数据仓库建设好以后，用户就可以编写Hive SQL语句对其进行访问并对其中数据进行分析。
在实际生产中，究竟需要哪些统计指标通常由数据需求相关部门人员提出，而且会不断有新的统计需求产生，以下为网站流量分析中的一些典型指标示例。
注：每一种统计指标都可以跟各维度表进行钻取。
1． 流量分析1.1． 多维度统计PV总量按时间维度
| --计算每小时pvs，注意gruop by语法 select count(*) as pvs,month,day,hour from ods_weblog_detail group by month,day,hour; |
方式一：直接在ods_weblog_detail单表上进行查询
| --计算该处理批次（一天）中的各小时pvs drop table dw_pvs_everyhour_oneday; create table dw_pvs_everyhour_oneday(month string,day string,hour string,pvs bigint) partitioned by(datestr string); insert into table dw_pvs_everyhour_oneday partition(datestr='20130918') select a.month as month,a.day as day,a.hour as hour,count(*) as pvs from ods_weblog_detail a where a.datestr='20130918' group by a.month,a.day,a.hour; --计算每天的pvs drop table dw_pvs_everyday; create table dw_pvs_everyday(pvs bigint,month string,day string); insert into table dw_pvs_everyday select count(*) as pvs,a.month as month,a.day as day from ods_weblog_detail a group by a.month,a.day; |
方式二：与时间维表关联查询
| --维度：日 drop table dw_pvs_everyday; create table dw_pvs_everyday(pvs bigint,month string,day string); insert into table dw_pvs_everyday select count(*) as pvs,a.month as month,a.day as day from (select distinct month, day from t_dim_time) a join ods_weblog_detail b on a.month=b.month and a.day=b.day group by a.month,a.day; --维度：月 drop table dw_pvs_everymonth; create table dw_pvs_everymonth (pvs bigint,month string); insert into table dw_pvs_everymonth select count(*) as pvs,a.month from (select distinct month from t_dim_time) a join ods_weblog_detail b on a.month=b.month group by a.month; --另外，也可以直接利用之前的计算结果。比如从之前算好的小时结果中统计每一天的 Insert into table dw_pvs_everyday Select sum(pvs) as pvs,month,day from dw_pvs_everyhour_oneday group by month,day having day='18'; |
按终端维度
数据中能够反映出用户终端信息的字段是http_user_agent。
User Agent也简称UA。它是一个特殊字符串头，是一种向访问网站提供所使用的浏览器类型及版本、操作系统及版本、浏览器内核、等信息的标识。例如：
| User-Agent,Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.276 Safari/537.36 |
上述UA信息就可以提取出以下的信息：
chrome 58.0、浏览器 chrome、浏览器版本 58.0、系统平台 windows
浏览器内核 webkit
这里不再拓展相关知识，感兴趣的可以查看参考资料如何解析UA。
可以用下面的语句进行试探性统计，当然这样的准确度不是很高。
| select distinct(http_user_agent) from ods_weblog_detail where http_user_agent like '%Chrome%' limit 200; |
按栏目维度
网站栏目可以理解为网站中内容相关的主题集中。体现在域名上来看就是不同的栏目会有不同的二级目录。比如某网站网址为www.xxxx.cn,旗下栏目可以通过如下方式访问：
栏目维度：../job
栏目维度：../news
栏目维度：../sports
栏目维度：../technology
那么根据用户请求url就可以解析出访问栏目，然后按照栏目进行统计分析。
按referer维度
| --统计每小时各来访url产生的pv量 drop table dw_pvs_referer_everyhour; create table dw_pvs_referer_everyhour(referer_url string,referer_host string,month string,day string,hour string,pv_referer_cnt bigint) partitioned by(datestr string); insert into table dw_pvs_referer_everyhour partition(datestr='20130918') select http_referer,ref_host,month,day,hour,count(1) as pv_referer_cnt from ods_weblog_detail group by http_referer,ref_host,month,day,hour having ref_host is not null order by hour asc,day asc,month asc,pv_referer_cnt desc; |
| --统计每小时各来访host的产生的pv数并排序 drop table dw_pvs_refererhost_everyhour; create table dw_pvs_refererhost_everyhour(ref_host string,month string,day string,hour string,ref_host_cnts bigint) partitioned by(datestr string); insert into table dw_pvs_refererhost_everyhour partition(datestr='20130918') select ref_host,month,day,hour,count(1) as ref_host_cnts from ods_weblog_detail group by ref_host,month,day,hour having ref_host is not null order by hour asc,day asc,month asc,ref_host_cnts desc; |
 |
注：还可以按来源地域维度、访客终端维度等计算
1.2． 人均浏览量
需求描述：统计今日所有来访者平均请求的页面数。
人均浏览量也称作人均浏览页数，该指标可以说明网站对用户的粘性。
人均页面浏览量表示用户某一时段平均浏览页面的次数。
计算方式：总页面请求数/去重总人数
remote_addr表示不同的用户。可以先统计出不同remote_addr的pv量，然后累加（sum）所有pv作为总的页面请求数，再count所有remote_addr作为总的去重总人数。
| --总页面请求数/去重总人数 drop table dw_avgpv_user_everyday; create table dw_avgpv_user_everyday( day string, avgpv string); insert into table dw_avgpv_user_everyday select '20130918',sum(b.pvs)/count(b.remote_addr) from (select remote_addr,count(1) as pvs from ods_weblog_detail where datestr='20130918' group by remote_addr) b; |
1.3． 统计pv总量最大的来源TOPN (分组TOP)
需求描述：统计每小时各来访host的产生的pvs数最多的前N个（topN）。
row_number()函数
Ø 语法：row_number() over (partition by xxx order by xxx) rank，rank为分组的别名，相当于新增一个字段为rank。
Ø partition by用于分组，比方说依照sex字段分组
Ø order by用于分组内排序，比方说依照sex分组，组内按照age排序
Ø 排好序之后，为每个分组内每一条分组记录从1开始返回一个数字
Ø 取组内某个数据，可以使用where 表名.rank>x之类的语法去取
以下语句对每个小时内的来访host次数倒序排序标号:
select ref_host,ref_host_cnts,concat(month,day,hour),
row_number() over (partition by concat(month,day,hour) order by ref_host_cnts desc) as od from dw_pvs_refererhost_everyhour;
效果如下：
根据上述row_number的功能，可编写hql取各小时的ref_host访问次数topn
| drop table dw_pvs_refhost_topn_everyhour; create table dw_pvs_refhost_topn_everyhour( hour string, toporder string, ref_host string, ref_host_cnts string )partitioned by(datestr string); insert into table dw_pvs_refhost_topn_everyhour partition(datestr='20130918') select t.hour,t.od,t.ref_host,t.ref_host_cnts from (select ref_host,ref_host_cnts,concat(month,day,hour) as hour, row_number() over (partition by concat(month,day,hour) order by ref_host_cnts desc) as od from dw_pvs_refererhost_everyhour) t where od<=3; |
结果如下：
2． 受访分析（从页面的角度分析）2.1． 各页面访问统计
主要是针对数据中的request进行统计分析，比如各页面PV ，各页面UV 等。
以上指标无非就是根据页面的字段group by。例如：
| --统计各页面pv select request as request,count(request) as request_counts from ods_weblog_detail group by request having request is not null order by request_counts desc limit 20; |
2.2． 热门页面统计
| --统计每日最热门的页面top10 drop table dw_hotpages_everyday; create table dw_hotpages_everyday(day string,url string,pvs string); insert into table dw_hotpages_everyday select '20130918',a.request,a.request_counts from (select request as request,count(request) as request_counts from ods_weblog_detail where datestr='20130918' group by request having request is not null) a order by a.request_counts desc limit 10; |
3． 访客分析3.1． 独立访客
需求描述：按照时间维度比如小时来统计独立访客及其产生的pv。
对于独立访客的识别，如果在原始日志中有用户标识，则根据用户标识即很好实现;此处，由于原始日志中并没有用户标识，以访客IP来模拟，技术上是一样的，只是精确度相对较低。
| --时间维度：时 drop table dw_user_dstc_ip_h; create table dw_user_dstc_ip_h( remote_addr string, pvs bigint, hour string); insert into table dw_user_dstc_ip_h select remote_addr,count(1) as pvs,concat(month,day,hour) as hour from ods_weblog_detail Where datestr='20130918' group by concat(month,day,hour),remote_addr; 在此结果表之上，可以进一步统计，如每小时独立访客总数： select count(1) as dstc_ip_cnts,hour from dw_user_dstc_ip_h group by hour; |
| --时间维度：日 select remote_addr,count(1) as counts,concat(month,day) as day from ods_weblog_detail Where datestr='20130918' group by concat(month,day),remote_addr; |
| --时间维度：月 select remote_addr,count(1) as counts,month from ods_weblog_detail group by month,remote_addr; |
3.2． 每日新访客
需求：将每天的新访客统计出来。
实现思路：创建一个去重访客累积表，然后将每日访客对比累积表。
| --历日去重访客累积表 drop table dw_user_dsct_history; create table dw_user_dsct_history( day string, ip string ) partitioned by(datestr string); --每日新访客表 drop table dw_user_new_d; create table dw_user_new_d ( day string, ip string ) partitioned by(datestr string); --每日新用户插入新访客表 insert into table dw_user_new_d partition(datestr='20130918') select tmp.day as day,tmp.today_addr as new_ip from ( select today.day as day,today.remote_addr as today_addr,old.ip as old_addr from (select distinct remote_addr as remote_addr,"20130918" as day from ods_weblog_detail where datestr="20130918") today left outer join dw_user_dsct_history old on today.remote_addr=old.ip ) tmp where tmp.old_addr is null; --每日新用户追加到累计表 insert into table dw_user_dsct_history partition(datestr='20130918') select day,ip from dw_user_new_d where datestr='20130918'; |
验证查看：
| select count(distinct remote_addr) from ods_weblog_detail; select count(1) from dw_user_dsct_history where datestr='20130918'; select count(1) from dw_user_new_d where datestr='20130918'; |
注：还可以按来源地域维度、访客终端维度等计算
4． 访客Visit分析（点击流模型）4.1． 回头/单次访客统计
需求：查询今日所有回头访客及其访问次数。
实现思路：上表中出现次数>1的访客，即回头访客；反之，则为单次访客。
| drop table dw_user_returning; create table dw_user_returning( day string, remote_addr string, acc_cnt string) partitioned by (datestr string); insert overwrite table dw_user_returning partition(datestr='20130918') select tmp.day,tmp.remote_addr,tmp.acc_cnt from (select '20130918' as day,remote_addr,count(session) as acc_cnt from ods_click_stream_visit group by remote_addr) tmp where tmp.acc_cnt>1; |
4.2． 人均访问频次
需求：统计出每天所有用户访问网站的平均次数（visit）
总visit数/去重总用户数
| select sum(pagevisits)/count(distinct remote_addr) from ods_click_stream_visit where datestr='20130918'; |
5． 关键路径转化率分析（漏斗模型）5.1． 需求分析
转化：在一条指定的业务流程中，各个步骤的完成人数及相对上一个步骤的百分比。
5.2． 模型设计
定义好业务流程中的页面标识，下例中的步骤为：
Step1、 /item
Step2、 /category
Step3、 /index
Step4、 /order
5.3． 开发实现
l 查询每一个步骤的总访问人数
| --查询每一步人数存入dw_oute_numbs create table dw_oute_numbs as select 'step1' as step,count(distinct remote_addr) as numbs from ods_click_pageviews where datestr='20130920' and request like '/item%' union select 'step2' as step,count(distinct remote_addr) as numbs from ods_click_pageviews where datestr='20130920' and request like '/category%' union select 'step3' as step,count(distinct remote_addr) as numbs from ods_click_pageviews where datestr='20130920' and request like '/order%' union select 'step4' as step,count(distinct remote_addr) as numbs from ods_click_pageviews where datestr='20130920' and request like '/index%'; |
注：UNION将多个SELECT语句的结果集合并为一个独立的结果集。
l 查询每一步骤相对于路径起点人数的比例
思路：级联查询，利用自join
| --dw_oute_numbs跟自己join select rn.step as rnstep,rn.numbs as rnnumbs,rr.step as rrstep,rr.numbs as rrnumbs from dw_oute_numbs rn inner join dw_oute_numbs rr; |
| --每一步的人数/第一步的人数==每一步相对起点人数比例 select tmp.rnstep,tmp.rnnumbs/tmp.rrnumbs as ratio from ( select rn.step as rnstep,rn.numbs as rnnumbs,rr.step as rrstep,rr.numbs as rrnumbs from dw_oute_numbs rn inner join dw_oute_numbs rr) tmp where tmp.rrstep='step1'; |
l 查询每一步骤相对于上一步骤的漏出率
| --自join表过滤出每一步跟上一步的记录 select rn.step as rnstep,rn.numbs as rnnumbs,rr.step as rrstep,rr.numbs as rrnumbs from dw_oute_numbs rn inner join dw_oute_numbs rr where cast(substr(rn.step,5,1) as int)=cast(substr(rr.step,5,1) as int)-1; |
| select tmp.rrstep as step,tmp.rrnumbs/tmp.rnnumbs as leakage_rate from ( select rn.step as rnstep,rn.numbs as rnnumbs,rr.step as rrstep,rr.numbs as rrnumbs from dw_oute_numbs rn inner join dw_oute_numbs rr) tmp where cast(substr(tmp.rnstep,5,1) as int)=cast(substr(tmp.rrstep,5,1) as int)-1; |
l 汇总以上两种指标
| select abs.step,abs.numbs,abs.rate as abs_ratio,rel.rate as leakage_rate from ( select tmp.rnstep as step,tmp.rnnumbs as numbs,tmp.rnnumbs/tmp.rrnumbs as rate from ( select rn.step as rnstep,rn.numbs as rnnumbs,rr.step as rrstep,rr.numbs as rrnumbs from dw_oute_numbs rn inner join dw_oute_numbs rr) tmp where tmp.rrstep='step1' ) abs left outer join ( select tmp.rrstep as step,tmp.rrnumbs/tmp.rnnumbs as rate from ( select rn.step as rnstep,rn.numbs as rnnumbs,rr.step as rrstep,rr.numbs as rrnumbs from dw_oute_numbs rn inner join dw_oute_numbs rr) tmp where cast(substr(tmp.rnstep,5,1) as int)=cast(substr(tmp.rrstep,5,1) as int)-1 ) rel on abs.step=rel.step; |