您身边的订单 "和面向用户的实时地理空间数据分析

Introduction

简介

By its nature, Uber’s business is highly real-time and contingent upon geospatial data. PBs of data are continuously being collected from our drivers, riders, restaurants, and eaters. Real-time analytics over this geospatial data could provide powerful insights.

就其性质而言，Uber的业务是高度实时的，并取决于地理空间数据。从我们的司机、乘客、餐厅和用餐者那里不断收集到PB级的数据。对这些地理空间数据进行实时分析可以提供强大的洞察力。

In this blog, we will highlight the Orders near you feature from the Uber Eats app, illustrating one example of how Uber generates insights across our geospatial data.

在这篇博客中，我们将强调Uber Eats应用程序中的 "您附近的订单"功能，说明Uber如何通过我们的地理空间数据产生洞察力的一个例子。

Orders near you was a recent collaboration between the Data and Uber Eats teams at Uber. The project’s goal was to create an engaging and unique social experience for eaters. We hoped to inspire new food and restaurant discovery by showing what your neighbors are ordering right now. Since this feature is part of our home feed, we needed it to be fast, personalized, and scalable.

你附近的订单是Uber的数据和Uber Eats团队最近的合作项目。这个项目的目标是为吃货们创造一个吸引人的、独特的社交体验。我们希望通过显示你的邻居现在正在订购的食物，来激发新的食物和餐厅的发现。由于这个功能是我们主页的一部分，我们需要它是快速的、个性化的和可扩展的。

Requirements 

要求

There were two main requirements of the project. Firstly, the orders needed to be near real time. The number of minutes since the order was placed is displayed in the subtext of each carousel item. It was important that we were displaying relevant, recent options to eaters.

该项目有两个主要要求。首先，订单需要接近实时。下单后的分钟数显示在每个转盘项目的子文本中。重要的是，我们要向用餐者显示相关的、最近的选择。

Another important consideration in our design was deliverability. The contents of the carousel needed to be deliverable to the eater. Hence, we needed an efficient and scalable way to retrieve geographically close orders.

在我们的设计中，另一个重要的考虑是可交付性。旋转木马的内容需要能够传递给食客。因此，我们需要一种有效和可扩展的方式来检索地理上接近的订单。

The core building block of Orders near you is Apache Pinot, a distributed, scalable OnLine Analytical Processing (OLAP) system. It is designed for delivering low latency, user-facing real-time analytics on TeraByte-scale data. Apache Pinot supports near-real-time table i...