hive基本操作与应用

1.启动hadoop

start-all.sh

 

2.Hdfs上创建文件夹

hdfs dfs -mkdir wcinput

hdfs dfs -ls /user/hadoop

 

 

3.上传文件至hdfs

hdfs dfs -put ./509.txt wcinput

hdfs dfs -ls /user/hadoop/wcinput

 

4.启动Hive

hive

 

 

5.创建原始文档表

create table docs(line string)

 

 

6.导入文件内容到表docs并查看

load data inpath '/user/hadoop/wcinput/509.txt' overwrite into table docs;

select *from docs;//查看表信息

 

 

 

7.用HQL进行词频统计，结果放在表word_count里

 

用一张表，记录文件数据，文件的一行就是表里一个字段的数据，所以使用换行符作为分隔符，并以文件名为分区

drop table file_data;
create table file_data(context string) partitioned by (file_name string)row format delimited fields terminated by '\n'stored as textfile;

 

从hdfs中把文件数据导入file_data

cat /home/hadoop/demo.txt

load data local inpath '/home/hadoop/demo.txt' overwrite into table file_data PARTITION(file_name='/home/hadoop/demo.txt');

 

查询file_data

select * from file_data;

 

将切分出来的每个单词作为一行 记录到结果表里面

select explode(split(context,' ')) from file_data where file_name='/home/hadoop/demo.txt';
drop table wordcount;
create table wordcount(context string) partitioned by (file_name string)row format delimited fields terminated by ' 'stored as textfile;
insert overwrite table wordcount partition(file_name='/home/hadoop/demo.txt') select explode(split(context,' ')) from file_data where file_name='/home/hadoop/demo.txt';

 

使用hql查询

select context, count(context) from wordcount where file_name='/home/hadoop/demo.txt' group by context;

转载于:https://www.cnblogs.com/ChalingStreet/p/9048603.html