1、创建表
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]
创建测试使用的数据库myhive3,使用该数据库。
1）、创建普通表
0: jdbc:hive2://localhost:10000> create database myhive3; No rows affected (0.204 seconds) 0: jdbc:hive2://localhost:10000> use myhive3; No rows affected (0.13 seconds) 0: jdbc:hive2://localhost:10000> create table t1(id int,name string) 0: jdbc:hive2://localhost:10000> row format delimited fields terminated by ',';//指定，分割，具体的参考前面说的那篇 No rows affected (0.117 seconds) 0: jdbc:hive2://localhost:10000> show tables ; +-----------+--+ | tab_name | +-----------+--+ | t1 | +-----------+--+ 0: jdbc:hive2://localhost:10000> desc t1; +-----------+------------+----------+--+ | col_name | data_type | comment | +-----------+------------+----------+--+ | id | int | | | name | string | | +-----------+------------+----------+--+
2）、创建外部表
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），Hive 创建内部表时，会将数据移动到数据仓库指向的路径；若创建外部表，仅记录数据所在的路径，不对数据的位置做任何改变。在删除表的时候，内部表的元数据和数据会被一起删除，而外部表只删除元数据，不删除数据。
STORED AS
SEQUENCEFILE|TEXTFILE|RCFILE
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 SEQUENCEFILE。
location当然是指定表（hdfs上）位置
0: jdbc:hive2://localhost:10000> create external table t2(id int,name string) 0: jdbc:hive2://localhost:10000> row format delimited fields terminated by ',' 0: jdbc:hive2://localhost:10000> stored as textfile 0: jdbc:hive2://localhost:10000> location '/mytable2'; No rows affected (0.133 seconds)
页面查看是否创建了该表
image.png
直接创建在根目录下的，区别于普通表创建在/user/hive/warehouse目录下。
3）、创建分区
创建分区，分区字段fields string，查看表信息的时候会显示该表下所有分区信息的。
0: jdbc:hive2://localhost:10000> create table t3(id int,name string) 0: jdbc:hive2://localhost:10000> partitioned by(fields string) 0: jdbc:hive2://localhost:10000> row format delimited fields terminated by ','; No rows affected (0.164 seconds) 0: jdbc:hive2://localhost:10000> load data local inpath '/root/sz.data' into table t3 partition (fields ='Chengdu'); INFO : Loading data to table myhive3.t3 partition (fields=Chengdu) from file:/root/sz.data INFO : Partition myhive3.t3{fields=Chengdu} stats: [numFiles=1, numRows=0, totalSize=91, rawDataSize=0] No rows affected (0.738 seconds) 0: jdbc:hive2://localhost:10000> load data local inpath '/root/sz.data' into table t3 partition (fields ='Wuhan'); INFO : Loading data to table myhive3.t3 partition (fields=Wuhan) from file:/root/sz.data INFO : Partition myhive3.t3{fields=Wuhan} stats: [numFiles=1, numRows=0, totalSize=91, rawDataSize=0] No rows affected (0.608 seconds) 0: jdbc:hive2://localhost:10000> select * from t3; +--------+-----------+------------+--+ | t3.id | t3.name | t3.fields | +--------+-----------+------------+--+ | 1 | zhangsan | Chengdu | | 2 | lisi | Chengdu | | 3 | wangwu | Chengdu | | 4 | furong | Chengdu | | 5 | fengjie | Chengdu | | 6 | aaa | Chengdu | | 7 | bbb | Chengdu | | 8 | ccc | Chengdu | | 9 | ddd | Chengdu | | 10 | eee | Chengdu | | 11 | fff | Chengdu | | 12 | ggg | Chengdu | | 1 | zhangsan | Wuhan | | 2 | lisi | Wuhan | | 3 | wangwu | Wuhan | | 4 | furong | Wuhan | | 5 | fengjie | Wuhan | | 6 | aaa | Wuhan | | 7 | bbb | Wuhan | | 8 | ccc | Wuhan | | 9 | ddd | Wuhan | | 10 | eee | Wuhan | | 11 | fff | Wuhan | | 12 | ggg | Wuhan | +--------+-----------+------------+--+
页面查看
image.png
这两个分区目录下都存放了文件sz.data。
2、修改表
1）、增加、删除表分区
语法
增加 ALTER TABLE table_name ADD [IF NOT EXISTS] partition_spec [ LOCATION 'location1' ] partition_spec [ LOCATION 'location2' ] ... 删除 ALTER TABLE table_name DROP partition_spec, partition_spec,...
还是对上面的分区表t3
增加分区fields=’Hefei’位置还是跟其他分区一致（可以省略不写）
由于hive客户端命令行可以使用hadoop命令查看文件系统（dfs），后面就不去页面查看了
0: jdbc:hive2://localhost:10000> alter table t3 add partition (fields='Hefei'); No rows affected (0.198 seconds) 0: jdbc:hive2://localhost:10000> dfs -ls /user/hive/warehouse/myhive3.db/t3; +---------------------------------------------------------------------------------------------------------------+--+ | DFS Output | +---------------------------------------------------------------------------------------------------------------+--+ | Found 3 items | | drwxr-xr-x - root supergroup 0 2017-10-19 05:17 /user/hive/warehouse/myhive3.db/t3/fields=Chengdu | | drwxr-xr-x - root supergroup 0 2017-10-19 05:28 /user/hive/warehouse/myhive3.db/t3/fields=Hefei | | drwxr-xr-x - root supergroup 0 2017-10-19 05:18 /user/hive/warehouse/myhive3.db/t3/fields=Wuhan | +---------------------------------------------------------------------------------------------------------------+--+ 0: jdbc:hive2://localhost:10000> alter table t3 drop partition (fields='Hefei'); INFO : Dropped the partition fields=Hefei No rows affected (0.536 seconds) 0: jdbc:hive2://localhost:10000> dfs -ls /user/hive/warehouse/myhive3.db/t3; +---------------------------------------------------------------------------------------------------------------+--+ | DFS Output | +---------------------------------------------------------------------------------------------------------------+--+ | Found 2 items | | drwxr-xr-x - root supergroup 0 2017-10-19 05:17 /user/hive/warehouse/myhive3.db/t3/fields=Chengdu | | drwxr-xr-x - root supergroup 0 2017-10-19 05:18 /user/hive/warehouse/myhive3.db/t3/fields=Wuhan | +---------------------------------------------------------------------------------------------------------------+--+
2）、重命名表
语法
alter table old_name rename to new_name
将t1改名为t4
0: jdbc:hive2://localhost:10000> alter table t1 rename to t4; No rows affected (0.183 seconds) 0: jdbc:hive2://localhost:10000> show tables; +-----------+--+ | tab_name | +-----------+--+ | t2 | | t3 | | t4 | +-----------+--+ 3 rows selected (0.127 seconds)
3）、添加、更新列
语法
alter table table_name add|replace columns(col_name data_type ...)
注：ADD是代表新增一字段，字段位置在所有列后面，REPLACE则是表示替换表中所有字段。
0: jdbc:hive2://localhost:10000> desc t4; +-----------+------------+----------+--+ | col_name | data_type | comment | +-----------+------------+----------+--+ | id | int | | | name | string | | +-----------+------------+----------+--+ 2 rows selected (0.315 seconds) 0: jdbc:hive2://localhost:10000> alter table t4 add columns (age int); No rows affected (0.271 seconds) 0: jdbc:hive2://localhost:10000> desc t4; +-----------+------------+----------+--+ | col_name | data_type | comment | +-----------+------------+----------+--+ | id | int | | | name | string | | | age | int | | +-----------+------------+----------+--+ 3 rows selected (0.199 seconds) 0: jdbc:hive2://localhost:10000> alter table t4 replace columns (no string,name string,scores int); No rows affected (0.406 seconds) 0: jdbc:hive2://localhost:10000> desc t4; +-----------+------------+----------+--+ | col_name | data_type | comment | +-----------+------------+----------+--+ | no | string | | | name | string | | | scores | int | | +-----------+------------+----------+--+
常用显示命令
show tables show databases show partitions show functions desc formatted table_name;//跟desc table_name一样，但是显示的内容更多
3、数据操作
1）、load导入数据
上面已经演示了将本地的文件sz.data导入到t3表中。
load也就是说将文件复制到指定的表（目录）下，指定了local的话那么会去查找本地文件系统中的文件路径。如果没指定会根据inpath指定的路径去查找。如果是hdfs的话，如下格式
hdfs://namenode:9000/user/hive/project/data1。
另外如果使用了 OVERWRITE 关键字，则目标表（或者分区）中的内容会被删除，然后再将 filepath 指向的文件/目录中的内容添加到表/分区中。
如果目标表（分区）已经有一个文件，并且文件名和 filepath 中的文件名冲突，那么现有的文件会被新文件所替代。
0: jdbc:hive2://localhost:10000> load data local inpath '/root/sz.data' overwrite into table t4 ; INFO : Loading data to table myhive3.t4 from file:/root/sz.data INFO : Table myhive3.t4 stats: [numFiles=1, numRows=0, totalSize=91, rawDataSize=0] No rows affected (0.7 seconds) 0: jdbc:hive2://localhost:10000> select * from t4; +--------+-----------+------------+--+ | t4.no | t4.name | t4.scores | +--------+-----------+------------+--+ | 1 | zhangsan | NULL | | 2 | lisi | NULL | | 3 | wangwu | NULL | | 4 | furong | NULL | | 5 | fengjie | NULL | | 6 | aaa | NULL | | 7 | bbb | NULL | | 8 | ccc | NULL | | 9 | ddd | NULL | | 10 | eee | NULL | | 11 | fff | NULL | | 12 | ggg | NULL | +--------+-----------+------------+--+
2）、插入语句
向表中插入语句的话
普通插入，查询其他表的表信息插入（自动数量要一致），将查询结果保存到一个目录中（目录会自动创建，由OutputFormat实现）。
insert into table t4 values('13','zhangsan',99);
0: jdbc:hive2://localhost:10000> truncate table t4;//清空表信息 0: jdbc:hive2://localhost:10000> insert into t4 0: jdbc:hive2://localhost:10000> select id,name from t3; 0: jdbc:hive2://localhost:10000> select * from t4; +--------+-----------+--+ | t4.no | t4.name | +--------+-----------+--+ | 1 | zhangsan | | 2 | lisi | | 3 | wangwu | | 4 | furong | | 5 | fengjie | | 6 | aaa | | 7 | bbb | | 8 | ccc | | 9 | ddd | | 10 | eee | | 11 | fff | | 12 | ggg | | 1 | zhangsan | | 2 | lisi | | 3 | wangwu | | 4 | furong | | 5 | fengjie | | 6 | aaa | | 7 | bbb | | 8 | ccc | | 9 | ddd | | 10 | eee | | 11 | fff | | 12 | ggg | +--------+-----------+--+
重新创建表t5，将表信息保存到本地目录/root/insertDir/test中
0: jdbc:hive2://localhost:10000> insert overwrite local directory '/root/insertDir/test' 0: jdbc:hive2://localhost:10000> select * from t5; 查看本地 [root@mini1 ~]# cd insertDir/test/ [root@mini1 test]# ll 总用量 4 -rw-r--r--. 1 root root 91 10月 19 06:15 000000_0 [root@mini1 test]# cat 000000_0 1zhangsan 2lisi 3wangwu 4furong 5fengjie 6aaa 7bbb 8ccc 9ddd 10eee 11fff 12ggg
4、数据查询SELECT
语法基本跟mysql一样，留意下分桶即可
SELECT [ALL | DISTINCT] select_expr, select_expr, ... FROM table_reference [WHERE where_condition] [GROUP BY col_list [HAVING condition]] [CLUSTER BY col_list | [DISTRIBUTE BY col_list] [SORT BY| ORDER BY col_list] ] [LIMIT number]
在前面做了很多测试，就不想再重复了，会mysql的查询这个肯定也会。
需要注意的是order by和sort by的区别:
1、order by 会对输入做全局排序，因此只有一个reducer，会导致当输入规模较大时，需要较长的计算时间。
2、sort by不是全局排序，其在数据进入reducer前完成排序。因此，如果用sort by进行排序，并且设置mapred.reduce.tasks>1，则sort by只保证每个reducer的输出有序，不保证全局有序。
主要介绍下join
5、Join查询
join查询其实跟mysql还是一样的
准备数据
a.txt中
1,a
2,b
3,c
4,d
7,y
8,u
b.txt中
2,bb
3,cc
7,yy
9,pp
创建表a和b，将a.txt导入到a表中，b.txt导入到b表中
1）、内连接
0: jdbc:hive2://localhost:10000> create table a(id int,name string) 0: jdbc:hive2://localhost:10000> row format delimited fields terminated by ','; No rows affected (0.19 seconds) 0: jdbc:hive2://localhost:10000> create table b(id int,name string) 0: jdbc:hive2://localhost:10000> row format delimited fields terminated by ','; No rows affected (0.071 seconds) 0: jdbc:hive2://localhost:10000> load data local inpath '/root/a.txt' into table a; 0: jdbc:hive2://localhost:10000> load data local inpath '/root/b.txt' into table b; 0: jdbc:hive2://localhost:10000> select * from a; +-------+---------+--+ | a.id | a.name | +-------+---------+--+ | 1 | a | | 2 | b | | 3 | c | | 4 | d | | 7 | y | | 8 | u | +-------+---------+--+ 6 rows selected (0.218 seconds) 0: jdbc:hive2://localhost:10000> select * from b; +-------+---------+--+ | b.id | b.name | +-------+---------+--+ | 2 | bb | | 3 | cc | | 7 | yy | | 9 | pp | +-------+---------+--+ 4 rows selected (0.221 seconds) 0: jdbc:hive2://localhost:10000> select * from a inner join b on a.id = b.id; ... +-------+---------+-------+---------+--+ | a.id | a.name | b.id | b.name | +-------+---------+-------+---------+--+ | 2 | b | 2 | bb | | 3 | c | 3 | cc | | 7 | y | 7 | yy | +-------+---------+-------+---------+--+
根据id进行连接，能连接到的则串起来。
2）、左外连接（outer可省）
0: jdbc:hive2://localhost:10000> select * from a left outer join b on a.id = b.id; ... +-------+---------+-------+---------+--+ | a.id | a.name | b.id | b.name | +-------+---------+-------+---------+--+ | 1 | a | NULL | NULL | | 2 | b | 2 | bb | | 3 | c | 3 | cc | | 4 | d | NULL | NULL | | 7 | y | 7 | yy | | 8 | u | NULL | NULL | +-------+---------+-------+---------+--+ 6 rows selected (16.453 seconds)
左边的表内容全列出来，右边的能连上的就显示，不能的则显示null。
右外连接则相反。
3）、全连接full outer
0: jdbc:hive2://localhost:10000> select * from a full outer join b on a.id = b.id; ... +-------+---------+-------+---------+--+ | a.id | a.name | b.id | b.name | +-------+---------+-------+---------+--+ | 1 | a | NULL | NULL | | 2 | b | 2 | bb | | 3 | c | 3 | cc | | 4 | d | NULL | NULL | | 7 | y | 7 | yy | | 8 | u | NULL | NULL | | NULL | NULL | 9 | pp | +-------+---------+-------+---------+--+
相当于左连接+右连接
4）、semi join
0: jdbc:hive2://localhost:10000> select * from a left semi join b on a.id = b.id; +-------+---------+--+ | a.id | a.name | +-------+---------+--+ | 2 | b | | 3 | c | | 7 | y | +-------+---------+--+ 3 rows selected (17.511 seconds)
相当于左外连接得到的信息的左半部分。
注：可以理解为exist in(…)，但是hive中没有该语法，所以使用LEFT SEMI JOIN代替IN/EXISTS的，前者为后者高效实现。
比如下面的例子
重写以下子查询为LEFT SEMI JOIN SELECT a.key, a.value FROM a WHERE a.key exist in (SELECT b.key FROM B); 可以被重写为： SELECT a.key, a.val FROM a LEFT SEMI JOIN b on (a.key = b.key)
