Apache Spark Python UDF失败

时间：2019-04-04 17:57:36

标签： python apache-spark pyspark user-defined-functions

我有一个用Python编写的简单udf，我在24小时内将其替换为Apache Spark一书中的代码示例。本书使用的是旧版本的Spark，而我正在运行2.3.3。

我确实找到了这个answer，但是我很难弄清楚为什么书中的例子不起作用，而且我不确定该答案是否确实解决了我的问题。我正在Windows 10上以本地模式运行它。

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *

spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .getOrCreate()

df = spark.read.csv("full201801.dat",header="true")

columntransform = udf(lambda x: 'Non-Fat Dry Milk' if x == '23040010' else 'foo', StringType())

df.select(df.PRODUCT_NC, columntransform(df.PRODUCT_NC).alias('COMMODITY')).show()


Py4JJavaError: An error occurred while calling o110.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 242, in main
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 144, in read_udfs
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 120, in read_single_udf
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 60, in read_command
  File "c:\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 171, in _read_with_length
    return self.loads(obj)
  File "c:\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 566, in loads
    return pickle.loads(obj, encoding=encoding)
TypeError: _fill_function() missing 4 required positional arguments: 'defaults', 'dict', 'module', and 'closure_values'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:332)
    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:286)
    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$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1661)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1649)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1648)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1648)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1882)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1831)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1820)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:363)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3278)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2489)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2489)
    at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3259)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3258)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2489)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:2703)
    at org.apache.spark.sql.Dataset.showString(Dataset.scala:254)
    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: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:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 242, in main
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 144, in read_udfs
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 120, in read_single_udf
  File "c:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 60, in read_command
  File "c:\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 171, in _read_with_length
    return self.loads(obj)
  File "c:\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 566, in loads
    return pickle.loads(obj, encoding=encoding)
TypeError: _fill_function() missing 4 required positional arguments: 'defaults', 'dict', 'module', and 'closure_values'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:332)
    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:286)
    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$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more

2 个答案:

答案 0 :(得分：1)

这里是在pyspark中调用UDF的解决方案

创建具有功能逻辑的方法

def getItem(it):
    return 'Non-Fat Dry Milk' if it == '23040010' else 'foo'

在Pyspark UDF中注册上述方法

from pyspark.sql import functions as F


columntransform = F.udf(getItem, StringType())

在数据框中调用UDF 

    ll = ["23040010", "23040011", "23040012", "23040013", "23040010"]
    n_rdd = sc.parallelize(ll).map(lambda row: Row(row))
    df = sql.createDataFrame(n_rdd, ["nums"])
    df.withColumn("NewItem",columntransform(df["nums"]))

在这里输出：

+--------+----------------+
|    nums|         NewItem|
+--------+----------------+
|23040010|Non-Fat Dry Milk|
|23040011|             foo|
|23040012|             foo|
|23040013|             foo|
|23040010|Non-Fat Dry Milk|
+--------+----------------+

答案 1 :(得分：0)

我不能完全确定真正的问题是什么，但是当我将所有这些都移到运行CENTOS 7的盒子中时，一切都能按预期进行。这不是代码的问题。设置了我的窗户。

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