动态图构建系统GUS实现毫秒级延迟，提升安全检测效率4倍。
Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality
- 基于ScaNN的动态图更新机制，支持实时数据流处理。
- 每请求延迟仅数十毫秒，较传统方法提速4倍以上。
- 适用于安卓安全等高时效场景，已在谷歌超10个部署。
近年来，大规模图学习与构建受到广泛关注，催生了众多系统、工具和算法。其中，Grale是专为离线环境设计的工具，已应用于谷歌50多个工业场景，因其能高效处理多类型特征数据而具备广泛适用性。然而，实际应用常需持续快速演化数据，且要求更新后的图能以低延迟可用，这使得Grale难以适用。尽管存在若干可处理动态更新的近似最近邻（ANN）系统，但大多局限于单一嵌入空间的相似性计算。本文提出Dynamic Grale Using ScaNN（Dynamic GUS），该系统兼具Grale的高质量建图能力与动态环境下的低延迟特性，单请求延迟仅为数十毫秒。系统已在谷歌超过10个场景中部署，其中一个应用在安卓安全与隐私领域，使有害应用的识别速度提升4倍，可在用户接触前完成拦截。
原文摘要 · Abstract (English)
Learning and constructing large-scale graphs has attracted attention in recent decades, resulting in a rich literature that introduced various systems, tools, and algorithms. Grale is one of such tools that is designed for offline environments and is deployed in more than 50 different industrial settings at Google. Grale is widely applicable because of its ability to efficiently learn and construct a graph on datasets with multiple types of features. However, it is often the case that applications require the underlying data to evolve continuously and rapidly and the updated graph needs to be available with low latency. Such setting make the use of Grale prohibitive. While there are Approximate Nearest Neighbor (ANN) systems that handle dynamic updates with low latency, they are mostly limited to similarities over a single embedding. In this work, we introduce a system that inherits the advantages and the quality of Grale, and maintains a graph construction in a dynamic setting with tens of milliseconds of latency per request. We call the system Dynamic Grale Using ScaNN (Dynamic GUS). Our system has a wide range of applications with over 10 deployments at Google. One of the applications is in Android Security and Privacy, where Dynamic Grale Using ScaNN enables capturing harmful applications 4 times faster, before they can reach users.
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