【发布时间】：2020-02-26 14:25:40
【问题描述】：
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）。我还确认该作业正在 python3 中运行：
sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)
如果在我的脚本中注释掉'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其他东西试图不正确地导入模块。
通过注释掉脚本中的代码，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，此调用会引发有关未安装 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 numNonzeroes()），但不知何故它无法找到 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
所以我在所有节点上安装了 Python2 版本的 numpy。不幸的是，这根本没有改变错误。
我添加了一行来检查 numpy 的位置 (print(np.file))，它给出了这个：
/home/ubuntu/.local/lib/python3.6/site-packages/numpy/__init__.py
一直向下的权限允许 Ubuntu 访问该目录，所以我认为这不是权限问题。
非常感谢任何提示！
完整的错误报告：
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
【问题讨论】：
-
你的python安装路径在所有工作节点和驱动上都一样吗？
-
名称节点：/usr/bin/python3，从节点1：/usr/bin/python3，从节点2：/usr/bin/python3，从节点3：/usr/bin/python3
-
尝试将其他参数传递给您的 spark-submit
--conf spark.pyspark.python=/usr/bin/python3 -
@lukaszKielar - 我试过了，结果是一样的。如果我问从应用程序内部运行的 python 版本，它会告诉我 v3.6.9。因此，不知何故，当 Vector 调用其调用 numpy 的方法时，它们会在其他地方寻找该安装。
标签： numpy apache-spark pyspark