目录
一、准备工作
Flume 要想将数据输出到 HDFS，必须持有 Hadoop 相关 jar 包。
将以下 jar 包拷贝到"/usr/local/flume/lib"目录下。
/usr/local/servers/hadoop/share/hadoop/common/lib/commons-configuration2-2.1.1.jar
/usr/local/servers/hadoop/share/hadoop/common/lib/commons-io-2.5.jar
/usr/local/servers/hadoop/share/hadoop/common/lib/hadoop-auth-3.1.3.jar
/usr/local/servers/hadoop/share/hadoop/common/lib/htrace-core4-4.1.0-incubating.jar
/usr/local/servers/hadoop/share/hadoop/common/lib/stax2-api-3.1.4.jar
/usr/local/servers/hadoop/share/hadoop/common/hadoop-common-3.1.3.jar
/usr/local/servers/hadoop/share/hadoop/hdfs/hadoop-hdfs-3.1.3.jar
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[root@bigdata common]# cd /usr/local/flume/lib
[root@bigdata lib]# cp /usr/local/servers/hadoop/share/hadoop/common/lib/commons-configuration2-2.1.1.jar .
[root@bigdata lib]# cp /usr/local/servers/hadoop/share/hadoop/common/lib/commons-io-2.5.jar .
[root@bigdata lib]# cp /usr/local/servers/hadoop/share/hadoop/common/lib/hadoop-auth-3.1.3.jar .
[root@bigdata lib]# cp /usr/local/servers/hadoop/share/hadoop/common/lib/htrace-core4-4.1.0-incubating.jar .
[root@bigdata lib]# cp /usr/local/servers/hadoop/share/hadoop/common/lib/stax2-api-3.1.4.jar .
[root@bigdata lib]# cp /usr/local/servers/hadoop/share/hadoop/common/hadoop-common-3.1.3.jar .
[root@bigdata lib]# cp /usr/local/servers/hadoop/share/hadoop/hdfs/hadoop-hdfs-3.1.3.jar .
二、实时读取本地文件到HDFS
（一）案例需求
实时监控Hive日志，并上传到HDFS中。
（二）需求分析
（三）实现步骤
1、在"/usr/local/flume/"目录下新建文件夹job
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[root@bigdata lib]# cd /usr/local/flume
[root@bigdata flume]# mkdir job
[root@bigdata flume]# cd job2、在job目录下新建文件flume-file-hdfs.conf
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[root@bigdata job]# vi flume-file-hdfs.conf
bash
# Name the components on this agent
a2.sources = r2
a2.sinks = k2
a2.channels = c2
# Describe/configure the source
a2.sources.r2.type = exec
a2.sources.r2.command = tail -F /usr/local/flume/datas/flume_tmp.log
a2.sources.r2.shell = /bin/bash -c
# Describe the sink
a2.sinks.k2.type = hdfs
a2.sinks.k2.hdfs.path = hdfs://bigdata:9000/flume/%Y%m%d/%H
#上传文件的前缀
a2.sinks.k2.hdfs.filePrefix = logs-
#是否按照时间滚动文件夹
a2.sinks.k2.hdfs.round = true
#多少时间单位创建一个新的文件夹
a2.sinks.k2.hdfs.roundValue = 1
#重新定义时间单位
a2.sinks.k2.hdfs.roundUnit = hour
#是否使用本地时间戳
a2.sinks.k2.hdfs.useLocalTimeStamp = true
#积攒多少个Event才flush到HDFS一次
a2.sinks.k2.hdfs.batchSize = 1000
#设置文件类型，可支持压缩
a2.sinks.k2.hdfs.fileType = DataStream
#多久生成一个新的文件
a2.sinks.k2.hdfs.rollInterval = 60
#设置每个文件的滚动大小
a2.sinks.k2.hdfs.rollSize = 134217700
#文件的滚动与Event数量无关
a2.sinks.k2.hdfs.rollCount = 0
#最小冗余数
a2.sinks.k2.hdfs.minBlockReplicas = 1
# Use a channel which buffers events in memory
a2.channels.c2.type = memory
a2.channels.c2.capacity = 1000
a2.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a2.sources.r2.channels = c2
a2.sinks.k2.channel = c2内容详解：
3、执行监控配置
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[root@bigdata zhc]# cd /usr/local/flume
[root@bigdata flume]# bin/flume-ng agent --conf conf/ --name a2 --conf-file job/flume-file-hdfs.conf
4、启动Hadoop和Hive并操作Hive产生日志
启动一个新的终端，输入：
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[root@bigdata zhc]# start-all.sh
[root@bigdata zhc]# cd /usr/local/hive
[root@bigdata hive]# bin/hive
再启动一个新的终端，写入日志：
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[root@bigdata hive]# echo 123 > /usr/local/flume/datas/flume_tmp.log然后就可以在HDFS上查看：
三、实时读取目录文件到HDFS
（一）案例需求
使用Flume监听整个目录的文件。
（二）需求分析
（三）实现步骤
1、在job目录下新建文件flume-dir-hdfs.conf
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[root@bigdata job]# vi flume-dir-hdfs.conf
bash
a3.sources = r3
a3.sinks = k3
a3.channels = c3
# Describe/configure the source
a3.sources.r3.type = spooldir
a3.sources.r3.spoolDir = /usr/local/flume/datas
a3.sources.r3.fileSuffix = .COMPLETED
a3.sources.r3.fileHeader = true
#忽略所有以.tmp结尾的文件，不上传
a3.sources.r3.ignorePattern = ([^ ]*\.tmp)
# Describe the sink
a3.sinks.k3.type = hdfs
a3.sinks.k3.hdfs.path = hdfs://bigdata:9000/flume/upload/%Y%m%d/%H
#上传文件的前缀
a3.sinks.k3.hdfs.filePrefix = upload-
#是否按照时间滚动文件夹
a3.sinks.k3.hdfs.round = true
#多少时间单位创建一个新的文件夹
a3.sinks.k3.hdfs.roundValue = 1
#重新定义时间单位
a3.sinks.k3.hdfs.roundUnit = hour
#是否使用本地时间戳
a3.sinks.k3.hdfs.useLocalTimeStamp = true
#积攒多少个Event才flush到HDFS一次
a3.sinks.k3.hdfs.batchSize = 100
#设置文件类型，可支持压缩
a3.sinks.k3.hdfs.fileType = DataStream
#多久生成一个新的文件
a3.sinks.k3.hdfs.rollInterval = 60
#设置每个文件的滚动大小大概是128M
a3.sinks.k3.hdfs.rollSize = 134217700
#文件的滚动与Event数量无关
a3.sinks.k3.hdfs.rollCount = 0
# Use a channel which buffers events in memory
a3.channels.c3.type = memory
a3.channels.c3.capacity = 1000
a3.channels.c3.transactionCapacity = 100
# Bind the source and sink to the channel
a3.sources.r3.channels = c3
a3.sinks.k3.channel = c3内容详解：
2、启动监控文件夹命令
启动一个新的终端：
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[root@bigdata hive]# cd /usr/local/flume
[root@bigdata flume]# bin/flume-ng agent --conf conf/ --name a3 --conf-file job/flume-dir-hdfs.conf
说明： 在使用Spooling Directory Source时
1.不要在监控目录中创建并持续修改文件
2.上传完成的文件会以.COMPLETED结尾
3.被监控文件夹每500毫秒扫描一次文件变动
3、向datas文件夹中添加文件
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[root@bigdata job]# cd /usr/local/flume/datas
[root@bigdata datas]# touch one.txt
[root@bigdata datas]# touch two.txt最后再到HDFS上查看：
