小贝子编程

PySpark-在群集模式下找不到Numpy-ModuleNotFoundError



我第一次在PySpark集群上运行作业。它在名称节点上以独立模式完美运行。但是，当它在集群中运行时：

spark-submit --master yarn 
--deploy-mode client 
--driver-memory 6g 
--executor-memory 6g 
--executor-cores 2 
--num-executors 10 
nearest_neighbor.py

它开始抱怨没有安装numpy：

from pyspark.ml.param.shared import *
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0024/container_1582692915671_0024_01_000002/pyspark.zip/pyspark/ml/param/__init__.py", line 26, in <module>
import numpy as np
ModuleNotFoundError: No module named 'numpy'

但是，该模块已确认安装在集群的所有节点上(使用pip3-install-numpy(。我还确认了该作业正在python3:中运行

sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)

如果注释掉我的脚本中对"import numpy as np"的调用，它仍然会抱怨没有安装numpy，所以我开始怀疑除了我的脚本之外还有什么东西试图不正确地导入模块。

通过注释脚本中的代码，我发现我正在调用的一些函数必须尝试在后端调用不同版本的numpy。具体来说，这个调用抛出了关于没有安装numpy的错误：

isNonZeroVector = udf(lambda x: x.numNonzeros() > 0, BooleanType())
trans_corpus_df = trans_corpus_df.select('id', 'features', 
isNonZeroVector('features').alias('non_zero'))
trans_corpus_df = trans_corpus_df.filter(trans_corpus_df.non_zero == True)

Vector似乎有基于Numpy的方法(在本例中为numNonzeros(((，但不知何故，它找不到Numpy模块。

我确认Python2没有安装numpy：

Python 2.7.15+ (default, Oct  7 2019, 17:39:04) 
[GCC 7.4.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: No module named numpy

因此，我在所有节点上安装了numpy的Python2版本。不幸的是，这根本没有改变错误。

我添加了一行来检查numpy在哪里(打印(np.文件((，它给出了：

/home/ubuntu/.local/lib/python3.6/site-packages/numpy/__init__.py

所有的权限都允许Ubuntu访问该目录，所以我不认为这是权限问题。

非常感谢任何提示！

完整错误报告：

20/02/26 14:21:19 ERROR TaskSetManager: Task 0 in stage 6.0 failed 4 times; aborting job
Traceback (most recent call last):
File "/home/ubuntu/server/nearest_neighbor.py", line 243, in <module>
main(args)
File "/home/ubuntu/server/nearest_neighbor.py", line 209, in main
hash_model = mh.fit(trans_corpus_df)
File "/home/ubuntu/server/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/ml/base.py", line 132, in fit
File "/home/ubuntu/server/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/ml/wrapper.py", line 295, in _fit
File "/home/ubuntu/server/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/ml/wrapper.py", line 292, in _fit_java
File "/home/ubuntu/server/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/home/ubuntu/server/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/home/ubuntu/server/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o160.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 6.0 failed 4 times, most recent failure: Lost task 0.3 in stage 6.0 (TID 28, ip-172-31-5-228.ec2.internal, executor 3): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 345, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
for obj in iterator:
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 334, in _batched
for item in iterator:
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 147, in load_stream
yield self._read_with_length(stream)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 172, in _read_with_length
return self.loads(obj)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 580, in loads
return pickle.loads(obj, encoding=encoding)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 869, in _parse_datatype_json_string
return _parse_datatype_json_value(json.loads(json_string))
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 886, in _parse_datatype_json_value
return _all_complex_types[tpe].fromJson(json_value)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 577, in fromJson
return StructType([StructField.fromJson(f) for f in json["fields"]])
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 577, in <listcomp>
return StructType([StructField.fromJson(f) for f in json["fields"]])
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 434, in fromJson
_parse_datatype_json_value(json["type"]),
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 888, in _parse_datatype_json_value
return UserDefinedType.fromJson(json_value)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 736, in fromJson
m = __import__(pyModule, globals(), locals(), [pyClass])
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/ml/__init__.py", line 22, in <module>
from pyspark.ml.base import Estimator, Model, Transformer, UnaryTransformer
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/ml/base.py", line 24, in <module>
from pyspark.ml.param.shared import *
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/ml/param/__init__.py", line 26, in <module>
import numpy as np
ModuleNotFoundError: No module named 'numpy'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at org.apache.spark.sql.execution.SparkPlan$$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$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$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$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$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$$collectFromPlan(Dataset.scala:3389)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
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:3369)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2557)
at org.apache.spark.ml.feature.LSH.fit(LSH.scala:328)
at org.apache.spark.ml.feature.LSH.fit(LSH.scala:304)
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 "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 345, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
for obj in iterator:
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 334, in _batched
for item in iterator:
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 147, in load_stream
yield self._read_with_length(stream)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 172, in _read_with_length
return self.loads(obj)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/serializers.py", line 580, in loads
return pickle.loads(obj, encoding=encoding)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 869, in _parse_datatype_json_string
return _parse_datatype_json_value(json.loads(json_string))
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 886, in _parse_datatype_json_value
return _all_complex_types[tpe].fromJson(json_value)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 577, in fromJson
return StructType([StructField.fromJson(f) for f in json["fields"]])
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 577, in <listcomp>
return StructType([StructField.fromJson(f) for f in json["fields"]])
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 434, in fromJson
_parse_datatype_json_value(json["type"]),
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 888, in _parse_datatype_json_value
return UserDefinedType.fromJson(json_value)
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/sql/types.py", line 736, in fromJson
m = __import__(pyModule, globals(), locals(), [pyClass])
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/ml/__init__.py", line 22, in <module>
from pyspark.ml.base import Estimator, Model, Transformer, UnaryTransformer
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/ml/base.py", line 24, in <module>
from pyspark.ml.param.shared import *
File "/tmp/hadoop-ubuntu/nm-local-dir/usercache/ubuntu/appcache/application_1582692915671_0010/container_1582692915671_0010_01_000004/pyspark.zip/pyspark/ml/param/__init__.py", line 26, in <module>
import numpy as np
ModuleNotFoundError: No module named 'numpy'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
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$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more

这最终解决了问题。我最终在集群的所有节点中向.bashrc添加了以下行：

export PYTHONPATH=/usr/bin/python3:/home/ubuntu/.local/lib/python3.6/site-packages:$PYTHONPATH
export PYSPARK_PYTHON=/usr/bin/python3:/home/ubuntu/.local/lib/python3.6/site-packages:$PYSPARK_PYTHON

其中第一个是到python3的路径，第二个是到已安装包的路径。

如果您通过Jupyter笔记本使用pyspark，您可以使用以下脚本(在创建spark会话之前(为pyspark python设置环境变量。

os.environ['PYSPARK_PYTHON'] = ".../bin/python"

右边的字符串是python的路径(安装在本地系统(节点(上，而不是pyspark的路径(。请注意，应该已经安装了Numpy包，脚本才能运行。

这与你的问题没有直接关系。然而，我把它放在这里是为了将来观察这个问题。

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