pabloem commented on a change in pull request #12580: URL: https://github.com/apache/beam/pull/12580#discussion_r474842645
########## File path: sdks/python/apache_beam/testing/benchmarks/nexmark/queries/query4.py ########## @@ -0,0 +1,81 @@ +# +# 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 4, 'Average Price for a Category'. Select the average of the wining bid +prices for all closed auctions in each category. In CQL syntax:: + + SELECT Istream(AVG(Q.final)) + FROM Category C, (SELECT Rstream(MAX(B.price) AS final, A.category) + FROM Auction A [ROWS UNBOUNDED], Bid B [ROWS UNBOUNDED] + WHERE A.id=B.auction + AND B.datetime < A.expires AND A.expires < CURRENT_TIME + GROUP BY A.id, A.category) Q + WHERE Q.category = C.id + GROUP BY C.id; + +For extra spiciness our implementation differs slightly from the above: + +* We select both the average winning price and the category. +* We don't bother joining with a static category table, since it's + contents are never used. +* We only consider bids which are above the auction's reserve price. +* We accept the highest-price, earliest valid bid as the winner. +* We calculate the averages oven a sliding window of size + window_size_sec and period window_period_sec. +""" + +from __future__ import absolute_import + +import apache_beam as beam +from apache_beam.testing.benchmarks.nexmark.queries import nexmark_query_util +from apache_beam.testing.benchmarks.nexmark.queries import winning_bids +from apache_beam.testing.benchmarks.nexmark.queries.nexmark_query_util import ResultNames +from apache_beam.transforms import window + + +def load(events, metadata=None): + # find winning bids for each closed auction + all_winning_bids = ( + events + | beam.Filter(nexmark_query_util.auction_or_bid) + | winning_bids.WinningBids()) + return ( + all_winning_bids + # key winning bids by auction category + | beam.Map(lambda auc_bid: (auc_bid.auction.category, auc_bid.bid.price)) + # re-window for sliding average + | beam.WindowInto( + window.SlidingWindows( + metadata.get('window_size_sec'), + metadata.get('window_period_sec'))) + # average for each category + | beam.CombinePerKey(beam.combiners.MeanCombineFn()) + # TODO(leiyiz): fanout with sliding window produces duplicated results, + # uncomment after it is fixed [BEAM-10617] + # .with_hot_key_fanout(metadata.get('fanout')) + # produce output + | beam.ParDo(ProjectToCategoryPriceFn())) + + +class ProjectToCategoryPriceFn(beam.DoFn): + def process(self, element, pane_info=beam.DoFn.PaneInfoParam): + yield { + ResultNames.CATEGORY: element[0], + ResultNames.PRICE: element[1], + ResultNames.IS_LAST: pane_info.is_last Review comment: (not a big deal. I'm just curious.) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
