Flume系列：Flume常用采集通道案例

目录

Apache Hadoop生态-目录汇总-持续更新

1: 文件->flume->kafka

2: kafka->flume->hdfs (方案一)

3: kafka->flume->hdfs (方案二)

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Apache Hadoop生态-目录汇总-持续更新

系统环境：centos7

Java环境：Java8

1: 文件->flume->kafka

tailDir source -> kafka channel

配置文件：file_flume_kafka.conf

# 1：定义组件
file_flume_kafka.sources = r1
file_flume_kafka.channels = c1

# 2：定义source
file_flume_kafka.sources.r1.type = TAILDIR
file_flume_kafka.sources.r1.positionFile = /usr/local/flume-1.9.0/project_v4/tail_dir.json
file_flume_kafka.sources.r1.fileSuffix = .COMPLETED
file_flume_kafka.sources.r1.filegroups = f1
file_flume_kafka.sources.r1.filegroups.f1 = /log/app.*.log
 ## 定义source拦截器(ETL数据清洗，判断数据是否完整)
file_flume_kafka.sources.r1.interceptors = i1
file_flume_kafka.sources.r1.interceptors.i1.type = com.wester.flume.interceptor.ETLInterceptor$Builder

# 3：定义channel
file_flume_kafka.channels.c1.type = org.apache.flume.channel.kafka.KafkaChannel
file_flume_kafka.channels.c1.kafka.bootstrap.servers = 192.168.5.103:9092,192.168.5.87:9092,192.168.5.114:9092
file_flume_kafka.channels.c1.kafka.topic = project_v4_topic_log
    ## 设置消费者组,保证每次消费时能够获取上次对应的Offset
file_flume_kafka.channels.c1.consumer.group.id = file_flume_kafka
    #设置不用flume组装的header
file_flume_kafka.channels.c1.parseAsFlumeEvent = false

# 4：定义sink - 这里不用

# 5：定义关联关系
file_flume_kafka.sources.r1.channels = c1

启动

nohup /usr/local/flume-1.9.0/bin/flume-ng agent \
  --conf-file /shucang_v4/project_files/flume_jobs/conf/file_flume_kafka.conf \
  -C /shucang_v4/project_files/flume_jobs/jar/project_v4_flume.jar \
  --name file_flume_kafka -Dflume.root.logger=INFO,LOGFILE >/usr/local/flume-1.9.0/logs/file_flume_kafka.log 2>&1  &

2: kafka->flume->hdfs (方案一)

kafka source -> file channel -> hdfs sink

缺点：相比方案二多一步source，性能差点

优点：有source可以多加一个拦截器，这个看需要吧

配置文件：kafka_flume_hdfs.conf

# 1：定义组件
kafka_flume_hdfs.sources = r1
kafka_flume_hdfs.channels = c1
kafka_flume_hdfs.sinks = k1

# 2：定义source
kafka_flume_hdfs.sources.r1.type = org.apache.flume.source.kafka.KafkaSource
kafka_flume_hdfs.sources.r1.kafka.bootstrap.servers = 192.168.5.103:9092,192.168.5.87:9092,192.168.5.114:9092
kafka_flume_hdfs.sources.r1.kafka.topics = project_v4_topic_log
kafka_flume_hdfs.sources.r1.batchSize = 5000
kafka_flume_hdfs.sources.r1.batchDurationMillis = 2000
#从头开始消费-非实时场景常使用
kafka_flume_hdfs.sources.r1.kafka.consumer.auto.offset.reset = earliest
  ## 配置时间连接器(解决零点漂移问题)
kafka_flume_hdfs.sources.r1.interceptors = i1
kafka_flume_hdfs.sources.r1.interceptors.i1.type = com.wester.flume.interceptor.TimeStampInterceptor$Builder

# 3：定义channel
kafka_flume_hdfs.channels.c1.type = file
    #file channel数据存储的路径
kafka_flume_hdfs.channels.c1.dataDirs = /usr/local/flume-1.9.0/data/project_v4
    #file checkpointDir路径
kafka_flume_hdfs.channels.c1.checkpointDir = /usr/local/flume-1.9.0/checkpointDir/project_v4

# 4：定义sink
kafka_flume_hdfs.sinks.k1.type = hdfs
kafka_flume_hdfs.sinks.k1.hdfs.path = hdfs://hadoop322ha/project/v4/log/topic_log/%Y-%m-%d
    # 上传文件的前缀
kafka_flume_hdfs.sinks.k1.hdfs.filePrefix = logs-
    # 设置是否需要滚动生成文件，比如1小时一个, 如果设置为true需要设置对应的，roundValue，roundUnit
kafka_flume_hdfs.sinks.k1.hdfs.round = false
 ## 控制生成的小文件
    # 控制多久滚动一次文件，防止凑不够rollSize卡住, 正常设置3600，这里测试为了快速写出
kafka_flume_hdfs.sinks.k1.hdfs.rollInterval = 20
    # 控制文件多大后，滚动文件,128M滚动文件
kafka_flume_hdfs.sinks.k1.hdfs.rollSize = 134217728
    # 多少个events滚动文件，一般不指定写0
kafka_flume_hdfs.sinks.k1.hdfs.rollCount = 0

 ## 配置输出类型CompressedStream(压缩流),DataStream(原样输出)，与压缩
    # 压缩流
kafka_flume_hdfs.sinks.k1.hdfs.fileType = CompressedStream
    # 压缩类型
kafka_flume_hdfs.sinks.k1.hdfs.codeC = lzop

# 5：定义关联关系
kafka_flume_hdfs.sources.r1.channels = c1
kafka_flume_hdfs.sinks.k1.channel = c1

启动

nohup /usr/local/flume-1.9.0/bin/flume-ng agent \
 --conf-file /shucang_v4/project_files/flume_jobs/conf/kafka_flume_hdfs.conf \
 -C /shucang_v4/project_files/flume_jobs/jar/project_v4_flume.jar \
 --name kafka_flume_hdfs -Dflume.root.logger=INFO,LOGFILE >/usr/local/flume-1.9.0/logs/kafka_flume_hdfs.log 2>&1  &

3: kafka->flume->hdfs (方案二)

kafka channel -> hdfs sink 不用source

优点：相比方案一少一步source，性能高些

缺点：没有source就不能加拦截器，数据要再到kafka之前处理好

配置文件：kafka_channel_flume_hdfs.conf

# 1：定义组件
kafka_channel_flume_hdfs.channels = c1
kafka_channel_flume_hdfs.sinks = k1

# 2：定义source - 这里不用

# 3：定义channel
kafka_channel_flume_hdfs.channels.c1.type = org.apache.flume.channel.kafka.KafkaChannel
kafka_channel_flume_hdfs.channels.c1.kafka.bootstrap.servers = 192.168.5.103:9092,192.168.5.87:9092,192.168.5.114:9092
kafka_channel_flume_hdfs.channels.c1.kafka.topic = project_v4_topic_log_v2
    ## 设置消费者组,保证每次消费时能够获取上次对应的Offset
kafka_channel_flume_hdfs.channels.c1.consumer.group.id = kafka_channel_flume_hdfs_v2
    #设置不用flume组装的header
    #kafka_channel_flume_hdfs.channels.c1.parseAsFlumeEvent = false

# 4：定义sink
kafka_channel_flume_hdfs.sinks.k1.type = hdfs
kafka_channel_flume_hdfs.sinks.k1.hdfs.path = hdfs://hadoop322ha/project/v4/log/topic_log_v2/%Y-%m-%d
    # 上传文件的前缀
kafka_channel_flume_hdfs.sinks.k1.hdfs.filePrefix = logs-
    # 设置是否需要滚动生成文件，比如1小时一个, 如果设置为true需要设置对应的，roundValue，roundUnit
kafka_channel_flume_hdfs.sinks.k1.hdfs.round = false
 ## 控制生成的小文件
    # 控制多久滚动一次文件，防止凑不够rollSize卡住, 正常设置3600，这里测试为了快速写出
kafka_channel_flume_hdfs.sinks.k1.hdfs.rollInterval = 20
    # 控制文件多大后，滚动文件,128M滚动文件
kafka_channel_flume_hdfs.sinks.k1.hdfs.rollSize = 134217728
    # 多少个events滚动文件，一般不指定写0
kafka_channel_flume_hdfs.sinks.k1.hdfs.rollCount = 0

 ## 配置输出类型CompressedStream(压缩流),DataStream(原样输出)，与压缩
    # 压缩流
kafka_channel_flume_hdfs.sinks.k1.hdfs.fileType = CompressedStream
    # 压缩类型
kafka_channel_flume_hdfs.sinks.k1.hdfs.codeC = lzop

# 5：定义关联关系
kafka_channel_flume_hdfs.sinks.k1.channel = c1

启动

nohup /usr/local/flume-1.9.0/bin/flume-ng agent \
 --conf-file /shucang_v4/project_files/flume_jobs/conf2/kafka_channel_flume_hdfs.conf \
 -C /shucang_v4/project_files/flume_jobs/jar/project_v4_flume.jar \
 --name kafka_channel_flume_hdfs -Dflume.root.logger=INFO,LOGFILE >/usr/local/flume-1.9.0/logs/kafka_channel_flume_hdfs.log 2>&1 

 Flume系列

        Apache Hadoop生态部署-Flume采集节点安装

        Flume系列：Flume组件架构

        Flume系列：Flume Source使用

        Flume系列：Flume Channel使用

        Flume系列：Flume Sink使用

        Flume系列：Flume 自定义Interceptor拦截器

        Flume系列：Flume通道拓扑结构

        Flume系列：Flume常用采集通道案例

        Flume系列：案例-Flume复制(Replicating)和多路复用(Multiplexing)

        Flume系列：案例-Flume负载均衡和故障转移

        Flume系列：案例-Flume 聚合拓扑(常见的日志收集结构)

        Flume系列：Flume数据监控Ganglia