统计文件信息：

$ /opt/cdh-5.3.6/hadoop-2.5.0/bin/hdfs dfs -text /user/hadoop/wordcount/input/wc.input

hadoop spark

spark hadoop

oracle mysql postgresql

postgresql oracle mysql

mysql mongodb

hdfs yarn mapreduce

yarn hdfs

zookeeper

针对于以上文件使用hive做词频统计：

create table docs (line string);

load data inpath '/user/hadoop/wordcount/input/wc.input' into table docs;

create table word_counts as

select word,count(1) as count from

(select explode(split(line,' ')) as word from docs) word

group by word

order by word;

分段解释：

--使用split函数对表中行按空格进行分隔：

select split(line,' ') from docs；

["hadoop","spark",""]

["spark","hadoop"]

["oracle","mysql","postgresql"]

["postgresql","oracle","mysql"]

["mysql","mongodb"]

["hdfs","yarn","mapreduce"]

["yarn","hdfs"]

["zookeeper"]

--使用explode函数对split的结果集进行行拆列：

select explode(split(line,' ')) as word from docs；

word

hadoop

spark

spark

hadoop

oracle

mysql

postgresql

postgresql

oracle

mysql

mysql

mongodb

hdfs

yarn

mapreduce

yarn

hdfs

zookeeper

--以上输出内容已经满足对其做统计分析，这时通过sql对其进行分析：

select word,count(1) as count from

(select explode(split(line,' ')) as word from docs) word

group by word

order by word;

word    count

     1

hadoop    2

hdfs    2

mapreduce    1

mongodb    1

mysql    3

oracle    2

postgresql    2

spark    2

yarn    2

zookeeper    1