数据流错误:“客户端具有非平凡的本地状态和不可选择的状态" [英] Dataflow Error: 'Clients have non-trivial state that is local and unpickleable'

问题描述

我有一个可以在本地执行而没有任何错误的管道.我曾经在本地运行的管道中遇到此错误

I have a pipeline that I can execute locally without any errors. I used to get this error in my locally run pipeline

    'Clients have non-trivial state that is local and unpickleable.'
     PicklingError: Pickling client objects is explicitly not supported.

我相信我通过降级到 apache-beam=2.3.0 解决了这个问题然后在本地它会完美运行.

I believe I fixed this by downgrading to apache-beam=2.3.0 Then locally it would run perfectly.

现在我正在使用 DataflowRunner 并且在 requirements.txt 文件中我有以下依赖项

Now I am using DataflowRunner and in the requirements.txt file I have the following dependencies

    apache-beam==2.3.0
    google-cloud-bigquery==1.1.0
    google-cloud-core==0.28.1
    google-cloud-datastore==1.6.0
    google-cloud-storage==1.10.0
    protobuf==3.5.2.post1
    pytz==2013.7

但我又犯了这个可怕的错误

but I get this dreaded error again

    'Clients have non-trivial state that is local and unpickleable.'
     PicklingError: Pickling client objects is explicitly not supported.

为什么它给了我 DataflowRunner 的错误而不是 DirectRunner 的错误?他们不应该使用相同的依赖项/环境吗?任何帮助，将不胜感激.

How come it's giving me the error with DataflowRunner but not DirectRunner? shouldn't they be using the same dependencies/environment? Any help would be appreciated.

我已经读到这是解决它的方法，但是当我尝试它时，我仍然遇到相同的错误

I had read that this is the way to solve it but when I try it I still get the same error

    class MyDoFn(beam.DoFn):

        def start_bundle(self, process_context):
            self._dsclient = datastore.Client()

        def process(self, context, *args, **kwargs):
        # do stuff with self._dsclient

来自 https://github.com/GoogleCloudPlatform/google-cloud-python/issues/3191

我之前在本地修复此问题的参考帖子:

My previous reference post where I fixed this locally:

使用 start_bundle()apache-beam 工作不起作用.Unpickleable storage.Client()

提前致谢！

推荐答案

在 start_bundle 方法中初始化 unpickleable 客户端是一种正确的方法，Beam IO 经常遵循该方法，请参阅 data.这是一个使用 DoFn 中的 GCS python 客户端执行简单操作的管道.我在 Apache Beam 2.16.0 上运行它没有问题.如果您仍然可以重现您的问题，请提供更多详细信息.

Initializing unpickleable clients in start_bundle method is a correct approach, and Beam IOs often follow that, see datastoreio.py as an example. Here is a pipeline that does a simple operation with a GCS python client in a DoFn. I ran it on Apache Beam 2.16.0 without issues. If you can still reproduce your issue, please provide additional details.

gcs_client.py 文件:

gcs_client.py file:

import argparse
import logging
import time

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from google.cloud import storage

class MyDoFn(beam.DoFn):
  def start_bundle(self):
    self.storage_client = storage.Client()

  def process(self, element):
    bucket = self.storage_client.get_bucket("existing-gcs-bucket")
    blob = bucket.blob(str(int(time.time())))
    blob.upload_from_string("payload")
    return element

logging.getLogger().setLevel(logging.INFO)
_, options = argparse.ArgumentParser().parse_known_args()

pipeline_options = PipelineOptions(options)
p = beam.Pipeline(options=pipeline_options)
_ = p | beam.Create([None]) | beam.ParDo(MyDoFn())

p.run().wait_until_finish()

requirements.txt 文件:

requirements.txt file:

google-cloud-storage==1.23.0

命令行:

python -m gcs_client \
    --project=insert_your_project \
    --runner=DataflowRunner \
    --temp_location gs://existing-gcs-bucket/temp/ \
    --requirements_file=requirements.txt \
    --save_main_session

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