hive基本用法

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; 
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. 
SQL 操作 
按先件查询 
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) 
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; 
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;