Hive常用的SQL命令操作

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 ' '

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(' ')

生成数据的周信息

weekday = datetime.datetime.fromtimestamp(float(unixtime)).isoweekday()

print ' '.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 ' ';

//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;