06 hive 面试题

教程 薪牛 ⋅ 于 2023-01-17 23:34:43 ⋅ 754 阅读
  • 数据准备
    1. 建表
      create table dept (deptid int ,deptname string,address string);
      create table emp(empid int ,empname string,salary DECIMAL ,ruzhidate date,mgr int,deptid int);
    2. 插入数据
      insert into dept values(1,'研发部','北京'),(2,'人事部','上海'),(3,'销售部','深圳'),(4,'公关部','东莞');
      insert into emp values(1001,'tom',18000,'2013-10-11',1005,1),(1002,'jerry',13000,'2021-11-11',1005,1),(1003,'jack',10000,'2020-09-11',1001,1),(1004,'rose',5000,'2020-10-11',1001,2),(1005,'bob',20000,'2018-08-11',null,2)
  • 面试题1:统计全公司工资最高的三个人

    select * from emp sort by salary desc limit 3 
  • 面试题2:查出部门的员工数和部门的名称
    select d.deptname,count(*) from
    dept d join emp e on d.deptid=e.deptid
    group by d.deptname
  • 面试题3:统计员工最高的工资是多少,以及这个人是谁
  1. 方式一:

    select e.empname,e.salary from 
    emp e
    join
    (select max(salary) as maxsalary from emp) t1
    on e.salary=t1.maxsalary;
  2. 方式二:
    select * from emp where salary =(select max(salary) from emp);
  • 面试题4:每个部门最高的工资,以及是谁

    select e.empname,e.salary,e.deptid from 
    emp  e
    join
    (select max(salary) as maxsalary ,deptid from emp group by deptid) t1
    on e.deptid=t1.deptid and e.salary=t1.maxsalary
  • 面试题5:部门平均工资大于公司总的平均工资的部门是哪个部门

    select t1.avg ,t1.deptid from 
    (select avg(salary) as avg ,deptid,'link' as link from emp group by deptid) t1
    join
    (select avg(salary) as totalavg ,'link' as link from emp ) t2
    on t1.link=t2.link where  t1.avg>t2.totalavg;
  • 面试题6:求哪个员工的工资大于本部门的平均工资
    select * from 
    (select avg(salary) as avg ,deptid from emp group by deptid) t1
    join
    emp t2
    on t1.deptid=t2.deptid where t2.salary>t1.avg;

数据准备
现有用户请求数据
用户id(uid) string
请求时间(req_time) string
请求url(req_url) string
处理日期(day) string

处理日期:20220919

uid req_time req_url
u0001 20200918102030 /index.jsp
u0001 20200918102035 /goods.action
u0002 20200918102030 /brand.action
u0001 20200918102130 /index.jsp
u0002 20200918102230 /index.jsp

处理日期:20220920

uid req_time req_url
u0001 20200919102030 /index.jsp
u0003 20200919102035 /goods.action
u0004 20200919102030 /brand.action
u0001 20200919102130 /index.jsp
u0003 20200919102230 /index.jsp

用户数据如下:

用户id(uid)
用户名称(uname)

uid Name
u0001 用户1
U0002 用户2
U0003 用户3
  • 面试题7:根据给的用户请求数据,创建textfile表 分区表hainiu.request_text ,创建每天处理日期的分区,并导入数据

    create table request_text(uid string,req_time string ,req_url string) partitioned by (day string) row format delim
    ited fields terminated by '\t';
    load data local inpath '/home/hadoop/19' into table request_text partition(day='20220919');
    load data local inpath '/home/hadoop/20' into table request_text partition(day='20220920');
  • 面试题8:创建parquet分区表 hainiu.request_p ,创建每天处理日期的分区,并将hainiu.request_text 表每天分区数据导入到 request_p 每天分区中
    create table request_p (uid string,req_time string ,req_url string) partitioned by (day string) stored as parquet;
  1. 普通方式
    insert into table request_p partition(day='20220919') select * from request_text where day='20220919';
    insert into table request_p partition(day='20220920') select * from request_text where day='20220920';
  2. 动态分区方式
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.exec.dynamic.partition=true;  
    insert into table request_p partition(day) select * from request_text;

数据准备
创建用户表 user_text表 存储格式是 字段两个,数据用已经提供的数据
u0001 用户1
u0002 用户2
u0003 用户3

  • 面试题9:创建parquet分区表 request_p2, 将用户信息表和request_p表中的数据导入到request_p2表中,格式要符合要求
    表结构:
    用户id
    用户名称
    请求日期 需要格式化 只保留 20200919 年月日
    请求url
    处理日期

    create table request_p2(uid string,uname string,req_day string,req_url string) partitioned by (day string) stored as parquet;

    动态分区的方式进行查询导入

    insert into table request_p2 partition(day)
    select t1.uid,t2.uname,t1.req_day,t1.req_url,t1.day from 
    (select uid,substring(req_time,1,8) as req_day,req_url,day from request_p) t1
    join
    user_text t2 on t1.uid=t2.uid; 
  • 面试题10: 用 request_p2 表 统计请求日期是20220918 ,处理日期是 20220919 请求的用户数(去重),导出到linux /home/hadoop/export/output1.txt文件中

    1. 方式1:
      insert overwrite directory '/home/hadoop/export/' select count(distinct(uid)) from request_p2 where req_day='20220918' and day='20220919';
      qqq
    2. 方式2:
      hive -e "use hainiu;set mapreduce.job.queuename=hainiu;select count(distinct(uid)) from request_p2 where req_day='20200918' and day='20220919';" > /home/hadoop/export/output1.txt
  • 面试题11:用 request_p2 表查询出每个请求日期每个用户的访问量,导出到linux /home/hadoop/export/output2.txt文件中

    hive -e "use hainiu;set mapreduce.job.queuename=hainiu;select req_day,uid ,count(*) from request_p2 group by req_day,uid;" > /home/hadoop/export/output2.txt
  • 面试题12:用request_p2 表 计算20号比19号的增量用户,导出到linux /home/hadoop/export/output3.txt文件中

    hive -e "use hainiu;set mapreduce.job.queuename=hainiu;select t2.uid from (select uid from request_p2 where day='20220919' group by uid) t1 right join (select uid from request_p2 where day='20220920' group by uid) t2 on t1.uid=t2.uid where t1.uid is null;" > /home/hadoop/export/output3.txt
  • 面试题13:查询request_p 处理日期是 20220919-20220920,每个用户的最后请求记录 导出到linux /home/hadoop/export/output4.txt文件中
    hive -e "use hainiu;set mapreduce.job.queuename=hainiu;select * from (select uid,req_time,req_url,row_number() over(partition by uid order by req_time desc ) as lastreq  from request_p) t1 where lastreq=1; " > /home/hadoop/export/output4.txt
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