ubuntu下安装protobuf

1.下载protobuf

下载地址：http://code.google.com/p/protobuf/downloads/list

2.编译protobuf
解压下载的zip包，cd到protobuf的目录下，执行以下指令：

./configure
make
make check
make install

3.检查安装是否成功
protoc –version
如果成功，则会输出版本号信息，例如：libprotoc 2.5.0
如果有问题，则会输出错误内容。

4.错误及解决方法
protoc: error while loading shared libraries: libprotoc.so.8: cannot open shared
错误原因：
protobuf的默认安装路径是/usr/local/lib，而/usr/local/lib 不在Ubuntu体系默认的 LD_LIBRARY_PATH 里，所以就找不到该lib
解决方法：
1. 创建文件 /etc/ld.so.conf.d/libprotobuf.conf 包含内容：

 /usr/local/lib  

1. 输入命令
   sudo ldconfig

这时，再运行protoc –version 就可以正常看到版本号了

转载 http://blog.csdn.net/xocoder/article/details/9155901

lzo

sudo apt-get install liblzo2-dev
sudo apt-get install lzop

（5）安装Hadoop-LZO

当然的还有一个前提，就是配置好maven和svn 或者Git（我使用的是SVN），这个就不说了，如果这些搞不定，其实也不必要进行下去了！

我这里使用https://github.com/twitter/hadoop-lzo

使用SVN从https://github.com/twitter/hadoop-lzo/trunk下载代码，修改pom.xml文件中的一部分。

从：

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  UTF-8  
  2.1.0-beta  
  1.0.4  
  

修改为：

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    UTF-8  
    2.2.0  
    1.0.4  
    

再依次执行:

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mvn clean package -Dmaven.test.skip=true    
tar -cBf - -C target/native/Linux-amd64-64/lib . | tar -xBvf - -C /home/hadoop/hadoop-2.2.0/lib/native/     
cp target/hadoop-lzo-0.4.20-SNAPSHOT.jar /home/hadoop/hadoop-2.2.0/share/hadoop/common/  

接下来就是将/home/hadoop/hadoop-2.2.0/share/hadoop/common/hadoop-lzo-0.4.20-SNAPSHOT.jar以及/home/hadoop/hadoop-2.2.0/lib/native/ 同步到其它所有的hadoop节点。注意，要保证目录/home/hadoop/hadoop-2.2.0/lib/native/ 下的jar包，你运行hadoop的用户都有执行权限。

（6）配置Hadoop

在文件$HADOOP_HOME/etc/hadoop/hadoop-env.sh中追加如下内容：

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export LD_LIBRARY_PATH=/usr/local/hadoop/lzo/lib  

在文件$HADOOP_HOME/etc/hadoop/core-site.xml中追加如下内容：

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        io.compression.codecs  
        org.apache.hadoop.io.compress.GzipCodec,  
                   org.apache.hadoop.io.compress.DefaultCodec,  
                   com.hadoop.compression.lzo.LzoCodec,  
                   com.hadoop.compression.lzo.LzopCodec,  
                   org.apache.hadoop.io.compress.BZip2Codec  
          
  
  
         io.compression.codec.lzo.class  
         com.hadoop.compression.lzo.LzoCodec  
  

在文件$HADOOP_HOME/etc/hadoop/mapred-site.xml中追加如下内容：

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    mapred.compress.map.output    
    true    
    
    
    mapred.map.output.compression.codec    
    com.hadoop.compression.lzo.LzoCodec    
    
    
    mapred.child.env  
    LD_LIBRARY_PATH=/usr/local/hadoop/lzo/lib    
   

（7）在Hive中体验lzo

A：首先创建nginx_lzo的表

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CREATE TABLE logs_app_nginx (  
ip STRING,  
user STRING,  
time STRING,  
request STRING,  
status STRING,  
size STRING,  
rt STRING,  
referer STRING,  
agent STRING,  
forwarded String  
)   
partitioned by (  
date string,  
host string  
)  
row format delimited   
fields terminated by '\t'  
STORED AS INPUTFORMAT "com.hadoop.mapred.DeprecatedLzoTextInputFormat"    
OUTPUTFORMAT "org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat";   

B：导入数据

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LOAD DATA Local INPATH '/home/hadoop/data/access_20131230_25.log.lzo' INTO TABLE logs_app_nginx PARTITION(date=20131229,host=25);  

/home/hadoop/data/access_20131219.log文件的格式如下:

221.207.93.109 - [23/Dec/2013:23:22:38 +0800] “GET /ClientGetResourceDetail.action?id=318880&token=Ocm HTTP/1.1” 200 199 0.008 “xxx.com” “Android4.1.2/LENOVO/Lenovo A706/ch_lenovo/80” “-“

直接采用lzop /home/hadoop/data/access_20131219.log即可生成lzo格式压缩文件/home/hadoop/data/access_20131219.log.lzo

C：索引LZO文件
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$HADOOP_HOME/bin/hadoop jar /home/hadoop/hadoop-2.2.0/share/hadoop/common/hadoop-lzo-0.4.20-SNAPSHOT.jar com.hadoop.compression.lzo.DistributedLzoIndexer /user/hive/warehouse/logs_app_nginx  

D：开始跑利用hive来跑map/reduce任务了

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set hive.exec.reducers.max=10;   
set mapred.reduce.tasks=10;  
select ip,rt from nginx_lzo limit 10;  

在hive的控制台能看到类似如下格式输出，就表示正确了!

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hive> set hive.exec.reducers.max=10;   
hive> set mapred.reduce.tasks=10;  
hive> select ip,rt from nginx_lzo limit 10;  
Total MapReduce 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_1388065803340_0009, Tracking URL = http://lrts216:8088/proxy/application_1388065803340_0009/  
Kill Command = /home/hadoop/hadoop-2.2.0/bin/hadoop job  -kill job_1388065803340_0009  
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0  
2013-12-27 09:13:39,163 Stage-1 map = 0%,  reduce = 0%  
2013-12-27 09:13:45,343 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.22 sec  
2013-12-27 09:13:46,369 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.22 sec  
MapReduce Total cumulative CPU time: 1 seconds 220 msec  
Ended Job = job_1388065803340_0009  
MapReduce Jobs Launched:   
Job 0: Map: 1   Cumulative CPU: 1.22 sec   HDFS Read: 63570 HDFS Write: 315 SUCCESS  
Total MapReduce CPU Time Spent: 1 seconds 220 msec  
OK  
221.207.93.109  "XXX.com"  
Time taken: 17.498 seconds, Fetched: 10 row(s)