我试图在数据流中创建我的第一条管道，当我使用交互式光束运行器执行时，我有相同的代码运行，但在数据流中，我得到了所有类型的错误，这对我来说没有什么意义。
我从pub sub得到的json格式如下。
{"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}
这是我的流水线的代码。
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
yield window.TimestampedValue(elem, elem['timestamp'])
def createTuples(elem):
yield (elem["sessionId"], 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):
"""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(AnalyzeSessions, self).__init__()
beam.DoFn.__init__(self)
def process(self, elem, window=beam.DoFn.WindowParam):
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"])
return {
"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)
| beam.Map(print)
| 'Window' >> beam.WindowInto(window.Sessions(15))
| 'group by key' >> beam.GroupByKey()
| 'analyze sessions' >> beam.ParDo(AnalyzeSessions())
| 'WriteTeamScoreSums' >> WriteToBigQuery(
args.table_name,
{
"uiud":'STRING',
"session_duration": 'INTEGER',
"window_start" : 'TIMESTAMP'
},
options.view_as(GoogleCloudOptions).project)
)
next1 = ( next
| 'Create Tuples' >> beam.Map(createTuples)
| beam.Map(print)
)
result = p.run()
# result.wait_till_termination()
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
run()
在下面的代码中，当我试图在管道中创建元组时，我得到了以下错误'生成器'对象不可下标。我得到的是使用yield创建生成器对象，即使是return也没有用，它只是让我的流水线发呆。
apache_beam.coders.coder_impl.SequenceCoderImpl.get_estimated_size_and_observables File "sessiontest1.py", line 23, in createTuples TypeError: 'generator' object is not subscriptable [while running 'generatedPtransform-148']
这是我用来执行管道的代码。
python3 sessiontest1.py --project phrasal-bond-xxxxx --region us-central1 --subscription projects/phrasal-bond-xxxxx/s
ubscriptions/xxxxxx --dataset sessions_beam --runner DataflowRunner --temp_location gs://webevents/sessions --service_account_email-xxxxxxxx-
[email protected]
任何关于这个问题的帮助将被感激。谢谢大家，又是第一次在数据流上工作，所以不知道我在这里错过了什么。
我之前得到的其他错误，现在已经解决了：-
a) 我从行名 beam.Map(lambda elem: window.TimestampedValue(elem, elem['timestamp']))中得到了widow没有定义的错误。
如果我去beam.window，那么它说beam没有定义，根据我的说法，beam应该由dataflow提供。
NameError: name 'window' is not defined [while running 'generatedPtransform-3820']
你只需要在函数本身导入模块。
获得一个 'generator' object is not subscriptable 的错误表明，当你试图进行 elem["sessionID"]，elem已经是一个生成器。前面的变换是setTimestamp，也是使用了 yield 因此输出一个生成器，作为元素传递给createTuples。
这里的解决方法是用以下方法实现setTimestamp和createTuples return 而不是 yield. 在下面的变换中返回你要接收的元素。
你应该在你的代码中设置save_main_session = True。( 试着在你的代码中取消这一行的comment).你应该在你的代码中设置save_main_session = True. 点击这里查看更多关于NameError的信息。https:/cloud.google.comdataflowdocsresourcesfaq）。