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自定义模块中的函数在PySpark中不起作用，但在交互模式下输入时起作用

pyspark

自定义模块中的函数在PySpark中不起作用，但在交互模式下输入时起作用,pyspark,pyspark-sql,Pyspark,Pyspark Sql,我编写了一个模块，其中包含作用于PySpark数据帧的函数。它们对数据帧中的列进行转换，然后返回一个新的数据帧。下面是一个代码示例，缩短为仅包含一个函数： from pyspark.sql import functions as F from pyspark.sql import types as t import pandas as pd import numpy as np metadta=pd.DataFrame(pd.read_csv("metadata.csv")) # this

我编写了一个模块，其中包含作用于PySpark数据帧的函数。它们对数据帧中的列进行转换，然后返回一个新的数据帧。下面是一个代码示例，缩短为仅包含一个函数：

from pyspark.sql import functions as F
from pyspark.sql import types as t

import pandas as pd
import numpy as np

metadta=pd.DataFrame(pd.read_csv("metadata.csv"))  # this contains metadata on my dataset

def str2num(text):
    if type(text)==None or text=='' or text=='NULL' or text=='null':
        return 0
    elif len(text)==1:
        return ord(text)
    else:
        newnum=''
        for lettr in text:
            newnum=newnum+str(ord(lettr))
        return int(newnum)

str2numUDF = F.udf(lambda s: str2num(s), t.IntegerType())

def letConvNum(df):    # df is a PySpark DataFrame
    #Get a list of columns that I want to transform, using the metadata Pandas DataFrame
    chng_cols=metadta[(metadta.comments=='letter conversion to num')].col_name.tolist()
    for curcol in chng_cols:
        df=df.withColumn(curcol, str2numUDF(df[curcol]))
    return df
这就是我的模块，称之为mymodule.py。如果启动PySpark shell，并执行以下操作：

import mymodule as mm
myf=sqlContext.sql("select * from tablename lim 10")
我检查了myf（PySpark数据帧），它是正常的。我通过尝试使用str2num函数检查是否已实际导入mymodule：

mm.str2num('a')
97
所以它实际上是在导入模块。那么，如果我尝试以下方法：

df2=mm.letConvNum(df)
并执行以下操作以检查其是否有效：

df2.show()
它尝试执行该操作，但随后崩溃：

    16/03/10 16:10:44 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 365)
    org.apache.spark.api.python.PythonException: Traceback (most recent call last):
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/worker.py", line 98, in main
        command = pickleSer._read_with_length(infile)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 164, in _read_with_length
        return self.loads(obj)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 422, in loads
        return pickle.loads(obj)
      File "test2.py", line 16, in <module>
        str2numUDF=F.udf(lambda s: str2num(s), t.IntegerType())
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1460, in udf
        return UserDefinedFunction(f, returnType)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1422, in __init__
        self._judf = self._create_judf(name)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1430, in _create_judf
        pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2317, in _prepare_for_python_RDD
        [x._jbroadcast for x in sc._pickled_broadcast_vars],
    AttributeError: 'NoneType' object has no attribute '_pickled_broadcast_vars'

            at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
            at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
            at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
            at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:397)
            at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:362)
            at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
            at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
            at org.apache.spark.scheduler.Task.run(Task.scala:88)
            at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
            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)
    16/03/10 16:10:44 ERROR TaskSetManager: Task 0 in stage 1.0 failed 1 times; aborting job
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/usr/hdp/2.3.4.0-3485/spark/python/pyspark/sql/dataframe.py", line 256, in show
        print(self._jdf.showString(n, truncate))
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
      File "/usr/hdp/2.3.4.0-3485/spark/python/pyspark/sql/utils.py", line 36, in deco
        return f(*a, **kw)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
    py4j.protocol.Py4JJavaError: An error occurred while calling o7299.showString.
    : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 365, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/worker.py", line 98, in main
        command = pickleSer._read_with_length(infile)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 164, in _read_with_length
        return self.loads(obj)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 422, in loads
        return pickle.loads(obj)
      File "test2.py", line 16, in <module>
        str2numUDF=F.udf(lambda s: str2num(s), t.IntegerType())
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1460, in udf
        return UserDefinedFunction(f, returnType)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1422, in __init__
        self._judf = self._create_judf(name)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1430, in _create_judf
        pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2317, in _prepare_for_python_RDD
        [x._jbroadcast for x in sc._pickled_broadcast_vars],
    AttributeError: 'NoneType' object has no attribute '_pickled_broadcast_vars'

            at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
            at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
            at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
            at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:397)
            at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:362)
            at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
            at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
            at org.apache.spark.scheduler.Task.run(Task.scala:88)
            at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
            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$$failJobAndIndependentStages(DAGScheduler.scala:1283)
            at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1271)
            at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1270)
            at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
            at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
            at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1270)
            at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
            at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
            at scala.Option.foreach(Option.scala:236)
            at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697)
            at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1496)
            at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1458)
            at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1447)
            at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
            at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
            at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
            at org.apache.spark.SparkContext.runJob(SparkContext.scala:1837)
            at org.apache.spark.SparkContext.runJob(SparkContext.scala:1850)
            at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:215)
            at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:207)
            at org.apache.spark.sql.DataFrame$$anonfun$collect$1.apply(DataFrame.scala:1385)
            at org.apache.spark.sql.DataFrame$$anonfun$collect$1.apply(DataFrame.scala:1385)
            at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
            at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:1903)
            at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:1384)
            at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1314)
            at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1377)
            at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:178)
            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:497)
            at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
            at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
            at py4j.Gateway.invoke(Gateway.java:259)
            at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
            at py4j.commands.CallCommand.execute(CallCommand.java:79)
            at py4j.GatewayConnection.run(GatewayConnection.java:207)
            at java.lang.Thread.run(Thread.java:745)
    Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/worker.py", line 98, in main
        command = pickleSer._read_with_length(infile)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 164, in _read_with_length
        return self.loads(obj)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 422, in loads
        return pickle.loads(obj)
      File "test2.py", line 16, in <module>
        str2numUDF=F.udf(lambda s: str2num(s), t.IntegerType())
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1460, in udf
        return UserDefinedFunction(f, returnType)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1422, in __init__
        self._judf = self._create_judf(name)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/sql/functions.py", line 1430, in _create_judf
        pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self)
      File "/usr/hdp/2.3.4.0-3485/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2317, in _prepare_for_python_RDD
        [x._jbroadcast for x in sc._pickled_broadcast_vars],
    AttributeError: 'NoneType' object has no attribute '_pickled_broadcast_vars'

            at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
            at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
            at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
            at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:397)
            at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:362)
            at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
            at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
            at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
            at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
            at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
            at org.apache.spark.scheduler.Task.run(Task.scala:88)
            at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
            ... 1 more
这一次，我得到了我所期望的转换后的数据帧

为什么在交互输入函数时，而在从模块读入函数时，它不起作用？我知道它正在读取模块，因为常规函数str2num起作用。

顺便问一下，您使用的是哪种spark版本

将函数转换为UDF，如下所示：

str2numUDF = F.udf(str2num, t.IntegerType())
这里不需要lambda函数。

我也有同样的错误，并遵循堆栈跟踪

在我的例子中，我构建了一个Egg文件，然后通过
--py files
选项将其传递给spark

关于这个错误，我认为可以归结为这样一个事实：当您调用
F.udf（str2num，t.IntegerType（））
时，一个
UserDefinedFunction
实例是在Spark运行之前创建的，因此它对一些
SparkContext
的引用是空的，请将其称为
sc
。运行UDF时，将引用
sc.\u pickled\u broadcast\u vars
，这将在输出中抛出
AttributeError

我的解决方法是在Spark运行之前避免创建UDF（因此有一个活动的
SparkContext
）

def letConvNum(df):    # df is a PySpark DataFrame
    #Get a list of columns that I want to transform, using the metadata Pandas DataFrame
    chng_cols=metadta[(metadta.comments=='letter conversion to num')].col_name.tolist()

    str2numUDF = F.udf(str2num, t.IntegerType()) # create UDF on demand
    for curcol in chng_cols:
        df=df.withColumn(curcol, str2numUDF(df[curcol]))
    return df
注意：我没有实际测试上面的代码，但是我自己的代码中的更改是类似的，并且一切都很好

另外，对于感兴趣的读者，请参见

如果您只在其他函数中使用UDF，您可以这样做

from pyspark.sql.functions import udf


class Udf(object):
    def __init__(s, func, spark_type):
        s.func, s.spark_type = func, spark_type

    def __call__(s, *args):
        return udf(s.func, s.spark_type)(*args)


myfunc_udf = Udf(myfunc, StringType())


def processing():
    df_new = df.select(myfunc_udf('somefield'))
我在这个问题上苦思冥想了整整20个小时。谢谢你们的解决方案

这是我的变体，以防有人对我如何解决相同的问题感兴趣。尽管它主要来自上面的代码/响应

这里的目的是简单地转换字符串列以显示其长度，但您当然可以做任何事情（我在主应用程序中执行数据类型检查和错误跟踪）

我使用udf的目的要复杂得多，但是这是我用来测试我的udf是否工作的

假设您的数据帧都是StringType（） 在我的例子中，我有4个字符串列

解决方案：

我创建了一个单独的.py文件，名为myfunctions

里面

from pyspark.sql import functions as F
from pyspark.sql.types import IntegerType
import logging

def str2num(text):
    if type(text) == None or text == '' or text == 'NULL' or text == 'null':
        return 0
   else:
        return len(text)


def letConvNum(df, columns):
    str2numUDF = F.udf(str2num, IntegerType())
    logging.info(columns)
    index = 0
    for curcol in columns:
        df = df.withColumn(curcol, str2numUDF(df[curcol]))
        index += 1
    return df
然后在我的主课里面 将新的.py文件添加到sparkContext

#my understanding is that this insures your function is added to a spark across all nodes
sc.addPyFile("./myfunctions.py")

#dynamically create headers based on config -simplified for example
schemaString = "YearMonth,IMEI,IMSI,MSISDN"
fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split(",")]
schema = StructType(fields)

df = sqlContext.read.format('com.databricks.spark.csv').options(header='false', inferschema='false', delimiter='|').load('/app/teacosy/invictus/kenya/SAF_QUALCOMM_IMEI_20170321.txt', schema=schema)

#read and write file to get parquet. please note this was to optimize MASSIVE files 50-200g
df.write.parquet("data.parquet", mode='overwrite')
dataframe = sqlContext.read.parquet("data.parquet")

df2 = mf.letConvNum(dataframe, schemaString.split(","))
df2.show()
输入：

+---------+---------------+---------------+------------+
|YearMonth|           IMEI|           IMSI|      MSISDN|
+---------+---------------+---------------+------------+
|   201609|869859025975610|639021005869699|254724884336|
|   201609|359521062182040|639021025339132|254721224577|
|   201609|353121070662770|639021025339132|254721224577|
|   201609|868096015837410|639021025339132|254721224577|
|   201609|866204020015610|639021025339132|254721224577|
|   201609|356051060479107|639028040455896|254710404131|
|   201609|353071062803703|639027641207269|254725555262|
|   201609|356899067316490|639027841002602|254711955201|
|   201609|860357020164930|639028550063234|254715570856|
|   201609|862245026673900|639028940332785|254728412070|
|   201609|352441075290910|639029340152407|254714582871|
|   201609|862074027499277|639029340152407|254714582871|
|   201609|357036073532528|639028500408346|254715408346|
|   201609|356546060475230|639021011628783|254722841516|
|   201609|356546060475220|639021011628783|254722841516|
|   201609|866838023727117|639028840277749|254718492024|
|   201609|354210053950950|639029440054836|254729308302|
|   201609|866912020393040|639029870328080|254725528182|
|   201609|357921070054540|639028340694869|254710255083|
|   201609|357977056264767|639027141561199|254721977494|
输出：

+---------+----+----+------+
|YearMonth|IMEI|IMSI|MSISDN|
+---------+----+----+------+
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
|        6|  15|  15|    12|
我希望这能帮助那些在pyspark应用程序冻结或挂起的情况下苦苦挣扎的人…如此令人沮丧的omg…

我认为一个更干净的解决方案是使用udf decorator来定义您的udf函数：

from pyspark.sql.functions as F

@F.udf
def str2numUDF(text):
    if type(text)==None or text=='' or text=='NULL' or text=='null':
        return 0
    elif len(text)==1:
        return ord(text)
    else:
        newnum=''
        for lettr in text:
            newnum=newnum+str(ord(lettr))
        return int(newnum)
使用此解决方案，udf不会引用任何其他函数，因此不会向您抛出任何错误

对于某些旧版本的spark，装饰器不支持类型化udf。有些情况下，您可能必须定义自定义装饰器，如下所示：

from pyspark.sql.functions as F
from pyspark.sql.types as t

# Custom udf decorator which accept return type
def udf_typed(returntype=t.StringType()):
    def _typed_udf_wrapper(func):
        return F.udf(func, returntype)
    return _typed_udf_wrapper

@udf_typed(t.IntegerType())
def my_udf(x)
    return int(x)
那这一堆错误到底是什么呢？我已经编辑了这个问题以包含错误。是的，这是真的。但是它并不能解决问题（我已经尝试了你提出的解决方案）。我不再试图解决这个问题，但我使用的是Spark 1.6。我没有创建类，而是为我的每个udf编写了lambda-myfunc_udf=lambda arg:udf（f=myfunc，returnType=StringType（））（arg） 
from pyspark.sql.functions as F
from pyspark.sql.types as t

# Custom udf decorator which accept return type
def udf_typed(returntype=t.StringType()):
    def _typed_udf_wrapper(func):
        return F.udf(func, returntype)
    return _typed_udf_wrapper

@udf_typed(t.IntegerType())
def my_udf(x)
    return int(x)