用近乎实时的功能为网络定价模式提供动力
Background
背景介绍
Drivers within the same area may have quite different earnings, depending on the trips they take. For example, consider two hypothetical drivers in downtown San Francisco. Two riders request two rides: one is within downtown San Francisco, and the other is to Oakland, as shown in the image above. The distances for the two trips are similar. If we just price the trip based on distance, they will make the same amount of money for the current trip, while the driver going to Oakland will be less likely to get more trips there. Drivers tend to reject these trips if they have other choices. To reduce the variance of earnings for colocated drivers and the cancellation rate for trips going to non-busy areas, we price these trips differently, based on the network effect.
同一地区的司机可能有相当不同的收入，这取决于他们的行程。例如，考虑在旧金山市中心的两个假设的司机。如上图所示，两名乘客要求乘坐两次：一次是在旧金山市中心，另一次是去奥克兰。这两个行程的距离是相似的。如果我们只是根据距离来定价，他们会在当前的行程中赚到同样的钱，而去奥克兰的司机则不可能在那里获得更多的行程。如果司机有其他选择，他们往往会拒绝这些行程。为了减少同地司机的收入差异和去往非繁忙地区的行程的取消率，我们根据网络效应，对这些行程进行不同的定价。
Both the rider and driver pricing flows are being changed to compute network adjustments in real time. Both these pricing systems receive adjustments based on a common network model, which returns the relative change in GB (Gross Bookings) of enabling a specific trip, compared with an average trip from that same origin.
乘车人和司机的定价流程都被改变，以实时计算网络调整。这两个定价系统都是根据一个共同的网络模型来接收调整，该模型会返回一个特定行程的GB（总预订量）的相对变化，与来自同一来源的平均行程相比。
The network model used requires some NRT (Near Real-Time) features. In this document, we will introduce some of the challenges we faced and how we solved them when building the real-time pipelines for computing and serving these features to online models.
所用的网络模型需要一些NRT（近实时）特征。在本文中，我们将介绍我们所面临的一些挑战，以及我们在建立实时管道以计算和提供这些功能给在线模型时如何解决这些问题。
Architecture
架构
The figure below shows the high-level architecture: Streaming Pipelines in Apache Flink are responsible for the feature computation and ingestion. For the rest of the article, we will discuss these pipelines in detail.
下图显示了高层架构。Apache Flink中的流管道负责特征计算和摄取。在...