PySpark-在群集模式下找不到Numpy-ModuleNotFoundError
我第一次在PySpark群集上运行作业。它在名称节点上以独立模式完美运行。但是，当它在群集中运行时：PySpark-在群集模式下找不到Numpy-ModuleNotFoundError,numpy,apache-spark,pyspark,Numpy,Apache Spark,Pyspark,我第一次在PySpark群集上运行作业。它在名称节点上以独立模式完美运行。但是，当它在群集中运行时： spark-submit --master yarn \ --deploy-mode client \ --driver-memory 6g \ --executor-memory 6g \ --executor-cores 2 \ --num-executors 10 \ nearest_neighbor.py export PYTHONPATH=/usr/bin/python3:/home
spark-submit --master yarn \
--deploy-mode client \
--driver-memory 6g \
--executor-memory 6g \
--executor-cores 2 \
--num-executors 10 \
nearest_neighbor.py
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
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'
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)
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
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'
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)
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
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'
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)
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
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。最后，我在集群所有节点的.bashrc中添加了以下行：
spark-submit --master yarn \
--deploy-mode client \
--driver-memory 6g \
--executor-memory 6g \
--executor-cores 2 \
--num-executors 10 \
nearest_neighbor.py
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。所有工作节点和驱动程序上的python安装路径是否相同？名称节点：/usr/bin/python3，从节点1:/usr/bin/python3，从节点2:/usr/bin/python3，从属节点3:/usr/bin/python3Try将附加参数传递给spark submit-conf spark.pyspark.python=/usr/bin/python3@lukaszKielar-我试过这个，结果是一样的。如果我问应用程序中运行的是哪个版本的python，它会告诉我v3.6.9。所以，不知何故，当Vector调用它的方法（调用numpy）时，他们正在其他地方寻找安装。