沃梦达 / 编程问答 / Python问题 / 正文

AttributeError：&#39；NoneType&#39；对象没有属性&#39；_JVM-PySpark UDF

Python问题
2022-01-01

AttributeError: #39;NoneType#39; object has no attribute #39;_jvm - PySpark UDF(AttributeError：#39；NoneType#39；对象没有属性#39；_JVM-PySpark UDF)

本文介绍了AttributeError：&#39；NoneType&#39；对象没有属性&#39；_JVM-PySpark UDF的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有杂志订阅及其创建时间的数据，还有一个包含与给定用户关联的所有订阅到期日期数组的列： 

 user_id           created_date             expiration_dates_for_user
 202394           '2018-05-04'           ['2019-1-03', '2018-10-06', '2018-07-05']
 202394           '2017-01-04'           ['2019-1-03', '2018-10-06', '2018-07-05']
 202394           '2016-05-04'           ['2019-1-03', '2018-10-06', '2018-07-05']

我正在尝试创建一个新列，该列由CREATED_DATE 45天内的所有到期日期组成，如下所示：

 user_id       created_date             expiration_dates_for_user                near_expiration_dates 
 202394       '2018-05-04'           ['2019-1-03', '2018-10-06', '2020-07-05']   []
 202394       '2019-01-04'           ['2019-1-03', '2018-10-06', '2020-07-05']   ['2019-1-03']
 202394       '2016-05-04'           ['2019-1-03', '2018-10-06', '2020-07-05']   []

以下是我使用的代码：

def check_if_sub_connected(created_at, expiration_array):
  if not expiration_array:
    return []
  if created_at == None:
    return []
  else:
    close_to_array = []
    for i in expiration_array:
      if datediff(created_at, i) < 45:
        if created_at != i:
          if datediff(created_at, i) > -45:
            close_to_array.append(i)
    return close_to_array

check_if_sub_connected = udf(check_if_sub_connected, ArrayType(TimestampType()))

但当我应用该函数创建列时...

df = df.withColumn('near_expiration-dates', check_if_sub_connected(df.created_date, df.expiration_dates_for_user)

我收到这个疯狂的错误：

AttributeError: 'NoneType' object has no attribute '_jvm'

at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:317)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:83)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:66)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:271)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage17.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:620)
at org.apache.spark.sql.execution.collect.UnsafeRowBatchUtils$.encodeUnsafeRows(UnsafeRowBatchUtils.scala:49)
at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:126)
at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:125)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:112)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:384)
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$$failJobAndIndependentStages(DAGScheduler.scala:1747)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1735)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1734)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1734)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:962)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:962)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:962)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1970)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1918)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1906)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2141)
at org.apache.spark.sql.execution.collect.Collector.runSparkJobs(Collector.scala:237)
at org.apache.spark.sql.execution.collect.Collector.collect(Collector.scala:247)
at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:64)
at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:70)
at org.apache.spark.sql.execution.ResultCacheManager.getOrComputeResult(ResultCacheManager.scala:497)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollectResult(limit.scala:48)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectResult(Dataset.scala:2775)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3350)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2504)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2504)
at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3334)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withCustomExecutionEnv$1.apply(SQLExecution.scala:89)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:175)
at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:84)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:126)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3333)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2504)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2718)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:259)
at sun.reflect.GeneratedMethodAccessor472.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:380)
at py4j.Gateway.invoke(Gateway.java:295)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:251)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/databricks/spark/python/pyspark/worker.py", line 262, in main
    process()
  File "/databricks/spark/python/pyspark/worker.py", line 257, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/databricks/spark/python/pyspark/worker.py", line 183, in <lambda>
    func = lambda _, it: map(mapper, it)
  File "<string>", line 1, in <lambda>
  File "/databricks/spark/python/pyspark/worker.py", line 77, in <lambda>
    return lambda *a: toInternal(f(*a))
  File "/databricks/spark/python/pyspark/util.py", line 55, in wrapper
    return f(*args, **kwargs)
  File "<command-30583>", line 9, in check_if_sub_connected
  File "/databricks/spark/python/pyspark/sql/functions.py", line 1045, in datediff
    return Column(sc._jvm.functions.datediff(_to_java_column(end), _to_java_column(start)))
AttributeError: 'NoneType' object has no attribute '_jvm'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:317)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:83)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:66)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:271)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage17.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:620)
at org.apache.spark.sql.execution.collect.UnsafeRowBatchUtils$.encodeUnsafeRows(UnsafeRowBatchUtils.scala:49)
at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:126)
at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:125)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:112)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:384)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more

在UDF中不允许使用Datediff函数吗？或者这是某种导入错误？我正在使用最新版本在Databricks上运行Spark。谢谢！

推荐答案

def check_if_sub_connected(created_at, expiration_array):
  if not expiration_array:
    return []
  else:
    close_to_array = []
    for i in expiration_array:
      if created_at - i < pd.Timedelta(days=45):
        if created_at - i > pd.Timedelta(days=-45):
          close_to_array.append(i)
    return close_to_array

check_if_sub_connected = udf(check_if_sub_connected, ArrayType(TimestampType()))
正如@user10465355指出的那样，pyspark.sql.functions在UDF中不起作用。因此，以下是我的替代解决方案。谢谢

这篇关于AttributeError：&#39；NoneType&#39；对象没有属性&#39；_JVM-PySpark UDF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编程学习网！

本文标题为：AttributeError：&#39；NoneType&#39；对象没有属性&#39；_JVM-PySpark UDF

上一篇： 《数据库：如何从R Dataframe切换到Pandas Dataframe》(R到Pandas Dataframe)
下一篇： 如何在Databricks笔记本中获取运行参数和运行ID？

基础教程推荐

学习HTML
学习jQuery
学习Laravel
学习CSS3
学习Vue.js
学习Bootstrap5
学习ThinkPHP
学习AJAX
猜你喜欢
最新文章
热门文章
编程基础
热门标签
网站首页 HTML/CSS 菜单 Layui Yii2