PySpark - Numpy Not Found in Cluster Mode - ModuleNotFoundError

Question

I'm running a job on a PySpark cluster for the first time. It runs perfectly in standalone mode on the name node. However, when it runs in the cluster:

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

It begins complaining that numpy isn't installed:

    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'

However, that module is confirmed to be installed on all nodes of the cluster (using pip3 install numpy). I've also confirmed that the job is running in python3:

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

If comment out the call in my script to 'import numpy as np', it still complains about numpy not being installed, so I began to suspect that something besides my script was trying to import the module improperly.

By commenting out code in the script, I discovered there are some functions I'm calling that must be trying to call a different version of numpy on the back end. Specifically, this call throws the error about numpy not being installed:

    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)

It appears that Vector has methods (in this case numNonzeroes()) that is based on Numpy, and somehow it's failing to find the numpy module.

I confirmed that Python2 doesn't have numpy installed:

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

So I installed the Python2 version of numpy on all the nodes. This didn't change the error at all, unfortunately.

I added a line to check where numpy is (print(np.file)) and it gives this:

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

The permissions all the way down allow Ubuntu to access that directory, so I don't think it's a permissions issue.

Any tips are greatly appreciated!

Full error report:

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

Does your python installation path is the same on all worker nodes and the driver? — lukaszKielar
– lukaszKielar, Commented Feb 26, 2020 at 20:53
name node: /usr/bin/python3, slave node 1: /usr/bin/python3, slave node 2: /usr/bin/python3, slave node 3: /usr/bin/python3 — SeaTea
– SeaTea, Commented Feb 27, 2020 at 2:00
Try to pass additional argument to your spark-submit --conf spark.pyspark.python=/usr/bin/python3 — lukaszKielar
– lukaszKielar, Commented Feb 27, 2020 at 5:58
@lukaszKielar - I have tried this and the result is the same. If I ask what version of python is running from inside the application it tells me v3.6.9. So, somehow when Vector calls its methods, which call numpy, they're looking somewhere else for that installation. — SeaTea
– SeaTea, Commented Feb 27, 2020 at 13:04

SeaTea · Accepted Answer · 2020-02-28 00:16:07Z

2

This ended up solving the problem. I ended up adding the following lines to .bashrc in all nodes of the cluster:

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

Where the first is the path to python3 and the second is the path to the installed packages.

answered Feb 28, 2020 at 0:16

SeaTea

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Comments

Sajad Safarveisi · Accepted Answer · 2021-07-26 13:11:14Z

2

If you are using pyspark via Jupyter notebook, you can use the following script (before you create a spark session) to set an environment variable for the pyspark python.

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

Where the string on the right is the path to python (installed on your local system (node) and not the one for pyspark). Note that the Numpy package should already be installed for the script to work.

This is not directly related to your question. However, I put it here for future observers of this issue.

answered Jul 26, 2021 at 13:11

Sajad Safarveisi

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