Sharding social networks

Quang-huy Duong, Sharad Goel, J. Hofman, Sergei Vassilvitskii
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引用次数: 29

Abstract

Online social networking platforms regularly support hundreds of millions of users, who in aggregate generate substantially more data than can be stored on any single physical server. As such, user data are distributed, or sharded, across many machines. A key requirement in this setting is rapid retrieval not only of a given user's information, but also of all data associated with his or her social contacts, suggesting that one should consider the topology of the social network in selecting a sharding policy. In this paper we formalize the problem of efficiently sharding large social network databases, and evaluate several sharding strategies, both analytically and empirically. We find that random sharding---the de facto standard---results in provably poor performance even when frequently accessed nodes are replicated to many shards. By contrast, we demonstrate that one can substantially reduce querying costs by identifying and assigning tightly knit communities to shards. In particular, our theoretical analysis motivates a novel, scalable sharding algorithm that outperforms both random and location-based sharding schemes.
社交网络分片
在线社交网络平台通常支持数亿用户,这些用户产生的数据总量远远超过任何一台物理服务器所能存储的数据量。因此,用户数据在许多机器上分布或分片。在这种设置中,一个关键的要求是不仅要快速检索给定用户的信息,而且要快速检索与他或她的社会联系人相关的所有数据,这表明在选择分片策略时应该考虑社会网络的拓扑结构。在本文中,我们形式化了大型社交网络数据库的高效分片问题,并从分析和经验两方面评估了几种分片策略。我们发现,即使频繁访问的节点被复制到许多分片,随机分片(事实上的标准)也会导致性能不佳。相比之下,我们证明可以通过识别和分配紧密结合的社区到分片来大幅降低查询成本。特别是,我们的理论分析激发了一种新颖的、可扩展的分片算法,该算法优于随机和基于位置的分片方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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