Clouds part 4

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Clouds Part 4

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  1. Elements Part 4: Cloudy!
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Publishers might do this to sell their remaining inventory or to reduce their management overhead. In RTB, the publisher's inventory is auctioned to buyers who bid for ad impressions. To auction, publishers use SSPs that work with ad exchanges and DSPs to automatically return the ad that won an auction. Read the bidder section in the overview for more detail. The following diagram depicts a possible architecture of a DSP system without integrated ad delivery. For details about administrative frontends such as campaign and bid managers, see user frontend in part 1.

Notable difference between this architecture and the one depicted for the ad server described in part 3 include the following:. The platform considerations section in part 1 covers most of what you need. However, bidders must fulfill a few requirements, as noted in this section. To minimize network latency, locate your bidders near major ad exchanges. Close proximity minimizes the round-trip time incurred during communication between bidders and ad exchanges. Ad exchanges often require a response within about milliseconds after the bid request is sent.

Timing out too often might affect the DSP's ability to bid:. Your frontend will be available behind a single global IP address, which allows for a simpler DNS setup. When you use Kubernetes, a load balancer distributes traffic to the VM instances, and kube-proxy programs iptables to distribute traffic to endpoints.

This method can affect network performance. For example, traffic could arrive at a node that doesn'tt contain the proper pod, which would add an extra hop. Consider, for example, this frequency-capping use case:. Because RTB bidders are dealing with billions of requests per day, if you don't establish affinity between the bid requests for a unique user and the DSP frontend worker processing these requests, you must centralize the incoming events by region to aggregate the counters per unique user.

The architecture shown previously in the overview depicts this centralized approach: collectors ingest events, Cloud Dataflow processes them, and then values such as counters finally get incremented through a master Redis node. An affinitized approach allows the same frontend worker to process all ad requests that pertain to the same unique user.

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The frontend can keep a local cache of its counters; this removes the dependency for this use case on the centralized processing. The result is less overhead and decreased latency. Affinity between a requestor and the processor is usually established in the load balancer by parsing the incoming request's headers. However, ad exchanges typically strip the headers from this user information, and we must therefore process the request payload.

Because this is not supported by Cloud Load Balancing, if you are considering setting up your own load balancer, you might want to consider software such as HAProxy.

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Ultimately, you must make a decision—you can choose a managed service that offers a global infrastructure or you can choose a custom build that can be adapted to specific use cases. Depending on your relationship with and proximity to the ad exchanges and SSPs, consider the following connection options:. Bid requests are received by a frontend that has the same scaling requirements as outlined in the frontends section. Bid requests are commonly serialized in JSON or protobuf format and often include the IP address, ad unit ID, ad size, user details, user agent, auction type, and maximum auction time.

But regardless of the standard or serialization used, your frontend code needs to parse the payload and extract the required fields and properties. Your frontend will either discard the bid request by responding with a no-bid response such as an HTTP status code , or it will proceed to the next step. Machine learning often facilitates these bidding tasks. For example, ML can be used to predict the optimal price and whether the bid could be won before making a bid decision. This article focuses on infrastructure decisions, however. For details about training and serving ML models, see the following:.

Each partnership implements a different process, even if all of these processes are quite similar. In real-time bidding, the cookie-matching process often happens well before the DSP receives a bid request. More often, the DSP initiates the sync that does not necessarily occur on a publisher property, but on an advertiser's property. How cookie matching works in real-time bidding is explained on Google Ad Manager website and quite extensively on the web. This article assumes that the DSP hosts the user-matching datastore.

This data store must:. NoSQL databases are well suited for such workload, because they can scale horizontally to support heavy loads and can retrieve single rows extremely quickly. If you want a fully managed service that can retrieve values using a specific key in single-digit milliseconds, consider Cloud Bigtable. It provides high availability, and its QPS and throughput scale linearly with the number of nodes. At a conceptual level, data is stored in Cloud Bigtable using a format similar to the following:. With another lookup, the system can extract user segments from the unique user profile store , order the segments by price, and filter for the most appropriate segment.

The following example shows the result of a lookup.

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The example is simplified for clarity. Depending on your ordering and filtering logic, you might want to promote some discrete fields such as the data provider name into the key. An efficient key design will both help you to scale as well as reduce the querying time. For advice on how to approach the key design, see Choosing a row key in the documentation for designing a Cloud Bigtable schema.

Although this article uses Cloud Bigtable as an example service for reading segments and for performing user ID matching, in-memory stores such as Redis or Aerospike might offer better performance, though at the cost of additional operational overhead. For more details, see heavy-read storing patterns in part 1. To get access to additional external user data, DSPs often work with data management platforms DMPs with whom they implement user matching techniques that are similar to those used with the SSP.

Third-party data can be loaded recurrently from an external location to Cloud Storage and then loaded to BigQuery. Or the data can be loaded in real time to your exposed endpoint, which fronts a messaging system. How to ingest and store events is covered in event management. In RTB, the following additional events are also collected:. Your bidder needs to make decisions for every bid request. This is different from ad serving, where prices are calculated for a batch of ads. For this reason, joining data as soon as possible can improve bid decisions.

These patterns can cause a few problems when you join data, because your system might have to wait for something that might never happen like a click after an impression. Your system might also have to wait a day or a week for an event to happen—for example, for a conversion after a click, or for a conversion not linked to a click called view-through conversion. Finally, the system might have won a bid that did not result in a rendered and billable impression.

Where you perform the join—in the data pipeline or after the data has been stored—is determined by whether you want to join data immediately or whether the join process can wait.

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If you decide to join the data immediately, you might implement a process similar to the following:. You can also improve the workflow using the timely and stateful processing functionality offered in Apache Beam. You could then get ordered events and not use Cloud Bigtable. If you decide to use offline joins because you can tolerate a delay, the process looks similar to the following:.

For infrastructure options about serving data, see heavy-read storing patterns. Although the concepts for exporting data are similar to ad serving, bidders need to return the bid response within a predefined deadline. For this reason, some bidders might prefer to use stores than can handle sub-millisecond reads and writes, even if these stores require more operational overhead.

Bidders commonly use Redis with local slaves or regional Aerospike.

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  • Learn more about infrastructure options to export data from real-time aggregations or offline analytics. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see our Site Policies. Last updated June 13, Send feedback. This article is part of the following series: Building advertising platforms overview Infrastructure options for serving advertising workloads part 1 Infrastructure options for data pipelines in advertising part 2 Infrastructure options for ad servers part 3 Infrastructure options for RTB bidders part 4, this article See the overview for ad-tech terminology used throughout this series.

    Overview Apart from direct sales to advertisers, publishers also have the option to expose their inventory to programmatic buyers, who buy impressions through a real-time bidding RTB system.

    Infrastructure options for RTB bidders (part 4)

    Notable difference between this architecture and the one depicted for the ad server described in part 3 include the following: Machine learning prediction happens offline. The predictions are copied into an in-memory store using locality-sensitive hashing to create keys based on unique feature combinations. See other options for quick machine learning serving in the ad serving article in part 3. Data to be read by the bidders is stored in an in-memory, clustered, NoSQL database for fast reads.

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    Platform considerations The platform considerations section in part 1 covers most of what you need.

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