google-cloud-dataflow - 谷歌数据流中的 Pardo 函数不产生任何输出

问题描述

我正在尝试在数据流中创建我的第一个管道，当我使用交互式光束运行器执行时，我有相同的代码运行，但是在数据流上我得到了各种各样的错误，这对我来说没有多大意义。

{"timestamp":1589992571906,"lastPageVisited":"https://kickassdataprojects.com/simple-and-complete-tutorial-on-simple-linear-regression/","pageUrl":"https://kickassdataprojects.com/","pageTitle":"Helping%20companies%20and%20developers%20create%20awesome%20data%20projects%20%7C%20Data%20Engineering/%20Data%20Science%20Blog","eventType":"Pageview","landingPage":0,"referrer":"direct","uiud":"31af5f22-4cc4-48e0-9478-49787dd5a19f","sessionId":322371}

这是我的代码

from __future__ import absolute_import
import apache_beam as beam
#from apache_beam.runners.interactive import interactive_runner
#import apache_beam.runners.interactive.interactive_beam as ib
import google.auth
from datetime import timedelta
import json
from datetime import datetime
from apache_beam import window
from apache_beam.transforms.trigger import AfterWatermark, AfterProcessingTime, AccumulationMode, AfterCount
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.options.pipeline_options import StandardOptions
import argparse
import logging
from time import mktime

def setTimestamp(elem):
     from apache_beam import window
     return window.TimestampedValue(elem, elem['timestamp'])

def createTuples(elem):
     return (elem["sessionId"], elem)

def checkOutput(elem):
     print(elem)
     return elem


class WriteToBigQuery(beam.PTransform):
  """Generate, format, and write BigQuery table row information."""
  def __init__(self, table_name, dataset, schema, project):
    """Initializes the transform.
    Args:
      table_name: Name of the BigQuery table to use.
      dataset: Name of the dataset to use.
      schema: Dictionary in the format {'column_name': 'bigquery_type'}
      project: Name of the Cloud project containing BigQuery table.
    """
    # TODO(BEAM-6158): Revert the workaround once we can pickle super() on py3.
    #super(WriteToBigQuery, self).__init__()
    beam.PTransform.__init__(self)
    self.table_name = table_name
    self.dataset = dataset
    self.schema = schema
    self.project = project

  def get_schema(self):
    """Build the output table schema."""
    return ', '.join('%s:%s' % (col, self.schema[col]) for col in self.schema)

  def expand(self, pcoll):
    return (
        pcoll
        | 'ConvertToRow' >>
        beam.Map(lambda elem: {col: elem[col]
                               for col in self.schema})
        | beam.io.WriteToBigQuery(
            self.table_name, self.dataset, self.project, self.get_schema()))


class ParseSessionEventFn(beam.DoFn):
  """Parses the raw game event info into a Python dictionary.
  Each event line has the following format:
    username,teamname,score,timestamp_in_ms,readable_time
  e.g.:
    user2_AsparagusPig,AsparagusPig,10,1445230923951,2015-11-02 09:09:28.224
  The human-readable time string is not used here.
  """
  def __init__(self):
    # TODO(BEAM-6158): Revert the workaround once we can pickle super() on py3.
    #super(ParseSessionEventFn, self).__init__()
    beam.DoFn.__init__(self)

  def process(self, elem):
          #timestamp = mktime(datetime.strptime(elem["timestamp"], "%Y-%m-%d %H:%M:%S").utctimetuple())
          elem['sessionId'] = int(elem['sessionId'])
          elem['landingPage'] = int(elem['landingPage'])
          yield elem



class AnalyzeSessions(beam.DoFn):
   def __init__(self):
    # TODO(BEAM-6158): Revert the workaround once we can pickle super() on py3.
    #super(AnalyzeSessions, self).__init__()
    from apache_beam import window    
    beam.DoFn.__init__(self)

  def process(self, elem, window=beam.DoFn.WindowParam):
          from apache_beam import window
          sessionId = elem[0]
          uiud = elem[1][0]["uiud"]
          count_of_events = 0
          pageUrl = []
          window_end = window.end.to_utc_datetime()
          window_start = window.start.to_utc_datetime()
          session_duration = window_end - window_start
          for rows in elem[1]:
             if rows["landingPage"] == 1:
                    referrer = rows["refererr"]
             pageUrl.append(rows["pageUrl"])       
          print({
             "pageUrl":pageUrl,
             "eventType":"pageview",
             "uiud":uiud,
             "sessionId":sessionId,
             "session_duration": session_duration,
              "window_start" : window_start
               })          
          yield {
             'pageUrl':pageUrl,
             'eventType':"pageview",
             'uiud':uiud,
             'sessionId':sessionId,
             'session_duration': session_duration,
             'window_start' : window_start,
               }

def run(argv=None, save_main_session=True):
    parser = argparse.ArgumentParser()
    parser.add_argument('--topic', type=str, help='Pub/Sub topic to read from')
    parser.add_argument(
          '--subscription', type=str, help='Pub/Sub subscription to read from')
    parser.add_argument(
          '--dataset',
          type=str,
          required=True,
          help='BigQuery Dataset to write tables to. '
          'Must already exist.')
    parser.add_argument(
          '--table_name',
          type=str,
          default='game_stats',
          help='The BigQuery table name. Should not already exist.')
    parser.add_argument(
          '--fixed_window_duration',
          type=int,
          default=60,
          help='Numeric value of fixed window duration for user '
          'analysis, in minutes')
    parser.add_argument(
          '--session_gap',
          type=int,
          default=5,
          help='Numeric value of gap between user sessions, '
          'in minutes')
    parser.add_argument(
          '--user_activity_window_duration',
          type=int,
          default=30,
          help='Numeric value of fixed window for finding mean of '
          'user session duration, in minutes')
    args, pipeline_args = parser.parse_known_args(argv)
    session_gap = args.session_gap * 60
    options = PipelineOptions(pipeline_args)
    # Set the pipeline mode to stream the data from Pub/Sub.
    options.view_as(StandardOptions).streaming = True

    options.view_as( StandardOptions).runner= 'DataflowRunner'
    options.view_as(SetupOptions).save_main_session = save_main_session
    p = beam.Pipeline(options=options)
    lines = (p
                | beam.io.ReadFromPubSub(
              subscription="projects/phrasal-bond-274216/subscriptions/rrrr")
             | 'decode' >> beam.Map(lambda x: x.decode('utf-8'))
             | beam.Map(lambda x: json.loads(x))
             | beam.ParDo(ParseSessionEventFn())
             )

    next = ( lines
                | 'AddEventTimestamps' >> beam.Map(setTimestamp)
                | 'Create Tuples' >> beam.Map(createTuples)
                | 'Window' >> beam.WindowInto(window.Sessions(15))
                | 'group by key' >> beam.GroupByKey() 
                | 'analyze sessions' >> beam.ParDo(AnalyzeSessions())
                | beam.Map(print)          
               | 'WriteTeamScoreSums' >> WriteToBigQuery(
              args.table_name, args.dataset,
           {

              "uiud":'STRING',
             "session_duration": 'INTEGER',
               "window_start" : 'TIMESTAMP'
                         },
             options.view_as(GoogleCloudOptions).project)
             )



    result = p.run()
#    result.wait_till_termination()

if __name__ == '__main__':
  logging.getLogger().setLevel(logging.INFO)
  run()

我面临的问题是AnalyzeSessions Pardo 函数不会产生任何输出或任何错误。我已经在交互式光束运行器中尝试过这段代码，它已经奏效了。

任何想法为什么管道不工作？

包括分析会话步骤在内的所有其他步骤都有输入

这是输出部分。

在此之后的步骤不起作用，我也没有在日志中看到任何内容，也不是打印语句在那里添加了一些东西。

编辑：如果有帮助的话，我的数据如何进入 WindoInto 和 GroupBy 关键步骤。

关于我应该尝试什么的任何想法。

标签： google-cloud-dataflowapache-beamdataflowapache-beam-io

google-cloud-dataflow - 谷歌数据流中的 Pardo 函数不产生任何输出

问题描述

解决方案

推荐阅读