Dataflow/apache beam - 传入模式时如何访问当前文件名？

Question

在堆栈溢出之前我已经看过这个问题的答案(/sf/ask/2098853501/),但不是因为apache beam为python添加了可拆分的dofn功能.在将文件模式传递给gcs存储桶时,如何访问当前正在处理的文件的文件名？

我想将文件名传递给我的转换函数:

with beam.Pipeline(options=pipeline_options) as p:                              
    lines = p | ReadFromText('gs://url to file')                                        


    data = (                                                                    
        lines                                                                   
        | 'Jsonify' >> beam.Map(jsonify)                                        
        | 'Unnest' >> beam.FlatMap(unnest)                                      
        | 'Write to BQ' >> beam.io.Write(beam.io.BigQuerySink(                  
            'project_id:dataset_id.table_name', schema=schema,                     
            create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,    
            write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND)       
        )                                                   

最后,我想要做的是在转换json的每一行时将文件名传递给我的转换函数(请参阅此内容,然后使用文件名在不同的BQ表中进行查找以获取值).我想一旦我设法知道如何获取文件名,我将能够找出侧输入部分,以便在bq表中进行查找并获得唯一值.

Answer 1

我试图用之前引用的案例实现一个解决方案。在那里，以及在其他方法中，例如这种方法，它们还获取文件名列表，但将所有文件加载到单个元素中，该元素可能无法很好地扩展大文件。因此，我考虑将文件名添加到每条记录中。

作为输入，我使用了两个 csv 文件：

$ gsutil cat gs://$BUCKET/countries1.csv
id,country
1,sweden
2,spain

gsutil cat gs://$BUCKET/countries2.csv
id,country
3,italy
4,france

使用GCSFileSystem.match我们可以访问metadata_list检索包含文件路径和大小（以字节为单位）的 FileMetadata。在我的例子中：

[FileMetadata(gs://BUCKET_NAME/countries1.csv, 29),
 FileMetadata(gs://BUCKET_NAME/countries2.csv, 29)]

代码是：

result = [m.metadata_list for m in gcs.match(['gs://{}/countries*'.format(BUCKET)])]

我们会将每个匹配的文件读入不同的 PCollection。由于我们不知道先验的文件数量，我们需要以编程方式为每个 PCollection 创建一个名称列表，(p0, p1, ..., pN-1)并确保我们为每个步骤都有唯一的标签('Read file 0', 'Read file 1', etc.)：

variables = ['p{}'.format(i) for i in range(len(result))]
read_labels = ['Read file {}'.format(i) for i in range(len(result))]
add_filename_labels = ['Add filename {}'.format(i) for i in range(len(result))]

然后我们继续将每个不同的文件读入其对应的 PCollection 中ReadFromText，然后我们调用AddFilenamesFnParDo 将每个记录与文件名相关联。

for i in range(len(result)):   
  globals()[variables[i]] = p | read_labels[i] >> ReadFromText(result[i].path) | add_filename_labels[i] >> beam.ParDo(AddFilenamesFn(), result[i].path)

在哪里AddFilenamesFn：

class AddFilenamesFn(beam.DoFn):
    """ParDo to output a dict with filename and row"""
    def process(self, element, file_path):
        file_name = file_path.split("/")[-1]
        yield {'filename':file_name, 'row':element}

我的第一种方法是直接使用 Map 函数，这会产生更简单的代码。但是，result[i].path在循环结束时被解析，并且每条记录都被错误地映射到列表的最后一个文件：

globals()[variables[i]] = p | read_labels[i] >> ReadFromText(result[i].path) | add_filename_labels[i] >> beam.Map(lambda elem: (result[i].path, elem))

最后，我们将所有 PCollections 合并为一个：

merged = [globals()[variables[i]] for i in range(len(result))] | 'Flatten PCollections' >> beam.Flatten()

我们通过记录元素来检查结果：

INFO:root:{'filename': u'countries2.csv', 'row': u'id,country'}
INFO:root:{'filename': u'countries2.csv', 'row': u'3,italy'}
INFO:root:{'filename': u'countries2.csv', 'row': u'4,france'}
INFO:root:{'filename': u'countries1.csv', 'row': u'id,country'}
INFO:root:{'filename': u'countries1.csv', 'row': u'1,sweden'}
INFO:root:{'filename': u'countries1.csv', 'row': u'2,spain'}

我在 Python SDK 2.8.0DirectRunner和DataflowRunnerPython SDK 2.8.0 上对此进行了测试。

我希望这解决了这里的主要问题，您现在可以继续将 BigQuery 集成到您的完整用例中。您可能需要为此使用 Python 客户端库，我写了一个类似的 Java示例。

完整代码：

import argparse, logging
from operator import add

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io import ReadFromText
from apache_beam.io.filesystem import FileMetadata
from apache_beam.io.filesystem import FileSystem
from apache_beam.io.gcp.gcsfilesystem import GCSFileSystem

class GCSFileReader:
  """Helper class to read gcs files"""
  def __init__(self, gcs):
      self.gcs = gcs

class AddFilenamesFn(beam.DoFn):
    """ParDo to output a dict with filename and row"""
    def process(self, element, file_path):
        file_name = file_path.split("/")[-1]
        # yield (file_name, element) # use this to return a tuple instead
        yield {'filename':file_name, 'row':element}

# just logging output to visualize results
def write_res(element):
  logging.info(element)
  return element

def run(argv=None):
  parser = argparse.ArgumentParser()
  known_args, pipeline_args = parser.parse_known_args(argv)

  p = beam.Pipeline(options=PipelineOptions(pipeline_args))
  gcs = GCSFileSystem(PipelineOptions(pipeline_args))
  gcs_reader = GCSFileReader(gcs)

  # in my case I am looking for files that start with 'countries'
  BUCKET='BUCKET_NAME'
  result = [m.metadata_list for m in gcs.match(['gs://{}/countries*'.format(BUCKET)])]
  result = reduce(add, result)

  # create each input PCollection name and unique step labels
  variables = ['p{}'.format(i) for i in range(len(result))]
  read_labels = ['Read file {}'.format(i) for i in range(len(result))]
  add_filename_labels = ['Add filename {}'.format(i) for i in range(len(result))]

  # load each input file into a separate PCollection and add filename to each row
  for i in range(len(result)):
    # globals()[variables[i]] = p | read_labels[i] >> ReadFromText(result[i].path) | add_filename_labels[i] >> beam.Map(lambda elem: (result[i].path, elem))
    globals()[variables[i]] = p | read_labels[i] >> ReadFromText(result[i].path) | add_filename_labels[i] >> beam.ParDo(AddFilenamesFn(), result[i].path)

  # flatten all PCollections into a single one
  merged = [globals()[variables[i]] for i in range(len(result))] | 'Flatten PCollections' >> beam.Flatten() | 'Write results' >> beam.Map(write_res)

  p.run()

if __name__ == '__main__':
  run()