Question: How do you implement horizontal scaling in Redis?


In the context of Redis, horizontal scaling can be achieved through a technique known as sharding, also referred to as partitioning. This approach allows data to be distributed across multiple Redis instances, thereby improving performance and capacity.

What is Sharding?

Sharding or partitioning refers to splitting your data into smaller parts and distributing those parts across different databases or servers. In the case of Redis, this means splitting your data across multiple Redis instances.

How does it work with Redis?

There are several strategies for data partitioning in Redis:

  1. Range partitioning: In this approach, ranges of data are stored on different nodes. For example, all usernames starting with A-M may go onto one node, N-Z into another. However, this can lead to uneven distribution if certain ranges have more data.

  2. Hash partitioning: With hash partitioning, a function hashes the key to determine which node the key-value pair should be stored on. This ensures an even distribution of data but requires knowing all keys ahead of time.

  3. Consistent hashing: Consistent hashing is similar to hash partitioning but makes handling new or dead nodes easier. When a new node is added, only the keys that hash to that node need to be moved rather than having to rehash all keys.

While Redis doesn't support automatic sharding, there exists a proxy called 'Redis Cluster' that provides automatic sharding and handles the key distribution among nodes.

Here's a simple example of how to set up a basic Redis cluster using redis-cli (Redis command line interface):

# Start three Redis instances as separate processes redis-server --port 7000 & redis-server --port 7001 & redis-server --port 7002 & # Create a Redis Cluster using the three nodes redis-cli --cluster create

When running these commands, you'll be asked if you agree with the configuration proposed by redis-cli. If everything looks correct, type 'yes' to proceed with creating the cluster.


With sharding, Redis can be horizontally scaled to distribute data across multiple nodes. However, careful planning and management is needed to ensure data distribution remains optimized as the data grows. Using Redis Cluster can simplify this task by automatically managing sharding and node operations.

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Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.