文章作者：foochane
原文链接：https://foochane.cn/article/2019062501.html
1 基本介绍
1.1 HIVE简单介绍
Hive是一个可以将SQL翻译为MR程序的工具，支持用户将HDFS上的文件映射为表结构，然后用户就可以输入SQL对这些表（HDFS上的文件）进行查询分析。Hive将用户定义的库、表结构等信息存储hive的元数据库（可以是本地derby，也可以是远程mysql）中。
1.2 Hive的用途
- 做数据分析，不用自己写大量的
MR程序，只需要写SQL脚本即可 - 用于构建大数据体系下的数据仓库
hive 2 以后 把底层引擎从MapReduce换成了Spark
启动hive前要先启动hdfs 和yarn
2 使用方式
2.1 方式1：直接使用hive服务端
输入命令 $ hive即可：
hadoop@Master:~$ hiveSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/usr/local/bigdata/hive-2.3.5/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/usr/local/bigdata/hadoop-2.7.1/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]Logging initialized using configuration in file:/usr/local/bigdata/hive-2.3.5/conf/hive-log4j2.properties Async: trueHive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.hive>show databases;OKdbtestdefaultTime taken: 3.539 seconds, Fetched: 2 row(s)hive>技巧：
让提示符显示当前库：
hive>set hive.cli.print.current.db=true;显示查询结果是显示自带名称：
hive>set hive.cli.print.header=true;这样设置只是对当前窗口有效，永久生效可以在当前用户目录下建一个.hiverc文件。
加入如下内容：
set hive.cli.print.current.db=true;set hive.cli.print.header=true;2.2 方式2：使用beeline客户端
将hive启动为一个服务端，然后可以在任意一台机器上使用beeline客户端连接hive服务，进行交互式查询
hive是一个单机的服务端可以在任何一台机器里安装，它访问的是hdfs集群。
启动hive服务 ：
$ nohup hiveserver2 1>/dev/null 2>&1 &启动后，可以用beeline去连接，beeline是一个客户端，可以在任意机器启动,只要能够跟hive服务端相连即可。
在本地启动beeline
$ beeline -u jdbc:hive2://localhost:10000 -n hadoop -p hadoop在启动机器上启动beeline
$ beeline -u jdbc:hive2://Master:10000 -n hadoop -p hadoop示例：
hadoop@Master:~$ beeline -u jdbc:hive2://Master:10000 -n hadoop -p hadoopConnecting to jdbc:hive2://Master:1000019/06/25 01:50:12 INFO jdbc.Utils: Supplied authorities: Master:1000019/06/25 01:50:12 INFO jdbc.Utils: Resolved authority: Master:1000019/06/25 01:50:13 INFO jdbc.HiveConnection: Will try to open client transport with JDBC Uri: jdbc:hive2://Master:10000Connected to: Apache Hive (version 2.3.5)Driver: Hive JDBC (version 1.2.1.spark2)Transaction isolation: TRANSACTION_REPEATABLE_READBeeline version 1.2.1.spark2 by Apache Hive0: jdbc:hive2://Master:10000> 参数说明
- u ：指定连接方式
- n ：登录的用户（系统用户）
- p ：用户密码
报错
errorMessage:Failed to open new session: java.lang.RuntimeException: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.authorize.AuthorizationException): User: hadoop is not allowed to impersonate hadoop), serverProtocolVersion:null)解决
在 hadoop配置文件中的core-site.xml 文件中添加如下内容,然后重启hadoop集群：
<property> <name>hadoop.proxyuser.hadoop.groups</name> <value>hadoop</value> <description>Allow the superuser oozie to impersonate any members of the group group1 and group2</description> </property> <property> <name>hadoop.proxyuser.hadoop.hosts</name> <value>Master,127.0.0.1,localhost</value> <description>The superuser can connect only from host1 and host2 to impersonate a user</description> </property>2.3 方式3：使用hive命令运行sql
接用 hive -e 在命令行中运行sql命令，该命令可以一起运行多条sql语句，用;隔开。
hive -e "sql1;sql2;sql3;sql4"另外，还可以使用 hive -f命令。
事先将sql语句写入一个文件比如 q.hql ，然后用hive -f命令执行：
bin/hive -f q.hql2.4 方式4：写脚本
可以将方式3写入一个xxx.sh脚本中,然后运行该脚本。
3 表的基本操作
3.1 新建数据库
create database db1;示例：
0: jdbc:hive2://Master:10000> create database db1;No rows affected (1.123 seconds)0: jdbc:hive2://Master:10000> show databases;+----------------+--+| database_name |+----------------+--+| db1 || dbtest || default |+----------------+--+成功后，hive就会在/user/hive/warehouse/下建一个文件夹： db1.db
3.2 删除数据库
drop database db1;示例：
0: jdbc:hive2://Master:10000> drop database db1;No rows affected (0.969 seconds)0: jdbc:hive2://Master:10000> show databases;+----------------+--+| database_name |+----------------+--+| dbtest || default |+----------------+--+3.3 建内部表
use db1;create table t_test(id int,name string,age int)row format delimitedfields terminated by ',';示例：
0: jdbc:hive2://Master:10000> use db1;No rows affected (0.293 seconds)0: jdbc:hive2://Master:10000> create table t_test(id int,name string,age int)0: jdbc:hive2://Master:10000> row format delimited0: jdbc:hive2://Master:10000> fields terminated by ',';No rows affected (1.894 seconds)0: jdbc:hive2://Master:10000> desc db1.t_test;+-----------+------------+----------+--+| col_name | data_type | comment |+-----------+------------+----------+--+| id | int | || name | string | || age | int | |+-----------+------------+----------+--+3 rows selected (0.697 seconds)建表后，hive会在仓库目录中建一个表目录： /user/hive/warehouse/db1.db/t_test
3.4 建外部表
create external table t_test1(id int,name string,age int)row format delimitedfields terminated by ','location '/user/hive/external/t_test1';这里的location指的是hdfs上的目录，可以直接在该目录下放入相应格式的文件，就可以在hive表中查看到。
示例：
0: jdbc:hive2://Master:10000> create external table t_test1(id int,name string,age int)0: jdbc:hive2://Master:10000> row format delimited0: jdbc:hive2://Master:10000> fields terminated by ','0: jdbc:hive2://Master:10000> location '/user/hive/external/t_test1';No rows affected (0.7 seconds)0: jdbc:hive2://Master:10000> desc db1.t_test1;+-----------+------------+----------+--+| col_name | data_type | comment |+-----------+------------+----------+--+| id | int | || name | string | || age | int | |+-----------+------------+----------+--+3 rows selected (0.395 seconds)本地创建测试文件user.data
1,xiaowang,282,xiaoli,183,xiaohong,23放入hdfs中：
$ hdfs dfs -mkdir -p /user/hive/external/t_test1$ hdfs dfs -put ./user.data /user/hive/external/t_test1此时在hive表中就可以查看到数据：
0: jdbc:hive2://Master:10000> select * from db1.t_test1;+-------------+---------------+--------------+--+| t_test1.id | t_test1.name | t_test1.age |+-------------+---------------+--------------+--+| 1 | xiaowang | 28 || 2 | xiaoli | 18 || 3 | xiaohong | 23 |+-------------+---------------+--------------+--+3 rows selected (8 seconds)注意：如果删除外部表，hdfs里的文件并不会删除
也就是如果包db1.t_test1删除，hdfs下/user/hive/external/t_test1/user.data文件并不会被删除。
3.5 导入数据
本质上就是把数据文件放入表目录；
可以用hive命令来做：
load data [local] inpath '/data/path' [overwrite] into table t_test;加local代表导入本地数据。
导入本地数据
load data local inpath '/home/hadoop/user.data' into table t_test;示例：
0: jdbc:hive2://Master:10000> load data local inpath '/home/hadoop/user.data' into table t_test;No rows affected (2.06 seconds)0: jdbc:hive2://Master:10000> select * from db1.t_test;+------------+--------------+-------------+--+| t_test.id | t_test.name | t_test.age |+------------+--------------+-------------+--+| 1 | xiaowang | 28 || 2 | xiaoli | 18 || 3 | xiaohong | 23 |+------------+--------------+-------------+--+导入hdfs中的数据
load data inpath '/user/hive/external/t_test1/user.data' into table t_test;示例：
0: jdbc:hive2://Master:10000> load data inpath '/user/hive/external/t_test1/user.data' into table t_test;No rows affected (1.399 seconds)0: jdbc:hive2://Master:10000> select * from db1.t_test;+------------+--------------+-------------+--+| t_test.id | t_test.name | t_test.age |+------------+--------------+-------------+--+| 1 | xiaowang | 28 || 2 | xiaoli | 18 || 3 | xiaohong | 23 || 1 | xiaowang | 28 || 2 | xiaoli | 18 || 3 | xiaohong | 23 |+------------+--------------+-------------+--+6 rows selected (0.554 seconds)注意：从本地导入数据，本地数据不是发生变化，从hdfs中导入数据，hdfs中的导入的文件会被移动到数据仓库相应的目录下
3.6 建分区表
分区的意义在于可以将数据分子目录存储，以便于查询时让数据读取范围更精准
create table t_test1(id int,name string,age int,create_time bigint)partitioned by (day string,country string)row format delimitedfields terminated by ',';插入数据到指定分区：
> load data [local] inpath '/data/path1' [overwrite] into table t_test partition(day='2019-06-04',country='China');> load data [local] inpath '/data/path2' [overwrite] into table t_test partition(day='2019-06-05',country='China');> load data [local] inpath '/data/path3' [overwrite] into table t_test partition(day='2019-06-04',country='England');导入完成后，形成的目录结构如下：
/user/hive/warehouse/db1.db/t_test1/day=2019-06-04/country=China/.../user/hive/warehouse/db1.db/t_test1/day=2019-06-04/country=England/.../user/hive/warehouse/db1.db/t_test1/day=2019-06-05/country=China/...4 查询语法
4.1 条件查询
select * from t_table where a<1000 and b>0;4.2 join关联查询
各类join
测试数据：
a.txt：
a,1b,2c,3d,4b.txt:
b,16c,17d,18e,19建表导入数据：
create table t_a(name string,num int)row format delimitedfields terminated by ',';create table t_b(name string,age int)row format delimitedfields terminated by ',';load data local inpath '/home/hadoop/a.txt' into table t_a;load data local inpath '/home/hadoop/b.txt' into table t_b;表数据如下：
0: jdbc:hive2://Master:10000> select * from t_a;+-----------+----------+--+| t_a.name | t_a.num |+-----------+----------+--+| a | 1 || b | 2 || c | 3 || d | 4 |+-----------+----------+--+4 rows selected (0.523 seconds)0: jdbc:hive2://Master:10000> select * from t_b;+-----------+----------+--+| t_b.name | t_b.age |+-----------+----------+--+| b | 16 || c | 17 || d | 18 || e | 19 |+-----------+----------+--+4 rows selected (0.482 seconds)4.3 内连接
指定join条件
select a.*,b.*from t_a a join t_b b on a.name=b.name;示例：
0: jdbc:hive2://Master:10000> select a.*,b.*0: jdbc:hive2://Master:10000> from0: jdbc:hive2://Master:10000> t_a a join t_b b on a.name=b.name;....+---------+--------+---------+--------+--+| a.name | a.num | b.name | b.age |+---------+--------+---------+--------+--+| b | 2 | b | 16 || c | 3 | c | 17 || d | 4 | d | 18 |+---------+--------+---------+--------+--+4.4 左外连接（左连接）
select a.*,b.*from t_a a left outer join t_b b on a.name=b.name;示例：
0: jdbc:hive2://Master:10000> select a.*,b.*0: jdbc:hive2://Master:10000> from0: jdbc:hive2://Master:10000> t_a a left outer join t_b b on a.name=b.name;...+---------+--------+---------+--------+--+| a.name | a.num | b.name | b.age |+---------+--------+---------+--------+--+| a | 1 | NULL | NULL || b | 2 | b | 16 || c | 3 | c | 17 || d | 4 | d | 18 |+---------+--------+---------+--------+--+4.5 右外连接（右连接）
select a.*,b.*from t_a a right outer join t_b b on a.name=b.name;示例：
0: jdbc:hive2://Master:10000> select a.*,b.*0: jdbc:hive2://Master:10000> from0: jdbc:hive2://Master:10000> t_a a right outer join t_b b on a.name=b.name;....+---------+--------+---------+--------+--+| a.name | a.num | b.name | b.age |+---------+--------+---------+--------+--+| b | 2 | b | 16 || c | 3 | c | 17 || d | 4 | d | 18 || NULL | NULL | e | 19 |+---------+--------+---------+--------+--+4.6 全外连接
select a.*,b.*fromt_a a full outer join t_b b on a.name=b.name;示例：
0: jdbc:hive2://Master:10000> select a.*,b.*0: jdbc:hive2://Master:10000> from0: jdbc:hive2://Master:10000> t_a a full outer join t_b b on a.name=b.name;WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.+---------+--------+---------+--------+--+| a.name | a.num | b.name | b.age |+---------+--------+---------+--------+--+| a | 1 | NULL | NULL || b | 2 | b | 16 || c | 3 | c | 17 || d | 4 | d | 18 || NULL | NULL | e | 19 |+---------+--------+---------+--------+--+4.7 左半连接
求存在于a表，且b表里也存在的数据。
select a.*from t_a a left semi join t_b b on a.name=b.name;示例：
0: jdbc:hive2://Master:10000> select a.*0: jdbc:hive2://Master:10000> from0: jdbc:hive2://Master:10000> t_a a left semi join t_b b on a.name=b.name;.....+---------+--------+--+| a.name | a.num |+---------+--------+--+| b | 2 || c | 3 || d | 4 |+---------+--------+--+4.8 group by分组聚合
构建测试数据
192.168.33.3,http://www.xxx.cn/stu,2019-08-04 15:30:20192.168.33.3,http://www.xxx.cn/teach,2019-08-04 15:35:20192.168.33.4,http://www.xxx.cn/stu,2019-08-04 15:30:20192.168.33.4,http://www.xxx.cn/job,2019-08-04 16:30:20192.168.33.5,http://www.xxx.cn/job,2019-08-04 15:40:20192.168.33.3,http://www.xxx.cn/stu,2019-08-05 15:30:20192.168.44.3,http://www.xxx.cn/teach,2019-08-05 15:35:20192.168.33.44,http://www.xxx.cn/stu,2019-08-05 15:30:20192.168.33.46,http://www.xxx.cn/job,2019-08-05 16:30:20192.168.33.55,http://www.xxx.cn/job,2019-08-05 15:40:20192.168.133.3,http://www.xxx.cn/register,2019-08-06 15:30:20192.168.111.3,http://www.xxx.cn/register,2019-08-06 15:35:20192.168.34.44,http://www.xxx.cn/pay,2019-08-06 15:30:20192.168.33.46,http://www.xxx.cn/excersize,2019-08-06 16:30:20192.168.33.55,http://www.xxx.cn/job,2019-08-06 15:40:20192.168.33.46,http://www.xxx.cn/excersize,2019-08-06 16:30:20192.168.33.25,http://www.xxx.cn/job,2019-08-06 15:40:20192.168.33.36,http://www.xxx.cn/excersize,2019-08-06 16:30:20192.168.33.55,http://www.xxx.cn/job,2019-08-06 15:40:20建分区表，导入数据：
create table t_pv(ip string,url string,time string)partitioned by (dt string)row format delimited fields terminated by ',';load data local inpath '/home/hadoop/pv.log.0804' into table t_pv partition(dt='2019-08-04');load data local inpath '/home/hadoop/pv.log.0805' into table t_pv partition(dt='2019-08-05');load data local inpath '/home/hadoop/pv.log.0806' into table t_pv partition(dt='2019-08-06');查看数据：
0: jdbc:hive2://Master:10000> select * from t_pv;+----------------+------------------------------+----------------------+-------------+--+| t_pv.ip | t_pv.url | t_pv.time | t_pv.dt |+----------------+------------------------------+----------------------+-------------+--+| 192.168.33.3 | http://www.xxx.cn/stu | 2019-08-04 15:30:20 | 2019-08-04 || 192.168.33.3 | http://www.xxx.cn/teach | 2019-08-04 15:35:20 | 2019-08-04 || 192.168.33.4 | http://www.xxx.cn/stu | 2019-08-04 15:30:20 | 2019-08-04 || 192.168.33.4 | http://www.xxx.cn/job | 2019-08-04 16:30:20 | 2019-08-04 || 192.168.33.5 | http://www.xxx.cn/job | 2019-08-04 15:40:20 | 2019-08-05 || 192.168.33.3 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 | 2019-08-05 || 192.168.44.3 | http://www.xxx.cn/teach | 2019-08-05 15:35:20 | 2019-08-05 || 192.168.33.44 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 | 2019-08-05 || 192.168.33.46 | http://www.xxx.cn/job | 2019-08-05 16:30:20 | 2019-08-05 || 192.168.33.55 | http://www.xxx.cn/job | 2019-08-05 15:40:20 | 2019-08-06 || 192.168.133.3 | http://www.xxx.cn/register | 2019-08-06 15:30:20 | 2019-08-06 || 192.168.111.3 | http://www.xxx.cn/register | 2019-08-06 15:35:20 | 2019-08-06 || 192.168.34.44 | http://www.xxx.cn/pay | 2019-08-06 15:30:20 | 2019-08-06 || 192.168.33.46 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | 2019-08-06 || 192.168.33.55 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | 2019-08-06 || 192.168.33.46 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | 2019-08-06 || 192.168.33.25 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | 2019-08-06 || 192.168.33.36 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | 2019-08-06 || 192.168.33.55 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | 2019-08-06 |+----------------+------------------------------+----------------------+-------------+--+查看表分区：
show partitions t_pv;0: jdbc:hive2://Master:10000> show partitions t_pv;+----------------+--+| partition |+----------------+--+| dt=2019-08-04 || dt=2019-08-05 || dt=2019-08-06 |+----------------+--+3 rows selected (0.575 seconds)每一行的url变成大写
- 针对每一行进行运算
select ip,upper(url),timefrom t_pv0: jdbc:hive2://Master:10000> select ip,upper(url),time0: jdbc:hive2://Master:10000> from t_pv+----------------+------------------------------+----------------------+--+| ip | _c1 | time |+----------------+------------------------------+----------------------+--+| 192.168.33.3 | HTTP://WWW.XXX.CN/STU | 2019-08-04 15:30:20 || 192.168.33.3 | HTTP://WWW.XXX.CN/TEACH | 2019-08-04 15:35:20 || 192.168.33.4 | HTTP://WWW.XXX.CN/STU | 2019-08-04 15:30:20 || 192.168.33.4 | HTTP://WWW.XXX.CN/JOB | 2019-08-04 16:30:20 || 192.168.33.5 | HTTP://WWW.XXX.CN/JOB | 2019-08-04 15:40:20 || 192.168.33.3 | HTTP://WWW.XXX.CN/STU | 2019-08-05 15:30:20 || 192.168.44.3 | HTTP://WWW.XXX.CN/TEACH | 2019-08-05 15:35:20 || 192.168.33.44 | HTTP://WWW.XXX.CN/STU | 2019-08-05 15:30:20 || 192.168.33.46 | HTTP://WWW.XXX.CN/JOB | 2019-08-05 16:30:20 || 192.168.33.55 | HTTP://WWW.XXX.CN/JOB | 2019-08-05 15:40:20 || 192.168.133.3 | HTTP://WWW.XXX.CN/REGISTER | 2019-08-06 15:30:20 || 192.168.111.3 | HTTP://WWW.XXX.CN/REGISTER | 2019-08-06 15:35:20 || 192.168.34.44 | HTTP://WWW.XXX.CN/PAY | 2019-08-06 15:30:20 || 192.168.33.46 | HTTP://WWW.XXX.CN/EXCERSIZE | 2019-08-06 16:30:20 || 192.168.33.55 | HTTP://WWW.XXX.CN/JOB | 2019-08-06 15:40:20 || 192.168.33.46 | HTTP://WWW.XXX.CN/EXCERSIZE | 2019-08-06 16:30:20 || 192.168.33.25 | HTTP://WWW.XXX.CN/JOB | 2019-08-06 15:40:20 || 192.168.33.36 | HTTP://WWW.XXX.CN/EXCERSIZE | 2019-08-06 16:30:20 || 192.168.33.55 | HTTP://WWW.XXX.CN/JOB | 2019-08-06 15:40:20 |+----------------+------------------------------+----------------------+--+求每条url的访问次数
select url ,count(1) --对分好组的数据进行逐行运算from t_pvgroup by url;0: jdbc:hive2://Master:10000> select url ,count(1)0: jdbc:hive2://Master:10000> from t_pv0: jdbc:hive2://Master:10000> group by url;·····+------------------------------+------+--+| url | _c1 |+------------------------------+------+--+| http://www.xxx.cn/excersize | 3 || http://www.xxx.cn/job | 7 || http://www.xxx.cn/pay | 1 || http://www.xxx.cn/register | 2 || http://www.xxx.cn/stu | 4 || http://www.xxx.cn/teach | 2 |+------------------------------+------+--+可以给_c1加入字段名称：
select url ,count(1) as countfrom t_pvgroup by url;求每个页面的访问者中ip最大的一个
select url,max(ip)from t_pvgroup by url;0: jdbc:hive2://Master:10000> select url,max(ip)0: jdbc:hive2://Master:10000> from t_pv0: jdbc:hive2://Master:10000> group by url;WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.+------------------------------+----------------+--+| url | _c1 |+------------------------------+----------------+--+| http://www.xxx.cn/excersize | 192.168.33.46 || http://www.xxx.cn/job | 192.168.33.55 || http://www.xxx.cn/pay | 192.168.34.44 || http://www.xxx.cn/register | 192.168.133.3 || http://www.xxx.cn/stu | 192.168.33.44 || http://www.xxx.cn/teach | 192.168.44.3 |+------------------------------+----------------+--+求每个用户访问同一个页面的所有记录中，时间最晚的一条
select ip,url,max(time)from t_pvgroup by ip,url;0: jdbc:hive2://Master:10000> select ip,url,max(time)0: jdbc:hive2://Master:10000> from t_pv0: jdbc:hive2://Master:10000> group by ip,url;.....+----------------+------------------------------+----------------------+--+| ip | url | _c2 |+----------------+------------------------------+----------------------+--+| 192.168.111.3 | http://www.xxx.cn/register | 2019-08-06 15:35:20 || 192.168.133.3 | http://www.xxx.cn/register | 2019-08-06 15:30:20 || 192.168.33.25 | http://www.xxx.cn/job | 2019-08-06 15:40:20 || 192.168.33.3 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 || 192.168.33.3 | http://www.xxx.cn/teach | 2019-08-04 15:35:20 || 192.168.33.36 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 || 192.168.33.4 | http://www.xxx.cn/job | 2019-08-04 16:30:20 || 192.168.33.4 | http://www.xxx.cn/stu | 2019-08-04 15:30:20 || 192.168.33.44 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 || 192.168.33.46 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 || 192.168.33.46 | http://www.xxx.cn/job | 2019-08-05 16:30:20 || 192.168.33.5 | http://www.xxx.cn/job | 2019-08-04 15:40:20 || 192.168.33.55 | http://www.xxx.cn/job | 2019-08-06 15:40:20 || 192.168.34.44 | http://www.xxx.cn/pay | 2019-08-06 15:30:20 || 192.168.44.3 | http://www.xxx.cn/teach | 2019-08-05 15:35:20 |+----------------+------------------------------+----------------------+--+求8月4号以后，每天http://www.xxx.cn/job的总访问...，及访问者中ip地址中最大的
select dt,'http://www.xxx.cn/job',count(1),max(ip)from t_pvwhere url='http://www.xxx.cn/job'group by dt having dt>'2019-08-04';select dt,max(url),count(1),max(ip)from t_pvwhere url='http://www.xxx.cn/job'group by dt having dt>'2019-08-04';select dt,url,count(1),max(ip)from t_pvwhere url='http://www.xxx.cn/job'group by dt,url having dt>'2019-08-04';select dt,url,count(1),max(ip)from t_pvwhere url='http://www.xxx.cn/job' and dt>'2019-08-04'group by dt,url;求8月4号以后，每天每个页面的总访问次数，及访问者中ip地址中最大的
select dt,url,count(1),max(ip)from t_pvwhere dt>'2019-08-04'group by dt,url;求8月4号以后，每天每个页面的总访问次数，及访问者中ip地址中最大的，且只查询出总访问次数>2 的记录
- 方式1：
select dt,url,count(1) as cnts,max(ip)from t_pvwhere dt>'2019-08-04'group by dt,url having cnts>2;- 方式2：用子查询
select dt,url,cnts,max_ipfrom(select dt,url,count(1) as cnts,max(ip) as max_ipfrom t_pvwhere dt>'2019-08-04'group by dt,url) tmpwhere cnts>2;5 基本数据类型
5.1 数字类型
TINYINT(1-byte signed integer, from -128 to 127)SMALLINT(2-byte signed integer, from -32,768 to 32,767)INT/INTEGER(4-byte signed integer, from -2,147,483,648 to 2,147,483,647)BIGINT(8-byte signed integer, from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807)FLOAT(4-byte single precision floating point number)DOUBLE(8-byte double precision floating point number)
示例：
create table t_test(a string ,b int,c bigint,d float,e double,f tinyint,g smallint)5.2 日期类型
TIMESTAMP(Note: Only available starting with Hive 0.8.0)DATE(Note: Only available starting with Hive 0.12.0)
示例，假如有以下数据文件：
1,zhangsan,1985-06-302,lisi,1986-07-103,wangwu,1985-08-09那么，就可以建一个表来对数据进行映射
create table t_customer(id int,name string,birthday date)row format delimited fields terminated by ',';然后导入数据
load data local inpath '/root/customer.dat' into table t_customer;然后，就可以正确查询
5.3 字符串类型
STRINGVARCHAR(Note: Only available starting with Hive 0.12.0)CHAR(Note: Only available starting with Hive 0.13.0)
5.4 杂类型
BOOLEANBINARY(Note: Only available starting with Hive 0.8.0)
5.5 复合类型
5.5.1 数组类型
有如下数据：
玩具总动员4,汤姆·汉克斯:蒂姆·艾伦:安妮·波茨,2019-06-21流浪地球,屈楚萧:吴京:李光洁:吴孟达,2019-02-05千与千寻,柊瑠美:入野自由:夏木真理:菅原文太,2019-06-21战狼2,吴京:弗兰克·格里罗:吴刚:张翰:卢靖姗,2017-08-16建表导入数据：
--建表映射：create table t_movie(movie_name string,actors array<string>,first_show date)row format delimited fields terminated by ','collection items terminated by ':';--导入数据load data local inpath '/home/hadoop/actor.dat' into table t_movie;0: jdbc:hive2://Master:10000> select * from t_movie;+---------------------+-----------------------------------+---------------------+--+| t_movie.movie_name | t_movie.actors | t_movie.first_show |+---------------------+-----------------------------------+---------------------+--+| 玩具总动员4 | ["汤姆·汉克斯","蒂姆·艾伦","安妮·波茨"] | 2019-06-21 || 流浪地球 | ["屈楚萧","吴京","李光洁","吴孟达"] | 2019-02-05 || 千与千寻 | ["柊瑠美","入野自由","夏木真理","菅原文太"] | 2019-06-21 || 战狼2 | ["吴京","弗兰克·格里罗","吴刚","张翰","卢靖姗"] | 2017-08-16 |+---------------------+-----------------------------------+---------------------+--+array[]
查询每部电影主演
select movie_name,actors[0],first_show from t_movie;0: jdbc:hive2://Master:10000> select movie_name,actors[0],first_show from t_movie;+-------------+---------+-------------+--+| movie_name | _c1 | first_show |+-------------+---------+-------------+--+| 玩具总动员4 | 汤姆·汉克斯 | 2019-06-21 || 流浪地球 | 屈楚萧 | 2019-02-05 || 千与千寻 | 柊瑠美 | 2019-06-21 || 战狼2 | 吴京 | 2017-08-16 |+-------------+---------+-------------+--+array_contains
查询包含'吴京'的电影
select movie_name,actors,first_showfrom t_movie where array_contains(actors,'吴京');0: jdbc:hive2://Master:10000> select movie_name,actors,first_show0: jdbc:hive2://Master:10000> from t_movie where array_contains(actors,'吴京');+-------------+-----------------------------------+-------------+--+| movie_name | actors | first_show |+-------------+-----------------------------------+-------------+--+| 流浪地球 | ["屈楚萧","吴京","李光洁","吴孟达"] | 2019-02-05 || 战狼2 | ["吴京","弗兰克·格里罗","吴刚","张翰","卢靖姗"] | 2017-08-16 |+-------------+-----------------------------------+-------------+--+size
每部电影查询列出的演员数量
select movie_name,size(actors) as actor_number,first_showfrom t_movie;0: jdbc:hive2://Master:10000> from t_movie;+-------------+---------------+-------------+--+| movie_name | actor_number | first_show |+-------------+---------------+-------------+--+| 玩具总动员4 | 3 | 2019-06-21 || 流浪地球 | 4 | 2019-02-05 || 千与千寻 | 4 | 2019-06-21 || 战狼2 | 5 | 2017-08-16 |+-------------+---------------+-------------+--+5.5.2 map类型
数据
1,zhangsan,father:xiaoming#mother:xiaohuang#brother:xiaoxu,282,lisi,father:mayun#mother:huangyi#brother:guanyu,223,wangwu,father:wangjianlin#mother:ruhua#sister:jingtian,294,mayun,father:mayongzhen#mother:angelababy,26导入数据
-- 建表映射上述数据create table t_family(id int,name string,family_members map<string,string>,age int)row format delimited fields terminated by ','collection items terminated by '#'map keys terminated by ':';-- 导入数据load data local inpath '/root/hivetest/fm.dat' into table t_family;0: jdbc:hive2://Master:10000> select * from t_family;+--------------+----------------+----------------------------------------------------------------+---------------+--+| t_family.id | t_family.name | t_family.family_members | t_family.age |+--------------+----------------+----------------------------------------------------------------+---------------+--+| 1 | zhangsan | {"father":"xiaoming","mother":"xiaohuang","brother":"xiaoxu"} | 28 || 2 | lisi | {"father":"mayun","mother":"huangyi","brother":"guanyu"} | 22 || 3 | wangwu | {"father":"wangjianlin","mother":"ruhua","sister":"jingtian"} | 29 || 4 | mayun | {"father":"mayongzhen","mother":"angelababy"} | 26 |+--------------+----------------+----------------------------------------------------------------+---------------+--+查出每个人的 爸爸、姐妹
select id,name,family_members["father"] as father,family_members["sister"] as sister,agefrom t_family;查出每个人有哪些亲属关系
select id,name,map_keys(family_members) as relations,agefrom t_family;查出每个人的亲人名字
select id,name,map_values(family_members) as relations,agefrom t_family;查出每个人的亲人数量
select id,name,size(family_members) as relations,agefrom t_family;查出所有拥有兄弟的人及他的兄弟是谁
-- 方案1：一句话写完select id,name,age,family_members['brother']from t_family where array_contains(map_keys(family_members),'brother');-- 方案2：子查询select id,name,age,family_members['brother']from(select id,name,age,map_keys(family_members) as relations,family_members from t_family) tmp where array_contains(relations,'brother');5.5.3 stuct类型
数据
1,zhangsan,18:male:深圳2,lisi,28:female:北京3,wangwu,38:male:广州4,laowang,26:female:上海5,yangyang,35:male:杭州导入数据：
-- 建表映射上述数据drop table if exists t_user;create table t_user(id int,name string,info struct<age:int,sex:string,addr:string>)row format delimited fields terminated by ','collection items terminated by ':';-- 导入数据load data local inpath '/home/hadoop/user.dat' into table t_user;0: jdbc:hive2://Master:10000> select * from t_user;+------------+--------------+----------------------------------------+--+| t_user.id | t_user.name | t_user.info |+------------+--------------+----------------------------------------+--+| 1 | zhangsan | {"age":18,"sex":"male","addr":"深圳"} || 2 | lisi | {"age":28,"sex":"female","addr":"北京"} || 3 | wangwu | {"age":38,"sex":"male","addr":"广州"} || 4 | laowang | {"age":26,"sex":"female","addr":"上海"} || 5 | yangyang | {"age":35,"sex":"male","addr":"杭州"} |+------------+--------------+----------------------------------------+--+查询每个人的id name和地址
select id,name,info.addrfrom t_user;0: jdbc:hive2://Master:10000> select id,name,info.addr0: jdbc:hive2://Master:10000> from t_user;+-----+-----------+-------+--+| id | name | addr |+-----+-----------+-------+--+| 1 | zhangsan | 深圳 || 2 | lisi | 北京 || 3 | wangwu | 广州 || 4 | laowang | 上海 || 5 | yangyang | 杭州 |+-----+-----------+-------+--+6 常用内置函数
测试函数
select substr("abcdef",1,3);0: jdbc:hive2://Master:10000> select substr("abcdef",1,3);+------+--+| _c0 |+------+--+| abc |+------+--+6.1 时间处理函数
from_unixtime(21938792183,'yyyy-MM-dd HH:mm:ss') 返回： '2017-06-03 17:50:30'
6.2 类型转换函数
select cast("8" as int);select cast("2019-2-3" as data)6.3 字符串截取和拼接
substr("abcde",1,3) --> 'abc'concat('abc','def') --> 'abcdef'0: jdbc:hive2://Master:10000> select substr("abcde",1,3);+------+--+| _c0 |+------+--+| abc |+------+--+1 row selected (0.152 seconds)0: jdbc:hive2://Master:10000> select concat('abc','def');+---------+--+| _c0 |+---------+--+| abcdef |+---------+--+1 row selected (0.165 seconds)6.4 Json数据解析函数
get_json_object('{\"key1\":3333，\"key2\":4444}' , '$.key1') 返回：3333
json_tuple('{\"key1\":3333，\"key2\":4444}','key1','key2') as(key1,key2)返回：3333, 4444
6.5 url解析函数
parse_url_tuple('http://www.xxxx.cn/bigdata?userid=8888','HOST','PATH','QUERY','QUERY:userid')返回： www.xxxx.cn /bigdata userid=8888 8888
7 自定义函数
7.1 问题
测试数据如下：
1,zhangsan:18-1999063117:30:00-beijing2,lisi:28-1989063117:30:00-shanghai3,wangwu:20-1997063117:30:00-tieling建表导入数据：
create table t_user_info(info string)row format delimited;导入数据：
load data local inpath '/root/udftest.data' into table t_user_info;需求：利用上表生成如下新表
t_user：uid,uname,age,birthday,address思路：可以自定义一个函数parse_user_info()，能传入一行上述数据，返回切分好的字段
然后可以通过如下sql完成需求：
create t_userasselect parse_user_info(info,0) as uid,parse_user_info(info,1) as uname,parse_user_info(info,2) as age,parse_user_info(info,3) as birthday_date,parse_user_info(info,4) as birthday_time,parse_user_info(info,5) as addressfrom t_user_info;实现关键： 自定义parse_user_info() 函数
7.2 实现步骤
1、写一个java类实现函数所需要的功能
public class UserInfoParser extends UDF{ // 1,zhangsan:18-1999063117:30:00-beijing public String evaluate(String line,int index) { String newLine = line.replaceAll(",", "\001").replaceAll(":", "\001").replaceAll("-", "\001"); StringBuilder sb = new StringBuilder(); String[] split = newLine.split("\001"); StringBuilder append = sb.append(split[0]) .append("\t") .append(split[1]) .append("\t") .append(split[2]) .append("\t") .append(split[3].substring(0, 8)) .append("\t") .append(split[3].substring(8, 10)).append(split[4]).append(split[5]) .append("\t") .append(split[6]); String res = append.toString(); return res.split("\t")[index]; }}2、将java类打成jar包: d:/up.jar
3、上传jar包到hive所在的机器上 /root/up.jar
4、在hive的提示符中添加jar包
hive> add jar /root/up.jar;5、创建一个hive的自定义函数名 跟 写好的jar包中的java类对应
hive> create temporary function parse_user_info as 'com.doit.hive.udf.UserInfoParser';