Spark之 Spark Streaming整合kafka(Java实现版本)

pom依赖

    <properties>
        <scala.version>2.11.8</scala.version>
        <hadoop.version>2.7.4</hadoop.version>
        <spark.version>2.1.3</spark.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-flume_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
    </dependencies>

demo代码

package com.blaze.kafka2streaming;

import com.blaze.conf.ConfigurationManager;
import com.blaze.constant.Constants;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.Optional;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaInputDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.dstream.DStream;
import org.apache.spark.streaming.kafka010.ConsumerStrategies;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;
import scala.Tuple2;

import java.util.*;

/**
 * create by zy 2019/3/15 9:26
 * TODO: kafka2streaming示例  使用的java8的lambda表达式(idea可以alt+enter将方法转换成非lambda表达式的java代码)
 */
public class BlazeDemo {
    public static void main(String[] args) {
        // 构建SparkStreaming上下文
        SparkConf conf = new SparkConf().setAppName("BlazeDemo").setMaster("local[2]");

        // 每隔5秒钟，sparkStreaming作业就会收集最近5秒内的数据源接收过来的数据
        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));
        //checkpoint目录
        //jssc.checkpoint(ConfigurationManager.getProperty(Constants.STREAMING_CHECKPOINT_DIR));
        jssc.checkpoint("/streaming_checkpoint");

        // 构建kafka参数map
        // 主要要放置的是连接的kafka集群的地址（broker集群的地址列表）
        Map<String, Object> kafkaParams = new HashMap<>();
        //Kafka服务监听端口
        kafkaParams.put("bootstrap.servers", ConfigurationManager.getProperty(Constants.KAFKA_BOOTSTRAP_SERVERS));
        //指定kafka输出key的数据类型及编码格式（默认为字符串类型编码格式为uft-8）
        kafkaParams.put("key.deserializer", StringDeserializer.class);
        //指定kafka输出value的数据类型及编码格式（默认为字符串类型编码格式为uft-8）
        kafkaParams.put("value.deserializer", StringDeserializer.class);
        //消费者ID，随意指定
        kafkaParams.put("group.id", ConfigurationManager.getProperty(Constants.GROUP_ID));
        //指定从latest(最新,其他版本的是largest这里不行)还是smallest(最早)处开始读取数据
        kafkaParams.put("auto.offset.reset", "latest");
        //如果true,consumer定期地往zookeeper写入每个分区的offset
        kafkaParams.put("enable.auto.commit", false);

        // 构建topic set
        String kafkaTopics = ConfigurationManager.getProperty(Constants.KAFKA_TOPICS);
        String[] kafkaTopicsSplited = kafkaTopics.split(",");

        Collection<String> topics = new HashSet<>();
        for (String kafkaTopic : kafkaTopicsSplited) {
            topics.add(kafkaTopic);
        }

        try {
            // 获取kafka的数据
            final JavaInputDStream<ConsumerRecord<String, String>> stream =
                    KafkaUtils.createDirectStream(
                            jssc,
                            LocationStrategies.PreferConsistent(),
                            ConsumerStrategies.<String, String>Subscribe(topics, kafkaParams)
                    );

            //获取words
            //JavaDStream<String> words = stream.flatMap(s -> Arrays.asList(s.value().split(",")).iterator());
            JavaDStream<String> words = stream.flatMap((FlatMapFunction<ConsumerRecord<String, String>, String>) s -> {
                List<String> list = new ArrayList<>();
                //todo 获取到kafka的每条数据 进行操作
                System.out.print("***************************" + s.value() + "***************************");
                list.add(s.value() + "23333");
                return list.iterator();
            });
            //获取word,1格式数据
            JavaPairDStream<String, Integer> wordsAndOne = words.mapToPair((PairFunction<String, String, Integer>) word -> new Tuple2<>(word, 1));

            //聚合本次5s的拉取的数据
            //JavaPairDStream<String, Integer> wordsCount = wordsAndOne.reduceByKey((Function2<Integer, Integer, Integer>) (a, b) -> a + b);
            //wordsCount.print();

            //历史累计 60秒checkpoint一次
            DStream<Tuple2<String, Integer>> result = wordsAndOne.updateStateByKey(((Function2<List<Integer>, Optional<Integer>, Optional<Integer>>) (values, state) -> {
                Integer updatedValue = 0;
                if (state.isPresent()) {
                    updatedValue = Integer.parseInt(state.get().toString());
                }
                for (Integer value : values) {
                    updatedValue += value;
                }
                return Optional.of(updatedValue);
            })).checkpoint(Durations.seconds(60));

            result.print();

            //开窗函数 5秒计算一次 计算前15秒的数据聚合
            JavaPairDStream<String, Integer> result2 = wordsAndOne.reduceByKeyAndWindow((Function2<Integer, Integer, Integer>) (x, y) -> x + y,
                    Durations.seconds(15), Durations.seconds(5));
            result2.print();

            jssc.start();
            jssc.awaitTermination();
            jssc.close();

        } catch (Exception e) {
            e.printStackTrace();
        }
    }

}

相关配置文件

package com.blaze.conf;

import java.io.InputStream;
import java.util.Properties;

/**
 * create by zy 2019/3/15 9:33
 * TODO:
 */
public class ConfigurationManager {
    //私有配置对象
    private static Properties prop = new Properties();

    /**
     * 静态代码块
     */
    static {
        try {
            //获取配置文件输入流
            InputStream in = ConfigurationManager.class
                    .getClassLoader().getResourceAsStream("blaze.properties");

            //加载配置对象
            prop.load(in);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    /**
     * 获取指定key对应的value
     *
     * @param key
     * @return value
     */
    public static String getProperty(String key) {
        return prop.getProperty(key);
    }

    /**
     * 获取整数类型的配置项
     *
     * @param key
     * @return value
     */
    public static Integer getInteger(String key) {
        String value = getProperty(key);
        try {
            return Integer.valueOf(value);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return 0;
    }

    /**
     * 获取布尔类型的配置项
     *
     * @param key
     * @return value
     */
    public static Boolean getBoolean(String key) {
        String value = getProperty(key);
        try {
            return Boolean.valueOf(value);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return false;
    }

    /**
     * 获取Long类型的配置项
     *
     * @param key
     * @return
     */
    public static Long getLong(String key) {
        String value = getProperty(key);
        try {
            return Long.valueOf(value);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return 0L;
    }
}

package com.blaze.constant;

/**
 * create by zy 2019/3/15 9:31
 * TODO:常量接口
 */
public interface Constants {

    String GROUP_ID = "group.id";
    String KAFKA_TOPICS = "kafka.topics";
    String KAFKA_BOOTSTRAP_SERVERS = "bootstrap.servers";
    String STREAMING_CHECKPOINT_DIR = "streaming.checkpoint.dir";

}

blaze.properties

bootstrap.servers=192.168.44.41:9092,192.168.44.42:9092,192.168.44.43:9092
kafka.topics=sparkDemo
group.id=blaze
streaming.checkpoint.dir=hdfs://192.168.44.41:9000/streaming_checkpoint