1.8 配送信息表

1.9 好友关系表

1.10 收藏信息表

大数据技术之 Hive (小白入门)

Hive 基本概念

什么是 Hive

Hive 安装

启动并使用 Hive

MySQL 安装

Hive 元数据配置到 MySQL

使用元数据服务的方式访问 Hive

Hive 常用交互命令

第1章环境准备