在Nextdoor搜索
In a thriving community, people are connected to their friends and local businesses. Nextdoor is the hyperlocal platform that mirrors these offline relationships. Every day, through active discussions on the platform, new relationships are formed and existing ones strengthened.
在一个繁荣的社区中，人们与他们的朋友和当地企业有联系。Nextdoor是反映这些线下关系的超本地平台。每天，通过在平台上的积极讨论，形成新的关系并加强现有关系。
For example, a Nextdoor user can create a post like “I really like @XYZ cafe. @John is a hard working business owner and we should all support him by buying a cup of delicious latte!” Here, the post is created by at-mentioning (via the @ symbol) nearby businesses and users. From this post, users in the neighborhood can contribute by at-mentioning others to be part of the comment threads. As a result, John’s cafe thrives and acts as a neighborhood hub where new friends are made.
例如，一个Nextdoor用户可以创建一个帖子，如 "我真的很喜欢@XYZ咖啡馆。@John是一个努力工作的企业主，我们都应该通过购买一杯美味的拿铁咖啡来支持他！"在这里，该帖子是通过at-mentioning（通过@符号）附近的企业和用户创建的。从这个帖子中，附近的用户可以通过at-mentioning他人来贡献自己的力量，成为评论线的一部分。因此，约翰的咖啡馆茁壮成长，并成为结交新朋友的社区中心。
Every month, millions of these mentions are created in various discussions (including lost dogs!). In addition to posts and comments, a user can type into the search box and see, among other things, nearby users and businesses. All these features are powered by the same autocomplete service — a set of APIs to ingest data and handle typeahead search of different entity types (businesses, users, keywords etc) on Nextdoor.
每个月，在各种讨论中都会产生数百万个这样的提法（包括丢失的狗！）。除了帖子和评论，用户可以在搜索框中输入，除其他外，还可以看到附近的用户和企业。所有这些功能都是由相同的自动完成服务提供的--一套API用于摄取数据并处理Nextdoor上不同实体类型（企业、用户、关键词等）的提前搜索。
This post focuses on how we built a proximity-based typeahead service to power typeahead use cases at Nextdoor.
这篇文章主要介绍了我们如何建立一个基于近距离的类型化服务，以支持Nextdoor的类型化用例。
Proximity-Based Typeahead Search as a Service
基于近距离的预先搜索服务
Any good search experience can be boiled down to two core components:
任何好的搜索体验都可以归结为两个核心部分。
- Relevance: Given a search query, whether the user sees relevant results or not. As a hyperlocal social network, relevancy is ...