如何使用镶木地板在火花中读取和写入同一个文件?
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【中文标题】如何使用镶木地板在火花中读取和写入同一个文件?【英文标题】:how to read and write to the same file in spark using parquet? 【发布时间】:2016-08-31 16:00:08 【问题描述】:我正在尝试从 spark 中的 parquet 文件中读取数据,与另一个 rdd 进行联合,然后将结果写入我读取过的同一文件中(基本上覆盖),这会引发以下错误:
couldnt write parquet to file: An error occurred while calling o102.parquet.
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:
TungstenExchange hashpartitioning(billID#42,200), None
+- Union
:- Scan ParquetRelation[units#35,price#36,priceSold#37,orderingTime#38,itemID#39,storeID#40,customerID#41,billID#42,sourceRef#43] InputPaths: hdfs://master-wat:8020/user/root/dataFile/parquet/general/NPM61LKK1C/Billbody
+- Project [units#22,price#23,priceSold#24,orderingTime#25,itemID#26,storeID#27,customerID#28,billID#29,2 AS sourceRef#30]
+- Scan ExistingRDD[units#22,price#23,priceSold#24,orderingTime#25,itemID#26,storeID#27,customerID#28,billID#29]
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
at org.apache.spark.sql.execution.Exchange.doExecute(Exchange.scala:247)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at org.apache.spark.sql.execution.Sort.doExecute(Sort.scala:64)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at org.apache.spark.sql.execution.Window.doExecute(Window.scala:245)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at org.apache.spark.sql.execution.Filter.doExecute(basicOperators.scala:70)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:109)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:70)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:256)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:148)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:139)
at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:334)
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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.FileNotFoundException: File does not exist: /user/root/dataFile/parquet/general/NPM61LKK1C/Billbody/part-r-00000-c51e45d3-6824-4fc2-9510-802e5379a86f.gz.parquet
at org.apache.hadoop.hdfs.server.namenode.INodeFile.valueOf(INodeFile.java:66)
at org.apache.hadoop.hdfs.server.namenode.INodeFile.valueOf(INodeFile.java:56)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocationsUpdateTimes(FSNamesystem.java:1934)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocationsInt(FSNamesystem.java:1875)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocations(FSNamesystem.java:1855)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocations(FSNamesystem.java:1827)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.getBlockLocations(NameNodeRpcServer.java:566)
at org.apache.hadoop.hdfs.server.namenode.AuthorizationProviderProxyClientProtocol.getBlockLocations(AuthorizationProviderProxyClientProtocol.java:88)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.getBlockLocations(ClientNamenodeProtocolServerSideTranslatorPB.java:361)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:617)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1073)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2086)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2082)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1693)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2080)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.ipc.RemoteException.instantiateException(RemoteException.java:106)
at org.apache.hadoop.ipc.RemoteException.unwrapRemoteException(RemoteException.java:73)
at org.apache.hadoop.hdfs.DFSClient.callGetBlockLocations(DFSClient.java:1222)
at org.apache.hadoop.hdfs.DFSClient.getLocatedBlocks(DFSClient.java:1210)
at org.apache.hadoop.hdfs.DFSClient.getBlockLocations(DFSClient.java:1260)
at org.apache.hadoop.hdfs.DistributedFileSystem$1.doCall(DistributedFileSystem.java:220)
at org.apache.hadoop.hdfs.DistributedFileSystem$1.doCall(DistributedFileSystem.java:216)
at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at org.apache.hadoop.hdfs.DistributedFileSystem.getFileBlockLocations(DistributedFileSystem.java:216)
at org.apache.hadoop.hdfs.DistributedFileSystem.getFileBlockLocations(DistributedFileSystem.java:208)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplits(FileInputFormat.java:395)
at org.apache.parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:294)
at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anonfun$buildInternalScan$1$$anon$1.getPartitions(ParquetRelation.scala:363)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.immutable.List.foreach(List.scala:318)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.ShuffleDependency.<init>(Dependency.scala:91)
at org.apache.spark.sql.execution.Exchange.prepareShuffleDependency(Exchange.scala:220)
at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:254)
at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:248)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48)
... 56 more
Caused by: org.apache.hadoop.ipc.RemoteException(java.io.FileNotFoundException): File does not exist: /user/root/dataFile/parquet/general/NPM61LKK1C/Billbody/part-r-00000-c51e45d3-6824-4fc2-9510-802e5379a86f.gz.parquet
at org.apache.hadoop.hdfs.server.namenode.INodeFile.valueOf(INodeFile.java:66)
at org.apache.hadoop.hdfs.server.namenode.INodeFile.valueOf(INodeFile.java:56)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocationsUpdateTimes(FSNamesystem.java:1934)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocationsInt(FSNamesystem.java:1875)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocations(FSNamesystem.java:1855)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocations(FSNamesystem.java:1827)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.getBlockLocations(NameNodeRpcServer.java:566)
at org.apache.hadoop.hdfs.server.namenode.AuthorizationProviderProxyClientProtocol.getBlockLocations(AuthorizationProviderProxyClientProtocol.java:88)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.getBlockLocations(ClientNamenodeProtocolServerSideTranslatorPB.java:361)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:617)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1073)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2086)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2082)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1693)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2080)
at org.apache.hadoop.ipc.Client.call(Client.java:1468)
at org.apache.hadoop.ipc.Client.call(Client.java:1399)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:232)
at com.sun.proxy.$Proxy20.getBlockLocations(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getBlockLocations(ClientNamenodeProtocolTranslatorPB.java:254)
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.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy21.getBlockLocations(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient.callGetBlockLocations(DFSClient.java:1220)
... 92 more
我假设这意味着在写入文件时,联合需要原始文件,并且 spark 无法再找到该文件。 我尝试缓存从镶木地板上读取的内容,以避免火花需要该文件,但这也不起作用。非常感谢有关 Hadoop 最佳实践的任何帮助。
【问题讨论】:
这个场景只适用于 Spark SQL,你可以只用你的文件指定目录并进行联合,或者将第二个文件写入目录,然后将它们都加载到单个 DataFrame 中。无论如何,将输出写入临时位置并在完成后将其移动到目标位置被认为是一种好习惯。 感谢 Vitaliy 的评论,我尝试使用 spark sql 执行文件中的所有操作:DFNew=hiveContext.sql("SELECT * FROM( SELECT *, ROW_NUMBER()OVER(PARTITION BY billID ORDER BY sourceRef DESC) rn FROM(SELECT * FROM new UNION All SELECT * FROM parquet.%s
) z) y WHERE rn = 1"%saveAddr)
你有没有得到任何解决方案...
【参考方案1】:
由于 spark 进行惰性转换,它基本上首先擦除您的目标目录,然后尝试从源位置读取。因此,您会收到此错误。
克服此问题的一种可能方法是在您的数据框上使用 collect 。为避免获取 OOM 异常过滤器数据并使用 collect()[1] 。这将强制 DAG 首先读取数据并指定输出到驱动程序。因此,您的数据将在被覆盖之前被读取。
【讨论】:
【参考方案2】:这会导致问题,因为您正在读取和写入您尝试覆盖的同一位置,这是 Spark 问题。
解决方法是将写入的数据存储在临时文件夹中,而不是在您正在处理的位置内,然后从它作为源读取到您的初始位置。
-
从根目录/我的文件夹中读取
进行数据转换
将转换后的数据写入 root/mytemp 文件夹
从根目录/mytempfolder 读取
写入根目录/我的文件夹
【讨论】:
【参考方案3】:您必须在模式下使用覆盖选项,请尝试使用追加代替
df.repartition(200).write.mode("append").parquet("path/parquet_name")
【讨论】:
【参考方案4】:刚刚遇到同样的问题...
你需要cache
union 之前的第一个 rdd。这将确保在您写入之前将其从磁盘读取到内存中。
val cached = first.cache()
cached.union(second).write.mode("overwrite").parquet("...")
【讨论】:
这不是一个实用的解决方案,因为缓存总是会导致内存问题。【参考方案5】:您可以使用 insertinto 而不是 save。它会起作用的。 df.write.mode("parquet").mode("overwrite").insertInto(file_path)
【讨论】:
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