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"));
    }
}

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
    }
}

~/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