Design consistent hashing
Last updated
Last updated
To achieve horizontal scaling, it is important to distribute requests/data efficiently and evenly across servers. Consistent hashing is a commonly used technique to achieve this goal. But first, let us take an in-depth look at the problem.
Quoted from Wikipedia: "Consistent hashing is a special kind of hashing such that when a hash table is re-sized and consistent hashing is used, only k/n keys need to be remapped on average, where k is the number of keys, and n is the number of slots. In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all keys to be remapped”.
If you have n cache servers, a common way to balance the load is to use the following hash method:
where N is the size of the server pool. Let us use an example to illustrate how it works.
To fetch the server where a key is stored, we perform the modular operation f(key) % 4. For instance, hash(key0) % 4 = 1 means a client must contact server 1 to fetch the cached data.
This approach works well when the size of the server pool is fixed, and the data distribution is even. However, problems arise when new servers are added, or existing servers are removed. For example, if server 1 goes offline, the size of the server pool becomes 3. Using the same hash function, we get the same hash value for a key. However applying modular operation gives us different server indexes because the number of servers is reduced by 1. We get the results as shown in the picture below by applying hash % 3:
Most keys are redistributed, not just the ones originally stored in the offline server (server 1). This means that when server 1 goes offline, most cache clients will connect to the wrong servers to fetch data. This causes a storm of cache misses. Consistent hashing is an effective technique to mitigate this problem.
