Source code for apache_beam.testing.benchmarks.nexmark.queries.query4

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"""
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


[docs]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()))
[docs]class ProjectToCategoryPriceFn(beam.DoFn):
[docs] 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 }