1、Hive不支持等值连接
•SQL中对两表内联可以写成：•select * from dual a,dual b where a.key = b.key;•Hive中应为•select * from dual a join dual b on a.key = b.key; 而不是传统的格式：SELECT t1.a1 as c1, t2.b1 as c2FROM t1, t2WHERE t1.a2 = t2.b22、分号字符•分号是SQL语句结束标记，在HiveQL中也是，但是在HiveQL中，对分号的识别没有那么智慧，例如：•select concat(key,concat(';',key)) from dual;•但HiveQL在解析语句时提示： FAILED: Parse Error: line 0:-1 mismatched input '<EOF>' expecting ) in function specification•解决的办法是，使用分号的八进制的ASCII码进行转义，那么上述语句应写成：•select concat(key,concat('\073',key)) from dual;3、IS [NOT] NULL•SQL中null代表空值, 值得警惕的是, 在HiveQL中String类型的字段若是空(empty)字符串, 即长度为0, 那么对它进行IS NULL的判断结果是False.4、Hive不支持将数据插入现有的表或分区中，仅支持覆盖重写整个表，示例如下：INSERT OVERWRITE TABLE t1
SELECT * FROM t2;5、hive不支持INSERT INTO 表 Values（）, UPDATE, DELETE操作
这样的话，就不要很复杂的锁机制来读写数据。 INSERT INTO syntax is only available starting in version 0.8。INSERT INTO就是在表或分区中追加数据。6、hive支持嵌入mapreduce程序，来处理复杂的逻辑如：FROM (
MAP doctext USING 'python wc_mapper.py' AS (word, cnt) FROM docs CLUSTER BY word ) a REDUCE word, cnt USING 'python wc_reduce.py';--doctext: 是输入
--word, cnt: 是map程序的输出--CLUSTER BY: 将wordhash后，又作为reduce程序的输入并且map程序、reduce程序可以单独使用，如：FROM (
FROM session_table SELECT sessionid, tstamp, data DISTRIBUTE BY sessionid SORT BY tstamp ) a REDUCE sessionid, tstamp, data USING 'session_reducer.sh';-DISTRIBUTE BY: 用于给reduce程序分配行数据
7、hive支持将转换后的数据直接写入不同的表，还能写入分区、hdfs和本地目录这样能免除多次扫描输入表的开销。FROM t1
INSERT OVERWRITE TABLE t2 SELECT t3.c2, count(1) FROM t3 WHERE t3.c1 <= 20 GROUP BY t3.c2 INSERT OVERWRITE DIRECTORY '/output_dir' SELECT t3.c2, avg(t3.c1) FROM t3 WHERE t3.c1 > 20 AND t3.c1 <= 30 GROUP BY t3.c2 INSERT OVERWRITE LOCAL DIRECTORY '/home/dir' SELECT t3.c2, sum(t3.c1) FROM t3 WHERE t3.c1 > 30 GROUP BY t3.c2;示例示例
实际实例创建一个表CREATE TABLE u_data (userid INT,movieid INT,rating INT,unixtime STRING)ROW FORMAT DELIMITEDFIELDS 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.gztar 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 sysimport datetimefor 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 DELIMITEDFIELDS 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_newSELECTTRANSFORM (userid, movieid, rating, unixtime)USING 'python weekday_mapper.py'AS (userid, movieid, rating, weekday)FROM u_data;SELECT weekday, COUNT(1)FROM u_data_newGROUP 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;