为什么自定义Python对象不能与ParDo FN一起使用？

Question

我目前刚开始使用Python中的Apache Beam和Dataflow Runner。我对创建一个发布到Google Cloud PubSub的批处理管道感兴趣，我对Beam Python API进行了修补，并找到了一个解决方案。然而，在我的探索中，我遇到了一些有趣的问题，让我好奇。

class PublishFn(beam.DoFn):
    def __init__(self, topic_path):
        self.topic_path = topic_path
        super(self.__class__, self).__init__()

    def process(self, element, **kwargs):
        from google.cloud import pubsub_v1
        publisher = pubsub_v1.PublisherClient()
        future = publisher.publish(self.topic_path, data=element.encode("utf-8"))
        return future.result()


def run_gcs_to_pubsub(argv):
    options = PipelineOptions(flags=argv)

    from datapipes.common.dataflow_utils import CsvFileSource
    from datapipes.protos import proto_schemas_pb2
    from google.protobuf.json_format import MessageToJson

    with beam.Pipeline(options=options) as p:
        normalized_data = (
                p |
                "Read CSV from GCS" >> beam.io.Read(CsvFileSource(
                    "gs://bucket/path/to/file.csv")) |
                "Normalize to Proto Schema" >> beam.Map(
                        lambda data: MessageToJson(
                            proto_schemas_pb2(data, proto_schemas_pb2.MySchema()),
                            indent=0,
                            preserving_proto_field_name=True)
                    )
        )
        (normalized_data |
            "Write to PubSub" >> beam.ParDo(
                    PublishFn(topic_path="projects/my-gcp-project/topics/mytopic"))
            )

在这里，我试图使发布服务器共享accrossdofn。我尝试了以下方法。

a.在DoFn中初始化publisher

class PublishFn(beam.DoFn):
    def __init__(self, topic_path):
        from google.cloud import pubsub_v1

        batch_settings = pubsub_v1.types.BatchSettings(
             max_bytes=1024,  # One kilobyte
             max_latency=1,  # One second
         )
        self.publisher = pubsub_v1.PublisherClient(batch_settings)
        self.topic_path = topic_path
        super(self.__class__, self).__init__()

    def process(self, element, **kwargs):
        future = self.publisher.publish(self.topic_path, data=element.encode("utf-8"))
        return future.result()

def run_gcs_to_pubsub(argv):
    ... ## same as 1

b.在DoFn外初始化Publisher并将其传递给DoFn

class PublishFn(beam.DoFn):
    def __init__(self, publisher, topic_path):
        self.publisher = publisher
        self.topic_path = topic_path
        super(self.__class__, self).__init__()

    def process(self, element, **kwargs):
        future = self.publisher.publish(self.topic_path, data=element.encode("utf-8"))
        return future.result()


def run_gcs_to_pubsub(argv):
    .... ## same as 1

    batch_settings = pubsub_v1.types.BatchSettings(
        max_bytes=1024,  # One kilobyte
        max_latency=1,  # One second
    )
    publisher = pubsub_v1.PublisherClient(batch_settings)

    with beam.Pipeline(options=options) as p:
        ... # same as 1
        (normalized_data | 
            "Write to PubSub" >> beam.ParDo(
                PublishFn(publisher=publisher, topic_path="projects/my-gcp-project/topics/mytopic"))
        )

  File "stringsource", line 2, in grpc._cython.cygrpc.Channel.__reduce_cython__

  File "stringsource", line 2, in grpc._cython.cygrpc.Channel.__reduce_cython__
TypeError: no default __reduce__ due to non-trivial __cinit__

谢谢，你的帮助将不胜感激。

好了，Ankur已经解释了为什么会出现我的问题，并讨论了如何在DOFN上进行序列化。基于这些知识，我现在明白了在DOFN中有两种使自定义对象共享/可重用的解决方案：

>

使自定义对象可序列化：这允许对象在DoFn对象创建期间初始化/可用（在__init__下）。这个对象必须是可序列化的，因为它将在管道提交过程中被序列化，在管道提交过程中创建DoFn对象（它调用__init__)。你如何实现这一点在下面我的回答中得到了回答。另外，我发现这个需求实际上与[1][2]中的Beam文档相关联。

[1]https://beam.apache.org/documentation/programming-guide/#core-beam-transforms

[2]https://beam.apache.org/documentation/programming-guide/#beam-transforms编写用户代码的要求

Answer 1

PublisherClient无法正确处理。这里有更多关于腌制的信息。在进程方法中初始化PublisherClient可避免对PublisherClient进行酸洗。

如果目的是重用publisherclient，我建议在process方法中初始化publisherclient，并使用以下代码将其存储在self中。

class PublishFn(beam.DoFn):
    def __init__(self, topic_path):
        self.topic_path = topic_path
        super(self.__class__, self).__init__()

    def process(self, element, **kwargs):
        if not hasattr(self, 'publish'):
            from google.cloud import pubsub_v1
            self.publisher = pubsub_v1.PublisherClient()
        future = self.publisher.publish(self.topic_path, data=element.encode("utf-8"))
        return future.result()