Yes, Memcached is thread-safe. This means that multiple threads can safely access a single instance of Memcached without any issues.
Memcached uses a multi-threaded architecture to handle incoming requests. When a request comes in, it's handled by a worker thread from the thread pool. The worker thread retrieves the requested data from the cache and sends it back to the client. All the threads in the pool share the same hash table, which contains the cached data.
To make sure that the hash table is accessed in a thread-safe way, Memcached uses a technique called "lock striping". In lock striping, the hash table is divided into a number of independent partitions, and each partition is protected by a separate lock. When a thread wants to access a particular item in the hash table, it first computes the hash code of the key and then locks the appropriate partition. This ensures that only one thread can modify or access a particular partition at any given time.
Here's an example of how to use Memcached in a multi-threaded environment using the Python
import threading import memcache mc = memcache.Client(['localhost:11211']) # Define a function that will be executed by a worker thread def worker(): # Access the shared Memcached instance value = mc.get('some_key') # Do some processing with the retrieved value ... # Start multiple worker threads threads =  for i in range(10): t = threading.Thread(target=worker) threads.append(t) t.start() # Wait for all the threads to finish for t in threads: t.join()
In this example, we create a
memcache.Client object that connects to a Memcached instance running on
localhost:11211. We then define a worker function that retrieves a value from the cache using the
get method. Multiple threads are started, and each thread executes the worker function concurrently. Because Memcached is thread-safe, there won't be any issues with concurrent access to the cache.
Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.