我一直在尝试将大量的json消息（每个约2kb）推送到cassandra，这些消息来自kafka，用于spark streaming。
模拟器---->Kafka---->sparkstreaming---->Cassandra。
每一个都运行在单独的ec2示例上，具有30gb的ram和8核处理器作为独立的单节点设置。
当我试图从模拟器中推送大约500万条消息时，在大约10万条消息之后，cassandra停止插入消息，spark streaming job继续创建批处理（如spark streaming web ui所示）。我甚至检查了日志，但没有发现任何问题。
另外，我也不确定在编写cassandra的代码中使用spark连接器的方式。
请看下面的代码，

/**
 * Spark Streaming to cassandra code
 */

package org.sparkexample;

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

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;

import com.datastax.spark.connector.japi.CassandraJavaUtil;
import com.datastax.spark.connector.japi.CassandraStreamingJavaUtil;

import scala.Tuple2;

public class SparkStreamingKafkaTest {

private SparkStreamingKafkaTest() {
}

public static void main(String[] args) {
    if (args.length < 6) {
        System.err.println("Usage: SparkStreamingKafka <zkQuorum> <group> <topics> <numThreads> <conc write> <cassandra ip>");
        System.exit(1);
    }

    SparkConf sparkConf = new SparkConf().setAppName("SparkStreamingKafka");

    //specific to cassandra

    sparkConf.set("spark.cassandra.output.concurrent.writes", args[4]);
    sparkConf.set("spark.cassandra.connection.host",args[5]);

    // Create the context with a 2 second batch size
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
    int numThreads = Integer.parseInt(args[3]);
    Map<String, Integer> topicMap = new HashMap<String, Integer>();
    String[] topics = args[2].split(",");
    for (String topic : topics) {
        topicMap.put(topic, numThreads);
    }

    JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1],
            topicMap);

    JavaDStream<WordCount> wc = messages.map(new Function<Tuple2<String, String>, WordCount>() {

        @Override
        public WordCount call(Tuple2<String, String> tuple2) {
            String key = System.currentTimeMillis()+ "_"+ Math.random();
            return new WordCount(key, tuple2._2());
        }
    });

    Map <String, String> map =  new HashMap<String, String>();
    map.put("word", "word");
    map.put("count", "count");

    CassandraStreamingJavaUtil.javaFunctions(wc).writerBuilder("mykeyspace", "wordcount",CassandraJavaUtil.mapToRow(WordCount.class, map)).saveToCassandra(); 

    jssc.start();
    jssc.awaitTermination();
  }

  }

字数.java

package org.sparkexample;

  import java.io.Serializable;

  public class WordCount implements Serializable{

  private String word;
  private String count;

  public WordCount(){

  }

  public String getWord() {
    return word;
  }
  public void setWord(String word) {
    this.word = word;
  }
  public String getCount() {
    return count;
  }
  public void setCount(String count) {
    this.count = count;
  }

  public WordCount(String key, String count) {
    this.word = key;
    this.count = count;
  }
  }

我一直在使用默认的cassandra.yml，它有以下主要依赖项，
spark-cassandra-连接器2.10-1.4.0-m3
spark-cassandra-connector-java\ U 2.10-1.4.0-m3
cassandra驱动核心-2.1.7.1
spark-streaming-Kafka2.10-1.4.1
Spark流2.10-1.4.1
Spark芯2.10-1.4.1
请提出可能的问题。
nodetool info和nodetool tpstats的输出如下所示。