如何从PCollection Apache Beam Python创建N个元素的组 [英] How to create groups of N elements from a PCollection Apache Beam Python

问题描述

我正在尝试完成以下操作: 在Beam/Dataflow中批处理PCollection

I am trying to accomplish something like this: Batch PCollection in Beam/Dataflow

以上链接的答案是Java，而我使用的语言是Python.因此，我需要一些帮助来获得类似的构造.

The answer in the above link is in Java, whereas the language I'm working with is Python. Thus, I require some help getting a similar construction.

具体地说，我有这个:

 p = beam.Pipeline (options = pipeline_options)
 lines = p | 'File reading' >> ReadFromText (known_args.input)

在此之后，由于我的用例需要一组行，因此我需要创建另一个PCollection，但要用N行的"c1"行作为List.我无法逐行操作.

After this, I need to create another PCollection but with a List of N rows of "lines" since my use case requires a group of rows. I can not operate line by line.

我尝试了ParDo函数，该函数使用变量将计数与计数器N行相关联，并在groupBy之后使用Map.但是这些每1000条记录会重置一次，所以这不是我想要的解决方案.我阅读了链接中的示例，但是我不知道如何在Python中执行类似的操作.

I tried a ParDo Function using variables for count associating with the counter N rows and after groupBy using Map. But these are reset every 1000 records, so it's not the solution I am looking for. I read the example in the link but I do not know how to do something like that in Python.

我尝试将计数器保存在数据存储区中，但是，使用数据存储区读取和写入数据流之间的速度差异非常明显.

I tried saving the counters in Datastore, however, the speed difference between Dataflow reading and writing with Datastore is quite significant.

执行此操作的正确方法是什么?我不知道该怎么办. 问候.

What is the correct way to do this? I don't know how else to approach it. Regards.

推荐答案

假设分组顺序并不重要，您可以将DoFn分组.

Assume the grouping order is not important, you can just group inside a DoFn.

class Group(beam.DoFn):
  def __init__(self, n):
     self._n = n
     self._buffer = []

  def process(self, element):
     self._buffer.append(element)
     if len(self._buffer) == self._n:
        yield list(self._buffer)
        self._buffer = []

  def finish_bundle(self):
     if len(self._buffer) != 0:
        yield list(self._buffer)
        self._buffer = []

lines = p | 'File reading' >> ReadFromText(known_args.input)
          | 'Group' >> beam.ParDo(Group(known_args.N)
          ...

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