Flume 学习笔记之 Flume NG+Kafka整合

Posted 2020-10-04 AK47Sonic

Flume NG集群+Kafka集群整合：

 

修改Flume配置文件（flume-kafka-server.conf），让Sink连上Kafka

hadoop1:

#set Agent name
a1.sources = r1
a1.channels = c1
a1.sinks = k1
#set channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# other node,nna to nns
a1.sources.r1.type = avro
a1.sources.r1.bind = hadoop1
a1.sources.r1.port = 52020
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = static
a1.sources.r1.interceptors.i1.key = Collector
a1.sources.r1.interceptors.i1.value = hadoop1
a1.sources.r1.channels = c1
#set sink to hdfs
a1.sinks.k1.type=org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.topic = ScalaTopic
a1.sinks.k1.brokerList = hadoop1:9092
a1.sinks.k1.requiredAcks = 1
a1.sinks.k1.batchSize = 20
a1.sinks.k1.channel=c1

hadoop2:

#set Agent name
a1.sources = r1
a1.channels = c1
a1.sinks = k1
#set channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# other node,nna to nns
a1.sources.r1.type = avro
a1.sources.r1.bind = hadoop2
a1.sources.r1.port = 52020
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = static
a1.sources.r1.interceptors.i1.key = Collector
a1.sources.r1.interceptors.i1.value = hadoop2
a1.sources.r1.channels = c1
#set sink to hdfs
a1.sinks.k1.type=org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.topic = ScalaTopic
a1.sinks.k1.brokerList = hadoop2:9092
a1.sinks.k1.requiredAcks = 1
a1.sinks.k1.batchSize = 20
a1.sinks.k1.channel=c1

 

集群测试：

1. 启动zookeeper（hadoop1，hadoop2，hadoop3）
2. 启动kafka server和consumer（hadoop1，hadoop2）
3. 启动Flume server（hadoop1，hadoop2）：flume-ng agent --conf conf --conf-file /usr/local/flume/conf/flume-kafka-server.conf --name a1 -Dflume.root.logger=INFO,console
4. 启动Flume client（hadoop3）：flume-ng agent --conf conf --conf-file /usr/local/flume/conf/flume-client.conf --name agent1 -Dflume.root.logger=INFO,console
5. 在hadoop3上追加一条日志记录
6. kafka consumer收到记录，从则测试完毕。

hadoop3：

hadoop1：

测试完毕，这样Flume+kafka就整合起来了，即Flume+Kafka+Spark Streaming的实时日志分析系统就孕育而生了。