创建DAG数据流（apache Beam）

Question

我在Dataflow（Apache beam）上创建了一个管道，以便在Google BigQuery上读写数据，但是我在创建DAG时遇到了问题，就像我在Airflow上做的那样。

这是我的代码中的一个示例：

# define pipeline
p = beam.Pipeline(argv=pipeline_args)
# execute query_1
query_result_gps = ( p | 'ReadFromBQ GPS_data' >> ... )
# write result from query_1 on BigQuery
output_gps = ( query_result_gps | 'WriteToBQ GPS_data' >> ... )
# execute query_2
query_result_temperature = (output_gps 
                                    | 'ReadFromBQ temperature_data' >> ... )
# write result from query_2
ouput_temperature = ( query_result_temperature | 'WriteToBQ temperature_data' >> ... )

我希望按顺序执行这些任务，而Dataflow是并行执行的

我如何让它们按顺序执行？

Answer 1

我假设您是从以下大查询中读取的：

count = (p | 'read' >> beam.io.Read(beam.io.BigQuerySource(known_args.input_table))

我深入研究了apache_beam源代码，看起来它们的源转换忽略了输入pcollection，这就是为什么它们是并行设置的。

请参见def展开（self,pbegin）的最后一行::

class Read(ptransform.PTransform):
  """A transform that reads a PCollection."""

  def __init__(self, source):
    """Initializes a Read transform.

    Args:
      source: Data source to read from.
    """
    super(Read, self).__init__()
    self.source = source

  def expand(self, pbegin):
    from apache_beam.options.pipeline_options import DebugOptions
    from apache_beam.transforms import util

    assert isinstance(pbegin, pvalue.PBegin)
    self.pipeline = pbegin.pipeline

    debug_options = self.pipeline._options.view_as(DebugOptions)
    if debug_options.experiments and 'beam_fn_api' in debug_options.experiments:
      source = self.source

      def split_source(unused_impulse):
        total_size = source.estimate_size()
        if total_size:
          # 1MB = 1 shard, 1GB = 32 shards, 1TB = 1000 shards, 1PB = 32k shards
          chunk_size = max(1 << 20, 1000 * int(math.sqrt(total_size)))
        else:
          chunk_size = 64 << 20  # 64mb
        return source.split(chunk_size)

      return (
          pbegin
          | core.Impulse()
          | 'Split' >> core.FlatMap(split_source)
          | util.Reshuffle()
          | 'ReadSplits' >> core.FlatMap(lambda split: split.source.read(
              split.source.get_range_tracker(
                  split.start_position, split.stop_position))))
    else:
      # Treat Read itself as a primitive.
      return pvalue.PCollection(self.pipeline)

# ... other methods

为什么你需要它们依次发生？你好像在给一张桌子写信，然后从另一张桌子读东西？

如果您真的需要顺序地执行此操作，也许像这样将其子类化read就可以了

class SequentialRead(Read):
  def expand(self, pbegin):
      return pbegin