storm复习笔记

1.storm基本介绍

　　0.storm是免费、开源、分布式、跨语言、可伸缩、低延迟、容错实时流计算系统。每个节点每秒处理百万元组

　　1.流计算，动态数据。mr操作的数据都是静态数据，启动mr读取文件，olap离线计算。实时监控实施舆情调查需要通过storm

2.storm名词解释

　　0.Tuple元组：storm中的主要数据结构，是一个有序元素列表，默认情况下，tuple支持所有的数据类型

　　1.Stream流:是tuple元组的序列

　　2.Spout:数据流的源头，一般 Spout 会从一个外部的数据源读取元组然后将他们发送到拓扑中，也可以读取kafka数据队列的消息。根据需求的不同，Spout 既可以定义为可靠的数据源，也可以定义为不可靠的数据源。一个可靠的 Spout能够在它发送的元组处理失败时重新发送该元组，以确保所有的元组都能得到正确的处理；相对应的，不可靠的 Spout 就不会在元组发送之后对元组进行任何其他的处理。一个 Spout可以发送多个数据流。

　　3.Bolt:转接头，逻辑处理单元。spout传递数据给bolt，bolt来进行处理并且产生新的数据输出流。bolt可以执行过滤、聚合、连接和交互

　　4.Topology:spout和bolt连接在一起形成了一个bolt，一个topology是一个有向图，顶点就是计算，边就是数据流

　　5.Task:storm中的每个spout或者bolt都是一个task

3.storm和hadoop对比

　　storm　　　　　　　　　　　　　　　　　　hadoop

实时流处理　　　　　　　　　　　　　　　　　　批处理

无状态　　　　　　　　　　　　　　　　　　　　有状态

使用zk协同的主从架构　　　　　　　　　　　　　无zk的主从架构

4.storm架构

　　storm是可以容错的、快速的、没有单点故障的分布式应用

　　1.Nimbus:master节点，核心组件，主要工作就是运行topology,分析拓扑并收集运行task，分发可用的task给supervisor，监控topology有没有失败，依靠zk监控top的运行状况

　　2.Supervisor:worker节点,每个supervisor有多个worker进程，负责代理task给worker,worker孵化执行线程，最终运行task，

　　　　storm使用内部消息系统在nimbus和supervisor之间进行通信

　　　　接收nimbus指令，管理worker进程完成task的派发

　　3.worker:执行特定的topology,worker本身不执行任务，而是孵化executors，让executors去执行任务

　　4.Executor:本质上是由worker进程孵化出来的一个线程而已。executor运行的task都属于同一个spout或者bolt

　　5.task：执行实际上的任务处理工作，或者是spout或者是bolt

5.storm的工作流程

　　1.nimbus等待提交的top

　　2.一旦拓扑提交，就会处理拓扑并且来收集所有任务

　　3.nimbus分发任务给所有可用的suoervisor

　　4.在特定时间间隔之内，所有的supervisor都会发送心跳给nimbus通知他们还活着

　　5.当一个supervisor挂掉之后，他就不再发送新桃给nimbus,这个时候nimbus回分派任务到另外一个supervisor

　　6.当nimbus本身挂掉之后，并不会影响supervisor继续执行自己的任务，但是不会接受新的任务

　　7.task完成之后，supervisor回继续等待新的task,同时挂掉的nimbus可以通过监控工具自动重启

6.安装和配置storm

[s201 ~ s204]
1.jdk
2.tar
3.环境变量
4.验证安装
$>source /etc/profile
$>./storm version
5.分发安装文件到其他节点。

6.配置
[storm/conf/storm.yaml]
storm.local.dir: "/home/centos/storm"
storm.zookeeper.servers:
- "s202"
- "s203"

storm.zookeeper.port: 2181

### nimbus.* configs are for the master
nimbus.seeds : ["s201"]

### ui.* configs are for the master
ui.host: 0.0.0.0
ui.port: 8080

supervisor.slots.ports:
- 6700
- 6701
- 6702
- 6703
7.分发

8.启动进程
a)启动s201 nimbus进程
$>storm nimbus &

b)启动s202 ~ s204 supervisor进程
$>storm supervisor &

c)启动s201的ui进程
$>storm ui &

9.通过webui查看
http://s201:8080/

7.编程实现CallLog日志统计

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

0.pom.xml
<?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>com.it18zhang</groupId>
<artifactId>StormDemo</artifactId>
<version>1.0-SNAPSHOT</version>

<dependencies>
<dependency>
<groupId>org.apache.storm</groupId>
<artifactId>storm-core</artifactId>
<version>1.0.3</version>
</dependency>
</dependencies>

</project>
1.创建Spout
package com.it18zhang.stormdemo;

import org.apache.storm.spout.SpoutOutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.IRichSpout;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Random;

/**
* Spout类,负责产生数据流
*/
public class CallLogSpout implements IRichSpout{

//Spout输出收集器
private SpoutOutputCollector collector;

//是否完成
private boolean completed = false;

//上下文
private TopologyContext context;

//随机发生器
private Random randomGenerator = new Random();

//
private Integer idx = 0;

public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) {
this.context = context;
this.collector = collector;
}

public void close() {
}

public void activate() {
}

public void deactivate() {

}

/**
* 下一个元组
*/
public void nextTuple() {
if (this.idx <= 1000) {
List<String> mobileNumbers = new ArrayList<String>();
mobileNumbers.add("1234123401");
mobileNumbers.add("1234123402");
mobileNumbers.add("1234123403");
mobileNumbers.add("1234123404");

Integer localIdx = 0;
while (localIdx++ < 100 && this.idx++ < 1000) {
//取出主叫
String caller = mobileNumbers.get(randomGenerator.nextInt(4));
//取出被叫
String callee = mobileNumbers.get(randomGenerator.nextInt(4));
while (caller == callee) {
//重新取出被叫
callee = mobileNumbers.get(randomGenerator.nextInt(4));
}
//模拟通话时长
Integer duration = randomGenerator.nextInt(60);

//输出元组
this.collector.emit(new Values(caller, callee, duration));
}
}
}

public void ack(Object msgId) {

}

public void fail(Object msgId) {

}

/**
* 定义输出的字段名称
*/
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("from", "to", "duration"));
}

public Map<String, Object> getComponentConfiguration() {
return null;
}
}

2.创建CreatorBolt
package com.it18zhang.stormdemo;

import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.IRichBolt;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;

import java.util.Map;

/**
* 创建CallLog日志的Bolt
*/
public class CallLogCreatorBolt implements IRichBolt {
//
private OutputCollector collector;

public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {
this.collector = collector ;
}

public void execute(Tuple tuple) {
//处理通话记录
String from = tuple.getString(0);
String to = tuple.getString(1);
Integer duration = tuple.getInteger(2);
//产生新的tuple
collector.emit(new Values(from + " - " + to, duration));
}

public void cleanup() {

}

/**
* 设置输出字段的名称
*/
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("call", "duration"));
}

public Map<String, Object> getComponentConfiguration() {
return null;
}
}

3.创建CounterBolt
package com.it18zhang.stormdemo;

import org.apache.storm.task.IBolt;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.IRichBolt;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;

import java.util.HashMap;
import java.util.Map;

/**
* 通话记录计数器Bolt
*/
public class CallLogCounterBolt implements IRichBolt{

Map<String, Integer> counterMap;
private OutputCollector collector;

public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {
this.counterMap = new HashMap<String, Integer>();
this.collector = collector;
}

public void execute(Tuple tuple) {
String call = tuple.getString(0);
Integer duration = tuple.getInteger(1);

if (!counterMap.containsKey(call)) {
counterMap.put(call, 1);
} else {
Integer c = counterMap.get(call) + 1;
counterMap.put(call, c);
}
collector.ack(tuple);
}

public void cleanup() {
for (Map.Entry<String, Integer> entry : counterMap.entrySet()) {
System.out.println(entry.getKey() + " : " + entry.getValue());
}
}

public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("call"));
}

public Map<String, Object> getComponentConfiguration() {
return null;
}
}

4.App
package com.it18zhang.stormdemo;

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;

/**
* App
*/
public class App {
public static void main(String[] args) throws InterruptedException {
TopologyBuilder builder = new TopologyBuilder();
//设置Spout
builder.setSpout("spout", new CallLogSpout());
//设置creator-Bolt
builder.setBolt("creator-bolt", new CallLogCreatorBolt()).shuffleGrouping("spout");
//设置counter-Bolt
builder.setBolt("counter-bolt", new CallLogCounterBolt()).fieldsGrouping("creator-bolt", new Fields("call"));

Config conf = new Config();
conf.setDebug(true);

LocalCluster cluster = new LocalCluster();
cluster.submitTopology("LogAnalyserStorm", conf, builder.createTopology());
Thread.sleep(10000);

//停止集群
cluster.shutdown();
}
}

5.在生产环境的集群上部署storm top
a)修改提交方式
[App.java]
public static void main(String[] args) throws Exception {
TopologyBuilder builder = new TopologyBuilder();
//设置Spout
builder.setSpout("spout", new CallLogSpout());
//设置creator-Bolt
builder.setBolt("creator-bolt", new CallLogCreatorBolt()).shuffleGrouping("spout");
//设置counter-Bolt
builder.setBolt("counter-bolt", new CallLogCounterBolt()).fieldsGrouping("creator-bolt", new Fields("call"));

Config conf = new Config();
conf.setDebug(true);

/**
* 本地模式storm
*/
// LocalCluster cluster = new LocalCluster();
// cluster.submitTopology("LogAnalyserStorm", conf, builder.createTopology());
// Thread.sleep(10000);
StormSubmitter.submitTopology("mytop", conf, builder.createTopology());
}
b)导入jar包.
maven ...

c)在centos上运行top
$>storm jar xxx.jar com.it18zhang.stormdemo.App

8.使用storm流计算实现wordCount

　　1.spout

package com.it18zhang.wc;

import com.it18zhang.util.Util;
import org.apache.storm.spout.SpoutOutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.IRichSpout;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Random;

/**
 * Created by stone on 2018/8/18.
 */
public class WordCountSpount implements IRichSpout {
    private  TopologyContext context;
    private SpoutOutputCollector collector;
    private Random r = new Random();
    private List<String> stats;

    public void open(Map map, TopologyContext context, SpoutOutputCollector collector) {
       Util.sendToClient(this,"open()",);
        this.context = context;
        this.collector = collector;
        stats  = new ArrayList<String>();
        stats.add("hello world tom");
        stats.add("hello world tomas");
        stats.add("hello world tomasLee");
        stats.add("hello world tomson");

    }

    public void close() {

    }

    public void activate() {

    }

    public void deactivate() {

    }

    public void nextTuple() {
        Util.sendToClient(this,"nextTuple()",);
        String line =stats.get(r.nextInt());
        collector.emit(new Values(line));
    }

    public void ack(Object o) {

    }

    public void fail(Object o) {

    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        //声明字段
        declarer.declare(new Fields("line"));
    }

    public Map<String, Object> getComponentConfiguration() {
        return null;
    }
}

　　2.splitBolt

　

package com.it18zhang.wc;

import com.it18zhang.util.Util;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.IRichBolt;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;

import java.util.Map;

/**
 * Created by stone on 2018/8/18.
 */
public class SplitBolt implements IRichBolt {
    private TopologyContext context;
    private OutputCollector collector;
    public void prepare(Map map, TopologyContext context, OutputCollector collector) {
        Util.sendToClient(this,"prepare()",);
        this.context=context;
        this.collector=collector;
    }

    public void execute(Tuple tuple) {
        Util.sendToClient(this,"execute()",);
        String line =tuple.getString();
        String[] arr = line.split(" ");
        for(String s:arr){
            collector.emit(new Values(s,));

        }
    }

    public void cleanup() {

    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("word","count"));
    }

    public Map<String, Object> getComponentConfiguration() {
        return null;
    }
}

　　3.counterBolt

　

package com.it18zhang.wc;

import com.it18zhang.util.Util;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.IRichBolt;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;

import java.util.HashMap;
import java.util.Map;

import static java.lang.System.out;

/**
 * Created by stone on 2018/8/18.
 */
public class CountBolt implements IRichBolt {
    private Map<String,Integer> map;
    private TopologyContext context;
    private OutputCollector collector;
    public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {
        Util.sendToClient(this,"prepare()",);
        this.context=context;
        this.collector=collector;
        map= new HashMap<String,Integer>();
    }

    public void execute(Tuple tuple) {
       Util.sendToClient(this,"execute("+tuple.toString()+")",);
        String word = tuple.getString();
        Integer count = tuple.getInteger();
        if(!map.containsKey(word)){
            map.put(word,);
        }
        else{
            map.put(word,map.get(word) + count);
        }
    }

    public void cleanup() {
        for(Map.Entry entry:map.entrySet()){
            out.println(entry.getKey()+":"+entry.getValue());
        }
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("word","count"));
    }

    public Map<String, Object> getComponentConfiguration() {
        return null;
    }
}

　　4.App

package com.it18zhang.wc;

import com.it18zhang.stormDemo.CallLogBolt;
import com.it18zhang.stormDemo.CallLogCounterBolt;
import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.StormSubmitter;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;

/**
 * Created by stone on 2018/8/18.
 */
public class App {
    public static void main(String[] args) throws Exception {
        TopologyBuilder builder = new TopologyBuilder();
        //设置spout
        builder.setSpout("wcspout",new WordCountSpount()).setNumTasks();
        //设置bolt
        builder.setBolt("split-bolt",new SplitBolt()).shuffleGrouping("wcspout").setNumTasks();//通过shuffle的方式随机发送过去
        //设置counterBolt
        builder.setBolt("counter-bolt",new CountBolt()).fieldsGrouping("split-bolt",new Fields("word")).setNumTasks();//按照字段来进行分组

        Config conf = new Config();
        conf.setDebug(true);

        //LocalCluster cluster = new LocalCluster();

        //cluster.submitTopology("wordCount",conf,builder.createTopology());
        //Thread.sleep(20000);
        //cluster.shutdown();
        StormSubmitter.submitTopology("mytop1", conf, builder.createTopology());
    }
}

9.设置topology的并发程度和任务

配置并发度

1.设置worker数据：conf.setNumWorkers(1);

2.设置并发暗示：

TopologyBuilder builder = new TopologyBuilder();
//设置spout的并发暗示
builder.setSpout("wcspout",new WordCountSpount(),3).setNumTasks(2);
//设置split-bolt的并发暗示

builder.setBolt("split-bolt",new SplitBolt(),4).shuffleGrouping("wcspout").setNumTasks(3);//通过shuffle的方式随机发送过去
//设置counterBolt
builder.setBolt("counter-bolt",new CountBolt(),5).fieldsGrouping("split-bolt",new Fields("word")).setNumTasks(4);//按照字段来进行分组

3.设置任务个数
4.并发度等于所有任务个数的总和