flume学习(四)

本文介绍了一个使用Nginx配置Web服务器及Flume进行日志收集的实例,详细展示了Nginx的基本配置文件,如何启动并测试Nginx服务,以及通过浏览器访问特定URL触发日志记录的过程。最后验证了Flume成功将日志数据写入HDFS。

接上章

nginx配置
worker_processes  1;

events {
    worker_connections  1024;
}

http {
    include       mime.types;
    default_type  application/octet-stream;

    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_user_agent" "$http_x_forwarded_for"';


    log_format  log_format   '$remote_addr^A$msec^A$http_host^A$request_uri';

    access_log /home/hadoop/access.log log_format;
    sendfile        on;
    keepalive_timeout  65;
    #include /etc/nginx/conf.d/*.conf;

server {
    listen       80;
    server_name  hh 0.0.0.0;

    location ~ .*(BfImg)\.(gif)$ {
      default_type image/gif;
      root /usr/local/nginx/www/source;  
   }
}
}
运行nginx
sbin/nginx
测试nginx
# curl http://dev-hadoop-single.com:80
<!DOCTYPE html>
<html>
<head>
<title>Welcome to nginx!</title>
<style>
    body {
        width: 35em;
        margin-left: 100px;
        margin-top:100px;
        font-family: Tahoma, Verdana, Arial, sans-serif;
    }
</style>
</head>
<body>
<h1>Welcome to nginx! backup</h1>
<p>this is the first page</p>
<p>If you see this page, the nginx web server is successfully installed and
working. Further configuration is required.</p>

<p>For online documentation and support please refer to
<a href="http://nginx.org/">nginx.org</a>.<br/>
Commercial support is available at
<a href="http://nginx.com/">nginx.com</a>.</p>

<p><em>Thank you for using nginx.</em></p>
</body>
</html>
启动flume进行测试

浏览器访问
http://dev-hadoop-single.com/a.gif?userid=xxx&ctime=xxxx&url=YYY
进行多次访问
查看/home/hadoop/access.log
“`
# tail -f /home/hadoop/access.log
192.168.56.1^A1476876997.488^Adev-hadoop-single.com^A/a.gif
192.168.56.1^A1476876999.153^Adev-hadoop-single.com^A/a.gif
192.168.56.1^A1476877028.421^Adev-hadoop-single.com^A/a.gif
192.168.56.1^A1476877030.573^Adev-hadoop-single.com^A/a.gif
192.168.56.1^A1476877064.052^Adev-hadoop-single.com^A/a.gif?userid=xxx&ctime=xxxx&url=YYY
192.168.56.1^A1476877065.791^Adev-hadoop-single.com^A/a.gif?userid=xxx&ctime=xxxx&url=YYY
192.168.56.1^A1476877067.537^Adev-hadoop-single.com^A/a.gif?userid=xxx&ctime=xxxx&url=YYY

查看flume日志

16/10/19 19:43:21 INFO hdfs.BucketWriter: Closing hdfs://dev-hadoop-single.com:8020/flume/events-02/2016-10-19/log-spool.1476876160609.tmp
16/10/19 19:43:21 INFO hdfs.BucketWriter: Renaming hdfs://dev-hadoop-single.com:8020/flume/events-02/2016-10-19/log-spool.1476876160609.tmp to hdfs://dev-hadoop-single.com:8020/flume/events-02/2016-10-19/log-spool.1476876160609
16/10/19 19:43:22 INFO hdfs.BucketWriter: Creating hdfs://dev-hadoop-single.com:8020/flume/events-02/2016-10-19/log-spool.1476876160610.tmp

查看hdfs目录

$ hdfs dfs -ls /flume/events-02/2016-10-19
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/modules/hadoop-2.5.0-cdh5.3.6/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/modules/hbase-0.98.6-cdh5.3.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
16/10/19 19:46:20 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
Found 33 items
-rw-r–r– 1 hadoop supergroup 10397 2016-10-19 19:43 /flume/events-02/2016-10-19/log-spool.1476876160609
-rw-r–r– 1 hadoop supergroup 10439 2016-10-19 19:43 /flume/events-02/2016-10-19/log-spool.1476876160610
-rw-r–r– 1 hadoop supergroup 10439 2016-10-19 19:43 /flume/events-02/2016-10-19/log-spool.1476876160611
-rw-r–r– 1 hadoop supergroup 10439 2016-10-19 19:44 /flume/events-02/2016-10-19/log-spool.1476876160612
“`

基于径向基函数神经网络RBFNN的自适应滑模控制学习(Matlab代码实现)内容概要:本文介绍了基于径向基函数神经网络(RBFNN)的自适应滑模控制方法,并提供了相应的Matlab代码实现。该方法结合了RBF神经网络的非线性逼近能力和滑模控制的强鲁棒性,用于解决复杂系统的控制问题,尤其适用于存在不确定性和外部干扰的动态系统。文中详细阐述了控制算法的设计思路、RBFNN的结构与权重更新机制、滑模面的构建以及自适应律的推导过程,并通过Matlab仿真验证了所提方法的有效性和稳定性。此外,文档还列举了大量相关的科研方向和技术应用,涵盖智能优化算法、机器学习、电力系统、路径规划等多个领域,展示了该技术的广泛应用前景。; 适合人群:具备一定自动控制理论基础和Matlab编程能力的研究生、科研人员及工程技术人员,特别是从事智能控制、非线性系统控制及相关领域的研究人员; 使用场景及目标:①学习和掌握RBF神经网络与滑模控制相结合的自适应控制策略设计方法;②应用于电机控制、机器人轨迹跟踪、电力电子系统等存在模型不确定性或外界扰动的实际控制系统中,提升控制精度与鲁棒性; 阅读建议:建议读者结合提供的Matlab代码进行仿真实践,深入理解算法实现细节,同时可参考文中提及的相关技术方向拓展研究思路,注重理论分析与仿真验证相结合。
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