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From "Rakesh Raushan (Jira)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-30288) Failed to write valid Parquet files when column names contains special characters like spaces
Date Wed, 18 Dec 2019 16:59:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-30288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16999349#comment-16999349
] 

Rakesh Raushan commented on SPARK-30288:
----------------------------------------

I am working on it. I will raise the PR soon.

> Failed to write valid Parquet files when column names contains special characters like
spaces
> ---------------------------------------------------------------------------------------------
>
>                 Key: SPARK-30288
>                 URL: https://issues.apache.org/jira/browse/SPARK-30288
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.4.3
>            Reporter: Jingyuan Wang
>            Priority: Major
>
> When I tried to write Parquet files using PySpark with columns containing some special
characters in their names, it threw the following exception:
> {code}
> org.apache.spark.sql.AnalysisException: Attribute name "col 1" contains invalid character(s)
among " ,;{}()\n\t=". Please use alias to rename it.;
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetSchemaConverter$.checkConversionRequirement(ParquetSchemaConverter.scala:583)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetSchemaConverter$.checkFieldName(ParquetSchemaConverter.scala:570)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport$$anonfun$setSchema$2.apply(ParquetWriteSupport.scala:444)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport$$anonfun$setSchema$2.apply(ParquetWriteSupport.scala:444)
> 	at scala.collection.immutable.List.foreach(List.scala:392)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport$.setSchema(ParquetWriteSupport.scala:444)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat.prepareWrite(ParquetFileFormat.scala:111)
> 	at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:103)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
> 	at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
> 	at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
> 	at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> 	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> 	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> 	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> 	at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
> 	at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
> 	at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
> 	at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
> 	at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
> 	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
> 	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
> 	at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
> 	at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
> 	at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
> 	at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
> 	at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:566)
> 	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> 	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> 	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> 	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
> 	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> 	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> 	at py4j.Gateway.invoke(Gateway.java:282)
> 	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> 	at py4j.commands.CallCommand.execute(CallCommand.java:79)
> 	at py4j.GatewayConnection.run(GatewayConnection.java:238)
> 	at java.base/java.lang.Thread.run(Thread.java:834)
> {code}
> However, it is supported by Pandas for both reading and writing. This validity check
of column names seems to be outdated and should be removed. 
> {code}
> >>> import pandas as pd
> >>> df = pd.DataFrame(data={'col(1)': [1, 2], 'col 2': [3, 4]})
> >>> df.to_parquet('special_columns.parquet')
> >>> df_written = pd.read_parquet('special_columns.parquet')
> >>> df_written
>    col(1)  col 2
> 0       1      3
> 1       2      4
> {code}



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