Apache Hive （七）Hive的DDL操作
转自：https://www.cnblogs.com/qingyunzong/p/8723271.html
库操作
1、创建库
语法结构
CREATE (DATABASE|SCHEMA) [IF NOT EXISTS] database_name
[COMMENT database_comment] //关于数据块的描述
[LOCATION hdfs_path] //指定数据库在HDFS上的存储位置
[WITH DBPROPERTIES (property_name=property_value, ...)]; //指定数据块属性
默认地址：/user/hive/warehouse/db_name.db/table_name/partition_name/…
创建库的方式
（1）创建普通的数据库
0: jdbc:hive2://hadoop3:10000> create database t1; No rows affected (0.308 seconds) 0: jdbc:hive2://hadoop3:10000> show databases; +----------------+ | database_name | +----------------+ | default | | myhive | | t1 | +----------------+ 3 rows selected (0.393 seconds) 0: jdbc:hive2://hadoop3:10000>
（2）创建库的时候检查存与否
0: jdbc:hive2://hadoop3:10000> create database if not exists t1; No rows affected (0.176 seconds) 0: jdbc:hive2://hadoop3:10000>
（3）创建库的时候带注释
0: jdbc:hive2://hadoop3:10000> create database if not exists t2 comment 'learning hive';
No rows affected (0.217 seconds)
0: jdbc:hive2://hadoop3:10000>
（4）创建带属性的库
0: jdbc:hive2://hadoop3:10000> create database if not exists t3 with dbproperties('creator'='hadoop','date'='2018-04-05');
No rows affected (0.255 seconds)
0: jdbc:hive2://hadoop3:10000>
2、查看库
查看库的方式
（1）查看有哪些数据库
0: jdbc:hive2://hadoop3:10000> show databases;
+----------------+
| database_name |
+----------------+
| default |
| myhive |
| t1 |
| t2 |
| t3 |
+----------------+
5 rows selected (0.164 seconds)
0: jdbc:hive2://hadoop3:10000>
（2）显示数据库的详细属性信息
语法
desc database [extended] dbname;
示例
0: jdbc:hive2://hadoop3:10000> desc database extended t3;
+----------+----------+------------------------------------------+-------------+-------------+------------------------------------+
| db_name | comment | location | owner_name | owner_type | parameters |
+----------+----------+------------------------------------------+-------------+-------------+------------------------------------+
| t3 | | hdfs://myha01/user/hive/warehouse/t3.db | hadoop | USER | {date=2018-04-05, creator=hadoop} |
+----------+----------+------------------------------------------+-------------+-------------+------------------------------------+
1 row selected (0.11 seconds)
0: jdbc:hive2://hadoop3:10000>
（3）查看正在使用哪个库
0: jdbc:hive2://hadoop3:10000> select current_database(); +----------+ | _c0 | +----------+ | default | +----------+ 1 row selected (1.36 seconds) 0: jdbc:hive2://hadoop3:10000>
（4）查看创建库的详细语句
0: jdbc:hive2://hadoop3:10000> show create database t3; +----------------------------------------------+ | createdb_stmt | +----------------------------------------------+ | CREATE DATABASE `t3` | | LOCATION | | 'hdfs://myha01/user/hive/warehouse/t3.db' | | WITH DBPROPERTIES ( | | 'creator'='hadoop', | | 'date'='2018-04-05') | +----------------------------------------------+ 6 rows selected (0.155 seconds) 0: jdbc:hive2://hadoop3:10000>
3、删除库
说明
删除库操作
drop database dbname; drop database if exists dbname;
默认情况下，hive 不允许删除包含表的数据库，有两种解决办法：
1、 手动删除库下所有表，然后删除库
2、 使用 cascade 关键字
drop database if exists dbname cascade;
默认情况下就是 restrict drop database if exists myhive ==== drop database if exists myhive restrict
示例
（1）删除不含表的数据库
0: jdbc:hive2://hadoop3:10000> show tables in t1; +-----------+ | tab_name | +-----------+ +-----------+ No rows selected (0.147 seconds) 0: jdbc:hive2://hadoop3:10000> drop database t1; No rows affected (0.178 seconds) 0: jdbc:hive2://hadoop3:10000> show databases; +----------------+ | database_name | +----------------+ | default | | myhive | | t2 | | t3 | +----------------+ 4 rows selected (0.124 seconds) 0: jdbc:hive2://hadoop3:10000>
（2）删除含有表的数据库
0: jdbc:hive2://hadoop3:10000> drop database if exists t3 cascade; No rows affected (1.56 seconds) 0: jdbc:hive2://hadoop3:10000>
4、切换库
语法
use database_name
示例
0: jdbc:hive2://hadoop3:10000> use t2; No rows affected (0.109 seconds) 0: jdbc:hive2://hadoop3:10000>
表操作
1、创建表
语法
CREATE [EXTERNAL] TABLE [IF NOT EXISTS] table_name
[(col_name data_type [COMMENT col_comment], ...)]
[COMMENT table_comment]
[PARTITIONED BY (col_name data_type [COMMENT col_comment], ...)]
[CLUSTERED BY (col_name, col_name, ...)
[SORTED BY (col_name [ASC|DESC], ...)] INTO num_buckets BUCKETS]
[ROW FORMAT row_format]
[STORED AS file_format]
[LOCATION hdfs_path]
详情请参见： https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualD DL-CreateTable
•CREATE TABLE 创建一个指定名字的表。如果相同名字的表已经存在，则抛出异常；用户可以用 IF NOT EXIST 选项来忽略这个异常 •EXTERNAL 关键字可以让用户创建一个外部表，在建表的同时指定一个指向实际数据的路径（LOCATION） •LIKE 允许用户复制现有的表结构，但是不复制数据 •COMMENT可以为表与字段增加描述
•PARTITIONED BY 指定分区
•ROW FORMAT
DELIMITED [FIELDS TERMINATED BY char] [COLLECTION ITEMS TERMINATED BY char]
MAP KEYS TERMINATED BY char] [LINES TERMINATED BY char]
| SERDE serde_name [WITH SERDEPROPERTIES
(property_name=property_value, property_name=property_value, ...)]
用户在建表的时候可以自定义 SerDe 或者使用自带的 SerDe。如果没有指定 ROW FORMAT 或者 ROW FORMAT DELIMITED，将会使用自带的 SerDe。在建表的时候，
用户还需要为表指定列，用户在指定表的列的同时也会指定自定义的 SerDe，Hive 通过 SerDe 确定表的具体的列的数据。
•STORED AS
SEQUENCEFILE //序列化文件
| TEXTFILE //普通的文本文件格式
| RCFILE //行列存储相结合的文件
| INPUTFORMAT input_format_classname OUTPUTFORMAT output_format_classname //自定义文件格式
如果文件数据是纯文本，可以使用 STORED AS TEXTFILE。如果数据需要压缩，使用 STORED AS SEQUENCE 。
•LOCATION指定表在HDFS的存储路径
最佳实践：
如果一份数据已经存储在HDFS上，并且要被多个用户或者客户端使用，最好创建外部表
反之，最好创建内部表。
如果不指定，就按照默认的规则存储在默认的仓库路径中。
示例
使用t2数据库进行操作
（1）创建默认的内部表
0: jdbc:hive2://hadoop3:10000> create table student(id int, name string, sex string, age int,department string) row format delimited fields terminated by ","; No rows affected (0.222 seconds) 0: jdbc:hive2://hadoop3:10000> desc student; +-------------+------------+----------+ | col_name | data_type | comment | +-------------+------------+----------+ | id | int | | | name | string | | | sex | string | | | age | int | | | department | string | | +-------------+------------+----------+ 5 rows selected (0.168 seconds) 0: jdbc:hive2://hadoop3:10000>
（2）外部表
0: jdbc:hive2://hadoop3:10000> create external table student_ext
(id int, name string, sex string, age int,department string) row format delimited fields terminated by "," location "/hive/student"; No rows affected (0.248 seconds) 0: jdbc:hive2://hadoop3:10000>
（3）分区表
0: jdbc:hive2://hadoop3:10000> create external table student_ptn(id int, name string, sex string, age int,department string) . . . . . . . . . . . . . . .> partitioned by (city string) . . . . . . . . . . . . . . .> row format delimited fields terminated by "," . . . . . . . . . . . . . . .> location "/hive/student_ptn"; No rows affected (0.24 seconds) 0: jdbc:hive2://hadoop3:10000>
添加分区
0: jdbc:hive2://hadoop3:10000> alter table student_ptn add partition(city="beijing"); No rows affected (0.269 seconds) 0: jdbc:hive2://hadoop3:10000> alter table student_ptn add partition(city="shenzhen"); No rows affected (0.236 seconds) 0: jdbc:hive2://hadoop3:10000>
如果某张表是分区表。那么每个分区的定义，其实就表现为了这张表的数据存储目录下的一个子目录
如果是分区表。那么数据文件一定要存储在某个分区中，而不能直接存储在表中。
（4）分桶表
0: jdbc:hive2://hadoop3:10000> create external table student_bck(id int, name string, sex string, age int,department string) . . . . . . . . . . . . . . .> clustered by (id) sorted by (id asc, name desc) into 4 buckets . . . . . . . . . . . . . . .> row format delimited fields terminated by "," . . . . . . . . . . . . . . .> location "/hive/student_bck"; No rows affected (0.216 seconds) 0: jdbc:hive2://hadoop3:10000>
（5）使用CTAS创建表
作用： 就是从一个查询SQL的结果来创建一个表进行存储
现象student表中导入数据
0: jdbc:hive2://hadoop3:10000> load data local inpath "/home/hadoop/student.txt" into table student; No rows affected (0.715 seconds) 0: jdbc:hive2://hadoop3:10000> select * from student; +-------------+---------------+--------------+--------------+---------------------+ | student.id | student.name | student.sex | student.age | student.department | +-------------+---------------+--------------+--------------+---------------------+ | 95002 | 刘晨 | 女 | 19 | IS | | 95017 | 王风娟 | 女 | 18 | IS | | 95018 | 王一 | 女 | 19 | IS | | 95013 | 冯伟 | 男 | 21 | CS | | 95014 | 王小丽 | 女 | 19 | CS | | 95019 | 邢小丽 | 女 | 19 | IS | | 95020 | 赵钱 | 男 | 21 | IS | | 95003 | 王敏 | 女 | 22 | MA | | 95004 | 张立 | 男 | 19 | IS | | 95012 | 孙花 | 女 | 20 | CS | | 95010 | 孔小涛 | 男 | 19 | CS | | 95005 | 刘刚 | 男 | 18 | MA | | 95006 | 孙庆 | 男 | 23 | CS | | 95007 | 易思玲 | 女 | 19 | MA | | 95008 | 李娜 | 女 | 18 | CS | | 95021 | 周二 | 男 | 17 | MA | | 95022 | 郑明 | 男 | 20 | MA | | 95001 | 李勇 | 男 | 20 | CS | | 95011 | 包小柏 | 男 | 18 | MA | | 95009 | 梦圆圆 | 女 | 18 | MA | | 95015 | 王君 | 男 | 18 | MA | +-------------+---------------+--------------+--------------+---------------------+ 21 rows selected (0.342 seconds) 0: jdbc:hive2://hadoop3:10000>
使用CTAS创建表
0: jdbc:hive2://hadoop3:10000> create table student_ctas as select * from student where id < 95012; 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. No rows affected (34.514 seconds) 0: jdbc:hive2://hadoop3:10000> select * from student_ctas . . . . . . . . . . . . . . .> ; +------------------+--------------------+-------------------+-------------------+--------------------------+ | student_ctas.id | student_ctas.name | student_ctas.sex | student_ctas.age | student_ctas.department | +------------------+--------------------+-------------------+-------------------+--------------------------+ | 95002 | 刘晨 | 女 | 19 | IS | | 95003 | 王敏 | 女 | 22 | MA | | 95004 | 张立 | 男 | 19 | IS | | 95010 | 孔小涛 | 男 | 19 | CS | | 95005 | 刘刚 | 男 | 18 | MA | | 95006 | 孙庆 | 男 | 23 | CS | | 95007 | 易思玲 | 女 | 19 | MA | | 95008 | 李娜 | 女 | 18 | CS | | 95001 | 李勇 | 男 | 20 | CS | | 95011 | 包小柏 | 男 | 18 | MA | | 95009 | 梦圆圆 | 女 | 18 | MA | +------------------+--------------------+-------------------+-------------------+--------------------------+ 11 rows selected (0.445 seconds) 0: jdbc:hive2://hadoop3:10000>
（6）复制表结构
0: jdbc:hive2://hadoop3:10000> create table student_copy like student; No rows affected (0.217 seconds) 0: jdbc:hive2://hadoop3:10000>
注意：
如果在table的前面没有加external关键字，那么复制出来的新表。无论如何都是内部表
如果在table的前面有加external关键字，那么复制出来的新表。无论如何都是外部表
2、查看表
（1）查看表列表
查看当前使用的数据库中有哪些表
0: jdbc:hive2://hadoop3:10000> show tables; +---------------+ | tab_name | +---------------+ | student | | student_bck | | student_copy | | student_ctas | | student_ext | | student_ptn | +---------------+ 6 rows selected (0.163 seconds) 0: jdbc:hive2://hadoop3:10000>
查看非当前使用的数据库中有哪些表
0: jdbc:hive2://hadoop3:10000> show tables in myhive; +-----------+ | tab_name | +-----------+ | student | +-----------+ 1 row selected (0.144 seconds) 0: jdbc:hive2://hadoop3:10000>
查看数据库中以xxx开头的表
0: jdbc:hive2://hadoop3:10000> show tables like 'student_c*'; +---------------+ | tab_name | +---------------+ | student_copy | | student_ctas | +---------------+ 2 rows selected (0.13 seconds) 0: jdbc:hive2://hadoop3:10000>
（2）查看表的详细信息
查看表的信息
0: jdbc:hive2://hadoop3:10000> desc student; +-------------+------------+----------+ | col_name | data_type | comment | +-------------+------------+----------+ | id | int | | | name | string | | | sex | string | | | age | int | | | department | string | | +-------------+------------+----------+ 5 rows selected (0.149 seconds) 0: jdbc:hive2://hadoop3:10000>
查看表的详细信息（格式不友好）
0: jdbc:hive2://hadoop3:10000> desc extended student;
查看表的详细信息（格式友好）
0: jdbc:hive2://hadoop3:10000> desc formatted student;
查看分区信息
0: jdbc:hive2://hadoop3:10000> show partitions student_ptn;
（3）查看表的详细建表语句
0: jdbc:hive2://hadoop3:10000> show create table student_ptn;
3、修改表
（1）修改表名
0: jdbc:hive2://hadoop3:10000> alter table student rename to new_student;
（2）修改字段定义
A. 增加一个字段
0: jdbc:hive2://hadoop3:10000> alter table new_student add columns (score int);
B. 修改一个字段的定义
0: jdbc:hive2://hadoop3:10000> alter table new_student change name new_name string;
C. 删除一个字段
不支持
D. 替换所有字段
0: jdbc:hive2://hadoop3:10000> alter table new_student replace columns (id int, name string, address string);
（3）修改分区信息
A. 添加分区
静态分区
添加一个
0: jdbc:hive2://hadoop3:10000> alter table student_ptn add partition(city="chongqing");
添加多个
0: jdbc:hive2://hadoop3:10000> alter table student_ptn add partition(city="chongqing2") partition(city="chongqing3") partition(city="chongqing4");
动态分区
先向student_ptn表中插入数据，数据格式如下图
0: jdbc:hive2://hadoop3:10000> load data local inpath "/home/hadoop/student.txt" into table student_ptn partition(city="beijing");
现在我把这张表的内容直接插入到另一张表student_ptn_age中，并实现sex为动态分区（不指定到底是哪中性别，让系统自己分配决定）
首先创建student_ptn_age并指定分区为age
0: jdbc:hive2://hadoop3:10000> create table student_ptn_age(id int,name string,sex string,department string) partitioned by (age int);
从student_ptn表中查询数据并插入student_ptn_age表中
0: jdbc:hive2://hadoop3:10000> insert overwrite table student_ptn_age partition(age) . . . . . . . . . . . . . . .> select id,name,sex,department，age from student_ptn; 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. No rows affected (27.905 seconds) 0: jdbc:hive2://hadoop3:10000>
B. 修改分区
修改分区，一般来说，都是指修改分区的数据存储目录
在添加分区的时候，直接指定当前分区的数据存储目录
0: jdbc:hive2://hadoop3:10000> alter table student_ptn add if not exists partition(city='beijing') . . . . . . . . . . . . . . .> location '/student_ptn_beijing' partition(city='cc') location '/student_cc'; No rows affected (0.306 seconds) 0: jdbc:hive2://hadoop3:10000>
修改已经指定好的分区的数据存储目录
0: jdbc:hive2://hadoop3:10000> alter table student_ptn partition (city='beijing') set location '/student_ptn_beijing';
此时原先的分区文件夹仍存在，但是在往分区添加数据时，只会添加到新的分区目录
C. 删除分区
0: jdbc:hive2://hadoop3:10000> alter table student_ptn drop partition (city='beijing');
4、删除表
0: jdbc:hive2://hadoop3:10000> drop table new_student;
5、清空表
0: jdbc:hive2://hadoop3:10000> truncate table student_ptn;
其他辅助命令
