Flume+Kafka+Strom基于伪分布式环境的结合使用

目录：

　　一、Flume、Kafka、Storm是什么,如何安装?

　　二、Flume、Kafka、Storm如何结合使用？

　　　　1) 原理是什么？

　　　　2) Flume和Kafka的整合

　　　　3) Kafka和Storm的整合

　　　　4) Flume、Kafka、Storm的整合

　　一、Flume、Kafka、Storm是什么，如何安装?

　　Flume的介绍，请参考这篇文章《Flume1.5.0的安装、部署、简单应用》

　　Kafka的介绍，请参考这篇文章《kafka2.9.2的分布式集群安装和demo(java api)测试》

　　Storm的介绍，请参考这篇文章《ubuntu12.04+storm0.9.2分布式集群的搭建》

　　在后面的例子中，我们也是使用以上三篇文章中的配置进行测试。

　　二、Flume、Kafka、Storm如何结合使用？

　　　　1) 原理是什么？

　　如何你仔细阅读过关于Flume、Kafka、Storm的介绍，就会知道，在他们各自之间对外交互发送消息的原理。

　　在后面的例子中，我们主要对Flume的sink进行重构，调用kafka的消费生产者(producer)发送消息;在Sotrm的spout中继承IRichSpout接口，调用kafka的消息消费者(Consumer)来接收消息，然后经过几个自定义的Bolt，将自定义的内容进行输出。

　　　　2) flume和kafka的整合

　　　　　#复制flume要用到的kafka相关jar到flume目录下的lib里面。

root@m1:/home/hadoop# cp /home/hadoop/kafka_2.9.2-0.8.1.1/libs/kafka_2.9.2-0.8.1.1.jar /home/hadoop/flume-1.5.0-bin/lib

root@m1:/home/hadoop# cp /home/hadoop/kafka_2.9.2-0.8.1.1/libs/scala-library-2.9.2.jar /home/hadoop/flume-1.5.0-bin/lib

root@m1:/home/hadoop# cp /home/hadoop/kafka_2.9.2-0.8.1.1/libs/metrics-core-2.2.0.jar /home/hadoop/flume-1.5.0-bin/lib

　　　　　#编写sink.java文件,然后在eclipse导出jar包，放到flume-1.5.1-bin/lib目录中,项目中要引用flume-ng-configuration-1.5.0.jar,flume-ng-sdk-1.5.0.jar,flume-ng-core-1.5.0.jar,zkclient-0.3.jar,commons-logging-1.1.1.jar,在flume目录中，可以找到这几个jar文件，如果找不到就用find命令搜一下。

package idoall.cloud.flume.sink;

import java.util.Properties;

import kafka.javaapi.producer.Producer;

import kafka.producer.KeyedMessage;

import kafka.producer.ProducerConfig;

import org.apache.commons.logging.Log;

import org.apache.commons.logging.LogFactory;

import org.apache.flume.Channel;

import org.apache.flume.Context;

import org.apache.flume.Event;

import org.apache.flume.EventDeliveryException;

import org.apache.flume.Transaction;

import org.apache.flume.conf.Configurable;

import org.apache.flume.sink.AbstractSink;

public class KafkaSink extends AbstractSink implements Configurable {

private static final Log logger = LogFactory.getLog(KafkaSink.class);

private String topic;

private Producer<String, String> producer;

public void configure(Context context) {

topic = "idoall_testTopic";

Properties props = new Properties();

props.setProperty("metadata.broker.list", "m1:9092,m2:9092,s1:9092,s2:9092");

props.setProperty("serializer.class", "kafka.serializer.StringEncoder");

props.put("partitioner.class", "idoall.cloud.kafka.Partitionertest");

props.put("zookeeper.connect", "m1:2181,m2:2181,s1:2181,s2:2181/kafka");

props.setProperty("num.partitions", "4"); //

props.put("request.required.acks", "1");

ProducerConfig config = new ProducerConfig(props);

producer = new Producer<String, String>(config);

logger.info("KafkaSink初始化完成.");

}

public Status process() throws EventDeliveryException {

Channel channel = getChannel();

Transaction tx = channel.getTransaction();

try {

tx.begin();

Event e = channel.take();

if (e == null) {

tx.rollback();

return Status.BACKOFF;

}

KeyedMessage<String, String> data = new KeyedMessage<String, String>(topic, new String(e.getBody()));

producer.send(data);

logger.info("flume向kafka发送消息：" + new String(e.getBody()));

tx.commit();

return Status.READY;

} catch (Exception e) {

logger.error("Flume KafkaSinkException:", e);

tx.rollback();

return Status.BACKOFF;

} finally {

tx.close();

}

　　　　　#在m1上配置flume和kafka交互的agent

root@m1:/home/hadoop/flume-1.5.0-bin# vi /home/hadoop/flume-1.5.0-bin/conf/kafka.conf

a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type = syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.host = localhost

a1.sources.r1.channels = c1

# Describe the sink

a1.sinks.k1.type = idoall.cloud.flume.sink.KafkaSink

# Use a channel which buffers events in memory

a1.channels.c1.type = memory

a1.channels.c1.capacity = 1000

a1.channels.c1.transactionCapacity = 100

# Bind the source and sink to the channel

a1.sources.r1.channels = c1

a1.sinks.k1.channel = c1

　　　　　#在m1,m2,s1,s2的机器上,分别启动kafka（如果不会请参考这篇文章介绍了kafka的安装、配置和启动《kafka2.9.2的分布式集群安装和demo(java api)测试》），然后在s1机器上再启动一个消息消费者consumer

1	`root@m1:/home/hadoop# /home/hadoop/kafka_2.9.2-0.8.1.1/bin/kafka-server-start.sh /home/hadoop/kafka_2.9.2-0.8.1.1/config/server.properties &`

　　　　　#在m1启动flume

root@m1:/home/hadoop

# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/kafka.conf -n a1 -Dflume.root.logger=INFO,console

#下面只截取部分日志信息

14/08/19 11:36:34 INFO sink.KafkaSink: KafkaSink初始化完成.

14/08/19 11:36:34 INFO node.AbstractConfigurationProvider: Channel c1 connected to [r1, k1]

14/08/19 11:36:34 INFO node.Application: Starting new configuration:{ sourceRunners:{r1=EventDrivenSourceRunner: { source:org.apache.flume.source

.SyslogTcpSource{name:r1,state:IDLE} }} sinkRunners:{k1=SinkRunner: { policy:org.apache.flume.sink.DefaultSinkProcessor@2a9e3ba7 counterGroup:{ name:null counters:{} } }} channels:{c1=org.apache.flume.channel.MemoryChannel{name: c1}} }

14/08/19 11:36:34 INFO node.Application: Starting Channel c1

14/08/19 11:36:34 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.

14/08/19 11:36:34 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started

14/08/19 11:36:34 INFO node.Application: Starting Sink k1

14/08/19 11:36:34 INFO node.Application: Starting Source r1

14/08/19 11:36:34 INFO source.SyslogTcpSource: Syslog TCP Source starting...

　　　　　#在m1上再打开一个窗口，测试向flume中发送syslog

1	`root@m1:/home/hadoop# echo "hello idoall.org syslog" \| nc localhost 5140`

　　　　　#m1打开的flume窗口中看最后一行的信息，Flume已经向kafka发送了消息

14/08/19 11:36:34 INFO sink.KafkaSink: KafkaSink初始化完成.

14/08/19 11:36:34 INFO node.AbstractConfigurationProvider: Channel c1 connected to [r1, k1]

14/08/19 11:36:34 INFO node.Application: Starting new configuration:{ sourceRunners:{r1=EventDrivenSourceRunner: { source:org.apache.flume.source

.SyslogTcpSource{name:r1,state:IDLE} }} sinkRunners:{k1=SinkRunner: { policy:org.apache.flume.sink.DefaultSinkProcessor@2a9e3ba7 counterGroup:{ name:null counters:{} } }} channels:{c1=org.apache.flume.channel.MemoryChannel{name: c1}} }

14/08/19 11:36:34 INFO node.Application: Starting Channel c1

14/08/19 11:36:34 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.

14/08/19 11:36:34 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started

14/08/19 11:36:34 INFO node.Application: Starting Sink k1

14/08/19 11:36:34 INFO node.Application: Starting Source r1

14/08/19 11:36:34 INFO source.SyslogTcpSource: Syslog TCP Source starting...

14/08/19 11:38:05 WARN source.SyslogUtils: Event created from Invalid Syslog data.

14/08/19 11:38:05 INFO client.ClientUtils$: Fetching metadata from broker id:3,host:s2,port:9092 with correlation id 0 for 1 topic(s) Set(idoall_testTopic)

14/08/19 11:38:05 INFO producer.SyncProducer: Connected to s2:9092 for producing

14/08/19 11:38:05 INFO producer.SyncProducer: Disconnecting from s2:9092

14/08/19 11:38:05 INFO producer.SyncProducer: Connected to m1:9092 for producing

14/08/19 11:38:05 INFO sink.KafkaSink: flume向kafka发送消息：hello idoall.org syslog

　　　　　#在刚才s1机器上打开的kafka消费端，同样可以看到从Flume中发出的信息，说明flume和kafka已经调试成功了。

root@s1:/home/hadoop

# /home/hadoop/kafka_2.9.2-0.8.1.1/bin/kafka-console-consumer.sh --zookeeper m1:2181 --topic flume-kafka-storm-001 --from-beginning

SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".

SLF4J: Defaulting to no-operation (NOP) logger implementation

SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.

[2014-08-11 14:22:12,165] INFO [ReplicaFetcherManager on broker 3] Removed fetcher for partitions [flume-kafka-storm-001,1] (kafka.server.ReplicaFetcherManager)

[2014-08-11 14:22:12,218] WARN [KafkaApi-3] Produce request with correlation id

2 from client  on partition [flume-kafka-storm-001,1] failed due to Topic flume-kafka-storm-001 either doesn't exist or is

in the process of being deleted (kafka.server.KafkaApis)

[2014-08-11 14:22:12,223] INFO Completed load of log flume-kafka-storm-001-1 with log end offset 0 (kafka.log.Log)

[2014-08-11 14:22:12,250] INFO Created log for partition [flume-kafka-storm-001,1] in /home/hadoop/kafka_2.9.2-0.8.1.1/kafka-logs with properties {segment.index.bytes -> 10485760, file

.delete.delay.ms -> 60000, segment.bytes -> 536870912, flush.ms -> 9223372036854775807, delete.retention.ms -> 86400000, index.interval.bytes -> 4096, retention.bytes -> -1, cleanup.policy -> delete, segment.ms -> 604800000, max.message.bytes -> 1000012, flush.messages -> 9223372036854775807, min.cleanable.dirty.ratio -> 0.5, retention.ms -> 604800000}. (kafka.log.LogManager)

[2014-08-11 14:22:12,267] WARN Partition [flume-kafka-storm-001,1] on broker 3: No checkpointed highwatermark is found for partition [flume-kafka-storm-001,1] (kafka.cluster.Partition)

[2014-08-11 14:22:12,375] INFO Closing socket connection to /192.168.1.50. (kafka.network.Processor)

hello idoall.org syslog

　　　　3) kafka和storm的整合

　　　　　#我们先在eclipse中写代码，在写代码之前，我们要先对maven进行配置，pom.xml配置文件内容如下：

<?xml version="1.0" encoding="utf-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

<modelVersion>4.0.0</modelVersion>

<groupId>idoall.cloud</groupId>

<artifactId>idoall.cloud</artifactId>

<version>0.0.1-SNAPSHOT</version>

<packaging>jar</packaging>

<name>idoall.cloud</name>

<url>http://maven.apache.org</url>

<properties>

<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>

</properties>

<repositories>

<repository>

<id>github-releases</id>

<url>http://oss.sonatype.org/content/repositories/github-releases/</url>

</repository>

<repository>

<id>clojars.org</id>

<url>http://clojars.org/repo</url>

</repository>

</repositories>

<dependencies>

<dependency>

<groupId>junit</groupId>

<artifactId>junit</artifactId>

<version>4.11</version>

<scope>test</scope>

</dependency>

<dependency>

<groupId>com.sksamuel.kafka</groupId>

<artifactId>kafka_2.10</artifactId>

<version>0.8.0-beta1</version>

</dependency>

<dependency>

<groupId>log4j</groupId>

<artifactId>log4j</artifactId>

<version>1.2.14</version>

</dependency>

<dependency>

<groupId>storm</groupId>

<artifactId>storm</artifactId>

<version>0.9.0.1</version>

<scope>provided</scope>

</dependency>

<dependency>

<groupId>commons-collections</groupId>

<artifactId>commons-collections</artifactId>

<version>3.2.1</version>

</dependency>

</dependencies>

</project>

　　　　　#编写KafkaSpouttest.java文件

package idoall.cloud.storm;

import java.text.SimpleDateFormat;

import java.util.Date;

import java.util.HashMap;

import java.util.List;

import java.util.Map;

import java.util.Properties;

import kafka.consumer.ConsumerConfig;

import kafka.consumer.ConsumerIterator;

import kafka.consumer.KafkaStream;

import kafka.javaapi.consumer.ConsumerConnector;

import backtype.storm.spout.SpoutOutputCollector;

import backtype.storm.task.TopologyContext;

import backtype.storm.topology.IRichSpout;

import backtype.storm.topology.OutputFieldsDeclarer;

import backtype.storm.tuple.Fields;

import backtype.storm.tuple.Values;

public class KafkaSpouttest implements IRichSpout {

private SpoutOutputCollector collector;

private ConsumerConnector consumer;

private String topic;

public KafkaSpouttest() {

}

public KafkaSpouttest(String topic) {

this.topic = topic;

}

public void nextTuple() {

}

public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) {

this.collector = collector;

}

public void ack(Object msgId) {

}

public void activate() {

<span style="font-size: 9pt; line-height: 25.2000007629395px;"> </span>consumer =kafka.consumer.Consumer.createJavaConsumerConnector(createConsumerConfig());

<span style="font-size: 9pt; line-height: 25.2000007629395px;"> </span>Map<String,Integer> topickMap = new HashMap<String, Integer>();

topickMap.put(topic, 1);

System.out.println("*********Results********topic:"+topic);

Map<String, List<KafkaStream<byte[],byte[]>>> streamMap=consumer.createMessageStreams(topickMap);

KafkaStream<byte[],byte[]>stream = streamMap.get(topic).get(0);

ConsumerIterator<byte[],byte[]> it =stream.iterator();

while(it.hasNext()){

String value =new String(it.next().message());

SimpleDateFormat formatter = new SimpleDateFormat ("yyyy年MM月dd日 HH:mm:ss SSS");

Date curDate = new Date(System.currentTimeMillis());//获取当前时间

String str = formatter.format(curDate);

System.out.println("storm接收到来自kafka的消息------->" + value);

collector.emit(new Values(value,1,str), value);

}

private static ConsumerConfig createConsumerConfig() {

Properties props = new Properties();

// 设置zookeeper的链接地址

props.put("zookeeper.connect","m1:2181,m2:2181,s1:2181,s2:2181");

// 设置group id

props.put("group.id", "1");

// kafka的group 消费记录是保存在zookeeper上的, 但这个信息在zookeeper上不是实时更新的, 需要有个间隔时间更新

props.put("auto.commit.interval.ms", "1000");

props.put("zookeeper.session.timeout.ms","10000");

return new ConsumerConfig(props);

}

public void close() {

}

public void deactivate() {

}

public void fail(Object msgId) {

}

public void declareOutputFields(OutputFieldsDeclarer declarer) {

declarer.declare(new Fields("word","id","time"));

}

public Map<String, Object> getComponentConfiguration() {

System.out.println("getComponentConfiguration被调用");

topic="idoall_testTopic";

return null;

}

　　　　　#编写KafkaTopologytest.java文件

package idoall.cloud.storm;

import java.util.HashMap;

import java.util.Map;

import backtype.storm.Config;

import backtype.storm.LocalCluster;

import backtype.storm.topology.BasicOutputCollector;

import backtype.storm.topology.OutputFieldsDeclarer;

import backtype.storm.topology.TopologyBuilder;

import backtype.storm.topology.base.BaseBasicBolt;

import backtype.storm.tuple.Fields;

import backtype.storm.tuple.Tuple;

import backtype.storm.tuple.Values;

import backtype.storm.utils.Utils;

public class KafkaTopologytest {

public static void main(String[] args) {

TopologyBuilder builder = new TopologyBuilder();

builder.setSpout("spout", new KafkaSpouttest(""), 1);

builder.setBolt("bolt1", new Bolt1(), 2).shuffleGrouping("spout");

builder.setBolt("bolt2", new Bolt2(), 2).fieldsGrouping("bolt1",new Fields("word"));

Map conf = new HashMap();

conf.put(Config.TOPOLOGY_WORKERS, 1);

conf.put(Config.TOPOLOGY_DEBUG, true);

LocalCluster cluster = new LocalCluster();

cluster.submitTopology("my-flume-kafka-storm-topology-integration", conf, builder.createTopology());

Utils.sleep(1000*60*5); // local cluster test ...

cluster.shutdown();

}

public static class Bolt1 extends BaseBasicBolt {

public void execute(Tuple input, BasicOutputCollector collector) {

try {

String msg = input.getString(0);

int id = input.getInteger(1);

String time = input.getString(2);

msg = msg+"bolt1";

System.out.println("对消息加工第1次-------[arg0]:"+ msg +"---[arg1]:"+id+"---[arg2]:"+time+"------->"+msg);

if (msg != null) {

collector.emit(new Values(msg));

}

} catch (Exception e) {

e.printStackTrace();

}

public void declareOutputFields(OutputFieldsDeclarer declarer) {

declarer.declare(new Fields("word"));

}

public static class Bolt2 extends BaseBasicBolt {

Map<String, Integer> counts = new HashMap<String, Integer>();

public void execute(Tuple tuple, BasicOutputCollector collector) {

String msg = tuple.getString(0);

msg = msg + "bolt2";

System.out.println("对消息加工第2次---------->"+msg);

collector.emit(new Values(msg,1));

}

public void declareOutputFields(OutputFieldsDeclarer declarer) {

declarer.declare(new Fields("word", "count"));

}

　　　　　#测试kafka和storm的结合

　　打开两个窗口(也可以在两台机器上分别打开，下面的例子中，我会打开m2和s1机器 )，分别m2上运行kafka的producer，在s1上运行kafka的consumer(如果刚才打开了就不用再打开),先测试kafka自运行是否正常。

　　如下所示，我在m2上运行producer，输入“hello welcome idoall.org”，在s1的机器上consumer同样收到了消息。说明kafka已经运行正常，并且消息通讯也没有问题。

　　m2机器输出的消息：

root@m2:/home/hadoop# /home/hadoop/kafka_2.9.2-0.8.1.1/bin/kafka-console-producer.sh --broker-st m1:9092 --sync --topic idoall_testTopic

SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".

SLF4J: Defaulting to no-operation (NOP) logger implementation

SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.

hello welcome idoall.org

　　s1机器接收的消息：

root@s1:/home/hadoop

# /home/hadoop/kafka_2.9.2-0.8.1.1/bin/kafka-console-consumer.sh --zookeeper m1:2181 --topic idoall_testTopic --from-beginning

SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".

SLF4J: Defaulting to no-operation (NOP) logger implementation

SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.

hello welcome idoall.org

　　　　　#我们再在Eclipse中运行KafkaTopologytest.java，可以看到在控制台，同样收到了刚才在m2上kafka发送的消息。说明kafka和storm也打通了。

#信息太多，我只截取重要部分：

*********Results********topic:idoall_testTopic

storm接收到来自kafka的消息------->hello welcome idoall.org

5268 [Thread-24-spout] INFO backtype.storm.daemon.task - Emitting: spout default [hello welcome idoall.org, 1, 2014年08月19日 11:21:15 051]

对消息加工第1次-------[arg0]:hello welcome idoall.orgbolt1---[arg1]:1---[arg2]:2014年08月19日 11:21:15 051------->hello welcome idoall.orgbolt1

5269 [Thread-18-bolt1] INFO backtype.storm.daemon.executor - Processing received message source: spout:6, stream: default, id: {-2000523200413433507=6673316475127546409}, [hello welcome idoall.org, 1, 2014年08月19日 11:21:15 051]

5269 [Thread-18-bolt1] INFO backtype.storm.daemon.task - Emitting: bolt1 default [hello welcome idoall.orgbolt1]

5269 [Thread-18-bolt1] INFO backtype.storm.daemon.task - Emitting: bolt1 __ack_ack [-2000523200413433507 4983764025617316501]

5269 [Thread-20-bolt2] INFO backtype.storm.daemon.executor - Processing received message source: bolt1:3, stream: default, id: {-2000523200413433507=1852530874180384956}, [hello welcome idoall.orgbolt1]

对消息加工第2次---------->hello welcome idoall.orgbolt1bolt2

5270 [Thread-20-bolt2] INFO backtype.storm.daemon.task - Emitting: bolt2 default [hello welcome idoall.orgbolt1bolt2, 1]

　　　　3) flume、kafka、storm的整合

　　从上面两个例子我们可以看到，flume和kafka之前已经完成了通讯和部署，kafka和storm之间可以正常通讯，只差把storm的相关文件打包成jar部署到storm中即可完成三者的通讯。

　　Storm的安装、配置、部署，如果不了解，可以参考这篇文章《ubuntu12.04+storm0.9.2分布式集群的搭建》

　　　　　#复制kafka相关的jar包到storm的lib里面。（因为在上面我们已经说过，kafka和storm的整合，主要是重写storm的spout，调用kafka的Consumer来接收消息并打印，所在需要用到这些jar包）

root@m1:/home/hadoop# cp /home/hadoop/kafka_2.9.2-0.8.1.1/libs/kafka_2.9.2-0.8.1.1.jar /home/hadoop/storm-0.9.2-incubating/lib

root@m1:/home/hadoop# cp /home/hadoop/kafka_2.9.2-0.8.1.1/libs/scala-library-2.9.2.jar /home/hadoop/storm-0.9.2-incubating/lib

root@m1:/home/hadoop# cp /home/hadoop/kafka_2.9.2-0.8.1.1/libs/metrics-core-2.2.0.jar /home/hadoop/storm-0.9.2-incubating/lib

root@m1:/home/hadoop# cp /home/hadoop/zookeeper-3.4.5/dist-maven/zookeeper-3.4.5.jar /home/hadoop/storm-0.9.2-incubating/lib

root@m1:/home/hadoop# cp /home/hadoop/kafka_2.9.2-0.8.1.1/libs/zkclient-0.3.jar /home/hadoop/storm-0.9.2-incubating/lib

　　　　　#在m1上启动storm nimbus

1	`root@m1:/home/hadoop# /home/hadoop/storm-0.9.2-incubating/bin/storm nimbus &`

　　　　　#在s1,s2上启动storm supervisor

1	`root@s1:/home/hadoop# /home/hadoop/storm-0.9.2-incubating/bin/storm supervisor &`

　　　　　#在m1上启动storm ui

1	`root@m1:/home/hadoop# /home/hadoop/storm-0.9.2-incubating/bin/storm ui &`

　　　　　#将Eclipse中的文件打包成jar复制到做任意目录，然后用storm来运行

root@m1:/home/hadoop/storm-0.9.2-incubating# ll

总用量 25768

drwxr-xr-x 11 root root 4096 Aug 19 11:53 ./

drwxr-xr-x 46 hadoop hadoop 4096 Aug 17 15:06 ../

drwxr-xr-x 2 root root 4096 Aug 1 14:38 bin/

-rw-r--r-- 1 502 staff 34239 Jun 13 08:46 CHANGELOG.md

drwxr-xr-x 2 root root 4096 Aug 2 12:31 conf/

-rw-r--r-- 1 502 staff 538 Mar 13 11:17 DISCLAIMER

drwxr-xr-x 3 502 staff 4096 May 6 03:13 examples/

drwxr-xr-x 3 root root 4096 Aug 1 14:38 external/

-rw-r--r-- 1 root root 26252342 Aug 19 11:36 idoall.cloud.jar

drwxr-xr-x 3 root root 4096 Aug 2 12:51 ldir/

drwxr-xr-x 2 root root 4096 Aug 19 11:53 lib/

-rw-r--r-- 1 502 staff 22822 Jun 12 04:07 LICENSE

drwxr-xr-x 2 root root 4096 Aug 1 14:38 logback/

drwxr-xr-x 2 root root 4096 Aug 1 15:07 logs/

-rw-r--r-- 1 502 staff 981 Jun 11 01:10 NOTICE

drwxr-xr-x 5 root root 4096 Aug 1 14:38 public/

-rw-r--r-- 1 502 staff 7445 Jun 10 02:24 README.markdown

-rw-r--r-- 1 502 staff 17 Jun 17 00:22 RELEASE

-rw-r--r-- 1 502 staff 3581 May 30 00:20 SECURITY.md

root@m1:/home/hadoop/storm-0.9.2-incubating# /home/hadoop/storm-0.9.2-incubating/bin/storm jar idoall.cloud.jar idoall.cloud.storm.KafkaTopologytest

　　　　　#在flume中发消息，在storm中看是否有接收到

　　　在flume中发送的消息：

1 2	`root@m1:/home/hadoop# echo "flume->kafka->storm message" \| nc localhost 5140` `root@m1:/home/hadoop#`

　　　storm中显示的内容：

#内容太多，只截取重要部分

storm接收到来自kafka的消息------->flume->kafka->storm message

174218 [Thread-16-spout] INFO  backtype.storm.daemon.task - Emitting: spout default [flume->kafka->storm message, 1, 2014年08月19日 12:06:39 360]

174220 [Thread-10-bolt1] INFO backtype.storm.daemon.executor - Processing received message source: spout:6, stream: default, id: {-2345821945306343027=-7738131487327750388}, [flume->kafka->storm message, 1, 2014年08月19日 12:06:39 360]

对消息加工第1次-------[arg0]:flume->kafka->storm messagebolt1---[arg1]:1---[arg2]:2014年08月19日 12:06:39 360------->flume->kafka->storm messagebolt1

174221 [Thread-10-bolt1] INFO backtype.storm.daemon.task - Emitting: bolt1 default [flume->kafka->storm messagebolt1]

174221 [Thread-10-bolt1] INFO  backtype.storm.daemon.task - Emitting: bolt1 __ack_ack [-2345821945306343027 -2191137958679040397]

174222 [Thread-20-__acker] INFO backtype.storm.daemon.executor - Processing received message source: bolt1:3, stream: __ack_ack, id: {}, [-2345821945306343027 -2191137958679040397]

174222 [Thread-12-bolt2] INFO backtype.storm.daemon.executor - Processing received message source: bolt1:3, stream: default, id: {-2345821945306343027=8433871885621516671}, [flume->kafka->storm messagebolt1]

对消息加工第2次---------->flume->kafka->storm messagebolt1bolt2

174223 [Thread-12-bolt2] INFO  backtype.storm.daemon.task - Emitting: bolt2 default [flume->kafka->storm messagebolt1bolt2, 1]

174223 [Thread-12-bolt2] INFO  backtype.storm.daemon.task - Emitting: bolt2 __ack_ack [-2345821945306343027 8433871885621516671]

174224 [Thread-20-__acker] INFO backtype.storm.daemon.executor - Processing received message source: bolt2:4, stream: __ack_ack, id: {}, [-2345821945306343027 8433871885621516671]

174228 [Thread-16-spout] INFO  backtype.storm.daemon.task - Emitting: spout __ack_init [-2345821945306343027 -7738131487327750388 6]

174228 [Thread-20-__acker] INFO backtype.storm.daemon.executor - Processing received message source: spout:6, stream: __ack_init, id: {}, [-2345821945306343027 -7738131487327750388 6]

174228 [Thread-20-__acker] INFO  backtype.storm.daemon.task - Emitting direct: 6; __acker __ack_ack [-2345821945306343027]

　　　通过以上实例，我们完成了flume、kafka、storm之间的通讯，结合之前介绍的《Flume1.5.0的安装、部署、简单应用(含分布式、与hadoop2.2.0、hbase0.96的案例)》和《Golang、Php、Python、Java基于Thrift0.9.1实现跨语言调用》.如果相互结合，相信在基于大数据实时计算，以及多语言之间的相互调用，能够解决你在项目中的大部分问题。希望最近一系列的文章能够对你有帮助。

---------------------------------------

博文作者：迦壹

博客地址：Flume+Kafka+Strom基于分布式环境的结合使用

---------------------------------------

Flume+Kafka+Strom基于伪分布式环境的结合使用的更多相关文章

基于Centos搭建 Hadoop 伪分布式环境
软硬件环境: CentOS 7.2 64 位, OpenJDK- 1.8,Hadoop- 2.7 关于本教程的说明云实验室云主机自动使用 root 账户登录系统,因此本教程中所有的操作都是以 roo ...
CentOS7下Hadoop伪分布式环境搭建
CentOS7下Hadoop伪分布式环境搭建前期准备 1.配置hostname(可选,了解) 在CentOS中,有三种定义的主机名:静态的(static),瞬态的(transient),和灵活的(p ...
CentOS5&period;4 搭建Hadoop2&period;5&period;2伪分布式环境
简介: Hadoop是处理大数据的主要工具,其核心部分是HDFS.MapReduce.为了学习的方便,我在虚拟机上搭建了一个伪分布式环境,来进行开发学习. 一.安装前准备: 1)linux服务器:Vm ...
Ubuntu 14&period;04 (32位)上搭建Hadoop 2&period;5&period;1单机和伪分布式环境
引言一直用的Ubuntu 32位系统(准备下次用Fedora,Ubuntu越来越不适合学习了),今天准备学习一下Hadoop,结果下载Apache官网上发布的最新的封装好的2.5.1版,配置完了根本 ...
linux环境下的伪分布式环境搭建
本文的配置环境是VMware10+centos2.5. 在学习大数据过程中,首先是要搭建环境,通过实验,在这里简短粘贴书写关于自己搭建大数据伪分布式环境的经验. 如果感觉有问题,欢迎咨询评论. 一:伪 ...
《OD大数据实战》Hadoop伪分布式环境搭建
一.安装并配置Linux 8. 使用当前root用户创建文件夹,并给/opt/下的所有文件夹及文件赋予775权限,修改用户组为当前用户 mkdir -p /opt/modules mkdir -p / ...
OS X Yosemite下安装Hadoop2&period;5&period;1伪分布式环境
最近开始学习Hadoop,一直使用的是公司配好的环境.用了一段时间后发现对Hadoop还是一知半解,故决定动手在本机上安装一个供学习研究使用.正好自己用的是mac,所以没啥说的,直接安装. 总体流程 ...
Hadoop 2&period;7 伪分布式环境搭建
1.安装环境 ①.一台Linux CentOS6.7 系统 hostname ipaddress subnet mask ...
Hadoop学习笔记1：伪分布式环境搭建
在搭建Hadoop环境之前,请先阅读如下博文,把搭建Hadoop环境之前的准备工作做好,博文如下: 1.CentOS 6.7下安装JDK , 地址: http://blog.csdn.net/yule ...

随机推荐

p2p网贷平台设计简析
以我之前主持开发的一个商业产品:p2p网贷为例进行分析.整个的概况,可以参见:www.huixinp2p.com(目的只会技术交流) 界面可以直接参考前期博客:http://www.cnblogs.c ...
【代码笔记】iOS-淡出淡入效果
一,效果图. 二,工程图. 三,代码. ViewController.h #import <UIKit/UIKit.h> @interface ViewController : UIVie ...
Hbase&amp&semi;Hadoop常用命令
Hbase中根据Rowkey的前缀Prefix查询数据: scan 'test_xiaomifeng_monitoring_log',{FILTER => "(PrefixFilter ...
开源分布式实时计算引擎 Iveely Computing 之 WordCount 详解(3)
WordCount是很多分布式计算中,最常用的例子,例如Hadoop.Storm,Iveely Computing也不例外.明白了WordCount在Iveely Computing上的运行原理,就很 ...
Carthage
Carthage Carthage - 一个简单.去集中化的Cocoa依赖管理器
NeHe OpenGL教程第十课：3D世界
转自[翻译]NeHe OpenGL 教程前言声明,此 NeHe OpenGL教程系列文章由51博客yarin翻译(2010-08-19),本博客为转载并稍加整理与修改.对NeHe的OpenGL管线 ...
USACO Section 4&period;2&colon; Drainage Ditches
最大流的模板题 /* ID: yingzho1 LANG: C++ TASK: ditch */ #include <iostream> #include <fstream> ...
基于visual Studio2013解决C语言竞赛题之0605strcat
题目
jQuery形式可以计算，它包含了无线电的变化价格，select价格变化，删除行动态计算加盟
jQuery能够计算的表单,包含单选改变价格,select改变价格,动态加入删除行计算各种表单情况的计算演示 JavaScript Code <script type="text/ ...
sublime text 我的常用配置
{ "color_scheme": "Packages/Color Scheme - Default/IDLE.tmTheme", "font_fac ...