我试图计算火花数据帧中每一行的随机向量的欧几里德距离。最初是一个叫做omatrix的pandas df，每一列都是一个单词，每一行都是一个表示为1和0＆s 39的矢量的句子，其中1表示该单词存在，0表示该单词。
import numpy as np
randv = np.random.rand(len(omatrix.columns))
def distance(x):
return np.linalg.norm(x-randv).item()
dist = udf(distance, FloatType())
df = spark.createDataFrame([[word] for word in omatrix.values.tolist()], schema=["features"])
df.printSchema()
df.show(5)
这成功输出：
root
|-- features: array (nullable = true)
| |-- element: long (containsNull = true)
+--------------------+
| features|
+--------------------+
|[0, 0, 0, 0, 0, 0...|
|[0, 0, 0, 0, 0, 0...|
|[0, 1, 0, 0, 1, 0...|
|[0, 0, 0, 0, 0, 1...|
|[0, 1, 0, 1, 0, 0...|
+--------------------+
only showing top 5 rows
现在我想我可以使用withColumn（）来计算距离：
df = df.withColumn('distance', dist(df.features))
df.select(df.distance).show(5)
第二行导致stacktrace：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-27-1a55784bd46e> in <module>()
----> 1 vdf.select(vdf.distance).show(5)
/home/jakeu123/spark/python/pyspark/sql/dataframe.pyc in show(self, n, truncate, vertical)
348 """
349 if isinstance(truncate, bool) and truncate:
--> 350 print(self._jdf.showString(n, 20, vertical))
351 else:
352 print(self._jdf.showString(n, int(truncate), vertical))
/home/jakeu123/spark/python/lib/py4j-0.10.6-src.zip/py4j/java_gateway.py in __call__(self, *args)
1158 answer = self.gateway_client.send_command(command)
1159 return_value = get_return_value(
-> 1160 answer, self.gateway_client, self.target_id, self.name)
1161
1162 for temp_arg in temp_args:
/home/jakeu123/spark/python/pyspark/sql/utils.pyc in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/home/jakeu123/spark/python/lib/py4j-0.10.6-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
318 raise Py4JJavaError(
319 "An error occurred while calling {0}{1}{2}.\n".
--> 320 format(target_id, ".", name), value)
321 else:
322 raise Py4JError(
Py4JJavaError: An error occurred while calling o311.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 11.0 failed 4 times, most recent failure: Lost task 0.3 in stage 11.0 (TID 24, 10.142.0.5, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 218, in main
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 138, in read_udfs
arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 118, in read_single_udf
f, return_type = read_command(pickleSer, infile)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 58, in read_command
command = serializer._read_with_length(file)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 170, in _read_with_length
return self.loads(obj)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 562, in loads
return pickle.loads(obj)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/cloudpickle.py", line 929, in subimport
__import__(name)
ImportError: ('No module named numpy.linalg.linalg', <function subimport at 0x7ff875f3c6e0>, ('numpy.linalg.linalg',))
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:83)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:66)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)
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:1820)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2027)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2048)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:363)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3272)
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:3253)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3252)
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:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 218, in main
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 138, in read_udfs
arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 118, in read_single_udf
f, return_type = read_command(pickleSer, infile)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/worker.py", line 58, in read_command
command = serializer._read_with_length(file)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 170, in _read_with_length
return self.loads(obj)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 562, in loads
return pickle.loads(obj)
File "/home/jakeu123/spark/python/lib/pyspark.zip/pyspark/cloudpickle.py", line 929, in subimport
__import__(name)
ImportError: ('No module named numpy.linalg.linalg', <function subimport at 0x7ff875f3c6e0>, ('numpy.linalg.linalg',))
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:83)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:66)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$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
为什么Py4J试图导入numpy.linalg.linalg？或者是造成这种情况的另一个问题？