How Many Hash Slots Are There in Redis?

Redis is an open-source, in-memory data structure store that is used as a database, cache, and message broker. If you are using Redis as a database, you may wonder how many hash slots there are in Redis.

Hash slots are a way to distribute data across multiple Redis instances. Each Redis instance has 16384 hash slots, which means that the maximum number of Redis instances in a cluster is 16384.

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When you add a key to the Redis instance, it gets assigned to one of the hash slots based on the key’s hash value. The hash slot can then be used to determine which Redis instance the key should be stored on.

This partitioning scheme allows for horizontal scaling of your Redis cluster. By adding more Redis instances to your cluster, you can increase the total number of hash slots available and distribute your data more evenly across multiple nodes.

Each Redis instance in a cluster is responsible for a subset of the total hash slots available. When you query a key that exists in the cluster, the client library will automatically route your request to the correct Redis instance based on its assigned hash slot.

To view information about your cluster’s hash slot distribution, you can use the `CLUSTER SLOTS` command in Redis CLI or another client library.

In addition to horizontal scaling through multiple instances, Redis also supports vertical scaling through sharding. Sharding allows you to split up large datasets across multiple machines by partitioning them into smaller subsets called shards.

Each shard can then be stored on a separate machine or set of machines. When querying data from a sharded dataset, your application must be aware of which shard contains the desired data and direct its request accordingly.

In conclusion, each Redis instance has 16384 hash slots, and by increasing the number of instances in your cluster, you can increase the total number of available hash slots and achieve horizontal scalability. Additionally, sharding can be used for vertical scalability by splitting large datasets into smaller subsets.