hive中有哪些基礎執行語句

小編給大家分享一下hive中有哪些基礎執行語句，希望大家閱讀完這篇文章之后都有所收獲，下面讓我們一起去探討吧！

hive簡單概念

hive是一種基于Hadoop的數據倉庫的處理工具，目前只支持簡單的類似傳統關系型數據庫的SQL查詢，修改操作功能，他可以直接將SQL轉化為MapReduce程序，開發人員不必一定要學會寫MR程序，提高了開發效率。

例子：基于mysql存儲的hive環境，hive元數據（hive相關表，表的各個字段屬性等信息）存放在mysql數據庫中，mysql數據存放在hdfs默認是/user/hive/warehouse/hive.db中

ddl 語句

mysql作為元數據存儲 數據庫（hive）結構目錄

創建表

hive> create table test (id int, name string);

引入分區的概念，因為hive 中的select 一般會掃描整個表，這樣會浪費很多時間，所以引入分區的概念

hive> create table test2 (id int, name string) partitioned by (ds string);

瀏覽表

hive>show tables;

引入正則表達式 類似like的功能

hive>show tables '.*t'

查看數據結構

hive> DESCRIBE test;或desc test;

修改或刪除表

hive>alter table test rename to test3;

hive>alter table add columns (new_column type comment '注釋')

hive>drop table test;

DML操作語句

1、倒入數據

LOAD DATA LOCAL INPATH '/home/hadoop/test.txt' OVERWRITE INTO TABLE test;

local 表示執行本地，如果去掉默認是取hdfs上的文件，overwrite表示導入數據覆蓋，如果去掉表示append

2、執行查詢

select ＊ from test2 where test2.ds='2014-08-26'

3、值得注意的是 select count（＊） from test 與我們平時關系型數據庫記錄查詢操作不同，他執行的是一個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=<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 mapred.reduce.tasks=<number>
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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