第四课 Hive中sql的使用

作者: Arroganter | 来源:发表于2018-09-26 16:36 被阅读3次

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)

第四课 Hive中sql的使用

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