文章目录

• • • 导入离线包
    • 开始安装
    • flume消费kafka数据到hdfs上

导入离线包

主节点创建/opt/cloudera/csd目录

mkdir -p /opt/cloudera/csd

上传KAFKA-1.2.0.jar到/opt/cloudera/csd目录，并修改所有者和所有者的组

[root@hadoop001 csd]# chown cloudera-scm:cloudera-scm /opt/cloudera/csd/ -R

上传KAFKA-4.0.0-1.4.0.0.p0.1-el6.parcel、KAFKA-4.0.0-1.4.0.0.p0.1-el6.parcel.sha1到/opt/cloudera/parcel-repo目录，并修改KAFKA-4.0.0-1.4.0.0.p0.1-el6.parcel.sha1名称为KAFKA-4.0.0-1.4.0.0.p0.1-el6.parcel.sha

[root@hadoop001 parcel-repo]# mv /opt/cloudera/parcel-repo/KAFKA-4.0.0-1.4.0.0.p0.1-el6.parcel.sha1 /opt/cloudera/parcel-repo/KAFKA-4.0.0-1.4.0.0.p0.1-el6.parcel.sha

ClouderManager中选择Parcel->检查Parcel->Kafka点击分配->激活

开始安装

点击CM–> 添加服务 --> 选择kafka–>主机全选

进入这个页面的时候不要着急

点击CM,这时看到kafka,修改Kafka的堆大小为256M,不然不够用,默认是50M,修改全部为256M

查看kafka的topic

[root@hadoop001 parcel-repo]# /opt/cloudera/parcels/KAFKA-4.0.0-1.4.0.0.p0.1/bin/kafka-topics --zookeeper hadoop001:2181 --list

进入到/opt/cloudera/parcels/KAFKA-4.0.0-1.4.0.0.p0.1目录下分别创建：启动日志主题、事件日志主题

创建启动日志主题

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-topics --zookeeper hadoop001:2181,hadoop002:2181,hadoop003:2181 --create --replication-factor 1 --partitions 1 --topic topic_start
WARNING: Due to limitations in metric names, topics with a period (’.’) or underscore (’_’) could collide. To avoid issues it is best to use either, but not both.
Created topic “topic_start”.

创建事件日志主题

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-topics --zookeeper hadoop001:2181,hadoop002:2181,hadoop003:2181 --create --replication-factor 1 --partitions 1 --topic topic_event
WARNING: Due to limitations in metric names, topics with a period (’.’) or underscore (’_’) could collide. To avoid issues it is best to use either, but not both.
Created topic “topic_event”

查看某个Topic的详情

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-topics --zookeeper hadoop001:2181 --describe --topic topic_event
Topic:topic_event PartitionCount:1 ReplicationFactor:1 Configs:
​ Topic: topic_event Partition: 0 Leader: 46 Replicas: 46 Isr: 46

删除刚刚创建的topic

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-topics --delete --zookeeper hadoop001:2181,hadoop002:2181,hadoop003:2181 --topic topic_start

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-topics --delete --zookeeper hadoop001:2181,hadoop002:2181,hadoop003:2181 --topic topic_event

生产消息

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-console-producer --broker-list hadoop001:9092 --topic topic_start

hello
nihao

消费消息

[root@hadoop002 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-console-consumer --bootstrap-server hadoop001:9092 --from-beginning --topic topic_start
[2019-07-21 18:07:45,771] WARN [Consumer clientId=consumer-1, groupId=console-consumer-39633] Error while fetching metadata with correlation id 2 : {topic_start=LEADER_NOT_AVAILABLE} (org.apache.kafka.clients.NetworkClient)
hello
nihao

测试与flume是否连通

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# mkdir -p /opt/module/flume
[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# cd /opt/module/flume/
[root@hadoop001 flume]# ls
[root@hadoop001 flume]# touch log_position.json
[root@hadoop001 flume]# chmod 777 log_position.json
[root@hadoop001 flume]# cd …
[root@hadoop001 module]# xsync flume/

将log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar上传都hadoop001的/opt/module目录下

分发log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar

[root@hadoop001 module]# xsync log-collector-1.0-SNAPSHOT-jar-with-dependencies.jar

选择一个窗口启动消费者

[root@hadoop001 KAFKA-4.0.0-1.4.0.0.p0.1]# bin/kafka-console-consumer --bootstrap-server hadoop001:9092 --from-beginning --topic topic_start

flume消费kafka数据到hdfs上

在CM管理页面hadoop003上Flume的配置中找到代理名称,修改hadoop003,为a1,配置文件为

## 组件
a1.sources=r1 r2
a1.channels=c1 c2
a1.sinks=k1 k2

## source1
a1.sources.r1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.r1.batchSize = 5000
a1.sources.r1.batchDurationMillis = 2000
a1.sources.r1.kafka.bootstrap.servers = hadoop001:9092,hadoop002:9092,hadoop003:9092
a1.sources.r1.kafka.topics=topic_start

## source2
a1.sources.r2.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.r2.batchSize = 5000
a1.sources.r2.batchDurationMillis = 2000
a1.sources.r2.kafka.bootstrap.servers = hadoop001:9092,hadoop002:9092,hadoop003:9092
a1.sources.r2.kafka.topics=topic_event

## channel1
a1.channels.c1.type=memory
a1.channels.c1.capacity=100000
a1.channels.c1.transactionCapacity=10000

## channel2
a1.channels.c2.type=memory
a1.channels.c2.capacity=100000
a1.channels.c2.transactionCapacity=10000

## sink1
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = /origin_data/gmall/log/topic_start/%Y-%m-%d
a1.sinks.k1.hdfs.filePrefix = logstart-
a1.sinks.k1.hdfs.round = true
a1.sinks.k1.hdfs.roundValue = 10
a1.sinks.k1.hdfs.roundUnit = second

##sink2
a1.sinks.k2.type = hdfs
a1.sinks.k2.hdfs.path = /origin_data/gmall/log/topic_event/%Y-%m-%d
a1.sinks.k2.hdfs.filePrefix = logevent-
a1.sinks.k2.hdfs.round = true
a1.sinks.k2.hdfs.roundValue = 10
a1.sinks.k2.hdfs.roundUnit = second

## 不要产生大量小文件
a1.sinks.k1.hdfs.rollInterval = 10
a1.sinks.k1.hdfs.rollSize = 134217728
a1.sinks.k1.hdfs.rollCount = 0

a1.sinks.k2.hdfs.rollInterval = 10
a1.sinks.k2.hdfs.rollSize = 134217728
a1.sinks.k2.hdfs.rollCount = 0

## 控制输出文件是原生文件。
a1.sinks.k1.hdfs.fileType = CompressedStream 
a1.sinks.k2.hdfs.fileType = CompressedStream 

a1.sinks.k1.hdfs.codeC = lzop
a1.sinks.k2.hdfs.codeC = lzop

## 拼装
a1.sources.r1.channels = c1
a1.sinks.k1.channel= c1

a1.sources.r2.channels = c2
a1.sinks.k2.channel= c2