Python Apache Beam:日期值超出范围

Python Apache Beam: date value out of range

应用 this or this 示例来构建我的程序,每次尝试插入到 Big Query 时,我都会遇到此错误:

OverflowError: 日期值超出范围 [while 运行 'Format']

我的 Beam 管道是这样的:

Bigquery = (transformation
            | 'Format' >> beam.ParDo(FormatBigQueryoFn())
            | 'Write to BigQuery' >> beam.io.Write(beam.io.BigQuerySink(
            'XXXX',
            schema=TABLE_SCHEMA,
            create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
            write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND
        )))

在classFormatBigQueryoFn中应该是window数据时间

的逻辑

例1的代码:

def timestamp2str(t, fmt='%Y-%m-%d %H:%M:%S.000'):
  """Converts a unix timestamp into a formatted string."""
    return datetime.fromtimestamp(t).strftime(fmt)

    class TeamScoresDict(beam.DoFn):
  """Formats the data into a dictionary of BigQuery columns with their values
  Receives a (team, score) pair, extracts the window start timestamp, and
  formats everything together into a dictionary. The dictionary is in the format
  {'bigquery_column': value}
  """

def process(self, team_score, window=beam.DoFn.WindowParam):
    team, score = team_score
    start = timestamp2str(int(window.start))
    yield {
        'team': team,
        'total_score': score,
        'window_start': start,
        'processing_time': timestamp2str(int(time.time()))
}

例2的代码:

class FormatDoFn(beam.DoFn):
  def process(self, element, window=beam.DoFn.WindowParam):
    ts_format = '%Y-%m-%d %H:%M:%S.%f UTC'
    window_start = window.start.to_utc_datetime().strftime(ts_format)
    window_end = window.end.to_utc_datetime().strftime(ts_format)
    return [{'word': element[0],
             'count': element[1],
             'window_start':window_start,
'window_end':window_end}]

我的管道可能出了什么问题?

编辑:

例如,如果我打印 window.start,我会得到:

Timestamp(-9223372036860)

问题是我之前从文件中读取数据以使用 Google Pub/Sub.

对其进行测试

当我从文件中读取数据时,元素没有时间戳。

元素中必须有时间戳。

Pub/Sub 自动附加此时间戳。

来自documentation

最简单的 windowing 形式是使用固定时间 windows:给定一个可能不断更新的带时间戳的 PCollection,每个 window 可能捕获(对于示例)时间戳落在五分钟间隔内的所有元素。