问题：
在lambda或DataFrame变换中不允许作业，这意味着我们通常必须为使用Spark的数据范围内完成的每个数据操作创建一个新结构。
示例（Python）：
我以前曾通过简单地在列表和词典中没有分配的情况下将修改的数据设在就地而解决这个问题，但是事实证明，Numpy Arithmetic非常麻烦。我已经进行了一些模拟，以将所有这些数据放入列表中，并且由于阵列非常大，因此将大大放慢速度。（例如，这些阵列约为每个元素，每个元素长，包含在每个db行的30个阵列中，几百万行）
a = np.zeros(5)
# Actual operation
a[1:3] += 7
print "{}".format(a)
>> [ 0. 7. 7. 0. 0.]
# Spark compatability - Create modified array in memory to avoid assignment
# Not sure if this is best "solution" performance-wise
c = np.concatenate([a[:1], a[1:3] + 7, a[3:]])
print "{}n".format(c)
>> [ 0. 7. 7. 0. 0.]
示例（pyspark）：
因此，现在您可以看到我期望的输出，这是火花版。
t = sc.parallelize(a)
t2 = t.map(lambda ar: np.concatenate([ar[:1], ar[1:3] + 7, ar[3:]]))
t2.take(1)
错误：
我认为这会起作用，但是我明白了。我认为问题是" AR [1：3] 7"，但是在没有此操作之后，它仍然给出了同样的错误。也许我缺少一些东西。
也许np.concatenate（）执行某种造成的作业 这。如果是这样，那将是一种解决方案？
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-46-4a4c467a0b3d> in <module>()
12 t = sc.parallelize(a)
13 t2 = t.map(lambda ar: np.concatenate([ar[:1], ar[1:3] + 7, ar[3:]]))
---> 14 t2.take(1)
/databricks/spark/python/pyspark/rdd.py in take(self, num)
1297
1298 p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1299 res = self.context.runJob(self, takeUpToNumLeft, p)
1300
1301 items += res
/databricks/spark/python/pyspark/context.py in runJob(self, rdd, partitionFunc, partitions, allowLocal)
914 # SparkContext#runJob.
915 mappedRDD = rdd.mapPartitions(partitionFunc)
--> 916 port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
917 return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
918
/databricks/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in __call__(self, *args)
536 answer = self.gateway_client.send_command(command)
537 return_value = get_return_value(answer, self.gateway_client,
--> 538 self.target_id, self.name)
539
540 for temp_arg in temp_args:
/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
34 def deco(*a, **kw):
35 try:
---> 36 return f(*a, **kw)
37 except py4j.protocol.Py4JJavaError as e:
38 s = e.java_exception.toString()
/databricks/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
298 raise Py4JJavaError(
299 'An error occurred while calling {0}{1}{2}.n'.
--> 300 format(target_id, '.', name), value)
301 else:
302 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 25.0 failed 1 times, most recent failure: Lost task 0.0 in stage 25.0 (TID 30, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/databricks/spark/python/pyspark/worker.py", line 111, in main
process()
File "/databricks/spark/python/pyspark/worker.py", line 106, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/databricks/spark/python/pyspark/serializers.py", line 263, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "/databricks/spark/python/pyspark/rdd.py", line 1295, in takeUpToNumLeft
yield next(iterator)
File "<ipython-input-46-4a4c467a0b3d>", line 13, in <lambda>
IndexError: invalid index to scalar variable.
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1283)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1271)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1270)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1270)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1496)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1458)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1447)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1827)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1840)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1853)
at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:393)
at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
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:497)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/databricks/spark/python/pyspark/worker.py", line 111, in main
process()
File "/databricks/spark/python/pyspark/worker.py", line 106, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/databricks/spark/python/pyspark/serializers.py", line 263, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "/databricks/spark/python/pyspark/rdd.py", line 1295, in takeUpToNumLeft
yield next(iterator)
File "<ipython-input-46-4a4c467a0b3d>", line 13, in <lambda>
IndexError: invalid index to scalar variable.
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
问题的来源要简单得多。当您执行sc.parallelize(a)输入数组时，将转换为列表，此列表的元素成为RDD的元素。因此，当您执行map时，它将该函数分别应用于输入的每个元素。因此，它等效于这样的东西：
f = lambda ar: np.concatenate([ar[:1], ar[1:3] + 7, ar[3:]])
[f(x) for x in list(a)]
## IndexError
## ...
## IndexError: invalid index to scalar variable.
因此，您看到的错误。您想要的很可能是这样：
sc.parallelize([a]).map(f).take(1)
## [array([ 0., 14., 14., 0., 0.])]
还有两件事值得注意：
- Spark在使用高阶功能时不需要lambda表达。唯一的要求是，您通过的函数不应修改其参数，最佳地应该是纯粹的。在实践中，如果您知道内部发生了什么，那么您可以在Pyspark中进行修改的数据（通常不是Spark），但实际上这不是您应该做的事情。因此，要回答标题中的问题，请不要尝试。
- Lambda表达式没有任何魔术保障，可以防止副作用。您根本无法直接使用其体内的语句。