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【慕课网实战】Spark Streaming实时流处理项目实战笔记十四之铭文升级版

铭文一级:

第11章 Spark Streaming整合Flume&Kafka打造通用流处理基础

streaming.conf

agent1.sources=avro-source
agent1.channels=logger-channel
agent1.sinks=log-sink

#define source
agent1.sources.avro-source.type=avro
agent1.sources.avro-source.bind=0.0.0.0
agent1.sources.avro-source.port=41414

#define channel
agent1.channels.logger-channel.type=memory

#define sink
agent1.sinks.log-sink.type=logger

agent1.sources.avro-source.channels=logger-channel
agent1.sinks.log-sink.channel=logger-channel

 

flume-ng agent \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/streaming.conf \
--name agent1 \
-Dflume.root.logger=INFO,console


java.lang.ClassNotFoundException: org.apache.flume.clients.log4jappender.Log4jAppender


./kafka-topics.sh --create --zookeeper hadoop000:2181 --replication-factor 1 --partitions 1 --topic streamingtopic


streaming2.conf
agent1.sources=avro-source
agent1.channels=logger-channel
agent1.sinks=kafka-sink

#define source
agent1.sources.avro-source.type=avro
agent1.sources.avro-source.bind=0.0.0.0
agent1.sources.avro-source.port=41414

#define channel
agent1.channels.logger-channel.type=memory

#define sink
agent1.sinks.kafka-sink.type=org.apache.flume.sink.kafka.KafkaSink
agent1.sinks.kafka-sink.topic = streamingtopic
agent1.sinks.kafka-sink.brokerList = hadoop000:9092
agent1.sinks.kafka-sink.requiredAcks = 1
agent1.sinks.kafka-sink.batchSize = 20

agent1.sources.avro-source.channels=logger-channel
agent1.sinks.kafka-sink.channel=logger-channel


flume-ng agent \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/streaming2.conf \
--name agent1 \
-Dflume.root.logger=INFO,console

 

我们现在是在本地进行测试的,在IDEA中运行LoggerGenerator,
然后使用Flume、Kafka以及Spark Streaming进行处理操作。

在生产上肯定不是这么干的,怎么干呢?
1) 打包jar,执行LoggerGenerator类
2) Flume、Kafka和我们的测试是一样的
3) Spark Streaming的代码也是需要打成jar包,然后使用spark-submit的方式进行提交到环境上执行
可以根据你们的实际情况选择运行模式:local/yarn/standalone/mesos

在生产上,整个流处理的流程都一样的,区别在于业务逻辑的复杂性

 

铭文二级:

第11章 Spark Streaming整合Flume&Kafka打造通用流处理基础

Flume整合log4j日志:streaming.conf=>avro-memory-logger

log4j.properties:需添加内容(上面四行即可):

#...
log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
log4j.appender.flume.Hostname = example.com
log4j.appender.flume.Port = 41414
log4j.appender.flume.UnsafeMode = true

# configure a class's logger to output to the flume appender
log4j.logger.org.example.MyClass = DEBUG,flume
#...

 

加上log4j.propertied内容为:

log4j.rootLogger=INFO,stdout,flume

log4j.appender.stdout = org.apache.log4j.ConsoleAppender
log4j.appender.stdout.target = System.out
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss,SSS} [%t] [%c] [%p] - %m%n

  

  

1.example.com改成hadoop000

2.log4j.rootLogger=INFO,stdout  //右侧添加flume

官网地址为:http://archive.cloudera.com/cdh5/cdh/5/flume-ng-1.6.0-cdh5.5.0/FlumeUserGuide.html

3.报错找不到类,根据内容添加依赖:

org.apache.flume.flume-ng-clients

flume-ng-log4jappender    //实际上打上这行就可以出现其他行

1.6.0

4.运行,若显示不全,将日志生成器的字符串减少一点

ps:运行前可以将不必要的进程kill掉先

Flume与Kafka整合=>

启动zk、启动kafka

修改类KafkaReceiverWordCount为KafkaStreamingApp

ToDo内容改成count().print() //简便测试总数

本地测试与生产环节使用拓展:

即将KafkaStreamingApp打包!!

 

第12章 Spark Streaming项目实战

课程目录、需求说明 //前面已经提过

posted on 2018-01-31 15:45  旷课小王子  阅读(240)  评论(0编辑  收藏  举报