Congratulations, you made it! We've been on a journey to expand our simple key-value store and have added integral features such as expiration times and LRU caching, learning from the popular data store, Redis.
Our key-value store has been revved up. It can now hold values for a specified time and uses LRU caching for handling keys when memory capacity becomes a potential concern while also preserving performance. We mimic Redis in two main aspects: fast access to stored data and optimized memory use.
The expanded key-value store can now be seen as a rudimentary version of Redis, the ultra-fast, in-memory data store with rich data structure support. Does it mean our key-value store is as powerful as Redis? Not entirely, because Redis has much more complex features (such as data replication, persistence options, various types of data structures, built-in Lua scripting, etc.). However, we've gained a glimpse into the inner workings of Redis and built something similar. This can be a great confidence booster, ever boosting your understanding of computer science and software development.
Here's the final snapshot of our key-value store implemented in Python:
xxxxxxxxxxif __name__ == '__main__': from collections import OrderedDict import time class LRU_Cache: def __init__(self, capacity): self.capacity = capacity self.cache = OrderedDict() def get(self, key): try: value = self.cache.pop(key) self.cache[key] = value return value except KeyError: return -1 def put(self, key, value): try: self.cache.pop(key) except KeyError: if len(self.cache) >= self.capacity: self.cache.popitem(last=False) self.cache[key] = value key_value_store = LRU_Cache(3) key_value_store.put(1, 'value1') key_value_store.put(2, 'value2') print(key_value_store.get(1)) # Output: 'value1'


