Я пытаюсь получить потоковые данные отКафка, В этом процессе я могу получать и хранить потоковые данные вJavaPairInputDStream, Теперь мне нужно проанализировать эти данные без сохранения в любой базе данных. Поэтому я хочу преобразовать этоJavaPairInputDStream вDataSet или жеDataFrame

Что я пробовал до сих пор:

import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.catalog.Function;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.AbstractJavaDStreamLike;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;


import kafka.serializer.StringDecoder;
import scala.Tuple2;

//Streaming Working Code

public class KafkaToSparkStreaming 
{
    public static  void main(String arr[]) throws InterruptedException
    {


        SparkConf conf = new SparkConf();
        conf.set("spark.app.name", "SparkReceiver"); //The name of application. This will appear in the UI and in log data.
        //conf.set("spark.ui.port", "7077");    //Port for application's dashboard, which shows memory and workload data.
        conf.set("dynamicAllocation.enabled","false");  //Which scales the number of executors registered with this application up and down based on the workload
        //conf.set("spark.cassandra.connection.host", "localhost"); //Cassandra Host Adddress/IP
        conf.set("spark.serializer","org.apache.spark.serializer.KryoSerializer");  //For serializing objects that will be sent over the network or need to be cached in serialized form.
        //conf.setMaster("local");
        conf.set("spark.streaming.stopGracefullyOnShutdown", "true");

        JavaSparkContext sc = new JavaSparkContext(conf);
        // Create the context with 2 seconds batch size
        JavaStreamingContext ssc = new JavaStreamingContext(sc, new Duration(2000));

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

        kafkaParams.put("zookeeper.connect", "localhost:2181"); //Make all kafka data for this cluster appear under a particular path. 
        kafkaParams.put("group.id", "testgroup");   //String that uniquely identifies the group of consumer processes to which this consumer belongs
        kafkaParams.put("metadata.broker.list", "localhost:9092"); //Producer can find a one or more Brokers to determine the Leader for each topic.
        kafkaParams.put("serializer.class", "kafka.serializer.StringEncoder"); //Serializer to use when preparing the message for transmission to the Broker.
        kafkaParams.put("request.required.acks", "1");  //Producer to require an acknowledgement from the Broker that the message was received.

        Set<String> topics = Collections.singleton("ny-2008.csv");

        //Create an input DStream for Receiving data from socket
        JavaPairInputDStream<String, String> directKafkaStream = KafkaUtils.createDirectStream(ssc,
                String.class, 
                String.class, 
                StringDecoder.class, 
                StringDecoder.class, 
                kafkaParams, topics);

    //System.out.println(directKafkaStream);
        directKafkaStream.print();
}
}