def load(events, metadata=None):
# find winning bids for each closed auction
return (events
# find winning bids
| beam.Filter(nexmark_query_util.auction_or_bid)
| winning_bids.WinningBids()
# (auction_bids -> (aution.seller, bid)
| beam.Map(lambda auc_bid: (auc_bid.auction.seller, auc_bid.bid))
# calculate and output mean as data arrives
| beam.WindowInto(
window.GlobalWindows(),
trigger=trigger.Repeatedly(trigger.AfterCount(1)),
accumulation_mode=trigger.AccumulationMode.ACCUMULATING,
allowed_lateness=0)
| beam.CombinePerKey(MovingMeanSellingPriceFn(10))
| beam.Map(lambda t: {
ResultNames.SELLER: t[0],
ResultNames.PRICE: t[1]
}))
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()))
def load(events, metadata=None):
return (events
| beam.Filter(nexmark_query_util.auction_or_bid)
| winning_bids.WinningBids())