Flume NG部署

　　本次配置单节点的Flume NG

1、下载flume安装包

　　下载地址：(http://flume.apache.org/download.html)

　　apache-flume-1.6.0-bin.tar.gz安装包上传解压到集群上的/usr/hadoop/目录下。

[hadoop@centpy hadoop]$ pwd
usr/hadoop
[hadoop@centpy hadoop]$ ls
hadoop-2.6.      　　　　　　   zookeeper-3.4.      hbase-0.98.  jdk1..0_60
[hadoop@centpy hadoop]$ rz

[hadoop@centpy hadoop]$ ls
apache-flume-1.8.0-bin.tar.gz  jdk1..0_60          hbase-0.98.  zookeeper-3.4.6     hadoop-2.6.
[hadoop@centpy hadoop]$ tar -zxf apache-flume-1.8.0-bin.tar.gz
[hadoop@centpy hadoop]$ ls
apache-flume-1.8.0-bin         hadoop-2.6.
apache-flume-1.8.-bin.tar.gz  jdk1..0_60hbase-0.98.19　　　　　　　　　　 zookeeper-3.4.
[hadoop@centpy hadoop]$ rm -f apache-flume-1.8.0-bin.tar.gz
[hadoop@centpy hadoop]$ mv apache-flume-1.8.0-bin/ flume-1.8.0
[hadoop@centpy hadoop]$ ls
jdk1..0_60          flume-1.8.0   hbase-0.98.  zookeeper-3.4.6  hadoop-2.6.0

2、配置flume

[hadoop@centpy hadoop]$ cd flume-1.8.0/conf/
[hadoop@centpy conf]$ ls
flume-conf.properties.template  flume-env.ps1.template  flume-env.sh.template  log4j.properties
[hadoop@centpy conf]$  cp flume-conf.properties.template flume-conf.properties    //需要通过flume-conf.properties.template复制一个flume-conf.properties配置文件
[hadoop@centpy conf]$ ls
flume-conf.properties  flume-conf.properties.template  flume-env.ps1.template  flume-env.sh.template  log4j.properties

[hadoop@centpy conf]$ vi flume-conf.properties

#Define sources, channels, sinks
agent1.sources = spool-source1
agent1.channels = ch1
agent1.sinks = hdfs-sink1

#Define and configure an Spool directory source
agent1.sources.spool-source1.channels = ch1
agent1.sources.spool-source1.type = spooldir
agent1.sources.spool-source1.spoolDir = /home/hadoop/test
agent1.sources.spool-source1.ignorePattern = event(_\d{}\-\d{}-\d{}_\d{}_\d{})?\.log(\.COMPLETED)?
agent1.sources.spool-source1.deserializer.maxLineLength = 

#Configure channels
agent1.sources.ch1.type = file
agent1.sources.ch1.checkpointDir = /home/hadoop/app/flume/checkpointDir
agent1.sources.ch1.dataDirs = /home/hadoop/app/flume/dataDirs

#Define and configure a hdfs sink
agent1.sinks.hdfs-sink1.channels = ch1
agent1.sinks.hdfs-sink1.type = hdfs
agent1.sinks.hdfs-sink1.hdfs.path = hdfs://centpy:9000/flume/%Y%m%d
agent1.sinks.hdfs-sink1.hdfs.useLocalTimeStamp = true
agent1.sinks.hdfs-sink1.hdfs.rollInterval =
agent1.sinks.hdfs-sink1.hdfs.rollSize =
agent1.sinks.hdfs-sink1.hdfs.rollCount =
#agent1.sinks.hdfs-sink1.hdfs.codeC = snappy

　　修改集群上的flume-conf.properties配置文件，这里收集日志文件到收集端。配置参数的详细说明可以参考官方文档(https://cwiki.apache.org/confluence/display/FLUME/Getting+Started)。

3、启动并测试Flume

　　1）首先启动Hadoop集群

[hadoop@centpy hadoop]$ cd hadoop-2.6.0
[hadoop@centpy hadoop-2.6.]$ sbin/start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [centpy]
centpy: starting namenode, logging to /usr/hadoop/hadoop-2.6./logs/hadoop-hadoop-namenode-centpy.out
centpy: starting datanode, logging to /usr/hadoop/hadoop-2.6./logs/hadoop-hadoop-datanode-centpy.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /usr/hadoop/hadoop-2.6./logs/hadoop-hadoop-secondarynamenode-centpy.out
starting yarn daemons
starting resourcemanager, logging to /usr/hadoop/hadoop-2.6./logs/yarn-hadoop-resourcemanager-centpy.out
centpy: starting nodemanager, logging to /usr/hadoop/hadoop-2.6./logs/yarn-hadoop-nodemanager-centpy.out
[hadoop@centpy hadoop-2.6.]$ jps
 ResourceManager
 SecondaryNameNode
 NameNode
 NodeManager
 DataNode
 Jps

　　2）启动Flume

[hadoop@centpy hadoop-2.6.]$ cd ../flume/
[hadoop@centpy flume]$ bin/flume-ng agent -n agent1 -f conf/flume-conf.properties

　　如上图，我们已经成功启动Flume。

　　3）测试Flume

　　先上传一个测试文件到我们配置的测试目录中（/home/hadoop/test）

[hadoop@centpy conf]$ cd /home/hadoop/test/
[hadoop@centpy test]$ ls
[hadoop@centpy test]$ rz

[hadoop@centpy test]$ ls
template.log

　　此时Flume会收集日志信息如下：

// :: INFO hdfs.BucketWriter: Creating hdfs://centpy:9000/flume/20180619/FlumeData.1529394914599.tmp
（此处会先在数据收集过程中先生成一个.tmp文件用于记录，等到30秒过后数据收集完成则会生成最终文件FlumeData.1529394914599）
// :: INFO file.EventQueueBackingStoreFile: Start checkpoint for /usr/hadoop/flume/checkpointDir/checkpoint, elements to sync =
// :: INFO file.EventQueueBackingStoreFile: Updating checkpoint metadata: logWriteOrderID: , queueSize: , queueHead:
// :: INFO file.Log: Updated checkpoint for file: /usr/hadoop/flume/dataDirs/log- position:  logWriteOrderID:
// :: INFO hdfs.BucketWriter: Closing hdfs://centpy:9000/flume/20180619/FlumeData.1529394914599.tmp
// :: INFO hdfs.BucketWriter: Renaming hdfs://centpy:9000/flume/20180619/FlumeData.1529394914599.tmp to hdfs://centpy:9000/flume/20180619/FlumeData.1529394914599
// :: INFO hdfs.HDFSEventSink: Writer callback called.

　　我们也可以在Web浏览器查看文件信息

 4、Flume 案例分析

　　下面我们看一下flume的实际应用场景，其示例图如下所示。

　　

　　在上面的应用场景中，主要可以分为以下几个步骤。
　　1、首先采用flume进行日志收集。
　　2、采用HDFS进行日志的存储。
　　3、采用MapReduce/Hive进行日志分析。
　　4、将分析后的格式化日志存储到Mysql数据库中。
　　5、最后前端查询，实现数据可视化展示。
　　flume的实际应用场景，相信大家有了一个初步的认识，大家可以根据复杂的业务需求，实现flume来收集数据。这里就不一一讲述，希望大家在以后的学习过程中，学会学习、学会解决实际的问题。

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