Hive用户行为数仓（一）

Hive用户行为离线仓库

1、基本说明

埋点数据基本格式
公共字段：基本所有安卓手机都包含的字段
业务字段：埋点上报的字段，有具体的业务类型
下面就是一个示例，表示业务字段的上传。
示例日志（服务器时间戳 | 日志）：

1540934156385|{
    "ap": "gmall", 
    "cm": {
        "uid": "1234", 
        "vc": "2", 
        "vn": "1.0", 
        "la": "EN", 
        "sr": "", 
        "os": "7.1.1", 
        "ar": "CN", 
        "md": "BBB100-1", 
        "ba": "blackberry", 
        "sv": "V2.2.1", 
        "g": "[email protected]", 
        "hw": "1620x1080", 
        "t": "1506047606608", 
        "nw": "WIFI", 
        "ln": 0
    }, 
        "et": [
            {
                "ett": "1506047605364",  //客户端事件产生时间
                "en": "display",  //事件名称
                "kv": {  //事件结果，以key-value形式自行定义
                    "goodsid": "236",
                    "action": "1",
                    "extend1": "1",
"place": "2",
"category": "75"
                }
            },{
                "ett": "1552352626835",
                "en": "active_background",
                "kv": {
                     "active_source": "1"
                }
            }
        ]
    }
}

2、日志采集Flume

2.2 Flume经验

1）Source
（1）Taildir Source相比Exec Source、Spooling Directory Source的优势

• TailDir Source：断点续传、多目录。Flume1.6以前需要自己自定义Source记录每次读取文件位置，实现断点续传。
• Exec Source：可以实时搜集数据，但是在Flume不运行或者Shell命令出错的情况下，数据将会丢失。
• Spooling Directory Source监控目录，不支持断点续传。

（2）batchSize大小如何设置？
答：Event 1K左右时，500-1000合适（默认为100）

2）Channel
保证数据的安全可靠，使用类型file，把数据缓存在磁盘中。

2.3 Flume的ETL和分类型拦截器

本项目中自定义了两个拦截器，分别是：ETL拦截器、日志类型区分拦截器。
ETL拦截器主要用于，过滤时间戳不合法和Json数据不完整的日志
日志类型区分拦截器主要用于，将启动日志和事件日志区分开来
1）创建Maven工程flume-interceptor
2）创建包名：com.bigdata.flume.interceptor
3）在pom.xml文件中添加如下配置

<dependencies>
    <dependency>
        <groupId>org.apache.flume</groupId>
        <artifactId>flume-ng-core</artifactId>
        <version>1.7.0</version>
    </dependency>
</dependencies>

<build>
    <plugins>
        <plugin>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>2.3.2</version>
            <configuration>
                <source>1.8</source>
                <target>1.8</target>
            </configuration>
        </plugin>
        <plugin>
            <artifactId>maven-assembly-plugin</artifactId>
            <configuration>
                <descriptorRefs>
                    <descriptorRef>jar-with-dependencies</descriptorRef>
                </descriptorRefs>
            </configuration>
            <executions>
                <execution>
                    <id>make-assembly</id>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

4）在com.bigdata.flume.interceptor包下创建LogETLInterceptor类名
Flume ETL拦截器LogETLInterceptor

import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;

import java.nio.charset.Charset;
import java.util.ArrayList;
import java.util.List;

public class LogETLInterceptor implements Interceptor {

    @Override
    public void initialize() {

    }

    @Override
    public Event intercept(Event event) {

        // 1 获取数据
        byte[] body = event.getBody();
        String log = new String(body, Charset.forName("UTF-8"));

        // 2 判断数据类型并向Header中赋值
        if (log.contains("start")) {
            if (LogUtils.validateStart(log)){
                return event;
            }
        }else {
            if (LogUtils.validateEvent(log)){
                return event;
            }
        }

        // 3 返回校验结果
        return null;
    }

    @Override
    public List<Event> intercept(List<Event> events) {

        ArrayList<Event> interceptors = new ArrayList<>();

        for (Event event : events) {
            Event intercept1 = intercept(event);

            if (intercept1 != null){
                interceptors.add(intercept1);
            }
        }

        return interceptors;
    }

    @Override
    public void close() {

    }

    public static class Builder implements Interceptor.Builder{

        @Override
        public Interceptor build() {
            return new LogETLInterceptor();
        }

        @Override
        public void configure(Context context) {

        }
    }
}

4）Flume日志过滤工具类

import org.apache.commons.lang.math.NumberUtils;

public class LogUtils {

    public static boolean validateEvent(String log) {
        // 服务器时间 | json
        // 1549696569054 | {"cm":{"ln":"-89.2","sv":"V2.0.4","os":"8.2.0","g":"[email protected]","nw":"4G","l":"en","vc":"18","hw":"1080*1920","ar":"MX","uid":"u8678","t":"1549679122062","la":"-27.4","md":"sumsung-12","vn":"1.1.3","ba":"Sumsung","sr":"Y"},"ap":"weather","et":[]}

        // 1 切割
        String[] logContents = log.split("\\|");

        // 2 校验
        if(logContents.length != 2){
            return false;
        }

        //3 校验服务器时间
        if (logContents[0].length()!=13 || !NumberUtils.isDigits(logContents[0])){
            return false;
        }

        // 4 校验json
        if (!logContents[1].trim().startsWith("{") || !logContents[1].trim().endsWith("}")){
            return false;
        }

        return true;
    }

    public static boolean validateStart(String log) {
 // {"action":"1","ar":"MX","ba":"HTC","detail":"542","en":"start","entry":"2","extend1":"","g":"[email protected]","hw":"640*960","l":"en","la":"-43.4","ln":"-98.3","loading_time":"10","md":"HTC-5","mid":"993","nw":"WIFI","open_ad_type":"1","os":"8.2.1","sr":"D","sv":"V2.9.0","t":"1559551922019","uid":"993","vc":"0","vn":"1.1.5"}

        if (log == null){
            return false;
        }

        // 校验json
        if (!log.trim().startsWith("{") || !log.trim().endsWith("}")){
            return false;
        }

        return true;
    }
}

5）Flume日志类型区分拦截器LogTypeInterceptor

import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;

import java.nio.charset.Charset;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

public class LogTypeInterceptor implements Interceptor {
    @Override
    public void initialize() {

    }

    @Override
    public Event intercept(Event event) {

        // 区分日志类型：   body  header
        // 1 获取body数据
        byte[] body = event.getBody();
        String log = new String(body, Charset.forName("UTF-8"));

        // 2 获取header
        Map<String, String> headers = event.getHeaders();

        // 3 判断数据类型并向Header中赋值
        if (log.contains("start")) {
            headers.put("topic","topic_start");
        }else {
            headers.put("topic","topic_event");
        }

        return event;
    }

    @Override
    public List<Event> intercept(List<Event> events) {

        ArrayList<Event> interceptors = new ArrayList<>();

        for (Event event : events) {
            Event intercept1 = intercept(event);

            interceptors.add(intercept1);
        }

        return interceptors;
    }

    @Override
    public void close() {

    }

    public static class Builder implements  Interceptor.Builder{

        @Override
        public Interceptor build() {
            return new LogTypeInterceptor();
        }

        @Override
        public void configure(Context context) {

        }
    }
}

6）打包
拦截器打包之后，只需要单独包，不需要将依赖的包上传。打包之后要放入Flume的lib文件夹下面。

注意：为什么不需要依赖包？因为依赖包在flume的lib目录下面已经存在了。

7）需要先将打好的包放入到node01、node02、node03的/kkb/install/flume-1.6.0-cdh5.14.2/lib文件夹下面。

[hadoop@node01 lib]$ ls flume-interceptor-1.0-SNAPSHOT.jar
flume-interceptor-1.0-SNAPSHOT.jar

[hadoop@node02 lib]$ ls flume-interceptor-1.0-SNAPSHOT.jar
flume-interceptor-1.0-SNAPSHOT.jar

[hadoop@node03 lib]$ ls flume-interceptor-1.0-SNAPSHOT.jar
flume-interceptor-1.0-SNAPSHOT.jar

2.4 日志采集Flume配置

1）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。
2）Flume的具体配置如下：
（1）在node01和node02主机上/kkb/install/flume-1.6.0-cdh5.14.2/myconf目录下创建flume-client.conf文件

#flume-client.conf
a1.sources = r1
a1.sinks = k1
a1.channels = c1

#配置source
a1.sources.r1.type = taildir
a1.sources.r1.positionFile = /kkb/bigdata/index/log_position.json
a1.sources.r1.filegroups = f1
a1.sources.r1.filegroups.f1 = /kkb/bigdata/weblog/app.+
a1.sources.r1.fileHeader = true
a1.sources.r1.channels = c1

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

#配置channel
a1.channels.c1.type = file
#检查点文件目录
a1.channels.c1.checkpointDir=/kkb/bigdata/flume_checkpoint
#缓存数据文件夹
a1.channels.c1.dataDirs=/kkb/bigdata/flume_data

#配置sink
a1.sinks.k1.channel = c1
a1.sinks.k1.type = avro
#node3
a1.sinks.k1.hostname = 192.168.52.120
a1.sinks.k1.port = 4141

在文件配置如下内容

注意：
com.bigdata.flume.interceptor.LogETLInterceptor
和 com.bigdata.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。

（2）在node03主机上/kkb/install/flume-1.6.0-cdh5.14.2/myconf目录下创建flume-hdfs.conf文件

#Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1

#配置source
a1.sources.r1.type = avro
a1.sources.r1.bind = 192.168.52.120
a1.sources.r1.port = 4141
a1.sources.r1.channels = c1

#配置channel
a1.channels.c1.type = file
#检查点文件目录
a1.channels.c1.checkpointDir=/kkb/bigdata/flume_checkpoint
#缓存数据文件夹
a1.channels.c1.dataDirs=/kkb/bigdata/flume_data

#配置sink
a1.sinks.k1.channel = c1
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://node01:8020/origin_data/gmall/log/%{topic}/%Y-%m-%d

a1.sinks.k1.hdfs.filePrefix = logevent-
a1.sinks.k1.hdfs.round = true
a1.sinks.k1.hdfs.roundValue = 10
a1.sinks.k1.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 = 1000
a1.sinks.k1.hdfs.useLocalTimeStamp = true
a1.sinks.k1.hdfs.minBlockReplicas=1
a1.sinks.k1.hdfs.fileType = CompressedStream
a1.sinks.k1.hdfs.codeC = lzop

这里需要配置hadoop支持lzo压缩
修改hadoop集群中的每台服务器配置文件core-site.xml文件，然后启动hadoop集群

    io.compression.codecs
    org.apache.hadoop.io.compress.GzipCodec,
            org.apache.hadoop.io.compress.DefaultCodec,
            org.apache.hadoop.io.compress.BZip2Codec,
            com.hadoop.compression.lzo.LzoCodec,
            com.hadoop.compression.lzo.LzopCodec
    


    io.compression.codec.lzo.class
    com.hadoop.compression.lzo.LzoCodec`在这里插入代码片`

2.5 日志采集Flume启动停止脚本

1）在/home/hadoop/bin目录下创建脚本flume.sh

[hadoop@node01 bin]$ vim flume.sh

在脚本中填写如下内容

#!/bin/bash

case $1 in 
"start" ){
 for i in node03 node02 node01
  do
    echo "-----------启动 $i 采集flume-------------"
    if [ "node03" = $i ];then
      ssh $i "source /etc/profile; nohup /kkb/install/apache-flume-1.6.0-cdh5.14.2-bin/bin/flume-ng agent -n a1 -c /kkb/install/apache-flume-1.6.0-cdh5.14.2-bin/myconf -f /kkb/install/apache-flume-1.6.0-cdh5.14.2-bin/myconf/flume-hdfs.conf -Dflume.root.logger=info,console > /dev/null 2>&1 & "
    else
      ssh $i "source /etc/profile; nohup /kkb/install/apache-flume-1.6.0-cdh5.14.2-bin/bin/flume-ng agent -n a1 -c /kkb/install/apache-flume-1.6.0-cdh5.14.2-bin/conf -f /kkb/install/apache-flume-1.6.0-cdh5.14.2-bin/myconf/flume-client.conf -Dflume.root.logger=info,console > /dev/null 2>&1 &  "
    fi
  done
};;
"stop"){
  for i in node03 node02 node01
    do
      echo "-----------停止 $i 采集flume-------------"
      ssh $i "source /etc/profile; ps -ef | grep flume | grep -v grep |awk '{print \$2}' | xargs kill"
    done
};;
esac

说明
1、nohup：该命令可以在你退出帐户/关闭终端之后继续运行相应的进程。nohup就是不挂起的意思，不挂断地运行命令。
2、/dev/null：代表linux的空设备文件，所有往这个文件里面写入的内容都会丢失，俗称“黑洞”。
标准输入0：从键盘获得输入 /proc/self/fd/0
标准输出1：输出到屏幕（即控制台） /proc/self/fd/1
错误输出2：输出到屏幕（即控制台） /proc/self/fd/2

2）增加脚本执行权限

[hadoop@node01 bin]$ chmod 777 flume.sh

3）flume集群启动脚本

[hadoop@node01 bin]$ ./flume.sh start

4）flume集群停止脚本

[hadoop@node01 bin]$ ./flume.sh stop

3、数仓搭建

数仓分层概念

• ods原始数据层：完整的保存hdfs的数据，启动日志表 、事件日志表
• dw数据仓库层：通过解析ods层的数据，将启动日志解析成为一张表
  将用户的事件日志，解析成为11张表
• dws层：主要就是针对12张表进行各种维度的统计分析
• app数据展示层：数据报表的展示

分层好处：复杂的事情给他简单化，每一层相互进行隔绝
离线的数仓做的是T+1的任务，在实际工作当中，每天都会分析前一天的数据，表示数据的分析结果需要延迟一天

创建数据库
1）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;
2）使用gmall数据库

hive (default)> use gmall;

3.1 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。

3.1.1 启动日志表ods_start_log

1）创建输入数据是lzo，输出是text，支持json解析的分区表

hive (gmall)> 
drop table if exists gmall.ods_start_log;
CREATE EXTERNAL TABLE gmall.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

2）加载数据

hive (gmall)> 
load data inpath '/origin_data/gmall/log/topic_start/2019-02-10'
into table gmall.ods_start_log
partition(dt='2019-02-10');

注意：时间格式都配置成YYYY-MM-DD格式，这是Hive默认支持的时间格式

3）查看是否加载成功

hive (gmall)> select * from gmall.ods_start_log limit 2;

3.1.2 事件日志表ods_event_log

1）创建输入数据是lzo输出是text，支持json解析的分区表

hive (gmall)> 
drop table if exists gmall.ods_event_log;
CREATE EXTERNAL TABLE gmall.ods_event_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_event_log';

2）加载数据

hive (gmall)> 
load data inpath '/origin_data/gmall/log/topic_event/2019-02-10' 
into table gmall.ods_event_log 
partition(dt='2019-02-10');

注意：时间格式都配置成YYYY-MM-DD格式，这是Hive默认支持的时间格式

3）查看是否加载成功

hive (gmall)> select * from gmall.ods_event_log limit 2;

3.1.3 ODS层加载数据脚本

1）在node03 的/home/hadoop/bin目录下创建脚本

[hadoop@node03 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
hive=/kkb/install/hive-1.1.0-cdh5.14.2/bin/hive

# 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');

load data inpath '/origin_data/gmall/log/topic_event/$do_date' 
into table "$APP".ods_event_log partition(dt='$do_date');
"

$hive -e "$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，
[hadoop@node03 ~]$ date --help

2）增加脚本执行权限
[hadoop@node03 bin]$ chmod 777 ods_log.sh

3）脚本使用
[hadoop@node03 bin]$ ./ods_log.sh 2019-02-11

4）查看导入数据

hive (gmall)> 
select * from gmall.ods_start_log where dt='2019-02-11' limit 2;
select * from gmall.ods_event_log where dt='2019-02-11' limit 2;

5）脚本执行时间
企业开发中一般在每日凌晨30分~1点

3.2、DWD层

对ODS层数据进行清洗（去除空值，脏数据，超过极限范围的数据，行式存储改为列存储，改压缩格式）。

3.2.1、启动表 dwd_start_log

1）创建启动表

hive (gmall)> 
drop table if exists gmall.dwd_start_log;
CREATE EXTERNAL TABLE gmall.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/';

2） 向启动表导入数据
开启hive本地模式以加快hive查询速度

set hive.exec.mode.local.auto=true;

hive (gmall)> 
insert overwrite table gmall.dwd_start_log
PARTITION (dt='2019-02-10')
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 gmall.ods_start_log 
where dt='2019-02-10';

3）测试

hive (gmall)> select * from gmall.dwd_start_log limit 2;

DWD层启动表加载数据脚本

1）在node03的/home/hadoop/bin目录下创建脚本

[hadoop@node03 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
hive=/kkb/install/hive-1.1.0-cdh5.14.2/bin/hive

# 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 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';
"
$hive -e "$sql"

2）增加脚本执行权限

[hadoop@node03 bin]$ chmod 777 dwd_start_log.sh

3）脚本使用

[hadoop@node03 bin]$ ./dwd_start_log.sh 2019-02-11

4）查询导入结果

hive (gmall)> 
select * from gmall.dwd_start_log where dt='2019-02-11' limit 2;

5）脚本执行时间
企业开发中一般在每日凌晨30分~1点

3.2.2、基础明细表 dwd_base_event_log

明细表用于存储ODS层原始表转换过来的明细数据。

1）创建事件日志基础明细表

hive (gmall)> 
drop table if exists gmall.dwd_base_event_log;
CREATE EXTERNAL TABLE gmall.dwd_base_event_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, 
`event_name` string, 
`event_json` string, 
`server_time` string)
PARTITIONED BY (`dt` string)
stored as parquet
location '/warehouse/gmall/dwd/dwd_base_event_log/';

2）说明：
其中event_name和event_json用来对应事件名和整个事件。
这个地方将原始日志1对多的形式拆分出来了。
操作的时候我们需要将原始日志展平，需要用到UDF和UDTF。

1️⃣ 自定义UDF函数（解析公共字段）

1）创建一个maven工程：hivefunction
2）创建包名：com.bigdata.udf
3）在pom.xml文件中添加如下内容

<properties>
    <project.build.sourceEncoding>UTF8</project.build.sourceEncoding>
    <hive.version>1.2.1</hive.version>
</properties>

<dependencies>
    <!--添加hive依赖-->
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-exec</artifactId>
        <version>${hive.version}</version>
    </dependency>
</dependencies>

<build>
    <plugins>
        <plugin>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>2.3.2</version>
            <configuration>
                <source>1.8</source>
                <target>1.8</target>
            </configuration>
        </plugin>
        <plugin>
            <artifactId>maven-assembly-plugin</artifactId>
            <configuration>
                <descriptorRefs>
                    <descriptorRef>jar-with-dependencies</descriptorRef>
                </descriptorRefs>
            </configuration>
            <executions>
                <execution>
                    <id>make-assembly</id>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

UDF代码：用于解析公共字段

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.hive.ql.exec.UDF;
import org.json.JSONException;
import org.json.JSONObject;

public class BaseFieldUDF extends UDF {

    public String evaluate(String line, String jsonkeysString) {
        
        // 0 准备一个sb
        StringBuilder sb = new StringBuilder();

        // 1 切割jsonkeys  mid uid vc vn l sr os ar md
        String[] jsonkeys = jsonkeysString.split(",");

        // 2 处理line   服务器时间 | json
        String[] logContents = line.split("\\|");

        // 3 合法性校验
        if (logContents.length != 2 || StringUtils.isBlank(logContents[1])) {
            return "";
        }

        // 4 开始处理json
        try {
            JSONObject jsonObject = new JSONObject(logContents[1]);

            // 获取cm里面的对象
            JSONObject base = jsonObject.getJSONObject("cm");

            // 循环遍历取值
            for (int i = 0; i < jsonkeys.length; i++) {
                String filedName = jsonkeys[i].trim();

                if (base.has(filedName)) {
                    sb.append(base.getString(filedName)).append("\t");
                } else {
                    sb.append("\t");
                }
            }

            sb.append(jsonObject.getString("et")).append("\t");
            sb.append(logContents[0]).append("\t");
        } catch (JSONException e) {
            e.printStackTrace();
        }

        return sb.toString();
    }

    public static void main(String[] args) {

        String line = "1541217850324|{\"cm\":{\"mid\":\"m7856\",\"uid\":\"u8739\",\"ln\":\"-74.8\",\"sv\":\"V2.2.2\",\"os\":\"8.1.3\",\"g\":\"[email protected]\",\"nw\":\"3G\",\"l\":\"es\",\"vc\":\"6\",\"hw\":\"640*960\",\"ar\":\"MX\",\"t\":\"1541204134250\",\"la\":\"-31.7\",\"md\":\"huawei-17\",\"vn\":\"1.1.2\",\"sr\":\"O\",\"ba\":\"Huawei\"},\"ap\":\"weather\",\"et\":[{\"ett\":\"1541146624055\",\"en\":\"display\",\"kv\":{\"goodsid\":\"n4195\",\"copyright\":\"ESPN\",\"content_provider\":\"CNN\",\"extend2\":\"5\",\"action\":\"2\",\"extend1\":\"2\",\"place\":\"3\",\"showtype\":\"2\",\"category\":\"72\",\"newstype\":\"5\"}},{\"ett\":\"1541213331817\",\"en\":\"loading\",\"kv\":{\"extend2\":\"\",\"loading_time\":\"15\",\"action\":\"3\",\"extend1\":\"\",\"type1\":\"\",\"type\":\"3\",\"loading_way\":\"1\"}},{\"ett\":\"1541126195645\",\"en\":\"ad\",\"kv\":{\"entry\":\"3\",\"show_style\":\"0\",\"action\":\"2\",\"detail\":\"325\",\"source\":\"4\",\"behavior\":\"2\",\"content\":\"1\",\"newstype\":\"5\"}},{\"ett\":\"1541202678812\",\"en\":\"notification\",\"kv\":{\"ap_time\":\"1541184614380\",\"action\":\"3\",\"type\":\"4\",\"content\":\"\"}},{\"ett\":\"1541194686688\",\"en\":\"active_background\",\"kv\":{\"active_source\":\"3\"}}]}";
        String x = new BaseFieldUDF().evaluate(line, "mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,nw,ln,la,t");
        System.out.println(x);
    }
}

注意：使用main函数主要用于模拟数据测试。

2️⃣自定义UDTF函数（解析具体事件字段）

1）创建包名：com.bigdata.udtf
2）在com.bigdata.udtf包下创建类名：EventJsonUDTF
3）用于展开业务字段

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDTF;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
import org.json.JSONArray;
import org.json.JSONException;

import java.util.ArrayList;

public class EventJsonUDTF extends GenericUDTF {

    //该方法中，我们将指定输出参数的名称和参数类型：
    @Override
    public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {

        ArrayList<String> fieldNames = new ArrayList<String>();
        ArrayList<ObjectInspector> fieldOIs = new ArrayList<ObjectInspector>();

        fieldNames.add("event_name");
        fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
        fieldNames.add("event_json");
        fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);

        return ObjectInspectorFactory.getStandardStructObjectInspector(fieldNames, fieldOIs);
    }

    //输入1条记录，输出若干条结果
    @Override
    public void process(Object[] objects) throws HiveException {

        // 获取传入的et
        String input = objects[0].toString();

        // 如果传进来的数据为空，直接返回过滤掉该数据
        if (StringUtils.isBlank(input)) {
            return;
        } else {

            try {
                // 获取一共有几个事件（ad/facoriters）
                JSONArray ja = new JSONArray(input);

                if (ja == null)
                    return;

                // 循环遍历每一个事件
                for (int i = 0; i < ja.length(); i++) {
                    String[] result = new String[2];

                    try {
                        // 取出每个的事件名称（ad/facoriters）
                        result[0] = ja.getJSONObject(i).getString("en");

                        // 取出每一个事件整体
                        result[1] = ja.getString(i);
                    } catch (JSONException e) {
                        continue;
                    }

                    // 将结果返回
                    forward(result);
                }
            } catch (JSONException e) {
                e.printStackTrace();
            }
        }
    }

    //当没有记录处理的时候该方法会被调用，用来清理代码或者产生额外的输出
    @Override
    public void close() throws HiveException {

    }
}

2）打包

3）将hive-function-1.0-SNAPSHOT.jar上传到node03的/kkb/install/hive-1.1.0-cdh5.14.2/lib

4）将jar包添加到Hive的classpath

hive (gmall)> 
add jar /kkb/install/hive-1.1.0-cdh5.14.2/lib/hive-function-1.0-SNAPSHOT.jar;

5）创建临时函数与开发好的java class关联

hive (gmall)> 
create temporary function base_analizer as 'com.bigdata.udf.BaseFieldUDF';

create temporary function flat_analizer as 'com.bigdata.udtf.EventJsonUDTF';

解析事件日志基础明细表，加载数据

hive (gmall)> 
insert overwrite table gmall.dwd_base_event_log 
PARTITION (dt='2019-02-10')
select
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
event_name,
event_json,
server_time
from
(
select
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[0]   as mid_id,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[1]   as user_id,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[2]   as version_code,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[3]   as version_name,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[4]   as lang,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[5]   as source,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[6]   as os,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[7]   as area,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[8]   as model,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[9]   as brand,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[10]   as sdk_version,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[11]  as gmail,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[12]  as height_width,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[13]  as app_time,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[14]  as network,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[15]  as lng,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[16]  as lat,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[17]  as ops,
split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[18]  as server_time
from gmall.ods_event_log where dt='2019-02-10'  and base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la')<>'' 
) sdk_log lateral view flat_analizer(ops) tmp_k as event_name, event_json;

2）测试

hive (gmall)> select * from gmall.dwd_base_event_log limit 2;

3.2.3、基础事件表（11张）

<1> 商品点击表 dwd_display_log

1）建表语句

hive (gmall)> 
drop table if exists gmall.dwd_display_log;
CREATE EXTERNAL TABLE gmall.dwd_display_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,
`action` string,
`goodsid` string,
`place` string,
`extend1` string,
`category` string,
`server_time` string
)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_display_log/';

2）导入数据

hive (gmall)> 
insert overwrite table gmall.dwd_display_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.action') action,
get_json_object(event_json,'$.kv.goodsid') goodsid,
get_json_object(event_json,'$.kv.place') place,
get_json_object(event_json,'$.kv.extend1') extend1,
get_json_object(event_json,'$.kv.category') category,
server_time
from gmall.dwd_base_event_log 
where dt='2019-02-10' and event_name='display';

<2> 商品详情页表 dwd_newsdetail_log

1）建表语句

drop table if exists gmall.dwd_newsdetail_log;
CREATE EXTERNAL TABLE gmall.dwd_newsdetail_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,
`action` string,
`goodsid` string,
`showtype` string,
`news_staytime` string,
`loading_time` string,
`type1` string,
`category` string,
`server_time` string)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_newsdetail_log/';

2）导入数据

insert overwrite table gmall.dwd_newsdetail_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.entry') entry,
get_json_object(event_json,'$.kv.action') action,
get_json_object(event_json,'$.kv.goodsid') goodsid,
get_json_object(event_json,'$.kv.showtype') showtype,
get_json_object(event_json,'$.kv.news_staytime') news_staytime,
get_json_object(event_json,'$.kv.loading_time') loading_time,
get_json_object(event_json,'$.kv.type1') type1,
get_json_object(event_json,'$.kv.category') category,
server_time
from gmall.dwd_base_event_log
where dt='2019-02-10' and event_name='newsdetail';

<3> 广告表 dwd_ad_log

1）建表语句

drop table if exists gmall.dwd_ad_log;
CREATE EXTERNAL TABLE gmall.dwd_ad_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,
`action` string,
`content` string,
`detail` string,
`ad_source` string,
`behavior` string,
`newstype` string,
`show_style` string,
`server_time` string)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_ad_log/';

2）导入数据

insert overwrite table gmall.dwd_ad_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.entry') entry,
get_json_object(event_json,'$.kv.action') action,
get_json_object(event_json,'$.kv.content') content,
get_json_object(event_json,'$.kv.detail') detail,
get_json_object(event_json,'$.kv.source') ad_source,
get_json_object(event_json,'$.kv.behavior') behavior,
get_json_object(event_json,'$.kv.newstype') newstype,
get_json_object(event_json,'$.kv.show_style') show_style,
server_time
from gmall.dwd_base_event_log 
where dt='2019-02-10' and event_name='ad';

<4> 消息通知表 dwd_notification_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_notification_log;
CREATE EXTERNAL TABLE gmall.dwd_notification_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, 
`action` string,
`noti_type` string,
`ap_time` string,
`content` string,
`server_time` string
)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_notification_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_notification_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.action') action,
get_json_object(event_json,'$.kv.noti_type') noti_type,
get_json_object(event_json,'$.kv.ap_time') ap_time,
get_json_object(event_json,'$.kv.content') content,
server_time
from gmall.dwd_base_event_log
where dt='2019-02-10' and event_name='notification';

<5> 商品列表页表 dwd_loading_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_loading_log;
CREATE EXTERNAL TABLE gmall.dwd_loading_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, 
`action` string,
`loading_time` string,
`loading_way` string,
`extend1` string,
`extend2` string,
`type` string,
`type1` string,
`server_time` string)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_loading_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_loading_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.action') action,
get_json_object(event_json,'$.kv.loading_time') loading_time,
get_json_object(event_json,'$.kv.loading_way') loading_way,
get_json_object(event_json,'$.kv.extend1') extend1,
get_json_object(event_json,'$.kv.extend2') extend2,
get_json_object(event_json,'$.kv.type') type,
get_json_object(event_json,'$.kv.type1') type1,
server_time
from gmall.dwd_base_event_log
where dt='2019-02-10' and event_name='loading';

<6> 用户后台活跃表 dwd_active_background_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_active_background_log;
CREATE EXTERNAL TABLE gmall.dwd_active_background_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,
`active_source` string,
`server_time` string
)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_background_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_active_background_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.active_source') active_source,
server_time
from gmall.dwd_base_event_log
where dt='2019-02-10' and event_name='active_background';

<7> 用户前台活跃表 dwd_active_foreground_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_active_foreground_log;
CREATE EXTERNAL TABLE gmall.dwd_active_foreground_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,
`push_id` string,
`access` string,
`server_time` string)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_foreground_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_active_foreground_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.push_id') push_id,
get_json_object(event_json,'$.kv.access') access,
server_time
from gmall.dwd_base_event_log
where dt='2019-02-10' and event_name='active_foreground';

<8> 评论表 dwd_comment_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_comment_log;
CREATE EXTERNAL TABLE gmall.dwd_comment_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,
`comment_id` int,
`userid` int,
`p_comment_id` int, 
`content` string,
`addtime` string,
`other_id` int,
`praise_count` int,
`reply_count` int,
`server_time` string
)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_comment_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_comment_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.comment_id') comment_id,
get_json_object(event_json,'$.kv.userid') userid,
get_json_object(event_json,'$.kv.p_comment_id') p_comment_id,
get_json_object(event_json,'$.kv.content') content,
get_json_object(event_json,'$.kv.addtime') addtime,
get_json_object(event_json,'$.kv.other_id') other_id,
get_json_object(event_json,'$.kv.praise_count') praise_count,
get_json_object(event_json,'$.kv.reply_count') reply_count,
server_time
from gmall.dwd_base_event_log
where dt='2019-02-10' and event_name='comment';

<9> 收藏表 dwd_favorites_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_favorites_log;
CREATE EXTERNAL TABLE gmall.dwd_favorites_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, 
`id` int, 
`course_id` int, 
`userid` int,
`add_time` string,
`server_time` string
)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_favorites_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_favorites_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.id') id,
get_json_object(event_json,'$.kv.course_id') course_id,
get_json_object(event_json,'$.kv.userid') userid,
get_json_object(event_json,'$.kv.add_time') add_time,
server_time
from gmall.dwd_base_event_log 
where dt='2019-02-10' and event_name='favorites';

3）测试
hive (gmall)> select * from gmall.dwd_favorites_log limit 2;

<10> 点赞表 dwd_praise_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_praise_log;
CREATE EXTERNAL TABLE gmall.dwd_praise_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, 
`id` string, 
`userid` string, 
`target_id` string,
`type` string,
`add_time` string,
`server_time` string
)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_praise_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_praise_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.id') id,
get_json_object(event_json,'$.kv.userid') userid,
get_json_object(event_json,'$.kv.target_id') target_id,
get_json_object(event_json,'$.kv.type') type,
get_json_object(event_json,'$.kv.add_time') add_time,
server_time
from gmall.dwd_base_event_log
where dt='2019-02-10' and event_name='praise';

<11> 错误日志表 dwd_error_log

1）建表语句
hive (gmall)>

drop table if exists gmall.dwd_error_log;
CREATE EXTERNAL TABLE gmall.dwd_error_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, 
`errorBrief` string, 
`errorDetail` string, 
`server_time` string)
PARTITIONED BY (dt string)
location '/warehouse/gmall/dwd/dwd_error_log/';

2）导入数据
hive (gmall)>

insert overwrite table gmall.dwd_error_log
PARTITION (dt='2019-02-10')
select 
mid_id,
user_id,
version_code,
version_name,
lang,
source,
os,
area,
model,
brand,
sdk_version,
gmail,
height_width,
app_time,
network,
lng,
lat,
get_json_object(event_json,'$.kv.errorBrief') errorBrief,
get_json_object(event_json,'$.kv.errorDetail') errorDetail,
server_time
from gmall.dwd_base_event_log 
where dt='2019-02-10' and event_name='error';

3）测试
hive (gmall)> select * from gmall.dwd_error_log limit 2;