Jingyuan Wang created SPARK-30288:
-------------------------------------
Summary: 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
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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