1 复制和多路复用
1.1 案例需求
使用 Flume-1 监控文件变动，Flume-1 将变动内容传递给 Flume-2，Flume-2 负责存储
到 HDFS。同时 Flume-1 将变动内容传递给 Flume-3，Flume-3 负责输出到 LocalFileSystem。
1.2 需求分析：单数据源多出口案例（选择器）
1.3 实现步骤
（1）准备工作
在/opt/module/flume/job 目录下创建 group1 文件夹
[bigdata@hadoop102 job]$ cd group1/
在/opt/module/datas/目录下创建 flume3 文件夹
[bigdata@hadoop102 datas]$ mkdir flume3
（2）创建 flume-file-flume.conf
配置 1 个接收日志文件的 source 和两个 channel、两个 sink，分别输送给 flume-flume-
hdfs 和 flume-flume-dir。
编辑配置文件
[bigdata@hadoop102 group1]$ vim flume-file-flume.conf
添加如下内容
# Name the components on this agent a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1 c2 # 将数据流复制给所有 channel a1.sources.r1.selector.type = replicating # Describe/configure the source a1.sources.r1.type = exec a1.sources.r1.command = tail -F /opt/module/hive/logs/hive.log a1.sources.r1.shell = /bin/bash -c # Describe the sink # sink 端的 avro 是一个数据发送者 a1.sinks.k1.type = avro a1.sinks.k1.hostname = hadoop102 a1.sinks.k1.port = 4141 a1.sinks.k2.type = avro a1.sinks.k2.hostname = hadoop102 a1.sinks.k2.port = 4142 # Describe the channel a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 a1.channels.c2.type = memory a1.channels.c2.capacity = 1000 a1.channels.c2.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 c2 a1.sinks.k1.channel = c1 a1.sinks.k2.channel = c2
（3）创建 flume-flume-hdfs.conf
配置上级 Flume 输出的 Source，输出是到 HDFS 的 Sink。
编辑配置文件
[bigdata@hadoop102 group1]$ vim flume-flume-hdfs.conf
添加如下内容
# Name the components on this agent a2.sources = r1 a2.sinks = k1 a2.channels = c1 # Describe/configure the source # source 端的 avro 是一个数据接收服务 a2.sources.r1.type = avro a2.sources.r1.bind = hadoop102 a2.sources.r1.port = 4141 # Describe the sink a2.sinks.k1.type = hdfs a2.sinks.k1.hdfs.path = hdfs://hadoop102:9820/flume2/%Y%m%d/%H #上传文件的前缀 a2.sinks.k1.hdfs.filePrefix = flume2- #是否按照时间滚动文件夹 a2.sinks.k1.hdfs.round = true #多少时间单位创建一个新的文件夹 a2.sinks.k1.hdfs.roundValue = 1 #重新定义时间单位 a2.sinks.k1.hdfs.roundUnit = hour #是否使用本地时间戳 a2.sinks.k1.hdfs.useLocalTimeStamp = true #积攒多少个 Event 才 flush 到 HDFS 一次 a2.sinks.k1.hdfs.batchSize = 100 #设置文件类型，可支持压缩 a2.sinks.k1.hdfs.fileType = DataStream #多久生成一个新的文件 a2.sinks.k1.hdfs.rollInterval = 30 #设置每个文件的滚动大小大概是 128M a2.sinks.k1.hdfs.rollSize = 134217700 #文件的滚动与 Event 数量无关 a2.sinks.k1.hdfs.rollCount = 0 # Describe the channel a2.channels.c1.type = memory a2.channels.c1.capacity = 1000 a2.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a2.sources.r1.channels = c1 a2.sinks.k1.channel = c1
（4）创建 flume-flume-dir.conf
配置上级 Flume 输出的 Source，输出是到本地目录的 Sink。
编辑配置文件
[bigdata@hadoop102 group1]$ vim flume-flume-dir.conf
添加如下内容
# Name the components on this agent a3.sources = r1 a3.sinks = k1 a3.channels = c2 # Describe/configure the source a3.sources.r1.type = avro a3.sources.r1.bind = hadoop102 a3.sources.r1.port = 4142 # Describe the sink a3.sinks.k1.type = file_roll a3.sinks.k1.sink.directory = /opt/module/data/flume3 # Describe the channel a3.channels.c2.type = memory a3.channels.c2.capacity = 1000 a3.channels.c2.transactionCapacity = 100 # Bind the source and sink to the channel a3.sources.r1.channels = c2 a3.sinks.k1.channel = c2
提示：输出的本地目录必须是已经存在的目录，如果该目录不存在，并不会创建新的目
录。
（5）执行配置文件
分别启动对应的 flume 进程：flume-flume-dir，flume-flume-hdfs，flume-file-flume。
[bigdata@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --namea3 --conf-file job/group1/flume-flume-dir.conf [bigdata@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --namea2 --conf-file job/group1/flume-flume-hdfs.conf [bigdata@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --namea1 --conf-file job/group1/flume-file-flume.conf
（6）启动 Hadoop 和 Hive
[bigdata@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh [bigdata@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh [bigdata@hadoop102 hive]$ bin/hive hive (default)>
（7）检查 HDFS 上数据
（8）检查/opt/module/datas/flume3 目录中数据
[bigdata@hadoop102 flume3]$ ll 总用量 8 -rw-rw-r--. 1 bigdata bigdata 5942 5 月 22 00:09 1526918887550-3
2 负载均衡和故障转移
2.1 案例需求
使用 Flume1 监控一个端口，其 sink 组中的 sink 分别对接 Flume2 和 Flume3，采用FailoverSinkProcessor，实现故障转移的功能。
2.2 需求分析:故障转移案例
2.3 实现步骤
（1）准备工作
在/opt/module/flume/job 目录下创建 group2 文件夹
[bigdata@hadoop102 job]$ cd group2/
（2）创建 flume-netcat-flume.conf
配置 1 个 netcat source 和 1 个 channel、1 个 sink group（2 个 sink），分别输送给
flume-flume-console1 和 flume-flume-console2。
编辑配置文件
[bigdata@hadoop102 group2]$ vim flume-netcat-flume.conf
添加如下内容
# Name the components on this agent a1.sources = r1 a1.channels = c1 a1.sinkgroups = g1 a1.sinks = k1 k2 # Describe/configure the source a1.sources.r1.type = netcat a1.sources.r1.bind = localhost a1.sources.r1.port = 44444 a1.sinkgroups.g1.processor.type = failover a1.sinkgroups.g1.processor.priority.k1 = 5 a1.sinkgroups.g1.processor.priority.k2 = 10 a1.sinkgroups.g1.processor.maxpenalty = 10000 # Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.hostname = hadoop102 a1.sinks.k1.port = 4141 a1.sinks.k2.type = avro a1.sinks.k2.hostname = hadoop102 a1.sinks.k2.port = 4142 # Describe the channel a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinkgroups.g1.sinks = k1 k2 a1.sinks.k1.channel = c1 a1.sinks.k2.channel = c1
（3）创建 flume-flume-console1.conf
配置上级 Flume 输出的 Source，输出是到本地控制台。
编辑配置文件
[bigdata@hadoop102 group2]$ vim flume-flume-console1.conf
添加如下内容
# Name the components on this agent a2.sources = r1 a2.sinks = k1 a2.channels = c1 # Describe/configure the source a2.sources.r1.type = avro a2.sources.r1.bind = hadoop102 a2.sources.r1.port = 4141 # Describe the sink a2.sinks.k1.type = logger # Describe the channel a2.channels.c1.type = memory a2.channels.c1.capacity = 1000 a2.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a2.sources.r1.channels = c1 a2.sinks.k1.channel = c1
（4）创建 flume-flume-console2.conf
配置上级 Flume 输出的 Source，输出是到本地控制台。
编辑配置文件
[bigdata@hadoop102 group2]$ vim flume-flume-console2.conf
添加如下内容
# Name the components on this agent a3.sources = r1 a3.sinks = k1 a3.channels = c2 # Describe/configure the source a3.sources.r1.type = avro a3.sources.r1.bind = hadoop102 a3.sources.r1.port = 4142 # Describe the sink a3.sinks.k1.type = logger # Describe the channel a3.channels.c2.type = memory a3.channels.c2.capacity = 1000 a3.channels.c2.transactionCapacity = 100 # Bind the source and sink to the channel a3.sources.r1.channels = c2 a3.sinks.k1.channel = c2
（5）执行配置文件
分别开启对应配置文件：flume-flume-console2，flume-flume-console1，flume-netcat-flume。
[bigdata@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --namea3 --conf-file job/group2/flume-flume-console2.conf - Dflume.root.logger=INFO,console [bigdata@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --namea2 --conf-file job/group2/flume-flume-console1.conf - Dflume.root.logger=INFO,console [bigdata@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --namea1 --conf-file job/group2/flume-netcat-flume.conf
（6）使用 netcat 工具向本机的 44444 端口发送内容
$ nc localhost 44444
（7）查看 Flume2 及 Flume3 的控制台打印日志
（8）将 Flume2 kill，观察 Flume3 的控制台打印情况。
注：使用 jps - -l ml 查看 e Flume 进程。
3 聚合
3.1 案例需求
hadoop102 上的 Flume-1 监控文件/opt/module/group.log，hadoop103 上的 Flume-2 监控某一个端口的数据流，Flume-1 与 Flume-2 将数据发送给 hadoop104 上的 Flume-3，Flume-3 将最终数据打印
到控制台。
3.2 需求分析:多数据源汇总案例
3.3 实现步骤
（1）准备工作
分发 Flume
[bigdata@hadoop102 module]$ xsync flume
在 hadoop102、hadoop103 以及 hadoop104 的/opt/module/flume/job 目录下创建一个
group3 文件夹。
[bigdata@hadoop102 job]$ mkdir group3 [bigdata@hadoop103 job]$ mkdir group3 [bigdata@hadoop104 job]$ mkdir group3
（2）创建 flume1-logger-flume.conf
配置 Source 用于监控 hive.log 文件，配置 Sink 输出数据到下一级 Flume。
在 hadoop102 上编辑配置文件
[bigdata@hadoop102 group3]$ vim flume1-logger-flume.conf
添加如下内容
# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = exec a1.sources.r1.command = tail -F /opt/module/group.log a1.sources.r1.shell = /bin/bash -c # Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.hostname = hadoop104 a1.sinks.k1.port = 4141 # Describe the channel a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1
（3）创建 flume2-netcat-flume.conf
配置 Source 监控端口 44444 数据流，配置 Sink 数据到下一级 Flume：
在 hadoop103 上编辑配置文件
[bigdata@hadoop102 group3]$ vim flume2-netcat-flume.conf
添加如下内容
# Name the components on this agent a2.sources = r1 a2.sinks = k1 a2.channels = c1 # Describe/configure the source a2.sources.r1.type = netcat a2.sources.r1.bind = hadoop103 a2.sources.r1.port = 44444 # Describe the sink a2.sinks.k1.type = avro a2.sinks.k1.hostname = hadoop104 a2.sinks.k1.port = 4141 # Use a channel which buffers events in memory a2.channels.c1.type = memory a2.channels.c1.capacity = 1000 a2.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a2.sources.r1.channels = c1 a2.sinks.k1.channel = c1
（4） 创建 flume3- - flume- - logger.conf
配置 source 用于接收 flume1 与 flume2 发送过来的数据流，最终合并后 sink 到控制
台。
在 hadoop104 上编辑配置文件
[bigdata@hadoop104 group3]$ touch flume3-flume-logger.conf [bigdata@hadoop104 group3]$ vim flume3-flume-logger.conf
添加如下内容
# Name the components on this agent a3.sources = r1 a3.sinks = k1 a3.channels = c1 # Describe/configure the source a3.sources.r1.type = avro a3.sources.r1.bind = hadoop104 a3.sources.r1.port = 4141 # Describe the sink # Describe the sink a3.sinks.k1.type = logger # Describe the channel a3.channels.c1.type = memory a3.channels.c1.capacity = 1000 a3.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a3.sources.r1.channels = c1 a3.sinks.k1.channel = c1
（5）执行配置文件
分别开启对应配置文件：flume3-flume-logger.conf，flume2-netcat-flume.conf，flume1-logger-flume.conf。
[bigdata@hadoop104 flume]$ bin/flume-ng agent --conf conf/ --namea3 --conf-file job/group3/flume3-flume-logger.conf - Dflume.root.logger=INFO,console [bigdata@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --namea2 --conf-file job/group3/flume1-logger-flume.conf [bigdata@hadoop103 flume]$ bin/flume-ng agent --conf conf/ --namea1 --conf-file job/group3/flume2-netcat-flume.conf
（6）在 hadoop103 上向/opt/module 目录下的 group.log 追加内容
[bigdata@hadoop103 module]$ echo 'hello' > group.log
（7）在 hadoop102 上向 44444 端口发送数据
[bigdata@hadoop102 flume]$ telnet hadoop102 44444
（8）检查 hadoop104 上数据
