Apache Beam：ReadFromText 与 ReadAllFromText

Posted 2023-03-31

【中文标题】Apache Beam：ReadFromText 与 ReadAllFromText

【英文标题】：Apache Beam: ReadFromText versus ReadAllFromText

【发布时间】：2019-01-06 21:59:14

【问题描述】：

我正在运行一个 Apache Beam 管道，从 Google Cloud Storage 读取文本文件，对这些文件执行一些解析并将解析后的数据写入 Bigquery。

为了简短起见，这里忽略了解析和 google_cloud_options，我的代码如下：（apache-beam 2.5.0 带有 GCP 附加组件和 Dataflow 作为运行器）

p = Pipeline(options=options)

lines = p | 'read from file' >> 
beam.io.ReadFromText('some_gcs_bucket_path*')  |  \
    'parse xml to dict' >> beam.ParDo(
        beam.io.WriteToBigQuery(
            'my_table',
            write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND,
            create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED)
    p.run()

这运行良好并成功地将相关数据附加到我的 Bigquery 表中以获取少量输入文件。但是，当我将输入文件的数量增加到 +- 800k 时，出现错误：

“BoundedSource.split() 操作返回的BoundedSource 对象的总大小大于允许的限制。”

我找到了Troubleshooting apache beam pipeline import errors [BoundedSource objects is larger than the allowable limit]，它建议使用 ReadAllFromText 而不是 ReadFromText。 但是，当我换出时，出现以下错误：

Traceback (most recent call last):
  File "/Users/richardtbenade/Repos/de_020/main_isolated.py", line 240, in <module>
    xmltobigquery.run_dataflow()
  File "/Users/richardtbenade/Repos/de_020/main_isolated.py", line 220, in run_dataflow
    'parse xml to dict' >> beam.ParDo(XmlToDictFn(), job_spec=self.job_spec) | \
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/transforms/ptransform.py", line 831, in __ror__
    return self.transform.__ror__(pvalueish, self.label)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/transforms/ptransform.py", line 488, in __ror__
    result = p.apply(self, pvalueish, label)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 464, in apply
    return self.apply(transform, pvalueish)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 500, in apply
    pvalueish_result = self.runner.apply(transform, pvalueish)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 187, in apply
    return m(transform, input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 193, in apply_PTransform
    return transform.expand(input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/io/textio.py", line 470, in expand
    return pvalue | 'ReadAllFiles' >> self._read_all_files
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pvalue.py", line 109, in __or__
    return self.pipeline.apply(ptransform, self)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 454, in apply
    label or transform.label)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 464, in apply
    return self.apply(transform, pvalueish)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 500, in apply
    pvalueish_result = self.runner.apply(transform, pvalueish)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 187, in apply
    return m(transform, input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 193, in apply_PTransform
    return transform.expand(input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/io/filebasedsource.py", line 416, in expand
    | 'ReadRange' >> ParDo(_ReadRange(self._source_from_file)))
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pvalue.py", line 109, in __or__
    return self.pipeline.apply(ptransform, self)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 454, in apply
    label or transform.label)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 464, in apply
    return self.apply(transform, pvalueish)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 500, in apply
    pvalueish_result = self.runner.apply(transform, pvalueish)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 187, in apply
    return m(transform, input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 193, in apply_PTransform
    return transform.expand(input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/transforms/util.py", line 568, in expand
    | 'RemoveRandomKeys' >> Map(lambda t: t[1]))
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pvalue.py", line 109, in __or__
    return self.pipeline.apply(ptransform, self)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pipeline.py", line 500, in apply
    pvalueish_result = self.runner.apply(transform, pvalueish)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 187, in apply
    return m(transform, input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/runners/runner.py", line 193, in apply_PTransform
    return transform.expand(input)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/transforms/util.py", line 494, in expand
    windowing_saved = pcoll.windowing
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pvalue.py", line 130, in windowing
    self.producer.inputs)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/transforms/ptransform.py", line 443, in get_windowing
    return inputs[0].windowing
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/pvalue.py", line 130, in windowing
    self.producer.inputs)
  File "/Users/richardtbenade/virtualenvs/de_020/lib/python2.7/site-packages/apache_beam/transforms/ptransform.py", line 443, in get_windowing
    return inputs[0].windowing
AttributeError: 'PBegin' object has no attribute 'windowing'. 

有什么建议吗？

【问题讨论】：

请同时分享出现此错误的代码的最终版本。这样我们就有了实际的上下文。

另外我发现this 回答提到了同样的错误并且添加PColl 修复了它。

原来使用“ReadAllFromText”还应该添加一个“Create”方法（不像“ReadFromText”示例那样自动包含）。这解决了我的问题，谢谢。

@richardt-benade-rezco，酷！随意分享你的代码到底是什么样子的。

@Richardt Benade REZCO 您能否将解决方案和工作代码发布为社区利益？

【参考方案1】：

我遇到了同样的问题。正如 Richardt 提到的，beam.Create 必须被显式调用。另一个挑战是如何将此模式与模板参数一起使用，因为beam.Create 仅支持in the documentation 所述的内存数据。

Google Cloud 支持在这种情况下帮助了我，我想与您分享解决方案。诀窍是使用虚拟字符串创建管道，然后使用映射 lambda 在运行时读取输入：

class AggregateOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):
        parser.add_value_provider_argument(
            '--input',
            help='Path of the files to read from')
        parser.add_value_provider_argument(
            '--output',
            help='Output files to write results to')

def run():
    logging.info('Starting main function')

    pipeline_options = PipelineOptions()
    pipeline = beam.Pipeline(options=pipeline_options)
    options = pipeline_options.view_as(AggregateOptions)

    steps = (
            pipeline
            | 'Create' >> beam.Create(['Start'])  # workaround to kickstart the pipeline
            | 'Read Input Parameter' >> beam.Map(lambda x: options.input.get())  # get the real input param
            | 'Read Data' >> beam.io.ReadAllFromText()
            | # ... other steps

希望这个答案有帮助。

【讨论】：

你在哪里指定输入/输出文件？运行管道脚本时作为解析器参数？

@Luiscri 出于测试目的，您可以在本地运行代码时添加参数。但是，为了在 GCloud DataFlow 中运行，通过创建元数据文件并运行所描述的 python 命令来生成custom template。将带有后缀 _metadata 的元数据文件上传到保存模板的同一 Cloud Storage 位置。那么模板就是selectable in Dataflow。