sdks/python/apache_beam/testing/benchmarks/nexmark/queries/query3.py - beam - Git at Google

 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
 # this work for additional information regarding copyright ownership.
 # The ASF licenses this file to You under the Apache License, Version 2.0
 # (the "License"); you may not use this file except in compliance with
 # the License.  You may obtain a copy of the License at
 #
 #    http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #

 """
 Query 3, 'Local Item Suggestion'. Who is selling in OR, ID or CA in category
 10, and for what auction ids? In CQL syntax::

   SELECT Istream(P.name, P.city, P.state, A.id)
   FROM Auction A [ROWS UNBOUNDED], Person P [ROWS UNBOUNDED]
   WHERE A.seller = P.id
     AND (P.state = `OR' OR P.state = `ID' OR P.state = `CA')
     AND A.category = 10;

 We'll implement this query to allow 'new auction' events to come before the
 'new person' events for the auction seller. Those auctions will be stored until
 the matching person is seen. Then all subsequent auctions for a person will use
 the stored person record.
 """

 import logging
 import typing

 import apache_beam as beam
 from apache_beam.testing.benchmarks.nexmark.models import nexmark_model
 from apache_beam.testing.benchmarks.nexmark.queries import nexmark_query_util
 from apache_beam.testing.benchmarks.nexmark.queries.nexmark_query_util import ResultNames
 from apache_beam.transforms import trigger
 from apache_beam.transforms import userstate
 from apache_beam.transforms import window
 from apache_beam.transforms.userstate import on_timer


 def load(events, metadata=None, pipeline_options=None):
   num_events_in_pane = 30
   windowed_events = (
       events
       | beam.WindowInto(
           window.GlobalWindows(),
           trigger=trigger.Repeatedly(trigger.AfterCount(num_events_in_pane)),
           accumulation_mode=trigger.AccumulationMode.DISCARDING))
   auction_by_seller_id = (
       windowed_events
       | nexmark_query_util.JustAuctions()
       | 'query3_filter_category' >> beam.Filter(lambda auc: auc.category == 10)
       | 'query3_key_by_seller' >> beam.ParDo(
           nexmark_query_util.AuctionBySellerFn()))
   person_by_id = (
       windowed_events
       | nexmark_query_util.JustPerson()
       | 'query3_filter_region' >>
       beam.Filter(lambda person: person.state in ['OR', 'ID', 'CA'])
       | 'query3_key_by_person_id' >> beam.ParDo(
           nexmark_query_util.PersonByIdFn()))
   return ({
       nexmark_query_util.AUCTION_TAG: auction_by_seller_id,
       nexmark_query_util.PERSON_TAG: person_by_id,
   }
           | beam.CoGroupByKey()
           | 'query3_join' >> beam.ParDo(
               JoinFn(metadata.get('max_auction_waiting_time')))
           | 'query3_output' >> beam.Map(
               lambda t: {
                   ResultNames.NAME: t[1].name,
                   ResultNames.CITY: t[1].city,
                   ResultNames.STATE: t[1].state,
                   ResultNames.AUCTION_ID: t[0].id
               }))


 class JoinFn(beam.DoFn):
   """
   Join auctions and person by person id and emit their product one pair at
   a time.

   We know a person may submit any number of auctions. Thus new person event
   must have the person record stored in persistent state in order to match
   future auctions by that person.

   However we know that each auction is associated with at most one person, so
   only need to store auction records in persistent state until we have seen the
   corresponding person record. And of course may have already seen that record.
   """

   AUCTIONS = 'auctions_state'
   PERSON = 'person_state'
   PERSON_EXPIRING = 'person_state_expiring'

   auction_spec = userstate.BagStateSpec(AUCTIONS, nexmark_model.Auction.CODER)
   person_spec = userstate.ReadModifyWriteStateSpec(
       PERSON, nexmark_model.Person.CODER)
   person_timer_spec = userstate.TimerSpec(
       PERSON_EXPIRING, userstate.TimeDomain.WATERMARK)

   def __init__(self, max_auction_wait_time):
     self.max_auction_wait_time = max_auction_wait_time

   def process(  # type: ignore
       self,
       element: typing.Tuple[
           str,
           typing.Dict[str,
                       typing.Union[typing.List[nexmark_model.Auction],
                                    typing.List[nexmark_model.Person]]]],
       auction_state=beam.DoFn.StateParam(auction_spec),
       person_state=beam.DoFn.StateParam(person_spec),
       person_timer=beam.DoFn.TimerParam(person_timer_spec)):
     # extract group with tags from element tuple
     _, group = element

     existing_person = person_state.read()
     if existing_person:
       # the person exists in person_state for this person id
       for auction in group[nexmark_query_util.AUCTION_TAG]:
         yield auction, existing_person
       return

     new_person = None
     for person in group[nexmark_query_util.PERSON_TAG]:
       if not new_person:
         new_person = person
       else:
         logging.error(
             'two new person wtih same key: %s and %s' % (person, new_person))
         continue
       # read all pending auctions for this person id, output and flush it
       pending_auctions = auction_state.read()
       if pending_auctions:
         for pending_auction in pending_auctions:
           yield pending_auction, new_person
         auction_state.clear()
       # output new auction for this person id
       for auction in group[nexmark_query_util.AUCTION_TAG]:
         yield auction, new_person
       # remember person for max_auction_wait_time seconds for future auctions
       person_state.write(new_person)
       person_timer.set(new_person.date_time + self.max_auction_wait_time)
     # we are done if we have seen a new person
     if new_person:
       return

     # remember auction until we see person
     for auction in group[nexmark_query_util.AUCTION_TAG]:
       auction_state.add(auction)

   @on_timer(person_timer_spec)
   def expiry(self, person_state=beam.DoFn.StateParam(person_spec)):
     person_state.clear()
	#
	# Licensed to the Apache Software Foundation (ASF) under one or more
	# contributor license agreements. See the NOTICE file distributed with
	# this work for additional information regarding copyright ownership.
	# The ASF licenses this file to You under the Apache License, Version 2.0
	# (the "License"); you may not use this file except in compliance with
	# the License. You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	#

	"""
	Query 3, 'Local Item Suggestion'. Who is selling in OR, ID or CA in category
	10, and for what auction ids? In CQL syntax::

	SELECT Istream(P.name, P.city, P.state, A.id)
	FROM Auction A [ROWS UNBOUNDED], Person P [ROWS UNBOUNDED]
	WHERE A.seller = P.id
	AND (P.state = `OR' OR P.state = `ID' OR P.state = `CA')
	AND A.category = 10;

	We'll implement this query to allow 'new auction' events to come before the
	'new person' events for the auction seller. Those auctions will be stored until
	the matching person is seen. Then all subsequent auctions for a person will use
	the stored person record.
	"""

	import logging
	import typing

	import apache_beam as beam
	from apache_beam.testing.benchmarks.nexmark.models import nexmark_model
	from apache_beam.testing.benchmarks.nexmark.queries import nexmark_query_util
	from apache_beam.testing.benchmarks.nexmark.queries.nexmark_query_util import ResultNames
	from apache_beam.transforms import trigger
	from apache_beam.transforms import userstate
	from apache_beam.transforms import window
	from apache_beam.transforms.userstate import on_timer


	def load(events, metadata=None, pipeline_options=None):
	num_events_in_pane = 30
	windowed_events = (
	events
	\| beam.WindowInto(
	window.GlobalWindows(),
	trigger=trigger.Repeatedly(trigger.AfterCount(num_events_in_pane)),
	accumulation_mode=trigger.AccumulationMode.DISCARDING))
	auction_by_seller_id = (
	windowed_events
	\| nexmark_query_util.JustAuctions()
	\| 'query3_filter_category' >> beam.Filter(lambda auc: auc.category == 10)
	\| 'query3_key_by_seller' >> beam.ParDo(
	nexmark_query_util.AuctionBySellerFn()))
	person_by_id = (
	windowed_events
	\| nexmark_query_util.JustPerson()
	\| 'query3_filter_region' >>
	beam.Filter(lambda person: person.state in ['OR', 'ID', 'CA'])
	\| 'query3_key_by_person_id' >> beam.ParDo(
	nexmark_query_util.PersonByIdFn()))
	return ({
	nexmark_query_util.AUCTION_TAG: auction_by_seller_id,
	nexmark_query_util.PERSON_TAG: person_by_id,
	}
	\| beam.CoGroupByKey()
	\| 'query3_join' >> beam.ParDo(
	JoinFn(metadata.get('max_auction_waiting_time')))
	\| 'query3_output' >> beam.Map(
	lambda t: {
	ResultNames.NAME: t[1].name,
	ResultNames.CITY: t[1].city,
	ResultNames.STATE: t[1].state,
	ResultNames.AUCTION_ID: t[0].id
	}))


	class JoinFn(beam.DoFn):
	"""
	Join auctions and person by person id and emit their product one pair at
	a time.

	We know a person may submit any number of auctions. Thus new person event
	must have the person record stored in persistent state in order to match
	future auctions by that person.

	However we know that each auction is associated with at most one person, so
	only need to store auction records in persistent state until we have seen the
	corresponding person record. And of course may have already seen that record.
	"""

	AUCTIONS = 'auctions_state'
	PERSON = 'person_state'
	PERSON_EXPIRING = 'person_state_expiring'

	auction_spec = userstate.BagStateSpec(AUCTIONS, nexmark_model.Auction.CODER)
	person_spec = userstate.ReadModifyWriteStateSpec(
	PERSON, nexmark_model.Person.CODER)
	person_timer_spec = userstate.TimerSpec(
	PERSON_EXPIRING, userstate.TimeDomain.WATERMARK)

	def __init__(self, max_auction_wait_time):
	self.max_auction_wait_time = max_auction_wait_time

	def process( # type: ignore
	self,
	element: typing.Tuple[
	str,
	typing.Dict[str,
	typing.Union[typing.List[nexmark_model.Auction],
	typing.List[nexmark_model.Person]]]],
	auction_state=beam.DoFn.StateParam(auction_spec),
	person_state=beam.DoFn.StateParam(person_spec),
	person_timer=beam.DoFn.TimerParam(person_timer_spec)):
	# extract group with tags from element tuple
	_, group = element

	existing_person = person_state.read()
	if existing_person:
	# the person exists in person_state for this person id
	for auction in group[nexmark_query_util.AUCTION_TAG]:
	yield auction, existing_person
	return

	new_person = None
	for person in group[nexmark_query_util.PERSON_TAG]:
	if not new_person:
	new_person = person
	else:
	logging.error(
	'two new person wtih same key: %s and %s' % (person, new_person))
	continue
	# read all pending auctions for this person id, output and flush it
	pending_auctions = auction_state.read()
	if pending_auctions:
	for pending_auction in pending_auctions:
	yield pending_auction, new_person
	auction_state.clear()
	# output new auction for this person id
	for auction in group[nexmark_query_util.AUCTION_TAG]:
	yield auction, new_person
	# remember person for max_auction_wait_time seconds for future auctions
	person_state.write(new_person)
	person_timer.set(new_person.date_time + self.max_auction_wait_time)
	# we are done if we have seen a new person
	if new_person:
	return

	# remember auction until we see person
	for auction in group[nexmark_query_util.AUCTION_TAG]:
	auction_state.add(auction)

	@on_timer(person_timer_spec)
	def expiry(self, person_state=beam.DoFn.StateParam(person_spec)):
	person_state.clear()