hive的基础配置优化与数仓流程

1.HDFS副本数

dfs.replication（HDFS）

文件副本数，通常设为3，不推荐修改。

如果测试环境只有二台虚拟机（2个datanode节点），此值要修改为2。

2.Yarn基础配置

  2.1NodeManager配置

    2.1.1CPU配置

配置项：yarn.nodemanager.resource.cpu-vcores

表示该节点服务器上yarn可以使用的虚拟CPU个数，默认值是8，推荐将值配置与物理CPU线程数相同，如果节点CPU核心不足8个，要调小这个值，yarn不会智能的去检测物理核心数。

2.1.2内存配置

配置项：yarn.nodemanager.resource.memory-mb

设置该nodemanager节点上可以为容器分配的总内存，默认为8G，如果节点内存资源不足8G，要减少这个值，yarn不会智能的去检测内存资源，一般按照服务器剩余可用内存资源进行配置。生产上根据经验一般要预留15-20%的内存，那么可用内存就是实际内存*0.8，比如实际内存是64G，那么64*0.8=51.2G，我们设置成50G就可以了（固定经验值）。

注意，要同时设置yarn.scheduler.maximum-allocation-mb为一样的值，yarn.app.mapreduce.am.command-opts（JVM内存）的值要同步修改为略小的值（-Xmx1024m）。

也就是:

yarn.nodemanager.resource.memory-mb
yarn.scheduler.maximum-allocation-mb : 与第一个保持一致
yarn.app.mapreduce.am.command-opts : 略小于第一个配置的值(0.9)

2.1.3本地目录

yarn.nodemanager.local-dirs（Yarn）

NodeManager 存储中间数据文件的本地文件系统中的目录列表。

如果单台服务器上有多个磁盘挂载，则配置的值应当是分布在各个磁盘上目录，这样可以充分利用节点的IO读写能力。

3.MapReduce内存配置

当MR内存溢出时，可以根据服务器配置进行调整。

1.mapreduce.map.memory.mb: 在运行MR的时候, 一个mapTask需要占用多大内存

为作业的每个 Map 任务分配的物理内存量(MiB)，默认为0，自动判断大小。

2.mapreduce.reduce.memory.mb::在运行MR的时候, 一个reduceTask需要占用多大内存

为作业的每个 Reduce 任务分配的物理内存量(MiB)，默认为0，自动判断大小。

3.mapreduce.reduce.java.opts: 在运行MR的时候, 一个reduceTask对应jvm需要占用多大内容

Map和Reduce的JVM配置选项。

注意：

mapreduce.map.java.opts一定要小于mapreduce.map.memory.mb；

mapreduce.reduce.java.opts一定要小于mapreduce.reduce.memory.mb，格式-Xmx4096m。

注意：

此部分所有配置均不能大于Yarn的NodeManager内存配置。

4.Hive基础配置

4.1hiveserver2的内存大小配置

配置项: HiveServer2 的 Java 堆栈大小（字节）

注意:Hiveserver2异常退出，导致连接失败的问题就是因为它

比如:

配置:HiveServer2 的 Java 堆栈大小（字节）

4.2动态生成分区的线程数

配置: hive.load.dynamic.partitions.thread

用于加载动态生成的分区的线程数。加载需要将文件重命名为它的最终位置，并更新关于新分区的一些元数据。默认值为 15 。

当有大量动态生成的分区时，增加这个值可以提高性能。根据服务器配置修改。

4.3监听输入文件线程数

hive.exec.input.listing.max.threads

Hive用来监听输入文件的最大线程数。默认值：15。

当需要读取大量分区时，增加这个值可以提高性能。根据服务器配置进行调整。

5.压缩配置

5.1Map输出压缩

除了创建表时指定保存数据时压缩，在查询分析过程中，Map的输出也可以进行压缩。由于map任务的输出需要写到磁盘并通过网络传输到reducer节点，所以通过使用LZO、LZ4或者Snappy这样的快速压缩方式，是可以获得性能提升的，因为需要传输的数据减少了。

MapReduce配置项：

  mapreduce.map.output.compress

设置是否启动map输出压缩，默认为false。在需要减少网络传输的时候，可以设置为true。

  mapreduce.map.output.compress.codec

设置map输出压缩编码解码器，默认为org.apache.hadoop.io.compress.DefaultCodec，推荐使用SnappyCodec：org.apache.hadoop.io.compress.SnappyCodec。

5.2Reduce结果压缩

是否对任务输出结果压缩，默认值false。对传输数据进行压缩，既可以减少文件的存储空间，又可以加快数据在网络不同节点之间的传输速度。

配置项：

  mapreduce.output.fileoutputformat.compress

是否启用 MapReduce 作业输出压缩。

  mapreduce.output.fileoutputformat.compress.codec

指定要使用的压缩编码解码器，推荐SnappyCodec。

  mapreduce.output.fileoutputformat.compress.type

指定MapReduce作业输出的压缩方式，默认值RECORD，可配置值有：NONE、RECORD、BLOCK。推荐使用BLOCK，即针对一组记录进行批量压缩，压缩效率更高。

5.3Hive执行过程通用压缩设置

主要包括压缩/解码器设置和压缩方式设置：

mapreduce.output.fileoutputformat.compress.codec（Yarn）

     map输出所用的压缩编码解码器，默认为org.apache.hadoop.io.compress.DefaultCodec；

        推荐使用SnappyCodec：org.apache.hadoop.io.compress.SnappyCodec

mapreduce.output.fileoutputformat.compress.type

     输出产生任务数据的压缩方式，默认值RECORD，可配置值有：NONE、RECORD、BLOCK。推荐使用BLOCK，即针对一组记录进行批量压缩，压缩效率更高。

5.4Hive多个Map-Reduce中间数据压缩

控制 Hive 在多个map-reduce作业之间生成的中间文件是否被压缩。压缩编解码器和其他选项由上面Hive通用压缩mapreduce.output.fileoutputformat.compress.*确定。

set hive.exec.compress.intermediate=true;

5.5Hive最终结果压缩

控制是否压缩查询的最终输出(到 local/hdfs 文件或 Hive table)。压缩编解码器和其他选项由 上面Hive通用压缩mapreduce.output.fileoutputformat.compress.*确定。

set hive.exec.compress.output=true;

6.全量流程

OLTP原始数据(mysql)——》数据采集(ODS)——》清洗转换(DWD)——》统计分析(DWS)——》导出至OLAP(Mysql)

7.操作

7.1.业务mysql->hive_ODS

code:

--访问咨询主表

sqoop import \
--connect jdbc:mysql://192.168.52.150:3306/nev \
--username root \
--password 123456 \
--query 'SELECT
id,create_date_time,session_id,sid,create_time,seo_source,seo_keywords,ip,
AREA,country,province,city,origin_channel,USER AS user_match,manual_time,begin_time,end_time,
last_customer_msg_time_stamp,last_agent_msg_time_stamp,reply_msg_count,
msg_count,browser_name,os_info, "2021-09-24" AS starts_time
FROM web_chat_ems_2019_07 where 1=1 and $CONDITIONS' \
--hcatalog-database itcast_ods \
--hcatalog-table web_chat_ems \
-m 1

--访问咨询附属表

sqoop import \
--connect jdbc:mysql://192.168.52.150:3306/nev \
--username root \
--password 123456 \
--query 'SELECT
  *, "2021-09-24" AS start_time
FROM web_chat_text_ems_2019_07 where 1=1 and $CONDITIONS' \
--hcatalog-database itcast_ods \
--hcatalog-table web_chat_text_ems \
-m 1

校验:

 查看mysql共计有多少条数据
    SELECT COUNT(1) FROM web_chat_ems_2019_07; 

    SELECT COUNT(1) FROM web_chat_text_ems_2019_07; 

 到hive中对表查询一下一共多少条数据
    SELECT COUNT(1) FROM itcast_ods.web_chat_ems; 

    SELECT COUNT(1) FROM itcast_ods.web_chat_text_ems; 

 查询其中一部分数据, 观察数据映射是否OK
    select * from itcast_ods.web_chat_ems limit 10;
    SELECT * FROM itcast_ods.web_chat_text_ems limit 10;

7.2.ODS->DWD

数据清洗与转换,并少量做维度退化

转换: 将create_time转换为 yearinfo,quarterinfo,monthinfo,dayinfo,hourinfo:
方案: 通过 **year() quarter() month() day() hour() quarter() **
select year(‘2019-07-01 23:45:00’) ; – 2019
select month(‘2019-07-01 23:45:00’) ; – 7
select day(‘2019-07-01 23:45:00’) ; – 1
select hour(‘2019-07-01 23:45:00’) ; – 23
select quarter(‘2019-07-01 23:45:00’); – 3

code:

select
    wce.session_id,
    wce.sid,
    unix_timestamp(wce.create_time) as create_time,  
    wce.seo_source,
    wce.ip,
    wce.area,
    wce.msg_count,
    wce.origin_channel,
    wcte.referrer,
    wcte.from_url,
    wcte.landing_page_url,
    wcte.url_title,
    wcte.platform_description,
    wcte.other_params,
    wcte.history,
    substr(wce.create_time,12,2) as hourinfo,
    substr(wce.create_time,1,4) as yearinfo,
    quarter(wce.create_time) as quarterinfo,
    substr(wce.create_time,6,2) as monthinfo,
    substr(wce.create_time,9,2) as dayinfo
from itcast_ods.web_chat_ems wce join itcast_ods.web_chat_text_ems wcte
    on wce.id = wcte.id;

报错:Error while processing statement: FAILED: Execution Error, return code 137 from org.apache.hadoop.hive.ql.exec.mr.MapredLocalTask

原因: 在执行转换操作的时候, 由于需要进行二表联查操作, 其中一个表数据量比较少, 此时hive会对其优化, 采用map join的方案进行处理, 而map join需要将小表的数据加载到内存中, 但是内存不足, 导致出现内存溢出错误,

解决:

关闭掉map join 让其采用reduce join即可

命令:

set hive.auto.convert.join= false;

执行后:

灌入DWD:可能出现分区问题,所以用的动态分区

解决如下:

--动态分区配置
set hive.exec.dynamic.partition=true;
set hive.exec.dynamic.partition.mode=nonstrict;
--hive压缩
set hive.exec.compress.intermediate=true;
set hive.exec.compress.output=true;
--写入时压缩生效
set hive.exec.orc.compression.strategy=COMPRESSION;

insert into table itcast_dwd.visit_consult_dwd partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select
    wce.session_id,
    wce.sid,
    unix_timestamp(wce.create_time) as create_time,  
    wce.seo_source,
    wce.ip,
    wce.area,
    wce.msg_count,
    wce.origin_channel,
    wcte.referrer,
    wcte.from_url,
    wcte.landing_page_url,
    wcte.url_title,
    wcte.platform_description,
    wcte.other_params,
    wcte.history,
    substr(wce.create_time,12,2) as hourinfo,
    substr(wce.create_time,1,4) as yearinfo,
    quarter(wce.create_time) as quarterinfo,
    substr(wce.create_time,6,2) as monthinfo,
    substr(wce.create_time,9,2) as dayinfo
from itcast_ods.web_chat_ems wce join itcast_ods.web_chat_text_ems wcte
    on wce.id = wcte.id;

7.3.DWD->DWS

DWS层作用: 细化维度统计操作

7.3.1以时间为基准, 统计总访问量

-- 统计每年的总访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  yearinfo as time_str,
  '-1' as from_url,
  '5' as grouptype,
  '5' as time_type,
  yearinfo,
  '-1' as quarterinfo,
  '-1' as monthinfo,
  '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo;

解释：

• Partition: 指定了分区列 yearinfo, quarterinfo, monthinfo, dayinfo。
• Select: 选择了需要统计的字段，包括用户ID、会话ID和IP地址的不同数量，以及一些固定的默认值。
• From: 数据来源于 itcast_dwd.visit_consult_dwd 表。
• Group By: 按 yearinfo 进行分组，以便统计每年的总访问量。

存放路径:

-- 统计每年每季度的总访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  concat(yearinfo,'_',quarterinfo) as time_str,
  '-1' as from_url,
  '5' as grouptype,
  '4' as time_type,
  yearinfo,
  quarterinfo,
  '-1' as monthinfo,
  '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo;
-- 统计每年每季度每月的总访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  concat(yearinfo,'-',monthinfo) as time_str,
  '-1' as from_url,
  '5' as grouptype,
  '3' as time_type,
  yearinfo,
  quarterinfo,
  monthinfo,
  '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo,monthinfo;
-- 统计每年每季度每月每天的总访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  concat(yearinfo,'-',monthinfo,'-',dayinfo) as time_str,
  '-1' as from_url,
  '5' as grouptype,
  '2' as time_type,
  yearinfo,
  quarterinfo,
  monthinfo,
  dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo,monthinfo,dayinfo;
-- 统计每年每季度每月每天每小时的总访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  hourinfo,
  concat(yearinfo,'-',monthinfo,'-',dayinfo,' ',hourinfo) as time_str,
  '-1' as from_url,
  '5' as grouptype,
  '1' as time_type,
  yearinfo,
  quarterinfo,
  monthinfo,
  dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo,monthinfo,dayinfo,hourinfo;

7.3.2基于时间统计各个受访页面的访问量

-- 统计每年各个受访页面的访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  yearinfo as time_str,
  from_url,
  '4' as grouptype,
  '5' as time_type,
  yearinfo,
  '-1' as quarterinfo,
  '-1' as monthinfo,
  '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,from_url;

-- 统计每年,每季度各个受访页面的访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  concat(yearinfo,'_',quarterinfo) as time_str,
  from_url,
  '4' as grouptype,
  '4' as time_type,
  yearinfo,
  quarterinfo,
  '-1' as monthinfo,
  '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo,from_url;

-- 统计每年,每季度,每月各个受访页面的访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  concat(yearinfo,'-',monthinfo) as time_str,
  from_url,
  '4' as grouptype,
  '3' as time_type,
  yearinfo,
  quarterinfo,
  monthinfo,
  '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo,monthinfo,from_url;

-- 统计每年,每季度,每月.每天各个受访页面的访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  '-1' as hourinfo,
  concat(yearinfo,'-',monthinfo,'-',dayinfo) as time_str,
  from_url,
  '4' as grouptype,
  '2' as time_type,
  yearinfo,
  quarterinfo,
  monthinfo,
  dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo,monthinfo,dayinfo,from_url;
-- 统计每年,每季度,每月.每天,每小时各个受访页面的访问量
insert into table itcast_dws.visit_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
  count(distinct sid) as  sid_total,
  count(distinct session_id) as sessionid_total,
  count(distinct ip) as ip_total,
  '-1' as area,
  '-1' as seo_source,
  '-1' as origin_channel,
  hourinfo,
  concat(yearinfo,'-',monthinfo,'-',dayinfo,' ',hourinfo) as time_str,
  from_url,
  '4' as grouptype,
  '1' as time_type,
  yearinfo,
  quarterinfo,
  monthinfo,
  dayinfo
from  itcast_dwd.visit_consult_dwd
group by yearinfo,quarterinfo,monthinfo,dayinfo,hourinfo,from_url;

 7.3.3基于时间统计总咨询量

-- 统计每年的总咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    '-1' as area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    yearinfo as time_str,
    '3' as grouptype,
    '5' as time_type,
    yearinfo,
    '-1' as quarterinfo,
    '-1' as monthinfo,
    '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo;
-- 统计每年每季度的总咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    '-1' as area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    concat(yearinfo,'_',quarterinfo) as time_str,
    '3' as grouptype,
    '4' as time_type,
    yearinfo,
    quarterinfo,
    '-1' as monthinfo,
    '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo;
-- 统计每年每季度每月的总咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    '-1' as area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    concat(yearinfo,'-',monthinfo) as time_str,
    '3' as grouptype,
    '3' as time_type,
    yearinfo,
    quarterinfo,
    monthinfo,
    '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo,monthinfo;
-- 统计每年每季度每月每天的总咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    '-1' as area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    concat(yearinfo,'-',monthinfo,'-',dayinfo) as time_str,
    '3' as grouptype,
    '2' as time_type,
    yearinfo,
    quarterinfo,
    monthinfo,
    dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo,monthinfo,dayinfo;
-- 统计每年每季度每月每天每小时的总咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    '-1' as area,
    '-1' as origin_channel,
    hourinfo,
    concat(yearinfo,'-',monthinfo,'-',dayinfo,' ',hourinfo) as time_str,
    '3' as grouptype,
    '1' as time_type,
    yearinfo,
    quarterinfo,
    monthinfo,
    dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo,monthinfo,dayinfo,hourinfo;

7.3.4基于时间,统计各个地区的咨询量

-- 统计每年各个地区的咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    yearinfo as time_str,
    '1' as grouptype,
    '5' as time_type,
    yearinfo,
    '-1' as quarterinfo,
    '-1' as monthinfo,
    '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,area;
-- 统计每年每季度各个地区的咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    concat(yearinfo,'_',quarterinfo) as time_str,
    '1' as grouptype,
    '4' as time_type,
    yearinfo,
    quarterinfo,
    '-1' as monthinfo,
    '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo,area;
-- 统计每年每季度每月各个地区的咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    concat(yearinfo,'-',monthinfo) as time_str,
    '1' as grouptype,
    '3' as time_type,
    yearinfo,
    quarterinfo,
    monthinfo,
    '-1' as dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo,monthinfo,area;
-- 统计每年每季度每月每天各个地区的咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    area,
    '-1' as origin_channel,
    '-1' as hourinfo,
    concat(yearinfo,'-',monthinfo,'-',dayinfo) as time_str,
    '1' as grouptype,
    '2' as time_type,
    yearinfo,
    quarterinfo,
    monthinfo,
    dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo,monthinfo,dayinfo,area;
-- 统计每年每季度每月每天每小时各个地区的咨询量
insert into table itcast_dws.consult_dws partition(yearinfo,quarterinfo,monthinfo,dayinfo)
select  
    count(distinct sid) as sid_total,
    count(distinct session_id) as sessionid_total,
    count(distinct ip) as ip_total,
    area,
    '-1' as origin_channel,
    hourinfo,
    concat(yearinfo,'-',monthinfo,'-',dayinfo,' ',hourinfo) as time_str,
    '1' as grouptype,
    '1' as time_type,
    yearinfo,
    quarterinfo,
    monthinfo,
    dayinfo
from  itcast_dwd.visit_consult_dwd where msg_count >= 1
group by yearinfo,quarterinfo,monthinfo,dayinfo,hourinfo,area;

7.4DWS->mysql

7.4.1MySQL中创建目标表

create database scrm_bi default character set utf8mb4 collate utf8mb4_general_ci;

-- 访问量的结果表:
CREATE TABLE IF NOT EXISTS scrm_bi.visit_dws (
  sid_total INT COMMENT '根据sid去重求count',
  sessionid_total INT COMMENT '根据sessionid去重求count',
  ip_total INT COMMENT '根据IP去重求count',
  area varchar(32) COMMENT '区域信息',
  seo_source varchar(32) COMMENT '搜索来源',
  origin_channel varchar(32) COMMENT '来源渠道',
  hourinfo varchar(32) COMMENT '创建时间，统计至小时',
  time_str varchar(32) COMMENT '时间明细',
  from_url varchar(32) comment '会话来源页面',
  groupType varchar(32) COMMENT '产品属性类型：1.地区；2.搜索来源；3.来源渠道；4.会话来源页面；5.总访问量',
  time_type varchar(32) COMMENT '时间聚合类型：1、按小时聚合；2、按天聚合；3、按月聚合；4、按季度聚合；5、按年聚合；',
  yearinfo varchar(32) COMMENT '年' ,
  quarterinfo varchar(32) COMMENT '季度',
  monthinfo varchar(32) COMMENT '月',
  dayinfo varchar(32) COMMENT '天'
)comment 'EMS访客日志dws表';

-- 咨询量的结果表:
CREATE TABLE IF NOT EXISTS scrm_bi.consult_dws
(
  sid_total INT COMMENT '根据sid去重求count',
  sessionid_total INT COMMENT '根据sessionid去重求count',
  ip_total INT COMMENT '根据IP去重求count',
  area varchar(32) COMMENT '区域信息',
  origin_channel varchar(32) COMMENT '来源渠道',
  hourinfo varchar(32) COMMENT '创建时间，统计至小时',
  time_str varchar(32) COMMENT '时间明细',
  groupType varchar(32) COMMENT '产品属性类型：1.地区；2.来源渠道',
  time_type varchar(32) COMMENT '时间聚合类型：1、按小时聚合；2、按天聚合；3、按月聚合；4、按季度聚合；5、按年聚合；',
  yearinfo varchar(32) COMMENT '年' ,
  quarterinfo varchar(32) COMMENT '季度',
  monthinfo varchar(32) COMMENT '月',
  dayinfo varchar(32) COMMENT '天'
)COMMENT '咨询量DWS宽表';

7.4.2DWS->sqoop->mysql

咨询量数据

sqoop export \
--connect jdbc:mysql://192.168.52.150:3306/scrm_bi \
--username root \
--password 123456 \
--table consult_dws \
--hcatalog-database itcast_dws \
--hcatalog-table consult_dws \
-m 1

(指定编码格式)

sqoop export \
--connect "jdbc:mysql://192.168.52.150:3306/scrm_bi?useUnicode=true&characterEncoding=utf-8" \
--username root \
--password 123456 \
--table consult_dws \
--hcatalog-database itcast_dws \
--hcatalog-table consult_dws \
-m 1

访问量数据导出

sqoop export \
--connect "jdbc:mysql://192.168.52.150:3306/scrm_bi?useUnicode=true&characterEncoding=utf-8" \
--username root \
--password 123456 \
--table visit_dws \
--hcatalog-database itcast_dws \
--hcatalog-table visit_dws \
-m 1