Hive实现wordcount的统计

1 所需环境

2 创建一个数据库

hive> create database wordcount;
OK
Time taken: 0.389 seconds

hive> show databases;
OK
default
wordcount
Time taken: 0.043 seconds, Fetched: 3 row(s)

3 创建表

hive> create table file_data(context string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\n';
OK
Time taken: 1.227 seconds

hive> show tables;
OK
file_data
Time taken: 0.195 seconds, Fetched: 1 row(s)

4 准备数据

[hadoop@zydatahadoop001 ~]$ pwd
/home/hadoop
[hadoop@zydatahadoop001 ~]$ ll
total 12
-rw-rw-r--. 1 hadoop hadoop    7 Dec 19 09:56 demo1.txt
-rw-rw-r--. 1 hadoop hadoop   27 Dec 22 14:40 helloword.txt
-rw-------. 1 hadoop hadoop 1008 Dec 19 15:00 nohup.out
[hadoop@zydatahadoop001 ~]$ vi wordcount.txt
hello world
hello hadoop
hello java
hello mysql
c
c++
lisi zhangsan wangwu

5 将数据加载到 file_data 表中

hive> load data local inpath '/home/hadoop/wordcount.txt' into table file_data;
Loading data to table wordcount.file_data
Table wordcount.file_data stats: [numFiles=1, totalSize=75]
OK
Time taken: 3.736 seconds

查看file_data
hive> select * from file_data;
OK
hello world
hello hadoop
hello java
hello mysql
c
c++
lisi zhangsan wangwu
Time taken: 0.736 seconds, Fetched: 7 row(s)

6 根据’ ‘切分数据，切分出来的每个单词作为一行 记录到结果表。

hive> create table words(word string);;
OK
Time taken: 0.606 seconds

hive> insert into table words select explode(split(word , " ")) from file_data;

查询 words
hive> select * from words;
OK
hello
world
hello
hadoop
hello
java
hello
mysql
c
c++
lisi
zhangsan
wangwu
Time taken: 0.304 seconds, Fetched: 13 row(s)

7 使用聚合函数count进行统计

hive> select word, count(word) from words group by word;

Query ID = hadoop_20171222143131_4636629a-1983-4b0b-8d96-39351f3cd53b
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1513893519773_0004, Tracking URL = http://zydatahadoop001:8088/proxy/application_1513893519773_0004/ Kill Command = /opt/software/hadoop-cdh/bin/hadoop job  -kill job_1513893519773_0004
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2017-12-22 15:47:06,871 Stage-1 map = 0%,  reduce = 0%
2017-12-22 15:47:50,314 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 22.96 sec
2017-12-22 15:48:09,496 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 25.59 sec
MapReduce Total cumulative CPU time: 25 seconds 590 msec
Ended Job = job_1513893519773_0004
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 25.59 sec   HDFS Read: 6944 HDFS Write: 77 SUCCESS
Total MapReduce CPU Time Spent: 25 seconds 590 msec
OK
c       1
c++     1
hadoop  1
hello   4
java    1
lisi    1
mysql   1
wangwu  1
world   1
zhangsan        1