一、静态分区
1、select查询中会扫描整个表内容，会消耗大量时间。由于相当多的时候人们只关心表中的一部分数据，
故建表时引入了分区概念。
2、hive分区表:是指在创建表时指定的partition的分区空间，若需要创建有分区的表，
需要在create表的时候调用可选参数partitioned by，详见表创建的语法结构。
二、实现创建、删除分区表
注意：
1、一个表可以拥有一个或者多个分区，每个分区以文件夹的形式单独存在表文件夹的目录下。
2、hive的表和列名不区分大小写(故建表时，都是小写)
3、分区是以字段的形式在表结构中存在，通过"desc table_name"命令可以查看到字段存在，该字段仅是分区的标识。
4、建表的语法（建分区可参见PARTITIONED BY参数）：
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]
5、分区建表分为2种，一种是单分区，也就是说在表文件夹目录下只有一级文件夹目录。另外一种是多分区，表文件夹下出现多文件夹嵌套模式。
a、单分区建表语句：create table test_table (id int, content string) partitioned by (dt string);
单分区表，按天分区，在表结构中存在id，content，dt三列。
b、双分区建表语句：create table test_table_2 (id int, content string) partitioned by (dt string, hour string);
双分区表，按天和小时分区，在表结构中新增加了dt和hour两列。
6、增加分区表语法（表已创建，在此基础上添加分区）：
ALTER TABLE table_name ADD partition_spec [ LOCATION 'location1' ] partition_spec [ LOCATION 'location2' ] ... partition_spec: : PARTITION (partition_col = partition_col_value, partition_col = partiton_col_value, ...)
用户可以用 ALTER TABLE ADD PARTITION 来向一个表中增加分区。当分区名是字符串时加引号。例：
ALTER TABLE test_table ADD PARTITION (dt='2016-08-08', hour='10') location '/path/uv1.txt' PARTITION (dt='2017-08-08', hour='12') location '/path/uv2.txt';
7、删除分区语法：
ALTER TABLE table_name DROP partition_spec, partition_spec,...
用户可以用 ALTER TABLE DROP PARTITION 来删除分区。分区的元数据和数据将被一并删除。例：
ALTER TABLE test_table DROP PARTITION (dt='2016-08-08', hour='10');
8、数据加载进分区表中语法：
LOAD DATA [LOCAL] INPATH 'filepath' [OVERWRITE] INTO TABLE tablename [PARTITION (partcol1=val1, partcol2=val2 ...)]
例：
LOAD DATA INPATH '/user/uv.txt' INTO TABLE test_table_2 PARTITION(dt='2016-08-08', hour='08');
LOAD DATA local INPATH '/user/hh/' INTO TABLE test_table partition(dt='2013-02- 07');
当数据被加载至表中时，不会对数据进行任何转换。Load操作只是将数据复制至Hive表对应的位置。数据加载时在表下自动创建一个目录，文件存放在该分区下。
9、基于分区的查询的语句：
SELECT test_table.* FROM test_table WHERE test_table.dt>= '2008-08-08';
10、查看双分区语句：
hive> show partitions test_table_2;
OK
dt=2016-08-08/hour=10
dt=2016-08-09/hour=10
dt=2008-08-09/hour=10
举例：
CREATE TABLE `incr_test_2`(
`ord_id` string,
`ord_no` string,
`creat_date` string,
`creat_time` string,
`time_stamp` string)
COMMENT 'Imported by sqoop on 2016/08/08 14:53:43'
PARTITIONED BY (
`log_time` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES (
'field.delim'='\u0001',
'line.delim'='\n',
'serialization.format'='\u0001')
STORED AS INPUTFORMAT
'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
;
查看对应的建表信息：
hive (origin_test)> show create table incr_test_2;
OK
CREATE TABLE `incr_test_2`(
`ord_id` string,
`ord_no` string,
`creat_date` string,
`creat_time` string,
`time_stamp` string)
COMMENT 'Imported by sqoop on 2016/08/04 14:53:43'
PARTITIONED BY (
`log_time` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES (
'field.delim'='\u0001',
'line.delim'='\n',
'serialization.format'='\u0001')
STORED AS INPUTFORMAT
'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
'hdfs://nameservice/user/hive/warehouse/origin_test.db/incr_test_2'
TBLPROPERTIES (
'transient_lastDdlTime'='1470293625')查看分区表：
-- 查看单分区：
hive (origin_test)>show partitions incr_test_2;
OK
log_time=20160917182510
log_time=20160917192512
log_time=20160917202512
log_time=20160917212512
log_time=20160917222510
log_time=20160917232511
log_time=20160918002525
log_time=20160918012514
log_time=20160918022513
log_time=20160918032510
log_time=20160918042510
log_time=20160918052511
log_time=20160918062513
log_time=20160918072510
log_time=20160918082510
log_time=20160918092511
log_time=20160918102510
log_time=20160918112511
log_time=20160918122512
log_time=20160918132511
Time taken: 0.264 seconds, Fetched: 20 row(s)
hive (origin_ennenergy_transport)>
分区：分区的字段不能是表中已有的字段
create table person4 (
id int,
name string,
likes ARRAY<string>,
address MAP<string,string>
)
PARTITIONED BY (sex string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
COLLECTION ITEMS TERMINATED BY '-'
MAP KEYS TERMINATED BY ':';
load data local inpath '/tmp/data/person0_data' into table person4 PARTITION (sex='1');
load data local inpath '/tmp/data/person0_data' into table person4 PARTITION (sex='2');hive> select * from person4;
1 Jed ["book","lol"] {"beijing":"chaoyang"} 1
2 Tom ["book","lol","sports"] {"beijing":"chaoyang","shanghai":"pudong"} 1
3 Cat ["book","lol","sports","music"]
{"beijing":"chaoyang","shanghai":"pudong","shanxi":"taiyuan"} 1
1 Jed ["book","lol"] {"beijing":"chaoyang"} 2
2 Tom ["book","lol","sports"] {"beijing":"chaoyang","shanghai":"pudong"} 2
3 Cat ["book","lol","sports","music"]
{"beijing":"chaoyang","shanghai":"pudong","shanxi":"taiyuan"} 2
hive> select * from person4 where sex="1";
1 Jed ["book","lol"] {"beijing":"chaoyang"} 1
2 Tom ["book","lol","sports"] {"beijing":"chaoyang","shanghai":"pudong"} 1
3 Cat ["book","lol","sports","music"]
{"beijing":"chaoyang","shanghai":"pudong","shanxi":"taiyuan"} 1
create table person6 (
id int,
name string,
likes ARRAY<string>,
address MAP<string,string>
)
PARTITIONED BY (sex string, age int)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
COLLECTION ITEMS TERMINATED BY '-'
MAP KEYS TERMINATED BY ':';
hive> load data local inpath '/tmp/data/person6_data' into table person6 PARTITION
(sex='1',age=18);
hive> load data local inpath '/tmp/data/person6_data' into table person6 PARTITION
(sex='2',age=18);
hive> load data local inpath '/tmp/data/person6_data' into table person6 PARTITION
(sex='3',age=20);
添加/删除分区，内部表删除分区后该分区下的数据也会被删除，外部表删除分区后该分
区下的数据不会被删除
ALTER TABLE person4 add PARTITION (sex='3');
ALTER TABLE person6 drop PARTITION (sex='1',age=18);
ALTER TABLE person6 drop PARTITION (sex='2');#会删除 sex='2'和 age=18
ALTER TABLE person4 drop PARTITION (age=20); #会删除 sex='3'和 age=20二、动态分区：
需求：按照不同部门作为分区导数据到目标表
以上需求如果用静态分区的话，数据量大你是不是很懵逼？？所以这个需求一般采用动态分区来实现。
1、创建目标表
hive (default)> create table emp_dynamic_partition(
> empno int,
> ename string,
> job string,
> mgr int,
> hiredate string,
> sal double,
> comm double)
> PARTITIONED BY(deptno int)
> row format delimited fields terminated by '\t';2、采用动态方式加载数据到目标表
加载之前先设置一下下面的参数
hive (default)> set hive.exec.dynamic.partition.mode=nonstrict开始加载数据：
insert into table emp_dynamic_partition partition(deptno)
select empno , ename , job , mgr , hiredate , sal , comm, deptno from emp;上面加载数据方式并没有指定具体的分区，只是指出了分区字段。在select最后一个字段必须跟你的分区字段，这样就会自行根据deptno的value来分区。
静态分区和动态分区的区别就是
动态分区不用指定目录，有系统自己分配，加载数据的时候会比静态分区更加方便。