how to read and write to the same file in spark using parquet?

Question

I am trying to read from a parquet file in spark, do a union with another rdd and then write the result into the same file I have read from (basically overwrite), this throws the following error:

 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

which I am assuming means that when writing to the file, the original file is needed for the union and spark can't find the file any more. I have tried caching what I have read from the parquet to avoid spark needing the file but that didn't work either. Any help on Hadoop's best practice for doing this is greatly appreciated.

this scenario just works with Spark SQL, you can just specify directory with you file and do union, or write second file into the directory, and then load them both into single DataFrame. Anyway, it's considered as the good practice to write output into temporary location, and move it to target location when you're done. — Vitalii Kotliarenko, May 05 '16 at 18:46
thank you Vitaliy for your comment, I tried to do everything in file with spark sql with this: 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) — roshan, May 05 '16 at 20:09

score 5 · Answer 1 · answered Apr 06 '20 at 03:51

As spark does lazy transformation, it basically first wiped your destination directory and then goes and tries to read from source location. Hence you are getting this error.

One possible way is to overcome this is to use collect on your data frame . To avoid getting OOM exception filter data and use collect()[1] . This will force DAG to first read data and specify output to driver. And hence your data will be read before it’s overwritten.

score 5 · Answer 2 · answered Jan 11 '22 at 09:53

This causes a problem as you are reading and writing to the same location that you are trying to overwrite, it is Spark issue.

The workaround is to store write your data in a temp folder, not inside the location you are working on, and read from it as the source to your initial location.

read from root/myfolder
make your data transformations
write transformed data into root/mytempfolder
read from root/mytempfolder
write into root/myfolder

score 0 · Answer 3 · edited Jan 02 '19 at 19:44

0

You must be using overwrite option in mode, please try to use append instead

df.repartition(200).write.mode("append").parquet("path/parquet_name")

edited Jan 02 '19 at 19:44

Gibolt

42,564
15
187
127

answered Jan 02 '19 at 19:21

Prateek Tandon

1

score 0 · Answer 4 · answered Jan 14 '19 at 03:00

0

Just ran into the same issue...

you need to cache the first rdd before union. This would ensure it was read from disk into memory before you write to it.

val cached = first.cache()
cached.union(second).write.mode("overwrite").parquet("...")

answered Jan 14 '19 at 03:00

user1509458

59
1
2

1

This is not a practical solution as cache can always lead to memory issues. – Deepesh Rehi Oct 15 '19 at 09:48

score 0 · Answer 5 · edited Jun 25 '22 at 18:24

0

Try this:

Df.write.format("parquet").mode("overwrite").insertInto(file_path)

edited Jun 25 '22 at 18:24

ChrisGPT was on strike

127,765
105
273
257

answered Jun 22 '22 at 08:03

Vinay Pathak

1

4

Welcome to Stack Overflow. Code is a lot more helpful when it is accompanied by an explanation. Stack Overflow is about learning, not providing snippets to blindly copy and paste. Please [edit] your answer and explain how it answers the specific question being asked. See [answer]. – ChrisGPT was on strike Jun 25 '22 at 18:24

score -2 · Answer 6 · answered Sep 03 '20 at 22:44

-2

You can use insertinto instead of save. It will work. Df.write.mode("parquet").mode("overwrite").insertInto(file_path)

answered Sep 03 '20 at 22:44

sarvan

91
6

how to read and write to the same file in spark using parquet?

6 Answers6

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