PySpark: org.apache.spark.sql.AnalysisException: 屬性名稱... 包含無效字符 (s) 在 ",;{}()\n\t=" 請使用別名重命名

Question

我正在嘗試將 Parquet 數據加載到PySpark中，其中一列的名稱中有空格：

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

即使我已為該列添加別名，我仍然收到此錯誤和從PySpark的JVM端傳播的錯誤。 我在下面附上了堆棧跟蹤。

有沒有辦法可以將此鑲木地板文件加載到PySpark中，而無需預處理 Scala 中的數據，並且無需修改源鑲木地板文件？

---------------------------------------------------------------------------
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.;'

Answer 1

你有沒有嘗試過，

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

當您選擇具有別名的列時，您仍然通過 select 子句傳遞了錯誤的列名。

Answer 2

@MaFF 的建議對我來說似乎通過了

df = spark.read.parquet("my_parquet_dump")
df2 = df.withColumnRenamed("Foo Bar", "foobar")
df2.registerTempTable("temp")
hc.sql("CREATE TABLE persistent STORED AS PARQUET AS SELECT * FROM temp")

您收到什么錯誤消息？

Answer 3

您可以使用正則表達式替換壞符號。 檢查我的答案 。

Answer 4

我嘗試了@ktang 的方法，它也對我有用。 我正在與 SQL 和 Python 合作，因此對您來說可能有所不同，但它仍然有效。

確保列名/標題中沒有空格。 盡管錯誤消息中提供了字符列表，但 Pyspark 似乎不接受空格 ( )。

我最初使用的是：

master_df = spark.sql(f'''
SELECT occ.num, 'Occ Event' AS `Event Type`,
...

將事件類型中的空格 ( ) 更改為下划線 (_) 有效：

master_df = spark.sql(f'''
SELECT occ.num, 'Occ Event' AS `Event_Type`,
...