In this article we will build a cache manager that stores frequently used data in memory using the Singleton pattern where the Singleton instance can ensure that the data is accessed efficiently and that memory usage is optimized.
UML Diagram of the Cache Manager
The cache manager is very simple approach, we need to save data with predefined key and then use this key to retrieve this data and this approach could be achieved through the set and get methods as shown below, we will also use the singleton pattern that require a static method and private constructor, so we could get only one instance from the class every time we call it
class CacheManager(object): _instance = None _cache_store =  def __init__(self): raise RuntimeError("Call get_instance() instead") @classmethod def get_instance(cls): if cls._instance is None: cls._instance = cls.__new__(cls) return cls._instance def set(self, key, value): self._cache_store.append((key, value)) def get(self, key): value = None for item in self._cache_store: if key in item: value = item return value
This is just a simple example of cache manager, where it has two methods for setting and getting cache values.
Testing the Cache Manager
We could test the cache manager as follows:
cache1 = CacheManager.get_instance() cache1.set("name", "Mustafa") cache2 = CacheManager.get_instance() cache2.set("role", "backend engineer") print(cache2.get("name")) # note we use cache2 to get the name print(cache2.get("role"))
And here is the result:
Mustafa backend engineer
As you can see, even creating multiple instances from the cache manager, but we still access only one instance.
This cache manager could be useful in any Python application, where it caches data in memory and optimize the application performance.
- Head First Design Patterns: A Brain-Friendly Guide
- Singleton Pattern – Design Patterns (ep 6) by Christopher Okhravi
- I used draw.io for drawing the UML diagram
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