Two issues in the basic approach
Last updated
Last updated
The consistent hashing algorithm was introduced by Karger et al. at MIT [1]. The basic steps are:
Map servers and keys on to the ring using a uniformly distributed hash function.
To find out which server a key is mapped to, go clockwise from the key position until the first server on the ring is found.
Two problems are identified with this approach. First, it is impossible to keep the same size of partitions on the ring for all servers considering a server can be added or removed. A partition is the hash space between adjacent servers. It is possible that the size of the partitions on the ring assigned to each server is very small or fairly large. In the picture below, if s1 is removed, s2’s partition (highlighted with the bidirectional arrows) is twice as large as s0 and s3’s partition.
Second, it is possible to have a non-uniform key distribution on the ring. For instance, if servers are mapped to positions listed below, most of the keys are stored on server 2. However, server 1 and server 3 have no data.
A technique called virtual nodes or replicas is used to solve these problems.
A virtual node refers to the real node, and each server is represented by multiple virtual nodes on the ring. In the picture below, both server 0 and server 1 have 3 virtual nodes. The 3 is arbitrarily chosen; and in real-world systems, the number of virtual nodes is much larger. Instead of using s0, we have s0_0, s0_1, and s0_2 to represent server 0 on the ring. Similarly, s1_0, s1_1, and s1_2 represent server 1 on the ring. With virtual nodes, each server is responsible for multiple partitions. Partitions (edges) with label s0 are managed by server 0. On the other hand, partitions with label s1 are managed by server 1.
To find which server a key is stored on, we go clockwise from the key’s location and find the first virtual node encountered on the ring. Inthe picture below, to find out which server k0 is stored on, we go clockwise from k0’s location and find virtual node s1_1, which refers to server 1.
As the number of virtual nodes increases, the distribution of keys becomes more balanced. This is because the standard deviation gets smaller with more virtual nodes, leading to a balanced data distribution. Standard deviation measures how data are spread out. The outcome of an experiment carried out by online research shows that with one or two hundred virtual nodes, the standard deviation is between 5% (200 virtual nodes) and 10% (100 virtual nodes) of the mean. The standard deviation will be smaller when we increase the number of virtual nodes. However, more spaces are needed to store data about virtual nodes. This is a tradeoff, and we can tune the number of virtual nodes to fit our system requirements.
When a server is added or removed, a fraction of data needs to be redistributed. How can we find the affected range to redistribute the keys? In the picture below, server 4 is added to the ring. The affected range starts from s4 (newly added node) and moves anticlockwise around the ring until a server is found (s3). Thus, keys located between s3 and s4 need to be redistributed to s4.
When a server (s1) is removed as shown below, the affected range starts from s1 (removed node) and moves anticlockwise around the ring until a server is found (s0). Thus, keys located between s0 and s1 must be redistributed to s2.
