PySpark:org.apache.spark.sql.AnalysisException:属性名称...在"，; {}()\ n \ t ="".请使用别名重命名 [英] PySpark: org.apache.spark.sql.AnalysisException: Attribute name ... contains invalid character(s) among " ,;{}()\n\t=". Please use alias to rename it

问题描述

我正在尝试将Parquet数据加载到PySpark中，其中一列的名称中有一个空格:

I'm trying to load Parquet data into PySpark, where a column has a space in the name:

df = spark.read.parquet('my_parquet_dump')
df.select(df['Foo Bar'].alias('foobar'))

即使我为该列添加了别名，我仍然会收到此错误以及从PySpark的JVM一侧传播的错误.我已经在下面附加了堆栈跟踪.

Even though I have aliased the column, I'm still getting this error and error propagating from the JVM side of PySpark. I've attached the stack trace below.

有没有一种方法可以将该镶木地板文件加载到PySpark中，而无需在Scala中预处理数据，也无需修改源镶木地板文件?

Is there a way I can load this parquet file into PySpark, without pre-processing the data in Scala, and without modifying the source parquet file?

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
/usr/local/python/pyspark/sql/utils.py in deco(*a, **kw)
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:

/usr/local/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    318                     "An error occurred while calling {0}{1}{2}.\n".
--> 319                     format(target_id, ".", name), value)
    320             else:

Py4JJavaError: An error occurred while calling o864.collectToPython.
: org.apache.spark.sql.AnalysisException: Attribute name "Foo Bar" contains invalid character(s) among " ,;{}()\n\t=". Please use alias to rename it.;
    at org.apache.spark.sql.execution.datasources.parquet.ParquetSchemaConverter$.checkConversionRequirement(ParquetSchemaConverter.scala:581)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetSchemaConverter$.checkFieldName(ParquetSchemaConverter.scala:567)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetSchemaConverter$$anonfun$checkFieldNames$1.apply(ParquetSchemaConverter.scala:575)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetSchemaConverter$$anonfun$checkFieldNames$1.apply(ParquetSchemaConverter.scala:575)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetSchemaConverter$.checkFieldNames(ParquetSchemaConverter.scala:575)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat.buildReaderWithPartitionValues(ParquetFileFormat.scala:293)
    at org.apache.spark.sql.execution.FileSourceScanExec.inputRDD$lzycompute(DataSourceScanExec.scala:285)
    at org.apache.spark.sql.execution.FileSourceScanExec.inputRDD(DataSourceScanExec.scala:283)
    at org.apache.spark.sql.execution.FileSourceScanExec.inputRDDs(DataSourceScanExec.scala:303)
    at org.apache.spark.sql.execution.ProjectExec.inputRDDs(basicPhysicalOperators.scala:42)
    at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:386)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
    at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:228)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:311)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2803)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2800)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2800)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
    at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2823)
    at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2800)
    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:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:280)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.lang.Thread.run(Thread.java:748)


During handling of the above exception, another exception occurred:

AnalysisException                         Traceback (most recent call last)
<ipython-input-37-9d7c55a5465c> in <module>()
----> 1 spark.sql("SELECT `Foo Bar` as hey FROM df limit 10").take(1)

/usr/local/python/pyspark/sql/dataframe.py in take(self, num)
    474         [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
    475         """
--> 476         return self.limit(num).collect()
    477 
    478     @since(1.3)

/usr/local/python/pyspark/sql/dataframe.py in collect(self)
    436         """
    437         with SCCallSiteSync(self._sc) as css:
--> 438             port = self._jdf.collectToPython()
    439         return list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
    440 

/usr/local/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1131         answer = self.gateway_client.send_command(command)
   1132         return_value = get_return_value(
-> 1133             answer, self.gateway_client, self.target_id, self.name)
   1134 
   1135         for temp_arg in temp_args:

/usr/local/python/pyspark/sql/utils.py in deco(*a, **kw)
     67                                              e.java_exception.getStackTrace()))
     68             if s.startswith('org.apache.spark.sql.AnalysisException: '):
---> 69                 raise AnalysisException(s.split(': ', 1)[1], stackTrace)
     70             if s.startswith('org.apache.spark.sql.catalyst.analysis'):
     71                 raise AnalysisException(s.split(': ', 1)[1], stackTrace)

AnalysisException: 'Attribute name "Foo Bar" contains invalid character(s) among " ,;{}()\\n\\t=". Please use alias to rename it.;'

推荐答案

您是否尝试过

df = df.withColumnRenamed("Foo Bar", "foobar")

当您选择带有别名的列时，您仍在通过选择子句传递错误的列名.

When you select the column with an alias you're still passing the wrong column name through a select clause.

这篇关于PySpark:org.apache.spark.sql.AnalysisException:属性名称...在"，; {}()\ n \ t ="".请使用别名重命名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！