hadoop的Hive实战操作sogou的日志

动手实战操作搜狗日志文件

本节中所用到的内容是来自搜狗实验室，网址为：

http://www.sogou.com/labs/dl/q.html

用户可以根据自己的Spark机器实际的内存配置等情况选择下载不同的数据版本，为了让所有的学习者都可以成功操作日志，我们使用的是迷你版本的tar.gz格式的文件，其大小为384K，下载地址：http://www.sogou.com/labs/resource/q.php

日志格式如下：

单挑记录：

00:00:002982199073774412[360安全卫士]8 3download.it.com.cn/softweb/software/firewall/antivirus/20067/17938.html

该文件的格式如下所示：

访问时间\t用户ID\t查询词\t该URL在返回结果中的排名\t用户点击的顺序号\t用户点击的URL

在hive中创建表格：

create table sogou(sTime string,userId string,Keyword string,Paiming int,Dianji int,URL string) row format delimited 

fields terminated by ‘\t‘ lines terminated by ‘\n‘ stored as textfile

结果如下：

hive> create table sogou(sTime string,userId string,Keyword string,Paiming int,Dianji int,URL string) row format delimited 

    > fields terminated by ‘\t‘ lines terminated by ‘\n‘ stored as textfile;

OK

Time taken: 4.422 seconds

导入数据：

load data local inpath  ‘/home/dyq/Documents/SogouQ.sample‘ overwrite into table sogou;

结果显示：

hive> load data local inpath  ‘/home/dyq/Documents/SogouQ.sample‘ overwrite into table sogou;

Loading data to table default.sogou

OK

Time taken: 3.567 seconds

查看表的建立情况：

hive> desc sogou;

OK

stime               string                                  

userid              string                                  

keyword             string                                  

paiming             int                                    

dianji              int                                    

url                 string                                  

Time taken: 2.515 seconds, Fetched: 6 row(s)

查看数据导入情况：

查看记录条数：

select count(*) from sogou;

可以看到查询过程很漫长，效率不高的说

hive> select count(*) from sogou;

WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. tez, spark) or using Hive 1.X releases.

Query ID = dyq_20160828114648_be63db30-cce4-47f0-aa1d-2e0f626c274c

Total jobs = 1

Launching Job 1 out of 1

Number of reduce tasks determined at compile time: 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_1472347049416_0004, Tracking URL = http://ubuntu:8088/proxy/application_1472347049416_0004/

Kill Command = /opt/hadoop-2.6.2/bin/hadoop job  -kill job_1472347049416_0004

Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1

2016-08-28 11:47:28,067 Stage-1 map = 0%,  reduce = 0%

2016-08-28 11:48:09,628 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 8.1 sec

2016-08-28 11:48:35,383 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 10.68 sec

MapReduce Total cumulative CPU time: 10 seconds 680 msec

Ended Job = job_1472347049416_0004

MapReduce Jobs Launched: 

Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 10.68 sec   HDFS Read: 906303 HDFS Write: 105 SUCCESS

Total MapReduce CPU Time Spent: 10 seconds 680 msec

OK

10000

Time taken: 109.492 seconds, Fetched: 1 row(s)

导出数据到外部文件

insert overwrite local directory ‘/home/dyq/Documents/SogouQdept‘ select a.Keyword,a.URL from sogou a; 

只导出2个字段，看看情况

hive> insert overwrite local directory ‘/home/dyq/Documents/SogouQdept‘ select a.Keyword,a.URL from sogou a; 

WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. tez, spark) or using Hive 1.X releases.

Query ID = dyq_20160828115140_2bc274b4-7845-46c2-9ac7-e45fcc5b7b23

Total jobs = 1

Launching Job 1 out of 1

Number of reduce tasks is set to 0 since there‘s no reduce operator

Starting Job = job_1472347049416_0005, Tracking URL = http://ubuntu:8088/proxy/application_1472347049416_0005/

Kill Command = /opt/hadoop-2.6.2/bin/hadoop job  -kill job_1472347049416_0005

Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0

2016-08-28 11:51:54,594 Stage-1 map = 0%,  reduce = 0%

2016-08-28 11:52:06,398 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.14 sec

MapReduce Total cumulative CPU time: 3 seconds 140 msec

Ended Job = job_1472347049416_0005

Moving data to local directory /home/dyq/Documents/SogouQdept

MapReduce Jobs Launched: 

Stage-Stage-1: Map: 1   Cumulative CPU: 3.14 sec   HDFS Read: 901894 HDFS Write: 178503 SUCCESS

Total MapReduce CPU Time Spent: 3 seconds 140 msec

OK

Time taken: 28.859 seconds

查看sogouQdept文件的内容：

原文：http://36006798.blog.51cto.com/988282/1843504