如何在Apache Beam/Google Cloud DataFlow中通过多个ParDo转换处理本地文件上的操作 [英] How to handle operations on local files over multiple ParDo transforms in Apache Beam / Google Cloud DataFlow

问题描述

我正在为Google Cloud Dataflow开发ETL管道，其中有多个分支 ParDo 转换，每个转换都需要本地音频文件.然后将分支结果合并并导出为文本.

I am developing an ETL pipeline for Google Cloud Dataflow where I have several branching ParDo transforms which each require a local audio file. The branched results are then combined and exported as text.

这最初是一个Python脚本，它在单台计算机上运行，​​我正尝试使用GC Dataflow来适应VM Worker并行化.

This was initially a Python script that ran on a single machine that I am attempting to adapt for VM worker parallelisation using GC Dataflow.

提取过程从单个GCS存储桶位置下载文件，然后在转换完成后将其删除以使存储处于受控状态.这是由于预处理模块需要本地访问文件.通过自己重写一些预处理库，可以将其重新设计为处理字节流而不是文件—但是，在此方面的一些尝试并不顺利，我想先探讨一下如何处理并行化本地为了更好地了解框架，在Apache Beam/GC Dataflow中进行文件操作.

The extraction process downloads the files from a single GCS bucket location then deletes them after the transform is completed to keep storage under control. This is due to the pre-processing module which requires local access to the files. This could be re-engineered to handle a byte stream instead of a file by rewriting some of the pre-processing libraries myself - however, some attempts at this aren't going well and I'd like to explore first how to handle parallelised local file operations in Apache Beam / GC Dataflow in order to understand the framework better.

在这个粗略的实现中，每个分支都下载和删除文件，并进行了很多双重处理.在我的实现中，我有8个分支，因此每个文件被下载和删除8次.可以将GCS存储桶安装在每个工作人员上，而不是从远程下载文件吗?

In this rough implementation each branch downloads and deletes the files, with lots of double handling. In my implementation I have 8 branches, so each file is being downloaded and deleted 8 times. Could a GCS bucket instead be mounted on every worker rather than downloading files from the remote?

或者还有另一种方法可以确保为工作人员传递对文件的正确引用，以便:

Or is there another way to ensure workers are being passed the correct reference to a file so that:

• 单个 DownloadFilesDoFn()可以下载一批
• 然后将 PCollection 中的本地文件引用散布到所有分支
• 然后最后一个 CleanUpFilesDoFn()可以将其删除
• 如何并行化本地文件引用?

• a single DownloadFilesDoFn() can download a batch
• then fan out the local file references in PCollection to all the branches
• and then a final CleanUpFilesDoFn() can remove them
• How can you parallelise local file references?

如果无法避免本地文件操作，那么对于Apache Beam/GC数据流而言，最好的分支 ParDo 策略是什么?

What is the best branched ParDo strategy for Apache Beam / GC Dataflow if local file operations cannot be avoided?

为简单起见，我现有实现的一些示例代码带有两个分支.

Some example code of my existing implementation with two branches for simplicity.

# singleton decorator
def singleton(cls):
  instances = {}
  def getinstance():
      if cls not in instances:
          instances[cls] = cls()
      return instances[cls]
  return getinstance

@singleton
class Predict():
  def __init__(self, model):
    '''
    Process audio, reads in filename 
    Returns Prediction
    '''
    self.model = model

  def process(self, filename):
      #simplified pseudocode
      audio = preprocess.load(filename=filename)
      prediction = inference(self.model, audio)
      return prediction

class PredictDoFn(beam.DoFn):
  def __init__(self, model):
    self.localfile, self.model = "", model
    
  def process(self, element):
    # Construct Predict() object singleton per worker
    predict = Predict(self.model)

    subprocess.run(['gsutil','cp',element['GCSPath'],'./'], cwd=cwd, shell=False)
    self.localfile = cwd + "/" + element['GCSPath'].split('/')[-1]

    res = predict.process(self.localfile)
    return [{
        'Index': element['Index'], 
        'Title': element['Title'],
        'File' : element['GCSPath'],
        self.model + 'Prediction': res
        }]    
  def finish_bundle(self):
    subprocess.run(['rm',self.localfile], cwd=cwd, shell=False)


# DoFn to split csv into elements (GSC bucket could be read as a PCollection instead maybe)
class Split(beam.DoFn):
    def process(self, element):
        Index,Title,GCSPath = element.split(",")
        GCSPath = 'gs://mybucket/'+ GCSPath
        return [{
            'Index': int(Index),
            'Title': Title,
            'GCSPath': GCSPath
        }]

管道的简化版本:

with beam.Pipeline(argv=pipeline_args) as p:
    files = 
        ( 
        p | 'Read From CSV' >> beam.io.ReadFromText(known_args.input)
          | 'Parse CSV into Dict' >> beam.ParDo(Split())
        )
    # prediction 1 branch
    preds1 = 
        (
          files | 'Prediction 1' >> beam.ParDo(PredictDoFn(model1))
        )
    # prediction 2 branch
    preds2 = 
        (
          files | 'Prediction 2' >> beam.ParDo(PredictDoFn(model2))
        )
    
    # join branches
    joined = { preds1, preds2 }

    # output to file
    output = 
        ( 
      joined | 'WriteToText' >> beam.io.Write(beam.io.textio.WriteToText(known_args.output))
        )

推荐答案

为避免重复下载文件，可以将文件内容放入pCollection中.

In order to avoid downloading the files repeatedly, the contents of the files can be put into the pCollection.

class DownloadFilesDoFn(beam.DoFn):
  def __init__(self):
     import re
     self.gcs_path_regex = re.compile(r'gs:\/\/([^\/]+)\/(.*)')

  def start_bundle(self):
     import google.cloud.storage
     self.gcs = google.cloud.storage.Client()

  def process(self, element):
     file_match = self.gcs_path_regex.match(element['GCSPath'])
     bucket = self.gcs.get_bucket(file_match.group(1))
     blob = bucket.get_blob(file_match.group(2))
     element['file_contents'] = blob.download_as_bytes()
     yield element
     

然后PredictDoFn变为:

Then PredictDoFn becomes:

class PredictDoFn(beam.DoFn):
  def __init__(self, model):
    self.model = model

  def start_bundle(self):
    self.predict = Predict(self.model)
    
  def process(self, element):
    res = self.predict.process(element['file_contents'])
    return [{
        'Index': element['Index'], 
        'Title': element['Title'],
        'File' : element['GCSPath'],
        self.model + 'Prediction': res
        }]   

和管道:

with beam.Pipeline(argv=pipeline_args) as p:
    files = 
        ( 
        p | 'Read From CSV' >> beam.io.ReadFromText(known_args.input)
          | 'Parse CSV into Dict' >> beam.ParDo(Split())
          | 'Read files' >> beam.ParDo(DownloadFilesDoFn())
        )
    # prediction 1 branch
    preds1 = 
        (
          files | 'Prediction 1' >> beam.ParDo(PredictDoFn(model1))
        )
    # prediction 2 branch
    preds2 = 
        (
          files | 'Prediction 2' >> beam.ParDo(PredictDoFn(model2))
        )
    
    # join branches
    joined = { preds1, preds2 }

    # output to file
    output = 
        ( 
      joined | 'WriteToText' >> beam.io.Write(beam.io.textio.WriteToText(known_args.output))
        )

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