我正在尝试在服务器上运行一个简单的熊猫UDF示例。来自here
我只是为了运行此代码而创建了一个新鲜的环境。
(PySparkEnv) $ conda list
# packages in environment at /home/shekhar/.conda/envs/PySparkEnv:
#
# Name Version Build Channel
arrow-cpp 0.10.0 py36h70250a7_0 conda-forge
blas 1.0 mkl
boost-cpp 1.67.0 h3a22d5f_0 conda-forge
bzip2 1.0.6 h470a237_2 conda-forge
ca-certificates 2018.8.24 ha4d7672_0 conda-forge
certifi 2018.8.24 py36_1 conda-forge
icu 58.2 hfc679d8_0 conda-forge
intel-openmp 2019.0 117
libffi 3.2.1 hfc679d8_5 conda-forge
libgcc-ng 7.2.0 hdf63c60_3 conda-forge
libgfortran-ng 7.2.0 hdf63c60_3 conda-forge
libstdcxx-ng 7.2.0 hdf63c60_3 conda-forge
mkl 2019.0 117
mkl_fft 1.0.6 py36_0 conda-forge
mkl_random 1.0.1 py36_0 conda-forge
ncurses 6.1 hfc679d8_1 conda-forge
numpy 1.15.0 py36h1b885b7_0
numpy-base 1.15.0 py36h3dfced4_0
openssl 1.0.2p h470a237_0 conda-forge
pandas 0.23.4 py36hf8a1672_0 conda-forge
parquet-cpp 1.5.0.pre h83d4a3d_0 conda-forge
pip 18.0 py36_1 conda-forge
py4j 0.10.7 py_1 conda-forge
pyarrow 0.10.0 py36hfc679d8_0 conda-forge
pyspark 2.3.1 py36_1 conda-forge
python 3.6.6 h5001a0f_0 conda-forge
python-dateutil 2.7.3 py_0 conda-forge
pytz 2018.5 py_0 conda-forge
readline 7.0 haf1bffa_1 conda-forge
setuptools 40.2.0 py36_0 conda-forge
six 1.11.0 py36_1 conda-forge
sqlite 3.24.0 h2f33b56_1 conda-forge
tk 8.6.8 0 conda-forge
wheel 0.31.1 py36_1 conda-forge
xz 5.2.4 h470a237_1 conda-forge
zlib 1.2.11 h470a237_3 conda-forge
然后我运行以下代码：
from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.types import *
from pyspark.sql.functions import col, pandas_udf, PandasUDFType
import pandas as pd
import os
os.environ['PYSPARK_PYTHON'] = '/usr/local/anaconda3/bin/python3'
SparkContext.setSystemProperty('spark.executor.memory', '30g')
SparkContext.setSystemProperty('spark.executor.cores', '5')
spark = SparkSession.builder.appName("Python Spark SQL basic example").getOrCreate()
# Declare the function and create the UDF
def multiply_func(a, b):
return a * b
multiply = pandas_udf(multiply_func, returnType=LongType())
# The function for a pandas_udf should be able to execute with local Pandas data
x = pd.Series([1, 2, 3])
print(multiply_func(x, x))
# 0 1
# 1 4
# 2 9
# dtype: int64
# Create a Spark DataFrame, 'spark' is an existing SparkSession
df = spark.createDataFrame(pd.DataFrame(x, columns=["x"]))
# Execute function as a Spark vectorized UDF
df.select(multiply(col("x"), col("x"))).show()
我遇到以下错误，无法寻求帮助。
ERROR Executor:91 - Exception in task 0.0 in stage 0.0 (TID 0)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 219, in main
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 139, in read_udfs
arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 127, in read_single_udf
return arg_offsets, wrap_scalar_pandas_udf(row_func, return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 79, in wrap_scalar_pandas_udf
arrow_return_type = to_arrow_type(return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/sql/types.py", line 1613, in to_arrow_type
import pyarrow as pa
ModuleNotFoundError: No module named 'pyarrow'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:171)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:121)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
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:38)
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:38)
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:38)
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:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
2018-09-13 11:55:39 WARN TaskSetManager:66 - Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 219, in main
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 139, in read_udfs
arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 127, in read_single_udf
return arg_offsets, wrap_scalar_pandas_udf(row_func, return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 79, in wrap_scalar_pandas_udf
arrow_return_type = to_arrow_type(return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/sql/types.py", line 1613, in to_arrow_type
import pyarrow as pa
ModuleNotFoundError: No module named 'pyarrow'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:171)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:121)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
2018-09-13 11:55:39 ERROR TaskSetManager:70 - Task 0 in stage 0.0 failed 1 times; aborting job
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/sql/dataframe.py", line 350, in show
print(self._jdf.showString(n, 20, vertical))
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/py4j/java_gateway.py", line 1257, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o58.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 219, in main
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 139, in read_udfs
arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 127, in read_single_udf
return arg_offsets, wrap_scalar_pandas_udf(row_func, return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 79, in wrap_scalar_pandas_udf
arrow_return_type = to_arrow_type(return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/sql/types.py", line 1613, in to_arrow_type
import pyarrow as pa
ModuleNotFoundError: No module named 'pyarrow'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:171)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:121)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1602)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1590)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1589)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1589)
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:1823)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1772)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1761)
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:3273)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2484)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2698)
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:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 219, in main
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 139, in read_udfs
arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 127, in read_single_udf
return arg_offsets, wrap_scalar_pandas_udf(row_func, return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 79, in wrap_scalar_pandas_udf
arrow_return_type = to_arrow_type(return_type)
File "/home/shekhar/.conda/envs/PySparkEnv/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/sql/types.py", line 1613, in to_arrow_type
import pyarrow as pa
ModuleNotFoundError: No module named 'pyarrow'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:171)
at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:121)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
更重要的是，这在我的本地计算机上有效。 我将为此提供任何帮助，将不胜感激。我已经被困了几天了。
答案 0 :(得分：0)
您解决了这个问题吗？您使用的是哪个IDE？
如果您使用的是IDE，则应将其安装在conda环境中并从那里使用。
答案 1 :(得分：0)
删除您的__pycache__文件。
我遇到了完全相同的问题，这为我解决了。
答案 2 :(得分：0)
AWS EMR附带的jupyter笔记本遇到相同的问题。仅在主节点上安装pyarrow无效。然后，我还同时在核心节点中安装了pandas和pyarrow，错误消失了。
答案 3 :(得分：0)
我在AWS EMR中遇到了同样的问题。我尝试了其他答案，但没有用。
唯一适用于我的解决方案是使用selection=null和pip安装pyarrow。
conda我真的不知道为什么，但是如果您遇到相同的问题，它可能会很有用。
答案 4 :(得分：0)
也许您的项目目录树中有一个名为 Pyarrow 的目录。