图示-实现hive的文件与hdfs的导入导出

 

已知一堆sql导入数据库。

Navite导出成文本

 

导为txt文件成功

导成结构,并修改成hive能看懂的sql

给hive中新建数据库并切换到数据库中：

hive> create database test1;
OK
Time taken: 2.757 seconds
hive> show databases;
OK
default
my_1
my_2
mydata
test
test1
test_1
Time taken: 0.122 seconds, Fetched: 7 row(s)
hive> use test1;
OK
Time taken: 0.105 seconds

 

创表a_users：

CREATE TABLE `a_users` (
  `id` int COMMENT '主键;自动递增',
  `regionId` int COMMENT '地区id',
  `parentId` int COMMENT '上一级,推荐Id',
  `level` int COMMENT '第几级',
  `email` varchar(255) COMMENT '邮箱',
  `password` varchar(255) COMMENT '密码',
  `nickName` varchar(255) COMMENT '昵称',
  `trueName` varchar(255) COMMENT '真实姓名',
  `qq` varchar(255) COMMENT 'qq',
  `phone` varchar(255) COMMENT '电话',
  `weiXin` varchar(255) COMMENT '微信',
  `birthday` timestamp COMMENT '生日',
  `education` tinyint COMMENT '最高学历;\r\n0:未知,1:小学,2:初中,3:高中,4:中专,5:大专,6:本科,7:研究生,8:博士生;9:其他',
  `loginCount` int COMMENT '登陆次数',
  `failedCount` int COMMENT '密码错误次数',
  `failedTime` timestamp COMMENT '失败次数',
  `address` varchar(255) COMMENT '详细地址',
  `photoPath` varchar(255) COMMENT '头像',
  `recomCode` varchar(255) COMMENT '邀请码',
  `souPass` varchar(255) COMMENT 'smm',
  `emailStatus` tinyint COMMENT '邮箱状态:0:未认证;1:已认证;2:认证不通过',
  `phoneStatus` tinyint COMMENT '手机状态:0:未认证;1:已认证;2:认证不通过',
  `idcardStatus` tinyint COMMENT '身份证状态:0:未认证;1:认证通过;2:认证不通过',
  `sex` tinyint COMMENT '性别:0:无,1:男;2:女',
  `usersType` tinyint COMMENT '用户类别:0:学生,1:老师,2:辅导员',
  `status` tinyint COMMENT '状态:0:禁用,1:启用',
  `createTime` timestamp COMMENT '创建时间',
  `updateTime` timestamp COMMENT '更新时间',
  `lastLoginTime` timestamp COMMENT '发布时间:用来排序'
)
-- 记录行的分隔符
row format delimited
-- 列的分隔符
fields terminated by ','
-- 存储文件的格式;textfile是默认的,写与不写都是一样的
stored as textfile ;

;

创表a_region:

CREATE TABLE `a_region`  (
  `id` int COMMENT '主键;自动递增',
  `parentId` int  COMMENT '上一级Id;0:表示国家;',
  `name` varchar(255)COMMENT '名称',
  `pinyin` varchar(255)COMMENT '拼音',
  `areacode` varchar(255)COMMENT '地区编码',
  `content` string COMMENT '内容',
  `leafStatus` tinyint  COMMENT '叶子节点状态:0:非叶子;1:叶子',
  `status` tinyint  COMMENT '状态:0:禁用,1:启用',
  `createTime` timestamp   COMMENT '创建时间',
  `updateTime` timestamp   COMMENT '更新时间',
  `pubTime` timestamp   COMMENT '发布时间:用来排序'
)
-- 记录行的分隔符
row format delimited
-- 列的分隔符
fields terminated by ','
-- 存储文件的格式;textfile是默认的,写与不写都是一样的
stored as textfile ;
;

 

查看创建的表：

hive> 
    > show tables;
OK
a_region
a_users
Time taken: 0.11 seconds, Fetched: 2 row(s)

 

 

导出一个目录到hdfs上：

hive> export table a_region to '/test1/a_region' ; 
OK
Time taken: 3.242 seconds
hive> export table a_users to '/test1/a_users' ; 
OK
Time taken: 0.546 seconds

node7-1:9870 和 node7-2:9870:

把txt文件上传到hdfs,两个

 导入并查询：

导入并查询：
hive> import table a_region from '/test1/a_region' ;
Copying data from hdfs://jh/test1/a_region/data
Copying file: hdfs://jh/test1/a_region/data/a_region.txt
Loading data to table test1.a_region
OK
Time taken: 5.895 seconds
hive>select *from a_region;

导入并查询：
hive> import table a_users from '/test1/a_users' ;
Copying data from hdfs://jh/test1/a_users/data
Copying file: hdfs://jh/test1/a_users/data/a_users.txt
Loading data to table test1.a_users
OK
Time taken: 0.946 seconds
hive>select *from a_users;

a_region查询完成，a_users同理：

题一：-- 统计不同学历的不同人数;请列出sql 语句+截图

hive> select education,count(*)  from a_users group by  education  ; 
Query ID = root_20200713211401_6f716ab4-e6c2-4b4b-984d-9571d37b0873
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1594600282321_0001, Tracking URL = http://node7-1:8088/proxy/application_1594600282321_0001/
Kill Command = /data/hadoop/hadoop/bin/mapred job  -kill job_1594600282321_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2020-07-13 21:14:33,100 Stage-1 map = 0%,  reduce = 0%
2020-07-13 21:14:48,146 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.0 sec
2020-07-13 21:15:07,026 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 8.87 sec
MapReduce Total cumulative CPU time: 8 seconds 870 msec
Ended Job = job_1594600282321_0001
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 8.87 sec   HDFS Read: 96758 HDFS Write: 271 SUCCESS
Total MapReduce CPU Time Spent: 8 seconds 870 msec
OK
NULL	81
0	107
1	1
2	6
3	14
4	14
5	27
6	10
7	1
8	4
9	3
Time taken: 67.004 seconds, Fetched: 11 row(s)

题二：-- 统计每天用户的注册人数;请列出sql 语句+截图

hive> select cast(createTime as date),count(*) from a_users GROUP BY cast( createTime as date);
Query ID = root_20200713211645_d85411cd-f6da-4d26-99ca-853c0a5861ed
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1594600282321_0002, Tracking URL = http://node7-1:8088/proxy/application_1594600282321_0002/
Kill Command = /data/hadoop/hadoop/bin/mapred job  -kill job_1594600282321_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2020-07-13 21:17:15,538 Stage-1 map = 0%,  reduce = 0%
2020-07-13 21:17:31,705 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.59 sec
2020-07-13 21:17:44,766 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 8.73 sec
MapReduce Total cumulative CPU time: 8 seconds 730 msec
Ended Job = job_1594600282321_0002
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 8.73 sec   HDFS Read: 96619 HDFS Write: 106 SUCCESS
Total MapReduce CPU Time Spent: 8 seconds 730 msec
OK
NULL	268
Time taken: 61.579 seconds, Fetched: 1 row(s)

题三：统计每天用户的注册人数(只统计河南的用户);

hive> select cast(createTime as date),count(*) from a_users  where address  like "%河南省%" GROUP BY cast( createTime as date);
Query ID = root_20200713211955_33340ba8-9656-4d32-87f9-ccbf6945206e
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1594600282321_0003, Tracking URL = http://node7-1:8088/proxy/application_1594600282321_0003/
Kill Command = /data/hadoop/hadoop/bin/mapred job  -kill job_1594600282321_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2020-07-13 21:20:19,162 Stage-1 map = 0%,  reduce = 0%
2020-07-13 21:20:29,926 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.8 sec
2020-07-13 21:20:41,126 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 9.38 sec
MapReduce Total cumulative CPU time: 9 seconds 380 msec
Ended Job = job_1594600282321_0003
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 9.38 sec   HDFS Read: 97826 HDFS Write: 104 SUCCESS
Total MapReduce CPU Time Spent: 9 seconds 380 msec
OK
NULL	7
Time taken: 48.073 seconds, Fetched: 1 row(s)

题四：-- 统计每天用户的注册人数;按照创建时间升序,每天只列出10个人;

hive> select cast(createTime as date),count(*),trueName from
    >  (select *, row_number() over (partition by cast(createTime as date)) as bh from a_users ) t 
    > where bh <11 GROUP BY cast(createTime as date),trueName;
Query ID = root_20200713212220_65a08e64-0b87-4d8d-b4bc-dbe2456713da
Total jobs = 2
Launching Job 1 out of 2
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1594600282321_0004, Tracking URL = http://node7-1:8088/proxy/application_1594600282321_0004/
Kill Command = /data/hadoop/hadoop/bin/mapred job  -kill job_1594600282321_0004
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2020-07-13 21:22:38,447 Stage-1 map = 0%,  reduce = 0%
2020-07-13 21:22:53,496 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.79 sec
2020-07-13 21:23:06,031 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 9.96 sec
MapReduce Total cumulative CPU time: 9 seconds 960 msec
Ended Job = job_1594600282321_0004
Launching Job 2 out of 2
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1594600282321_0005, Tracking URL = http://node7-1:8088/proxy/application_1594600282321_0005/
Kill Command = /data/hadoop/hadoop/bin/mapred job  -kill job_1594600282321_0005
Hadoop job information for Stage-2: number of mappers: 1; number of reducers: 1
2020-07-13 21:23:34,372 Stage-2 map = 0%,  reduce = 0%
2020-07-13 21:23:46,039 Stage-2 map = 100%,  reduce = 0%, Cumulative CPU 3.12 sec
2020-07-13 21:23:56,749 Stage-2 map = 100%,  reduce = 100%, Cumulative CPU 7.29 sec
MapReduce Total cumulative CPU time: 7 seconds 290 msec
Ended Job = job_1594600282321_0005
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 9.96 sec   HDFS Read: 97476 HDFS Write: 224 SUCCESS
Stage-Stage-2: Map: 1  Reduce: 1   Cumulative CPU: 7.29 sec   HDFS Read: 8662 HDFS Write: 243 SUCCESS
Total MapReduce CPU Time Spent: 17 seconds 250 msec
OK
NULL	6	
NULL	1	嵇荟茹
NULL	1	李瑶
NULL	1	牛真真
NULL	1	王向旗
Time taken: 99.565 seconds, Fetched: 5 row(s)