Question: How can I scale Memcached in AWS ElastiCache?

Answer

AWS ElastiCache automatically scales the underlying hardware infrastructure, but in order to take full advantage of this, you should also follow some best practices for scaling Memcached.

1. Increase Node Size: If your workload has outgrown the current node type, you can change to a larger node type with more memory and CPU capacity.
2. Add Nodes or Shards: If you need more cache space, you can increase the number of nodes in your cluster. But remember that Memcached doesn't support automatic sharding so you'll need to manage the distribution of keys across nodes in your application code.
3. Partition Data: You can partition data across multiple clusters if your workload has grown too large for a single Memcached cluster.
4. Auto Scaling: AWS ElastiCache does not directly support auto-scaling for Memcached. However, you could implement a custom solution using other AWS services, like CloudWatch and Lambda.

Here's a Python example showing how you might distribute keys across multiple Memcached nodes:

import memcache

# Define your servers
servers = ["node1:11211", "node2:11211", "node3:11211"]

# Create memcache client
mc = memcache.Client(servers)

# Function to choose the right server based on the key
def get_server(key):
    return mc.get_stats()[hash(key) % len(mc.get_stats())][0].split(':')[0]

# Now you can set a value
server_for_key = get_server('mykey')
mc.set('mykey', 'myvalue', server_for_key)

# And retrieve it
print(mc.get('mykey', server_for_key))

Remember that when you add or remove nodes, you'll need to update the servers list in your application code and redistribute keys as necessary. Keep in mind that changing the number of nodes can cause cache misses if not handled properly. Always test these changes carefully before applying them to your production environment.