  Consistent hashing (CH) has been pivotal as a data router and load balancer
in diverse fields, including distributed databases, cloud infrastructure, and
peer-to-peer networks. However, existing CH algorithms often fall short in
simultaneously meeting various critical requirements, such as load balance,
minimal disruption, statelessness, high lookup rate, small memory footprint,
and low update overhead. To address these limitations, we introduce DxHash, a
scalable consistent hashing algorithm based on pseudo-random sequences. To
adjust workloads on heterogeneous nodes and enhance flexibility, we propose
weighted DxHash. Through comprehensive evaluations, DxHash demonstrates
substantial improvements across all six requirements compared to
state-of-the-art alternatives. Notably, even when confronted with a 50% failure
ratio in a cluster of one million nodes, DxHash maintains remarkable processing
capabilities, handling up to 13.3 million queries per second.
