Rendezvous hashing

"rendezvous"的意思是约会

wikipedia Rendezvous hashing

Rendezvous or highest random weight (HRW) hashing[1][2] is an algorithm that allows clients to achieve distributed agreement on a set of k options out of a possible set of n options. A typical application is when clients need to agree on which sites (or proxies) objects are assigned to. As shown below, Rendezvous hashing is more general than Consistent hashing, which becomes a special case (for k = 1) of Rendezvous hashing.

NOTE:

集合或最高随机权重（HRW）散列[1] [2]是一种算法，允许客户端在一组可能的n个选项中实现对一组k个选项的分布式协议。 典型的应用是当客户需要就将objects分配了哪些站点（或代理）达成一致。 如下所示，Rendezvous哈希比一致哈希更通用，它Rendezvous哈希的一种特殊情况（对于k = 1）。

History

Rendezvous hashing was invented by David Thaler and Chinya Ravishankar at the University of Michigan in 1996[1] . Consistent hashing appeared a year later in the literature. One of the first applications of rendezvous hashing was to enable multicast clients on the Internet (in contexts such as the MBONE) to identify multicast rendezvous points in a distributed fashion.[3][4] It was used in 1998 by Microsoft's Cache Array Routing Protocol(CARP) for distributed cache coordination and routing.[5][6] Some Protocol Independent Multicast routing protocols use rendezvous hashing to pick a rendezvous point.[1]

NOTE:

Rendezvous哈希是1996年由密歇根大学的David Thaler和Chinya Ravishankar发明的。 一年后，文献中出现了一致的散列。 集合散列的第一个应用之一是使Internet上的多播客户端能够以分布式方式识别多播集合点。 它于1998年被微软的缓存阵列路由协议用于分布式缓存协调和路由。 某些协议独立多播路由协议使用集合散列来选择集合点。

Given its simplicity and generality, rendezvous hashing has been applied in a wide variety of applications, including mobile caching,[7] router design,[8] secure key establishment,[9] and sharding and distributed databases.[10]

NOTE: 上述这些领域是非常重要的

The HRW algorithm for rendezvous hashing

NOTE:

未读懂

TODO

https://github.com/drainingsun/ared

https://en.wikipedia.org/wiki/Distributed_hash_table

https://medium.com/@dgryski/consistent-hashing-algorithmic-tradeoffs-ef6b8e2fcae8

https://link.springer.com/chapter/10.1007/978-3-030-29859-3_3

https://blog.kevingomez.fr/2019/04/11/clusters-and-data-sharding-introducing-rendezvous-hashing/