Hive的查询语句整理

select查询

hive (default)> 
              > select * from emp;
OK
emp.empno       emp.ename       emp.job emp.mgr emp.hiredate    emp.sal emp.comm        emp.deptno
7839    KING    PRESIDENT       NULL    1981-11-17 00:00:00     5000.0  NULL    10
7782    CLARK   MANAGER 7839    1981-06-09 00:00:00     2450.0  NULL    10
7934    MILLER  CLERK   7782    1982-01-23 00:00:00     1300.0  NULL    10
7902    FORD    ANALYST 7566    1981-12-03 00:00:00     3000.0  NULL    20
7788    SCOTT   ANALYST 7566    1982-12-09 00:00:00     3000.0  NULL    20
7566    JONES   MANAGER 7839    1981-04-02 00:00:00     2975.0  NULL    20
7876    ADAMS   CLERK   7788    1983-01-12 00:00:00     1100.0  NULL    20
7369    SMITH   CLERK   7902    1980-12-17 00:00:00     800.0   NULL    20
7698    BLAKE   MANAGER 7839    1981-05-01 00:00:00     2850.0  NULL    30
7499    ALLEN   SALESMAN        7698    1981-02-20 00:00:00     1600.0  300.0   30
7844    TURNER  SALESMAN        7698    1981-09-08 00:00:00     1500.0  0.0     30
7654    MARTIN  SALESMAN        7698    1981-09-28 00:00:00     1250.0  1400.0  30
7521    WARD    SALESMAN        7698    1981-02-22 00:00:00     1250.0  500.0   30
7900    JAMES   CLERK   7698    981-12-03 00:00:00      950.0   NULL    30
Time taken: 0.229 seconds, Fetched: 14 row(s)

hive (default)> select  empno,ename  from emp ;
	
	OK
	empno   ename
	7839    KING
	7782    CLARK
	7934    MILLER
	7902    FORD
	7788    SCOTT
	7566    JONES
	7876    ADAMS
	7369    SMITH
	7698    BLAKE
	7499    ALLEN
	7844    TURNER
	7654    MARTIN
	7521    WARD
	7900    JAMES
	Time taken: 0.047 seconds, Fetched: 14 row(s)
	hive (default)> 
hive (default)> select * from emp  where sal between 1000 and 2000;
OK
emp.empno       emp.ename       emp.job emp.mgr emp.hiredate    emp.sal emp.comm        emp.deptno
7934    MILLER  CLERK   7782    1982-01-23 00:00:00     1300.0  NULL    10
7876    ADAMS   CLERK   7788    1983-01-12 00:00:00     1100.0  NULL    20
7499    ALLEN   SALESMAN        7698    1981-02-20 00:00:00     1600.0  300.0   30
7844    TURNER  SALESMAN        7698    1981-09-08 00:00:00     1500.0  0.0     30
7654    MARTIN  SALESMAN        7698    1981-09-28 00:00:00     1250.0  1400.0  30
7521    WARD    SALESMAN        7698    1981-02-22 00:00:00     1250.0  500.0   30
Time taken: 0.032 seconds, Fetched: 6 row(s)
hive (default)> select * from emp  where sal>=3000;
OK
emp.empno       emp.ename       emp.job emp.mgr emp.hiredate    emp.sal emp.comm        emp.deptno
7839    KING    PRESIDENT       NULL    1981-11-17 00:00:00     5000.0  NULL    10
7902    FORD    ANALYST 7566    1981-12-03 00:00:00     3000.0  NULL    20
7788    SCOTT   ANALYST 7566    1982-12-09 00:00:00     3000.0  NULL    20
Time taken: 0.074 seconds, Fetched: 3 row(s)
hive (default)> 
hive (default)> 
          > select * from emp  limit 2;
OK
emp.empno       emp.ename       emp.job emp.mgr emp.hiredate    emp.sal emp.comm        emp.deptno
7839    KING    PRESIDENT       NULL    1981-11-17 00:00:00     5000.0  NULL    10
7782    CLARK   MANAGER 7839    1981-06-09 00:00:00     2450.0  NULL    10
Time taken: 0.035 seconds, Fetched: 2 row(s)
hive (default)> select  * from emp where comm is null;
OK
emp.empno       emp.ename       emp.job emp.mgr emp.hiredate    emp.sal emp.comm        emp.deptno
7839    KING    PRESIDENT       NULL    1981-11-17 00:00:00     5000.0  NULL    10
7782    CLARK   MANAGER 7839    1981-06-09 00:00:00     2450.0  NULL    10
7934    MILLER  CLERK   7782    1982-01-23 00:00:00     1300.0  NULL    10
7902    FORD    ANALYST 7566    1981-12-03 00:00:00     3000.0  NULL    20
7788    SCOTT   ANALYST 7566    1982-12-09 00:00:00     3000.0  NULL    20
7566    JONES   MANAGER 7839    1981-04-02 00:00:00     2975.0  NULL    20
7876    ADAMS   CLERK   7788    1983-01-12 00:00:00     1100.0  NULL    20
7369    SMITH   CLERK   7902    1980-12-17 00:00:00     800.0   NULL    20
7698    BLAKE   MANAGER 7839    1981-05-01 00:00:00     2850.0  NULL    30
7900    JAMES   CLERK   7698    981-12-03 00:00:00      950.0   NULL    30
Time taken: 0.046 seconds, Fetched: 10 row(s)
hive (default)> 

聚合函数

max/min/count/sum/ave 特点:多进一出,进来很多条记录出去只有一条记录

  • 查询部门编号为10的有多少人数

     hive (default)> select  deptno,count(1) sum  from emp group by  deptno having deptno=10;
    
  • 求最大工资,最小工资,平均工资,工资的和

     hive (default)>  
           > select max(sal),min(sal),avg(sal),sum(sal) from emp;
    

分组函数 group by

使用group by函数需注意:1.group by中出现的字段,select中也一定要出现,不然会报错;
2.group by 一般和having比较函数一起使用同时也和聚合函数一起使用
3.group by后面可以跟多个字段,只不过需要用","隔开

  • 求部门的平均工资

     hive (default)> 
               > select deptno,avg(sal) avgsal from emp group by deptno;
    
  • 求每个部门(deptno)、工作岗位(job)的最高工资(salary),很简单还是注意group by后面的字段内容

     hive (default)> select deptno,job,max(sal) maxsal from emp group by deptno,job;
    
  • 每个部门的那些工作平均工资大于2000

     hive (default)> select deptno,job,avg(sal) avgsal from emp group by deptno,job having avg(sal)>2000 ;
    

case when then end 根据已有的字段取一个常用的标识

	hive (default)> 
              > 
              > select  ename,sal,  
              > case
              > when  sal>1  and sal<=1000 then  'lower'
              > when  sal>1000 and sal<=2000 then  'just so so'
              > when  sal>2000  and sal<=3000 then  'ok'
              > else  'high'
              > end
              > from  emp ;
OK
ename   sal     _c2
KING    5000.0  high
CLARK   2450.0  ok
MILLER  1300.0  just so so
FORD    3000.0  ok
SCOTT   3000.0  ok
JONES   2975.0  ok
ADAMS   1100.0  just so so
SMITH   800.0   lower
BLAKE   2850.0  ok
ALLEN   1600.0  just so so
TURNER  1500.0  just so so
MARTIN  1250.0  just so so
WARD    1250.0  just so so
JAMES   950.0   lower
Time taken: 0.048 seconds, Fetched: 14 row(s)
hive (default)> 

order by 、 sort by 、distribute by 和 cluster by的区别

  • order by 全局排序 ,在1个reduce中排序 ,一般不建议生产上使用
  • sort by 局部排序 ,每个reduce内是有序
  • distribute by 按照一定的规则把数据分散到某个reducer
  • cluster by 等同于 distribute by xxx sort by xxxx(前提是xxx必须和xxxx是相同的),这个几乎很少用到

默认执行reduce任务的就1个,如果需要多个reduce执行任务,可以设置mapred.reduce.tasks参数
set mapred.reduce.tasks=3 这种设置只是临时会话种有效,如果需要永久有效,则在应在hive_site.xml中设置。
用order by排序的时候如果hive.mapred.mode=strict后面不加limit会报错,但系统默认的是hive.mapred.mode=nonstrict不严格模式,所以加不加limit都不会报错。

		hive (default)> set mapred.reduce.tasks;
	mapred.reduce.tasks=3
	hive (default)> insert overwrite local directory '/home/hadoop/data/sort/' row format delimited fields terminated by ','  select * from emp  sort by empno;
	Query ID = hadoop_20190719141717_d0b35106-40a0-4d0a-a1f7-e17f4e544cdf
	Total jobs = 1
	Launching Job 1 out of 1
	Number of reduce tasks not specified. Defaulting to jobconf value of: 3
	In order to change the average load for a reducer (in bytes):
	  set hive.exec.reducers.bytes.per.reducer=<number>
	In order to limit the maximum number of reducers:
	  set hive.exec.reducers.max=<number>
	In order to set a constant number of reducers:
	  set mapreduce.job.reduces=<number>
	Starting Job = job_1562553101223_0020, Tracking URL = http://hadoop001:8078/proxy/application_1562553101223_0020/
	Kill Command = /home/hadoop/app/hadoop/bin/hadoop job  -kill job_1562553101223_0020
	Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 3
	2019-07-19 15:36:47,245 Stage-1 map = 0%,  reduce = 0%
	2019-07-19 15:36:51,327 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.77 sec
	2019-07-19 15:36:56,557 Stage-1 map = 100%,  reduce = 33%, Cumulative CPU 2.09 sec
	2019-07-19 15:36:58,644 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 5.0 sec
	MapReduce Total cumulative CPU time: 5 seconds 0 msec
	Ended Job = job_1562553101223_0020
	Copying data to local directory /home/hadoop/data/sort
	MapReduce Jobs Launched: 
	Stage-Stage-1: Map: 1  Reduce: 3   Cumulative CPU: 5.0 sec   HDFS Read: 15426 HDFS Write: 802 SUCCESS
	Total MapReduce CPU Time Spent: 5 seconds 0 msec
	OK
	emp.empno       emp.ename       emp.job emp.mgr emp.hiredate    emp.sal emp.comm        emp.deptno
	Time taken: 18.827 seconds
	查看/home/hadoop/data/sort,看是否是3个文件
	[hadoop@hadoop001 ~]$ cd /home/hadoop/data/sort
	[hadoop@hadoop001 sort]$ ll
	total 12
	-rw-r--r-- 1 hadoop hadoop 341 Jul 19 15:37 000000_0
	-rw-r--r-- 1 hadoop hadoop 345 Jul 19 15:37 000001_0
	-rw-r--r-- 1 hadoop hadoop 116 Jul 19 15:37 000002_0
	[hadoop@hadoop001 sort]$ 

hive的内置函数

hive的内置函数较多,这里没有必要记,只需要知道怎么查找内置函数就行
show functions;查看说有的内置函数
desc function length(函数名);查看length函数的说明,没有例子
desc function extended length(函数名);查看length函数的说明并且还有案例

hive (default)> 
          > create table dual(x string); 
           > insert into table values('');//创建一张空表dual,用来做下面测试

常用有关时间的内置函数

hive (default)> select  ename,lower(ename) from emp;
OK
ename   _c1
KING    king
CLARK   clark
MILLER  miller
FORD    ford
SCOTT   scott
JONES   jones
ADAMS   adams
SMITH   smith
BLAKE   blake
ALLEN   allen
TURNER  turner
MARTIN  martin
WARD    ward
JAMES   james
Time taken: 0.034 seconds, Fetched: 14 row(s)
hive (default)> 

date内置函数

	hive.mapred.mode=nonstrict
hive (default)> select current_date from dual;
OK
_c0
2019-07-19
Time taken: 0.034 seconds, Fetched: 1 row(s)
hive (default)> 
             
              > select current_timestamp  from dual;
OK
_c0
2019-07-19 16:37:01.49
Time taken: 0.036 seconds, Fetched: 1 row(s)
hive (default)> 
hive (default)> select unix_timestamp()  from dual;//当前时间的时间戳
OK
_c0
1563525550
Time taken: 0.035 seconds, Fetched: 1 row(s)

hive (default)> select unix_timestamp("2019-07-19 16:42:00")  from dual;
OK
_c0
1563525720
Time taken: 0.031 seconds, Fetched: 1 row(s)

hive (default)> select unix_timestamp("20190719 164200","yyyyMMdd HHmmss")  from dual;//如果时间格式不对,需要先修改格式
OK
_c0
1563525720
Time taken: 0.033 seconds, Fetched: 1 row(s)
hive (default)> 

hive (default)> 
              > 
              > select year("2019-07-19 16:42:00")  from dual;
		OK
		_c0
		2019
		Time taken: 0.032 seconds, Fetched: 1 row(s)
		hive (default)> select month("2019-07-19 16:42:00")  from dual;
		OK
		_c0
		7
		Time taken: 0.031 seconds, Fetched: 1 row(s)
		hive (default)> select day("2019-07-19 16:42:00")  from dual;
		OK
		_c0
		19
		Time taken: 0.03 seconds, Fetched: 1 row(s)
		hive (default)> 
		hive (default)> select date_add("2019-07-19 16:52:57.04",10)  from dual; //向上加10天
	OK
	_c0
	2019-07-29
	Time taken: 0.036 seconds, Fetched: 1 row(s)
	hive (default)> select date_sub("2019-07-19 16:52:57.04",10)  from dual;//减10天
OK
_c0
2019-07-09
Time taken: 0.032 seconds, Fetched: 1 row(s)
hive (default)> 

转换函数 cast

	hive (default)> select  cast("5"as int) from dual;
	OK
	_c0
	5
	Time taken: 0.029 seconds, Fetched: 1 row(s)
	hive (default)> select  cast("5a"as int) from dual; //若果前面的5a不满足后面的类型,将会为null
	OK
	_c0
	NULL
	Time taken: 0.027 seconds, Fetched: 1 row(s)
	hive (default)> select  cast("2018-8-8"as date) from dual;
	OK
	_c0
	2018-08-08
	Time taken: 0.028 seconds, Fetched: 1 row(s)
	hive (default)> select  cast(532 as string) from dual;
	OK
	_c0
	532
	Time taken: 0.029 seconds, Fetched: 1 row(s)
	hive (default)> 

内置数字函数

	Time taken: 0.029 seconds, Fetched: 1 row(s)
	hive (default)> 
	              > 
	              > 
	              > select  round(5.4) from dual; //round函数,四舍五入
	OK
	_c0
	5.0
	Time taken: 0.035 seconds, Fetched: 1 row(s)
	hive (default)> select  round(3.145654,2) from dual;
	OK
	_c0
	3.15
	Time taken: 0.027 seconds, Fetched: 1 row(s)
	hive (default)> 
	hive (default)> select  least(3,4,5) from dual; 取最小的数
	OK
	_c0
	3
	Time taken: 0.034 seconds, Fetched: 1 row(s)
	hive (default)> select  greatest(3,4,5) from dual;取最大的数
	OK
	_c0
	5
	Time taken: 0.025 seconds, Fetched: 1 row(s)
	hive (default)> 

字符串函数

	hive (default)> select concat("www",".","baicu.com") from dual;
OK
_c0
www.baicu.com
Time taken: 0.037 seconds, Fetched: 1 row(s)
hive (default)> select concat_ws(".","192","168","1","1") from dual;
OK
	_c0
	192.168.1.1
	Time taken: 0.027 seconds, Fetched: 1 row(s)
	hive (default)> 

Hive来完成wc统计

[hadoop@hadoop001 data]$ cat /home/hadoop/data/wc.txt 
hello   hello   hello
world   world
welcome
[hadoop@hadoop001 data]$ 
hive (default)> create table if not exists wc(sentence string);
OK
Time taken: 0.268 seconds
hive (default)> load data local inpath '/home/hadoop/data/wc.txt' overwrite into table wc;
Loading data to table default.wc
Table default.wc stats: [numFiles=1, numRows=0, totalSize=44, rawDataSize=0]
OK
Time taken: 0.198 seconds

hive (default)> select word,count(1) as cnt from  
          > (select  explode(split(sentence,'\t')) as word from  wc) t
          > group by word order by cnt desc;
Query ID = hadoop_20190719160606_75417f04-c6d4-45c9-9309-3d0cadf21313
Total jobs = 2
Launching Job 1 out of 2
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1562553101223_0022, Tracking URL = http://hadoop001:8078/proxy/application_1562553101223_0022/
Kill Command = /home/hadoop/app/hadoop/bin/hadoop job  -kill job_1562553101223_0022
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2019-07-19 17:43:54,749 Stage-1 map = 0%,  reduce = 0%
2019-07-19 17:43:58,883 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.51 sec
2019-07-19 17:44:03,008 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 2.74 sec
MapReduce Total cumulative CPU time: 2 seconds 740 msec
Ended Job = job_1562553101223_0022
Launching Job 2 out of 2
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1562553101223_0023, Tracking URL = http://hadoop001:8078/proxy/application_1562553101223_0023/
Kill Command = /home/hadoop/app/hadoop/bin/hadoop job  -kill job_1562553101223_0023
Hadoop job information for Stage-2: number of mappers: 1; number of reducers: 1
2019-07-19 17:44:09,024 Stage-2 map = 0%,  reduce = 0%
2019-07-19 17:44:13,185 Stage-2 map = 100%,  reduce = 0%, Cumulative CPU 0.65 sec
2019-07-19 17:44:18,307 Stage-2 map = 100%,  reduce = 100%, Cumulative CPU 1.95 sec
MapReduce Total cumulative CPU time: 1 seconds 950 msec
Ended Job = job_1562553101223_0023
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 2.74 sec   HDFS Read: 6753 HDFS Write: 169 SUCCESS
Stage-Stage-2: Map: 1  Reduce: 1   Cumulative CPU: 1.95 sec   HDFS Read: 4655 HDFS Write: 25 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 690 msec
OK
word    cnt
hello   3
world   2
welcom  1
Time taken: 31.204 seconds, Fetched: 3 row(s)
hive (default)> 

注意:explode:一进多出,就是将行变成列。
split是hive内置的函数。

<think> 嗯,用户这次询问的是Hive建表语句的相关信息,结合之前的对话历史,用户之前问过character varying字段在Hive中的替代方案,现在显然是想深入理解Hive的表创建机制。 用户可能正在搭建数据仓库,需要从PostgreSQL迁移到Hive,所以既关心字段类型的映射(上次的问题),又需要全面了解建表语法(本次的问题)。从用户两次提问的关联性看,应该是个务实的技术人员,关注点很实际——就是要马上能用的代码示例和避坑指南。 翻看之前的回复,已经详细解释过字符串类型的对应关系,这次要侧重建表语句本身。引用[1][2][3][4]都提到关键点:内部表/外部表的区别、分区设定、存储格式指定、location用法。特别要注意引用[3]中提到的分区语法和引用[4]强调的外部表路径设置,这些都是实际开发中的高频需求。 用户没有明确说使用场景,但根据经验推测可能有这些潜在需求: 1 需要创建分区表处理时间序列数据 2 可能要对接外部存储系统所以需要external table 3 或许会从现有表结构复制(CREATE TABLE AS/LIKE) 回复时要突出三个层次:基础语法结构(必选参数)、进阶功能(分区/分桶)、实战技巧(避免数据重复加载)。分区语法示例尤其重要,因为这是用户最可能遇到的性能优化点。 最后生成的三个相关问题也很有针对性:数据类型映射延续上次话题,分区策略关乎性能,存储格式影响查询效率——完全覆盖数据仓库建设的核心痛点。这样用户接下来无论遇到哪个环节的问题,都能找到延伸阅读方向。 </think> 以下是Hive建表语句的核心语法及参数详解(附示例),结合官方文档与实际开发高频场景整理: ### 一、基础建表语法 ```sql CREATE [EXTERNAL] TABLE [IF NOT EXISTS] 表名( 列名1 数据类型 [COMMENT &#39;列注释&#39;], 列名2 数据类型, ... ) [PARTITIONED BY (分区列名 数据类型, ...)] -- 分区字段不能与主字段重复 [CLUSTERED BY (分桶列名) INTO 分桶数 BUCKETS] -- 分桶存储 [ROW FORMAT row_format] -- 数据格式定义 [STORED AS file_format] -- 存储格式 [LOCATION &#39;hdfs_path&#39;] -- 数据存储路径(外部表必选) [TBLPROPERTIES (属性名=值,...)]; -- 表级元数据 ``` --- ### 二、关键参数详解(附示例) #### 1. **`EXTERNAL` 关键字** - **内部表**(默认):数据由Hive管理,删除表时**同时删除HDFS数据** ```sql CREATE TABLE internal_table (id INT, name STRING); ``` - **外部表**:删除表时**只删元数据,保留HDFS数据** ```sql CREATE EXTERNAL TABLE external_table ( dept_no INT, addr STRING ) LOCATION &#39;/user/ext/dept&#39;; -- 必须指定路径[^3][^4] ``` #### 2. **`PARTITIONED BY` 分区** 加速查询,按目录存储数据(分区字段虚拟存在,不占用实际存储): ```sql CREATE TABLE logs ( ip STRING, url STRING ) PARTITIONED BY (dt STRING); -- 按日期分区[^3] ``` > 📌 **加载分区数据示例**: > ```sql > ALTER TABLE logs ADD PARTITION (dt=&#39;2023-10-01&#39;); > ``` #### 3. **`ROW FORMAT` 数据格式定义** 指定字段分隔符(常用文本格式): ```sql CREATE TABLE csv_data ( id INT, name STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY &#39;,&#39; -- 列分隔符(逗号) LINES TERMINATED BY &#39;\n&#39;; -- 行分隔符(换行符)[^4] ``` #### 4. **`STORED AS` 存储格式** | 格式 | 特点 | 适用场景 | |------------|--------------------------|-----------------------| | `TEXTFILE` | 默认格式,可读性强 | 原始日志(兼容性好) | | `ORC` | 列式存储,高压缩比 | 大数据量分析 | | `PARQUET` | 列式存储,Schema演进支持 | 跨系统数据交换 | ```sql CREATE TABLE orc_table ( id INT, name STRING ) STORED AS ORC; -- 使用ORC格式存储[^2] ``` #### 5. **快速建表方式** - **`LIKE` 复制表结构**: ```sql CREATE TABLE new_table LIKE existing_table; -- 仅复制结构[^1] ``` - **`AS SELECT` 复制结构+数据**: ```sql CREATE TABLE dept_backup AS SELECT * FROM dept; -- 备份数据[^1][^4] ``` --- ### 三、最佳实践建议 1. **路径规范** - 内部表默认路径:`/user/hive/warehouse/库名.db/表名` - 外部表路径需自定义:`LOCATION &#39;/project/user/table_name&#39;` 2. **字符集问题** 中文乱码时需在`TBLPROPERTIES`中指定: ```sql TBLPROPERTIES (&#39;serialization.encoding&#39;=&#39;UTF-8&#39;) ``` 3. **避免分区陷阱** 分区字段不要与实际字段同名,否则报错: ```sql -- ❌ 错误:addr已存在于主字段 CREATE TABLE dept(dept_no INT, addr STRING) PARTITIONED BY (addr STRING); ``` --- ### 四、完整示例 ```sql CREATE EXTERNAL TABLE user_behavior ( user_id BIGINT, item_id BIGINT, behavior_type STRING ) PARTITIONED BY (dt STRING) -- 按天分区 ROW FORMAT DELIMITED FIELDS TERMINATED BY &#39;\t&#39; -- 列分隔符为Tab STORED AS ORC LOCATION &#39;/data/user_behavior&#39; TBLPROPERTIES (&#39;orc.compress&#39;=&#39;SNAPPY&#39;); ``` > 参考文档:[Hive官方建表文档](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-CreateTable)[^2]
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