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Question: What are the differences between a centralized cache and a distributed cache?


Centralized cache and Distributed cache are two different caching strategies that are used for different reasons. Here's a comprehensive comparison between them:

Centralized Cache

In a centralized cache, there is a single cache storage that all instances of an application connect to. This setup is very useful for synchronizing data across all instances because if one instance puts something into the cache then all other instances would be able to retrieve it.

An example of a centralized cache is Memcached, which allows different instances of your application to use a single machine (or perhaps several, in a failover configuration) as a cache server.

# Example usage of Memcached in Python using pymemcache library from pymemcache.client import base client = base.Client(('localhost', 11211)) client.set('key', 'some value') result = client.get('key')


  • Simplicity of design and implementation.
  • All nodes have equal access to cached data.


  • Single point of failure - if the cache server goes down, all instances lose access to the cache.
  • Can become a network bottleneck with heavy traffic.

Distributed Cache

In contrast to a centralized cache, a distributed cache spreads its data out over multiple nodes. Each node only stores a subset of the cache data. This approach can provide high availability and data redundancy by replicating the cache data over multiple nodes.

Redis Cluster is a well-known example of a distributed cache.

# Example usage of Redis Cluster in Python using redis-py-cluster library from rediscluster import RedisCluster startup_nodes = [{"host": "", "port": "7000"}] rc = RedisCluster(startup_nodes=startup_nodes, decode_responses=True) rc.set("foo", "bar") print(rc.get("foo")) # Outputs: 'bar'


  • Can handle more traffic.
  • No single point of failure - if one node fails, others can still serve data.
  • Data is closer to the consumer and hence lower latency since it's spread across various points in the network.


  • More complex to implement and manage.
  • Consistency can be harder to ensure as changes need to propagate through the network.
  • The system needs to decide where to put each key/value pair based on hashing or some other distribution strategy.

Choosing between these two types of caching largely depends on your specific application requirements, such as the amount of traffic you expect, the scale at which your application operates, the complexity of implementation you can handle, and the level of fault-tolerance required.

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