在pandas_udf中使用pd.shift时出现“索引处的值为空”错误

时间：2019-07-22 18:57:45

标签： pyspark apache-spark-sql

对于pyspark中的DataFrame，如果使用F.lit（1）（或任何其他值）初始化了列，则在pandas_udf内为其分配了移位列，这将导致“索引处的值为空”错误。 谁能提供一些暗示为什么会这样？是pyspark中的错误吗？

请参见下面的代码和错误。

spark = SparkSession.builder.appName('test').getOrCreate()
df = spark.createDataFrame([Row(id=1, name='a', c='3'),
Row(id=2, name='b', c='6'),
Row(id=3, name='a', c='2'),
Row(id=4, name='b', c='9'),
Row(id=5, name='c', c='7')])

df = df.withColumn('f', F.lit(1))

df.show()

@pandas_udf(df.schema, PandasUDFType.GROUPED_MAP)
def shift_test(pdf):
    pdf['f'] = pdf['c'].shift(1)
return pdf

df = df.groupby(['name']).apply(shift_test)
df.show()

如果我将列f设置为c，则不会出现此类错误 请参见下面的输出。

+---+---+----+---+
|  c| id|name|  f|
+---+---+----+---+
|  3|  1|   a|  1|
|  6|  2|   b|  1|
|  2|  3|   a|  1|
|  9|  4|   b|  1|
|  7|  5|   c|  1|
+---+---+----+---+


---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-242-a457c8d5f1f2> in <module>
     16 df = df.groupby(['name']).apply(shift_test)
     17 
---> 18 df.show()

/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
    376         """
    377         if isinstance(truncate, bool) and truncate:
--> 378             print(self._jdf.showString(n, 20, vertical))
    379         else:
    380             print(self._jdf.showString(n, int(truncate), vertical))

/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o4911.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 97 in stage 671.0 failed 4 times, most recent failure: Lost task 97.3 in stage 671.0 (TID 16149, 10.122.97.240, executor 3): java.lang.IllegalStateException: Value at index is null
    at org.apache.arrow.vector.IntVector.get(IntVector.java:101)
    at org.apache.spark.sql.vectorized.ArrowColumnVector$IntAccessor.getInt(ArrowColumnVector.java:299)
    at org.apache.spark.sql.vectorized.ArrowColumnVector.getInt(ArrowColumnVector.java:84)
    at org.apache.spark.sql.execution.vectorized.MutableColumnarRow.getInt(MutableColumnarRow.java:117)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1887)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1875)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1874)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1874)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2108)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2057)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2046)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3384)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
    at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3365)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3364)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2545)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:2759)
    at org.apache.spark.sql.Dataset.getRows(Dataset.scala:255)
    at org.apache.spark.sql.Dataset.showString(Dataset.scala:292)
    at sun.reflect.GeneratedMethodAccessor75.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.IllegalStateException: Value at index is null
    at org.apache.arrow.vector.IntVector.get(IntVector.java:101)
    at org.apache.spark.sql.vectorized.ArrowColumnVector$IntAccessor.getInt(ArrowColumnVector.java:299)
    at org.apache.spark.sql.vectorized.ArrowColumnVector.getInt(ArrowColumnVector.java:84)
    at org.apache.spark.sql.execution.vectorized.MutableColumnarRow.getInt(MutableColumnarRow.java:117)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more

2 个答案:

答案 0 :(得分：1)

当pyspark从pandas_udf中出来时，似乎无法处理缺失值。在进入pandas_udf之前，它希望每一列都具有某种数据类型（如 @pandas_udf(df.schema, PandasUDFType.GROUPED_MAP)。

如果有任何遗漏值（如shift可能在此处生成），则将其抛出，因为Java无法处理遗漏值（Java例外：java.lang.IllegalStateException）。

要解决此问题，需要用适当类型的值（此处为integer）替换此类丢失的值。

此新的pandas_udf函数可解决此问题：

@pandas_udf(df.schema, PandasUDFType.GROUPED_MAP)
def shift_test(pdf):
    pdf['f'] = pdf['c'].shift(1)
    pdf['f'].fillna(value=-1, inplace=True) #replace missing values with -1
    return pdf

这是输出

+---+---+----+---+
|  c| id|name|  f|
+---+---+----+---+
|  7|  5|   c| -1|
|  6|  2|   b| -1|
|  9|  4|   b|  6|
|  2|  3|   a| -1|
|  3|  1|   a|  2|
+---+---+----+---+

答案 1 :(得分：0)

您收到错误的原因是 null 函数引入的 shift 值，其次是因为您没有对返回的架构进行任何更改以接受 null价值。

当您将返回架构指定为 df.schema 时，spark 默认从原始架构中获取 nullable = False。因此，您需要在此处为​​列 f 提供新架构，您需要在其中设置 nullable = True 以避免此错误。

# Schema of output DataFrame
new_schema = StructType([
    StructField("c", IntegerType(), False), 
    StructField("id", IntegerType(), False), 
    StructField("name", StringType(), False), 
    StructField("f", IntegerType(), True)
  ])

@pandas_udf(new_schema, PandasUDFType.GROUPED_MAP)
def shift_test(pdf):
    pdf['f'] = pdf['c'].shift(1)
    return pdf
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