Feature parity, sometimes referred to as functional parity or feature equivalence, is a state where a recreated or custom-made system has all the important functionalities of its comparative, often established system. In the development of custom datastores, reaching feature parity means developing a datastore that has all the features of a pre-existing system like PostgreSQL or MongoDB.

As a senior engineer, think of it like designing a new, customized AI model that performs as well as, if not better than, existing solutions in the finance industry. Or like constructing an algorithm that can do exactly what Quicksort does (a commonly referenced sorting algorithm in computer science), but perhaps with your own modifications.

For example, if you're creating your own version of Redis, a popular in-memory data structure store, feature parity would entail building data storage and retrieval, along with other features like Pub/Sub capabilities, transactional operations, and replication. Let's consider a simple Python datastore example which, at its current stage, is far from achieving feature parity with Redis.

Our BasicDatastore supports two fundamental operations: 'put' and 'get'. In this case, our simulated real-world use of storing and retrieving stock price data just barely scratches the surface of Redis' robust feature set.

Reaching feature parity here would involve iterative development to add many more features like data expiration, clustering, real-time analytics, etc. Remember, the path to feature parity is sometimes not about cramming every possible feature but achieving a refined set of features that serve your users' needs effectively.

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if __name__ == "__main__":

  # A simplified example illustrating feature parity

  class BasicDatastore:

    def __init__(self):

      self.data = {}

    def put(self, key, value):

      self.data[key] = value

    def get(self, key):

      return self.data.get(key, None)

​

  # Let's simulate a real-world usage

  datastore = BasicDatastore()

  datastore.put('Apple', {'stock_price': 150, 'sector': 'tech'})

  print(datastore.get('Apple'))