Hive提供了很多的函数，可以在命令行下show functions罗列所有的函数，你会发现这些函数名与mysql的很相近，绝大多数相同的，可通过describe function functionName 查看函数使用方法。

hive支持的数据类型很简单就INT(4 byte integer),BIGINT(8 byte integer),FLOAT(single precision),DOUBLE(double precision),BOOLEAN,STRING等原子类型，连日期时间类型也不支持，但通过to_date、unix_timestamp、date_diff、date_add、date_sub等函数就能完成mysql同样的时间日期复杂操作。
如下示例：

select * from tablename where to_date(cz_time) > to_date('2050-12-31');
select * from tablename where unix_timestamp(cz_time) > unix_timestamp('2050-12-31 15:32:28');

分区
hive与mysql分区有些区别，mysql分区是用表结构中的字段来分区(range,list,hash等)，而hive不同，他需要手工指定分区列，这个列是独立于表结构，但属于表中一列，在加载数据时手动指定分区。

创建表

hive> CREATE TABLE pokes (foo INT, bar STRING 
COMMENT 'This is bar'
); 

创建表并创建索引字段ds

hive> CREATE TABLE invites (foo INT, bar STRING) PARTITIONED BY (ds STRING); 

显示所有表

hive> SHOW TABLES;

按正条件（正则表达式）显示表，

hive> SHOW TABLES '.*s';

**表添加一列 **

hive> ALTER TABLE pokes ADD COLUMNS (new_col INT);

添加一列并增加列字段注释

hive> ALTER TABLE invites ADD COLUMNS (new_col2 INT COMMENT 'a comment');

更改表名
hive> ALTER TABLE events RENAME TO 3koobecaf;

删除列

hive> DROP TABLE pokes;

元数据存储#

将本地文件中的数据加载到表中

hive> LOAD DATA LOCAL INPATH './examples/files/kv1.txt' OVERWRITE INTO TABLE pokes;

加载本地数据，同时给定分区信息

hive> LOAD DATA LOCAL INPATH './examples/files/kv2.txt' OVERWRITE INTO TABLE invites PARTITION (ds='2008-08-15');

加载DFS数据 ，同时给定分区信息

hive> LOAD DATA INPATH '/user/myname/kv2.txt' OVERWRITE INTO TABLE invites PARTITION (ds='2008-08-15');
The above command will load data from an HDFS file/directory to the table. Note that loading data from HDFS will result in moving the file/directory. As a result, the operation is almost instantaneous. 

SQL 操作#

按先件查询

hive> SELECT a.foo FROM invites a WHERE a.ds='';

将查询数据输出至目录

hive> INSERT OVERWRITE DIRECTORY '/tmp/hdfs_out' SELECT a.* FROM invites a WHERE a.ds='';

将查询结果输出至本地目录

hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/local_out' SELECT a.* FROM pokes a;

**选择所有列到本地目录 **

hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a;
hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a WHERE a.key < 100; 
hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/reg_3' SELECT a.* FROM events a;
hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_4' select a.invites, a.pokes FROM profiles a;
hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_5' SELECT COUNT(1) FROM invites a WHERE a.ds='';
hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_5' SELECT a.foo, a.bar FROM invites a;
hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/sum' SELECT SUM(a.pc) FROM pc1 a;

将一个表的统计结果插入另一个表中

hive> FROM invites a INSERT OVERWRITE TABLE events SELECT a.bar, count(1) WHERE a.foo > 0 GROUP BY a.bar;
hive> INSERT OVERWRITE TABLE events SELECT a.bar, count(1) FROM invites a WHERE a.foo > 0 GROUP BY a.bar;
JOIN
hive> FROM pokes t1 JOIN invites t2 ON (t1.bar = t2.bar) INSERT OVERWRITE TABLE events SELECT t1.bar, t1.foo, t2.foo;

将多表数据插入到同一表中

FROM src
INSERT OVERWRITE TABLE dest1 SELECT src.* WHERE src.key < 100
INSERT OVERWRITE TABLE dest2 SELECT src.key, src.value WHERE src.key >= 100 and src.key < 200
INSERT OVERWRITE TABLE dest3 PARTITION(ds='2008-04-08', hr='12') SELECT src.key WHERE src.key >= 200 and src.key < 300
INSERT OVERWRITE LOCAL DIRECTORY '/tmp/dest4.out' SELECT src.value WHERE src.key >= 300;

将文件流直接插入文件

hive> FROM invites a INSERT OVERWRITE TABLE events SELECT TRANSFORM(a.foo, a.bar) AS (oof, rab) USING '/bin/cat' WHERE a.ds > '2008-08-09';
This streams the data in the map phase through the script /bin/cat (like hadoop streaming). Similarly - streaming can be used on the reduce side (please see the Hive Tutorial or examples) 

实际示例#

创建一个表

CREATE TABLE u_data (
userid INT,
movieid INT,
rating INT,
unixtime STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE;

下载示例数据文件，并解压缩

wget http://www.grouplens.org/system/files/ml-data.tar__0.gz
tar xvzf ml-data.tar__0.gz

加载数据到表中

LOAD DATA LOCAL INPATH 'ml-data/u.data'
OVERWRITE INTO TABLE u_data;

统计数据总量

SELECT COUNT(1) FROM u_data;

现在做一些复杂的数据分析#

**创建一个 weekday_mapper.py: 文件，作为数据按周进行分割 **

import sys
import datetime
for line in sys.stdin:
line = line.strip()
userid, movieid, rating, unixtime = line.split('\t')

生成数据的周信息

weekday = datetime.datetime.fromtimestamp(float(unixtime)).isoweekday()
print '\t'.join([userid, movieid, rating, str(weekday)])

使用映射脚本

//创建表，按分割符分割行中的字段值

CREATE TABLE u_data_new (
userid INT,
movieid INT,
rating INT,
weekday INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t';

//将python文件加载到系统

add FILE weekday_mapper.py;

//将数据按周进行分割

INSERT OVERWRITE TABLE u_data_new
SELECT
TRANSFORM (userid, movieid, rating, unixtime)
USING 'python weekday_mapper.py'
AS (userid, movieid, rating, weekday)
FROM u_data;
SELECT weekday, COUNT(1)
FROM u_data_new
GROUP BY weekday;

Hive2.0 HPL/SQL
HPL/SQL Reference
Hive 语言手册
HiveQL(Hive SQL)跟普通SQL最大区别