hive数据库和表的创建
给dim添加最新状态记录
任务
接着上一篇数据抽取的任务继续 需用到上篇ods数据抽取的数据继续练习
hive数据库和表的创建
1、创建dwd数据库
create database dwd;2、创建dim_user_info 表，分区字段etl_date
CREATE TABLE `dim_user_info` (
`id` bigint,
`login_name` string,
`nick_name` string,
`passwd` string,
`name` string,
`phone_num` string,
`email` string,
`head_img` string,
`user_level` string,
`birthday` timestamp,
`gender` string,
`create_time` timestamp,
`operate_time` timestamp,
`dwd_insert_user` string,
`dwd_insert_time` timestamp,
`dwd_modify_user` string,
`dwd_modify_time` timestamp
) PARTITIONED BY ( `etl_date` string)
row format delimited
fields terminated by "\001"
stored as textfile;3、创建dim_sku_info 表，分区字段 etl_dat
CREATE TABLE `dim_sku_info` (
`id` bigint,
`spu_id` bigint,
`price` decimal(10, 0),
`sku_name` string,
`sku_desc` string,
`weight` decimal(10, 2),
`tm_id` bigint,
`category3_id` bigint,
`sku_default_img` string,
`create_time` timestamp,
`dwd_insert_user` string,
`dwd_insert_time` timestamp,
`dwd_modify_user` string,
`dwd_modify_time` timestamp
) PARTITIONED BY ( `etl_date` string)
row format delimited
fields terminated by "\001"
stored as textfile;4、创建dim_base_province 表，分区字段 etl_date
CREATE TABLE `dim_base_province` (
`id` bigint,
`name` string,
`region_id` string,
`area_code` string,
`iso_code` string,
`create_time` timestamp,
`dwd_insert_user` string,
`dwd_insert_time` timestamp,
`dwd_modify_user` string,
`dwd_modify_time` timestamp
) PARTITIONED BY ( `etl_date` string)
row format delimited
fields terminated by "\001"
stored as textfile;5、 创建dim_base_region 表，分区字段是 etl_date
CREATE TABLE `dim_base_region` (
`id` string,
`region_name` string,
`create_time` timestamp,
`dwd_insert_user` string,
`dwd_insert_time` timestamp,
`dwd_modify_user` string,
`dwd_modify_time` timestamp
) PARTITIONED BY ( `etl_date` string)
row format delimited
fields terminated by "\001"
stored as textfile;6、 创建fact_order_info 表 分区字段etl_date
CREATE TABLE `fact_order_info` (
`id` bigint,
`consignee` string,
`consignee_tel`string,
`final_total_amount` decimal(16, 2),
`order_status` string,
`user_id` bigint,
`delivery_address` string,
`order_comment` string,
`out_trade_no` string,
`trade_body` string,
`create_time` timestamp,
`operate_time` timestamp,
`expire_time` timestamp,
`tracking_no` string,
`parent_order_id` bigint,
`img_url` string,
`province_id` int,
`benefit_reduce_amount` decimal(16, 2),
`original_total_amount` decimal(16, 2),
`feight_fee` decimal(16, 2),
`dwd_insert_user` string,
`dwd_insert_time` timestamp,
`dwd_modify_user` string,
`dwd_modify_time` timestamp
) PARTITIONED BY ( `etl_date` string)
row format delimited
fields terminated by "\001"
stored as textfile;7、创建fact_order_detail 表 分区字段是etl_date
CREATE TABLE `fact_order_detail` (
`id` bigint,
`order_id` bigint,
`sku_id` bigint,
`sku_name` string,
`img_url` string,
`order_price` decimal(10, 2),
`sku_num` string,
`create_time` timestamp,
`source_type` string,
`source_id` bigint,
`dwd_insert_user` string,
`dwd_insert_time` timestamp,
`dwd_modify_user` string,
`dwd_modify_time` timestamp
) PARTITIONED BY ( `etl_date` string)
row format delimited
fields terminated by "\001"
stored as textfile;
给dim添加最新状态记录
这里可以自己给dwd数据库中dim打头的所有表 自己给里面添加最新状态(分区时间)的数据方便实验测试结果。测试完成可以在hive的dwd数据库的操作表中使用select查看按题目要求添加后的新状态记录以及题目要求的其余内容。
1、dim_user_info表
INSERT INTO `dim_user_info` VALUES (82, 'rz4gxf1', '阿仁+1', NULL, '康仁', '13437298274', 'rz4gxf1@0355.net', NULL, '1', '2004-04-26', 'M', '2020-04-26 18:57:55', '2020-04-26 06:02:36','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');
INSERT INTO `dim_user_info` VALUES (85, 'cte9ov7mv', '波宁+1', NULL, '华良海', '13751595688', 'p6vilkg81l9w@qq.com', NULL, '1', '1989-04-26', 'M', '2020-04-26 18:57:55', '2020-04-26 23:54:52','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');2、dim_sku_info表
INSERT INTO `dim_sku_info` VALUES (1, 1, 2220, '荣耀10青春版+1 幻彩渐变 2400万AI自拍 全网通版4GB+64GB 渐变蓝 移动联通电信4G全面屏手机 双卡双待', 'new sku_desc', 0.24, 2, 61, 'http://AOvKmfRQEBRJJllwCwCuptVAOtBBcIjWeJRsmhbJ','2021-01-01 12:21:13','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');
INSERT INTO `dim_sku_info` VALUES (2, 2, 3321, 'TCL 55A950C 55英寸32核人工智能+1 HDR曲面超薄4K电视金属机身（枪色）', 'new sku_desc', 15.24, 4, 86, 'http://JfJSvAnPkErYPcUsbgCuokhjxKiLeqpDXakZqFeE','2021-01-01 12:21:13','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');3、dim_base_province表
INSERT INTO `dim_base_province` VALUES (1, '北京+1', '1', '110000', 'CN-11','2024-01-01 12:21:13','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');
INSERT INTO `dim_base_province` VALUES (2, '天津市+1', '1', '120000', 'CN-12','2024-01-01 12:21:13','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');4、dim_base_region表
INSERT INTO `dim_base_region` VALUES ('1', '华北+1','2024-01-01 12:21:13','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');
INSERT INTO `dim_base_region` VALUES ('2', '华东+1','2024-01-01 12:21:13','user1','2020-04-26 18:57:55','user1','2020-04-26 18:57:55','20230403');任务
要求：使用Scala编写spark工程代码，将ods库中相应表数据全量抽取到Hive的dwd库中对应表中。表中有涉及到timestamp类型的，均要求按照yyyy-MM-dd HH:mm:ss，不记录毫秒数，若原数据中只有年月日，则在时分秒的位置添加00:00:00，添加之后使其符合yyyy-MM-dd HH:mm:ss。
1、抽取ods库中user_info表中昨天的分区（任务一生成的分区）数据，并结合dim_user_info最新分区现有的数据，根据id合并数据到dwd库中dim_user_info的分区表（合并是指对dwd层数据进行插入或修改，需修改的数据以id为合并字段，根据operate_time排序取最新的一条），分区字段为etl_date且值与ods库的相对应表该值相等，同时若operate_time为空，则用create_time填充，并添加dwd_insert_user、dwd_insert_time、dwd_modify_user、dwd_modify_time四列,其中dwd_insert_user、dwd_modify_user均填写“user1”。若该条记录第一次进入数仓dwd层则dwd_insert_time、dwd_modify_time均存当前操作时间，并进行数据类型转换。若该数据在进入dwd层时发生了合并修改，则dwd_insert_time时间不变，dwd_modify_time存当前操作时间，其余列存最新的值。使用hive cli执行show partitions dwd.dim_user_info命令
def main(args: Array[String]): Unit = {
//添加hdfs用户名
System.getProperty("HADOOP_user_name","root")
System.getProperty("user.name","root")
//创建Session对象
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("sparkOneHive")
.enableHiveSupport()
.getOrCreate()
//结构 插入最终查询结果字段(字段开窗过滤(dos.user_info的查询字段 union all dwd.dim_user_info)条件 开窗排序=1）
spark.sql(
"""
|insert overwrite table dwd.dim_user_info
|partition (etl_date = '20230403')
|select
|id,
|login_name,
|nick_name,
|passwd,
|name,
|phone_num,
|email,
|head_img,
|user_level,
|birthday,
|gender,
|create_time,
|operate_time,
|dwd_insert_user,
|insert_time,
|dwd_modify_user,
|dwd_modify_time
|from
|(select
|id,
|login_name,
|nick_name,
|passwd,
|name,
|phone_num,
|email,
|head_img,
|user_level,
|birthday,
|gender,
|create_time,
|operate_time,
|dwd_insert_user,
|dwd_insert_time,
|dwd_modify_user,
|dwd_modify_time,
|min(dwd_insert_time) over(partition by id) insert_time,
|row_number() over(partition by id order by operate_time desc) row
|from
|(select
|id,
|login_name,
|nick_name,
|passwd,
|name,
|phone_num,
|email,
|head_img,
|user_level,
|birthday,
|gender,
|create_time,
|if(operate_time is null,create_time,operate_time) operate_time,
|"user1" dwd_insert_user,
|cast(date_format(current_timestamp(), 'yyyy-MM-dd HH:mm:ss') as timestamp) dwd_insert_time,
|"user1" dwd_modify_user,
|cast(date_format(current_timestamp(), 'yyyy-MM-dd HH:mm:ss') as timestamp) dwd_modify_time
|from ods.user_info
|where etl_date = '20230330'
|union all
|select
|id,
|login_name,
|nick_name,
|passwd,
|name,
|phone_num,
|email,
|head_img,
|user_level,
|birthday,
|gender,
|create_time,
|if(operate_time is null,create_time,operate_time) operate_time,
|dwd_insert_user,
|dwd_insert_time,
|dwd_modify_user,
|dwd_modify_time
|from dwd.dim_user_info
|where etl_date = '20230403'
|)dw
|)tmp where row = 1
|""".stripMargin)
spark.sql("select * from dwd.dim_user_info").show()
spark.sql("show partitions dwd.dim_user_info").show()
spark.close()
}
2、抽取ods库sku_info表中昨天的分区（任务一生成的分区）数据，并结合dim_sku_info最新分区现有的数据，根据id合并数据到dwd库中dim_sku_info的分区表（合并是指对dwd层数据进行插入或修改，需修改的数据以id为合并字段，根据create_time排序取最新的一条），分区字段为etl_date且值与ods库的相对应表该值相等，并添加dwd_insert_user、dwd_insert_time、dwd_modify_user、dwd_modify_time四列,其中dwd_insert_user、dwd_modify_user均填写“user1”。若该条数据第一次进入数仓dwd层则dwd_insert_time、dwd_modify_time均填写当前操作时间，并进行数据类型转换。若该数据在进入dwd层时发生了合并修改，则dwd_insert_time时间不变，dwd_modify_time存当前操作时间，其余列存最新的值。使用hive cli查询表dim_sku_info的字段id、sku_desc、dwd_insert_user、dwd_modify_time、etl_date，条件为最新分区的数据，id大于等于15且小于等于20，并且按照id升序排序
def main(args: Array[String]): Unit = {
//添加hdfs用户名
System.getProperty("HADOOP_user_name","root")
System.getProperty("user.name","root")
//创建Session对象
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("sparkOneHive")
.enableHiveSupport()
.getOrCreate()
spark.sql("""insert overwrite table dwd.dim_sku_info partition (etl_date = '20230403')
|select
|id,
|spu_id,
|price,
|sku_name,
|sku_desc,
|weight,
|tm_id,
|category3_id,
|sku_default_img,
|create_time,
|dwd_insert_user,
|insert_time,
|dwd_modify_user,
|dwd_modify_time
|from
| (select
| id,
| spu_id,
| price,
| sku_name,
| sku_desc,
| weight,
| tm_id,
| category3_id,
| sku_default_img,
| create_time,
| dwd_insert_user,
| dwd_insert_time,
| dwd_modify_user,
| dwd_modify_time,
| min(dwd_insert_time) over(partition by id) insert_time,
| row_number() over(partition by id order by create_time desc) rw
|from
| (select
| id,
| spu_id,
| price,
| sku_name,
| sku_desc,
| weight,
| tm_id,
| category3_id,
| sku_default_img,
| cast(date_format(create_time , 'yyyy-MM-dd HH-mm-ss') as timestamp) create_time,
| 'user1' dwd_insert_user,
| cast(date_format(current_timestamp() , 'yyyy-MM-dd HH:mm:ss') as timestamp) dwd_insert_time,
| 'user1' dwd_modify_user,
| cast(date_format(current_timestamp() , 'yyyy-MM-dd HH:mm:ss') as timestamp) dwd_modify_time
| from ods.sku_info
| where etl_date = '20230330'
| union all
| select
| id,
| spu_id,
| price,
| sku_name,
| sku_desc,
| weight,
| tm_id,
| category3_id,
| sku_default_img,
| cast(create_time as timestamp) create_time,
| dwd_insert_user,
| dwd_insert_time,
| dwd_modify_user,
| dwd_modify_time
| from dwd.dim_sku_info
| where etl_date = '20230403'
| ) dw
| )tmp where rw = 1""".stripMargin)
spark.sql("select id,sku_desc,dwd_insert_user,dwd_modify_time from dwd.dim_sku_info where id >= 15 and id <= 20 order by id").show()
spark.sql("show partitions dwd.dim_sku_info").show()
spark.close()
}
3、抽取ods库base_province表中昨天的分区（任务一生成的分区）数据，并结合dim_province最新分区现有的数据，根据id合并数据到dwd库中dim_province的分区表（合并是指对dwd层数据进行插入或修改，需修改的数据以id为合并字段，根据create_time排序取最新的一条），分区字段为etl_date且值与ods库的相对应表该值相等，并添加dwd_insert_user、dwd_insert_time、dwd_modify_user、dwd_modify_time四列,其中dwd_insert_user、dwd_modify_user均填写“user1”。若该条数据第一次进入数仓dwd层则dwd_insert_time、dwd_modify_time均填写当前操作时间，并进行数据类型转换。若该数据在进入dwd层时发生了合并修改，则dwd_insert_time时间不变，dwd_modify_time存当前操作时间，其余列存最新的值。使用hive cli在表dwd.dim_province最新分区中，查询该分区中数据的条数
def main(args: Array[String]): Unit = {
//添加hdfs用户名
System.getProperty("HADOOP_user_name","root")
System.getProperty("user.name","root")
//创建Session对象
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("sparkOneHive")
.enableHiveSupport()
.getOrCreate()
spark.sql("""
|insert overwrite table dwd.dim_base_province
|partition (etl_date = '20230403')
|select
|id,
|name,
|region_id,
|area_code,
|iso_code,
|create_time,
|dwd_insert_user,
|dwd_insert_time,
|dwd_modify_user,
|dwd_modify_time
|from
| (select
| id,
| name,
| region_id,
| area_code,
| iso_code,
| create_time,
| dwd_insert_user,
| dwd_insert_time,
| dwd_modify_user,
| dwd_modify_time,
| min(dwd_insert_time) over(partition by id) insert_time,
| row_number() over(partition by id order by create_time desc) rw
| from
| (select
| id,
| name,
| region_id,
| area_code,
| iso_code,
| create_time,
| 'user1' dwd_insert_user,
| cast(date_format(current_timestamp() , 'yyyy-MM-dd HH-mm-ss') as timestamp) dwd_insert_time,
| 'user1' dwd_modify_user,
| cast(date_format(current_timestamp() , 'yyyy-MM-dd HH-mm-ss') as timestamp) dwd_modify_time
| from ods.base_province where etl_date = '20230405'
| union all
| select
| id,
| name,
| region_id,
| area_code,
| iso_code,
| create_time,
| dwd_insert_user,
| dwd_insert_time,
| dwd_modify_user,
| dwd_modify_time
| from dwd.dim_base_province where etl_date = '20230403'
| )dw
| )tmp where rw = 1
| """.stripMargin)
spark.sql("select count(*) from dwd.dim_base_province").show()
spark.sql("show partitions dwd.dim_base_province").show()
spark.close()
}
4、抽取ods库base_region表中昨天的分区（任务一生成的分区）数据，并结合dim_region最新分区现有的数据，根据id合并数据到dwd库中dim_region的分区表（合并是指对dwd层数据进行插入或修改，需修改的数据以id为合并字段，根据create_time排序取最新的一条），分区字段为etl_date且值与ods库的相对应表该值相等，并添加dwd_insert_user、dwd_insert_time、dwd_modify_user、dwd_modify_time四列,其中dwd_insert_user、dwd_modify_user均填写“user1”。若该条数据第一次进入数仓dwd层则dwd_insert_time、dwd_modify_time均填写当前操作时间，并进行数据类型转换。若该数据在进入dwd层时发生了合并修改，则dwd_insert_time时间不变，dwd_modify_time存当前操作时间，其余列存最新的值。使用hive cli在表dwd.dim_region最新分区中，查询该分区中数据的条数
def main(args: Array[String]): Unit = {
//添加hdfs用户名
System.getProperty("HADOOP_user_name","root")
System.getProperty("user.name","root")
//创建Session对象
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("sparkOneHive")
.enableHiveSupport()
.getOrCreate()
spark.sql(
"""
|insert overwrite table dwd.dim_base_region partition (etl_date = '20230403')
|select
|id,
|region_name,
|create_time,
|dwd_insert_user,
|insert_time,
|dwd_modify_user,
|dwd_modify_time
|from
| (select
| id,
| region_name,
| create_time,
| dwd_insert_user,
| dwd_insert_time,
| dwd_modify_user,
| dwd_modify_time,
| min(dwd_insert_time) over(partition by id) insert_time,
| row_number() over(partition by id order by create_time desc) rw
| from
| (select
| id,
| region_name,
| create_time,
| 'user1' dwd_insert_user,
| cast(date_format(current_timestamp(), 'yyyy-MM-dd HH:mm:ss') as timestamp) dwd_insert_time,
| 'user1' dwd_modify_user,
| cast(date_format(current_timestamp(), 'yyyy-MM-dd HH:mm:ss') as timestamp) dwd_modify_time
| from ods.base_region where etl_date = '20230401'
| union all
| select
| id,
| region_name,
| create_time,
| dwd_insert_user,
| dwd_insert_time,
| dwd_modify_user,
| dwd_modify_time
| from dwd.dim_base_region
| where etl_date = '20230403'
| )dw
| )tmp where rw = 1
|""".stripMargin)
spark.sql("select * from dwd.dim_base_region").show()
spark.sql("show partitions dwd.dim_base_region").show()
spark.close()
}
5、将ods库中order_info表昨天的分区（任务一生成的分区）数据抽取到dwd库中fact_order_info的动态分区表，分区字段为etl_date，类型为String，取create_time值并将格式转换为yyyyMMdd，同时若operate_time为空，则用create_time填充，并添加dwd_insert_user、dwd_insert_time、dwd_modify_user、dwd_modify_time四列，其中dwd_insert_user、dwd_modify_user均填写“user1”，dwd_insert_time、dwd_modify_time均填写当前操作时间，并进行数据类型转换。使用hive cli执行show partitions dwd.fact_order_info命令
使用动态分区需要在hive-site.xml配置文件种添加如下
<!--配置动态分区-->
<property>
<name>hive.exec.dynamic.partition.mode</name>
<value>nonstrict</value>
</property>def main(args: Array[String]): Unit = {
//添加hdfs用户名
System.getProperty("HADOOP_user_name","root")
System.getProperty("user.name","root")
//创建Session对象
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("sparkOneHive")
.enableHiveSupport()
.getOrCreate()
spark.sql("""
|insert overwrite table dwd.fact_order_info partition(etl_date)
|select
| id,
| consignee,
| consignee_tel,
| final_total_amount,
| order_status,
| user_id,
| delivery_address,
| order_comment,
| out_trade_no,
| trade_body,
| cast(date_format(create_time,'yyyyMMdd') as timestamp) create_time,
| cast(date_format(if(operate_time is null,create_time,operate_time),'yyyyMMdd') as timestamp) operate_time,
| expire_time,
| tracking_no,
| parent_order_id,
| img_url,
| province_id,
| benefit_reduce_amount,
| original_total_amount,
| feight_fee,
| 'user1' dwd_insert_user,
| cast(date_format(current_timestamp() , 'yyyy-MM-dd') as timestamp) dwd_insert_time,
| 'user1' dwd_modify_user,
| cast(date_format(current_timestamp() , 'yyyy-MM-dd') as timestamp) dwd_modify_time,
| etl_date
| from ods.order_info where etl_date = '20230401'""".stripMargin)
spark.sql("select * from dwd.fact_order_info").show()
spark.sql("show partitions dwd.fact_order_info").show()
spark.close()
}
6、将ods库中order_detail表昨天的分区（任务一中生成的分区）数据抽取到dwd库中fact_order_detail的动态分区表，分区字段为etl_date，类型为String，取create_time值并将格式转换为yyyyMMdd，并添加dwd_insert_user、dwd_insert_time、dwd_modify_user、dwd_modify_time四列，其中dwd_insert_user、dwd_modify_user均填写“user1”，dwd_insert_time、dwd_modify_time均填写当前操作时间，并进行数据类型转换。使用hive cli执行show partitions dwd.fact_order_detail命令 文章来源：https://www.toymoban.com/news/detail-406718.html
def main(args: Array[String]): Unit = {
//添加hdfs用户名
System.getProperty("HADOOP_user_name","root")
System.getProperty("user.name","root")
//创建Session对象
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("sparkOneHive")
.enableHiveSupport()
.getOrCreate()
spark.sql(
"""
|insert overwrite table dwd.fact_order_detail partition(etl_date)
|select
|id,
|order_id,
|sku_id,
|sku_name,
|img_url,
|order_price,
|sku_num,
|cast(date_format(create_time,'yyyyMMdd') as timestamp) create_time,
|source_type,
|source_id,
|'user1' dwd_insert_user,
|cast(date_format(current_timestamp() , 'yyyy-MM-dd HH-mm-ss') as timestamp) dwd_insert_time,
|'user1' dwd_modify_user,
|cast(date_format(current_timestamp() , 'yyyy-MM-dd HH-mm-ss') as timestamp) dwd_modify_time,
|etl_date
|from ods.order_detail where etl_date = '20230401'
|""".stripMargin)
spark.sql("select * from dwd.fact_order_detail").show()
spark.sql("show partitions dwd.fact_order_detail").show()
spark.close()
}
文章来源地址https://www.toymoban.com/news/detail-406718.html
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