stom实时单词统计

注：我是用一起写office写的，发到博客上格式就变了，，，变了，，，

1.微批处理可以根据数据的条数或者间隔时间来定。

实时处理有两种方式。

一是持续流处理，

二是微批处理。

2数据纪录处理情况

一是至少一次，  存在重复处理

二是有且仅有一次 严格

三是至多一次    存在漏出里

3.配置

stom.yaml(python格式，注意文件的配置)

# Licensed to the Apache Software Foundation (ASF) under one

# or more contributor license agreements.  See the NOTICE file

# distributed with this work for additional information

# regarding copyright ownership.  The ASF licenses this file

# to you under the Apache License, Version 2.0 (the

# "License"); you may not use this file except in compliance

# with the License.  You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

########### These MUST be filled in for a storm configuration

storm.zookeeper.servers:

    - "hadoop02-linux.alibaba.com"

#     - "server2"

#

nimbus.host: "hadoop02-linux.alibaba.com"

#

#

storm.local.dir: "/opt/modules/apache-storm-0.9.6/worspace"

# ##### These may optionally be filled in:

#    

## List of custom serializations

# topology.kryo.register:

#     - org.mycompany.MyType

#     - org.mycompany.MyType2: org.mycompany.MyType2Serializer

#

## List of custom kryo decorators

# topology.kryo.decorators:

#     - org.mycompany.MyDecorator

#

## Locations of the drpc servers

# drpc.servers:

#     - "server1"

#     - "server2"

supervisor.slots.ports:

   - 6700

   - 6701

   - 6702

   - 6703

ui.port: 8082

## Metrics Consumers

# topology.metrics.consumer.register:

#   - class: "backtype.storm.metric.LoggingMetricsConsumer"

#     parallelism.hint: 1

#   - class: "org.mycompany.MyMetricsConsumer"

#     parallelism.hint: 1

#     argument:

#       - endpoint: "metrics-collector.mycompany.org"

4.nimbus

当集群中没有人提交任务，没有supervisor节点挂掉，主节点短时间挂掉是没有问题的，只要配合运维，监控重新启动就可以。

启动，并查看进程

运行官方示例程序

bin/storm jar  examples/storm-starter/storm-starter-topologies-0.9.6.jar storm.starter.WordCountTopology wordcount

注意的是： 停止Storm集群之前，一定要先将运行在Storm集群上的Topology先停掉，否则下次重启Storm集群，Storm会将这些Topology都启动起来

5.停止集群，没有脚本文件，可以自行编写，或者使用kill

跑bolt是线程跑的。

6.编写word topology

---

SentenceSpout:

package com.ibeifeng.bigdata.storm.topo;

import java.util.Map;

import java.util.Random;

import org.slf4j.Logger;

import org.slf4j.LoggerFactory;

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;

/**

* spout采集数据源

* @author lm

*

*/

public class SentenceSpout implements IRichSpout{

//抽象类 BaseRichSpout

//BaseRichSpout

private static final Logger log = LoggerFactory.getLogger(SentenceSpout.class);

private SpoutOutputCollector collector;

private static final String[] Sentence =

  {"hadoop yarn mapreduce",

"flume error liangman yunkjhhj",

"abc dec abc abd fff",

"dhjfh jkdshg hjdkfgh"};

@Override

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

this.collector = collector;

}

@Override

public void close() {

// TODO Auto-generated method stub

}

@Override

public void activate() {

// 和页面激活有关，一般不写

}

@Override

public void deactivate() {

//  和页面上的失效有关，一般不写

}

@Override

public void nextTuple() {

//random get the data

String tmp = Sentence[ new Random().nextInt(Sentence.length)];

if(tmp.contains("error")){

log.error("数据错误");

}else{

//告诉发送的value是什么，但是没告诉key是什么，declareOutputFields会告诉key的名称

//key and value 是一一对应的

this.collector.emit(new Values(tmp));

}

}

@Override

public void ack(Object msgId) {

// TODO Auto-generated method stub

}

@Override

public void fail(Object msgId) {

// TODO Auto-generated method stub

}

@Override

public void declareOutputFields(OutputFieldsDeclarer declarer) {

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

}

@Override

public Map<String, Object> getComponentConfiguration() {

//针对本组件单独的配置

return null;

}

}

---

SplitBolt

package com.ibeifeng.bigdata.storm.topo;

import java.util.Map;

import org.apache.commons.lang.StringUtils;

import backtype.storm.task.OutputCollector;

import backtype.storm.task.TopologyContext;

import backtype.storm.topology.IRichBolt;

import backtype.storm.topology.OutputFieldsDeclarer;

import backtype.storm.tuple.Fields;

import backtype.storm.tuple.Tuple;

import backtype.storm.tuple.Values;

/**

* 开发一个bolt

* @author lm

*

*/

public class SplitBolt implements IRichBolt{

private OutputCollector collector;

@Override

public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {

this.collector = collector;

}

/**

*接收到前面的一条tuple就调用一次

*

*/

@Override

public void execute(Tuple input) {

//

String sentence = input.getStringByField("seq");

if(StringUtils.isNotBlank(sentence)){

String[] words = sentence.split(" ");

for(String word:words){

this.collector.emit(new Values(word));

}

}

}

@Override

public void cleanup() {

// TODO Auto-generated method stub

}

@Override

public void declareOutputFields(OutputFieldsDeclarer declarer) {

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

}

@Override

public Map<String, Object> getComponentConfiguration() {

// TODO Auto-generated method stub

return null;

}

}

---

CountBolt

package com.ibeifeng.bigdata.storm.topo;

import java.util.HashMap;

import java.util.Map;

import backtype.storm.task.OutputCollector;

import backtype.storm.task.TopologyContext;

import backtype.storm.topology.OutputFieldsDeclarer;

import backtype.storm.topology.base.BaseRichBolt;

import backtype.storm.tuple.Fields;

import backtype.storm.tuple.Tuple;

import backtype.storm.tuple.Values;

/**

* 计数bolt

* @author lm

*

*/

public class CountBolt  extends BaseRichBolt{

private  OutputCollector collector;

private Map<String ,Integer> map;

@Override

public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {

// TODO Auto-generated method stub

this.collector=collector;

map = new HashMap<String,Integer>();

}

@Override

public void execute(Tuple input) {

String word = input.getStringByField("word");

int count = 1;

if(map.containsKey(word)){

count = map.get(word) + 1;

}

this.collector.emit(new Values(word,count));

}

@Override

public void declareOutputFields(OutputFieldsDeclarer declarer) {

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

}

}

---

PrintBolt

package com.ibeifeng.bigdata.storm.topo;

import java.util.Map;

import org.slf4j.Logger;

import org.slf4j.LoggerFactory;

import backtype.storm.task.OutputCollector;

import backtype.storm.task.TopologyContext;

import backtype.storm.topology.OutputFieldsDeclarer;

import backtype.storm.topology.base.BaseRichBolt;

import backtype.storm.tuple.Tuple;

public class PrintBolt extends BaseRichBolt{

private static final Logger logger = LoggerFactory.getLogger(PrintBolt.class);

@Override

public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {

}

@Override

public void execute(Tuple input) {

String word = input.getStringByField("word");

Integer value = input.getIntegerByField("count");

logger.info("单次"+word+"数量"+value); //单词 数量

}

@Override

public void declareOutputFields(OutputFieldsDeclarer declarer) {

// TODO Auto-generated method stub

}

}

---

主程序：

package com.ibeifeng.bigdata.storm.topo;

import backtype.storm.Config;

import backtype.storm.LocalCluster;

import backtype.storm.StormSubmitter;

import backtype.storm.generated.AlreadyAliveException;

import backtype.storm.generated.InvalidTopologyException;

import backtype.storm.topology.TopologyBuilder;

import backtype.storm.tuple.Fields;

/**

* 一个实时计数单词的例子

* @author lm

*

*/

public class WordCountToplogy {

public static final String SPOUT_ID ="sentenceSpout";

public static final String SPILT_ID ="spiltBolt";

public static final String COUNT_ID ="countBolt";

public static final String PRINT_ID ="printBolt";

public static void main(String[] args){

//点和边的连接

//边就是数据流分组的问题

TopologyBuilder builder = new TopologyBuilder();

builder.setSpout(SPOUT_ID, new SentenceSpout());

builder.setBolt(SPILT_ID, new SplitBolt()).shuffleGrouping(SPOUT_ID);

builder.setBolt(COUNT_ID, new CountBolt()).fieldsGrouping(SPILT_ID,new Fields("word"));

builder.setBolt(PRINT_ID, new PrintBolt()).globalGrouping(COUNT_ID);

Config conf = new Config();

if(args == null || args.length == 0){

//本地执行

LocalCluster lc = new LocalCluster();

lc.submitTopology("wordcount", conf, builder.createTopology());

}else{

//指定多少个进程执行

conf.setNumWorkers(1);

try {

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

} catch (AlreadyAliveException | InvalidTopologyException e) {

// TODO Auto-generated catch block

e.printStackTrace();

}

}

}

}

打包：在集群上运行

工作节点：

打印节点的输出信息：