Distributed caching is the method of storing a website or application’s data across a network of interconnected servers, promoting high availability, horizontal scaling, and fault tolerance. Here are some of the popular distributed caching solutions:
import redis r = redis.Redis(host='localhost', port=6379, db=0) r.set('foo', 'bar') value = r.get('foo')
from pymemcache.client import base client = base.Client(('localhost', 11211)) client.set('key', 'value') result = client.get('key')
import redis r = redis.Redis(host='localhost', port=6379, db=0) r.set('foo', 'bar') value = r.get('foo')
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance(); IMap<Integer, String> map = hazelcastInstance.getMap("map"); map.put(1, "Value"); String value = map.get(1);
from couchbase.cluster import Cluster, QueryOptions cluster = Cluster.connect("couchbase://localhost", "username", "password") bucket = cluster.bucket("bucket-name") coll = bucket.default_collection() coll.upsert("key", {"name": "value"}) result = coll.get("key")
All these systems have their own benefits and trade-offs. Therefore, users need to make the choice based on their specific use case.
Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.