从BigQuery缓慢更改查找缓存 - Dataflow Python Streaming SDK

问题描述 投票：1回答：1

我试图在DataFlow上使用Python SDK for Apache Beam来跟踪缓慢变化的查找缓存（https://cloud.google.com/blog/products/gcp/guide-to-common-cloud-dataflow-use-case-patterns-part-1）的设计模式。

我们的查找缓存参考表位于BigQuery中，我们能够读取并将其作为侧输入传递给ParDo操作，但无论我们如何设置触发器/窗口，它都不会刷新。

class FilterAlertDoFn(beam.DoFn):
  def process(self, element, alertlist):

    print len(alertlist)
    print alertlist

    …  # function logic

alert_input = (p | beam.io.Read(beam.io.BigQuerySource(query=ALERT_QUERY))
                        | ‘alert_side_input’ >> beam.WindowInto(
                            beam.window.GlobalWindows(),
                            trigger=trigger.RepeatedlyTrigger(trigger.AfterWatermark(
                                late=trigger.AfterCount(1)
                            )),
                            accumulation_mode=trigger.AccumulationMode.ACCUMULATING
                          )
                       | beam.Map(lambda elem: elem[‘SOMEKEY’])
)

...


main_input | ‘alerts’ >> beam.ParDo(FilterAlertDoFn(), beam.pvalue.AsList(alert_input))

基于此处的I / O页面（https://beam.apache.org/documentation/io/built-in/），它说Python SDK仅支持BigQuery Sink的流式传输，这是否意味着BQ读取是有限的源，因此无法在此方法中刷新？

尝试在源上设置非全局窗口会导致侧输入中的空PCollection。

更新：当试图实现Pablo的答案所建议的策略时，使用侧输入的ParDo操作不会运行。

有一个输入源可以转到两个输出端，然后使用侧输入端。 Non-SideInput仍然会到达它的目的地，而SideInput管道不会进入FilterAlertDoFn（）。

通过将侧输入替换为虚拟值，管道将进入该功能。它可能正在等待一个不存在的合适窗口吗？

使用与上面相同的FilterAlertDoFn（），我的side_input和call现在看起来像这样：

def refresh_side_input(_):
   query = 'select col from table'
   client = bigquery.Client(project='gcp-project')
   query_job = client.query(query)

   return query_job.result()


trigger_input = ( p | 'alert_ref_trigger' >> beam.io.ReadFromPubSub(
            subscription=known_args.trigger_subscription))


bigquery_side_input = beam.pvalue.AsSingleton((trigger_input
         | beam.WindowInto(beam.window.GlobalWindows(),
                           trigger=trigger.Repeatedly(trigger.AfterCount(1)),
                           accumulation_mode=trigger.AccumulationMode.DISCARDING)
         | beam.Map(refresh_side_input)
        ))

...

# Passing this as side input doesn't work
main_input | 'alerts' >> beam.ParDo(FilterAlertDoFn(), bigquery_side_input)

# Passing dummy variable as side input does work
main_input | 'alerts' >> beam.ParDo(FilterAlertDoFn(), [1])

我尝试了几个不同版本的refresh_side_input（），它们在检查函数内部的返回时报告了期望结果。

更新2：

我对Pablo的代码做了一些小修改，我得到了相同的行为 - DoFn永远不会执行。

在下面的例子中，每当我发布到some_other_topic时我都会看到'in_load_conversion_data'，但在发布到some_topic时永远不会看到'in_DoFn'

import apache_beam as beam
import apache_beam.transforms.window as window

from apache_beam.transforms import trigger
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.options.pipeline_options import StandardOptions


def load_my_conversion_data():
    return {'EURUSD': 1.1, 'USDMXN': 4.4}


def load_conversion_data(_):
    # I will suppose that these are currency conversions. E.g.
    # {'EURUSD': 1.1, 'USDMXN' 20,}
    print 'in_load_conversion_data'
    return load_my_conversion_data()


class ConvertTo(beam.DoFn):
    def __init__(self, target_currency):
        self.target_currency = target_currency

    def process(self, elm, rates):
        print 'in_DoFn'
        elm = elm.attributes
        if elm['currency'] == self.target_currency:
            yield elm
        elif ' % s % s' % (elm['currency'], self.target_currency) in rates:
            rate = rates[' % s % s' % (elm['currency'], self.target_currency)]
            result = {}.update(elm).update({'currency': self.target_currency,
            'value': elm['value']*rate})
             yield result
         else:
             return  # We drop that value


pipeline_options = PipelineOptions()
pipeline_options.view_as(StandardOptions).streaming = True
p = beam.Pipeline(options=pipeline_options)

some_topic = 'projects/some_project/topics/some_topic'
some_other_topic = 'projects/some_project/topics/some_other_topic'

with beam.Pipeline(options=pipeline_options) as p:

    table_pcv = beam.pvalue.AsSingleton((
      p
      | 'some_other_topic' >>  beam.io.ReadFromPubSub(topic=some_other_topic,  with_attributes=True)
      | 'some_other_window' >> beam.WindowInto(window.GlobalWindows(),
                        trigger=trigger.Repeatedly(trigger.AfterCount(1)),
                        accumulation_mode=trigger.AccumulationMode.DISCARDING)
      | beam.Map(load_conversion_data)))


    _ = (p | 'some_topic' >> beam.io.ReadFromPubSub(topic=some_topic)
         | 'some_window' >> beam.WindowInto(window.FixedWindows(1))
         | beam.ParDo(ConvertTo('USD'), rates=table_pcv))
python streaming apache-beam dataflow
1个回答
1
投票

正如您所指出的，Java SDK允许您使用更多流式实用程序，如计时器和状态。这些实用程序有助于实现这些管道。

Python SDK缺少一些这些实用程序，特别是计时器。出于这个原因，我们需要使用hack，其中可以通过在PubSub中将消息插入我们的some_other_topic来触发侧输入的重新加载。

这也意味着您必须手动执行对BigQuery的查找。您可以使用apache_beam.io.gcp.bigquery_tools.BigQueryWrapper类直接在BigQuery中执行查找。

以下是刷新某些货币转换数据的管道示例。我还没有对它进行过测试，但我90％肯定它只需要很少的调整即可。如果这有帮助，请告诉我。

pipeline_options = PipelineOptions()
p = beam.Pipeline(options=pipeline_options)

def load_conversion_data(_):
  # I will suppose that these are currency conversions. E.g. 
  # {‘EURUSD’: 1.1, ‘USDMXN’ 20, …}
  return external_service.load_my_conversion_data()

table_pcv = beam.pvalue.AsSingleton((
  p
  | beam.io.gcp.ReadFromPubSub(topic=some_other_topic)
  | WindowInto(window.GlobalWindow(),
               trigger=trigger.Repeatedly(trigger.AfterCount(1),
               accumulation_mode=trigger.AccumulationMode.DISCARDING)
  | beam.Map(load_conversion_data)))


class ConvertTo(beam.DoFn):
  def __init__(self, target_currency):
    self.target_currenct = target_currency

  def process(self, elm, rates):
    if elm[‘currency’] == self.target_currency:
      yield elm
    elif ‘%s%s’ % (elm[‘currency’], self.target_currency) in rates:
      rate = rates[‘%s%s’ % (elm[‘currency’], self.target_currency)]
      result = {}.update(elm).update({‘currency’: self.target_currency,
                                      ‘value’: elm[‘value’]*rate})
      yield result
    else:
      return  # We drop that value


_ = (p 
     | beam.io.gcp.ReadFromPubSub(topic=some_topic)
     | beam.WindowInto(window.FixedWindows(1))
     | beam.ParDo(ConvertTo(‘USD’), rates=table_pcv))
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