SPARK-5063 RDD转换和操作只能由驱动程序调用 [英] SPARK-5063 RDD transformations and actions can only be invoked by the driver
本文介绍了SPARK-5063 RDD转换和操作只能由驱动程序调用的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我尝试看到一个RDD [Row]：
I have a RDD[Row] which I am trying to see:
val pairMap = itemMapping.map(x=> {
val countryInfo = MappingUtils.getCountryInfo(x);
(countryInfo.getId(), countryInfo)
})
pairMap：org.apache.spark.rdd.RDD [（String，com.model.item.CountryInfo）] = MapPartitionsRDD [8]
pairMap: org.apache.spark.rdd.RDD[(String, com.model.item.CountryInfo)] = MapPartitionsRDD[8]
val itemList = df.filter(not($"newItemType" === "Unknown Type")).map(row => {
val customerId = row.getAs[String](0);
val itemId = row.getAs[String](1);
val itemType = row.getAs[String](4);
val priceType = if (StringUtils.isNotBlank(pairMap.lookup(itemType).head.getpriceType)) pairMap.lookup(itemType).head.getpriceType else "unknown"
val kidsAdults = if (pairMap.lookup(itemType).head.getItems.size() > 0) "Kids" else "Adults"
val tvMovie = if (pairMap.lookup(itemType).head.getbarCode != barCode) "TV" else "Movie"
Row(customerId ,itemId,itemType,priceType,kidsAdults,tvMovie)
})
：
itemList.first（）
When I did : itemList.first()
但请继续获取此错误：
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 10.0 failed 4 times, most recent failure: Lost task 0.3 in stage 10.0 (TID 34, ip-172-31-0-28.ec2.internal): org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.
at org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$sc(RDD.scala:87)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.PairRDDFunctions.lookup(PairRDDFunctions.scala:928)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:89)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:83)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1314)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1314)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1314)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.take(RDD.scala:1288)
at org.apache.spark.rdd.RDD$$anonfun$first$1.apply(RDD.scala:1328)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.first(RDD.scala:1327)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:86)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:91)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:93)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:95)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:97)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:99)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:101)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:103)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:105)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:107)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:109)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:111)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:113)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:115)
at $iwC$$iwC$$iwC.<init>(<console>:117)
at $iwC$$iwC.<init>(<console>:119)
at $iwC.<init>(<console>:121)
at <init>(<console>:123)
at .<init>(<console>:127)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
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:483)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1346)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.zeppelin.spark.SparkInterpreter.interpretInput(SparkInterpreter.java:664)
at org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:629)
at org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:622)
at org.apache.zeppelin.interpreter.ClassloaderInterpreter.interpret(ClassloaderInterpreter.java:57)
at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:93)
at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:276)
at org.apache.zeppelin.scheduler.Job.run(Job.java:170)
at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:118)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
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)
Caused by: org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.
at org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$sc(RDD.scala:87)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.PairRDDFunctions.lookup(PairRDDFunctions.scala:928)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:89)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:83)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
at scala.collection.Iterator$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$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1314)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1314)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
... 3 more
我也尝试过：
val tvMovieUDF = udf {( itemType: String) => if (StringUtils.isNotBlank(pairMap.lookup(itemType).head.getpriceType)) pairMap.lookup(itemType).head.getpriceType else "unknown" }
val priceUDF = udf {( itemType: String) => if (pairMap.lookup(itemType).head.getItems.size() > 0) "Kids" else "Adults" }
val kidsUDF = udf {( itemType: String) => if (pairMap.lookup(itemType).head.getbarCode != barCode) "TV" else "Movie" }
val broDF = df.filter(not($"newItemType" === "Unknown Type")).withColumn("tvMovie",tvMovieUDF($"newItemType")).withColumn("priceType",priceUDF($"newItemType")).withColumn("kids",kidsUDF($"newItemType"))
但仍然是相同的错误。有人可以告诉我如何解决吗？我想查看数据还希望将其保存为压缩文件：
But still same error. Can someone tell me how do I resolve it ? I want to see the data also want to save it as gzipped file :
val json = itemList.toJSON
json.saveAsTextFile("s3://...", classOf[GzipCodec])
推荐答案
好吧，您不能从转换访问另一个RDD。那是不允许的。我认为您要尝试实现的是将pairMap发送给该函数，以便可以完成查找。如果是，那么您可以使用广播。
Well, you cannot access another RDD from a transformation. That is not allowed. I think what you are trying to achieve is to send out pairMap to the function, so that the lookup can be done. If yes, then you can use a broadcast.
b = sc.broadcast(pairMap.collect())
并且，除了 pairMap.lookup 外，您还可以使用 b.value.lookup
And, instead of pairMap.lookup, you can use b.value.lookup
这篇关于SPARK-5063 RDD转换和操作只能由驱动程序调用的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
