hive学习（2）--- hive sql 常用语法

来源：互联网发布：软件学报小木虫编辑：程序博客网时间：2024/06/03 18:31

hql语法参考文章：http://blog.csdn.net/acmilanvanbasten/article/details/17252673

一、单表select

1、and、sort by 、limit的使用

hive> select * from weather where city='hangzhou' and weath='fine' and minTemperat=-16 sort by pmvalue DESC limit 5;

//查询城市是hangzhou，天气fine并且最低温度是-16° 并且pm值最大的5条记录，结果如下

2014-05-23|07:34:58 China hangzhou fine -16 -10 496
2014-05-23|07:34:58 China hangzhou fine -16 -6 496
2014-05-23|07:34:58 China hangzhou fine -16 14 496
2014-05-23|07:34:58 China hangzhou fine -16 0 496
2014-05-23|07:34:58 China hangzhou fine -16 -8 496
Time taken: 29.266 seconds, Fetched: 5 row(s)

2、in

hive> select * from weather where city='hangzhou' and weath='fine' and minTemperat in (-16,-17) sort by pmvalue DESC limit 10;

2014-05-23|07:34:57 China hangzhou fine -17 -12 498
2014-05-23|07:34:57 China hangzhou fine -17 10 498
2014-05-23|07:34:57 China hangzhou fine -17 -14 498
2014-05-23|07:34:58 China hangzhou fine -17 -7 496
2014-05-23|07:34:58 China hangzhou fine -16 -6 496
2014-05-23|07:34:58 China hangzhou fine -16 6 496
2014-05-23|07:34:58 China hangzhou fine -16 -12 496
2014-05-23|07:34:58 China hangzhou fine -16 1 496
2014-05-23|07:34:58 China hangzhou fine -17 2 496
2014-05-23|07:34:58 China hangzhou fine -17 11 496

Time taken: 29.277 seconds, Fetched: 10 row(s)

3、group by

select * from weather where city='hangzhou' and weath='fine'
and minTemperat in (-16,-17) group by pmvalue;

4、把select的结果输出到文件中：

INSERT OVERWRITE LOCAL DIRECTORY '/home/hadoop/selectResult'
select * from weather where city='hangzhou' and weath='fine' and
minTemperat in (-16,-17) sort by pmvalue DESC limit 100;

二、多表联合select

ps：准备工作，再创建两张表，一张是城市名称与区号的对照表，一张是pm值对应的空气状况表,表结构如下：

create table cityinfo
(name string, number string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ' '
STORED AS TEXTFILE;

create table pminfo
(pmvalue string, pmlevel string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ' '
STORED AS TEXTFILE;

1、3表联合查询，返回所有字段，前5个结果

select *
from cityinfo join weather on (cityinfo.name=weather.city)
join pminfo on (pminfo.pmvalue=weather.pmvalue)
where city='hangzhou' and weath='fine' and minTemperat=-16 limit 5

Job 1: Map: 2 Reduce: 1 Cumulative CPU: 3.34 sec HDFS Read: 229134 HDFS Write: 336 SUCCESS
Total MapReduce CPU Time Spent: 9 seconds 300 msec
OK
hangzhou 0571 2014-05-23|07:33:59 China hangzhou fine -16 11 0 0 A
hangzhou 0571 2014-05-23|07:35:44 China hangzhou fine -16 -5 0 0 A
hangzhou 0571 2014-05-23|07:35:44 China hangzhou fine -16 -14 0 0 A
hangzhou 0571 2014-05-23|07:35:44 China hangzhou fine -16 12 0 0 A
hangzhou 0571 2014-05-23|07:35:44 China hangzhou fine -16 18 0 0 A
Time taken: 34.448 seconds, Fetched: 5 row(s)

2、3表联合查询，使用别名，只返回部分列

select cy.number,wh.*,pm.pmlevel
from cityinfo cy join weather wh on (cy.name=wh.city)
join pminfo pm on (pm.pmvalue=wh.pmvalue)
where wh.city='hangzhou' and wh.weath='fine' and wh.minTemperat=-16 limit 5

Total MapReduce CPU Time Spent: 6 seconds 790 msec
OK
0571 2014-05-23|07:33:59 China hangzhou fine -16 11 0 A
0571 2014-05-23|07:35:44 China hangzhou fine -16 -5 0 A
0571 2014-05-23|07:35:44 China hangzhou fine -16 -14 0 A
0571 2014-05-23|07:35:44 China hangzhou fine -16 12 0 A
0571 2014-05-23|07:35:44 China hangzhou fine -16 18 0 A

3、LEFT，RIGHT 和 FULLOUTER 、聚合等高级特性

//待续

补充知识：join时mp如何工作？

join 时，每次map/reduce 任务的逻辑：

reducer 会缓存 join 序列中除了最后一个表的所有表的记录，再通过最后一个表将结果序列化到文件系统。这一实现有助于在 reduce 端减少内存的使用量。实践中，应该把最大的那个表写在最后（否则会因为缓存浪费大量内存）。例如：

SELECT a.val, b.val, c.val FROM a

JOIN b ON (a.key = b.key1)JOIN c ON (c.key = b.key1)

所有表都使用同一个 join key（使用 1 次map/reduce 任务计算）。Reduce 端会缓存 a 表和 b 表的记录，然后每次取得一个 c 表的记录就计算一次 join 结果，类似的还有：

SELECT a.val, b.val, c.val FROMa

JOIN b ON (a.key = b.key1)JOIN c ON (c.key = b.key2)

这里用了 2 次 map/reduce 任务。第一次缓存 a 表，用 b 表序列化[王黎17] ；第二次缓存第一次 map/reduce 任务的结果，然后用 c 表序列化。

hive hql 语法参考网址：http://blog.csdn.net/hguisu/article/details/7256833

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