Question: How can Redis be scaled horizontally?

Answer

"Horizontally scaling" means adding more nodes to a system to handle increased load, as opposed to "vertically scaling," which involves adding more power (CPU, RAM) to an existing node. In the context of Redis, horizontal scaling is achieved through two main strategies: sharding and replication.

Sharding is accomplished by partitioning data across several Redis instances. Each shard holds a portion of the dataset, thus distributing the load and increasing the capacity for read/write operations. Here's an example:

import redis

# Create a connection pool with multiple Redis servers (shards)
pool = redis.ConnectionPool(nodes=[
    {'host': '127.0.0.1', 'port': 6379},
    {'host': '127.0.0.2', 'port': 6379}
])

r = redis.Redis(connection_pool=pool)

# Now when you set a key, it will be stored on one of the shards.
r.set('foo', 'bar')

Replication involves creating duplicates of your Redis data. Redis uses a master-slave model for replication, where write operations are performed on the master and read from the slaves. This increases read performance and provides redundancy.

import redis

# Connect to the master node
master = redis.Redis(host='127.0.0.1', port=6379)

# Write data to the master node
master.set('foo', 'bar')

# Connect to the slave node
slave = redis.Redis(host='127.0.0.2', port=6379)

# Read data from the slave node
print(slave.get('foo'))

Remember that while these methods can increase throughput, they also add complexity to your system. You'll need to manage multiple instances of Redis and handle issues like consistency between nodes. Additionally, not all types of data are well-suited for sharding, so it's important to consider the nature of your workload before implementing these strategies.