Consistent hashing is used in distributed systems and networking applications to spread data evenly and efficiently across a cluster of nodes. In this paper, we present MementoHash, a novel consistent hashing algorithm that eliminates known limitations of state-of-the-art algorithms while keeping optimal performance and minimal memory usage. We describe the algorithm in detail, provide a pseudo-code implementation, and formally establish its solid theoretical guarantees. To measure the efficacy of MementoHash, we compare its performance, in terms of memory usage and lookup time, to that of state-of-the-art algorithms, namely, AnchorHash, DxHash, and JumpHash. Unlike JumpHash, MementoHash can handle random failures. Moreover, MementoHash does not require fixing the overall capacity of the cluster (as AnchorHash and DxHash do), allowing it to scale indefinitely. The number of removed nodes affects the performance of all the considered algorithms. Therefore, we conduct experiments considering three different scenarios: stable (no removed nodes), one-shot removals (90% of the nodes removed at once), and incremental removals. We report experimental results that averaged a varying number of nodes from ten to one million. Results indicate that our algorithm shows optimal lookup performance and minimal memory usage in its best-case scenario. It behaves better than AnchorHash and DxHash in its average-case scenario and at least as well as those two algorithms in its worst-case scenario. However, the worst-case scenario for MementoHash occurs when more than 70% of the nodes fail, which describes a unlikely scenario. Therefore, MementoHash shows the best performance during the regular life cycle of a cluster.

翻译：一致性哈希被广泛应用于分布式系统和网络应用中，以高效均匀地将数据分布在节点集群上。本文提出MementoHash——一种新颖的一致性哈希算法，它在消除现有最先进算法已知局限性的同时，保持了最优性能和最小内存使用。我们详细描述了该算法，提供了伪代码实现，并正式确立了其坚实的理论保证。为了衡量MementoHash的有效性，我们从内存使用和查找时间两方面，将其性能与当前最先进的算法（即AnchorHash、DxHash和JumpHash）进行了比较。与JumpHash不同，MementoHash能处理随机故障。此外，MementoHash无需像AnchorHash和DxHash那样固定集群总容量，从而能够无限扩展。移除节点的数量会影响所有被考虑算法的性能。因此，我们设计了三种不同场景进行实验：稳定状态（无节点移除）、一次性移除（一次性移除90%节点）和增量式移除。我们报告了从十个到一百万个不同节点数量的平均实验结果。结果表明，我们的算法在最佳场景下展现了最优查找性能和最小内存使用。在平均场景下，其表现优于AnchorHash和DxHash，在最差场景下至少与这两种算法相当。然而，MementoHash的最差场景发生在超过70%节点失效时，这描述了一种不太可能发生的情况。因此，在集群的正常生命周期内，MementoHash展现了最佳性能。