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Storm架构和容错机制

概述

Apache Storm提供了一种基于Topology流计算的概念，Topology等价于hadoop中的mapreduce计算，MapReduce端最终会停止，Topology会一直运行，除非用户执行storm kill指令才会停止该计算。Storm的单个阶段每秒钟可以处理100万条数据/Tuple。
目前的主流流计算框架:Kafka Streaming、Apache Storm、Spark Streaming、Flink DataStream等。

  • Kafka Streaming:是一套基于Kafka-Streaming库的一套流计算工具jar包，具有简单容易集成等特点。
  • Apache Storm/Jstorm:流处理框架实现对流数据流的处理和状态管理等操作。
  • Spark Streaming:构建在Spark批处理之上的流处理框架，微观批处理，因此诟病 延迟较高。
  • Flink DataStream/Blink:属于第三代流计算框架，吸取了Spark和Storm设计经验，在实时性和应用性上以及性能都有很大的提升，是目前为止最强的流计算引擎。
Storm-1.2.2之前的版本在和消息队列Kafka整合时会报错：STORM-3046，最好使用高一点的版本

Storm架构原理

Storm核心组件

Storm架构和容错机制-LMLPHP
Nimbus：负责Nimbus的资源分配和任务调度。
Supervisor：负责接收Nimbus分配的任务，启动和关闭属于自己管理的worker进程。。
Worker：负责运行具体处理组件逻辑的进程。worker运行的任务类型只有两种，一种是Spout任务，一种是Bolt任务。
Task：worker中每一个spout或者bolt的线程称为一个task，在storm0.8之后，task不再与物理线程对应，不同spout/bolt的task可能会共享一个物理线程，该线程称为executor。

Storm编程

Topology：Storm中运行的实时应用程序（拓扑）。
Spout：在一个topology中获取的组件。通常情况下spout会从外部数据源中读取数据，并且将数据封装成Tuple,然后转换为topology内部的源数据（将封装Tuple发射|emit到Topology中）。
Bolt：接受数据后执行处理的组件，用户可以在其中执行自己想要的操作。
Tuple：一次消息传递的基本单元，可以理解为一组消息就是一个Tuple。
Stream：数据流，无限制的Tuple序列，以分布式方式并行处理和创建。

字符统计案例(入门)：

  • 需要依赖：
<dependency>
	<groupId>org.apache.storm</groupId>
	<artifactId>storm-core</artifactId>
	<version>2.0.0</version>
	<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.storm</groupId>
	<artifactId>storm-client</artifactId>
	<version>2.0.0</version>
	<scope>provided</scope>
</dependency>

Spout：

public class WordCountSpout extends BaseRichSpout {
    private String[] lines={"this is a demo","hello Storm","ni hao"};
     //该类负责将数据发送给下游
    private SpoutOutputCollector collector;

    public void open(Map<String, Object> conf, TopologyContext context, SpoutOutputCollector collector) {
        this.collector=collector;
    }
    //向下游发送Tuple ，改Tuple的Schemal在declareOutputFields声明
    public void nextTuple() {
        Utils.sleep(1000);//休息1s钟
        String line=lines[new Random().nextInt(lines.length)];//随机获取一个lines数组中的数据
        collector.emit(new Values(line));//发送数据给下游
    }
    //对emit中的tuple做字段的描述
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("line"));
    }
}

LineSplitBolt:

public class LineSplitBolt extends BaseRichBolt {
     //该类负责将数据发送给下游
    private OutputCollector collector;
    public void prepare(Map<String, Object> topoConf, TopologyContext context, OutputCollector collector) {
        this.collector=collector;
    }

    public void execute(Tuple input) {
        String line = input.getStringByField("line");//接受来自上游的数据
        String[] tokens = line.split("\\W+");//把一行数据分割，这些单词组成一个数组
        for (String token : tokens) {
            collector.emit(new Values(token,1));//向下游发送tuple
        }
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("word","count"));//描述向下游发送的数据
    }
}

WordCountBolt

public class WordCountBolt extends BaseRichBolt {
    //存储状态
    private Map<String,Integer> keyValueState;
    //该类负责将数据发送给下游
    private OutputCollector collector;

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

    public void execute(Tuple input) {
        String key = input.getStringByField("word");
        int count=0;
        if(keyValueState.containsKey(key)){
            count=keyValueState.get(key);//获取以前的count
        }
        //更新状态
        int currentCount=count+1;
        keyValueState.put(key,currentCount);
        //将最后结果输出给下游
        collector.emit(new Values(key,currentCount));
    }

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

WordPrintBolt

public class WordPrintBolt extends BaseRichBolt {

    public void prepare(Map<String, Object> topoConf, TopologyContext context, OutputCollector collector) {
    }
	//打印数据
    public void execute(Tuple input) {
        String word=input.getStringByField("key");
        Integer result=input.getIntegerByField("result");
        System.out.println(input+"\t"+word+" , "+result);
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {

    }
}

Topology

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

public class WordCountTopology {
    public static void main(String[] args) throws Exception {
        //1.创建TopologyBuilder
        TopologyBuilder builder = new TopologyBuilder();

        //2.编织流处理逻辑- 重点（Spout、Bolt、连接方式）

        builder.setSpout("WordCountSpout",new WordCountSpout(),1);

        builder.setBolt("LineSplitBolt",new LineSplitBolt(),3)
                .shuffleGrouping("WordCountSpout");//设置 LineSplitBolt 接收上游数据通过 随机

        builder.setBolt("WordCountBolt",new WordCountBolt(),3)
                .fieldsGrouping("LineSplitBolt",new Fields("word"));

        builder.setBolt("WordPrintBolt",new WordPrintBolt(),4)
                .fieldsGrouping("WordCountBolt",new Fields("key"));

        //3.提交流计算
        Config conf= new Config();
        conf.setNumWorkers(3); //设置Topology运行所需的Worker资源，JVM个数
        conf.setNumAckers(0);  //关闭Storm应答，可靠性有关
        StormSubmitter.submitTopology("worldcount",conf,builder.createTopology());
    }
}

Storm程序并发机制

Workers(JVMs):在一个物理节点上可以运行一个或多个独立JVM进程。一个Topology可以包含一个或多个worker（并行的跑在不同的物理机上），所以worker process就是执行一个topology的子集。。为提高并发效果，Worker的数目至少应该大于machines的数目。
Executors(线程)：在一个worker JVM进程中运行着多个Java线程。一个Executors线程可以执行一个或多个tasks。只能在代码中配置executor的个数。
Tasks(spout|bolt)：Task是具体的处理逻辑对象，每一个Spout和Bolt会被当做很多task在整个集群里执行。stream grouping是定义怎么从一对task发射tuple到另外一对task。通过TopologyBuilder.setSpout 和TopBuilder.setBolt可以设置并行度，也就是多少个task。默认一个executor中一个task。

Storm消息容错机制

概念：

  • Storm中，可靠的信息处理机制是从spout开始的。

  • 提供了可靠信息处理机制的spout需要记录它发射出去的tuple，当下游bolt处理tuple或者子tuple失败时，spout能够重新发射。

  • Storm通过调用Spout的nextTuple()发送一个tuple。为了实现可靠的消息处理，首先要给每个发出的tuple带上唯一的ID，并且将ID作为参数传递给SpoutOutputCollector的emit()方法：;messageid就是用来标示唯一tuple的，而rootid是随机生成的。
    给每个tuple指定ID告诉Storm系统，无论处理成功还是失败，spout都要接受tuple树上所有节点返回的通知。如果处理成功，spout的ack()方法将会对编号是msgId的消息应答确认；如果处理失败或超时，会调用fail()方法。

实现：
Storm系统中有一组叫做“acker”的特殊任务，负责跟踪有向无环图（DAG）中的每个消息。

acker任务保存了spout id 到一对值的映射，第一个值就是spout的任务id，通过这个id，Acker就知道消息处理完成时该通知哪个spout任务。第二个值是一个64bit的数字，我们称之为“ack val”，他是树中所有消息的随机id异或计算的结果。

ack val 表示了整棵树的状态，当消息被创建和被应答的时候都会有相同的消息id发送过来做异或。当acker发现一棵树的ack val值为0的时候，就知道这棵树已经被完全处理了。
bolt的可靠消息处理机制包含两个步骤：
a、当发射衍生的tuple时，需要锚定读入的tuple
b、当处理消息时，需要应答或报错
可以通过OutputCollector中emit()的一个重载函数锚定tuple： 并且需要调用一次==this.collector.ack(tuple)==应答。
异或计算处理流程如下图:Storm架构和容错机制-LMLPHP

spout端:

  • 发射tuple时必须提供msgId
  • 覆盖ack和fail方法
public class WordCountSpout extends BaseRichSpout {
    private String[] lines={"this is a demo","hello Storm","ni hao"};
    private SpoutOutputCollector collector;

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

    public void nextTuple() {
        Utils.sleep(5000);//休息1s钟
        int msgId = new Random().nextInt(lines.length);
        String line=lines[msgId];
        //发送 Tuple 指定 msgId
        collector.emit(new Values(line),msgId);
    }
    //对emit中的tuple做字段的描述
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("line"));
    }

    //发送成功回调 AckerBolt
    @Override
    public void ack(Object msgId) {
        System.out.println("发送成功："+msgId);
    }
    //发送失败回调 AckerBolt
    @Override
    public void fail(Object msgId) {
        String line = lines[(Integer) msgId];
        System.out.println("发送失败："+msgId+"\t"+line);
    }
}

bolt端：

  • 将当前字tuple锚定到父tuple上，
  • 向上游应答当前父tuple的状态 collector.ack(); | collector.fail();

BasicBolt|BaseBasicBolt
许多Bolt遵循读取输入元组的共同模式（锚定、ack出错fail），基于它发出元组，然后在执行方法结束时执行元组。因此Storm给我们提供了一套规范，如果用户使用Ack机制，在编写Bolt的时候只需要实现接口或者继承类即可。

public class WordCountBolt extends BaseBasicBolt {
    //存储状态
    private Map<String,Integer> keyValueState;

    @Override
    public void prepare(Map<String, Object> topoConf, TopologyContext context) {
        keyValueState=new HashMap<String, Integer>();
    }

    public void execute(Tuple input, BasicOutputCollector collector) {
        String key = input.getStringByField("word");
        int count=0;
        if(keyValueState.containsKey(key)){
            count=keyValueState.get(key);
        }
        //更新状态
        int currentCount=count+1;
        keyValueState.put(key,currentCount);
        //将最后结果输出给下游
        collector.emit(new Values(key,currentCount));
    }

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

关闭ack机制

  • 设置NumAcker数目为0
  • Spout端不提供msgID
  • Bolt不使用锚定
    好处：提升Storm处理性能，减少延迟

Storm状态管理

Storm提供了一种Bolt存储和查询自己操作状态的状态管理机制，目前Storm提供了一个默认的实现，该实现基于内存实现。此外还提供了基于Redis/Memcached和Hbase等的实现。提供了StateFulBolt | BaseStatefulBolt 用于实现Bolt的状态管理。

代码如下：

public class WordCountBolt extends BaseStatefulBolt<KeyValueState<String,Integer>> {
    private KeyValueState<String,Integer> state;
    private OutputCollector collector;
    public void initState(KeyValueState<String,Integer> state) {
        this.state=state;
    }
    @Override
    public void prepare(Map<String, Object> topoConf, TopologyContext context, OutputCollector collector) {
        this.collector=collector;
    }
    public void execute(Tuple input) {
        String key = input.getStringByField("word");
        Integer count=input.getIntegerByField("count");
        Integer historyCount = state.get(key, 0);

        Integer currentCount=historyCount+count;
        //更新状态
        state.put(key,currentCount);

        //必须锚定当前的input
        collector.emit(input,new Values(key,currentCount));
        collector.ack(input);

    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("key","result"));
    }
}
含有State的bolt的Topology必须开启Ack机制

集成Redis实现状态持久化

  • 引入maven依赖
<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-redis</artifactId>
    <version>2.0.0</version>
</dependency>
  • 安装Redis
[root@CentOSA ~]# yum install -y gcc-c++
[root@CentOSA ~]# tar -zxf redis-3.2.9.tar.gz
[root@CentOSA ~]# cd redis-3.2.9
[root@CentOSA redis-3.2.9]# vi redis.conf
bind CentOSA
protected-mode no
daemonize yes
[root@CentOSA redis-3.2.9]# ./src/redis-server redis.conf
[root@CentOSA redis-3.2.9]# ps -aux | grep redis-server

出现如下信息表示安装成功
Warning: bad syntax, perhaps a bogus '-'? See /usr/share/doc/procps-3.2.8/FAQ
root     41601  0.1  0.5 135648  5676 ?        Ssl  14:45   0:00 ./src/redis-server CentOSA:6379
root     41609  0.0  0.0 103260   888 pts/1    S+   14:45   0:00 grep redis-server
  • 配置topology，添加配置信息
package demo4;

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;
import com.fasterxml.jackson.databind.ObjectMapper;

import java.util.HashMap;

public class WordCountTopology {
    public static void main(String[] args) throws Exception {
        //创建TopologyBuilder
        TopologyBuilder builder = new TopologyBuilder();

        //编织流处理逻辑
        builder.setSpout("WordCountSpout",new WordCountSpout(),1);

        builder.setBolt("LineSplitBolt",new LineSplitBolt(),3)
                .shuffleGrouping("WordCountSpout");
        builder.setBolt("WordCountBolt",new WordCountBolt(),3)
                .fieldsGrouping("LineSplitBolt",new Fields("word"));
        builder.setBolt("WordPrintBolt",new WordPrintBolt(),3)
                .fieldsGrouping("WordCountBolt",new Fields("key"));

        //提交流计算
        Config config = new Config();

        //配置redis
        config.put(Config.TOPOLOGY_STATE_PROVIDER,"org.apache.storm.redis.state.RedisKeyValueStateProvider");
        HashMap<String, Object> stateConfig = new HashMap<String, Object>();
        HashMap<String, Object> redisConfig = new HashMap<String, Object>();
        redisConfig.put("host","Redis");
        redisConfig.put("port",6379);
        stateConfig.put("jedisPoolConfig",redisConfig);
        ObjectMapper objectMapper = new ObjectMapper();
        System.out.println(objectMapper.writeValueAsString(stateConfig));
        config.put(Config.TOPOLOGY_STATE_PROVIDER_CONFIG,objectMapper.writeValueAsString(stateConfig));
        config.put(Config.TOPOLOGY_STATE_CHECKPOINT_INTERVAL,1000);
        config.setNumWorkers(3);//设置Topology运行所需的Worker资源，jvm个数

        config.setNumAckers(3);//关闭Storm应答，可靠性有关
        config.setMessageTimeoutSecs(5000);
        config.setMaxSpoutPending(10);
        LocalCluster localCluster = new LocalCluster();
        localCluster.submitTopology("wordcount",config,builder.createTopology());
    }
}

集成Hbase实现持久化

  • 导入依赖
<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-hbase</artifactId>
    <version>2.0.0</version>
</dependency>
  • 安装Hbase(略)
  • 配置topology，添加配置信息
config.put(Config.TOPOLOGY_STATE_PROVIDER,"org.apache.storm.hbase.state.HBaseKeyValueStateProvider");
Map<String,Object> hbaseConfig = new HashMap<String,Object>();
hbaseConfig.put("hbase.zookeeper.quorum", "CentOS-hbase");//Hbase zookeeper连接参数
config.put("hbase.conf",hbaseConfig);
ObjectMapper objectMapper = new ObjectMapper();
Map<String,Object> stateConfig = new HashMap<String,Object>();
stateConfig.put("hbaseConfigKey","hbase.conf");
stateConfig.put("tableName","demo1:wordcountstate");
stateConfig.put("columnfamily","cf");
config.put(Config.TOPOLOGY_STATE_PROVIDER_CONFIG,objectMapper.writeValueAsString(stateConfig));

Kafka Storm集成（kafka作为storm数据来源）

  • 所需依赖
<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-kafka-client</artifactId>
    <version>2.0.0</version>
</dependency>
<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>2.2.0</version>
</dependency>
  • 构建Kafkaspout
public class KafkaTopologyDemo {
    public static void main(String[] args) throws Exception {
        TopologyBuilder builder = new TopologyBuilder();

        String boostrapServers="CentOSA:9092,CentOSB:9092,CentOSC:9092";
                String topic="topic01";

        KafkaSpout<String, String> kafkaSpout = buildKafkaSpout(boostrapServers,topic);

        //默认输出的Tuple格式 new Fields(new String[]{"topic", "partition", "offset", "key", "value"});
        builder.setSpout("KafkaSpout",kafkaSpout,3);

        builder.setBolt("KafkaPrintBlot",new KafkaPrintBlot(),1)
                .shuffleGrouping("KafkaSpout");


        Config conf = new Config();
        conf.setNumWorkers(3);
        LocalCluster cluster = new LocalCluster();
        cluster.submitTopology("kafkaspout",conf,builder.createTopology());
    }
    public static KafkaSpout<String, String> buildKafkaSpout(String boostrapServers,String topic){

        KafkaSpoutConfig<String,String> kafkaspoutConfig=KafkaSpoutConfig.builder(boostrapServers,topic)
                .setProp(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer")
                .setProp(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer")
                .setProp(ConsumerConfig.GROUP_ID_CONFIG,"g1")
                .setEmitNullTuples(false)
                .setFirstPollOffsetStrategy(FirstPollOffsetStrategy.LATEST)
                .setProcessingGuarantee(KafkaSpoutConfig.ProcessingGuarantee.AT_LEAST_ONCE)
                .setMaxUncommittedOffsets(10)//一旦分区积压有10个未提交offset，Spout停止poll数据，解决Storm背压问题
                .setRecordTranslator(new MyRecordTranslator<String, String>())
                .build();
        return new KafkaSpout<String, String>(kafkaspoutConfig);
    }
}

MyRecordTranslator

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.storm.kafka.spout.DefaultRecordTranslator;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;

import java.util.List;

public class MyRecordTranslator<K, V>  extends DefaultRecordTranslator<K, V> {
    @Override
    public List<Object> apply(ConsumerRecord<K, V> record) {
        return new Values(new Object[]{record.topic(),record.partition(),record.offset(),record.key(),record.value(),record.timestamp()});
    }

    @Override
    public Fields getFieldsFor(String stream) {
        return new Fields("topic","partition","offset","key","value","timestamp");
    }
}

Kafka Hbase Redis 整合(从Kafka获取数据，Redis存储状态，Hbase存储最终输出)

  • 所需依赖
<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-core</artifactId>
    <version>2.0.0</version>
    <scope>provide</scope>
</dependency>

<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-client</artifactId>
    <version>2.0.0</version>
    <scope>provide</scope>
</dependency>

<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-redis</artifactId>
    <version>2.0.0</version>
</dependency>

<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-hbase</artifactId>
    <version>2.0.0</version>
</dependency>
<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-kafka-client</artifactId>
    <version>2.0.0</version>
</dependency>
<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>2.2.0</version>
</dependency>
  • WordCounrTopology
public class WodCountTopology {
    public static void main(String[] args) throws Exception {

        TopologyBuilder builder=new TopologyBuilder();
        Config conf = new Config();

        //Redis 状态管理
        conf.put(Config.TOPOLOGY_STATE_PROVIDER,"org.apache.storm.redis.state.RedisKeyValueStateProvider");
        Map<String,Object> stateConfig=new HashMap<String,Object>();
        Map<String,Object> redisConfig=new HashMap<String,Object>();
        redisConfig.put("host","CentOSA");
        redisConfig.put("port",6379);
        stateConfig.put("jedisPoolConfig",redisConfig);
        ObjectMapper objectMapper=new ObjectMapper();
        System.out.println(objectMapper.writeValueAsString(stateConfig));
        conf.put(Config.TOPOLOGY_STATE_PROVIDER_CONFIG,objectMapper.writeValueAsString(stateConfig));

        //配置Hbase连接参数
        Map<String, Object> hbaseConfig = new HashMap<String, Object>();
        hbaseConfig.put("hbase.zookeeper.quorum", "CentOSA");
        conf.put("hbase.conf", hbaseConfig);

        //构建KafkaSpout
        KafkaSpout<String, String> kafkaSpout = KafkaSpoutUtils.buildKafkaSpout("CentOSA:9092,CentOSB:9092,CentOSC:9092", "topic01");

        builder.setSpout("KafkaSpout",kafkaSpout,3);
        builder.setBolt("LineSplitBolt",new LineSplitBolt(),3)
                .shuffleGrouping("KafkaSpout");
        builder.setBolt("WordCountBolt",new WordCountBolt(),3)
                .fieldsGrouping("LineSplitBolt",new Fields("word"));

        SimpleHBaseMapper mapper = new SimpleHBaseMapper()
                .withRowKeyField("key")
                .withColumnFields(new Fields("key"))
                .withCounterFields(new Fields("result"))//要求改field的值必须是数值类型
                .withColumnFamily("cf1");

        HBaseBolt haseBolt = new HBaseBolt("baizhi:t_words", mapper)
                .withConfigKey("hbase.conf");
        builder.setBolt("HBaseBolt",haseBolt,3)
                .fieldsGrouping("WordCountBolt",new Fields("key"));

        StormSubmitter.submitTopology("wordcount1",conf,builder.createTopology());
    }
}
  • LineSplitBolt
public class LineSplitBolt extends BaseBasicBolt {
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("word","count"));
    }
    public void execute(Tuple input, BasicOutputCollector collector) {
        String line = input.getStringByField("value");
        String[] tokens = line.split("\\W+");
        for (String token : tokens) {
            //锚定当前Tuple
            collector.emit(new Values(token,1));
        }
    }
}
  • WordVountBolt
public class WordCountBolt extends BaseStatefulBolt<KeyValueState<String,Integer>> {
    private KeyValueState<String,Integer> state;
    private OutputCollector collector;
    public void initState(KeyValueState<String,Integer> state) {
        this.state=state;
    }
    @Override
    public void prepare(Map<String, Object> topoConf, TopologyContext context, OutputCollector collector) {
        this.collector=collector;
    }
    public void execute(Tuple input) {
        String key = input.getStringByField("word");
        Integer count=input.getIntegerByField("count");
        Integer historyCount = state.get(key, 0);

        Integer currentCount=historyCount+count;
        //更新状态
        state.put(key,currentCount);

        //必须锚定当前的input
        collector.emit(input,new Values(key,currentCount));
        collector.ack(input);

    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("key","result"));
    }
}
  • maven远程下载
[root@CentOS ~]# storm jar storm-lowlevel-1.0-SNAPSHOT.jar com.baizhi.demo09.WodCountTopology --artifacts 'org.apache.storm:storm-redis:2.0.0,org.apache.storm:storm-hbase:2.0.0,org.apache.storm:storm-kafka-client:2.0.0,org.apache.kafka:kafka-clients:2.2.0' --artifactRepositories  'local^http://192.168.111.1:8081/nexus/content/groups/public/'
  • 在项目中添加插件
<plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-shade-plugin</artifactId>
    <version>2.4.1</version>
    <executions>
        <execution>
            <phase>package</phase>
            <goals>
                <goal>shade</goal>
            </goals>
            <configuration>
                <filters>
                    <filter>
                        <artifact>*:*</artifact>
                        <excludes>
                            <exclude>META-INF/*.SF</exclude>
                            <exclude>META-INF/*.DSA</exclude>
                            <exclude>META-INF/*.RSA</exclude>
                        </excludes>
                    </filter>
                </filters>
            </configuration>
        </execution>
    </executions>
</plugin>
07-16 04:44
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