Flume多Sink方案修正
在实际项目中采用http://www.cnblogs.com/moonandstar08/p/6091384.html方案进行布署时，由于系统产生的消费比较大按照原方案进行布署时，随着国外局点不断增加，那么SZ局点的Channel会不断增加，另一方面，在Kafaka集群中创建Partitation时由于无法保证Channel均匀的分布到Kafka集群时，那么在实际的生产环境上布署时会发现：SZ Kafka中的数据会保存N(海外局点数)份在SZ的环境上，很容易造成磁盘中存了N份冗余数据，此时Flume的模型如下图所示：
因此需要对此方案进行修正，修正的思路主要有二种：
一、采用Flume load balance模式
模型原型如下所示：
采用此方案时，SZ本地的Flume配置如下：
#list the sources,sinks and channels int the agent agent.sources = kafkaSrc agent.channels = kafkaChannel_sz agent.sinks = kafkaSink_sz #source configure agent.sources.kafkaSrc.type = org.apache.flume.source.kafka.KafkaSource agent.sources.kafkaSrc.zookeeperConnect = XXX:2181,YYY:2182 agent.sources.kafkaSrc.topic = test_produce agent.sources.kakkaSrc.groupId = test agent.sources.kakkaSrc.kafka.consumer.timeout.ms = 100 #use a channel which buffers events in memory agent.channels.kafkaChannel_sz.type = memory agent.channels.kafkaChannel_sz.capacity = 1000000 agent.channels.kafkaChannel_sz.transactionCapacity = 100 #sink agent.sources.kafkaSink_sz.type = org.apache.flume.sink.kafka.KafkaSink agent.sources.kafkaSink_sz.topic = test_consume agent.sources.kafkaSink_sz.brokerList= XXX:9092 agent.sources.kafkaSink_sz.batchSize = 5 #bind the source and sink to the channel agent.sources.kafkaSrc.channels = kafkaChannel_sz agent.sinks.kafkaSink_sz.channel = kafkaChannel_sz
此方案的实质是通过memory共享数据，当数据量比较大时很容易造成内存溢出。另外，当memory中数据丢失时也无法恢复。
此模型的使用如下所示： 
可以参见：http://www.cnblogs.com/lishouguang/p/4558790.html
二、Kafka + Flume Souce groupID来处理
Kafka + Flume Souce groupID方案的模型如下图所示：
Flume相关配置如下：
A.SZ本地搭建Kafka集群，不进行Flume配置；
B.UK本地Flume配置如下：
#Source agent.sources = kafkaSrc agent.channels = kafkaChannel_sz agent.sinks = kafkaSink_uk #Source configure agent.sources.kafkaSrc.type = org.apache.flume.source.kafka.KafkaSource agent.sources.kafkaSrc.channels = kafkaChannel_sz agent.sources.kafkaSrc.zookeeperConnect = XXX:2181,YYY:2182 (SZ ZOO) agent.sources.kafkaSrc.topic = k_produce agent.sources.kafkaSrc.groupId = k_uk #Channel agent.channels.kafkaChannel_sz.type = org.apache.flume.channel.kafka.KafkaChannel agent.channels.kafkaChannel_sz.brokeList = XXX:9092,YYY:9093(UK brokeList) agent.channels.kafkaChannel_sz.topic = k_uk agent.channels.kafkaChannel_sz.zookeeperConnect = XXX:2181,YYY:2182(UK ZOO) agent.channels.kafkaChannel_sz.capacity = 10000 agent.channels.kafkaChannel_sz.transactionCapacity = 1000 #Sink agent.sinks.kafkaSink_uk.channel = kafkaChannel_sz agent.sinks.kafkaSink_uk.type = org.apache.flume.sink.kafka.KafkaSink agent.sinks.kafkaSink_uk.topic = t_uk_consume agent.sinks,kafkaSink_uk.brokeList = XXX:9092,YYY:9093(UK brokeList) agent.sinks.kafkaSink_uk.bachSize = 20
C.BR本地Flume配置如下：
#Source agent.sources = kafkaSrc agent.channels = kafkaChannel_sz agent.sinks = kafkaSink_br #Source configure agent.sources.kafkaSrc.type = org.apache.flume.source.kafka.KafkaSource agent.sources.kafkaSrc.channels = kafkaChannel_sz agent.sources.kafkaSrc.zookeeperConnect = XXX:2181,YYY:2182 (SZ ZOO) agent.sources.kafkaSrc.topic = k_produce agent.sources.kafkaSrc.groupId = k_br #Channel agent.channels.kafkaChannel_sz.type = org.apache.flume.channel.kafka.KafkaChannel agent.channels.kafkaChannel_sz.brokeList = XXX:9092,YYY:9093(BR brokeList) agent.channels.kafkaChannel_sz.topic = k_br agent.channels.kafkaChannel_sz.zookeeperConnect = XXX:2181,YYY:2182(BR ZOO) agent.channels.kafkaChannel_sz.capacity = 10000 agent.channels.kafkaChannel_sz.transactionCapacity = 1000 #Sink agent.sinks.kafkaSink_uk.channel = kafkaChannel_sz agent.sinks.kafkaSink_uk.type = org.apache.flume.sink.kafka.KafkaSink agent.sinks.kafkaSink_uk.topic = t_br_consume agent.sinks,kafkaSink_uk.brokeList = XXX:9092,YYY:9093(BR brokeList) agent.sinks.kafkaSink_uk.bachSize = 20
