Centos7 搭建 Flume 搭配 Hadoop 采集 Nginx 日志

本文目的是根据前文的博文，打造一个Hadoop、Sprak的服务器闭环。也是经验归纳。

版本信息

CentOS: Linux localhost.localdomain 3.10.0-862.el7.x86_64 #1 SMP Fri Apr 20 16:44:24 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

JDK: Oracle jdk1.8.0_241 , https://www.oracle.com/java/technologies/javase-jdk8-downloads.html

Hadoop : hadoop-3.2.1.tar.gz

Flume：apache-flume-1.9.0-bin.tar.gz , http://flume.apache.org/download.html

服务器搭建

Hadoop：CentOS7 部署 Hadoop 3.2.1 (伪分布式)

Nginx：请参考 CentOS 6.7 配置 yum 安装 nginx

搭建 Flume

1.下载Flume 的 bin包并解压到指定目录

mkdir /data/server/flume/
wget https://mirrors.tuna.tsinghua.edu.cn/apache/flume/1.9.0/apache-flume-1.9.0-bin.tar.gz
tar zxvf apache-flume-1.9.0-bin.tar.gz
mv apache-flume-1.9.0-bin 1.9.0

2. 安装JDK

下载Java SDK，前往 https://www.oracle.com/java/technologies/javase-jdk8-downloads.html 下载

rz #选择你下载好的文件，上传到当前目录下
tar zxvf jdk-8u241-linux-x64.tar.gz

编辑env文件

cp 1.9.0/conf/flume-env.sh.template 1.9.0/conf/flume-env.sh
vi 1.9.0/conf/flume-env.sh

在文件末尾添加如下内容：

export JAVA_HOME=/data/server/flume/jdk1.8.0_241/

3. 配置Flume

新建配置文件 flume.conf

cp 1.9.0/conf/flume-conf.properties.template 1.9.0/conf/flume.conf
vi 1.9.0/conf/flume.conf

添加如下内容：

##配置Agent
myagent.sources = r1
myagent.sinks = k1
myagent.channels = c1

# # 配置Source
myagent.sources.r1.type = exec
myagent.sources.r1.channels = c1
myagent.sources.r1.deserializer.outputCharset = UTF-8
# # 配置需要监控的日志输出文件
myagent.sources.r1.command = tail -F /usr/local/nginx/logs/flume-test.access.log
# # 配置Sink
myagent.sinks.k1.type = hdfs
myagent.sinks.k1.channel = c1
myagent.sinks.k1.hdfs.useLocalTimeStamp = true
myagent.sinks.k1.hdfs.path = hdfs://172.16.1.126:9000/flume/nginx_logs/%Y%m%d
myagent.sinks.k1.hdfs.filePrefix = %Y-%m-%d-%H
myagent.sinks.k1.hdfs.fileSuffix = .log
myagent.sinks.k1.hdfs.minBlockReplicas = 1
myagent.sinks.k1.hdfs.fileType = DataStream
myagent.sinks.k1.hdfs.writeFormat = Text
myagent.sinks.k1.hdfs.rollInterval = 86400
myagent.sinks.k1.hdfs.rollSize = 1000000
myagent.sinks.k1.hdfs.rollCount = 10000
myagent.sinks.k1.hdfs.idleTimeout = 0
# # 配置Channel
myagent.channels.c1.type = memory
myagent.channels.c1.capacity = 1000
myagent.channels.c1.transactionCapacity = 100
# # 将三者连接
myagent.sources.r1.channel = c1
myagent.sinks.k1.channel = c1

4. 编写启动、关闭脚本

start_flume.sh

#!/usr/bin/env bash

CURRENT_DIR=$(pwd)

BIN_DIR="/data/server/flume/1.9.0/"

CHECK_PID="ps aux | grep \"${BIN_DIR}\" | grep ‘flume‘ | grep -v grep | awk ‘{print \$2}‘"

cd ${BIN_DIR}

FLUME_PID=$(eval ${CHECK_PID})

if [ ""x != "${FLUME_PID}"x ] ;then
  echo "Flume is running, please kill the flume process"
  cd ${CURRENT_DIR}
  exit 0
fi

nohup ./bin/flume-ng agent --conf ./conf -f ./conf/flume.conf --name myagent > ../nohup.out 2>&1 &

#等3秒后执行下一条
sleep 3

FLUME_PID=$(eval ${CHECK_PID})

if [ ""x != "${FLUME_PID}"x ] ;then
  echo "Flume is running!"
fi

cd ${CURRENT_DIR}

stop_flume.sh

#!/usr/bin/env bash

CURRENT_DIR=$(pwd)

BIN_DIR="/data/server/flume/1.9.0/"

CHECK_PID="ps aux | grep \"${BIN_DIR}\" | grep ‘flume‘ | grep -v grep | awk ‘{print \$2}‘"

cd ${BIN_DIR}

FLUME_PID=$(eval ${CHECK_PID})

if [ ""x == "${FLUME_PID}"x ] ;then
  echo "Flume is no runnig"
  cd ${CURRENT_DIR}
  exit 0
fi

kill -9 $FLUME_PID

echo "Flume is stop!"

cd ${CURRENT_DIR}

5.安装Nginx

1.安装请参考：请参考 CentOS 6.7 配置 yum 安装 nginx

2.设置 Nginx 日志打印格式为JSON字符串

编辑 /etc/nginx/nginx.cnf , 在 http{} 节点查找关键字 log_format ，另起一行增加如下内容：、

log_format post_json ‘{"remote_addr":"$remote_addr","http_x_forwarded_for":"$http_x_forwarded_for","remote_user":"$remote_user","time_local":"$time_local","server_protocol":"$server_protocol","request_time":"$request_time","request_method":"$request_method","request_uri":"$request_uri","status":$status,"body_bytes_sent":$body_bytes_sent,"http_token":"$http_token","http_referer":"$http_referer","http_user_agent":"$http_user_agent","request_body":"$request_body"}‘;

增加一个你的测试web， /etc/nginx/conf.d/test.flume.conf

server {
    listen 8881;

    access_log logs/flume-test.access.log post_json;

    location / {
         root   /data/www/test;
         index  index.html index.htm;
    }

}

重载配置

nginx -s reload

至此配置完毕！

服务验证

1.启动服务：

./1.9.0/bin/flume-ng agent --conf 1.9.0/conf/ -f 1.9.0/conf/flume.conf --name myagent

出现如下报错：

Info: Sourcing environment configuration script /data/server/flume/1.9.0/conf/flume-env.sh
Info: Including Hadoop libraries found via (/data/server/hadoop/3.2.1/bin/hadoop) for HDFS access
Info: Including Hive libraries found via () for Hive access
+ exec /data/server/flume/jdk1.8.0_241//bin/java -Xmx20m -cp ‘/data/server/flume/1.9.0/conf:/data/server/flume/1.9.0/lib/*:/data/server/hadoop/3.2.1/etc/hadoop:/data/server/hadoop/3.2.1/share/hadoop/common/lib/*:/data/server/hadoop/3.2.1/share/hadoop/common/*:/data/server/hadoop/3.2.1/share/hadoop/hdfs:/data/server/hadoop/3.2.1/share/hadoop/hdfs/lib/*:/data/server/hadoop/3.2.1/share/hadoop/hdfs/*:/data/server/hadoop/3.2.1/share/hadoop/mapreduce/lib/*:/data/server/hadoop/3.2.1/share/hadoop/mapreduce/*:/data/server/hadoop/3.2.1/share/hadoop/yarn:/data/server/hadoop/3.2.1/share/hadoop/yarn/lib/*:/data/server/hadoop/3.2.1/share/hadoop/yarn/*:/lib/*‘ -Djava.library.path=:/data/server/hadoop/3.2.1/lib/native org.apache.flume.node.Application -f 1.9.0/conf/flume.conf --name myagent
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/data/server/flume/1.9.0/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/data/server/hadoop/3.2.1/share/hadoop/common/lib/slf4j-log4j12-1.7.25.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]
Exception in thread "SinkRunner-PollingRunner-DefaultSinkProcessor" java.lang.NoSuchMethodError: com.google.common.base.Preconditions.checkArgument(ZLjava/lang/String;Ljava/lang/Object;)V
    at org.apache.hadoop.conf.Configuration.set(Configuration.java:1357)
    at org.apache.hadoop.conf.Configuration.set(Configuration.java:1338)
    at org.apache.hadoop.conf.Configuration.setBoolean(Configuration.java:1679)
    at org.apache.flume.sink.hdfs.BucketWriter.open(BucketWriter.java:221)
    at org.apache.flume.sink.hdfs.BucketWriter.append(BucketWriter.java:572)
    at org.apache.flume.sink.hdfs.HDFSEventSink.process(HDFSEventSink.java:412)
    at org.apache.flume.sink.DefaultSinkProcessor.process(DefaultSinkProcessor.java:67)
    at org.apache.flume.SinkRunner$PollingRunner.run(SinkRunner.java:145)
    at java.lang.Thread.run(Thread.java:748)

原因是 guava jar包版本过低，前往 Maven 仓库下载一个最新的包：guava-28.1-jre.jar ，参考：https://blog.csdn.net/GQB1226/article/details/102555820

移除旧版本

mv 1.9.0/lib/guava-11.0.2.jar ./
wget https://repo1.maven.org/maven2/com/google/guava/guava/28.1-jre/guava-28.1-jre.jar -P 1.9.0/lib/

再次手动启动，控制台输出，显示新建了一个零时文件，hdfs://172.16.1.126:9000/flume/nginx_logs/20200331/2020-03-31-05.1585647655051.log.tmp：

31 Mar 2020 05:40:50,916 INFO  [lifecycleSupervisor-1-0] (org.apache.flume.node.PollingPropertiesFileConfigurationProvider.start:62)  - Configuration provider starting
31 Mar 2020 05:40:50,921 INFO  [conf-file-poller-0] (org.apache.flume.node.PollingPropertiesFileConfigurationProvider$FileWatcherRunnable.run:138)  - Reloading configuration file:./conf/flume.conf
31 Mar 2020 05:40:50,927 INFO  [conf-file-poller-0] (org.apache.flume.conf.FlumeConfiguration$AgentConfiguration.addComponentConfig:1203)  - Processing:k131 Mar 2020 05:40:50,929 INFO  [conf-file-poller-0] (org.apache.flume.conf.FlumeConfiguration$AgentConfiguration.addProperty:1117)  - Added sinks: k1 Agent: myagent
31 Mar 2020 05:40:50,929 INFO  [conf-file-poller-0] (org.apache.flume.conf.FlumeConfiguration$AgentConfiguration.addComponentConfig:1203)  - Processing:r131 Mar 2020 05:40:50,935 WARN  [conf-file-poller-0] (org.apache.flume.conf.FlumeConfiguration$AgentConfiguration.validateConfigFilterSet:623)  - Agent configuration for ‘myagent‘ has no configfilters.
31 Mar 2020 05:40:50,956 INFO  [conf-file-poller-0] (org.apache.flume.conf.FlumeConfiguration.validateConfiguration:163)  - Post-validation flume configuration contains configuration for agents: [myagent]
31 Mar 2020 05:40:50,957 INFO  [conf-file-poller-0] (org.apache.flume.node.AbstractConfigurationProvider.loadChannels:151)  - Creating channels
31 Mar 2020 05:40:50,963 INFO  [conf-file-poller-0] (org.apache.flume.channel.DefaultChannelFactory.create:42)  - Creating instance of channel c1 type memory
31 Mar 2020 05:40:50,967 INFO  [conf-file-poller-0] (org.apache.flume.node.AbstractConfigurationProvider.loadChannels:205)  - Created channel c1
31 Mar 2020 05:40:50,968 INFO  [conf-file-poller-0] (org.apache.flume.source.DefaultSourceFactory.create:41)  - Creating instance of source r1, type exec
31 Mar 2020 05:40:50,974 INFO  [conf-file-poller-0] (org.apache.flume.sink.DefaultSinkFactory.create:42)  - Creating instance of sink: k1, type: hdfs
31 Mar 2020 05:40:50,983 INFO  [conf-file-poller-0] (org.apache.flume.node.AbstractConfigurationProvider.getConfiguration:120)  - Channel c1 connected to [r1, k1]
31 Mar 2020 05:40:50,985 INFO  [conf-file-poller-0] (org.apache.flume.node.Application.startAllComponents:162)  - Starting new configuration:{ sourceRunners:{r1=EventDrivenSourceRunner: { source:org.apache.flume.source.ExecSource{name:r1,state:IDLE} }} sinkRunners:{k1=SinkRunner: { policy:[email protected] counterGroup:{ name:null counters:{} } }} channels:{c1=org.apache.flume.channel.MemoryChannel{name: c1}} }
31 Mar 2020 05:40:50,986 INFO  [conf-file-poller-0] (org.apache.flume.node.Application.startAllComponents:169)  - Starting Channel c1
31 Mar 2020 05:40:51,032 INFO  [lifecycleSupervisor-1-0] (org.apache.flume.instrumentation.MonitoredCounterGroup.register:119)  - Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.
31 Mar 2020 05:40:51,032 INFO  [lifecycleSupervisor-1-0] (org.apache.flume.instrumentation.MonitoredCounterGroup.start:95)  - Component type: CHANNEL, name: c1 started
31 Mar 2020 05:40:51,034 INFO  [conf-file-poller-0] (org.apache.flume.node.Application.startAllComponents:196)  - Starting Sink k1
31 Mar 2020 05:40:51,035 INFO  [conf-file-poller-0] (org.apache.flume.node.Application.startAllComponents:207)  - Starting Source r1
31 Mar 2020 05:40:51,035 INFO  [lifecycleSupervisor-1-4] (org.apache.flume.source.ExecSource.start:170)  - Exec source starting with command: tail -F /usr/local/nginx/logs/hadoop.access.log
31 Mar 2020 05:40:51,036 INFO  [lifecycleSupervisor-1-1] (org.apache.flume.instrumentation.MonitoredCounterGroup.register:119)  - Monitored counter group for type: SINK, name: k1: Successfully registered new MBean.
31 Mar 2020 05:40:51,036 INFO  [lifecycleSupervisor-1-1] (org.apache.flume.instrumentation.MonitoredCounterGroup.start:95)  - Component type: SINK, name: k1 started
31 Mar 2020 05:40:51,036 INFO  [lifecycleSupervisor-1-4] (org.apache.flume.instrumentation.MonitoredCounterGroup.register:119)  - Monitored counter group for type: SOURCE, name: r1: Successfully registered new MBean.
31 Mar 2020 05:40:51,037 INFO  [lifecycleSupervisor-1-4] (org.apache.flume.instrumentation.MonitoredCounterGroup.start:95)  - Component type: SOURCE, name:r1 started
31 Mar 2020 05:40:55,050 INFO  [SinkRunner-PollingRunner-DefaultSinkProcessor] (org.apache.flume.sink.hdfs.HDFSDataStream.configure:57)  - Serializer = TEXT, UseRawLocalFileSystem = false
31 Mar 2020 05:40:55,167 INFO  [SinkRunner-PollingRunner-DefaultSinkProcessor] (org.apache.flume.sink.hdfs.BucketWriter.open:246)  - Creating hdfs://172.16.1.126:9000/flume/nginx_logs/20200331/2020-03-31-05.1585647655051.log.tmp
31 Mar 2020 05:40:59,225 INFO  [Thread-9] (org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.checkTrustAndSend:239)  - SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false

登陆Hadoop查看： http://172.16.1.126:9870/explorer.html#/flume/nginx_logs/20200331

编写一个测试脚本，mockRequest2NginxForTestFlume.sh：

#!/bin/bash
step=1 #间隔的秒数，不能大于60

user_agent_list=("Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:74.0) Gecko/20100101 Firefox/74.0")
user_agent_list[1]="Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36"

referer_list=("https://www.baidu.com" "https://www.qq.com" "https://www.sina.com" "https://weibo.com/")

while [ 1 ]
do
    random=$((RANDOM))
    num=$(((RANDOM%7)+1))
    agent=$(((RANDOM%2)))
    referer=$(((RANDOM%4)))
    url="http://172.16.1.126:8881/"$num".html?r="$random;
    url="http://172.16.1.126:8881/888.html";
    echo " `date +%Y-%m-%d\ %H:%M:%S` get $url"

    #curl http://192.168.75.137/1.html #调用链接
    curl -s -A "${user_agent_list[$agent]}" -e "${referer_list[$referer]}" $url > /dev/null

    sleep $step
done

监控 HDFS文件：

[[email protected] lib]# hadoop fs -tail -f /flume/nginx_logs/20200331/2020-03-31-05.1585647655051.log.tmp
2020-03-31 05:47:31,491 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36","request_body":"-"}
{"remote_addr":"172.16.39.19","http_x_forwarded_for":"-","remote_user":"-","time_local":"31/Mar/2020:04:53:30 -0400","server_protocol":"HTTP/1.1","request_time":"0.000","request_method":"GET","request_uri":"/999.html","status":404,"body_bytes_sent":193,"http_token":"-","http_referer":"-","http_user_agent":"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36","request_body":"-"}
{"remote_addr":"172.16.39.19","http_x_forwarded_for":"-","remote_user":"-","time_local":"31/Mar/2020:04:54:05 -0400","server_protocol":"HTTP/1.1","request_time":"0.000","request_method":"GET","request_uri":"/888.html","status":404,"body_bytes_sent":193,"http_token":"-","http_referer":"-","http_user_agent":"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36","request_body":"-"}

可见日志已经收集进Hadoop里！

Ok, 完结撒花！！！

PS：

大数据可视化之Nginx日志分析及web图表展示（HDFS+Flume+Spark+Nginx+Highcharts）大数据可视化之Nginx日志分析及web图表展示（HDFS+Flume+Spark+Nginx+Highcharts）

flume使用之flume+hive 实现日志离线收集、分析flume使用之flume+hive 实现日志离线收集、分析

Hadoop之——Flume采集Nginx日志到Hive的事务表

原文地址：https://www.cnblogs.com/phpdragon/p/12606769.html

时间： 2024-11-13 21:51:45

Centos7 搭建 Flume 搭配 Hadoop 采集 Nginx 日志的相关文章

Flume采集Nginx日志到HDFS

下载apache-flume-1.7.0-bin.tar.gz,用 tar -zxvf 解压,在/etc/profile文件中增加设置: export FLUME_HOME=/opt/apache-flume-1.7.0-bin export PATH=$PATH:$FLUME_HOME/bin 修改$FLUME_HOME/conf/下的两个文件,在flume-env.sh中增加JAVA_HOME: JAVA_HOME=/opt/jdk1.8.0_121 最重要的,修改flume-conf.pr

Window7 开发 Spark 分析 Nginx 日志

通过上文 Window7 开发 Spark 应用 ,展示了如何开发一个Spark应用,但文中使用的测试数据都是自己手动录入的. 所以本文讲解一下如何搭建一个开发闭环,本里使用了Nginx日志采集分析为例,分析页面访问最多的10个,404页面的10. 如果把这些开发成果最终展示到一个web网页中,在这篇文章中就不描述了,本博其他文章给出的示例已经足够你把Spark的应用能力暴露到Web中. 版本信息 OS: Window7 JAVA:1.8.0_181 Hadoop:3.2.1 Spark: 3.

Nginx日志分析

日志服务支持通过数据接入向导配置采集Nginx日志,并自动创建索引和Nginx日志仪表盘,达到快速采集并分析Nginx日志. 很多个人站长选取Nginx作为服务器搭建网站,在对网站访问情况进行分析时,需要对Nginx访问日志统计分析,从中获得网站的访问量,访问时段等访问情况,传统模式下利用CNZZ模式,在前端页面插入js,用户访问的时候触发js,但只能记录页面的访问请求,像ajax之类的请求是无法记录的,还有爬虫信息也不会记录.或者利用流计算.离线统计分析Nginx访问日志,从日志中挖掘有用信息

大数据技术之_18_大数据离线平台_02_Nginx+Mysql+数据收集+Web 工程 JS/JAVA SDK 讲解+Flume 故障后-如何手动上传 Nginx 日志文件至 HDFS 上

十一.Nginx11.1.介绍11.2.常见其他 Web 服务器11.3.版本11.4.Nginx 安装11.5.目录结构11.6.操作命令十二.Mysql12.1.介绍12.2.关系型数据库(SQL)种类12.3.特征12.4.术语12.4.与非关系型数据库比较(Not Only SQL)12.4.1.种类12.4.2.特征12.4.3.总结十三.数据收集13.1.收集方式13.2.数据的事件类型13.2.1.Launch 事件13.2.2.PageView 事件13.3.Nginx 日志收集

centos7搭建ELK Cluster集群日志分析平台（三）

续 centos7搭建ELK Cluster集群日志分析平台(一) 续 centos7搭建ELK Cluster集群日志分析平台(二) 已经安装好elasticsearch 5.4集群和logstash 5.4 安装kibana步骤 1.下载安装Kibana ~]#wget https://artifacts.elastic.co/downloads/kibana/kibana-5.4.0-x86_64.rpm 如果链接失效,官网下载:https://www.elastic.co/down

分布式实时日志系统（二）环境搭建之 flume 集群搭建/flume ng资料

最近公司业务数据量越来越大,以前的基于消息队列的日志系统越来越难以满足目前的业务量,表现为消息积压,日志延迟,日志存储日期过短,所以,我们开始着手要重新设计这块,业界已经有了比较成熟的流程,即基于流式处理,采用 flume 收集日志,发送到 kafka 队列做缓冲,storm 分布式实时框架进行消费处理,短期数据落地到 hbase.mongo中,长期数据进入 hadoop 中存储. 接下来打算将这其间所遇到的问题.学习到的知识记录整理下,作为备忘,作为分享,带给需要的人. 学习flume ng的

centos7搭建ELK Cluster集群日志分析平台（二）

续 centos7搭建ELK Cluster集群日志分析平台(一) 已经安装完Elasticsearch 5.4 集群. 安装Logstash步骤 1. 安装Java 8 官方说明:需要安装Java 8 ,不支持Java 9... //自行安装,略过 2. 安装Logstash 可以同elasticsearch一样建立repo文件通过yum安装,也可以去官网直接下载rpm包进行本地安装: ~]# rpm -ivh logstash-5.4.0.rpm //这里直接下载好进行本地安装 3.

ELKR分布式搭建nginx日志分析系统

ELKR分布式搭建nginx日志分析系统一.名词介绍 1.什么是elk ELK 其实并不是一款软件,而是一整套解决方案,是三个软件产品的首字母缩写,Elasticsearch,Logstash 和 Kibana.这三款软件都是开源软件,通常是配合使用. 2.Elasticsearch 2.1.Elasticsearch介绍 Elasticsearch 是一个实时的分布式搜索和分析引擎,它可以用于全文搜索,结构化搜索以及分析.它是一个建立在全文搜索引擎 Apache Lucene 基础上的搜索引

Flume采集处理日志文件

Flume简介 Flume是Cloudera提供的一个高可用的,高可靠的,分布式的海量日志采集.聚合和传输的系统,Flume支持在日志系统中定制各类数据发送方,用于收集数据:同时,Flume提供对数据进行简单处理,并写到各种数据接受方(可定制)的能力. 系统功能日志收集 Flume最早是Cloudera提供的日志收集系统,目前是Apache下的一个孵化项目,Flume支持在日志系统中定制各类数据发送方,用于收集数据. 数据处理 Flume提供对数据进行简单处理,并写到各种数据接受方(可定制)的