Flume+Kafka+Storm实战：一、Kakfa与Storm整合

本文主要是介绍Flume+Kafka+Storm实战：一、Kakfa与Storm整合，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

文章目录

• 0x00 文章内容
• 0x01 Kafka准备
• • • • • 1. 启动Kafka
        • 2. 创建Topic
        • 3. 启动消费者与消费者
• 0x02 Storm准备
• • • • • 1. 构建Maven项目
        • 2. 编写代码
• 0x03 校验结果
• • • • • 1. 打包Storm代码
        • 2. 执行ZK与Storm
        • 3. 执行Storm作业
        • 4. 校验过程
• 0xFF 总结

1. Kafka准备
2. Storm准备
3. 校验结果

1. 启动Kafka

a. 后台启动Kafka（三台都要启动）

nohup ~/bigdata/kafka_2.11-1.0.0/bin/kafka-server-start.sh ~/bigdata/kafka_2.11-1.0.0/config/server.properties >~/bigdata/kafka_2.11-1.0.0/logs/server.log 2>&1 &

2. 创建Topic

a. 创建Topic：word-count-input

~/bigdata/kafka_2.11-1.0.0/bin/kafka-topics.sh --create --zookeeper master:2181 --replication-factor 1 --partitions 1 --topic word-count-input

b. 创建Topic：word-count-output

~/bigdata/kafka_2.11-1.0.0/bin/kafka-topics.sh --create --zookeeper master:2181 --replication-factor 1 --partitions 1 --topic word-count-output

3. 启动消费者与消费者

a. 启动一个producer，向word-count-input发送消息

进入到$KAFKA_HOME路径：
cd ~/bigdata/kafka_2.11-1.0.0

启动：

bin/kafka-console-producer.sh --broker-list master:9092 --topic word-count-input

b. 启动一个consumer，消费word-count-output的消息

bin/kafka-console-consumer.sh --bootstrap-server master:9092 --topic word-count-output --property print.key=true

1. 构建Maven项目

a. 引入Storm依赖

<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-core</artifactId>
    <version>1.2.2</version>
    <scope>provided</scope>
</dependency>

b. 引入Kafka依赖

<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-kafka-client</artifactId>
    <version>1.2.2</version>
</dependency>

c. 引入额外打包插件

<plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-compiler-plugin</artifactId>
    <version>3.1</version>
    <configuration>
        <source>1.8</source>
        <target>1.8</target>
        <testExcludes>
            <testExclude>/src/test/**</testExclude>
        </testExcludes>
        <encoding>utf-8</encoding>
    </configuration>
</plugin>

<plugin>
    <artifactId>maven-assembly-plugin</artifactId>
    <configuration>
        <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
        </descriptorRefs>
    </configuration>
    <executions>
        <execution>
            <id>make-assembly</id> <!-- this is used for inheritance merges -->
            <phase>package</phase> <!-- 指定在打包节点执行jar包合并操作 -->
            <goals>
                <goal>single</goal>
            </goals>
        </execution>
    </executions>
</plugin>

d. 完整的pom.xml文件

<?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.shaonaiyi</groupId>
    <artifactId>stormlearning</artifactId>
    <version>1.0-SNAPSHOT</version>

    <dependencies>
        <!-- https://mvnrepository.com/artifact/org.apache.storm/storm-core -->
        <dependency>
            <groupId>org.apache.storm</groupId>
            <artifactId>storm-core</artifactId>
            <version>1.2.2</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.storm</groupId>
            <artifactId>storm-kafka-client</artifactId>
            <version>1.2.2</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.1</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <testExcludes>
                        <testExclude>/src/test/**</testExclude>
                    </testExcludes>
                    <encoding>utf-8</encoding>
                </configuration>
            </plugin>

            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id> <!-- this is used for inheritance merges -->
                        <phase>package</phase> <!-- 指定在打包节点执行jar包合并操作 -->
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

</project>

2. 编写代码

a. 项目代码结构

b. KafkaSpoutBuilder

package com.shaonaiyi.kafka;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.storm.kafka.spout.KafkaSpout;
import org.apache.storm.kafka.spout.KafkaSpoutConfig;

import java.util.List;

/**
 * @author: shaonaiyi
 * @createTime: 2019/07/14 13:32
 * @description: KafkaSpout构建器
 */

public class KafkaSpoutBuilder {

    private List<String> brokers;
    private String topic;

    public KafkaSpoutBuilder brokers(List<String> v) {
        brokers = v;
        return this;
    }

    public KafkaSpoutBuilder topic(String v) {
        topic = v;
        return this;
    }


    public KafkaSpout build() {
        /** 配置kafka
         * 1. 需要设置consumer group（注意一个partition中的消息只能被同一group中的一个consumer消费）
         * 2. 起始消费策略：根据业务需要配置
         */
        String allBrokers = String.join(",", brokers);
        KafkaSpoutConfig<String, String> conf = KafkaSpoutConfig
                .builder(allBrokers, topic)
                .setProp(ConsumerConfig.GROUP_ID_CONFIG, "word-count-storm")
                //消费最新的数据
                .setFirstPollOffsetStrategy(KafkaSpoutConfig.FirstPollOffsetStrategy.LATEST)
                .build();
        return new KafkaSpout(conf);
    }

}

c. KafkaSplitSentenceBolt

package com.shaonaiyi.kafka;

/**
 * @author: shaonaiyi
 * @createTime: 2019/07/14 13:38
 * @description: 语句分割bolt
 */

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

import java.util.Map;

/**
 * 如，接收的Tuple是：Tuple("sentence" -> "I love teacher shao")
 * 则，输出的Tuple为：
 *      Tuple("word" -> "I")
 *      Tuple("word" -> "love")
 *      Tuple("word" -> "teacher")
 *      Tuple("word" -> "shao")
 */
public class KafkaSplitSentenceBolt extends BaseRichBolt {

    private OutputCollector collector;

    @Override
    public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {
        this.collector = outputCollector;
    }

    @Override
    public void execute(Tuple tuple) { // 实时接收SentenceSpout中输出的Tuple流
        String sentence = tuple.getStringByField("value"); // 根据key获取Tuple中的语句,"value"是Kafka中固定了的
        String[] words = sentence.split(" "); // 将语句按照空格进行切割
        for (String word: words) {
            this.collector.emit(new Values(word)); // 将切割之后的每一个单词作为Tuple的value输出到下一个bolt中
        }
        this.collector.ack(tuple); // 表示成功处理kafka-spout输出的消息，需要应答，要不然，kafka-spout会不断的重复发送消息
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        outputFieldsDeclarer.declare(new Fields("word")); // 输出Tuple的key
    }

}

d. KafkaWordCountBolt

package com.shaonaiyi.kafka;

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

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

/**
 * @author: shaonaiyi
 * @createTime: 2019/07/14 13:42
 * @description: 单词计数bolt
 */

public class KafkaWordCountBolt extends BaseRichBolt {

    private OutputCollector collector;
    private HashMap<String, Long> counts = null; // 用于统计每隔单词的计数

    @Override
    public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {
        this.collector = outputCollector;
        this.counts = new HashMap<String, Long>();
    }

    @Override
    public void execute(Tuple tuple) { // 实时接收SplitSentenceBolt中输出的Tuple流
        String word = tuple.getStringByField("word"); // 根据key获取Tuple中的单词
        // 统计每一个单词总共出现的次数
        Long count = counts.getOrDefault(word, 0L);
        count++;
        this.counts.put(word, count);

        // 将每一个单词以及这个单词出现的次数作为Tuple中的value输出到下一个bolt中
        this.collector.emit(new Values(word, count.toString()));
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        // 输出Tuple的key，有两个key，是因为每次输出的value也有两个
        outputFieldsDeclarer.declare(new Fields("key", "message"));
    }

}

e. WordCountKafkaTopology

package com.shaonaiyi.kafka;

import org.apache.storm.Config;
import org.apache.storm.StormSubmitter;
import org.apache.storm.generated.AlreadyAliveException;
import org.apache.storm.generated.AuthorizationException;
import org.apache.storm.generated.InvalidTopologyException;
import org.apache.storm.kafka.bolt.KafkaBolt;
import org.apache.storm.kafka.bolt.mapper.FieldNameBasedTupleToKafkaMapper;
import org.apache.storm.kafka.bolt.selector.DefaultTopicSelector;
import org.apache.storm.kafka.spout.KafkaSpout;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;

import java.util.Arrays;
import java.util.Properties;

/**
 * @author: shaonaiyi
 * @createTime: 2019/07/15 22:54
 * @description: Kafka之WordCountTopology
 */

public class WordCountKafkaTopology {

    private static final String SENTENCE_SPOUT_ID = "sentence-spout";
    private static final String SPLIT_BOLT_ID = "split-bolt";
    private static final String COUNT_BOLT_ID = "count-bolt";
    private static final String KAFKA_BOLT_ID = "kafka-bolt";
    private static final String TOPOLOGY_NAME = "word-count-topology";

    public static void main(String[] args) throws InvalidTopologyException, AuthorizationException, AlreadyAliveException {

        int workers = Integer.parseInt(args[0]);

        // 从Kafka中消费数据
        KafkaSpout kafkaSpout = new KafkaSpoutBuilder()
                .brokers(Arrays.asList("master:9092"))
                .topic("word-count-input")
                .build();

        KafkaSplitSentenceBolt splitSentenceBolt = new KafkaSplitSentenceBolt();
        KafkaWordCountBolt wordCountBolt = new KafkaWordCountBolt();

        Properties props = new Properties();
        props.put("bootstrap.servers", "master:9092");
        // 此配置是表明当一次produce请求被认为完成时的确认值。
        // 特别是，多少个其他brokers必须已经提交了数据到他们的log并且向他们的leader确认了这些信息。典型的值包括：
        // 0： 表示producer从来不等待来自broker的确认信息（和0.7一样的行为）。
        // 这个选择提供了最小的时延但同时风险最大（因为当server宕机时，数据将会丢失）。
        // 1：表示获得leader replica已经接收了数据的确认信息。这个选择时延较小同时确保了server确认接收成功。
        // -1：producer会获得所有同步replicas都收到数据的确认
        props.put("acks", "1");
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        KafkaBolt kafkaBolt = new KafkaBolt()
                .withProducerProperties(props)
                .withTopicSelector(new DefaultTopicSelector("word-count-output"))
                .withTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper());

        TopologyBuilder builder = new TopologyBuilder();
        builder.setSpout(SENTENCE_SPOUT_ID, kafkaSpout);
        builder.setBolt(SPLIT_BOLT_ID, splitSentenceBolt).shuffleGrouping(SENTENCE_SPOUT_ID);
        builder.setBolt(COUNT_BOLT_ID, wordCountBolt).fieldsGrouping(SPLIT_BOLT_ID, new Fields("word"));
        builder.setBolt(KAFKA_BOLT_ID, kafkaBolt).shuffleGrouping(COUNT_BOLT_ID);

        // 3、提交Topology
        Config config = new Config(); // 用来配置Topology运行时行为，对Topology所有组件全局生效的配置参数集合
        config.setNumWorkers(workers);
        StormSubmitter.submitTopology(TOPOLOGY_NAME, config, builder.createTopology()); // 提交Topology

    }

}

1. 打包Storm代码

a. 打包

b. 上传到集群

2. 执行ZK与Storm

此步骤与教程：实时流处理框架之Storm的安装与部署
=>
0x03 启动并校验Storm 步骤一样

即：
a. 启动集群上的三台Zookeeper（查看进程是否存在，如果Kafka已经启动，应该还有Kafka的进程）

b. 启动Storm
在master上启动Nimbus和Web UI
cd ~/bigdata/apache-storm-1.2.2
nohup bin/storm nimbus 2>&1 &
然后回车，切换终端2，执行：
nohup bin/storm ui 2>&1 &
然后回车
在slave1和slave2上启动Supervisor
cd ~/bigdata/apache-storm-1.2.2
nohup bin/storm supervisor 2>&1 &

3. 执行Storm作业

a. 执行Storm作业

~/bigdata/apache-storm-1.2.2/bin/storm jar /home/hadoop-sny/jar/stormlearning-1.0-SNAPSHOT-jar-with-dependencies.jar com.shaonaiyi.kafka.WordCountKafkaTopology 1

b. 查看Web UI界面（master:8080）

4. 校验过程

a. 目前各节点的进程情况

b. 发送消息到Kafka

c. 查看消费者信息

d. 查看Storm的Web UI界面

1. 在生产者端多发送几个语句，你会发现这种统计的结果，并不是我们真正想要的结果，思考应该怎样才能想我们前面学习WordCount那种表现形式，请看后面的教程。
2. 内容比较多，请大家认真操作。

作者简介：邵奈一
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