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：
00:00:0029821990737744128 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>
Query> 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=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapreduce.job.reduces=
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: 1Reduce: 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>
Query> 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：
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