Flume-复制和多路复用案例

逃离我推掉我的手 2023-01-23 14:47 165阅读 0赞

### 复制和多路复用 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NsZWFybHhq_size_16_color_FFFFFF_t_70]

分析：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

1-2：taildir source -> memory channel -> avro sink

2-HDFS：avro source -> memory channel -> hdfs sink

1-3: taildir source -> memory channel -> avro sink

3-Local FileSystem：avro source -> memory channel -> file roll sink

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NsZWFybHhq_size_16_color_FFFFFF_t_70 1]

Avro 是由 Hadoop 创始人 Doug Cutting 创建的一种语言无关的数据序列化和 RPC 框架。  
RPC(Remote Procedure Call)—远程过程调用,它是一种通过网络从远程计算机程序上请求服务,而不需要了解底层网络技术的协议

Flume-1的配置文件

# Name the components on this agent
    a1.sources = r1
    a1.sinks = k1 k2
    a1.channels = c1 c2
    
    # Describe/configure the source
    a1.sources.r1.type = TAILDIR
    a1.sources.r1.filegroups = f1
    a1.sources.r1.filegroups.f1 = /home/bd/tmp/group1Test/log
    a1.sources.r1.positionFile = /home/bd/tmp/group1Position/position.json
    # set channel selector
    a1.sources.r1.selector.type = replicating
    
    # Describe the sink
    a1.sinks.k1.type = avro
    a1.sinks.k1.hostname = hadoop113
    a1.sinks.k1.port = 4441
    a1.sinks.k2.type = avro
    a1.sinks.k2.hostname = hadoop113
    a1.sinks.k2.port = 4442
    
    # Use a channel which buffers events in memory
    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

Flume-2配置文件

# Name the components on this agent
    a2.sources = r2
    a2.sinks = k2
    a2.channels = c2
    
    # Describe/configure the source
    a2.sources.r2.type = avro
    a2.sources.r2.bind = hadoop113
    a2.sources.r2.port = 4441
    
    # Describe the sink
    a2.sinks.k2.type = hdfs
    a2.sinks.k2.hdfs.path = hdfs://hadoop113:9000/group1/%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,到了一定时间了也会去flush
    a2.sinks.k2.hdfs.batchSize = 500
    #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,60s
    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
    
    # 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

Flume-3配置文件

# Name the components on this agent
    a3.sources = r2
    a3.sinks = k2
    a3.channels = c2
    
    # Describe/configure the source
    a3.sources.r2.type = avro
    a3.sources.r2.bind = hadoop113
    a3.sources.r2.port = 4442
    
    # Sink
    a3.sinks.k2.type = file_roll
    # 这个目录要提前创建好，否则会报错
    a3.sinks.k2.sink.directory = /home/bd/tmp/group1FileRoll
    
    
    # Use a channel which buffers events in memory
    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.r2.channels = c2
    a3.sinks.k2.channel = c2

启动时，要先启动下游的Flume，不然如果上游是avro sink的话，先启动上游的话，会出现连接不上avro的错误，因为avro sink相当于客户端，avro source相当于服务端，虽然会有重试的机制，但是最好还是先启动下游Flume，再启动上游Flume。

本案例，先启动Flume-2和Flume-3，最后启动Flume-1。

为log文件依次追加hello和word

![在这里插入图片描述][20210715150433741.png]

查看本地文件内容：  
![在这里插入图片描述][2021071515042815.png]

查看HDFS文件内容：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NsZWFybHhq_size_16_color_FFFFFF_t_70 2]

均传输成功了。

备注：本地文件默认30s滚动一次，就算没有内容也会滚动一个空的文件，而HDFS则不会滚动空文件。

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NsZWFybHhq_size_16_color_FFFFFF_t_70 1]: /images/20221004/9e4fbadf388042d584a92933d6555eeb.png
[20210715150433741.png]: /images/20221004/765bb01cab4947b0a598a944ea53fd4e.png
[2021071515042815.png]: /images/20221004/f1eda6ad47484f56ae7ba68eaa32ba75.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NsZWFybHhq_size_16_color_FFFFFF_t_70 2]: /images/20221004/913630a2b64048f194be8f3ebc053e62.png