在自我实现的对象/类的功能上使用 Pysparks rdd.parallelize().map()

Question

我有一些我想并行计算的对象，因此我想我可以求助于 pyspark。

考虑这个例子，一个 class 的对象确实有一个数字i ，可以用square()平方：

class MyMathObject():
    def __init__(self, i):
        self.i = i
    def square(self):
        return self.i ** 2


print(MyMathObject(3).square()) # Test one instance with regular python - works

此外，我设置了 pyspark（在 jupyter 笔记本中），现在我想在我的对象上并行计算 0 到 4 的平方：

import findspark
findspark.init()
from pyspark import SparkContext

sc = SparkContext("local[2]")

rdd = sc.parallelize([MyMathObject(i) for i in range(5)])
rdd.map(lambda obj: obj.square()).collect() # This fails

这不起作用 - 它会导致很长的错误消息，对我来说大多是无用的。 我觉得有点有趣的唯一一行是：

AttributeError：无法从 '/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark 获取 <module 'pyspark.daemon' 上的属性 'MyMathObject' /daemon.py'>

因此，这似乎与属性square()的调用方式有关。 我在最后复制了完整的消息。

Pyspark 本身似乎工作； 例如，在普通的 python 列表上执行以下操作会按预期返回平方数。

rdd = sc.parallelize([i for i in range(5)])
rdd.map(lambda i: i**2).collect()

因此，我创建或操作对象的方式似乎存在缺陷，但我无法追查错误。

完整的错误信息：

Py4JJavaError Traceback（最近一次调用最后一次）在 1 rdd = sc.parallelize([MyMathObject(i) for i in range(5)]) ----> 2 rdd.map(lambda obj: obj.square())。搜集（）

/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/pyspark/rdd.py in collect(self) 887 """ 888 with SCCallSiteSync(self.context) as css: -- > 889 sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) 890 返回列表(_load_from_socket(sock_info, self._jrdd_deserializer)) 891

/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in call (self, *args) 1302 1303 answer = self.gateway_client.send_command（command）-> 1304 return_value = get_return_value（1305 answer，self.gateway_client，self.target_id，self.name）1306

/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name ) 324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client) 325 if answer[1] == REFERENCE_TYPE: --> 326 raise Py4JJavaError( 327 "调用 {0}{1}{2} 时出错.\n". 328 格式(target_id, ".", name), value)

Py4JJavaError：调用 z:org.apache.spark.api.python.PythonRDD.collectAndServe 时出错。 ：org.apache.spark.SparkException：作业因阶段故障而中止：阶段 1.0 中的任务 0 失败 1 次，最近一次失败：阶段 1.0 中丢失的任务 0.0（TID 2，192.168.2.108，执行程序驱动程序）：org.ZB06EFD606E3Z1418D .spark.api.python.PythonException：回溯（最近一次调用）：文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/ worker.py”，第 605 行，在主进程（）文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/worker.py”中，第 597 行，在进程中 serializer.dump_stream(out_iter, outfile) 文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/serializers.py ”，第 271 行，在 dump_stream vs = list(itertools.islice(iterator, batch)) 文件中“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip /pyspark/serializers.py"，第 147 行，在 load_stream 中产生 self._read_with_length(stream ) 文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/serializers.py”，第 172 行，在 _read_with_length 返回 self.loads(obj ) 文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/serializers.py”，第 458 行，加载返回 pickle.loads(obj , encoding=encoding) AttributeError: Can't get attribute 'MyMathObject' on <module 'pyspark.daemon' from '/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/ pyspark.zip/pyspark/daemon.py'>

at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:503) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:638) at org .apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:621) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:456) at org.apache .spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) 在 ZBAAD2C48E66FBC14C61337D0B25 78221Z.collection.Iterator.foreach(Iterator.scala:941) at scala.collection.Iterator.foreach$(Iterator.scala:941) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala. collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer .$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:315 ) 在 ZBAAD2C4 8E66FBC14C61337D0B2578221Z.collection.TraversableOnce.to$(TraversableOnce.scala:313) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307) at scala. collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294) at scala.collection. TraversableOnce.toArray$(TraversableOnce.scala:288) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleI terator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2154) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner .$anonfun$run$3(Executor.scala:462) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) at org.apache.spark.executor.Executor$TaskRunner.run(Executor. scala:465) 在 java.base/java.util.concurrent。 ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:第834章）

Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059) at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2008) at org.apache. spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2007) at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) at scala.collection.mutable.ResizableArray.foreach$(ResizableArray .scala:55) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala :2007) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:973) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:973 ) at scala.Option.foreach(Option.scala:407) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala: 2239) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.ZBAAD2C48E66FBC14 C61337D0B2578221Z:2177) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775) at org.apache .spark.SparkContext.runJob(SparkContext.scala:2114) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2135) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2154) at org. apache.spark.SparkContext.runJob(SparkContext.scala:2179) at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1004) at org.apache.spark.rdd.RDDOperationScope$.withScope (RDDOperationScope.ZBAAD2C48E66FB C14C61337D0B2578221Z:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:388) at org.apache.spark. rdd.RDD.collect(RDD.scala:1003) at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:168) at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD .scala) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.Z93F725A07423FE1C889F448B33 D21F46Z:62) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:566) 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.base/java.lang.Thread.run(Thread.java:834 ) 引起：org.ZB6EFD606D118D0F62066E31419FF 04CCZ.spark.api.python.PythonException：回溯（最近一次调用）：文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark /worker.py”，第 605 行，在主进程（）文件中“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/worker.py ”，第 597 行，正在处理 serializer.dump_stream(out_iter, outfile) 文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/serializers. py”，第 271 行，在 dump_stream vs = list(itertools.islice(iterator, batch)) 文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark. zip/pyspark/serializers.py”，第 147 行，在 load_stream 中产生 self._read_with_length(stream) 文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark. zip/pyspark/serializers.py”，第 172 行，在 _read_with_length 返回 self.loads(obj) 文件“/opt/apache-spark-3/spark-3.0.2-bin-hadoop3.2/python/lib/pyspark. zip/pyspark/serializers.py",第 458 行，在负载中返回 pickle.loads(obj, encoding=encoding) AttributeError: Can't get attribute 'MyMathObject' on <module 'pyspark.daemon' from '/opt/apache-spark-3/spark-3.0.2 -bin-hadoop3.2/python/lib/pyspark.zip/pyspark/daemon.py'>

at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:503) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:638) at org .apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:621) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:456) at org.apache .spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) 在 ZBAAD2C48E66FBC14C61337D0B25 78221Z.collection.Iterator.foreach(Iterator.scala:941) at scala.collection.Iterator.foreach$(Iterator.scala:941) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala. collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer .$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:315 ) 在 ZBAAD2C4 8E66FBC14C61337D0B2578221Z.collection.TraversableOnce.to$(TraversableOnce.scala:313) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307) at scala. collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294) at scala.collection. TraversableOnce.toArray$(TraversableOnce.scala:288) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleI terator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2154) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner .$anonfun$run$3(Executor.scala:462) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) at org.apache.spark.executor.Executor$TaskRunner.run(Executor. scala:465) 在 java.base/java.util.concurrent。 ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)... 1 more

Answer 1

它看起来不像您的错误，但解决此问题的一种方法是将模块放在单独的 Python 文件中并导入它：

例如打开一个文件mymodule.py

class MyMathObject():
    def __init__(self, i):
        self.i = i
    def square(self):
        return self.i ** 2

在你的主脚本中，你可以做

from mymodule import MyMathObject

rdd = sc.parallelize([MyMathObject(i) for i in range(5)])
rdd.map(lambda obj: obj.square()).collect()

这应该给出 [0, 1, 4, 9, 16]。