1. 建表

以纯文本数据建表：

create table default.calendar_table ( day_cal date ,week_cal string ,montn_cal string ,year_cal string ) row format delimited fields terminated by ',' stored as textfile;

若未指定为外部表（external table），则默认为托管表（managed table）。二者的区别在于load与drop操作：托管表用load data inpath加载数据（路径可为本地目录，也可是HDFS目录），该操作会将该文件放在HDFS目录：/user/hive/warehouse/ 下；而外部表的数据是在location中指定，一般配合partition描述数据的生成信息；drop托管表时会将元数据与/user/hive/warehouse/下的数据一起删掉，而drop外部表时只会删除元数据。将本地文件加载到托管表：

load data local inpath 'cal.csv' overwrite into table default.calendar_table;

以orc file数据建外部表表：

create external table default.ad_base ( uid string ,adx string ,exposure string ,click string ) partitioned by (day_time date) stored as orc location '/
     
      /
      
       ';

2. Partition

增加partition并指定location：

alter table DEFAULT.ad_base add if not exists partition (day_time=date '2016-05-20') location '2016-05-20/xxx';

重新设置partition的location：

alter table DEFAULT.ad_base partition (day_time=date '2016-05-20') set location 'hdfs://
     
      /
      
       /'; -- must be an absolute path

删除partition

alter table DEFAULT.ad_base drop if exists partition (day_time=date '2016-05-20') ignore protection;

查看所有的paritition，以及查看某一partition的详细信息：

show partitions ad_base;describe formatted ad_base partition(day_time = '2016-05-20');

3. UDF

Hive的非常丰富，基本能满足大部分的需求。

正则匹配获取相应字符串：

regexp_extract(b.dvc, '(.*)_(.*)', 2) as imei

复杂数据类型map、struct、指定schema的struct、array、union的构造如下：

map(key1, value1, key2, value2, ...)struct(val1, val2, val3, ...)named_struct(name1, val1, name2, val2, ...)array(val1, val2, ...)create_union(tag, val1, val2, ...)

获取复杂数据类型的某列值：

array: A[n]map: M[key]struct: S.x

条件判断case when，比如，在left join中指定默认值：

select uid, media,     case when b.tag is NULL then array(named_struct('tag','EMPTY', 'label','EMPTY')) else b.tag end as tags from ad_base a left outer join ad_tag b on (a.uid = regexp_extract(b.dvc, '(.*)_(.*)', 2) and exposure = '1');

4. UDTF

UDTF主要用来对复杂数据类型进行平铺操作，比如，explode平铺array与map，inline平铺array<struct>；这种内置的UDTF要与lateral view配合使用：

select myCol1, col2 FROM baseTable lateral view explode(col1) myTable1 AS myCol1; select uid, tag, label from ad_tag lateral view inline(tags) tag_tb; -- tags: array
     
      
       >

5. 多维分析

提供grouping set、rollup、cube关键字进行多维数据分析，可以解决自定义的维度组合、上钻维度（

SELECT a, b, SUM( c ) FROM tab1 GROUP BY a, b GROUPING SETS ( (a, b), a, b, ( ) ) -- equivalent aggregate query with group by SELECT a, b, SUM( c ) FROM tab1 GROUP BY a, b UNION SELECT a, null, SUM( c ) FROM tab1 GROUP BY a, null UNION SELECT null, b, SUM( c ) FROM tab1 GROUP BY null, b UNION SELECT null, null, SUM( c ) FROM tab1 GROUP BY a, b, c, WITH ROLLUP -- is equivalent to GROUP BY a, b, c GROUPING SETS ( (a, b, c), (a, b), (a), ( )) GROUP BY a, b, c WITH CUBE -- is equivalent to GROUP BY a, b, c GROUPING SETS ( (a, b, c), (a, b), (b, c), (a, c), (a), (b), (c), ( ))

此外，Hive还提供了GROUPING__ID函数对每一组合的维度进行编号，以区分该统计属于哪一维度组合，比如：

select tag, media, grouping__id, count(*) as pv from ad_base group by tag, media with rollup;

以指定分隔符保存结果到本地目录：

explainINSERT OVERWRITE LOCAL DIRECTORY '/home/
     
      /
      
       ' ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' select media, count(distinct uid) as uv from ad_base where day_time = '2016-05-20' and exposure = '1' group by media;

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