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一、DDL Operations
创建表：

hive> CREATE TABLE pokes (foo INT, bar STRING); 

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;

二、元数据存储

将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.

三、SQL 操作

按条件查询：

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

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='<DATE>';

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='<DATE>';
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)

四、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;

五、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;

处理Apache Weblog 数据：
将WEB日志先用正则表达式进行组合，再按需要的条件进行组合输入到表中

add jar ../build/contrib/hive_contrib.jar;

CREATE TABLE apachelog (
host STRING,
identity STRING,
user STRING,
time STRING,
request STRING,
status STRING,
size STRING,
referer STRING,
agent STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
"input.regex" = "([^ ]*) ([^ ]*) ([^ ]*) (-|//[[^//]]*//]) ([^ /"]*|/"[^/"]*/") (-|[0-9]*) (-|[0-9]*)(?: ([^ /"]*|/"[^/"]*/") ([^ /"]*|/"[^/"]*/"))?",
"output.format.string" = "%1$s %2$s %3$s %4$s %5$s %6$s %7$s %8$s %9$s"
)
STORED AS TEXTFILE;