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Flume 高可用配置案例+load balance负载均衡+ 案例:日志的采集及汇总

作者：互联网

高可用配置案例
（一）、failover故障转移

在完成单点的Flume NG搭建后，下面我们搭建一个高可用的Flume NG集群，架构图如下所示：

 

 

（1）节点分配

Flume的Agent和Collector分布如下表所示：

名称

Ip地址

        Host

角色

Agent1

192.168.137.188

hadoop-001

    WebServer

Collector1

192.168.137.189

hadoop-002

AgentMstr1

Collector2

192.168.137.190

hadoop-003

AgentMstr2

Agent1数据分别流入到Collector1和Collector2，Flume NG本身提供了Failover机制，可以自动切换和恢复。下面我们开发配置Flume NG集群。

（2）配置

在下面单点Flume中，基本配置都完成了，我们只需要新添加两个配置文件，它们是flume-client.conf和flume-server.conf，其配置内容如下所示：

 

1、hadoop-001上的flume-client.conf配置

#agent1 name

agent1.channels = c1

agent1.sources = r1

agent1.sinks = k1 k2

 

#set gruop

agent1.sinkgroups = g1

#set sink group

agent1.sinkgroups.g1.sinks = k1 k2

 

#set channel

agent1.channels.c1.type = memory

agent1.channels.c1.capacity = 1000

agent1.channels.c1.transactionCapacity = 100

 

agent1.sources.r1.channels = c1

agent1.sources.r1.type = exec

agent1.sources.r1.command = tail -F /root/log/test.log

 

agent1.sources.r1.interceptors = i1 i2

agent1.sources.r1.interceptors.i1.type = static

agent1.sources.r1.interceptors.i1.key = Type

agent1.sources.r1.interceptors.i1.value = LOGIN

agent1.sources.r1.interceptors.i2.type = timestamp

 

 

# set sink1

agent1.sinks.k1.channel = c1

agent1.sinks.k1.type = avro

agent1.sinks.k1.hostname = hadoop-002

agent1.sinks.k1.port = 52020

 

# set sink2

agent1.sinks.k2.channel = c1

agent1.sinks.k2.type = avro

agent1.sinks.k2.hostname = hadoop-003

agent1.sinks.k2.port = 52020

 

#set failover

agent1.sinkgroups.g1.processor.type = failover

agent1.sinkgroups.g1.processor.priority.k1 = 10

agent1.sinkgroups.g1.processor.priority.k2 = 5

agent1.sinkgroups.g1.processor.maxpenalty = 10000

#这里首先要申明一个sinkgroups,然后再设置2个sink ,k1与k2,其中2个优先级是10和5，#而processor的maxpenalty被设置为10秒，默认是30秒。‘

 

启动命令：

bin/flume-ng agent -n agent1 -c conf -f conf/flume-client.conf

-Dflume.root.logger=DEBUG,console

 

 

2、Hadoop-002和hadoop-003上的flume-server.conf配置

#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 = 0.0.0.0

a1.sources.r1.port = 52020

a1.sources.r1.channels = c1

a1.sources.r1.interceptors = i1 i2

a1.sources.r1.interceptors.i1.type = timestamp

a1.sources.r1.interceptors.i2.type = host

a1.sources.r1.interceptors.i2.hostHeader=hostname

 

#set sink to hdfs

a1.sinks.k1.type=hdfs

a1.sinks.k1.hdfs.path=/data/flume/logs/%{hostname}

a1.sinks.k1.hdfs.filePrefix=%Y-%m-%d

a1.sinks.k1.hdfs.fileType=DataStream

a1.sinks.k1.hdfs.writeFormat=TEXT

a1.sinks.k1.hdfs.rollInterval=10

a1.sinks.k1.channel=c1

 

启动命令：

bin/flume-ng agent -n agent1 -c conf -f conf/flume-server.conf

-Dflume.root.logger=DEBUG,console

（3）测试failover

1、先在hadoop-002和hadoop-003上启动脚本

bin/flume-ng agent -n a1 -c conf -f conf/flume-server.conf

-Dflume.root.logger=DEBUG,console

2、然后启动hadoop-001上的脚本

bin/flume-ng agent -n agent1 -c conf -f conf/flume-client.conf

-Dflume.root.logger=DEBUG,console

3、Shell脚本生成数据

 while true;do date >> test.log; sleep 1s ;done

 

4、观察HDFS上生成的数据目录。只观察到hadoop-002在接受数据

 

5、Hadoop-002上的agent被干掉之后，继续观察HDFS上生成的数据目录，hadoop-003对应的ip目录出现，此时数据收集切换到hadoop-003上

 

6、Hadoop-002上的agent重启后，继续观察HDFS上生成的数据目录。此时数据收集切换到hadoop-002上，又开始继续工作！

 

 

load balance负载均衡

（1）节点分配

如failover故障转移的节点分配

（2）配置

在failover故障转移的配置上稍作修改

hadoop-001上的flume-client-loadbalance.conf配置

#agent1 name

agent1.channels = c1

agent1.sources = r1

agent1.sinks = k1 k2

 

#set gruop

agent1.sinkgroups = g1

 

#set channel

agent1.channels.c1.type = memory

agent1.channels.c1.capacity = 1000

agent1.channels.c1.transactionCapacity = 100

agent1.sources.r1.channels = c1

agent1.sources.r1.type = exec

agent1.sources.r1.command = tail -F /root/log/test.log

 

# set sink1

agent1.sinks.k1.channel = c1

agent1.sinks.k1.type = avro

agent1.sinks.k1.hostname = hadoop-002

agent1.sinks.k1.port = 52020

 

# set sink2

agent1.sinks.k2.channel = c1

agent1.sinks.k2.type = avro

agent1.sinks.k2.hostname = hadoop-003

agent1.sinks.k2.port = 52020

 

#set sink group

agent1.sinkgroups.g1.sinks = k1 k2

 

#set load-balance

agent1.sinkgroups.g1.processor.type = load_balance

# 默认是round_robin，还可以选择random

agent1.sinkgroups.g1.processor.selector = round_robin

#如果backoff被开启，则 sink processor会屏蔽故障的sink

agent1.sinkgroups.g1.processor.backoff = true

 

 

Hadoop-002和hadoop-003上的flume-server-loadbalance.conf配置

#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 = 0.0.0.0

a1.sources.r1.port = 52020

a1.sources.r1.channels = c1

a1.sources.r1.interceptors = i1 i2

a1.sources.r1.interceptors.i1.type = timestamp

a1.sources.r1.interceptors.i2.type = host

a1.sources.r1.interceptors.i2.hostHeader=hostname

a1.sources.r1.interceptors.i2.useIP=false

#set sink to hdfs

a1.sinks.k1.type=hdfs

a1.sinks.k1.hdfs.path=/data/flume/loadbalance/%{hostname}

a1.sinks.k1.hdfs.fileType=DataStream

a1.sinks.k1.hdfs.writeFormat=TEXT

a1.sinks.k1.hdfs.rollInterval=10

a1.sinks.k1.channel=c1

a1.sinks.k1.hdfs.filePrefix=%Y-%m-%d

（3）测试load balance

1、先在hadoop-002和hadoop-003上启动脚本

bin/flume-ng agent -n a1 -c conf -f conf/flume-server-loadbalance.conf

-Dflume.root.logger=DEBUG,console

2、然后启动hadoop-001上的脚本

bin/flume-ng agent -n agent1 -c conf -f conf/flume-client-loadbalance.conf

-Dflume.root.logger=DEBUG,console

3、Shell脚本生成数据

 while true;do date >> test.log; sleep 1s ;done

4、观察HDFS上生成的数据目录，由于轮训机制都会收集到数据

 

       5、Hadoop-002上的agent被干掉之后，hadoop-002上不在产生数据

 

       6、Hadoop-002上的agent重新启动后，两者都可以接受到数据

 

 

 

1. 案例场景:日志的采集及汇总
A、B两台日志服务机器实时生产日志主要类型为access.log、nginx.log、web.log
现在要求：

把A、B 机器中的access.log、nginx.log、web.log 采集汇总到C机器上然后统一收集到hdfs中。
但是在hdfs中要求的目录为：

/source/logs/access/20190101/**
/source/logs/nginx/20190101/**
/source/logs/web/20190101/**


2. 场景分析

 

图一
3. 数据流程处理分析

 

 


4. 实现

服务器A对应的IP为 192.168.137.188
服务器B对应的IP为 192.168.137.189
服务器C对应的IP为 192.168.137.190

 


①　在服务器A和服务器B上的$FLUME_HOME/conf 创建配置文件 exec_source_avro_sink.conf 文件内容为

# Name the components on this agent
a1.sources = r1 r2 r3
a1.sinks = k1
a1.channels = c1

# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /root/data/access.log
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = static
## static拦截器的功能就是往采集到的数据的header中插入自己定## 义的key-value对
a1.sources.r1.interceptors.i1.key = type
a1.sources.r1.interceptors.i1.value = access

a1.sources.r2.type = exec
a1.sources.r2.command = tail -F /root/data/nginx.log
a1.sources.r2.interceptors = i2
a1.sources.r2.interceptors.i2.type = static
a1.sources.r2.interceptors.i2.key = type
a1.sources.r2.interceptors.i2.value = nginx

a1.sources.r3.type = exec
a1.sources.r3.command = tail -F /root/data/web.log
a1.sources.r3.interceptors = i3
a1.sources.r3.interceptors.i3.type = static
a1.sources.r3.interceptors.i3.key = type
a1.sources.r3.interceptors.i3.value = web

# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = 192.168.200.101
a1.sinks.k1.port = 41414

# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 20000
a1.channels.c1.transactionCapacity = 10000

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sources.r2.channels = c1
a1.sources.r3.channels = c1
a1.sinks.k1.channel = c1

 


②　在服务器C上的$FLUME_HOME/conf 创建配置文件 avro_source_hdfs_sink.conf 文件内容为

 

 

#定义agent名， source、channel、sink的名称
a1.sources = r1
a1.sinks = k1
a1.channels = c1


#定义source
a1.sources.r1.type = avro
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port =41414

#添加时间拦截器
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = org.apache.flume.interceptor.TimestampInterceptor$Builder


#定义channels
a1.channels.c1.type = memory
a1.channels.c1.capacity = 20000
a1.channels.c1.transactionCapacity = 10000

#定义sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path=hdfs://192.168.200.101:9000/source/logs/%{type}/%Y%m%d
a1.sinks.k1.hdfs.filePrefix =events
a1.sinks.k1.hdfs.fileType = DataStream
a1.sinks.k1.hdfs.writeFormat = Text
#时间类型
a1.sinks.k1.hdfs.useLocalTimeStamp = true
#生成的文件不按条数生成
a1.sinks.k1.hdfs.rollCount = 0
#生成的文件按时间生成
a1.sinks.k1.hdfs.rollInterval = 30
#生成的文件按大小生成
a1.sinks.k1.hdfs.rollSize = 10485760
#批量写入hdfs的个数
a1.sinks.k1.hdfs.batchSize = 10000
flume操作hdfs的线程数（包括新建，写入等）
a1.sinks.k1.hdfs.threadsPoolSize=10
#操作hdfs超时时间
a1.sinks.k1.hdfs.callTimeout=30000

#组装source、channel、sink
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

 


③　配置完成之后，在服务器A和B上的/root/data有数据文件access.log、nginx.log、web.log。先启动服务器C上的flume，启动命令
在flume安装目录下执行 ：

bin/flume-ng agent -c conf -f conf/avro_source_hdfs_sink.conf -name a1 -Dflume.root.logger=DEBUG,console

 

然后在启动服务器上的A和B，启动命令
在flume安装目录下执行 ：

bin/flume-ng agent -c conf -f conf/exec_source_avro_sink.conf -name a1 -Dflume.root.logger=DEBUG,console

 



 

 

 

标签：Flume,load,sinks,sources,a1,案例,k1,agent1,r1
来源： https://blog.51cto.com/u_15241496/2869402