具有repartitionByCassandraReplica函数的cassandra-spark-connector错误

Question

我试图使用1.2版本中的新联接功能，但在repl中的repartitionByCassandraReplica函数出现了一个错误。

我尝试复制该网站的示例，并创建了一个cassandra表(shopping_history)，其中包含几个元素:https://github.com/datastax/spark-cassandra-connector/blob/master/doc/2_loading.mde

import com.datastax.spark.connector.rdd._
import com.datastax.spark.connector.cql.CassandraConnector
import com.datastax.spark.connector._
import com.datastax.driver.core._

case class CustomerID(cust_id: Int)
val idsOfInterest = sc.parallelize(1 to 1000).map(CustomerID(_))
val repartitioned =  idsOfInterest.repartitionByCassandraReplica("cim_dev", "shopping_history", 10)
repartitioned.first()

我得到这个错误：

15/04/13 18:35:43 WARN TaskSetManager: Lost task 0.0 in stage 1.0 (TID 2, dev2-cim.aid.fr): java.lang.ClassNotFoundException: $line31.$read$$iwC$$iwC$CustomerID
    at java.net.URLClassLoader$1.run(URLClassLoader.java:372)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:360)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:344)
    at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:59)
    at java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1613)
    at java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1518)
    at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1774)
    at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
    at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1993)
    at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1918)
    at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
    at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
    at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
    at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
    at org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133)
    at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
    at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    at org.apache.spark.rdd.RDD$$anonfun$27.apply(RDD.scala:1098)
    at org.apache.spark.rdd.RDD$$anonfun$27.apply(RDD.scala:1098)
    at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1353)
    at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1353)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
    at org.apache.spark.scheduler.Task.run(Task.scala:56)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

谢谢

Answer 1

我将在这里回答你的第二个问题，如果我能根据更多的信息找到一些东西，我将继续第一部分。

我也很好奇：joinWithCassandraTable/cassandraTable与In子句/foreachPartition（withSessionDo）语法之间的差异。

带有in子句的CassandRatable将创建单个火花分区。因此，对于非常小的in子句可能没有问题，但是该子句必须从驱动程序序列化到spark应用程序。对于大in子句来说，这可能非常糟糕，而且一般来说，如果没有必要，我们不希望将数据从spark驱动程序来回发送到执行程序。