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NOTICE: This file is deprecated and has been replaced by an Excel file downloadable on Github here.
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Log TypeDescriptionDelivery Method(s)Expected Delivery TimingDelivered ViaS3 Bucket RequirementsDelivery Format OptionsLog Headers Google DocsLog Headers GitHubMore information
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WinsThe winning auctions, impressions, clicks, and events (video plays, etc)Batch - HourlyBatched logs are created via two different processes: the processing of the bid (which can take up to 4 hours), and the joining of the bid with clicks and events (which can take up to three hours). As a result, at most we expect a 7.5 hour window from bid_time to log delivery (including 30 min for file writing). Events after 3 hours are not included in logs.S3Single bucket across regionsGzipped CSVGoogle Docauction_log_headers.csvBatch files are joined, which means bid, click and activity data matched against impression data will be included.
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Batch - DailyBetween 6-7am ET (6am during daylight savings time, 7am during non-DST periods)S3Single bucket across regionsGzipped CSV
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Realtime - UnjoinedRealtimeHTTP or Kinesis StreamN/AProtobuf (or JSON for HTTP)GitHub ProtoDoes not include the RequestLogMessage (Auction Message) mentioned in GitHub.

New information (clicks, activities) are sent as separate messages and are never "joined" back an impression.
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Realtime - JoinedRealtimeHTTP or Kinesis Stream

(Kinesis Streams must be in the US-EAST-1 region)
N/AProtobuf or JSONRequestLogMessage will be included 99%+ of the time and sent alongside the ImpressionLogMessage.

As more information is received about the win event (clicks, activities), the message will be-resent joined with the additional information. i.e., It is possible receive new messages for the same Auction ID multiple times.
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Attributed ConversionsThe conversions events recorded by Beeswax. Conversions will be attributed to an auction.Batch~8 Hour delayS3Single bucket across regionsGzipped CSVGoogle Docconversion_log_headers.csvThese logs are unjoined, and will need to be joined by the client with the win logs for custom attribution.
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ConversionsThe conversions events recorded by Beeswax. Conversions will not be attributed.BatchNear real-timeS3Buckets must be co-located in US-EAST-1, US-WEST-2, EU-WEST-1, or AP-NORTHEAST-1Gzipped CSVGoogle Docconversion_log_headers.csvThese logs are unjoined, and will need to be joined by the client with the win logs for custom attribution.
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Bid Response Feedback (aka "Loss Logs")Loss logs provided by a limited number of exchanges (currently only Google)Batch~1 Hour delayS3Single bucket across regionsGzipped CSVGoogle Docbid_response_feedback_log.csvGoogle documentation on feedback logs.
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BidsThe bids returned from Beeswax to the exchange, whether the auction was won or not.BatchNear real-timeS3Buckets must be co-located in US-EAST-1, US-WEST-2, EU-WEST-1, or AP-NORTHEAST-1Gzipped CSVGoogle Docbid_log_headers.csvBid logs are mainly used to calculate win rates on any dimension that comes through on the bid & win logs, which you can then use to improve your bidding strategies. Often, clients that use a custom bidding agent will log the bids themselves.
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AuctionsThe auction request from the exchange, normalized to OpenRTB fields.BatchNear real-timeS3Buckets must be co-located in US-EAST-1, US-WEST-2, EU-WEST-1, or AP-NORTHEAST-1Gzipped CSVGoogle Docauction_log_headers.csvAuction Logs can be used for inventory analytics. You can use the data to inform future bidding strategies. Importantly, per your agreement with Beeswax, these logs can never be used to create derivative segments for your use or resale. For example, you may not record the User IDs that come through on the bid requests and retarget those users in a different auction.
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SegmentsAll 1st party and 3rd party segments available on the auctionBatchNear real-timeS3Buckets must be co-located in US-EAST-1, US-WEST-2, EU-WEST-1, or AP-NORTHEAST-1Gzipped CSVGoogle Docsegment_log_headers.csvSegment logs have two primary use cases. The first is for Audience Forecasting, and analyzing how segmented users appear in auctions. The second is for understanding and attribution of user segments that were not explicitly targeted on a line item but which were nevertheless available.
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