【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