python zipWithIndex()在非常大的数据集上抛出IllegalArgumentException

vyswwuz2  于 2023-08-02  发布在  Python
关注(0)|答案(1)|浏览(76)

我正在Azure Databricks中运行Python Notebook。当我尝试用rdd.zipWithIndex（）添加行号时，出现IllegalArgumentException错误。该文件是2.72 GB和1238951行（我认为，文本编辑器的行为与这么大的文件不稳定）。它运行了4个多小时才失败。我想知道我们是否达到了某种大小限制，因为Exception是IllegalArgumentException。我想知道如何防止这种异常，和/或任何方法，使它更快。我在想我可能得把它分成小文件。任何帮助都是感激的。
代码片段

runKey = "cca2e0f0-bec0-408a-a5cb-341d26e8b7e0" # this is new id for every file
filePath = "/mnt/my_file_path/my_file.txt"
rdd = sc.textFile(filePath)
rdd = rdd.zipWithIndex().map(lambda line: "{}{}{}{}{}".format(str(runKey), delimiter, str(line[1]+1), delimiter, line[0]))

字符串
错误输出

File "<command-3893172145851236>", line 26, in OpenFileRDD
    rdd = rdd.zipWithIndex().map(lambda line: "{}{}{}{}{}".format(str(runKey), delimiter, str(line[1]+1), delimiter, line[0]))
  File "/databricks/spark/python/pyspark/rdd.py", line 2524, in zipWithIndex
    nums = self.mapPartitions(lambda it: [sum(1 for i in it)]).collect()
  File "/databricks/spark/python/pyspark/rdd.py", line 967, in collect
    sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
  File "/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1304, in __call__
    return_value = get_return_value(
  File "/databricks/spark/python/pyspark/sql/utils.py", line 117, in deco
    return f(*a, **kw)
  File "/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 326, in get_return_value
    raise Py4JJavaError(
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 4 in stage 1234573.0 failed 4 times, most recent failure: Lost task 4.3 in stage 1234573.0 (TID 46064376) (10.0.2.5 executor 5455): java.lang.IllegalArgumentException
    at java.nio.CharBuffer.allocate(CharBuffer.java:334)
    at java.nio.charset.CharsetDecoder.decode(CharsetDecoder.java:810)
    at org.apache.hadoop.io.Text.decode(Text.java:412)
    at org.apache.hadoop.io.Text.decode(Text.java:389)
    at org.apache.hadoop.io.Text.toString(Text.java:280)
    at org.apache.spark.SparkContext.$anonfun$textFile$2(SparkContext.scala:1065)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
    at scala.collection.Iterator.foreach(Iterator.scala:941)
    at scala.collection.Iterator.foreach$(Iterator.scala:941)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:442)
    at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:797)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread.$anonfun$run$1(PythonRunner.scala:521)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2241)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:313)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2978)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2925)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2919)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2919)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1357)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1357)
    at scala.Option.foreach(Option.scala:407)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1357)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:3186)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:3127)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:3115)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:1123)
    at org.apache.spark.SparkContext.runJobInternal(SparkContext.scala:2500)
    at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1071)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:165)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:125)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:454)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:1069)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:260)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    at sun.reflect.GeneratedMethodAccessor6189.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
    at py4j.Gateway.invoke(Gateway.java:295)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:251)
    at java.lang.Thread.run(Thread.java:750)
Caused by: java.lang.IllegalArgumentException
    at java.nio.CharBuffer.allocate(CharBuffer.java:334)
    at java.nio.charset.CharsetDecoder.decode(CharsetDecoder.java:810)
    at org.apache.hadoop.io.Text.decode(Text.java:412)
    at org.apache.hadoop.io.Text.decode(Text.java:389)
    at org.apache.hadoop.io.Text.toString(Text.java:280)
    at org.apache.spark.SparkContext.$anonfun$textFile$2(SparkContext.scala:1065)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
    at scala.collection.Iterator.foreach(Iterator.scala:941)
    at scala.collection.Iterator.foreach$(Iterator.scala:941)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:442)
    at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:797)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread.$anonfun$run$1(PythonRunner.scala:521)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2241)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:313)

型

python

1条答案

按热度按时间
vof42yt1

vof42yt11#

我试过在单行中使用超过965个字符，效果很好。一行有15295个字符。
x1c 0d1x的数据
因此，当字符串对象的组成超过最大限制时，会发生此错误。即2^31 - 1。单行中的字符数可以超过此最大限制。好好检查一下。
所以，我创建了一个文件有单行长度2^31-1，并尝试你的代码花了1.2小时，并给出内核重启错误.
我会建议分裂线有更多的长度和添加您的信息。

with  open("/dbfs/output.txt", 'r') as file_data:
    for line in file_data:
        print(f"before split {len(line)}")
        data = line.split("0")
        print(f"After split {len(data)}")

字符串
在这里，我使用0作为分割条件，它根据您的需求有意义地分割行。


的
这只是文本文件中的一行数据。在你的文件中检查字符串有多大，或者你可以根据下面的记录大小来分割行。

charset = "UTF-8"
recordSize = 500
df = sc.binaryRecords("dbfs:/output.txt", recordSize).map(lambda record: (str(record, charset),)).toDF(schema=["value"])

型


的

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