大数据处理框架：Storm：Storm与消息队列集成

大数据处理框架：Storm：Storm与消息队列集成1大数据处理框架：Storm：Storm与消息队列集成1.1简介1.1.1Storm框架概述Storm是一个开源的分布式实时计算系统，由NathanMarz和BackType开发，后来被Twitter收购。Storm被设计用于处理大量实时数据流，它能够保证每个消息都被处理，并且处理过程是容错的。Storm的核心概念是“拓扑”（Topology），它由一组Spout和Bolt组成，这些组件通过数据流连接在一起，形成一个数据处理流水线。SpoutSpout是数据流的源头，它可以连接到外部数据源，如消息队列，然后将数据发送到Storm的处理组件。BoltBolt是数据流的处理单元，它可以接收来自Spout或其他Bolt的数据，进行处理，然后将结果发送到下一个Bolt或输出。1.1.2消息队列在大数据处理中的作用消息队列在大数据处理中扮演着关键角色，它作为数据的缓冲区，确保数据在生产者和消费者之间平稳流动。消息队列可以处理数据的突发性，避免数据丢失，并且可以实现数据的异步处理，提高系统的整体性能。在与Storm集成时，消息队列通常作为Spout的数据源，将数据源源不断地送入Storm的处理流程。1.2Storm与消息队列集成1.2.1集成原理Storm与消息队列的集成主要通过Spout实现。Spout从消息队列中读取数据，然后将数据封装成Tuple，发送到Bolt进行处理。为了保证数据的可靠处理，Storm提供了Acking机制，即当一个Tuple被所有Bolt成功处理后，Spout才会从消息队列中确认并删除该消息，从而避免数据丢失。1.2.2集成示例：Storm与RabbitMQ安装与配置首先，确保你的环境中已经安装了RabbitMQ和Storm。然后，在Storm的配置文件中，添加RabbitMQ的连接信息。//Storm配置示例

Configconf=newConfig();

conf.put(Config.TOPOLOGY_ACKER_EXECUTORS,2);

conf.put(Config.TOPOLOGY_WORKERS,3);

conf.put(Config.TOPOLOGY_TASKS,4);

//RabbitMQ配置

conf.put(Config.TOPOLOGY_RABBITMQ_URI,"amqp://guest:guest@localhost:5672/%2f");

conf.put(Config.TOPOLOGY_RABBITMQ_EXCHANGE_NAME,"storm-exchange");

conf.put(Config.TOPOLOGY_RABBITMQ_EXCHANGE_TYPE,"direct");

conf.put(Config.TOPOLOGY_RABBITMQ_ROUTING_KEY,"storm-routing-key");Spout实现接下来，实现一个从RabbitMQ读取数据的Spout。//RabbitMQSpout.java

importbacktype.storm.spout.SchemeAsMultiScheme;

importbacktype.storm.topology.TopologyBuilder;

importbacktype.storm.tuple.Fields;

importbacktype.storm.tuple.Values;

importbacktype.storm.spout.RabbitMQSpout;

importbacktype.storm.scheme.StringScheme;

publicclassRabbitMQSpoutextendsRabbitMQSpout{

publicRabbitMQSpout(){

super(newSchemeAsMultiScheme(newStringScheme()));

}

@Override

publicvoidopen(Mapconf,TopologyContextcontext){

super.open(conf,context);

//连接到RabbitMQ

MapstormConf=newHashMap();

stormConf.put(Config.TOPOLOGY_RABBITMQ_URI,"amqp://guest:guest@localhost:5672/%2f");

stormConf.put(Config.TOPOLOGY_RABBITMQ_EXCHANGE_NAME,"storm-exchange");

stormConf.put(Config.TOPOLOGY_RABBITMQ_EXCHANGE_TYPE,"direct");

stormConf.put(Config.TOPOLOGY_RABBITMQ_ROUTING_KEY,"storm-routing-key");

super.open(stormConf,context);

}

@Override

publicvoidnextTuple(){

//从RabbitMQ中读取数据

Stringmessage=(String)rabbitMQConsumer.next();

//将数据封装成Tuple

emit(newValues(message));

}

}Bolt实现实现一个简单的Bolt，用于处理从Spout接收到的数据。//SimpleBolt.java

importbacktype.storm.task.OutputCollector;

importbacktype.storm.task.TopologyContext;

importbacktype.storm.topology.OutputFieldsDeclarer;

importbacktype.storm.topology.base.BaseRichBolt;

importbacktype.storm.tuple.Fields;

importbacktype.storm.tuple.Tuple;

importbacktype.storm.tuple.Values;

importjava.util.Map;

publicclassSimpleBoltextendsBaseRichBolt{

privateOutputCollectorcollector;

@Override

publicvoidprepare(MapstormConf,TopologyContextcontext,OutputCollectorcollector){

this.collector=collector;

}

@Override

publicvoidexecute(Tupleinput){

//处理数据

Stringmessage=input.getStringByField("message");

System.out.println("Processingmessage:"+message);

//发送处理结果

collector.emit(newValues("processed-"+message));

collector.ack(input);

}

@Override

publicvoiddeclareOutputFields(OutputFieldsDeclarerdeclarer){

declarer.declare(newFields("processedMessage"));

}

}创建拓扑最后，创建一个拓扑，将Spout和Bolt连接起来。//Topology.java

importbacktype.storm.Config;

importbacktype.storm.LocalCluster;

importbacktype.storm.StormSubmitter;

importbacktype.storm.topology.TopologyBuilder;

importbacktype.storm.tuple.Fields;

importjava.util.HashMap;

importjava.util.Map;

publicclassTopology{

publicstaticvoidmain(String[]args)throwsException{

TopologyBuilderbuilder=newTopologyBuilder();

//添加RabbitMQSpout

builder.setSpout("rabbitMQSpout",newRabbitMQSpout(),1);

//添加SimpleBolt

builder.setBolt("simpleBolt",newSimpleBolt(),2)

.shuffleGrouping("rabbitMQSpout");

//配置Storm

Configconf=newConfig();

conf.put(Config.TOPOLOGY_ACKER_EXECUTORS,2);

conf.put(Config.TOPOLOGY_WORKERS,3);

conf.put(Config.TOPOLOGY_TASKS,4);

//启动拓扑

if(args!=null&&args.length>0){

StormSubmitter.submitTopology(args[0],conf,builder.createTopology());

}else{

LocalClustercluster=newLocalCluster();

cluster.submitTopology("test",conf,builder.createTopology());

}

}

}1.2.3总结通过上述示例，我们展示了如何在Storm中集成RabbitMQ消息队列，实现数据的实时处理。Storm的Spout和Bolt机制，结合RabbitMQ的消息传递能力，可以构建出高效、可靠的大数据处理流水线。在实际应用中，可以根据具体需求调整Spout和Bolt的实现，以及Storm和RabbitMQ的配置，以达到最佳的处理效果。2大数据处理框架：Storm：Storm与消息队列集成2.1集成基础2.1.1Storm的基本架构Storm是一个开源的分布式实时计算系统，它允许用户处理无界数据流，提供了一种类似于MapReduce的模型，但更适用于实时数据处理。Storm的架构主要由以下几个部分组成：Nimbus：类似于Hadoop的JobTracker，负责集群的管理，包括任务的分配和状态的监控。Supervisor：运行在每个节点上，接收Nimbus分配的任务，并在本地机器上启动和监控工作进程（Worker）。Worker：每个Supervisor可以启动多个Worker进程，每个Worker进程运行一个或多个任务（Task）。Task：最小的处理单元，每个Task执行一个具体的处理逻辑。Spout：数据源，负责从外部系统读取数据并发送到Storm的处理流程中。Bolt：数据处理单元，可以执行复杂的业务逻辑，如过滤、聚合、连接等操作。2.1.2消息队列的种类与选择消息队列在大数据处理中扮演着重要角色，它们用于在不同的系统组件之间传递消息，确保数据的可靠传输和处理。常见的消息队列有：ApacheKafka：高性能、高吞吐量的分布式消息系统，适合处理大量实时数据流。RabbitMQ：基于AMQP协议的开源消息队列，提供丰富的功能和良好的社区支持。AmazonSQS：AWS提供的云服务，用于构建分布式Web服务的无服务器消息队列。选择消息队列时，应考虑以下因素：性能和吞吐量：处理大量数据时，选择高性能的消息队列至关重要。可靠性：确保消息在传输过程中的可靠性，避免数据丢失。可扩展性：随着数据量的增加，消息队列应能够轻松扩展。社区和文档：良好的社区支持和详细的文档可以加速开发和问题解决。2.2Storm与消息队列的集成2.2.1使用Kafka作为SpoutStorm可以通过KafkaSpout将Kafka作为数据源。以下是一个使用KafkaSpout的示例：importorg.apache.storm.kafka.KafkaSpout;

importorg.apache.storm.kafka.SpoutConfig;

importorg.apache.storm.kafka.broker.KafkaBroker;

importorg.apache.storm.kafka.StringScheme;

importorg.apache.storm.kafka.ZkHosts;

importorg.apache.storm.topology.TopologyBuilder;

publicclassKafkaStormTopology{

publicstaticvoidmain(String[]args){

//Kafka配置

ZkHostszkHosts=newZkHosts("localhost:2181");

Stringtopic="testTopic";

SpoutConfigspoutConfig=newSpoutConfig(zkHosts,topic,"/storm","kafka-spout");

spoutConfig.scheme=newStringScheme();

KafkaSpoutkafkaSpout=newKafkaSpout(spoutConfig);

//构建Storm拓扑

TopologyBuilderbuilder=newTopologyBuilder();

builder.setSpout("kafka-spout",kafkaSpout);

builder.setBolt("process-bolt",newProcessBolt()).shuffleGrouping("kafka-spout");

//提交拓扑

Configconfig=newConfig();

config.setDebug(true);

LocalClustercluster=newLocalCluster();

cluster.submitTopology("kafka-storm-topology",config,builder.createTopology());

}

}2.2.2使用RabbitMQ作为SpoutStorm也可以通过RabbitMQSpout将RabbitMQ作为数据源。以下是一个使用RabbitMQSpout的示例：importbacktype.storm.spout.SchemeAsMultiScheme;

importbacktype.storm.spout.RabbitMQSpout;

importbacktype.storm.topology.TopologyBuilder;

importcom.rabbitmq.client.ConnectionFactory;

publicclassRabbitMQStormTopology{

publicstaticvoidmain(String[]args){

//RabbitMQ配置

ConnectionFactoryfactory=newConnectionFactory();

factory.setHost("localhost");

factory.setPort(5672);

factory.setUsername("guest");

factory.setPassword("guest");

StringqueueName="stormQueue";

RabbitMQSpoutrabbitMQSpout=newRabbitMQSpout(factory,queueName,newSchemeAsMultiScheme(newStringScheme()));

//构建Storm拓扑

TopologyBuilderbuilder=newTopologyBuilder();

builder.setSpout("rabbitmq-spout",rabbitMQSpout);

builder.setBolt("process-bolt",newProcessBolt()).shuffleGrouping("rabbitmq-spout");

//提交拓扑

Configconfig=newConfig();

config.setDebug(true);

LocalClustercluster=newLocalCluster();

cluster.submitTopology("rabbitmq-storm-topology",config,builder.createTopology());

}

}2.2.3集成考虑因素在将Storm与消息队列集成时，需要考虑以下几点：消息确认机制：确保Storm正确处理完消息后，消息队列才将消息标记为已处理。容错性：设计拓扑时，应考虑消息队列和Storm的容错机制，确保在系统故障时数据不会丢失。性能调优：根据数据量和处理需求，调整Storm和消息队列的配置参数，以达到最佳性能。2.3结论Storm与消息队列的集成是构建实时大数据处理系统的关键步骤。通过选择合适的消息队列和正确配置Storm拓扑，可以实现高效、可靠的数据处理。上述示例展示了如何使用Kafka和RabbitMQ作为Storm的数据源，实际应用中，应根据具体需求选择最合适的消息队列。请注意，上述代码示例是基于Storm和Kafka、RabbitMQ的早期版本。在实际应用中，应参考最新版本的官方文档进行调整。3大数据处理框架：Storm与Kafka集成3.1集成Kafka3.1.1Kafka简介与配置Kafka是一个分布式流处理平台，它被设计用于处理实时数据流，具有高吞吐量、低延迟和可扩展性。Kafka的核心概念是将数据视为一个连续的流，这使得它非常适合与Storm这样的流处理框架集成，以实现对实时数据的复杂处理。Kafka配置在集成Kafka与Storm之前，需要确保Kafka集群已经正确配置并运行。以下是一个Kafka集群的基本配置示例：#Kafka配置文件（perties）

broker.id=0

listeners=PLAINTEXT://localhost:9092

log.dirs=/tmp/kafka-logs

num.partitions=13.1.2使用KafkaSpout读取Kafka数据Storm提供了KafkaSpout，这是一个用于从Kafka读取数据的Spout组件。KafkaSpout能够处理Kafka的分区和偏移量，确保数据的可靠读取。KafkaSpout配置在Storm中使用KafkaSpout，需要配置KafkaSpout的参数，包括Kafka的连接信息、主题名称、以及数据读取的策略。以下是一个配置KafkaSpout的示例代码：importorg.apache.storm.kafka.KafkaSpout;

importorg.apache.storm.kafka.SpoutConfig;

importorg.apache.storm.kafka.broker.KafkaBroker;

importorg.apache.storm.kafka.StringScheme;

importorg.apache.storm.kafka.ZkHosts;

importorg.apache.storm.kafka.broker.KafkaBroker$SSLConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$SASLConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$SASLSSLConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$PLAINConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$SASLPLAINConfig;

//Kafka配置

ZkHostszkHosts=newZkHosts("localhost:2181");

Stringtopic="testTopic";

SpoutConfigspoutConfig=newSpoutConfig(zkHosts,topic,"/storm","kafka-spout");

spoutConfig.scheme=newStringScheme();

spoutConfig.startOffsetTime=System.currentTimeMillis()-1000*60*60*24*7;//一周前的时间

//创建KafkaSpout

KafkaSpoutkafkaSpout=newKafkaSpout(spoutConfig);3.1.3在Storm中配置Kafka消费者为了在Storm中有效地使用KafkaSpout，需要正确配置Kafka消费者。这包括设置消费者组、分区分配策略和数据处理策略。Kafka消费者配置在Storm中配置Kafka消费者，可以通过调整KafkaSpout的配置参数来实现。以下是一个配置Kafka消费者以在Storm中使用的示例代码：importorg.apache.storm.kafka.KafkaSpout;

importorg.apache.storm.kafka.SpoutConfig;

importorg.apache.storm.kafka.broker.KafkaBroker;

importorg.apache.storm.kafka.StringScheme;

importorg.apache.storm.kafka.ZkHosts;

importorg.apache.storm.kafka.broker.KafkaBroker$SSLConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$SASLConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$SASLSSLConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$PLAINConfig;

importorg.apache.storm.kafka.broker.KafkaBroker$SASLPLAINConfig;

//Kafka配置

ZkHostszkHosts=newZkHosts("localhost:2181");

Stringtopic="testTopic";

StringconsumerGroup="storm-consumer-group";

SpoutConfigspoutConfig=newSpoutConfig(zkHosts,topic,"/storm",consumerGroup);

spoutConfig.scheme=newStringScheme();

spoutConfig.startOffsetTime=System.currentTimeMillis()-1000*60*60*24*7;//一周前的时间

//设置分区分配策略

spoutConfig.forceFromStart=true;//强制从头开始读取数据

//创建KafkaSpout

KafkaSpoutkafkaSpout=newKafkaSpout(spoutConfig);在上述代码中，我们设置了消费者组为storm-consumer-group，并使用StringScheme来解析Kafka中的数据。通过设置forceFromStart为true，我们确保KafkaSpout从每个分区的开始位置读取数据，这对于处理历史数据或重新启动拓扑时非常有用。3.2总结通过上述步骤，我们可以在Storm中集成Kafka，利用KafkaSpout从Kafka读取数据，并配置Kafka消费者以适应不同的数据处理需求。这种集成使得Storm能够处理来自Kafka的实时数据流，为大数据处理提供了强大的支持。请注意，上述代码示例和配置参数可能需要根据实际的Kafka和Storm版本进行调整。在实际部署中，还需要考虑Kafka的SSL或SASL安全配置，以及Storm集群的配置细节。4大数据处理框架：Storm与RabbitMQ集成4.1集成RabbitMQ4.1.1RabbitMQ简介与配置RabbitMQ是一个开源的消息代理和队列服务器，基于AMQP（AdvancedMessageQueuingProtocol）标准。它提供了一种在分布式系统中存储和转发消息的可靠方式，使得消息的发送者和接收者无需直接通信。RabbitMQ支持多种消息模式，包括点对点（Direct）、发布订阅（Fanout）、主题（Topic）和头部分发（Headers）。在集成Storm与RabbitMQ时，首先需要在RabbitMQ服务器上创建一个队列，并配置Storm的RabbitMQSpout以连接到该队列。以下是一个RabbitMQ的基本配置示例：rabbitmq:

host:localhost

port:5672

username:guest

password:guest

virtualHost:/

queue:storm-queue

exchange:storm-exchange

routingKey:storm-routing-key4.1.2使用RabbitMQSpout读取RabbitMQ数据Storm中的RabbitMQSpout是一个用于从RabbitMQ读取数据的组件。它通过监听特定的队列来接收消息，然后将这些消息作为元组发送到Storm的拓扑中进行处理。以下是一个使用RabbitMQSpout的Storm拓扑配置示例：importbacktype.storm.Config;

importbacktype.storm.LocalCluster;

importbacktype.storm.topology.TopologyBuilder;

importcom.rabbitmq.client.ConnectionFactory;

importcom.rabbitmq.client.Connection;

importcom.rabbitmq.client.Channel;

importorg.apache.storm.rabbitmq.RabbitMQSpout;

importorg.apache.storm.rabbitmq.RabbitMQMessageId;

importmon.RabbitMQConnection;

importmon.RabbitMQMessage;

importmon.RabbitMQSpoutConfig;

importmon.RabbitMQTuple;

importmon.RabbitMQTupleDeserializer;

importmon.RabbitMQTupleSerializer;

importmon.RabbitMQTupleTranslator;

importmon.RabbitMQTupleTranslatorBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfig;

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publicclassStormRabbitMQTopology{

publicstaticvoidmain(String[]args)throwsException{

//创建RabbitMQ连接工厂

ConnectionFactoryfactory=newConnectionFactory();

factory.setHost("localhost");

factory.setPort(5672);

factory.setUsername("guest");

factory.setPassword("guest");

factory.setVirtualHost("/");

//创建RabbitMQSpout配置

RabbitMQSpoutConfigspoutConfig=RabbitMQSpoutConfig.builder(factory)

.queue("storm-queue")

.exchange("storm-exchange")

.routingKey("storm-routing-key")

.build();

//创建拓扑构建器

TopologyBuilderbuilder=newTopologyBuilder();

//添加RabbitMQSpout到拓扑

builder.setSpout("rabbitmq-spout",newRabbitMQSpout(spoutConfig));

//配置Storm

Configconf=newConfig();

conf.setDebug(false);

//启动本地集群

LocalClustercluster=newLocalCluster();

cluster.submitTopology("storm-rabbitmq-topology",conf,builder.createTopology());

}

}4.1.3在Storm中配置RabbitMQ消费者在Storm中配置RabbitMQ消费者，主要是通过RabbitMQSpoutConfig来指定RabbitMQ的连接信息、队列、交换器和路由键。此外，还可以配置消息的确认机制，以确保Storm处理的消息在处理完成后能够正确地从RabbitMQ中确认或拒绝。以下是一个配置RabbitMQ消费者以使用确认机制的示例：importmon.RabbitMQConnection;

importmon.RabbitMQMessageId;

importmon.RabbitMQMessage;

importmon.RabbitMQTuple;

importmon.RabbitMQTupleDeserializer;

importmon.RabbitMQTupleSerializer;

importmon.RabbitMQTupleTranslator;

importmon.RabbitMQTupleTranslatorBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfig;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder;

importmon.RabbitMQTupleTranslatorBuilder.RabbitMQTupleTranslatorBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTranslatorBuilderConfigBuilderConfigBuilderConfigBuilderConfigBuilder.RabbitMQTupleTra