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Handling workload skew in a consistent hashing based partitioning implementation

机译:处理基于散列的一致散列执行工作负载偏移

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Consistent hashing is used for distributing the data uniformly over a given set of servers in a topology. However, uniform distribution of the data over a given set of servers does not guarantee a uniform distribution of the workload associated with the data over the set of servers. When the workload is skewed over a small subset of data items the traditional re-partitioning approach used for handling overloads on a partition fails. This paper analyzes the effect of workload skew on a traditional consistent hashing implementation. A novel approach is then proposed that enables the creation of a uniform distribution even in the presence of skew. The proposed approach is then experimentally verified for correctness.
机译:一致的散列用于在拓扑中均匀地在给定的一组服务器上分发数据。然而,通过给定的一组服务器的数据的统一分布不保证与该组服务器上的数据相关联的工作量的均匀分布。当工作负载偏斜在一个小数据项上时,用于处理分区上的过载的传统重新分区方法失败。本文分析了工作量偏差对传统一致散列实施的影响。然后提出了一种新的方法,使得即使在歪斜的情况下也能够创建均匀的分布。然后通过实验验证所提出的方法以进行正确性。

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