spark第十篇：Spark与Kafka整合

spark与kafka整合需要引入spark-streaming-kafka.jar，该jar根据kafka版本有2个分支，分别是spark-streaming-kafka-0-8和spark-streaming-kafka-0-10。

jar包分支选择原则：0.10.0>kafka版本>=0.8.2.1，选择spark-streaming-kafka-0-8；kafka版本>=0.10.0，选择spark-streaming-kafka-0-10。

kafka0.8.2.1及之后版本依次是0.8.2.1(2015年3月11号发布)、0.8.2.2(2015年10月2号发布)、0.9.x、0.10.x(0.10.0.0于2016年5月22号发布)、0.11.x、1.0.x(1.0.0版本于2017年11月1号发布)、1.1.x、2.0.x(2.0.0版本于2018年7月30日发布)。

本次学习使用kafka1.0.0版本，故需要引入spark-streaming-kafka-0-10的jar，如下

<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10 -->

<dependency>

    <groupId>org.apache.spark</groupId>

    <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>

    <version>2.2.1</version>

</dependency>

PS：从jar包的groupId可看出，该jar是由spark项目组开发的。

简单用例1：本例在spark2.4.0(scala2.12)、kafka2.2.0(scala2.12)环境测试通过

import org.apache.commons.collections.MapUtils;

import org.apache.kafka.clients.consumer.ConsumerRecord;

import org.apache.spark.SparkConf;

import org.apache.spark.sql.*;

import org.apache.spark.storage.StorageLevel;

import org.apache.spark.streaming.Durations;

import org.apache.spark.streaming.api.java.*;

import org.apache.spark.streaming.kafka010.*;

import com.alibaba.fastjson.JSON;

import java.util.*;

public class SparkConsumerTest {

    public static void main(String[] args) throws Exception {

        System.setProperty("hadoop.home.dir", "C:/Users/lenovo/Downloads/winutils-master/winutils-master/hadoop-2.7.1");

        SparkConf conf = new SparkConf().setAppName("heihei").setMaster("local[*]");

        conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");

        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(10));

        Properties props = new Properties();

        props.setProperty("bootstrap.servers", "192.168.56.100:9092");

        props.setProperty("group.id", "my-test-consumer-group");

        props.setProperty("enable.auto.commit", "true");

        props.setProperty("auto.commit.interval.ms", "1000");

        props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        Map kafkaParams = new HashMap(8);

        kafkaParams.putAll(props);

        JavaInputDStream<ConsumerRecord<String, String>> javaInputDStream = KafkaUtils.createDirectStream(jssc,

                LocationStrategies.PreferConsistent(),

                ConsumerStrategies.Subscribe(Arrays.asList("test"), kafkaParams));

        javaInputDStream.persist(StorageLevel.MEMORY_AND_DISK_SER());

        SparkSession spark = SparkSession.builder().config(conf).getOrCreate();

        javaInputDStream.foreachRDD(rdd -> {

            Dataset<Row> df = spark.createDataFrame(rdd.map(consumerRecord -> {

                Map testMap = JSON.parseObject(consumerRecord.value(), Map.class);

                return new DemoBean(MapUtils.getString(testMap, "id"),

                        MapUtils.getString(testMap, "name"),

                        MapUtils.getIntValue(testMap, "age"));

            }), DemoBean.class);

            DataFrameWriter writer = df.write();

            String url = "jdbc:postgresql://192.168.56.100/postgres";

            String table = "test";

            Properties connectionProperties = new Properties();

            connectionProperties.put("user", "postgres");

            connectionProperties.put("password", "abc123");

            connectionProperties.put("driver", "org.postgresql.Driver");

            connectionProperties.put("batchsize", "3000");

            writer.mode(SaveMode.Append).jdbc(url, table, connectionProperties);

        });

        jssc.start();

        jssc.awaitTermination();

    }

}

DemoBean是另外的一个实体类。

相应pom.xml：

    <dependencies>

        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka -->

        <dependency>

            <groupId>org.apache.kafka</groupId>

            <artifactId>kafka_2.12</artifactId>

            <version>2.2.0</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka-streams -->

        <dependency>

            <groupId>org.apache.kafka</groupId>

            <artifactId>kafka-streams</artifactId>

            <version>2.2.0</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.commons/commons-lang3 -->

        <dependency>

            <groupId>org.apache.commons</groupId>

            <artifactId>commons-lang3</artifactId>

            <version>3.9</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/commons-io/commons-io -->

        <dependency>

            <groupId>commons-io</groupId>

            <artifactId>commons-io</artifactId>

            <version>2.6</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10 -->

        <dependency>

            <groupId>org.apache.spark</groupId>

            <artifactId>spark-streaming-kafka-0-10_2.12</artifactId>

            <version>2.4.0</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->

        <dependency>

            <groupId>org.apache.spark</groupId>

            <artifactId>spark-streaming_2.12</artifactId>

            <version>2.4.0</version>

            <!--            <scope>provided</scope>-->

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->

        <dependency>

            <groupId>org.apache.spark</groupId>

            <artifactId>spark-sql_2.12</artifactId>

            <version>2.4.0</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/com.fasterxml.jackson.core/jackson-databind -->

        <dependency>

            <groupId>com.fasterxml.jackson.core</groupId>

            <artifactId>jackson-databind</artifactId>

            <version>2.6.7.2</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->

        <dependency>

            <groupId>com.alibaba</groupId>

            <artifactId>fastjson</artifactId>

            <version>1.2.58</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/com.thoughtworks.paranamer/paranamer -->

        <dependency>

            <groupId>com.thoughtworks.paranamer</groupId>

            <artifactId>paranamer</artifactId>

            <version>2.8</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.postgresql/postgresql -->

        <dependency>

            <groupId>org.postgresql</groupId>

            <artifactId>postgresql</artifactId>

            <version>42.2.5</version>

        </dependency>

    </dependencies>

简单用例2：本例在spark1.6.0(scala2.11)、kafka0.10.2.0(scala2.11)环境测试通过

import com.alibaba.fastjson.JSON;

import kafka.common.TopicAndPartition;

import kafka.message.MessageAndMetadata;

import kafka.serializer.StringDecoder;

import kafka.utils.ZKGroupTopicDirs;

import org.apache.commons.lang3.StringUtils;

import org.apache.curator.framework.CuratorFramework;

import org.apache.curator.framework.CuratorFrameworkFactory;

import org.apache.curator.retry.RetryUntilElapsed;

import org.apache.spark.SparkConf;

import org.apache.spark.SparkException;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.sql.DataFrame;

import org.apache.spark.sql.SQLContext;

import org.apache.spark.streaming.Durations;

import org.apache.spark.streaming.api.java.JavaInputDStream;

import org.apache.spark.streaming.api.java.JavaStreamingContext;

import org.apache.spark.streaming.kafka.HasOffsetRanges;

import org.apache.spark.streaming.kafka.KafkaCluster;

import org.apache.spark.streaming.kafka.KafkaUtils;

import java.nio.charset.StandardCharsets;

import java.util.*;

import org.apache.spark.api.java.function.Function;

import org.apache.spark.streaming.kafka.OffsetRange;

import scala.collection.JavaConversions;

import scala.collection.Map$;

import scala.collection.immutable.Set;

import scala.collection.mutable.ArrayBuffer;

import scala.util.Either;

public class SparkConsumerTest {

    public static CuratorFramework curatorFramework;

    static {

        curatorFramework = CuratorFrameworkFactory.builder().connectString("192.168.56.103:2181")

                .connectionTimeoutMs(30000)

                .sessionTimeoutMs(30000)

                .retryPolicy(new RetryUntilElapsed(1000, 1000))

                .build();

        curatorFramework.start();

    }

    public static void main(String[] args) throws Exception {

        String topic = "test";

        String groupId = "spark-test-consumer-group";

        System.setProperty("hadoop.home.dir", "C:/Users/lenovo/Downloads/winutils-master/winutils-master/hadoop-2.7.1");

        SparkConf conf = new SparkConf().setAppName("heihei").setMaster("local[*]");

        conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");

        // 每5s一个批次

        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(30));

        Map<String, String> kafkaParams = new HashMap(4);

        kafkaParams.put("bootstrap.servers", "192.168.56.103:9092");

        // 生成fromOffsets,KafkaUtils.createDirectStream要使用

        Map<TopicAndPartition, Long> fromOffsets = getFromOffsets(kafkaParams, topic, groupId);

        SQLContext sqlContext = new SQLContext(jssc.sparkContext());

        // Function不是jdk的类，是spark中的类

        Function<MessageAndMetadata<String, String>, String> function = MessageAndMetadata::message;

        kafkaParams.put("group.id", groupId);

        JavaInputDStream<String> messages = KafkaUtils.createDirectStream(

                jssc,

                String.class,

                String.class,

                StringDecoder.class,

                StringDecoder.class,

                String.class,

                kafkaParams,

                fromOffsets,

                function

        );

        messages.foreachRDD(rdd -> {

            if (!rdd.isEmpty()) {

                String firstValue = rdd.first();

                System.out.println("message:" + firstValue);

                JavaRDD<Person> personJavaRDD = rdd.mapPartitions(it -> {

                    List<String> list = new ArrayList();

                    while (it.hasNext()) {

                        list.add(it.next());

                    }

                    return list;

                }).map(p -> {

                    try {

                        Person person = JSON.parseObject(StringUtils.deleteWhitespace(p), Person.class);

                        return person;

                    } catch (Exception e) {

                        e.printStackTrace();

                    }

                    return new Person();

                }).filter(p -> StringUtils.isNotBlank(p.getId())

                        || StringUtils.isNotBlank(p.getName())

                        || p.getAge() != 0

                );

                if (!personJavaRDD.isEmpty()) {

                    DataFrame df = sqlContext.createDataFrame(personJavaRDD, Person.class).select("id", "name", "age");

                    df.show(5);

                }

                // 设置zookeeper 消费偏移量

                OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();

                Arrays.asList(offsetRanges).forEach(offsetRange -> {

                    String consumerOffsetDir = new ZKGroupTopicDirs(groupId, topic).consumerOffsetDir()

                            + "/" + offsetRange.partition();

                    try {

                        curatorFramework.setData().forPath(consumerOffsetDir, String.valueOf(offsetRange.untilOffset()).getBytes(StandardCharsets.UTF_8));

                    } catch (Exception e) {

                        e.printStackTrace();

                    }

                });

            }

        });

        jssc.start();

        jssc.awaitTermination();

    }

    public static scala.collection.immutable.Map jMap2sMap(Map<String, String> map) {

        scala.collection.mutable.Map mapTest = JavaConversions.mapAsScalaMap(map);

        Object objTest = Map$.MODULE$.newBuilder().$plus$plus$eq(mapTest.toSeq());

        Object resultTest = ((scala.collection.mutable.Builder) objTest).result();

        scala.collection.immutable.Map resultTest2 = (scala.collection.immutable.Map) resultTest;

        return resultTest2;

    }

    public static Map<TopicAndPartition, Long> getFromOffsets(Map kafkaParams, String topic, String groupId) throws Exception {

        // kafkaParams只有bootstrap.servers -> broker列表

        KafkaCluster kc = new KafkaCluster(jMap2sMap(kafkaParams));

        ArrayBuffer<String> arrayBuffer = new ArrayBuffer();

        arrayBuffer.$plus$eq(topic);

        Either<ArrayBuffer<Throwable>, Set<TopicAndPartition>> either = kc.getPartitions(arrayBuffer.toSet());

        if (either.isLeft()) {

            throw new SparkException("get partitions failed", either.left().toOption().get().last());

        }

        scala.collection.immutable.Set<TopicAndPartition> topicAndPartitions = either.right().get();

        Either<ArrayBuffer<Throwable>, scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset>> either2 = kc.getEarliestLeaderOffsets(topicAndPartitions);

        if (either2.isLeft()) {

            throw new SparkException("get earliestLeaderOffsets failed", either2.left().toOption().get().last());

        }

        scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset> earliestLeaderOffsets = either2.right().get();

        Either<ArrayBuffer<Throwable>, scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset>> either3 = kc.getLatestLeaderOffsets(topicAndPartitions);

        if (either3.isLeft()) {

            throw new SparkException("get latestLeaderOffsets failed", either3.left().toOption().get().last());

        }

        scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset> latestLeaderOffsets = either3.right().get();

        Map<TopicAndPartition, Long> fromOffsets = new HashMap();

        ZKGroupTopicDirs zKGroupTopicDirs = new ZKGroupTopicDirs(groupId, topic);

        // 从0分区开始

        for (int i = 0; i < topicAndPartitions.size(); i++) {

            TopicAndPartition topicAndPartition = new TopicAndPartition(topic, i);

            // 路径是/consumers/$group/offsets/$topic

            String consumerOffsetDir = zKGroupTopicDirs.consumerOffsetDir() + "/" + i;

            long zookeeperConsumerOffset = 0;

            // 没有消费组偏移量目录，说明没有开始消费

            if (curatorFramework.checkExists().forPath(consumerOffsetDir) == null) {

                System.out.println(consumerOffsetDir + "目录不存在");

                // 如果目录不存在的话，就创建目录，并设值为0

                curatorFramework.create().creatingParentsIfNeeded().forPath(consumerOffsetDir, "0".getBytes(StandardCharsets.UTF_8));

            } else {

                // 拿到zookeeper节点存储的值

                byte[] zookeeperConsumerOffsetBytes = curatorFramework.getData().forPath(consumerOffsetDir);

                if (zookeeperConsumerOffsetBytes != null) {

                    zookeeperConsumerOffset = Long.parseLong(new String(zookeeperConsumerOffsetBytes, StandardCharsets.UTF_8));

                }

            }

            long earliestLeaderOffset = earliestLeaderOffsets.get(topicAndPartition).get().offset();

            long latestLeaderOffset = latestLeaderOffsets.get(topicAndPartition).get().offset();

            long fromOffset;

            if (zookeeperConsumerOffset < earliestLeaderOffset) {

                fromOffset = earliestLeaderOffset;

            } else if (zookeeperConsumerOffset > latestLeaderOffset) {

                fromOffset = latestLeaderOffset;

            } else {

                fromOffset = zookeeperConsumerOffset;

            }

            fromOffsets.put(topicAndPartition, fromOffset);

        }

        return fromOffsets;

    }

}

pom.xml：

    <dependencies>

        <dependency>

            <groupId>com.alibaba</groupId>

            <artifactId>fastjson</artifactId>

            <version>1.2.58</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka -->

        <dependency>

            <groupId>org.apache.spark</groupId>

            <artifactId>spark-streaming-kafka_2.11</artifactId>

            <version>1.6.0</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->

        <dependency>

            <groupId>org.apache.spark</groupId>

            <artifactId>spark-streaming_2.11</artifactId>

            <version>1.6.0</version>

            <scope>provided</scope>

        </dependency>

        <!--  https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->

        <dependency>

            <groupId>org.apache.spark</groupId>

            <artifactId>spark-sql_2.11</artifactId>

            <version>1.6.0</version>

            <scope>provided</scope>

        </dependency>

    </dependencies>

这里用了spark-streaming-kafka_2.11-1.6.0.jar，而没有用spark-streaming-kafka-assembly_2.11-1.6.0.jar。这两个jar包是完全一样的，但是后面的assembly包死活找不到源码。注意，这里kafka服务器虽然是0.10.2.0版本，但是没有引用kafka_2.11-0.10.2.0.jar，因为实测会报java.lang.ClassCastException: kafka.cluster.BrokerEndPoint cannot be cast to kafka.cluster.Broker。根本原因是在kafka_2.11-0.8.2.1.jar中，kafka.api.PartitionMetadata类定义是case class PartitionMetadata(partitionId: Int, val leader: Option[Broker], replicas: Seq[Broker], isr: Seq[Broker] = Seq.empty, errorCode: Short = ErrorMapping.NoError)，但是从kafka_2.11-0.10.0.0.jar开始，变成了case class PartitionMetadata(partitionId: Int, leader: Option[BrokerEndPoint], replicas: Seq[BrokerEndPoint], isr: Seq[BrokerEndPoint] = Seq.empty, errorCode: Short = Errors.NONE.code)，成员变量的类型发生了变化。但是spark-streaming-kafka_2.11-1.6.0.jar包是和kafka_2.11-0.8.2.1.jar兼容的，所以在kafka_2.11-0.10.2.0.jar时会发生类型转换错误。

需要特别提醒的是，spark-streaming-kafka_2.11-1.6.0.jar包的KafkaCluster的内部类都是private的，引用KafkaCluster.LeaderOffset时一直报错。大量搜索后，发现从spark-streaming-kafka_2.11-2.0.jar版本开始，内部类才不是private的。解决办法是在项目中创建一个名为org.apache.spark.streaming.kafka的package，把spark-streaming-kafka_2.11-1.6.0.jar中的KafkaCluster类拷贝到这个包下，同时修改源码，把LeaderOffset的private[spark]标识符去掉。这样，引用的KafkaCluster类就是我们自己的了，不是spark-streaming-kafka_2.11-1.6.0.jar包中的了，KafkaCluster.LeaderOffset就可以用了。

以上场景都是kafka的一条消息对应数据库中的一条记录。如果一条kafka消息对应数据库中的多条记录呢？

简单用例3：和例2一样环境，pom也一样

import com.alibaba.fastjson.JSON;

import kafka.common.TopicAndPartition;

import kafka.message.MessageAndMetadata;

import kafka.serializer.StringDecoder;

import kafka.utils.ZKGroupTopicDirs;

import org.apache.commons.lang3.StringUtils;

import org.apache.curator.framework.CuratorFramework;

import org.apache.curator.framework.CuratorFrameworkFactory;

import org.apache.curator.retry.RetryUntilElapsed;

import org.apache.spark.SparkConf;

import org.apache.spark.SparkException;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.api.java.JavaSparkContext;

import org.apache.spark.sql.*;

import org.apache.spark.sql.types.DataTypes;

import org.apache.spark.sql.types.Metadata;

import org.apache.spark.sql.types.StructField;

import org.apache.spark.sql.types.StructType;

import org.apache.spark.streaming.Durations;

import org.apache.spark.streaming.api.java.JavaInputDStream;

import org.apache.spark.streaming.api.java.JavaStreamingContext;

import org.apache.spark.streaming.kafka.HasOffsetRanges;

import org.apache.spark.streaming.kafka.KafkaCluster;

import org.apache.spark.streaming.kafka.KafkaUtils;

import java.nio.charset.StandardCharsets;

import java.util.*;

import org.apache.spark.api.java.function.Function;

import org.apache.spark.streaming.kafka.OffsetRange;

import scala.collection.JavaConversions;

import scala.collection.Map$;

import scala.collection.immutable.Set;

import scala.collection.mutable.ArrayBuffer;

import scala.util.Either;

public class SparkConsumerTest {

    public static CuratorFramework curatorFramework;

    static {

        curatorFramework = CuratorFrameworkFactory.builder().connectString("192.168.56.103:2181")

                .connectionTimeoutMs(30000)

                .sessionTimeoutMs(30000)

                .retryPolicy(new RetryUntilElapsed(1000, 1000))

                .build();

        curatorFramework.start();

    }

    public static void main(String[] args) throws Exception {

        String topic = "test";

        String groupId = "spark-test-consumer-group";

        System.setProperty("hadoop.home.dir", "C:/Users/lenovo/Downloads/winutils-master/winutils-master/hadoop-2.7.1");

        SparkConf conf = new SparkConf().setAppName("heihei").setMaster("local[*]");

        conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");

        conf.set("spark.streaming.kafka.maxRatePerPartition", "10000");

        // 每5s一个批次

        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(10));

        Map<String, String> kafkaParams = new HashMap(4);

        kafkaParams.put("bootstrap.servers", "192.168.56.103:9092");

        // 生成fromOffsets,KafkaUtils.createDirectStream要使用

        Map<TopicAndPartition, Long> fromOffsets = getFromOffsets(kafkaParams, topic, groupId);

        JavaSparkContext jsc = jssc.sparkContext();

        // Function不是jdk的类，是spark中的类

        Function<MessageAndMetadata<String, String>, String> messageHandler = MessageAndMetadata::message;

        kafkaParams.put("group.id", groupId);

        StructType schema = new StructType(new StructField[]{

                new StructField("id", DataTypes.StringType, false, Metadata.empty()),

                new StructField("msg", DataTypes.StringType, false, Metadata.empty()),

                new StructField("createdDate", DataTypes.StringType, false, Metadata.empty()),

                new StructField("updatedDate", DataTypes.StringType, false, Metadata.empty())

        });

        SQLContext sqlContext = new SQLContext(jssc.sparkContext());

        JavaInputDStream<String> messages = KafkaUtils.createDirectStream(

                jssc,

                String.class,

                String.class,

                StringDecoder.class,

                StringDecoder.class,

                String.class,

                kafkaParams,

                fromOffsets,

                messageHandler

        );

        messages.foreachRDD(rdd -> {

            if (!rdd.isEmpty()) {

                JavaRDD<Object> javaRDD = rdd.mapPartitions(it -> {

                    List<Object> list = new ArrayList<>();

                    while (it.hasNext()) {

                        String str = it.next();

                        if (StringUtils.isNotBlank(str)) {

                            str = StringUtils.deleteWhitespace(str);

                            try {

                                List<Person> personList = JSON.parseArray(str, Person.class);

                                list.addAll(personList);

                            } catch (Exception e) {

                                list.add(str);

                            }

                        }

                    }

                    return list;

                });

                JavaRDD<Person> personRDD = javaRDD.filter(p -> p instanceof Person).map(p -> (Person) p);

                DataFrame df;

                DataFrameWriter writer;

                if (!personRDD.isEmpty()) {

                    df = sqlContext.createDataFrame(personRDD, Person.class)

                            .select("id", "name", "age");

                    df.show(2);

                }

                JavaRDD<Row> rowRDD = javaRDD.filter(p -> p instanceof String)

                        .map(p -> RowFactory.create(UUID.randomUUID().toString(), p, String.valueOf(System.currentTimeMillis()), String.valueOf(System.currentTimeMillis())));

                if (!rowRDD.isEmpty()) {

                    df = sqlContext.createDataFrame(rowRDD, schema);

                    df.show(2);

                }

                // 设置zookeeper 消费偏移量

                OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();

                Arrays.asList(offsetRanges).forEach(offsetRange -> {

                    String consumerOffsetDir = new ZKGroupTopicDirs(groupId, topic).consumerOffsetDir()

                            + "/" + offsetRange.partition();

                    try {

                        curatorFramework.setData().forPath(consumerOffsetDir, String.valueOf(offsetRange.untilOffset()).getBytes(StandardCharsets.UTF_8));

                    } catch (Exception e) {

                        e.printStackTrace();

                    }

                });

            }

        });

        jssc.start();

        jssc.awaitTermination();

    }

    public static scala.collection.immutable.Map jMap2sMap(Map<String, String> map) {

        scala.collection.mutable.Map mapTest = JavaConversions.mapAsScalaMap(map);

        Object objTest = Map$.MODULE$.newBuilder().$plus$plus$eq(mapTest.toSeq());

        Object resultTest = ((scala.collection.mutable.Builder) objTest).result();

        scala.collection.immutable.Map resultTest2 = (scala.collection.immutable.Map) resultTest;

        return resultTest2;

    }

    public static Map<TopicAndPartition, Long> getFromOffsets(Map kafkaParams, String topic, String groupId) throws

            Exception {

        // kafkaParams只有bootstrap.servers -> broker列表

        KafkaCluster kc = new KafkaCluster(jMap2sMap(kafkaParams));

        ArrayBuffer<String> arrayBuffer = new ArrayBuffer();

        arrayBuffer.$plus$eq(topic);

        Either<ArrayBuffer<Throwable>, Set<TopicAndPartition>> either = kc.getPartitions(arrayBuffer.toSet());

        if (either.isLeft()) {

            throw new SparkException("get partitions failed", either.left().toOption().get().last());

        }

        scala.collection.immutable.Set<TopicAndPartition> topicAndPartitions = either.right().get();

        Either<ArrayBuffer<Throwable>, scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset>> either2 = kc.getEarliestLeaderOffsets(topicAndPartitions);

        if (either2.isLeft()) {

            throw new SparkException("get earliestLeaderOffsets failed", either2.left().toOption().get().last());

        }

        scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset> earliestLeaderOffsets = either2.right().get();

        Either<ArrayBuffer<Throwable>, scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset>> either3 = kc.getLatestLeaderOffsets(topicAndPartitions);

        if (either3.isLeft()) {

            throw new SparkException("get latestLeaderOffsets failed", either3.left().toOption().get().last());

        }

        scala.collection.immutable.Map<TopicAndPartition, KafkaCluster.LeaderOffset> latestLeaderOffsets = either3.right().get();

        Map<TopicAndPartition, Long> fromOffsets = new HashMap();

        ZKGroupTopicDirs zKGroupTopicDirs = new ZKGroupTopicDirs(groupId, topic);

        // 从0分区开始

        for (int i = 0; i < topicAndPartitions.size(); i++) {

            TopicAndPartition topicAndPartition = new TopicAndPartition(topic, i);

            // 路径是/consumers/$group/offsets/$topic

            String consumerOffsetDir = zKGroupTopicDirs.consumerOffsetDir() + "/" + i;

            long zookeeperConsumerOffset = 0;

            // 没有消费组偏移量目录，说明没有开始消费

            if (curatorFramework.checkExists().forPath(consumerOffsetDir) == null) {

                System.out.println(consumerOffsetDir + "目录不存在");

                // 如果目录不存在的话，就创建目录，并设值为0

                curatorFramework.create().creatingParentsIfNeeded().forPath(consumerOffsetDir, "0".getBytes(StandardCharsets.UTF_8));

            } else {

                // 拿到zookeeper节点存储的值

                byte[] zookeeperConsumerOffsetBytes = curatorFramework.getData().forPath(consumerOffsetDir);

                if (zookeeperConsumerOffsetBytes != null) {

                    String zookeeperConsumerOffsetStr = new String(zookeeperConsumerOffsetBytes, StandardCharsets.UTF_8);

                    zookeeperConsumerOffset = Long.parseLong(zookeeperConsumerOffsetStr);

                }

            }

            long earliestLeaderOffset = earliestLeaderOffsets.get(topicAndPartition).get().offset();

            long latestLeaderOffset = latestLeaderOffsets.get(topicAndPartition).get().offset();

            long fromOffset;

            if (zookeeperConsumerOffset < earliestLeaderOffset) {

                fromOffset = earliestLeaderOffset;

            } else if (zookeeperConsumerOffset > latestLeaderOffset) {

                fromOffset = latestLeaderOffset;

            } else {

                fromOffset = zookeeperConsumerOffset;

            }

            fromOffsets.put(topicAndPartition, fromOffset);

        }

        System.out.println("fromOffsets= " + fromOffsets);

        return fromOffsets;

    }

}

Receiver DStream和Direct DStream分别是什么？spark从kafka拉取数据有哪几种方式？在oppo面试的时候有问过。

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