由org.apache.spark.sql.Dataset上的java.lang.NullPointerExceptio

Question

下面我提供了我的代码。我迭代DataFrameprodRows，对于每个product_PK，我从prodRows中找到一些匹配的product_PK子列表。

numRecProducts = 10
var listOfProducts: Map[Long,Array[(Long, Int)]] = Map()
prodRows.foreach{ row : Row =>
val product_PK = row.get(row.fieldIndex("product_PK")).toString.toLong
val gender = row.get(row.fieldIndex("gender_PK")).toString
val selection = prodRows.filter($"gender_PK" === gender || $"gender_PK" === "UNISEX").limit(numRecProducts).select($"product_PK")
var productList: Array[(Long, Int)] = Array()
if (!selection.rdd.isEmpty()) {
productList = selection.rdd.map(x => (x(0).toString.toLong,1)).collect()
}
listOfProducts = listOfProducts + (product_PK -> productList)
}

但当我执行它时，它会给我以下错误。在某些迭代中，selection看起来是空的。然而，我不明白如何处理这个错误：

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1690)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1678)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1677)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1677)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1905)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1860)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1849)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:671)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:918)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:916)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.foreach(RDD.scala:916)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply$mcV$sp(Dataset.scala:2325)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2823)
at org.apache.spark.sql.Dataset.foreach(Dataset.scala:2324)
at org.test.ComputeNumSim.run(ComputeNumSim.scala:69)
at org.test.ComputeNumSimRunner$.main(ComputeNumSimRunner.scala:19)
at org.test.ComputeNumSimRunner.main(ComputeNumSimRunner.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:635)
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:170)
at org.apache.spark.sql.Dataset$.apply(Dataset.scala:61)
at org.apache.spark.sql.Dataset.withTypedPlan(Dataset.scala:2877)
at org.apache.spark.sql.Dataset.filter(Dataset.scala:1304)
at org.test.ComputeNumSim$$anonfun$run$1.apply(ComputeNumSim.scala:74)
at org.test.ComputeNumSim$$anonfun$run$1.apply(ComputeNumSim.scala:69)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)

这意味着什么？我该如何处理？

Answer 1

您不能从传递给Spark的DataFrame/RDD转换的函数中访问Spark的任何"驱动程序端"抽象(RDD、DataFrames、Datasets、SparkSession…)。您也不能从这些函数中更新驱动程序端的可变对象。

在您的情况下，您试图在传递给DataFrame.foreach的函数中使用prodRows和selection(两者都是DataFrames)。您还试图从同一函数中更新listOfProducts(本地驱动程序侧变量)。

为什么

• DataFrames、RDD和SparkSession仅存在于驱动程序应用程序中。它们充当访问分布在工作机器集群上的数据的"句柄">
• 传递给RDD/DataFrame转换的函数被序列化并发送到该集群，在每个工作机器的数据分区上执行。当串行化的DataFrames/RDD在这些机器上被反序列化时，它们是无用的，它们仍然不能表示集群上的数据，因为它们只是驱动程序应用程序上创建的数据的空心副本，而驱动程序实际上维护着与集群机器的连接
• 出于同样的原因，尝试更新驱动程序端变量将失败：变量(在大多数情况下开始为空)将在每个工作线程上序列化、反序列化，在工作线程上本地更新，并停留在那里。。。原始驾驶员侧变量将保持不变

如何解决此问题使用Spark时，尤其是使用DataFrame时，应尽量避免对数据进行"迭代"，而应使用DataFrame的声明性操作。在大多数情况下，当您想为DataFrame中的每个记录引用另一个DataFrame的数据时，您会希望使用join来创建一个新的DataFrame，其中的记录将来自两个DataFrames的数据组合在一起。

在这个特定的案例中，如果我能够正确地得出结论，这里有一个大致等效的解决方案，它可以完成你想要做的事情。尝试使用这个并阅读DataFrame文档来了解细节：

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
import spark.implicits._
val numRecProducts = 10
val result = prodRows.as("left")
// self-join by gender:
.join(prodRows.as("right"), $"left.gender_PK" === $"right.gender_PK" || $"right.gender_PK" === "UNISEX")
// limit to 10 results per record:
.withColumn("rn", row_number().over(Window.partitionBy($"left.product_PK").orderBy($"right.product_PK")))
.filter($"rn" <= numRecProducts).drop($"rn")
// group and collect_list to create products column:
.groupBy($"left.product_PK" as "product_PK")
.agg(collect_list(struct($"right.product_PK", lit(1))) as "products")

Answer 2

问题是您试图从prodRows.foreach中访问prodRows。不能在转换中使用数据帧，数据帧只存在于驱动程序中。