大数据之CDH数仓(8) | 数仓之用户行为数仓搭建

浅浅的花香味﹌ 2022-12-11 15:07 358阅读 0赞

### 目录 ###

*  用户行为日志生成
 *  日志数据导入数仓
 *   *  日志采集Flume配置
     *  Flume拦截器
     *  消费Kafka Flume配置
     *  模拟生成日志
 *  ODS层
 *   *  创建数据库
     *  创建启动日志表ods\_start\_log
     *  ODS层加载数据脚本
 *  DWD层启动表数据解析
 *   *  创建启动表
     *  DWD层启动表加载数据脚本
 *  DWS层（需求：用户日活跃）
 *   *  每日活跃设备明细
     *  DWS层加载数据脚本
 *  ADS层（需求：用户日活跃）
 *   *  活跃设备数
     *  ADS层加载数据脚本

# 用户行为日志生成 #

安装包

链接：https://pan.baidu.com/s/17RE9B2SSDsHNbSQysOU8hg 
    提取码：rpyk

1）将log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar上传到hadoop102的/opt/module目录  
2）分发log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar到hadoop103

[root@hadoop102 module]# xsync log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar

3）在/root/bin目录下创建脚本lg.sh

[root@hadoop102 bin]$ vim lg.sh

4）在脚本中编写如下内容

#! /bin/bash
    
    	for i in hadoop102 hadoop103 
    	do
    		ssh $i " source /etc/profile ; java -classpath /opt/module/log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar com.atguigu.appclient.AppMain $1 $2 >/opt/module/test.log &"
    	done

5）修改脚本执行权限

[root@hadoop102 bin]$ chmod +x lg.sh

6）启动脚本

[root@hadoop102 module]$ lg.sh

# 日志数据导入数仓 #

## 日志采集Flume配置 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center]  
1）集群规划

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>服务器hadoop102</th> 
   <th>服务器hadoop103</th> 
   <th>服务器hadoop104</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>Flume（日志采集）</td> 
   <td>Flume</td> 
   <td>Flume</td> 
   <td></td> 
  </tr> 
 </tbody> 
</table>

2）Flume配置分析  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 1]  
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  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 2]  
（2）点击实例  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 3]

（3）点击hadoop102的Agent选项  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 4]

（4）点击配置  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 5]  
（5）对Flume Agent进行具体配置  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 6]

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 = hadoop102:9092,hadoop103:9092,hadoop104:9092
    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 = hadoop102:9092,hadoop103:9092,hadoop104:9092
    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需要根据用户自定义的拦截器做相应修改。  
（6）在hadoop103上重复相同的操作名。

## Flume拦截器 ##

1）采用root用户将flume-interceptor-1.0-SNAPSHOT.jar包放入到hadoop102的/opt/cloudera/parcels/CDH/lib/flume-ng/lib文件夹下面。  
2）分发Flume到hadoop103

[root@hadoop102 lib]$ xsync flume-interceptor-1.0-SNAPSHOT.jar

## 消费Kafka Flume配置 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 7]  
1）集群规划

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>服务器hadoop102</th> 
   <th>服务器hadoop103</th> 
   <th>服务器hadoop104</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>Flume（消费Kafka）</td> 
   <td></td> 
   <td></td> 
   <td>Flume</td> 
  </tr> 
 </tbody> 
</table>

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 = hadoop102:9092,hadoop103:9092,hadoop104:9092
    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

## 模拟生成日志 ##

1）确保Flume、Kafka等服务正常运行  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 9]

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）调用日志生成脚本

lg.sh

# ODS层 #

原始数据层，存放原始数据，直接加载原始日志、数据，数据保持原貌不做处理。

## 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  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 10]

3）启动Hive客户端（注意要以hive用户启动）

sudo -u hive hive

4）创建gmall数据库

hive (default)> create database gmall;

说明：如果数据库存在且有数据，需要强制删除时执行：drop database gmall cascade;  
5）使用gmall数据库

hive (default)> use gmall;

## 创建启动日志表ods\_start\_log ##

hive (gmall)> 
    drop table if exists ods_start_log;
    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';

说明Hive的LZO压缩：[https://cwiki.apache.org/confluence/display/Hive/LanguageManual+LZO][https_cwiki.apache.org_confluence_display_Hive_LanguageManual_LZO]

## ODS层加载数据脚本 ##

1）在hadoop102的/root/bin目录下创建脚本

[root@hadoop102 bin]$ vim ods_log.sh

在脚本中编写如下内容

#!/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://hadoop102:10000/" -n hive -e "$sql"

说明1：  
\[ -n 变量值 \] 判断变量的值，是否为空  
– 变量的值，非空，返回true  
– 变量的值，为空，返回false  
说明2：  
查看date命令的使用，\[root@hadoop102 ~\]$ date --help  
2）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 ods_log.sh

3）脚本使用

[root@hadoop102 module]$ ods_log.sh 2019-09-03

# DWD层启动表数据解析 #

## 创建启动表 ##

1）建表语句

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/';

## DWD层启动表加载数据脚本 ##

1）在hadoop102的/root/bin目录下创建脚本

[root@hadoop102 bin]$ vim dwd_start_log.sh

在脚本中编写如下内容

#!/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://hadoop102:10000/" -n hive -e "$sql"

2）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 dwd_start_log.sh

3）脚本使用

[root@hadoop102 module]$ dwd_start_log.sh 2019-09-03

# DWS层（需求：用户日活跃） #

目标：统计当日、当周、当月活动的每个设备明细

## 每日活跃设备明细 ##

1）建表语句

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'
    ;

## DWS层加载数据脚本 ##

1）在hadoop102的/root/bin目录下创建脚本

[root@hadoop102 bin]$ vim dws_log.sh

在脚本中编写如下内容

#!/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://hadoop102:10000/" -n hive -e "$sql"

2）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 dws_log.sh

3）脚本使用

[root@hadoop102 module]$ dws_log.sh 2019-09-03

# ADS层（需求：用户日活跃） #

目标：当日活跃设备数

## 活跃设备数 ##

1）建表语句

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/'
    ;

## ADS层加载数据脚本 ##

1）在hadoop102的/root/bin目录下创建脚本

[root@hadoop102 bin]$ vim ads_uv_log.sh

在脚本中编写如下内容

#!/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://hadoop102:10000/" -n hive -e "$sql"

2）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 ads_uv_log.sh

3）脚本使用

[root@hadoop102 module]$ ads_uv_log.sh 2019-09-03

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[https_cwiki.apache.org_confluence_display_Hive_LanguageManual_LZO]: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+LZO