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如何修改Spark數據框中的numpy數組？

2016-06-22 34 views
0

問題：
在lambda或數據幀轉換內部不允許分配，這意味着我們通常必須爲使用Spark在Dataframe中完成的每個數據操作創建新結構。如何修改Spark數據框中的numpy數組？

示例（Python）：
我以前解決這個問題，通過簡單地創建就地修改後的數據，而不在列表和字典的任務，但是numpy的算法被證明是相當麻煩得到。我已經將所有這些數據放入列表中進行了一些模擬，並且由於數組非常大，所以它會顯着減慢。 （出這些陣列是3K左右的元素各家之長，包含在每分貝排30點的陣列中，列出了幾百萬行）

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.]

例（pyspark）：
所以，現在你可以看到輸出我期待着，這裏是一個Spark版本。

t = sc.parallelize(a) 
t2 = t.map(lambda ar: np.concatenate([ar[:1], ar[1:3] + 7, ar[3:]])) 
t2.take(1)

錯誤：
我想這會工作，但我得到這個。我認爲這個問題是這個「ar [1：3] + 7」，但是沒有運行後，它仍然給出了同樣的錯誤。也許有一些我錯過了。

Maybe the np.concatenate() does some sort of assignment that causes this. If that is the case what would be a way around it?

--------------------------------------------------------------------------- 
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

來源

2016-06-22 alfredox

回答

0

問題的根源是比簡單得多。當您執行sc.parallelize(a)輸入數組轉換爲列表並且此列表的元素成爲RDD的元素時。因此，當您執行map時，它會將該函數分別應用於輸入的每個元素。所以它相當於這樣的東西：

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，你看到的錯誤。你需要的是最有可能的是：

sc.parallelize([a]).map(f).take(1) 
## [array([ 0., 14., 14., 0., 0.])]

還有值得一提的兩兩件事：

  • 星火與高階函數工作時並不需要lambda表達式。唯一的要求是你傳遞的函數不應該修改它的參數，最好是純粹的。在實踐中，如果你知道內部發生了什麼，但在實踐中你不應該這樣做，你可以在PySpark中修改數據（一般不是Spark）。所以要回答標題中的問題，請不要嘗試。
  • Lambda表達式沒有任何防禦副作用的魔法保護。你不能直接在它的內部使用語句。

來源

2016-06-22 21:51:11 zero323

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