CDH数仓项目(二) —— 用户行为数仓和业务数仓搭建

0 说明

本文基于《CDH数仓项目(一) —— CDH安装部署搭建详细流程》开始搭建数仓

1 数仓搭建环境准备

1.1 Flume安装部署

1）添加服务

2) 选择Flume
3）选择依赖

4)选择部署节点

5) 安装完成

1.2 安装Sqoop

1）添加服务
2）选择Sqoop
3）选择部署节点

4）完成安装部署

1.3 配置Hadoop支持LZO

1）点击主机，在下拉菜单中点击Parcel

2）点击配置

3）任意选一个加上parcel库的url
本地url: http://chen102:8900/cloudera-repos/gplextras6/6.2.1/parcels/
远程url: https://archive.cloudera.com/gplextras6/6.2.1/parcels/

4）静待片刻，Parcel列表中出现了GPLEXTERAS，依次点击下载、分配、激活。

5）修改HDFS配置
在HDFS配置项中搜索“压缩编码解码器”，加入com.hadoop.compression.lzo.LzopCodec

6）修改Hive配置
在Hive配置项中搜索“Hive 辅助 JAR 目录”，加入/opt/cloudera/parcels/GPLEXTRAS/lib/hadoop/lib

7）修改Sqoop配置
在Sqoop的配置项中搜索“sqoop-conf/sqoop-env.sh 的 Sqoop 1 Client 客户端高级配置代码段（安全阀）”，加入以下字段

HADOOP_CLASSPATH=$HADOOP_CLASSPATH:/opt/cloudera/parcels/GPLEXTRAS/lib/hadoop/lib/*
JAVA_LIBRARY_PATH=$JAVA_LIBRARY_PATH:/opt/cloudera/parcels/GPLEXTRAS/lib/hadoop/lib/native

1.4 修改yarn配置

1）在yarn配置项中搜索“yarn.nodemanager.resource.memory-mb”，修改成4G。

2）在yarn配置项中搜索“yarn.scheduler.maximum-allocation-mb”，修改成2G。

3）重启相关组件

2 HUE使用概述

2.1 来源

HUE=Hadoop User Experience（Hadoop用户体验），直白来说就一个开源的Apache Hadoop UI系统，由Cloudera Desktop演化而来，最后Cloudera公司将其贡献给Apache基金会的Hadoop社区，它是基于Python Web框架Django实现的。通过使用HUE我们可以在浏览器端的Web控制台上与Hadoop集群进行交互来分析处理数据。

2.2 Hue用户管理

HUE的初始管理用户为admin，密码为admin。
1）在HUE中新建一个用户组——hive，并在该组下新建一个用户——hive。
（1）创建hive组

（2）创建hive用户

2）切换为hive用户

3 用户行为数仓搭建

3.1 日志采集Flume

3.1.1 用户行为日志生成

1）将log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar上传到chen102
该jar包百度云连接如下：

链接：https://pan.baidu.com/s/1aoFH-Uu8OhG1siRqHQQZSQ
提取码：zx1q

2）分发log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar到chen103

3.1.2 日志采集Flume配置

1）集群规划

服务器chen102	服务器chen103	服务器chen104
Flume（日志采集）	Flume	Flume

2）Flume配置分析

Flume直接读log日志的数据，log日志的格式是app-yyyy-mm-dd.log。
3）Flume的具体配置如下：
（1）在CM管理页面上点击Flume
（2）点击实例
（3）点击chen102的Agent选项

（4）点击配置
（5）对Flume Agent进行具体配置
内容如下：

a1.sources=r1
a1.channels=c1 c2 
a1.sinks=k1 k2 

# configure source
a1.sources.r1.type = TAILDIR
a1.sources.r1.filegroups = f1
a1.sources.r1.filegroups.f1 = /tmp/logs/app.+
a1.sources.r1.fileHeader = true
a1.sources.r1.channels = c1 c2

#interceptor
a1.sources.r1.interceptors = i1 i2
a1.sources.r1.interceptors.i1.type = com.atguigu.flume.interceptor.LogETLInterceptor$Builder
a1.sources.r1.interceptors.i2.type = com.atguigu.flume.interceptor.LogTypeInterceptor$Builder

# selector
a1.sources.r1.selector.type = multiplexing
a1.sources.r1.selector.header = topic
a1.sources.r1.selector.mapping.topic_start = c1
a1.sources.r1.selector.mapping.topic_event = c2

# configure channel
a1.channels.c1.type = memory
a1.channels.c1.capacity=10000
a1.channels.c1.byteCapacityBufferPercentage=20

a1.channels.c2.type = memory
a1.channels.c2.capacity=10000
a1.channels.c2.byteCapacityBufferPercentage=20

# configure sink
# start-sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.topic = topic_start
a1.sinks.k1.kafka.bootstrap.servers = chen102:9092,chen103:9092,chen104:9092
a1.sinks.k1.kafka.flumeBatchSize = 2000
a1.sinks.k1.kafka.producer.acks = 1
a1.sinks.k1.channel = c1

# event-sink
a1.sinks.k2.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k2.kafka.topic = topic_event
a1.sinks.k2.kafka.bootstrap.servers = chen102:9092,chen103:9092,chen104:9092
a1.sinks.k2.kafka.flumeBatchSize = 2000
a1.sinks.k2.kafka.producer.acks = 1
a1.sinks.k2.channel = c2

注意：com.atguigu.flume.interceptor.LogETLInterceptor和com.atguigu.flume.interceptor.LogTypeInterceptor是自定义的拦截器的全类名。需要根据用户自定义的拦截器做相应修改。
（6）在chen103上重复相同的操作

4）将自定义的拦截器flume-interceptor-1.0-SNAPSHOT.jar包放入到chen102的/opt/cloudera/parcels/CDH/lib/flume-ng/lib文件夹下面。如果没有自定义拦截器可以取消
5）分发Flume的jar包到chen103

3.1.3 消费Kafka Flume配置

1）集群规划

chen102	chen103	chen104
		Flume（消费kafka）

2）Flume配置分析

3）Flume的具体配置如下：
（1）在CM管理页面chen104上Flume的配置中找到代理名称

a1

在配置文件如下内容(kafka-hdfs)

## 组件
a1.sources=r1 r2
a1.channels=c1 c2
a1.sinks=k1 k2

## source1
a1.sources.r1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.r1.batchSize = 5000
a1.sources.r1.batchDurationMillis = 2000
a1.sources.r1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sources.r1.kafka.topics=topic_start

## source2
a1.sources.r2.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.r2.batchSize = 5000
a1.sources.r2.batchDurationMillis = 2000
a1.sources.r2.kafka.bootstrap.servers = chen102:9092,chen103:9092,chen104:9092
a1.sources.r2.kafka.topics=topic_event

## channel1
a1.channels.c1.type=memory
a1.channels.c1.capacity=100000
a1.channels.c1.transactionCapacity=10000

## channel2
a1.channels.c2.type=memory
a1.channels.c2.capacity=100000
a1.channels.c2.transactionCapacity=10000

## sink1
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.proxyUser=hive
a1.sinks.k1.hdfs.path = /origin_data/gmall/log/topic_start/%Y-%m-%d
a1.sinks.k1.hdfs.filePrefix = logstart-
a1.sinks.k1.hdfs.round = true
a1.sinks.k1.hdfs.roundValue = 10
a1.sinks.k1.hdfs.roundUnit = second

##sink2
a1.sinks.k2.type = hdfs
a1.sinks.k2.hdfs.proxyUser=hive
a1.sinks.k2.hdfs.path = /origin_data/gmall/log/topic_event/%Y-%m-%d
a1.sinks.k2.hdfs.filePrefix = logevent-
a1.sinks.k2.hdfs.round = true
a1.sinks.k2.hdfs.roundValue = 10
a1.sinks.k2.hdfs.roundUnit = second

## 不要产生大量小文件
a1.sinks.k1.hdfs.rollInterval = 10
a1.sinks.k1.hdfs.rollSize = 134217728
a1.sinks.k1.hdfs.rollCount = 0

a1.sinks.k2.hdfs.rollInterval = 10
a1.sinks.k2.hdfs.rollSize = 134217728
a1.sinks.k2.hdfs.rollCount = 0

## 控制输出文件是原生文件。
a1.sinks.k1.hdfs.fileType = CompressedStream 
a1.sinks.k2.hdfs.fileType = CompressedStream 

a1.sinks.k1.hdfs.codeC = lzop
a1.sinks.k2.hdfs.codeC = lzop

## 拼装
a1.sources.r1.channels = c1
a1.sinks.k1.channel= c1

a1.sources.r2.channels = c2
a1.sinks.k2.channel= c2

3.1.4 模拟生成日志

1）确保Flume、Kafka等服务正常运行

2）在HDFS创建/origin_data路径，并修改所有者为hive
sudo -u hdfs hadoop fs -mkdir /origin_data
sudo -u hdfs hadoop fs -chown hive:hive /origin_data

3）调用命令或脚本生成日志

4）登录HDFS-WEBUI
可以发现能够正常采集到相应的日志数据，并生成了目录

3.2 数仓ODS层

3.2.1 创建数据库

1）创建数据仓库目录，并修改所有者

sudo -u hdfs hadoop fs -mkdir /warehouse
sudo -u hdfs hadoop fs -chown hive:hive /warehouse

2）修改hive配置

3）使用hue组件的hive用户进行操作
4）创建gmall数据库

5）使用gmall数据库

3.2.2 创建启动日志表

CREATE EXTERNAL TABLE ods_start_log (`line` string)
PARTITIONED BY (`dt` string)
STORED AS
  INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_start_log';

3.2.3 ods层加载数据脚本

#!/bin/bash

# 定义变量方便修改
APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
   do_date=$1
else
   do_date=`date -d "-1 day" +%F`
fi

echo "===日志日期为 $do_date==="
sql="
load data inpath '/origin_data/gmall/log/topic_start/$do_date' into table "$APP".ods_start_log partition(dt='$do_date');

"
beeline -u "jdbc:hive2://chen102:10000/" -n hive -e "$sql"

3.2.4 执行脚本

sh ods_log.sh 2023-01-27

可在hue页面查看数据是否导入成功

3.3 DWD层

3.3.1 创建启动表

drop table if exists dwd_start_log;
CREATE EXTERNAL TABLE dwd_start_log(
`mid_id` string,
`user_id` string, 
`version_code` string, 
`version_name` string, 
`lang` string, 
`source` string, 
`os` string, 
`area` string, 
`model` string,
`brand` string, 
`sdk_version` string, 
`gmail` string, 
`height_width` string,  
`app_time` string,
`network` string, 
`lng` string, 
`lat` string, 
`entry` string, 
`open_ad_type` string, 
`action` string, 
`loading_time` string, 
`detail` string, 
`extend1` string
)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_start_log/';

3.3.2 DWD数据导入脚本

#!/bin/bash

# 定义变量方便修改
APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
	do_date=$1
else 
	do_date=`date -d "-1 day" +%F`  
fi 

sql="
set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table "$APP".dwd_start_log
PARTITION (dt='$do_date')
select 
    get_json_object(line,'$.mid') mid_id,
    get_json_object(line,'$.uid') user_id,
    get_json_object(line,'$.vc') version_code,
    get_json_object(line,'$.vn') version_name,
    get_json_object(line,'$.l') lang,
    get_json_object(line,'$.sr') source,
    get_json_object(line,'$.os') os,
    get_json_object(line,'$.ar') area,
    get_json_object(line,'$.md') model,
    get_json_object(line,'$.ba') brand,
    get_json_object(line,'$.sv') sdk_version,
    get_json_object(line,'$.g') gmail,
    get_json_object(line,'$.hw') height_width,
    get_json_object(line,'$.t') app_time,
    get_json_object(line,'$.nw') network,
    get_json_object(line,'$.ln') lng,
    get_json_object(line,'$.la') lat,
    get_json_object(line,'$.entry') entry,
    get_json_object(line,'$.open_ad_type') open_ad_type,
    get_json_object(line,'$.action') action,
    get_json_object(line,'$.loading_time') loading_time,
    get_json_object(line,'$.detail') detail,
    get_json_object(line,'$.extend1') extend1
from "$APP".ods_start_log 
where dt='$do_date';
"

beeline -u "jdbc:hive2://chen102:10000/" -n hive -e "$sql"

3.3.3 执行脚本并验证

3.4 DWS层

目标：统计当日、当周、当月活动的每个设备明细

3.4.1 每日设备活跃明细

建表

drop table if exists dws_uv_detail_day;
create external table dws_uv_detail_day
(
    `mid_id` string COMMENT '设备唯一标识',
    `user_id` string COMMENT '用户标识', 
    `version_code` string COMMENT '程序版本号', 
    `version_name` string COMMENT '程序版本名', 
    `lang` string COMMENT '系统语言', 
    `source` string COMMENT '渠道号', 
    `os` string COMMENT '安卓系统版本', 
    `area` string COMMENT '区域', 
    `model` string COMMENT '手机型号', 
    `brand` string COMMENT '手机品牌', 
    `sdk_version` string COMMENT 'sdkVersion', 
    `gmail` string COMMENT 'gmail', 
    `height_width` string COMMENT '屏幕宽高',
    `app_time` string COMMENT '客户端日志产生时的时间',
    `network` string COMMENT '网络模式',
    `lng` string COMMENT '经度',
    `lat` string COMMENT '纬度'
)
partitioned by(dt string)
stored as parquet
location '/warehouse/gmall/dws/dws_uv_detail_day'
;

3.4.2 数据导入脚本

#!/bin/bash

# 定义变量方便修改
APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
	do_date=$1
else 
	do_date=`date -d "-1 day" +%F`  
fi 


sql="
  set hive.exec.dynamic.partition.mode=nonstrict;

  insert overwrite table "$APP".dws_uv_detail_day partition(dt='$do_date')
  select  
    mid_id,
    concat_ws('|', collect_set(user_id)) user_id,
    concat_ws('|', collect_set(version_code)) version_code,
    concat_ws('|', collect_set(version_name)) version_name,
    concat_ws('|', collect_set(lang)) lang,
    concat_ws('|', collect_set(source)) source,
    concat_ws('|', collect_set(os)) os,
    concat_ws('|', collect_set(area)) area, 
    concat_ws('|', collect_set(model)) model,
    concat_ws('|', collect_set(brand)) brand,
    concat_ws('|', collect_set(sdk_version)) sdk_version,
    concat_ws('|', collect_set(gmail)) gmail,
    concat_ws('|', collect_set(height_width)) height_width,
    concat_ws('|', collect_set(app_time)) app_time,
    concat_ws('|', collect_set(network)) network,
    concat_ws('|', collect_set(lng)) lng,
    concat_ws('|', collect_set(lat)) lat
  from "$APP".dwd_start_log
  where dt='$do_date'  
  group by mid_id;
"

beeline -u "jdbc:hive2://chen102:10000/" -n hive -e "$sql"

3.4.3 执行并验证

3.5 ADS层

目标：当日活跃设备数

3.5.1 建表

drop table if exists ads_uv_count;
create external table ads_uv_count( 
    `dt` string COMMENT '统计日期',
    `day_count` bigint COMMENT '当日用户数量'
) COMMENT '活跃设备数' 
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_uv_count/'
;

3.5.2 ADS层加载脚本

#!/bin/bash

# 定义变量方便修改
APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
	do_date=$1
else 
	do_date=`date -d "-1 day" +%F`  
fi 

sql="
  set hive.exec.dynamic.partition.mode=nonstrict;

insert into table "$APP".ads_uv_count 
select  
  '$do_date' dt,
   daycount.ct
from 
(
   select  
      '$do_date' dt,
       count(*) ct
   from "$APP".dws_uv_detail_day
   where dt='$do_date'  
)daycount;
"
beeline -u "jdbc:hive2://chen102:10000/" -n hive -e "$sql"

3.5.3 执行并验证

4 业务数仓搭建

4.1 业务数据生成

1）连接Mysql创建数据库gmall
2）设置数据库编码

3）导入建表语句（1建表脚本）
sql脚本如下：

链接：https://pan.baidu.com/s/1WX3xVMQvAApSUZMobWLiLQ 
提取码：8emk 

4. 生成数据

CALL init_data('2019-02-10',1000,200,300,TRUE);

4.2 Sqoop导入脚本

#!/bin/bash
export HADOOP_USER_NAME=hive
db_date=$2
echo $db_date
db_name=gmall

import_data() {
sqoop import \
--connect jdbc:mysql://chen102:3306/$db_name \
--username root \
--password Chen.123456 \
--target-dir  /origin_data/$db_name/db/$1/$db_date \
--delete-target-dir \
--num-mappers 1 \
--fields-terminated-by "\t" \
--query "$2"' and  $CONDITIONS;'
}

import_sku_info(){
  import_data  "sku_info"  "select 
id, spu_id, price, sku_name, sku_desc, weight, tm_id,
category3_id, create_time 
  from sku_info  where 1=1"
}

import_user_info(){
  import_data "user_info" "select 
id, name, birthday, gender, email, user_level, 
create_time 
from user_info where 1=1"
}

import_base_category1(){
  import_data "base_category1" "select 
id, name from base_category1 where 1=1"
}

import_base_category2(){
  import_data "base_category2" "select 
id, name, category1_id from base_category2 where 1=1"
}

import_base_category3(){
  import_data "base_category3" "select id, name, category2_id from base_category3 where 1=1"
}

import_order_detail(){
  import_data   "order_detail"   "select 
    od.id, 
    order_id, 
    user_id, 
    sku_id, 
    sku_name, 
    order_price, 
    sku_num, 
    o.create_time  
  from order_info o , order_detail od 
  where o.id=od.order_id 
  and DATE_FORMAT(create_time,'%Y-%m-%d')='$db_date'"
}

import_payment_info(){
  import_data "payment_info"   "select 
    id,  
    out_trade_no, 
    order_id, 
    user_id, 
    alipay_trade_no, 
    total_amount,  
    subject, 
    payment_type, 
    payment_time 
  from payment_info 
  where DATE_FORMAT(payment_time,'%Y-%m-%d')='$db_date'"
}

import_order_info(){
  import_data   "order_info"   "select 
    id, 
    total_amount, 
    order_status, 
    user_id, 
    payment_way, 
    out_trade_no, 
    create_time, 
    operate_time  
  from order_info 
  where  (DATE_FORMAT(create_time,'%Y-%m-%d')='$db_date' or DATE_FORMAT(operate_time,'%Y-%m-%d')='$db_date')"
}

case $1 in
  "base_category1")
     import_base_category1
;;
  "base_category2")
     import_base_category2
;;
  "base_category3")
     import_base_category3
;;
  "order_info")
     import_order_info
;;
  "order_detail")
     import_order_detail
;;
  "sku_info")
     import_sku_info
;;
  "user_info")
     import_user_info
;;
  "payment_info")
     import_payment_info
;;
   "all")
   import_base_category1
   import_base_category2
   import_base_category3
   import_order_info
   import_order_detail
   import_sku_info
   import_user_info
   import_payment_info
;;
esac

执行结果如下：

修改/orgin_data/gmall/db路径的访问权限

sudo -u hdfs hadoop fs -chmod -R 777 /origin_data/gmall/db

4.3 ODS层

4.3.1 创建订单表

drop table if exists ods_order_info;
create external table ods_order_info ( 
    `id` string COMMENT '订单编号',
    `total_amount` decimal(10,2) COMMENT '订单金额', 
    `order_status` string COMMENT '订单状态', 
    `user_id` string COMMENT '用户id' ,
    `payment_way` string COMMENT '支付方式',  
    `out_trade_no` string COMMENT '支付流水号',  
    `create_time` string COMMENT '创建时间',  
    `operate_time` string COMMENT '操作时间' 
) COMMENT '订单表'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_order_info/'
;

4.3.2 订单详情表

drop table if exists ods_order_detail;
create external table ods_order_detail( 
    `id` string COMMENT '订单编号',
    `order_id` string  COMMENT '订单号', 
    `user_id` string COMMENT '用户id' ,
    `sku_id` string COMMENT '商品id',  
    `sku_name` string COMMENT '商品名称',  
    `order_price` string COMMENT '商品价格',  
    `sku_num` string COMMENT '商品数量',  
    `create_time` string COMMENT '创建时间'
) COMMENT '订单明细表'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_order_detail/'

4.3.3 创建商品表

drop table if exists ods_sku_info;
create external table ods_sku_info( 
    `id` string COMMENT 'skuId',
    `spu_id` string   COMMENT 'spuid', 
    `price` decimal(10,2) COMMENT '价格' ,
    `sku_name` string COMMENT '商品名称',  
    `sku_desc` string COMMENT '商品描述',  
    `weight` string COMMENT '重量',  
    `tm_id` string COMMENT '品牌id',  
    `category3_id` string COMMENT '品类id',  
    `create_time` string COMMENT '创建时间'
) COMMENT '商品表'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_sku_info/'
;

4.3.4 创建用户表

drop table if exists ods_user_info;
create external table ods_user_info( 
    `id` string COMMENT '用户id',
    `name`  string COMMENT '姓名', 
    `birthday` string COMMENT '生日' ,
    `gender` string COMMENT '性别',  
    `email` string COMMENT '邮箱',  
    `user_level` string COMMENT '用户等级',  
    `create_time` string COMMENT '创建时间'
) COMMENT '用户信息'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_user_info/'
;

4.3.5 创建一级分类表

drop table if exists ods_base_category1;
create external table ods_base_category1( 
    `id` string COMMENT 'id',
    `name`  string COMMENT '名称'
) COMMENT '商品一级分类'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_base_category1/'
;

4.3.6 创建二级分类表

drop table if exists ods_base_category2;
create external table ods_base_category2( 
    `id` string COMMENT ' id',
    `name`  string COMMENT '名称',
    category1_id string COMMENT '一级品类id'
) COMMENT '商品二级分类'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_base_category2/'
;

4.3.7 创建三级分类表

drop table if exists ods_base_category3;
create external table ods_base_category3( 
    `id` string COMMENT ' id',
    `name`  string COMMENT '名称',
    category2_id string COMMENT '二级品类id'
) COMMENT '商品三级分类'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_base_category3/'
;

4.3.8 创建支付流水表

drop table if exists `ods_payment_info`;
create external table  `ods_payment_info`(
    `id`   bigint COMMENT '编号',
    `out_trade_no`    string COMMENT '对外业务编号',
    `order_id`        string COMMENT '订单编号',
    `user_id`         string COMMENT '用户编号',
    `alipay_trade_no` string COMMENT '支付宝交易流水编号',
    `total_amount`    decimal(16,2) COMMENT '支付金额',
    `subject`         string COMMENT '交易内容',
    `payment_type` string COMMENT '支付类型',
    `payment_time`   string COMMENT '支付时间'
   )  COMMENT '支付流水表'
PARTITIONED BY ( `dt` string)
row format delimited  fields terminated by '\t' 
location '/warehouse/gmall/ods/ods_payment_info/'
;

4.3.8 ODS数据导入脚本

#!/bin/bash

APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
	do_date=$1
else 
	do_date=`date -d "-1 day" +%F`  
fi

sql=" 
load data inpath '/origin_data/$APP/db/order_info/$do_date'  OVERWRITE into table "$APP".ods_order_info partition(dt='$do_date');

load data inpath '/origin_data/$APP/db/order_detail/$do_date'  OVERWRITE into table "$APP".ods_order_detail partition(dt='$do_date');

load data inpath '/origin_data/$APP/db/sku_info/$do_date'  OVERWRITE into table "$APP".ods_sku_info partition(dt='$do_date');

load data inpath '/origin_data/$APP/db/user_info/$do_date' OVERWRITE into table "$APP".ods_user_info partition(dt='$do_date');

load data inpath '/origin_data/$APP/db/payment_info/$do_date' OVERWRITE into table "$APP".ods_payment_info partition(dt='$do_date');

load data inpath '/origin_data/$APP/db/base_category1/$do_date' OVERWRITE into table "$APP".ods_base_category1 partition(dt='$do_date');

load data inpath '/origin_data/$APP/db/base_category2/$do_date' OVERWRITE into table "$APP".ods_base_category2 partition(dt='$do_date');

load data inpath '/origin_data/$APP/db/base_category3/$do_date' OVERWRITE into table "$APP".ods_base_category3 partition(dt='$do_date'); 
"
beeline -u "jdbc:hive2://hadoop102:10000/" -n hive -e "$sql"

执行结果如下：

4.4 DWD层

4.4.1 创建订单表

drop table if exists dwd_order_info;
create external table dwd_order_info ( 
    `id` string COMMENT '',
    `total_amount` decimal(10,2) COMMENT '', 
    `order_status` string COMMENT ' 1  2  3  4  5', 
    `user_id` string COMMENT 'id' ,
    `payment_way` string COMMENT '',  
    `out_trade_no` string COMMENT '',  
    `create_time` string COMMENT '',  
    `operate_time` string COMMENT '' 
) 
PARTITIONED BY ( `dt` string)
stored as  parquet
location '/warehouse/gmall/dwd/dwd_order_info/'

4.4.2 创建订单详情表

drop table if exists dwd_order_detail;
create external table dwd_order_detail( 
    `id` string COMMENT '',
    `order_id` decimal(10,2) COMMENT '', 
    `user_id` string COMMENT 'id' ,
    `sku_id` string COMMENT 'id',  
    `sku_name` string COMMENT '',  
    `order_price` string COMMENT '',  
    `sku_num` string COMMENT '', 
    `create_time` string COMMENT ''
) 
PARTITIONED BY (`dt` string)
stored as parquet
location '/warehouse/gmall/dwd/dwd_order_detail/'
;

4.4.3 创建用户表

drop table if exists dwd_user_info;
create external table dwd_user_info( 
    `id` string COMMENT 'id',
    `name`  string COMMENT '', 
    `birthday` string COMMENT '' ,
    `gender` string COMMENT '',  
    `email` string COMMENT '',  
    `user_level` string COMMENT '',  
    `create_time` string COMMENT ''
) 
PARTITIONED BY (`dt` string)
stored as  parquet
location '/warehouse/gmall/dwd/dwd_user_info/'
;

4.4.4 创建支付流水表

drop table if exists `dwd_payment_info`;
create external  table  `dwd_payment_info`(
    `id`   bigint COMMENT '',
    `out_trade_no`    string COMMENT '',
    `order_id`        string COMMENT '',
    `user_id`         string COMMENT '',
    `alipay_trade_no` string COMMENT '',
    `total_amount`    decimal(16,2) COMMENT '',
    `subject`         string COMMENT '',
    `payment_type`    string COMMENT '',
    `payment_time`    string COMMENT ''
   )  
PARTITIONED BY ( `dt` string)
stored as  parquet
location '/warehouse/gmall/dwd/dwd_payment_info/'
;

4.4.5 创建商品表

drop table if exists dwd_sku_info;
create external table dwd_sku_info( 
    `id` string COMMENT 'skuId',
    `spu_id` string COMMENT 'spuid', 
    `price` decimal(10,2) COMMENT '' ,
    `sku_name` string COMMENT '',  
    `sku_desc` string COMMENT '',  
    `weight` string COMMENT '',  
    `tm_id` string COMMENT 'id',  
    `category3_id` string COMMENT '1id',  
    `category2_id` string COMMENT '2id',  
    `category1_id` string COMMENT '3id',  
    `category3_name` string COMMENT '3',  
    `category2_name` string COMMENT '2',  
    `category1_name` string COMMENT '1',  
    `create_time` string COMMENT ''
) 
PARTITIONED BY ( `dt` string)
stored as  parquet
location '/warehouse/gmall/dwd/dwd_sku_info/'
;

4.4.6 DWD层数据导入脚本

#!/bin/bash

# 定义变量方便修改
APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
	do_date=$1
else 
	do_date=`date -d "-1 day" +%F`  
fi 

sql="

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table   "$APP".dwd_order_info partition(dt)
select * from "$APP".ods_order_info 
where dt='$do_date'  and id is not null;
 
insert overwrite table   "$APP".dwd_order_detail partition(dt)
select * from "$APP".ods_order_detail 
where dt='$do_date'   and id is not null;

insert overwrite table   "$APP".dwd_user_info partition(dt)
select * from "$APP".ods_user_info
where dt='$do_date'   and id is not null;
 
insert overwrite table   "$APP".dwd_payment_info partition(dt)
select * from "$APP".ods_payment_info
where dt='$do_date'  and id is not null;

insert overwrite table   "$APP".dwd_sku_info partition(dt)
select  
    sku.id,
    sku.spu_id, 
    sku.price,
    sku.sku_name,  
    sku.sku_desc,  
    sku.weight,  
    sku.tm_id,  
    sku.category3_id,  
    c2.id category2_id ,  
    c1.id category1_id,  
    c3.name category3_name,  
    c2.name category2_name,  
    c1.name category1_name,  
    sku.create_time,
    sku.dt
from
    "$APP".ods_sku_info sku 
join "$APP".ods_base_category3 c3 on sku.category3_id=c3.id 
    join "$APP".ods_base_category2 c2 on c3.category2_id=c2.id 
    join "$APP".ods_base_category1 c1 on c2.category1_id=c1.id 
where sku.dt='$do_date'  and c2.dt='$do_date'  
and  c3.dt='$do_date' and  c1.dt='$do_date' 
and sku.id is not null;
"
beeline -u "jdbc:hive2://chen102:10000/" -n hive -e "$sql"

执行结果如下：

4.5 DWS层

1）为什么要建宽表
需求目标，把每个用户单日的行为聚合起来组成一张多列宽表，以便之后关联用户维度信息后进行，不同角度的统计分析。

4.5.1 创建用户行为宽表

drop table if exists dws_user_action;
create external table dws_user_action 
(   
    user_id          string      comment '用户 id',
    order_count     bigint      comment '下单次数 ',
    order_amount    decimal(16,2)  comment '下单金额 ',
    payment_count   bigint      comment '支付次数',
    payment_amount  decimal(16,2) comment '支付金额 '
) COMMENT '每日用户行为宽表'
PARTITIONED BY (`dt` string)
stored as parquet
location '/warehouse/gmall/dws/dws_user_action/'
tblproperties ("parquet.compression"="snappy");

4.5.2 用户行为宽表导入脚本

#!/bin/bash

# 定义变量方便修改
APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
	do_date=$1
else 
	do_date=`date -d "-1 day" +%F`  
fi 

sql="
with  
tmp_order as
(
    select 
        user_id, 
        count(*)  order_count,
        sum(oi.total_amount) order_amount
    from "$APP".dwd_order_info oi
    where date_format(oi.create_time,'yyyy-MM-dd')='$do_date'
    group by user_id
) ,
tmp_payment as
(
    select
        user_id, 
        sum(pi.total_amount) payment_amount, 
        count(*) payment_count 
    from "$APP".dwd_payment_info pi 
    where date_format(pi.payment_time,'yyyy-MM-dd')='$do_date'
    group by user_id
)
insert overwrite table "$APP".dws_user_action partition(dt='$do_date')
select
    user_actions.user_id,
    sum(user_actions.order_count),
    sum(user_actions.order_amount),
    sum(user_actions.payment_count),
    sum(user_actions.payment_amount)
from 
(
    select
        user_id,
        order_count,
        order_amount,
        0 payment_count,
        0 payment_amount
    from tmp_order

    union all
    select
        user_id,
        0 order_count,
        0 order_amount,
        payment_count,
        payment_amount
    from tmp_payment
 ) user_actions
group by user_id;
"
beeline -u "jdbc:hive2://chen102:10000/" -n hive -e "$sql"

执行结果如下：

4.6 ADS层

求GMV成交总额

4.6.1 建表

drop table if exists ads_gmv_sum_day;
create external table ads_gmv_sum_day(
    `dt` string COMMENT '统计日期',
    `gmv_count`  bigint COMMENT '当日gmv订单个数',
    `gmv_amount`  decimal(16,2) COMMENT '当日gmv订单总金额',
    `gmv_payment`  decimal(16,2) COMMENT '当日支付金额'
) COMMENT 'GMV'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_gmv_sum_day/'
;

4.6.2 数据导入脚本

#!/bin/bash

# 定义变量方便修改
APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$1" ] ;then
	do_date=$1
else 
	do_date=`date -d "-1 day" +%F`
fi 

sql="
insert into table "$APP".ads_gmv_sum_day 
select 
    '$do_date' dt,
    sum(order_count)  gmv_count,
    sum(order_amount) gmv_amount,
    sum(payment_amount) payment_amount 
from "$APP".dws_user_action 
where dt ='$do_date'
group by dt;
"

beeline -u "jdbc:hive2://chen102:10000/" -n hive -e "$sql"

执行结果如下：

4.6.3 数据导出脚本

1）在MySQL中创建ads_gmv_sum_day表

DROP TABLE IF EXISTS ads_gmv_sum_day;
CREATE TABLE ads_gmv_sum_day(
  `dt` varchar(200) DEFAULT NULL COMMENT '统计日期',
  `gmv_count` bigint(20) DEFAULT NULL COMMENT '当日gmv订单个数',
  `gmv_amount` decimal(16, 2) DEFAULT NULL COMMENT '当日gmv订单总金额',
  `gmv_payment` decimal(16, 2) DEFAULT NULL COMMENT '当日支付金额'
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci COMMENT = '每日活跃用户数量' ROW_FORMAT = Dynamic;

#!/bin/bash

export HADOOP_USER_NAME=hive

db_name=gmall

export_data() {
    
    
sqoop export \
--connect "jdbc:mysql://chen102:3306/${db_name}?useUnicode=true&characterEncoding=utf-8"  \
--username root \
--password Atguigu.123456 \
--table $1 \
--num-mappers 1 \
--export-dir /warehouse/$db_name/ads/$1 \
--input-fields-terminated-by "\t" \
--update-mode allowinsert \
--update-key $2 \
--input-null-string '\\N'    \
--input-null-non-string '\\N'
}

case $1 in
  "ads_gmv_sum_day")
     export_data "ads_gmv_sum_day" "dt"
;;
   "all")
     export_data "ads_gmv_sum_day" "dt"
;;
esac

执行结果如下：

4.6.4 执行脚本并验证

select * from ads_gmv_sum_day

至此，用户行为数仓和业务数仓搭建完成。接下来是CDH即席查询——Impala

5 Oozie基于Hue实现GMV指标全流程调度

5.1 在Hue中创建Oozie任务GMV

1）生成新的业务数据

CALL init_data('2023-01-29',300,200,300,FALSE);

2）将oozie调度脚本上传到HDFS

sudo -u hive hadoop fs -mkdir /user/hive/bin/
cp /root/bin/*.sh /var/lib/hive/
sudo -u hive hadoop fs -put /var/lib/hive/*.sh /user/hive/bin

3）编辑workflow

4）添加工作流

5）保存并执行workflow
等待执行成功


6) 查看Mysql执行结果

6 即席查询

6.1 Impala安装

1）添加服务

2）角色分配

注意：最好将StateStore和CataLog Sever单独部署在同一节点上。

3）配置并启动Impala

6.2 配置Hue支持Impala

1）进入HUE配置页面，搜索“impala”，开启HUE中的impala服务

2）搜索“hue_safety_valve.ini 的 Hue 服务高级配置代码段（安全阀）”，输入以下代码段，确定HUE支持impala搜索引擎

6.3 Impala基于Hue查询

1. 打开hue
2. 进入impala查询
   分别用impala和hive查询以下SQL语句，对比查询速度。

select  
    sku.id,
    sku.spu_id, 
    sku.price,
    sku.sku_name,  
    sku.sku_desc,  
    sku.weight,  
    sku.tm_id,  
    sku.category3_id,  
    c2.id category2_id ,  
    c1.id category1_id,  
    c3.name category3_name,  
    c2.name category2_name,  
    c1.name category1_name,  
    sku.create_time,
    sku.dt
from
    ods_sku_info sku 
join ods_base_category3 c3 on sku.category3_id=c3.id 
    join ods_base_category2 c2 on c3.category2_id=c2.id 
    join ods_base_category1 c1 on c2.category1_id=c1.id 
where sku.dt='2023-01-27'  and c2.dt='2023-01-29';  

hive查询耗时22秒

impala耗时2秒左右

接下来是Kerberos认证和Sentry权限管理相关内容，详见《CDH数仓项目(四) —— 集群性能测试/资源管理/清理CDH集群》