Flume整合Kafka完成實時數據採集

agent選擇

agent1 exec source + memory channel + avro sink

agent2 avro source + memory channel 

模擬實際工做中的場景，agent1 爲A機器，agent2 爲B機器。

 

avro source: 監聽avro端口，而且接收來自外部avro信息，

avro sink：通常用於跨節點傳輸，主要綁定數據移動目的地的ip和port

 

 

在建立agent2配置文件

cd /app/flume/flume/conf

vi test-avro-memory-kafka.conf

avro-memory-kafka.sources = avro-source
avro-memory-kafka.sinks = kafka-sink
avro-memory-kafka.channels = memory-channel
 
avro-memory-kafka.sources.avro-source.type = avro
avro-memory-kafka.sources.avro-source.bind= dblab-VirtualBox
avro-memory-kafka.sources.avro-source.port=44444
 
avro-memory-kafka.sinks.kafka-sink.type = org.apache.flume.sink.kafka.KafkaSink
avro-memory-kafka.sinks.kafka-sink.kafka.bootstrap.servers = dblab-VirtualBox:9092
avro-memory-kafka.sinks.kafka-sink.kafka.topic = hello_topic
avro-memory-kafka.sinks.kafka-sink.batchSize = 5
avro-memory-kafka.sinks.kafka-sink.requiredAcks = 1 

avro-memory-kafka.channels.memory-channel.type = memory
 
avro-memory-kafka.sources.avro-source.channels = memory-channel
avro-memory-kafka.sinks.kafka-sink.channel = memory-channel

 

啓動agent2

flume-ng agent --name avro-memory-kafka -c conf -f conf/test-avro-memory-kafka.conf -Dflume.root.logger=INFO,console

 

 

這裏必定要等agent2的avro-source啓動成功，已經監聽了本身的44444端口，才能去啓動agent1,否則agent1啓動會被拒絕鏈接

 

建立agent1配置文件

cd /app/flume/flume/conf

vi test-exec-memory-avro.conf

exec-memory-avro.sources = exec-source
exec-memory-avro.sinks = avro-sink
exec-memory-avro.channels = memory-channel

exec-memory-avro.sources.exec-source.type = exec
exec-memory-avro.sources.exec-source.command = tail -F /home/hadoop/data/data.log
exec-memory-avro.sources.exec-source.shell = /bin/sh -c

exec-memory-avro.sinks.avro-sink.type = avro
exec-memory-avro.sinks.avro-sink.hostname = dblab-VirtualBox
exec-memory-avro.sinks.avro-sink.port = 44444

exec-memory-avro.channels.memory-channel.type = memory

exec-memory-avro.sources.exec-source.channels = memory-channel
exec-memory-avro.sinks.avro-sink.channel = memory-channel

 

啓動agent2

flume-ng agent --name exec-memory-avro -c conf -f conf/test-exec-memory-avro.conf -Dflume.root.logger=INFO,console

 

接下來對Kafka進行配置

先啓動Kafka

$ kafka-server-start.sh $KAFKA_HOME/config/server.properties

建立hello_topic

$ kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test

 啓動生產者

kafka-console-producer.sh --broker-list localhost:9092 --topic hello_topic

 

啓動一個Kafka的客戶端來消費，測試是否啓動成功

kafka-console-consumer.sh --zookeeper localhost:2181 --topic hello_topic

 

向agent1的exec-source監聽的文件中寫數據

 

查看Kafka的客戶端是否經過flume消費到數據

 

至此完成Flume整合Kafka完成實時數據採集