小编给大家分享一下hive中有哪些基础执行语句，希望大家阅读完这篇文章之后都有所收获，下面让我们一起去探讨吧！
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hive是一种基于Hadoop的数据仓库的处理工具，目前只支持简单的类似传统关系型数据库的SQL查询，修改操作功能，他可以直接将SQL转化为MapReduce程序，开发人员不必一定要学会写MR程序，提高了开发效率。
例子：基于MySQL存储的hive环境，hive元数据（hive相关表，表的各个字段属性等信息）存放在mysql数据库中，mysql数据存放在hdfs默认是/user/hive/warehouse/hive.db中
mysql作为元数据存储 数据库（hive）结构目录
创建表
hive> create table test (id int, name string);
引入分区的概念，因为hive 中的select 一般会扫描整个表，这样会浪费很多时间，所以引入分区的概念
hive> create table test2 (id int, name string) partitioned by (ds string);
浏览表
hive>show tables;
引入正则表达式 类似like的功能
hive>show tables '.*t'
查看数据结构
hive> DESCRIBE test;或desc test;
修改或删除表
hive>alter table test rename to test3;
hive>alter table add columns (new_column type comment '注释')
1、倒入数据
LOAD DATA LOCAL INPATH '/home/hadoop/test.txt' OVERWRITE INTO TABLE test;
local 表示执行本地，如果去掉默认是取hdfs上的文件，overwrite表示导入数据覆盖，如果去掉表示append
2、执行查询
select ＊ from test2 where test2.ds='2014-08-26'
3、值得注意的是 select count（＊） from test 与我们平时关系型数据库记录查询操作不同，他执行的是一个mr
hive> select count(*) from test2;
Total MapReduce jobs = 1
Launching Job 1 out of 1
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=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapred.reduce.tasks=
Starting Job = job_1411720827309_0004, Tracking URL = http://master:8031/proxy/application_1411720827309_0004/
Kill Command = /usr/local/cloud/hadoop/bin/hadoop job -kill job_1411720827309_0004
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.93 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.3 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.3 sec
MapReduce Total cumulative CPU time: 2 seconds 300 msec
Ended Job = job_1411720827309_0004
MapReduce Jobs Launched:
Job 0: Map: 1 Reduce: 1 Cumulative CPU: 2.3 sec HDFS Read: 245 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 300 msec
OK
3
Time taken: 27.508 seconds, Fetched: 1 row(s)
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